Q5-632 - College Writers

excel work

Homework 5 need to be worked. Provide me the excel file with calculation. Week5 load approve is the file need to add a new sheet and work.

Question. Using data in excel sheet “data” in the attached Excel file “Week5-Loan_Approval”. Banks have to assess risk before approving loans by looking at many factors to deciding whether or not the applicant’s profile is relevant for granting with loan.

Using Logistic Regression model, predict whether the candidate’s profile is relevant or not using key features like Gender, Married, Dependents, Education, Self_Employed, ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term, Credit_History, and Property_Area.

Based on the features below, predict the applicant’s approval acceptance:

You can use Loan_Status as dependent variable and features as independent variables. Convert all textual categorical variables to numeric, and clean data if necessary.

Gender: Male

Married: Yes

Dependents: 2

Education: Graduate

Self_Employed: No

ApplicantIncome: 5000

CoapplicantIncome: 1000

LoanAmount: 800

Loan_Amount_Term: 240

Credit_History: 1

Property_Area: Urban

data

Loan_ID	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status
LP001002	Male	No	0	Graduate	No	5849	0		360	1	Urban	Y
LP001003	Male	Yes	1	Graduate	No	4583	1508	128	360	1	Rural	N
LP001005	Male	Yes	0	Graduate	Yes	3000	0	66	360	1	Urban	Y
LP001006	Male	Yes	0	Not Graduate	No	2583	2358	120	360	1	Urban	Y
LP001008	Male	No	0	Graduate	No	6000	0	141	360	1	Urban	Y
LP001011	Male	Yes	2	Graduate	Yes	5417	4196	267	360	1	Urban	Y
LP001013	Male	Yes	0	Not Graduate	No	2333	1516	95	360	1	Urban	Y
LP001014	Male	Yes	3	Graduate	No	3036	2504	158	360	0	Semiurban	N
LP001018	Male	Yes	2	Graduate	No	4006	1526	168	360	1	Urban	Y
LP001020	Male	Yes	1	Graduate	No	12841	10968	349	360	1	Semiurban	N
LP001024	Male	Yes	2	Graduate	No	3200	700	70	360	1	Urban	Y
LP001027	Male	Yes	2	Graduate	Yes	2500	1840	109	360	1	Urban	Y
LP001028	Male	Yes	2	Graduate	No	3073	8106	200	360	1	Urban	Y
LP001029	Male	No	0	Graduate	No	1853	2840	114	360	1	Rural	N
LP001030	Male	Yes	2	Graduate	No	1299	1086	17	120	1	Urban	Y
LP001032	Male	No	0	Graduate	No	4950	0	125	360	1	Urban	Y
LP001034	Male	No	1	Not Graduate	No	3596	0	100	240		Urban	Y
LP001036	Female	No	0	Graduate	No	3510	0	76	360	0	Urban	N
LP001038	Male	Yes	0	Not Graduate	No	4887	0	133	360	1	Rural	N
LP001041	Male	Yes	0	Graduate	Yes	2600	3500	115		1	Urban	Y
LP001043	Male	Yes	0	Not Graduate	No	7660	0	104	360	0	Urban	N
LP001046	Male	Yes	1	Graduate	No	5955	5625	315	360	1	Urban	Y
LP001047	Male	Yes	0	Not Graduate	No	2600	1911	116	360	0	Semiurban	N
LP001052	Male	Yes	1	Graduate	Yes	3717	2925	151	360		Semiurban	N
LP001066	Male	Yes	0	Graduate	Yes	9560	0	191	360	1	Semiurban	Y
LP001068	Male	Yes	0	Graduate	No	2799	2253	122	360	1	Semiurban	Y
LP001073	Male	Yes	2	Not Graduate	No	4226	1040	110	360	1	Urban	Y
LP001086	Male	No	0	Not Graduate	No	1442	0	35	360	1	Urban	N
LP001087	Female	No	2	Graduate	Yes	3750	2083	120	360	1	Semiurban	Y
LP001091	Male	Yes	1	Graduate	Yes	4166	3369	201	360		Urban	N
LP001095	Male	No	0	Graduate	No	3167	0	74	360	1	Urban	N
LP001097	Male	No	1	Graduate	Yes	4692	0	106	360	1	Rural	N
LP001098	Male	Yes	0	Graduate	No	3500	1667	114	360	1	Semiurban	Y
LP001100	Male	No	3	Graduate	No	12500	3000	320	360	1	Rural	N
LP001106	Male	Yes	0	Graduate	No	2275	2067		360	1	Urban	Y
LP001109	Male	Yes	0	Graduate	No	1828	1330	100		0	Urban	N
LP001112	Female	Yes	0	Graduate	No	3667	1459	144	360	1	Semiurban	Y
LP001114	Male	No	0	Graduate	No	4166	7210	184	360	1	Urban	Y
LP001116	Male	No	0	Not Graduate	No	3748	1668	110	360	1	Semiurban	Y
LP001119	Male	No	0	Graduate	No	3600	0	80	360	1	Urban	N
LP001120	Male	No	0	Graduate	No	1800	1213	47	360	1	Urban	Y
LP001123	Male	Yes	0	Graduate	No	2400	0	75	360		Urban	Y
LP001131	Male	Yes	0	Graduate	No	3941	2336	134	360	1	Semiurban	Y
LP001136	Male	Yes	0	Not Graduate	Yes	4695	0	96		1	Urban	Y
LP001137	Female	No	0	Graduate	No	3410	0	88		1	Urban	Y
LP001138	Male	Yes	1	Graduate	No	5649	0	44	360	1	Urban	Y
LP001144	Male	Yes	0	Graduate	No	5821	0	144	360	1	Urban	Y
LP001146	Female	Yes	0	Graduate	No	2645	3440	120	360	0	Urban	N
LP001151	Female	No	0	Graduate	No	4000	2275	144	360	1	Semiurban	Y
LP001155	Female	Yes	0	Not Graduate	No	1928	1644	100	360	1	Semiurban	Y
LP001157	Female	No	0	Graduate	No	3086	0	120	360	1	Semiurban	Y
LP001164	Female	No	0	Graduate	No	4230	0	112	360	1	Semiurban	N
LP001179	Male	Yes	2	Graduate	No	4616	0	134	360	1	Urban	N
LP001186	Female	Yes	1	Graduate	Yes	11500	0	286	360	0	Urban	N
LP001194	Male	Yes	2	Graduate	No	2708	1167	97	360	1	Semiurban	Y
LP001195	Male	Yes	0	Graduate	No	2132	1591	96	360	1	Semiurban	Y
LP001197	Male	Yes	0	Graduate	No	3366	2200	135	360	1	Rural	N
LP001198	Male	Yes	1	Graduate	No	8080	2250	180	360	1	Urban	Y
LP001199	Male	Yes	2	Not Graduate	No	3357	2859	144	360	1	Urban	Y
LP001205	Male	Yes	0	Graduate	No	2500	3796	120	360	1	Urban	Y
LP001206	Male	Yes	3	Graduate	No	3029	0	99	360	1	Urban	Y
LP001207	Male	Yes	0	Not Graduate	Yes	2609	3449	165	180	0	Rural	N
LP001213	Male	Yes	1	Graduate	No	4945	0		360	0	Rural	N
LP001222	Female	No	0	Graduate	No	4166	0	116	360	0	Semiurban	N
LP001225	Male	Yes	0	Graduate	No	5726	4595	258	360	1	Semiurban	N
LP001228	Male	No	0	Not Graduate	No	3200	2254	126	180	0	Urban	N
LP001233	Male	Yes	1	Graduate	No	10750	0	312	360	1	Urban	Y
LP001238	Male	Yes	3	Not Graduate	Yes	7100	0	125	60	1	Urban	Y
LP001241	Female	No	0	Graduate	No	4300	0	136	360	0	Semiurban	N
LP001243	Male	Yes	0	Graduate	No	3208	3066	172	360	1	Urban	Y
LP001245	Male	Yes	2	Not Graduate	Yes	1875	1875	97	360	1	Semiurban	Y
LP001248	Male	No	0	Graduate	No	3500	0	81	300	1	Semiurban	Y
LP001250	Male	Yes	3	Not Graduate	No	4755	0	95		0	Semiurban	N
LP001253	Male	Yes	3	Graduate	Yes	5266	1774	187	360	1	Semiurban	Y
LP001255	Male	No	0	Graduate	No	3750	0	113	480	1	Urban	N
LP001256	Male	No	0	Graduate	No	3750	4750	176	360	1	Urban	N
LP001259	Male	Yes	1	Graduate	Yes	1000	3022	110	360	1	Urban	N
LP001263	Male	Yes	3	Graduate	No	3167	4000	180	300	0	Semiurban	N
LP001264	Male	Yes	3	Not Graduate	Yes	3333	2166	130	360		Semiurban	Y
LP001265	Female	No	0	Graduate	No	3846	0	111	360	1	Semiurban	Y
LP001266	Male	Yes	1	Graduate	Yes	2395	0		360	1	Semiurban	Y
LP001267	Female	Yes	2	Graduate	No	1378	1881	167	360	1	Urban	N
LP001273	Male	Yes	0	Graduate	No	6000	2250	265	360		Semiurban	N
LP001275	Male	Yes	1	Graduate	No	3988	0	50	240	1	Urban	Y
LP001279	Male	No	0	Graduate	No	2366	2531	136	360	1	Semiurban	Y
LP001280	Male	Yes	2	Not Graduate	No	3333	2000	99	360		Semiurban	Y
LP001282	Male	Yes	0	Graduate	No	2500	2118	104	360	1	Semiurban	Y
LP001289	Male	No	0	Graduate	No	8566	0	210	360	1	Urban	Y
LP001310	Male	Yes	0	Graduate	No	5695	4167	175	360	1	Semiurban	Y
LP001316	Male	Yes	0	Graduate	No	2958	2900	131	360	1	Semiurban	Y
LP001318	Male	Yes	2	Graduate	No	6250	5654	188	180	1	Semiurban	Y
LP001319	Male	Yes	2	Not Graduate	No	3273	1820	81	360	1	Urban	Y
LP001322	Male	No	0	Graduate	No	4133	0	122	360	1	Semiurban	Y
LP001325	Male	No	0	Not Graduate	No	3620	0	25	120	1	Semiurban	Y
LP001326	Male	No	0	Graduate	Yes	6782	0		360		Urban	N
LP001327	Female	Yes	0	Graduate	No	2484	2302	137	360	1	Semiurban	Y
LP001333	Male	Yes	0	Graduate	No	1977	997	50	360	1	Semiurban	Y
LP001334	Male	Yes	0	Not Graduate	No	4188	0	115	180	1	Semiurban	Y
LP001343	Male	Yes	0	Graduate	No	1759	3541	131	360	1	Semiurban	Y
LP001345	Male	Yes	2	Not Graduate	No	4288	3263	133	180	1	Urban	Y
LP001349	Male	No	0	Graduate	No	4843	3806	151	360	1	Semiurban	Y
LP001350	Male	Yes		Graduate	No	13650	0		360	1	Urban	Y
LP001356	Male	Yes	0	Graduate	No	4652	3583		360	1	Semiurban	Y
LP001367	Male	Yes	1	Graduate	No	3052	1030	100	360	1	Urban	Y
LP001369	Male	Yes	2	Graduate	No	11417	1126	225	360	1	Urban	Y
LP001370	Male	No	0	Not Graduate	Yes	7333	0	120	360	1	Rural	N
LP001379	Male	Yes	2	Graduate	No	3800	3600	216	360	0	Urban	N
LP001384	Male	Yes	3	Not Graduate	No	2071	754	94	480	1	Semiurban	Y
LP001385	Male	No	0	Graduate	No	5316	0	136	360	1	Urban	Y
LP001387	Female	Yes	0	Graduate	Yes	2929	2333	139	360	1	Semiurban	Y
LP001391	Male	Yes	0	Not Graduate	No	3572	4114	152		0	Rural	N
LP001392	Female	No	1	Graduate	Yes	7451	0		360	1	Semiurban	Y
LP001398	Male	No	0	Graduate	Yes	5050	0	118	360	1	Semiurban	Y
LP001401	Male	Yes	1	Graduate	No	14583	0	185	180	1	Rural	Y
LP001404	Female	Yes	0	Graduate	No	3167	2283	154	360	1	Semiurban	Y
LP001405	Male	Yes	1	Graduate	No	2214	1398	85	360		Urban	Y
LP001421	Male	Yes	0	Graduate	No	5568	2142	175	360	1	Rural	N
LP001422	Female	No	0	Graduate	No	10408	0	259	360	1	Urban	Y
LP001426	Male	Yes		Graduate	No	5667	2667	180	360	1	Rural	Y
LP001430	Female	No	0	Graduate	No	4166	0	44	360	1	Semiurban	Y
LP001431	Female	No	0	Graduate	No	2137	8980	137	360	0	Semiurban	Y
LP001432	Male	Yes	2	Graduate	No	2957	0	81	360	1	Semiurban	Y
LP001439	Male	Yes	0	Not Graduate	No	4300	2014	194	360	1	Rural	Y
LP001443	Female	No	0	Graduate	No	3692	0	93	360		Rural	Y
LP001449	Male	No	0	Graduate	No	3865	1640		360	1	Rural	Y
LP001451	Male	Yes	1	Graduate	Yes	10513	3850	160	180	0	Urban	N
LP001465	Male	Yes	0	Graduate	No	6080	2569	182	360		Rural	N
LP001469	Male	No	0	Graduate	Yes	20166	0	650	480		Urban	Y
LP001473	Male	No	0	Graduate	No	2014	1929	74	360	1	Urban	Y
LP001478	Male	No	0	Graduate	No	2718	0	70	360	1	Semiurban	Y
LP001482	Male	Yes	0	Graduate	Yes	3459	0	25	120	1	Semiurban	Y
LP001487	Male	No	0	Graduate	No	4895	0	102	360	1	Semiurban	Y
LP001488	Male	Yes	3	Graduate	No	4000	7750	290	360	1	Semiurban	N
LP001489	Female	Yes	0	Graduate	No	4583	0	84	360	1	Rural	N
LP001491	Male	Yes	2	Graduate	Yes	3316	3500	88	360	1	Urban	Y
LP001492	Male	No	0	Graduate	No	14999	0	242	360	0	Semiurban	N
LP001493	Male	Yes	2	Not Graduate	No	4200	1430	129	360	1	Rural	N
LP001497	Male	Yes	2	Graduate	No	5042	2083	185	360	1	Rural	N
LP001498	Male	No	0	Graduate	No	5417	0	168	360	1	Urban	Y
LP001504	Male	No	0	Graduate	Yes	6950	0	175	180	1	Semiurban	Y
LP001507	Male	Yes	0	Graduate	No	2698	2034	122	360	1	Semiurban	Y
LP001508	Male	Yes	2	Graduate	No	11757	0	187	180	1	Urban	Y
LP001514	Female	Yes	0	Graduate	No	2330	4486	100	360	1	Semiurban	Y
LP001516	Female	Yes	2	Graduate	No	14866	0	70	360	1	Urban	Y
LP001518	Male	Yes	1	Graduate	No	1538	1425	30	360	1	Urban	Y
LP001519	Female	No	0	Graduate	No	10000	1666	225	360	1	Rural	N
LP001520	Male	Yes	0	Graduate	No	4860	830	125	360	1	Semiurban	Y
LP001528	Male	No	0	Graduate	No	6277	0	118	360	0	Rural	N
LP001529	Male	Yes	0	Graduate	Yes	2577	3750	152	360	1	Rural	Y
LP001531	Male	No	0	Graduate	No	9166	0	244	360	1	Urban	N
LP001532	Male	Yes	2	Not Graduate	No	2281	0	113	360	1	Rural	N
LP001535	Male	No	0	Graduate	No	3254	0	50	360	1	Urban	Y
LP001536	Male	Yes	3	Graduate	No	39999	0	600	180	0	Semiurban	Y
LP001541	Male	Yes	1	Graduate	No	6000	0	160	360		Rural	Y
LP001543	Male	Yes	1	Graduate	No	9538	0	187	360	1	Urban	Y
LP001546	Male	No	0	Graduate	Yes	2980	2083	120	360	1	Rural	Y
LP001552	Male	Yes	0	Graduate	No	4583	5625	255	360	1	Semiurban	Y
LP001560	Male	Yes	0	Not Graduate	No	1863	1041	98	360	1	Semiurban	Y
LP001562	Male	Yes	0	Graduate	No	7933	0	275	360	1	Urban	N
LP001565	Male	Yes	1	Graduate	No	3089	1280	121	360	0	Semiurban	N
LP001570	Male	Yes	2	Graduate	No	4167	1447	158	360	1	Rural	Y
LP001572	Male	Yes	0	Graduate	No	9323	0	75	180	1	Urban	Y
LP001574	Male	Yes	0	Graduate	No	3707	3166	182		1	Rural	Y
LP001577	Female	Yes	0	Graduate	No	4583	0	112	360	1	Rural	N
LP001578	Male	Yes	0	Graduate	No	2439	3333	129	360	1	Rural	Y
LP001579	Male	No	0	Graduate	No	2237	0	63	480	0	Semiurban	N
LP001580	Male	Yes	2	Graduate	No	8000	0	200	360	1	Semiurban	Y
LP001581	Male	Yes	0	Not Graduate	Yes	1820	1769	95	360	1	Rural	Y
LP001586	Male	Yes	3	Not Graduate	No	3522	0	81	180	1	Rural	N
LP001594	Male	Yes	0	Graduate	No	5708	5625	187	360	1	Semiurban	Y
LP001603	Male	Yes	0	Not Graduate	Yes	4344	736	87	360	1	Semiurban	N
LP001606	Male	Yes	0	Graduate	No	3497	1964	116	360	1	Rural	Y
LP001608	Male	Yes	2	Graduate	No	2045	1619	101	360	1	Rural	Y
LP001610	Male	Yes	3	Graduate	No	5516	11300	495	360	0	Semiurban	N
LP001616	Male	Yes	1	Graduate	No	3750	0	116	360	1	Semiurban	Y
LP001630	Male	No	0	Not Graduate	No	2333	1451	102	480	0	Urban	N
LP001633	Male	Yes	1	Graduate	No	6400	7250	180	360	0	Urban	N
LP001634	Male	No	0	Graduate	No	1916	5063	67	360		Rural	N
LP001636	Male	Yes	0	Graduate	No	4600	0	73	180	1	Semiurban	Y
LP001637	Male	Yes	1	Graduate	No	33846	0	260	360	1	Semiurban	N
LP001639	Female	Yes	0	Graduate	No	3625	0	108	360	1	Semiurban	Y
LP001640	Male	Yes	0	Graduate	Yes	39147	4750	120	360	1	Semiurban	Y
LP001641	Male	Yes	1	Graduate	Yes	2178	0	66	300	0	Rural	N
LP001643	Male	Yes	0	Graduate	No	2383	2138	58	360		Rural	Y
LP001647	Male	Yes	0	Graduate	No	9328	0	188	180	1	Rural	Y
LP001653	Male	No	0	Not Graduate	No	4885	0	48	360	1	Rural	Y
LP001656	Male	No	0	Graduate	No	12000	0	164	360	1	Semiurban	N
LP001657	Male	Yes	0	Not Graduate	No	6033	0	160	360	1	Urban	N
LP001658	Male	No	0	Graduate	No	3858	0	76	360	1	Semiurban	Y
LP001664	Male	No	0	Graduate	No	4191	0	120	360	1	Rural	Y
LP001665	Male	Yes	1	Graduate	No	3125	2583	170	360	1	Semiurban	N
LP001666	Male	No	0	Graduate	No	8333	3750	187	360	1	Rural	Y
LP001669	Female	No	0	Not Graduate	No	1907	2365	120		1	Urban	Y
LP001671	Female	Yes	0	Graduate	No	3416	2816	113	360		Semiurban	Y
LP001673	Male	No	0	Graduate	Yes	11000	0	83	360	1	Urban	N
LP001674	Male	Yes	1	Not Graduate	No	2600	2500	90	360	1	Semiurban	Y
LP001677	Male	No	2	Graduate	No	4923	0	166	360	0	Semiurban	Y
LP001682	Male	Yes	3	Not Graduate	No	3992	0		180	1	Urban	N
LP001688	Male	Yes	1	Not Graduate	No	3500	1083	135	360	1	Urban	Y
LP001691	Male	Yes	2	Not Graduate	No	3917	0	124	360	1	Semiurban	Y
LP001692	Female	No	0	Not Graduate	No	4408	0	120	360	1	Semiurban	Y
LP001693	Female	No	0	Graduate	No	3244	0	80	360	1	Urban	Y
LP001698	Male	No	0	Not Graduate	No	3975	2531	55	360	1	Rural	Y
LP001699	Male	No	0	Graduate	No	2479	0	59	360	1	Urban	Y
LP001702	Male	No	0	Graduate	No	3418	0	127	360	1	Semiurban	N
LP001708	Female	No	0	Graduate	No	10000	0	214	360	1	Semiurban	N
LP001711	Male	Yes	3	Graduate	No	3430	1250	128	360	0	Semiurban	N
LP001713	Male	Yes	1	Graduate	Yes	7787	0	240	360	1	Urban	Y
LP001715	Male	Yes	3	Not Graduate	Yes	5703	0	130	360	1	Rural	Y
LP001716	Male	Yes	0	Graduate	No	3173	3021	137	360	1	Urban	Y
LP001720	Male	Yes	3	Not Graduate	No	3850	983	100	360	1	Semiurban	Y
LP001722	Male	Yes	0	Graduate	No	150	1800	135	360	1	Rural	N
LP001726	Male	Yes	0	Graduate	No	3727	1775	131	360	1	Semiurban	Y
LP001732	Male	Yes	2	Graduate	Yes	5000	0	72	360	0	Semiurban	N
LP001734	Female	Yes	2	Graduate	No	4283	2383	127	360		Semiurban	Y
LP001736	Male	Yes	0	Graduate	No	2221	0	60	360	0	Urban	N
LP001743	Male	Yes	2	Graduate	No	4009	1717	116	360	1	Semiurban	Y
LP001744	Male	No	0	Graduate	No	2971	2791	144	360	1	Semiurban	Y
LP001749	Male	Yes	0	Graduate	No	7578	1010	175		1	Semiurban	Y
LP001750	Male	Yes	0	Graduate	No	6250	0	128	360	1	Semiurban	Y
LP001751	Male	Yes	0	Graduate	No	3250	0	170	360	1	Rural	N
LP001754	Male	Yes		Not Graduate	Yes	4735	0	138	360	1	Urban	N
LP001758	Male	Yes	2	Graduate	No	6250	1695	210	360	1	Semiurban	Y
LP001761	Male	No	0	Graduate	Yes	6400	0	200	360	1	Rural	Y
LP001765	Male	Yes	1	Graduate	No	2491	2054	104	360	1	Semiurban	Y
LP001768	Male	Yes	0	Graduate	Yes	3716	0	42	180	1	Rural	Y
LP001770	Male	No	0	Not Graduate	No	3189	2598	120		1	Rural	Y
LP001776	Female	No	0	Graduate	No	8333	0	280	360	1	Semiurban	Y
LP001778	Male	Yes	1	Graduate	No	3155	1779	140	360	1	Semiurban	Y
LP001784	Male	Yes	1	Graduate	No	5500	1260	170	360	1	Rural	Y
LP001786	Male	Yes	0	Graduate	Yes	5746	0	255	360		Urban	N
LP001788	Female	No	0	Graduate	Yes	3463	0	122	360		Urban	Y
LP001790	Female	No	1	Graduate	No	3812	0	112	360	1	Rural	Y
LP001792	Male	Yes	1	Graduate	No	3315	0	96	360	1	Semiurban	Y
LP001798	Male	Yes	2	Graduate	No	5819	5000	120	360	1	Rural	Y
LP001800	Male	Yes	1	Not Graduate	No	2510	1983	140	180	1	Urban	N
LP001806	Male	No	0	Graduate	No	2965	5701	155	60	1	Urban	Y
LP001807	Male	Yes	2	Graduate	Yes	6250	1300	108	360	1	Rural	Y
LP001811	Male	Yes	0	Not Graduate	No	3406	4417	123	360	1	Semiurban	Y
LP001813	Male	No	0	Graduate	Yes	6050	4333	120	180	1	Urban	N
LP001814	Male	Yes	2	Graduate	No	9703	0	112	360	1	Urban	Y
LP001819	Male	Yes	1	Not Graduate	No	6608	0	137	180	1	Urban	Y
LP001824	Male	Yes	1	Graduate	No	2882	1843	123	480	1	Semiurban	Y
LP001825	Male	Yes	0	Graduate	No	1809	1868	90	360	1	Urban	Y
LP001835	Male	Yes	0	Not Graduate	No	1668	3890	201	360	0	Semiurban	N
LP001836	Female	No	2	Graduate	No	3427	0	138	360	1	Urban	N
LP001841	Male	No	0	Not Graduate	Yes	2583	2167	104	360	1	Rural	Y
LP001843	Male	Yes	1	Not Graduate	No	2661	7101	279	180	1	Semiurban	Y
LP001844	Male	No	0	Graduate	Yes	16250	0	192	360	0	Urban	N
LP001846	Female	No	3	Graduate	No	3083	0	255	360	1	Rural	Y
LP001849	Male	No	0	Not Graduate	No	6045	0	115	360	0	Rural	N
LP001854	Male	Yes	3	Graduate	No	5250	0	94	360	1	Urban	N
LP001859	Male	Yes	0	Graduate	No	14683	2100	304	360	1	Rural	N
LP001864	Male	Yes	3	Not Graduate	No	4931	0	128	360		Semiurban	N
LP001865	Male	Yes	1	Graduate	No	6083	4250	330	360		Urban	Y
LP001868	Male	No	0	Graduate	No	2060	2209	134	360	1	Semiurban	Y
LP001870	Female	No	1	Graduate	No	3481	0	155	36	1	Semiurban	N
LP001871	Female	No	0	Graduate	No	7200	0	120	360	1	Rural	Y
LP001872	Male	No	0	Graduate	Yes	5166	0	128	360	1	Semiurban	Y
LP001875	Male	No	0	Graduate	No	4095	3447	151	360	1	Rural	Y
LP001877	Male	Yes	2	Graduate	No	4708	1387	150	360	1	Semiurban	Y
LP001882	Male	Yes	3	Graduate	No	4333	1811	160	360	0	Urban	Y
LP001883	Female	No	0	Graduate	Yes	3418	0	135	360	1	Rural	N
LP001884	Female	No	1	Graduate	No	2876	1560	90	360	1	Urban	Y
LP001888	Female	No	0	Graduate	No	3237	0	30	360	1	Urban	Y
LP001891	Male	Yes	0	Graduate	No	11146	0	136	360	1	Urban	Y
LP001892	Male	No	0	Graduate	No	2833	1857	126	360	1	Rural	Y
LP001894	Male	Yes	0	Graduate	No	2620	2223	150	360	1	Semiurban	Y
LP001896	Male	Yes	2	Graduate	No	3900	0	90	360	1	Semiurban	Y
LP001900	Male	Yes	1	Graduate	No	2750	1842	115	360	1	Semiurban	Y
LP001903	Male	Yes	0	Graduate	No	3993	3274	207	360	1	Semiurban	Y
LP001904	Male	Yes	0	Graduate	No	3103	1300	80	360	1	Urban	Y
LP001907	Male	Yes	0	Graduate	No	14583	0	436	360	1	Semiurban	Y
LP001908	Female	Yes	0	Not Graduate	No	4100	0	124	360		Rural	Y
LP001910	Male	No	1	Not Graduate	Yes	4053	2426	158	360	0	Urban	N
LP001914	Male	Yes	0	Graduate	No	3927	800	112	360	1	Semiurban	Y
LP001915	Male	Yes	2	Graduate	No	2301	985.7999878	78	180	1	Urban	Y
LP001917	Female	No	0	Graduate	No	1811	1666	54	360	1	Urban	Y
LP001922	Male	Yes	0	Graduate	No	20667	0		360	1	Rural	N
LP001924	Male	No	0	Graduate	No	3158	3053	89	360	1	Rural	Y
LP001925	Female	No	0	Graduate	Yes	2600	1717	99	300	1	Semiurban	N
LP001926	Male	Yes	0	Graduate	No	3704	2000	120	360	1	Rural	Y
LP001931	Female	No	0	Graduate	No	4124	0	115	360	1	Semiurban	Y
LP001935	Male	No	0	Graduate	No	9508	0	187	360	1	Rural	Y
LP001936	Male	Yes	0	Graduate	No	3075	2416	139	360	1	Rural	Y
LP001938	Male	Yes	2	Graduate	No	4400	0	127	360	0	Semiurban	N
LP001940	Male	Yes	2	Graduate	No	3153	1560	134	360	1	Urban	Y
LP001945	Female	No		Graduate	No	5417	0	143	480	0	Urban	N
LP001947	Male	Yes	0	Graduate	No	2383	3334	172	360	1	Semiurban	Y
LP001949	Male	Yes	3	Graduate	Yes	4416	1250	110	360	1	Urban	Y
LP001953	Male	Yes	1	Graduate	No	6875	0	200	360	1	Semiurban	Y
LP001954	Female	Yes	1	Graduate	No	4666	0	135	360	1	Urban	Y
LP001955	Female	No	0	Graduate	No	5000	2541	151	480	1	Rural	N
LP001963	Male	Yes	1	Graduate	No	2014	2925	113	360	1	Urban	N
LP001964	Male	Yes	0	Not Graduate	No	1800	2934	93	360	0	Urban	N
LP001972	Male	Yes		Not Graduate	No	2875	1750	105	360	1	Semiurban	Y
LP001974	Female	No	0	Graduate	No	5000	0	132	360	1	Rural	Y
LP001977	Male	Yes	1	Graduate	No	1625	1803	96	360	1	Urban	Y
LP001978	Male	No	0	Graduate	No	4000	2500	140	360	1	Rural	Y
LP001990	Male	No	0	Not Graduate	No	2000	0		360	1	Urban	N
LP001993	Female	No	0	Graduate	No	3762	1666	135	360	1	Rural	Y
LP001994	Female	No	0	Graduate	No	2400	1863	104	360	0	Urban	N
LP001996	Male	No	0	Graduate	No	20233	0	480	360	1	Rural	N
LP001998	Male	Yes	2	Not Graduate	No	7667	0	185	360		Rural	Y
LP002002	Female	No	0	Graduate	No	2917	0	84	360	1	Semiurban	Y
LP002004	Male	No	0	Not Graduate	No	2927	2405	111	360	1	Semiurban	Y
LP002006	Female	No	0	Graduate	No	2507	0	56	360	1	Rural	Y
LP002008	Male	Yes	2	Graduate	Yes	5746	0	144	84		Rural	Y
LP002031	Male	Yes	1	Not Graduate	No	3399	1640	111	180	1	Urban	Y
LP002035	Male	Yes	2	Graduate	No	3717	0	120	360	1	Semiurban	Y
LP002036	Male	Yes	0	Graduate	No	2058	2134	88	360		Urban	Y
LP002043	Female	No	1	Graduate	No	3541	0	112	360		Semiurban	Y
LP002050	Male	Yes	1	Graduate	Yes	10000	0	155	360	1	Rural	N
LP002051	Male	Yes	0	Graduate	No	2400	2167	115	360	1	Semiurban	Y
LP002053	Male	Yes	3	Graduate	No	4342	189	124	360	1	Semiurban	Y
LP002054	Male	Yes	2	Not Graduate	No	3601	1590		360	1	Rural	Y
LP002055	Female	No	0	Graduate	No	3166	2985	132	360		Rural	Y
LP002065	Male	Yes	3	Graduate	No	15000	0	300	360	1	Rural	Y
LP002067	Male	Yes	1	Graduate	Yes	8666	4983	376	360	0	Rural	N
LP002068	Male	No	0	Graduate	No	4917	0	130	360	0	Rural	Y
LP002082	Male	Yes	0	Graduate	Yes	5818	2160	184	360	1	Semiurban	Y
LP002086	Female	Yes	0	Graduate	No	4333	2451	110	360	1	Urban	N
LP002087	Female	No	0	Graduate	No	2500	0	67	360	1	Urban	Y
LP002097	Male	No	1	Graduate	No	4384	1793	117	360	1	Urban	Y
LP002098	Male	No	0	Graduate	No	2935	0	98	360	1	Semiurban	Y
LP002100	Male	No		Graduate	No	2833	0	71	360	1	Urban	Y
LP002101	Male	Yes	0	Graduate	Yes	63337	0	490	180	1	Urban	Y
LP002106	Male	Yes		Graduate	Yes	5503	4490	70		1	Semiurban	Y
LP002110	Male	Yes	1	Graduate	Yes	5250	688	160	360	1	Rural	Y
LP002112	Male	Yes	2	Graduate	Yes	2500	4600	176	360	1	Rural	Y
LP002113	Female	No	3	Not Graduate	No	1830	0		360	0	Urban	N
LP002114	Female	No	0	Graduate	No	4160	0	71	360	1	Semiurban	Y
LP002115	Male	Yes	3	Not Graduate	No	2647	1587	173	360	1	Rural	N
LP002116	Female	No	0	Graduate	No	2378	0	46	360	1	Rural	N
LP002119	Male	Yes	1	Not Graduate	No	4554	1229	158	360	1	Urban	Y
LP002126	Male	Yes	3	Not Graduate	No	3173	0	74	360	1	Semiurban	Y
LP002128	Male	Yes	2	Graduate	Yes	2583	2330	125	360	1	Rural	Y
LP002129	Male	Yes	0	Graduate	No	2499	2458	160	360	1	Semiurban	Y
LP002130	Male	Yes		Not Graduate	No	3523	3230	152	360	0	Rural	N
LP002131	Male	Yes	2	Not Graduate	No	3083	2168	126	360	1	Urban	Y
LP002137	Male	Yes	0	Graduate	No	6333	4583	259	360		Semiurban	Y
LP002138	Male	Yes	0	Graduate	No	2625	6250	187	360	1	Rural	Y
LP002139	Male	Yes	0	Graduate	No	9083	0	228	360	1	Semiurban	Y
LP002140	Male	No	0	Graduate	No	8750	4167	308	360	1	Rural	N
LP002141	Male	Yes	3	Graduate	No	2666	2083	95	360	1	Rural	Y
LP002142	Female	Yes	0	Graduate	Yes	5500	0	105	360	0	Rural	N
LP002143	Female	Yes	0	Graduate	No	2423	505	130	360	1	Semiurban	Y
LP002144	Female	No		Graduate	No	3813	0	116	180	1	Urban	Y
LP002149	Male	Yes	2	Graduate	No	8333	3167	165	360	1	Rural	Y
LP002151	Male	Yes	1	Graduate	No	3875	0	67	360	1	Urban	N
LP002158	Male	Yes	0	Not Graduate	No	3000	1666	100	480	0	Urban	N
LP002160	Male	Yes	3	Graduate	No	5167	3167	200	360	1	Semiurban	Y
LP002161	Female	No	1	Graduate	No	4723	0	81	360	1	Semiurban	N
LP002170	Male	Yes	2	Graduate	No	5000	3667	236	360	1	Semiurban	Y
LP002175	Male	Yes	0	Graduate	No	4750	2333	130	360	1	Urban	Y
LP002178	Male	Yes	0	Graduate	No	3013	3033	95	300		Urban	Y
LP002180	Male	No	0	Graduate	Yes	6822	0	141	360	1	Rural	Y
LP002181	Male	No	0	Not Graduate	No	6216	0	133	360	1	Rural	N
LP002187	Male	No	0	Graduate	No	2500	0	96	480	1	Semiurban	N
LP002188	Male	No	0	Graduate	No	5124	0	124		0	Rural	N
LP002190	Male	Yes	1	Graduate	No	6325	0	175	360	1	Semiurban	Y
LP002191	Male	Yes	0	Graduate	No	19730	5266	570	360	1	Rural	N
LP002194	Female	No	0	Graduate	Yes	15759	0	55	360	1	Semiurban	Y
LP002197	Male	Yes	2	Graduate	No	5185	0	155	360	1	Semiurban	Y
LP002201	Male	Yes	2	Graduate	Yes	9323	7873	380	300	1	Rural	Y
LP002205	Male	No	1	Graduate	No	3062	1987	111	180	0	Urban	N
LP002209	Female	No	0	Graduate	Yes	2764	1459	110	360	1	Urban	Y
LP002211	Male	Yes	0	Graduate	No	4817	923	120	180	1	Urban	Y
LP002219	Male	Yes	3	Graduate	No	8750	4996	130	360	1	Rural	Y
LP002223	Male	Yes	0	Graduate	No	4310	0	130	360		Semiurban	Y
LP002224	Male	No	0	Graduate	No	3069	0	71	480	1	Urban	N
LP002225	Male	Yes	2	Graduate	No	5391	0	130	360	1	Urban	Y
LP002226	Male	Yes	0	Graduate	Yes	3333	2500	128	360	1	Semiurban	Y
LP002229	Male	No	0	Graduate	No	5941	4232	296	360	1	Semiurban	Y
LP002231	Female	No	0	Graduate	No	6000	0	156	360	1	Urban	Y
LP002234	Male	No	0	Graduate	Yes	7167	0	128	360	1	Urban	Y
LP002236	Male	Yes	2	Graduate	No	4566	0	100	360	1	Urban	N
LP002237	Male	No	1	Graduate	Yes	3667	0	113	180	1	Urban	Y
LP002239	Male	No	0	Not Graduate	No	2346	1600	132	360	1	Semiurban	Y
LP002243	Male	Yes	0	Not Graduate	No	3010	3136		360	0	Urban	N
LP002244	Male	Yes	0	Graduate	No	2333	2417	136	360	1	Urban	Y
LP002250	Male	Yes	0	Graduate	No	5488	0	125	360	1	Rural	Y
LP002255	Male	No	3	Graduate	No	9167	0	185	360	1	Rural	Y
LP002262	Male	Yes	3	Graduate	No	9504	0	275	360	1	Rural	Y
LP002263	Male	Yes	0	Graduate	No	2583	2115	120	360		Urban	Y
LP002265	Male	Yes	2	Not Graduate	No	1993	1625	113	180	1	Semiurban	Y
LP002266	Male	Yes	2	Graduate	No	3100	1400	113	360	1	Urban	Y
LP002272	Male	Yes	2	Graduate	No	3276	484	135	360		Semiurban	Y
LP002277	Female	No	0	Graduate	No	3180	0	71	360	0	Urban	N
LP002281	Male	Yes	0	Graduate	No	3033	1459	95	360	1	Urban	Y
LP002284	Male	No	0	Not Graduate	No	3902	1666	109	360	1	Rural	Y
LP002287	Female	No	0	Graduate	No	1500	1800	103	360	0	Semiurban	N
LP002288	Male	Yes	2	Not Graduate	No	2889	0	45	180	0	Urban	N
LP002296	Male	No	0	Not Graduate	No	2755	0	65	300	1	Rural	N
LP002297	Male	No	0	Graduate	No	2500	20000	103	360	1	Semiurban	Y
LP002300	Female	No	0	Not Graduate	No	1963	0	53	360	1	Semiurban	Y
LP002301	Female	No	0	Graduate	Yes	7441	0	194	360	1	Rural	N
LP002305	Female	No	0	Graduate	No	4547	0	115	360	1	Semiurban	Y
LP002308	Male	Yes	0	Not Graduate	No	2167	2400	115	360	1	Urban	Y
LP002314	Female	No	0	Not Graduate	No	2213	0	66	360	1	Rural	Y
LP002315	Male	Yes	1	Graduate	No	8300	0	152	300	0	Semiurban	N
LP002317	Male	Yes	3	Graduate	No	81000	0	360	360	0	Rural	N
LP002318	Female	No	1	Not Graduate	Yes	3867	0	62	360	1	Semiurban	N
LP002319	Male	Yes	0	Graduate	Yes	6256	0	160	360		Urban	Y
LP002328	Male	Yes	0	Not Graduate	No	6096	0	218	360	0	Rural	N
LP002332	Male	Yes	0	Not Graduate	No	2253	2033	110	360	1	Rural	Y
LP002335	Female	Yes	0	Not Graduate	No	2149	3237	178	360	0	Semiurban	N
LP002337	Female	No	0	Graduate	No	2995	0	60	360	1	Urban	Y
LP002341	Female	No	1	Graduate	No	2600	0	160	360	1	Urban	N
LP002342	Male	Yes	2	Graduate	Yes	1600	20000	239	360	1	Urban	N
LP002345	Male	Yes	0	Graduate	No	1025	2773	112	360	1	Rural	Y
LP002347	Male	Yes	0	Graduate	No	3246	1417	138	360	1	Semiurban	Y
LP002348	Male	Yes	0	Graduate	No	5829	0	138	360	1	Rural	Y
LP002357	Female	No	0	Not Graduate	No	2720	0	80		0	Urban	N
LP002361	Male	Yes	0	Graduate	No	1820	1719	100	360	1	Urban	Y
LP002362	Male	Yes	1	Graduate	No	7250	1667	110		0	Urban	N
LP002364	Male	Yes	0	Graduate	No	14880	0	96	360	1	Semiurban	Y
LP002366	Male	Yes	0	Graduate	No	2666	4300	121	360	1	Rural	Y
LP002367	Female	No	1	Not Graduate	No	4606	0	81	360	1	Rural	N
LP002368	Male	Yes	2	Graduate	No	5935	0	133	360	1	Semiurban	Y
LP002369	Male	Yes	0	Graduate	No	2920	16.12000084	87	360	1	Rural	Y
LP002370	Male	No	0	Not Graduate	No	2717	0	60	180	1	Urban	Y
LP002377	Female	No	1	Graduate	Yes	8624	0	150	360	1	Semiurban	Y
LP002379	Male	No	0	Graduate	No	6500	0	105	360	0	Rural	N
LP002386	Male	No	0	Graduate	Yes	12876	0	405	360	1	Semiurban	Y
LP002387	Male	Yes	0	Graduate	No	2425	2340	143	360	1	Semiurban	Y
LP002390	Male	No	0	Graduate	No	3750	0	100	360	1	Urban	Y
LP002398	Male	No	0	Graduate	No	1926	1851	50	360	1	Semiurban	Y
LP002401	Male	Yes	0	Graduate	No	2213	1125		360	1	Urban	Y
LP002403	Male	No	0	Graduate	Yes	10416	0	187	360	0	Urban	N
LP002407	Female	Yes	0	Not Graduate	Yes	7142	0	138	360	1	Rural	Y
LP002408	Male	No	0	Graduate	No	3660	5064	187	360	1	Semiurban	Y
LP002409	Male	Yes	0	Graduate	No	7901	1833	180	360	1	Rural	Y
LP002418	Male	No	3	Not Graduate	No	4707	1993	148	360	1	Semiurban	Y
LP002422	Male	No	1	Graduate	No	37719	0	152	360	1	Semiurban	Y
LP002424	Male	Yes	0	Graduate	No	7333	8333	175	300		Rural	Y
LP002429	Male	Yes	1	Graduate	Yes	3466	1210	130	360	1	Rural	Y
LP002434	Male	Yes	2	Not Graduate	No	4652	0	110	360	1	Rural	Y
LP002435	Male	Yes	0	Graduate	Yes	3539	1376	55	360	1	Rural	N
LP002443	Male	Yes	2	Graduate	No	3340	1710	150	360	0	Rural	N
LP002444	Male	No	1	Not Graduate	Yes	2769	1542	190	360		Semiurban	N
LP002446	Male	Yes	2	Not Graduate	No	2309	1255	125	360	0	Rural	N
LP002447	Male	Yes	2	Not Graduate	No	1958	1456	60	300		Urban	Y
LP002448	Male	Yes	0	Graduate	No	3948	1733	149	360	0	Rural	N
LP002449	Male	Yes	0	Graduate	No	2483	2466	90	180	0	Rural	Y
LP002453	Male	No	0	Graduate	Yes	7085	0	84	360	1	Semiurban	Y
LP002455	Male	Yes	2	Graduate	No	3859	0	96	360	1	Semiurban	Y
LP002459	Male	Yes	0	Graduate	No	4301	0	118	360	1	Urban	Y
LP002467	Male	Yes	0	Graduate	No	3708	2569	173	360	1	Urban	N
LP002472	Male	No	2	Graduate	No	4354	0	136	360	1	Rural	Y
LP002473	Male	Yes	0	Graduate	No	8334	0	160	360	1	Semiurban	N
LP002484	Male	Yes	3	Graduate	No	7740	0	128	180	1	Urban	Y
LP002487	Male	Yes	0	Graduate	No	3015	2188	153	360	1	Rural	Y
LP002489	Female	No	1	Not Graduate	Yes	5191	0	132	360	1	Semiurban	Y
LP002493	Male	No	0	Graduate	No	4166	0	98	360	0	Semiurban	N
LP002494	Male	No	0	Graduate	No	6000	0	140	360	1	Rural	Y
LP002500	Male	Yes	3	Not Graduate	No	2947	1664	70	180	0	Urban	N
LP002502	Female	Yes	2	Not Graduate	Yes	210	2917	98	360	1	Semiurban	Y
LP002505	Male	Yes	0	Graduate	No	4333	2451	110	360	1	Urban	N
LP002515	Male	Yes	1	Graduate	Yes	3450	2079	162	360	1	Semiurban	Y
LP002517	Male	Yes	1	Not Graduate	No	2653	1500	113	180	0	Rural	N
LP002519	Male	Yes	3	Graduate	No	4691	0	100	360	1	Semiurban	Y
LP002522	Female	No	0	Graduate	Yes	2500	0	93	360		Urban	Y
LP002524	Male	No	2	Graduate	No	5532	4648	162	360	1	Rural	Y
LP002527	Male	Yes	2	Graduate	Yes	16525	1014	150	360	1	Rural	Y
LP002529	Male	Yes	2	Graduate	No	6700	1750	230	300	1	Semiurban	Y
LP002531	Male	Yes	1	Graduate	Yes	16667	2250	86	360	1	Semiurban	Y
LP002533	Male	Yes	2	Graduate	No	2947	1603		360	1	Urban	N
LP002534	Female	No	0	Not Graduate	No	4350	0	154	360	1	Rural	Y
LP002536	Male	Yes	3	Not Graduate	No	3095	0	113	360	1	Rural	Y
LP002537	Male	Yes	0	Graduate	No	2083	3150	128	360	1	Semiurban	Y
LP002541	Male	Yes	0	Graduate	No	10833	0	234	360	1	Semiurban	Y
LP002543	Male	Yes	2	Graduate	No	8333	0	246	360	1	Semiurban	Y
LP002544	Male	Yes	1	Not Graduate	No	1958	2436	131	360	1	Rural	Y
LP002545	Male	No	2	Graduate	No	3547	0	80	360	0	Rural	N
LP002547	Male	Yes	1	Graduate	No	18333	0	500	360	1	Urban	N
LP002555	Male	Yes	2	Graduate	Yes	4583	2083	160	360	1	Semiurban	Y
LP002556	Male	No	0	Graduate	No	2435	0	75	360	1	Urban	N
LP002560	Male	No	0	Not Graduate	No	2699	2785	96	360		Semiurban	Y
LP002562	Male	Yes	1	Not Graduate	No	5333	1131	186	360		Urban	Y
LP002571	Male	No	0	Not Graduate	No	3691	0	110	360	1	Rural	Y
LP002582	Female	No	0	Not Graduate	Yes	17263	0	225	360	1	Semiurban	Y
LP002585	Male	Yes	0	Graduate	No	3597	2157	119	360	0	Rural	N
LP002586	Female	Yes	1	Graduate	No	3326	913	105	84	1	Semiurban	Y
LP002587	Male	Yes	0	Not Graduate	No	2600	1700	107	360	1	Rural	Y
LP002588	Male	Yes	0	Graduate	No	4625	2857	111	12		Urban	Y
LP002600	Male	Yes	1	Graduate	Yes	2895	0	95	360	1	Semiurban	Y
LP002602	Male	No	0	Graduate	No	6283	4416	209	360	0	Rural	N
LP002603	Female	No	0	Graduate	No	645	3683	113	480	1	Rural	Y
LP002606	Female	No	0	Graduate	No	3159	0	100	360	1	Semiurban	Y
LP002615	Male	Yes	2	Graduate	No	4865	5624	208	360	1	Semiurban	Y
LP002618	Male	Yes	1	Not Graduate	No	4050	5302	138	360		Rural	N
LP002619	Male	Yes	0	Not Graduate	No	3814	1483	124	300	1	Semiurban	Y
LP002622	Male	Yes	2	Graduate	No	3510	4416	243	360	1	Rural	Y
LP002624	Male	Yes	0	Graduate	No	20833	6667	480	360		Urban	Y
LP002626	Male	Yes	0	Graduate	Yes	2479	3013	188	360	1	Urban	Y
LP002634	Female	No	1	Graduate	No	13262	0	40	360	1	Urban	Y
LP002637	Male	No	0	Not Graduate	No	3598	1287	100	360	1	Rural	N
LP002640	Male	Yes	1	Graduate	No	6065	2004	250	360	1	Semiurban	Y
LP002643	Male	Yes	2	Graduate	No	3283	2035	148	360	1	Urban	Y
LP002648	Male	Yes	0	Graduate	No	2130	6666	70	180	1	Semiurban	N
LP002652	Male	No	0	Graduate	No	5815	3666	311	360	1	Rural	N
LP002659	Male	Yes	3	Graduate	No	3466	3428	150	360	1	Rural	Y
LP002670	Female	Yes	2	Graduate	No	2031	1632	113	480	1	Semiurban	Y
LP002682	Male	Yes		Not Graduate	No	3074	1800	123	360	0	Semiurban	N
LP002683	Male	No	0	Graduate	No	4683	1915	185	360	1	Semiurban	N
LP002684	Female	No	0	Not Graduate	No	3400	0	95	360	1	Rural	N
LP002689	Male	Yes	2	Not Graduate	No	2192	1742	45	360	1	Semiurban	Y
LP002690	Male	No	0	Graduate	No	2500	0	55	360	1	Semiurban	Y
LP002692	Male	Yes	3	Graduate	Yes	5677	1424	100	360	1	Rural	Y
LP002693	Male	Yes	2	Graduate	Yes	7948	7166	480	360	1	Rural	Y
LP002697	Male	No	0	Graduate	No	4680	2087		360	1	Semiurban	N
LP002699	Male	Yes	2	Graduate	Yes	17500	0	400	360	1	Rural	Y
LP002705	Male	Yes	0	Graduate	No	3775	0	110	360	1	Semiurban	Y
LP002706	Male	Yes	1	Not Graduate	No	5285	1430	161	360	0	Semiurban	Y
LP002714	Male	No	1	Not Graduate	No	2679	1302	94	360	1	Semiurban	Y
LP002716	Male	No	0	Not Graduate	No	6783	0	130	360	1	Semiurban	Y
LP002717	Male	Yes	0	Graduate	No	1025	5500	216	360		Rural	Y
LP002720	Male	Yes	3	Graduate	No	4281	0	100	360	1	Urban	Y
LP002723	Male	No	2	Graduate	No	3588	0	110	360	0	Rural	N
LP002729	Male	No	1	Graduate	No	11250	0	196	360		Semiurban	N
LP002731	Female	No	0	Not Graduate	Yes	18165	0	125	360	1	Urban	Y
LP002732	Male	No	0	Not Graduate	Yes	2550	2042	126	360	1	Rural	Y
LP002734	Male	Yes	0	Graduate	No	6133	3906	324	360	1	Urban	Y
LP002738	Male	No	2	Graduate	No	3617	0	107	360	1	Semiurban	Y
LP002739	Male	Yes	0	Not Graduate	No	2917	536	66	360	1	Rural	N
LP002740	Male	Yes	3	Graduate	No	6417	0	157	180	1	Rural	Y
LP002741	Female	Yes	1	Graduate	No	4608	2845	140	180	1	Semiurban	Y
LP002743	Female	No	0	Graduate	No	2138	0	99	360	0	Semiurban	N
LP002753	Female	No	1	Graduate	Yes	3652	0	95	360	1	Semiurban	Y
LP002755	Male	Yes	1	Not Graduate	No	2239	2524	128	360	1	Urban	Y
LP002757	Female	Yes	0	Not Graduate	No	3017	663	102	360		Semiurban	Y
LP002767	Male	Yes	0	Graduate	No	2768	1950	155	360	1	Rural	Y
LP002768	Male	No	0	Not Graduate	No	3358	0	80	36	1	Semiurban	N
LP002772	Male	No	0	Graduate	No	2526	1783	145	360	1	Rural	Y
LP002776	Female	No	0	Graduate	No	5000	0	103	360	0	Semiurban	N
LP002777	Male	Yes	0	Graduate	No	2785	2016	110	360	1	Rural	Y
LP002778	Male	Yes	2	Graduate	Yes	6633	0		360	0	Rural	N
LP002784	Male	Yes	1	Not Graduate	No	2492	2375		360	1	Rural	Y
LP002785	Male	Yes	1	Graduate	No	3333	3250	158	360	1	Urban	Y
LP002788	Male	Yes	0	Not Graduate	No	2454	2333	181	360	0	Urban	N
LP002789	Male	Yes	0	Graduate	No	3593	4266	132	180	0	Rural	N
LP002792	Male	Yes	1	Graduate	No	5468	1032	26	360	1	Semiurban	Y
LP002794	Female	No	0	Graduate	No	2667	1625	84	360		Urban	Y
LP002795	Male	Yes	3	Graduate	Yes	10139	0	260	360	1	Semiurban	Y
LP002798	Male	Yes	0	Graduate	No	3887	2669	162	360	1	Semiurban	Y
LP002804	Female	Yes	0	Graduate	No	4180	2306	182	360	1	Semiurban	Y
LP002807	Male	Yes	2	Not Graduate	No	3675	242	108	360	1	Semiurban	Y
LP002813	Female	Yes	1	Graduate	Yes	19484	0	600	360	1	Semiurban	Y
LP002820	Male	Yes	0	Graduate	No	5923	2054	211	360	1	Rural	Y
LP002821	Male	No	0	Not Graduate	Yes	5800	0	132	360	1	Semiurban	Y
LP002832	Male	Yes	2	Graduate	No	8799	0	258	360	0	Urban	N
LP002833	Male	Yes	0	Not Graduate	No	4467	0	120	360		Rural	Y
LP002836	Male	No	0	Graduate	No	3333	0	70	360	1	Urban	Y
LP002837	Male	Yes	3	Graduate	No	3400	2500	123	360	0	Rural	N
LP002840	Female	No	0	Graduate	No	2378	0	9	360	1	Urban	N
LP002841	Male	Yes	0	Graduate	No	3166	2064	104	360	0	Urban	N
LP002842	Male	Yes	1	Graduate	No	3417	1750	186	360	1	Urban	Y
LP002847	Male	Yes		Graduate	No	5116	1451	165	360	0	Urban	N
LP002855	Male	Yes	2	Graduate	No	16666	0	275	360	1	Urban	Y
LP002862	Male	Yes	2	Not Graduate	No	6125	1625	187	480	1	Semiurban	N
LP002863	Male	Yes	3	Graduate	No	6406	0	150	360	1	Semiurban	N
LP002868	Male	Yes	2	Graduate	No	3159	461	108	84	1	Urban	Y
LP002874	Male	No	0	Graduate	No	3229	2739	110	360	1	Urban	Y
LP002877	Male	Yes	1	Graduate	No	1782	2232	107	360	1	Rural	Y
LP002888	Male	No	0	Graduate	Yes	3182	2917	161	360	1	Urban	Y
LP002892	Male	Yes	2	Graduate	No	6540	0	205	360	1	Semiurban	Y
LP002893	Male	No	0	Graduate	No	1836	33837	90	360	1	Urban	N
LP002894	Female	Yes	0	Graduate	No	3166	0	36	360	1	Semiurban	Y
LP002898	Male	Yes	1	Graduate	No	1880	0	61	360		Rural	N
LP002911	Male	Yes	1	Graduate	No	2787	1917	146	360	0	Rural	N
LP002912	Male	Yes	1	Graduate	No	4283	3000	172	84	1	Rural	N
LP002916	Male	Yes	0	Graduate	No	2297	1522	104	360	1	Urban	Y
LP002917	Female	No	0	Not Graduate	No	2165	0	70	360	1	Semiurban	Y
LP002926	Male	Yes	2	Graduate	Yes	2726	0	106	360	0	Semiurban	N
LP002928	Male	Yes	0	Graduate	No	3000	3416	56	180	1	Semiurban	Y
LP002931	Male	Yes	2	Graduate	Yes	6000	0	205	240	1	Semiurban	N
LP002936	Male	Yes	0	Graduate	No	3859	3300	142	180	1	Rural	Y
LP002938	Male	Yes	0	Graduate	Yes	16120	0	260	360	1	Urban	Y
LP002940	Male	No	0	Not Graduate	No	3833	0	110	360	1	Rural	Y
LP002941	Male	Yes	2	Not Graduate	Yes	6383	1000	187	360	1	Rural	N
LP002943	Male	No		Graduate	No	2987	0	88	360	0	Semiurban	N
LP002945	Male	Yes	0	Graduate	Yes	9963	0	180	360	1	Rural	Y
LP002948	Male	Yes	2	Graduate	No	5780	0	192	360	1	Urban	Y
LP002949	Female	No	3	Graduate	Yes	416	41667	350	180		Urban	N
LP002950	Male	Yes	0	Not Graduate	Yes	2894	2792	155	360	1	Rural	Y
LP002953	Male	Yes	3	Graduate	No	5703	0	128	360	1	Urban	Y
LP002958	Male	No	0	Graduate	No	3676	4301	172	360	1	Rural	Y
LP002959	Female	Yes	1	Graduate	No	12000	0	496	360	1	Semiurban	Y
LP002960	Male	Yes	0	Not Graduate	No	2400	3800		180	1	Urban	N
LP002961	Male	Yes	1	Graduate	No	3400	2500	173	360	1	Semiurban	Y
LP002964	Male	Yes	2	Not Graduate	No	3987	1411	157	360	1	Rural	Y
LP002974	Male	Yes	0	Graduate	No	3232	1950	108	360	1	Rural	Y
LP002978	Female	No	0	Graduate	No	2900	0	71	360	1	Rural	Y
LP002979	Male	Yes	3	Graduate	No	4106	0	40	180	1	Rural	Y
LP002983	Male	Yes	1	Graduate	No	8072	240	253	360	1	Urban	Y
LP002984	Male	Yes	2	Graduate	No	7583	0	187	360	1	Urban	Y
LP002990	Female	No	0	Graduate	Yes	4583	0	133	360	0	Semiurban	N

TOC0

Real Statistics Using Excel – Examples Workbook Regression 1

Concise Table of Contents

Linear Regression

Multiple Regression

Tables

Real Statistics Using Excel – Examples Workbook Regression 1
Charles Zaiontz, 13 April 2021
Copyright © 2013 – 2021 Charles Zaiontz
Table of Contents
Linear Regression
Method of least squares
	Regression line (Example 1)
Regression analysis
	Using regression line for prediction (Example 1)
	Significance vs effect size (re correlation coefficient)
Hypothesis testing whether the regression line is a good fit for the data
	Testing fit of regression line (Example 1)
Hypothesis testing of the significance of the slope of the regression line
	Testing slope of regression line (Example 1)
	LINEST function (Figure 2)
	Regression data analysis tool (Figures 3 and 4)
	Comparing the slopes of two independent samples (Example 1 of Detail)
Confidence and prediction intervals for forecasted values
	Confidence/prediction Intervals (Example 1)
	Testing intercept of regression line (Example 2)
	Plot
Exponential regression
	Exponential Regression – linear regression (Example 1)
	LOGEST and GROWTH functions (Figure 4)
	Nonlinear regression via Solver, before (Example 1)
	Nonlinear regression via Solver, after (Example 1)
	Nonlinear regression via Newton's method (Example 1)
	Data analysis tool (Example 1)
Power regression
	Log-log Regression (Example 1)
Linear regression models for comparing means
	Regression to Compare Means (Example 1)
	Full results from data analysis tool for Example 1
	Regression to Compare Means (Example 2)
	Full results from data analysis tool for Example 2
Total least squares regression
	Total least squares regression (Example 1)
	Comparison with ordinary linear regression
Deming regression
	Deming regression with known variances (Example 1)
	Residuals
	Deming regression with unknown variances (Example 2)
	Standard error using jackknifing
	Hypothesis testing
	Prediction interval
	Prediction interval function
	Real Statistics data analysis tool for Example 1
	Real Statistics data analysis tool for Example 2
Passing-Bablok regression
	Excel example
	Linearity test
	Real Statistics support
Multiple Regression
Method of least squares
	Method of least squares (Example 1)
	Method of least squares using covariance matrix (Example 2)
	Method of least squares using hat matrix (Example 3)
	Method of least squares using Real Statistics functions (Example 3)
Multiple regression analysis
	Sample size requirements for multiple regression (Figure 1)
	TREND and LINEST function (Example 1)
	Data for Example 1 is normal via QQ plot (extra worksheet)
	Regression data analysis tool (Example 2)
	Real Statistics regression data analysis tool (Example 2)
	Formulas for regression analysis (for Figure 5 and 6)
	Real Statistics functions (for Figure 5)
	Alternative approach to multiple regression (Example 1 of Detail)
	Coding categorical data (Example 4)
Confidence and prediction intervals
	Confidence and prediction intervals (Example 1)
Shapely-Owen Decomposition
	Shapely-Owen (Example 1)
Seasonal Forecasts
	Seasonality (Example 5)
Polynomial regression
	Polynomial regression (Example 1)
	Full data analysis for quadratic model (Example 1)
	Full data analysis for linear model (Example 1)
	Real Statistics data analysis (Example 2)
	Real Statistics data analysis with optimization (Example 3)
	Real Statistics functions
	Using Extract Columns data analysis tool
Multiple regression with log transformations
	Log-level transformation (Example 1)
	Log-log transformation (Example 2)
Interaction
	Regression with interaction (Example 1)
Comparing two or more slopes and intercepts
	Example with two slopes and intercepts
ANOVA using regression
	One factor ANOVA via Regression model (Example 1)
	One factor ANOVA via Regression model (Example 1 alternative coding)
	Group means and group effect sizes (Figure 5)
	Two factor ANOVA via Regression model (Example 2)
Unbalanced factorial ANOVA
	Unbalanced ANOVA via Regression model (Example 1)
	Unbalanced ANOVA via Regression model using Real Statistics analysis tool (Example 1)
Three factor ANOVA using regression
	Balanced model, including data format conversion (Example 1)
	Unbalanced model (Example 2)
Unbalanced repeated measures ANOVA using regression
	Regression model
	Real Statistics data analysis, input in Excel format
	Real Statistics data analysis, input in standard format
Other measures of effect size for ANOVA
	Omega square effect size for 1 factor ANOVA (Example 3 of Basic Concepts for ANOVA)
	Omega square effect size for 2 factor ANOVA (Example 2 of ANOVA using Regression)
Residuals
	Studentized residuals and hat matrix (Example 1)
	Plot of studentized residuals (Figure 2)
	Normality test
Outliers and influencers
	Outliers and influencers: Cook's distance (Example 1)
	Outliers and influencers: Cook's distance (Example 2)
	Outliers and influencers: Cook's distance via Real Statistics data analysis tool
Linear Regression without Intercept
	Linear Regression without Intercept (Example 1)
	Use of LINEST
	Matrix solution (Example 2)
	Forecast
Heteroskedasticity Testing
	Graphic Approach
	Breusch-Pagan Test
	Shortened White Test
	Full White Test
	Real Statistics data analysis tool
Weighted linear regression
	Weighted Linear Regression (Example 1)
	Weighted Linear Regression (Example 2)
	Weighted Linear Regression (Example 3)
	Weighted Linear Regression (Example 4)
	Weighted Linear Regression (Example 5)
	Weighted Linear Regression using regression through the origin (Example 2)
	Weighted Linear Regression (extra example)
Robust standard errors and heteroscedasticity
	Huber-White robust standard errors (Example 1)
Autocorrelation
	Introduction and graphical detection of autocorrelation
	Detecting autocorrelation using the runs test
	Durbin-Watson test (Example 1)
	Breusch-Godfrey Test
	FGLS using Durbin-Watson
	FGLS using linear regression
	Cochrane-Orcutt regression
	Cochrane-Orcutt regression data analysis tool
	Newey-West standard errors
	Breusch-Godfrey and Newey-West data analysis tool
Collinearity
	Collinearity (Figure 1)
	Tolerance and VIF (Example 1)
Testing the significance of extra variables
	Testing significance of extra variables (Example 1)
	Testing significance of extra variables using R Square (Figure 2)
	Akaike’s Information Criterion (Example 2)
Stepwise Regression
	Stepwise regression (Example 1)
	Real Statistics data analysis tool
Multiple Correlation
	Partial correlation coefficient (Example 1)
	Partial correlation matrix (Example 2)
	Coefficient of determination
Statistical Power and Sample Size
	Statistical Power (Example 1)
	Statistical Power (other example)
	Statistical Power (other example)
	Sample Size Requirement (Example 2)
	Confidence interval for effect size and power (Example 3)
Least Absolute Deviation (LAD) Regression
	Using Simplex method (Example 1)
	Using Iteratively reweighted least squares (IRLS) method (Example 2)
	Real Statistics data analysis tool
	Real Statistics data analysis tool (no intercept)
	Real Statistics data analysis tool with standard errors
	Standard errors via bootstrapping
Lp Regression
	Lp Regression data anaysis tool and functions
	Lp Regression details
Total Least Squares Multiple Regression
	TLS regression (Example 1)
Ridge and LASSO Regression
	Ridge Regression and multicollinearity
	Ridge Regression
	Predictions using Ridge regression
	LASSO Regression
Mediation Analysis
	Mediation Analysis Example
Cross Validation
	Cross Validation Example
Tables
	Durbin-Watson table 1
	Durbin-Watson table 2
	Durbin-Watson table 3
	Durbin-Watson table 4

Reg 1

Regression
		cov(x,y)	-90.9466666667	=COVAR(A4:A18,B4:B18)					Prediction using y = a + b * x
Cig (x)	Life Exp (y)	varp(x)	144.7733333333	=VARP(A4:A18)	Data		Prediction
5	80	slope b	-0.6282004052	=E2/E3	Cig (x)	Life Exp (y)	Life Exp (ŷ)	Error (e)	x	ŷ
23	78	intercept a	85.7204211948	=AVERAGE(B4:B18)-E4*AVERAGE(A4:A18)	5	80	82.5794191687	-2.5794191687	4	83.2076195739
25	60				23	78	71.2718118745	6.7281881255	24	70.6436114693
48	53				25	60	70.015411064	-10.015411064	44	58.0796033646
17	85				48	53	55.5668017437	-2.5668017437
8	84				17	85	75.0410143059	9.9589856941	Prediction using FORECAST
4	73				8	84	80.694817953	3.305182047
26	79				4	73	83.2076195739	-10.2076195739	x	ŷ
11	81				26	79	69.3872106588	9.6127893412	4	83.2076195739
19	75				11	81	78.8102167373	2.1897832627	24	70.6436114693
14	68				19	75	73.7846134954	1.2153865046	44	58.0796033646
35	72				14	68	76.9256155216	-8.9256155216
29	58				35	72	63.7334070117	8.2665929883	Prediction using TREND
4	92				29	58	67.5026094431	-9.5026094431
23	65				4	92	83.2076195739	8.7923804261	x	ŷ
					23	65	71.2718118745	-6.2718118745	4	83.2076195739
									24	70.6436114693
					19.4	73.5333333333			44	58.0796033646
					12.454488577	10.966616008

Longevity vs Smoking

Life Exp (y) 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 80 78 60 53 85 84 73 79 81 75 68 72 58 92 65

Cigarettes smoked (per day)

Life Expectancy (years)

Reg 2

Regression Testing						Alternative 1								Alternative 2
Cig (x)	Life Exp (y)	n	15			Data		Prediction			SS	df	MS	Data		Prediction	SS				SS	df	MS	Alternative ways to calculate SS-Res		Correlations		Use of LINEST
5	80	r	-0.7134301744			Cig (x)	Life Exp (y)	Life Exp (ŷ)	Error (e)	T	1683.7333333333	14	120.2666666667	Cig (x)	Life Exp (y)	Life Exp (ŷ)	T	Reg	Res	T	1683.7333333333	14	120.2666666667
23	78					5	80	82.5794191687	-2.5794191687	Reg	856.9909928164	1	856.9909928164	5	80	82.5794191687	41.8177777778	81.8316689401	6.6534032477	Reg	856.9909928164	1	856.9909928164	826.742340517	=AB4-AB5	x and y	-0.7134301744	Slope (b)	-0.6282004052	85.7204211948	Intercept (a)
25	60		SS	df	MS	23	78	71.2718118745	6.7281881255	Res	826.742340517	13	63.5955646552	23	78	71.2718118745	19.9511111111	5.1144793088	45.2685154521	Res	826.742340517	13	63.5955646552	826.742340517	=SUMXMY2(U5:U19,V5:V19)	x and ŷ	-1	S.E. of slope (sb)	0.1711289546	3.9065908076	S.E. of intercept (sa)
48	53	T	1683.7333333333	14	120.2666666667	25	60	70.015411064	-10.015411064					25	60	70.015411064	183.1511111111	12.3757770928	100.3084587817							y and ŷ	0.7134301744	R Square	0.5089826137	7.9746827307	S.E. of estimate (sRes)
17	85	Reg	856.9909928164	1	856.9909928164	48	53	55.5668017437	-2.5668017437					48	53	55.5668017437	421.6177777778	322.7962573605	6.5884711916									F	13.4756409109	13	dfRes
8	84	Res	826.742340517	13	63.5955646552	17	85	75.0410143059	9.9589856941					17	85	75.0410143059	131.4844444444	2.273101915	99.1813960555									SSReg	856.9909928164	826.742340517	SSRes
4	73					8	84	80.694817953	3.305182047					8	84	80.694817953	109.5511111111	51.2868619573	10.924228364
26	79	F	13.4756409109		=G8/G9	4	73	83.2076195739	-10.2076195739					4	73	83.2076195739	0.2844444444	93.5918142643	104.1954973653
11	81	alpha	0.05			26	79	69.3872106588	9.6127893412					26	79	69.3872106588	29.8844444444	17.1903332322	92.4057189181
19	75	F-crit	4.6671927318		=FINV(E12,F8,F9)	11	81	78.8102167373	2.1897832627					11	81	78.8102167373	55.7511111111	27.8454984588	4.7951507377
14	68	p-value	0.002822343		=FDIST(E11,F8,F9)	19	75	73.7846134954	1.2153865046					19	75	73.7846134954	2.1511111111	0.0631417199	1.4771643555
35	72	sig	yes		=IF(E14<E12,"yes","no")	14	68	76.9256155216	-8.9256155216					14	68	76.9256155216	30.6177777778	11.5075784447	79.6666124391
29	58					35	72	63.7334070117	8.2665929883					35	72	63.7334070117	2.3511111111	96.0385559089	68.3365596338
4	92					29	58	67.5026094431	-9.5026094431					29	58	67.5026094431	241.2844444444	36.3696306401	90.2995862284
23	65					4	92	83.2076195739	8.7923804261					4	92	83.2076195739	341.0177777778	93.5918142643	77.3059535574
						23	65	71.2718118745	-6.2718118745					23	65	71.2718118745	72.8177777778	5.1144793088	39.3356241891
																	1683.7333333333	856.9909928164	826.742340517
						mean	73.5333333333	73.5333333333	0					n	15
						std dev	10.966616008	7.823914771	7.6845965621					mean y	73.5333333333

Reg 3

Test the slope of the regression line
Cig (x)	Life Exp (y)	n	15	=COUNT(A4:A18)	n	15	=COUNT(A4:A18)
5	80	r	-0.7134301744	=CORREL(A4:A18,B4:B18)	sx	12.454488577	=STDEV(A4:A18)
23	78	sx	12.454488577	=STDEV(A4:A18)	b	-0.6282004052	=SLOPE(B4:B18,A4:A18)
25	60	sy	10.966616008	=STDEV(B4:B18)	sy∙x	7.9746827307	=STEYX(B4:B18,A4:A18)
48	53	b	-0.6282004052	=E4*E6/E5	sb	0.1711289546	=J6/(J4*SQRT(J3-1))
17	85	sy∙x	7.9746827307	=E6SQRT((1-E4^2)(E3-1)/(E3-2))	t	-3.6709182653	=J5/J7
8	84	sb	0.1711289546	=E8/(E5*SQRT(E3-1))	df	13	=J3-2
4	73	t	-3.6709182653	=E7/E9	p-value	0.002822343	=TDIST(ABS(J8),J9,2)
26	79	df	13	=E3-2	alpha	0.05
11	81	p-value	0.002822343	=TDIST(ABS(E10),E11,2)	t-crit	2.1603686565	=TINV(0.05,J9)
19	75	alpha	0.05		sig	yes	=IF(J10<J11,"yes","no")
14	68	t-crit	2.1603686565	=TINV(0.05,E11)
35	72	sig	yes	=IF(E12<E13,"yes","no")	Confidence interval
29	58
4	92	Confidence interval			lower	-0.997902035	=J5-J12*J7
23	65				upper	-0.2584987755	=J5+J12*J7
		lower	-0.997902035	=E7-E14*E9
		upper	-0.2584987755	=E7+E14*E9

Reg 4

SUMMARY OUTPUT
							RESIDUAL OUTPUT				PROBABILITY OUTPUT
Regression Statistics
Multiple R	0.7134301744						Observation	Predicted Life Exp	Residuals	Standard Residuals	Percentile	Life Exp
R Square	0.5089826137						1	82.5794191687	-2.5794191687	-0.3356609742	3.3333333333	53
Adjusted R Square	0.4712120456						2	71.2718118745	6.7281881255	0.8755421408	10	58
Standard Error	7.9746827307						3	70.015411064	-10.015411064	-1.3033099374	16.6666666667	60
Observations	15						4	55.5668017437	-2.5668017437	-0.3340190631	23.3333333333	65
							5	75.0410143059	9.9589856941	1.2959672786	30	68
ANOVA							6	80.694817953	3.305182047	0.4301048234	36.6666666667	72
	df	SS	MS	F	Significance F		7	83.2076195739	-10.2076195739	-1.328322117	43.3333333333	73
Regression	1	856.9909928164	856.9909928164	13.4756409109	0.002822343		8	69.3872106588	9.6127893412	1.2509165918	50	75
Residual	13	826.742340517	63.5955646552				9	78.8102167373	2.1897832627	0.2849574789	56.6666666667	78
Total	14	1683.7333333333					10	73.7846134954	1.2153865046	0.1581587914	63.3333333333	79
							11	76.9256155216	-8.9256155216	-1.1614943542	70	80
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	12	63.7334070117	8.2665929883	1.0757354562	76.6666666667	81
Intercept	85.7204211948	3.9065908076	21.9425134131	0	77.2807448605	94.1600975291	13	67.5026094431	-9.5026094431	-1.2365788323	83.3333333333	84
Cig (x)	-0.6282004052	0.1711289546	-3.6709182653	0.002822343	-0.997902035	-0.2584987755	14	83.2076195739	8.7923804261	1.1441564115	90	85
							15	71.2718118745	-6.2718118745	-0.8161536944	96.6666666667	92

Cig (x) Residual Plot

5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 -2.5794191686621133 6.7281881254988747 -10.015411064038801 -2.5668017437219817 9.958985694111874 3.30518204703138 -10.207619573893282 9.6127893411923679 2.1897832627248874 1.2153865045742123 -8.9256155215816193 8.2665929882728619 -9.5026094431141246 8.7923804261067176 -6.2718118745011253

Cig (x)

Residuals

Normal Probability Plot

3.3333333333333335 10 16.666666666666668 23.333333333333332 30 36.666666666666671 43.333333333333336 50.000000000000007 56.666666666666671 63.333333333333336 70 76.666666666666671 83.333333333333329 90 96.666666666666671 53 58 60 65 68 72 73 75 78 79 80 81 84 85 92

Sample Percentile

Life Exp (y)

Cig (x) Line Fit Plot

Life Exp (y) 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 80 78 60 53 85 84 73 79 81 75 68 72 58 92 65 Predicted Life Exp (y) 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 82.579419168662113 71.271811874501125 70.015411064038801 55.566801743721982 75.041014305888126 80.69481795296862 83.207619573893282 69.387210658807632 78.810216737275113 73.784613495425788 76.925615521581619 63.733407011727138 67.502609443114125 83.207619573893282 71.271811874501125

Cig (x)

Life Exp (y)

Reg 5

Testing two slopes				Comparison of two slopes
Men		Women			Men	Women
Cig (x)	Life Exp (y)	Cig (x)	Life Exp (y)	n	15	16	=COUNT(x)
5	80	22	88	b	-0.6282004052	-0.4678596247	=SLOPE(y,x)
23	78	7	95	sy∙x	7.9746827307	8.3792452579	=STEYX(y,x)
25	60	20	86	sx	12.454488577	13.8515943727	=STDEV(x)
48	53	23	60	sb	0.1711289546	0.1561922595	= sy∙x / (sx * SQRT(n-1))	Using pooled error variance
17	85	15	82					sRes2	67.0261798446	= ((n1-2)sy.x12+(n2-2)sy.x22)/(n1+n2-4)
8	84	34	75	sb1-b2	0.2316919097		= SQRT(sb12+sb22)	sb1-b2	0.2327101955	= sRes*SQRT(1/(sx12(n1-1))+1/(sx22(n2-1)))
4	73	4	80	t	-0.69204307		= (b1-b2)/(sb1-b2)	t	-0.6890148501	= (b1-b2)/(sb1-b2)
26	79	40	68	df	27		= n1+n2-4	df	27	= n1+n2-4
11	81	8	93	alpha	0.05			alpha	0.05
19	75	16	77	p-value	0.4948191745		= TDIST(\|t\|,df,2)	p-value	0.4966927316	= TDIST(\|t\|,df,2)
14	68	11	72	t-crit	2.0518305165		= TINV(α,df)	t-crit	2.0518305165	= TINV(α,df)
35	72	52	67	sig	no		= yes if p-value < α	sig	no	= yes if p-value < α
29	58	3	90
4	92	31	66
23	65	18	72	Using Real Statistics functions
		8	78
				sb1-b2	0.2316919097			sb1-b2	0.2327101955
				t	-0.69204307			t	-0.6890148501
				df	27			df	27
				p-value	0.4948191745			p-value	0.4966927316
				Using Real Statistics functions
				with and w/o labels
					no pool	pooled
				std err	0.2316919097	0.2327101955
				t	-0.69204307	-0.6890148501
				df	27	27
				p-value	0.4948191745	0.4966927316

