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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 06 Nov 2014 19:59:50 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/06/t1415304118snebeg1as5lk701.htm/, Retrieved Fri, 17 May 2024 03:22:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=252919, Retrieved Fri, 17 May 2024 03:22:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-11-06 19:59:50] [63a9f0ea7bb98050796b649e85481845] [Current]
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Dataseries X:
1	0	149	96	18	68	86	7,5	1,8	2,1	1,5	9.0	5.0	4.0	2.0	0.0	1.0	11.0	8.0	7.0	18.0	12.0	20.0	4.0
1	0	152	75	7	55	62	2,5	1,6	1,5	1,8	9.0	6.0	6.0	2.0	1.0	2.0	15.0	18.0	18.0	23.0	20.0	25.0	9.0
1	1	139	70	31	39	70	6,0	2,1	2,0	2,1	8.0	5.0	5.0	1.0	1.0	2.0	19.0	18.0	20.0	23.0	20.0	19.0	4.0
1	0	148	88	39	32	71	6,5	2,2	2,0	2,1	8.0	4.0	6.0	2.0	0.0	2.0	16.0	12.0	9.0	22.0	14.0	18.0	5.0
1	1	158	114	46	62	108	1,0	2,3	2,1	1,9	8.0	5.0	5.0	0.0	0.0	0.0	24.0	24.0	19.0	22.0	25.0	24.0	4.0
1	1	128	69	31	33	64	1,0	2,1	2,0	1,6	8.0	7.0	4.0	0.0	2.0	2.0	15.0	16.0	12.0	19.0	15.0	20.0	4.0
1	1	224	176	67	52	119	5,5	2,7	2,3	2,1	7.0	3.0	0.0	0.0	1.0	1.0	17.0	19.0	16.0	25.0	20.0	20.0	9.0
1	0	159	114	35	62	97	8,5	2,1	2,1	2,1	8.0	4.0	5.0	2.0	1.0	0.0	19.0	16.0	17.0	28.0	21.0	24.0	8.0
1	1	105	121	52	77	129	6,5	2,4	2,1	2,2	9.0	4.0	3.0	2.0	2.0	2.0	19.0	15.0	9.0	16.0	15.0	21.0	11.0
1	1	159	110	77	76	153	4,5	2,9	2,2	1,5	8.0	7.0	5.0	0.0	1.0	0.0	28.0	28.0	28.0	28.0	28.0	28.0	4.0
1	1	167	158	37	41	78	2,0	2,2	2,1	1,9	7.0	6.0	2.0	2.0	1.0	1.0	26.0	21.0	20.0	21.0	11.0	10.0	4.0
1	1	165	116	32	48	80	5,0	2,1	2,1	2,2	9.0	6.0	3.0	3.0	0.0	1.0	15.0	18.0	16.0	22.0	22.0	22.0	6.0
1	1	159	181	36	63	99	0,5	2,2	2,1	1,6	7.0	2.0	4.0	0.0	1.0	1.0	26.0	22.0	22.0	24.0	22.0	19.0	4.0
1	1	119	77	38	30	68	5,0	2,2	2,0	1,5	8.0	4.0	6.0	0.0	1.0	1.0	16.0	19.0	17.0	24.0	27.0	27.0	8.0
1	0	176	141	69	78	147	5,0	2,7	2,3	1,9	8.0	4.0	3.0	2.0	0.0	2.0	24.0	22.0	12.0	26.0	24.0	23.0	4.0
1	0	54	35	21	19	40	2,5	1,9	1,8	0,1	8.0	5.0	4.0	1.0	0.0	0.0	25.0	25.0	18.0	28.0	23.0	24.0	4.0
0	0	91	80	26	31	57	5,0	2,0	2,0	2,2	8.0	3.0	1.0	2.0	0.0	1.0	22.0	20.0	20.0	24.0	24.0	24.0	11.0
1	1	163	152	54	66	120	5,5	2,5	2,2	1,8	8.0	4.0	5.0	1.0	1.0	1.0	15.0	16.0	12.0	20.0	21.0	25.0	4.0
1	0	124	97	36	35	71	3,5	2,2	2,0	1,6	6.0	7.0	4.0	1.0	1.0	2.0	21.0	19.0	16.0	26.0	20.0	24.0	4.0
0	1	137	99	42	42	84	3,0	2,3	2,1	2,2	9.0	5.0	4.0	1.0	0.0	2.0	22.0	18.0	16.0	21.0	19.0	21.0	6.0
1	0	121	84	23	45	68	4,0	1,9	2,0	2,1	7.0	2.0	4.0	0.0	0.0	2.0	27.0	26.0	21.0	28.0	25.0	28.0	6.0
1	1	153	68	34	21	55	0,5	2,1	1,8	1,9	7.0	3.0	3.0	2.0	0.0	1.0	26.0	24.0	15.0	27.0	16.0	28.0	4.0
1	1	148	101	112	25	137	6,5	3,5	2,2	1,6	8.0	6.0	6.0	1.0	0.0	2.0	26.0	20.0	17.0	23.0	24.0	22.0	8.0
1	0	221	107	35	44	79	4,5	2,1	2,2	1,9	7.0	6.0	5.0	1.0	0.0	2.0	22.0	19.0	17.0	24.0	21.0	26.0	5.0
1	1	188	88	47	69	116	7,5	2,3	1,7	2,2	8.0	2.0	5.0	1.0	0.0	2.0	21.0	19.0	17.0	24.0	22.0	26.0	4.0
1	1	149	112	47	54	101	5,5	2,3	2,1	1,8	8.0	7.0	6.0	2.0	2.0	0.0	22.0	23.0	18.0	22.0	25.0	21.0	9.0
1	1	244	171	37	74	111	4,0	2,2	2,3	2,4	3.0	2.0	4.0	0.0	0.0	0.0	20.0	18.0	15.0	21.0	23.0	26.0	4.0
0	1	148	137	109	80	189	7,5	3,5	2,7	2,4	9.0	10.0	6.0	1.0	2.0	2.0	21.0	16.0	20.0	25.0	20.0	23.0	7.0
0	0	92	77	24	42	66	7,0	1,9	1,9	2,5	8.0	4.0	5.0	2.0	0.0	1.0	20.0	18.0	13.0	20.0	21.0	20.0	10.0
1	1	150	66	20	61	81	4,0	1,9	2,0	1,9	8.0	4.0	6.0	3.0	0.0	2.0	22.0	21.0	21.0	21.0	22.0	24.0	4.0
1	0	153	93	22	41	63	5,5	1,9	2,0	2,1	6.0	2.0	5.0	2.0	0.0	1.0	21.0	20.0	12.0	26.0	25.0	25.0	4.0
1	0	94	105	23	46	69	2,5	1,9	1,9	1,9	5.0	4.0	4.0	1.0	0.0	2.0	8.0	15.0	6.0	23.0	23.0	24.0	7.0
1	0	156	131	32	39	71	5,5	2,1	2,0	2,1	8.0	4.0	4.0	0.0	1.0	2.0	22.0	19.0	13.0	21.0	19.0	20.0	12.0
1	1	146	89	7	63	70	0,5	1,6	2,0	1,9	8.0	6.0	6.0	2.0	1.0	2.0	18.0	27.0	6.0	27.0	27.0	23.0	4.0
1	1	132	102	30	34	64	3,5	2,0	2,0	1,5	8.0	7.0	6.0	2.0	1.0	1.0	20.0	19.0	19.0	27.0	21.0	24.0	7.0
1	1	161	161	92	51	143	2,5	3,2	2,1	1,9	9.0	2.0	4.0	2.0	0.0	1.0	24.0	7.0	12.0	25.0	19.0	25.0	5.0
1	1	105	120	43	42	85	4,5	2,3	2,0	2,1	7.0	6.0	6.0	3.0	0.0	1.0	17.0	20.0	14.0	23.0	25.0	23.0	8.0
1	1	97	127	55	31	86	4,5	2,5	1,8	1,5	7.0	3.0	6.0	3.0	1.0	1.0	20.0	20.0	13.0	25.0	16.0	21.0	5.0
1	0	151	77	16	39	55	4,5	1,8	2,0	2,1	3.0	3.0	3.0	1.0	0.0	0.0	23.0	19.0	12.0	23.0	24.0	23.0	4.0
0	1	131	108	49	20	69	6,0	2,4	2,2	2,1	7.0	2.0	4.0	0.0	1.0	0.0	20.0	19.0	17.0	19.0	24.0	21.0	9.0
1	1	166	85	71	49	120	2,5	2,8	2,2	1,8	8.0	5.0	5.0	2.0	1.0	1.0	22.0	20.0	19.0	22.0	18.0	18.0	7.0
1	0	157	168	43	53	96	5,0	2,3	2,1	2,4	8.0	7.0	6.0	2.0	1.0	2.0	19.0	18.0	10.0	24.0	28.0	24.0	4.0
1	1	111	48	29	31	60	0,0	2,0	1,8	2,1	7.0	6.0	6.0	2.0	1.0	1.0	15.0	14.0	10.0	19.0	15.0	18.0	4.0
1	1	145	152	56	39	95	5,0	2,5	1,9	1,9	8.0	4.0	6.0	2.0	1.0	2.0	20.0	17.0	11.0	21.0	17.0	21.0	4.0
1	1	162	75	46	54	100	6,5	2,3	2,1	2,1	8.0	6.0	6.0	1.0	1.0	2.0	22.0	17.0	11.0	27.0	18.0	23.0	4.0
1	1	163	107	19	49	68	5,0	1,8	2,0	1,9	9.0	4.0	6.0	3.0	0.0	2.0	17.0	8.0	10.0	25.0	26.0	25.0	4.0
0	1	59	62	23	34	57	6,0	1,9	1,9	2,4	6.0	3.0	5.0	2.0	0.0	2.0	14.0	9.0	7.0	25.0	18.0	22.0	7.0
1	0	187	121	59	46	105	4,5	2,6	2,2	2,1	9.0	5.0	5.0	2.0	1.0	1.0	24.0	22.0	22.0	23.0	22.0	22.0	4.0
1	1	109	124	30	55	85	5,5	2,0	2,0	2,2	8.0	2.0	3.0	0.0	0.0	2.0	17.0	20.0	12.0	17.0	19.0	23.0	7.0
0	1	90	72	61	42	103	1,0	2,6	2,0	2,2	8.0	3.0	5.0	0.0	1.0	2.0	23.0	20.0	18.0	28.0	17.0	24.0	4.0
1	0	105	40	7	50	57	7,5	1,6	1,7	1,8	8.0	5.0	1.0	0.0	0.0	2.0	25.0	22.0	20.0	25.0	26.0	25.0	4.0
0	1	83	58	38	13	51	6,0	2,2	2,0	2,1	7.0	7.0	5.0	3.0	1.0	2.0	16.0	22.0	9.0	20.0	21.0	22.0	4.0
0	1	116	97	32	37	69	5,0	2,1	2,2	2,4	8.0	4.0	6.0	2.0	2.0	1.0	18.0	22.0	16.0	25.0	26.0	24.0	4.0
0	1	42	88	16	25	41	1,0	1,8	1,7	2,2	7.0	3.0	6.0	0.0	0.0	1.0	20.0	16.0	14.0	21.0	21.0	21.0	8.0
1	1	148	126	19	30	49	5,0	1,8	2,0	2,1	7.0	2.0	4.0	2.0	2.0	2.0	18.0	14.0	11.0	24.0	12.0	24.0	4.0
0	1	155	104	22	28	50	6,5	1,9	2,2	1,5	9.0	5.0	6.0	0.0	0.0	1.0	23.0	24.0	20.0	28.0	20.0	25.0	4.0
1	1	125	148	48	45	93	7,0	2,4	2,0	1,9	7.0	4.0	6.0	0.0	1.0	0.0	24.0	21.0	17.0	20.0	20.0	23.0	4.0
1	1	116	146	23	35	58	4,5	1,9	1,9	1,8	9.0	6.0	6.0	2.0	2.0	2.0	23.0	20.0	14.0	19.0	24.0	27.0	4.0
0	0	128	80	26	28	54	0,0	2,0	2,0	1,8	7.0	4.0	5.0	3.0	0.0	2.0	13.0	20.0	8.0	24.0	24.0	27.0	7.0
1	1	138	97	33	41	74	8,5	2,1	2,0	1,6	6.0	4.0	2.0	0.0	0.0	1.0	20.0	18.0	16.0	21.0	22.0	23.0	12.0
0	0	49	25	9	6	15	3,5	1,7	1,6	1,2	3.0	2.0	2.0	1.0	0.0	0.0	20.0	14.0	11.0	24.0	21.0	18.0	4.0
0	1	96	99	24	45	69	7,5	1,9	2,1	1,8	9.0	9.0	6.0	2.0	1.0	2.0	19.0	19.0	10.0	23.0	20.0	20.0	4.0
1	1	164	118	34	73	107	3,5	2,1	2,1	1,5	9.0	8.0	6.0	2.0	2.0	1.0	22.0	24.0	15.0	18.0	23.0	23.0	4.0
1	0	162	58	48	17	65	6,0	2,4	2,0	2,1	7.0	8.0	5.0	0.0	1.0	2.0	22.0	19.0	15.0	27.0	19.0	24.0	5.0
1	0	99	63	18	40	58	1,5	1,8	1,9	2,4	6.0	3.0	6.0	3.0	1.0	2.0	15.0	16.0	10.0	25.0	24.0	26.0	15.0
1	1	202	139	43	64	107	9,0	2,3	2,2	2,4	9.0	2.0	5.0	2.0	0.0	1.0	17.0	16.0	10.0	20.0	21.0	20.0	5.0
1	0	186	50	33	37	70	3,5	2,1	2,1	1,5	8.0	4.0	4.0	0.0	1.0	2.0	19.0	16.0	18.0	21.0	16.0	23.0	10.0
0	1	66	60	28	25	53	3,5	2,0	1,8	1,8	8.0	2.0	5.0	3.0	0.0	2.0	20.0	14.0	10.0	23.0	17.0	22.0	9.0
1	0	183	152	71	65	136	4,0	2,8	2,3	2,1	7.0	2.0	4.0	2.0	1.0	2.0	22.0	22.0	22.0	27.0	23.0	23.0	8.0
1	1	214	142	26	100	126	6,5	2,0	2,3	2,2	9.0	1.0	5.0	2.0	0.0	2.0	21.0	21.0	16.0	24.0	20.0	17.0	4.0
1	1	188	94	67	28	95	7,5	2,7	2,2	2,1	5.0	4.0	4.0	3.0	1.0	0.0	21.0	15.0	10.0	27.0	19.0	20.0	5.0
0	0	104	66	34	35	69	6,0	2,1	2,1	1,9	6.0	5.0	6.0	0.0	1.0	1.0	16.0	14.0	7.0	24.0	18.0	22.0	4.0
1	0	177	127	80	56	136	5,0	2,9	2,2	2,1	8.0	8.0	5.0	1.0	1.0	2.0	20.0	15.0	16.0	23.0	18.0	18.0	9.0
1	0	126	67	29	29	58	5,5	2,0	1,9	1,9	8.0	4.0	4.0	2.0	0.0	1.0	21.0	14.0	16.0	24.0	21.0	19.0	4.0
0	0	76	90	16	43	59	3,5	1,8	1,8	1,6	8.0	6.0	5.0	1.0	1.0	1.0	20.0	20.0	16.0	21.0	20.0	19.0	10.0
0	1	99	75	59	59	118	7,5	2,6	2,1	2,4	8.0	5.0	5.0	2.0	1.0	2.0	23.0	21.0	22.0	23.0	17.0	16.0	4.0
1	1	157	96	58	52	110	1,0	2,5	1,8	1,9	9.0	6.0	6.0	2.0	1.0	2.0	15.0	17.0	13.0	22.0	20.0	24.0	7.0
1	0	139	128	32	50	82	6,5	2,1	2,0	1,9	7.0	3.0	4.0	0.0	0.0	0.0	18.0	14.0	5.0	27.0	25.0	26.0	4.0
1	1	78	41	47	3	50	NA	2,3	1,7	1,9	8.0	8.0	6.0	3.0	1.0	2.0	22.0	19.0	18.0	24.0	15.0	14.0	6.0
1	0	162	146	43	59	102	6,5	2,3	2,1	2,1	9.0	4.0	2.0	0.0	1.0	0.0	16.0	16.0	10.0	25.0	17.0	25.0	7.0
0	1	108	69	38	27	65	6,5	2,2	2,1	1,8	9.0	6.0	5.0	2.0	1.0	1.0	17.0	13.0	8.0	19.0	17.0	23.0	5.0
1	0	159	186	29	61	90	7,0	2,0	2,1	2,1	8.0	4.0	6.0	1.0	0.0	1.0	24.0	26.0	16.0	24.0	24.0	18.0	4.0
0	0	74	81	36	28	64	3,5	2,2	1,8	2,4	4.0	3.0	5.0	0.0	0.0	0.0	13.0	13.0	8.0	25.0	21.0	22.0	4.0
1	1	110	85	32	51	83	1,5	2,1	2,0	2,1	7.0	8.0	5.0	0.0	2.0	2.0	19.0	18.0	16.0	23.0	22.0	26.0	4.0
0	0	96	54	35	35	70	4,0	2,1	2,1	2,2	8.0	6.0	3.0	1.0	2.0	2.0	20.0	15.0	14.0	23.0	18.0	25.0	4.0