Men

5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 80 78 60 53 85 84 73 79 81 75 68 72 58 92 65

Cigarettes

Longevity

Women

22 7 20 23 15 34 4 40 8 16 11 52 3 31 18 8 88 95 86 60 82 75 80 68 93 77 72 67 90 66 72 78

Cigarettes

Longevity

Reg 6

Confidence and Prediction Intervals
Cig (x)	Life Exp (y)	Confidence interval for the forecasted value
5	80
23	78	n	15
25	60	df	13	= n – 2
48	53	mean(x)	19.40	= AVERAGE(x)
17	85	x0	20
8	84	ŷ0	73.1564130902	= FORECAST(y,x,x0)	Prediction interval		Test for y-intercept
4	73	sRes	7.9746827307	= STEYX(y,x)
26	79	SSx	2171.6	= DEVSQ(x)	ŷ0	73.1564130902	ŷ0	85.7204211948
11	81	se	2.0616127069	= sRes*SQRT(1/n+(x0-x̄)^2/SSx)	se	8.236856901	se	3.9065908076
19	75	t-crit	2.1603686565	= TINV(0.05,df)	t-crit	2.1603686565	t-crit	2.1603686565
14	68	lower	68.7025696165	= ŷ0 – t-crit * se	lower	55.3617656134	lower	77.2807448605
35	72	upper	77.6102565639	= ŷ0 + t-crit * se	upper	90.951060567	upper	94.1600975291
29	58
4	92
23	65

Reg 7

Confidence and Prediction Intervals
Cig (x)	Life Exp (y)	alpha	0.05	x	y	lower	upper	conf se	pred	lower	upper	pred se
5	80	n	15	0	85.7204211948	77.2807448605	94.1600975291	3.9065908076	85.7204211948	66.5360292638	104.9048131258	8.8801473182
23	78	mean(x)	19.4	5	82.5794191687	75.6418882127	89.5169501246	3.2112718055	82.5794191687	64.0068024198	101.1520359175	8.5969663989
25	60	devsq(x)	2171.6	10	79.4384171425	73.7935522501	85.0832820349	2.6129174183	79.4384171425	61.3089588829	97.5678754021	8.3918354423
48	53	steyx	7.9746827307	15	76.2974151164	71.5609999852	81.0338302475	2.1924105948	76.2974151164	58.4299478804	94.1648823523	8.2705639996
17	85	t-crit	2.1603686565	20	73.1564130902	68.7025696165	77.6102565639	2.0616127069	73.1564130902	55.3617656134	90.951060567	8.236856901
8	84			25	70.015411064	65.1089072339	74.9219148942	2.2711419255	70.015411064	52.1021049211	87.928717207	8.2917820944
4	73			30	66.8744090379	60.9461038219	72.8027142539	2.7441173979	66.8744090379	48.6547065192	85.0941115566	8.4336080623
26	79			35	63.7334070117	56.4498783074	71.016935716	3.3714286136	63.7334070117	45.0287940106	82.4380200128	8.6580653469
11	81			40	60.5924049856	51.7726123128	69.4121976583	4.0825405638	60.5924049856	41.2377802172	79.947029754	8.9589453682
19	75			45	57.4514029594	46.9937904237	67.9090154952	4.8406611087	57.4514029594	37.2976336573	77.6051722615	9.328856555
14	68			50	54.3104009333	42.154392466	66.4664094006	5.6268213441	54.3104009333	33.2253043247	75.3954975418	9.7599530272
35	72
29	58
4	92
23	65

Confidence Interval

y 0 5 10 15 20 25 30 35 40 45 50 85.720421194817945 82.579419168662113 79.438417142506282 76.29741511635045 73.15 6413090194633 70.015411064038801 66.87440903788297 63.733407011727138 60.592404985571306 57.451402959415475 54.310400933259643 lower 77.280744860496497 75.64188821274449 73.793552250134994 71.560999985205697 68.702569616457737 65.108907233900567 60.946103821855864 56.4498783074391 51.772612312803879 46.993790423677837 42.154392465952931 upper 94.160097529139392 89.516950124579736 85.08328203487757 81.033830247495203 77.610256563931529 74.921914894177036 72.802714253910068 71.016935716015183 69.412197658338741 67.909015495153113 66.466409400566349

Prediction Interval

pred 0 5 10 15 20 25 30 35 40 45 50 85.720421194817945 82.579419168662113 79.438417142506282 76.29741511635045 73 .156413090194633 70.015411064038801 66.87440903788297 63.733407011727138 60.592404985571306 57.451402959415475 54.310400933259643 lower 66.536029263796109 64.006802419822819 61.308958882864985 58.429947880351747 55.36176561339704 52.102104921115 48.654706519199394 45.028794010621283 41.237780217171576 37.297633657308054 33.225304324702627 upper 104.90481312583978 101.15203591750141 97.567875402147578 94.164882352349153 90.951060566992226 87.928717206962602 85.094111556566546 82.438020012832993 79.947029753971037 77.605172261522895 75.395497541816667

Exp Reg

Exponential Regression
Original Data		Log Transformed Data		SUMMARY OUTPUT							Use of LOGEST
x	y	x	ln y	Regression Statistics							Slope: Exp(b)	1.0162211359	14.0513516508	Intercept: Exp(a)
45	33	45	3.4965075615	Multiple R	0.9389424341						S.E. of slope	0.0019655063	0.1210836594	S.E. of intercept (sa)
99	72	99	4.276666119	R Square	0.8816128946						R-Squared	0.8816128946	0.1866581997	S.E. of estimate (sRes)
31	19	31	2.9444389792	Adjusted R Square	0.8684587718						F	67.0217928281	9	dfRes
57	27	57	3.295836866	Standard Error	0.1866581997						SSReg	2.3351252862	0.3135715517	SSRes
37	23	37	3.1354942159	Observations	11
85	62	85	4.127134385
21	24	21	3.1780538303	ANOVA							Use of GROWTH
64	32	64	3.4657359028		df	SS	MS	F	Significance F
17	18	17	2.8903717579	Regression	1	2.3351252862	2.3351252862	67.0217928281	0.0000184007		x	y
41	36	41	3.5835189385	Residual	9	0.3135715517	0.0348412835				25	21.0098855506
103	76	103	4.3307333403	Total	10	2.6486968379					35	24.677770455
					Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Use of slope and intercept for prediction
r	0.9389424341			Intercept	2.6427185941	0.1210836594	21.8255593522	0.0000000042	2.3688083267	2.9166288614
				x	0.0160909789	0.0019655063	8.1866838725	0.0000184007	0.0116446947	0.0205372631	x	y
											25	21.0098855506
				ea	14.0513516508		x	26			35	24.677770455
				eb	1.0162211359		y	21.3506897597
											Use of TREND
											x	y
											25	21.0098855506
											35	24.677770455

Log Transformation

45 99 31 57 37 85 21 64 17 41 103 3.4965075614664802 4.2766661190160553 2.9444389791664403 3.2958368660043291 3.1354942159291 497 4.1271343850450917 3.1780538303479458 3.4657359027997265 2.8903717578961645 3.5835189384561099 4.3307333402863311

Original Data

45 99 31 57 37 85 21 64 17 41 103 33 72 19 27 23 62 24 32 18 36 76

Exp Reg1

Exponential Regression
x	y	pred y	residual	resid-sq
45	33	28.9859910546	4.0140089454	16.1122678135
99	72	69.1118017662	2.8881982338	8.3416890375
31	19	23.1394497215	-4.1394497215	17.1350439968
57	27	35.159835473	-8.159835473	66.5829149469
37	23	25.4848667369	-2.4848667369	6.1745627002
85	62	55.17179175	6.82820825	46.624427906
21	24	19.7002071658	4.2997928342	18.4882184174
64	32	39.3517814588	-7.3517814588	54.0486906183
17	18	18.4721692767	-0.4721692767	0.2229438259
41	36	27.1791118189	8.8208881811	77.8080683033
103	76	73.7063845615	2.2936154385	5.2606717796
				316.7994993455
α	14.0513516508
β	0.0160909789

Exp Reg2

Exponential Regression
x	y	pred y	residual	resid-sq
45	33	28.8316597251	4.1683402749	17.3750606478
99	72	71.6356829087	0.3643170913	0.132726943
31	19	22.7716704812	-3.7716704812	14.2254982191
57	27	35.2943466049	-8.2943466049	68.796185603
37	23	25.1949001766	-2.1949001766	4.8175867852
85	62	56.5789198906	5.4210801094	29.3881095528
21	24	19.239715179	4.760284821	22.6603115767
64	32	39.7138986214	-7.7138986214	59.5042319411
17	18	17.9854015153	0.0145984847	0.0002131158
41	36	26.9520089919	9.0479910081	81.866141283
103	76	76.6316022831	-0.6316022831	0.398921444
				299.1649871115
α	13.5047487581
β	0.0168540599

Exp Reg3

Exponential Regression
x	y	Exp(bx)	F1	F2	J11	J12	J22		pred-y	resid-sq
45	33	2.0628614083	8.2803441459	5235.7512322647	-4.2553971898	-2318.1182181756	-1465771.24132193		28.8316805954	17.3748866584
99	72	4.918516274	14.2056500154	19761.2497955878	-24.1918023378	-32246.4652981831	-44857535.9164534		71.6357970789	0.1326437678
31	19	1.6467774985	-6.8167526574	-2969.3222498662	-2.7118761295	-1392.5906113559	-606601.191956597		22.7716818294	14.2255838219
57	27	2.5022386705	-20.4178558654	-16353.2129449696	-6.261198364	-6178.5808825226	-4948592.52292722		35.2943789762	68.7967226008
37	23	1.8136950359	-4.5067904657	-2343.0804130274	-3.2894896833	-1876.9569700266	-975829.949509399		25.1949151675	4.8176525926
85	62	3.9264401832	26.8105512523	32021.581105746	-15.4169325123	-16134.5460526302	-19270535.3264136		56.5789973038	29.3872702326
21	24	1.4020150983	6.0283744731	1778.8430016919	-1.9656463358	-453.4238816675	-133795.586571344		19.2397216677	22.6602498012
64	32	2.8005691151	-20.5891720947	-18515.5646272478	-7.8431873686	-8370.9795758959	-7527908.97165889		39.7139395244	59.5048629868
17	18	1.3146186741	-0.6207225485	-148.2738437078	-1.7282222583	-423.377880929	-101133.535263427		17.985406422	0.0002129725
41	36	1.9342702748	17.0619818061	9829.5001548538	-3.741401496	-1455.9004175168	-838752.118135151		26.952026765	81.8658196621
103	76	5.2455013862	12.0311629618	17412.5724590365	-27.5152847923	-38583.3652807775	-55841288.6432481		76.6317293534	0.3990819759
			31.466771023	45710.043670362	-98.9204384678	-109434.305069681	-136567745.003459			299.1649870725
	B	F		J			J-1			(JTJ)-1		ANOVA
a	14.0513516508	31.466771023		-98.9204384678	-109434.305069681		-0.0890577453	0.0000713636		0.007931287	-0.0000063555		df	SS	MS	F	Significance F
b	0.0160909789	45710.0436703619		-109434.305069681	-136567745.003459		0.0000713636	-0.0000000645		-0.0000063555	0.0000000051	Regression	1	4333.005913813	4333.005913813	130.3529988784	0.0000011753
												Residual	9	299.1649870725	33.2405541192
a	13.5916758681	-2.2474664484		-110.9372140004	-123761.053402971		-0.1065929302	0.0000874679		0.01136206	-0.0000093235	Total	10	4542.5454545455
b	0.0167940318	-1730.7024143965		-123761.053402971	-150821608.241333		0.0000874679	-0.0000000784		-0.0000093235	0.0000000077
													Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
a	13.5034927852	0.0361874464		-112.050970217	-124181.147520747		-0.1045645629	0.0000862978		0.0109337556	-0.0000090237	Intercept	13.504748744	0.6030423849	22.3943607989	0.0000000033	12.1405720936	14.8689253945
b	0.0168549178	29.2968801829		-124181.147520747	-150466713.313504		0.0000862978	-0.0000000779		-0.0000090237	0.0000000074	x	0.016854076	0.0004976538	33.8670728844	0.0000000001	0.015728305	0.017979847
a	13.5047484532	0.0000075825		-112.0354919564	-124176.072237456		-0.1045956294	0.0000863163		0.010940253	-0.0000090283
b	0.0168540762	0.0058190187		-124176.072237456	-150472969.273923		0.0000863163	-0.0000000779		-0.0000090283	0.0000000075
a	13.504748744	0		-112.0354882541	-124176.07084749		-0.1045956361	0.0000863163		0.0109402544	-0.0000090283
b	0.016854076	0.0000000001		-124176.07084749	-150472970.45024		0.0000863163	-0.0000000779		-0.0000090283	0.0000000075

Exp Reg4

Exponential Regression
x	y	pred y	residual	resid-sq	ANOVA
45	33	28.8316805954	4.1683194046	17.3748866584		df	SS	MS	F	Sig F
99	72	71.6357970789	0.3642029211	0.1326437678	Regression	1	4333.005913813	4333.005913813	130.3529988784	0.0000011753
31	19	22.7716818294	-3.7716818294	14.2255838219	Residual	9	299.1649870725	33.2405541192
57	27	35.2943789762	-8.2943789762	68.7967226008	Total	10	4542.5454545455
37	23	25.1949151675	-2.1949151675	4.8176525926
85	62	56.5789973038	5.4210026962	29.3872702326		Coeff	Std Error	t Stat	P-value	Lower 95%	Upper 95%
21	24	19.2397216677	4.7602783323	22.6602498012	Intercept	13.504748744	0.6030423742	22.3943611938	0.0000000033	12.1405721177	14.8689253704
64	32	39.7139395244	-7.7139395244	59.5048629868	x	0.016854076	0.0004976537	33.8670734816	0.0000000001	0.015728305	0.017979847
17	18	17.985406422	0.014593578	0.0002129725
41	36	26.952026765	9.047973235	81.8658196621
103	76	76.6317293534	-0.6317293534	0.3990819759		Coeff	Std err	SSE/MSE	SSR/dfT		x	pred y	x	pred y
				299.1649870725	alpha	13.504748744	0.6030423742	299.1649870725	4333.005913813		45	28.8316805954	45	28.8316805954
α	13.504748744				beta	0.016854076	0.0004976537	33.2405541192	10		50	31.3666486436	50	31.3666486436
β	0.016854076

Exp Reg5

Exponential Regression
x	y	Exponential Regression Analysis
45	33
99	72	ANOVA				Alpha	0.05
31	19		df	SS	MS	F	p-value	sig
57	27	Regression	1	4333.005913813	4333.005913813	130.3529988784	0.0000011753	yes
37	23	Residual	9	299.1649870725	33.2405541192
85	62	Total	10	4542.5454545455
21	24
64	32		coeff	std err	t stat	p-value	lower	upper
17	18	Intercept	13.504748744	0.6030423742	22.3943611938	0.0000000033	12.1405721177	14.8689253704
41	36	x	0.016854076	0.0004976537	33.8670734816	0.0000000001	0.015728305	0.017979847
103	76

Pow Reg

Power Regression
Original Data		Log Transformed Data		SUMMARY OUTPUT							Use of slope and intercept for prediction
x	y	ln x	ln y	Regression Statistics							x	y
8.1	33	2.0918640617	3.4965075615	Multiple R	0.753806894						25	35.421097965
69.9	49	4.2470656492	3.8918202981	R Square	0.5682248335						35	38.3276081227
4.2	19	1.4350845253	2.9444389792	Adjusted R Square	0.520249815
14.1	27	2.6461747974	3.295836866	Standard Error	0.2108597743						Use of TREND
5.6	23	1.7227665977	3.1354942159	Observations	11
52.1	51	3.9531649488	3.9318256327								x	y
44.6	34	3.797733859	3.5263605246	ANOVA							25	35.421097965
19.6	32	2.9755295662	3.4657359028		df	SS	MS	F	Significance F		35	38.3276081227
33	28	3.4965075615	3.3322045102	Regression	1	0.5266141616	0.5266141616	11.8441816443	0.0073725828
6.7	36	1.9021075264	3.5835189385	Residual	9	0.4001565998	0.0444618444				26	35.7482114052
30.1	43	3.4045251718	3.7612001157	Total	10	0.9267707613
					Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
r	0.753806894			Intercept	2.8128629027	0.20614121	13.6453206142	0.0000002559	2.3465390878	3.2791867175
				ln x	0.2343814327	0.068103695	3.441537686	0.0073725828	0.0803201714	0.3884426941
				ea	16.6575389321		x	26
				eb	1.2641265795		y	35.7482114052

Original Data

8.1 69.900000000000006 4.2 14.1 5.6 52.1 44.6 19.600000000000001 33 6.7 30.1 33 49 18.999999999999996 27 23 51 34 32 28 36 43

Log-Log Transformation

2.0918640616783932 4.2470656492397643 1.4350845252893227 2.6461747973841225 1.7227665977411035 3.9531649487593215 3.7977338590260183 2.9755295662364718 3.4965075614664802 1.9021075263969205 3.4045251717548299 3.4965075614664802 3.8918202981106265 2.9444389791664403 3.2958368660043291 3.1354942159291497 3.9318256327243257 3.5263605246161616 3.4657359027997265 3.3322045101752038 3.5835189384561099 3.7612001156935624

Reg T 1

Two Sample t-Test by Regression
New	Old	x	y	SUMMARY OUTPUT							t-Test: Two-Sample Assuming Equal Variances			Comparison
13	12	0	13
17	8	0	17	Regression Statistics								New	Old	F	4.738317757
19	6	0	19	Multiple R	0.4564917417						Mean	15	11.1	t2	4.738317757
11	16	0	11	R Square	0.2083847102						Variance	13.3333333333	18.7666666667
20	12	0	20	Adjusted R Square	0.164406083						Observations	10	10	Sig F	0.0430527165
15	14	0	15	Standard Error	4.0062451248						Pooled Variance	16.05		P(T<t) two-tail	0.0430527153
18	10	0	18	Observations	20						Hypothesized Mean Difference	0
9	18	0	9								df	18		Multiple R	0.4564917417
12	4	0	12	ANOVA							t Stat	2.1767677315		df	18
16	11	0	16		df	SS	MS	F	Significance F		P(T<=t) one-tail	0.0215263576		√(F/(F+df))	0.4564917417
		1	12	Regression	1	76.05	76.05	4.738317757	0.0430527165		t Critical one-tail	1.7340635923
		1	8	Residual	18	288.9	16.05				P(T<=t) two-tail	0.0430527153
		1	6	Total	19	364.95					t Critical two-tail	2.1009220369
		1	16
		1	12		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
		1	14	Intercept	15	1.2668859459	11.8400555695	0.0000000006	12.3383713937	17.6616286063
		1	10	x	-3.9	1.7916472867	-2.1767677315	0.0430527165	-7.664111273	-0.135888727
		1	18
		1	4
		1	11

x Residual Plot

0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 -2 2 4 -4 5 0 3 -6 -3 1 0.89999999999999858 -3.1000000000000014 -5.1000000000000014 4.8999999999999986 0.89999999999999858 2.8999999999999986 -1.1000000000000014 6.8999999999999986 -7.1000000000000014 -0.10000000000000142

Residuals

Reg T 1A

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.4564917417
R Square	0.2083847102
Adjusted R Square	0.164406083
Standard Error	4.0062451248
Observations	20
ANOVA
	df	SS	MS	F	Significance F
Regression	1	76.05	76.05	4.738317757	0.0430527165
Residual	18	288.9	16.05
Total	19	364.95
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	15	1.2668859459	11.8400555695	0.0000000006	12.3383713937	17.6616286063
x	-3.9	1.7916472867	-2.1767677315	0.0430527165	-7.664111273	-0.135888727
RESIDUAL OUTPUT					PROBABILITY OUTPUT
Observation	Predicted y	Residuals	Standard Residuals		Percentile	y
1	15	-2	-0.5129003852		2.5	4
2	15	2	0.5129003852		7.5	6
3	15	4	1.0258007704		12.5	8
4	15	-4	-1.0258007704		17.5	9
5	15	5	1.282250963		22.5	10
6	15	0	0		27.5	11
7	15	3	0.7693505778		32.5	11
8	15	-6	-1.5387011556		37.5	12
9	15	-3	-0.7693505778		42.5	12
10	15	1	0.2564501926		47.5	12
11	11.1	0.9	0.2308051733		52.5	13
12	11.1	-3.1	-0.794995597		57.5	14
13	11.1	-5.1	-1.3078959822		62.5	15
14	11.1	4.9	1.2566059437		67.5	16
15	11.1	0.9	0.2308051733		72.5	16
16	11.1	2.9	0.7437055585		77.5	17
17	11.1	-1.1	-0.2820952119		82.5	18
18	11.1	6.9	1.7695063289		87.5	18
19	11.1	-7.1	-1.8207963674		92.5	19
20	11.1	-0.1	-0.0256450193		97.5	20

x Residual Plot

Residuals

x Line Fit Plot

y 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 13 17 19 11 20 15 18 9 12 16 12 8 6 16 12 14 10 18 4 11 Predicted y 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 15 15 15 15 15 15 15 15 15 15 11.100000000000001 11.100000000000001 11.100000000000001 11.100000000000001 11.100000000000001 11.100000000000001 11.100000000000001 11.100000000000001 11.100000000000001 11.100000000000001

Normal Probability Plot

2.5 7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5 92.5 97.5 4 6 8 9 10 11 11 12 12 12 13 14 15 16 16 17 18 18 19 20

Sample Percentile

Reg T 2

Two Sample t-Test by Regression
New	Old	x	y	SUMMARY OUTPUT							t-Test: Two-Sample Assuming Equal Variances			t-Test: Two-Sample Assuming Unequal Variances
34	12	0	34
52	8	0	52	Regression Statistics								New	Old		New	Old
17	6	0	17	Multiple R	0.6314395224						Mean	33.5	11.1	Mean	33.5	11.1
45	16	0	45	R Square	0.3987158704						Variance	401.6111111111	18.7666666667	Variance	401.6111111111	18.7666666667
5	12	0	5	Adjusted R Square	0.3653111966						Observations	10	10	Observations	10	10
29	14	0	29	Standard Error	14.4978925672						Pooled Variance	210.1888888889		Hypothesized Mean Difference	0
31	10	0	31	Observations	20						Hypothesized Mean Difference	0		df	10
6	18	0	6								df	18		t Stat	3.4548416236
52	4	0	52	ANOVA							t Stat	3.4548416236		P(T<=t) one-tail	0.0030879404
64	11	0	64		df	SS	MS	F	Significance F		P(T<=t) one-tail	0.0014131154		t Critical one-tail	1.8124611022
		1	12	Regression	1	2508.8	2508.8	11.9359306444	0.0028262308		t Critical one-tail	1.7340635923		P(T<=t) two-tail	0.0061758809
		1	8	Residual	18	3783.4	210.1888888889				P(T<=t) two-tail	0.0028262308		t Critical two-tail	2.2281388424
		1	6	Total	19	6292.2					t Critical two-tail	2.1009220369
		1	16
		1	12		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
		1	14	Intercept	33.5	4.5846361785	7.3070138384	0.0000008687	23.8680368062	43.1319631938
		1	10	x	-22.4	6.4836546621	-3.4548416236	0.0028262308	-36.021652981	-8.778347019
		1	18
		1	4
		1	11

x Residual Plot

0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0.5 18.5 -16.5 11.5 -28.5 -4.5 -2.5 -27.5 18.5 30.5 0.89999999999999503 -3.100000000000005 -5.100000000000005 4.899999999999995 0.89999999999999503 2.899999999999995 -1.100000000000005 6.899999999999995 -7.100000000000005 -0.10000000000000497

Residuals

Reg T 2A

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.6314395224
R Square	0.3987158704
Adjusted R Square	0.3653111966
Standard Error	14.4978925672
Observations	20
ANOVA
	df	SS	MS	F	Significance F
Regression	1	2508.8	2508.8	11.9359306444	0.0028262308
Residual	18	3783.4	210.1888888889
Total	19	6292.2
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	33.5	4.5846361785	7.3070138384	0.0000008687	23.8680368062	43.1319631938
x	-22.4	6.4836546621	-3.4548416236	0.0028262308	-36.021652981	-8.778347019
RESIDUAL OUTPUT					PROBABILITY OUTPUT
Observation	Predicted y	Residuals	Standard Residuals		Percentile	y
1	33.5	0.5	0.0354328165		2.5	4
2	33.5	18.5	1.3110142104		7.5	5
3	33.5	-16.5	-1.1692829444		12.5	6
4	33.5	11.5	0.8149547794		17.5	6
5	33.5	-28.5	-2.0196705403		22.5	8
6	33.5	-4.5	-0.3188953485		27.5	10
7	33.5	-2.5	-0.1771640825		32.5	11
8	33.5	-27.5	-1.9488049073		37.5	12
9	33.5	18.5	1.3110142104		42.5	12
10	33.5	30.5	2.1614018063		47.5	14
11	11.1	0.9	0.0637790697		52.5	16
12	11.1	-3.1	-0.2196834623		57.5	17
13	11.1	-5.1	-0.3614147283		62.5	18
14	11.1	4.9	0.3472416017		67.5	29
15	11.1	0.9	0.0637790697		72.5	31
16	11.1	2.9	0.2055103357		77.5	34
17	11.1	-1.1	-0.0779521963		82.5	45
18	11.1	6.9	0.4889728676		87.5	52
19	11.1	-7.1	-0.5031459942		92.5	52
20	11.1	-0.1	-0.0070865633		97.5	64

x Residual Plot

Residuals

x Line Fit Plot

y 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 34 52 17 45 5 29 31 6 52 64 12 8 6 16 12 14 10 18 4 11 Predicted y 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 33.5 33.5 33.5 33.5 33.5 33.5 33.5 33.5 33.5 33.5 11.100000000000005 11.100000000000005 11.100000000000005 11.100000000000005 11.100000000000005 11.100000000000005 11.100000000000005 11.100000000000005 11.100000000000005 11.100000000000005

Normal Probability Plot

2.5 7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5 92.5 97.5 4 5 6 6 8 10 11 12 12 14 16 17 18 29 31 34 45 52 52 64

Sample Percentile

TReg

Total Least Squares Regression
Cig	Life Exp				Regression Analysis							TLS
5	80	x̅	19.4	=AVERAGE(A4:A18)
23	78	y̅	73.5333333333	=AVERAGE(B4:B18)	OVERALL FIT							Cig	Life Exp
25	60	n	15	=COUNT(A4:A18)	Multiple R	0.7134301744		AIC	64.1416432922			5	85.5868574373
48	53	w	-487.8666666667	=DEVSQ(B4:B18)-DEVSQ(A4:A18)	R Square	0.5089826137		AICc	66.323461474			23	70.5199523073
17	85	r	-2728.4	=2COVAR(A4:A18,B4:B18)E6	Adjusted R Square	0.4712120456		SBC	65.5577436944			25	68.8458517374
8	84				Standard Error	7.9746827307						48	49.5936951824
4	73	a	89.7721088623	=E5-E4*E11	Observations	15						17	75.5422540173
26	79	b	-0.837050285	=(E7+SQRT(E7^2+E8^2))/E8								8	83.0757065823
11	81				ANOVA				Alpha	0.05		4	86.4239077223
19	75	a	89.7721088623	=TRegCoeff0(A4:A18,B4:B18)		df	SS	MS	F	p-value	sig	26	68.0088014524
14	68	b	-0.837050285		Regression	1	856.9909928164	856.9909928164	13.4756409109	0.002822343	yes	11	80.5645557273
35	72				Residual	13	826.742340517	63.5955646552				19	73.8681534473
29	58	a	89.7721088623	=TRegCoeff(A4:A18,B4:B18)	Total	14	1683.7333333333					14	78.0534048723
4	92	b	-0.837050285									35	60.4753488874
23	65					coeff	std err	t stat	p-value	lower	upper	29	65.4976505974
					Intercept	85.7204211948	3.9065908076	21.9425134131	0	77.2807448605	94.1600975291	4	86.4239077223
					Cig	-0.6282004052	0.1711289546	-3.6709182653	0.002822343	-0.997902035	-0.2584987755	23	70.5199523073

Life Expectancy vs. Smoking

Life Exp 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 80 78 60 53 85 84 73 79 81 75 68 72 58 92 65 TLS 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 85.586857437276166 70.519952307347609 68.845851737355559 49.593695182446851 75.542254017323799 83.075706582288078 86.42390772227219 68.008801452359521 80.564555727299989 73.868153447331736 78.053404872311887 60.475348887395249 65.497650597371432 86.42390772227219 70.519952307347609

Dem 1

Deming Regression – Known Variances
Subject	x	y	Regression coefficients			Residuals Report										Shapiro-Wilk Test		QQ Plot – opt res
1	5.1	5.4
2	5.6	5.6	x̅	5.6	=AVERAGE(B4:B13)	Subject	x	y	pred y	x-hat	y-hat	raw res	x-res	y-res	opt res		opt res	Count	10	20
3	6.8	6.3	y̅	5.53	=AVERAGE(C4:C13)	1	5.1	5.4	5.0210000822	5.3686176266	5.2944527819	0.3789999178	-0.2686176266	0.1055472181	0.3162372012	W	0.9337800184	Mean	0
4	5.9	6.1	u	7.92	=DEVSQ(B4:B13)	2	5.6	5.6	5.53	5.649612765	5.5805057866	0.07	-0.049612765	0.0194942134	0.058407939	p-value	0.4860841799	Std Dev	0.356215109
5	4.0	4.7	v	7.481	=DEVSQ(C4:C13)	3	6.8	6.3	6.7515998028	6.4799269301	6.4257654702	-0.4515998028	0.3200730699	-0.1257654702	-0.376814482	alpha	0.05
6	5.6	5.1	r	6.9	=SUMPRODUCT(B4:B13-F5,C4:C13-F6)	4	5.9	6.1	5.8353999507	6.0875362867	6.0263118597	0.2646000493	-0.1875362867	0.0736881403	0.2207820506	normal	yes	Interval	Data	Std Norm	Std Data
7	6.6	6.6	n	10	=COUNT(B4:B13)	5	4.0	4.7	3.901200263	4.5661523378	4.4775432498	0.798799737	-0.5661523378	0.2224567502	0.6665178046			1	-0.4362239194	-1.644853627	-1.2246081323
8	6.7	6.8	λ	2.5	=B15/C15	6	5.6	5.1	5.53	5.2952358721	5.2197501678	-0.43	0.3047641279	-0.1197501678	-0.3587916254			3	-0.376814482	-1.0364333895	-1.057828465
9	5.2	4.6	b0	-0.1707990796	=F6-F5*F13	7	6.6	6.6	6.5479998357	6.6368553134	6.5855185386	0.0520001643	-0.0368553134	0.0144814614	0.0433888918			5	-0.3587916254	-0.6744897502	-1.007233035
10	4.5	4.1	b1	1.0179998357	=(F11F8-F7+SQRT((F11F8-F7)^2+4F11F9^2))/(2F11F9)	8	6.7	6.8	6.6497998192	6.8064549468	6.7581709375	0.1502001808	-0.1064549468	0.0418290625	0.1253269			7	-0.2588307606	-0.3853204664	-0.7266136501
						9	5.2	4.6	5.1228000657	4.8294634741	4.7455939433	-0.5228000657	0.3705365259	-0.1455939433	-0.4362239194			9	0.0433888918	-0.1256613469	0.1218053101
var	0.05	0.02				10	4.5	4.1	4.4102001808	4.2801444475	4.1863872644	-0.3102001808	0.2198555525	-0.0863872644	-0.2588307606			11	0.058407939	0.1256613469	0.1639681685
			intercept	-0.1707990796	=DRegCoeff(B4:B13,C4:C13,F11,TRUE)							0	-9.76996261670138E-16	5.32907051820075E-16	0			13	0.1253269	0.3853204664	0.351829265
			slope	1.0179998357														15	0.2207820506	0.6744897502	0.6197997924
						Real Statistics Function												17	0.3162372012	1.0364333895	0.8877703197
																		19	0.6665178046	1.644853627	1.8711104267
						Subject	x	y	pred	x-hat	y-hat	res	x-res	y-res	opt-res
						1	5.1	5.4	5.0210000822	5.3686176266	5.2944527819	0.3789999178	-0.2686176266	0.1055472181	0.3162372012
						2	5.6	5.6	5.53	5.649612765	5.5805057866	0.07	-0.049612765	0.0194942134	0.058407939
						3	6.8	6.3	6.7515998028	6.4799269301	6.4257654702	-0.4515998028	0.3200730699	-0.1257654702	-0.376814482
						4	5.9	6.1	5.8353999507	6.0875362867	6.0263118597	0.2646000493	-0.1875362867	0.0736881403	0.2207820506
						5	4.0	4.7	3.901200263	4.5661523378	4.4775432498	0.798799737	-0.5661523378	0.2224567502	0.6665178046
						6	5.6	5.1	5.53	5.2952358721	5.2197501678	-0.43	0.3047641279	-0.1197501678	-0.3587916254
						7	6.6	6.6	6.5479998357	6.6368553134	6.5855185386	0.0520001643	-0.0368553134	0.0144814614	0.0433888918
						8	6.7	6.8	6.6497998192	6.8064549468	6.7581709375	0.1502001808	-0.1064549468	0.0418290625	0.1253269
						9	5.2	4.6	5.1228000657	4.8294634741	4.7455939433	-0.5228000657	0.3705365259	-0.1455939433	-0.4362239194
						10	4.5	4.1	4.4102001808	4.2801444475	4.1863872644	-0.3102001808	0.2198555525	-0.0863872644	-0.2588307606
												0	-9.76996261670138E-16	2.66453525910038E-16	0

QQ Plot – Optimized Residuals

-0.43622391935638849 -0.37681448197851175 -0.35879162535668324 -0.25883076057969279 4.3388891826697341E-2 5.8407939011554652E-2 0.12532689998185859 0.22078205060339232 0.31623720122492682 0.66651780462285926 -1.6448536269514726 -1.0364333894937898 -0.67448975019608193 -0.38532046640756784 -0.12566134685507402 0.12566134685507416 0.38532046640756784 0.67448975019608193 1.0364333894937898 1.6448536269514715

Data

Std Normal

Dem 2

Deming Regression – Unknown variances
						Deviations					Regression coeff
Subject	x1	x2	x3	y1	y2	x	y	Subject	x	y			Subject	x	y	intercept	-15.9116828011
1	96	110	104	80	75	98.6666666667	12.5	1	103.3	77.5	x̅	243.1	1	103.3	77.5	slope	0.772981007
2	124	130	132	89	80	34.6666666667	40.5	2	128.7	84.5	y̅	172	2	128.7	84.5
3	146	150	160	93	102	104	40.5	3	152.0	97.5	u	94254.2333333333	3	152.0	97.5	λ	2.4111675127
4	184	188	192	111	111	32	0	4	188.0	111.0	v	56154.5	4	188.0	111.0
5	224	230	220	163	170	50.6666666667	24.5	5	224.7	166.5	r	72170	5	224.7	166.5
6	256	252	246	188	177	50.6666666667	60.5	6	251.3	182.5	n	10	6	251.3	182.5
7	284	284	288	201	196	10.6666666667	12.5	7	285.3	198.5	λ	2.4111675127	7	285.3	198.5
8	332	326	346	232	234	210.6666666667	2	8	334.7	233.0	b0	-15.9116828011	8	334.7	233.0
9	352	344	369	269	271	326	2	9	355.0	270.0	b1	0.772981007	9	355.0	270.0
10	412	404	408	300	298	32	2	10	408.0	299.0			10	408.0	299.0
								var	47.5	19.7

Dem 3

Deming Regression – Standard Error
Subject	x	y	x̄	y̅	u	v	r	b0	b1
1	5.1	5.4	5.6555555556	5.5444444444	7.6422222222	7.4622222222	6.8277777778	-0.2953965503	1.0325848517
2	5.6	5.6	5.6	5.5222222222	7.92	7.4755555556	6.9	-0.1753892271	1.017430616
3	6.8	6.3	5.4666666667	5.4444444444	6.32	6.8222222222	5.8733333333	-0.5474420159	1.0960767915
4	5.9	6.1	5.5666666667	5.4666666667	7.82	7.12	6.71	-0.0798732549	0.996384417
5	4.0	4.7	5.7777777778	5.6222222222	5.0755555556	6.7155555556	5.4244444444	-1.2979027152	1.1977139315
6	5.6	5.1	5.6	5.5777777778	7.92	7.2755555556	6.9	-0.0034319233	0.9966445895
7	6.6	6.6	5.4888888889	5.4111111111	6.8088888889	6.2088888889	5.7111111111	-0.1181361696	1.0073527435
8	6.7	6.8	5.4777777778	5.3888888889	6.5755555556	5.6888888889	5.3477777778	0.020224907	0.9800806458
9	5.2	4.6	5.6444444444	5.6333333333	7.7422222222	6.52	6.4866666667	0.2759297187	0.9491463097
10	4.5	4.1	5.7222222222	5.6888888889	6.5755555556	5.2088888889	5.1522222222	0.3648604423	0.9304127382
			5.6	5.53	7.92	7.481	6.9	-0.1707990796	1.0179998357
λ	2.5
							var	17.8884133159	0.4809261294
							s.e.	1.3374757312	0.2193002803
			Coefficient Report
								alpha	0.05
				coeff	s.e.	df	t stat	p-value	lower	upper
			intercept	-0.1707990796	1.3374757312	9	-0.1277025636	0.9011922209	-3.1963793851	2.8547812258	intercept	-0.1707990796	1.3374757312
			slope	1.0179998357	0.2193002803	9	4.6420361808	0.0012157505	0.5219081357	1.5140915356	slope	1.0179998357	0.2193002803
			Deming Regression
								alpha	0.05
				coeff	std err	df	t stat	p-value	lower	upper
			intercept	-0.1707990796	1.3374757312	9	-0.1277025636	0.9011922209	-3.1963793851	2.8547812258
			slope	1.0179998357	0.2193002803	9	4.6420361808	0.0012157505	0.5219081357	1.5140915356

Dem 4

Deming Regression – Hypothesis Testing
Subject	x	y	x̅	y̅	y̅ – x̅	Hypothesis Testing										Deming Regression
1	5.1	5.4	5.6555555556	5.5444444444	-0.1111111111						alpha	0.025									alpha	0.05
2	5.6	5.6	5.6	5.5222222222	-0.0777777778		param	s.e.	df	t stat	p-value	lower	upper				coeff	std err	df	t stat	p-value	lower	upper
3	6.8	6.3	5.4666666667	5.4444444444	-0.0222222222	test 1	-0.0179998357	0.2193002803	9	-0.082078489	0.9363807236	-0.606823467	0.5708237957	stat	-0.07	intercept	-0.1707990796	1.3374757312	9	-0.1277025636	0.9011922209	-3.1963793851	2.8547812258
4	5.9	6.1	5.5666666667	5.4666666667	-0.1	test 2	-0.07	0.1333749935	9	-0.5248360144	0.6123784972	-0.4281133042	0.2881133042	s.e.	0.1333749935	slope	1.0179998357	0.2193002803	9	4.6420361808	0.0012157505	0.5219081357	1.5140915356
5	4.0	4.7	5.7777777778	5.6222222222	-0.1555555556
6	5.6	5.1	5.6	5.5777777778	-0.0222222222											Hypothesis Testing
7	6.6	6.6	5.4888888889	5.4111111111	-0.0777777778									stat	s.e.						alpha	0.025
8	6.7	6.8	5.4777777778	5.3888888889	-0.0888888889									-0.07	0.1333749935	test	param	std err	df	t stat	p-value	lower	upper
9	5.2	4.6	5.6444444444	5.6333333333	-0.0111111111											slope = 1	-0.0179998357	0.2193002803	9	-0.082078489	0.9363807236	-0.606823467	0.5708237957
10	4.5	4.1	5.7222222222	5.6888888889	-0.0333333333											identity	-0.07	0.1333749935	9	-0.5248360144	0.6123784972	-0.4281133042	0.2881133042
			5.6	5.53
λ	2.5
				var	0.1778888889
				s.e.	0.1333749935

Dem 5

Deming Regression – Prediction Interval
Subject	x	y	x̄	y̅	u	v	r	b0	b1	p	pred	5.9371999343
1	5.1	5.4	5.6555555556	5.5444444444	7.6422222222	7.4622222222	6.8277777778	-0.2953965503	1.0325848517	5.90011256	s.e	0.127217116
2	5.6	5.6	5.6	5.5222222222	7.92	7.4755555556	6.9	-0.1753892271	1.017430616	5.9291944686	lower	5.6494148241
3	6.8	6.3	5.4666666667	5.4444444444	6.32	6.8222222222	5.8733333333	-0.5474420159	1.0960767915	6.0290187333	upper	6.2249850445
4	5.9	6.1	5.5666666667	5.4666666667	7.82	7.12	6.71	-0.0798732549	0.996384417	5.8984332474
5	4.0	4.7	5.7777777778	5.6222222222	5.0755555556	6.7155555556	5.4244444444	-1.2979027152	1.1977139315	5.8883808737
6	5.6	5.1	5.6	5.5777777778	7.92	7.2755555556	6.9	-0.0034319233	0.9966445895	5.9764356136
7	6.6	6.6	5.4888888889	5.4111111111	6.8088888889	6.2088888889	5.7111111111	-0.1181361696	1.0073527435	5.9259802911
8	6.7	6.8	5.4777777778	5.3888888889	6.5755555556	5.6888888889	5.3477777778	0.020224907	0.9800806458	5.9007087817
9	5.2	4.6	5.6444444444	5.6333333333	7.7422222222	6.52	6.4866666667	0.2759297187	0.9491463097	5.9708075768
10	4.5	4.1	5.7222222222	5.6888888889	6.5755555556	5.2088888889	5.1522222222	0.3648604423	0.9304127382	5.9473368717
			5.6	5.53	7.92	7.481	6.9	-0.1707990796	1.0179998357	5.9371999343
λ	2.5
									var	0.161841946
new	6	5.9371999343							s.e.	0.127217116

Dem 6

Deming Regression – Prediction Intervals
Confidence Interval for Sample Data Predictions							Prediction Intervals
Subject	x	y	pred y	s.e.	lower	upper	x	4	5	6	7
1	5.1	5.4	5.0210000822	0.2476644337	4.4607442094	5.5812559549	pred	3.901200263	4.9192000986	5.9371999343	6.9551997699
2	5.6	5.6	5.53	0.1631989095	5.1608184178	5.8991815822	s.e	0.4715177657	0.2667657888	0.127217116	0.2395613263
3	6.8	6.3	6.7515998028	0.2036018764	6.2910203597	7.2121792459	lower	2.8345529719	4.3157339588	5.6494148241	6.4132743998
4	5.9	6.1	5.8353999507	0.131733969	5.5373970092	6.1334028922	upper	4.9678475541	5.5226662384	6.2249850445	7.4971251401
5	4	4.7	3.901200263	0.4715177657	2.8345529719	4.9678475541
6	5.6	5.1	5.53	0.1631989095	5.1608184178	5.8991815822
7	6.6	6.6	6.5479998357	0.1713628553	6.1603501252	6.9356495461
8	6.7	6.8	6.6497998192	0.1868918136	6.2270211644	7.072578474
9	5.2	4.6	5.1228000657	0.229070316	4.6046070095	5.640993122
10	4.5	4.1	4.4102001808	0.3670467838	3.5798826698	5.2405176918
lambda	2.5
alpha	0.05
df	9
crit	2.2621571628

Dem 7

Deming Regression
					alpha	0.05
	coeff	std err	df	t stat	p-value	lower	upper
intercept	-0.1707990796	1.3374757312	9	-0.1277025636	0.9011922209	-3.1963793851	2.8547812258
slope	1.0179998357	0.2193002803	9	4.6420361808	0.0012157505	0.5219081357	1.5140915356
Hypothesis Testing
					alpha	0.025
test	param	std err	df	t stat	p-value	lower	upper
slope = 1	-0.0179998357	0.2193002803	9	-0.082078489	0.9363807236	-0.606823467	0.5708237957
identity	-0.07	0.1333749935	9	-0.5248360144	0.6123784972	-0.4281133042	0.2881133042

Dem 8

Deming Regression
					alpha	0.05
	coeff	std err	df	t stat	p-value	lower	upper
intercept	-15.9116828011	10.5432160207	19	-1.5091868335	0.1477014771	-37.9788875432	6.1555219411
slope	0.772981007	0.0367007765	19	21.061707128	0	0.6961653989	0.8497966151
Hypothesis Testing
					alpha	0.025
test	param	std err	df	t stat	p-value	lower	upper
slope = 1	0.227018993	0.0367007765	19	6.1856727395	0.0000060676	0.1377098476	0.3163281384
identity	-71.1	8.2115997449	19	-8.6584833904	0.0000000508	-91.0824370189	-51.1175629811

PB

																																				Passing-Bablok Regression
		x	347	249	369	286	329	410	267	295	500	286	271	506	117	329	132	274	277	198	n	18	=D22	x	y	res	r	d			r	d	c	alpha	0.05
		y	371	283	373	341	353	454	214	230	510	295	286	453	114	328	109	203	305	154	N	153	=COMBIN(Y2,2)	347	371	13.4	1	532.9590100943	n+	9	1	188.0725275705	1	slope	1.1273584906	alpha	0.05	alpha	0.05
x	y		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	M	153	=COUNT(E5:V22)	249	283	35.9	1	402.0947745829	n-	9	-1	194.2858242479	0	intercept	-33.6179245283	slope	1.1273584906	h-stat	1.2649110641
347	371	1																			m	77	=INT(Y4/2)+1	369	373	-9.4	-1	549.0541090945	pos	1	-1	271.7470095761	-1	b-lower	0.9197860963	intercept	-33.6179245283	h-crit	1.3580986393
249	283	2	0.8979591837																		k	13	=COUNTIF(E5:V22,"<-1")	286	341	52.2	1	470.0372520038			-1	358.8364571612	-2	b-upper	1.4563758389	b-lower	0.9197860963	p-value	0.0815188864
369	373	3	0.0909090909	0.75																				329	353	15.7	1	507.5486612644	alpha	0.05	-1	362.4204494512	-3	a-lower	-134.3624161074	b-upper	1.4563758389
286	341	4	0.4918032787	1.5675675676	0.3855421687																b	1.1273584906	=SMALL(E5:V22,Y5+Y6)	410	454	25.4	1	636.8572250133	h-stat	1.2649110641	-1	392.9704623337	-4	a-upper	32.7700534759	a-lower	-134.3624161074
329	353	5	1	0.875	0.5	0.2790697674															a	-33.6179245283	=MEDIAN(C5:C22-Y8*B5:B22)	267	214	-53.4	-1	362.4204494512	h-crit	1.3580986393	1	402.0947745829	-3			a-upper	32.7700534759
410	454	6	1.3174603175	1.0621118012	1.9756097561	0.9112903226	1.2469135802																	295	230	-69.0	-1	392.9704623337	p-val	0.0815188864	1	418.9379732838	-2	=PBRegCoeff(B5:B22,C5:C22,TRUE,AQ2)
267	214	7	1.9625	-3.8333333333	1.5588235294	6.6842105263	2.2419354839	1.6783216783													α	0.05		500	510	-20.1	-1	738.4735793388			1	435.6246657712	-1			=PBRegCoeff(B5:B22,C5:C22,TRUE,AV3)
295	230	8	2.7115384615	-1.152173913	1.9324324324	-12.3333333333	3.6176470588	1.947826087	0.5714285714												z-crit	1.9599639845	=NORMSINV(1-Y11/2)	286	295	6.2	1	435.6246657712			1	437.1333856697	0	alpha	0.05	=PBTEST(B5:B22,C5:C22,TRUE,AY3)
500	510	9	0.908496732	0.9043824701	1.0458015267	0.7897196262	0.918128655	0.6222222222	1.2703862661	1.3658536585											c	51.7445339918	=SQRT(Y2(Y2-1)(2Y2+5)/18)Y12	271	286	14.1	1	418.9379732838			1	470.0372520038	1	h-stat	1.2649110641
286	295	10	1.2459016393	0.3243243243	0.9397590361	-1000	1.3488372093	1.2822580645	4.2631578947	-7.2222222222	1.0046728972										m1	51	=ROUND((Y3-Y13)/2,0)	506	453	-83.8	-1	699.8134144708			-1	488.8461687467	0	h-crit	1.3580986393
271	286	11	1.1184210526	0.1363636364	0.887755102	3.6666666667	1.1551724138	1.2086330935	18	-2.3333333333	0.9781659389	0.6									m2	103	=Y3-Y14+1	117	114	15.7	1	188.0725275705			1	507.5486612644	1	p-value	0.0815188864
506	453	12	0.5157232704	0.6614785992	0.5839416058	0.5090909091	0.5649717514	-0.0104166667	1	1.0568720379	-9.5	0.7181818182	0.7106382979											329	328	-9.3	-1	488.8461687467			1	532.9590100943	2
117	114	13	1.1173913043	1.2803030303	1.0277777778	1.3431952663	1.1273584906	1.1604095563	0.6666666667	0.6516853933	1.0339425587	1.0710059172	1.1168831169	0.8714652956							b-low	0.9197860963	=SMALL(E5:V22,Y14+Y6)	132	109	-6.2	-1	194.2858242479			-1	549.0541090945	1	=PBTEST(B5:B22,C5:C22,TRUE,AQ12)
329	328	14	2.3888888889	0.5625	1.125	-0.3023255814	-1000	1.5555555556	1.8387096774	2.8823529412	1.0643274854	0.7674418605	0.724137931	0.7062146893	1.0094339623						b-up	1.4563758389	=SMALL(E5:V22,Y15+Y6)	274	203	-72.3	-1	358.8364571612			1	636.8572250133	2
132	109	15	1.2186046512	1.4871794872	1.1139240506	1.5064935065	1.2385786802	1.2410071942	0.7777777778	0.7423312883	1.089673913	1.2077922078	1.273381295	0.9197860963	-0.3333333333	1.1116751269								277	305	26.3	1	437.1333856697			-1	699.8134144708	1
274	203	16	2.301369863	-3.2	1.7894736842	11.5	2.7272727273	1.8455882353	-1.5714285714	1.2857142857	1.3584070796	7.6666666667	-27.6666666667	1.0775862069	0.5668789809	2.2727272727	0.661971831				a-low	-134.3624161074	=MEDIAN(C5:C22-Y18*B5:B22)	198	154	-35.6	-1	271.7470095761			-1	738.4735793388	0
277	305	17	0.9428571429	0.7857142857	0.7391304348	4	0.9230769231	1.1203007519	9.1	-4.1666666667	0.9192825112	-1.1111111111	3.1666666667	0.6462882096	1.19375	0.4423076923	1.3517241379	34			a-up	32.7700534759	=MEDIAN(C5:C22-Y17*B5:B22)									max	4
198	154	18	1.4563758389	2.5294117647	1.2807017544	2.125	1.5190839695	1.4150943396	0.8695652174	0.7835051546	1.178807947	1.6022727273	1.8082191781	0.9707792208	0.4938271605	1.3282442748	0.6818181818	0.6447368421	1.9113924051

Mult Reg 1

Method of Least Squares
Color	Quality	Price		Covariance matrix
7	5	65
3	7	38			Color	Quality	Price
5	8	51		Color	5.8	-2.1	20.5
8	1	38		Quality	-2.1	6.8181818182	15.3454545455
9	3	55		Price	20.5	15.3454545455	185.7636363636
5	4	43
4	0	25		Equations
2	6	33			b1	b2	const
8	7	71			5.8	-2.1	20.5
6	4	51			-2.1	6.8181818182	15.3454545455
9	2	49
6	4.2727272727	47.1818181818	mean	A			C	A-1C
				5.8	-2.1		20.5	4.8952883645
n	11			-2.1	6.8181818182		15.3454545455	3.7584154829
				b0	1.7514036586
				b1	4.8952883645
				b2	3.7584154829
				Covariance matrix using COV function
					Color	Quality	Price
				Color	5.8	-2.1	20.5
				Quality	-2.1	6.8181818182	15.3454545455
				Price	20.5	15.3454545455	185.7636363636

Mult Reg 2

Method of Least Squares
Color	Quality	Price		X			Y		B	Ŷ		E	H = X(XTX)-1XT												Stud E
7	5	65		1	7	5	65		1.7514036586	54.81050		10.1895003752	0.1277393982	0.0686951797	0.1341819969	0.0795622138	0.1392791534	0.0622525809	-0.0334290667	0.0269605941	0.1874563378	0.0860048125	0.1212967994	1	1.8529159932
3	7	38		1	3	7	38		4.8952883645	42.74618		-4.7461771327	0.0686951797	0.2905379182	0.233796476	-0.0815803772	-0.0693161531	0.1254366219	0.0589924707	0.3053636575	0.0809594039	0.083520919	-0.0964061166	2	-0.9569854101
5	8	51		1	5	8	51		3.7584154829	56.29517		-5.2951693446	0.1341819969	0.233796476	0.2950917229	-0.0851509742	0.0288235142	0.07288675	-0.1521902248	0.1753732309	0.2481564853	0.0757587518	-0.026727729	3	-1.0711234254
8	1	38		1	8	1	38			44.67213		-6.6721260576	0.0795622138	-0.0815803772	-0.0851509742	0.2670961733	0.2022044555	0.0831328107	0.2321664209	-0.0587596057	0.014670496	0.1023829853	0.2442754017	4	-1.3236303506
9	3	55		1	9	3	55		s.e.	57.08425		-2.084245388	0.1392791534	-0.0693161531	0.0288235142	0.2022044555	0.2466557996	0.0411394861	0.002846128	-0.1168464902	0.1837304976	0.0917488163	0.2497347926	5	-0.4078289894
5	4	43		1	5	4	43		6.960	41.26151		1.7384925871	0.0622525809	0.1254366219	0.07288675	0.0831328107	0.0411394861	0.1148024528	0.1777536288	0.1569510207	0.0202592564	0.0937669797	0.0516184119	6	0.3138186219
4	0	25		1	4	0	25		0.820	21.33256		3.6674428834	-0.0334290667	0.0589924707	-0.1521902248	0.2321664209	0.002846128	0.1777536288	0.5720458485	0.2058268002	-0.2627493596	0.1134052627	0.0853320914	7	0.9521178465
2	6	33		1	2	6	33		0.757	34.09247		-1.0924732852	0.0269605941	0.3053636575	0.1753732309	-0.0587596057	-0.1168464902	0.1569510207	0.2058268002	0.3680560946	-0.0311262905	0.0896530311	-0.1214520427	8	-0.2333982146
8	7	71		1	8	7	71			67.22262		3.7773810448	0.1874563378	0.0809594039	0.2481564853	0.014670496	0.1837304976	0.0202592564	-0.2627493596	-0.0311262905	0.3565163394	0.0753706435	0.1267561903	9	0.7997384993
6	4	51		1	6	4	51			46.15680		4.8432042226	0.0860048125	0.083520919	0.0757587518	0.1023829853	0.0917488163	0.0937669797	0.1134052627	0.0896530311	0.0753706435	0.0921369246	0.0962508732	10	0.8632738279
9	2	49		1	9	2	49			53.32583		-4.325829905	0.1212967994	-0.0964061166	-0.026727729	0.2442754017	0.2497347926	0.0516184119	0.0853320914	-0.1214520427	0.1267561903	0.0962508732	0.2693213278	11	-0.8594729086
				(XTX)-1			MSRes(XTX)-1
				1.3973194649	-0.141970038	-0.1063934384	48.4444212216	-4.9220357234	-3.688611426		SSRes	277.3563093482
				-0.141970038	0.019405418	0.0059768687	-4.9220357234	0.6727768895	0.207215282		dfRes	8
				-0.1063934384	0.0059768687	0.0165075422	-3.688611426	0.207215282	0.572308874		MSRes	34.6695386685
Normality
QQ Tables – stud. res														QQ Tables – y
Count	11	22												Count	11	22
Mean	-0.0064158645													Mean	47.1818181818
Std Dev	1.0294316605													Std Dev	13.6295134309
Interval	Data	Std Norm	Std Data											Interval	Data	Std Norm	Std Data
1	-1.3236303506	-1.6906216296	-1.2795550561											1	25	-1.6906216296	-1.6274842308
3	-1.0711234254	-1.0968035621	-1.0342673552											3	33	-1.0968035621	-1.0405227049
5	-0.9569854101	-0.7478585948	-0.923392569											5	38	-0.7478585948	-0.6736717513
7	-0.8594729086	-0.472789121	-0.8286679697											7	38	-0.472789121	-0.6736717513
9	-0.4078289894	-0.2298841176	-0.389936642											9	43	-0.2298841176	-0.3068207976
11	-0.2333982146	0	-0.2204928785											11	49	0	0.1334003468
13	0.3138186219	0.2298841176	0.3110789173											13	51	0.2298841176	0.2801407282
15	0.7997384993	0.472789121	0.7831062467											15	51	0.472789121	0.2801407282
17	0.8632738279	0.7478585948	0.8448250873											17	55	0.7478585948	0.5736214912
19	0.9521178465	1.0968035621	0.9311290374											19	65	1.0968035621	1.3073233985
21	1.8529159932	1.6906216296	1.8061731819											21	71	1.6906216296	1.7475445429
Homogeneity of variances
Ŷ	Stud. E
54.81050	1.8529159932
42.74618	-0.9569854101
56.29517	-1.0711234254
44.67213	-1.3236303506
57.08425	-0.4078289894
41.26151	0.3138186219
21.33256	0.9521178465
34.09247	-0.2333982146
67.22262	0.7997384993
46.15680	0.8632738279
53.32583	-0.8594729086

QQ Plot

-1.3236303505689102 -1.0711234253885886 -0.95698541007981308 -0.85947290855377767 -0.40782898942708506 -0.23339821457085794 0.31381862189921916 0.79973849933219099 0.86327382787517848 0.95211784652177578 1.8529159932154711 -1.6906216295848977 -1.096803562093513 -0.74785859476330196 -0.47278912099226744 -0.22988411757923208 0 0.22988411757923222 0.47278912099226728 0.74785859476330196 1.096803562093513 1.6906216295848984

Data

Std Normal

QQ Plot

25 33 38 38 43 49 51 51 55 65 71 -1.6906216295848977 -1.096803562093513 -0.74785859476330196 -0.47278912099226744 -0.22988411757923208 0 0.22988411757923222 0.47278912099226728 0.74785859476330196 1.096803562093513 1.6906216295848984

Data

Std Normal

54.810499624828537 42.746177132655433 56.295169344614322 44.672126057595207 57.08424538797891 41.261507412869634 21.332557116613586 34.092473285207895 67.222618955212241 46.156795777380999 53.325829905042738 1.8529159932154711 -0.95698541007981308 -1.0711234253885886 -1.3236303505689102 -0.40782898942708506 0.31381862189921916 0.9521178465217 7578 -0.23339821457085794 0.79973849933219099 0.86327382787517848 -0.85947290855377767

Predicted values of price (y variable)

Studentized residuals

Mult Reg 2A

Method of Least Squares (using Real Statistics functions)
Color	Quality	Price	X			Y		B	Ŷ		E	H = X(XTX)-1XT												Stud E
7	5	65	1	7	5	65		1.7514036586	54.81050		10.1895003752	0.1277393982	0.0686951797	0.1341819969	0.0795622138	0.1392791534	0.0622525809	-0.0334290667	0.0269605941	0.1874563378	0.0860048125	0.1212967994	1	1.8529159932
3	7	38	1	3	7	38		4.8952883645	42.74618		-4.7461771327	0.0686951797	0.2905379182	0.233796476	-0.0815803772	-0.0693161531	0.1254366219	0.0589924707	0.3053636575	0.0809594039	0.083520919	-0.0964061166	2	-0.9569854101
5	8	51	1	5	8	51		3.7584154829	56.29517		-5.2951693446	0.1341819969	0.233796476	0.2950917229	-0.0851509742	0.0288235142	0.07288675	-0.1521902248	0.1753732309	0.2481564853	0.0757587518	-0.026727729	3	-1.0711234254
8	1	38	1	8	1	38			44.67213		-6.6721260576	0.0795622138	-0.0815803772	-0.0851509742	0.2670961733	0.2022044555	0.0831328107	0.2321664209	-0.0587596057	0.014670496	0.1023829853	0.2442754017	4	-1.3236303506
9	3	55	1	9	3	55		s.e.	57.08425		-2.084245388	0.1392791534	-0.0693161531	0.0288235142	0.2022044555	0.2466557996	0.0411394861	0.002846128	-0.1168464902	0.1837304976	0.0917488163	0.2497347926	5	-0.4078289894
5	4	43	1	5	4	43		ERROR:#NAME?	41.26151		1.7384925871	0.0622525809	0.1254366219	0.07288675	0.0831328107	0.0411394861	0.1148024528	0.1777536288	0.1569510207	0.0202592564	0.0937669797	0.0516184119	6	0.3138186219
4	0	25	1	4	0	25		ERROR:#NAME?	21.33256		3.6674428834	-0.0334290667	0.0589924707	-0.1521902248	0.2321664209	0.002846128	0.1777536288	0.5720458485	0.2058268002	-0.2627493596	0.1134052627	0.0853320914	7	0.9521178465
2	6	33	1	2	6	33		ERROR:#NAME?	34.09247		-1.0924732852	0.0269605941	0.3053636575	0.1753732309	-0.0587596057	-0.1168464902	0.1569510207	0.2058268002	0.3680560946	-0.0311262905	0.0896530311	-0.1214520427	8	-0.2333982146
8	7	71	1	8	7	71			67.22262		3.7773810448	0.1874563378	0.0809594039	0.2481564853	0.014670496	0.1837304976	0.0202592564	-0.2627493596	-0.0311262905	0.3565163394	0.0753706435	0.1267561903	9	0.7997384993
6	4	51	1	6	4	51			46.15680		4.8432042226	0.0860048125	0.083520919	0.0757587518	0.1023829853	0.0917488163	0.0937669797	0.1134052627	0.0896530311	0.0753706435	0.0921369246	0.0962508732	10	0.8632738279
9	2	49	1	9	2	49			53.32583		-4.325829905	0.1212967994	-0.0964061166	-0.026727729	0.2442754017	0.2497347926	0.0516184119	0.0853320914	-0.1214520427	0.1267561903	0.0962508732	0.2693213278	11	-0.8594729086
			(XTX)-1			MSRes(XTX)-1
			1.3973194649	-0.141970038	-0.1063934384	48.4444212216	-4.9220357234	-3.688611426		SSRes	277.3563093482	277.3563093482	=MMULT(TRANSPOSE(I4:I14-M4:M14),I4:I14-M4:M14)
			-0.141970038	0.019405418	0.0059768687	-4.9220357234	0.6727768895	0.207215282		dfRes	8
			-0.1063934384	0.0059768687	0.0165075422	-3.688611426	0.207215282	0.572308874		MSRes	34.6695386685

Mult Reg 2B

Finding regression coefficients using Solver
Color	Quality	Price (Y)		B	Ŷ	E
7	5	65	intercept	1.7516320388	54.8104836021	10.1895163979
3	7	38	color	4.8952632584	42.7462340705	-4.7462340705
5	8	51	quality	3.7584017509	56.2951623382	-5.2951623382
8	1	38			44.6721398567	-6.6721398567
9	3	55	SSE	277.3563093872	57.084206617	-2.084206617
5	4	43			41.2615553344	1.7384446656
4	0	25			21.3326850723	3.6673149277
2	6	33			34.0925690612	-1.0925690612
8	7	71			67.2225503624	3.7774496376
6	4	51			46.1568185928	4.8431814072
9	2	49			53.325804866	-4.325804866

Mult Reg 3

Sample size requirements
	Significance level α = .01				Significance level α = .05
k	2	5	10	20	2	5	10	20
20	0.45	0.56	0.71	N/A	0.39	0.48	0.64	N/A
50	0.23	0.29	0.36	0.49	0.19	0.23	0.29	0.42
100	0.13	0.16	0.20	0.26	0.10	0.12	0.15	0.21
250	0.05	0.07	0.08	0.11	0.04	0.05	0.06	0.09
500	0.03	0.03	0.04	0.06	0.03	0.04	0.05	0.08
1000	0.01	0.02	0.02	0.03	0.01	0.01	0.02	0.02
Table lists the minimum value of R2 that can be detected by a given sample size and # of dependent variables k
N/A = not applicable

Mult Reg 4

Multiple Regression											Model with all three independent variables							Model only with Infant mortality							Test for significance of eliminating White and Crime					AIC/SBC for the two models
	Poverty	Infant Mort	White	Crime	Forecast using TREND						SUMMARY OUTPUT							SUMMARY OUTPUT								complete	reduced	difference			complete	reduced
Alabama	15.7	9.0	71.0	448																					SSE	280.6984404336	288.4098852946	7.711444861	=AE4-AD4	n	50	50
Alaska	8.4	6.9	70.6	661	Infant Mort	White	Crime	Poverty			Regression Statistics							Regression Statistics							dfE	46	48	2	=AE5-AD5	k	3	1
Arizona	14.7	6.4	86.5	483	7.0	80	400	12.867466231			Multiple R	0.5803450584						Multiple R	0.5644295679						MSE	6.1021400094		3.8557224305	=AF4/AF5	SSE	280.6984404336	288.4098852946
Arkansas	17.3	8.5	80.8	529	7.5	70	500	13.2860317256			R Square	0.3368003868						R Square	0.3185807372						F			0.6318639731	=AF6/AD6	AIC	94.2628960967	91.6179837864
California	13.3	5.0	76.6	523	8.0	75	450	14.0362762181			Adjusted R Square	0.2935482381						Adjusted R Square	0.3043845025						α			0.05		AICc	95.6265324603	92.1397229169
Colorado	11.4	5.7	89.7	348							Standard Error	2.4702510013						Standard Error	2.4512321956						p-value			0.5361505196	=FDIST(AF7,AF5,AD5)	SBC	101.9109881184	95.4420297973
Connecticut	9.3	6.2	84.3	256	Output from LINEST						Observations	50						Observations	50						sig			no	=IF(AF9<AF8,"yes","no")
Delaware	10.0	8.3	74.3	689																										AIC/SBC using the Real Statistics functions
Florida	13.2	7.3	79.8	723		Crime	White	Infant Mort	intercept		ANOVA							ANOVA
Georgia	14.7	8.1	65.4	493	Slope (b)	0.001421499	0.0363269231	1.279369653	0.4371252188	Intercept (a)		df	SS	MS	F	Significance F			df	SS	MS	F	Significance F			complete	reduced	difference		AIC	94.2628960967	91.6179837864
Hawaii	9.1	5.6	29.7	273	S.E. of slope (sb)	0.0022421017	0.0336025319	0.300672909	3.9875336905	S.E. of intercept (sa)	Regression	3	142.5503595664	47.5167865221	7.7869053232	0.0002622132		Regression	1	134.8389147054	134.8389147054	22.4412138275	0.0000196073		R-Square	0.3368003868	0.3185807372	0.0182196497	=AD14-AE14	AICc	95.6265324603	92.1397229169
Idaho	12.6	6.8	94.6	239	R Square	0.3368003868	2.4702510013	ERROR:#N/A	ERROR:#N/A	S.E. of estimate (sRes)	Residual	46	280.6984404336	6.1021400094				Residual	48	288.4098852946	6.008539277				dfE	46	48	2	=AE15-AD15	SBC	101.9109881184	95.4420297973
Illinois	12.2	7.3	79.1	533	F	7.7869053232	46	ERROR:#N/A	ERROR:#N/A	dfRes	Total	49	423.2488					Total	49	423.2488					F			0.6318639731	=AF14AD15/(AF15(1-AD14))
Indiana	13.1	8.0	88.0	334	SSReg	142.5503595664	280.6984404336	ERROR:#N/A	ERROR:#N/A	SSRes															α			0.05		Augmented versions
Iowa	11.5	5.1	94.2	295								Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	p-value			0.5361505196	=FDIST(AF16,AF15,AD15)
Kansas	11.3	7.1	88.7	453							Intercept	0.4371252188	3.9875336905	0.1096229531	0.9131852533	-7.5893637974	8.4636142349	Intercept	4.2690992314	1.819794213	2.3459241715	0.0231583219	0.610157735	7.9280407278	sig			no	=IF(AF18<AF17,"yes","no")	AIC	236.1567494172	233.5118371069
Kentucky	17.3	7.5	89.9	295							Infant Mort	1.279369653	0.300672909	4.2550213694	0.0001016276	0.6741464778	1.8845928283	Infant Mort	1.2390777114	0.2615624357	4.7372158308	0.0000196073	0.7131711868	1.764984236						AICc	237.5203857808	234.0335762373
Louisiana	17.3	9.9	64.8	730							White	0.0363269231	0.0336025319	1.0810769602	0.2852981526	-0.0313114656	0.1039653117								Using Rsquare Test			0.5361505196	=RSquareTest(C4:E53,C4:C53,B4:B53)	SBC	243.8048414389	237.3358831178
Maine	12.3	6.3	96.4	118							Crime	0.001421499	0.0022421017	0.6340029143	0.5292192176	-0.0030916176	0.0059346156
Maryland	8.1	8.0	63.4	642																					Alternative form of test
Massachusetts	10.0	4.8	86.2	432
Michigan	14.4	7.4	81.2	536																						complete	reduced	difference
Minnesota	9.6	5.2	89.0	289																					SSReg	142.5503595664	134.8389147054	7.711444861
Mississippi	21.2	10.6	60.6	291																					dfReg	3	1	2
Missouri	13.4	7.4	85.0	505																					MSE	6.1021400094
Montana	14.8	5.8	90.5	288																					dfE	46
Nebraska	10.8	5.6	91.4	302																					F			0.6318639731
Nevada	11.3	6.4	80.9	751																					α			0.05
New Hampshire	7.6	6.1	95.5	137																					p-value			0.5361505196
New Jersey	8.7	5.5	76.0	329																					sig			no
New Mexico	17.1	5.8	84.0	664
New York	13.6	5.6	73.4	414
North Carolina	14.6	8.1	73.9	466
North Dakota	12.0	5.8	91.4	142
Ohio	13.4	7.8	84.8	343
Oklahoma	15.9	8.0	78.1	500
Oregon	13.6	5.5	90.1	288
Pennsylvania	12.1	7.6	85.4	417
Rhode Island	11.7	6.1	88.5	227
South Carolina	15.7	8.4	68.7	788
South Dakota	12.5	6.9	88.2	169
Tennessee	15.5	8.7	80.4	753
Texas	15.8	6.2	82.4	511
Utah	9.6	5.1	92.9	235
Vermont	10.6	5.5	96.4	124
Virginia	10.2	7.1	73.0	270
Washington	11.3	4.7	84.3	333
West Virginia	17.0	7.4	94.5	275
Wisconsin	10.4	6.4	89.7	291
Wyoming	9.4	7.0	93.9	239
Poverty – % below poverty level
Infant Mort – infant mortality per 1,000 births, death prior to 1 yr, excludes fetal death, residents only
White – % of the population that is white
Crime – violent crime (murder, forcible rape, robbery, and aggravated assault) per 100,000 people

Mult Reg 4A

Multiple Regression
	Poverty	Infant Mort	White	Crime	Doctors	Traf Deaths	University	Unemployed	Income	QQ Tables
Alabama	15.7	9.0	71.0	448	218.2	1.81	22.0	5.0	42,666
Alaska	8.4	6.9	70.6	661	228.5	1.63	27.3	6.7	68,460	Count	50	100
Arizona	14.7	6.4	86.5	483	209.7	1.69	25.1	5.5	50,958	Mean	12.732
Arkansas	17.3	8.5	80.8	529	203.4	1.96	18.8	5.1	38,815	Std Dev	2.9390016353
California	13.3	5.0	76.6	523	268.7	1.21	29.6	7.2	61,021
Colorado	11.4	5.7	89.7	348	259.7	1.14	35.6	4.9	56,993	Interval	Data	Std Norm	Std Data
Connecticut	9.3	6.2	84.3	256	376.4	0.86	35.6	5.7	68,595	1	7.6	-2.326347874	-1.7461711958
Delaware	10.0	8.3	74.3	689	250.9	1.23	27.5	4.8	57,989	3	8.1	-1.8807936082	-1.5760453973
Florida	13.2	7.3	79.8	723	247.9	1.56	25.8	6.2	47,778	5	8.4	-1.644853627	-1.4739699182
Georgia	14.7	8.1	65.4	493	217.4	1.46	27.5	6.2	50,861	7	8.7	-1.4757910282	-1.3718944391
Hawaii	9.1	5.6	29.7	273	317.0	1.33	29.1	3.9	67,214	9	9.1	-1.3407550337	-1.2357938003
Idaho	12.6	6.8	94.6	239	168.8	1.60	24.0	4.9	47,576	11	9.3	-1.22652812	-1.1677434809
Illinois	12.2	7.3	79.1	533	280.2	1.16	29.9	6.5	56,235	13	9.4	-1.126391129	-1.1337183212
Indiana	13.1	8.0	88.0	334	216.9	1.26	22.9	5.9	47,966	15	9.6	-1.0364333895	-1.0656680018
Iowa	11.5	5.1	94.2	295	189.3	1.42	24.3	4.1	48,980	17	9.6	-0.9541652531	-1.0656680018
Kansas	11.3	7.1	88.7	453	222.5	1.38	29.6	4.4	50,177	19	10	-0.8778962951	-0.929567363
Kentucky	17.3	7.5	89.9	295	232.3	1.80	19.7	6.4	41,538	21	10	-0.806421247	-0.929567363
Louisiana	17.3	9.9	64.8	730	262.7	2.17	20.3	4.6	43,733	23	10.2	-0.7388468492	-0.8615170436
Maine	12.3	6.3	96.4	118	278.4	1.22	25.4	5.4	46,581	25	10.4	-0.6744897502	-0.7934667242
Maryland	8.1	8.0	63.4	642	421.4	1.09	35.2	4.4	70,545	27	10.6	-0.612812991	-0.7254164048
Massachusetts	10.0	4.8	86.2	432	469.0	0.76	38.1	5.3	65,401	29	10.8	-0.5533847196	-0.6573660854
Michigan	14.4	7.4	81.2	536	250.2	1.04	24.7	8.4	48,591	31	11.3	-0.4958503473	-0.4872402869
Minnesota	9.6	5.2	89.0	289	293.2	0.88	31.5	5.4	57,288	33	11.3	-0.4399131657	-0.4872402869
Mississippi	21.2	10.6	60.6	291	177.9	2.04	19.4	6.9	37,790	35	11.3	-0.3853204664	-0.4872402869
Missouri	13.4	7.4	85.0	505	246.0	1.43	25.0	6.1	46,867	37	11.4	-0.3318533464	-0.4532151272
Montana	14.8	5.8	90.5	288	220.6	2.45	27.1	4.5	43,654	39	11.5	-0.2793190344	-0.4191899675
Nebraska	10.8	5.6	91.4	302	245.4	1.32	27.1	3.3	49,693	41	11.7	-0.2275449766	-0.3511396481
Nevada	11.3	6.4	80.9	751	187.8	1.68	21.9	6.7	56,361	43	12	-0.1763741648	-0.249064169
New Hampshire	7.6	6.1	95.5	137	274.9	0.96	33.3	3.8	63,731	45	12.1	-0.1256613469	-0.2150390093
New Jersey	8.7	5.5	76.0	329	316.3	0.95	34.4	5.5	70,378	47	12.2	-0.0752698621	-0.1810138496
New Mexico	17.1	5.8	84.0	664	243.6	1.54	24.7	4.2	43,508	49	12.3	-0.0250689083	-0.1469886899
New York	13.6	5.6	73.4	414	395.9	0.97	31.9	5.4	56,033	51	12.5	0.0250689083	-0.0789383705
North Carolina	14.6	8.1	73.9	466	254.2	1.62	26.1	6.3	46,549	53	12.6	0.0752698621	-0.0449132108
North Dakota	12.0	5.8	91.4	142	244.4	1.42	26.9	3.2	45,685	55	13.1	0.1256613469	0.1252125877
Ohio	13.4	7.8	84.8	343	266.7	1.14	24.1	6.5	47,988	57	13.2	0.1763741648	0.1592377474
Oklahoma	15.9	8.0	78.1	500	173.5	1.58	22.2	3.8	42,822	59	13.3	0.2275449766	0.1932629071
Oregon	13.6	5.5	90.1	288	274.5	1.31	28.1	6.4	50,169	61	13.4	0.2793190344	0.2272880668
Pennsylvania	12.1	7.6	85.4	417	305.3	1.37	26.3	5.4	50,713	63	13.4	0.3318533464	0.2272880668
Rhode Island	11.7	6.1	88.5	227	375.5	0.80	30.0	7.8	55,701	65	13.6	0.3853204664	0.2953383862
South Carolina	15.7	8.4	68.7	788	229.8	2.09	23.7	6.9	44,625	67	13.6	0.4399131657	0.2953383862
South Dakota	12.5	6.9	88.2	169	219.1	1.62	25.1	3.0	46,032	69	14.4	0.4958503473	0.5675396638
Tennessee	15.5	8.7	80.4	753	263.6	1.70	22.9	6.4	43,614	71	14.6	0.5533847196	0.6355899832
Texas	15.8	6.2	82.4	511	214.2	1.38	25.3	4.9	50,043	73	14.7	0.612812991	0.6696151429
Utah	9.6	5.1	92.9	235	208.1	1.11	29.1	3.4	56,633	75	14.7	0.6744897502	0.6696151429
Vermont	10.6	5.5	96.4	124	373.7	0.86	32.1	4.8	52,104	77	14.8	0.7388468492	0.7036403026
Virginia	10.2	7.1	73.0	270	274.5	1.25	33.7	4.0	61,233	79	15.5	0.806421247	0.9418164205
Washington	11.3	4.7	84.3	333	270.0	1.00	30.7	5.3	58,078	81	15.7	0.8778962951	1.0098667399
West Virginia	17.0	7.4	94.5	275	232.1	2.10	17.1	4.3	37,989	83	15.7	0.9541652531	1.0098667399
Wisconsin	10.4	6.4	89.7	291	259.1	1.27	25.7	4.7	52,094	85	15.8	1.0364333895	1.0438918996
Wyoming	9.4	7.0	93.9	239	184.4	1.60	23.6	3.1	53,207	87	15.9	1.126391129	1.0779170593
										89	17	1.22652812	1.452193816
Poverty – % below poverty level										91	17.1	1.3407550337	1.4862189757
Infant Mort – infant mortality per 1,000 births, death prior to 1 yr, excludes fetal death, residents only										93	17.3	1.4757910282	1.5542692951
White – % of the population that is white										95	17.3	1.644853627	1.5542692951
Crime – violent crime (murder, forcible rape, robbery, and aggravated assault) per 100,000 people										97	17.3	1.8807936082	1.5542692951
Doctors – # doctors per 100,000 residents, excludes some categories of doctors										99	21.2	2.326347874	2.8812505234
Traf Deaths – # of traffic fatalities per 100 million vehicle miles
University – % of residents 25 yrs or older with at least a bachelor's degree
Unemployment – % of civilian labor force
Income – median household income

QQ Plot

7.6 8.1 8.4 8.6999999999999993 9.1 9.3000000000000007 9.4 9.6 9.6 10 10 10.199999999999999 10.4 10.6 10.8 11.3 11.3 11.3 11.4 11.5 11.7 12 12.1 12.2 12.3 12.5 12.6 13.1 13.2 13.3 13.4 13.4 13.6 13.6 14.4 14.6 14.7 14.7 14.8 15.5 15.7 15.7 15.8 15.9 17 17.100000000000001 17.3 17.3 17.3 21.2 -2.3263478740408408 -1.8807936081512509 -1.6448536269514726 -1.4757910281791702 -1.3407550336902161 -1.2265281200366105 -1.1263911290388013 -1.0364333894937898 -0.95416525314619549 -0.87789629505122846 -0.80642124701824058 -0.73884684918521393 -0.67448975019608193 -0.61281299101662734 -0.55338471955567303 -0.49585034734745354 -0.43991316567323374 -0.38532046640756784 -0.33185334643681658 -0.27931903444745415 -0.2275449766411495 -0.17637416478086138 -0.12566134685507402 -7.5269862099829901E-2 -2.506890825871106E-2 2.506890825871106E-2 7.5269862099829901E-2 0.12566134685507416 0.17637416478086121 0.22754497664114934 0.27931903444745415 0.33185334643681658 0.38532046640756784 0.43991316567323396 0.49585034734745331 0.5533847195556727 0.61281299101662734 0.67448975019608193 0.73884684918521393 0.80642124701824058 0.87789629505122857 0.95416525314619549 1.0364333894937898 1.1263911290388013 1.2265281200366105 1.3407550336902161 1.4757910281791713 1.6448536269514715 1.8807936081512504 2.3263478740408408

Data

Std Normal

Mult Reg 5

Multiple Regression			Regression data analysis tool output
Color	Quality	Price	SUMMARY OUTPUT
7	5	65
3	7	38	Regression Statistics
5	8	51	Multiple R	0.9223307274
8	1	38	R Square	0.8506939707
9	3	55	Adjusted R Square	0.8133674634
5	4	43	Standard Error	5.8880844651
4	0	25	Observations	11
2	6	33
8	7	71	ANOVA
6	4	51		df	SS	MS	F	Significance F
9	2	49	Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462
			Residual	8	277.3563093482	34.6695386685
			Total	10	1857.6363636364
				Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
			Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998
			Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255
			Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481
			RESIDUAL OUTPUT					PROBABILITY OUTPUT
			Observation	Predicted Price	Residuals	Standard Residuals		Percentile	Price
			1	54.8104996248	10.1895003752	1.9347901368		4.5454545455	25
			2	42.7461771327	-4.7461771327	-0.9012077497		13.6363636364	33
			3	56.2951693446	-5.2951693446	-1.0054508115		22.7272727273	38
			4	44.6721260576	-6.6721260576	-1.2669084069		31.8181818182	38
			5	57.084245388	-2.084245388	-0.3957581109		40.9090909091	43
			6	41.2615074129	1.7384925871	0.3301063042		50	49
			7	21.3325571166	3.6674428834	0.6963768641		59.0909090909	51
			8	34.0924732852	-1.0924732852	-0.2074396643		68.1818181818	51
			9	67.2226189552	3.7773810448	0.7172520064		77.2727272727	55
			10	46.1567957774	4.8432042226	0.9196313279		86.3636363636	65
			11	53.325829905	-4.325829905	-0.8213918961		95.4545454545	71	Problem plots for homogeneity of variances
										1	-1
										2	7
										3	8
										4	7
										5	0
										6	-7
										7	-4
										8	1
										9	8
										10	5
										11	6
										12	-2
										13	-1
										14	7
										15	0
										16	-4
										17	6
										18	5
										19	-8
										20	-3
										1	-1
										2	1
										3	-2
										4	1
										5	0
										6	-3
										7	-1
										8	0
										9	4
										10	3
										11	3
										12	-2
										13	-1
										14	4
										15	0
										16	-4
										17	8
										18	5
										19	-8
										20	-3
										1	-1
										2	1
										3	3
										4	6
										5	6
										6	4
										7	3
										8	1
										9	2
										10	1
										11	1
										12	-2
										13	-1
										14	2
										15	0
										16	6
										17	8
										18	-3
										19	6
										20	4

Color Residual Plot

7 3 5 8 9 5 4 2 8 6 9 10.18950037517142 -4.7461771326554327 -5.2951693446143437 -6.6721260575952854 -2.0842453879789957 1.7384925871303238 3.66744288338 63679 -1.0924732852078947 3.7773810447877167 4.843204222618958 -4.3258299050428306

Color

Residuals

Quality Residual Plot

5 7 8 1 3 4 0 6 7 4 2 10.18950037517142 -4.7461771326554327 -5.2951693446143437 -6.6721260575952854 -2.0842453879789957 1.7384925871303238 3.667442883 3863679 -1.0924732852078947 3.7773810447877167 4.843204222618958 -4.3258299050428306

Quality

Residuals

Color Line Fit Plot

Price 7 3 5 8 9 5 4 2 8 6 9 65 38 51 38 55 43 25 33 71 51 49 Predicted Price 7 3 5 8 9 5 4 2 8 6 9 54.81049962482858 42.746177132655433 56.295169344614344 44.672126057595285 57.084245387978996 41.261507412869676 21.332557116613632 34.092473285207895 67.222618955212283 46.156795777381042 53.325829905042831

Color

Price

Quality Line Fit Plot

Price 5 7 8 1 3 4 0 6 7 4 2 65 38 51 38 55 43 25 33 71 51 49 Predicted Price 5 7 8 1 3 4 0 6 7 4 2 54.81049962482858 42.746177132655433 56.295169344614344 44.672126057595285 57.084245387978996 41.261507412869676 21.332557116613632 34.092473285207895 67.222618955212283 46.156795777381042 53.325829905042831

Quality

Price

Normal Probability Plot

4.5454545454545459 13.636363636363637 22.72727272727273 31.81818181818182 40.909090909090914 50.000000000000007 59.090909090909093 68.181818181818187 77.27272727272728 86.363636363636374 95.454545454545467 25 33 38 38 43 49 51 51 55 65 71

Sample Percentile

Price

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -1 7 8 7 0 -7 -4 1 8 5 6 -2 -1 7 0 -4 6 5 -8 -3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -1 1 -2 1 0 -3 -1 0 4 3 3 -2 -1 4 0 -4 8 5 -8 -3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -1 1 3 6 6 4 3 1 2 1 1 -2 -1 2 0 6 8 -3 6 4

Mult Reg 5A

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.5803450584
R Square	0.3368003868
Adjusted R Square	0.2935482381
Standard Error	2.4702510013
Observations	50
ANOVA
	df	SS	MS	F	Significance F
Regression	3	142.5503595664	47.5167865221	7.7869053232	0.0002622132
Residual	46	280.6984404336	6.1021400094
Total	49	423.2488
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	0.4371252188	3.9875336905	0.1096229531	0.9131852533	-7.5893637974	8.4636142349
Infant Mort	1.279369653	0.300672909	4.2550213694	0.0001016276	0.6741464778	1.8845928283
White	0.0363269231	0.0336025319	1.0810769602	0.2852981526	-0.0313114656	0.1039653117
Crime	0.001421499	0.0022421017	0.6340029143	0.5292192176	-0.0030916176	0.0059346156
RESIDUAL OUTPUT					PROBABILITY OUTPUT
Observation	Predicted Poverty	Residuals	Standard Residuals		Percentile	Poverty
1	15.1684824653	0.5315175347	0.2220729371		1	7.6
2	12.7701940963	-4.3701940963	-1.8259074734		3	8.1
3	12.4537343514	2.2462656486	0.9385105431		5	8.4
4	14.9989413874	2.3010586126	0.9614035498		7	8.7
5	10.3609461346	2.9390538654	1.2279638614		9	9.1
6	11.4836930525	-0.0836930525	-0.0349677306		11	9.3
7	11.7947049281	-2.4947049281	-1.0423107696		13	9.4
8	14.7334477711	-4.7334477711	-1.9776782151		15	9.6
9	13.7029894019	-0.5029894019	-0.2101536196		17	9.6
10	13.8764373211	0.8235626789	0.3440920969		19	10
11	9.0671031505	0.0328968495	0.0137446077		21	10
12	12.9136966318	-0.3136966318	-0.1310653513		23	10.2
13	13.4091293344	-1.2091293344	-0.5051854081		25	10.4
14	14.3430637586	-1.2430637586	-0.5193635241		27	10.6
15	10.8017494322	0.6982505678	0.2917355389		29	10.8
16	13.3864954336	-2.0864954336	-0.8717570711		31	11.3
17	13.7176874014	3.5823125986	1.4967233037		33	11.3
18	16.4952894117	0.8047105883	0.3362155192		35	11.3
19	12.1664376795	0.1335623205	0.0558035716		37	11.4
20	13.8875976694	-5.7875976694	-2.4181117828		39	11.5
21	10.3229904388	-0.3229904388	-0.1349483897		41	11.7
22	13.6155275636	0.7844724364	0.3277598323		43	12
23	10.7349852544	-1.1349852544	-0.4742073264		45	12.1
24	16.6138715883	4.5861284117	1.9161268255		47	12.2
25	13.711013367	-0.311013367	-0.1299442584		49	12.3
26	11.552564407	3.247435593	1.3568085966		51	12.5
27	11.3507375248	-0.5507375248	-0.2301032266		53	12.6
28	12.6305975325	-1.3305975325	-0.5559359436		55	13.1
29	11.9052076088	-4.3052076088	-1.7987555184		57	13.2
30	10.7037708049	-2.0037708049	-0.8371939568		59	13.3
31	11.8529639831	5.2470360169	2.1922601297		61	13.4
32	10.8574525756	2.7425474244	1.1458616546		63	13.4
33	14.1489227581	0.4510772419	0.1884642395		65	13.6
34	11.3799356587	0.6200643413	0.2590686111		67	13.6
35	13.9832908902	-0.5832908902	-0.2437043234		69	14.4
36	14.2208941012	1.6791058988	0.7015459592		71	14.6
37	11.1570064788	2.4429935212	1.0207052661		73	14.7
38	13.8556004574	-1.7556004574	-0.7335060926		75	14.7
39	11.7787339485	-0.0787339485	-0.0328957712		77	14.8
40	14.8018062071	0.8981937929	0.3752736658		79	15.5
41	12.7089571716	-0.2089571716	-0.087304237		81	15.7
42	15.5581228066	-0.0581228066	-0.0242842456		83	15.7
43	12.0884701877	3.7115298123	1.5507114496		85	15.8
44	10.6710112494	-1.0710112494	-0.447478396		87	15.9
45	11.1525859779	-0.5525859779	-0.2308755274		89	17
46	12.5570521009	-2.3570521009	-0.9847981465		91	17.1
47	9.9856930227	1.3143069773	0.5491295991		93	17.3
48	13.7294518141	3.2705481859	1.3664652514		95	17.3
49	12.296174498	-1.896174498	-0.7922392256		97	17.3
50	13.1427872096	-3.7427872096	-1.5637710789		99	21.2

Infant Mort Residual Plot

9 6.9 6.4 8.5 5 5.7 6.2 8.3000000000000007 7.3 8.1 5.6 6.8 7.3 8 5.0999999999999996 7.1 7.5 9.9 6.3 8 4.8 7.4 5.2 10.6 7.4 5.8 5.6 6.4 6.1 5.5 5.8 5.6 8.1 5.8 7.8 8 5.5 7.6 6.1 8.4 6.9 8.6999999999999993 6.2 5.0999999999999996 5.5 7.1 4.7 7.4 6.4 7 0.53151753471999008 -4.3701940963279213 2.2462656486003425 2.3010586126454893 2.9390538653623732 -8.3693052508371224E-2 -2.4947049280746594 -4.733447771089935 -0.5029894018649923 0.82356267893254831 3.2896849450136045E-2 -0.31369663181623331 -1.2091293343592771 -1.243063758629301 0.69825056776613792 -2.0864954336057906 3.5823125986140916 0.80471058 834260845 0.13356232046473338 -5.7875976693676634 -0.32299043884211009 0.78447243635617347 -1.1349852544480097 4.5861284116821146 -0.31101336697906667 3.2474355930402812 -0.55073752484253724 -1.3305975324749575 -4.3052076088216076 -2.0037708049472513 5.247036016924115 2.7425474244084391 0.45107724189759857 0.62006434129559196 -0.58329089015636626 1.6791058988210104 2.4429935212216041 -1.7556004573916262 -7.8733948462614833E-2 0.89819379286492129 -0.20895717155026716 -5.8122806587869391E-2 3.7115298123194638 -1.0710112494406534 -0.55258597785637065 -2.3570521008724015 1.31430697733256 3.2705481858594432 -1.8961744980388389 -3.742787209565174

Infant Mort

Residuals

White Residual Plot

71.027177760140717 70.62318863808899 86.505696765320337 80.783955956979611 76.640052174481781 89.734092175332663 84.278652322083644 74.266056727126113 79.811068541941054 65.387718279566343 29.667333748383395 94.600660447193093 79.13310968601246 88.000000627274659 94.170464820794294 88.703716524620162 89.904327345935869 64.839543701408999 96.389852756187835 63.398020838196281 86.203669547721617 81.183409037427396 89.045556531854984 60.598179144073846 85.028888093842554 90.467729264863962 91.372477335833381 80.891227371165002 95.487186970145373 76.032117342828414 83.996520785584835 73.422529169257928 73.937344387272134 91.393509706444945 84.764237226305966 78.141238608693655 90.140446325387984 85.424563507935517 88.483880668602993 68.748136077503446 88.189790025789804 80.371971305033981 82.402677784750395 92.915461931338129 96.408807764739961 73.031896017666924 84.290902250404002 94.524786328554711 89.673695670212709 93.867286940458214 0.53151753471999008 -4.3701940963279213 2.2462656486003425 2.3010586126454893 2.9390538653623732 -8.3693052508371224E-2 -2.4947049280746594 -4.733447771089935 -0.5029894018649923 0.82356267893254831 3.2896849450136045E-2 -0.31369663181623331 -1.2091293343592771 -1.243063758629301 0.69825056776613792 -2.0864954336057906 3.5823125986140916 0.80471058834260845 0.13356232046473338 -5.7875976693676634 -0.32299043884211009 0.78447243635617347 -1.1349852544480097 4.5861284116821146 -0.31101336697906667 3.2474355930402812 -0.55073752484253724 -1.3305975324749575 -4.3052076088216076 -2.0037708049472513 5.247036016924115 2.7425474244084391 0.45107724189759857 0.62006434129559196 -0.58329089015636626 1.6791058988210104 2.4429935212216041 -1.7556004573916262 -7.8733948462614833E-2 0.89819379286492129 -0.20895717155026716 -5.8122806587869391E-2 3.7115298123194638 -1.0710 112494406534 -0.55258597785637065 -2.3570521008724015 1.31430697733256 3.2705481858594432 -1.8961744980388389 -3.742787209565174

White

Residuals

Crime Residual Plot

448 661.2 482.7 529.4 522.6 347.8 256 689.2 722.6 493.2 272.8 239.4 533.20000000000005 333.6 294.7 452.7 295 729.5 118 641.9 431.5 536 288.7 291.3 504.9 287.5 302.39999999999998 750.6 137.30000000000001 329.3 664.2 414.1 466.4 142.4 343.2 499.6 287.60000000000002 416.5 227.3 788.3 169.2 753.3 510.6 234.8 124.3 269.7 333.1 275.2 290.89999999999998 239.3 0.53151753471999008 -4.3701940963279213 2.2462656486003425 2.3010586126454893 2.9390538653623732 -8.3693052508371224E-2 -2.4947049280746594 -4.733447771089935 -0.5029894018649923 0.82356267893254831 3.2896849450136045E-2 -0.31369663181623331 -1.2091293343592771 -1.243063758629301 0.69825056776613792 -2.086495433 6057906 3.5823125986140916 0.80471058834260845 0.13356232046473338 -5.7875976693676634 -0.32299043884211009 0.78447243635617347 -1.1349852544480097 4.5861284116821146 -0.31101336697906667 3.2474355930402812 -0.55073752484253724 -1.3305975324749575 -4.3052076088216076 -2.0037708049472513 5.247036016924115 2.7425474244084391 0.45107724189759857 0.62006434129559196 -0.58329089015636626 1.6791058988210104 2.4429935212216041 -1.7556004573916262 -7.8733948462614833E-2 0.89819379286492129 -0.20895717155026716 -5.8122806587869391E-2 3.7115298123194638 -1.0710112494406534 -0.55258597785637065 -2.3570521008724015 1.31430697733256 3.2705481858594432 -1.8961744980388389 -3.742787209565174

Crime

Residuals

Infant Mort Line Fit Plot

Poverty 9 6.9 6.4 8.5 5 5.7 6.2 8.3000000000000007 7.3 8.1 5.6 6.8 7.3 8 5.0999999999999996 7.1 7.5 9.9 6.3 8 4.8 7.4 5.2 10.6 7.4 5.8 5.6 6.4 6.1 5.5 5.8 5.6 8.1 5.8 7.8 8 5.5 7.6 6.1 8.4 6.9 8.6999999999999993 6.2 5.0999999999999996 5.5 7.1 4.7 7.4 6.4 7 15.7 8.4 14.7 17.3 13.3 11.4 9.3000000000000007 10 13.2 14.7 9.1 12.6 12.2 13.1 11.5 11.3 17.3 17.3 12.3 8.1 10 14.4 9. 6 21.2 13.4 14.8 10.8 11.3 7.6 8.6999999999999993 17.100000000000001 13.6 14.6 12 13.4 15.9 13.6 12.1 11.7 15.7 12.5 15.5 15.8 9.6 10.6 10.199999999999999 11.3 17 10.4 9.4 Predicted Poverty 9 6.9 6.4 8.5 5 5.7 6.2 8.3000000000000007 7.3 8.1 5.6 6.8 7.3 8 5.0999999999999996 7.1 7.5 9.9 6.3 8 4.8 7.4 5.2 10.6 7.4 5.8 5.6 6.4 6.1 5.5 5.8 5.6 8.1 5.8 7.8 8 5.5 7.6 6.1 8.4 6.9 8.6999999999999993 6.2 5.0999999999999996 5.5 7.1 4.7 7.4 6.4 7 15.168482465280009 12.770194096327922 12.453734351399657 14.998941387354511 10.360946134637627 11.483693052508372 11.79470492807466 14.733447771089935 13.702989401864992 13.876437321067451 9.0671031505498636 12.913696631816233 13.409129334359276 14.343063758629301 10.801749432233862 13.386495433605791 13.717687401385909 16.495289411657392 12.166437679535267 13.887597669367663 10.32299043884211 13.615527563643827 10.734985254448009 16.613871588317885 13.711013366979067 11.55256440695972 11.350737524842538 12.630597532474958 11.905207608821607 10.703770804947251 11.852963983075886 10.857452575591561 14.148922758102401 11.379935658704408 13.983290890156367 14.22089410117899 11.157006478778396 13.855600457391626 11.778733948462614 14.801806207135078 12.708957171550267 15.558122806587869 12.088470187680537 10.671011249440653 11.15258597785637 12.557052100872401 9.9856930226674407 13.729451814140557 12.296174498038839 13.142787209565174

Infant Mort

Poverty

White Line Fit Plot

Poverty 71.027177760140717 70.62318863808899 86.505696765320337 80.783955956979611 76.640052174481781 89.734092175332663 84.278652322083644 74.266056727126113 79.811068541941054 65.387718279566343 29.667333748383395 94.600660447193093 79.13310968601246 88.000000627274659 94.170464820794294 88.703716524620162 89. 904327345935869 64.839543701408999 96.389852756187835 63.398020838196281 86.203669547721617 81.183409037427396 89.045556531854984 60.598179144073846 85.028888093842554 90.467729264863962 91.372477335833381 80.891227371165002 95.487186970145373 76.032117342828414 83.996520785584835 73.422529169257928 73.937344387272134 91.393509706444945 84.764237226305966 78.141238608693655 90.140446325387984 85.424563507935517 88.483880668602993 68.748136077503446 88.189790025789804 80.371971305033981 82.402677784750395 92.915461931338129 96.408807764739961 73.031896017666924 84.290902250404002 94.524786328554711 89.673695670212709 93.867286940458214 15.7 8.4 14.7 17.3 13.3 11.4 9.3000000000000007 10 13.2 14.7 9.1 12.6 12.2 13.1 11.5 11.3 17.3 17.3 12.3 8.1 10 14.4 9.6 21.2 13.4 14.8 10.8 11.3 7.6 8.6999999999999993 17.100000000000001 13.6 14.6 12 13.4 15.9 13.6 12.1 11.7 15.7 12.5 15.5 15.8 9.6 10.6 10.199999999999999 11.3 17 10.4 9.4 Predicted Poverty 71.027177760140717 70.62318863808899 86.505696765320337 80.783955956979611 76.640052174481781 89.734092175332663 84.278652322083644 74.266056727126113 79.811068541941054 65.387718279566343 29.667333748383395 94.600660447193093 79.13310968601246 88.000000627274659 94.170464820794294 88.703716524620162 89.904327345935869 64.839543701408999 96.389852756187835 63.398020838196281 86.203669547721617 81.183409037427396 89.045556531854984 60.598179144073846 85.028888093842554 90.467729264863962 91.372477335833381 80.891227371165002 95.487186970145373 76.032117342828414 83.996520785584835 73.422529169257928 73.937344387272134 91.393509706444945 84.764237226305966 78.141 238608693655 90.140446325387984 85.424563507935517 88.483880668602993 68.748136077503446 88.189790025789804 80.371971305033981 82.402677784750395 92.915461931338129 96.408807764739961 73.031896017666924 84.290902250404002 94.524786328554711 89.673695670212709 93.867286940458214 15.168482465280009 12.770194096327922 12.453734351399657 14.998941387354511 10.360946134637627 11.483693052508372 11.79470492807466 14.733447771089935 13.702989401864992 13.876437321067451 9.0671031505498636 12.913696631816233 13.409129334359276 14.343063758629301 10.801749432233862 13.386495433605791 13.717687401385909 16.495289411657392 12.166437679535267 13.887597669367663 10.32299043884211 13.615527563643827 10.734985254448009 16.613871588317885 13.711013366979067 11.55256440695972 11.350737524842538 12.630597532474958 11.905207608821607 10.703770804947251 11.852963983075886 10.857452575591561 14.148922758102401 11.379935658704408 13.983290890156367 14.22089410117899 11.157006478778396 13.855600457391626 11.778733948462614 14.801806207135078 12.708957171550267 15.558122806587869 12.088470187680537 10.671011249440653 11.15258597785637 12.557052100872401 9.9856930226674407 13.729451814140557 12.296174498038839 13.142787209565174

White

Poverty

Crime Line Fit Plot

Poverty 448 661.2 482.7 529.4 522.6 347.8 256 689.2 722.6 493.2 272.8 239.4 533.20000000000005 333.6 294.7 452.7 295 729.5 118 641.9 431.5 536 288.7 291.3 504.9 287.5 302.39999999999998 750.6 137.30000000000001 329.3 664.2 414.1 466.4 142.4 343.2 499.6 287.60000000000002 416.5 227.3 788.3 169.2 753.3 510.6 234.8 124.3 269.7 333.1 275.2 290.89999999999998 239.3 15.7 8.4 14.7 17.3 13.3 11.4 9.3000000000000007 10 13.2 14.7 9.1 12.6 12.2 13.1 11.5 11.3 17.3 17.3 12.3 8.1 10 14.4 9.6 21.2 13.4 14.8 10.8 11.3 7.6 8.6999999999999993 17.100000000000001 13.6 14.6 12 13.4 15.9 13.6 12.1 11.7 15.7 12.5 15.5 15.8 9.6 10.6 10.199999999999999 11.3 17 10.4 9.4 Predicted Poverty 448 661.2 482.7 529.4 522.6 347.8 256 689.2 722.6 493.2 272.8 239.4 533.20000000000005 333.6 294.7 452.7 295 729.5 118 641.9 431.5 536 288.7 291.3 504.9 287.5 302.39999999999998 750.6 137.30000000000001 329.3 664.2 414.1 466.4 142.4 343.2 499.6 287.60000000000002 416.5 227.3 788.3 169.2 753.3 510.6 234.8 124.3 269.7 333.1 275.2 290.89999999999998 239.3 15.168482465280009 12.770194096327922 12.453734351399657 14.998941387354511 10.360946134637627 11.483693052508372 11.79470492807466 14.733447771089935 13.702989401864992 13.876437321067451 9.0671031505498636 12.913696631816233 13.409129334359276 14.343063758629301 10.801749432233862 13.386495433605791 13.717687401385909 16.495289411657392 12.166437679535267 13.887597669367663 10.32299043884211 13.615527563643827 10.734985254448009 16.613871588317885 13.711013366979067 11.55256440695972 11.350737524842538 12.630597532474958 11.905207608821607 10.703770804947251 11.852963983075886 10.857452575591561 14.148922758102401 11.379935658704408 13.983290890156367 14.22089410117899 11.157006478778396 13.855600457391626 11.778733948462614 14.801806207135078 12.708957171550267 15.558122806587869 12.088470187680537 10.671011249440653 11.15258597785637 12.557052100872401 9.9856930226674407 13.729451814140557 12.296174498038839 13.142787209565174

Crime

Poverty

Normal Probability Plot

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 7.6 8.1 8.4 8.6999999999999993 9.1 9.3000000000000007 9.4 9.6 9.6 10 10 10.199999999999999 10.4 10.6 10.8 11.3 11.3 11.3 11.4 11.5 11.7 12 12.1 12.2 12.3 12.5 12.6 13.1 13.2 13.3 13.4 13.4 13.6 13.6 14.4 14.6 14.7 14.7 14.8 15.5 15.7 15.7 15.8 15.9 17 17.100000000000001 17.3 17.3 17.3 21.2

Sample Percentile

Poverty

Mult Reg 5B

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.5644295679
R Square	0.3185807372
Adjusted R Square	0.3043845025
Standard Error	2.4512321956
Observations	50
ANOVA
	df	SS	MS	F	Significance F
Regression	1	134.8389147054	134.8389147054	22.4412138275	0.0000196073
Residual	48	288.4098852946	6.008539277
Total	49	423.2488
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	4.2690992314	1.819794213	2.3459241715	0.0231583219	0.610157735	7.9280407278
Infant Mort	1.2390777114	0.2615624357	4.7372158308	0.0000196073	0.7131711868	1.764984236
RESIDUAL OUTPUT					PROBABILITY OUTPUT
Observation	Predicted Poverty	Residuals	Standard Residuals		Percentile	Poverty
1	15.4207986336	0.2792013664	0.1150828235		1	7.6
2	12.8187354398	-4.4187354398	-1.8213397634		3	8.1
3	12.1991965841	2.5008034159	1.0307955215		5	8.4
4	14.801259778	2.498740222	1.0299451024		7	8.7
5	10.4644877882	2.8355122118	1.1687577162		9	9.1
6	11.3318421862	0.0681578138	0.0280936793		11	9.3
7	11.9513810418	-2.6513810418	-1.0928614726		13	9.4
8	14.5534442357	-4.5534442357	-1.876864809		15	9.6
9	13.3143665243	-0.1143665243	-0.0471402511		17	9.6
10	14.3056286934	0.3943713066	0.1625542312		19	10
11	11.207934415	-2.107934415	-0.8688605193		21	10
12	12.6948276687	-0.0948276687	-0.0390866133		23	10.2
13	13.3143665243	-1.1143665243	-0.4593259971		25	10.4
14	14.1817209223	-1.0817209223	-0.4458699453		27	10.6
15	10.5883955594	0.9116044406	0.3757503564		29	10.8
16	13.0665509821	-1.7665509821	-0.7281471343		31	11.3
17	13.5621820666	3.7378179334	1.5406752731		33	11.3
18	16.5359685739	0.7640314261	0.3149228633		35	11.3
19	12.075288813	0.224711187	0.0926227482		37	11.4
20	14.1817209223	-6.0817209223	-2.5067986751		39	11.5
21	10.2166722459	-0.2166722459	-0.0893092113		41	11.7
22	13.4382742955	0.9617257045	0.3964096269		43	12
23	10.7123033305	-1.1123033305	-0.458475578		45	12.1
24	17.4033229718	3.7966770282	1.564936153		47	12.2
25	13.4382742955	-0.0382742955	-0.015776119		49	12.3
26	11.4557499573	3.3442500427	1.3784521985		51	12.5
27	11.207934415	-0.407934415	-0.1681447512		53	12.6
28	12.1991965841	-0.8991965841	-0.3706360148		55	13.1
29	11.8274732707	-4.2274732707	-1.7425042236		57	13.2
30	11.0840266439	-2.3840266439	-0.9826618006		59	13.3
31	11.4557499573	5.6442500427	2.3264794142		61	13.4
32	11.207934415	2.392065585	0.9859753375		63	13.4
33	14.3056286934	0.2943713066	0.1213356566		65	13.6
34	11.4557499573	0.5442500427	0.2243321098		67	13.6
35	13.93390538	-0.53390538	-0.2200681873		69	14.4
36	14.1817209223	1.7182790777	0.7082501434		71	14.6
37	11.0840266439	2.5159733561	1.0370483546		73	14.7
38	13.6860898377	-1.5860898377	-0.6537636229		75	14.7
39	11.8274732707	-0.1274732707	-0.0525426652		77	14.8
40	14.6773520068	1.0226479932	0.4215209259		79	15.5
41	12.8187354398	-0.3187354398	-0.131378205		81	15.7
42	15.0490753202	0.4509246798	0.1858647255		83	15.7
43	11.9513810418	3.8486189582	1.5863458762		85	15.8
44	10.5883955594	-0.9883955594	-0.4074025609		87	15.9
45	11.0840266439	-0.4840266439	-0.1995088833		89	17
46	13.0665509821	-2.8665509821	-1.1815514549		91	17.1
47	10.0927644748	1.2072355252	0.4976052755		93	17.3
48	13.4382742955	3.5617257045	1.4680925664		95	17.3
49	12.1991965841	-1.7991965841	-0.7416031862		97	17.3
50	12.9426432109	-3.5426432109	-1.4602270346		99	21.2

Infant Mort Residual Plot

9 6.9 6.4 8.5 5 5.7 6.2 8.3000000000000007 7.3 8.1 5.6 6.8 7.3 8 5.0999999999999996 7.1 7.5 9.9 6.3 8 4.8 7.4 5.2 10.6 7.4 5.8 5.6 6.4 6.1 5.5 5.8 5.6 8.1 5.8 7.8 8 5.5 7.6 6.1 8.4 6.9 8.6999999999999993 6.2 5.0999999999999996 5.5 7.1 4.7 7.4 6.4 7 0.27920136635354176 -4.41873543979505 2.5008034158838548 2.4987402220324508 2.8355122117847955 6.8157813834327285E-2 -2.65138104184458 -4.5534442356959897 -0.11436652433817507 0.39437130657557518 -2.1079344150298933 -9.4827668659268838E-2 -1.1143665243381751 -1.0817209222886444 0.91160444064901469 -1.7665509820666117 3.7378179333902626 0.7640314261 3151331 0.22471118701963988 -6.0817209222886444 -0.21667224594364143 0.96172570452604411 -1.1123033304867675 3.7966770281810405 -3.8274295473955888E-2 3.3442500426985458 -0.40793441502989225 -0.89919658411614378 -4.2274732707087992 -2.3840266438941118 5.6442500426985465 2.3920655849701067 0.29437130657557553 0.54425004269854504 -0.53390538001708165 1.7182790777113564 2.5159733561058886 -1.5860898377455186 -0.12747327070879955 1.0226479931682295 -0.31873543979505037 0.45092467976088813 3.84861895815542 -0.98839555935098566 -0.48402664389411143 -2.8665509820666131 1.2072355251921394 3.5617257045260438 -1.7991965841161441 -3.5426432109308301

Infant Mort

Residuals

Infant Mort Line Fit Plot

Poverty 9 6.9 6.4 8.5 5 5.7 6.2 8.3000000000000007 7.3 8.1 5.6 6.8 7.3 8 5.0999999999999996 7.1 7.5 9.9 6.3 8 4.8 7.4 5.2 10.6 7.4 5.8 5.6 6.4 6.1 5.5 5.8 5.6 8.1 5.8 7.8 8 5.5 7.6 6.1 8.4 6.9 8.6999999999999993 6.2 5.0999999999999996 5.5 7.1 4.7 7.4 6.4 7 15.7 8.4 14.7 17.3 13.3 11.4 9.3000000000000007 10 13.2 14.7 9.1 12.6 12.2 13.1 11.5 11.3 17.3 17.3 12.3 8.1 10 14.4 9. 6 21.2 13.4 14.8 10.8 11.3 7.6 8.6999999999999993 17.100000000000001 13.6 14.6 12 13.4 15.9 13.6 12.1 11.7 15.7 12.5 15.5 15.8 9.6 10.6 10.199999999999999 11.3 17 10.4 9.4 Predicted Poverty 9 6.9 6.4 8.5 5 5.7 6.2 8.3000000000000007 7.3 8.1 5.6 6.8 7.3 8 5.0999999999999996 7.1 7.5 9.9 6.3 8 4.8 7.4 5.2 10.6 7.4 5.8 5.6 6.4 6.1 5.5 5.8 5.6 8.1 5.8 7.8 8 5.5 7.6 6.1 8.4 6.9 8.6999999999999993 6.2 5.0999999999999996 5.5 7.1 4.7 7.4 6.4 7 15.420798633646458 12.81873543979505 12.199196584116144 14.80125977796755 10.464487788215205 11.331842186165673 11.951381041844581 14.55344423569599 13.314366524338174 14.305628693424424 11.207934415029893 12.694827668659268 13.314366524338174 14.181720922288644 10.588395559350985 13.066550982066612 13.562182066609738 16.535968573868487 12.075288812980361 14.181720922288644 10.216672245943641 13.438274295473956 10.712303330486767 17.403322971818959 13.438274295473956 11.455749957301455 11.207934415029893 12.199196584116144 11.827473270708799 11.084026643894111 11.455749957301455 11.207934415029893 14.305628693424424 11.455749957301455 13.933905380017082 14.181720922288644 11.084026643894111 13.686089837745518 11.827473270708799 14.67735200683177 12.81873543979505 15.049075320239112 11.951381041844581 10.588395559350985 11.084026643894111 13.066550982066612 10.092764474807861 13.438274295473956 12.199196584116144 12.94264321093083

Infant Mort

Poverty

Normal Probability Plot

Sample Percentile

Poverty

Mult Reg 5C

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.9223307274
R Square	0.8506939707
Adjusted R Square	0.8133674634
Standard Error	5.8880844651
Observations	11
ANOVA
	df	SS	MS	F	Significance F
Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462
Residual	8	277.3563093482	34.6695386685
Total	10	1857.6363636364
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998
Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255
Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481
RESIDUAL OUTPUT					PROBABILITY OUTPUT
Observation	Predicted Price	Residuals	Standard Residuals		Percentile	Price
1	54.8104996248	10.1895003752	1.9347901368		4.5454545455	25
2	42.7461771327	-4.7461771327	-0.9012077497		13.6363636364	33
3	56.2951693446	-5.2951693446	-1.0054508115		22.7272727273	38
4	44.6721260576	-6.6721260576	-1.2669084069		31.8181818182	38
5	57.084245388	-2.084245388	-0.3957581109		40.9090909091	43
6	41.2615074129	1.7384925871	0.3301063042		50	49
7	21.3325571166	3.6674428834	0.6963768641		59.0909090909	51
8	34.0924732852	-1.0924732852	-0.2074396643		68.1818181818	51
9	67.2226189552	3.7773810448	0.7172520064		77.2727272727	55
10	46.1567957774	4.8432042226	0.9196313279		86.3636363636	65
11	53.325829905	-4.325829905	-0.8213918961		95.4545454545	71

Color Residual Plot

7 3 5 8 9 5 4 2 8 6 9 10.18950037517142 -4.7461771326554327 -5.2951693446143437 -6.6721260575952854 -2.0842453879789957 1.7384925871303238 3.6674428833 863679 -1.0924732852078947 3.7773810447877167 4.843204222618958 -4.3258299050428306

Color

Residuals

Quality Residual Plot

5 7 8 1 3 4 0 6 7 4 2 10.18950037517142 -4.7461771326554327 -5.2951693446143437 -6.6721260575952854 -2.0842453879789957 1.7384925871303238 3.66744288 33863679 -1.0924732852078947 3.7773810447877167 4.843204222618958 -4.3258299050428306

Quality

Residuals

Color Line Fit Plot

Color

Price

Quality Line Fit Plot

Quality

Price

Normal Probability Plot

Sample Percentile

Price

Mult Reg 6

Multiple Regression			Regression data analysis (formulas inserted)
Color	Quality	Price	SUMMARY OUTPUT								Alternative derivations of Multiple R
7	5	65
3	7	38	Regression Statistics								Price	Predicted Price	Pairwise Correlations
5	8	51	Multiple R	0.9223307274							65	54.8104996248	Price – Color	0.6245389264
8	1	38	R Square	0.8506939707							38	42.7461771327	Price – Quality	0.4311864613
9	3	55	Adjusted R Square	0.8133674634							51	56.2951693446	Color – Quality	-0.3339419731
5	4	43	Standard Error	5.8880844651							38	44.6721260576
4	0	25	Observations	11							55	57.084245388	Multiple R	0.9223307274
2	6	33									43	41.2615074129
8	7	71	ANOVA								25	21.3325571166
6	4	51		df	SS	MS	F	Significance F			33	34.0924732852
9	2	49	Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462			71	67.2226189552
			Residual	8	277.3563093482	34.6695386685					51	46.1567957774
			Total	10	1857.6363636364						49	53.325829905
											Multiple R	0.9223307274
				Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
			Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998		SSRes = SST (1 – r2)	277.3563093482
			Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255
			Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481
			RESIDUAL OUTPUT					PROBABILITY OUTPUT							QQ Tables
			Observation	Predicted Price	Residuals	Std Residuals		Percentile	Price						Count	11	22
			1	54.8104996248	10.1895003752	1.9347901368		4.5454545455	25						Mean	47.1818181818
			2	42.7461771327	-4.7461771327	-0.9012077497		13.6363636364	33						Std Dev	13.6295134309			QQ Table reversing axes
			3	56.2951693446	-5.2951693446	-1.0054508115		22.7272727273	38
			4	44.6721260576	-6.6721260576	-1.2669084069		31.8181818182	38						Interval	Data	Std Norm	Std Data	Std Norm	Data
			5	57.084245388	-2.084245388	-0.3957581109		40.9090909091	43						1	25	-1.6906216296	-1.6274842308	-1.6906216296	25
			6	41.2615074129	1.7384925871	0.3301063042		50	49						3	33	-1.0968035621	-1.0405227049	-1.0968035621	33
			7	21.3325571166	3.6674428834	0.6963768641		59.0909090909	51						5	38	-0.7478585948	-0.6736717513	-0.7478585948	38
			8	34.0924732852	-1.0924732852	-0.2074396643		68.1818181818	51						7	38	-0.472789121	-0.6736717513	-0.472789121	38
			9	67.2226189552	3.7773810448	0.7172520064		77.2727272727	55						9	43	-0.2298841176	-0.3068207976	-0.2298841176	43
			10	46.1567957774	4.8432042226	0.9196313279		86.3636363636	65						11	49	0	0.1334003468	0	49
			11	53.325829905	-4.325829905	-0.8213918961		95.4545454545	71						13	51	0.2298841176	0.2801407282	0.2298841176	51
															15	51	0.472789121	0.2801407282	0.472789121	51
															17	55	0.7478585948	0.5736214912	0.7478585948	55
				Residual Plots											19	65	1.0968035621	1.3073233985	1.0968035621	65
															21	71	1.6906216296	1.7475445429	1.6906216296	71
				Color	Residuals		Quality	Residuals
				7	10.1895003752		5	10.1895003752
				3	-4.7461771327		7	-4.7461771327
				5	-5.2951693446		8	-5.2951693446
				8	-6.6721260576		1	-6.6721260576
				9	-2.084245388		3	-2.084245388
				5	1.7384925871		4	1.7384925871
				4	3.6674428834		0	3.6674428834
				2	-1.0924732852		6	-1.0924732852
				8	3.7773810448		7	3.7773810448
				6	4.8432042226		4	4.8432042226
				9	-4.325829905		2	-4.325829905
				Fit Plots
				Color	Price	Pred Price		Quality	Price	Pred Price
				7	65	54.8104996248		5	65	54.8104996248
				3	38	42.7461771327		7	38	42.7461771327
				5	51	56.2951693446		8	51	56.2951693446
				8	38	44.6721260576		1	38	44.6721260576
				9	55	57.084245388		3	55	57.084245388
				5	43	41.2615074129		4	43	41.2615074129
				4	25	21.3325571166		0	25	21.3325571166
				2	33	34.0924732852		6	33	34.0924732852
				8	71	67.2226189552		7	71	67.2226189552
				6	51	46.1567957774		4	51	46.1567957774
				9	49	53.325829905		2	49	53.325829905

Normal Probability Plot

4.5454545454545459 13.636363636363637 22.727272727272727 31.81818181818182 40.909090909090914 50.000000000000007 59.090909090909101 68.181818181818187 77.27272727272728 86.363636363636374 95.454545454545467 25 33 38 38 43 49 51 51 55 65 71

Sample Percentile

Price

Color Residuals

Residuals 7 3 5 8 9 5 4 2 8 6 9 10.189500 375171463 -4.7461771326554327 -5.2951693446143224 -6.6721260575952073 -2.0842453879789105 1.7384925871303665 3.6674428833864141 -1.0924732852078947 3.7773810447877594 4.8432042226190006 -4.3258299050427382

Color

Residuals

Quality Residuals

5 7 8 1 3 4 0 6 7 4 2 10.189500375171463 -4.7461771326554327 -5.2951693446143224 -6.6721260575952073 -2.0842453879789105 1.7384925871303665 3.667442883386 4141 -1.0924732852078947 3.7773810447877594 4.8432042226190006 -4.3258299050427382

Quality

Residuals

QQ Plot

25 33 38 38 43 49 51 51 55 65 71 -1.6906216295848977 -1.096803562093513 -0.74785859476330196 -0.47278912099226744 -0.2298841 1757923208 0 0.22988411757923222 0.47278912099226728 0.74785859476330196 1.096803562093513 1.6906216295848984

Data

Std Normal

Revised QQ Plot

-1.6906216295848977 -1.096803562093513 -0.74785859476330196 -0.47278912099226744 -0.22988411757923208 0 0.22988411757923222 0.47278912099226728 0.74785859476330196 1.096803562093513 1.6906216295848984 25 33 38 38 43 49 51 51 55 65 71

Standard Normal

Data

Color Line Fit Plot

Price 7 3 5 8 9 5 4 2 8 6 9 65 38 51 38 55 43 25 33 71 51 49 Pred Price 7 3 5 8 9 5 4 2 8 6 9 54.810499624828537 42.746177132655433 56.295169344614322 44.672126057595207 57.08424538797891 41.261507412869634 21.332557116613586 34.092473285207895 67.222618955212241 46.156795777380999 53.325829905042738

Color

Price

Quality Line Fit Plot

Price 5 7 8 1 3 4 0 6 7 4 2 65 38 51 38 55 43 25 33 71 51 49 Pred Price 5 7 8 1 3 4 0 6 7 4 2 54.810499624828537 42.746177132655433 56.295169344614322 44.672126057595207 57.08424538797891 41.261507412869634 21.332557116613586 34.092473285207895 67.222618955212241 46.156795777380999 53.325829905042738

Quality

Price

Mult Reg 6A

Multiple Regression			Regression data analysis (Real Statistics formulas inserted)
Color	Quality	Price	SUMMARY OUTPUT
7	5	65
3	7	38	Regression Statistics
5	8	51	Multiple R	0.9223307274
8	1	38	R Square	0.8506939707
9	3	55	Adjusted R Square	0.8133674634
5	4	43	Standard Error	5.8880844651
4	0	25	Observations	11
2	6	33
8	7	71	ANOVA
6	4	51		df	SS	MS	F	Significance F
9	2	49	Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462
			Residual	8	277.3563093482	34.6695386685
			Total	10	1857.6363636364
				Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
			Intercept	1.7514036586	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
			Color	4.8952883645	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
			Quality	3.7584154829	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?

Mult Reg 6B

Multiple Regression (Real Statistics data analysis tool)
Color	Quality	Price	Regression Analysis
7	5	65
3	7	38	OVERALL FIT
5	8	51	Multiple R	0.9223307274		AIC	41.5014849434
8	1	38	R Square	0.8506939707		AICc	48.1681516101
9	3	55	Adjusted R Square	0.8133674634		SBC	42.6951707618
5	4	43	Standard Error	5.8880844651
4	0	25	Observations	11
2	6	33
8	7	71	ANOVA				Alpha	0.05
6	4	51		df	SS	MS	F	p-value	sig
9	2	49	Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462	yes
			Residual	8	277.3563093482	34.6695386685
			Total	10	1857.6363636364
				coeff	std err	t stat	p-value	lower	upper	vif
			Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998
			Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255	1.1255142436
			Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481	1.1255142436

Mult Reg 6C

Multiple Regression with Categorical Variables									Regression Analysis
Age	Party	Gender	Income	Age	Party 1	Party 2	Gender 1	Income	OVERALL FIT
20	Rep	Male	45000	20	1	0	1	45000	Multiple R	0.9396376559		AIC	274.4946993725
25	Dem	Male	39000	25	0	1	1	39000	R Square	0.8829189244		AICc	283.8280327058
45	Ind	Male	56000	45	0	0	1	56000	Adjusted R Square	0.8403439878		SBC	278.3576429837
35	Rep	Female	49000	35	1	0	0	49000	Standard Error	4688.2097873178
50	Dem	Female	41000	50	0	1	0	41000	Observations	16
55	Ind	Female	42000	55	0	0	0	42000
39	Rep	Male	58000	39	1	0	1	58000	ANOVA				Alpha	0.05
48	Dem	Male	55000	48	0	1	1	55000		df	SS	MS	F	p-value	sig
30	Ind	Male	46000	30	0	0	1	46000	Regression	4	1823227578.89107	455806894.722769	20.7379974066	0.0000440841	yes
27	Rep	Female	42000	27	1	0	0	42000	Residual	11	241772421.108926	21979311.0099023
47	Dem	Female	37000	47	0	1	0	37000	Total	15	2065000000
21	Ind	Female	25000	21	0	0	0	25000
48	Rep	Male	75000	48	1	0	1	75000		coeff	std err	t stat	p-value	lower	upper	vif
24	Ind	Male	43000	24	0	0	1	43000	Intercept	13994.703836425	4506.4962103634	3.1054511494	0.0100063421	4075.9725534059	23913.4351194441
28	Ind	Female	40000	28	0	0	0	40000	Age	625.6118840172	112.0174686862	5.5849493062	0.0001639552	379.063097768	872.1606702665	1.1405086249
40	Dem	Female	31000	40	0	1	0	31000	Party 1	10453.1015786022	2848.823840062	3.6692692021	0.0036943351	4182.8825829105	16723.3205742939	1.2692869721
									Party 2	-5141.4115686091	2985.1823269143	-1.7223107353	0.1129733721	-11711.7535703152	1428.9304330971	1.393703389
Age	Party	Gender	Income	Age	Party 1	Party 2	Gender 1	Income	Gender 1	13677.5215086503	2384.7867074809	5.7353227715	0.000131174	8428.641355501	18926.4016617996	1.0350110861
25	Dem	Female	?	25	0	1	0	24494
40	Ind	Male	?	40	0	0	1	52697
									Regression Analysis
				Age	Party 1	Party 2	Gender 1	Income	OVERALL FIT
				20	1	0	1	45000	Multiple R	0.9396376559		AIC	274.4946993725
				25	0	1	1	39000	R Square	0.8829189244		AICc	283.8280327058
				45	-1	-1	1	56000	Adjusted R Square	0.8403439878		SBC	278.3576429837
				35	1	0	-1	49000	Standard Error	4688.2097873178
				50	0	1	-1	41000	Observations	16
				55	-1	-1	-1	42000
				39	1	0	1	58000	ANOVA				Alpha	0.05
				48	0	1	1	55000		df	SS	MS	F	p-value	sig
				30	-1	-1	1	46000	Regression	4	1823227578.89107	455806894.722769	20.7379974066	0.0000440841	yes
				27	1	0	-1	42000	Residual	11	241772421.108926	21979311.0099023
				47	0	1	-1	37000	Total	15	2065000000
				21	-1	-1	-1	25000
				48	1	0	1	75000		coeff	std err	t stat	p-value	lower	upper	vif
				24	-1	-1	1	43000	Intercept	22604.0279274145	4259.2928779761	5.3069907553	0.0002497186	13229.3875105052	31978.6683443238
				28	-1	-1	-1	40000	Age	625.6118840172	112.0174686862	5.5849493062	0.0001639552	379.063097768	872.1606702665	1.1405086249
				40	0	1	-1	31000	Party 1	8682.5382419378	1728.1785713815	5.0240978483	0.0003875359	4878.8428523388	12486.2336315369	1.4862135729
									Party 2	-6911.9749052735	1803.2673842189	-3.833028294	0.0027798129	-10880.9396576164	-2943.0101529305	1.6181703787
									Gender 1	6838.7607543251	1192.3933537405	5.7353227715	0.000131174	4214.3206777505	9463.2008308998	1.0350110861

Mult Reg 7

Multiple Regression (Alternative Approach)
Color	Quality	Price	n	11
7	5	65	k	2
3	7	38	R Square	0.8506939707
5	8	51	df1	2
8	1	38	df2	8
9	3	55	α	0.05
5	4	43	F	22.7906126672
4	0	25	p-value	0.0004969462
2	6	33	F-crit	4.4589701075
8	7	71	sig	yes
6	4	51
9	2	49

Mult Reg 8

Multiple Regression – confidence and prediction intervals
	Poverty	Infant Mort	White	Crime	Regression Analysis							Confidence and prediction Intervals			Real Statistics function
Alabama	15.7	9.0	71.0	448
Alaska	8.4	6.9	70.6	661	OVERALL FIT			Core Matrix					Conf	Pred
Arizona	14.7	6.4	86.5	483	Multiple R	0.5803450584		2.6057128988	-0.1197888874	-0.0194978505	-0.000415727	alpha	0.05	0.05	pred	12.867466231
Arkansas	17.3	8.5	80.8	529	R Square	0.3368003868		-0.1197888874	0.0148151629	0.0004013913	-0.0000350934	poverty	12.867466231	12.867466231	se-conf	0.3590320902
California	13.3	5.0	76.6	523	Adjusted R Square	0.2935482381		-0.0194978505	0.0004013913	0.0001850384	0.0000039009	MSRes	6.1021400094	6.1021400094	lower-conf	12.1447721167
Colorado	11.4	5.7	89.7	348	Standard Error	2.4702510013		-0.000415727	-0.0000350934	0.0000039009	0.0000008238	dfRes	46	46	upper-conf	13.5901603453
Connecticut	9.3	6.2	84.3	256	Observations	50						t-crit	2.0128955989	2.0128955989	se-pred	2.4962059313
Delaware	10.0	8.3	74.3	689								s.e.	0.3590320902	2.4962059313	lower-pred	7.842864298
Florida	13.2	7.3	79.8	723	ANOVA				Alpha	0.05		lower	12.1447721167	7.842864298	upper-pred	17.892068164
Georgia	14.7	8.1	65.4	493		df	SS	MS	F	p-value	sig	upper	13.5901603453	17.892068164
Hawaii	9.1	5.6	29.7	273	Regression	3	142.5503595664	47.5167865221	7.7869053232	0.0002622132	yes
Idaho	12.6	6.8	94.6	239	Residual	46	280.6984404336	6.1021400094
Illinois	12.2	7.3	79.1	533	Total	49	423.2488
Indiana	13.1	8.0	88.0	334
Iowa	11.5	5.1	94.2	295		coeff	std err	t stat	p-value	lower	upper	data
Kansas	11.3	7.1	88.7	453	Intercept	0.4371252188	3.9875336905	0.1096229531	0.9131852533	-7.5893637974	8.4636142349	1
Kentucky	17.3	7.5	89.9	295	Infant Mort	1.279369653	0.300672909	4.2550213694	0.0001016276	0.6741464778	1.8845928283	7
Louisiana	17.3	9.9	64.8	730	White	0.0363269231	0.0336025319	1.0810769602	0.2852981526	-0.0313114656	0.1039653117	80
Maine	12.3	6.3	96.4	118	Crime	0.001421499	0.0022421017	0.6340029143	0.5292192176	-0.0030916176	0.0059346156	400
Maryland	8.1	8.0	63.4	642
Massachusetts	10.0	4.8	86.2	432
Michigan	14.4	7.4	81.2	536
Minnesota	9.6	5.2	89.0	289
Mississippi	21.2	10.6	60.6	291
Missouri	13.4	7.4	85.0	505
Montana	14.8	5.8	90.5	288
Nebraska	10.8	5.6	91.4	302
Nevada	11.3	6.4	80.9	751
New Hampshire	7.6	6.1	95.5	137
New Jersey	8.7	5.5	76.0	329
New Mexico	17.1	5.8	84.0	664
New York	13.6	5.6	73.4	414
North Carolina	14.6	8.1	73.9	466
North Dakota	12.0	5.8	91.4	142
Ohio	13.4	7.8	84.8	343
Oklahoma	15.9	8.0	78.1	500
Oregon	13.6	5.5	90.1	288
Pennsylvania	12.1	7.6	85.4	417
Rhode Island	11.7	6.1	88.5	227
South Carolina	15.7	8.4	68.7	788
South Dakota	12.5	6.9	88.2	169
Tennessee	15.5	8.7	80.4	753
Texas	15.8	6.2	82.4	511
Utah	9.6	5.1	92.9	235
Vermont	10.6	5.5	96.4	124
Virginia	10.2	7.1	73.0	270
Washington	11.3	4.7	84.3	333
West Virginia	17.0	7.4	94.5	275
Wisconsin	10.4	6.4	89.7	291
Wyoming	9.4	7.0	93.9	239
Poverty – % below poverty level
Infant Mort – infant mortality per 1,000 births, death prior to 1 yr, excludes fetal death, residents only
White – % of the population that is white
Crime – violent crime (murder, forcible rape, robbery, and aggravated assault) per 100,000 people

Mult Reg 8a

Simple Regression – confidence and prediction intervals
Cig (x)	Life Exp (y)	Regression Analysis							Confidence and prediction Intervals			Real Statistics function
5	80
23	78	OVERALL FIT			Core Matrix					Conf	Pred
25	60	Multiple R	0.7134301744		0.2399766685	-0.0089335052			alpha	0.05	0.05	pred	73.1564130902
48	53	R Square	0.5089826137		-0.0089335052	0.00046049			poverty	73.1564130902	73.1564130902	se-conf	2.0616127069
17	85	Adjusted R Square	0.4712120456						MSRes	63.5955646552	63.5955646552	lower-conf	68.7025696165
8	84	Standard Error	7.9746827307						dfRes	13	13	upper-conf	77.6102565639
4	73	Observations	15						t-crit	2.1603686565	2.1603686565	se-pred	8.236856901
26	79								s.e.	2.0616127069	8.236856901	lower-pred	55.3617656134
11	81	ANOVA				Alpha	0.05		lower	68.7025696165	55.3617656134	upper-pred	90.951060567
19	75		df	SS	MS	F	p-value	sig	upper	77.6102565639	90.951060567
14	68	Regression	1	856.9909928164	856.9909928164	13.4756409109	0.002822343	yes
35	72	Residual	13	826.742340517	63.5955646552
29	58	Total	14	1683.7333333333
4	92
23	65		coeff	std err	t stat	p-value	lower	upper	data
		Intercept	85.7204211948	3.9065908076	21.9425134131	0	77.2807448605	94.1600975291	1
		Cig (x)	-0.6282004052	0.1711289546	-3.6709182653	0.002822343	-0.997902035	-0.2584987755	20

Stepwise 1

Stepwise Regression
input data						variables to retain				p-values					alpha	0.15
																	Regression Analysis
y	x1	x2	x3	x4		1	2	3	4	x1	x2	x3	x4		min p	var #
49.25	13	7	72	11	1a					0.0078766523	0.0552154832	0.0005079645	0.0006648249		0.0005079645	3	OVERALL FIT
47.15	1	20	57	14	1b			3				0.0005079645					Multiple R	0.9874428388		AIC	9.4445992771
62.15	21	10	16	41	2a			3		0.0000062378	0.0000455338	x	0.6872232266		0.0000062378	1	R Square	0.97504336		AICc	14.4445992771
53.8	24	10	51	16	2b	1		3		0.0000062378		0.0000005693					Adjusted R Square	0.970052032		SBC	11.1394473495
57.95	13	7	32	37	3a	1		3		x	0.0298591256	x	0.0281660886		0.0281660886	4	Standard Error	1.3016942951
64.6	21	11	18	40	3b	1		3	4	0.000001581		0.265654844	0.0281660886				Observations	13
61.35	5	22	1	56	4a	1			4	x	0.3732955056	0.265654844	x		0.265654844	3
46.25	2	29	47	16													ANOVA				Alpha	0.05
56.55	3	24	18	39														df	SS	MS	F	p-value	sig
67.95	41	4	23	32													Regression	2	661.9966888522	330.9983444261	195.3474824431	0.0000000097	yes
51.9	1	31	34	25													Residual	10	16.9440803785	1.6944080379
66.65	21	11	5	51													Total	12	678.9407692308
64.7	18	10	5	53
																		coeff	std err	t stat	p-value	lower	upper	vif
										=RegStepwise(B6:E18,A6:A18)							Intercept	41.2354301358	0.9297619044	44.3505266672	0	39.1637915135	43.3070687582
																	x1	0.3598664277	0.0323529028	11.1231573211	0.0000005943	0.287779668	0.4319531874	1.0363842788
										1			4	4			x4	0.3433271126	0.02458355	13.9657255526	0.0000000693	0.2885515497	0.3981026755	1.0363842788

Stepwise 2

Stepwise Regression
input data					Stepwise Regression											Regression Analysis
y	x1	x2	x3	x4	Alpha	0.15										OVERALL FIT
49.25	13	7	72	11												Multiple R	0.9874428388		AIC	9.4445992771
47.15	1	20	57	14	Step	1	2	3	4	x1	x2	x3	x4	min p	var #	R Square	0.97504336		AICc	14.4445992771
62.15	21	10	16	41	1a					0.0078766523	0.0552154832	0.0005079645	0.0006648249	0.0005079645	3	Adjusted R Square	0.970052032		SBC	11.1394473495
53.8	24	10	51	16	1b			3				0.0005079645				Standard Error	1.3016942951
57.95	13	7	32	37	2a			3		0.0000062378	0.0000455338	x	0.6872232266	0.0000062378	1	Observations	13
64.6	21	11	18	40	2b	1		3		0.0000062378		0.0000005693
61.35	5	22	1	56	3a	1		3		x	0.0298591256	x	0.0281660886	0.0281660886	4	ANOVA				Alpha	0.05
46.25	2	29	47	16	3b	1		3	4	0.000001581		0.265654844	0.0281660886				df	SS	MS	F	p-value	sig
56.55	3	24	18	39	4a	1			4	x	0.3732955056	0.265654844	x	0.265654844	3	Regression	2	661.9966888522	330.9983444261	195.3474824431	0.0000000097	yes
67.95	41	4	23	32												Residual	10	16.9440803785	1.6944080379
51.9	1	31	34	25												Total	12	678.9407692308
66.65	21	11	5	51
64.7	18	10	5	53													coeff	std err	t stat	p-value	lower	upper	vif
																Intercept	41.2354301358	0.9297619044	44.3505266672	0	39.1637915135	43.3070687582
																x1	0.3598664277	0.0323529028	11.1231573211	0.0000005943	0.287779668	0.4319531874	1.0363842788
																x4	0.3433271126	0.02458355	13.9657255526	0.0000000693	0.2885515497	0.3981026755	1.0363842788

Season

Regression Forecast with Seasonality
Year	Quarter	Rev ($M)	t	Q1	Q2	Q3	Pred	Regression Analysis
2012	1	10.5	1	1	0	0	10.8125
	2	9.2	2	0	1	0	9.6125	OVERALL FIT
	3	13.1	3	0	0	1	13.4125	Multiple R	0.9908852074		AIC	-12.6783696987
	4	16.0	4	0	0	0	16.4625	R Square	0.9818534942		AICc	-3.3450363654
2013	1	13.6	5	1	0	0	13.0375	Adjusted R Square	0.9752547648		SBC	-8.8154260875
	2	12.2	6	0	1	0	11.8375	Standard Error	0.5937171044
	3	15.6	7	0	0	1	15.6375	Observations	16
	4	19.4	8	0	0	0	18.6875
2014	1	15.9	9	1	0	0	15.2625	ANOVA				Alpha	0.05
	2	14.7	10	0	1	0	14.0625		df	SS	MS	F	p-value	sig
	3	18.3	11	0	0	1	17.8625	Regression	4	209.8	52.45	148.7943262411	0.0000000017	yes
	4	20.5	12	0	0	0	20.9125	Residual	11	3.8775	0.3525
2015	1	16.6	13	1	0	0	17.4875	Total	15	213.6775
	2	15.7	14	0	1	0	16.2875
	3	20.0	15	0	0	1	20.0875		coeff	std err	t stat	p-value	lower	upper	vif
	4	23.3	16	0	0	0	23.1375	Intercept	14.2375	0.4452878283	31.9737012698	0	13.257428098	15.217571902
2016	1		17	1	0	0	19.7125	t	0.55625	0.0331897951	16.7596695917	0.0000000035	0.4831997535	0.6293002465	1.0625
	2		18	0	1	0	18.5125	Q1	-3.98125	0.4314673365	-9.2272338201	0.0000016413	-4.9309032048	-3.0315967952	1.584375
	3		19	0	0	1	22.3125	Q2	-5.7375	0.4250367631	-13.4988323314	0.0000000344	-6.6729996081	-4.8020003919	1.5375
	4		20	0	0	0	25.3625	Q3	-2.49375	0.4211312889	-5.9215500383	0.0000999485	-3.4206537173	-1.5668462827	1.509375
				MAE	MSE
				0.4390625	0.24234375

Forecast

Actual 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 2012 2013 2014 2015 2016 10.5 9.1999999999999993 13.1 16 13.6 12.2 15.6 19.399999999999999 15.9 14.7 18.3 20.5 16.600000000000001 15.7 20 23.3 Forecast 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 2012 2013 2014 2015 2016 10.812499999999998 9.6124999999999972 13.412499999999998 16.462499999999999 13.0375 11.837499999999997 15.637499999999999 18.6875 15.262500000000001 14.062499999999998 17.862500000000001 20.912500000000001 17.487500000000001 16.287499999999998 20.087500000000002 23.137500000000003 19.712500000000002 18.512499999999999 22.312500000000004 25.362500000000004

Revenue ($M)

Shapely

Shapely-Owen
x1	x2	x3	y		r-sq		w/o	with	r-sq w/o	r-sq w/	diff	weight	w*d
7	3	6	23	0	0		0	1	0	0.844963993	0.844963993	0.3333333333	0.2816546643
9	4	8	45	1	0.844963993	=RSquare(A4:A8,D4:D8)	2	12	0.7030406608	0.8828222336	0.1797815728	0.1666666667	0.0299635955
12	5	9	68	2	0.7030406608	=RSquare(B4:B8,D4:D8)	3	13	0.7337566068	0.8496171435	0.1158605367	0.1666666667	0.0193100895
10	8	12	59	3	0.7337566068	=RSquare(C4:C8,D4:D8)	23	123	0.7639738714	0.9861249212	0.2221510498	0.3333333333	0.0740503499
20	9	23	89	12	0.8828222336	=RSquare(A4:B8,D4:D8)		1					0.4049786992	0.4106768732
				23	0.7639738714	=RSquare(B4:C8,D4:D8)
	x1	x3	y	13	0.8496171435	=RSquare(B11:C15,D11:D15)	0	2	0	0.7030406608	0.7030406608	0.3333333333	0.2343468869
	7	6	23	123	0.9861249212	=RSquare(A4:C8,D4:D8)	1	12	0.844963993	0.8828222336	0.0378582405	0.1666666667	0.0063097068
	9	8	45				3	23	0.7337566068	0.7639738714	0.0302172646	0.1666666667	0.0050362108
	12	9	68	1	ERROR:#NAME?	=Shapely(A4:C8,D4:D8)	13	123	0.8496171435	0.9861249212	0.1365077777	0.3333333333	0.0455025926
	10	12	59	2	ERROR:#NAME?			2					0.291195397	0.2952926052
	20	23	89	3	ERROR:#NAME?
				tot	ERROR:#NAME?	=SUM(G13:G15)	0	3	0	0.7337566068	0.7337566068	0.3333333333	0.2445855356
							1	13	0.844963993	0.8496171435	0.0046531505	0.1666666667	0.0007755251
							2	23	0.7030406608	0.7639738714	0.0609332106	0.1666666667	0.0101555351
							12	123	0.8828222336	0.9861249212	0.1033026876	0.3333333333	0.0344342292
								3					0.289950825	0.2940305217
								tot					0.9861249212

MCorr

Multiple Correlation					Correlation matrix							Regression Analysis
	Crime	Doctors	Traf Deaths	University		Crime	Doctors	Traf Deaths	University			OVERALL FIT
Alabama	448	218.2	1.81	22.0	Crime	1	-0.1178039695	0.285978323	-0.1726907951			Multiple R	0.3213904657
Alaska	661	228.5	1.63	27.3	Doctors	-0.1178039695	1	-0.7222585485	0.7595710863			R Square	0.1032918314
Arizona	483	209.7	1.69	25.1	Traf Deaths	0.285978323	-0.7222585485	1	-0.8321261296			Adjusted R Square	-0.1412649418
Arkansas	529	203.4	1.96	18.8	University	-0.1726907951	0.7595710863	-0.8321261296	1			Standard Error	170.9949239105
California	523	268.7	1.21	29.6								Observations	15
Colorado	348	259.7	1.14	35.6	Inverse of correlation matrix					Sqrt Diag	Alternative
Connecticut	256	376.4	0.86	35.6								ANOVA				Alpha	0.05
Delaware	689	250.9	1.23	27.5	1	1.1151900195	-0.154914032	-0.5613101182	-0.1568295455	1.0560255771	1.0560255771		df	SS	MS	F	p-value	sig
Florida	723	247.9	1.56	25.8	2	-0.154914032	2.5429579314	0.8174327834	-1.2781023675	1.5946654607	1.5946654607	Regression	3	37048.785298642	12349.5950995473	0.4223634048	0.7407241199	no
Georgia	493	217.4	1.46	27.5	3	-0.5613101182	0.8174327834	3.7507191542	2.4032400151	1.936677349	1.936677349	Residual	11	321631.904034691	29239.2640031538
Hawaii	273	317.0	1.33	29.1	4	-0.1568295455	-1.2781023675	2.4032400151	3.943525397	1.9858311602	1.9858311602	Total	14	358680.689333333
Idaho	239	168.8	1.60	24.0
Illinois	533	280.2	1.16	29.9	Partial correlation matrix								coeff	std err	t stat	p-value	lower	upper
Indiana	334	216.9	1.26	22.9								Intercept	-172.4023863141	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
Iowa	295	189.3	1.42	24.3		Crime	Doctors	Traf Deaths	University			Doctors	0.4218812772	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
					Crime	-1	0.091991295	0.2744550086	0.0747844193			Traf Deaths	276.5930156608	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
Partial correlation Crime x Doctors					Doctors	0.091991295	-1	-0.264682477	0.403602391			University	4.8750086384	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
					Traf Deaths	0.2744550086	-0.264682477	-1	-0.6248813346
	0.091991295				University	0.0747844193	0.403602391	-0.6248813346	-1			Other ways of calculating R-square
	0.091991295
					Alternative formula							=RSquare(C4:E18,B4:B18)		0.1032918314
												=RSquare(B4:E18,1)		0.1032918314
						Crime	Doctors	Traf Deaths	University			=1-1/H11		0.1032918314
					Crime	-1	0.091991295	0.2744550086	0.0747844193
					Doctors	0.091991295	-1	-0.264682477	0.403602391
					Traf Deaths	0.2744550086	-0.264682477	-1	-0.6248813346
					University	0.0747844193	0.403602391	-0.6248813346	-1
					Real Statistics formula
						Crime	Doctors	Traf Deaths	University
					Crime	1	0.091991295	0.2744550086	0.0747844193
					Doctors	0.091991295	1	-0.264682477	0.403602391
					Traf Deaths	0.2744550086	-0.264682477	1	-0.6248813346
					University	0.0747844193	0.403602391	-0.6248813346	1

Mult Reg Pow

Multiple regression – Statistical Power
Color	Quality	Price	Regression Analysis							n	11	=COUNT(A4:A14)
7	5	65								k	2	=COUNTA(A3:B3)
3	7	38	OVERALL FIT							dfRes	8	=N3-N4-1
5	8	51	Multiple R	0.9223307274						dfReg	2	=N4
8	1	38	R Square	0.8506939707
9	3	55	Adjusted R Square	0.8133674634						R-square	0.8506939707	=F7
5	4	43	Standard Error	5.8880844651						f-square	5.6976531668	=N8/(1-N8)
4	0	25	Observations	11						λ	62.6741848347	=N9*N3
2	6	33
8	7	71	ANOVA				Alpha	0.05		α	0.05
6	4	51		df	SS	MS	F	p-value	sig	F-crit	4.4589701075	=FINV(N12,N6,N5)
9	2	49	Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462	yes	β	0.0000339272	=NF_DIST(N13,N6,N5,N10,TRUE)
			Residual	8	277.3563093482	34.6695386685				1-β	0.9999660728	=1-N14
			Total	10	1857.6363636364
										SSRes	277.3563093482	=G15
				coeff	std err	t stat	p-value	lower	upper	SSReg	1580.2800542881	=G14
			Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998	f-square	5.6976531668	=N18/N17
			Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255
			Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481

Mult Reg Pow 1

Multiple Regression – power															Confidence interval of effect size and power
	Poverty	Infant Mort	White	Crime	Regression Analysis							n	50	=COUNT(B4:B53)	F	7.7869053232	=K14
Alabama	15.7	9.0	71.0	448								k	3	=COUNTA(C3:E3)	dfReg	3	=H14
Alaska	8.4	6.9	70.6	661	OVERALL FIT							dfRes	46	=P3-P4-1	dfRes	46	=H15
Arizona	14.7	6.4	86.5	483	Multiple R	0.5803450584						dfReg	3	=P4	SSReg	142.5503595664	=I14
Arkansas	17.3	8.5	80.8	529	R Square	0.3368003868									SSRes	280.6984404336	=I15
California	13.3	5.0	76.6	523	Adjusted R Square	0.2935482381						R-square	0.3368003868	=H7	k	3	=U4
Colorado	11.4	5.7	89.7	348	Standard Error	2.4702510013						f-square	0.5078416515	=P8/(1-P8)	n	50	=U4+U5+1
Connecticut	9.3	6.2	84.3	256	Observations	50						λ	25.3920825756	=P9*P3	alpha	0.05
Delaware	10.0	8.3	74.3	689											λ	25.3920825756	=U14*U9/(1-U14)
Florida	13.2	7.3	79.8	723	ANOVA				Alpha	0.05		α	0.05		λ lower	5.3045349824	=NF_NCP(1-U10/2,U4,U5,U3)
Georgia	14.7	8.1	65.4	493		df	SS	MS	F	p-value	sig	F-crit	2.8068449288	=FINV(P12,P6,P5)	λ upper	46.7616636582	=NF_NCP(U10/2,U4,U5,U3)
Hawaii	9.1	5.6	29.7	273	Regression	3	142.5503595664	47.5167865221	7.7869053232	0.0002622132	yes	β	0.0106387263	=NF_DIST(P13,P6,P5,P10,TRUE)	R-sq	0.3368003868	=H7
Idaho	12.6	6.8	94.6	239	Residual	46	280.6984404336	6.1021400094				1-β	0.9893612737	=1-P14	R-sq lower	0.0959150092	=U12/(U9+U12)
Illinois	12.2	7.3	79.1	533	Total	49	423.2488								R-sq upper	0.483266429	=U13/(U9+U13)
Indiana	13.1	8.0	88.0	334								SSRes	280.6984404336	=I15	α	0.05
Iowa	11.5	5.1	94.2	295		coeff	std err	t stat	p-value	lower	upper	SSReg	142.5503595664	=I14	1-β	0.9893612737	=REG_POWER(U14,U9,U8,2,U17)
Kansas	11.3	7.1	88.7	453	Intercept	0.4371252188	3.9875336905	0.1096229531	0.9131852533	-7.5893637974	8.4636142349	f-square	0.5078416515	=P18/P17	1-β lower	0.430500998	=REG_POWER(U15,U9,U8,2,U17)
Kentucky	17.3	7.5	89.9	295	Infant Mort	1.279369653	0.300672909	4.2550213694	0.0001016276	0.6741464778	1.8845928283				1-β upper	0.9999652526	=REG_POWER(U16,U9,U8,2,U17)
Louisiana	17.3	9.9	64.8	730	White	0.0363269231	0.0336025319	1.0810769602	0.2852981526	-0.0313114656	0.1039653117
Maine	12.3	6.3	96.4	118	Crime	0.001421499	0.0022421017	0.6340029143	0.5292192176	-0.0030916176	0.0059346156
Maryland	8.1	8.0	63.4	642
Massachusetts	10.0	4.8	86.2	432
Michigan	14.4	7.4	81.2	536
Minnesota	9.6	5.2	89.0	289
Mississippi	21.2	10.6	60.6	291
Missouri	13.4	7.4	85.0	505
Montana	14.8	5.8	90.5	288
Nebraska	10.8	5.6	91.4	302
Nevada	11.3	6.4	80.9	751
New Hampshire	7.6	6.1	95.5	137
New Jersey	8.7	5.5	76.0	329
New Mexico	17.1	5.8	84.0	664
New York	13.6	5.6	73.4	414
North Carolina	14.6	8.1	73.9	466
North Dakota	12.0	5.8	91.4	142
Ohio	13.4	7.8	84.8	343
Oklahoma	15.9	8.0	78.1	500
Oregon	13.6	5.5	90.1	288
Pennsylvania	12.1	7.6	85.4	417
Rhode Island	11.7	6.1	88.5	227
South Carolina	15.7	8.4	68.7	788
South Dakota	12.5	6.9	88.2	169
Tennessee	15.5	8.7	80.4	753
Texas	15.8	6.2	82.4	511
Utah	9.6	5.1	92.9	235
Vermont	10.6	5.5	96.4	124
Virginia	10.2	7.1	73.0	270
Washington	11.3	4.7	84.3	333
West Virginia	17.0	7.4	94.5	275
Wisconsin	10.4	6.4	89.7	291
Wyoming	9.4	7.0	93.9	239
Poverty – % below poverty level
Infant Mort – infant mortality per 1,000 births, death prior to 1 yr, excludes fetal death, residents only
White – % of the population that is white
Crime – violent crime (murder, forcible rape, robbery, and aggravated assault) per 100,000 people