0	0	116	46	21	29	50	7,5	1,9	1,9	2,1	6.0	3.0	3.0	0.0	0.0	0.0	22.0	18.0	15.0	25.0	22.0	26.0	4.0
0	0	87	106	29	48	77	4,5	2,0	2,1	2,2	7.0	5.0	5.0	2.0	1.0	2.0	19.0	21.0	9.0	26.0	20.0	26.0	4.0
0	1	97	34	12	25	37	0,0	1,7	1,0	1,6	7.0	4.0	6.0	1.0	0.0	2.0	21.0	17.0	21.0	26.0	21.0	24.0	6.0
0	0	127	60	37	44	81	3,5	2,2	2,2	2,4	3.0	3.0	2.0	2.0	0.0	0.0	15.0	18.0	7.0	16.0	21.0	22.0	10.0
0	1	106	95	37	64	101	5,5	2,2	2,1	2,1	8.0	7.0	6.0	1.0	0.0	1.0	21.0	20.0	17.0	23.0	20.0	21.0	7.0
0	1	80	57	47	32	79	5,0	2,3	1,9	1,9	8.0	2.0	4.0	1.0	1.0	1.0	24.0	18.0	18.0	26.0	18.0	22.0	4.0
0	0	74	62	51	20	71	4,5	2,4	2,0	2,4	8.0	4.0	5.0	3.0	0.0	2.0	22.0	25.0	16.0	25.0	25.0	28.0	4.0
0	0	91	36	32	28	60	2,5	2,1	1,9	2,1	8.0	6.0	6.0	2.0	1.0	1.0	20.0	20.0	16.0	23.0	23.0	22.0	7.0
0	0	133	56	21	34	55	7,5	1,9	2,0	1,8	5.0	6.0	5.0	0.0	0.0	2.0	21.0	19.0	14.0	26.0	21.0	26.0	4.0
0	1	74	54	13	31	44	7,0	1,7	1,8	2,1	6.0	6.0	5.0	2.0	1.0	1.0	19.0	18.0	15.0	22.0	20.0	20.0	8.0
0	1	114	64	14	26	40	0,0	1,8	2,0	1,8	6.0	4.0	6.0	1.0	1.0	2.0	14.0	12.0	8.0	20.0	21.0	24.0	11.0
0	1	140	76	-2	58	56	4,5	1,5	2,0	1,9	7.0	6.0	5.0	0.0	0.0	2.0	25.0	22.0	22.0	27.0	20.0	21.0	6.0
0	0	95	98	20	23	43	3,0	1,9	2,0	1,9	7.0	5.0	6.0	1.0	1.0	2.0	11.0	16.0	5.0	20.0	22.0	23.0	14.0
0	1	98	88	24	21	45	1,5	1,9	1,8	2,4	7.0	5.0	5.0	0.0	0.0	2.0	17.0	18.0	13.0	22.0	15.0	23.0	5.0
0	0	121	35	11	21	32	3,5	1,7	2,0	1,8	8.0	6.0	4.0	0.0	0.0	2.0	22.0	23.0	22.0	24.0	24.0	23.0	4.0
0	1	126	102	23	33	56	2,5	1,9	1,1	1,8	9.0	8.0	5.0	1.0	1.0	2.0	20.0	20.0	18.0	21.0	22.0	22.0	8.0
0	1	98	61	24	16	40	5,5	1,9	1,8	2,1	8.0	5.0	5.0	2.0	2.0	1.0	22.0	20.0	15.0	24.0	21.0	23.0	9.0
0	1	95	80	14	20	34	8,0	1,8	1,8	2,1	8.0	6.0	5.0	2.0	1.0	2.0	15.0	16.0	11.0	26.0	17.0	21.0	4.0
0	1	110	49	52	37	89	1,0	2,4	2,0	2,4	7.0	4.0	5.0	2.0	1.0	2.0	23.0	22.0	19.0	24.0	23.0	27.0	4.0
0	1	70	78	15	35	50	5,0	1,8	1,9	1,9	9.0	3.0	4.0	2.0	1.0	2.0	20.0	19.0	19.0	24.0	22.0	23.0	5.0
0	0	102	90	23	33	56	4,5	1,9	2,1	1,8	7.0	3.0	5.0	3.0	0.0	1.0	22.0	23.0	21.0	27.0	23.0	26.0	4.0
0	1	86	45	19	27	46	3,0	1,8	1,6	1,8	6.0	2.0	0.0	0.0	0.0	0.0	16.0	6.0	4.0	25.0	16.0	27.0	5.0
0	1	130	55	35	41	76	3,0	2,1	2,2	2,2	7.0	4.0	5.0	0.0	0.0	1.0	25.0	19.0	17.0	27.0	18.0	27.0	4.0
0	1	96	96	24	40	64	8,0	1,9	1,9	2,4	8.0	5.0	6.0	0.0	0.0	1.0	18.0	24.0	10.0	19.0	25.0	23.0	4.0
0	0	102	43	39	35	74	2,5	2,2	2,0	1,8	6.0	3.0	1.0	0.0	1.0	0.0	19.0	19.0	13.0	22.0	18.0	23.0	7.0
0	0	100	52	29	28	57	7,0	2,0	2,1	2,4	2.0	4.0	1.0	0.0	1.0	0.0	25.0	15.0	15.0	22.0	14.0	23.0	10.0
0	0	94	60	13	32	45	0,0	1,7	1,3	1,8	4.0	5.0	3.0	3.0	0.0	0.0	21.0	18.0	11.0	25.0	20.0	28.0	4.0
0	0	52	54	8	22	30	1,0	1,7	1,8	1,9	8.0	3.0	3.0	2.0	0.0	2.0	22.0	18.0	20.0	23.0	19.0	24.0	5.0
0	0	98	51	18	44	62	3,5	1,8	1,9	2,4	6.0	5.0	6.0	0.0	0.0	1.0	21.0	22.0	13.0	24.0	18.0	20.0	4.0
0	0	118	51	24	27	51	5,5	1,9	2,1	2,1	8.0	4.0	4.0	2.0	0.0	1.0	22.0	23.0	18.0	24.0	22.0	23.0	4.0
0	1	99	38	19	17	36	5,5	1,8	1,8	1,9	6.0	4.0	5.0	2.0	0.0	2.0	23.0	18.0	20.0	23.0	21.0	22.0	4.0
1	1	48	41	23	12	34	0.5	1	0.75	2.1	7	6	6	0	2	2	20	17	15	22	14	15	6
1	1	50	146	16	45	61	7.5	1	1.5	2.7	7	3	6	2	2	2	6	6	4	24	5	27	4
1	1	150	182	33	37	70	9	4	3	2.1	7	4	6	1	2	1	15	22	9	19	25	23	8
1	1	154	192	32	37	69	9.5	4	2.25	2.1	9	3	6	3	2	2	18	20	18	25	21	23	5
0	0	109	263	37	108	145	8.5	3	3	2.1	7	10	6	3	2	2	24	16	12	26	11	20	4
0	1	68	35	14	10	23	7	2	1.5	2.1	6	4	6	0	0	1	22	16	17	18	20	18	17
1	1	194	439	52	68	120	8	4	3	2.1	8	8	5	3	1	2	21	17	12	24	9	22	4
1	0	158	214	75	72	147	10	4	3	2.1	8	3	6	2	2	2	23	20	16	28	15	20	4
1	1	159	341	72	143	215	7	4	3	2.1	9	5	5	2	0	2	20	23	17	23	23	21	8
1	0	67	58	15	9	24	8.5	2	0.75	2.1	7	4	6	0	0	1	20	18	14	19	21	25	4
1	0	147	292	29	55	84	9	4	3	2.4	6	3	5	2	0	2	18	13	13	19	9	19	7
1	1	39	85	13	17	30	9.5	1	2.25	1.95	8	5	5	3	0	2	25	22	20	27	24	25	4
1	1	100	200	40	37	77	4	3	1.5	2.1	6	3	6	2	0	2	16	20	16	24	16	24	4
1	1	111	158	19	27	46	6	3	1.5	2.1	6	3	4	0	0	2	20	20	15	26	20	22	5
1	1	138	199	24	37	61	8	4	2.25	1.95	9	4	5	0	0	2	14	13	10	21	15	28	7
1	1	101	297	121	58	178	5.5	3	3	2.1	6	3	6	0	0	1	22	16	16	25	18	22	4
0	1	131	227	93	66	160	9.5	4	3	2.4	9	6	6	1	1	1	26	25	21	28	22	21	4
1	1	101	108	36	21	57	7.5	3	1.5	2.1	8	6	5	2	2	2	20	16	15	19	21	23	7
1	1	114	86	23	19	42	7	3	2.25	2.25	8	4	6	3	2	2	17	15	16	20	21	19	11
1	0	165	302	85	78	163	7.5	4	2.25	2.4	9	4	6	0	0	0	22	19	19	26	21	21	7
1	1	114	148	41	35	75	8	3	1.5	2.25	6	4	6	2	0	1	22	19	9	27	20	25	4
1	1	111	178	46	48	94	7	3	2.25	2.55	4	3	4	2	1	2	20	24	19	23	24	23	4
1	1	75	120	18	27	45	7	2	1.5	1.95	8	2	6	1	2	0	17	9	7	18	15	28	4
1	1	82	207	35	43	78	6	2	2.25	2.4	5	5	5	2	0	2	22	22	23	23	24	14	4
1	1	121	157	17	30	47	10	3	2.25	2.1	7	4	6	2	2	2	17	15	14	21	18	23	4
1	1	32	128	4	25	29	2.5	1	3	2.1	9	4	6	2	0	0	22	22	10	23	24	24	4
1	0	150	296	28	69	97	9	4	3	2.4	9	4	5	2	0	2	21	22	16	22	24	25	6
1	1	117	323	44	72	116	8	3	3	2.1	8	3	4	2	1	1	25	24	12	21	15	15	8
0	1	71	79	10	23	32	6	2	1.5	2.1	6	4	5	2	2	1	11	12	10	14	19	23	23
1	1	165	70	38	13	50	8.5	4	3	2.25	8	2	6	1	1	2	19	21	7	24	20	26	4
1	1	154	146	57	61	118	6	4	3	2.25	3	0	0	0	0	0	24	25	20	26	26	21	8
1	1	126	246	23	43	66	9	4	2.25	2.4	8	4	6	2	1	2	17	26	9	24	26	26	6
1	0	138	145	26	22	48	8	4	1.5	2.1	7	3	4	0	0	0	22	19	14	26	18	15	4
1	0	149	196	36	51	86	8	4	2.25	2.1	7	6	6	0	2	2	22	21	12	22	23	23	4
1	0	145	199	22	67	89	9	4	2.25	2.4	9	4	4	2	0	1	17	14	10	20	13	15	7
1	1	120	127	40	36	76	5.5	3	3	2.1	4	4	6	0	0	1	26	28	19	20	16	16	4
1	0	138	91	18	21	39	5	4	0.75	1.95	7	2	4	0	0	0	19	16	16	20	19	20	4
1	0	109	153	31	44	75	7	3	2.25	2.1	6	4	5	0	1	0	20	21	11	18	22	20	4
1	0	132	299	11	45	57	5.5	4	3	2.25	3	2	1	0	1	0	19	16	15	18	21	20	4
1	1	172	228	38	34	72	9	4	3	2.25	8	4	5	3	2	2	21	16	14	25	11	21	10
1	0	169	190	24	36	60	2	4	1.5	2.4	8	3	5	0	0	1	24	25	11	28	23	28	6
1	1	114	180	37	72	109	8.5	3	2.25	2.25	9	6	5	2	2	0	21	21	14	23	18	19	5
1	1	156	212	37	39	76	9	4	3	2.25	8	6	5	3	0	2	19	22	15	20	19	21	5
1	0	172	269	22	43	65	8.5	4	2.25	2.1	8	4	5	0	0	2	13	9	7	22	15	22	4
0	1	68	130	15	25	40	9	2	1.5	2.1	9	5	6	2	2	1	24	20	22	27	8	27	4
0	1	89	179	2	56	58	7.5	2	2.25	2.1	8	4	5	0	1	2	28	19	19	24	15	20	5
1	1	167	243	43	80	123	10	4	2.25	2.7	9	6	6	3	2	2	27	24	22	23	21	17	5
1	0	113	190	31	40	71	9	3	1.5	2.1	7	6	5	2	1	2	22	22	11	20	25	26	5
0	0	115	299	29	73	102	7.5	3	2.25	2.1	7	9	6	2	1	2	23	22	19	22	14	21	5
0	0	78	121	45	34	80	6	2	1.5	2.25	6	4	5	2	1	0	19	12	9	21	21	24	4
0	0	118	137	25	72	97	10.5	3	2.25	2.7	8	8	6	3	1	2	18	17	11	24	18	21	6
0	1	87	305	4	42	46	8.5	2	3	2.4	6	5	5	3	0	2	23	18	17	26	18	25	4
1	0	173	157	31	61	93	8	4	3	2.1	7	4	5	3	0	0	21	10	12	24	12	22	4
1	1	2	96	-4	23	19	10	1	3	2.1	8	4	6	2	2	1	22	22	17	18	24	17	4
0	0	162	183	66	74	140	10.5	4	3	2.4	8	7	6	3	2	1	17	24	10	17	17	14	9
0	1	49	52	61	16	78	6.5	1	1.5	1.95	7	4	6	1	2	2	15	18	17	23	20	23	18
0	0	122	238	32	66	98	9.5	4	2.25	2.7	9	8	6	2	1	2	21	18	13	21	24	28	6
0	1	96	40	31	9	40	8.5	3	1.5	2.1	9	4	6	3	2	1	20	23	11	21	22	24	5
0	0	100	226	39	41	80	7.5	3	2.25	2.25	9	3	6	2	0	1	26	21	19	24	15	22	4
0	0	82	190	19	57	76	5	2	2.25	2.1	6	5	6	2	1	2	19	21	21	22	22	24	11
0	1	100	214	31	48	79	8	3	2.25	2.7	8	8	6	2	2	2	28	28	24	24	26	25	4
0	0	115	145	36	51	87	10	3	3	2.1	9	4	5	1	0	1	21	17	13	24	17	21	10
0	1	141	119	42	53	95	7	4	1.5	2.1	9	10	6	3	1	0	19	21	16	24	23	22	6
1	1	165	222	21	29	49	7.5	4	2.25	1.65	8	5	6	2	2	2	22	21	13	23	19	16	8
1	1	165	222	21	29	49	7.5	4	2.25	1.65	8	5	6	2	2	2	21	20	15	21	21	18	8
0	1	110	159	25	55	80	9.5	3	3	2.1	8	3	6	1	0	2	20	18	15	24	23	27	6
1	1	118	165	32	54	86	6	3	2.25	2.1	8	3	5	1	1	2	19	17	11	19	19	17	8
1	0	158	249	26	43	69	10	4	3	2.1	8	3	3	0	0	2	11	7	7	19	18	25	4
0	1	146	125	28	51	79	7	4	2.25	2.1	9	4	4	1	1	1	17	17	13	23	16	24	4
1	0	49	122	32	20	52	3	1	1.5	2.1	6	5	6	1	0	2	19	14	13	25	23	21	9
0	0	90	186	41	79	120	6	2	3	2.4	9	5	4	2	1	2	20	18	12	24	13	21	9
0	0	121	148	29	39	69	7	3	1.5	2.4	8	4	6	0	0	0	17	14	8	21	18	19	5
1	1	155	274	33	61	94	10	4	3	2.1	8	7	6	3	1	0	21	23	7	18	23	27	4
0	0	104	172	17	55	72	7	3	3	2.25	8	5	3	1	0	1	21	20	17	23	21	28	4
0	1	147	84	13	30	43	3.5	4	3	2.4	8	4	4	1	2	0	12	14	9	20	23	19	15
0	0	110	168	32	55	87	8	3	3	2.1	9	7	4	3	0	2	23	17	18	23	16	23	10
0	0	108	102	30	22	52	10	3	2.25	2.1	9	7	4	3	0	2	22	21	17	23	17	25	9
0	0	113	106	34	37	71	5.5	3	2.25	2.4	9	7	4	3	0	2	22	23	17	23	20	26	7
0	0	115	2	59	2	61	6	3	0.75	2.4	8	7	4	3	0	2	21	24	18	23	18	25	9
0	1	61	139	13	38	51	6.5	1	3	2.1	8	7	4	0	0	0	20	21	12	27	20	25	6
0	1	60	95	23	27	50	6.5	1	0.75	2.1	8	7	6	2	1	2	18	14	14	19	19	24	4
0	1	109	130	10	56	67	8.5	3	1.5	2.4	3	1	4	1	1	0	21	24	22	25	26	24	7
0	1	68	72	5	25	30	4	2	1.5	2.1	6	2	4	2	1	2	24	16	19	25	9	24	4
0	0	111	141	31	39	70	9.5	3	3	2.7	5	3	2	1	0	2	22	21	21	21	23	22	7
0	0	77	113	19	33	52	8	2	1.5	2.1	4	6	5	1	0	1	20	8	10	25	9	21	4
0	1	73	206	32	43	75	8.5	2	2.25	2.1	9	8	6	3	2	2	17	17	16	17	13	17	15
1	0	151	268	30	57	87	5.5	4	3	2.25	8	8	6	1	1	1	19	18	11	22	27	23	4