Mult Reg Pow 2

Multiple Regression Power and Sample Size
n	100	100	α	0.05
k	10	10	k	8
dfRes	89	=B3-B4-1	R-square	0.2
dfReg	10	=B4	1-β	0.9
R-square	0.15		n	85	=REG_SIZE(H5,H4,H6,2,H3)
f-square	0.1764705882	=B8/(1-B8)	f-square	0.25	=H5/(1-H5)
λ	17.6470588235	=B9*B3	λ	21.25	=H9*H8
			dfRes	76	=H8-H4-1
α	0.05		dfReg	8	=H4
F-crit	1.9387913095	=FINV(B12,B6,B5)	F-crit	2.0627389208	=FINV(H3,H12,H11)
β	0.2082822301	=NF_DIST(B13,B6,B5,B10,TRUE)	actual 1-β	0.9025941714	=1-NF_DIST(H13,H12,H11,H10,TRUE)
1-β	0.7917177699	=1-B14
1-β	0.7917177699	=REG_POWER(B8,B3,B4,2,B12)

Poly Reg

Polynomial Regression
							Month	MonSq	Use	Quadratic Regression Model
	Hours per Month						1	1	7
Person	1	2	3	4	5	6	2	4	11	SUMMARY OUTPUT
1	7	11	23	59	120	180	3	9	23
2	3	8	20	48	88	140	4	16	59	Regression Statistics
3	4	11	21	58	128	195	5	25	120	Multiple R	0.9774119772
4	5	9	16	45	111	156	6	36	180	R Square	0.9553341731
5	2	5	12	38	78	145	1	1	3	Adjusted R Square	0.9520255934
							2	4	8	Standard Error	13.2174487849
							3	9	20	Observations	30
							4	16	48
							5	25	88	ANOVA
							6	36	140		df	SS	MS	F	Significance F
							1	1	4	Regression	2	100887.874285714	50443.9371428571	288.744488541	5.95206420043254E-19
							2	4	11	Residual	27	4716.9257142857	174.700952381
							3	9	21	Total	29	105604.8
							4	16	58
							5	25	128		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Month	3
							6	36	195	Intercept	21.92	10.5739590279	2.0730173005	0.0478397665	0.2240281865	43.6159718135	Hours of Use	20.7885714286
							1	1	5	Month	-24.5485714286	6.9177729301	-3.548623477	0.0014411959	-38.7426690327	-10.3544738244
							2	4	9	MonSq	8.0571428571	0.9674181925	8.3285004557	0.0000000061	6.0721646876	10.0421210267
							3	9	16
							4	16	45	Linear Regression Model
							5	25	111
							6	36	156	SUMMARY OUTPUT
							1	1	2
							2	4	5	Regression Statistics
							3	9	12	Multiple R	0.91683485
							4	16	38	R Square	0.8405861422
							5	25	78	Adjusted R Square	0.8348927901
							6	36	145	Standard Error	24.5203039566
										Observations	30
										ANOVA
											df	SS	MS	F	Significance F
										Regression	1	88769.9314285714	88769.9314285714	147.6434502268	0
										Residual	28	16834.8685714286	601.2453061224
										Total	29	105604.8
											Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
										Intercept	-53.28	10.2086166086	-5.2191204786	0.0000152374	-74.1914031689	-32.3685968311
										Month	31.8514285714	2.621330755	12.1508621187	0	26.4818759319	37.220981211

Internet use per month

Use 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 7 11 23 59 120 180 3 8 20 48 88 140 4 11 21 58 128 195 5 9 16 45 111 156 2 5 12 38 78 145

Month

Hours of use per month

Poly Reg 1

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.9774119772
R Square	0.9553341731
Adjusted R Square	0.9520255934
Standard Error	13.2174487849
Observations	30
ANOVA
	df	SS	MS	F	Significance F
Regression	2	100887.874285714	50443.9371428571	288.744488541	5.95206420043254E-19
Residual	27	4716.9257142857	174.700952381
Total	29	105604.8
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	21.92	10.5739590279	2.0730173005	0.0478397665	0.2240281865	43.6159718135
Month	-24.5485714286	6.9177729301	-3.548623477	0.0014411959	-38.7426690327	-10.3544738244
MonSq	8.0571428571	0.9674181925	8.3285004557	0.0000000061	6.0721646876	10.0421210267
RESIDUAL OUTPUT					PROBABILITY OUTPUT
Observation	Predicted Hours	Residuals	Standard Residuals		Percentile	Hours
1	5.4285714286	1.5714285714	0.1232151544		1.6666666667	2
2	5.0514285714	5.9485714286	0.4664253665		5	3
3	20.7885714286	2.2114285714	0.1733973264		8.3333333333	4
4	52.64	6.36	0.4986853342		11.6666666667	5
5	100.6057142857	19.3942857143	1.5206990334		15	5
6	164.6857142857	15.3142857143	1.200787687		18.3333333333	7
7	5.4285714286	-2.4285714286	-0.1904234205		21.6666666667	8
8	5.0514285714	2.9485714286	0.2311964352		25	9
9	20.7885714286	-0.7885714286	-0.0618316048		28.3333333333	11
10	52.64	-4.64	-0.3638207469		31.6666666667	11
11	100.6057142857	-12.6057142857	-0.9884095662		35	12
12	164.6857142857	-24.6857142857	-1.9355980626		38.3333333333	16
13	5.4285714286	-1.4285714286	-0.1120137768		41.6666666667	20
14	5.0514285714	5.9485714286	0.4664253665		45	21
15	20.7885714286	0.2114285714	0.016578039		48.3333333333	23
16	52.64	5.36	0.4202756904		51.6666666667	38
17	100.6057142857	27.3942857143	2.1479761833		55	45
18	164.6857142857	30.3142857143	2.376932343		58.3333333333	48
19	5.4285714286	-0.4285714286	-0.033604133		61.6666666667	58
20	5.0514285714	3.9485714286	0.309606079		65	59
21	20.7885714286	-4.7885714286	-0.3754701797		68.3333333333	78
22	52.64	-7.64	-0.5990496782		71.6666666667	88
23	100.6057142857	10.3942857143	0.8150122398		75	111
24	164.6857142857	-8.6857142857	-0.6810437627		78.3333333333	120
25	5.4285714286	-3.4285714286	-0.2688330642		81.6666666667	128
26	5.0514285714	-0.0514285714	-0.004032496		85	140
27	20.7885714286	-8.7885714286	-0.6891087547		88.3333333333	145
28	52.64	-14.64	-1.1479171843		91.6666666667	156
29	100.6057142857	-22.6057142857	-1.7725060036		95	180
30	164.6857142857	-19.6857142857	-1.5435498439		98.3333333333	195

Month Residual Plot

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1.5714285714285303 5.9485714285714124 2.21142857142857 6.3600000000000136 19.394285714285743 15.314285714285717 -2.4285714285714697 2.9485714285714124 -0.78857142857143003 -4.6399999999999864 -12.605714285714257 -24.685714285714283 -1.4285714285714697 5.9485714285714124 0.21142857142856997 5.3600000000000136 27.394285714285743 30.314285714285717 -0.42857142857146968 3.9485714285714124 -4.78857142857143 -7.6399999999999864 10.394285714285743 -8.6857142857142833 -3.4285714285714697 -5.1428571428587588E-2 -8.78857142857143 -14.639999999999986 -22.605714285714257 -19.685714285714283

Month

Residuals

MonSq Residual Plot

1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 1.5714285714285303 5.9485714285714124 2.21142857142857 6.3600000000000136 19.394285714285743 15.314285714285717 -2.4285714285714697 2.9485714285714124 -0.78857142857143003 -4.6399999999999864 -12.605714285714257 -24.685714285714283 -1.4285714285714697 5.9485714285714124 0.21142857142856997 5.3600000000000136 27.394285714285743 30.314285714285717 -0.42857142857146968 3.9485714285714124 -4.78857142857143 -7.6399999999999864 10.394285714285743 -8.6857142857142833 -3.4285714285714697 -5.1428571428587588E-2 -8.78857142857143 -14.639999999999986 -22.605714285714257 -19.685714285714283

MonSq

Residuals

Month Line Fit Plot

Hours 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 7 11 23 59 120 180 3 8 20 48 88 140 4 11 21 58 128 195 5 9 16 45 111 156 2 5 12 38 78 145 Predicted Hours 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.7885714 2857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428

Month

Hours

MonSq Line Fit Plot

Hours 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 7 11 23 59 120 180 3 8 20 48 88 140 4 11 21 58 128 195 5 9 16 45 111 156 2 5 12 38 78 145 Predicted Hours 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 1 4 9 16 25 36 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428 5.4285714285714697 5.0514285714285876 20.78857142857143 52.639999999999986 100.60571428571426 164.68571428571428

MonSq

Hours

Normal Probability Plot

1.6666666666666667 5 8.3333333333333339 11.666666666666666 15 18.333333333333336 21.666666666666668 25.000000000000004 28.333333333333336 31.666666666666668 35 38.333333333333336 41.666666666666664 45 48.333333333333336 51.666666666666664 55 58.333333333333336 61. 666666666666664 65 68.333333333333343 71.666666666666671 75.000000000000014 78.333333333333343 81.666666666666671 85.000000000000014 88.333333333333343 91.666666666666671 95.000000000000014 98.333333333333343 2 3 4 5 5 7 8 9 11 11 12 16 20 21 23 38 45 48 58 59 78 88 111 120 128 140 145 156 180 195

Sample Percentile

Hours

Poly Reg 2

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.91683485
R Square	0.8405861422
Adjusted R Square	0.8348927901
Standard Error	24.5203039566
Observations	30
ANOVA
	df	SS	MS	F	Significance F
Regression	1	88769.9314285714	88769.9314285714	147.6434502268	0
Residual	28	16834.8685714286	601.2453061224
Total	29	105604.8
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-53.28	10.2086166086	-5.2191204786	0.0000152374	-74.1914031689	-32.3685968311
Month	31.8514285714	2.621330755	12.1508621187	0	26.4818759319	37.220981211

Poly Reg 3

X	Y	Polynomial Regression										Polynomial Regression										Real Statistics Functions			x	x2	x3
1.0	7																								1	1	1
1.2	12	OVERALL FIT							degree	R-square	p-value	OVERALL FIT							degree	R-square	p-value	-37.5595059927	24.0165058767	=PolyCoeff(A2:A31,B2:B31,3)	1.2	1.44	1.728
1.4	18	Multiple R	0.9973068353		AIC	169.9350585482			1	0.9162042429		Multiple R	0.9985315265		AIC	153.7586417861			1	0.9162042429		47.7508734854	23.0871427154		1.4	1.96	2.744
1.6	27	R Square	0.9946209237		AICc	171.5350585482			2	0.9946209237	0	R Square	0.9970652094		AICc	156.2586417861			2	0.9946209237	0	-7.9966647646	6.4841952157		1.6	2.56	4.096
1.8	38	Adjusted R Square	0.9942224736		SBC	174.1386506932						Adjusted R Square	0.9967265797		SBC	159.3634313127			3	0.9970652094	0.0000839077	2.557643313	0.5496242453		1.8	3.24	5.832
2.0	51	Standard Error	16.1987039124						opt deg	2		Standard Error	12.1929890467						4	0.9970707413	0.8297496047				2	4	8
2.2	56	Observations	30									Observations	30						5	0.9970707665	0.9886625212		3	=PolyDeg(A2:A31,B2:B31,8)	2.2	4.84	10.648
2.4	64																		6	0.9971095463	0.5839164442				2.4	5.76	13.824
2.6	76	ANOVA				Alpha	0.05					ANOVA				Alpha	0.05		7	0.997356011	0.1661845337		0.9970707665	=PolyRSquare($A$2:$A$31,$B$2:$B$31,5)	2.6	6.76	17.576
2.8	95		df	SS	MS	F	p-value	sig					df	SS	MS	F	p-value	sig	8	0.9973622212	0.8261865046				2.8	7.84	21.952
3.0	111	Regression	2	1310008.72043872	655004.360219358	2496.2245868665	2.31501597830315E-31	yes				Regression	3	1313228.07313742	437742.691045807	2944.4117089444	5.00670792940562E-33	yes							3	9	27
3.2	107	Residual	27	7084.7462279517	262.3980084427							Residual	26	3865.3935292463	148.6689818941				opt deg	3					3.2	10.24	32.768
3.4	121	Total	29	1317093.46666667								Total	29	1317093.46666667											3.4	11.56	39.304
3.6	143																								3.6	12.96	46.656
3.8	168		coeff	std err	t stat	p-value	lower	upper					coeff	std err	t stat	p-value	lower	upper							3.8	14.44	54.872
4.0	197	Intercept	60.4330168441	15.3414377989	3.9392016339	0.0005196843	28.9549866016	91.9110470866				Intercept	-37.5595059927	24.0165058767	-1.5639038495	0.1299313851	-86.9261408355	11.8071288501							4	16	64
4.2	229	Degree 1	-55.1789240029	8.7886334541	-6.2784418409	0.0000010192	-73.2117103222	-37.1461376836				Degree 1	47.7508734854	23.0871427154	2.068288574	0.048694859	0.2945719797	95.207174991							4.2	17.64	74.088
4.4	241	Degree 2	21.9277619975	1.1052538857	19.8395701487	1.24933674803336E-17	19.6599683463	24.1955556486				Degree 2	-7.9966647646	6.4841952157	-1.2332547831	0.2285146172	-21.3251189165	5.3317893872							4.4	19.36	85.184
4.6	249											Degree 3	2.557643313	0.5496242453	4.6534397545	0.0000839077	1.4278744966	3.6874121293							4.6	21.16	97.336
4.8	303																								4.8	23.04	110.592
5.0	339																								5	25	125
5.2	317																								5.2	27.04	140.608
5.4	387																								5.4	29.16	157.464
5.6	430																								5.6	31.36	175.616
5.8	478																								5.8	33.64	195.112
6.0	510																								6	36	216
6.2	560																								6.2	38.44	238.328
6.4	600																								6.4	40.96	262.144
6.6	690																								6.6	43.56	287.496
6.8	710																								6.8	46.24	314.432

Chart

1 1.2 1.4 1.6 1.8 2 2.2000000000000002 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4000000000000004 4.5999999999999996 4.8 5 5.2 5.4 5.6 5.8 6 6.2 6.4 6.6 6.8 7 12 18 27 38 51 56 64 76 95 111 107 121 143 168 197 229 241 249 303 339 317 387 430 478 510 560 600 690 710

Poly Reg 4

X	Y	Y	X	X^2	X^3
1.0	7	7	1.0	1	1
1.2	12	12	1.2	1.44	1.728
1.4	18	18	1.4	1.96	2.744
1.6	27	27	1.6	2.56	4.096
1.8	38	38	1.8	3.24	5.832
2.0	51	51	2.0	4	8
2.2	56	56	2.2	4.84	10.648
2.4	64	64	2.4	5.76	13.824
2.6	76	76	2.6	6.76	17.576
2.8	95	95	2.8	7.84	21.952
3.0	111	111	3.0	9	27
3.2	107	107	3.2	10.24	32.768
3.4	121	121	3.4	11.56	39.304
3.6	143	143	3.6	12.96	46.656
3.8	168	168	3.8	14.44	54.872
4.0	197	197	4.0	16	64
4.2	229	229	4.2	17.64	74.088
4.4	241	241	4.4	19.36	85.184
4.6	249	249	4.6	21.16	97.336
4.8	303	303	4.8	23.04	110.592
5.0	339	339	5.0	25	125
5.2	317	317	5.2	27.04	140.608
5.4	387	387	5.4	29.16	157.464
5.6	430	430	5.6	31.36	175.616
5.8	478	478	5.8	33.64	195.112
6.0	510	510	6.0	36	216
6.2	560	560	6.2	38.44	238.328
6.4	600	600	6.4	40.96	262.144
6.6	690	690	6.6	43.56	287.496
6.8	710	710	6.8	46.24	314.432

Log Reg

Multiple Regression with Log Transformations
Log-level transformation
Color	Quality	Price	Color	Quality	Ln Price	Regression Analysis							Use of LOGEST					Use of GROWTH
7	5	58	7	5	4.0604430105
3	7	11	3	7	2.3978952728	OVERALL FIT							Slope: Exp(b)	1.2586149756	1.3490959793	1.1934517063	Intercept: Exp(a)	Color	Quality	Price
5	8	24	5	8	3.1780538303	Multiple R	0.9195011916						S.E. of slope	0.0472285564	0.0512064847	0.4345215466	S.E. of intercept (sa)	7	7	48.5685401339	=GROWTH(C6:C16,A6:B16,W8:X9)
8	1	11	8	1	2.3978952728	R Square	0.8454824413						R-Squared	0.8454824413	0.3675898087	ERROR:#N/A	S.E. of estimate (sRes)	4	5	12.4864998427
9	3	31	9	3	3.4339872045	Adjusted R Square	0.8068530517						F	21.8870256199	8	ERROR:#N/A	dfRes
5	4	15	5	4	2.7080502011	Standard Error	0.3675898087						SSReg	5.9148490598	1.0809781397	ERROR:#N/A	SSRes	Use of slope and intercept for prediction
4	0	5	4	0	1.6094379124	Observations	11
2	6	8	2	6	2.0794415417													Color	Quality	Price
8	7	84	8	7	4.4308167988	ANOVA				Alpha	0.05							7	7	48.5685401339	=$T$7$S$7^W14$R$7^X14
6	4	24	6	4	3.1780538303		df	SS	MS	F	p-value	sig						4	5	12.4864998427	=$T$7$S$7^W15$R$7^X15
9	2	21	9	2	3.0445224377	Regression	2	5.9148490598	2.9574245299	21.8870256199	0.0005700479	yes
						Residual	8	1.0809781397	0.1351222675									Use of TREND
						Total	10	6.9958271995
																		Color	Quality	Price
							coeff	std err	t stat	p-value	lower	upper						7	7	48.5685401339	=EXP(TREND(LN(C6:C16),A6:B16,W20:X21))
						Intercept	0.176849702	0.4345215466	0.406998694	0.6946808829	-0.8251587813	1.1788581854						4	5	12.4864998427
						Color	0.2994347232	0.0512064847	5.8475938083	0.0003838232	0.1813523576	0.4175170887
						Quality	0.2300118907	0.0472285564	4.8701867751	0.0012396784	0.1211026444	0.338921137
							exp(coeff)
						Intercept	1.1934517063
						Color	1.3490959793
						Quality	1.2586149756
Log-log transformation
Color	Quality	Price	Ln Color	Ln Quality	Ln Price	Regression Analysis												Use of slope and intercept for prediction
6	3	58	1.7917594692	1.0986122887	4.0604430105
2	6	11	0.6931471806	1.7917594692	2.3978952728	OVERALL FIT												Color	Quality	Price
3	7	24	1.0986122887	1.9459101491	3.1780538303	Multiple R	0.9255505335											7	7	84.4379340736	=EXP($J$51)EXP($J$52)^LN(W38)EXP($J$53)^LN(X38)
7	1	11	1.9459101491	0	2.3978952728	R Square	0.85664379											4	5	29.5256118318	=EXP($J$51)EXP($J$52)^LN(W39)EXP($J$53)^LN(X39)
9	2	31	2.1972245773	0.6931471806	3.4339872045	Adjusted R Square	0.8208047375
3	3	15	1.0986122887	1.0986122887	2.7080502011	Standard Error	0.3540648375											Use of TREND
3	1	5	1.0986122887	0	1.6094379124	Observations	11
2	4	8	0.6931471806	1.3862943611	2.0794415417													Color	Quality	Price
7	6	84	1.9459101491	1.7917594692	4.4308167988	ANOVA				Alpha	0.05							7	7	84.4379340736	=EXP(TREND(LN(C36:C46),LN(A36:B46),LN(W44:X45)))
4	3	24	1.3862943611	1.0986122887	3.1780538303		df	SS	MS	F	p-value	sig						4	5	29.5256118318
9	2	21	2.1972245773	0.6931471806	3.0445224377	Regression	2	5.9929319266	2.9964659633	23.902523377	0.0004223437	yes
						Residual	8	1.0028952729	0.1253619091
						Total	10	6.9958271995
							coeff	std err	t stat	p-value	lower	upper
						Intercept	0.0345722857	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
						Ln Color	1.2982396078	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
						Ln Quality	0.9636554098	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
							coeff
						Exp(Intercept)	1.0351768541
						Color	3.6628429485
						Quality	2.6212607644

Interaction Reg

Regression Model – Interaction							Regression analysis on interaction model
Original Data			Interaction Model				SUMMARY OUTPUT
Votes	Money	Quality	Votes	Money	Quality	Interaction
70.4	10.8	4.3	70.4	10.8	4.3	46.44	Regression Statistics
41.8	8.1	3.5	41.8	8.1	3.5	28.35	Multiple R	0.9989747694
7.2	10.7	1.3	7.2	10.7	1.3	13.91	R Square	0.9979505899
57.4	2.8	7.8	57.4	2.8	7.8	21.84	Adjusted R Square	0.997182061
48.3	6.2	4.6	48.3	6.2	4.6	28.52	Standard Error	1.0190748819
19.6	4.5	3.0	19.6	4.5	3.0	13.50	Observations	12
72.1	6.8	5.7	72.1	6.8	5.7	38.76
40.8	2.1	6.8	40.8	2.1	6.8	14.28	ANOVA
55.5	7.9	4.3	55.5	7.9	4.3	33.97		df	SS	MS	F	Significance F
50.8	3.1	6.8	50.8	3.1	6.8	21.08	Regression	3	4045.5943910813	1348.5314636938	1298.5207362097	0
37.7	7.6	3.4	37.7	7.6	3.4	25.84	Residual	8	8.3081089186	1.0385136148
60.9	4.6	6.4	60.9	4.6	6.4	29.44	Total	11	4053.9025
								Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
							Intercept	-12.2156916164	2.798644494	-4.3648600751	0.0023969573	-18.6693773926	-5.7620058402
							Money	-0.8552031262	0.3166892915	-2.7004485125	0.0270552378	-1.585489942	-0.1249163104
							Quality	4.8619416857	0.4258145649	11.4179788265	0.0000031286	3.8800115383	5.8438718331
							Interaction	1.5569687934	0.0583279131	26.6933738898	0.0000000042	1.4224643845	1.6914732022
							Regression analysis without interaction
							SUMMARY OUTPUT
							Regression Statistics
							Multiple R	0.9030037224
							R Square	0.8154157226
							Adjusted R Square	0.7743969943
							Standard Error	9.1182762971
							Observations	12
							ANOVA
								df	SS	MS	F	Significance F
							Regression	2	3305.6158363318	1652.8079181659	19.8791078149	0.0004987423
							Residual	9	748.2866636682	83.1429626298
							Total	11	4053.9025
								Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
							Intercept	-60.8893585362	18.9965216875	-3.2052898703	0.0107397598	-103.8624761398	-17.9162409326
							Money	6.3778321963	1.4666631843	4.3485322769	0.0018542219	3.0600095685	9.6956548241
							Quality	14.0511109028	2.2424521701	6.2659579054	0.0001468123	8.978331664	19.1238901417

Slopes

Testing two slopes
Men		Women
Cig	Life Exp	Cig	Life Exp	x	g	xg	y
5	80	22	88	5	0	0	80	Regression Analysis
23	78	7	95	23	0	0	78
25	60	20	86	25	0	0	60	OVERALL FIT
48	53	23	60	48	0	0	53	Multiple R	0.6551984619		AIC	79.0115940777
17	85	15	82	17	0	0	85	R Square	0.4292850245		AICc	84.4661395322
8	84	34	75	8	0	0	84	Adjusted R Square	0.2975815686		SBC	82.3444474539
4	73	4	80	4	0	0	73	Standard Error	9.2323007828
26	79	40	68	26	0	0	79	Observations	17
		8	78	22	1	22	88
				7	1	7	95	ANOVA				Alpha	0.05
std err				20	1	20	86		df	SS	MS	F	p-value	sig
				23	1	23	60	Regression	3	833.4695010798	277.8231670266	3.2594818534	0.0562869467	no
				15	1	15	82	Residual	13	1108.0599106849	85.235377745
				34	1	34	75	Total	16	1941.5294117647
				4	1	4	80
				40	1	40	68		coeff	std err	t stat	p-value	lower	upper	vif
				8	1	8	78	Intercept	85.0622476447	5.6978204024	14.9289099406	0.0000000015	72.7528550373	97.3716402521
								x	-0.567294751	0.2394972552	-2.3686899904	0.034019057	-1.0846971143	-0.0498923877	1.8061119468
								g	2.7361379556	8.2604044017	0.3312353515	0.7457453845	-15.1093808036	20.5816567148	3.390519947
								xg	0.1153556157	0.3585155529	0.3217590277	0.7527503799	-0.6591701477	0.8898813792	4.1657554786

LAD 1

LAD Regression using Simplex method
Color	Quality	Price	Pred	Res	T	-T
7	5	65	51.8888888889	13.1111111111	13.1111111111	-13.1111111111
3	7	38	40.1111111111	-2.1111111111	2.1111111111	-2.1111111111
5	8	51	51.5555555556	-0.5555555556	0.5555555556	-0.5555555556
8	1	38	45.1111111111	-7.1111111111	7.1111111111	-7.1111111111
9	3	55	55	0	0	-0
5	4	43	40.4444444444	2.5555555556	2.5555555556	-2.5555555556
4	0	25	25	0	-1.11022302462516E-16	1.11022302462516E-16
2	6	33	33	0	0	0
8	7	71	61.7777777778	9.2222222222	9.2222222222	-9.2222222222
6	4	51	44.7777777778	6.2222222222	6.2222222222	-6.2222222222
9	2	49	52.2222222222	-3.2222222222	3.2222222222	-3.2222222222
				b0	7.6666666667
				b1	4.3333333333
				b2	2.7777777778
				sum T	44.1111111111

LAD 2

LAD Regression using Reweighted Least Squares
Color	Quality	Price		0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	Real Stats
7	5	65		1	0.0981402388	0.0931175853	0.088667789	0.0849768433	0.0820834352	0.0799356941	0.0784345607	0.0774575989	0.0768734736	0.0765563118	0.0764000743	0.0763286371	0.0762970012	0.0762829531	0.0762766164	0.076273714	0.0762723696	0.0762717422	0.076271448	0.0762713097	0.0762712446	0.0762712139	0.0762711994	0.0762711926	0.0762711893	0.0762711893
3	7	38		1	0.2106958868	0.2383819708	0.2706314118	0.3062934329	0.343268888	0.3787631241	0.4099031234	0.4345404291	0.4518701998	0.4625668092	0.4683604756	0.4711997953	0.4725272047	0.4731429018	0.4734299532	0.4735645025	0.4736277778	0.4736575883	0.4736716464	0.4736782796	0.4736814103	0.4736828883	0.4736835862	0.4736839157	0.4736840714	0.4736840714
5	8	51		1	0.1888513728	0.2295471881	0.2878589562	0.370654506	0.4864178349	0.6434722741	0.8447583884	1.078938577	1.3146519324	1.5113948193	1.6456324848	1.7228685672	1.7627440825	1.7822621076	1.7916028562	1.796033192	1.7981273559	1.7991160809	1.7995827531	1.7998030234	1.7999069982	1.7999560868	1.799979265	1.7999902098	1.7999953785	1.7999953785
8	1	38		1	0.1498772642	0.1470041546	0.1451550325	0.1441475442	0.1436842006	0.1434387008	0.1431536027	0.142714994	0.1421632959	0.1416242074	0.1412047994	0.1409341432	0.1407812151	0.1407015501	0.140661886	0.1406426124	0.140633368	0.1406289651	0.140626876	0.1406258868	0.140625419	0.1406251979	0.1406250935	0.1406250442	0.1406250209	0.1406250209
9	3	55		1	0.4797899546	0.5781620311	0.7392729777	1.0091037388	1.4836473359	2.3791124243	4.2261583599	8.4562960802	19.3291574984	50.6923539941	150.3378274695	487.5442973914	1666.7640967948	5854.5534491544	20834.0095090284	74596.4230512111	267864.478275434	963120.308419483	3465823.23944976	12501269.1897399	43493339.7063291	145592111.728814	490853405.257143	1561806289.45455	2863311530.66667	2863311530.66667
5	4	43		1	0.5752109658	0.5261594055	0.4861652702	0.4543391475	0.4300328965	0.4125494489	0.4010183703	0.3943460019	0.3912330776	0.3903092669	0.3903880199	0.3907122956	0.3909774775	0.3911372915	0.3912221826	0.3912647087	0.3912854134	0.3912953504	0.3913000842	0.3913023305	0.3913033942	0.3913038973	0.391304135	0.3913042473	0.3913043003	0.3913043003
4	0	25		1	0.2726695498	0.3382791377	0.4374784782	0.586818088	0.812786589	1.1605402562	1.7138542008	2.6422349576	4.3116210778	7.5350231759	14.1033788975	27.8597128484	56.9270335004	118.4625743182	248.7704719088	524.7185437712	1109.0818730772	2346.5596452488	4967.10006506	10516.460825273	22268.0234762231	47154.4338240945	99855.0938342788	211470.570950271	447765.564637198	447765.564637198
2	6	33		1	0.9153541908	1.2017788131	1.6279289044	2.2939285276	3.3993569818	5.3771532438	9.2731425722	17.9632430021	40.611292411	111.2265831778	373.9813959369	1498.2873561652	6753.7401078409	32533.3204279252	162117.909530611	821324.808509717	4194208.00219722	21476514.3326822	110215680.410585	602802427.508772	1227133513.14286	1431655765.33333	2863311530.66667	8589934592	4294967296	4294967296
8	7	71		1	0.264733684	0.2078129359	0.1719821091	0.148936523	0.1338971508	0.1240486976	0.1176697098	0.1136491967	0.1112245911	0.1098479428	0.1091196404	0.1087582557	0.1085859393	0.108505059	0.1084672095	0.1084494738	0.1084411457	0.1084372279	0.1084353825	0.1084345124	0.1084341019	0.1084339082	0.1084338167	0.1084337736	0.1084337532	0.1084337532
6	4	51		1	0.2064748778	0.1960031687	0.1866076238	0.1786728394	0.1723626522	0.1676654944	0.1644400316	0.1624457368	0.1613738837	0.160897435	0.1607348503	0.1606989172	0.1606991768	0.1607049329	0.1607092684	0.1607117556	0.1607130479	0.1607136896	0.1607140011	0.1607141504	0.1607142216	0.1607142554	0.1607142714	0.1607142789	0.1607142825	0.1607142825
9	2	49		1	0.2311695147	0.2400743034	0.2518910084	0.2655163728	0.2794308962	0.2919009403	0.3014530575	0.3074477834	0.3103117557	0.311158227	0.311105285	0.3108401856	0.3106214573	0.3104878571	0.3104158413	0.3103793193	0.3103613778	0.3103527149	0.3103485719	0.3103466011	0.3103456665	0.3103452241	0.310345015	0.3103449161	0.3103448694	0.3103448694
			b0	1.7514036586	2.8494359298	3.9218699651	4.8791847069	5.6749343307	6.2995505852	6.7668914852	7.1017630524	7.3311425862	7.4795608674	7.568583527	7.6176629132	7.64289425	7.6553077283	7.6612770309	7.6641170907	7.6654620617	7.6660977823	7.6663980444	7.6665398285	7.666606775	7.6666383864	7.6666533127	7.6666603612	7.6666636891	7.6666652607	7.6666652607
			b1	4.8952883645	4.7986063798	4.6980758258	4.604177419	4.5236826021	4.4596954402	4.4124070775	4.3799423675	4.3592315216	4.3469313849	4.3401278697	4.3366107406	4.3348848509	4.3340626968	4.3336757999	4.3334942814	4.3334090729	4.3333690155	4.3333501577	4.3333412706	4.3333370794	4.3333351017	4.3333341682	4.3333337275	4.3333335194	4.3333334212	4.3333334212
			b2	3.7584154829	3.5642418642	3.3827093039	3.2247323047	3.0953122912	2.9945050908	2.9193554445	2.8656702413	2.8291696773	2.8059278366	2.7923057772	2.7849638391	2.7812473524	2.779432936	2.778562923	2.7781492607	2.7779533385	2.7778607056	2.7778169415	2.777796272	2.7777865112	2.7777819018	2.7777797252	2.7777786973	2.777778212	2.7777779828	2.7777779828
						Color	Quality	Price	Pred	Res	Abs(Res)
				coeff		7	5	65	51.8888891236	13.1111108764	13.1111108764
			b0	7.6666652607		3	7	38	40.1111114043	-2.1111114043	2.1111114043
			b1	4.3333334212		5	8	51	51.5555562296	-0.5555562296	0.5555562296
			b2	2.7777779828		8	1	38	45.1111106134	-7.1111106134	7.1111106134
						9	3	55	55.0000000003	-0.0000000003	0.0000000003
						5	4	43	40.4444442982	2.5555557018	2.5555557018
						4	0	25	24.9999989456	0.0000010544	0.0000010544
						2	6	33	33.0000000002	-0.0000000002	0.0000000002
						8	7	71	61.7777785105	9.2222214895	9.2222214895
						6	4	51	44.7777777195	6.2222222805	6.2222222805
						9	2	49	52.2222220175	-3.2222220175	3.2222220175
											44.111111668

LAD 3

LAD Regression Tool					No intercept
Color	Quality	Price	LAD Regression		LAD Regression
7	5	65
3	7	38	intercept	7.6666652607	intercept	0
5	8	51	Color	4.3333334212	Color	4.8125063997
8	1	38	Quality	2.7777779828	Quality	3.8958312001
9	3	55
5	4	43	LAD	44.111111668	LAD	47.1458567989
4	0	25
2	6	33
8	7	71
6	4	51
9	2	49

LAD 4

LAD Regression Tool with standard errors
Color	Quality	Price	LAD Regression
7	5	65						alpha	0.05
3	7	38		coeff	std err	df	t stat	p-value	lower	upper
5	8	51	intercept	7.6666652607	10.6032525212	9	0.7230484463	0.4880139508	-16.3195583791	31.6528889005
8	1	38	Color	4.3333334212	1.2908731473	9	3.3569010482	0.0084305669	1.4131754848	7.2534913577
9	3	55	Quality	2.7777779828	1.4250858522	9	1.9492004489	0.0830733155	-0.4459901853	6.001546151
5	4	43
4	0	25	LAD Regression
2	6	33						alpha	0.05
8	7	71		coeff	std err	df	t stat	p-value	lower	upper
6	4	51	Color	4.8125063997	0.7690568545	10	6.2576731116	0.0000941092	3.0989409428	6.5260718566
9	2	49	Quality	3.8958312001	0.9166882998	10	4.2498973762	0.00168978	1.8533223842	5.9383400161

LAD 5

	LAD Regression Standard Errors via Bootstrapping
	Color	Quality	Price		C	Q	P		C	Q	P		C	Q	P		C	Q	P		C	Q	P
1	7	5	65	9	8	7	71	5	4	0	25	9	5	4	43	5	8	7	71	4	2	6	33
2	3	7	38	1	7	5	65	11	5	4	43	7	9	3	55	9	9	3	55	4	2	6	33
3	5	8	51	6	5	4	43	1	8	7	71	5	2	6	33	2	9	3	55	8	9	3	55
4	8	1	38	2	3	7	38	7	9	3	55	6	5	4	43	3	2	6	33	10	5	4	43
5	9	3	55	7	4	0	25	9	2	6	33	3	8	7	71	5	8	7	71	4	2	6	33
6	5	4	43	2	3	7	38	3	5	4	43	11	9	3	55	8	5	4	43	11	5	4	43
7	4	0	25	5	9	3	55	7	9	3	55	8	5	4	43	9	9	3	55	10	5	4	43
8	2	6	33	3	5	8	51	11	5	4	43	10	5	4	43	2	9	3	55	4	2	6	33
9	8	7	71	8	2	6	33	3	5	4	43	11	9	3	55	1	5	4	43	4	2	6	33
10	6	4	51	5	9	3	55	3	5	4	43	3	8	7	71	7	5	4	43	7	9	3	55
11	9	2	49	6	5	4	43	10	9	3	55	11	9	3	55	8	5	4	43	1	8	7	71
	Inter	7.6666652607	7.4768210354		I	7.563288785			I	9.4705808153			I	1.399998394			I	1.3999988618			I	-9.6677832007
	Color	4.3333334212	0.5914959956		C	4.3591778078			C	3.8823532955			C	4.2666668314			C	4.2666667834			C	5.4509463031
	Quality	2.7777779828	1.1332025682		Q	2.7548302169			Q	3.5294131767			Q	5.0666667078			Q	5.0666666958			Q	5.2943150923

Lp Reg A

LAD Regression Tool
			LAD Regression			LAD Regression			Lp Reg	1.5	Lp Reg	1.5
Color	Quality	Price
7	5	65	intercept	7.6666652607		intercept	0		intercept	3.0853468371	intercept	0
3	7	38	Color	4.3333334212		Color	4.8125063997		Color	4.7601976112	Color	5.0409729452
5	8	51	Quality	2.7777779828		Quality	3.8958312001		Quality	3.5458272747	Quality	3.8486408692
8	1	38
9	3	55	LAD	44.111111668		LAD	47.1458567989		Lp	23.2226733705	Lp	23.4381768933
5	4	43
4	0	25
2	6	33	Lp Regression		1.5
8	7	71						alpha	0.05
6	4	51		coeff	std err	df	t stat	p-value	lower	upper
9	2	49	intercept	3.0853468371	8.4611601932	9	0.3646482003	0.7237950233	-16.0551272996	22.2258209738
			Color	4.7601976112	0.9936890437	9	4.7904298042	0.0009868024	2.5123168233	7.008078399
			Quality	3.5458272747	1.0658626939	9	3.3267205006	0.0088451435	1.1346783471	5.9569762024
			Lp Regression		1.5
								alpha	0.05
				coeff	std err	df	t stat	p-value	lower	upper
			Color	5.0409729452	0.6047157262	10	8.336103605	0.0000081966	3.6935823413	6.3883635492
			Quality	3.8486408692	0.7526840826	10	5.1132220786	0.0004551633	2.1715562216	5.5257255168

Lp Reg B

Lp Regression
					Lp Reg
Color	Quality	Price
7	5	65	54.1358664889	35.8090369414	p	1.5
3	7	38	42.1867305937	8.5666795933
5	8	51	55.2529530908	8.7707329694	intercept	3.0853468371
8	1	38	44.7127550011	17.3920668165	Color	4.7601976112
9	3	55	56.5646071618	1.9570772601	Quality	3.5458272747
5	4	43	41.0696439918	2.6819835977
4	0	25	22.1261372817	4.8719074289	Lp	23.2226733705
2	6	33	33.8807057078	0.8265063917
8	7	71	65.9877186495	11.2215580741
6	4	51	45.829841603	11.755897019
9	2	49	53.018779887	8.0564057377
				111.9098518298
			Lp	23.2226733705

TLS Reg

Total Least Squares							Regression Analysis
Color	Quality	Price	0.1097213548	-0.6095211992	-0.785140199	=SVD_V(A4:C14-A15:C15)	OVERALL FIT
7	5	65	0.0820351671	0.7927675872	-0.6039782976		Multiple R	0.9223307274		AIC	41.5014849434
3	7	38	0.9905712774	0.0018602097	0.1369857072		R Square	0.8506939707		AICc	48.1681516101
5	8	51					Adjusted R Square	0.8133674634		SBC	42.6951707618
8	1	38	b0	-6.0461855849		=C15-MMULT(A15:B15,F8:F9)	Standard Error	5.8880844651
9	3	55	b1	5.7315483132		=-G3/G$5	Observations	11
5	4	43	b2	4.4090606971		=-G4/G$5
4	0	25					ANOVA				Alpha	0.05
2	6	33	b0	-6.0461855849		=TRegCoeff(A4:B14,C4:C14)		df	SS	MS	F	p-value	sig
8	7	71	b1	5.7315483132			Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462	yes
6	4	51	b2	4.4090606971			Residual	8	277.3563093482	34.6695386685
9	2	49					Total	10	1857.6363636364
6	4.2727272727	47.1818181818
								coeff	std err	t stat	p-value	lower	upper	vif
							Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998
							Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255	1.1255142436
							Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481	1.1255142436

Reg Anova 1

ANOVA using Regression
			Model with Dummy Variables			SUMMARY OUTPUT							Effect size
Flavor 1	Flavor 2	Flavor 3	Score	t1	t2
16	7	15	16	1	0	Regression Statistics							ω2	-0.0388821385
8	14	12	8	1	0	Multiple R	0.28502265						ω	0.1971855434
14	10	11	14	1	0	R Square	0.081237911
12	12	17	12	1	0	Adjusted R Square	-0.0412637008
15	8	20	15	1	0	Standard Error	3.9791121288
7	18	9	7	1	0	Observations	18
			7	0	1
			14	0	1	ANOVA
			10	0	1		df	SS	MS	F	Significance F
			12	0	1	Regression	2	21	10.5	0.6631578947	0.5296914941
			8	0	1	Residual	15	237.5	15.8333333333
			18	0	1	Total	17	258.5
			15	0	0
			12	0	0		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
			11	0	0	Intercept	14	1.6244657241	8.6182181575	0.0000003403	10.5375332705	17.4624667295
			17	0	0	t1	-2	2.2973414587	-0.8705715001	0.3977069129	-6.8966674081	2.8966674081
			20	0	0	t2	-2.5	2.2973414587	-1.0882143752	0.2936767453	-7.3966674081	2.3966674081
			9	0	0
						Anova: Single Factor
						SUMMARY
						Groups	Count	Sum	Average	Variance
						Flavor 1	6	72	12	14
						Flavor 2	6	69	11.5	16.7
						Flavor 3	6	84	14	16.8
						ANOVA
						Source of Variation	SS	df	MS	F	P-value	F crit
						Between Groups	21	2	10.5	0.6631578947	0.5296914941	3.6823203437
						Within Groups	237.5	15	15.8333333333
						Total	258.5	17

Reg Anova 2

ANOVA using Regression
			Model with Dummy Variables			SUMMARY OUTPUT								Analysis of means
Flavor 1	Flavor 2	Flavor 3	Score	t1	t2
16	7	15	16	1	0	Regression Statistics								Flavor 1	Flavor 2	Flavor 3
8	14	12	8	1	0	Multiple R	0.28502265							16	7	15
14	10	11	14	1	0	R Square	0.081237911							8	14	12
12	12	17	12	1	0	Adjusted R Square	-0.0412637008							14	10	11
15	8	20	15	1	0	Standard Error	3.9791121288							12	12	17
7	18	9	7	1	0	Observations	18							15	8	20
			7	0	1									7	18	9
			14	0	1	ANOVA							mean	12	11.5	14	12.5
			10	0	1		df	SS	MS	F	Significance F		grp effect	-0.5	-1	1.5
			12	0	1	Regression	2	21	10.5	0.6631578947	0.5296914941
			8	0	1	Residual	15	237.5	15.8333333333
			18	0	1	Total	17	258.5
			15	-1	-1
			12	-1	-1		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
			11	-1	-1	Intercept	12.5	0.9378857231	13.3278497496	0.000000001	10.5009439017	14.4990560983
			17	-1	-1	t1	-0.5	1.3263707096	-0.3769685175	0.7114765762	-3.3270922462	2.3270922462
			20	-1	-1	t2	-1	1.3263707096	-0.7539370349	0.4625588773	-3.8270922462	1.8270922462
			9	-1	-1
						Anova: Single Factor
						SUMMARY
						Groups	Count	Sum	Average	Variance
						Flavor 1	6	72	12	14
						Flavor 2	6	69	11.5	16.7
						Flavor 3	6	84	14	16.8
						ANOVA
						Source of Variation	SS	df	MS	F	P-value	F crit
						Between Groups	21	2	10.5	0.6631578947	0.5296914941	3.6823203437
						Within Groups	237.5	15	15.8333333333
						Total	258.5	17

Reg Anova 3

Two-way ANOVA using regression
	Crop			t1	t2	t3	t1*t2	t1*t3	y	Anova: Two-Factor With Replication							SUMMARY OUTPUT
Fertilizer	Corn	Soy	Rice	1	1	0	1	0	128
Blend X	128	166	151	1	1	0	1	0	150	SUMMARY	Corn	Soy	Rice	Total			Regression Statistics
	150	178	125	1	1	0	1	0	174	Blend X							Multiple R	0.6171786337
	174	187	117	1	1	0	1	0	116	Count	5	5	5	15			R Square	0.3809094658
	116	153	155	1	1	0	1	0	109	Sum	677	879	706	2262			Adjusted R Square	0.2519322712
	109	195	158	0	1	0	0	0	175	Average	135.4	175.8	141.2	150.8			Standard Error	21.2210587232
Blend Y	175	140	167	0	1	0	0	0	132	Variance	707.8	278.7	354.2	723.8857142857			Observations	30
	132	145	183	0	1	0	0	0	120
	120	159	142	0	1	0	0	0	187	Blend Y							ANOVA
	187	131	167	0	1	0	0	0	184	Count	5	5	5	15				df	SS	MS	F	Significance F
	184	126	168	1	0	1	0	1	166	Sum	798	701	827	2326			Regression	5	6649.8666666667	1329.9733333333	2.9533086603	0.0323741792
				1	0	1	0	1	178	Average	159.6	140.2	165.4	155.0666666667			Residual	24	10808	450.3333333333
t1 = 1 if Blend X, = 0 otherwise				1	0	1	0	1	187	Variance	978.3	165.7	217.3	513.3523809524			Total	29	17457.8666666667
t2 = 1 if Corn, = 0 otherwise				1	0	1	0	1	153
t3 = 1 if Soy, = 0 otherwise				1	0	1	0	1	195	Total								Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
				0	0	1	0	0	140	Count	10	10	10				Intercept	165.4	9.4903459719	17.4282371253	0	145.8128885992	184.9871114008
				0	0	1	0	0	145	Sum	1475	1580	1533				t1	-24.2	13.4213759851	-1.8030938129	0.0839444093	-51.9003585907	3.5003585907
				0	0	1	0	0	159	Average	147.5	158	153.3				t2	-5.8	13.4213759851	-0.432146451	0.6694929262	-33.5003585907	21.9003585907
				0	0	1	0	0	131	Variance	912.0555555556	549.5555555556	416.6777777778				t3	-25.2	13.4213759851	-1.8776018217	0.0726414191	-52.9003585907	2.5003585907
				0	0	1	0	0	126								t1*t2	0	18.9806919438	0	1	-39.1742228016	39.1742228016
				1	0	0	0	0	151								t1*t3	59.8	18.9806919438	3.1505700728	0.0043278129	20.6257771984	98.9742228016
				1	0	0	0	0	125	ANOVA
				1	0	0	0	0	117	Source of Variation	SS	df	MS	F	P-value	F crit	R	0.3809094658
				1	0	0	0	0	155	Rows	136.5333333333	1	136.5333333333	0.3031828275	0.586983179	4.2596772727
				1	0	0	0	0	158	Columns	553.2666666667	2	276.6333333333	0.6142857143	0.5493169371	3.4028261054	Regression (Row)
				0	0	0	0	0	167	Interaction	5960.0666666667	2	2980.0333333333	6.6173945226	0.0051421901	3.4028261054
				0	0	0	0	0	183	Within	10808	24	450.3333333333				SUMMARY OUTPUT
				0	0	0	0	0	142
				0	0	0	0	0	167	Total	17457.8666666667	29					Regression Statistics
				0	0	0	0	0	168								Multiple R	0.0884349145
																	R Square	0.0078207341
				Calculating coefficients from ANOVA						Calculation of elements of the regression model from the ANOVA results							Adjusted R Square	-0.0276142397
																	Standard Error	24.8720535465
				t1	t2	t3	t1*t2	t1*t3	mean	MST	601.9954022989						Observations	30
				0	0	0	0	0	165.4	R	0.3809094658
				0	0	1	0	0	140.2								ANOVA
				0	1	0	0	0	159.6	Calculation of omega square								df	SS	MS	F	Significance F
				1	0	0	0	0	141.2								Regression	1	136.5333333333	136.5333333333	0.2207066431	0.6421403428
				1	0	1	0	1	175.8	ω2	0.2455969891						Residual	28	17321.3333333333	618.619047619
				1	1	0	1	0	135.4								Total	29	17457.8666666667
				b0	165.4													Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
				b3	-25.2												Intercept	155.0666666667	6.4219366114	24.146402565	2.74870043674344E-20	141.9119258476	168.2214074857
				b2	-5.8												t1	-4.2666666667	9.0819898526	-0.4697942561	0.6421403428	-22.8702795424	14.3369462091
				b1	-24.2
				b5	59.8												Regression (Columns)
				b4	0
																	SUMMARY OUTPUT
																	Regression Statistics
																	Multiple R	0.1780211764
																	R Square	0.0316915392
																	Adjusted R Square	-0.0400350134
																	Standard Error	25.0219163194
																	Observations	30
																	ANOVA
																		df	SS	MS	F	Significance F
																	Regression	2	553.2666666667	276.6333333333	0.4418383162	0.6474188037
																	Residual	27	16904.6	626.0962962963
																	Total	29	17457.8666666667
																		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
																	Intercept	153.3	7.9126246992	19.3741022516	2.27730898401226E-17	137.0646351768	169.5353648232
																	t2	-5.8	11.1901411635	-0.5183133899	0.6084614762	-28.760273123	17.160273123
																	t3	4.7	11.1901411635	0.4200125746	0.6777999373	-18.260273123	27.660273123

Reg Anova 4

Two-way ANOVA using regression
										SUMMARY OUTPUT
	Crop			t1	t2	t3	t1*t2	t1*t3	y								Anova: Two-Factor With Replication
Fertilizer	Corn	Soy	Rice	1	1	0	1	0	128	Regression Statistics
Blend X	128	166	151	1	1	0	1	0	150	Multiple R	0.5602916494						SUMMARY	Corn	Soy	Rice	Total
	150	178	125	1	1	0	1	0	174	R Square	0.3139267324						Blend X
	174	187	117	1	1	0	1	0	116	Adjusted R Square	0.1505759544						Count	5	5	5	15
	116	153	155	1	1	0	1	0	109	Standard Error	21.9161823231						Sum	677	879	706	2262
	109		158	-1	1	0	-1	0	175	Observations	27						Average	135.4	175.8	141.2	150.8
Blend Y	175	140	167	-1	1	0	-1	0	132								Variance	707.8	278.7	354.2	723.8857142857
	132	145	183	-1	1	0	-1	0	120	ANOVA
	120	159	142	-1	1	0	-1	0	187		df	SS	MS	F	Significance F		Blend Y
	187	131	167	-1	1	0	-1	0	184	Regression	5	4615.3740740741	923.0748148148	1.9217951472	0.1333573079		Count	5	5	5	15
	184			1	0	1	0	1	166	Residual	21	10086.7	480.319047619				Sum	798	701	827	2326
				1	0	1	0	1	178	Total	26	14702.0740740741					Average	159.6	140.2	165.4	155.0666666667
t1 = 1 if Blend X, = -1 Blend Y				1	0	1	0	1	187								Variance	978.3	165.7	217.3	513.3523809524
t2 = 1 if Corn, -1 if Rice; = 0 otherwise				1	0	1	0	1	153		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
t3 = 1 if Soy, -1 if Rice; = 0 otherwise				-1	0	1	0	-1	140	Intercept	152.6166666667	4.2440504575	35.9601442526	2.37929000771869E-20	143.7906805775	161.4426527558	Total
				-1	0	1	0	-1	145	t1	-3.4166666667	4.2440504575	-0.8050485499	0.4298128072	-12.2426527558	5.4093194225	Count	10	10	10
				-1	0	1	0	-1	159	t2	-5.1166666667	5.8328894389	-0.8772096095	0.3902981081	-17.2468242986	7.0134909652	Sum	1475	1580	1533
				-1	0	1	0	-1	131	t3	4.7583333333	6.1664623518	0.7716471879	0.4489201173	-8.0655271465	17.5821938132	Average	147.5	158	153.3
				1	-1	-1	-1	-1	151	t1*t2	-8.6833333333	5.8328894389	-1.4886847118	0.1514350809	-20.8134909652	3.4468242986	Variance	912.0555555556	549.5555555556	416.6777777778
				1	-1	-1	-1	-1	125	t1*t3	17.0416666667	6.1664623518	2.7636050777	0.0116400567	4.2178061868	29.8655271465
				1	-1	-1	-1	-1	117
				1	-1	-1	-1	-1	155	α + β model							ANOVA
				1	-1	-1	-1	-1	158								Source of Variation	SS	df	MS	F	P-value	F crit
				-1	-1	-1	1	1	167	SUMMARY OUTPUT							Rows	136.5333333333	1	136.5333333333	0.3031828275	0.586983179	4.2596772727
				-1	-1	-1	1	1	183								Columns	553.2666666667	2	276.6333333333	0.6142857143	0.5493169371	3.4028261054
				-1	-1	-1	1	1	142	Regression Statistics							Interaction	5960.0666666667	2	2980.0333333333	6.6173945226	0.0051421901	3.4028261054
				-1	-1	-1	1	1	167	Multiple R	0.2528429948						Within	10808	24	450.3333333333
										R Square	0.06392958
				Equally weighted means						Adjusted R Square	-0.0581665617						Total	17457.8666666667	29
										Standard Error	24.4613063152
					Corn	Soy	Rice			Observations	27							MST	601.9954022989
				Blend X	135.4	171	141.2	149.2										R	0.3809094658
				Blend Y	159.6	143.75	164.75	156.0333333333		ANOVA
					147.5	157.375	152.975	152.6166666667			df	SS	MS	F	Significance F
										Regression	3	939.8974211815	313.2991403938	0.5236003294	0.6703789375
				Calculation of coefficients						Residual	23	13762.1766528926	598.3555066475
										Total	26	14702.0740740741
				b0	152.6166666667
				b1	-3.4166666667						Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
				b2	-5.1166666667					Intercept	152.3412534435	4.7303824279	32.2048493471	1.22707316649507E-20	142.5557118339	162.1267950531
				b3	4.7583333333					t1	-4.3388429752	4.7172969742	-0.9197731241	0.367237884	-14.0973152616	5.4196293111
				b4	-8.6833333333					t2	-4.8412534435	6.5055131595	-0.7441770272	0.4643051569	-18.2989327507	8.6164258636
				b5	17.0416666667					t3	5.0337465565	6.8780809357	0.7318533474	0.4716500212	-9.1946479162	19.2621410291
				t2	t3	t1	t1*t2	t1*t3	y	α + αβ model
				1	0	1	1	0	128
				1	0	1	1	0	150	SUMMARY OUTPUT
				1	0	1	1	0	174
				1	0	1	1	0	116	Regression Statistics
				1	0	1	1	0	109	Multiple R	0.5328886108
				1	0	-1	-1	0	175	R Square	0.2839702715
				1	0	-1	-1	0	132	Adjusted R Square	0.1905750895
				1	0	-1	-1	0	120	Standard Error	21.3939468353
				1	0	-1	-1	0	187	Observations	27
				1	0	-1	-1	0	184
				0	1	1	0	1	166	ANOVA
				0	1	1	0	1	178		df	SS	MS	F	Significance F
				0	1	1	0	1	187	Regression	3	4174.9519666361	1391.6506555453	3.0405237776	0.0493882353
				0	1	1	0	1	153	Residual	23	10527.122107438	457.700961193
				0	1	-1	0	-1	140	Total	26	14702.0740740741
				0	1	-1	0	-1	145
				0	1	-1	0	-1	159		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
				0	1	-1	0	-1	131	Intercept	152.2479338843	4.1257649683	36.9017467199	5.67336085447165E-22	143.7131387839	160.7827289847
				-1	-1	1	-1	-1	151	t1	-3.389738292	4.1372095533	-0.8193296105	0.4210077559	-11.9482083203	5.1687317362
				-1	-1	1	-1	-1	125	t1*t2	-8.710261708	5.6897453013	-1.5308702317	0.1394408867	-20.4803966269	3.059873211
				-1	-1	1	-1	-1	117	t1*t3	17.014738292	6.0155944237	2.8284384042	0.0095257547	4.5705331062	29.4589434778
				-1	-1	1	-1	-1	155
				-1	-1	1	-1	-1	158	β + αβ model
				-1	-1	-1	1	1	167
				-1	-1	-1	1	1	183	SUMMARY OUTPUT
				-1	-1	-1	1	1	142
				-1	-1	-1	1	1	167	Regression Statistics
										Multiple R	0.5410666328
										R Square	0.2927531011
										Adjusted R Square	0.1641627558
										Standard Error	21.7401976828
										Observations	27
										ANOVA
											df	SS	MS	F	Significance F
										Regression	4	4304.0777777778	1076.0194444444	2.2766336036	0.0934594583
										Residual	22	10397.9962962963	472.6361952862
										Total	26	14702.0740740741
											Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
										Intercept	152.4901234568	4.2070826901	36.2460485538	4.09900291372637E-21	143.7651679712	161.2150789423
										t2	-4.9901234568	5.7839506053	-0.8627534703	0.3975808162	-16.9853028432	7.0050559296
										t3	4.8848765432	6.1149586724	0.7988404836	0.4329229412	-7.7967715589	17.5665246453
										t1*t2	-9.062962963	5.7671122854	-1.5714906377	0.1303409667	-21.0232218113	2.8972958854
										t1*t3	17.4212962963	6.0990342738	2.856402426	0.009177412	4.7726733756	30.069919217
										Summary
										ANOVA
											dfReg	SSReg	R Square		R Square x SST
										α+β+αβ	5	4615.3740740741	0.3139267324
										α+αβ	3	4174.9519666361	0.2839702715
										β+αβ	4	4304.0777777778	0.2927531011
										α+β	3	939.8974211815	0.06392958
										A	1	311.2962962963	0.0211736313		311.2962962963
										B	2	440.422107438	0.0299564609		440.422107438
										AB	2	3675.4766528926	0.2499971524		3675.4766528926
										ANOVA Results
										ANOVA
										Source of Variation	SS	df	MS	F	P-value
										Blend (Rows)	311.2962962963	1	311.2962962963	0.6481031678	0.4298128072
										Crop (Columns)	440.422107438	2	220.211053719	0.4584682927	0.6384312823
										Interaction	3675.4766528926	2	1837.7383264463	3.8260783859	0.0382957648
										Within	10086.7	21	480.319047619
										Total	14702.0740740741	26
										Total (as sum)	14513.8950566269

Reg Anova 5

Two-way ANOVA Real Statistics tool				Input data in standard format			Descriptive Statistics					Two Factor Anova (via Regression)							Input data with rows and columns exchanged
	Crop			Blend X	Corn	128	COUNT	unbalanced				ANOVA				Alpha	0.05			Blend X	Blend Y
Fertilizer	Corn	Soy	Rice	Blend X	Soy	166		Corn	Rice	Soy			SS	df	MS	F	p-value	sig	Corn	128	175
Blend X	128	166	151	Blend X	Rice	151	Blend X	5	5	4	14	Rows	311.2962962963	1	311.2962962963	0.6481031678	0.4298128072	no		150	132
	150	178	125	Blend X	Corn	150	Blend Y	5	4	4	13	Columns	440.422107438	2	220.211053719	0.4584682927	0.6384312823	no		174	120
	174	187	117	Blend X	Soy	178		10	9	8	27	Inter	3675.4766528926	2	1837.7383264463	3.8260783859	0.0382957648	yes		116	187
	116	153	155	Blend X	Rice	125						Within	10086.7	21	480.319047619					109	184
	109		158	Blend X	Corn	174	MEAN					Total	14702.074074074	26	565.4643874644				Soy	166	140
Blend Y	175	140	167	Blend X	Soy	187		Corn	Rice	Soy										178	145
	132	145	183	Blend X	Rice	117	Blend X	135.4	141.2	171	149.2									187	159
	120	159	142	Blend X	Corn	116	Blend Y	159.6	164.75	143.75	156.0333333333									153	131
	187	131	167	Blend X	Soy	153		147.5	152.975	157.375	152.6166666667
	184			Blend X	Rice	155													Rice	151	167
				Blend X	Corn	109	VARIANCE													125	183
				Blend X	Rice	158		Corn	Rice	Soy										117	142
				Blend Y	Corn	175	Blend X	707.8	354.2	218	618.5549450549									155	167
				Blend Y	Soy	140	Blend Y	978.3	286.9166666667	136.9166666667	512.8974358974									158
				Blend Y	Rice	167		912.0555555556	438.75	364.2678571429	565.4643874644
				Blend Y	Corn	132
				Blend Y	Soy	145
				Blend Y	Rice	183
				Blend Y	Corn	120
				Blend Y	Soy	159
				Blend Y	Rice	142
				Blend Y	Corn	187
				Blend Y	Soy	131
				Blend Y	Rice	167
				Blend Y	Corn	184

Reg An3.1

Three Factor ANOVA – row format, balanced															Input data in column format				Descriptive Statistics					Three Factor Anova (via Regression)							Three Factor ANOVA															Input data in column format
Gender	Country	Position	Sample Scores												Male	Italian	Seated	23				Count	Mean	ANOVA				Alpha	0.05		Gender	Country	Position	Sample Scores												Male	Italian	Seated	23	Male	Foreign	Seated	16	Female	Italian	Seated	19	Female	Foreign	Seated	28
Male	Italian	Seated	23	18	26	32	13	31	26	34	17	23	28	26	Male	Italian	Seated	18	Female			48	24.75		SS	df	MS	F	p-value	sig	Male	Italian	Seated	23	18	26	32	13	31	26	34	17	23	28	26	Male	Italian	Seated	18	Male	Foreign	Seated	31	Female	Italian	Seated	28	Female	Foreign	Seated	37
Male	Italian	Prone	24	25	14	17	30	18	11	16	25	18	14	25	Male	Italian	Seated	26	Male			48	24.5625	A	0.84375	1	0.84375	0.01749357	0.895078017	no	Male	Italian	Prone	24	25	14	17	30	18	11	16	25	18	14	25	Male	Italian	Seated	26	Male	Foreign	Seated	17	Female	Italian	Seated	31	Female	Foreign	Seated	22
Male	Foreign	Seated	16	31	17	11	34	24	24	19	31	16	11	19	Male	Italian	Seated	32		Foreign		48	27.5625	B	810.84375	1	810.84375	16.8113207547	0.0000917793	yes	Male	Foreign	Seated	16	31	17	11	34	24	24	19	31	16	11	19	Male	Italian	Seated	32	Male	Foreign	Seated	11	Female	Italian	Seated	23	Female	Foreign	Seated	44
Male	Foreign	Prone	31	29	40	31	35	25	18	29	36	42	40	36	Male	Italian	Seated	13		Italian		48	21.75	C	25.0104166667	1	25.0104166667	0.5185439695	0.4733717827	no	Male	Foreign	Prone	31	29	40	31	35	25	18	29	36	42	40	36	Male	Italian	Seated	13	Male	Foreign	Seated	34	Female	Italian	Seated	19	Female	Foreign	Seated	37
Female	Italian	Seated	19	28	31	23	19	14	4	29	18	22	18	24	Male	Italian	Seated	31			Prone	48	24.1458333333	A x B	33.84375	1	33.84375	0.7016865294	0.4044868976	no	Female	Italian	Seated	19	28	31	23	19	14	4	29	18	22	18	24	Male	Italian	Seated	31	Male	Foreign	Seated	24	Female	Italian	Seated	14	Female	Foreign	Seated	42
Female	Italian	Prone	29	25	17	12	26	35	10	23	26	18	24	16	Male	Italian	Seated	26			Seated	48	25.1666666667	A x C	446.34375	1	446.34375	9.2540985216	0.0030965103	yes	Female	Italian	Prone	29	25	17	12	26	35	10	23	26	18	24	16	Male	Italian	Seated	26	Male	Foreign	Seated	24	Female	Italian	Seated	4	Female	Foreign	Seated	30
Female	Foreign	Seated	28	37	22	44	37	42	30	37	25	38	41	28	Male	Italian	Seated	34	Female	Foreign		24	28.25	B x C	23.0104166667	1	23.0104166667	0.4770777296	0.4915668605	no	Female	Foreign	Seated	28	37	22	44	37	42	30	37	25	38	41	28	Male	Italian	Seated	34	Male	Foreign	Seated	19	Female	Italian	Seated	29	Female	Foreign	Seated	37
Female	Foreign	Prone	35	23	24	11	23	30	26	16	23	14	19	25	Male	Italian	Seated	17	Female	Italian		24	21.25	A x B x C	1283.34375	1	1283.34375	26.6077199458	0.0000015229	yes	Female	Foreign	Prone	35	23	24	11	23	30	26	16	23	14	19	25	Male	Italian	Seated	17	Male	Foreign	Seated	31	Female	Italian	Seated	18	Female	Foreign	Seated	25
															Male	Italian	Seated	23	Male	Foreign		24	26.875	Within	4244.4166666667	88	48.2320075758																			Male	Italian	Seated	23	Male	Foreign	Seated	16	Female	Italian	Seated	22	Female	Foreign	Seated	38
															Male	Italian	Seated	28	Male	Italian		24	22.25	Total	6867.65625	95	72.2911184211																			Male	Italian	Seated	28	Male	Foreign	Seated	11	Female	Italian	Seated	18	Female	Foreign	Seated	41
															Male	Italian	Seated	26	Female		Prone	24	22.0833333333																							Male	Italian	Seated	26	Male	Foreign	Seated	19	Female	Italian	Seated	24	Female	Foreign	Seated	28
															Male	Italian	Prone	24	Female		Seated	24	27.4166666667																							Male	Italian	Prone	24	Male	Foreign	Prone	31	Female	Italian	Prone	29	Female	Foreign	Prone	35
															Male	Italian	Prone	25	Male		Prone	24	26.2083333333																							Male	Italian	Prone	25	Male	Foreign	Prone	29	Female	Italian	Prone	25	Female	Foreign	Prone	23
															Male	Italian	Prone	14	Male		Seated	24	22.9166666667																							Male	Italian	Prone	14	Male	Foreign	Prone	40	Female	Italian	Prone	17	Female	Foreign	Prone	24
															Male	Italian	Prone	17		Foreign	Prone	24	27.5416666667																							Male	Italian	Prone	17	Male	Foreign	Prone	31	Female	Italian	Prone	12	Female	Foreign	Prone	11
															Male	Italian	Prone	30		Foreign	Seated	24	27.5833333333																							Male	Italian	Prone	30	Male	Foreign	Prone	35	Female	Italian	Prone	26	Female	Foreign	Prone	23
															Male	Italian	Prone	18		Italian	Prone	24	20.75																							Male	Italian	Prone	18	Male	Foreign	Prone	25	Female	Italian	Prone	35	Female	Foreign	Prone	30
															Male	Italian	Prone	11		Italian	Seated	24	22.75																							Male	Italian	Prone	11	Male	Foreign	Prone	18	Female	Italian	Prone	10	Female	Foreign	Prone	26
															Male	Italian	Prone	16	Female	Foreign	Prone	12	22.4166666667																							Male	Italian	Prone	16	Male	Foreign	Prone	29	Female	Italian	Prone	23	Female	Foreign	Prone	16
															Male	Italian	Prone	25	Female	Foreign	Seated	12	34.0833333333																							Male	Italian	Prone	25	Male	Foreign	Prone	36	Female	Italian	Prone	26	Female	Foreign	Prone	23
															Male	Italian	Prone	18	Female	Italian	Prone	12	21.75																							Male	Italian	Prone	18	Male	Foreign	Prone	42	Female	Italian	Prone	18	Female	Foreign	Prone	14
															Male	Italian	Prone	14	Female	Italian	Seated	12	20.75																							Male	Italian	Prone	14	Male	Foreign	Prone	40	Female	Italian	Prone	24	Female	Foreign	Prone	19
															Male	Italian	Prone	25	Male	Foreign	Prone	12	32.6666666667																							Male	Italian	Prone	25	Male	Foreign	Prone	36	Female	Italian	Prone	16	Female	Foreign	Prone	25
															Male	Foreign	Seated	16	Male	Foreign	Seated	12	21.0833333333
															Male	Foreign	Seated	31	Male	Italian	Prone	12	19.75
															Male	Foreign	Seated	17	Male	Italian	Seated	12	24.75
															Male	Foreign	Seated	11				96	24.65625
															Male	Foreign	Seated	34				SST	6867.65625
															Male	Foreign	Seated	24
															Male	Foreign	Seated	24
															Male	Foreign	Seated	19
															Male	Foreign	Seated	31
															Male	Foreign	Seated	16
															Male	Foreign	Seated	11
															Male	Foreign	Seated	19
															Male	Foreign	Prone	31
															Male	Foreign	Prone	29
															Male	Foreign	Prone	40
															Male	Foreign	Prone	31
															Male	Foreign	Prone	35
															Male	Foreign	Prone	25
															Male	Foreign	Prone	18
															Male	Foreign	Prone	29
															Male	Foreign	Prone	36
															Male	Foreign	Prone	42
															Male	Foreign	Prone	40
															Male	Foreign	Prone	36
															Female	Italian	Seated	19
															Female	Italian	Seated	28
															Female	Italian	Seated	31
															Female	Italian	Seated	23
															Female	Italian	Seated	19
															Female	Italian	Seated	14
															Female	Italian	Seated	4
															Female	Italian	Seated	29
															Female	Italian	Seated	18
															Female	Italian	Seated	22
															Female	Italian	Seated	18
															Female	Italian	Seated	24
															Female	Italian	Prone	29
															Female	Italian	Prone	25
															Female	Italian	Prone	17
															Female	Italian	Prone	12
															Female	Italian	Prone	26
															Female	Italian	Prone	35
															Female	Italian	Prone	10
															Female	Italian	Prone	23
															Female	Italian	Prone	26
															Female	Italian	Prone	18
															Female	Italian	Prone	24
															Female	Italian	Prone	16
															Female	Foreign	Seated	28
															Female	Foreign	Seated	37
															Female	Foreign	Seated	22
															Female	Foreign	Seated	44
															Female	Foreign	Seated	37
															Female	Foreign	Seated	42
															Female	Foreign	Seated	30
															Female	Foreign	Seated	37
															Female	Foreign	Seated	25
															Female	Foreign	Seated	38
															Female	Foreign	Seated	41
															Female	Foreign	Seated	28
															Female	Foreign	Prone	35
															Female	Foreign	Prone	23
															Female	Foreign	Prone	24
															Female	Foreign	Prone	11
															Female	Foreign	Prone	23
															Female	Foreign	Prone	30
															Female	Foreign	Prone	26
															Female	Foreign	Prone	16
															Female	Foreign	Prone	23
															Female	Foreign	Prone	14
															Female	Foreign	Prone	19
															Female	Foreign	Prone	25

Reg An3.2

Three Factor ANOVA – row format, unbalanced															Input data in column format				Descriptive Statistics					Three Factor Anova (via Regression)
Gender	Country	Position	Sample Scores												Male	Italian	Seated	23				Count	Mean	ANOVA				Alpha	0.05
Male	Italian	Seated	23	18	26	32	13	31	26	34	17	23	28	26	Male	Italian	Seated	18	Female			45	24.7787878788		SS	df	MS	F	p-value	sig
Male	Italian	Prone	24	25	14	17	30	18	11	16	25				Male	Italian	Seated	26	Male			44	24.6723484848	A	0.2495922367	1	0.2495922367	0.0048965713	0.9443856267	no
Male	Foreign	Seated	16	31	17	11	34	24	24	19	31	16	11		Male	Italian	Seated	32		Foreign		46	27.5511363636	B	703.5546876976	1	703.5546876976	13.8025355528	0.0003720251	yes
Male	Foreign	Prone	31	29	40	31	35	25	18	29	36	42	40	36	Male	Italian	Seated	13		Italian		43	21.9	C	21.0242793021	1	21.0242793021	0.4124602787	0.5225369673	no
Female	Italian	Seated	19	28	31	23	19	14	4	29	18	22	18	24	Male	Italian	Seated	31			Prone	42	24.2371212121	A x B	24.5875616386	1	24.5875616386	0.4823657629	0.4893400316	no
Female	Italian	Prone	29	25	17	12	26	35	10	23	26	18			Male	Italian	Seated	26			Seated	47	25.2140151515	A x C	407.1304874194	1	407.1304874194	7.9872014578	0.0059314596	yes
Female	Foreign	Seated	28	37	22	44	37	42	30	37	25	38	41	28	Male	Italian	Seated	34	Female	Foreign		23	28.1325757576	B x C	11.51941143	1	11.51941143	0.225991083	0.6357919268	no
Female	Foreign	Prone	35	23	24	11	23	30	26	16	23	14	19		Male	Italian	Seated	17	Female	Italian		22	21.425	A x B x C	1189.7800006322	1	1189.7800006322	23.3414417471	0.000006325	yes
															Male	Italian	Seated	23	Male	Foreign		23	26.9696969697	Within	4128.8015151515	81	50.9728582117
															Male	Italian	Seated	28	Male	Italian		21	22.375	Total	6590.9887640449	88	74.8975995914
															Male	Italian	Seated	26	Female		Prone	21	22.1409090909
															Male	Italian	Prone	24	Female		Seated	24	27.4166666667
															Male	Italian	Prone	25	Male		Prone	21	26.3333333333
															Male	Italian	Prone	14	Male		Seated	23	23.0113636364
															Male	Italian	Prone	17		Foreign	Prone	23	27.4242424242
															Male	Italian	Prone	30		Foreign	Seated	23	27.678030303
															Male	Italian	Prone	18		Italian	Prone	19	21.05
															Male	Italian	Prone	11		Italian	Seated	24	22.75
															Male	Italian	Prone	16	Female	Foreign	Prone	11	22.1818181818
															Male	Italian	Prone	25	Female	Foreign	Seated	12	34.0833333333
															Male	Foreign	Seated	16	Female	Italian	Prone	10	22.1
															Male	Foreign	Seated	31	Female	Italian	Seated	12	20.75
															Male	Foreign	Seated	17	Male	Foreign	Prone	12	32.6666666667
															Male	Foreign	Seated	11	Male	Foreign	Seated	11	21.2727272727
															Male	Foreign	Seated	34	Male	Italian	Prone	9	20
															Male	Foreign	Seated	24	Male	Italian	Seated	12	24.75
															Male	Foreign	Seated	24				89	24.7255681818
															Male	Foreign	Seated	19				SST	6590.9887640449
															Male	Foreign	Seated	31
															Male	Foreign	Seated	16
															Male	Foreign	Seated	11
															Male	Foreign	Prone	31
															Male	Foreign	Prone	29
															Male	Foreign	Prone	40
															Male	Foreign	Prone	31
															Male	Foreign	Prone	35
															Male	Foreign	Prone	25
															Male	Foreign	Prone	18
															Male	Foreign	Prone	29
															Male	Foreign	Prone	36
															Male	Foreign	Prone	42
															Male	Foreign	Prone	40
															Male	Foreign	Prone	36
															Female	Italian	Seated	19
															Female	Italian	Seated	28
															Female	Italian	Seated	31
															Female	Italian	Seated	23
															Female	Italian	Seated	19
															Female	Italian	Seated	14
															Female	Italian	Seated	4
															Female	Italian	Seated	29
															Female	Italian	Seated	18
															Female	Italian	Seated	22
															Female	Italian	Seated	18
															Female	Italian	Seated	24
															Female	Italian	Prone	29
															Female	Italian	Prone	25
															Female	Italian	Prone	17
															Female	Italian	Prone	12
															Female	Italian	Prone	26
															Female	Italian	Prone	35
															Female	Italian	Prone	10
															Female	Italian	Prone	23
															Female	Italian	Prone	26
															Female	Italian	Prone	18
															Female	Foreign	Seated	28
															Female	Foreign	Seated	37
															Female	Foreign	Seated	22
															Female	Foreign	Seated	44
															Female	Foreign	Seated	37
															Female	Foreign	Seated	42
															Female	Foreign	Seated	30
															Female	Foreign	Seated	37
															Female	Foreign	Seated	25
															Female	Foreign	Seated	38
															Female	Foreign	Seated	41
															Female	Foreign	Seated	28
															Female	Foreign	Prone	35
															Female	Foreign	Prone	23
															Female	Foreign	Prone	24
															Female	Foreign	Prone	11
															Female	Foreign	Prone	23
															Female	Foreign	Prone	30
															Female	Foreign	Prone	26
															Female	Foreign	Prone	16
															Female	Foreign	Prone	23
															Female	Foreign	Prone	14
															Female	Foreign	Prone	19

ANOVA Match 2.5

Repeated Measures ANOVA using Regression																						Regression Analysis (S,D,A)								Two Factor Anova with Repeated Measures
				subjects													days		age
Age	Day 1	Day 2	Day 3	2	3	4	5	6	7	8	9	10	11	12	13	14	2	3	mid	old	yield	OVERALL FIT								ANOVA				Alpha	0.05
Young	250	278	442	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	250	Multiple R	0.9559875706		AIC	345.0614638532					SS	df	MS	F	P value	F crit
	65	207	341	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	65	R Square	0.9139122351		AICc	379.6069183987				Between Subjects	192393.809523809	13
	251	261	384	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	251	Adjusted R Square	0.8529334016		SBC	376.3395169823				– Rows	122054.892857143	2	61027.4464285714	9.5438193041	0.0039493461	3.9822979571
	103	286	401	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	103	Standard Error	52.4107139477							– Error	70338.9166666666	11	6394.446969697
	230	306	432	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	230	Observations	42							Within Subjects	573396.666666667	28
Middle	54	172	307	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	54									– Columns	507471.476190476	2	253735.738095238	104.4198873423	0	3.4433567794
	20	116	425	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	20	ANOVA				Alpha	0.05			– Interaction	12466.1571428572	4	3116.5392857143	1.2825496462	0.3070609813	2.8167083396
	41	168	378	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	41		df	SS	MS	F	p-value	sig		– Error	53459.0333333332	22	2429.956060606
	29	81	193	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	29	Regression	17	699865.285714285	41168.5462184874	14.9873682898	0.0000000072	yes		Total	765790.476190476	41	18677.8164924506
	3	54	285	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	3	Residual	24	65925.1904761909	2746.882936508
Old	118	124	365	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	118	Total	41	765790.476190476
	83	266	382	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	83
	38	207	289	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	38		coeff	std err	t stat	p-value	lower	upper	vif
	71	285	471	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	71	Intercept	199.6666666667	31.0795247196	6.4243796669	0.0000012116	135.5216803019	263.8116530315
				0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	278	2	-119	41.114346637	-2.8943668022	0.0079649051	-203.8558408864	-34.1441591136	1.8571428571
				1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	207	3	-24.6666666667	41.114346637	-0.5999527825	0.5541606487	-109.5225075531	60.1891742198	1.8571428571
				0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	261	4	-60	41.114346637	-1.4593446061	0.1574360851	-144.8558408864	24.8558408864	1.8571428571
				0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	286	5	-0.6666666667	41.114346637	-0.0162149401	0.9871969633	-85.5225075531	84.1891742198	1.8571428571
				0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	306	6	63.6666666667	41.114346637	1.5485267765	0.1345823747	-21.1891742198	148.5225075531	3.00239975158033E+15
				0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	172	7	73	41.114346637	1.7755359375	0.0884931489	-11.8558408864	157.8558408864	-1.50119987579017E+15
				0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	116	8	81.6666666667	41.114346637	1.9863301584	0.0585307717	-3.1891742198	166.5225075531	-1.12589990684262E+15
				0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	168	9	-13	41.114346637	-0.3161913313	0.7545908747	-97.8558408864	71.8558408864	-4.5035996273705E+15
				0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	81	10	0	0	ERROR:#DIV/0!	ERROR:#DIV/0!	0	0	3.00239975158033E+15
				0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	54	11	24.3333333333	41.114346637	0.5918453125	0.5594887147	-60.5225075531	109.1891742198	3.00239975158033E+15
				0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	1	124	12	65.6666666667	41.114346637	1.5971715967	0.1233119202	-19.1891742198	150.5225075531	3.00239975158033E+15
				0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	266	13	0	0	ERROR:#DIV/0!	ERROR:#DIV/0!	0	0	1.50119987579017E+15
				0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	1	207	14	97.6666666667	41.114346637	2.37548872	0.025858123	12.8108257802	182.5225075531	3.00239975158033E+15
				0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1	285	2	103.9285714286	19.0322442528	5.4606577158	0.0000130096	64.6479498907	143.2091929665	1.3333333333
				0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	442	3	267.0714285714	19.0322442528	14.0325767694	0	227.7908070335	306.3520501093	1.3333333333
				1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	341	mid	-209.3333333333	41.114346637	-5.0914911814	0.0000329725	-294.1891742198	-124.4774924469	-2.25179981368525E+15
				0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	384	old	-145.3333333333	41.114346637	-3.5348569349	0.0016900167	-230.1891742198	-60.4774924469	-1.50119987579017E+15
				0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	401
				0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	432
				0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	307
				0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	425	Regression Analysis (S)
				0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	378
				0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	193	OVERALL FIT
				0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	285	Multiple R	0.5012340507		AIC	427.9098628359
				0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	1	365	R Square	0.2512355736		AICc	446.3714012974
				0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	382	Adjusted R Square	-0.096405053		SBC	452.2372374918
				0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	289	Standard Error	143.1029432994
				0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1	471	Observations	42
																						ANOVA				Alpha	0.05
																							df	SS	MS	F	p-value	sig
																						Regression	13	192393.809523809	14799.5238095238	0.7226876101	0.7267173612	no
																						Residual	28	573396.666666667	20478.4523809524
																						Total	41	765790.476190476
																							coeff	std err	t stat	p-value	lower	upper	vif
																						Intercept	323.3333333333	82.6205228357	3.913474791	0.0005296789	154.0928642977	492.5738023689
																						2	-119	116.8430639247	-1.0184601122	0.3171792412	-358.3421666125	120.3421666125	1.8571428571
																						3	-24.6666666667	116.8430639247	-0.211109379	0.8343308894	-264.0088332792	214.6754999458	1.8571428571
																						4	-60	116.8430639247	-0.5135093003	0.6116236375	-299.3421666125	179.3421666125	1.8571428571
																						5	-0.6666666667	116.8430639247	-0.0057056589	0.9954880257	-240.0088332792	238.6754999458	1.8571428571
																						6	-145.6666666667	116.8430639247	-1.2466864679	0.2228413238	-385.0088332792	93.6754999458	1.8571428571
																						7	-136.3333333333	116.8430639247	-1.1668072434	0.2531298742	-375.6754999458	103.0088332792	1.8571428571
																						8	-127.6666666667	116.8430639247	-1.0926336778	0.2838668101	-367.0088332792	111.6754999458	1.8571428571
																						9	-222.3333333333	116.8430639247	-1.9028372405	0.0673910084	-461.6754999458	17.0088332792	1.8571428571
																						10	-209.3333333333	116.8430639247	-1.7915768921	0.0840160741	-448.6754999458	30.0088332792	1.8571428571
																						11	-121	116.8430639247	-1.0355770889	0.3092609744	-360.3421666125	118.3421666125	1.8571428571
																						12	-79.6666666667	116.8430639247	-0.6818262376	0.5009503068	-319.0088332792	159.6754999458	1.8571428571
																						13	-145.3333333333	116.8430639247	-1.2438336385	0.2238738563	-384.6754999458	94.0088332792	1.8571428571
																						14	-47.6666666667	116.8430639247	-0.4079546108	0.6864097608	-287.0088332792	191.6754999458	1.8571428571
																						Regression Analysis(D,A)
																						OVERALL FIT
																						Multiple R	0.9066757133		AIC	349.5565863569
																						R Square	0.822060849		AICc	351.9565863569
																						Adjusted R Square	0.8028241841		SBC	358.2449344483
																						Standard Error	60.6861904107
																						Observations	42
																						ANOVA				Alpha	0.05
																							df	SS	MS	F	p-value	sig
																						Regression	4	629526.369047618	157381.592261905	42.7340628122	0	yes
																						Residual	37	136264.107142858	3682.8137065637
																						Total	41	765790.476190476
																							coeff	std err	t stat	p-value	lower	upper	vif
																						Intercept	158.8	20.5156768115	7.7404221883	0.000000003	117.2312902705	200.3687097295
																						2	103.9285714286	22.9372239775	4.5310004179	0.0000594444	57.4533410825	150.4038017746	1.3333333333
																						3	267.0714285714	22.9372239775	11.6435811427	0	220.5961982254	313.5466589175	1.3333333333
																						mid	-127.4	22.1594636113	-5.7492366347	0.0000013726	-172.299338154	-82.500661846	1.2857142857
																						old	-57.55	23.5036604805	-2.4485547708	0.0192037481	-105.1729397193	-9.9270602807	1.2857142857
																						Regression Analysis (A)
																						OVERALL FIT
																						Multiple R	0.3992294923		AIC	410.7696961394
																						R Square	0.1593841875		AICc	411.8507772204
																						Adjusted R Square	0.1162756843		SBC	415.9827049942
																						Standard Error	128.475836632
																						Observations	42
																						ANOVA				Alpha	0.05
																							df	SS	MS	F	p-value	sig
																						Regression	2	122054.892857143	61027.4464285714	3.6972795544	0.0338573002	yes
																						Residual	39	643735.583333333	16506.0405982906
																						Total	41	765790.476190476
																							coeff	std err	t stat	p-value	lower	upper	vif
																						Intercept	282.4666666667	33.1723183777	8.5151319076	0.0000000002	215.3693194875	349.5640138458
																						mid	-127.4	46.9127425451	-2.7156800709	0.0098054701	-222.2899783801	-32.5100216199	1.2857142857
																						old	-57.55	49.7584775666	-1.1565868333	0.2544768414	-158.1960207688	43.0960207688	1.2857142857
																						Regression Analysis (S,D)
																						OVERALL FIT
																						Multiple R	0.9559875706		AIC	341.0614638532
																						R Square	0.9139122351		AICc	366.5614638532
																						Adjusted R Square	0.8642462169		SBC	368.8641777458
																						Standard Error	50.3545851843
																						Observations	42
																						ANOVA				Alpha	0.05
																							df	SS	MS	F	p-value	sig
																						Regression	15	699865.285714286	46657.6857142857	18.4011577336	0.0000000004	yes
																						Residual	26	65925.1904761901	2535.5842490842
																						Total	41	765790.476190476
																							coeff	std err	t stat	p-value	lower	upper	vif
																						Intercept	199.6666666667	31.0795247196	6.4243796669	0.0000008314	135.7817886666	263.5515446668
																						2	-119	41.114346637	-2.8943668022	0.0075939969	-203.511749863	-34.488250137	1.8571428571
																						3	-24.6666666667	41.114346637	-0.5999527825	0.5537311215	-109.1784165296	59.8450831963	1.8571428571
																						4	-60	41.114346637	-1.4593446061	0.1564475725	-144.511749863	24.511749863	1.8571428571
																						5	-0.6666666667	41.114346637	-0.0162149401	0.9871867067	-85.1784165296	83.8450831963	1.8571428571
																						6	-145.6666666667	41.114346637	-3.5429644049	0.0015205769	-230.1784165296	-61.1549168037	1.8571428571
																						7	-136.3333333333	41.114346637	-3.315955244	0.002698641	-220.8450831963	-51.8215834704	1.8571428571
																						8	-127.6666666667	41.114346637	-3.1051610231	0.0045525818	-212.1784165296	-43.1549168037	1.8571428571
																						9	-222.3333333333	41.114346637	-5.4076825128	0.0000114962	-306.8450831963	-137.8215834704	1.8571428571
																						10	-209.3333333333	41.114346637	-5.0914911814	0.0000264012	-293.8450831963	-124.8215834704	1.8571428571
																						11	-121	41.114346637	-2.9430116224	0.0067556855	-205.511749863	-36.488250137	1.8571428571
																						12	-79.6666666667	41.114346637	-1.9376853382	0.0635947803	-164.1784165296	4.8450831963	1.8571428571
																						13	-145.3333333333	41.114346637	-3.5348569349	0.0015523048	-229.8450831963	-60.8215834704	1.8571428571
																						14	-47.6666666667	41.114346637	-1.1593682149	0.2568431531	-132.1784165296	36.8450831963	1.8571428571
																						2	103.9285714286	19.0322442528	5.4606577158	0.0000100062	64.8072330835	143.0499097736	1.3333333333
																						3	267.0714285714	19.0322442528	14.0325767694	0	227.9500902264	306.1927669165	1.3333333333
																						Regression Analysis (S,A)
				2	3	4	5	6	7	8	9	10	11	12	13	14	mid	old	yield
				0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	250			OVERALL FIT
				1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	65			Multiple R	0.5012340507		AIC	431.9098628359
				0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	251			R Square	0.2512355736		AICc	457.4098628359
				0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	103			Adjusted R Square	-0.1807439032		SBC	459.7125767284
				0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	230			Standard Error	148.504942506
				0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	54			Observations	42
				0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	20
				0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	41			ANOVA				Alpha	0.05
				0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	29				df	SS	MS	F	p-value	sig
				0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	3			Regression	15	192393.80952381	12826.253968254	0.5815914576	0.8627043748	no
				0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	118			Residual	26	573396.666666667	22053.7179487179
				0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	83			Total	41	765790.476190476
				0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	38
				0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	71				coeff	std err	t stat	p-value	lower	upper	vif
				0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	278			Intercept	323.3333333333	82.6205228357	3.913474791	0.0005856193	153.5044164084	493.1622502583
				1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	207			2	-119	116.8430639247	-1.0184601122	0.3178433426	-359.1743575984	121.1743575984	1.8571428571
				0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	261			3	-24.6666666667	116.8430639247	-0.211109379	0.8344478656	-264.841024265	215.5076909317	1.8571428571
				0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	286			4	-60	116.8430639247	-0.5135093003	0.6119315381	-300.1743575984	180.1743575984	1.8571428571
				0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	306			5	-0.6666666667	116.8430639247	-0.0057056589	0.9954911214	-240.841024265	239.5076909317	1.8571428571
				0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	172			6	76.6666666667	116.8430639247	0.6561507726	0.5174923952	-163.5076909317	316.841024265	ERROR:#VALUE!
				0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	116			7	86	116.8430639247	0.7360299971	0.4683013907	-154.1743575984	326.1743575984	2.25179981368525E+15
				0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	168			8	94.6666666667	116.8430639247	0.8102035627	0.4251770868	-145.5076909317	334.841024265	-4.5035996273705E+15
				0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	81			9	0	0	ERROR:#DIV/0!	ERROR:#DIV/0!	0	0	1.50119987579017E+15
				0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	54			10	13	116.8430639247	0.1112603484	0.9122643593	-227.1743575984	253.1743575984	9.00719925474099E+15
				0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	124			11	24.3333333333	116.8430639247	0.2082565496	0.8366514755	-215.841024265	264.5076909317	-4.5035996273705E+15
				0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	266			12	65.6666666667	116.8430639247	0.5620074009	0.5789242838	-174.5076909317	305.841024265	-4.5035996273705E+15
				0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	207			13	0	0	ERROR:#DIV/0!	ERROR:#DIV/0!	0	0	-900719925474099
				0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	285			14	97.6666666667	116.8430639247	0.8358790277	0.4108392406	-142.5076909317	337.841024265	-900719925474099
				0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	442			mid	-222.3333333333	116.8430639247	-1.9028372405	0.0681911141	-462.5076909317	17.841024265	3.00239975158033E+15
				1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	341			old	-145.3333333333	116.8430639247	-1.2438336385	0.2246588449	-385.5076909317	94.841024265	2.25179981368525E+15
				0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	384
				0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	401
				0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	432
				0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	307
				0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	425
				0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	378
				0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	193
				0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	285
				0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	365
				0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	382
				0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	289
				0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	471
																						Regression Analysis (A,D,DA)
						2	3	mid	old	mid2	old2	mid3	old3	yield
						0	0	0	0	0	0	0	0	250								OVERALL FIT
						0	0	0	0	0	0	0	0	65								Multiple R	0.9156089003		AIC	353.5269313975
						0	0	0	0	0	0	0	0	251								R Square	0.8383396584		AICc	360.6237055911
						0	0	0	0	0	0	0	0	103								Adjusted R Square	0.7991492725		SBC	369.1659579621
						0	0	0	0	0	0	0	0	230								Standard Error	61.2491063633
						0	0	1	0	0	0	0	0	54								Observations	42
						0	0	1	0	0	0	0	0	20
						0	0	1	0	0	0	0	0	41								ANOVA				Alpha	0.05
						0	0	1	0	0	0	0	0	29									df	SS	MS	F	p-value	sig
						0	0	1	0	0	0	0	0	3								Regression	8	641992.526190476	80249.0657738094	21.3914622216	0.0000000001	yes
						0	0	0	1	0	0	0	0	118								Residual	33	123797.950000001	3751.4530303031
						0	0	0	1	0	0	0	0	83								Total	41	765790.476190476
						0	0	0	1	0	0	0	0	38
						0	0	0	1	0	0	0	0	71									coeff	std err	t stat	p-value	lower	upper	vif
						1	0	0	0	0	0	0	0	278								Intercept	179.8	27.3914330779	6.564096135	0.0000001852	124.071710384	235.528289616
						1	0	0	0	0	0	0	0	207								2	87.8	38.7373361516	2.2665471796	0.0300987428	8.9882970172	166.6117029828	3.7333333333
						1	0	0	0	0	0	0	0	261								3	220.2	38.7373361516	5.6844383707	0.0000024452	141.3882970172	299.0117029828	3.7333333333
						1	0	0	0	0	0	0	0	286								mid	-150.4	38.7373361516	-3.8825591778	0.0004689849	-229.2117029828	-71.5882970172	3.8571428571
						1	0	0	0	0	0	0	0	306								old	-102.3	41.0871496168	-2.4898295685	0.0179935893	-185.892434424	-18.707565576	3.8571428571
						1	0	1	0	1	0	0	0	172								mid2	1	54.7828661558	0.0182538825	0.9855462394	-110.4565792321	112.4565792321	3.5238095238
						1	0	1	0	1	0	0	0	116								old2	55.2	58.1060042274	0.9499878839	0.3490244398	-63.0175544743	173.4175544743	3.2571428571
						1	0	1	0	1	0	0	0	168								mid3	68	54.7828661558	1.2412640077	0.2232594523	-43.4565792321	179.4565792321	3.5238095238
						1	0	1	0	1	0	0	0	81								old3	79.05	58.1060042274	1.3604446055	0.1829100402	-39.1675544743	197.2675544743	3.2571428571
						1	0	1	0	1	0	0	0	54
						1	0	0	1	0	1	0	0	124
						1	0	0	1	0	1	0	0	266								Regression Analysis (D)
						1	0	0	1	0	1	0	0	207
						1	0	0	1	0	1	0	0	285								OVERALL FIT
						0	1	0	0	0	0	0	0	442								Multiple R	0.8140495449		AIC	372.4197984709
						0	1	0	0	0	0	0	0	341								R Square	0.6626766615		AICc	373.500879552
						0	1	0	0	0	0	0	0	384								Adjusted R Square	0.6453780288		SBC	377.6328073257
						0	1	0	0	0	0	0	0	401								Standard Error	81.3852818547
						0	1	0	0	0	0	0	0	432								Observations	42
						0	1	1	0	0	0	1	0	307
						0	1	1	0	0	0	1	0	425								ANOVA				Alpha	0.05
						0	1	1	0	0	0	1	0	378									df	SS	MS	F	p-value	sig
						0	1	1	0	0	0	1	0	193								Regression	2	507471.476190476	253735.738095238	38.3080369067	0.0000000006	yes
						0	1	1	0	0	0	1	0	285								Residual	39	258319	6623.5641025641
						0	1	0	1	0	0	0	1	365								Total	41	765790.476190476
						0	1	0	1	0	0	0	1	382
						0	1	0	1	0	0	0	1	289									coeff	std err	t stat	p-value	lower	upper	vif
						0	1	0	1	0	0	0	1	471								Intercept	96.8571428571	21.7511315019	4.4529703132	0.0000691252	52.8613266678	140.8529590465
																						2	103.9285714286	30.7607451669	3.3786103316	0.0016639847	41.7090914859	166.1480513712	1.3333333333
																						3	267.0714285714	30.7607451669	8.6822158281	0.0000000001	204.8519486288	329.2909085141	1.3333333333

ANOVA Match 2.5A

Repeated Measures ANOVA using Regression
Age	Day 1	Day 2	Day 3	Input data in stacked format			Descriptive Statistics					Two Factor Anova with Repeated Measures
Young	250	278	442
	65	207	341	Young	Day 1	250	COUNT	unbalanced				ANOVA				Alpha	0.05
	251	261	384	Young	Day 2	278		Day 1	Day 2	Day 3			SS	df	MS	F	P value	F crit
	103	286	401	Young	Day 3	442	Middle	5	5	5	15	Between Subjects	158686.666666667	12
Middle	54	172	307	Young	Day 1	65	Old	4	4	4	12	– Rows	94407.9000000003	2	47203.9500000001	7.3436303227	0.0109052465	4.1028210151
	20	116	425	Young	Day 2	207	Young	4	4	4	12	– Error	64278.7666666666	10	6427.8766666667
	41	168	378	Young	Day 3	341		13	13	13	39	Within Subjects	552578	26
	29	81	193	Young	Day 1	251						– Columns	488948.051282051	2	244474.025641026	91.8282431488	0.0000000001	3.4928284767
	3	54	285	Young	Day 2	261	MEAN					– Interaction	10384.0153846153	4	2596.0038461538	0.9750993864	0.4431231318	2.866081402
Old	118	124	365	Young	Day 3	384		Day 1	Day 2	Day 3		– Error	53245.9333333332	20	2662.2966666667
	83	266	382	Young	Day 1	103	Middle	29.4	118.2	317.6	155.0666666667	Total	711264.666666667	38	18717.4912280702
	38	207	289	Young	Day 2	286	Old	77.5	220.5	376.75	224.9166666667
	71	285	471	Young	Day 3	401	Young	167.25	258	392	272.4166666667
				Middle	Day 1	54		91.3833333333	198.9	362.1166666667	217.4666666667	Greenhouse and Geisser				Alpha	0.05
				Middle	Day 2	172						Sources	SS	df	MS	F	P value	F crit
				Middle	Day 3	307	VARIANCE					Columns	488948.051282051	1.8601201679	262858.314057886	91.8282431488	0.0000000003	3.613636761
				Middle	Day 1	20		Day 1	Day 2	Day 3		Interaction	10384.0153846153	3.7202403358	2791.221654319	0.9750993864	0.4400589074	2.9626271633
				Middle	Day 2	116	Middle	381.3	2721.2	7970.8	18723.9238095238	Error	53245.9333333332	18.6012016791	2862.4996520059
				Middle	Day 3	425	Old	1091	5241.6666666667	5582.9166666667	19542.2651515151
				Middle	Day 1	41	Young	9481.5833333333	1264.6666666667	1748.6666666667	12705.1742424242	Huyhn and Feldt				Alpha	0.05
				Middle	Day 2	168		6329.2564102564	6525.2307692308	5671.8974358974	18717.4912280702	Sources	SS	df	MS	F	P value	F crit
				Middle	Day 3	378						Columns	488948.051282051	2	244474.025641026	91.8282431488	0.0000000001	3.4928284767
				Middle	Day 1	29	GG epsilon	0.930060084				Interaction	10384.0153846153	4	2596.0038461538	0.9750993864	0.4431231318	2.866081402
				Middle	Day 2	81	HF epsilon	1				Error	53245.9333333332	20	2662.2966666667
				Middle	Day 3	193
				Middle	Day 1	3
				Middle	Day 2	54
				Middle	Day 3	285
				Old	Day 1	118
				Old	Day 2	124
				Old	Day 3	365
				Old	Day 1	83
				Old	Day 2	266
				Old	Day 3	382
				Old	Day 1	38
				Old	Day 2	207
				Old	Day 3	289
				Old	Day 1	71
				Old	Day 2	285
				Old	Day 3	471

ANOVA Match 2.5B

Repeated Measures ANOVA using Regression
Age	Day	Sleep	Input data in Excel Format				Descriptive Statistics					Two Factor Anova with Repeated Measures
Young	Day 1	250
Young	Day 2	278		Day 1	Day 2	Day 3	COUNT	unbalanced				ANOVA				Alpha	0.05
Young	Day 3	442	Middle	54	172	307		Day 1	Day 2	Day 3			SS	df	MS	F	P value	F crit
Young	Day 1	65		20	116	425	Middle	5	5	5	15	Between Subjects	158686.666666667	12
Young	Day 2	207		41	168	378	Old	4	4	4	12	– Rows	94407.9	2	47203.95	7.3436303227	0.0109052465	4.1028210151
Young	Day 3	341		29	81	193	Young	4	4	4	12	– Error	64278.7666666667	10	6427.8766666667
Young	Day 1	251		3	54	285		13	13	13	39	Within Subjects	552578	26
Young	Day 2	261	Old	118	124	365						– Columns	488948.051282051	2	244474.025641026	91.8282431488	0.0000000001	3.4928284767
Young	Day 3	384		83	266	382	MEAN					– Interaction	10384.0153846154	4	2596.0038461539	0.9750993864	0.4431231318	2.866081402
Young	Day 1	103		38	207	289		Day 1	Day 2	Day 3		– Error	53245.9333333334	20	2662.2966666667
Young	Day 2	286		71	285	471	Middle	29.4	118.2	317.6	155.0666666667	Total	711264.666666667	38	18717.4912280702
Young	Day 3	401					Old	77.5	220.5	376.75	224.9166666667
Middle	Day 1	54	Young	250	278	442	Young	167.25	258	392	272.4166666667
Middle	Day 2	172		65	207	341		91.3833333333	198.9	362.1166666667	217.4666666667	Greenhouse and Geisser				Alpha	0.05
Middle	Day 3	307		251	261	384						Sources	SS	df	MS	F	P value	F crit
Middle	Day 1	20		103	286	401	VARIANCE					Columns	488948.051282051	1.8601201679	262858.314057886	91.8282431488	0.0000000003	3.613636761
Middle	Day 2	116						Day 1	Day 2	Day 3		Interaction	10384.0153846154	3.7202403358	2791.2216543191	0.9750993864	0.4400589074	2.9626271633
Middle	Day 3	425					Middle	381.3	2721.2	7970.8	18723.9238095238	Error	53245.9333333334	18.6012016791	2862.4996520059
Middle	Day 1	41					Old	1091	5241.6666666667	5582.9166666667	19542.2651515151
Middle	Day 2	168					Young	9481.5833333333	1264.6666666667	1748.6666666667	12705.1742424242	Huyhn and Feldt				Alpha	0.05
Middle	Day 3	378						6329.2564102564	6525.2307692308	5671.8974358974	18717.4912280702	Sources	SS	df	MS	F	P value	F crit
Middle	Day 1	29										Columns	488948.051282051	2	244474.025641026	91.8282431488	0.0000000001	3.4928284767
Middle	Day 2	81					GG epsilon	0.930060084				Interaction	10384.0153846154	4	2596.0038461539	0.9750993864	0.4431231318	2.866081402
Middle	Day 3	193					HF epsilon	1				Error	53245.9333333334	20	2662.2966666667
Middle	Day 1	3
Middle	Day 2	54
Middle	Day 3	285
Old	Day 1	118
Old	Day 2	124
Old	Day 3	365
Old	Day 1	83
Old	Day 2	266
Old	Day 3	382
Old	Day 1	38
Old	Day 2	207
Old	Day 3	289
Old	Day 1	71
Old	Day 2	285
Old	Day 3	471

Res 1

Residuals
Method of Least Squares
Cig	Life Exp	X		Y		B	Ŷ		E		H = X(XTX)-1XT
5	80	1	5	80		85.7204211948	82.57942		-2.5794191687		0.162153865	0.0427948671	0.0295327562	-0.122981519	0.0825811997	0.1422606987	0.1687849205	0.0229017007	0.1223675324	0.0693190888	0.1024743661	-0.0367777982	0.0030085344	0.1687849205	0.0427948671
23	78	1	23	78		-0.6282004052	71.27181		6.7281881255		0.0427948671	0.0726346166	0.0759501443	0.1140787131	0.0626880334	0.0477681587	0.0411371032	0.0776079081	0.0527414502	0.0660035611	0.0577147418	0.0925277829	0.0825811997	0.0411371032	0.0726346166
25	60	1	25	60			70.01541		-10.015411064		0.0295327562	0.0759501443	0.0811076319	0.1404187389	0.0604776816	0.0372689875	0.0269540124	0.0836863756	0.0450052189	0.0656351692	0.0527414502	0.1068950697	0.091422607	0.0269540124	0.0759501443
48	53	1	48	53			55.56680		-2.5668017437		-0.122981519	0.1140787131	0.1404187389	0.4433290354	0.0350586357	-0.0834714803	-0.1361515319	0.1535887518	-0.0439614416	0.0613986615	-0.004451403	0.2721188678	0.1930987904	-0.1361515319	0.1140787131
17	85	1	17	85		s.e.	75.04101		9.9589856941		0.0825811997	0.0626880334	0.0604776816	0.0350586357	0.0693190888	0.079265672	0.0836863756	0.0593725057	0.0759501443	0.067108737	0.0726346166	0.0494259225	0.056056978	0.0836863756	0.0626880334
8	84	1	8	84		3.907	80.69482		3.305182047		0.1422606987	0.0477681587	0.0372689875	-0.0834714803	0.079265672	0.126511942	0.1475102843	0.032019402	0.1107631854	0.0687665009	0.0950144287	-0.0152268681	0.0162706453	0.1475102843	0.0477681587
4	73	1	4	73		0.171	83.20762		-10.2076195739		0.1687849205	0.0411371032	0.0269540124	-0.1361515319	0.0836863756	0.1475102843	0.1758764659	0.019862467	0.1262356481	0.0695032848	0.1049610118	-0.0439614416	-0.0014121692	0.1758764659	0.0411371032
26	79	1	26	79			69.38721		9.6127893412		0.0229017007	0.0776079081	0.0836863756	0.1535887518	0.0593725057	0.032019402	0.019862467	0.0867256094	0.0411371032	0.0654509732	0.0502548044	0.1140787131	0.0958433106	0.019862467	0.0776079081
11	81	1	11	81			78.81022		2.1897832627		0.1223675324	0.0527414502	0.0450052189	-0.0439614416	0.0759501443	0.1107631854	0.1262356481	0.0411371032	0.0991588383	0.0682139129	0.0875544913	0.0063240621	0.0295327562	0.1262356481	0.0527414502
19	75	1	19	75			73.78461		1.2153865046		0.0693190888	0.0660035611	0.0656351692	0.0613986615	0.067108737	0.0687665009	0.0695032848	0.0654509732	0.0682139129	0.0667403451	0.067661325	0.0637932093	0.0648983852	0.0695032848	0.0660035611
14	68	1	14	68			76.92562		-8.9256155216		0.1024743661	0.0577147418	0.0527414502	-0.004451403	0.0726346166	0.0950144287	0.1049610118	0.0502548044	0.0875544913	0.067661325	0.0800945539	0.0278749923	0.0427948671	0.1049610118	0.0577147418
35	72	1	35	72			63.73341		8.2665929883		-0.0367777982	0.0925277829	0.1068950697	0.2721188678	0.0494259225	-0.0152268681	-0.0439614416	0.1140787131	0.0063240621	0.0637932093	0.0278749923	0.1787315037	0.1356296433	-0.0439614416	0.0925277829
29	58	1	29	58			67.50261		-9.5026094431		0.0030085344	0.0825811997	0.091422607	0.1930987904	0.056056978	0.0162706453	-0.0014121692	0.0958433106	0.0295327562	0.0648983852	0.0427948671	0.1356296433	0.1091054215	-0.0014121692	0.0825811997
4	92	1	4	92			83.20762		8.7923804261		0.1687849205	0.0411371032	0.0269540124	-0.1361515319	0.0836863756	0.1475102843	0.1758764659	0.019862467	0.1262356481	0.0695032848	0.1049610118	-0.0439614416	-0.0014121692	0.1758764659	0.0411371032
23	65	1	23	65			71.27181		-6.2718118745		0.0427948671	0.0726346166	0.0759501443	0.1140787131	0.0626880334	0.0477681587	0.0411371032	0.0776079081	0.0527414502	0.0660035611	0.0577147418	0.0925277829	0.0825811997	0.0411371032	0.0726346166
		(XTX)-1		MSRes(XTX)-1				SSRes	826.742340517
		0.2399766685	-0.0089335052	15.2614517379	-0.5681313107			dfRes	13
		-0.0089335052	0.00046049	-0.5681313107	0.0292851191			MSRes	63.5955646552
										Testing homogeneity of variances and linearity
										Residuals			Standardized			Studentized
										X	E		X	E/√MSRes		X	E/s.e.
										5	-2.5794191687		5	-0.3234510081	1	5	-0.3533673127
										23	6.7281881255		23	0.8436935177	2	23	0.8761112802
										25	-10.015411064		25	-1.2559008806	3	25	-1.3101560984
										48	-2.5668017437		48	-0.3218688229	4	48	-0.4313994897
										17	9.9589856941		17	1.2488253176	5	17	1.2944977249
										8	3.305182047		8	0.4144593783	6	8	0.4434590123
										4	-10.2076195739		4	-1.28000322	7	4	-1.4099863362
										26	9.6127893412		26	1.2054133896	8	26	1.2613493026
										11	2.1897832627		11	0.2745918975	9	11	0.2893101078
										19	1.2153865046		19	0.1524056249	10	19	0.1577610362
										14	-8.9256155216		14	-1.1192439653	11	14	-1.166952467
										35	8.2665929883		35	1.0366046233	12	35	1.1438539063
										29	-9.5026094431		29	-1.1915971787	13	29	-1.2624561673
										4	8.7923804261		4	1.1025367056	14	4	1.2144982651
	Testing for normality									23	-6.2718118745		23	-0.7864653788	15	23	-0.8166842288
										QQ Tables
										Count	15	30
										Mean	-0
										Std Dev	7.6845965621
										Interval	Data	Std Norm	Std Data
										1	-10.2076195739	-1.8339146358	-1.328322117
										3	-10.015411064	-1.2815515655	-1.3033099374
										5	-9.5026094431	-0.9674215661	-1.2365788323
										7	-8.9256155216	-0.7279132909	-1.1614943542
										9	-6.2718118745	-0.5244005127	-0.8161536944
										11	-2.5794191687	-0.3406948271	-0.3356609742
										13	-2.5668017437	-0.1678940048	-0.3340190631
										15	1.2153865046	0	0.1581587914
										17	2.1897832627	0.1678940048	0.2849574789
										19	3.305182047	0.3406948271	0.4301048234
										21	6.7281881255	0.5244005127	0.8755421408
										23	8.2665929883	0.7279132909	1.0757354562
										25	8.7923804261	0.9674215661	1.1441564115
										27	9.6127893412	1.2815515655	1.2509165918
										29	9.9589856941	1.8339146358	1.2959672786
										QQ Tables
										Count	15	30
										Mean	73.5333333333
										Std Dev	10.966616008
										Interval	Data	Std Norm	Std Data
										1	53	-1.8339146358	-1.8723490745
										3	58	-1.2815515655	-1.4164199168
										5	60	-0.9674215661	-1.2340482537
										7	65	-0.7279132909	-0.7781190959
										9	68	-0.5244005127	-0.5045616013
										11	72	-0.3406948271	-0.139818275
										13	73	-0.1678940048	-0.0486324435
										15	75	0	0.1337392196
										17	78	0.1678940048	0.4072967143
										19	79	0.3406948271	0.4984825458
										21	80	0.5244005127	0.5896683774
										23	81	0.7279132909	0.6808542089
										25	84	0.9674215661	0.9544117036
										27	85	1.2815515655	1.0455975351
										29	92	1.8339146358	1.683898356
										QQ Tables
										Count	15	30
										Mean	-0.004677431
										Std Dev	1.0268089615
										Interval	Data	Std Norm	Std Data
										1	-4.4098569763	-1.8339146358	-4.2901646854
										3	-3.9547885483	-1.2815515655	-3.8469776418
										5	-3.6074996745	-0.9674215661	-3.5087561353
										7	-3.0521593018	-0.7279132909	-2.9679151479
										9	-2.9181511633	-0.5244005127	-2.8374058287
										11	-0.8032391292	-0.3406948271	-0.7777120459
										13	-0.4834102156	-0.1678940048	-0.4662335474
										15	0.589938544	0	0.5790911428
										17	0.8720110795	0.1678940048	0.8537990448
										19	1.1652422148	0.3406948271	1.1393742065
										21	2.4519555167	0.5244005127	2.3924927029
										23	2.6289915595	0.7279132909	2.5649065106
										25	3.1304940898	0.9674215661	3.0533153082
										27	4.0931940221	1.2815515655	3.990880102
										29	4.7432418797	1.8339146358	4.6239558561

Residuals

5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 -2.5794191686621275 6.7281881254988463 -10.015411064038815 -2.5668017437220101 9.9589856941118597 3.3051820470313658 -10.207619573893297 9.6127893411923537 2.189783262724859 1.2153865045741838 -8.9256155215816477 8.2665929882728335 -9.502609443114153 8.7923804261067033 -6.2718118745011537

Std Residuals

5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 -0.32345100811793859 0.84369351768774359 -1.2559008806076084 -0.32186882292434321 1.2488253176250446 0.41445937834140428 -1.2800032200166485 1.2054133895758909 0.27459189746795337 0.1524056248528837 -1.1192439653094886 1.0366046233363364 -1.1915971787247441 1.1025367056009348 -0.78646537878744494

Studentized Residuals

5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 -0.35336731270605498 0.87611128018264017 -1.3101560983825884 -0.43139948969891356 1.2944977248762728 0.44345901226089324 -1.4099863361999463 1.2613493026239915 0.28931010782196626 0.15776103618215834 -1.1669524669732534 1.1438539062603683 -1.2624561672637942 1.2144982651184277 -0.81668422879101499

QQ Plot – Residuals

-10.207619573893297 -10.015411064038815 -9.502609443114153 -8.9256155215816477 -6.2718118745011537 -2.5794191686621275 -2.5668017437220101 1.2153865045741838 2.189783262724859 3.3051820470313658 6.7281881254988463 8.2665929882728335 8.7923804261067033 9.6127893411923537 9.9589856941118597 -1.8339146358159142 -1.2815515655446006 -0.96742156610170071 -0.72791329088164469 -0.52440051270804089 -0.34069482708779553 -0.16789400478810546 0 0.16789400478810546 0.34069482708779542 0.52440051270804078 0.72791329088164458 0.96742156610170071 1.2815515655446006 1.8339146358159142

Data

Std Normal

QQ Plot – y

53 58 60 65 68 72 73 75 78 79 80 81 84 85 92 -1.8339146358159142 -1.2815515655446006 -0.96742156610170071 -0.72791329088164469 -0.52440051270804089 -0.34069482708779553 -0.16789400478810546 0 0.16789400478810546 0.34069482708779542 0.52440051270804078 0.72791329088164458 0.96742156610170071 1.2815515655446006 1.8339146358159142

Data

Std Normal

QQ Plot – Studentized Residuals

-4.4098569762515689 -3.9547885483193022 -3.6074996744649384 -3.0521593017915123 -2.9181511633249473 -0.80323912920398621 -0.48341021558419511 0.58993854401092605 0.87201107950729451 1.1652422147908958 2.4519555167106906 2.6289915595077131 3.1304940898048206 4.0931940220702412 4.7432418796866473 -1.8339146358159142 -1.2815515655446006 -0.96742156610170071 -0.72791329088164469 -0.52440051270804089 -0.34069482708779553 -0.16789400478810546 0 0.16789400478810546 0.34069482708779542 0.52440051270804078 0.72791329088164458 0.96742156610170071 1.2815515655446006 1.8339146358159142

Data

Std Normal

Res 2

Residuals
Least Squares Method
Color	Quality	Price		X			Y	B		s.e.	Ŷ		E	H = X(XTX)-1XT
7	5	65		1	7	5	65	1.7514036586	1	16.6138348971	54.8104996248		10.1895003752	0.1277393982	0.0686951797	0.1341819969	0.0795622138	0.1392791534	0.0622525809	-0.0334290667	0.0269605941	0.1874563378	0.0860048125	0.1212967994
3	7	38		1	3	7	38	4.8952883645	2	1.9578685796	42.7461771327		-4.7461771327	0.0686951797	0.2905379182	0.233796476	-0.0815803772	-0.0693161531	0.1254366219	0.0589924707	0.3053636575	0.0809594039	0.083520919	-0.0964061166
5	8	51		1	5	8	51	3.7584154829	3	1.8057733712	56.2951693446		-5.2951693446	0.1341819969	0.233796476	0.2950917229	-0.0851509742	0.0288235142	0.07288675	-0.1521902248	0.1753732309	0.2481564853	0.0757587518	-0.026727729
8	1	38		1	8	1	38				44.6721260576		-6.6721260576	0.0795622138	-0.0815803772	-0.0851509742	0.2670961733	0.2022044555	0.0831328107	0.2321664209	-0.0587596057	0.014670496	0.1023829853	0.2442754017
9	3	55		1	9	3	55	(XTX)-1			57.084245388		-2.084245388	0.1392791534	-0.0693161531	0.0288235142	0.2022044555	0.2466557996	0.0411394861	0.002846128	-0.1168464902	0.1837304976	0.0917488163	0.2497347926
5	4	43		1	5	4	43	1.3973194649	-0.141970038	-0.1063934384	41.2615074129		1.7384925871	0.0622525809	0.1254366219	0.07288675	0.0831328107	0.0411394861	0.1148024528	0.1777536288	0.1569510207	0.0202592564	0.0937669797	0.0516184119
4	0	25		1	4	0	25	-0.141970038	0.019405418	0.0059768687	21.3325571166		3.6674428834	-0.0334290667	0.0589924707	-0.1521902248	0.2321664209	0.002846128	0.1777536288	0.5720458485	0.2058268002	-0.2627493596	0.1134052627	0.0853320914
2	6	33		1	2	6	33	-0.1063934384	0.0059768687	0.0165075422	34.0924732852		-1.0924732852	0.0269605941	0.3053636575	0.1753732309	-0.0587596057	-0.1168464902	0.1569510207	0.2058268002	0.3680560946	-0.0311262905	0.0896530311	-0.1214520427
8	7	71		1	8	7	71				67.2226189552		3.7773810448	0.1874563378	0.0809594039	0.2481564853	0.014670496	0.1837304976	0.0202592564	-0.2627493596	-0.0311262905	0.3565163394	0.0753706435	0.1267561903
6	4	51		1	6	4	51	MSRes(XTX)-1			46.1567957774		4.8432042226	0.0860048125	0.083520919	0.0757587518	0.1023829853	0.0917488163	0.0937669797	0.1134052627	0.0896530311	0.0753706435	0.0921369246	0.0962508732
9	2	49		1	9	2	49	276.0195099868	-28.0440524266	-21.0164285726	53.325829905		-4.325829905	0.1212967994	-0.0964061166	-0.026727729	0.2442754017	0.2497347926	0.0516184119	0.0853320914	-0.1214520427	0.1267561903	0.0962508732	0.2693213278
								-28.0440524266	3.8332493749	1.1806408075
								-21.0164285726	1.1806408075	3.2608174682		SSRes	1580.2800542881
												dfRes	8
												MSRes	197.535006786
Normality														Residual plots
QQ Tables														Residuals			Standardized			Studentized
Count	11	22												Color	E		Color	E/√MSRes		Price	E/s.e.
Mean	47.1818181818													7	10.1895003752		7	0.7249880595	1	0.776260925	65
Std Dev	13.6295134309													3	-4.7461771327		3	-0.3376928821	2	-0.400919622	38
														5	-5.2951693446		5	-0.3767539532	3	-0.4487366206	51
Interval	Data	Std Norm	Std Data											8	-6.6721260576		8	-0.4747251136	4	-0.5545219125	38
1	25	-1.6906216296	-1.6274842308											9	-2.084245388		9	-0.1482951041	5	-0.1708559426	55
3	33	-1.0968035621	-1.0405227049											5	1.7384925871		5	0.123694619	6	0.1314712241	43
5	38	-0.7478585948	-0.6736717513											4	3.6674428834		4	0.2609403996	7	0.3988804042	25
7	38	-0.472789121	-0.6736717513											2	-1.0924732852		2	-0.0777300219	8	-0.0977798857	33
9	43	-0.2298841176	-0.3068207976											8	3.7773810448		8	0.2687625549	9	0.3350425759	71
11	49	0	0.1334003468											6	4.8432042226		6	0.3445964082	10	0.3616600767	51
13	51	0.2298841176	0.2801407282											9	-4.325829905		9	-0.3077849662	11	-0.3600677189	49
15	51	0.472789121	0.2801407282
17	55	0.7478585948	0.5736214912
19	65	1.0968035621	1.3073233985
21	71	1.6906216296	1.7475445429
														Residual plots
														Residuals			Standardized			Studentized
														Color	E		Color	E/√MSRes		Color	E/s.e.
														5	10.1895003752		5	0.7249880595	1	5	0.7249880595
														7	-4.7461771327		7	-0.3376928821	2	7	-0.3376928821
														8	-5.2951693446		8	-0.3767539532	3	8	-0.3767539532
														1	-6.6721260576		1	-0.4747251136	4	1	-0.4747251136
														3	-2.084245388		3	-0.1482951041	5	3	-0.1482951041
														4	1.7384925871		4	0.123694619	6	4	0.123694619
														0	3.6674428834		0	0.2609403996	7	0	0.2609403996
														6	-1.0924732852		6	-0.0777300219	8	6	-0.0777300219
														7	3.7773810448		7	0.2687625549	9	7	0.2687625549
														4	4.8432042226		4	0.3445964082	10	4	0.3445964082
														2	-4.325829905		2	-0.3077849662	11	2	-0.3077849662

QQ Plot

Data

Std Normal

Residuals

7 3 5 8 9 5 4 2 8 6 9 10.189500375171463 -4.7461771326554327 -5.2951693446143224 -6.6721260575952073 -2.0842453879789105 1.7384925871303665 3.6674428833864141 -1.0924732852078947 3.7773810447877594 4.8432042226190006 -4.3258299050427382

Std Residuals

7 3 5 8 9 5 4 2 8 6 9 0.72498805947326206 -0.33769288214608184 -0.37675395322507593 -0.47472511359277891 -0.14829510414258162 0.12369461904368265 0.26094039956405612 -7.7730021876163222E-2 0.26876255485742384 0.34459640823460619 -0.30778496619031398

Studentized Residuals

65 38 51 38 55 43 25 33 71 51 49 0.77626092500939581 -0.40091962202771336 -0.44873662056771335 -0.55452191252345118 -0.17085594259939102 0.13147122406662365 0.3 9888040416573833 -9.7779885651430545E-2 0.33504257588057895 0.36166007666644429 -0.36006771891297884

Residuals

5 7 8 1 3 4 0 6 7 4 2 10.189500375171463 -4.7461771326554327 -5.2951693446143224 -6.6721260575952073 -2.0842453879789105 1.7384925871303665 3.6674428833864141 -1.0924732852078947 3.7773810447877594 4.8432042226190006 -4.3258299050427382

Std Residuals

5 7 8 1 3 4 0 6 7 4 2 0.72498805947326206 -0.33769288214608184 -0.37675395322507593 -0.47472511359277891 -0.14829510414258162 0.12369461904368265 0.26094039956405612 -7.7730021876163222E-2 0.26876255485742384 0.34459640823460619 -0.30778496619031398

Studentized Residuals

5 7 8 1 3 4 0 6 7 4 2 0.72498805947326206 -0.33769288214608184 -0.37675395322507593 -0.47472511359277891 -0.14829510414258162 0.12369461904368265 0.260940399564 05612 -7.7730021876163222E-2 0.26876255485742384 0.34459640823460619 -0.30778496619031398

Res 3

Residuals
	Poverty	Infant Mort	White	Crime	X				Y	B			s.e.	Ŷ	E	H = X(XTX)-1XT
Alabama	15.7	9.0	71.0	448	1	9	71.0271777601	448	15.7	0.4371252188		1	2.8416365105	15.1684824653	0.5315175347	0.0845684163	0.0114528702	-0.0022684899	0.054739347	-0.0327307491	-0.0123912675	0.0140465988	0.0423060127	0.0076002632	0.0626162357	0.051146074	0.020995996	0.0244547275	0.0511186367	-0.0280703379	0.0165421945	0.0393536908	0.0903789577	0.0164189816	0.0492593213	-0.0397307989	0.0248091146	-0.019825168	0.150467963	0.0236398165	-0.0053363976	-0.0127765354	-0.0195682995	0.0104012628	-0.002445074	-0.0310782753	-0.0044593353	0.0563991764	0.0061379872	0.0482508942	0.0467467542	-0.0129199371	0.0360649931	0.0096807971	0.0419638072	0.0360050735	0.0412891749	-0.0058366695	-0.0217045707	-0.0052064141	0.0477681714	-0.0320551777	0.0338144498	0.0109644374	0.0270013
Alaska	8.4	6.9	70.6	661.2	1	6.9	70.6231886381	661.2	8.4	1.279369653		2	0.2142685635	12.7701940963	-4.3701940963	0.0114528702	0.0725812779	0.0326474007	0.020330129	0.0671773341	0.0159537056	0.0007386702	0.0558000681	0.067648462	0.0360646015	0.0699503982	-0.0205659035	0.0376360462	-0.0130440086	0.0099907701	0.0167072144	-0.0151581864	0.0526588127	-0.035996531	0.0635958564	0.044535362	0.0346322432	0.0133145314	-0.0225837089	0.0254270031	0.004130145	0.0080549639	0.0822045259	-0.0294037307	0.0302458995	0.072274844	0.0455902594	0.0224724149	-0.0204246298	-0.0055038369	0.0245595967	0.0082262996	0.008165249	-0.0072238027	0.076609516	-0.0262736478	0.054637004	0.0440956702	0.0016238169	-0.025057959	0.0039413774	0.0318717736	-0.0221247694	-0.0019119083	-0.0222734897
Arizona	14.7	6.4	86.5	482.7	1	6.4	86.5056967653	482.7	14.7	0.0363269231		3	0.0239461755	12.4537343514	2.2462656486	-0.0022684899	0.0326474007	0.0345847538	0.0184165681	0.0389509033	0.029932941	0.0123953363	0.0287374653	0.0444202384	0.0030665873	-0.034175507	0.0164271748	0.0258169786	0.0103865085	0.033476212	0.0288212209	0.0121521828	0.011997121	0.0102342254	0.015967706	0.0409437349	0.02733364	0.0272640716	-0.0386442813	0.0280810569	0.0242036357	0.0279230328	0.0545819931	0.0126339067	0.0164508832	0.0536899622	0.0212619846	0.0087418224	0.0113402865	0.0096249069	0.0166470365	0.0260552623	0.0186447345	0.0144347963	0.0321093158	0.0028943071	0.03780473	0.0347243188	0.0265927983	0.0166006016	-0.0035831554	0.0304980232	0.0154342055	0.0194496284	0.0142752338
Arkansas	17.3	8.5	80.8	529.4	1	8.5	80.783955957	529.4	17.3	0.001421499		4	0.0015977891	14.9989413874	2.3010586126	0.054739347	0.020330129	0.0184165681	0.056823865	-0.0172282048	0.002395416	0.0039120862	0.0527332399	0.0391311431	0.0332044833	-0.058093816	0.0248576535	0.0314495642	0.0462300017	-0.0074607789	0.033328832	0.0365646461	0.0774731515	0.0120022071	0.0348980946	-0.015750144	0.0354537488	-0.0104375867	0.0712137474	0.0378614033	0.0028289736	0.0002252558	0.0231866256	0.0078863921	-0.014996316	0.0108561365	-0.0122595263	0.040134238	-0.0017158627	0.0395895831	0.0432699064	-0.0034670395	0.0389339391	0.0047439411	0.0533057519	0.0182977234	0.068777009	0.0116534375	-0.0108550691	-0.0037356419	0.011982344	-0.0231867818	0.0381129493	0.0142118267	0.0281714071
California	13.3	5.0	76.6	522.6	1	5	76.6400521745	522.6	13.3					10.3609461346	2.9390538654	-0.0327307491	0.0671773341	0.0389509033	-0.0172282048	0.1036137199	0.0394946248	0.0170871425	0.0198135848	0.0506757309	0.0106488095	0.1087910561	-0.0183011436	0.0253436888	-0.0368328516	0.0464868757	0.0087053012	-0.0279462842	-0.0158970649	-0.0207193373	0.0370715052	0.0855182549	0.0197961436	0.0488413889	-0.0944120609	0.0106077924	0.0268938325	0.0344626625	0.0831364947	-0.0102449252	0.0610082059	0.0872151061	0.0727234325	-0.0039234328	0.0056501816	-0.0247347619	-0.0013457052	0.0373106799	-0.0087786803	0.0111025187	0.0372027195	-0.0231910228	0.0076023039	0.0546820577	0.0397978187	0.006760034	0.0033142973	0.0776697049	-0.0332277774	0.0083990585	-0.0240409635
Colorado	11.4	5.7	89.7	347.8	1	5.7	89.7340921753	347.8	11.4	(XTX)-1				11.4836930525	-0.0836930525	-0.0123912675	0.0159537056	0.029932941	0.002395416	0.0394946248	0.0376211563	0.0258486868	0.0031311965	0.0195204068	-0.0053098377	-0.0079012774	0.0263381882	0.0150699285	0.0095054806	0.0467659084	0.022888922	0.0158972637	-0.0215573442	0.0309204872	-0.0025569638	0.0470877902	0.0155151789	0.041638913	-0.0419321813	0.0177562558	0.0357644274	0.0390599114	0.0312908588	0.0329500275	0.0292508554	0.0387429203	0.0279025395	0.0005538896	0.0334403303	0.0095808379	0.0055551466	0.0389795309	0.0139080053	0.0295628506	-0.0001039244	0.0189093813	0.0044392936	0.0297377337	0.0445768297	0.0402893875	0.007310098	0.0447564685	0.0201084998	0.0283211258	0.023479397
Connecticut	9.3	6.2	84.3	256	1	6.2	84.2786523221	256	9.3	2.6057128988	-0.1197888874	-0.0194978505	-0.000415727	11.7947049281	-2.4947049281	0.0140465988	0.0007386702	0.0123953363	0.0039120862	0.0170871425	0.0258486868	0.0351686079	-0.0077241807	-0.0100674616	0.0149398066	0.0581568023	0.0305771786	0.0079507395	0.0207923208	0.0308306173	0.0121276933	0.0251602211	-0.0125226266	0.0427467327	0.0021511629	0.0222596362	0.0065270132	0.0332144155	0.0281129919	0.0078404132	0.0308814091	0.0297279224	-0.0103575821	0.041932732	0.033896408	-0.0017038204	0.0267313975	0.0138945502	0.0440843153	0.0218567756	0.0093465739	0.0319351492	0.0153360692	0.0364166837	-0.0150190258	0.0395076694	-0.0175016721	0.0121066979	0.0369617649	0.0446209439	0.0357810155	0.0325786099	0.0254022062	0.0290391356	0.0302734662
Delaware	10.0	8.3	74.3	689.2	1	8.3	74.2660567271	689.2	10	-0.1197888874	0.0148151629	0.0004013913	-0.0000350934	14.7334477711	-4.7334477711	0.0423060127	0.0558000681	0.0287374653	0.0527332399	0.0198135848	0.0031311965	-0.0077241807	0.0732966705	0.0709877275	0.0396967729	-0.0249096546	-0.0022138674	0.0423083282	0.0207769298	-0.0089668147	0.0309643351	0.0110716643	0.090950085	-0.0244090039	0.0606667129	0.0068726952	0.044151572	-0.0103441039	0.0300506382	0.0404880821	-0.0048685992	-0.0041474547	0.0675662275	-0.0235034463	-0.0050339913	0.0501055619	0.0079167482	0.037923594	-0.0264580426	0.0196062507	0.0431504244	-0.007580154	0.0290506157	-0.0118114838	0.0876305764	-0.0135916064	0.088076081	0.0300912957	-0.0183120338	-0.0304962722	-0.0007147547	-0.0093197394	0.0084333423	0.0007133739	-0.0006626692
Florida	13.2	7.3	79.8	722.6	1	7.3	79.8110685419	722.6	13.2	-0.0194978505	0.0004013913	0.0001850384	0.0000039009	13.7029894019	-0.5029894019	0.0076002632	0.067648462	0.0444202384	0.0391311431	0.0506757309	0.0195204068	-0.0100674616	0.0709877275	0.0894400949	0.0169265998	-0.0588090367	-0.0064199653	0.0442939268	0.0030092112	0.0145554134	0.0361462038	-0.0016292634	0.0629010408	-0.0306102562	0.0502969994	0.0400328966	0.0465447376	0.0074275337	-0.0477471992	0.0431645064	0.0056186684	0.0110303235	0.1015781607	-0.0260080232	0.0022049349	0.0874404546	0.0189529407	0.0193502574	-0.0274950097	0.0029567364	0.0319236809	0.0067969274	0.0218266718	-0.0120215859	0.0881279076	-0.029927291	0.0902718595	0.0477791292	-0.0007832001	-0.025143256	-0.0227799863	0.0154834327	-0.0010712014	0.0026306703	-0.0081831564
Georgia	14.7	8.1	65.4	493.2	1	8.1	65.3877182796	493.2	14.7	-0.000415727	-0.0000350934	0.0000039009	0.0000008238	13.8764373211	0.8235626789	0.0626162357	0.0360646015	0.0030665873	0.0332044833	0.0106488095	-0.0053098377	0.0149398066	0.0396967729	0.0169265998	0.0651732975	0.1302230633	-0.0018781628	0.0257459583	0.0201433988	-0.0186148637	0.0057440364	0.0128235384	0.0737429847	-0.0057454534	0.0629412876	-0.0089283481	0.0219944119	-0.0060732951	0.1064967656	0.0146450079	-0.003697872	-0.0081151085	0.0052072624	-0.0063139461	0.0240390063	-0.0038526565	0.0274825756	0.0471986704	-0.0001545203	0.025136802	0.0356548037	-0.0057196305	0.0190084215	0.0057826869	0.0490546727	0.0160011258	0.0273069875	0.0092817593	-0.0135119846	-0.0133523623	0.047653721	-0.0017918543	0.0023995998	0.0034274843	0.0015866697
Hawaii	9.1	5.6	29.7	272.8	1	5.6	29.6673337484	272.8	9.1					9.0671031506	0.0328968494	0.051146074	0.0699503982	-0.034175507	-0.058093816	0.1087910561	-0.0079012774	0.0581568023	-0.0249096546	-0.0588090367	0.1302230633	0.6584228545	-0.0684323218	-0.0000815731	-0.0648188627	-0.0202287471	-0.0736751236	-0.0574526231	0.0097833889	-0.0373308867	0.1145401416	0.0381531735	-0.0262220869	0.0327705329	0.1501033686	-0.0589019732	-0.0028088435	-0.0096380183	-0.0469323516	-0.0260232877	0.1505848573	-0.0359718176	0.1520015918	0.0460575889	0.0267245456	-0.025919475	-0.0044827729	0.011011464	-0.0460064076	0.0244255771	0.0037289399	0.0157704905	-0.1214373121	0.0090266569	0.0094749664	-0.0116544778	0.1436916092	0.0888051196	-0.0980441497	-0.0159332402	-0.0674586166
Idaho	12.6	6.8	94.6	239.4	1	6.8	94.6006604472	239.4	12.6	MSRes(XTX)-1				12.9136966318	-0.3136966318	0.020995996	-0.0205659035	0.0164271748	0.0248576535	-0.0183011436	0.0263381882	0.0305771786	-0.0022138674	-0.0064199653	-0.0018781628	-0.0684323218	0.0555949649	0.0091358187	0.0488999093	0.0321118515	0.0301090493	0.0502193014	-0.0046697384	0.0640176096	-0.019355966	0.0035943849	0.0133660901	0.0251331262	0.0342235096	0.0225292292	0.0339290855	0.0320224419	-0.0165551992	0.0587044517	0.0029776013	-0.010070685	-0.007800358	0.0147631888	0.0482602119	0.0405445745	0.0178069854	0.0302162077	0.0330840285	0.039077248	-0.0191094418	0.052125064	0.0065465286	0.0050108854	0.03529351	0.0550743769	0.0200574718	0.008017197	0.0586287245	0.0386181239	0.0564838099
Illinois	12.2	7.3	79.1	533.2	1	7.3	79.133109686	533.2	12.2	8.0748980576	-0.371216282	-0.0604222956	-0.0012883049	13.4091293344	-1.2091293344	0.0244547275	0.0376360462	0.0258169786	0.0314495642	0.0253436888	0.0150699285	0.0079507395	0.0423083282	0.0442939268	0.0257459583	-0.0000815731	0.0091358187	0.0297867114	0.016972155	0.0108715393	0.0248656652	0.0136253655	0.0461735386	-0.0005250066	0.0366036275	0.0196155248	0.0304586462	0.009762545	0.0131845268	0.0286896114	0.0107396639	0.0117301925	0.0453184286	0.0005200512	0.011257437	0.0383816284	0.0174427053	0.0250327876	-0.0001504418	0.0169181114	0.0280673902	0.0102725527	0.0223115632	0.00627861	0.0491303491	0.0029457368	0.048693982	0.027020108	0.006122225	-0.0011736892	0.0085218767	0.0116953788	0.0126943392	0.0117231797	0.009297251
Indiana	13.1	8.0	88.0	333.6	1	8	88.0000006273	333.6	13.1	-0.371216282	0.0459110173	0.0012438799	-0.0001087516	14.3430637586	-1.2430637586	0.0511186367	-0.0130440086	0.0103865085	0.0462300017	-0.0368328516	0.0095054806	0.0207923208	0.0207769298	0.0030092112	0.0201433988	-0.0648188627	0.0488999093	0.016972155	0.0597671418	0.0060166673	0.0312339909	0.0540901406	0.0411393046	0.049562351	0.0036726301	-0.021749385	0.0216488005	0.0020694693	0.0856183152	0.0290775183	0.0174181212	0.0129552668	-0.0178959687	0.0424086371	-0.0112995324	-0.0205028731	-0.0190976565	0.0333411582	0.0299910209	0.050345218	0.0339612562	0.0102811597	0.040988398	0.0262651432	0.0080830985	0.0483147605	0.0326181369	-0.0015952592	0.0090819186	0.0312150789	0.0252332115	-0.0187555109	0.0594044589	0.0295296236	0.0524253598
Iowa	11.5	5.1	94.2	294.7	1	5.1	94.1704648208	294.7	11.5	-0.0604222956	0.0012438799	0.0005734193	0.0000120887	10.8017494322	0.6982505678	-0.0280703379	0.0099907701	0.033476212	-0.0074607789	0.0464868757	0.0467659084	0.0308306173	-0.0089668147	0.0145554134	-0.0186148637	-0.0202287471	0.0321118515	0.0108715393	0.0060166673	0.0609978522	0.0237419203	0.0159488418	-0.0457795172	0.0406372705	-0.0168798731	0.0591858757	0.011541835	0.0534674531	-0.069048927	0.0153843807	0.0449828398	0.0495845123	0.0312984012	0.0432752868	0.034434357	0.0435204964	0.0311319387	-0.009418518	0.0437849798	0.0058826397	-0.0025037287	0.0496488284	0.0111105107	0.0367919833	-0.0153175965	0.0221456255	-0.00782465	0.0326497001	0.0586631589	0.0541752642	0.0033036671	0.057195358	0.0225142264	0.0340781726	0.0279311217
Kansas	11.3	7.1	88.7	452.7	1	7.1	88.7037165246	452.7	11.3	-0.0012883049	-0.0001087516	0.0000120887	0.0000025529	13.3864954336	-2.0864954336	0.0165421945	0.0167072144	0.0288212209	0.033328832	0.0087053012	0.022888922	0.0121276933	0.0309643351	0.0361462038	0.0057440364	-0.0736751236	0.0301090493	0.0248656652	0.0312339909	0.0237419203	0.0341471587	0.0295047846	0.0268932814	0.0237259255	0.0102262412	0.017402873	0.0286689516	0.01607998	0.0002825804	0.0328767837	0.0212633824	0.0224314394	0.0347095007	0.0223637368	-0.0001204455	0.0317230889	0.0009802058	0.0173891407	0.0148347002	0.0257739473	0.0251113776	0.0192324653	0.0297257551	0.0165046032	0.0283839673	0.0170058178	0.0458290803	0.0230225229	0.0185805767	0.0200021291	0.0002485318	0.0086404597	0.0349989361	0.0233012593	0.0300038053
Kentucky	17.3	7.5	89.9	295	1	7.5	89.9043273459	295	17.3					13.7176874014	3.5823125986	0.0393536908	-0.0151581864	0.0121521828	0.0365646461	-0.0279462842	0.0158972637	0.0251602211	0.0110716643	-0.0016292634	0.0128235384	-0.0574526231	0.0502193014	0.0136253655	0.0540901406	0.0159488418	0.0295047846	0.0513229355	0.0226868973	0.054356425	-0.0041352656	-0.0111729095	0.0177563038	0.0115080798	0.0664494159	0.02540148	0.0236933296	0.0201875165	-0.0179645833	0.0481277359	-0.003738888	-0.0168053297	-0.0126474077	0.0262135975	0.0372573862	0.0456568617	0.0271406366	0.0181419039	0.0368583024	0.0313623807	-0.0028333479	0.049577261	0.0203667541	0.0010332285	0.0194214572	0.0401970608	0.0247354954	-0.0069808194	0.0573063167	0.0326075982	0.0526869068
Louisiana	17.3	9.9	64.8	729.5	1	9.9	64.8395437014	729.5	17.3	SSRes	142.5503595664			16.4952894117	0.8047105883	0.0903789577	0.0526588127	0.011997121	0.0774731515	-0.0158970649	-0.0215573442	-0.0125226266	0.090950085	0.0629010408	0.0737429847	0.0097833889	-0.0046697384	0.0461735386	0.0411393046	-0.0457795172	0.0268932814	0.0226868973	0.1447132292	-0.0305582555	0.0858615737	-0.0361345621	0.0477629979	-0.0401056637	0.1269457206	0.0423235248	-0.0247984426	-0.0295232466	0.0401311071	-0.0336537184	-0.0193728508	0.0137926686	-0.0071702805	0.065492384	-0.0385969378	0.0387512958	0.0629138391	-0.0327586249	0.0394928877	-0.0207197764	0.1064296867	-0.0039471651	0.103534758	0.0118601218	-0.0503616226	-0.0518447028	0.0209505657	-0.0464937376	0.0150148796	-0.007565529	0.001281603
Maine	12.3	6.3	96.4	118	1	6.3	96.3898527562	118	12.3	dfRes	46			12.1664376795	0.1335623205	0.0164189816	-0.035996531	0.0102342254	0.0120022071	-0.0207193373	0.0309204872	0.0427467327	-0.0244090039	-0.0306102562	-0.0057454534	-0.0373308867	0.0640176096	-0.0005250066	0.049562351	0.0406372705	0.0237259255	0.054356425	-0.0305582555	0.0822045783	-0.0335220514	0.0052564288	0.0025473383	0.0356274112	0.0411676408	0.0127382832	0.0425684165	0.0397123583	-0.0410436002	0.0762301769	0.0141446923	-0.0276455018	-0.0023816628	0.0099846592	0.0675346366	0.0420786915	0.0093224992	0.0396890369	0.0293443135	0.0522128869	-0.0471342662	0.06780403	-0.0235543509	-0.0013941159	0.0487711752	0.0747225492	0.0328665975	0.0180062977	0.0629176126	0.0458158726	0.064679881
Maryland	8.1	8.0	63.4	641.9	1	8	63.3980208382	641.9	8.1	MSRes	3.0989208601			13.8875976694	-5.7875976694	0.0492593213	0.0635958564	0.015967706	0.0348980946	0.0370715052	-0.0025569638	0.0021511629	0.0606667129	0.0502969994	0.0629412876	0.1145401416	-0.019355966	0.0366036275	0.0036726301	-0.0168798731	0.0102262412	-0.0041352656	0.0858615737	-0.0335220514	0.0787009511	0.0098642475	0.0327854795	-0.0066779002	0.0608213402	0.0224190396	-0.0086992349	-0.0096994585	0.0488228454	-0.030463677	0.023750554	0.03457874	0.0360235293	0.0432660731	-0.022154744	0.0107451586	0.0371199716	-0.0085158171	0.0148975277	-0.0089275508	0.0800454414	-0.0116287088	0.0539059882	0.0262769936	-0.0190756431	-0.0343818979	0.027804656	0.0057863992	-0.015357066	-0.0058073462	-0.0175286321
Massachusetts	10.0	4.8	86.2	431.5	1	4.8	86.2036695477	431.5	10					10.3229904388	-0.3229904388	-0.0397307989	0.044535362	0.0409437349	-0.015750144	0.0855182549	0.0470877902	0.0222596362	0.0068726952	0.0400328966	-0.0089283481	0.0381531735	0.0035943849	0.0196155248	-0.021749385	0.0591858757	0.017402873	-0.0111729095	-0.0361345621	0.0052564288	0.0098642475	0.0821648724	0.0171242912	0.0552954619	-0.1039934615	0.0140212495	0.0377255653	0.0452289863	0.0694088571	0.0131191881	0.0500770084	0.0778407628	0.0561129968	-0.0112721393	0.0221748892	-0.0150772419	-0.0045137847	0.046480932	-0.001306884	0.022359212	0.0142635772	-0.007283416	0.0024663083	0.0495297959	0.0526630116	0.0293093443	-0.0032716516	0.0743806555	-0.0100897328	0.0205107403	-0.0023061249
Michigan	14.4	7.4	81.2	536	1	7.4	81.1834090374	536	14.4					13.6155275636	0.7844724364	0.0248091146	0.0346322432	0.02733364	0.0354537488	0.0197961436	0.0155151789	0.0065270132	0.044151572	0.0465447376	0.0219944119	-0.0262220869	0.0133660901	0.0304586462	0.0216488005	0.011541835	0.0286689516	0.0177563038	0.0477629979	0.0025473383	0.0327854795	0.0171242912	0.0322527737	0.0084066956	0.0115149171	0.0319276719	0.0113115646	0.0123515444	0.0462388564	0.0028937535	0.005237769	0.0387278568	0.0110966247	0.024779275	-0.0001660573	0.0198259265	0.0296284114	0.0100423872	0.0255604714	0.0062493555	0.0494924753	0.0041600604	0.0544835681	0.0265189886	0.0058452647	0.0001846443	0.0043357327	0.0074915111	0.018458963	0.0133001326	0.0136524107
Minnesota	9.6	5.2	89.0	288.7	1	5.2	89.0455565319	288.7	9.6					10.7349852544	-1.1349852544	-0.019825168	0.0133145314	0.0272640716	-0.0104375867	0.0488413889	0.041638913	0.0332144155	-0.0103441039	0.0074275337	-0.0060732951	0.0327705329	0.0251331262	0.009762545	0.0020694693	0.0534674531	0.01607998	0.0115080798	-0.0401056637	0.0356274112	-0.0066779002	0.0552954619	0.0084066956	0.0508454343	-0.0472152339	0.0095515161	0.0408510886	0.0442591096	0.0226963562	0.0386303566	0.0426709469	0.0347022325	0.0393509818	-0.0040792324	0.0429180416	0.0047497255	-0.0019885135	0.0459286252	0.0073505041	0.03611116	-0.0144098647	0.023099359	-0.0168593091	0.0295392262	0.0540056879	0.0492605625	0.01544642	0.0579222274	0.0143688732	0.0303704	0.0215654271
Mississippi	21.2	10.6	60.6	291.3	1	10.6	60.5981791441	291.3	21.2					16.6138715883	4.5861284117	0.150467963	-0.0225837089	-0.0386442813	0.0712137474	-0.0944120609	-0.0419321813	0.0281129919	0.0300506382	-0.0477471992	0.1064967656	0.1501033686	0.0342235096	0.0131845268	0.0856183152	-0.069048927	0.0002825804	0.0664494159	0.1269457206	0.0411676408	0.0608213402	-0.1039934615	0.0115149171	-0.0472152339	0.3116001449	0.0100699918	-0.0201454265	-0.0376732333	-0.1058082035	0.0275001334	-0.0067923416	-0.1187051232	-0.0205080765	0.0901003025	0.0223525971	0.0817223643	0.0624630685	-0.0346352943	0.0467189636	0.0197445203	0.0202986359	0.0797672142	0.0129650052	-0.0457283144	-0.0467673137	-0.0019308947	0.1026104768	-0.0729566885	0.0541083382	0.0119053623	0.0466474037
Missouri	13.4	7.4	85.0	504.9	1	7.4	85.0288880938	504.9	13.4					13.711013367	-0.311013367	0.0236398165	0.0254270031	0.0280810569	0.0378614033	0.0106077924	0.0177562558	0.0078404132	0.0404880821	0.0431645064	0.0146450079	-0.0589019732	0.0225292292	0.0286896114	0.0290775183	0.0153843807	0.0328767837	0.02540148	0.0423235248	0.0127382832	0.0224190396	0.0140212495	0.0319276719	0.0095515161	0.0100699918	0.0342026046	0.0148895229	0.0157783017	0.0408273796	0.011819959	-0.0009391652	0.0344858229	0.0027423521	0.0227608275	0.0054992428	0.0247158224	0.0294752139	0.0126356234	0.0295347123	0.0100861476	0.0417831655	0.0105482248	0.0547973079	0.0240213536	0.0095831699	0.0082119166	0.0012367845	0.0040409494	0.0287781032	0.0179542798	0.0229107329
Montana	14.8	5.8	90.5	287.5	1	5.8	90.4677292649	287.5	14.8					11.552564407	3.247435593	-0.0053363976	0.004130145	0.0242036357	0.0028289736	0.0268938325	0.0357644274	0.0308814091	-0.0048685992	0.0056186684	-0.003697872	-0.0028088435	0.0339290855	0.0107396639	0.0174181212	0.0449828398	0.0212633824	0.0236933296	-0.0247984426	0.0425684165	-0.0086992349	0.0377255653	0.0113115646	0.0408510886	-0.0201454265	0.0148895229	0.0372878789	0.0390014359	0.0124130627	0.0429018517	0.0283597048	0.0216935337	0.0232880219	0.0030072209	0.0422107708	0.0164239467	0.0056990872	0.0393497328	0.0162448053	0.0354011197	-0.0123741424	0.0308025871	-0.0055916815	0.0220694385	0.0458825978	0.0485054563	0.0158236388	0.040070347	0.0282375964	0.0320558666	0.0318972667
Nebraska	10.8	5.6	91.4	302.4	1	5.6	91.3724773358	302.4	10.8					11.3507375248	-0.5507375248	-0.0127765354	0.0080549639	0.0279230328	0.0002252558	0.0344626625	0.0390599114	0.0297279224	-0.0041474547	0.0110303235	-0.0081151085	-0.0096380183	0.0320224419	0.0117301925	0.0129552668	0.0495845123	0.0224314394	0.0201875165	-0.0295232466	0.0397123583	-0.0096994585	0.0452289863	0.0123515444	0.0442591096	-0.0376732333	0.0157783017	0.0390014359	0.0418110291	0.0214311495	0.0409797075	0.0298671877	0.0310644572	0.0260395401	-0.0007797599	0.0407472105	0.012334722	0.0035927539	0.0419548612	0.0145489665	0.0345157402	-0.0105050567	0.026050268	-0.0037697615	0.0264541468	0.0489348817	0.0482941482	0.0105386459	0.0453624303	0.0252633297	0.0318580213	0.0292572587
Nevada	11.3	6.4	80.9	750.6	1	6.4	80.8912273712	750.6	11.3					12.6305975325	-1.3305975325	-0.0195682995	0.0822045259	0.0545819931	0.0231866256	0.0831364947	0.0312908588	-0.0103575821	0.0675662275	0.1015781607	0.0052072624	-0.0469323516	-0.0165551992	0.0453184286	-0.0178959687	0.0312984012	0.0347095007	-0.0179645833	0.0401311071	-0.0410436002	0.0488228454	0.0694088571	0.0462388564	0.0226963562	-0.1058082035	0.0408273796	0.0124130627	0.0214311495	0.1278025203	-0.0324816659	0.0163775637	0.1167465872	0.0368651074	0.0056074455	-0.0292901286	-0.0144380439	0.0213041825	0.0177790862	0.0114887538	-0.0128321708	0.0894393916	-0.0452487171	0.0847520361	0.0625737345	0.0124992633	-0.0241005865	-0.0341715126	0.0405551636	-0.0174247486	0.0013369996	-0.0210625666
New Hampshire	7.6	6.1	95.5	137.3	1	6.1	95.4871869701	137.3	7.6					11.9052076088	-4.3052076088	0.0104012628	-0.0294037307	0.0126339067	0.0078863921	-0.0102449252	0.0329500275	0.041932732	-0.0235034463	-0.0260080232	-0.0063139461	-0.0260232877	0.0587044517	0.0005200512	0.0424086371	0.0432752868	0.0223637368	0.0481277359	-0.0336537184	0.0762301769	-0.030463677	0.0131191881	0.0028937535	0.0386303566	0.0275001334	0.011819959	0.0429018517	0.0409797075	-0.0324816659	0.0715859443	0.0189729068	-0.0188920427	0.00414005	0.0074256358	0.0648943126	0.0364683304	0.0070845768	0.0412815075	0.0258426976	0.0505190819	-0.0437575469	0.061831721	-0.0240568348	0.0030530673	0.0504631929	0.0720692141	0.0306505902	0.0246896862	0.0559103255	0.0439502514	0.0586904054
New Jersey	8.7	5.5	76.0	329.3	1	5.5	76.0321173428	329.3	8.7					10.7037708049	-2.0037708049	-0.002445074	0.0302458995	0.0164508832	-0.014996316	0.0610082059	0.0292508554	0.033896408	-0.0050339913	0.0022049349	0.0240390063	0.1505848573	0.0029776013	0.011257437	-0.0112995324	0.034434357	-0.0001204455	-0.003738888	-0.0193728508	0.0141446923	0.023750554	0.0500770084	0.005237769	0.0426709469	-0.0067923416	-0.0009391652	0.0283597048	0.0298671877	0.0163775637	0.0189729068	0.0604128967	0.0254997867	0.0600675493	0.0085755519	0.0324959148	-0.0015562937	0.0010112719	0.0347924218	-0.0019618251	0.0288589656	-0.0003309347	0.0163281992	-0.0272162554	0.0271259953	0.0393126933	0.0293250357	0.0377100618	0.0597368154	-0.009944214	0.0181527551	0.000533434
New Mexico	17.1	5.8	84.0	664.2	1	5.8	83.9965207856	664.2	17.1					11.8529639831	5.2470360169	-0.0310782753	0.072274844	0.0536899622	0.0108561365	0.0872151061	0.0387429203	-0.0017038204	0.0501055619	0.0874404546	-0.0038526565	-0.0359718176	-0.010070685	0.0383816284	-0.0205028731	0.0435204964	0.0317230889	-0.0168053297	0.0137926686	-0.0276455018	0.03457874	0.0778407628	0.0387278568	0.0347022325	-0.1187051232	0.0344858229	0.0216935337	0.0310644572	0.1167465872	-0.0188920427	0.0254997867	0.1117307104	0.0423117577	-0.0024618786	-0.0144977539	-0.0164771024	0.0124202821	0.0284400061	0.0075836752	-0.0026403047	0.0677691712	-0.0365361047	0.0629908239	0.0615568479	0.0270021403	-0.0072535268	-0.0301511371	0.0531943674	-0.015067021	0.0077244912	-0.0154939667
New York	13.6	5.6	73.4	414.1	1	5.6	73.4225291693	414.1	13.6					10.8574525756	2.7425474244	-0.0044593353	0.0455902594	0.0212619846	-0.0122595263	0.0727234325	0.0279025395	0.0267313975	0.0079167482	0.0189529407	0.0274825756	0.1520015918	-0.007800358	0.0174427053	-0.0190976565	0.0311319387	0.0009802058	-0.0126474077	-0.0071702805	-0.0023816628	0.0360235293	0.0561129968	0.0110966247	0.0393509818	-0.0205080765	0.0027423521	0.0232880219	0.0260395401	0.0368651074	0.00414005	0.0600675493	0.0423117577	0.0645233498	0.0095567516	0.0191703158	-0.0079446127	0.0036409408	0.0305255514	-0.0037984128	0.019865343	0.0185497324	0.002070515	-0.0123652282	0.0344718925	0.0327861631	0.0151839261	0.0302858153	0.0607139557	-0.0202279106	0.01183581	-0.0106764254
North Carolina	14.6	8.1	73.9	466.4	1	8.1	73.9373443873	466.4	14.6					14.1489227581	0.4510772419	0.0563991764	0.0224724149	0.0087418224	0.040134238	-0.0039234328	0.0005538896	0.0138945502	0.037923594	0.0193502574	0.0471986704	0.0460575889	0.0147631888	0.0250327876	0.0333411582	-0.009418518	0.0173891407	0.0262135975	0.065492384	0.0099846592	0.0432660731	-0.0112721393	0.024779275	-0.0040792324	0.0901003025	0.0227608275	0.0030072209	-0.0007797599	0.0056074455	0.0074256358	0.0085755519	-0.0024618786	0.0095567516	0.0415536834	0.00628262	0.0327876116	0.036157969	-0.0008086335	0.0275867248	0.0101897349	0.0406833759	0.0228164838	0.0370265977	0.0083350074	-0.0068451329	-0.0010210212	0.0340812545	-0.0085082108	0.0219557843	0.0116210995	0.0180178105
North Dakota	12.0	5.8	91.4	142.4	1	5.8	91.3935097064	142.4	12					11.3799356587	0.6200643413	0.0061379872	-0.0204246298	0.0113402865	-0.0017158627	0.0056501816	0.0334403303	0.0440843153	-0.0264580426	-0.0274950097	-0.0001545203	0.0267245456	0.0482602119	-0.0001504418	0.0299910209	0.0437849798	0.0148347002	0.0372573862	-0.0385969378	0.0675346366	-0.022154744	0.0221748892	-0.0001660573	0.0429180416	0.0223525971	0.0054992428	0.0422107708	0.0407472105	-0.0292901286	0.0648943126	0.0324959148	-0.0144977539	0.0191703158	0.00628262	0.0635887823	0.0280684691	0.0029200887	0.0427511222	0.0180387384	0.0497247852	-0.0427272345	0.05634843	-0.0348365083	0.0064986296	0.0521239487	0.0683629535	0.0367949975	0.0361352159	0.0417267905	0.0401762295	0.0476221931
Ohio	13.4	7.8	84.8	343.2	1	7.8	84.7642372263	343.2	13.4					13.9832908902	-0.5832908902	0.0482508942	-0.0055038369	0.0096249069	0.0395895831	-0.0247347619	0.0095808379	0.0218567756	0.0196062507	0.0029567364	0.025136802	-0.025919475	0.0405445745	0.0169181114	0.050345218	0.0058826397	0.0257739473	0.0456568617	0.0387512958	0.0420786915	0.0107451586	-0.0150772419	0.0198259265	0.0047497255	0.0817223643	0.0247158224	0.0164239467	0.012334722	-0.0144380439	0.0364683304	-0.0015562937	-0.0164771024	-0.0079446127	0.0327876116	0.0280684691	0.0439411354	0.0312470108	0.0108768114	0.0352493583	0.0250253769	0.0102210812	0.0434507766	0.0258383299	0.001259563	0.0096150061	0.0274127677	0.0294059579	-0.0106231116	0.0484210722	0.0262934715	0.0436205575
Oklahoma	15.9	8.0	78.1	499.6	1	8	78.1412386087	499.6	15.9					14.2208941012	1.6791058988	0.0467467542	0.0245595967	0.0166470365	0.0432699064	-0.0013457052	0.0055551466	0.0093465739	0.0431504244	0.0319236809	0.0356548037	-0.0044827729	0.0178069854	0.0280673902	0.0339612562	-0.0025037287	0.0251113776	0.0271406366	0.0629138391	0.0093224992	0.0371199716	-0.0045137847	0.0296284114	-0.0019885135	0.0624630685	0.0294752139	0.0056990872	0.0035927539	0.0213041825	0.0070845768	0.0010112719	0.0124202821	0.0036409408	0.036157969	0.0029200887	0.0312470108	0.0360730176	0.0018918845	0.0302747226	0.0080283452	0.0457122178	0.0168869255	0.0501944237	0.0141922323	-0.0038495648	-0.0006016423	0.0195636475	-0.0079630773	0.0260501255	0.0132094918	0.0202290183
Oregon	13.6	5.5	90.1	287.6	1	5.5	90.1404463254	287.6	13.6					11.1570064788	2.4429935212	-0.0129199371	0.0082262996	0.0260552623	-0.0034670395	0.0373106799	0.0389795309	0.0319351492	-0.007580154	0.0067969274	-0.0057196305	0.011011464	0.0302162077	0.0102725527	0.0102811597	0.0496488284	0.0192324653	0.0181419039	-0.0327586249	0.0396890369	-0.0085158171	0.046480932	0.0100423872	0.0459286252	-0.0346352943	0.0126356234	0.0393497328	0.0419548612	0.0177790862	0.0412815075	0.0347924218	0.0284400061	0.0305255514	-0.0008086335	0.0427511222	0.0108768114	0.0018918845	0.0428454513	0.0121784683	0.0358855152	-0.0136222053	0.0271342627	-0.0106393675	0.0258383131	0.0502161358	0.0493449972	0.0149147867	0.0487106425	0.0221249106	0.0315451914	0.0274000081
Pennsylvania	12.1	7.6	85.4	416.5	1	7.6	85.4245635079	416.5	12.1					13.8556004574	-1.7556004574	0.0360649931	0.008165249	0.0186447345	0.0389339391	-0.0087786803	0.0139080053	0.0153360692	0.0290506157	0.0218266718	0.0190084215	-0.0460064076	0.0330840285	0.0223115632	0.040988398	0.0111105107	0.0297257551	0.0368583024	0.0394928877	0.0293443135	0.0148975277	-0.001306884	0.0255604714	0.0073505041	0.0467189636	0.0295347123	0.0162448053	0.0145489665	0.0114887538	0.0258426976	-0.0019618251	0.0075836752	-0.0037984128	0.0275867248	0.0180387384	0.0352493583	0.0302747226	0.0121784683	0.0329576682	0.0184019427	0.0242644075	0.0284488231	0.0396620813	0.0119407419	0.010239626	0.0193417413	0.0154134807	-0.0038388484	0.0402787389	0.0229297443	0.0348585143
Rhode Island	11.7	6.1	88.5	227.3	1	6.1	88.4838806686	227.3	11.7					11.7787339485	-0.0787339485	0.0096807971	-0.0072238027	0.0144347963	0.0047439411	0.0111025187	0.0295628506	0.0364166837	-0.0118114838	-0.0120215859	0.0057826869	0.0244255771	0.039077248	0.00627861	0.0262651432	0.0367919833	0.0165046032	0.0313623807	-0.0207197764	0.0522128869	-0.0089275508	0.022359212	0.0062493555	0.03611116	0.0197445203	0.0100861476	0.0354011197	0.0345157402	-0.0128321708	0.0505190819	0.0288589656	-0.0026403047	0.019865343	0.0101897349	0.0497247852	0.0250253769	0.0080283452	0.0358855152	0.0184019427	0.0402830544	-0.0228089884	0.0443775144	-0.0175201084	0.0112307515	0.0423608714	0.053104582	0.0310294004	0.0317263798	0.0344494137	0.0337772376	0.038557514
South Carolina	15.7	8.4	68.7	788.3	1	8.4	68.7481360775	788.3	15.7					14.8018062071	0.8981937929	0.0419638072	0.076609516	0.0321093158	0.0533057519	0.0372027195	-0.0001039244	-0.0150190258	0.0876305764	0.0881279076	0.0490546727	0.0037289399	-0.0191094418	0.0491303491	0.0080830985	-0.0153175965	0.0283839673	-0.0028333479	0.1064296867	-0.0471342662	0.0800454414	0.0142635772	0.0494924753	-0.0144098647	0.0202986359	0.0417831655	-0.0123741424	-0.0105050567	0.0894393916	-0.0437575469	-0.0003309347	0.0677691712	0.0185497324	0.0406833759	-0.0427272345	0.0102210812	0.0457122178	-0.0136222053	0.0242644075	-0.0228089884	0.1104323065	-0.0310719756	0.1007251834	0.0383231029	-0.0273076273	-0.0495073179	-0.0043400694	-0.0059409578	-0.0089173146	-0.0086544625	-0.0179702729
South Dakota	12.5	6.9	88.2	169.2	1	6.9	88.1897900258	169.2	12.5					12.7089571716	-0.2089571716	0.0360050735	-0.0262736478	0.0028943071	0.0182977234	-0.0231910228	0.0189093813	0.0395076694	-0.0135916064	-0.029927291	0.0160011258	0.0157704905	0.052125064	0.0029457368	0.0483147605	0.0221456255	0.0170058178	0.049577261	-0.0039471651	0.06780403	-0.0116287088	-0.007283416	0.0041600604	0.023099359	0.0797672142	0.0105482248	0.0308025871	0.026050268	-0.0452487171	0.061831721	0.0163281992	-0.0365361047	0.002070515	0.0228164838	0.05634843	0.0434507766	0.0168869255	0.0271342627	0.0284488231	0.0443775144	-0.0310719756	0.0639572693	-0.018487657	-0.0053793534	0.0322816683	0.0571924372	0.0450095829	0.0082150513	0.0529983381	0.0371486192	0.0543382684
Tennessee	15.5	8.7	80.4	753.3	1	8.7	80.371971305	753.3	15.5					15.5581228066	-0.0581228066	0.0412891749	0.054637004	0.03780473	0.068777009	0.0076023039	0.0044392936	-0.0175016721	0.088076081	0.0902718595	0.0273069875	-0.1214373121	0.0065465286	0.048693982	0.0326181369	-0.00782465	0.0458290803	0.0203667541	0.103534758	-0.0235543509	0.0539059882	0.0024663083	0.0544835681	-0.0168593091	0.0129650052	0.0547973079	-0.0055916815	-0.0037697615	0.0847520361	-0.0240568348	-0.0272162554	0.0629908239	-0.0123652282	0.0370265977	-0.0348365083	0.0258383299	0.0501944237	-0.0106393675	0.0396620813	-0.0175201084	0.1007251834	-0.018487657	0.118724108	0.0333588384	-0.0228521277	-0.034120069	-0.0214449927	-0.0234440019	0.0229939659	0.0026563969	0.0081872419
Texas	15.8	6.2	82.4	510.6	1	6.2	82.4026777848	510.6	15.8					12.0884701877	3.7115298123	-0.0058366695	0.0440956702	0.0347243188	0.0116534375	0.0546820577	0.0297377337	0.0121066979	0.0300912957	0.0477791292	0.0092817593	0.0090266569	0.0050108854	0.027020108	-0.0015952592	0.0326497001	0.0230225229	0.0010332285	0.0118601218	-0.0013941159	0.0262769936	0.0495297959	0.0265189886	0.0295392262	-0.0457283144	0.0240213536	0.0220694385	0.0264541468	0.0625737345	0.0030530673	0.0271259953	0.0615568479	0.0344718925	0.0083350074	0.0064986296	0.001259563	0.0141922323	0.0258383131	0.0119407419	0.0112307515	0.0383231029	-0.0053793534	0.0333588384	0.0393701285	0.0258448172	0.0095178381	-0.0004926418	0.0396632195	0.0012210304	0.0145897572	0.0022755792
Utah	9.6	5.1	92.9	234.8	1	5.1	92.9154619313	234.8	9.6					10.6710112494	-1.0710112494	-0.0217045707	0.0016238169	0.0265927983	-0.0108550691	0.0397978187	0.0445768297	0.0369617649	-0.0183120338	-0.0007832001	-0.0135119846	0.0094749664	0.03529351	0.006122225	0.0090819186	0.0586631589	0.0185805767	0.0194214572	-0.0503616226	0.0487711752	-0.0190756431	0.0526630116	0.0058452647	0.0540056879	-0.0467673137	0.0095831699	0.0458825978	0.0489348817	0.0124992633	0.0504631929	0.0393126933	0.0270021403	0.0327861631	-0.0068451329	0.0521239487	0.0096150061	-0.0038495648	0.0502161358	0.010239626	0.0423608714	-0.0273076273	0.0322816683	-0.0228521277	0.0258448172	0.0601622575	0.0606799355	0.0153699566	0.0567412157	0.0247483764	0.0361224477	0.0317795448
Vermont	10.6	5.5	96.4	124.3	1	5.5	96.4088077647	124.3	10.6					11.1525859779	-0.5525859779	-0.0052064141	-0.025057959	0.0166006016	-0.0037356419	0.006760034	0.0402893875	0.0446209439	-0.0304962722	-0.025143256	-0.0133523623	-0.0116544778	0.0550743769	-0.0011736892	0.0312150789	0.0541752642	0.0200021291	0.0401970608	-0.0518447028	0.0747225492	-0.0343818979	0.0293093443	0.0001846443	0.0492605625	-0.0019308947	0.0082119166	0.0485054563	0.0482941482	-0.0241005865	0.0720692141	0.0293250357	-0.0072535268	0.0151839261	-0.0010210212	0.0683629535	0.0274127677	-0.0006016423	0.0493449972	0.0193417413	0.053104582	-0.0495073179	0.0571924372	-0.034120069	0.0095178381	0.0606799355	0.0770974875	0.0276341335	0.0405457465	0.0479507716	0.0448595731	0.0535350926
Virginia	10.2	7.1	73.0	269.7	1	7.1	73.0318960177	269.7	10.2					12.5570521009	-2.3570521009	0.0477681714	0.0039413774	-0.0035831554	0.011982344	0.0033142973	0.007310098	0.0357810155	-0.0007147547	-0.0227799863	0.047653721	0.1436916092	0.0200574718	0.0085218767	0.0252332115	0.0033036671	0.0002485318	0.0247354954	0.0209505657	0.0328665975	0.027804656	-0.0032716516	0.0043357327	0.01544642	0.1026104768	0.0012367845	0.0158236388	0.0105386459	-0.0341715126	0.0306505902	0.0377100618	-0.0301511371	0.0302858153	0.0340812545	0.0367949975	0.0294059579	0.0195636475	0.0149147867	0.0154134807	0.0310294004	-0.0043400694	0.0450095829	-0.0214449927	-0.0004926418	0.0153699566	0.0276341335	0.061760396	0.015715272	0.0177147361	0.0200894624	0.0226499637
Washington	11.3	4.7	84.3	333.1	1	4.7	84.2909022504	333.1	11.3					9.9856930227	1.3143069773	-0.0320551777	0.0318717736	0.0304980232	-0.0231867818	0.0776697049	0.0447564685	0.0325786099	-0.0093197394	0.0154834327	-0.0017918543	0.0888051196	0.008017197	0.0116953788	-0.0187555109	0.057195358	0.0086404597	-0.0069808194	-0.0464937376	0.0180062977	0.0057863992	0.0743806555	0.0074915111	0.0579222274	-0.0729566885	0.0040409494	0.040070347	0.0453624303	0.0405551636	0.0246896862	0.0597368154	0.0531943674	0.0607139557	-0.0085082108	0.0361352159	-0.0106231116	-0.0079630773	0.0487106425	-0.0038388484	0.0317263798	-0.0059409578	0.0082150513	-0.0234440019	0.0396632195	0.0567412157	0.0405457465	0.015715272	0.0763295548	-0.0079291044	0.0239065123	0.0029364802
West Virginia	17.0	7.4	94.5	275.2	1	7.4	94.5247863286	275.2	17					13.7294518141	3.2705481859	0.0338144498	-0.0221247694	0.0154342055	0.0381129493	-0.0332277774	0.0201084998	0.0254022062	0.0084333423	-0.0010712014	0.0023995998	-0.0980441497	0.0586287245	0.0126943392	0.0594044589	0.0225142264	0.0349989361	0.0573063167	0.0150148796	0.0629176126	-0.015357066	-0.0100897328	0.018458963	0.0143688732	0.0541083382	0.0287781032	0.0282375964	0.0252633297	-0.0174247486	0.0559103255	-0.009944214	-0.015067021	-0.0202279106	0.0219557843	0.0417267905	0.0484210722	0.0260501255	0.0221249106	0.0402787389	0.0344494137	-0.0089173146	0.0529983381	0.0229939659	0.0012210304	0.0247483764	0.0479507716	0.0177147361	-0.0079291044	0.0664874887	0.0372247196	0.0607684716
Wisconsin	10.4	6.4	89.7	290.9	1	6.4	89.6736956702	290.9	10.4					12.296174498	-1.896174498	0.0109644374	-0.0019119083	0.0194496284	0.0142118267	0.0083990585	0.0283211258	0.0290391356	0.0007133739	0.0026306703	0.0034274843	-0.0159332402	0.0386181239	0.0117231797	0.0295296236	0.0340781726	0.0233012593	0.0326075982	-0.007565529	0.0458158726	-0.0058073462	0.0205107403	0.0133001326	0.0303704	0.0119053623	0.0179542798	0.0320558666	0.0318580213	0.0013369996	0.0439502514	0.0181527551	0.0077244912	0.01183581	0.0116210995	0.0401762295	0.0262934715	0.0132094918	0.0315451914	0.0229297443	0.0337772376	-0.0086544625	0.0371486192	0.0026563969	0.0145897572	0.0361224477	0.0448595731	0.0200894624	0.0239065123	0.0372247196	0.0317367953	0.0382000563
Wyoming	9.4	7.0	93.9	239.3	1	7	93.8672869405	239.3	9.4					13.1427872096	-3.7427872096	0.0270013	-0.0222734897	0.0142752338	0.0281714071	-0.0240409635	0.023479397	0.0302734662	-0.0006626692	-0.0081831564	0.0015866697	-0.0674586166	0.0564838099	0.009297251	0.0524253598	0.0279311217	0.0300038053	0.0526869068	0.001281603	0.064679881	-0.0175286321	-0.0023061249	0.0136524107	0.0215654271	0.0466474037	0.0229107329	0.0318972667	0.0292572587	-0.0210625666	0.0586904054	0.000533434	-0.0154939667	-0.0106764254	0.0180178105	0.0476221931	0.0436205575	0.0202290183	0.0274000081	0.0348585143	0.038557514	-0.0179702729	0.0543382684	0.0081872419	0.0022755792	0.0317795448	0.0535350926	0.0226499637	0.0029364802	0.0607684716	0.0382000563	0.0579490181