0	0	89	175	25	43	69	7	2	3	2.1	3	0	1	0	0	0	16	17	15	23	22	17	9
0	0	78	77	48	23	72	9	2	1.5	2.4	6	3	4	1	0	2	19	16	12	27	12	25	4
0	0	110	125	35	44	79	8	3	3	2.25	6	6	5	1	1	2	23	22	21	27	18	19	4
1	1	220	255	67	54	121	10	4	3	2.25	9	5	5	2	0	2	8	17	22	5	6	8	28
0	1	65	111	15	28	43	8	2	1.5	2.1	7	7	6	1	0	1	22	21	20	19	17	14	4
1	0	141	132	22	36	58	6	4	1.5	2.1	6	3	5	0	1	2	23	20	15	24	22	22	4
0	0	117	211	18	39	57	8	3	2.25	2.4	9	3	6	2	0	0	15	20	9	23	22	25	4
1	1	122	92	33	16	50	5	4	1.5	2.25	7	4	6	2	0	1	17	19	15	28	23	28	5
0	0	63	76	46	23	69	9	2	1.5	2.1	8	4	5	3	0	2	21	8	14	25	19	25	4
1	1	44	171	24	40	64	4.5	1	2.25	2.1	8	1	5	0	0	2	25	19	11	27	20	24	4
0	1	52	83	14	24	38	8.5	1	1.5	1.65	8	5	6	2	0	2	18	11	9	16	17	15	12
0	1	62	119	23	29	53	7	1	2.25	1.65	7	3	4	1	0	1	23	15	18	23	18	25	5
0	0	131	266	12	78	90	9.5	4	3	2.7	0	0	0	0	0	0	20	13	12	25	24	24	4
0	1	101	186	38	57	96	8.5	3	3	2.1	6	4	6	1	1	0	21	18	11	26	20	28	6
0	1	42	50	12	37	49	7.5	1	0.75	1.95	9	6	5	2	2	1	21	19	14	24	18	24	6
1	1	152	117	28	27	56	7.5	4	1.5	2.25	9	4	6	1	1	2	24	23	10	23	23	25	5
1	0	107	219	41	61	102	5	3	1.5	2.4	6	1	2	0	1	2	22	20	18	24	27	23	4
0	0	77	246	12	27	40	7	2	2.25	1.95	8	3	5	0	0	2	22	22	11	27	25	26	4
1	0	154	279	31	69	100	8	4	2.25	2.1	8	7	5	2	0	2	23	19	14	25	24	26	4
1	1	103	148	33	34	67	5.5	3	1.5	2.4	5	3	1	0	0	2	17	16	16	19	12	22	10
0	1	96	137	34	44	78	8.5	3	2.25	2.1	6	5	5	1	1	0	15	11	11	19	16	25	7
1	0	154	130	41	21	62	7.5	4	0.75	2.1	6	3	4	1	0	0	24	11	8	14	16	20	4
1	1	175	181	21	34	55	9.5	4	2.25	2.4	9	3	5	2	2	2	22	21	16	24	24	22	4
0	1	57	98	20	39	59	7	1	0.75	2.4	9	6	4	2	1	2	19	14	13	20	23	26	7
0	0	112	226	44	51	96	8	3	2.25	2.4	9	9	6	3	0	2	18	21	12	21	24	20	4
1	0	143	234	52	34	86	8.5	4	3	2.25	6	4	5	0	1	2	21	20	17	28	24	26	4
0	0	49	138	7	31	38	3.5	1	0.75	2.4	4	3	6	0	1	1	20	21	23	26	26	26	12
1	1	110	85	29	13	43	6.5	3	0.75	2.1	8	9	6	2	2	2	19	20	14	19	19	21	5
1	1	131	66	11	12	23	6.5	4	3	2.1	4	5	6	0	1	0	19	19	10	23	28	21	8
1	0	167	236	26	51	77	10.5	4	3	1.8	5	3	6	3	1	1	16	19	16	23	23	24	6
0	0	56	106	24	24	48	8.5	1	3	2.7	8	6	5	2	0	1	18	18	11	21	21	21	17
1	0	137	135	7	19	26	8	4	3	2.1	6	2	6	1	0	1	23	20	16	26	19	18	4
0	1	86	122	60	30	91	10	2	1.5	2.1	8	4	5	3	1	2	22	21	19	25	23	23	5
1	1	121	218	13	81	94	10	3	3	2.4	9	5	5	2	1	1	23	22	17	25	23	26	4
1	0	149	199	20	42	62	9.5	4	3	2.55	7	4	5	2	0	1	20	19	12	24	20	23	5
1	0	168	112	52	22	74	9	4	3	2.55	4	0	0	0	0	0	24	23	17	23	18	25	5
1	0	140	278	28	85	114	10	4	3	2.1	8	2	6	1	1	2	25	16	11	22	20	20	6
0	1	88	94	25	27	52	7.5	2	1.5	2.1	8	5	6	2	1	2	25	23	19	27	28	25	4
1	1	168	113	39	25	64	4.5	4	2.25	2.1	8	3	6	2	0	1	20	18	12	26	21	26	4
1	1	94	84	9	22	31	4.5	2	0.75	2.25	4	0	0	0	0	0	23	23	8	23	25	19	4
1	1	51	86	19	19	38	0.5	1	0.75	2.25	9	5	5	3	0	2	21	20	17	22	18	21	6
0	0	48	62	13	14	27	6.5	1	2.25	2.1	8	6	5	1	0	2	23	20	13	26	24	23	8
1	1	145	222	60	45	105	4.5	4	3	2.1	6	3	5	0	1	1	23	23	17	22	28	24	10
1	1	66	167	19	45	64	5.5	2	2.25	1.95	3	0	0	0	0	0	11	13	7	17	9	6	4
0	1	85	82	34	28	62	5	2	3	2.4	7	3	4	0	1	0	21	21	23	25	22	22	5
1	0	109	207	14	51	65	6	3	2.25	2.1	8	5	6	2	1	2	27	26	18	22	26	21	4
0	0	63	184	17	41	58	4	2	3	2.4	7	4	4	0	0	2	19	18	13	28	28	28	4
0	1	102	83	45	31	76	8	3	1.5	2.4	7	5	5	2	0	1	21	19	17	22	18	24	4
0	0	162	183	66	74	140	10.5	4	3	2.4	8	7	6	3	2	1	16	18	13	21	23	14	16
1	1	128	85	24	24	48	8.5	4	3	2.25	8	4	5	3	1	2	22	19	13	21	22	17	4
0	1	86	89	48	19	68	6.5	2	0.75	1.95	7	8	6	2	1	2	21	18	8	24	15	20	7
0	1	114	225	29	51	80	8	3	1.5	2.1	7	6	6	1	1	2	22	19	16	26	24	28	4
1	0	164	237	-2	73	71	8.5	4	3	2.1	6	4	5	1	0	1	16	13	14	26	12	19	4
1	1	119	102	51	24	76	5.5	3	3	2.55	8	5	5	1	1	0	18	10	13	24	12	24	14
1	0	126	221	2	61	63	7	4	3	2.1	8	5	6	0	1	2	23	21	19	27	20	21	5
1	1	132	128	24	23	46	5	4	2.25	2.1	7	3	6	1	0	2	24	24	15	22	25	21	5
1	1	142	91	40	14	53	3.5	4	2.25	2.1	9	6	6	0	1	2	20	21	15	23	24	26	5
1	0	83	198	20	54	74	5	2	3	1.95	9	3	4	2	0	1	20	23	8	22	23	24	5
0	1	94	204	19	51	70	9	2	1.5	2.25	7	6	5	3	1	1	18	18	14	23	18	26	7
0	0	81	158	16	62	78	8.5	2	2.25	2.4	7	3	2	1	0	2	4	11	7	15	20	25	19
1	1	166	138	20	36	56	5	4	2.25	1.95	8	7	6	2	2	2	14	16	11	20	22	23	16
0	0	110	226	40	59	100	9.5	3	2.25	2.1	8	7	6	3	0	2	22	20	17	22	20	24	4
0	1	64	44	27	24	51	3	2	0.75	2.1	6	6	4	3	1	2	17	20	19	25	25	24	4
1	0	93	196	25	26	52	1.5	2	2.25	1.95	9	5	6	1	1	0	23	26	17	27	28	26	7
0	0	104	83	49	54	102	6	3	1.5	2.1	6	5	5	1	0	1	20	21	12	24	25	23	9
0	1	105	79	39	39	78	0.5	3	2.25	2.1	5	4	4	0	0	2	18	12	12	21	14	20	5
0	1	49	52	61	16	78	6.5	1	1.5	1.95	7	4	6	1	2	2	19	15	18	17	16	16	14
0	0	88	105	19	36	55	7.5	2	0.75	2.1	9	7	6	3	0	2	20	18	16	26	24	24	4
0	1	95	116	67	31	98	4.5	2	1.5	1.95	6	2	1	0	1	0	15	14	15	20	13	20	16
0	1	102	83	45	31	76	8	3	1.5	2.4	7	5	5	2	0	1	24	18	20	22	19	23	10
0	0	99	196	30	42	73	9	3	2.25	2.4	5	4	5	2	1	0	21	16	16	24	18	23	5
0	1	63	153	8	39	47	7.5	2	1.5	2.4	9	2	6	2	2	2	19	19	12	23	16	18	6
0	0	76	157	19	25	45	8.5	2	1.5	1.95	8	5	4	2	0	0	19	7	10	22	8	21	4
0	0	109	75	52	31	83	7	3	3	2.7	4	4	3	0	0	2	27	21	28	28	27	25	4
0	1	117	106	22	38	60	9.5	3	2.25	2.1	9	7	4	3	2	2	23	24	19	21	23	23	4
0	1	57	58	17	31	48	6.5	1	1.5	1.95	8	6	5	2	2	0	23	21	18	24	20	26	5
0	0	120	75	33	17	50	9.5	3	0.75	2.1	7	4	5	0	0	0	20	20	19	28	20	26	4
0	1	73	74	34	22	56	6	2	2.25	1.95	8	5	6	2	2	2	17	22	8	25	26	24	4
0	0	91	185	22	55	77	8	2	3	2.1	1	0	1	0	0	0	21	17	17	24	23	23	5
0	0	108	265	30	62	91	9.5	3	3	2.25	8	7	6	2	1	2	23	19	16	24	24	21	4
0	1	105	131	25	51	76	8	3	1.5	2.7	8	4	4	2	0	2	22	20	18	21	21	23	4
1	0	117	139	38	30	68	8	3	1.5	2.1	9	5	4	3	0	2	16	16	12	20	15	20	5
0	0	119	196	26	49	74	9	3	2.25	2.4	8	6	5	2	0	1	20	20	17	26	22	23	8
0	1	31	78	13	16	29	5	1	0.75	1.35	9	8	3	2	1	1	16	16	13	16	25	24	15




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 20 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252919&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]20 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252919&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252919&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Exam.10[t] = + 1.00099 -0.698832Cohort[t] -0.32004Gender[t] -0.0051615LFM[t] + 0.00931549B[t] -0.334439PRH[t] -0.328142CH[t] + 0.323378H[t] + 0.813286PR.4[t] + 0.396289PE.3[t] + 1.59065PA.3[t] + 0.163347Calculation[t] -0.0203334Algebraic_Reasoning[t] + 0.0505018Graphical_Interpretation[t] + 0.31386Proportionality_and_Ratio[t] + 0.187921Probability_and_Sampling[t] -0.215637Estimation[t] + 0.0730074AMS.I1[t] -0.0830373AMS.I2[t] + 0.0232483AMS.I3[t] -0.0668768AMS.E1[t] -0.0101557AMS.E2[t] -0.0321283AMS.E3[t] -0.0143755AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Exam.10[t] =  +  1.00099 -0.698832Cohort[t] -0.32004Gender[t] -0.0051615LFM[t] +  0.00931549B[t] -0.334439PRH[t] -0.328142CH[t] +  0.323378H[t] +  0.813286PR.4[t] +  0.396289PE.3[t] +  1.59065PA.3[t] +  0.163347Calculation[t] -0.0203334Algebraic_Reasoning[t] +  0.0505018Graphical_Interpretation[t] +  0.31386Proportionality_and_Ratio[t] +  0.187921Probability_and_Sampling[t] -0.215637Estimation[t] +  0.0730074AMS.I1[t] -0.0830373AMS.I2[t] +  0.0232483AMS.I3[t] -0.0668768AMS.E1[t] -0.0101557AMS.E2[t] -0.0321283AMS.E3[t] -0.0143755AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252919&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Exam.10[t] =  +  1.00099 -0.698832Cohort[t] -0.32004Gender[t] -0.0051615LFM[t] +  0.00931549B[t] -0.334439PRH[t] -0.328142CH[t] +  0.323378H[t] +  0.813286PR.4[t] +  0.396289PE.3[t] +  1.59065PA.3[t] +  0.163347Calculation[t] -0.0203334Algebraic_Reasoning[t] +  0.0505018Graphical_Interpretation[t] +  0.31386Proportionality_and_Ratio[t] +  0.187921Probability_and_Sampling[t] -0.215637Estimation[t] +  0.0730074AMS.I1[t] -0.0830373AMS.I2[t] +  0.0232483AMS.I3[t] -0.0668768AMS.E1[t] -0.0101557AMS.E2[t] -0.0321283AMS.E3[t] -0.0143755AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252919&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252919&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Exam.10[t] = + 1.00099 -0.698832Cohort[t] -0.32004Gender[t] -0.0051615LFM[t] + 0.00931549B[t] -0.334439PRH[t] -0.328142CH[t] + 0.323378H[t] + 0.813286PR.4[t] + 0.396289PE.3[t] + 1.59065PA.3[t] + 0.163347Calculation[t] -0.0203334Algebraic_Reasoning[t] + 0.0505018Graphical_Interpretation[t] + 0.31386Proportionality_and_Ratio[t] + 0.187921Probability_and_Sampling[t] -0.215637Estimation[t] + 0.0730074AMS.I1[t] -0.0830373AMS.I2[t] + 0.0232483AMS.I3[t] -0.0668768AMS.E1[t] -0.0101557AMS.E2[t] -0.0321283AMS.E3[t] -0.0143755AMS.A[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.000991.947980.51390.6077810.303891
Cohort-0.6988320.333119-2.0980.03687680.0184384
Gender-0.320040.282574-1.1330.2584230.129211
LFM-0.00516150.00502343-1.0270.3051390.15257
B0.009315490.002868253.2480.001314740.00065737