Res 4

Normality Test
				Shapiro-Wilk Test		Shapiro-Wilk Test
Color	Quality	Price	Residuals
7	5	65	10.1895003752		Residuals	W-stat	0.939487119
3	7	38	-4.7461771327	W-stat	0.939487119	p-value	0.514487484
5	8	51	-5.2951693446	p-value	0.514487484	alpha	0.05
8	1	38	-6.6721260576	alpha	0.05	normal	yes
9	3	55	-2.084245388	normal	yes
5	4	43	1.7384925871			d'Agostino-Pearson
4	0	25	3.6674428834	d'Agostino-Pearson
2	6	33	-1.0924732852			DA-stat	0.7577965383
8	7	71	3.7773810448	DA-stat	0.7577965383	p-value	0.6846152556
6	4	51	4.8432042226	p-value	0.6846152556	alpha	0.05
9	2	49	-4.325829905	alpha	0.05	normal	yes
				normal	yes

Cooks 1

Cook's Distance
Version 1
Cig	Life Exp		Obs	X	Y	Pred Y	Residual	Leverage	Mod MSE	RStudent	T-test	Cook's D	DFFITS
5	80		1	5	80	82.5794191687	-2.5794191687	0.162153865	68.2334383702	-0.3411466881	0.738443913	0.0120833063	-0.1500799501
23	78		2	23	78	71.2718118745	6.7281881255	0.0726346166	64.8273525777	0.867747843	0.4012671794	0.0300594698	0.242851017
25	60		3	25	60	70.015411064	-10.015411064	0.0811076319	59.7983317742	-1.3511137768	0.1997057817	0.0757553251	-0.4014121011
48	53		4	48	53	55.5668017437	-2.5668017437	0.4433290354	67.9089045755	-0.4174742358	0.6831440172	0.074106596	-0.3725576467
17	85		5	17	85	75.0410143059	9.9589856941	0.0693190888	60.0144753046	1.3325597725	0.2055631574	0.0624057528	0.3636743893
8	84		6	8	84	80.694817953	3.305182047	0.126511942	67.852991483	0.4293212641	0.6747190403	0.014241362	0.1633878187
4	73		7	4	73	83.2076195739	-10.2076195739	0.1758764659	58.3592040728	-1.4718841812	0.1648436567	0.2121364156	-0.6799568367
26	79		8	26	79	69.3872106588	9.6127893412	0.0867256094	60.4634721615	1.2936066625	0.2183141928	0.0755417128	0.3986348687
11	81		9	11	81	78.8102167373	2.1897832627	0.0991588383	68.4516141858	0.2788593265	0.7847394663	0.0046065992	0.0925181155
19	75		10	19	75	73.7846134954	1.2153865046	0.0667403451	68.7632949561	0.1517172019	0.8817391788	0.0008899292	0.0405721295
14	68		11	14	68	76.9256155216	-8.9256155216	0.0800945539	61.6782746538	-1.1849512189	0.2572472177	0.0592838138	-0.3496474543
35	72		12	35	72	63.7334070117	8.2665929883	0.1787315037	61.9611493222	1.1588420375	0.267365231	0.142372814	0.5406076832
29	58		13	29	58	67.5026094431	-9.5026094431	0.1091054215	60.4486675856	-1.2949003726	0.2178807108	0.0975938916	-0.4531545693
4	92		14	4	92	83.2076195739	8.7923804261	0.1758764659	61.0782082324	1.2392735155	0.2371518853	0.157390754	0.5724991886
23	65	n	15	23	65	71.2718118745	-6.2718118745	0.0726346166	65.3604839778	-0.8055823697	0.4349718611	0.0261198759	-0.2254531652
		k	1
		Mean		19.4
		SS		2171.6			826.742340517
		df					13
		MSE					63.5955646552
Version 2
Cig	Life Exp		Obs	X	Y	Pred Y	Residual	Leverage	Mod MSE	RStudent	T-test	Cook's D	DFFITS
5	80		1	5	80	78.8899735986	1.1100264014	0.162153865	68.7726428464	0.1462323018	0.8859812606	0.0015761862	0.0643316711
23	78		2	23	78	74.694173267	3.305826733	0.0726346166	67.9131575974	0.4165601158	0.6837959485	0.0051114512	0.1165800049
25	60		3	25	60	74.2279732302	-14.2279732302	0.0811076319	50.5365684639	-2.0878910978	0.0570518483	0.1076863518	-0.6203065699
48	83		4	48	83	68.8666728065	14.1333271935	0.4433290354	38.9925909511	3.0335704243	0.0095986524	1.5825593871	2.7071846867
17	85		5	17	85	76.0927733775	8.9072266225	0.0693190888	61.7911950619	1.1745697222	0.2612342316	0.0351623903	0.3205566723
8	84		6	8	84	78.1906735433	5.8093264567	0.126511942	65.6755103192	0.7670003613	0.4567854492	0.0309892665	0.2918991591
4	73		7	4	73	79.123073617	-6.123073617	0.1758764659	65.104093652	-0.8359285807	0.4182950942	0.0537655648	-0.386168532
26	79		8	26	79	73.9948732118	5.0051267882	0.0867256094	66.6093455014	0.6417218995	0.5322062898	0.0144250431	0.197751552
11	81		9	11	81	77.4913734881	3.5086265119	0.0991588383	67.7564019791	0.4490946297	0.6607588286	0.0083301231	0.1489976662
19	75		10	19	75	75.6265733407	-0.6265733407	0.0667403451	68.8601392265	-0.0781603902	0.9388907131	0.0001665979	-0.0209016079
14	68		11	14	68	76.7920734328	-8.7920734328	0.0800945539	61.8926134572	-1.1651995306	0.2648737308	0.0405173818	-0.3438192586
35	72		12	35	72	71.896973046	0.103026954	0.1787315037	68.8941179945	0.0136967398	0.9892798958	0.0000155767	0.0063896222
29	58		13	29	58	73.2955731565	-15.2955731565	0.1091054215	47.011333975	-2.3634766178	0.0343517159	0.1781014123	-0.827106279
4	92		14	4	92	79.123073617	12.876926383	0.1758764659	52.1283688118	1.9646219279	0.0712001496	0.2377878589	0.9075837138
23	65	n	15	23	65	74.694173267	-9.694173267	0.0726346166	60.4503938703	-1.2947495537	0.2179312099	0.0439547279	-0.3623532441
		k	1
		Mean		19.4
		SS		2171.6			1173.7381040093
		df					13
		MSE					90.2875464623
														Version 1		Version 2
														X	RStudent	X	RStudent
														5	-0.3411466881	5	0.1462323018
														23	0.867747843	23	0.4165601158
														25	-1.3511137768	25	-2.0878910978
														48	-0.4174742358	48	3.0335704243
														17	1.3325597725	17	1.1745697222
														8	0.4293212641	8	0.7670003613
														4	-1.4718841812	4	-0.8359285807
														26	1.2936066625	26	0.6417218995
														11	0.2788593265	11	0.4490946297
														19	0.1517172019	19	-0.0781603902
														14	-1.1849512189	14	-1.1651995306
														35	1.1588420375	35	0.0136967398
														29	-1.2949003726	29	-2.3634766178
														4	1.2392735155	4	1.9646219279
														23	-0.8055823697	23	-1.2947495537

Longevity vs Smoking – Version 1

Life Exp 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 80 78 60 53 85 84 73 79 81 75 68 72 58 92 65

Cigarettes smoked (per day)

Life Expectancy (years)

Longevity vs Smoking – Version 2

Life Exp 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 80 78 60 83 85 84 73 79 81 75 68 72 58 92 65

Cigarettes smoked (per day)

Life Expectancy (years)

Studentized Residuals – Version 2

RStudent 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 0.1462 3230177453703 0.41656011578665819 -2.0878910978328102 3.0335704242792341 1.1745697221543929 0.76700036126600379 -0.8359285807354262 0.64172189954042136 0.44909462967616182 -7.8160390167444527E-2 -1.1651995306055807 1.3696739817148742E-2 -2.3634766178487125 1.9646219279288331 -1.294749553741537

Studentized Residuals – Version 1

RStudent 5 23 25 48 17 8 4 26 11 19 14 35 29 4 23 -0.34114668806182424 0.8677478429535036 -1.3511137768141768 -0.41747423579741882 1.3325597725103162 0.42932126412379101 -1.4718841811600343 1.2936066624599454 0.27885932646467648 0.1517172019269904 -1.1849512188942048 1.1588420374683235 -1.2949003726146766 1.2392735154674501 -0.80558236972443698

Cooks 2

Cook's Distance																									Real Statistics data analysis tool
Color	Quality	Price	X			Y		B	Ŷ		E	H = X(XTX)-1XT												Stud E	Cook's D
7	5	65	1	7	5	65		1.7514036586	54.81050		10.1895003752	0.1277393982	0.0686951797	0.1341819969	0.0795622138	0.1392791534	0.0622525809	-0.0334290667	0.0269605941	0.1874563378	0.0860048125	0.1212967994	1	1.8529159932
3	7	38	1	3	7	38		4.8952883645	42.74618		-4.7461771327	0.0686951797	0.2905379182	0.233796476	-0.0815803772	-0.0693161531	0.1254366219	0.0589924707	0.3053636575	0.0809594039	0.083520919	-0.0964061166	2	-0.9569854101	Obs	Color	Quality	Price	Pred Y	Residual	Leverage	SResidual	Mod MSE	RStudent	T-Test	Cook's D	DFFITS
5	8	51	1	5	8	51		3.7584154829	56.29517		-5.2951693446	0.1341819969	0.233796476	0.2950917229	-0.0851509742	0.0288235142	0.07288675	-0.1521902248	0.1753732309	0.2481564853	0.0757587518	-0.026727729	3	-1.0711234254	1	7	5	65	54.8104996248	10.1895003752	0.1277393982	1.7305289072	22.617923249	2.294052741	0.0509415662	0.1675979932	0.8778952571	SSE	277.3563093482
8	1	38	1	8	1	38			44.67213		-6.6721260576	0.0795622138	-0.0815803772	-0.0851509742	0.2670961733	0.2022044555	0.0831328107	0.2321664209	-0.0587596057	0.014670496	0.1023829853	0.2442754017	4	-1.3236303506	2	3	7	38	42.7461771327	-4.7461771327	0.2905379182	-0.8060647161	35.0864593102	-0.9512826499	0.3693123249	0.1250152547	-0.608760397	dfE	8
9	3	55	1	9	3	55		s.e.	57.08425		-2.084245388	0.1392791534	-0.0693161531	0.0288235142	0.2022044555	0.2466557996	0.0411394861	0.002846128	-0.1168464902	0.1837304976	0.0917488163	0.2497347926	5	-0.4078289894	3	5	8	51	56.2951693446	-5.2951693446	0.2950917229	-0.899302545	33.9399658116	-1.0825746382	0.3105451274	0.1600966329	-0.7004385778	MSE	34.6695386685
5	4	43	1	5	4	43		6.960	41.26151		1.7384925871	0.0622525809	0.1254366219	0.07288675	0.0831328107	0.0411394861	0.1148024528	0.1777536288	0.1569510207	0.0202592564	0.0937669797	0.0516184119	6	0.3138186219	4	8	1	38	44.6721260576	-6.6721260576	0.2670961733	-1.1331573277	30.9450530053	-1.4010225736	0.1987845562	0.2128300052	-0.8457762215	k	2
4	0	25	1	4	0	25		0.820	21.33256		3.6674428834	-0.0334290667	0.0589924707	-0.1521902248	0.2321664209	0.002846128	0.1777536288	0.5720458485	0.2058268002	-0.2627493596	0.1134052627	0.0853320914	7	0.9521178465	5	9	3	55	57.084245388	-2.084245388	0.2466557996	-0.3539768154	38.7985594568	-0.3855177015	0.7099025803	0.0181523482	-0.2205937813
2	6	33	1	2	6	33		0.757	34.09247		-1.0924732852	0.0269605941	0.3053636575	0.1753732309	-0.0587596057	-0.1168464902	0.1569510207	0.2058268002	0.3680560946	-0.0311262905	0.0896530311	-0.1214520427	8	-0.2333982146	6	5	4	43	41.2615074129	1.7384925871	0.1148024528	0.2952560544	39.1345684889	0.2953741382	0.7752290803	0.0042574262	0.1063721532
8	7	71	1	8	7	71			67.22262		3.7773810448	0.1874563378	0.0809594039	0.2481564853	0.014670496	0.1837304976	0.0202592564	-0.2627493596	-0.0311262905	0.3565163394	0.0753706435	0.1267561903	9	0.7997384993	7	4	0	25	21.3325571166	3.6674428834	0.5720458485	0.6228584024	35.1324840214	0.945823953	0.3719256665	0.4039185058	1.0935209444
6	4	51	1	6	4	51			46.15680		4.8432042226	0.0860048125	0.083520919	0.0757587518	0.1023829853	0.0917488163	0.0937669797	0.1134052627	0.0896530311	0.0753706435	0.0921369246	0.0962508732	10	0.8632738279	8	2	6	33	34.0924732852	-1.0924732852	0.3680560946	-0.1855396762	39.3525279584	-0.2190711776	0.8320804424	0.010575704	-0.1671871847
9	2	49	1	9	2	49			53.32583		-4.325829905	0.1212967994	-0.0964061166	-0.026727729	0.2442754017	0.2497347926	0.0516184119	0.0853320914	-0.1214520427	0.1267561903	0.0962508732	0.2693213278	11	-0.8594729086	9	8	7	71	67.2226189552	3.7773810448	0.3565163394	0.6415296973	36.4546154288	0.7799123082	0.4578933444	0.1181181583	0.5805193919
																									10	6	4	51	46.1567957774	4.8432042226	0.0921369246	0.8225432654	35.9313033353	0.8479809981	0.421093593	0.0252109525	0.2701424288
			(XTX)-1			MSRes(XTX)-1																			11	9	2	49	53.325829905	-4.325829905	0.2693213278	-0.7346752464	35.9637343181	-0.8438666678	0.4232576676	0.0907585283	-0.5123255217
			1.3973194649	-0.141970038	-0.1063934384	48.4444212216	-4.9220357234	-3.688611426		SSRes	277.3563093482
			-0.141970038	0.019405418	0.0059768687	-4.9220357234	0.6727768895	0.207215282		dfRes	8
			-0.1063934384	0.0059768687	0.0165075422	-3.688611426	0.207215282	0.572308874		MSRes	34.6695386685
										k	2
												Obs	Color	Quality	Price (Y)	Pred Y	Residual	SResidual	Leverage	Mod MSE	RStudent	T-test	Cook's D	DFFITS
												1	7	5	65	54.81050	10.1895003752	1.7305289072	0.1277393982	22.617923249	2.294052741	0.0509415662	0.1675979932	0.8778952571
												2	3	7	38	42.74618	-4.7461771327	-0.8060647161	0.2905379182	35.0864593102	-0.9512826499	0.3693123249	0.1250152547	-0.608760397
												3	5	8	51	56.29517	-5.2951693446	-0.899302545	0.2950917229	33.9399658116	-1.0825746382	0.3105451274	0.1600966329	-0.7004385778
												4	8	1	38	44.67213	-6.6721260576	-1.1331573277	0.2670961733	30.9450530053	-1.4010225736	0.1987845562	0.2128300052	-0.8457762215
												5	9	3	55	57.08425	-2.084245388	-0.3539768154	0.2466557996	38.7985594568	-0.3855177015	0.7099025803	0.0181523482	-0.2205937813
												6	5	4	43	41.26151	1.7384925871	0.2952560544	0.1148024528	39.1345684889	0.2953741382	0.7752290803	0.0042574262	0.1063721532
												7	4	0	25	21.33256	3.6674428834	0.6228584024	0.5720458485	35.1324840214	0.945823953	0.3719256665	0.4039185058	1.0935209444
												8	2	6	33	34.09247	-1.0924732852	-0.1855396762	0.3680560946	39.3525279584	-0.2190711776	0.8320804424	0.010575704	-0.1671871847
												9	8	7	71	67.22262	3.7773810448	0.6415296973	0.3565163394	36.4546154288	0.7799123082	0.4578933444	0.1181181583	0.5805193919
												10	6	4	51	46.15680	4.8432042226	0.8225432654	0.0921369246	35.9313033353	0.8479809981	0.421093593	0.0252109525	0.2701424288
												11	9	2	49	53.32583	-4.325829905	-0.7346752464	0.2693213278	35.9637343181	-0.8438666678	0.4232576676	0.0907585283	-0.5123255217

Cooks 3

Cook's Distance (Real Statistics)			Cook's D
Color	Quality	Price	Obs	Color	Quality	Price	Pred Y	Residual	Leverage	SResidual	Mod MSE	RStudent	T-Test	Cook's D	DFFITS
7	5	65	1	7	5	65	54.8104996248	10.1895003752	0.1277393982	1.7305289072	22.617923249	2.294052741	0.0509415662	0.1675979932	0.8778952571		SSE	277.3563093482
3	7	38	2	3	7	38	42.7461771327	-4.7461771327	0.2905379182	-0.8060647161	35.0864593102	-0.9512826499	0.3693123249	0.1250152547	-0.608760397		dfE	8
5	8	51	3	5	8	51	56.2951693446	-5.2951693446	0.2950917229	-0.899302545	33.9399658116	-1.0825746382	0.3105451274	0.1600966329	-0.7004385778		MSE	34.6695386685
8	1	38	4	8	1	38	44.6721260576	-6.6721260576	0.2670961733	-1.1331573277	30.9450530053	-1.4010225736	0.1987845562	0.2128300052	-0.8457762215		k	2
9	3	55	5	9	3	55	57.084245388	-2.084245388	0.2466557996	-0.3539768154	38.7985594568	-0.3855177015	0.7099025803	0.0181523482	-0.2205937813
5	4	43	6	5	4	43	41.2615074129	1.7384925871	0.1148024528	0.2952560544	39.1345684889	0.2953741382	0.7752290803	0.0042574262	0.1063721532
4	0	25	7	4	0	25	21.3325571166	3.6674428834	0.5720458485	0.6228584024	35.1324840214	0.945823953	0.3719256665	0.4039185058	1.0935209444	*
2	6	33	8	2	6	33	34.0924732852	-1.0924732852	0.3680560946	-0.1855396762	39.3525279584	-0.2190711776	0.8320804424	0.010575704	-0.1671871847
8	7	71	9	8	7	71	67.2226189552	3.7773810448	0.3565163394	0.6415296973	36.4546154288	0.7799123082	0.4578933444	0.1181181583	0.5805193919
6	4	51	10	6	4	51	46.1567957774	4.8432042226	0.0921369246	0.8225432654	35.9313033353	0.8479809981	0.421093593	0.0252109525	0.2701424288
9	2	49	11	9	2	49	53.325829905	-4.325829905	0.2693213278	-0.7346752464	35.9637343181	-0.8438666678	0.4232576676	0.0907585283	-0.5123255217

Studentized Residuals Plot

SResidual 54.810499624828587 42.746177132655426 56.295169344614344 44.672126057595285 57.084245387978996 41.261507412869676 21.332557116613632 34.092473285207895 67.222618955212283 46.156795777381042 53.325829905042831 1.7305289072382328 -0.80606471608232444 -0.89930254499084783 -1.1331573276684697 -0.3539768154348728 0.2952560543968572 0.62285840244041202 -0.18553967621851886 0.64152969733306831 0.82254326535214306 -0.73467524636567916

Fitted Values

Studentized Residuals

Residuals Plot

Residual 54.810499624828587 42.746177132655426 56.295169344614344 44.672126057595285 57.084245387978996 41.261507412869676 21.332557116613632 34.092473285207895 67.222618955212283 46.156795777381042 53.325829905042831 10.189500375171413 -4.7461771326554256 -5.2951693446143437 -6.6721260575952854 -2.0842453879789957 1.7384925871303238 3.6674428833863679 -1.0924732852078947 3.7773810447877167 4.843204222618958 -4.3258299050428306

Fitted Values

Residuals

Reg No Const

Regression w/o Intercept			Regression Analysis								Cook's D
Color	Quality	Price	OVERALL FIT								Obs	Color	Quality	Price	Pred Y	Residual	Leverage	SResidual	Mod MSE	RStudent	T-Test	Cook's D	DFFITS
7	5	65	Multiple R	0.9946802605		AIC	39.58820477				1	7	5	65	54.9714841218	10.0285158782	0.1159337098	1.7993969908	20.7239757875	2.3429213657	0.0438101457	0.2401397506	0.8484381025	SSE	279.5515230071
3	7	38	R Square	0.9893888205		AICc	43.0167761986				2	3	7	38	42.4620868438	-4.4620868438	0.2537727988	-0.8006235157	31.6087934243	-0.918752474	0.3821880798	0.1460594316	-0.5357787916	dfE	9
5	8	51	Adjusted R Square	0.9870307807		SBC	40.3839953156				3	5	8	51	56.5003240441	-5.5003240441	0.2759188964	-0.986912386	29.7211890088	-1.1856638518	0.2661141174	0.2562915101	-0.7319117085	MSE	31.0612803341
8	1	38	Standard Error	5.5732647823							4	8	1	38	44.4776409592	-6.4776409592	0.2498657532	-1.1622704487	27.9518865246	-1.4146265784	0.190829627	0.2999253295	-0.8164426516	k	2
9	3	55	Observations	11							5	9	3	55	57.3344134802	-2.3344134802	0.2181464679	-0.4188592452	34.0726956233	-0.4522846352	0.6617681107	0.0313042711	-0.2389038925
5	4	43									6	5	4	43	40.9332469216	2.0667530784	0.0657161374	0.3708334628	34.3724507492	0.3647068498	0.7237527259	0.005176571	0.0967253948
4	0	25	ANOVA				Alpha	0.05			7	4	0	25	20.2929358393	4.7070641607	0.0796963244	0.8445793165	31.9345460856	0.868268741	0.4078032074	0.0335603798	0.2555099011
2	6	33		df	SS	MS	F	p-value	sig		8	2	6	33	33.4970836034	-0.4970836034	0.2065734654	-0.0891907388	34.905012369	-0.0944565719	0.9268160063	0.0013051806	-0.048196565
8	7	71	Regression	2	26065.4484769929	13032.7242384964	419.5810378163	0.0000000013	yes		9	8	7	71	67.8282566429	3.1717433571	0.1894269049	0.5690997074	33.392575671	0.6096449594	0.5571728875	0.046687866	0.2947146514
6	4	51	Residual	9	279.5515230071	31.0612803341					10	6	4	51	46.0064808814	4.9935191186	0.0818442737	0.8959773694	31.5491958254	0.9278000606	0.377721991	0.0389690384	0.2770067631
9	2	49	Total	11	26345						11	9	2	49	53.4426441996	-4.4426441996	0.263105268	-0.79713496	31.5959235853	-0.9207108896	0.3812181513	0.1539403103	-0.5501553312
				coeff	std err	t stat	p-value	lower	upper	vif
			Color	5.0732339598	0.3933406503	12.8978125108	0.0000004155	4.1834355904	5.9630323292	1.1255142436
			Quality	3.8917692806	0.5109996678	7.6159918012	0.0000327191	2.7358077218	5.0477308394	1.1255142436
			Regression Analysis (with intercept)								Cook's D
			OVERALL FIT								Obs	Color	Quality	Price	Pred Y	Residual	Leverage	SResidual	Mod MSE	RStudent	T-Test	Cook's D	DFFITS
			Multiple R	0.9223307274		AIC	41.5014849434				1	7	5	65	54.8104996248	10.1895003752	0.1277393982	1.7305289072	22.617923249	2.294052741	0.0509415662	0.1675979932	0.8778952571	SSE	277.3563093482
			R Square	0.8506939707		AICc	48.1681516101				2	3	7	38	42.7461771327	-4.7461771327	0.2905379182	-0.8060647161	35.0864593102	-0.9512826499	0.3693123249	0.1250152547	-0.608760397	dfE	8
			Adjusted R Square	0.8133674634		BSC	42.6951707618				3	5	8	51	56.2951693446	-5.2951693446	0.2950917229	-0.899302545	33.9399658116	-1.0825746382	0.3105451274	0.1600966329	-0.7004385778	MSE	34.6695386685
			Standard Error	5.8880844651							4	8	1	38	44.6721260576	-6.6721260576	0.2670961733	-1.1331573277	30.9450530053	-1.4010225736	0.1987845562	0.2128300052	-0.8457762215	k	2
			Observations	11							5	9	3	55	57.084245388	-2.084245388	0.2466557996	-0.3539768154	38.7985594568	-0.3855177015	0.7099025803	0.0181523482	-0.2205937813
											6	5	4	43	41.2615074129	1.7384925871	0.1148024528	0.2952560544	39.1345684889	0.2953741382	0.7752290803	0.0042574262	0.1063721532
			ANOVA				Alpha	0.05			7	4	0	25	21.3325571166	3.6674428834	0.5720458485	0.6228584024	35.1324840214	0.945823953	0.3719256665	0.4039185058	1.0935209444
				df	SS	MS	F	p-value	sig		8	2	6	33	34.0924732852	-1.0924732852	0.3680560946	-0.1855396762	39.3525279584	-0.2190711776	0.8320804424	0.010575704	-0.1671871847
			Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462	yes		9	8	7	71	67.2226189552	3.7773810448	0.3565163394	0.6415296973	36.4546154288	0.7799123082	0.4578933444	0.1181181583	0.5805193919
			Residual	8	277.3563093482	34.6695386685					10	6	4	51	46.1567957774	4.8432042226	0.0921369246	0.8225432654	35.9313033353	0.8479809981	0.421093593	0.0252109525	0.2701424288
			Total	10	1857.6363636364						11	9	2	49	53.325829905	-4.325829905	0.2693213278	-0.7346752464	35.9637343181	-0.8438666678	0.4232576676	0.0907585283	-0.5123255217
				coeff	std err	t stat	p-value	lower	upper	vif
			Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998
			Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255	1.1255142436
			Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481	1.1255142436

Reg No Const 1

Regression w/o Intercept			Regression Analysis								Matrix Calculations								LINEST				TREND
Color	Quality	Price	OVERALL FIT								X		Y		B	Ŷ		E		b2	b1		Color	Quality	Price 0	Price 1
7	5	65	Multiple R	0.9946802605		AIC	39.58820477				7	5	65		5.0732339598	54.97148		10.0285158782	Slope (b)	3.8917692806	5.0732339598		7	5	54.9714841218	54.8104996248
3	7	38	R Square	0.9893888205		AICc	43.0167761986				3	7	38		3.8917692806	42.46209		-4.4620868438	S.E. of slope (sb)	0.5109996678	0.3933406503		4	4	35.8600129618	36.3662190484
5	8	51	Adjusted R Square	0.9870307807		SBC	40.3839953156				5	8	51			56.50032		-5.5003240441	R Square	0.9893888205	5.5732647823	S.E. of estimate (sRes)	9	8	76.7932598833	75.8763228027
8	1	38	Standard Error	5.5732647823							8	1	38			44.47764		-6.4776409592	F	419.5810378163	9	dfRes
9	3	55	Observations	11							9	3	55		s.e.	57.33441		-2.3344134802	SSReg	26065.4484769929	279.5515230071	SSRes
5	4	43									5	4	43		0.3933406503	40.93325		2.0667530784
4	0	25	ANOVA				Alpha	0.05			4	0	25		0.5109996678	20.29294		4.7070641607
2	6	33		df	SS	MS	F	p-value	sig		2	6	33			33.49708		-0.4970836034
8	7	71	Regression	2	26065.4484769929	13032.7242384964	419.5810378163	0.0000000013	yes		8	7	71			67.82826		3.1717433571
6	4	51	Residual	9	279.5515230071	31.0612803341					6	4	51			46.00648		4.9935191186
9	2	49	Total	11	26345						9	2	49			53.44264		-4.4426441996
				coeff	std err	t stat	p-value	lower	upper	vif	(XTX)-1		MSRes(XTX)-1
			Color	5.0732339598	0.3933406503	12.8978125108	0.0000004155	4.1834355904	5.9630323292	1.1255142436	0.0049810203	-0.0048328858	0.1547168671	-0.150115622			SSRes	279.5515230071
			Quality	3.8917692806	0.5109996678	7.6159918012	0.0000327191	2.7358077218	5.0477308394	1.1255142436	-0.0048328858	0.008406629	-0.150115622	0.2611206605			dfRes	9
																	MSRes	31.0612803341

Heter 1

x	y
7	9.8
10	14.5
11	19.8
13	22.4
14	26.1
14	22.6
16	30.0
18	29.9
20	30.6
22	32.9
23	33.5
24	36.8
26	43.1
28	43.4
x	y
7	12.4
10	14.4
11	20.0
13	19.6
14	24.1
14	25.3
16	28.2
18	27.5
20	27.4
22	29.1
23	48.0
24	51.2
26	37.1
28	60.2

Example 1

7 10 11 13 14 14 16 18 20 22 23 24 26 28 9.8000000000000007 14.5 19.8 22.4 26.1 22.6 30 29.9 30.6 32.9 33.5 36.799999999999997 43.1 43.4

Example 2

7 10 11 13 14 14 16 18 20 22 23 24 26 28 12.4 14.4 20 19.600000000000001 24.1 25.3 28.2 27.5 27.4 29.1 48 51.2 37.1 60.2

Heter 2

Breusch-Pagan Test
	Poverty	Inf Mort	White	Crime	pred	resid	res-sq	Regression Analysis								LM	2.2435405218	=K10*K7	2.2435405218	=BPagStat(C4:E53,B4:B53)
Alabama	15.7	9.0	71.0	448	15.1684824653	0.5315175347	0.2825108897									df	3	=K14
Alaska	8.4	6.9	70.6	661	12.7701940963	-4.3701940963	19.0985964396	OVERALL FIT								p-value	0.5234232393	=CHISQ.DIST.RT(S3,S4)	0.5234232393	=BPagTest(C4:E53,B4:B53)
Arizona	14.7	6.4	86.5	483	12.4537343514	2.2462656486	5.0457093641	Multiple R	0.2118273128		AIC	212.1405293022
Arkansas	17.3	8.5	80.8	529	14.9989413874	2.3010586126	5.2948707388	R Square	0.0448708104		AICc	213.5041656658				F	0.7203413956	=(K7/K14)/((1-K7)/K15)	0.7203413956	=BPagStat(C4:E53,B4:B53,FALSE)
California	13.3	5.0	76.6	523	10.3609461346	2.9390538654	8.6380376235	Adjusted R Square	-0.0174202237		SBC	219.7886213239				df1	3	=K14
Colorado	11.4	5.7	89.7	348	11.4836930525	-0.0836930525	0.007004527	Standard Error	8.0292899347							df2	46	=K15
Connecticut	9.3	6.2	84.3	256	11.7947049281	-2.4947049281	6.2235526782	Observations	50							p-value	0.5449728467	=F.DIST.RT(S7,S8,S9)	0.5449728467	=BPagTest(C4:E53,B4:B53,FALSE)
Delaware	10.0	8.3	74.3	689	14.7334477711	-4.7334477711	22.4055278016
Florida	13.2	7.3	79.8	723	13.7029894019	-0.5029894019	0.2529983384	ANOVA				Alpha	0.05
Georgia	14.7	8.1	65.4	493	13.8764373211	0.8235626789	0.6782554861		df	SS	MS	F	p-value	sig
Hawaii	9.1	5.6	29.7	273	9.0671031505	0.0328968495	0.0010822027	Regression	3	139.3201420119	46.4400473373	0.7203413956	0.5449728467	no
Idaho	12.6	6.8	94.6	239	12.9136966318	-0.3136966318	0.0984055768	Residual	46	2965.5968553824	64.4694968561
Illinois	12.2	7.3	79.1	533	13.4091293344	-1.2091293344	1.4619937472	Total	49	3104.9169973943
Indiana	13.1	8.0	88.0	334	14.3430637586	-1.2430637586	1.545207508
Iowa	11.5	5.1	94.2	295	10.8017494322	0.6982505678	0.4875538554		coeff	std err	t stat	p-value	lower	upper	vif
Kansas	11.3	7.1	88.7	453	13.3864954336	-2.0864954336	4.3534631945	Intercept	4.2014141315	12.9610570378	0.3241567504	0.7472887646	-21.8878405372	30.2906688002
Kentucky	17.3	7.5	89.9	295	13.7176874014	3.5823125986	12.8329635542	Inf Mort	0.3883396519	0.9773055292	0.3973574694	0.6929417367	-1.5788743465	2.3555536504	1.3011416808
Louisiana	17.3	9.9	64.8	730	16.4952894117	0.8047105883	0.647559131	White	-0.0439635007	0.1092214804	-0.4025169827	0.6891680823	-0.263814938	0.1758879365	1.299581918
Maine	12.3	6.3	96.4	118	12.1664376795	0.1335623205	0.0178388934	Crime	0.0058010882	0.0072877146	0.7960092415	0.430115539	-0.0088683205	0.0204704968	1.36048401
Maryland	8.1	8.0	63.4	642	13.8875976694	-5.7875976694	33.4962867825
Massachusetts	10.0	4.8	86.2	432	10.3229904388	-0.3229904388	0.1043228236
Michigan	14.4	7.4	81.2	536	13.6155275636	0.7844724364	0.6153970034
Minnesota	9.6	5.2	89.0	289	10.7349852544	-1.1349852544	1.2881915278
Mississippi	21.2	10.6	60.6	291	16.6138715883	4.5861284117	21.0325738084
Missouri	13.4	7.4	85.0	505	13.711013367	-0.311013367	0.0967293144
Montana	14.8	5.8	90.5	288	11.552564407	3.247435593	10.5458379309
Nebraska	10.8	5.6	91.4	302	11.3507375248	-0.5507375248	0.3033118213
Nevada	11.3	6.4	80.9	751	12.6305975325	-1.3305975325	1.7704897934
New Hampshire	7.6	6.1	95.5	137	11.9052076088	-4.3052076088	18.5348125551
New Jersey	8.7	5.5	76.0	329	10.7037708049	-2.0037708049	4.0150974388
New Mexico	17.1	5.8	84.0	664	11.8529639831	5.2470360169	27.5313869629
New York	13.6	5.6	73.4	414	10.8574525756	2.7425474244	7.5215663751
North Carolina	14.6	8.1	73.9	466	14.1489227581	0.4510772419	0.2034706782
North Dakota	12.0	5.8	91.4	142	11.3799356587	0.6200643413	0.3844797873
Ohio	13.4	7.8	84.8	343	13.9832908902	-0.5832908902	0.3402282625
Oklahoma	15.9	8.0	78.1	500	14.2208941012	1.6791058988	2.8193966195
Oregon	13.6	5.5	90.1	288	11.1570064788	2.4429935212	5.9682173447
Pennsylvania	12.1	7.6	85.4	417	13.8556004574	-1.7556004574	3.082132966
Rhode Island	11.7	6.1	88.5	227	11.7787339485	-0.0787339485	0.0061990346
South Carolina	15.7	8.4	68.7	788	14.8018062071	0.8981937929	0.8067520895
South Dakota	12.5	6.9	88.2	169	12.7089571716	-0.2089571716	0.0436630995
Tennessee	15.5	8.7	80.4	753	15.5581228066	-0.0581228066	0.0033782606
Texas	15.8	6.2	82.4	511	12.0884701877	3.7115298123	13.7754535477
Utah	9.6	5.1	92.9	235	10.6710112494	-1.0710112494	1.1470650964
Vermont	10.6	5.5	96.4	124	11.1525859779	-0.5525859779	0.3053512629
Virginia	10.2	7.1	73.0	270	12.5570521009	-2.3570521009	5.5556946062
Washington	11.3	4.7	84.3	333	9.9856930227	1.3143069773	1.7274028307
West Virginia	17.0	7.4	94.5	275	13.7294518141	3.2705481859	10.696485436
Wisconsin	10.4	6.4	89.7	291	12.296174498	-1.896174498	3.595477727
Wyoming	9.4	7.0	93.9	239	13.1427872096	-3.7427872096	14.0084560961

Heter 3

Shortened White Test
	Poverty	Inf Mort	White	Crime	ypred	ypred-sq	resid	res-sq	Regression Analysis
Alabama	15.7	9.0	71.0	448	15.1684824653	230.0828602995	0.5315175347	0.2825108897
Alaska	8.4	6.9	70.6	661	12.7701940963	163.0778572579	-4.3701940963	19.0985964396	OVERALL FIT								LM	1.2210411526	=L10*L7	1.2210411526	=WhiteStat(C4:E53,B4:B53)
Arizona	14.7	6.4	86.5	483	12.4537343514	155.0954992952	2.2462656486	5.0457093641	Multiple R	0.1562716323		AIC	211.1997649977				df	2	=L14
Arkansas	17.3	8.5	80.8	529	14.9989413874	224.9682427413	2.3010586126	5.2948707388	R Square	0.0244208231		AICc	212.0886538866				p-value	0.5430680871	=CHISQ.DIST.RT(T5,T6)	0.5430680871	=WhiteTest(C4:E53,B4:B53)
California	13.3	5.0	76.6	523	10.3609461346	107.3492048049	2.9390538654	8.6380376235	Adjusted R Square	-0.0170931845		SBC	216.935834014
Colorado	11.4	5.7	89.7	348	11.4836930525	131.8752061242	-0.0836930525	0.007004527	Standard Error	8.027999365							F	0.5882550133	=(L7/L14)/((1-L7)/L15)	0.5593314864	=WhiteTest(C4:E53,B4:B53,FALSE)
Connecticut	9.3	6.2	84.3	256	11.7947049281	139.1150643403	-2.4947049281	6.2235526782	Observations	50							df1	2	=L14
Delaware	10.0	8.3	74.3	689	14.7334477711	217.0744832234	-4.7334477711	22.4055278016									df2	47	=L15
Florida	13.2	7.3	79.8	723	13.7029894019	187.7719185476	-0.5029894019	0.2529983384	ANOVA				Alpha	0.05			p-value	0.5593314864	=F.DIST.RT(T9,T10,T11)	0.5593314864	=WhiteTest(C4:E53,B4:B53,FALSE)
Georgia	14.7	8.1	65.4	493	13.8764373211	192.5555127255	0.8235626789	0.6782554861		df	SS	MS	F	p-value	sig
Hawaii	9.1	5.6	29.7	273	9.0671031505	82.2123595427	0.0328968495	0.0010822027	Regression	2	75.8246285855	37.9123142928	0.5882550133	0.5593314864	no
Idaho	12.6	6.8	94.6	239	12.9136966318	166.7635606986	-0.3136966318	0.0984055768	Residual	47	3029.0923688088	64.4487738044
Illinois	12.2	7.3	79.1	533	13.4091293344	179.8047495056	-1.2091293344	1.4619937472	Total	49	3104.9169973943
Indiana	13.1	8.0	88.0	334	14.3430637586	205.7234779841	-1.2430637586	1.545207508
Iowa	11.5	5.1	94.2	295	10.8017494322	116.6777907968	0.6982505678	0.4875538554		coeff	std err	t stat	p-value	lower	upper	vif
Kansas	11.3	7.1	88.7	453	13.3864954336	179.1982599939	-2.0864954336	4.3534631945	Intercept	-28.6040793645	54.0452214702	-0.5292619511	0.5991147855	-137.3290409695	80.1208822406
Kentucky	17.3	7.5	89.9	295	13.7176874014	188.1749476421	3.5823125986	12.8329635542	ypred	4.7460892812	8.4548440149	0.5613455757	0.5772288838	-12.2628629608	21.7550415233	158.1120091789
Louisiana	17.3	9.9	64.8	730	16.4952894117	272.0945727743	0.8047105883	0.647559131	ypred-sq	-0.1588868926	0.3270914817	-0.4857567425	0.6293966329	-0.8169100781	0.4991362928	158.1120091789
Maine	12.3	6.3	96.4	118	12.1664376795	148.02220581	0.1335623205	0.0178388934
Maryland	8.1	8.0	63.4	642	13.8875976694	192.8653690262	-5.7875976694	33.4962867825
Massachusetts	10.0	4.8	86.2	432	10.3229904388	106.5641316004	-0.3229904388	0.1043228236
Michigan	14.4	7.4	81.2	536	13.6155275636	185.3825908363	0.7844724364	0.6153970034
Minnesota	9.6	5.2	89.0	289	10.7349852544	115.2399084132	-1.1349852544	1.2881915278
Mississippi	21.2	10.6	60.6	291	16.6138715883	276.0207291531	4.5861284117	21.0325738084
Missouri	13.4	7.4	85.0	505	13.711013367	187.9918875495	-0.311013367	0.0967293144
Montana	14.8	5.8	90.5	288	11.552564407	133.461744377	3.247435593	10.5458379309
Nebraska	10.8	5.6	91.4	302	11.3507375248	128.8392423579	-0.5507375248	0.3033118213
Nevada	11.3	6.4	80.9	751	12.6305975325	159.5319940274	-1.3305975325	1.7704897934
New Hampshire	7.6	6.1	95.5	137	11.9052076088	141.7339682091	-4.3052076088	18.5348125551
New Jersey	8.7	5.5	76.0	329	10.7037708049	114.5707094448	-2.0037708049	4.0150974388
New Mexico	17.1	5.8	84.0	664	11.8529639831	140.4927551841	5.2470360169	27.5313869629
New York	13.6	5.6	73.4	414	10.8574525756	117.8842764312	2.7425474244	7.5215663751
North Carolina	14.6	8.1	73.9	466	14.1489227581	200.1920152147	0.4510772419	0.2034706782
North Dakota	12.0	5.8	91.4	142	11.3799356587	129.5029355963	0.6200643413	0.3844797873
Ohio	13.4	7.8	84.8	343	13.9832908902	195.5324241187	-0.5832908902	0.3402282625
Oklahoma	15.9	8.0	78.1	500	14.2208941012	202.2338290369	1.6791058988	2.8193966195
Oregon	13.6	5.5	90.1	288	11.1570064788	124.4787935675	2.4429935212	5.9682173447
Pennsylvania	12.1	7.6	85.4	417	13.8556004574	191.9776640349	-1.7556004574	3.082132966
Rhode Island	11.7	6.1	88.5	227	11.7787339485	138.7385734287	-0.0787339485	0.0061990346
South Carolina	15.7	8.4	68.7	788	14.8018062071	219.0934669936	0.8981937929	0.8067520895
South Dakota	12.5	6.9	88.2	169	12.7089571716	161.5175923883	-0.2089571716	0.0436630995
Tennessee	15.5	8.7	80.4	753	15.5581228066	242.0551852649	-0.0581228066	0.0033782606
Texas	15.8	6.2	82.4	511	12.0884701877	146.1311114784	3.7115298123	13.7754535477
Utah	9.6	5.1	92.9	235	10.6710112494	113.8704810857	-1.0710112494	1.1470650964
Vermont	10.6	5.5	96.4	124	11.1525859779	124.3801739935	-0.5525859779	0.3053512629
Virginia	10.2	7.1	73.0	270	12.5570521009	157.679557464	-2.3570521009	5.5556946062
Washington	11.3	4.7	84.3	333	9.9856930227	99.7140651429	1.3143069773	1.7274028307
West Virginia	17.0	7.4	94.5	275	13.7294518141	188.4978471168	3.2705481859	10.696485436
Wisconsin	10.4	6.4	89.7	291	12.296174498	151.1959072862	-1.896174498	3.595477727
Wyoming	9.4	7.0	93.9	239	13.1427872096	172.7328556359	-3.7427872096	14.0084560961

Heter 4

Full White Test
	Poverty	Inf Mort	White	Crime	I-sq	W-sq	C-sq	I*W	I*C	W*C	pred	resid	res-sq	Regression Analysis								LM	16.3597671107	=Q10*Q7
Alabama	15.7	9.0	71.0	448	81	5,045	200,704	639	4,032	31,820	15.1684824653	0.5315175347	0.2825108897									df	9	=Q14
Alaska	8.4	6.9	70.6	661	48	4,988	437,185	487	4,562	46,696	12.7701940963	-4.3701940963	19.0985964396	OVERALL FIT								p-value	0.0597383327	=CHISQ.DIST.RT(Y3,Y4)
Arizona	14.7	6.4	86.5	483	41	7,483	232,999	554	3,089	41,756	12.4537343514	2.2462656486	5.0457093641	Multiple R	0.5720099144		AIC	206.6209504652
Arkansas	17.3	8.5	80.8	529	72	6,526	280,264	687	4,500	42,767	14.9989413874	2.3010586126	5.2948707388	R Square	0.3271953422		AICc	213.5683188863				F	2.1614022794	=(Q7/Q14)/((1-Q7)/Q15)
California	13.3	5.0	76.6	523	25	5,874	273,111	383	2,613	40,052	10.3609461346	2.9390538654	8.6380376235	Adjusted R Square	0.1758142942		SBC	225.7411805195				df1	9	=Q14
Colorado	11.4	5.7	89.7	348	32	8,052	120,965	511	1,982	31,210	11.4836930525	-0.0836930525	0.007004527	Standard Error	7.2266911825							df2	40	=Q15
Connecticut	9.3	6.2	84.3	256	38	7,103	65,536	523	1,587	21,575	11.7947049281	-2.4947049281	6.2235526782	Observations	50							p-value	0.0462078845	=F.DIST.RT(Y7,Y8,Y9)
Delaware	10.0	8.3	74.3	689	69	5,515	474,997	616	5,720	51,184	14.7334477711	-4.7334477711	22.4055278016
Florida	13.2	7.3	79.8	723	53	6,370	522,151	583	5,275	57,671	13.7029894019	-0.5029894019	0.2529983384	ANOVA				Alpha	0.05
Georgia	14.7	8.1	65.4	493	66	4,276	243,246	530	3,995	32,249	13.8764373211	0.8235626789	0.6782554861		df	SS	MS	F	p-value	sig
Hawaii	9.1	5.6	29.7	273	31	880	74,420	166	1,528	8,093	9.0671031505	0.0328968495	0.0010822027	Regression	9	1015.9143795078	112.8793755009	2.1614022794	0.0462078845	yes
Idaho	12.6	6.8	94.6	239	46	8,949	57,312	643	1,628	22,647	12.9136966318	-0.3136966318	0.0984055768	Residual	40	2089.0026178865	52.2250654472
Illinois	12.2	7.3	79.1	533	53	6,262	284,302	578	3,892	42,194	13.4091293344	-1.2091293344	1.4619937472	Total	49	3104.9169973943
Indiana	13.1	8.0	88.0	334	64	7,744	111,289	704	2,669	29,357	14.3430637586	-1.2430637586	1.545207508
Iowa	11.5	5.1	94.2	295	26	8,868	86,848	480	1,503	27,752	10.8017494322	0.6982505678	0.4875538554		coeff	std err	t stat	p-value	lower	upper	vif
Kansas	11.3	7.1	88.7	453	50	7,868	204,937	630	3,214	40,156	13.3864954336	-2.0864954336	4.3534631945	Intercept	-146.9999327556	87.3416558332	-1.6830449498	0.1001570937	-323.5240039087	29.5241383975
Kentucky	17.3	7.5	89.9	295	56	8,083	87,025	674	2,213	26,522	13.7176874014	3.5823125986	12.8329635542	Inf Mort	18.7054023064	21.7436107616	0.8602712085	0.3947666412	-25.2400743003	62.6508789132	795.0647438321
Louisiana	17.3	9.9	64.8	730	98	4,204	532,170	642	7,222	47,300	16.4952894117	0.8047105883	0.647559131	White	0.3748405021	0.818715262	0.4578398858	0.6495449382	-1.2798447656	2.0295257698	90.1422933476
Maine	12.3	6.3	96.4	118	40	9,291	13,924	607	743	11,374	12.1664376795	0.1335623205	0.0178388934	Crime	0.3710330048	0.1328770041	2.7923041104	0.0079880669	0.1024785618	0.6395874478	558.3231960422
Maryland	8.1	8.0	63.4	642	64	4,019	412,036	507	5,135	40,695	13.8875976694	-5.7875976694	33.4962867825	I-sq	-0.146352435	0.8780874478	-0.1666718222	0.8684679748	-1.9210333663	1.6283284964	272.3588933546
Massachusetts	10.0	4.8	86.2	432	23	7,431	186,192	414	2,071	37,197	10.3229904388	-0.3229904388	0.1043228236	W-sq	0.006607247	0.0062561585	1.0561188711	0.2972510746	-0.0060369209	0.019251415	109.2654255172
Michigan	14.4	7.4	81.2	536	55	6,591	287,296	601	3,966	43,514	13.6155275636	0.7844724364	0.6153970034	C-sq	-0.0000009105	0.0000421102	-0.0216210962	0.9828577024	-0.0000860184	0.0000841975	47.1642787816
Minnesota	9.6	5.2	89.0	289	27	7,929	83,348	463	1,501	25,707	10.7349852544	-1.1349852544	1.2881915278	I*W	-0.0887309931	0.1388499859	-0.6390421469	0.5264382534	-0.3693572825	0.1918952963	185.2246174768
Mississippi	21.2	10.6	60.6	291	112	3,672	84,856	642	3,088	17,652	16.6138715883	4.5861284117	21.0325738084	I*C	-0.0239267962	0.0065120713	-3.6742221153	0.0006993183	-0.0370881832	-0.0107654093	107.0575815728
Missouri	13.4	7.4	85.0	505	55	7,230	254,924	629	3,736	42,931	13.711013367	-0.311013367	0.0967293144	W*C	-0.0023678853	0.0010610885	-2.2315624853	0.0313133352	-0.0045124251	-0.0002233454	188.934235076
Montana	14.8	5.8	90.5	288	34	8,184	82,656	525	1,668	26,009	11.552564407	3.247435593	10.5458379309
Nebraska	10.8	5.6	91.4	302	31	8,349	91,446	512	1,693	27,631	11.3507375248	-0.5507375248	0.3033118213
Nevada	11.3	6.4	80.9	751	41	6,543	563,400	518	4,804	60,717	12.6305975325	-1.3305975325	1.7704897934
New Hampshire	7.6	6.1	95.5	137	37	9,118	18,851	582	838	13,110	11.9052076088	-4.3052076088	18.5348125551
New Jersey	8.7	5.5	76.0	329	30	5,781	108,438	418	1,811	25,037	10.7037708049	-2.0037708049	4.0150974388
New Mexico	17.1	5.8	84.0	664	34	7,055	441,162	487	3,852	55,790	11.8529639831	5.2470360169	27.5313869629
New York	13.6	5.6	73.4	414	31	5,391	171,479	411	2,319	30,404	10.8574525756	2.7425474244	7.5215663751
North Carolina	14.6	8.1	73.9	466	66	5,467	217,529	599	3,778	34,484	14.1489227581	0.4510772419	0.2034706782
North Dakota	12.0	5.8	91.4	142	34	8,353	20,278	530	826	13,014	11.3799356587	0.6200643413	0.3844797873
Ohio	13.4	7.8	84.8	343	61	7,185	117,786	661	2,677	29,091	13.9832908902	-0.5832908902	0.3402282625
Oklahoma	15.9	8.0	78.1	500	64	6,106	249,600	625	3,997	39,039	14.2208941012	1.6791058988	2.8193966195
Oregon	13.6	5.5	90.1	288	30	8,125	82,714	496	1,582	25,924	11.1570064788	2.4429935212	5.9682173447
Pennsylvania	12.1	7.6	85.4	417	58	7,297	173,472	649	3,165	35,579	13.8556004574	-1.7556004574	3.082132966
Rhode Island	11.7	6.1	88.5	227	37	7,829	51,665	540	1,387	20,112	11.7787339485	-0.0787339485	0.0061990346
South Carolina	15.7	8.4	68.7	788	71	4,726	621,417	577	6,622	54,194	14.8018062071	0.8981937929	0.8067520895
South Dakota	12.5	6.9	88.2	169	48	7,777	28,629	609	1,167	14,922	12.7089571716	-0.2089571716	0.0436630995
Tennessee	15.5	8.7	80.4	753	76	6,460	567,461	699	6,554	60,544	15.5581228066	-0.0581228066	0.0033782606
Texas	15.8	6.2	82.4	511	38	6,790	260,712	511	3,166	42,075	12.0884701877	3.7115298123	13.7754535477
Utah	9.6	5.1	92.9	235	26	8,633	55,131	474	1,197	21,817	10.6710112494	-1.0710112494	1.1470650964
Vermont	10.6	5.5	96.4	124	30	9,295	15,450	530	684	11,984	11.1525859779	-0.5525859779	0.3053512629
Virginia	10.2	7.1	73.0	270	50	5,334	72,738	519	1,915	19,697	12.5570521009	-2.3570521009	5.5556946062
Washington	11.3	4.7	84.3	333	22	7,105	110,956	396	1,566	28,077	9.9856930227	1.3143069773	1.7274028307
West Virginia	17.0	7.4	94.5	275	55	8,935	75,735	699	2,036	26,013	13.7294518141	3.2705481859	10.696485436
Wisconsin	10.4	6.4	89.7	291	41	8,041	84,623	574	1,862	26,086	12.296174498	-1.896174498	3.595477727
Wyoming	9.4	7.0	93.9	239	49	8,811	57,264	657	1,675	22,462	13.1427872096	-3.7427872096	14.0084560961

Heter 5

Heteroskedascity Testing
Sample size	50
Indep var	3
Breusch-Pagan		White Test
LM stat	2.2435405218	LM stat	1.2210411526
df	3	df	2
p-value	0.5234232393	p-value	0.5430680871
F stat	0.7203413956	F stat	0.5882550133
df1	3	df1	2
df2	46	df2	47
p-value	0.5449728467	p-value	0.5593314864

WReg A

Weighted Regression
			Regression Analysis							Weighted Regression Analysis
			OVERALL FIT							OVERALL FIT							X	Y
X	Y	W	Multiple R	0.9248759136		AIC	-20.5879359752			Multiple R	0.9181482769		AIC	0.6578869179			15	15.8746008585
21	17.26	20	R Square	0.8553954556		AICc	-12.5879359752			R Square	0.8429962583		AICc	3.6578869179			21	17.0819364393
20	17.07	26	Adj R Square	0.8264745468		SBC	-20.6961156771			Adj R Square	0.816828968		BSC	0.603797067
19	16.37	27	Standard Error	0.2043246716						Standard Error	1.0750540057
18	16.40	24	Observations	7						Observations	7
17	16.13	36
16	16.17	39	ANOVA				Alpha	0.05		ANOVA				Alpha	0.05
15	15.98	32		df	SS	MS	F	p-value	sig		df	SS	MS	F	p-value	sig
			Regression	1	1.2348	1.2348	29.5770599507	0.0028523045	yes	Regression	1	31.0274591302	31.0274591302	26.8463747839	0.00352135	yes
			Residual	5	0.2087428571	0.0417485714				Residual	5	5.7787055757	1.1557411151
			Total	6	1.4435428571					Total	6	36.8061647059
				coeff	std err	t stat	p-value	lower	upper		coeff	std err	t stat	p-value	lower	upper
			Intercept	12.7028571429	0.6993244554	18.1644686459	0.0000092937	10.9051864007	14.500527885	Intercept	12.8562619064	0.6896503999	18.6417087672	0.0000081767	11.0834591155	14.6290646974
			X	0.21	0.0386137334	5.4384795624	0.0028523045	0.1107402383	0.3092597617	X	0.2012225968	0.0388359493	5.1813487421	0.00352135	0.1013916111	0.3010535825

OLS (blue) vs. WLS (red)

Y 21 20 19 18 17 16 15 17.260000000000002 17.07 16.37 16.400000000000002 16.13 16.170000000000002 15.98 15 21 15.8746008584788 17.081936439295475

WReg B

Weighted Linear Regression
Company size ($ million)				Wages ($ thousands)			Weighted Regression Analysis
band	lower	upper	Ln(mean)	Mean	Std Dev	Weight
1	2	25	2.6026896854	266.7	60.5	0.0002732054	OVERALL FIT
2	25	50	3.624340933	342.5	68.3	0.0002143673	Multiple R	0.9748883807		AIC	-9.506743992
3	50	100	4.3174881135	418.1	81.4	0.0001509215	R Square	0.9504073548		AICc	-7.106743992
4	100	250	5.1647859739	494.2	98.8	0.0001024439	Adjusted R Square	0.9433226912		BSC	-9.4273024503
5	250	500	5.926926026	608.3	110.6	0.0000817504	Standard Error	0.5625189462
6	500	1000	6.6200732065	798.3	145.6	0.0000471712	Observations	8
7	1000	5000	8.0063675677	950.6	173.1	0.0000333738
8	5000	10000	8.9226582995	1216.5	238.3	0.0000176097	ANOVA				Alpha	0.05
								df	SS	MS	F	p-value	sig
							Regression	1	36.3846393646	36.3846393646	114.985682044	0.0000388462	yes
							Residual	6	1.8985653892	0.3164275649
							Total	7	38.2832047539
								coeff	std err	t stat	p-value	lower	upper
							Intercept	-100.8456364708	53.2964541876	-1.892164085	0.1073295893	-231.2573618464	29.5660889049
							Ln(mean)	126.8453092593	11.829122488	10.7231376958	0.0000388462	97.9004892549	155.7901292638

WReg C

Weighted Regression
Ad	Clients	Regression Analysis							Cook's D																Weighted Regression Analysis
26	61
24	59	OVERALL FIT							Obs	Ad	Clients	Pred Y	Residual	Leverage	SResidual	Mod MSE	RStudent	T-Test	Cook's D	DFFITS	Ad	Abs Res	Pred Res	Weights	OVERALL FIT
32	74	Multiple R	0.9311416939		AIC	41.9964747346			1	26	61	61.2268907563	-0.2268907563	0.0945378151	-0.0425262455	31.6220675432	-0.0424020167	0.9670129126	0.0001042675	-0.0137010589	26	0.2268907563	4.750582586	0.0443104596	Multiple R	0.9316980087		AIC	6.0227491392
20	39	R Square	0.8670248542		AICc	44.9964747346			2	24	59	54.3865546218	4.6134453782	0.0861344538	0.8647003249	29.0406130268	0.8955324783	0.3915491971	0.0385579152	0.2749341205	24	4.6134453782	3.7213473625	0.0722103715	R Square	0.8680611793		AICc	7.3560824726
24	51	Adjusted R Square	0.8537273396		SBC	42.9662880342			3	32	74	81.7478991597	-7.7478991597	0.4222689076	-1.4521925311	20.0832323232	-2.2745960624	0.0462082465	1.3340005982	-1.9446254481	32	7.7478991597	7.8382882565	0.0162763694	Adj R Square	0.8560667411		BSC	6.507655789
27	59	Standard Error	5.3353112579						4	20	39	40.7058823529	-1.7058823529	0.2205882353	-0.3197343642	31.2135369871	-0.3458550378	0.736617363	0.0185608161	-0.1839932119	20	1.7058823529	1.6628769155	0.3616427735	Standard Error	1.2953427421
20	44	Observations	12						5	24	51	54.3865546218	-3.3865546218	0.0861344538	-0.63474359	30.2339719029	-0.6442721079	0.533897353	0.0207768138	-0.1977956016	24	3.3865546218	3.7213473625	0.0722103715	Observations	12
23	52								6	27	59	64.6470588235	-5.6470588235	0.1176470588	-1.0584309988	27.6126984127	-1.1440545115	0.2792473275	0.0846430891	-0.417749642	27	5.6470588235	5.2652001977	0.036072
29	81	ANOVA				Alpha	0.05		7	20	44	40.7058823529	3.2941176471	0.2205882353	0.6174180826	30.0814615154	0.6803089129	0.5117512841	0.0692113191	0.3619210603	20	3.2941176471	1.6628769155	0.3616427735	ANOVA				Alpha	0.05
22	46		df	SS	MS	F	p-value	sig	8	23	52	50.9663865546	1.0336134454	0.1008403361	0.1937306739	31.4963655244	0.1942269637	0.8498870529	0.0023406013	0.0650441519	23	1.0336134454	3.2067297507	0.0972467907		df	SS	MS	F	p-value	sig
28	74	Regression	1	1856.0112044818	1856.0112044818	65.202023184	0.0000108524	yes	9	29	81	71.487394958	9.512605042	0.2016806723	1.7829522182	19.0339181287	2.4403214935	0.034829811	0.502991222	1.226565834	29	9.512605042	6.2944354212	0.0252398306	Regression	1	110.394421723	110.394421723	65.7927041474	0.000010431	yes
21	40	Residual	10	284.6554621849	28.4655462185				10	22	46	47.5462184874	-1.5462184874	0.1281512605	-0.2898084878	31.3236947791	-0.2958787654	0.7733756547	0.0070799925	-0.1134369829	22	1.5462184874	2.692112139	0.1379792269	Residual	10	16.7791281957	1.6779128196
		Total	11	2140.6666666667					11	28	74	68.0672268908	5.9327731092	0.1533613445	1.1119825672	27.0090984285	1.2406624818	0.2430487971	0.1322774099	0.5280348129	28	5.9327731092	5.7798178095	0.0299344789	Total	11	127.1735499187
									12	21	40	44.1260504202	-4.1260504202	0.1680672269	-0.7733476494	29.354657688	-0.8349335334	0.4232534478	0.0726149616	-0.3752747156	21	4.1260504202	2.1774945272	0.2109045046
			coeff	std err	t stat	p-value	lower	upper																		coeff	std err	t stat	p-value	lower	upper
		Intercept	-27.6974789916	10.5607728758	-2.6226753778	0.0254804579	-51.2283473432	-4.16661064											SSE	284.6554621849					Intercept	-28.7116020328	9.3290880501	-3.0776429463	0.0116875323	-49.4981055708	-7.9250984948
		Ad	3.4201680672	0.4235619235	8.0747769743	0.0000108524	2.4764132892	4.3639228452											dfE	10					Ad	3.4592303452	0.4264720892	8.1112701932	0.000010431	2.508991314	4.4094693764
																			MSE	28.4655462185
																			k	1

Residual Analysis

Residual 26 24 32 20 24 27 20 23 29 22 28 21 -0.22689075630251665 4.6134453781512619 -7.7478991596638593 -1.7058823529411811 -3.3865546218487381 -5.6470588235293917 3.2941176470588189 1.0336134453781582 9.5126050420168156 -1.5462184873949596 5.9327731092436977 -4.1260504201680632

Ad Spend

Residuals

Impact of Ad Budget

Clients 26 24 32 20 24 27 20 23 29 22 28 21 61 59 74 39 51 59 44 52 81 46 74 40

Ad spend

Number of New Clients

WReg D

Weighted Regression
			Regression Analysis								Color	Quality	Price	Pred	Res	Abs Res	Pred Res	Weight	Weighted Regression Analysis
Color	Quality	Price									7	5	65	55.0265642953	9.9734357047	9.9734357047	4.2356330869	0.0557395343
7	5	65	OVERALL FIT								3	7	38	42.6894237427	-4.6894237427	4.6894237427	3.5348805123	0.0800295774	OVERALL FIT
3	7	38	Multiple R	0.9624427357		AIC	50.2150488568				5	8	51	56.5671184093	-5.5671184093	5.5671184093	4.3231369341	0.0535059422	Multiple R	0.969002431		AIC	8.5062078757
5	8	51	R Square	0.9262960195		AICc	54.2150488568				8	1	38	44.6198817505	-6.6198817505	6.6198817505	3.6445309952	0.07528643	R Square	0.9389657113		AICc	10.6880260575
8	1	38	Adjusted R Square	0.9140120227		SBC	52.3391994601				9	3	55	57.3405723765	-2.3405723765	2.3405723765	4.3670693069	0.052434826	Adjusted R Square	0.9295758207		BSC	9.9223082779
9	3	55	Standard Error	4.8812763732							5	4	43	41.1488696287	1.8511303713	1.8511303713	3.4473766651	0.0841438782	Standard Error	1.2992389953
5	4	43	Observations	15							2	3	20	22.2596087264	-2.2596087264	2.2596087264	2.374462066	0.1773656556	Observations	15
2	3	20									8	6	65	63.8926927262	1.1073072738	1.1073072738	4.7392313314	0.0445229758
8	6	65	ANOVA				Alpha	0.05			3	1	20	19.5620505718	0.4379494282	0.4379494282	2.2212401088	0.2026791089	ANOVA				Alpha	0.05
3	1	20		df	SS	MS	F	p-value	sig		1	5	26	24.957166881	1.042833119	1.042833119	2.5276840232	0.1565144478		df	SS	MS	F	p-value	sig
1	5	26	Regression	2	3593.4110249562	1796.7055124781	75.4067294438	0.0000001603	yes		4	0	25	20.7190546124	4.2809453876	4.2809453876	2.2869582188	0.1911980854	Regression	2	311.6270765795	155.8135382897	92.305397287	0.0000000517	yes
4	0	25	Residual	12	285.9223083772	23.8268590314					2	6	33	33.8232953119	-0.8232953119	0.8232953119	3.0312822678	0.1088296524	Residual	12	20.2562636036	1.688021967
2	6	33	Total	14	3879.3333333333						8	7	71	67.7472549214	3.2527450786	3.2527450786	4.9581713987	0.0406777505	Total	14	331.8833401831
8	7	71									6	4	51	46.1604358645	4.8395641355	4.8395641355	3.7320348424	0.071797384
6	4	51		coeff	std err	t stat	p-value	lower	upper	vif	9	2	49	53.4860101813	-4.4860101813	4.4860101813	4.1481292397	0.0581159635		coeff	std err	t stat	p-value	lower	upper	vif
9	2	49	Intercept	0.6727896695	3.7254870443	0.1805910641	0.8597025902	-7.4443492993	8.7899286383										Intercept	1.9172425562	2.6876052111	0.713364652	0.4892618484	-3.9385461589	7.7730312712
			Color	5.0115662357	0.4772022063	10.5019762463	0.0000002104	3.9718319462	6.0513005253	1.006708924									Color	4.9696452468	0.4332697359	11.470095496	0.0000000799	4.0256315874	5.9136589062	1.0035854268
			Quality	3.8545621951	0.5352247238	7.2017640882	0.0000108408	2.6884077001	5.0207166902	1.006708924									Quality	3.5955530326	0.4519327618	7.9559468504	0.0000039784	2.6108761331	4.5802299321	1.0035854268
																				0.9389657113

Residual Plot

Res 55.026564295334694 42.689423742742569 56.567118409338214 44.619881750458461 57.340572376480964 41.14886962873905 22.259608726420481 63.892692726207414 19.562050571843827 24.957166880997136 20.719054612416969 33.823295311869856 67.747254921357211 46.160435864461974 53.486010181331174 9.9734357046653059 -4.6894237427425693 -5.5671184093382138 -6.6198817504584611 -2.3405723764809636 1.8511303712609504 -2.2596087264204812 1.1073072737925855 0.43794942815617333 1.0428331190028644 4.2809453875830314 -0.82329531186985605 3.2527450786427892 4.8395641355380263 -4.4860101813311744

Forecasted Price

Residuals

WReg E

Weighted Regression
Score	Gender	Stress	Regression Analysis								Score	Gender	Stress	Pred	Res						Score	Gender	Stress	Weight	Weighted Regression Analysis
5.08	0	3.35									5.08	0	3.35	3.4648189777	-0.1148189777						5.08	0	3.35	109.9231083898
5.16	0	3.61	OVERALL FIT								5.16	0	3.61	3.4880909975	0.1219090025						5.16	0	3.61	109.9231083898	OVERALL FIT
4.85	0	3.30	Multiple R	0.468773476		AIC	-64.4529360561				4.85	0	3.30	3.3979119209	-0.0979119209						4.85	0	3.30	109.9231083898	Multiple R	0.6385836363		AIC	1.8869793874
4.98	0	3.41	R Square	0.2197485718		AICc	-61.9529360561				4.98	0	3.41	3.435728953	-0.025728953						4.98	0	3.41	109.9231083898	R Square	0.4077890605		AICc	3.2987440933
4.90	0	3.58	Adjusted R Square	0.1330539686		SBC	-61.3193687429				4.90	0	3.58	3.4124569332	0.1675430668						4.90	0	3.58	109.9231083898	Adj R Square	0.3454510669		BSC	3.9760242629
5.21	0	3.47	Standard Error	0.2018207163							5.21	0	3.47	3.5026360099	-0.0326360099						5.21	0	3.47	109.9231083898	Standard Error	1.0271267338
5.08	0	3.45	Observations	21							5.08	0	3.45	3.4648189777	-0.0148189777						5.08	0	3.45	109.9231083898	Observations	21
4.94	0	3.36									4.94	0	3.36	3.4240929431	-0.0640929431						4.94	0	3.36	109.9231083898
5.46	0	3.67	ANOVA				Alpha	0.05			5.46	0	3.67	3.5753610717	0.0946389283						5.46	0	3.67	109.9231083898	ANOVA				Alpha	0.05
6.04	0	3.71		df	SS	MS	F	p-value	sig		6.04	0	3.71	3.7440832151	-0.0340832151						6.04	0	3.71	109.9231083898		df	SS	MS	F	p-value	sig
6.41	1	3.92	Regression	2	0.2064883151	0.1032441575	2.5347433843	0.1071793579	no		6.41	1	3.92	3.6011519598	0.3188480402						6.41	1	3.92	15.3540628348	Regression	2	13.0761446714	6.5380723357	6.1972876559	0.0089595837	yes
6.01	1	3.16	Residual	18	0.7331688278	0.0407316015					6.01	1	3.16	3.4847918609	-0.3247918609						6.01	1	3.16	15.3540628348	Residual	18	18.989807893	1.0549893274
6.15	1	3.58	Total	20	0.9396571429						6.15	1	3.58	3.5255178955	0.0544821045						6.15	1	3.58	15.3540628348	Total	20	32.0659525643
5.80	1	3.22									5.80	1	3.22	3.423702809	-0.203702809						5.80	1	3.22	15.3540628348
5.94	1	3.30		coeff	std err	t stat	p-value	lower	upper	vif	5.94	1	3.30	3.4644288436	-0.1644288436						5.94	1	3.30	15.3540628348		coeff	std err	t stat	p-value	lower	upper	vif
5.32	1	3.13	Intercept	1.9870457217	0.6996385479	2.8401032615	0.0108604699	0.5171596763	3.4569317671		5.32	1	3.13	3.2840706903	-0.1540706903						5.32	1	3.13	15.3540628348	Intercept	1.9430365812	0.4488198031	4.3292131224	0.0004040699	1.0001011648	2.8859719976
6.12	1	3.46	Score	0.2909002473	0.1347623915	2.158615947	0.0446299377	0.0077749688	0.5740255257	2.0398868153	6.12	1	3.46	3.5167908881	-0.0567908881		Min	Max	Var	Weight	6.12	1	3.46	15.3540628348	Score	0.2994126535	0.0866052865	3.4572098964	0.0028114236	0.1174616983	0.4813636087	1.4634364177
5.34	1	3.64	Gender	-0.2505643468	0.1259453343	-1.9894690674	0.0620642703	-0.5151656755	0.0140369819	2.0398868153	5.34	1	3.64	3.2898886952	0.3501113048	Males	-0.3247918609	0.3501113048	0.0651293414	15.3540628348	5.34	1	3.64	15.3540628348	Gender	-0.2562444434	0.102692713	-2.4952543947	0.0225291003	-0.4719938274	-0.0404950593	1.4634364177
5.76	1	3.55									5.76	1	3.55	3.4120667991	0.1379332009	Females	-0.1148189777	0.1675430668	0.0090972682	109.9231083898	5.76	1	3.55	15.3540628348
5.65	1	3.72									5.65	1	3.72	3.3800677719	0.3399322281						5.65	1	3.72	15.3540628348								1.4634364177
5.71	1	3.10									5.71	1	3.10	3.3975217867	-0.2975217867						5.71	1	3.10	15.3540628348								1.4634364177
																																ERROR:#N/A

Residual Chart

Female 3.4648189777471519 3.4880909975274603 3.3979119208787636 3.4357289530217656 3.4124569332414572 3.5026360098901534 3.4648189777471519 3.4240929431316114 3.5753610717036191 3.7440832151108578 -0.11481897774715177 0.12190900247253955 -9.791192087876377E-2 -2.5728953021765477E-2 0.16754306675854291 -3.263600989015325E-2 -1.4818977747151685E-2 -6.4092943131611513E-2 9.4638928296380875E-2 -3.4083215110857878E-2 Male 3.6011519597908466 3.4847918608893025 3.5255178955048425 3.4237028089659916 3.4644288435815325 3.2840706902841381 3.5167908880872272 3.2898886952292159 3.4120667990758369 3.3800677718779122 3.3975217867131438 0.31884804020915336 -0.32479186088930234 5.4482104495157557E-2 -0.20370280896599136 -0.16442884358153265 -0.15407069028413822 -5.6790888087227209E-2 0.35011130477078423 0.13793320092416295 0.33993222812208801 -0.29752178671314367

Forecasted Stress

Residuals

Female

Stres s 5.08 5.16 4.8499999999999996 4.9800000000000004 4.9000000000000004 5.21 5.08 4.9400000000000004 5.46 6.04 3.35 3.61 3.3 3.41 3.58 3.47 3.45 3.36 3.67 3.71

Male

6.41 6.01 6.15 5.8 5.94 5.32 6.12 5.34 5.76 5.65 5.71 3.92 3.16 3.58 3.22 3.3 3.13 3.46 3.64 3.55 3.72 3.1

WReg B0

Weighted Linear Regression								Regression Analysis								Regression Analysis								Prediction
Company size ($ million)					Wages ($ thousands)			OVERALL FIT								OVERALL FIT								Size	Wage1	Wage2
band	lower	upper	Ln(mean)		Mean	Std Dev	Weight	Multiple R	0.9955230042		AIC	-7.506743992				Multiple R	0.9815910093		AIC	69.1326869395				200	571.2210684431	429.9790187993
1	2	25	2.6026896854		266.7	60.5	0.0002732054	R Square	0.991066052		AICc	-1.506743992				R Square	0.9635209096		AICc	75.1326869395
2	25	50	3.624340933		342.5	68.3	0.0002143673	Adjusted R Square	0.9880880693		BSC	-7.3478609086				Adjusted R Square	0.9574410612		BSC	69.2915700228
3	50	100	4.3174881135		418.1	81.4	0.0001509215	Standard Error	0.5625189462							Standard Error	67.6693923057
4	100	250	5.1647859739		494.2	98.8	0.0001024439	Observations	8							Observations	8
5	250	500	5.926926026		608.3	110.6	0.0000817504
6	500	1000	6.6200732065		798.3	145.6	0.0000471712	ANOVA				Alpha	0.05			ANOVA				Alpha	0.05
7	1000	5000	8.0063675677		950.6	173.1	0.0000333738		df	SS	MS	F	p-value	sig			df	SS	MS	F	p-value	sig
8	5000	10000	8.9226582995		1216.5	238.3	0.0000176097	Regression	2	210.612787784	105.306393892	332.7977887569	0.0000007131	yes		Regression	1	725693.020069862	725693.020069862	158.4777852166	0.0000153821	yes
								Residual	6	1.8985653892	0.3164275649					Residual	6	27474.879930138	4579.146655023
								Total	8	212.5113531733						Total	7	753167.9
									coeff	std err	t stat	p-value	lower	upper	vif		coeff	std err	t stat	p-value	lower	upper	vif
								Weight	-100.8456364708	53.2964541876	-1.892164085	0.1073295893	-231.2573618464	29.5660889049	1.2050094115	Intercept	-204.7607563255	71.0096839497	-2.883561015	0.0279274635	-378.5151935282	-31.0063191229
								Ln(mean)	126.8453092593	11.829122488	10.7231376958	0.0000388462	97.9004892549	155.7901292638	1.2050094115	Ln(mean)	149.0148700824	11.8371025993	12.5887960193	0.0000153821	120.050523449	177.9792167158	1
								Regression Analysis
					y/s	1/s	x/s	OVERALL FIT
				1	4.4082644628	0.0165289256	0.0430196642	Multiple R	ERROR:#NAME?		AIC	ERROR:#NAME?
				2	5.0146412884	0.0146412884	0.053065021	R Square	ERROR:#NAME?		AICc	ERROR:#NAME?
				3	5.1363636364	0.0122850123	0.0530403945	Adjusted R Square	ERROR:#NAME?		BSC	ERROR:#NAME?
				4	5.0020242915	0.0101214575	0.0522751617	Standard Error	ERROR:#NAME?
				5	5.5	0.0090415913	0.0535888429	Observations	8
				6	5.4828296703	0.0068681319	0.0454675358
				7	5.4916233391	0.0057770075	0.0462528456	ANOVA				Alpha	0.05
				8	5.1049097776	0.0041963911	0.0374429639		df	SS	MS	F	p-value	sig
								Regression	2	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?
								Residual	6	ERROR:#NAME?	ERROR:#NAME?
								Total	8	212.5113531733
									coeff	std err	t stat	p-value	lower	upper	vif
								1/s	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	1.2050094115
								x/s	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	ERROR:#NAME?	1.2050094115

WReg0

Weighted Regression Tool
Color	Quality	Price	Weight	Regression Analysis
7	5	65	1
3	7	38	2	OVERALL FIT
5	8	51	2	Multiple R	0.995217326		AIC	42.7530648814	31.2166477305	73.9697126119
8	1	38	1	R Square	0.990457526		AICc	49.419731548
9	3	55	1	Adjusted R Square	0.9868790983		BSC	43.9467506997	31.2166477305	75.1633984302
5	4	43	1	Standard Error	6.2327691712
4	0	25	1	Observations	11
2	6	33	3
8	7	71	1	ANOVA				Alpha	0.05
6	4	51	1		df	SS	MS	F	p-value	sig
9	2	49	1	Regression	3	32257.2207076682	10752.4069025561	276.7856718349	0.0000000203	yes
				Residual	8	310.7792923318	38.8474115415
				Total	11	32568
					coeff	std err	t stat	p-value	lower	upper	vif
				Weight	1.8989103375	6.46549489	0.2936991475	0.7764631419	-13.0105476151	16.8083682901	4.2883187586
				Color	4.9784720238	0.7271421707	6.8466281072	0.0001314382	3.3016791713	6.6552648763	1.5462820521
				Quality	3.4756152308	0.7331135766	4.740896011	0.0014622691	1.7850522916	5.16617817	3.3406425556

Robust

Huber-White Robust Standard Errors					Regression Analysis
	Poverty	Infant Mort	White	Crime	OVERALL FIT
Alabama	15.7	9.0	71.0	448	Multiple R	0.5803450584		AIC	94.2628960967
Alaska	8.4	6.9	70.6	661	R Square	0.3368003868		AICc	95.6265324604
Arizona	14.7	6.4	86.5	483	Adjusted R Square	0.2935482381		SBC	101.9109881184
Arkansas	17.3	8.5	80.8	529	Standard Error	2.4702510013
California	13.3	5.0	76.6	523	Observations	50
Colorado	11.4	5.7	89.7	348
Connecticut	9.3	6.2	84.3	256	ANOVA				Alpha	0.05
Delaware	10.0	8.3	74.3	689		df	SS	MS	F	p-value	sig
Florida	13.2	7.3	79.8	723	Regression	3	142.5503595664	47.5167865221	7.7869053232	0.0002622132	yes
Georgia	14.7	8.1	65.4	493	Residual	46	280.6984404336	6.1021400094
Hawaii	9.1	5.6	29.7	273	Total	49	423.2488
Idaho	12.6	6.8	94.6	239
Illinois	12.2	7.3	79.1	533		coeff	std err	t stat	p-value	lower	upper	vif	OLS SE	p-value
Indiana	13.1	8.0	88.0	334	Intercept	0.4371252188	2.6143551952	0.167201924	0.8679440475	-4.8252988477	5.6995492852		3.9875336905	0.9131852533
Iowa	11.5	5.1	94.2	295	Infant Mort	1.279369653	0.4294296022	2.9792302315	0.0046023977	0.4149726968	2.1437666093	1.3011416808	0.300672909	0.0001016276
Kansas	11.3	7.1	88.7	453	White	0.0363269231	0.0298615072	1.2165133819	0.2299947432	-0.0237811733	0.0964350194	1.299581918	0.0336025319	0.2852981526
Kentucky	17.3	7.5	89.9	295	Crime	0.001421499	0.0031327053	0.4537608358	0.6521345985	-0.0048843098	0.0077273078	1.36048401	0.0022421017	0.5292192176
Louisiana	17.3	9.9	64.8	730
Maine	12.3	6.3	96.4	118
Maryland	8.1	8.0	63.4	642
Massachusetts	10.0	4.8	86.2	432
Michigan	14.4	7.4	81.2	536
Minnesota	9.6	5.2	89.0	289
Mississippi	21.2	10.6	60.6	291
Missouri	13.4	7.4	85.0	505
Montana	14.8	5.8	90.5	288
Nebraska	10.8	5.6	91.4	302
Nevada	11.3	6.4	80.9	751
New Hampshire	7.6	6.1	95.5	137
New Jersey	8.7	5.5	76.0	329
New Mexico	17.1	5.8	84.0	664
New York	13.6	5.6	73.4	414
North Carolina	14.6	8.1	73.9	466
North Dakota	12.0	5.8	91.4	142
Ohio	13.4	7.8	84.8	343
Oklahoma	15.9	8.0	78.1	500
Oregon	13.6	5.5	90.1	288
Pennsylvania	12.1	7.6	85.4	417
Rhode Island	11.7	6.1	88.5	227
South Carolina	15.7	8.4	68.7	788
South Dakota	12.5	6.9	88.2	169
Tennessee	15.5	8.7	80.4	753
Texas	15.8	6.2	82.4	511
Utah	9.6	5.1	92.9	235
Vermont	10.6	5.5	96.4	124
Virginia	10.2	7.1	73.0	270
Washington	11.3	4.7	84.3	333
West Virginia	17.0	7.4	94.5	275
Wisconsin	10.4	6.4	89.7	291
Wyoming	9.4	7.0	93.9	239

Autocorr

First-order autocorrelation
								Runs Test
Year	Rainfall	Temp	Yield	Pred	Residuals	Res lag 0	Res lag 1
2000	30	20	65	62.6787196489	2.3212803511	2.3212803511		n1	6
2001	23	27	62	60.68520727	1.31479273	1.31479273	2.3212803511	n2	5
2002	34	28	70	69.5963364022	0.4036635978	0.4036635978	1.31479273	mean	6.4545454545
2003	31	21	64	63.9265849354	0.0734150646	0.0734150646	0.4036635978	std dev	1.5587662
2004	17	23	52	54.161093355	-2.161093355	-2.161093355	0.0734150646	runs	3
2005	36	24	68	69.2028335639	-1.2028335639	-1.2028335639	-2.161093355	tails	2
2006	38	20	68	68.8093307256	-0.8093307256	-0.8093307256	-1.2028335639	z-stat	2.2162050054
2007	40	26	72	73.231216906	-1.231216906	-1.231216906	-0.8093307256	p-value	0.0266774652
2008	37	27	71	71.4137766541	-0.4137766541	-0.4137766541	-1.231216906	p-exact	0.0476190476
2009	34	24	69	67.6701807947	1.3298192053	1.3298192053	-0.4137766541
2010	38	30	74	73.6247197443	0.3752802557	0.3752802557	1.3298192053
				correl	0.585986946	correl	0.585986946

Residuals 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2.3212803510947921 1.3147927300481896 0.40366359776750471 7.3415064637707417E-2 -2.1610933549587301 -1.202833563898281 -0.80933072556408092 -1.2312169059771918 -0.41377665410485065 1.3298192052664319 0.37528025568860812

Scatter Plot

1.3147927300481896 0.40366359776750471 7.3415064637707417E-2 -2.1610933549587301 -1.202833563898281 -0.80933072556408092 -1.2312169059771918 -0.41377665410485065 1.3298192052664319 0.37528025568860812 2.3212803510947921 1.3147927300481896 0.40366359776750471 7.3415064637707417E-2 -2.1610933549587301 -1.202833563898281 -0.80933072556408092 -1.2312169059771918 -0.41377665410485065 1.3298192052664319

Runs

Runs Test for Autocorrelation
Year	Rainfall	Temp	Yield	Pred	Residuals	Runs Test			++++—–++		+-+-+—++–
2000	30	20	65	62.6787196489	2.3212803511
2001	23	27	62	60.68520727	1.31479273	n1	6	=COUNTIF(G4:G14,">0")	n1	6	n1	5
2002	34	28	70	69.5963364022	0.4036635978	n2	5	=COUNT(G4:G14)-J5	n2	5	n2	7
2003	31	21	64	63.9265849354	0.0734150646	n	11	=J5+J6	mean	6.4545454545	mean	6.8333333333
2004	17	23	52	54.161093355	-2.161093355	mean	6.4545454545	=2J5J6/J7+1	std dev	1.5587662	std dev	1.6009782363
2005	36	24	68	69.2028335639	-1.2028335639	var	2.4297520661	=2J5J6(2J5*J6-J7)/J7^2/(J7-1)	runs	3	runs	8
2006	38	20	68	68.8093307256	-0.8093307256	stdev	1.5587662	=SQRT(J9)	tails	2	tails	2
2007	40	26	72	73.231216906	-1.231216906	runs	3	{=SUM(IF(G4:G13*G5:G14>0,0,1))+1}	z-stat	2.2162050054	z-stat	0.7287211283
2008	37	27	71	71.4137766541	-0.4137766541	tails	2		p-value	0.0266774652	p-value	0.4661722659
2009	34	24	69	67.6701807947	1.3298192053	z-stat	2.2162050054	=ABS(J11-J8)/J10	p-exact	0.0476190476	p-exact	0.2929292929
2010	38	30	74	73.6247197443	0.3752802557	p-value	0.0266774652	=N13*(1-NORM.S.DIST(J13,TRUE))

Durbin

Durbin-Watson Test									Real Statistics functions
Year	Rainfall	Temp	Yield	Pred	Residuals	d	0.7259508658	=SUMXMY2(G4:G13,G5:G14)/SUMSQ(G4:G14)	d	0.7259508658	=DURBIN(F4:F14)
2000	30	20	65	62.6787196489	2.3212803511	d-lower	0.75798	=DLowerCRIT(COUNT(A4:A14),COUNTA(B3:C3))	d	0.7259508658	=DURBIN(A4:B14,C4:C14)
2001	23	27	62	60.68520727	1.31479273	d-upper	1.60439	=DUpperCRIT(COUNT(A4:A14),COUNTA(B3:C3))
2002	34	28	70	69.5963364022	0.4036635978	sig	yes	=IF(J3<J4,"yes",IF(J3>J5,"no","unclear"))	d-lower	0.75798	=DLowerCRIT(11,2)
2003	31	21	64	63.9265849354	0.0734150646				d-upper	1.60439	=DUpperCRIT(11,2)
2004	17	23	52	54.161093355	-2.161093355				sig	yes	=IF(J3<O6,"yes",IF(J3>O7,"no","unclear"))
2005	36	24	68	69.2028335639	-1.2028335639
2006	38	20	68	68.8093307256	-0.8093307256				D-stat	0.7259508658	=DURBIN(B4:C14,D4:D14,TRUE)
2007	40	26	72	73.231216906	-1.231216906				D-lower	0.75798
2008	37	27	71	71.4137766541	-0.4137766541				D-upper	1.60439
2009	34	24	69	67.6701807947	1.3298192053				sig	yes
2010	38	30	74	73.6247197443	0.3752802557
									D-stat	0.7259508658	=DURBIN(G4:G14,2,TRUE)
									D-lower	0.75798
									D-upper	1.60439
									sig	yes
									Data analysis tool
									Durbin-Watson Test
									Alpha	0.05
									D-stat	0.7259508658
									D-lower	0.75798
									D-upper	1.60439
									sig	yes

BG

Breusch-Godfrey Test
Year	Invest	GNP	Interest	Pred	Res (ε)	εi	εi-1	εi-2	εi-3	εi-4	GNP	Interest	Regression Analysis										Durbin-Watson Test		Regression Analysis
1995	112.2	860.8	4.62	128.2955514328	-16.0955514328	-16.0955514328	0	0	0	0	860.8	4.62							Breusch-Godfrey test		Alternative BG test
1996	132.4	866.9	4.10	130.0739667532	2.3260332468	2.3260332468	-16.0955514328	0	0	0	866.9	4.10	OVERALL FIT										Alpha	0.05	OVERALL FIT
1997	153.4	919.2	4.04	139.2580446261	14.1419553739	14.1419553739	2.3260332468	-16.0955514328	0	0	919.2	4.04	Multiple R	0.783397785		AIC	105.5806024801		χ2-stat	12.2742417904	p	4			Multiple R	0.915211057		AIC	116.6040485895
1998	162.3	974.6	3.42	149.7536158337	12.5463841663	12.5463841663	14.1419553739	2.3260332468	-16.0955514328	0	974.6	3.42	R Square	0.6137120895		AICc	118.671511571		df	4	k	2	D-stat	1.2181447117	R Square	0.8376112788		AICc	119.2707152562
1999	156.5	1001.2	3.35	154.4791093679	2.0208906321	2.0208906321	12.5463841663	14.1419553739	2.3260332468	-16.0955514328	1001.2	3.35	Adjusted R Square	0.4354253616		SBC	112.550728395		p-value	0.0154242947	n	20	D-lower	1.1004	Adjusted R Square	0.8185067234		SBC	119.5912454102
2000	162.7	1048.5	2.99	163.206660815	-0.506660815	-0.506660815	2.0208906321	12.5463841663	14.1419553739	2.3260332468	1048.5	2.99	Standard Error	12.242414492							LM*	5.1634137047	D-upper	1.53668	Standard Error	17.2249804081
2001	166.2	1077.0	2.81	168.4144441992	-2.2144441992	-2.2144441992	-0.506660815	2.0208906321	12.5463841663	14.1419553739	1077.0	2.81	Observations	20							df1	4	sig	unclear	Observations	20
2002	158.5	1075.2	2.76	168.1701350034	-9.6701350034	-9.6701350034	-2.2144441992	-0.506660815	2.0208906321	12.5463841663	1075.2	2.76									df2	13
2003	174.1	1112.5	2.06	175.6261936429	-1.5261936429	-1.5261936429	-9.6701350034	-2.2144441992	-0.506660815	2.0208906321	1112.5	2.06	ANOVA				Alpha	0.05			p-value	0.0103026027	Breusch-Godfrey Test		ANOVA				Alpha	0.05
2004	197.9	1175.9	2.44	186.1353272197	11.7646727803	11.7646727803	-1.5261936429	-9.6701350034	-2.2144441992	-0.506660815	1175.9	2.44		df	SS	MS	F	p-value	sig							df	SS	MS	F	p-value	sig
2005	217.6	1244.1	2.86	197.4246227387	20.1753772613	20.1753772613	11.7646727803	-1.5261936429	-9.6701350034	-2.2144441992	1244.1	2.86	Regression	6	3095.5018872784	515.9169812131	3.4422758031	0.0292607651	yes				p	p-value	Regression	2	26016.7503490017	13008.3751745008	43.843536785	0.0000001949	yes
2006	198.3	1236.8	1.12	198.5530511677	-0.2530511677	-0.2530511677	20.1753772613	11.7646727803	-1.5261936429	-9.6701350034	1236.8	1.12	Residual	13	1948.3972637199	149.8767125938							5	0.031034314	Residual	17	5043.8991509983	296.6999500587
2007	162.4	1218.3	0.36	196.3813880669	-33.9813880669	-33.9813880669	-0.2530511677	20.1753772613	11.7646727803	-1.5261936429	1218.3	0.36	Total	19	5043.8991509983								4	0.0154242947	Total	19	31060.6495
2008	194.8	1288.5	2.40	205.7853713329	-10.9853713329	-10.9853713329	-33.9813880669	-0.2530511677	20.1753772613	11.7646727803	1288.5	2.40											3	0.0171422832
2009	221.1	1359.6	1.79	218.9993048983	2.1006951017	2.1006951017	-10.9853713329	-33.9813880669	-0.2530511677	20.1753772613	1359.6	1.79		coeff	std err	t stat	p-value	lower	upper	vif			2	0.0883301628		coeff	std err	t stat	p-value	lower	upper	vif
2010	257.4	1428.0	2.27	230.240687944	27.159312056	27.159312056	2.1006951017	-10.9853713329	-33.9813880669	-0.2530511677	1428.0	2.27	Intercept	-11.5826771037	18.7175162777	-0.6188148541	0.5467293051	-52.0194125968	28.8540583895				1	0.1882545398	Intercept	-15.1335543844	24.8912168782	-0.6079877275	0.5512324402	-67.6494315053	37.3823227365
2011	258.5	1469.8	3.73	235.5020376093	22.9979623907	22.9979623907	27.159312056	2.1006951017	-10.9853713329	-33.9813880669	1469.8	3.73	εi-1	-0.2011364788	0.292037724	-0.6887345787	0.5030972158	-0.8320456242	0.4297726666	2.1716829636					GNP	0.1740221981	0.0186448434	9.3335296247	0.0000000421	0.1346850171	0.2133593791	1.0012420236
2012	226.1	1465.8	4.73	233.4273336015	-7.3273336015	-7.3273336015	22.9979623907	27.159312056	2.1006951017	-10.9853713329	1465.8	4.73	εi-2	-0.3526879715	0.2960046864	-1.191494553	0.2547586108	-0.9921672182	0.2867912752	2.2327026256			Modified Test		Interest	-1.3786152155	3.2263163966	-0.4273031675	0.6745216904	-8.185547808	5.428317377	1.0012420236
2013	242.1	1502.0	4.11	240.5816786058	1.5183213942	1.5183213942	-7.3273336015	22.9979623907	27.159312056	2.1006951017	1502.0	4.11	εi-3	-0.5626557234	0.3130943933	-1.7970801632	0.0955813693	-1.2390550372	0.1137435904	2.44542246
2014	200.4	1475.5	5.11	234.591475141	-34.191475141	-34.191475141	1.5183213942	-7.3273336015	22.9979623907	27.159312056	1475.5	5.11	εi-4	-0.6334239927	0.3369575101	-1.8798334324	0.0827271517	-1.3613764361	0.0945284506	2.4813723298			p	p-value
													GNP	0.0077881525	0.0137789378	0.5652215412	0.5815469552	-0.0219794329	0.0375557378	1.0825211327			5	0.0264333473
													Interest	1.6042385398	2.4293657429	0.6603528285	0.5205572105	-3.6440870662	6.8525641459	1.1238135896			4	0.0103026027
																							3	0.0163725206
																							2	0.1243512185
																							1	0.2359844187

FGLS 1

FGLS															Regression Analysis
Year	Rainfall	Temp	Yield	Pred	Res (εi)			Year	Res (εi)	Res (δi)					OVERALL FIT
2000	30	20	65	62.6787196489	2.3212803511	D-stat	0.7259508658	2000	2.3212803511		Year	Rainfall'	Temp'	Yield'	Multiple R	0.9923686812		AIC	1.2404034888
2001	23	27	62	60.68520727	1.31479273	D-lower	0.75798	2001	1.31479273	-0.1639198808	2001	3.8892629863	14.2595086576	20.5934031371	R Square	0.9847955995		AICc	9.2404034888
2002	34	28	70	69.5963364022	0.4036635978	D-upper	1.60439	2002	0.4036635978	-0.4338916719	2002	19.3484349562	10.8003366877	30.5044768385	Adjusted R Square	0.9804514851		SBC	2.1481587678
2003	31	21	64	63.9265849354	0.0734150646	sig	yes	2003	0.0734150646	-0.183728564	2003	9.3411647179	3.1633121206	19.4082803015	Standard Error	0.9421014125
2004	17	23	52	54.161093355	-2.161093355			2004	-2.161093355	-2.2078605547	2004	-2.7477615808	9.6224840904	11.2304277042	Observations	10
2005	36	24	68	69.2028335639	-1.2028335639	r	0.6370245671	2005	-1.2028335639	0.1738359951	2005	25.1705823589	9.3484349562	34.8747225097
2006	38	20	68	68.8093307256	-0.8093307256			2006	-0.8093307256	-0.0430961952	2006	15.0671155836	4.7114103891	24.6823294357	ANOVA				Alpha	0.05
2007	40	26	72	73.231216906	-1.231216906			2007	-1.231216906	-0.7156533509	2007	15.7930664494	13.2595086576	28.6823294357		df	SS	MS	F	p-value	sig
2008	37	27	71	71.4137766541	-0.4137766541			2008	-0.4137766541	0.3705387625	2008	11.5190173151	10.4373612548	25.1342311672	Regression	2	402.4112829034	201.2056414517	226.6965148604	0.0000004334	yes
2009	34	24	69	67.6701807947	1.3298192053			2009	1.3298192053	1.5934050992	2009	10.4300910165	6.8003366877	23.7712557344	Residual	7	6.2128855004	0.8875550715
2010	38	30	74	73.6247197443	0.3752802557			2010	0.3752802557	-0.4718472479	2010	16.3411647179	14.7114103891	30.0453048686	Total	9	408.6241684038
																coeff	std err	t stat	p-value	lower	upper	vif
															Intercept'	10.7636728044	0.9598090699	11.2143895514	0.0000100027	8.4940850009	13.0332606079
															Rainfall'	0.8151226565	0.0397819864	20.4897424847	0.0000001655	0.7210532067	0.9091921063	1.0010828793
															Temp'	0.4128217274	0.0806864205	5.1163718126	0.001374271	0.2220286608	0.6036147941	1.0010828793
															Intercept	29.6539981207	2.6442810805	11.2143895514	0.0000100027	23.4012669496	35.9067292918
															Regression Analysis
											Year	Rainfall'	Temp'	Yield'	OVERALL FIT
											2000	23.1253049882	15.4168699921	50.1048274743	Multiple R	0.9281025018		AIC	33.7230867491
											2001	3.8892629863	14.2595086576	20.5934031371	R Square	0.8613742539		AICc	40.3897534158
											2002	19.3484349562	10.8003366877	30.5044768385	Adjusted R Square	0.8267178174		SBC	34.9167725675
											2003	9.3411647179	3.1633121206	19.4082803015	Standard Error	4.1345031842
											2004	-2.7477615808	9.6224840904	11.2304277042	Observations	11
											2005	25.1705823589	9.3484349562	34.8747225097
											2006	15.0671155836	4.7114103891	24.6823294357	ANOVA				Alpha	0.05
											2007	15.7930664494	13.2595086576	28.6823294357		df	SS	MS	F	p-value	sig
											2008	11.5190173151	10.4373612548	25.1342311672	Regression	2	849.7372141742	424.8686070871	24.8546688618	0.0003692968	yes
											2009	10.4300910165	6.8003366877	23.7712557344	Residual	8	136.7529326421	17.0941165803
											2010	16.3411647179	14.7114103891	30.0453048686	Total	10	986.4901468163
																coeff	std err	t stat	p-value	lower	upper	vif
															Intercept'	5.933394982	3.83242772	1.5482079286	0.1601626795	-2.9041991883	14.7709891523
															Rainfall'	0.9853218893	0.1633626214	6.031501458	0.0003122569	0.6086070087	1.3620367698	1.0392976676
															Temp'	0.7877702633	0.3270737612	2.4085400808	0.0426051457	0.0335368174	1.5420037091	1.0392976676
															Intercept	16.3465470237	10.5583666907	1.5482079286	0.1601626795	-8.001090226	40.6941842735

Res (ε)

Res (εi) 2.3212803510947921 1.3147927300481896 0.40366359776750471 7.3415064637707417E-2 -2.1610933549587301 -1.202833563898281 -0.80933072556408092 -1.2312169059771918 -0.41377665410485065 1.3298192052664319 0.37528025568860812

Res (δ)

Res (δi) -0.16391988077614794 -0.43389167194624101 -0.18372856399293741 -2.2078605547297796 0.17383599505395142 -4.3096195202290688E-2 -0.71565335086641746 0.37053876245822581 1.5934050992326523 -0.47184724789640375

FGLS 2

FGLS							Regression Analysis											Regression Analysis
Year	Rainfall	Temp	Yield	Pred	Res (εi)	Res (εi-1)	OVERALL FIT							Year	Rainfall'	Temp'	Yield'	OVERALL FIT
2000	30	20	65	62.6787196489	2.3212803511		Multiple R	0.5786842595		AIC	-0.0357369975			2000	26.2472527357	17.4981684904	56.8690475939	Multiple R	0.9196237135		AIC	32.7347456433
2001	23	27	62	60.68520727	1.31479273	2.3212803511	R Square	0.3348754722		AICc	1.6785487168			2001	8.4713481757	17.3142321172	30.5212543808	R Square	0.8457077744		AICc	39.4014123099
2002	34	28	70	69.5963364022	0.4036635978	1.31479273	Adjusted R Square	0.2609727468		BSC	0.2668480955			2002	22.8613669347	14.9242133582	39.9741195632	Adjusted R Square	0.807134718		SBC	33.9284314617
2003	31	21	64	63.9265849354	0.0734150646	0.4036635978	Standard Error	0.9520796402						2003	14.5341945992	7.439924964	30.0998124101	Standard Error	3.952872685
2004	17	23	52	54.161093355	-2.161093355	0.0734150646	Observations	10						2004	1.9870597816	12.829943723	21.0055427749	Observations	11
2005	36	24	68	69.2028335639	-1.2028335639	-2.161093355								2005	27.7670972996	12.8613669347	42.8170035046
2006	38	20	68	68.8093307256	-0.8093307256	-1.2028335639	ANOVA				Alpha	0.05		2006	20.5656178109	8.3770785406	35.0683891984	ANOVA				Alpha	0.05
2007	40	26	72	73.231216906	-1.231216906	-0.8093307256		df	SS	MS	F	p-value	sig	2007	21.5970410226	16.3142321172	39.0683891984		df	SS	MS	F	p-value	sig
2008	37	27	71	71.4137766541	-0.4137766541	-1.231216906	Regression	1	4.1074230965	4.1074230965	4.5313007164	0.0621541701	no	2008	17.6284642343	14.4085017523	36.1312356218	Regression	2	685.159872303	342.5799361515	21.9248318184	0.00056673	yes
2009	34	24	69	67.6701807947	1.3298192053	-0.4137766541	Residual	9	8.1581007711	0.9064556412				2009	16.0813294168	10.9242133582	34.6155240159	Residual	8	125.0016197118	15.625202464
2010	38	30	74	73.6247197443	0.3752802557	1.3298192053	Total	10	12.2655238675					2010	21.5341945992	18.3770785406	40.5841008042	Total	10	810.1614920148
								coeff	std err	t stat	p-value	lower	upper						coeff	std err	t stat	p-value	lower	upper	vif
							Res (εi-1)	0.4842883941	0.2275058765	2.1286852084	0.0621541701	-0.030365654	0.9989424423					Intercept'	9.2474640542	5.3101859131	1.7414576826	0.1197786999	-2.9978466201	21.4927747285
																		Rainfall'	0.9649341813	0.1662711807	5.803376012	0.0004036071	0.5815121511	1.3483562114	1.0296615894
																		Temp'	0.7453286666	0.3454013055	2.1578629111	0.0629988205	-0.0511681722	1.5418255055	1.0296615894
																		Intercept	17.9314639212	10.2968128946	1.7414576826	0.1197786999	-5.8130291932	41.6759570356
																		Regression Analysis
																		OVERALL FIT
																		Multiple R	0.9911904515		AIC	1.8096275754
																		R Square	0.9824585111		AICc	9.8096275754
																		Adjusted R Square	0.9774466571		SBC	2.7173828544
																		Standard Error	0.9692999692
																		Observations	10
																		ANOVA				Alpha	0.05
																			df	SS	MS	F	p-value	sig
																		Regression	2	368.3512973588	184.1756486794	196.0269624253	0.0000007149	yes
																		Residual	7	6.576797012	0.9395424303
																		Total	9	374.9280943708
																			coeff	std err	t stat	p-value	lower	upper	vif
																		Intercept'	15.0196683152	1.3999588787	10.7286496366	0.0000134321	11.7092915994	18.3300450309
																		Rainfall'	0.8107105139	0.0430239494	18.8432379079	0.0000002946	0.7089750398	0.912445988	1.0032510695
																		Temp'	0.4441425779	0.0888443137	4.9991109083	0.0015668464	0.2340591591	0.6542259968	1.0032510695
																		Intercept	29.1241619241	2.7146158101	10.7286496366	0.0000134321	22.7051155459	35.5432083023

OC

Cochrane-Orcutt Method
Year	Rainfall	Temp	Yield	Pred	Res (εi)	ρ	0.4842883941		Pred	Res (εi)	ρ	0.5917565524		Pred	Res (εi)	ρ	0.6306787145		Pred	Res (εi)	ρ	0.6427680187		Pred	Res (εi)	ρ	0.6464093455		Pred	Res (εi)	ρ	0.6474996971		Pred	Res (εi)	ρ	0.647825738		Pred	Res (εi)	ρ	0.647923195		Pred	Res (εi)	ρ	0.6479523227		Pred	Res (εi)	ρ	0.6479610281		Pred	Res (εi)	ρ	0.6479636298		Pred	Res (εi)	ρ	0.6479644074		Pred	Res (εi)	ρ	0.6479646397		Pred	Res (εi)	ρ	0.6479647092
2000	30	20	65	62.6787196489	2.3212803511				62.3283289005	2.6716710995				62.3646596579	2.6353403421				62.3649031132	2.6350968868				62.3631747519	2.6368252481				62.3624673505	2.6375326495				62.3622380801	2.6377619199				62.3621679435	2.6378320565				62.3621468373	2.6378531627				62.3621405165	2.6378594835				62.3621386263	2.6378613737				62.3621380613	2.6378619387				62.3621378924	2.6378621076				62.3621378419	2.6378621581
2001	23	27	62	60.68520727	1.31479273		coeff	std err	59.7623533486	2.2376466514		coeff	std err	59.6131801141	2.3868198859		coeff	std err	59.5574061814	2.4425938186		coeff	std err	59.5393991237	2.4606008763		coeff	std err	59.5338896204	2.4661103796		coeff	std err	59.5322314386	2.4677685614		coeff	std err	59.531734829	2.468265171		coeff	std err	59.5315863175	2.4684136825		coeff	std err	59.5315419245	2.4684580755		coeff	std err	59.5315286563	2.4684713437		coeff	std err	59.5315246909	2.4684753091		coeff	std err	59.5315235057	2.4684764943		coeff	std err	59.5315231515	2.4684768485		coeff	std err
2002	34	28	70	69.5963364022	0.4036635978	Intercept	29.1241619241	2.7146158101	69.1243115797	0.8756884203	Intercept	29.5165427443	2.6414616581	68.9903884971	1.0096115029	Intercept	29.6358634645	2.6423131174	68.9364454601	1.0635545399	Intercept	29.6700718902	2.6465548731	68.9186505801	1.0813494199	Intercept	29.6800919351	2.6482477329	68.913173345	1.086826655	Intercept	29.6830661687	2.6487935057	68.911522017	1.088477983	Intercept	29.683953177	2.648960225	68.9110272066	1.0889727934	Intercept	29.6842181019	2.6490103749	68.9108792105	1.0891207895	Intercept	29.6842972631	2.6490253918	68.9108349696	1.0891650304	Intercept	29.6843209201	2.6490298824	68.9108217466	1.0891782534	Intercept	29.6843279902	2.6490312247	68.9108177947	1.0891822053	Intercept	29.6843301032	2.6490316259	68.9108166136	1.0891833864	Intercept	29.6843307347	2.6490317458	68.9108162606	1.0891837394	Intercept	29.6843309234	2.6490317816
2003	31	21	64	63.9265849354	0.0734150646	Intercept'	15.0196683152	1.3999588787	63.5831819923	0.4168180077	Intercept'	12.0499351721	1.0783594141	63.5999706332	0.4000293668	Intercept'	10.9451551922	0.9758624773	63.5938505011	0.4061494989	Intercept'	10.5990985663	0.9454340409	63.5902195628	0.4097804372	Intercept'	10.4946031343	0.9363956493	63.5889458605	0.4110541395	Intercept'	10.4632898146	0.933700513	63.5885476239	0.4114523761	Intercept'	10.4539243028	0.9328956123	63.5884270166	0.4115729834	Intercept'	10.4511246674	0.9326551091	63.5883908291	0.4116091709	Intercept'	10.4502879026	0.9325832362	63.5883800012	0.4116199988	Intercept'	10.4500378191	0.9325617564	63.588376764	0.411623236	Intercept'	10.4499630778	0.9325553369	63.5883757964	0.4116242036	Intercept'	10.4499407403	0.9325534184	63.5883755072	0.4116244928	Intercept'	10.4499340644	0.932552845	63.5883754208	0.4116245792	Intercept'	10.4499320692	0.9325526736
2004	17	23	52	54.161093355	-2.161093355	Rainfall	0.8107105139	0.0430239494	53.1215199533	-1.1215199533	Rainfall	0.8141897408	0.0405758377	53.0435567313	-1.0435567313	Rainfall	0.8150091891	0.0398838237	53.0115982516	-1.0115982516	Rainfall	0.8152206646	0.0396925347	53.0007785514	-1.0007785514	Rainfall	0.8152805215	0.0396371683	52.9974145374	-0.9974145374	Rainfall	0.8152981035	0.0396207934	52.9963970563	-0.9963970563	Rainfall	0.8153033304	0.0396159151	52.9960918757	-0.9960918757	Rainfall	0.8153048901	0.0396144586	52.9960005704	-0.9960005704	Rainfall	0.815305356	0.0396140234	52.9959732738	-0.9959732738	Rainfall	0.8153054953	0.0396138934	52.9959651151	-0.9959651151	Rainfall	0.8153055369	0.0396138545	52.9959626767	-0.9959626767	Rainfall	0.8153055493	0.0396138429	52.9959619479	-0.9959619479	Rainfall	0.815305553	0.0396138394	52.9959617301	-0.9959617301	Rainfall	0.8153055541	0.0396138384
2005	36	24	68	69.2028335639	-1.2028335639	Temp	0.4441425779	0.0888443137	68.9691622958	-0.9691622958	Temp	0.4211212345	0.0826539067	68.9342830406	-0.9342830406	Temp	0.4139381988	0.0809382692	68.9107110429	-0.9107110429	Temp	0.4118241462	0.080465262	68.9017953243	-0.9017953243	Temp	0.4111979886	0.0803283905	68.8989424336	-0.8989424336	Temp	0.4110114404	0.0802879108	68.8980724624	-0.8980724624	Temp	0.4109557427	0.0802758516	68.8978108968	-0.8978108968	Temp	0.4109391016	0.080272251	68.8977325844	-0.8977325844	Temp	0.4109341286	0.0802711753	68.8977091672	-0.8977091672	Temp	0.4109326424	0.0802708538	68.8977021675	-0.8977021675	Temp	0.4109321982	0.0802707577	68.8977000755	-0.8977000755	Temp	0.4109320655	0.080270729	68.8976994502	-0.8976994502	Temp	0.4109320258	0.0802707204	68.8976992634	-0.8976992634	Temp	0.410932014	0.0802707179
2006	38	20	68	68.8093307256	-0.8093307256				68.8140130118	-0.8140130118				68.8781775841	-0.8781775841				68.8849766258	-0.8849766258				68.8849400684	-0.8849400684				68.8847115221	-0.8847115221				68.8846229077	-0.8846229077				68.8845945871	-0.8845945871				68.8845859584	-0.8845859584				68.8845833648	-0.8845833648				68.8845825884	-0.8845825884				68.8845823563	-0.8845823563				68.8845822869	-0.8845822869				68.8845822661	-0.8845822661
2007	40	26	72	73.231216906	-1.231216906				73.1002895073	-1.1002895073				73.0332844728	-1.0332844728				72.9986241969	-0.9986241969				72.986326275	-0.986326275				72.9824604966	-0.9824604966				72.981287757	-0.981287757				72.9809357039	-0.9809357039				72.9808303481	-0.9808303481				72.9807988486	-0.9807988486				72.9807894334	-0.9807894334				72.9807866195	-0.9807866195				72.9807857785	-0.9807857785				72.9807855271	-0.9807855271
2008	37	27	71	71.4137766541	-0.4137766541				71.1123005435	-0.1123005435				71.0118364849	-0.0118364849				70.9675348285	0.0324651715				70.9524884276	0.0475115724				70.9478169208	0.0521830792				70.946404887	0.053595113				70.9459814552	0.0540185448				70.9458547793	0.0541452207				70.9458169091	0.0541830909				70.94580559	0.05419441				70.9458022071	0.0541977929				70.945801196	0.054198804				70.9458008939	0.0541991061
2009	34	24	69	67.6701807947	1.3298192053				67.3477412679	1.6522587321				67.3059035591	1.6940964409				67.2806926648	1.7193073352				67.2713539952	1.7286460048				67.2683813907	1.7316186093				67.2674762555	1.7325237445				67.2672042359	1.7327957641				67.2671228042	1.7328771958				67.2670984551	1.7329015449				67.267091177	1.732908823				67.2670890017	1.7329109983				67.2670883516	1.7329116484				67.2670881573	1.7329118427
2010	38	30	74	73.6247197443	0.3752802557				73.2554387912	0.7445612088				73.0893899293	0.9106100707				73.024358614	0.975641386				73.0031815309	0.9968184691				72.996691408	1.003308592				72.9947373116	1.0052626884				72.9941520136	1.0058479864				72.9939769742	1.0060230258				72.993924651	1.006075349				72.9939090125	1.0060909875				72.9939043387	1.0060956613				72.9939029418	1.0060970582				72.9939025244	1.0060974756
						1	0.4842883941				2	0.5917565524				3	0.6306787145				4	0.6427680187				5	0.6464093455				6	0.6474996971				7	0.647825738				8	0.647923195				9	0.647923195				10	0.647923195				11	0.647923195				12	0.647923195				13	0.647923195				14	0.647923195
							29.1241619241	2.7146158101				29.5165427443	2.6414616581				29.6358634645	2.6423131174				29.6700718902	2.6465548731				29.6800919351	2.6482477329				29.6830661687	2.6487935057				29.683953177	2.648960225				29.6842181019	2.6490103749				29.6842972631	2.6490253918				29.6843209201	2.6490298824				29.6843279902	2.6490312247				29.6843301032	2.6490316259				29.6843307347	2.6490317458				29.6843309234	2.6490317816
							0.8107105139	0.0430239494				0.8141897408	0.0405758377				0.8150091891	0.0398838237				0.8152206646	0.0396925347				0.8152805215	0.0396371683				0.8152981035	0.0396207934				0.8153033304	0.0396159151				0.8153048901	0.0396144586				0.815305356	0.0396140234				0.8153054953	0.0396138934				0.8153055369	0.0396138545				0.8153055493	0.0396138429				0.815305553	0.0396138394				0.8153055541	0.0396138384
							0.4441425779	0.0888443137				0.4211212345	0.0826539067				0.4139381988	0.0809382692				0.4118241462	0.080465262				0.4111979886	0.0803283905				0.4110114404	0.0802879108				0.4109557427	0.0802758516				0.4109391016	0.080272251				0.4109341286	0.0802711753				0.4109326424	0.0802708538				0.4109321982	0.0802707577				0.4109320655	0.080270729				0.4109320258	0.0802707204				0.410932014	0.0802707179
																																														ρ	0.647923195
																																															coeff	std err
																																														Intercept	29.6842972631	2.6490253918
																																														Rainfall	0.815305356	0.0396140234
																																														Temp	0.4109341286	0.0802711753

OC 1

Cochrane-Orcutt Method				Cochrane-Orcutt Regression			Regression Analysis								Durbin-Watson Test
Year	Rainfall	Temp	Yield	Rho	0.647923195		OVERALL FIT								Alpha	0.05
2000	30	20	65				Multiple R	0.9924258957		AIC	1.237584618
2001	23	27	62	Rainfall	Temp	Yield	R Square	0.9849091585		AICc	9.237584618				D-stat	1.4721820737
2002	34	28	70	3.5623041491	14.0415360994	19.8849923232	Adjusted R Square	0.9805974895		SBC	2.145339897				D-lower	0.69715
2003	31	21	64	19.0977665143	10.5060737342	29.8287619082	Standard Error	0.9419686388							D-upper	1.64134
2004	17	23	52	8.970611369	2.8581505392	18.645376348	Observations	10							sig	unclear
2005	36	24	68	-3.0856190459	9.3936129044	10.5329155182
2006	38	20	68	24.9853056845	9.0977665143	34.3079938585	ANOVA				Alpha	0.05
2007	40	26	72	14.674764979	4.4498433193	23.9412227381		df	SS	MS	F	p-value	sig
2008	37	27	71	15.3789185889	13.0415360994	27.9412227381	Regression	2	405.3719053046	202.6859526523	228.4287496868	0.0000004222	yes
2009	34	24	69	11.0830721989	10.1539969293	24.349529958	Residual	7	6.211134415	0.8873049164
2010	38	30	74	10.0268417839	6.5060737342	22.997453153	Total	9	411.5830397197
				15.970611369	14.4498433193	29.293299543
								coeff	std err	t stat	p-value	lower	upper	vif
							Intercept	29.6842181019	2.6490103749	11.2057764602	0.0000100541	23.4203039267	35.9481322771
							Rainfall	0.8153048901	0.0396144586	20.5809928655	0.0000001605	0.7216315806	0.9089781997	1.0009884408
							Temp	0.4109391016	0.080272251	5.1193170273	0.0013697852	0.22112539	0.6007528131	1.0009884408

Newey

Newey-West						W0					W0 weighted							Regression Analysis
						5043.8991509983	6553087.87914907	14956.2316512213	i	0	5043.8991509983	6553087.87914907	14956.2316512213
Constant	GNP	Interest	Invest	Pred	Res (ε)	6553087.87914907	8691982792.84052	19856622.3643662	h	3	6553087.87914907	8691982792.84052	19856622.3643662					OVERALL FIT
1	860.8	4.62	112.2	128.2955514328	-16.0955514328	14956.2316512213	19856622.3643662	59473.7194865715	mult	1	14956.2316512213	19856622.3643662	59473.7194865715					Multiple R	0.915211057		AIC	116.6040485895
1	866.9	4.10	132.4	130.0739667532	2.3260332468													R Square	0.8376112788		AICc	119.2707152562
1	919.2	4.04	153.4	139.2580446261	14.1419553739	W1					W1 weighted							Adjusted R Square	0.8185067234		SBC	119.5912454102
1	974.6	3.42	162.3	149.7536158337	12.5463841663	2515.4754764874	3162032.66612114	5706.8899711301	i	1	1886.6066073655	2371524.49959085	4280.1674783476					Standard Error	17.2249804081
1	1001.2	3.35	156.5	154.4791093679	2.0208906321	3162032.66612114	4036354456.11019	6923388.7828722	h	3	2371524.49959085	3027265842.08265	5192541.58715415					Observations	20
1	1048.5	2.99	162.7	163.206660815	-0.506660815	5706.8899711301	6923388.7828722	11688.6067477006	mult	0.75	4280.1674783476	5192541.58715415	8766.4550607754
1	1077.0	2.81	166.2	168.4144441992	-2.2144441992													ANOVA				Alpha	0.05
1	1075.2	2.76	158.5	168.1701350034	-9.6701350034	W2					W2 weighted								df	SS	MS	F	p-value	sig
1	1112.5	2.06	174.1	175.6261936429	-1.5261936429	-2475.4747342322	-2910945.48918001	-4449.8704448531	i	2	-1237.7373671161	-1455472.74459	-2224.9352224265					Regression	2	26016.7503490017	13008.3751745008	43.843536785	0.0000001949	yes
1	1175.9	2.44	197.9	186.1353272197	11.7646727803	-2910945.48918001	-3449333375.87822	-4603058.04477489	h	3	-1455472.74459	-1724666687.93911	-2301529.02238744					Residual	17	5043.8991509983	296.6999500587
1	1244.1	2.86	217.6	197.4246227387	20.1753772613	-4449.8704448531	-4603058.04477489	-4097.2598786455	mult	0.5	-2224.9352224265	-2301529.02238744	-2048.6299393228					Total	19	31060.6495
1	1236.8	1.12	198.3	198.5530511677	-0.2530511677
1	1218.3	0.36	162.4	196.3813880669	-33.9813880669	W3					W3 weighted			(X'X)-1					coeff	std err	t stat	p-value	lower	upper	vif
1	1288.5	2.40	194.8	205.7853713329	-10.9853713329	-6059.4207591757	-7879805.23575025	-16210.6817895706	i	3	-1514.8551897939	-1969951.30893756	-4052.6704473926	2.088212949	-0.0014160863	-0.1156234295		Intercept	-15.1335543844	24.8912168782	-0.6079877275	0.5512324402	-67.6494315053	37.3823227365
1	1359.6	1.79	221.1	218.9993048983	2.1006951017	-7879805.23575024	-10351005351.2715	-21203070.8594089	h	3	-1969951.30893756	-2587751337.81788	-5300767.71485224	-0.0014160863	0.0000011717	0.0000071407		GNP	0.1740221981	0.0186448434	9.3335296247	0.0000000421	0.1346850171	0.2133593791	1.0012420236
1	1428.0	2.27	257.4	230.240687944	27.159312056	-16210.6817895705	-21203070.8594089	-49992.3386260805	mult	0.25	-4052.6704473926	-5300767.71485224	-12498.0846565201	-0.1156234295	0.0000071407	0.0350829769		Interest	-1.3786152155	3.2263163966	-0.4273031675	0.6745216904	-8.185547808	5.428317377	1.0012420236
1	1469.8	3.73	258.5	235.5020376093	22.9979623907
1	1465.8	4.73	226.1	233.4273336015	-7.3273336015						X'SX			Cov(B)			s.e		coeff	std err		coeff	std err
1	1502.0	4.11	242.1	240.5816786058	1.5183213942				n	20	4915.1920017104	6469633.32377924	15245.6393644114	848.7501534211	-0.5067052663	-91.2995046904	29.133316897	Intercept	-15.1335543844	24.8912168782		-15.1335543844	29.133316897
1	1475.5	5.11	200.4	234.591475141	-34.191475141				k	3	6469633.32377924	8713918363.72491	20525726.1344479	-0.5067052663	0.0003886707	0.0264021111	0.0197147332	GNP	0.1740221981	0.0186448434		0.1740221981	0.0197147332
									mult	1.1764705882	15245.6393644114	20525726.1344479	63168.7764135342	-91.2995046904	0.0264021111	19.8192833867	4.4518853744	Interest	-1.3786152155	3.2263163966		-1.3786152155	4.4518853744

Newey 1

Newey-West				Breusch-Godfrey Test		Regression Analysis
Constant	GNP	Interest	Invest	Lags	3	OVERALL FIT
1	860.8	4.62	112.2	LM*	4.8320957868	Multiple R	0.915211057		AIC	116.6040485895
1	866.9	4.10	132.4	df1	3	R Square	0.8376112788		AICc	119.2707152562
1	919.2	4.04	153.4	df2	14	Adjusted R Square	0.8185067234		SBC	119.5912454102
1	974.6	3.42	162.3	p-value	0.0163725206	Standard Error	17.2249804081
1	1001.2	3.35	156.5			Observations	20
1	1048.5	2.99	162.7	LM	10.1741586032
1	1077.0	2.81	166.2	p-value	0.0171422832	ANOVA				Alpha	0.05
1	1075.2	2.76	158.5				df	SS	MS	F	p-value	sig
1	1112.5	2.06	174.1			Regression	2	26016.7503490017	13008.3751745008	43.843536785	0.0000001949	yes
1	1175.9	2.44	197.9			Residual	17	5043.8991509983	296.6999500587
1	1244.1	2.86	217.6			Total	19	31060.6495
1	1236.8	1.12	198.3
1	1218.3	0.36	162.4			ols	coeff	std err	t stat	p-value	lower	upper	vif
1	1288.5	2.40	194.8			Intercept	-15.1335543844	24.8912168782	-0.6079877275	0.5512324402	-67.6494315053	37.3823227365
1	1359.6	1.79	221.1			GNP	0.1740221981	0.0186448434	9.3335296247	0.0000000421	0.1346850171	0.2133593791	1.0012420236
1	1428.0	2.27	257.4			Interest	-1.3786152155	3.2263163966	-0.4273031675	0.6745216904	-8.185547808	5.428317377	1.0012420236
1	1469.8	3.73	258.5
1	1465.8	4.73	226.1			newey-west	coeff	std err	t stat	p-value
1	1502.0	4.11	242.1			Intercept	-15.1335543844	29.133316897	-0.5194586815	0.6101383655
1	1475.5	5.11	200.4			GNP	0.1740221981	0.0197147332	8.8270125954	0.0000000933
						Interest	-1.3786152155	4.4518853744	-0.3096699712	0.7605755801

Collinearity

Multicollinearity										Regression with X1 and X2 variables
X1	X2	Y	X			Y	B			SUMMARY OUTPUT							RESIDUAL OUTPUT
34	17	4	1	34	17	4	ERROR:#NUM!
45	22.5	7	1	45	22.5	7	ERROR:#NUM!			Regression Statistics							Observation	Predicted Y	Residuals	Std Residuals
89	44.5	17	1	89	44.5	17	ERROR:#NUM!			Multiple R	0.9922590279						1	4.9030715344	-0.9030715344	-1.4197639369
54	27	10	1	54	27	10				R Square	0.9845779785						2	7.4985854776	-0.4985854776	-0.7838511719
65	32.5	13	1	65	32.5	13		(XTX)-1		Adjusted R Square	0.815340975						3	17.8806412502	-0.8806412502	-1.3845001649
24	12	3	1	24	12	3	ERROR:#NUM!	ERROR:#NUM!	ERROR:#NUM!	Standard Error	0.7344722315						4	9.6221877947	0.3778122053	0.5939774686
74	37	15	1	74	37	15	ERROR:#NUM!	ERROR:#NUM!	ERROR:#NUM!	Observations	8						5	12.2177017378	0.7822982622	1.2298902337
81	40.5	16	1	81	40.5	16	ERROR:#NUM!	ERROR:#NUM!	ERROR:#NUM!								6	2.5435134043	0.4564865957	0.7176654136
										ANOVA							7	14.341304055	0.658695945	1.0355688475
											df	SS	MS	F	Significance F		8	15.9929947461	0.0070052539	0.0110133102
										Regression	2	206.6383032467	103.3191516233	383.0540560226	0.0000033856
										Residual	6	3.2366967533	0.5394494589
										Total	8	209.875					Linest
																		b2	b1	intercept
											Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Slope (b)	0	0.235955813	-3.119426108	Intercept (a)
										Intercept	-3.119426108	0.748730402	-4.1662874912	0.0059028935	-4.951503402	-1.2873488141	S.E. of slope (sb)	0	0.0120559281	0.748730402	S.E. of intercept (sa)
										X1	0.235955813	0.0120559281	19.5717668089	0.0000011529	0.2064560197	0.2654556063	R Square	0.9845779785	0.7344722315	ERROR:#N/A	S.E. of estimate (sRes)
										X2	0	0	65535	ERROR:#NUM!	0	0	F	383.0540560226	6	ERROR:#N/A	dfRes
																	SSReg	206.6383032467	3.2366967533	ERROR:#N/A	SSRes
										Regression with only X1 variable
										SUMMARY OUTPUT							RESIDUAL OUTPUT
										Regression Statistics							Observation	Predicted Y	Residuals	Std Residuals
										Multiple R	0.9922590279						1	4.9030715344	-0.9030715344	-1.3280675555
										R Square	0.9845779785						2	7.4985854776	-0.4985854776	-0.7332256318
										Adjusted R Square	0.9820076416						3	17.8806412502	-0.8806412502	-1.2950813173
										Standard Error	0.7344722315						4	9.6221877947	0.3778122053	0.5556150458
										Observations	8						5	12.2177017378	0.7822982622	1.1504569695
																	6	2.5435134043	0.4564865957	0.671314524
										ANOVA							7	14.341304055	0.658695945	0.968685957
											df	SS	MS	F	Significance F		8	15.9929947461	0.0070052539	0.0103020083
										Regression	1	206.6383032467	206.6383032467	383.0540560226	0.0000011529
										Residual	6	3.2366967533	0.5394494589
										Total	7	209.875					Linest
																		b	intercept
											Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Slope (b)	0.235955813	-3.119426108	Intercept (a)
										Intercept	-3.119426108	0.748730402	-4.1662874912	0.0059028935	-4.951503402	-1.2873488141	S.E. of slope (sb)	0.0120559281	0.748730402	S.E. of intercept (sa)
										X1	0.235955813	0.0120559281	19.5717668089	0.0000011529	0.2064560197	0.2654556063	R Square	0.9845779785	0.7344722315	S.E. of estimate (sRes)
																	F	383.0540560226	6	dfRes
																	SSReg	206.6383032467	3.2366967533	SSRes

VIF

VIF and Tolerance										Using regression data analysis
	Poverty	Infant Mort	White	Crime	Doctors	Traf Deaths	University	Unemployed	Income	Infant Mort	White	Doctors	Traf Deaths	University	Unemployed	Income	Crime	SUMMARY OUTPUT
Alabama	15.7	9.0	71.0	448	218.2	1.81	22.0	5.0	42,666	9.0	71.0	218.2	1.81	22.0	5.0	42,666	448
Alaska	8.4	6.9	70.6	661	228.5	1.63	27.3	6.7	68,460	6.9	70.6	228.5	1.63	27.3	6.7	68,460	661	Regression Statistics
Arizona	14.7	6.4	86.5	483	209.7	1.69	25.1	5.5	50,958	6.4	86.5	209.7	1.69	25.1	5.5	50,958	483	Multiple R	0.7197661778
Arkansas	17.3	8.5	80.8	529	203.4	1.96	18.8	5.1	38,815	8.5	80.8	203.4	1.96	18.8	5.1	38,815	529	R Square	0.5180633507
California	13.3	5.0	76.6	523	268.7	1.21	29.6	7.2	61,021	5.0	76.6	268.7	1.21	29.6	7.2	61,021	523	Adjusted R Square	-0.3253257857
Colorado	11.4	5.7	89.7	348	259.7	1.14	35.6	4.9	56,993	5.7	89.7	259.7	1.14	35.6	4.9	56,993	348	Standard Error	192.835089928
Connecticut	9.3	6.2	84.3	256	376.4	0.86	35.6	5.7	68,595	6.2	84.3	376.4	0.86	35.6	5.7	68,595	256	Observations	12
Delaware	10.0	8.3	74.3	689	250.9	1.23	27.5	4.8	57,989	8.3	74.3	250.9	1.23	27.5	4.8	57,989	689
Florida	13.2	7.3	79.8	723	247.9	1.56	25.8	6.2	47,778	7.3	79.8	247.9	1.56	25.8	6.2	47,778	723	ANOVA
Georgia	14.7	8.1	65.4	493	217.4	1.46	27.5	6.2	50,861	8.1	65.4	217.4	1.46	27.5	6.2	50,861	493		df	SS	MS	F	Significance F
Hawaii	9.1	5.6	29.7	273	317.0	1.33	29.1	3.9	67,214	5.6	29.7	317.0	1.33	29.1	3.9	67,214	273	Regression	7	159891.374869812	22841.6249814018	0.6142637228	0.7311117201
Idaho	12.6	6.8	94.6	239	168.8	1.60	24.0	4.9	47,576	6.8	94.6	168.8	1.60	24.0	4.9	47,576	239	Residual	4	148741.487630188	37185.3719075469
																		Total	11	308632.8625
j		1	2	3	4	5	6	7	8
Variable		Infant Mort	White	Crime	Doctors	Traf Deaths	University	Unemployed	Income										Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Tolerance		0.3876265239	0.4803800099	0.4819366493	0.2613873066	0.1042879801	0.1025025192	0.5185422255	0.2243943886									Intercept	-374.5549161667	2180.1320240233	-0.171803777	0.8719334281	-6427.571802951	5678.4619706176
VIF		2.5798028215	2.0816852897	2.0749615149	3.8257404802	9.5888327606	9.7558577828	1.928483257	4.4564394242									Infant Mort	65.7589974708	65.3242119732	1.0066558093	0.3710529616	-115.6100911132	247.1280860548
																		White	-1.0926639699	4.9989674664	-0.2185779318	0.8376780411	-14.9720227231	12.7866947833
R Square		0.6123734761		0.5180633507														Doctors	-0.4650133743	2.040368857	-0.2279065242	0.8308948868	-6.1299855	5.1999587515
Tolerance		0.3876265239		0.4819366493														Traf Deaths	-26.6948482634	574.8420550903	-0.0464385791	0.9651867047	-1622.7122583808	1569.3225618541
VIF		2.5798028215		2.0749615149														University	-5.8433433693	36.9048158802	-0.1583355242	0.8818645357	-108.3075387782	96.6208520395
																		Unemployed	101.5536318	70.336160288	1.4438324666	0.2222777807	-93.7308561499	296.8381197499
																		Income	0.0040754607	0.0119971113	0.3397035039	0.7511675882	-0.0292338602	0.0373847817

Ridge 1

Ridge Regression
X1	X2	X3	X4	Y	Regression Analysis								Correlation Matrix
3	6	2	8	3
7	7	11	14	15	OVERALL FIT								1	0.9887990395	0.9337766092	0.3291347093	0.9615413012
11	11	23	33	19	Multiple R	0.9744016579		AIC	66.1192188769				0.9887990395	1	0.9193005987	0.3137204537	0.9424967132
15	12	26	34	27	R Square	0.949458591		AICc	73.7555825132				0.9337766092	0.9193005987	1	0.621243176	0.9222345486
21	16	12	5	23	Adjusted R Square	0.9339073882		SBC	70.5710776664				0.3291347093	0.3137204537	0.621243176	1	0.3303511405
23	17	16	10	23	Standard Error	5.5933499952							0.9615413012	0.9424967132	0.9222345486	0.3303511405	1
28	22	22	15	31	Observations	18
31	16	28	24	39
38	21	34	31	47	ANOVA				Alpha	0.05
39	27	27	8	51		df	SS	MS	F	p-value	sig
42	24	31	16	47	Regression	4	7640.3987769202	1910.0996942301	61.053707836	0.0000000269	yes
49	32	40	25	51	Residual	13	406.7123341909	31.2855641685
57	29	42	21	55	Total	17	8047.1111111111
68	36	35	9	63
71	42	39	15	67		coeff	std err	t stat	p-value	lower	upper	vif
89	51	51	23	71	Intercept	14.9697910435	4.1248574103	3.6291657031	0.0030565915	6.058578382	23.881003705
95	53	60	29	71	X1	0.300586968	0.4118921473	0.7297710578	0.4784744725	-0.5892519169	1.1904258529	83.3507398375
97	55	68	40	75	X2	-0.5202933853	0.5872814647	-0.8859353079	0.3917460717	-1.7890378542	0.7484510837	45.2048896839
					X3	1.4228769391	0.6019743343	2.3636837288	0.0343384414	0.1223904553	2.7233634229	56.9697921539
					X4	-0.793632504	0.3718119154	-2.1344999207	0.0524178862	-1.5968833122	0.0096183042	8.1924225771