PRH-0.3344390.353436-0.94630.3448920.172446
CH-0.3281420.352379-0.93120.3525970.176298
H0.3233780.3526340.9170.3599680.179984
PR.40.8132860.2198113.70.0002628050.000131403
PE.30.3962890.2623171.5110.1320650.0660323
PA.31.590650.4939483.220.001442360.000721181
Calculation0.1633470.09843691.6590.09823090.0491155
Algebraic_Reasoning-0.02033340.0836735-0.2430.8081890.404095
Graphical_Interpretation0.05050180.1089010.46370.643220.32161
Proportionality_and_Ratio0.313860.1335872.3490.01954260.00977128
Probability_and_Sampling0.1879210.1922170.97770.3291480.164574
Estimation-0.2156370.180094-1.1970.2322490.116125
AMS.I10.07300740.05083681.4360.1521630.0760817
AMS.I2-0.08303730.044758-1.8550.06468340.0323417
AMS.I30.02324830.03887450.5980.5503330.275167
AMS.E1-0.06687680.0534378-1.2510.2118720.105936
AMS.E2-0.01015570.037414-0.27140.7862660.393133
AMS.E3-0.03212830.044234-0.72630.4682870.234143
AMS.A-0.01437550.0456134-0.31520.7528910.376445

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.00099 & 1.94798 & 0.5139 & 0.607781 & 0.303891 \tabularnewline
Cohort & -0.698832 & 0.333119 & -2.098 & 0.0368768 & 0.0184384 \tabularnewline
Gender & -0.32004 & 0.282574 & -1.133 & 0.258423 & 0.129211 \tabularnewline
LFM & -0.0051615 & 0.00502343 & -1.027 & 0.305139 & 0.15257 \tabularnewline
B & 0.00931549 & 0.00286825 & 3.248 & 0.00131474 & 0.00065737 \tabularnewline
PRH & -0.334439 & 0.353436 & -0.9463 & 0.344892 & 0.172446 \tabularnewline
CH & -0.328142 & 0.352379 & -0.9312 & 0.352597 & 0.176298 \tabularnewline
H & 0.323378 & 0.352634 & 0.917 & 0.359968 & 0.179984 \tabularnewline
PR.4 & 0.813286 & 0.219811 & 3.7 & 0.000262805 & 0.000131403 \tabularnewline
PE.3 & 0.396289 & 0.262317 & 1.511 & 0.132065 & 0.0660323 \tabularnewline
PA.3 & 1.59065 & 0.493948 & 3.22 & 0.00144236 & 0.000721181 \tabularnewline
Calculation & 0.163347 & 0.0984369 & 1.659 & 0.0982309 & 0.0491155 \tabularnewline
Algebraic_Reasoning & -0.0203334 & 0.0836735 & -0.243 & 0.808189 & 0.404095 \tabularnewline
Graphical_Interpretation & 0.0505018 & 0.108901 & 0.4637 & 0.64322 & 0.32161 \tabularnewline
Proportionality_and_Ratio & 0.31386 & 0.133587 & 2.349 & 0.0195426 & 0.00977128 \tabularnewline
Probability_and_Sampling & 0.187921 & 0.192217 & 0.9777 & 0.329148 & 0.164574 \tabularnewline
Estimation & -0.215637 & 0.180094 & -1.197 & 0.232249 & 0.116125 \tabularnewline
AMS.I1 & 0.0730074 & 0.0508368 & 1.436 & 0.152163 & 0.0760817 \tabularnewline
AMS.I2 & -0.0830373 & 0.044758 & -1.855 & 0.0646834 & 0.0323417 \tabularnewline
AMS.I3 & 0.0232483 & 0.0388745 & 0.598 & 0.550333 & 0.275167 \tabularnewline
AMS.E1 & -0.0668768 & 0.0534378 & -1.251 & 0.211872 & 0.105936 \tabularnewline
AMS.E2 & -0.0101557 & 0.037414 & -0.2714 & 0.786266 & 0.393133 \tabularnewline
AMS.E3 & -0.0321283 & 0.044234 & -0.7263 & 0.468287 & 0.234143 \tabularnewline
AMS.A & -0.0143755 & 0.0456134 & -0.3152 & 0.752891 & 0.376445 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252919&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.00099[/C][C]1.94798[/C][C]0.5139[/C][C]0.607781[/C][C]0.303891[/C][/ROW]
[ROW][C]Cohort[/C][C]-0.698832[/C][C]0.333119[/C][C]-2.098[/C][C]0.0368768[/C][C]0.0184384[/C][/ROW]
[ROW][C]Gender[/C][C]-0.32004[/C][C]0.282574[/C][C]-1.133[/C][C]0.258423[/C][C]0.129211[/C][/ROW]
[ROW][C]LFM[/C][C]-0.0051615[/C][C]0.00502343[/C][C]-1.027[/C][C]0.305139[/C][C]0.15257[/C][/ROW]
[ROW][C]B[/C][C]0.00931549[/C][C]0.00286825[/C][C]3.248[/C][C]0.00131474[/C][C]0.00065737[/C][/ROW]
[ROW][C]PRH[/C][C]-0.334439[/C][C]0.353436[/C][C]-0.9463[/C][C]0.344892[/C][C]0.172446[/C][/ROW]
[ROW][C]CH[/C][C]-0.328142[/C][C]0.352379[/C][C]-0.9312[/C][C]0.352597[/C][C]0.176298[/C][/ROW]
[ROW][C]H[/C][C]0.323378[/C][C]0.352634[/C][C]0.917[/C][C]0.359968[/C][C]0.179984[/C][/ROW]
[ROW][C]PR.4[/C][C]0.813286[/C][C]0.219811[/C][C]3.7[/C][C]0.000262805[/C][C]0.000131403[/C][/ROW]
[ROW][C]PE.3[/C][C]0.396289[/C][C]0.262317[/C][C]1.511[/C][C]0.132065[/C][C]0.0660323[/C][/ROW]
[ROW][C]PA.3[/C][C]1.59065[/C][C]0.493948[/C][C]3.22[/C][C]0.00144236[/C][C]0.000721181[/C][/ROW]
[ROW][C]Calculation[/C][C]0.163347[/C][C]0.0984369[/C][C]1.659[/C][C]0.0982309[/C][C]0.0491155[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-0.0203334[/C][C]0.0836735[/C][C]-0.243[/C][C]0.808189[/C][C]0.404095[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]0.0505018[/C][C]0.108901[/C][C]0.4637[/C][C]0.64322[/C][C]0.32161[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.31386[/C][C]0.133587[/C][C]2.349[/C][C]0.0195426[/C][C]0.00977128[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]0.187921[/C][C]0.192217[/C][C]0.9777[/C][C]0.329148[/C][C]0.164574[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.215637[/C][C]0.180094[/C][C]-1.197[/C][C]0.232249[/C][C]0.116125[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0730074[/C][C]0.0508368[/C][C]1.436[/C][C]0.152163[/C][C]0.0760817[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0830373[/C][C]0.044758[/C][C]-1.855[/C][C]0.0646834[/C][C]0.0323417[/C][/ROW]
[ROW][C]AMS.I3[/C][C]0.0232483[/C][C]0.0388745[/C][C]0.598[/C][C]0.550333[/C][C]0.275167[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0668768[/C][C]0.0534378[/C][C]-1.251[/C][C]0.211872[/C][C]0.105936[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0101557[/C][C]0.037414[/C][C]-0.2714[/C][C]0.786266[/C][C]0.393133[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0321283[/C][C]0.044234[/C][C]-0.7263[/C][C]0.468287[/C][C]0.234143[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0143755[/C][C]0.0456134[/C][C]-0.3152[/C][C]0.752891[/C][C]0.376445[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252919&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252919&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.000991.947980.51390.6077810.303891
Cohort-0.6988320.333119-2.0980.03687680.0184384
Gender-0.320040.282574-1.1330.2584230.129211
LFM-0.00516150.00502343-1.0270.3051390.15257
B0.009315490.002868253.2480.001314740.00065737
PRH-0.3344390.353436-0.94630.3448920.172446
CH-0.3281420.352379-0.93120.3525970.176298
H0.3233780.3526340.9170.3599680.179984
PR.40.8132860.2198113.70.0002628050.000131403
PE.30.3962890.2623171.5110.1320650.0660323
PA.31.590650.4939483.220.001442360.000721181
Calculation0.1633470.09843691.6590.09823090.0491155
Algebraic_Reasoning-0.02033340.0836735-0.2430.8081890.404095
Graphical_Interpretation0.05050180.1089010.46370.643220.32161
Proportionality_and_Ratio0.313860.1335872.3490.01954260.00977128
Probability_and_Sampling0.1879210.1922170.97770.3291480.164574
Estimation-0.2156370.180094-1.1970.2322490.116125
AMS.I10.07300740.05083681.4360.1521630.0760817
AMS.I2-0.08303730.044758-1.8550.06468340.0323417
AMS.I30.02324830.03887450.5980.5503330.275167
AMS.E1-0.06687680.0534378-1.2510.2118720.105936
AMS.E2-0.01015570.037414-0.27140.7862660.393133
AMS.E3-0.03212830.044234-0.72630.4682870.234143
AMS.A-0.01437550.0456134-0.31520.7528910.376445







Multiple Linear Regression - Regression Statistics
Multiple R0.620028
R-squared0.384435
Adjusted R-squared0.330396
F-TEST (value)7.11413
F-TEST (DF numerator)23
F-TEST (DF denominator)262
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09217
Sum Squared Residuals1146.82

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.620028 \tabularnewline
R-squared & 0.384435 \tabularnewline
Adjusted R-squared & 0.330396 \tabularnewline
F-TEST (value) & 7.11413 \tabularnewline
F-TEST (DF numerator) & 23 \tabularnewline
F-TEST (DF denominator) & 262 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.09217 \tabularnewline
Sum Squared Residuals & 1146.82 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252919&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.620028[/C][/ROW]
[ROW][C]R-squared[/C][C]0.384435[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.330396[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.11413[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]23[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]262[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.09217[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1146.82[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252919&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252919&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.620028
R-squared0.384435
Adjusted R-squared0.330396
F-TEST (value)7.11413
F-TEST (DF numerator)23
F-TEST (DF denominator)262
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09217
Sum Squared Residuals1146.82







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.54.844712.65529