Ridge 2

X1	X2	X3	X4	Y	Ridge Regression						Regression Analysis										Ridge Trace
3	6	2	8	3
7	7	11	14	15	Lambda	0.17					OVERALL FIT										Lambda	0	0.0017	0.017	0.17	1.7	17	170
11	11	23	33	19							Multiple R	0.9681766905		AIC	-42.8969276723						X1	0.4154257012	0.4160248142	0.4201954837	0.4202090382	0.3639769802	0.2537897266	0.0742801508
15	12	26	34	27	X1	X2	X3	X4	Y	map	R Square	0.9373661041		AICc	-37.8969276723						X2	-0.3714041171	-0.3678251978	-0.3368525304	-0.123697832	0.2303976344	0.2384547438	0.0725129201
21	16	12	5	23	-1.3487504511	-1.3199573955	-1.7343236198	-1.1490815243	-1.8487173746	1	Adjusted R Square	0.9194707052		BSC	-39.3354406407						X3	1.1124069143	1.1072546389	1.063881741	0.7988384671	0.3903990537	0.2283358662	0.0702441161
23	17	16	10	23	-1.2157230093	-1.2555692299	-1.2052079392	-0.5745407622	-1.2971663347	2	Standard Error	0.2757815169									X4	-0.3809380132	-0.3790192644	-0.3628386822	-0.2627927184	-0.0947813777	0.0150799518	0.0217741364
28	22	22	15	31	-1.0826955676	-0.9980165673	-0.499720365	1.244838318	-1.113315988	3	Observations	18
31	16	28	24	39	-0.9496681258	-0.9336284017	-0.3233484715	1.3405951117	-0.7456152947	4											Ridge Cross Validation
38	21	34	31	47	-0.7501269632	-0.6760757392	-1.146417308	-1.4363519054	-0.9294656414	5	ANOVA				Alpha	0.05
39	27	27	8	51	-0.6836132423	-0.6116875735	-0.9112547833	-0.9575679369	-0.9294656414	1		df	SS	MS	F	p-value	sig		MSE	33.2055367264	# of Groups	5
42	24	31	16	47	-0.5173289401	-0.2897467454	-0.5585109962	-0.4787839685	-0.5617649481	2	Regression	4	15.9352237694	3.9838059423	52.3802857247	0.0000000286	yes		VIF Goal	1	Lambda	0.1063962004
49	32	40	25	51	-0.4175583588	-0.6760757392	-0.2057672091	0.3830271748	-0.1940642548	3	Residual	14	1.0647762306	0.076055445					Lambda	1.5586300492	CV Error	0.281573555
57	29	42	21	55	-0.1847603358	-0.354134911	0.1469765779	1.0533247306	0.1736364385	4	Total	18	17
68	36	35	9	63	-0.1515034753	0.0321940828	-0.2645578403	-1.1490815243	0.3574867851	5									coef unstd	std err
71	42	39	15	67	-0.051732894	-0.1609704141	-0.0293953156	-0.3830271748	0.1736364385	1		coeff	std err	t stat	p-value	lower	upper	vif	13.3347224663
89	51	51	23	71	0.181065129	0.354134911	0.499720365	0.4787839685	0.3574867851	2	X1	0.4202090382	0.2610564649	1.6096480827	0.1297846015	-0.1397013925	0.9801194688	15.2330726977	0.3040480171	0.1888909883
95	53	60	29	71	0.4471200126	0.1609704141	0.6173016274	0.0957567937	0.5413371318	3	X2	-0.123697832	0.2538567289	-0.4872741903	0.6336052399	-0.6681663648	0.4207707008	14.4044263915	-0.1732860806	0.3556233514
97	55	68	40	75	0.8129454774	0.6116875735	0.2057672091	-1.0533247306	0.9090378251	4	X3	0.7988384671	0.2679401933	2.9814058774	0.0099100123	0.2241639074	1.3735130269	16.0470180869	1.0217923121	0.3427216401
					0.9127160587	0.9980165673	0.4409297338	-0.4787839685	1.0928881717	5	X4	-0.2627927184	0.1173247289	-2.239874926	0.0418447552	-0.514429235	-0.0111562017	3.0767890963	-0.547492862	0.2444301044
					1.5113395466	1.577510058	1.146417308	0.2872703811	1.2767385184	1
					1.7108807092	1.7062863893	1.6755329886	0.8618111432	1.2767385184	2	R Square	0.9373661041
					1.7773944301	1.8350627206	2.1458580381	1.9151358739	1.460588865	3

Ridge Trace

X1 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 0.41542570119959465 0.41602481418138454 0.42019548374079296 0.42020903815928212 0.36397698018790275 0.25378972657159399 7.4280150801580888E-2 X2 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 -0.37140411710341692 -0.36782519780234801 -0.33685253043124175 -0.12369783202344081 0.23039763439912436 0.23845474376746628 7.2512920140047579E-2 X3 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 1.1124069143399349 1.1072546389330469 1.0638817410262469 0.79883846712574091 0.39039905370277894 0.22833586621233812 7.0244116059912778E-2 X4 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 -0.38093801321675191 -0.37901926441711931 -0.36283868218831522 -0.26279271836474805 -9.4781377718283322E-2 1.507995175083722E-2 2.1774136428059303E-2

Ridge 3

X1	X2	X3	X4	Y		coeff	Pred	Res	Pred
3	6	2	8	3	Intercept	11.4207086876	13.4863417821	-10.4863417821	13.4863417821	11.4207086876
7	7	11	14	15	X1	0.2646839177	18.2057259795	-3.2057259795	18.2057259795	0.2646839177
11	11	23	33	19	X2	0.3154820192	22.7348879072	-3.7348879072	22.7348879072	0.3154820192
15	12	26	34	27	X3	0.5081532784	25.4288632661	1.5711367339	25.4288632661	0.5081532784
21	16	12	5	23	X4	-0.2047021663	27.1011117763	-4.1011117763	27.1011117763	-0.2047021663
23	17	16	10	23			28.9550639127	-5.9550639127	28.9550639127
28	22	22	15	31	MSE	42.6854055991	33.881302436	-2.881302436	33.881302436
31	16	28	24	39	Lambda	1.6	33.9890622469	5.0109377531	33.9890622469
38	21	34	31	47			39.0352642729	7.9647357271	39.0352642729
39	27	27	8	51			42.3439171835	8.6560828165	42.3439171835
42	24	31	16	47			42.5865186616	4.4134813384	42.5865186616
49	32	40	25	51			49.6942222478	1.3057777522	49.6942222478
57	29	42	21	55			52.700362754	2.299637246	52.700362754
68	36	35	9	63			56.7196130313	6.2803869687	56.7196130313
71	42	39	15	67			60.2109570153	6.7890429847	60.2109570153
89	51	51	23	71			72.2748277172	-1.2748277172	72.2748277172
95	53	60	29	71			77.8390617692	-6.8390617692	77.8390617692
97	55	68	40	75			80.8128960403	-5.8128960403	80.8128960403
								554.9102727885
50	20	30	25	41.091589151	41.091589151
50	20	30	25	41.091589151	41.091589151
30	30	20	20	34.8947090369	34.8947090369

LASSO

X1	X2	X3	X4	Y	LASSO Trace
3	6	2	8	3
7	7	11	14	15	Lambda	0	0.0017	0.017	0.17	1.7	17	170
11	11	23	33	19	X1	0.4154257012	0.4121080601	0.3822492905	0.1654940652	0.7579895279	0.4615413012	0
15	12	26	34	27	X2	-0.3714041171	-0.366861744	-0.3259803865	0	0	0	0
21	16	12	5	23	X3	1.1124069143	1.1108565591	1.0969033624	0.9578989429	0.1644416575	0	0
23	17	16	10	23	X4	-0.3809380132	-0.3802579501	-0.3741373825	-0.3142068821	0	0	0
28	22	22	15	31
31	16	28	24	39
38	21	34	31	47
39	27	27	8	51
42	24	31	16	47
49	32	40	25	51
57	29	42	21	55
68	36	35	9	63
71	42	39	15	67
89	51	51	23	71
95	53	60	29	71
97	55	68	40	75

LASSO Trace

X1 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 0.41542570119956729 0.41210806013226747 0.38224929052649564 0.16549406515640327 0.75798952785145657 0.46154130117260894 0 X2 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 -0.37140411710341636 -0.36686174404260619 -0.3259803864953138 0 0 0 0 X3 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 1.1124069143399722 1.110856559147732 1.0969033624176578 0.95789894290687272 0.16444165747482023 0 0 X4 0 1.7000000000000001E-3 1.7000000000000001E-2 0.17 1.7000000000000002 17 170 -0.38093801321676335 -0.38025795014245478 -0.3741373824737077 -0.31420688207632508 0 0 0

Med

Mediation Analysis
Id	Support	Training	Perform	Mediation Analysis
1	4.23	1.50	2.00
2	3.92	1.82	1.70				Count	20
3	4.45	2.08	2.26		coeff	std err	t-stat	p-value	corr	semi-part
4	4.50	2.68	2.70	X => M	0.6740994527	0.2462987071	2.7369183571	0.013544944	0.542089223
5	4.45	2.54	2.65	M => Y	0.3324711541	0.0961937486	3.4562657038	0.0028173177	0.6315959851	0.4471577258
6	3.86	2.53	2.18	X => Y	0.3089338252	0.1360238728	2.2711735733	0.0356439643	0.4719518039	0.1541915971
7	4.05	2.13	2.25	X	0.1201111527	0.1435500201	0.8367198597	0.4137210299
8	3.90	2.61	2.30	M	0.280111001	0.1154383356	2.426498957	0.0259725122
9	2.13	2.04	1.55
10	3.31	1.59	2.01		coeff	std err	t-stat	p-value
11	4.18	2.95	1.90	X => M	0.542089223	0.1980655439	2.7369183571
12	4.41	2.99	2.65	M => Y	0.6315959851	0.1827394185	3.4562657038
13	3.36	2.17	1.55	X=>M=>Y	0.3423813768	0.1554104959	2.203077565	0.0416715405
14	2.69	1.40	1.90
15	4.47	2.50	2.14
16	3.62	2.75	2.50
17	3.00	1.21	1.95
18	4.32	2.81	2.00
19	4.21	2.07	2.48
20	3.73	2.27	2.00

CV

Cross Validation
Color	Quality	Price	Regression Analysis									Color	Quality	Price	Res	Pred	Res	Hat
7	5	65									1	7	5	65	11.6817157095	54.8104996248	10.1895003752	0.1277393982	SST	1857.6363636364	=DEVSQ(Q4:Q14)
3	7	38	OVERALL FIT								2	3	7	38	-6.6898249453	42.7461771327	-4.7461771327	0.2905379182	PRESS	506.4298346752	=R17
5	8	51	Multiple R	0.9223307274		AIC	41.5014849434				3	5	8	51	-7.5118558215	56.2951693446	-5.2951693446	0.2950917229	Pred R-sq	0.7273794567	=1-Y5/Y4
8	1	38	R Square	0.8506939707		AICc	48.1681516101				4	8	1	38	-9.1036856598	44.6721260576	-6.6721260576	0.2670961733
9	3	55	Adjusted R Square	0.8133674634		SBC	42.6951707618				5	9	3	55	-2.7666575079	57.084245388	-2.084245388	0.2466557996		0.7273794567	=PredRSquare(O4:P14,Q4:Q14)
5	4	43	Standard Error	5.8880844651							6	5	4	43	1.963960014	41.2615074129	1.7384925871	0.1148024528
4	0	25	Observations	11							7	4	0	25	8.5697097944	21.3325571166	3.6674428834	0.5720458485
2	6	33									8	2	6	33	-1.7287504094	34.0924732852	-1.0924732852	0.3680560946
8	7	71	ANOVA				Alpha	0.05			9	8	7	71	5.8702050663	67.2226189552	3.7773810448	0.3565163394
6	4	51		df	SS	MS	F	p-value	sig		10	6	4	51	5.3347298222	46.1567957774	4.8432042226	0.0921369246
9	2	49	Regression	2	1580.2800542881	790.1400271441	22.7906126672	0.0004969462	yes		11	9	2	49	-5.9202903683	53.325829905	-4.325829905	0.2693213278
			Residual	8	277.3563093482	34.6695386685								CV	46.0390758796		CV	46.0390758796
			Total	10	1857.6363636364
														PRESS	506.4298346752	=PRESS(O4:P14,Q4:Q14)
				coeff	std err	t stat	p-value	lower	upper	vif				CV	46.0390758796	=R17/N14
			Intercept	1.7514036586	6.960202671	0.2516311293	0.8076696241	-14.2988524827	17.8016597998
			Color	4.8952883645	0.8202297785	5.9681914666	0.0003350836	3.0038351036	6.7867416255	1.1255142436					46.0390758796	=RegCV(O4:P14,Q4:Q14)
			Quality	3.7584154829	0.7565109874	4.9680910731	0.0010957202	2.0138980178	5.5029329481	1.1255142436

DW Table 1

Durbin-Watson Table
Alpha = .01
nk	1		2		3		4		5		6		7		8		9		10		11		12		13		14		15		16		17		18		19		20
6	0.390	1.142
7	0.435	1.036	0.294	1.676
8	0.497	1.003	0.345	1.489	0.229	2.102
9	0.554	0.998	0.408	1.389	0.279	1.875	0.183	2.433
10	0.604	1.001	0.466	1.333	0.340	1.733	0.230	2.193	0.150	2.690
11	0.653	1.010	0.519	1.297	0.396	1.640	0.286	2.030	0.193	2.453	0.124	2.892
12	0.697	1.023	0.569	1.274	0.449	1.575	0.339	1.913	0.244	2.280	0.164	2.665	0.105	3.053
13	0.738	1.038	0.616	1.261	0.499	1.526	0.391	1.826	0.294	2.150	0.211	2.490	0.140	2.838	0.090	3.182
14	0.776	1.054	0.660	1.254	0.547	1.490	0.441	1.757	0.343	2.049	0.257	2.354	0.183	2.667	0.122	2.981	0.078	3.287
15	0.811	1.070	0.700	1.252	0.591	1.465	0.487	1.705	0.390	1.967	0.303	2.244	0.226	2.530	0.161	2.817	0.107	3.101	0.068	3.374
16	0.844	1.086	0.738	1.253	0.633	1.447	0.532	1.664	0.437	1.901	0.349	2.153	0.269	2.416	0.200	2.681	0.142	2.944	0.094	3.201	0.060	3.446
17	0.873	1.102	0.773	1.255	0.672	1.432	0.574	1.631	0.481	1.847	0.393	2.078	0.313	2.319	0.241	2.566	0.179	2.811	0.127	3.053	0.084	3.286	0.053	3.506
18	0.902	1.118	0.805	1.259	0.708	1.422	0.614	1.604	0.522	1.803	0.435	2.015	0.355	2.238	0.282	2.467	0.216	2.697	0.160	2.925	0.113	3.146	0.075	3.358	0.047	3.557
19	0.928	1.133	0.835	1.264	0.742	1.416	0.650	1.583	0.561	1.767	0.476	1.963	0.396	2.169	0.322	2.381	0.255	2.597	0.196	2.813	0.145	3.023	0.102	3.227	0.067	3.420	0.043	3.601
20	0.952	1.147	0.862	1.270	0.774	1.410	0.684	1.567	0.598	1.736	0.515	1.918	0.436	2.110	0.362	2.308	0.294	2.510	0.232	2.174	0.178	2.914	0.131	3.109	0.092	3.297	0.061	3.474	0.038	3.639
21	0.975	1.161	0.889	1.276	0.803	1.408	0.718	1.554	0.634	1.712	0.552	1.881	0.474	2.059	0.400	2.244	0.331	2.434	0.268	2.625	0.212	2.817	0.162	3.004	0.119	3.185	0.084	3.358	0.055	3.521	0.035	3.671
22	0.997	1.174	0.915	1.284	0.832	1.407	0.748	1.543	0.666	1.691	0.587	1.849	0.510	2.015	0.437	2.188	0.368	2.367	0.304	2.548	0.246	2.729	0.194	2.909	0.148	3.084	0.109	3.252	0.077	3.412	0.050	3.562	0.032	3.700
23	1.017	1.186	0.938	1.290	0.858	1.407	0.777	1.535	0.699	1.674	0.620	1.821	0.545	1.977	0.473	2.140	0.404	2.308	0.340	2.479	0.281	2.651	0.227	2.822	0.178	2.991	0.136	3.155	0.100	3.311	0.070	3.459	0.046	3.597	0.029	3.725
24	1.037	1.199	0.959	1.298	0.881	1.407	0.805	1.527	0.728	1.659	0.652	1.797	0.578	1.944	0.507	2.097	0.439	2.255	0.375	2.417	0.315	2.580	0.260	2.744	0.209	2.906	0.165	3.065	0.125	3.218	0.092	3.363	0.065	3.501	0.043	3.629	0.027	3.747
25	1.055	1.210	0.981	1.305	0.906	1.408	0.832	1.521	0.756	1.645	0.682	1.776	0.610	1.915	0.540	2.059	0.473	2.209	0.409	2.362	0.348	2.517	0.292	2.674	0.240	2.829	0.194	2.982	0.152	3.131	0.116	3.274	0.085	3.410	0.060	3.538	0.039	3.657	0.025	3.766
26	1.072	1.222	1.000	1.311	0.928	1.410	0.855	1.517	0.782	1.635	0.711	1.759	0.640	1.889	0.572	2.026	0.505	2.168	0.441	2.313	0.381	2.460	0.324	2.610	0.272	2.758	0.224	2.906	0.180	3.050	0.141	3.191	0.107	3.325	0.079	3.452	0.055	3.572	0.036	3.682
27	1.088	1.232	1.019	1.318	0.948	1.413	0.878	1.514	0.808	1.625	0.738	1.743	0.669	1.867	0.602	1.997	0.536	2.131	0.473	2.269	0.413	2.409	0.356	2.552	0.303	2.694	0.253	2.836	0.208	2.976	0.167	3.113	0.131	3.245	0.100	3.371	0.073	3.490	0.051	3.602
28	1.104	1.244	1.036	1.325	0.969	1.414	0.901	1.512	0.832	1.618	0.764	1.729	0.696	1.847	0.630	1.970	0.566	2.098	0.504	2.229	0.444	2.363	0.387	2.499	0.333	2.635	0.283	2.772	0.237	2.907	0.194	3.040	0.156	3.169	0.122	3.294	0.093	3.412	0.068	3.524
29	1.119	1.254	1.053	1.332	0.988	1.418	0.921	1.511	0.855	1.611	0.788	1.718	0.723	1.830	0.658	1.947	0.595	2.068	0.533	2.193	0.474	2.321	0.417	2.451	0.363	2.582	0.313	2.713	0.266	2.843	0.222	2.972	0.182	3.098	0.146	3.220	0.114	3.338	0.087	3.450
30	1.134	1.264	1.070	1.339	1.006	1.421	0.941	1.510	0.877	1.606	0.812	1.707	0.748	1.814	0.684	1.925	0.622	2.041	0.562	2.160	0.503	2.283	0.447	2.407	0.393	2.533	0.342	2.659	0.294	2.785	0.249	2.909	0.208	3.032	0.171	3.152	0.137	3.267	0.107	3.379
31	1.147	1.274	1.085	1.345	1.022	1.425	0.960	1.509	0.897	1.601	0.834	1.698	0.772	1.800	0.710	1.906	0.649	2.017	0.589	2.131	0.531	2.248	0.475	2.367	0.422	2.487	0.371	2.609	0.322	2.730	0.277	2.851	0.234	2.970	0.193	3.087	0.160	3.201	0.128	3.311
32	1.160	1.283	1.100	1.351	1.039	1.428	0.978	1.509	0.917	1.597	0.856	1.690	0.794	1.788	0.734	1.889	0.674	1.995	0.615	2.104	0.558	2.216	0.503	2.330	0.450	2.446	0.399	2.563	0.350	2.680	0.304	2.797	0.261	2.912	0.221	3.026	0.184	3.137	0.151	3.246
33	1.171	1.291	1.114	1.358	1.055	1.432	0.995	1.510	0.935	1.594	0.876	1.683	0.816	1.776	0.757	1.874	0.698	1.975	0.641	2.080	0.585	2.187	0.530	2.296	0.477	2.408	0.426	2.520	0.377	2.633	0.331	2.746	0.287	2.858	0.246	2.969	0.209	3.078	0.174	3.184
34	1.184	1.298	1.128	1.364	1.070	1.436	1.012	1.511	0.954	1.591	0.896	1.677	0.837	1.766	0.779	1.860	0.722	1.957	0.665	2.057	0.610	2.160	0.556	2.266	0.503	2.373	0.452	2.481	0.404	2.590	0.357	2.699	0.313	2.808	0.272	2.915	0.233	3.022	0.197	3.126
35	1.195	1.307	1.141	1.370	1.085	1.439	1.028	1.512	0.971	1.589	0.914	1.671	0.857	1.757	0.800	1.847	0.744	1.940	0.689	2.037	0.634	2.136	0.581	2.237	0.529	2.340	0.478	2.444	0.430	2.550	0.383	2.655	0.339	2.761	0.297	2.865	0.257	2.969	0.221	3.071
36	1.205	1.315	1.153	1.376	1.098	1.442	1.043	1.513	0.987	1.587	0.932	1.666	0.877	1.749	0.821	1.836	0.766	1.925	0.711	2.018	0.658	2.113	0.605	2.210	0.554	2.310	0.504	2.410	0.455	2.512	0.409	2.614	0.364	2.717	0.322	2.818	0.282	2.919	0.244	3.019
37	1.217	1.322	1.164	1.383	1.112	1.446	1.058	1.514	1.004	1.585	0.950	1.662	0.895	1.742	0.841	1.825	0.787	1.911	0.733	2.001	0.680	2.092	0.628	2.186	0.578	2.282	0.528	2.379	0.480	2.477	0.434	2.576	0.389	2.675	0.347	2.774	0.306	2.872	0.268	2.969
38	1.227	1.330	1.176	1.388	1.124	1.449	1.072	1.515	1.019	1.584	0.966	1.658	0.913	1.735	0.860	1.816	0.807	1.899	0.754	1.985	0.702	2.073	0.651	2.164	0.601	2.256	0.552	2.350	0.504	2.445	0.458	2.540	0.414	2.637	0.371	2.733	0.330	2.828	0.291	2.923
39	1.237	1.337	1.187	1.392	1.137	1.452	1.085	1.517	1.033	1.583	0.982	1.655	0.930	1.729	0.878	1.807	0.826	1.887	0.774	1.970	0.723	2.055	0.673	2.143	0.623	2.232	0.575	2.323	0.528	2.414	0.482	2.507	0.438	2.600	0.395	2.694	0.354	2.787	0.315	2.879
40	1.246	1.344	1.197	1.398	1.149	1.456	1.098	1.518	1.047	1.583	0.997	1.652	0.946	1.724	0.895	1.799	0.844	1.876	0.749	1.956	0.744	2.039	0.694	2.123	0.645	2.210	0.597	2.297	0.551	2.386	0.505	2.476	0.461	2.566	0.418	2.657	0.377	2.748	0.338	2.838
45	1.288	1.376	1.245	1.424	1.201	1.474	1.156	1.528	1.111	1.583	1.065	1.643	1.019	1.704	0.974	1.768	0.927	1.834	0.881	1.902	0.835	1.972	0.790	2.044	0.744	2.118	0.700	2.193	0.655	2.269	0.612	2.346	0.570	2.424	0.528	2.503	0.488	2.582	0.448	2.661
50	1.324	1.403	1.285	1.445	1.245	1.491	1.206	1.537	1.164	1.587	1.123	1.639	1.081	1.692	1.039	1.748	0.997	1.805	0.955	1.864	0.913	1.925	0.871	1.987	0.829	2.051	0.787	2.116	0.746	2.182	0.705	2.250	0.665	2.318	0.625	2.387	0.586	2.456	0.548	2.526
55	1.356	1.428	1.320	1.466	1.284	1.505	1.246	1.548	1.209	1.592	1.172	1.638	1.134	1.685	1.095	1.734	1.057	1.785	1.018	1.837	0.979	1.891	0.940	1.945	0.902	2.002	0.863	2.059	0.825	2.117	0.786	2.176	0.748	2.237	0.711	2.298	0.674	2.359	0.637	2.421
60	1.382	1.449	1.351	1.484	1.317	1.520	1.283	1.559	1.248	1.598	1.214	1.639	1.179	1.682	1.144	1.726	1.108	1.771	1.072	1.817	1.037	1.865	1.001	1.914	0.965	1.964	0.929	2.015	0.893	2.067	0.857	2.120	0.822	2.173	0.786	2.227	0.751	2.283	0.716	2.338
65	1.407	1.467	1.377	1.500	1.346	1.534	1.314	1.568	1.283	1.604	1.251	1.642	1.218	1.680	1.186	1.720	1.153	1.761	1.120	1.802	1.087	1.845	1.053	1.889	1.020	1.934	0.986	1.980	0.953	2.027	0.919	2.075	0.886	2.123	0.852	2.172	0.819	2.221	0.789	2.272
70	1.429	1.485	1.400	1.514	1.372	1.546	1.343	1.577	1.313	1.611	1.283	1.645	1.253	1.680	1.223	1.716	1.192	1.754	1.162	1.792	1.131	1.831	1.099	1.870	1.068	1.911	1.037	1.953	1.005	1.995	0.974	2.038	0.943	2.082	0.911	2.127	0.880	2.172	0.849	2.217
75	1.448	1.501	1.422	1.529	1.395	1.557	1.368	1.586	1.340	1.617	1.313	1.649	1.284	1.682	1.256	1.714	1.227	1.748	1.199	1.783	1.170	1.819	1.141	1.856	1.111	1.893	1.082	1.931	1.052	1.970	1.023	2.009	0.993	2.049	0.964	2.090	0.934	2.131	0.905	2.172
80	1.465	1.514	1.440	1.541	1.416	1.568	1.390	1.595	1.364	1.624	1.338	1.653	1.312	1.683	1.285	1.714	1.259	1.745	1.232	1.777	1.205	1.810	1.177	1.844	1.150	1.878	1.122	1.913	1.094	1.949	1.066	1.984	1.039	2.022	1.011	2.059	0.983	2.097	0.955	2.135
85	1.481	1.529	1.458	1.553	1.434	1.577	1.411	1.603	1.386	1.630	1.362	1.657	1.337	1.685	1.312	1.714	1.287	1.743	1.262	1.773	1.236	1.803	1.210	1.834	1.184	1.866	1.158	1.898	1.132	1.931	1.106	1.965	1.080	1.999	1.053	2.033	1.027	2.068	1.000	2.104
90	1.496	1.541	1.474	1.563	1.452	1.587	1.429	1.611	1.406	1.636	1.383	1.661	1.360	1.687	1.336	1.714	1.312	1.741	1.288	1.769	1.264	1.798	1.240	1.827	1.215	1.856	1.191	1.886	1.166	1.917	1.141	1.948	1.116	1.979	1.091	2.012	1.066	2.044	1.041	2.077
95	1.510	1.552	1.489	1.573	1.468	1.596	1.446	1.618	1.425	1.641	1.403	1.666	1.381	1.690	1.358	1.715	1.336	1.741	1.313	1.767	1.290	1.793	1.267	1.821	1.244	1.848	1.221	1.876	1.197	1.905	1.174	1.943	1.150	1.963	1.126	1.993	1.102	2.023	1.079	2.054
100	1.522	1.562	1.502	1.582	1.482	1.604	1.461	1.625	1.441	1.647	1.421	1.670	1.400	1.693	1.378	1.717	1.357	1.741	1.335	1.765	1.314	1.790	1.292	1.816	1.270	1.841	1.248	1.868	1.225	1.895	1.203	1.922	1.181	1.949	1.158	1.977	1.136	2.006	1.113	2.034
150	1.611	1.637	1.598	1.651	1.584	1.665	1.571	1.679	1.557	1.693	1.543	1.708	1.530	1.722	1.515	1.737	1.501	1.752	1.486	1.767	1.473	1.783	1.458	1.799	1.444	1.814	1.429	1.830	1.414	1.847	1.400	1.863	1.385	1.880	1.370	1.897	1.355	1.913	1.340	1.931
200	1.664	1.684	1.653	1.693	1.643	1.704	1.633	1.715	1.623	1.725	1.613	1.735	1.603	1.746	1.592	1.757	1.582	1.768	1.571	1.779	1.561	1.791	1.550	1.801	1.539	1.813	1.528	1.824	1.518	1.836	1.507	1.847	1.495	1.860	1.484	1.871	1.474	1.883	1.462	1.896
https://www3.nd.edu/~wevans1/econ30331/Durbin_Watson_tables.pdf

https://www3.nd.edu/~wevans1/econ30331/Durbin_Watson_tables.pdf

DW Table 2

Durbin-Watson Table
Alpha = .05
nk	1		2		3		4		5		6		7		8		9		10		11		12		13		14		15		16		17		18		19		20
6	0.610	1.400
7	0.700	1.356	0.467	1.896
8	0.763	1.332	0.559	1.777	0.367	2.287
9	0.824	1.320	0.629	1.699	0.455	2.128	0.296	2.588
10	0.879	1.320	0.697	1.641	0.525	2.016	0.376	2.414	0.243	2.822
11	0.927	1.324	0.758	1.604	0.595	1.928	0.444	2.283	0.315	2.645	0.203	3.004
12	0.971	1.331	0.812	1.579	0.658	1.864	0.512	2.177	0.380	2.506	0.268	2.832	0.171	3.149
13	1.010	1.340	0.861	1.562	0.715	1.816	0.574	2.094	0.444	2.390	0.328	2.692	0.230	2.985	0.147	3.266
14	1.045	1.350	0.905	1.551	0.767	1.779	0.632	2.030	0.505	2.296	0.389	2.572	0.286	2.848	0.200	3.111	0.127	3.360
15	1.077	1.361	0.946	1.543	0.814	1.750	0.685	1.977	0.562	2.220	0.447	2.471	0.343	2.727	0.251	2.979	0.175	3.216	0.111	3.438
16	1.106	1.371	0.982	1.539	0.857	1.728	0.734	1.935	0.615	2.157	0.502	2.388	0.398	2.624	0.304	2.860	0.222	3.090	0.155	3.304	0.098	3.503
17	1.133	1.381	1.015	1.536	0.897	1.710	0.779	1.900	0.664	2.104	0.554	2.318	0.451	2.537	0.356	2.757	0.272	2.975	0.198	3.184	0.138	3.378	0.087	3.557
18	1.158	1.391	1.046	1.535	0.933	1.696	0.820	1.872	0.710	2.060	0.603	2.258	0.502	2.461	0.407	2.668	0.321	2.873	0.244	3.073	0.177	3.265	0.123	3.441	0.078	3.603
19	1.180	1.401	1.074	1.536	0.967	1.685	0.859	1.848	0.752	2.023	0.649	2.206	0.549	2.396	0.456	2.589	0.369	2.783	0.290	2.974	0.220	3.159	0.160	3.335	0.111	3.496	0.070	3.642
20	1.201	1.411	1.100	1.537	0.998	1.676	0.894	1.828	0.792	1.991	0.691	2.162	0.595	2.339	0.502	2.521	0.416	2.704	0.336	2.885	0.263	3.063	0.200	3.234	0.145	3.395	0.100	3.542	0.063	3.676
21	1.221	1.420	1.125	1.538	1.026	1.669	0.927	1.812	0.829	1.964	0.731	2.124	0.637	2.290	0.546	2.461	0.461	2.633	0.380	2.806	0.307	2.976	0.240	3.141	0.182	3.300	0.132	3.448	0.091	3.583	0.058	3.705
22	1.239	1.429	1.147	1.541	1.053	1.664	0.958	1.797	0.863	1.940	0.769	2.090	0.677	2.246	0.588	2.407	0.504	2.571	0.424	2.735	0.349	2.897	0.281	3.057	0.220	3.211	0.166	3.358	0.120	3.495	0.083	3.619	0.052	3.731
23	1.257	1.437	1.168	1.543	1.078	1.660	0.986	1.785	0.895	1.920	0.804	2.061	0.715	2.208	0.628	2.360	0.545	2.514	0.465	2.670	0.391	2.826	0.322	2.979	0.259	3.128	0.202	3.272	0.153	3.409	0.110	3.535	0.076	3.650	0.048	3.753
24	1.273	1.446	1.188	1.546	1.101	1.656	1.013	1.775	0.925	1.902	0.837	2.035	0.750	2.174	0.666	2.318	0.584	2.464	0.506	2.613	0.431	2.761	0.362	2.908	0.297	3.053	0.239	3.193	0.186	3.327	0.141	3.454	0.101	3.572	0.070	3.678	0.044	3.773
25	1.288	1.454	1.206	1.550	1.123	1.654	1.038	1.767	0.953	1.886	0.868	2.013	0.784	2.144	0.702	2.280	0.621	2.419	0.544	2.560	0.470	2.702	0.400	2.844	0.335	2.983	0.275	3.119	0.221	3.251	0.172	3.376	0.130	3.494	0.094	3.604	0.065	3.702	0.041	3.790
26	1.302	1.461	1.224	1.553	1.143	1.652	1.062	1.759	0.979	1.873	0.897	1.992	0.816	2.117	0.735	2.246	0.657	2.379	0.581	2.513	0.508	2.649	0.438	2.784	0.373	2.919	0.312	3.051	0.256	3.179	0.205	3.303	0.160	3.420	0.120	3.531	0.087	3.632	0.060	3.724
27	1.316	1.469	1.240	1.556	1.162	1.651	1.084	1.753	1.004	1.861	0.925	1.974	0.845	2.093	0.767	2.216	0.691	2.342	0.616	2.470	0.544	2.600	0.475	2.730	0.409	2.859	0.348	2.987	0.291	3.112	0.238	3.233	0.191	3.349	0.149	3.460	0.112	3.563	0.081	3.658
28	1.328	1.476	1.255	1.560	1.181	1.650	1.104	1.747	1.028	1.850	0.951	1.959	0.874	2.071	0.798	2.188	0.723	2.309	0.649	2.431	0.578	2.555	0.510	2.680	0.445	2.805	0.383	2.928	0.325	3.050	0.271	3.168	0.222	3.283	0.178	3.392	0.138	3.495	0.104	3.592
29	1.341	1.483	1.270	1.563	1.198	1.650	1.124	1.743	1.050	1.841	0.975	1.944	0.900	2.052	0.826	2.164	0.753	2.278	0.681	2.396	0.612	2.515	0.544	2.634	0.479	2.755	0.418	2.874	0.359	2.992	0.305	3.107	0.254	3.219	0.208	3.327	0.166	3.431	0.129	3.528
30	1.352	1.489	1.284	1.567	1.214	1.650	1.143	1.739	1.071	1.833	0.998	1.931	0.926	2.034	0.854	2.141	0.782	2.251	0.712	2.363	0.643	2.477	0.577	2.592	0.512	2.708	0.451	2.823	0.392	2.937	0.337	3.050	0.286	3.160	0.238	3.266	0.195	3.368	0.156	3.465
31	1.363	1.496	1.297	1.570	1.229	1.650	1.160	1.735	1.090	1.825	1.020	1.920	0.950	2.018	0.879	2.120	0.810	2.226	0.741	2.333	0.674	2.443	0.608	2.553	0.545	2.665	0.484	2.776	0.425	2.887	0.370	2.996	0.317	3.103	0.269	3.208	0.224	3.309	0.183	3.406
32	1.373	1.502	1.309	1.574	1.244	1.650	1.177	1.732	1.109	1.819	1.041	1.909	0.972	2.004	0.904	2.102	0.836	2.203	0.769	2.306	0.703	2.411	0.638	2.517	0.576	2.625	0.515	2.733	0.457	2.840	0.401	2.946	0.349	3.050	0.299	3.153	0.253	3.252	0.211	3.348
33	1.383	1.508	1.321	1.577	1.258	1.651	1.193	1.730	1.127	1.813	1.061	1.900	0.994	1.991	0.927	2.085	0.861	2.181	0.796	2.281	0.731	2.382	0.668	2.484	0.606	2.588	0.546	2.692	0.488	2.796	0.432	2.899	0.379	3.000	0.329	3.100	0.283	3.198	0.239	3.293
34	1.393	1.514	1.333	1.580	1.271	1.652	1.208	1.728	1.144	1.808	1.079	1.891	1.015	1.978	0.950	2.069	0.885	2.162	0.821	2.257	0.758	2.355	0.695	2.454	0.634	2.554	0.575	2.654	0.518	2.754	0.462	2.854	0.409	2.954	0.359	3.051	0.312	3.147	0.267	3.240
35	1.402	1.519	1.343	1.584	1.283	1.653	1.222	1.726	1.160	1.803	1.097	1.884	1.034	1.967	0.971	2.054	0.908	2.144	0.845	2.236	0.783	2.330	0.722	2.425	0.662	2.521	0.604	2.619	0.547	2.716	0.492	2.813	0.439	2.910	0.388	3.005	0.340	3.099	0.295	3.190
36	1.411	1.525	1.354	1.587	1.295	1.654	1.236	1.724	1.175	1.799	1.114	1.876	1.053	1.957	0.991	2.041	0.930	2.127	0.868	2.216	0.808	2.306	0.748	2.398	0.689	2.492	0.631	2.586	0.575	2.680	0.520	2.774	0.467	2.868	0.417	2.961	0.369	3.053	0.323	3.142
37	1.419	1.530	1.364	1.590	1.307	1.655	1.249	1.723	1.190	1.795	1.131	1.870	1.071	1.948	1.011	2.029	0.951	2.112	0.891	2.197	0.831	2.285	0.772	2.374	0.714	2.464	0.657	2.555	0.602	2.646	0.548	2.738	0.495	2.829	0.445	2.920	0.397	3.009	0.351	3.097
38	1.427	1.535	1.373	1.594	1.318	1.656	1.261	1.722	1.204	1.792	1.146	1.864	1.088	1.939	1.029	2.017	0.970	2.098	0.912	2.180	0.854	2.265	0.796	2.351	0.739	2.438	0.683	2.526	0.628	2.614	0.575	2.703	0.522	2.792	0.472	2.880	0.424	2.968	0.378	3.054
39	1.435	1.540	1.382	1.597	1.328	1.658	1.273	1.722	1.218	1.789	1.161	1.859	1.104	1.932	1.047	2.007	0.990	2.085	0.932	2.164	0.875	2.246	0.819	2.329	0.763	2.413	0.707	2.499	0.653	2.585	0.600	2.671	0.549	2.757	0.499	2.843	0.451	2.929	0.404	3.013
40	1.442	1.544	1.391	1.600	1.338	1.659	1.285	1.721	1.230	1.786	1.175	1.854	1.120	1.924	1.064	1.997	1.008	2.072	0.952	2.149	0.896	2.228	0.840	2.309	0.785	2.391	0.731	2.473	0.678	2.557	0.626	2.641	0.575	2.724	0.525	2.808	0.477	2.829	0.430	2.974
45	1.475	1.566	1.430	1.615	1.383	1.666	1.336	1.720	1.287	1.776	1.238	1.835	1.189	1.895	1.139	1.958	1.089	2.022	1.038	2.088	0.988	2.156	0.938	2.225	0.887	2.296	0.838	2.367	0.788	2.439	0.740	2.512	0.692	2.586	0.644	2.659	0.598	2.733	0.553	2.807
50	1.503	1.585	1.462	1.628	1.421	1.674	1.378	1.721	1.335	1.771	1.291	1.822	1.246	1.875	1.201	1.930	1.156	1.986	1.110	2.044	1.064	2.103	1.019	2.163	0.973	2.225	0.927	2.287	0.882	2.350	0.836	2.414	0.792	2.479	0.747	2.544	0.703	2.610	0.660	2.675
55	1.528	1.601	1.490	1.641	1.452	1.681	1.414	1.724	1.374	1.768	1.334	1.814	1.294	1.861	1.253	1.909	1.212	1.959	1.170	2.010	1.129	2.062	1.087	2.116	1.045	2.170	1.003	2.225	0.961	2.281	0.919	2.338	0.877	2.396	0.836	2.454	0.795	2.512	0.754	2.571
60	1.549	1.616	1.514	1.652	1.480	1.689	1.444	1.727	1.408	1.767	1.372	1.808	1.335	1.850	1.298	1.894	1.260	1.939	1.222	1.984	1.184	2.031	1.145	2.079	1.106	2.127	1.068	2.177	1.029	2.227	0.990	2.278	0.951	2.330	0.913	2.382	0.874	2.434	0.836	2.487
65	1.567	1.629	1.536	1.662	1.503	1.696	1.471	1.731	1.438	1.767	1.404	1.805	1.370	1.843	1.336	1.882	1.301	1.923	1.266	1.964	1.231	2.006	1.195	2.049	1.160	2.093	1.124	2.138	1.088	2.183	1.052	2.229	1.016	2.276	0.980	2.323	0.944	2.371	0.908	2.419
70	1.583	1.641	1.554	1.672	1.525	1.703	1.494	1.735	1.464	1.768	1.433	1.802	1.401	1.838	1.369	1.874	1.337	1.910	1.305	1.948	1.272	1.987	1.239	2.026	1.206	2.066	1.172	2.106	1.139	2.148	1.105	2.189	1.072	2.232	1.038	2.275	1.005	2.318	0.971	2.362
75	1.598	1.652	1.571	1.680	1.543	1.709	1.515	1.739	1.487	1.770	1.458	1.801	1.428	1.834	1.399	1.867	1.369	1.901	1.339	1.935	1.308	1.970	1.277	2.006	1.247	2.043	1.215	2.080	1.184	2.118	1.153	2.156	1.121	2.195	1.090	2.235	1.058	2.275	1.027	2.315
80	1.611	1.662	1.586	1.688	1.560	1.715	1.534	1.743	1.507	1.772	1.480	1.801	1.453	1.831	1.425	1.861	1.397	1.893	1.369	1.925	1.340	1.957	1.311	1.991	1.283	2.024	1.253	2.059	1.224	2.093	1.195	2.129	1.165	2.165	1.136	2.201	1.106	2.238	1.076	2.275
85	1.624	1.671	1.600	1.696	1.575	1.721	1.550	1.747	1.525	1.774	1.500	1.801	1.474	1.829	1.448	1.857	1.422	1.886	1.396	1.916	1.369	1.946	1.342	1.977	1.315	2.009	1.287	2.040	1.260	2.073	1.232	2.105	1.205	2.139	1.177	2.172	1.149	2.206	1.121	2.241
90	1.635	1.679	1.612	1.703	1.589	1.726	1.566	1.751	1.542	1.776	1.518	1.801	1.494	1.827	1.469	1.854	1.445	1.881	1.420	1.909	1.395	1.937	1.369	1.966	1.344	1.995	1.318	2.025	1.292	2.055	1.266	2.085	1.240	2.116	1.213	2.148	1.187	2.179	1.160	2.211
95	1.645	1.687	1.623	1.709	1.602	1.732	1.579	1.755	1.557	1.778	1.535	1.802	1.512	1.827	1.489	1.852	1.465	1.877	1.442	1.903	1.418	1.930	1.394	1.956	1.370	1.984	1.345	2.012	1.321	2.040	1.296	2.068	1.271	2.097	1.247	2.126	1.222	2.156	1.197	2.186
100	1.654	1.694	1.634	1.715	1.613	1.736	1.592	1.758	1.571	1.780	1.550	1.803	1.528	1.826	1.506	1.850	1.484	1.874	1.462	1.898	1.439	1.923	1.416	1.948	1.393	1.974	1.371	2.000	1.347	2.026	1.324	2.053	1.301	2.080	1.277	2.108	1.253	2.135	1.229	2.164
150	1.720	1.747	1.706	1.760	1.693	1.774	1.679	1.788	1.665	1.802	1.651	1.817	1.637	1.832	1.622	1.846	1.608	1.862	1.593	1.877	1.579	1.892	1.564	1.908	1.550	1.924	1.535	1.940	1.519	1.956	1.504	1.972	1.489	1.989	1.474	2.006	1.458	2.023	1.443	2.040
200	1.758	1.779	1.748	1.789	1.738	1.799	1.728	1.809	1.718	1.820	1.707	1.831	1.697	1.841	1.686	1.852	1.675	1.863	1.665	1.874	1.654	1.885	1.643	1.896	1.632	1.908	1.621	1.919	1.610	1.931	1.599	1.943	1.588	1.955	1.576	1.967	1.565	1.979	1.554	1.991

DW Table 3

Durbin-Watson Table
Alpha = .01
nk	1		2		3		4		5		6		7		8		9		10		11		12		13		14		15		16		17		18		19		20
250	1.700	1.716	1.692	1.724	1.684	1.732	1.676	1.740	1.667	1.748	1.659	1.757	1.651	1.765	1.643	1.774	1.634	1.782	1.626	1.791	1.618	1.800	1.609	1.808	1.601	1.817	1.592	1.826	1.583	1.835	1.575	1.844	1.566	1.854	1.557	1.863	1.549	1.872	1.540	1.882
300	1.726	1.739	1.720	1.746	1.713	1.753	1.706	1.760	1.699	1.767	1.692	1.774	1.686	1.781	1.679	1.788	1.672	1.795	1.665	1.802	1.658	1.809	1.651	1.816	1.644	1.823	1.637	1.831	1.630	1.838	1.623	1.846	1.615	1.853	1.608	1.861	1.601	1.868	1.594	1.876
350	1.747	1.758	1.741	1.764	1.735	1.770	1.730	1.775	1.724	1.781	1.718	1.787	1.712	1.793	1.706	1.799	1.700	1.805	1.694	1.811	1.688	1.817	1.682	1.823	1.676	1.830	1.670	1.836	1.664	1.842	1.658	1.848	1.652	1.855	1.646	1.861	1.640	1.867	1.634	1.874
400	1.763	1.773	1.758	1.778	1.753	1.783	1.748	1.788	1.743	1.794	1.738	1.799	1.733	1.804	1.728	1.809	1.723	1.814	1.718	1.820	1.712	1.825	1.707	1.830	1.702	1.835	1.697	1.841	1.691	1.846	1.686	1.852	1.681	1.857	1.676	1.863	1.670	1.868	1.665	1.874
450	1.777	1.786	1.773	1.790	1.768	1.795	1.764	1.799	1.759	1.804	1.755	1.808	1.750	1.813	1.746	1.818	1.741	1.822	1.736	1.827	1.732	1.832	1.727	1.836	1.723	1.841	1.718	1.846	1.713	1.850	1.709	1.855	1.704	1.860	1.699	1.865	1.695	1.870	1.690	1.875
500	1.789	1.797	1.785	1.801	1.781	1.805	1.777	1.809	1.773	1.813	1.768	1.817	1.764	1.821	1.760	1.825	1.756	1.829	1.752	1.833	1.748	1.838	1.744	1.842	1.740	1.846	1.736	1.850	1.731	1.854	1.727	1.859	1.723	1.863	1.719	1.867	1.715	1.872	1.710	1.876
550	1.799	1.806	1.795	1.809	1.791	1.813	1.788	1.817	1.784	1.820	1.780	1.824	1.777	1.828	1.773	1.832	1.769	1.835	1.765	1.839	1.762	1.843	1.758	1.847	1.754	1.851	1.750	1.854	1.747	1.858	1.743	1.862	1.739	1.866	1.735	1.870	1.731	1.874	1.728	1.878
600	1.807	1.814	1.804	1.817	1.801	1.821	1.797	1.824	1.794	1.827	1.790	1.831	1.787	1.834	1.784	1.838	1.780	1.841	1.777	1.844	1.773	1.848	1.770	1.851	1.767	1.855	1.763	1.858	1.760	1.862	1.756	1.865	1.753	1.869	1.749	1.872	1.746	1.876	1.742	1.880
650	1.815	1.821	1.812	1.824	1.809	1.827	1.806	1.830	1.803	1.833	1.799	1.837	1.796	1.840	1.793	1.843	1.790	1.846	1.787	1.849	1.784	1.852	1.781	1.856	1.777	1.859	1.774	1.862	1.771	1.865	1.768	1.868	1.765	1.872	1.761	1.875	1.758	1.878	1.755	1.881
700	1.822	1.827	1.819	1.830	1.816	1.833	1.813	1.836	1.810	1.839	1.807	1.842	1.804	1.845	1.802	1.848	1.799	1.851	1.796	1.854	1.793	1.856	1.790	1.859	1.787	1.862	1.784	1.865	1.781	1.868	1.778	1.871	1.775	1.874	1.772	1.877	1.769	1.880	1.766	1.883
750	1.828	1.833	1.825	1.836	1.822	1.838	1.820	1.841	1.817	1.844	1.814	1.847	1.812	1.849	1.809	1.852	1.806	1.855	1.804	1.857	1.801	1.860	1.798	1.863	1.795	1.866	1.793	1.868	1.790	1.871	1.787	1.874	1.784	1.877	1.782	1.880	1.779	1.882	1.776	1.885
800	1.833	1.838	1.831	1.841	1.828	1.843	1.826	1.846	1.823	1.848	1.821	1.851	1.818	1.853	1.816	1.856	1.813	1.859	1.811	1.861	1.808	1.864	1.806	1.866	1.803	1.869	1.800	1.871	1.798	1.874	1.795	1.877	1.793	1.879	1.790	1.882	1.787	1.884	1.785	1.887
850	1.838	1.843	1.836	1.845	1.834	1.848	1.831	1.850	1.829	1.853	1.827	1.855	1.824	1.857	1.822	1.860	1.819	1.862	1.817	1.865	1.815	1.867	1.812	1.869	1.810	1.872	1.807	1.874	1.805	1.877	1.803	1.879	1.800	1.882	1.798	1.884	1.795	1.886	1.793	1.889
900	1.843	1.847	1.841	1.850	1.839	1.852	1.836	1.854	1.834	1.856	1.832	1.859	1.830	1.861	1.827	1.863	1.825	1.865	1.823	1.868	1.821	1.870	1.818	1.872	1.816	1.874	1.814	1.877	1.811	1.879	1.809	1.881	1.807	1.884	1.805	1.886	1.802	1.888	1.800	1.891
950	1.847	1.851	1.845	1.854	1.843	1.856	1.841	1.858	1.839	1.860	1.837	1.862	1.835	1.864	1.832	1.866	1.830	1.868	1.828	1.871	1.826	1.873	1.824	1.875	1.822	1.877	1.820	1.879	1.817	1.881	1.815	1.884	1.813	1.886	1.811	1.888	1.809	1.890	1.807	1.892
1000	1.851	1.855	1.849	1.857	1.847	1.859	1.845	1.861	1.843	1.863	1.841	1.865	1.839	1.867	1.837	1.869	1.835	1.871	1.833	1.873	1.831	1.875	1.829	1.877	1.827	1.879	1.825	1.881	1.823	1.884	1.821	1.886	1.819	1.888	1.817	1.890	1.815	1.892	1.812	1.894
1050	1.855	1.859	1.853	1.860	1.851	1.862	1.849	1.864	1.847	1.866	1.845	1.868	1.843	1.870	1.841	1.872	1.839	1.874	1.837	1.876	1.836	1.878	1.834	1.880	1.832	1.882	1.830	1.884	1.828	1.886	1.826	1.888	1.824	1.890	1.822	1.892	1.820	1.893	1.818	1.895
1100	1.858	1.862	1.856	1.864	1.854	1.865	1.853	1.867	1.851	1.869	1.849	1.871	1.847	1.873	1.845	1.875	1.843	1.876	1.842	1.878	1.840	1.880	1.838	1.882	1.836	1.884	1.834	1.886	1.832	1.888	1.831	1.889	1.829	1.891	1.827	1.893	1.825	1.895	1.823	1.897
1150	1.861	1.865	1.860	1.866	1.858	1.868	1.856	1.870	1.854	1.872	1.853	1.873	1.851	1.875	1.849	1.877	1.847	1.879	1.845	1.881	1.844	1.882	1.842	1.884	1.840	1.886	1.838	1.888	1.837	1.889	1.835	1.891	1.833	1.893	1.831	1.895	1.830	1.897	1.828	1.898
1200	1.864	1.868	1.863	1.869	1.861	1.871	1.859	1.873	1.858	1.874	1.856	1.876	1.854	1.878	1.852	1.879	1.851	1.881	1.849	1.883	1.847	1.884	1.846	1.886	1.844	1.888	1.842	1.889	1.841	1.891	1.839	1.893	1.837	1.895	1.836	1.896	1.834	1.898	1.832	1.900
1250	1.867	1.870	1.865	1.872	1.864	1.873	1.862	1.875	1.861	1.877	1.859	1.878	1.857	1.880	1.856	1.881	1.854	1.883	1.852	1.885	1.851	1.886	1.849	1.888	1.848	1.890	1.846	1.891	1.844	1.893	1.843	1.894	1.841	1.896	1.839	1.898	1.838	1.899	1.836	1.901
1300	1.870	1.873	1.868	1.874	1.867	1.876	1.865	1.877	1.863	1.879	1.862	1.880	1.860	1.882	1.859	1.883	1.857	1.885	1.856	1.887	1.854	1.888	1.853	1.890	1.851	1.891	1.849	1.893	1.848	1.894	1.846	1.896	1.845	1.898	1.843	1.899	1.842	1.901	1.840	1.902
1350	1.872	1.875	1.871	1.876	1.869	1.878	1.868	1.879	1.866	1.881	1.865	1.882	1.863	1.884	1.862	1.885	1.860	1.887	1.859	1.888	1.857	1.890	1.856	1.891	1.854	1.893	1.853	1.894	1.851	1.896	1.850	1.898	1.848	1.899	1.847	1.901	1.845	1.902	1.844	1.904
1400	1.874	1.877	1.873	1.879	1.872	1.880	1.870	1.882	1.869	1.883	1.867	1.884	1.866	1.886	1.864	1.887	1.863	1.889	1.861	1.890	1.860	1.892	1.859	1.893	1.857	1.895	1.856	1.896	1.854	1.897	1.853	1.899	1.851	1.900	1.850	1.902	1.848	1.903	1.847	1.905
1450	1.877	1.879	1.875	1.881	1.874	1.882	1.872	1.883	1.871	1.885	1.870	1.886	1.868	1.888	1.867	1.889	1.865	1.890	1.864	1.892	1.863	1.893	1.861	1.895	1.860	1.896	1.859	1.897	1.857	1.899	1.856	1.900	1.854	1.902	1.853	1.903	1.851	1.904	1.850	1.906
1500	1.879	1.881	1.877	1.883	1.876	1.884	1.875	1.885	1.873	1.887	1.872	1.888	1.871	1.889	1.869	1.891	1.868	1.892	1.867	1.893	1.865	1.895	1.864	1.896	1.863	1.897	1.861	1.899	1.860	1.900	1.858	1.902	1.857	1.903	1.856	1.904	1.854	1.906	1.853	1.907
1550	1.881	1.883	1.879	1.885	1.878	1.886	1.877	1.887	1.875	1.888	1.874	1.890	1.873	1.891	1.872	1.892	1.870	1.894	1.869	1.895	1.868	1.896	1.866	1.898	1.865	1.899	1.864	1.900	1.862	1.901	1.861	1.903	1.860	1.904	1.859	1.905	1.857	1.907	1.856	1.908
1600	1.883	1.885	1.881	1.886	1.880	1.888	1.879	1.889	1.878	1.890	1.876	1.891	1.875	1.893	1.874	1.894	1.873	1.895	1.871	1.896	1.870	1.898	1.869	1.899	1.867	1.900	1.866	1.901	1.865	1.903	1.864	1.904	1.862	1.905	1.861	1.907	1.860	1.908	1.859	1.909
1650	1.884	1.887	1.883	1.888	1.882	1.889	1.881	1.890	1.880	1.892	1.878	1.893	1.877	1.894	1.876	1.895	1.875	1.897	1.873	1.898	1.872	1.899	1.871	1.900	1.870	1.901	1.868	1.903	1.867	1.904	1.866	1.905	1.865	1.906	1.864	1.908	1.862	1.909	1.861	1.910
1700	1.886	1.888	1.885	1.890	1.884	1.891	1.883	1.892	1.881	1.893	1.880	1.894	1.879	1.896	1.878	1.897	1.877	1.898	1.875	1.899	1.874	1.900	1.873	1.901	1.872	1.903	1.871	1.904	1.869	1.905	1.868	1.906	1.867	1.907	1.866	1.909	1.865	1.910	1.864	1.911
1750	1.888	1.890	1.887	1.891	1.885	1.892	1.884	1.893	1.883	1.895	1.882	1.896	1.881	1.897	1.880	1.898	1.879	1.899	1.877	1.900	1.876	1.902	1.875	1.903	1.874	1.904	1.873	1.905	1.872	1.906	1.870	1.907	1.869	1.908	1.868	1.910	1.867	1.911	1.866	1.912
1800	1.889	1.892	1.888	1.893	1.887	1.894	1.886	1.895	1.885	1.896	1.884	1.897	1.883	1.898	1.882	1.899	1.880	1.900	1.879	1.902	1.878	1.903	1.877	1.904	1.876	1.905	1.875	1.906	1.874	1.907	1.873	1.908	1.871	1.909	1.870	1.911	1.869	1.912	1.868	1.913
1850	1.891	1.893	1.890	1.894	1.889	1.895	1.888	1.896	1.887	1.897	1.885	1.898	1.884	1.900	1.883	1.901	1.882	1.902	1.881	1.903	1.880	1.904	1.879	1.905	1.878	1.906	1.877	1.907	1.876	1.908	1.875	1.909	1.873	1.910	1.872	1.912	1.871	1.913	1.870	1.914
1900	1.892	1.894	1.891	1.895	1.890	1.897	1.889	1.898	1.888	1.899	1.887	1.900	1.886	1.901	1.885	1.902	1.884	1.903	1.883	1.904	1.882	1.905	1.881	1.906	1.880	1.907	1.879	1.908	1.877	1.909	1.876	1.910	1.875	1.911	1.874	1.912	1.873	1.914	1.872	1.915
1950	1.894	1.896	1.893	1.897	1.892	1.898	1.891	1.899	1.890	1.900	1.889	1.901	1.888	1.902	1.887	1.903	1.885	1.904	1.884	1.905	1.883	1.906	1.882	1.907	1.881	1.908	1.880	1.909	1.879	1.910	1.878	1.911	1.877	1.912	1.876	1.913	1.875	1.914	1.874	1.915
2000	1.895	1.897	1.894	1.898	1.893	1.899	1.892	1.900	1.891	1.901	1.890	1.902	1.889	1.903	1.888	1.904	1.887	1.905	1.886	1.906	1.885	1.907	1.884	1.908	1.883	1.909	1.882	1.910	1.881	1.911	1.880	1.912	1.879	1.913	1.878	1.914	1.877	1.915	1.876	1.916

DW Table 4

Durbin-Watson Table
Alpha = .05
nk	1		2		3		4		5		6		7		8		9		10		11		12		13		14		15		16		17		18		19		20
250	1.785	1.801	1.777	1.809	1.769	1.817	1.760	1.825	1.752	1.834	1.744	1.842	1.736	1.851	1.727	1.859	1.719	1.868	1.710	1.876	1.702	1.885	1.693	1.894	1.685	1.903	1.676	1.912	1.667	1.921	1.658	1.930	1.650	1.939	1.641	1.948	1.632	1.958	1.623	1.967
300	1.804	1.817	1.797	1.824	1.791	1.831	1.784	1.838	1.777	1.845	1.770	1.852	1.763	1.859	1.756	1.866	1.749	1.873	1.742	1.880	1.735	1.887	1.728	1.894	1.721	1.902	1.714	1.909	1.707	1.916	1.699	1.924	1.692	1.931	1.685	1.939	1.678	1.946	1.670	1.954
350	1.819	1.830	1.813	1.836	1.807	1.842	1.802	1.848	1.796	1.854	1.790	1.860	1.784	1.866	1.778	1.872	1.772	1.878	1.766	1.884	1.760	1.890	1.754	1.896	1.748	1.902	1.742	1.908	1.736	1.914	1.730	1.921	1.724	1.927	1.717	1.933	1.711	1.940	1.705	1.946
400	1.831	1.841	1.826	1.846	1.821	1.851	1.816	1.856	1.811	1.861	1.806	1.866	1.800	1.872	1.795	1.877	1.790	1.882	1.785	1.887	1.780	1.893	1.774	1.898	1.769	1.903	1.764	1.909	1.759	1.914	1.753	1.919	1.748	1.925	1.743	1.930	1.737	1.936	1.732	1.941
450	1.841	1.850	1.836	1.854	1.832	1.859	1.827	1.863	1.823	1.868	1.818	1.872	1.814	1.877	1.809	1.882	1.805	1.886	1.800	1.891	1.795	1.895	1.791	1.900	1.786	1.905	1.781	1.910	1.777	1.914	1.772	1.919	1.767	1.924	1.763	1.929	1.758	1.934	1.753	1.938
500	1.849	1.857	1.845	1.861	1.841	1.865	1.837	1.869	1.833	1.873	1.829	1.877	1.825	1.882	1.821	1.886	1.817	1.890	1.812	1.894	1.808	1.898	1.804	1.902	1.800	1.907	1.796	1.911	1.792	1.915	1.787	1.919	1.783	1.924	1.779	1.928	1.775	1.932	1.770	1.937
550	1.856	1.864	1.853	1.867	1.849	1.871	1.845	1.875	1.842	1.878	1.838	1.882	1.834	1.886	1.831	1.890	1.827	1.893	1.823	1.897	1.819	1.901	1.816	1.905	1.812	1.908	1.808	1.912	1.804	1.916	1.800	1.920	1.797	1.924	1.793	1.928	1.789	1.932	1.785	1.936
600	1.863	1.869	1.859	1.873	1.856	1.876	1.853	1.879	1.849	1.883	1.846	1.886	1.842	1.890	1.839	1.893	1.836	1.896	1.832	1.900	1.829	1.903	1.825	1.907	1.822	1.910	1.818	1.914	1.815	1.917	1.811	1.921	1.808	1.924	1.804	1.928	1.801	1.931	1.797	1.935
650	1.868	1.874	1.865	1.877	1.862	1.880	1.859	1.884	1.856	1.887	1.853	1.890	1.849	1.893	1.846	1.896	1.843	1.899	1.840	1.902	1.837	1.906	1.834	1.909	1.830	1.912	1.827	1.915	1.824	1.918	1.821	1.922	1.818	1.925	1.814	1.928	1.811	1.931	1.808	1.935
700	1.873	1.879	1.870	1.882	1.867	1.884	1.864	1.887	1.861	1.890	1.859	1.893	1.856	1.896	1.853	1.899	1.850	1.902	1.847	1.905	1.844	1.908	1.841	1.911	1.838	1.914	1.835	1.917	1.832	1.920	1.829	1.923	1.826	1.926	1.823	1.929	1.820	1.932	1.817	1.935
750	1.877	1.883	1.875	1.885	1.872	1.888	1.869	1.891	1.867	1.893	1.864	1.896	1.861	1.899	1.859	1.902	1.856	1.904	1.853	1.907	1.850	1.910	1.848	1.913	1.845	1.915	1.842	1.918	1.839	1.921	1.837	1.924	1.834	1.926	1.831	1.929	1.828	1.932	1.825	1.935
800	1.881	1.886	1.879	1.889	1.876	1.891	1.874	1.894	1.871	1.896	1.869	1.899	1.866	1.901	1.864	1.904	1.861	1.907	1.859	1.909	1.856	1.912	1.853	1.914	1.851	1.917	1.848	1.919	1.846	1.922	1.843	1.925	1.841	1.927	1.838	1.930	1.835	1.933	1.833	1.935
850	1.885	1.890	1.883	1.892	1.880	1.894	1.878	1.897	1.875	1.899	1.873	1.902	1.871	1.904	1.868	1.906	1.866	1.909	1.864	1.911	1.861	1.914	1.859	1.916	1.856	1.918	1.854	1.921	1.851	1.923	1.849	1.926	1.847	1.928	1.844	1.931	1.842	1.933	1.839	1.936
900	1.888	1.893	1.886	1.895	1.884	1.897	1.882	1.899	1.879	1.902	1.877	1.904	1.875	1.906	1.873	1.908	1.870	1.911	1.868	1.913	1.866	1.915	1.863	1.918	1.861	1.920	1.859	1.922	1.857	1.924	1.854	1.927	1.852	1.929	1.850	1.931	1.847	1.934	1.845	1.936
950	1.891	1.895	1.889	1.898	1.887	1.900	1.885	1.902	1.883	1.904	1.881	1.906	1.879	1.908	1.876	1.910	1.874	1.913	1.872	1.915	1.870	1.917	1.868	1.919	1.866	1.921	1.864	1.923	1.861	1.925	1.859	1.928	1.857	1.930	1.855	1.932	1.853	1.934	1.850	1.936
1000	1.894	1.898	1.892	1.900	1.890	1.902	1.888	1.904	1.886	1.906	1.884	1.908	1.882	1.910	1.880	1.912	1.878	1.914	1.876	1.916	1.874	1.918	1.872	1.920	1.870	1.922	1.868	1.924	1.866	1.927	1.864	1.929	1.862	1.931	1.859	1.933	1.857	1.935	1.855	1.937
1050	1.897	1.900	1.895	1.902	1.893	1.904	1.891	1.906	1.889	1.908	1.887	1.910	1.885	1.912	1.883	1.914	1.881	1.916	1.879	1.918	1.877	1.920	1.875	1.922	1.874	1.924	1.872	1.926	1.870	1.928	1.868	1.930	1.866	1.932	1.864	1.933	1.862	1.935	1.860	1.937
1100	1.899	1.903	1.897	1.905	1.895	1.906	1.894	1.908	1.892	1.910	1.890	1.912	1.888	1.914	1.886	1.916	1.884	1.917	1.883	1.919	1.881	1.921	1.879	1.923	1.877	1.925	1.875	1.927	1.873	1.929	1.871	1.930	1.870	1.932	1.868	1.934	1.866	1.936	1.864	1.938
1150	1.901	1.905	1.900	1.907	1.898	1.908	1.896	1.910	1.894	1.912	1.893	1.914	1.891	1.915	1.889	1.917	1.887	1.919	1.886	1.921	1.884	1.922	1.882	1.924	1.880	1.926	1.878	1.928	1.877	1.930	1.875	1.931	1.873	1.933	1.871	1.935	1.870	1.937	1.868	1.939
1200	1.903	1.907	1.902	1.908	1.900	1.910	1.898	1.912	1.897	1.913	1.895	1.915	1.893	1.917	1.892	1.919	1.890	1.920	1.888	1.922	1.887	1.924	1.885	1.925	1.883	1.927	1.882	1.929	1.880	1.930	1.878	1.932	1.876	1.934	1.875	1.936	1.873	1.937	1.871	1.939
1250	1.905	1.909	1.904	1.910	1.902	1.912	1.901	1.913	1.899	1.915	1.897	1.917	1.896	1.918	1.894	1.920	1.893	1.922	1.891	1.923	1.889	1.925	1.888	1.926	1.886	1.928	1.884	1.930	1.883	1.931	1.881	1.933	1.879	1.935	1.878	1.936	1.876	1.938	1.875	1.940
1300	1.907	1.910	1.906	1.912	1.904	1.913	1.903	1.915	1.901	1.917	1.900	1.918	1.898	1.920	1.896	1.921	1.895	1.923	1.893	1.924	1.892	1.926	1.890	1.927	1.889	1.929	1.887	1.931	1.886	1.932	1.884	1.934	1.882	1.935	1.881	1.937	1.879	1.939	1.878	1.940
1350	1.909	1.912	1.908	1.913	1.906	1.915	1.905	1.916	1.903	1.918	1.902	1.919	1.900	1.921	1.899	1.922	1.897	1.924	1.896	1.925	1.894	1.927	1.893	1.928	1.891	1.930	1.890	1.932	1.888	1.933	1.887	1.935	1.885	1.936	1.884	1.938	1.882	1.939	1.880	1.941
1400	1.911	1.914	1.909	1.915	1.908	1.916	1.906	1.918	1.905	1.919	1.904	1.921	1.902	1.922	1.901	1.924	1.899	1.925	1.898	1.927	1.896	1.928	1.895	1.929	1.893	1.931	1.892	1.932	1.891	1.934	1.889	1.935	1.888	1.937	1.886	1.938	1.885	1.940	1.883	1.941
1450	1.912	1.915	1.911	1.916	1.910	1.918	1.908	1.919	1.907	1.921	1.905	1.922	1.904	1.923	1.903	1.925	1.901	1.926	1.900	1.928	1.898	1.929	1.897	1.930	1.896	1.932	1.894	1.933	1.893	1.935	1.891	1.936	1.890	1.937	1.889	1.939	1.887	1.940	1.886	1.942
1500	1.914	1.916	1.912	1.918	1.911	1.919	1.910	1.920	1.908	1.922	1.907	1.923	1.906	1.924	1.904	1.926	1.903	1.927	1.902	1.929	1.900	1.930	1.899	1.931	1.898	1.933	1.896	1.934	1.895	1.935	1.894	1.937	1.892	1.938	1.891	1.939	1.889	1.941	1.888	1.942
1550	1.915	1.918	1.914	1.919	1.913	1.920	1.911	1.922	1.910	1.923	1.909	1.924	1.907	1.926	1.906	1.927	1.905	1.928	1.904	1.929	1.902	1.931	1.901	1.932	1.900	1.933	1.898	1.935	1.897	1.936	1.896	1.937	1.894	1.939	1.893	1.940	1.892	1.941	1.890	1.943
1600	1.917	1.919	1.915	1.920	1.914	1.922	1.913	1.923	1.912	1.924	1.910	1.925	1.909	1.927	1.908	1.928	1.907	1.929	1.905	1.930	1.904	1.932	1.903	1.933	1.901	1.934	1.900	1.935	1.899	1.937	1.898	1.938	1.896	1.939	1.895	1.941	1.894	1.942	1.893	1.943
1650	1.918	1.920	1.917	1.921	1.915	1.923	1.914	1.924	1.913	1.925	1.912	1.926	1.911	1.928	1.909	1.929	1.908	1.930	1.907	1.931	1.906	1.932	1.904	1.934	1.903	1.935	1.902	1.936	1.901	1.937	1.899	1.939	1.898	1.940	1.897	1.941	1.896	1.942	1.895	1.944
1700	1.919	1.921	1.918	1.923	1.917	1.924	1.916	1.925	1.914	1.926	1.913	1.927	1.912	1.929	1.911	1.930	1.910	1.931	1.908	1.932	1.907	1.933	1.906	1.934	1.905	1.936	1.904	1.937	1.902	1.938	1.901	1.939	1.900	1.940	1.899	1.942	1.898	1.943	1.896	1.944
1750	1.920	1.923	1.919	1.924	1.918	1.925	1.917	1.926	1.916	1.927	1.915	1.928	1.913	1.929	1.912	1.931	1.911	1.932	1.910	1.933	1.909	1.934	1.908	1.935	1.906	1.936	1.905	1.938	1.904	1.939	1.903	1.940	1.902	1.941	1.901	1.942	1.899	1.943	1.898	1.945
1800	1.921	1.924	1.920	1.925	1.919	1.926	1.918	1.927	1.917	1.928	1.916	1.929	1.915	1.930	1.914	1.931	1.912	1.933	1.911	1.934	1.910	1.935	1.909	1.936	1.908	1.937	1.907	1.938	1.906	1.939	1.905	1.940	1.903	1.942	1.902	1.943	1.901	1.944	1.900	1.945
1850	1.922	1.925	1.921	1.926	1.920	1.927	1.919	1.928	1.918	1.929	1.917	1.930	1.916	1.931	1.915	1.932	1.914	1.933	1.913	1.934	1.912	1.936	1.911	1.937	1.909	1.938	1.908	1.939	1.907	1.940	1.906	1.941	1.905	1.942	1.904	1.943	1.903	1.944	1.902	1.945
1900	1.924	1.926	1.922	1.927	1.921	1.928	1.920	1.929	1.919	1.930	1.918	1.931	1.917	1.932	1.916	1.933	1.915	1.934	1.914	1.935	1.913	1.936	1.912	1.937	1.911	1.938	1.910	1.939	1.909	1.940	1.908	1.942	1.907	1.943	1.905	1.944	1.904	1.945	1.903	1.946
1950	1.925	1.927	1.923	1.928	1.922	1.929	1.921	1.930	1.920	1.931	1.919	1.932	1.918	1.933	1.917	1.934	1.916	1.935	1.915	1.936	1.914	1.937	1.913	1.938	1.912	1.939	1.911	1.940	1.910	1.941	1.909	1.942	1.908	1.943	1.907	1.944	1.906	1.945	1.905	1.946
2000	1.925	1.927	1.924	1.928	1.923	1.929	1.922	1.930	1.921	1.931	1.920	1.932	1.919	1.934	1.918	1.935	1.917	1.936	1.916	1.937	1.915	1.938	1.914	1.939	1.913	1.940	1.912	1.941	1.911	1.942	1.910	1.943	1.909	1.944	1.908	1.945	1.907	1.946	1.906	1.947

https://www3.nd.edu/~wevans1/econ30331/Durbin_Watson_tables.pdf

Introduction to Supervised Learning: Logistic Regression

Supervised machine learning algorithms derive insights, patterns, and relationships

from a labeled training dataset. It means the dataset already contains a known value for

the target variable for each record. It is called supervised learning because the process

of an algorithm learning from the training dataset is like an instructor supervising the

learning process. You know the correct answers, the algorithm iteratively makes

predictions on the training data and the instructor corrects it. Learning ends when the

algorithm achieves the desired level of performance and accuracy.

Supervised learning problems can be further classified into regression and

classification problems.

• Classification: In a classification problem, the output variable is a category,

such as “red” or “blue,” “disease” or “no disease,” “true” or “false,” etc.

• Regression: In a regression problem, the output variable is a real continuous

value, such as “dollars” or “weight.”

The following is an example of a supervised learning method where we have labeled

data to identify dogs and cats. The algorithm learns from this data and trains a model to

predict the new input.

Now that we learned the basics of supervised learning, let's have a look at a popular

supervised machine learning algorithm: logistic regression.

What is Logistic Regression?

Logistic regression is a statistical method that is used for building machine learning

models where the dependent variable is dichotomous: i.e. binary. Logistic regression is

used to describe data and the relationship between one dependent variable and one or

more independent variables. The independent variables can be nominal, ordinal, or of

interval type.

The name “logistic regression” is derived from the concept of the logistic function that it

uses. The logistic function is also known as the sigmoid function. The value of this

logistic function lies between zero and one.

The following is an example of a logistic function we can use to find the probability of a

vehicle breaking down, depending on how many years it has been since it was serviced

last.

Here is how you can interpret the results from the graph to decide whether the vehicle

will break down or not.

Advantages of the Logistic Regression Algorithm

• Logistic regression performs better when the data is linearly separable

• It does not require too many computational resources as it’s highly

interpretable

• There is no problem scaling the input features—It does not require tuning

• It is easy to implement and train a model using logistic regression

• It gives a measure of how relevant a predictor (coefficient size) is, and its

direction of association (positive or negative)

How Does the Logistic Regression Algorithm Work?

Consider the following example: An organization wants to determine an employee’s

salary increase based on their performance.

For this purpose, a linear regression algorithm will help them decide. Plotting a

regression line by considering the employee’s performance as the independent variable,

and the salary increase as the dependent variable will make their task easier.

Now, what if the organization wants to know whether an employee would get a

promotion or not based on their performance? The above linear graph won’t be suitable

in this case. As such, we clip the line at zero and one, and convert it into a sigmoid curve

(S curve).

Based on the threshold values, the organization can decide whether an employee will

get a salary increase or not.

To understand logistic regression, let’s go over the odds of success.

Odds (𝜃) = Probability of an event happening / Probability of an event not happening

𝜃 = p / 1 – p

The values of odds range from zero to ∞ and the values of probability lies between zero

and one.

Consider the equation of a straight line:

𝑦 = 𝛽0 + 𝛽1* 𝑥

Here, 𝛽0 is the y-intercept

𝛽1 is the slope of the line

x is the value of the x coordinate

y is the value of the prediction

Now to predict the odds of success, we use the following formula:

Exponentiating both the sides, we have:

Let Y = e 𝛽0+𝛽1 * 𝑥

Then p(x) / 1 – p(x) = Y

p(x) = Y(1 – p(x))

p(x) = Y – Y(p(x))

p(x) + Y(p(x)) = Y

p(x)(1+Y) = Y

p(x) = Y / 1+Y

The equation of the sigmoid function is:

The sigmoid curve obtained from the above equation is as follows:

Now that you know more about logistic regression algorithms, let’s look at

the difference between linear regression and logistic regression.

Linear Regression vs. Logistic Regression

Linear Regression Logistic Regression

Used to solve regression problems Used to solve classification problems

The response variables are continuous in nature The response variable is categorical in nature

It helps estimate the dependent variable when

there is a change in the independent variable

It helps to calculate the possibility of a particular

event taking place

It is a straight line It is an S-curve (S = Sigmoid)

Now, let’s look at some logistic regression algorithm examples.

https://www.simplilearn.com/tutorials/machine-learning-tutorial/linear-regression-vs-logistic-regression

Applications of Logistic Regression

• Using the logistic regression algorithm, banks can predict whether a customer

would default on loans or not

• To predict the weather conditions of a certain place (sunny, windy, rainy,

humid, etc.)

• Ecommerce companies can identify buyers if they are likely to purchase a

certain product

• Companies can predict whether they will gain or lose money in the next

quarter, year, or month based on their current performance

• To classify objects based on their features and attributes

Now, let’s look at the assumptions you need to take to build a logistic regression model.

Assumption in a Logistic Regression Algorithm

• In a binary logistic regression, the dependent variable must be binary

• For a binary regression, the factor level one of the dependent variables should

represent the desired outcome

• Only meaningful variables should be included

• The independent variables should be independent of each other. This means

the model should have little or no multicollinearity

• The independent variables are linearly related to the log odds

• Logistic regression requires quite large sample sizes

Let’s now jump into understanding the logistics Regression algorithm in Python.

Use Case: Predict the Digits in Images Using a Logistic

Regression Classifier in Python

We’ll be using the digits dataset in the scikit learn library to predict digit values from

images using the logistic regression model in Python.

• Importing libraries and their associated methods

• Determining the total number of images and labels

• Displaying some of the images and their labels

• Dividing dataset into “training” and “test” set

• Importing the logistic regression model

• Making an instance of the model and training it

• Predicting the output of the first element of the test set

• Predicting the output of the first 10 elements of the test set

• Prediction for the entire dataset

• Determining the accuracy of the model

• Representing the confusion matrix in a heat map

• Presenting predictions and actual output

The images above depict the actual numbers and the predicted digit values from our

logistic regression model.

Conclusion

We hoped that this article has helped you get acquainted with the basics of supervised

learning and logistic regression. We covered the logistic regression algorithm and went

into detail with an elaborate example. Then, we looked at the different applications of

logistic regression, followed by the list of assumptions you should make to create a

logistic regression model. Finally, we built a model using the logistic regression

algorithm to predict the digits in images.