22.54.01618-1.51618
364.70521.2948
46.55.538460.961543
514.25417-3.25417
613.74303-2.74303
75.54.367391.13261
88.55.516792.98321
96.56.316970.183029
104.53.44961.0504
1126.07793-4.07793
1255.37811-0.37811
130.54.6791-4.1791
1453.114341.88566
1554.700630.299374
162.51.398271.10173
1756.10055-1.10055
185.54.811240.688764
193.53.79478-0.294783
2036.01173-3.01173
2143.731120.268882
220.53.76794-3.26794
236.54.463742.03626
244.54.018060.481944
257.54.057093.44291
265.55.101660.39834
2744.50679-0.506788
287.57.123550.376453
2976.873250.126752
3044.60382-0.603817
315.54.520260.979744
322.53.29646-0.79646
335.55.120670.379332
340.53.00351-2.50351
353.54.06321-0.563213
362.56.60067-4.10067
374.55.19597-0.695969
384.54.86088-0.360879
394.54.17060.329403
4065.989510.0104867
412.55.02221-2.52221
4256.10773-1.10773
4305.0988-5.0988
4455.56058-0.560581
456.54.345232.15477
4655.18737-0.187367
4765.796680.203323
484.56.05093-1.55093
495.54.575550.924445
5015.23684-4.23684
517.53.146254.35375
5265.419960.580041
5356.26395-1.26395
5415.8443-4.8443
5555.36461-0.364612
566.53.946712.55329
5775.356491.64351
584.55.61158-1.11158
5904.42923-4.42923
608.53.240835.25917
613.53.74172-0.241718
627.55.443472.05653
633.54.56358-1.06358
6464.004671.99533
651.54.97545-3.47545
6695.987513.01249
673.53.404140.0958579
683.55.68545-2.18545
6945.29423-1.29423
706.55.121991.37801
717.55.294852.20515
7264.868371.13163
7355.61564-0.615636
745.55.314620.185382
753.55.32059-1.82059
767.56.705640.794363
7714.42057-3.42057
786.54.307482.19252
79NANA1.38627
806.55.996010.503985
816.55.009131.49087
8278.91305-1.91305
833.56.23788-2.73788
841.53.54723-2.04723
8541.341472.65853
867.58.64457-1.14457
874.58.11709-3.61709
8802.05116-2.05116
893.53.58308-0.0830765
905.56.01669-0.516692
9156.60673-1.60673
924.57.86044-3.36044
932.5-1.405143.90514
947.55.966611.53339
95711.5335-4.53353
960-0.4704230.470423
974.56.45763-1.95763
9836.68269-3.68269
991.52.03712-0.537125
1003.55.81213-2.31213
1012.52.85362-0.353615
1025.52.903542.59646
103812.6096-4.60961
10411.63301-0.633012
10555.95518-0.955179
1064.55.70713-1.20713
10734.84718-1.84718
10830.2646082.73539
10989.99738-1.99738
1102.51.207341.29266
111711.6463-4.64629
11204.75655-4.75655
11312.58235-1.58235
1143.53.52009-0.0200893
1155.54.913650.58635
1165.58.41679-2.91679
1170.5-1.195081.69508
1187.55.582541.91746
11997.404821.59518
1209.510.2148-0.714808
1218.56.620811.87919
12278.98802-1.98802
12385.876362.12364
1241010.8441-0.844072
12573.147763.85224
1268.59.27204-0.772041
12794.175674.82433
1289.511.2867-1.78672
12943.03420.9658
13064.756551.24345
13188.79535-0.795349
1325.54.778730.721275
1339.58.509590.990409
1347.57.72312-0.223121
13577.84883-0.84883
1367.54.924012.57599
13787.226840.773159
13876.310010.689989
13977.41979-0.419791
14063.035752.96425
1411013.4093-3.40934
1422.52.71795-0.217952
14399.53181-0.531814
14487.847670.152334
14563.242592.75741
1468.57.985820.514177
14764.539951.46005
14897.913471.08653
14986.788531.21147
15088.06913-0.0691276
15199.00361-0.00360575
1525.56.79526-1.29526
15354.468130.531868
154710.8924-3.89237
1555.55.448940.051055
156913.5506-4.55057
15721.044370.955625
1588.57.657240.842764
15998.480380.519616
1608.56.56771.9323
16198.629670.370328
1627.57.124080.375924
163107.524742.47526
164910.2778-1.27783
1657.58.80969-1.30969
16663.929592.07041
16710.510.9004-0.400389
1688.59.59065-1.09065
16984.582993.41701
170108.707691.29231
17110.57.874512.62549
1726.57.06102-0.561018
1739.57.675731.82427
1748.510.0725-1.57248
1757.59.29795-1.79795
17655.77968-0.779679
17785.653162.34684
1781010.6155-0.615468
17976.658850.341146
1807.57.264570.235434
1817.54.978062.52194
1829.510.1459-0.645942
18364.132471.86753
1841010.5037-0.503652
18578.36583-1.36583
18634.73033-1.73033
18766.94726-0.947262
18875.881871.11813
1891010.838-0.837966
190711.8203-4.82029
1913.53.99402-0.49402
19285.283222.71678
1931011.9562-1.95623
1945.55.000440.499561
19564.642151.35785
1966.55.360741.13926
1976.54.544681.95532
1988.510.6868-2.18681
19942.280761.71924
2009.57.76511.7349
20187.690210.30979
2028.511.7764-3.27637
2035.54.878180.621822
20473.590023.40998
20598.073780.926218
20686.691021.30898
207108.420571.57943
20888.27967-0.279674
20966.48794-0.487939
21089.22981-1.22981
21152.783732.21627
21299.2107-0.210702
2134.51.366223.13378
2148.56.868451.63155
21576.450530.549472
2169.58.562850.937152
2178.55.857472.64253
2187.56.78930.710705
2197.58.90322-1.40322
22054.726820.273182
22177.15162-0.151623
22288.07034-0.0703445
2235.54.280941.21906
2248.58.7163-0.216302
2257.56.188181.31182
2269.58.274921.22508
22777.87248-0.872477
22886.694061.30594
2298.59.8446-1.3446
2303.53.065010.434993
2316.55.996640.503358
2326.53.719132.78087
23310.59.004251.49575
2348.58.130510.36949
23584.21563.7844
236107.975082.02492
237109.23280.767195
2389.57.091372.40863
23998.5490.451005
240108.057361.94264
2417.59.46269-1.96269
2424.53.726490.773508
2434.58.89263-4.39263
2440.5-1.030381.53038
2456.58.84392-2.34392
2464.54.290340.209656
2475.56.75197-1.25197
24856.59751-1.59751
24968.55298-2.55298
25042.708371.29163
25186.773581.22642
25210.510.02970.470266
2538.57.185621.31438
2546.55.355131.14487
25587.755090.244906
2568.510.694-2.19397
2575.56.56664-1.06664
25878.18417-1.18417
25957.03325-2.03325
2603.54.86855-1.36855
26153.118721.88128
26296.74292.2571
2638.510.2237-1.7237
26454.12560.874404
2659.511.1376-1.63765
26637.75358-4.75358
2671.50.4865621.01344
268611.2713-5.27132
2690.5-0.8368161.33682
2706.55.074791.42521
2717.57.82215-0.322153
2724.53.51590.984102
27388.03772-0.0377168
27499.07323-0.0732305
2757.57.176570.323431
2768.58.00080.4992
27774.921442.07856
2789.58.18011.3199
2796.52.298894.20111
2809.58.548720.95128
28163.986182.01382
28287.413470.58653
2839.59.242460.257538
28486.799441.20056
28586.745031.25497
28698.175470.824528
2875NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 4.84471 & 2.65529 \tabularnewline
2 & 2.5 & 4.01618 & -1.51618 \tabularnewline
3 & 6 & 4.7052 & 1.2948 \tabularnewline
4 & 6.5 & 5.53846 & 0.961543 \tabularnewline
5 & 1 & 4.25417 & -3.25417 \tabularnewline
6 & 1 & 3.74303 & -2.74303 \tabularnewline
7 & 5.5 & 4.36739 & 1.13261 \tabularnewline
8 & 8.5 & 5.51679 & 2.98321 \tabularnewline
9 & 6.5 & 6.31697 & 0.183029 \tabularnewline
10 & 4.5 & 3.4496 & 1.0504 \tabularnewline
11 & 2 & 6.07793 & -4.07793 \tabularnewline
12 & 5 & 5.37811 & -0.37811 \tabularnewline
13 & 0.5 & 4.6791 & -4.1791 \tabularnewline
14 & 5 & 3.11434 & 1.88566 \tabularnewline
15 & 5 & 4.70063 & 0.299374 \tabularnewline
16 & 2.5 & 1.39827 & 1.10173 \tabularnewline
17 & 5 & 6.10055 & -1.10055 \tabularnewline
18 & 5.5 & 4.81124 & 0.688764 \tabularnewline
19 & 3.5 & 3.79478 & -0.294783 \tabularnewline
20 & 3 & 6.01173 & -3.01173 \tabularnewline
21 & 4 & 3.73112 & 0.268882 \tabularnewline
22 & 0.5 & 3.76794 & -3.26794 \tabularnewline
23 & 6.5 & 4.46374 & 2.03626 \tabularnewline
24 & 4.5 & 4.01806 & 0.481944 \tabularnewline
25 & 7.5 & 4.05709 & 3.44291 \tabularnewline
26 & 5.5 & 5.10166 & 0.39834 \tabularnewline
27 & 4 & 4.50679 & -0.506788 \tabularnewline
28 & 7.5 & 7.12355 & 0.376453 \tabularnewline
29 & 7 & 6.87325 & 0.126752 \tabularnewline
30 & 4 & 4.60382 & -0.603817 \tabularnewline
31 & 5.5 & 4.52026 & 0.979744 \tabularnewline
32 & 2.5 & 3.29646 & -0.79646 \tabularnewline
33 & 5.5 & 5.12067 & 0.379332 \tabularnewline
34 & 0.5 & 3.00351 & -2.50351 \tabularnewline
35 & 3.5 & 4.06321 & -0.563213 \tabularnewline
36 & 2.5 & 6.60067 & -4.10067 \tabularnewline
37 & 4.5 & 5.19597 & -0.695969 \tabularnewline
38 & 4.5 & 4.86088 & -0.360879 \tabularnewline
39 & 4.5 & 4.1706 & 0.329403 \tabularnewline
40 & 6 & 5.98951 & 0.0104867 \tabularnewline
41 & 2.5 & 5.02221 & -2.52221 \tabularnewline
42 & 5 & 6.10773 & -1.10773 \tabularnewline
43 & 0 & 5.0988 & -5.0988 \tabularnewline
44 & 5 & 5.56058 & -0.560581 \tabularnewline
45 & 6.5 & 4.34523 & 2.15477 \tabularnewline
46 & 5 & 5.18737 & -0.187367 \tabularnewline
47 & 6 & 5.79668 & 0.203323 \tabularnewline
48 & 4.5 & 6.05093 & -1.55093 \tabularnewline
49 & 5.5 & 4.57555 & 0.924445 \tabularnewline
50 & 1 & 5.23684 & -4.23684 \tabularnewline
51 & 7.5 & 3.14625 & 4.35375 \tabularnewline
52 & 6 & 5.41996 & 0.580041 \tabularnewline
53 & 5 & 6.26395 & -1.26395 \tabularnewline
54 & 1 & 5.8443 & -4.8443 \tabularnewline
55 & 5 & 5.36461 & -0.364612 \tabularnewline
56 & 6.5 & 3.94671 & 2.55329 \tabularnewline
57 & 7 & 5.35649 & 1.64351 \tabularnewline
58 & 4.5 & 5.61158 & -1.11158 \tabularnewline
59 & 0 & 4.42923 & -4.42923 \tabularnewline
60 & 8.5 & 3.24083 & 5.25917 \tabularnewline
61 & 3.5 & 3.74172 & -0.241718 \tabularnewline
62 & 7.5 & 5.44347 & 2.05653 \tabularnewline
63 & 3.5 & 4.56358 & -1.06358 \tabularnewline
64 & 6 & 4.00467 & 1.99533 \tabularnewline
65 & 1.5 & 4.97545 & -3.47545 \tabularnewline
66 & 9 & 5.98751 & 3.01249 \tabularnewline
67 & 3.5 & 3.40414 & 0.0958579 \tabularnewline
68 & 3.5 & 5.68545 & -2.18545 \tabularnewline
69 & 4 & 5.29423 & -1.29423 \tabularnewline
70 & 6.5 & 5.12199 & 1.37801 \tabularnewline
71 & 7.5 & 5.29485 & 2.20515 \tabularnewline
72 & 6 & 4.86837 & 1.13163 \tabularnewline
73 & 5 & 5.61564 & -0.615636 \tabularnewline
74 & 5.5 & 5.31462 & 0.185382 \tabularnewline
75 & 3.5 & 5.32059 & -1.82059 \tabularnewline
76 & 7.5 & 6.70564 & 0.794363 \tabularnewline
77 & 1 & 4.42057 & -3.42057 \tabularnewline
78 & 6.5 & 4.30748 & 2.19252 \tabularnewline
79 & NA & NA & 1.38627 \tabularnewline
80 & 6.5 & 5.99601 & 0.503985 \tabularnewline
81 & 6.5 & 5.00913 & 1.49087 \tabularnewline
82 & 7 & 8.91305 & -1.91305 \tabularnewline
83 & 3.5 & 6.23788 & -2.73788 \tabularnewline
84 & 1.5 & 3.54723 & -2.04723 \tabularnewline
85 & 4 & 1.34147 & 2.65853 \tabularnewline
86 & 7.5 & 8.64457 & -1.14457 \tabularnewline
87 & 4.5 & 8.11709 & -3.61709 \tabularnewline
88 & 0 & 2.05116 & -2.05116 \tabularnewline
89 & 3.5 & 3.58308 & -0.0830765 \tabularnewline
90 & 5.5 & 6.01669 & -0.516692 \tabularnewline
91 & 5 & 6.60673 & -1.60673 \tabularnewline
92 & 4.5 & 7.86044 & -3.36044 \tabularnewline
93 & 2.5 & -1.40514 & 3.90514 \tabularnewline
94 & 7.5 & 5.96661 & 1.53339 \tabularnewline
95 & 7 & 11.5335 & -4.53353 \tabularnewline
96 & 0 & -0.470423 & 0.470423 \tabularnewline
97 & 4.5 & 6.45763 & -1.95763 \tabularnewline
98 & 3 & 6.68269 & -3.68269 \tabularnewline
99 & 1.5 & 2.03712 & -0.537125 \tabularnewline
100 & 3.5 & 5.81213 & -2.31213 \tabularnewline
101 & 2.5 & 2.85362 & -0.353615 \tabularnewline
102 & 5.5 & 2.90354 & 2.59646 \tabularnewline
103 & 8 & 12.6096 & -4.60961 \tabularnewline
104 & 1 & 1.63301 & -0.633012 \tabularnewline
105 & 5 & 5.95518 & -0.955179 \tabularnewline
106 & 4.5 & 5.70713 & -1.20713 \tabularnewline
107 & 3 & 4.84718 & -1.84718 \tabularnewline
108 & 3 & 0.264608 & 2.73539 \tabularnewline
109 & 8 & 9.99738 & -1.99738 \tabularnewline
110 & 2.5 & 1.20734 & 1.29266 \tabularnewline
111 & 7 & 11.6463 & -4.64629 \tabularnewline
112 & 0 & 4.75655 & -4.75655 \tabularnewline
113 & 1 & 2.58235 & -1.58235 \tabularnewline
114 & 3.5 & 3.52009 & -0.0200893 \tabularnewline
115 & 5.5 & 4.91365 & 0.58635 \tabularnewline
116 & 5.5 & 8.41679 & -2.91679 \tabularnewline
117 & 0.5 & -1.19508 & 1.69508 \tabularnewline
118 & 7.5 & 5.58254 & 1.91746 \tabularnewline
119 & 9 & 7.40482 & 1.59518 \tabularnewline
120 & 9.5 & 10.2148 & -0.714808 \tabularnewline
121 & 8.5 & 6.62081 & 1.87919 \tabularnewline
122 & 7 & 8.98802 & -1.98802 \tabularnewline
123 & 8 & 5.87636 & 2.12364 \tabularnewline
124 & 10 & 10.8441 & -0.844072 \tabularnewline
125 & 7 & 3.14776 & 3.85224 \tabularnewline
126 & 8.5 & 9.27204 & -0.772041 \tabularnewline
127 & 9 & 4.17567 & 4.82433 \tabularnewline
128 & 9.5 & 11.2867 & -1.78672 \tabularnewline
129 & 4 & 3.0342 & 0.9658 \tabularnewline
130 & 6 & 4.75655 & 1.24345 \tabularnewline
131 & 8 & 8.79535 & -0.795349 \tabularnewline
132 & 5.5 & 4.77873 & 0.721275 \tabularnewline
133 & 9.5 & 8.50959 & 0.990409 \tabularnewline
134 & 7.5 & 7.72312 & -0.223121 \tabularnewline
135 & 7 & 7.84883 & -0.84883 \tabularnewline
136 & 7.5 & 4.92401 & 2.57599 \tabularnewline
137 & 8 & 7.22684 & 0.773159 \tabularnewline
138 & 7 & 6.31001 & 0.689989 \tabularnewline
139 & 7 & 7.41979 & -0.419791 \tabularnewline
140 & 6 & 3.03575 & 2.96425 \tabularnewline
141 & 10 & 13.4093 & -3.40934 \tabularnewline
142 & 2.5 & 2.71795 & -0.217952 \tabularnewline
143 & 9 & 9.53181 & -0.531814 \tabularnewline
144 & 8 & 7.84767 & 0.152334 \tabularnewline
145 & 6 & 3.24259 & 2.75741 \tabularnewline
146 & 8.5 & 7.98582 & 0.514177 \tabularnewline
147 & 6 & 4.53995 & 1.46005 \tabularnewline
148 & 9 & 7.91347 & 1.08653 \tabularnewline
149 & 8 & 6.78853 & 1.21147 \tabularnewline
150 & 8 & 8.06913 & -0.0691276 \tabularnewline
151 & 9 & 9.00361 & -0.00360575 \tabularnewline
152 & 5.5 & 6.79526 & -1.29526 \tabularnewline
153 & 5 & 4.46813 & 0.531868 \tabularnewline
154 & 7 & 10.8924 & -3.89237 \tabularnewline
155 & 5.5 & 5.44894 & 0.051055 \tabularnewline
156 & 9 & 13.5506 & -4.55057 \tabularnewline
157 & 2 & 1.04437 & 0.955625 \tabularnewline
158 & 8.5 & 7.65724 & 0.842764 \tabularnewline
159 & 9 & 8.48038 & 0.519616 \tabularnewline
160 & 8.5 & 6.5677 & 1.9323 \tabularnewline
161 & 9 & 8.62967 & 0.370328 \tabularnewline
162 & 7.5 & 7.12408 & 0.375924 \tabularnewline
163 & 10 & 7.52474 & 2.47526 \tabularnewline
164 & 9 & 10.2778 & -1.27783 \tabularnewline
165 & 7.5 & 8.80969 & -1.30969 \tabularnewline
166 & 6 & 3.92959 & 2.07041 \tabularnewline
167 & 10.5 & 10.9004 & -0.400389 \tabularnewline
168 & 8.5 & 9.59065 & -1.09065 \tabularnewline
169 & 8 & 4.58299 & 3.41701 \tabularnewline
170 & 10 & 8.70769 & 1.29231 \tabularnewline
171 & 10.5 & 7.87451 & 2.62549 \tabularnewline
172 & 6.5 & 7.06102 & -0.561018 \tabularnewline
173 & 9.5 & 7.67573 & 1.82427 \tabularnewline
174 & 8.5 & 10.0725 & -1.57248 \tabularnewline
175 & 7.5 & 9.29795 & -1.79795 \tabularnewline
176 & 5 & 5.77968 & -0.779679 \tabularnewline
177 & 8 & 5.65316 & 2.34684 \tabularnewline
178 & 10 & 10.6155 & -0.615468 \tabularnewline
179 & 7 & 6.65885 & 0.341146 \tabularnewline
180 & 7.5 & 7.26457 & 0.235434 \tabularnewline
181 & 7.5 & 4.97806 & 2.52194 \tabularnewline
182 & 9.5 & 10.1459 & -0.645942 \tabularnewline
183 & 6 & 4.13247 & 1.86753 \tabularnewline
184 & 10 & 10.5037 & -0.503652 \tabularnewline
185 & 7 & 8.36583 & -1.36583 \tabularnewline
186 & 3 & 4.73033 & -1.73033 \tabularnewline
187 & 6 & 6.94726 & -0.947262 \tabularnewline
188 & 7 & 5.88187 & 1.11813 \tabularnewline
189 & 10 & 10.838 & -0.837966 \tabularnewline
190 & 7 & 11.8203 & -4.82029 \tabularnewline
191 & 3.5 & 3.99402 & -0.49402 \tabularnewline
192 & 8 & 5.28322 & 2.71678 \tabularnewline
193 & 10 & 11.9562 & -1.95623 \tabularnewline
194 & 5.5 & 5.00044 & 0.499561 \tabularnewline
195 & 6 & 4.64215 & 1.35785 \tabularnewline
196 & 6.5 & 5.36074 & 1.13926 \tabularnewline
197 & 6.5 & 4.54468 & 1.95532 \tabularnewline
198 & 8.5 & 10.6868 & -2.18681 \tabularnewline
199 & 4 & 2.28076 & 1.71924 \tabularnewline
200 & 9.5 & 7.7651 & 1.7349 \tabularnewline
201 & 8 & 7.69021 & 0.30979 \tabularnewline
202 & 8.5 & 11.7764 & -3.27637 \tabularnewline
203 & 5.5 & 4.87818 & 0.621822 \tabularnewline
204 & 7 & 3.59002 & 3.40998 \tabularnewline
205 & 9 & 8.07378 & 0.926218 \tabularnewline
206 & 8 & 6.69102 & 1.30898 \tabularnewline
207 & 10 & 8.42057 & 1.57943 \tabularnewline
208 & 8 & 8.27967 & -0.279674 \tabularnewline
209 & 6 & 6.48794 & -0.487939 \tabularnewline
210 & 8 & 9.22981 & -1.22981 \tabularnewline
211 & 5 & 2.78373 & 2.21627 \tabularnewline
212 & 9 & 9.2107 & -0.210702 \tabularnewline
213 & 4.5 & 1.36622 & 3.13378 \tabularnewline
214 & 8.5 & 6.86845 & 1.63155 \tabularnewline
215 & 7 & 6.45053 & 0.549472 \tabularnewline
216 & 9.5 & 8.56285 & 0.937152 \tabularnewline
217 & 8.5 & 5.85747 & 2.64253 \tabularnewline
218 & 7.5 & 6.7893 & 0.710705 \tabularnewline
219 & 7.5 & 8.90322 & -1.40322 \tabularnewline
220 & 5 & 4.72682 & 0.273182 \tabularnewline
221 & 7 & 7.15162 & -0.151623 \tabularnewline
222 & 8 & 8.07034 & -0.0703445 \tabularnewline
223 & 5.5 & 4.28094 & 1.21906 \tabularnewline
224 & 8.5 & 8.7163 & -0.216302 \tabularnewline
225 & 7.5 & 6.18818 & 1.31182 \tabularnewline
226 & 9.5 & 8.27492 & 1.22508 \tabularnewline
227 & 7 & 7.87248 & -0.872477 \tabularnewline
228 & 8 & 6.69406 & 1.30594 \tabularnewline
229 & 8.5 & 9.8446 & -1.3446 \tabularnewline
230 & 3.5 & 3.06501 & 0.434993 \tabularnewline
231 & 6.5 & 5.99664 & 0.503358 \tabularnewline
232 & 6.5 & 3.71913 & 2.78087 \tabularnewline
233 & 10.5 & 9.00425 & 1.49575 \tabularnewline
234 & 8.5 & 8.13051 & 0.36949 \tabularnewline
235 & 8 & 4.2156 & 3.7844 \tabularnewline
236 & 10 & 7.97508 & 2.02492 \tabularnewline
237 & 10 & 9.2328 & 0.767195 \tabularnewline
238 & 9.5 & 7.09137 & 2.40863 \tabularnewline
239 & 9 & 8.549 & 0.451005 \tabularnewline
240 & 10 & 8.05736 & 1.94264 \tabularnewline
241 & 7.5 & 9.46269 & -1.96269 \tabularnewline
242 & 4.5 & 3.72649 & 0.773508 \tabularnewline
243 & 4.5 & 8.89263 & -4.39263 \tabularnewline
244 & 0.5 & -1.03038 & 1.53038 \tabularnewline
245 & 6.5 & 8.84392 & -2.34392 \tabularnewline
246 & 4.5 & 4.29034 & 0.209656 \tabularnewline
247 & 5.5 & 6.75197 & -1.25197 \tabularnewline
248 & 5 & 6.59751 & -1.59751 \tabularnewline
249 & 6 & 8.55298 & -2.55298 \tabularnewline
250 & 4 & 2.70837 & 1.29163 \tabularnewline
251 & 8 & 6.77358 & 1.22642 \tabularnewline
252 & 10.5 & 10.0297 & 0.470266 \tabularnewline
253 & 8.5 & 7.18562 & 1.31438 \tabularnewline
254 & 6.5 & 5.35513 & 1.14487 \tabularnewline
255 & 8 & 7.75509 & 0.244906 \tabularnewline
256 & 8.5 & 10.694 & -2.19397 \tabularnewline
257 & 5.5 & 6.56664 & -1.06664 \tabularnewline
258 & 7 & 8.18417 & -1.18417 \tabularnewline
259 & 5 & 7.03325 & -2.03325 \tabularnewline
260 & 3.5 & 4.86855 & -1.36855 \tabularnewline
261 & 5 & 3.11872 & 1.88128 \tabularnewline
262 & 9 & 6.7429 & 2.2571 \tabularnewline
263 & 8.5 & 10.2237 & -1.7237 \tabularnewline
264 & 5 & 4.1256 & 0.874404 \tabularnewline
265 & 9.5 & 11.1376 & -1.63765 \tabularnewline
266 & 3 & 7.75358 & -4.75358 \tabularnewline
267 & 1.5 & 0.486562 & 1.01344 \tabularnewline
268 & 6 & 11.2713 & -5.27132 \tabularnewline
269 & 0.5 & -0.836816 & 1.33682 \tabularnewline
270 & 6.5 & 5.07479 & 1.42521 \tabularnewline
271 & 7.5 & 7.82215 & -0.322153 \tabularnewline
272 & 4.5 & 3.5159 & 0.984102 \tabularnewline
273 & 8 & 8.03772 & -0.0377168 \tabularnewline
274 & 9 & 9.07323 & -0.0732305 \tabularnewline
275 & 7.5 & 7.17657 & 0.323431 \tabularnewline
276 & 8.5 & 8.0008 & 0.4992 \tabularnewline
277 & 7 & 4.92144 & 2.07856 \tabularnewline
278 & 9.5 & 8.1801 & 1.3199 \tabularnewline
279 & 6.5 & 2.29889 & 4.20111 \tabularnewline
280 & 9.5 & 8.54872 & 0.95128 \tabularnewline
281 & 6 & 3.98618 & 2.01382 \tabularnewline
282 & 8 & 7.41347 & 0.58653 \tabularnewline
283 & 9.5 & 9.24246 & 0.257538 \tabularnewline
284 & 8 & 6.79944 & 1.20056 \tabularnewline
285 & 8 & 6.74503 & 1.25497 \tabularnewline
286 & 9 & 8.17547 & 0.824528 \tabularnewline
287 & 5 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252919&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]7.5[/C][C]4.84471[/C][C]2.65529[/C][/ROW]
[ROW][C]2[/C][C]2.5[/C][C]4.01618[/C][C]-1.51618[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]4.7052[/C][C]1.2948[/C][/ROW]
[ROW][C]4[/C][C]6.5[/C][C]5.53846[/C][C]0.961543[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]4.25417[/C][C]-3.25417[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]3.74303[/C][C]-2.74303[/C][/ROW]
[ROW][C]7[/C][C]5.5[/C][C]4.36739[/C][C]1.13261[/C][/ROW]
[ROW][C]8[/C][C]8.5[/C][C]5.51679[/C][C]2.98321[/C][/ROW]
[ROW][C]9[/C][C]6.5[/C][C]6.31697[/C][C]0.183029[/C][/ROW]
[ROW][C]10[/C][C]4.5[/C][C]3.4496[/C][C]1.0504[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]6.07793[/C][C]-4.07793[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]5.37811[/C][C]-0.37811[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]4.6791[/C][C]-4.1791[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]3.11434[/C][C]1.88566[/C][/ROW]
[ROW][C]15[/C][C]5[/C][C]4.70063[/C][C]0.299374[/C][/ROW]
[ROW][C]16[/C][C]2.5[/C][C]1.39827[/C][C]1.10173[/C][/ROW]
[ROW][C]17[/C][C]5[/C][C]6.10055[/C][C]-1.10055[/C][/ROW]
[ROW][C]18[/C][C]5.5[/C][C]4.81124[/C][C]0.688764[/C][/ROW]
[ROW][C]19[/C][C]3.5[/C][C]3.79478[/C][C]-0.294783[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]6.01173[/C][C]-3.01173[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.73112[/C][C]0.268882[/C][/ROW]
[ROW][C]22[/C][C]0.5[/C][C]3.76794[/C][C]-3.26794[/C][/ROW]
[ROW][C]23[/C][C]6.5[/C][C]4.46374[/C][C]2.03626[/C][/ROW]
[ROW][C]24[/C][C]4.5[/C][C]4.01806[/C][C]0.481944[/C][/ROW]
[ROW][C]25[/C][C]7.5[/C][C]4.05709[/C][C]3.44291[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]5.10166[/C][C]0.39834[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]4.50679[/C][C]-0.506788[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]7.12355[/C][C]0.376453[/C][/ROW]
[ROW][C]29[/C][C]7[/C][C]6.87325[/C][C]0.126752[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.60382[/C][C]-0.603817[/C][/ROW]
[ROW][C]31[/C][C]5.5[/C][C]4.52026[/C][C]0.979744[/C][/ROW]
[ROW][C]32[/C][C]2.5[/C][C]3.29646[/C][C]-0.79646[/C][/ROW]
[ROW][C]33[/C][C]5.5[/C][C]5.12067[/C][C]0.379332[/C][/ROW]
[ROW][C]34[/C][C]0.5[/C][C]3.00351[/C][C]-2.50351[/C][/ROW]
[ROW][C]35[/C][C]3.5[/C][C]4.06321[/C][C]-0.563213[/C][/ROW]
[ROW][C]36[/C][C]2.5[/C][C]6.60067[/C][C]-4.10067[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]5.19597[/C][C]-0.695969[/C][/ROW]
[ROW][C]38[/C][C]4.5[/C][C]4.86088[/C][C]-0.360879[/C][/ROW]
[ROW][C]39[/C][C]4.5[/C][C]4.1706[/C][C]0.329403[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]5.98951[/C][C]0.0104867[/C][/ROW]
[ROW][C]41[/C][C]2.5[/C][C]5.02221[/C][C]-2.52221[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]6.10773[/C][C]-1.10773[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]5.0988[/C][C]-5.0988[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]5.56058[/C][C]-0.560581[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]4.34523[/C][C]2.15477[/C][/ROW]
[ROW][C]46[/C][C]5[/C][C]5.18737[/C][C]-0.187367[/C][/ROW]
[ROW][C]47[/C][C]6[/C][C]5.79668[/C][C]0.203323[/C][/ROW]
[ROW][C]48[/C][C]4.5[/C][C]6.05093[/C][C]-1.55093[/C][/ROW]
[ROW][C]49[/C][C]5.5[/C][C]4.57555[/C][C]0.924445[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]5.23684[/C][C]-4.23684[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]3.14625[/C][C]4.35375[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]5.41996[/C][C]0.580041[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]6.26395[/C][C]-1.26395[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]5.8443[/C][C]-4.8443[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]5.36461[/C][C]-0.364612[/C][/ROW]
[ROW][C]56[/C][C]6.5[/C][C]3.94671[/C][C]2.55329[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]5.35649[/C][C]1.64351[/C][/ROW]
[ROW][C]58[/C][C]4.5[/C][C]5.61158[/C][C]-1.11158[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]4.42923[/C][C]-4.42923[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]3.24083[/C][C]5.25917[/C][/ROW]
[ROW][C]61[/C][C]3.5[/C][C]3.74172[/C][C]-0.241718[/C][/ROW]
[ROW][C]62[/C][C]7.5[/C][C]5.44347[/C][C]2.05653[/C][/ROW]
[ROW][C]63[/C][C]3.5[/C][C]4.56358[/C][C]-1.06358[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]4.00467[/C][C]1.99533[/C][/ROW]
[ROW][C]65[/C][C]1.5[/C][C]4.97545[/C][C]-3.47545[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]5.98751[/C][C]3.01249[/C][/ROW]
[ROW][C]67[/C][C]3.5[/C][C]3.40414[/C][C]0.0958579[/C][/ROW]
[ROW][C]68[/C][C]3.5[/C][C]5.68545[/C][C]-2.18545[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]5.29423[/C][C]-1.29423[/C][/ROW]
[ROW][C]70[/C][C]6.5[/C][C]5.12199[/C][C]1.37801[/C][/ROW]
[ROW][C]71[/C][C]7.5[/C][C]5.29485[/C][C]2.20515[/C][/ROW]
[ROW][C]72[/C][C]6[/C][C]4.86837[/C][C]1.13163[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.61564[/C][C]-0.615636[/C][/ROW]
[ROW][C]74[/C][C]5.5[/C][C]5.31462[/C][C]0.185382[/C][/ROW]
[ROW][C]75[/C][C]3.5[/C][C]5.32059[/C][C]-1.82059[/C][/ROW]
[ROW][C]76[/C][C]7.5[/C][C]6.70564[/C][C]0.794363[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]4.42057[/C][C]-3.42057[/C][/ROW]
[ROW][C]78[/C][C]6.5[/C][C]4.30748[/C][C]2.19252[/C][/ROW]
[ROW][C]79[/C][C]NA[/C][C]NA[/C][C]1.38627[/C][/ROW]
[ROW][C]80[/C][C]6.5[/C][C]5.99601[/C][C]0.503985[/C][/ROW]
[ROW][C]81[/C][C]6.5[/C][C]5.00913[/C][C]1.49087[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]8.91305[/C][C]-1.91305[/C][/ROW]
[ROW][C]83[/C][C]3.5[/C][C]6.23788[/C][C]-2.73788[/C][/ROW]
[ROW][C]84[/C][C]1.5[/C][C]3.54723[/C][C]-2.04723[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]1.34147[/C][C]2.65853[/C][/ROW]
[ROW][C]86[/C][C]7.5[/C][C]8.64457[/C][C]-1.14457[/C][/ROW]
[ROW][C]87[/C][C]4.5[/C][C]8.11709[/C][C]-3.61709[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]2.05116[/C][C]-2.05116[/C][/ROW]
[ROW][C]89[/C][C]3.5[/C][C]3.58308[/C][C]-0.0830765[/C][/ROW]
[ROW][C]90[/C][C]5.5[/C][C]6.01669[/C][C]-0.516692[/C][/ROW]
[ROW][C]91[/C][C]5[/C][C]6.60673[/C][C]-1.60673[/C][/ROW]
[ROW][C]92[/C][C]4.5[/C][C]7.86044[/C][C]-3.36044[/C][/ROW]
[ROW][C]93[/C][C]2.5[/C][C]-1.40514[/C][C]3.90514[/C][/ROW]
[ROW][C]94[/C][C]7.5[/C][C]5.96661[/C][C]1.53339[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]11.5335[/C][C]-4.53353[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]-0.470423[/C][C]0.470423[/C][/ROW]
[ROW][C]97[/C][C]4.5[/C][C]6.45763[/C][C]-1.95763[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]6.68269[/C][C]-3.68269[/C][/ROW]
[ROW][C]99[/C][C]1.5[/C][C]2.03712[/C][C]-0.537125[/C][/ROW]
[ROW][C]100[/C][C]3.5[/C][C]5.81213[/C][C]-2.31213[/C][/ROW]
[ROW][C]101[/C][C]2.5[/C][C]2.85362[/C][C]-0.353615[/C][/ROW]
[ROW][C]102[/C][C]5.5[/C][C]2.90354[/C][C]2.59646[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]12.6096[/C][C]-4.60961[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.63301[/C][C]-0.633012[/C][/ROW]
[ROW][C]105[/C][C]5[/C][C]5.95518[/C][C]-0.955179[/C][/ROW]
[ROW][C]106[/C][C]4.5[/C][C]5.70713[/C][C]-1.20713[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]4.84718[/C][C]-1.84718[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]0.264608[/C][C]2.73539[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]9.99738[/C][C]-1.99738[/C][/ROW]
[ROW][C]110[/C][C]2.5[/C][C]1.20734[/C][C]1.29266[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]11.6463[/C][C]-4.64629[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]4.75655[/C][C]-4.75655[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]2.58235[/C][C]-1.58235[/C][/ROW]
[ROW][C]114[/C][C]3.5[/C][C]3.52009[/C][C]-0.0200893[/C][/ROW]
[ROW][C]115[/C][C]5.5[/C][C]4.91365[/C][C]0.58635[/C][/ROW]
[ROW][C]116[/C][C]5.5[/C][C]8.41679[/C][C]-2.91679[/C][/ROW]
[ROW][C]117[/C][C]0.5[/C][C]-1.19508[/C][C]1.69508[/C][/ROW]
[ROW][C]118[/C][C]7.5[/C][C]5.58254[/C][C]1.91746[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]7.40482[/C][C]1.59518[/C][/ROW]
[ROW][C]120[/C][C]9.5[/C][C]10.2148[/C][C]-0.714808[/C][/ROW]
[ROW][C]121[/C][C]8.5[/C][C]6.62081[/C][C]1.87919[/C][/ROW]
[ROW][C]122[/C][C]7[/C][C]8.98802[/C][C]-1.98802[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]5.87636[/C][C]2.12364[/C][/ROW]
[ROW][C]124[/C][C]10[/C][C]10.8441[/C][C]-0.844072[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]3.14776[/C][C]3.85224[/C][/ROW]
[ROW][C]126[/C][C]8.5[/C][C]9.27204[/C][C]-0.772041[/C][/ROW]
[ROW][C]127[/C][C]9[/C][C]4.17567[/C][C]4.82433[/C][/ROW]
[ROW][C]128[/C][C]9.5[/C][C]11.2867[/C][C]-1.78672[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]3.0342[/C][C]0.9658[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]4.75655[/C][C]1.24345[/C][/ROW]
[ROW][C]131[/C][C]8[/C][C]8.79535[/C][C]-0.795349[/C][/ROW]
[ROW][C]132[/C][C]5.5[/C][C]4.77873[/C][C]0.721275[/C][/ROW]
[ROW][C]133[/C][C]9.5[/C][C]8.50959[/C][C]0.990409[/C][/ROW]
[ROW][C]134[/C][C]7.5[/C][C]7.72312[/C][C]-0.223121[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]7.84883[/C][C]-0.84883[/C][/ROW]
[ROW][C]136[/C][C]7.5[/C][C]4.92401[/C][C]2.57599[/C][/ROW]
[ROW][C]137[/C][C]8[/C][C]7.22684[/C][C]0.773159[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]6.31001[/C][C]0.689989[/C][/ROW]
[ROW][C]139[/C][C]7[/C][C]7.41979[/C][C]-0.419791[/C][/ROW]
[ROW][C]140[/C][C]6[/C][C]3.03575[/C][C]2.96425[/C][/ROW]
[ROW][C]141[/C][C]10[/C][C]13.4093[/C][C]-3.40934[/C][/ROW]
[ROW][C]142[/C][C]2.5[/C][C]2.71795[/C][C]-0.217952[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]9.53181[/C][C]-0.531814[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]7.84767[/C][C]0.152334[/C][/ROW]
[ROW][C]145[/C][C]6[/C][C]3.24259[/C][C]2.75741[/C][/ROW]
[ROW][C]146[/C][C]8.5[/C][C]7.98582[/C][C]0.514177[/C][/ROW]
[ROW][C]147[/C][C]6[/C][C]4.53995[/C][C]1.46005[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]7.91347[/C][C]1.08653[/C][/ROW]
[ROW][C]149[/C][C]8[/C][C]6.78853[/C][C]1.21147[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]8.06913[/C][C]-0.0691276[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]9.00361[/C][C]-0.00360575[/C][/ROW]
[ROW][C]152[/C][C]5.5[/C][C]6.79526[/C][C]-1.29526[/C][/ROW]
[ROW][C]153[/C][C]5[/C][C]4.46813[/C][C]0.531868[/C][/ROW]
[ROW][C]154[/C][C]7[/C][C]10.8924[/C][C]-3.89237[/C][/ROW]
[ROW][C]155[/C][C]5.5[/C][C]5.44894[/C][C]0.051055[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]13.5506[/C][C]-4.55057[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.04437[/C][C]0.955625[/C][/ROW]
[ROW][C]158[/C][C]8.5[/C][C]7.65724[/C][C]0.842764[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]8.48038[/C][C]0.519616[/C][/ROW]
[ROW][C]160[/C][C]8.5[/C][C]6.5677[/C][C]1.9323[/C][/ROW]
[ROW][C]161[/C][C]9[/C][C]8.62967[/C][C]0.370328[/C][/ROW]
[ROW][C]162[/C][C]7.5[/C][C]7.12408[/C][C]0.375924[/C][/ROW]
[ROW][C]163[/C][C]10[/C][C]7.52474[/C][C]2.47526[/C][/ROW]
[ROW][C]164[/C][C]9[/C][C]10.2778[/C][C]-1.27783[/C][/ROW]
[ROW][C]165[/C][C]7.5[/C][C]8.80969[/C][C]-1.30969[/C][/ROW]
[ROW][C]166[/C][C]6[/C][C]3.92959[/C][C]2.07041[/C][/ROW]
[ROW][C]167[/C][C]10.5[/C][C]10.9004[/C][C]-0.400389[/C][/ROW]
[ROW][C]168[/C][C]8.5[/C][C]9.59065[/C][C]-1.09065[/C][/ROW]
[ROW][C]169[/C][C]8[/C][C]4.58299[/C][C]3.41701[/C][/ROW]
[ROW][C]170[/C][C]10[/C][C]8.70769[/C][C]1.29231[/C][/ROW]
[ROW][C]171[/C][C]10.5[/C][C]7.87451[/C][C]2.62549[/C][/ROW]
[ROW][C]172[/C][C]6.5[/C][C]7.06102[/C][C]-0.561018[/C][/ROW]
[ROW][C]173[/C][C]9.5[/C][C]7.67573[/C][C]1.82427[/C][/ROW]
[ROW][C]174[/C][C]8.5[/C][C]10.0725[/C][C]-1.57248[/C][/ROW]
[ROW][C]175[/C][C]7.5[/C][C]9.29795[/C][C]-1.79795[/C][/ROW]
[ROW][C]176[/C][C]5[/C][C]5.77968[/C][C]-0.779679[/C][/ROW]
[ROW][C]177[/C][C]8[/C][C]5.65316[/C][C]2.34684[/C][/ROW]
[ROW][C]178[/C][C]10[/C][C]10.6155[/C][C]-0.615468[/C][/ROW]
[ROW][C]179[/C][C]7[/C][C]6.65885[/C][C]0.341146[/C][/ROW]
[ROW][C]180[/C][C]7.5[/C][C]7.26457[/C][C]0.235434[/C][/ROW]
[ROW][C]181[/C][C]7.5[/C][C]4.97806[/C][C]2.52194[/C][/ROW]
[ROW][C]182[/C][C]9.5[/C][C]10.1459[/C][C]-0.645942[/C][/ROW]
[ROW][C]183[/C][C]6[/C][C]4.13247[/C][C]1.86753[/C][/ROW]
[ROW][C]184[/C][C]10[/C][C]10.5037[/C][C]-0.503652[/C][/ROW]
[ROW][C]185[/C][C]7[/C][C]8.36583[/C][C]-1.36583[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]4.73033[/C][C]-1.73033[/C][/ROW]
[ROW][C]187[/C][C]6[/C][C]6.94726[/C][C]-0.947262[/C][/ROW]
[ROW][C]188[/C][C]7[/C][C]5.88187[/C][C]1.11813[/C][/ROW]
[ROW][C]189[/C][C]10[/C][C]10.838[/C][C]-0.837966[/C][/ROW]
[ROW][C]190[/C][C]7[/C][C]11.8203[/C][C]-4.82029[/C][/ROW]
[ROW][C]191[/C][C]3.5[/C][C]3.99402[/C][C]-0.49402[/C][/ROW]
[ROW][C]192[/C][C]8[/C][C]5.28322[/C][C]2.71678[/C][/ROW]
[ROW][C]193[/C][C]10[/C][C]11.9562[/C][C]-1.95623[/C][/ROW]
[ROW][C]194[/C][C]5.5[/C][C]5.00044[/C][C]0.499561[/C][/ROW]
[ROW][C]195[/C][C]6[/C][C]4.64215[/C][C]1.35785[/C][/ROW]
[ROW][C]196[/C][C]6.5[/C][C]5.36074[/C][C]1.13926[/C][/ROW]
[ROW][C]197[/C][C]6.5[/C][C]4.54468[/C][C]1.95532[/C][/ROW]
[ROW][C]198[/C][C]8.5[/C][C]10.6868[/C][C]-2.18681[/C][/ROW]
[ROW][C]199[/C][C]4[/C][C]2.28076[/C][C]1.71924[/C][/ROW]
[ROW][C]200[/C][C]9.5[/C][C]7.7651[/C][C]1.7349[/C][/ROW]
[ROW][C]201[/C][C]8[/C][C]7.69021[/C][C]0.30979[/C][/ROW]
[ROW][C]202[/C][C]8.5[/C][C]11.7764[/C][C]-3.27637[/C][/ROW]
[ROW][C]203[/C][C]5.5[/C][C]4.87818[/C][C]0.621822[/C][/ROW]
[ROW][C]204[/C][C]7[/C][C]3.59002[/C][C]3.40998[/C][/ROW]
[ROW][C]205[/C][C]9[/C][C]8.07378[/C][C]0.926218[/C][/ROW]
[ROW][C]206[/C][C]8[/C][C]6.69102[/C][C]1.30898[/C][/ROW]
[ROW][C]207[/C][C]10[/C][C]8.42057[/C][C]1.57943[/C][/ROW]
[ROW][C]208[/C][C]8[/C][C]8.27967[/C][C]-0.279674[/C][/ROW]
[ROW][C]209[/C][C]6[/C][C]6.48794[/C][C]-0.487939[/C][/ROW]
[ROW][C]210[/C][C]8[/C][C]9.22981[/C][C]-1.22981[/C][/ROW]
[ROW][C]211[/C][C]5[/C][C]2.78373[/C][C]2.21627[/C][/ROW]
[ROW][C]212[/C][C]9[/C][C]9.2107[/C][C]-0.210702[/C][/ROW]
[ROW][C]213[/C][C]4.5[/C][C]1.36622[/C][C]3.13378[/C][/ROW]
[ROW][C]214[/C][C]8.5[/C][C]6.86845[/C][C]1.63155[/C][/ROW]
[ROW][C]215[/C][C]7[/C][C]6.45053[/C][C]0.549472[/C][/ROW]
[ROW][C]216[/C][C]9.5[/C][C]8.56285[/C][C]0.937152[/C][/ROW]
[ROW][C]217[/C][C]8.5[/C][C]5.85747[/C][C]2.64253[/C][/ROW]
[ROW][C]218[/C][C]7.5[/C][C]6.7893[/C][C]0.710705[/C][/ROW]
[ROW][C]219[/C][C]7.5[/C][C]8.90322[/C][C]-1.40322[/C][/ROW]
[ROW][C]220[/C][C]5[/C][C]4.72682[/C][C]0.273182[/C][/ROW]
[ROW][C]221[/C][C]7[/C][C]7.15162[/C][C]-0.151623[/C][/ROW]
[ROW][C]222[/C][C]8[/C][C]8.07034[/C][C]-0.0703445[/C][/ROW]
[ROW][C]223[/C][C]5.5[/C][C]4.28094[/C][C]1.21906[/C][/ROW]
[ROW][C]224[/C][C]8.5[/C][C]8.7163[/C][C]-0.216302[/C][/ROW]
[ROW][C]225[/C][C]7.5[/C][C]6.18818[/C][C]1.31182[/C][/ROW]
[ROW][C]226[/C][C]9.5[/C][C]8.27492[/C][C]1.22508[/C][/ROW]
[ROW][C]227[/C][C]7[/C][C]7.87248[/C][C]-0.872477[/C][/ROW]
[ROW][C]228[/C][C]8[/C][C]6.69406[/C][C]1.30594[/C][/ROW]
[ROW][C]229[/C][C]8.5[/C][C]9.8446[/C][C]-1.3446[/C][/ROW]
[ROW][C]230[/C][C]3.5[/C][C]3.06501[/C][C]0.434993[/C][/ROW]
[ROW][C]231[/C][C]6.5[/C][C]5.99664[/C][C]0.503358[/C][/ROW]
[ROW][C]232[/C][C]6.5[/C][C]3.71913[/C][C]2.78087[/C][/ROW]
[ROW][C]233[/C][C]10.5[/C][C]9.00425[/C][C]1.49575[/C][/ROW]
[ROW][C]234[/C][C]8.5[/C][C]8.13051[/C][C]0.36949[/C][/ROW]
[ROW][C]235[/C][C]8[/C][C]4.2156[/C][C]3.7844[/C][/ROW]
[ROW][C]236[/C][C]10[/C][C]7.97508[/C][C]2.02492[/C][/ROW]
[ROW][C]237[/C][C]10[/C][C]9.2328[/C][C]0.767195[/C][/ROW]
[ROW][C]238[/C][C]9.5[/C][C]7.09137[/C][C]2.40863[/C][/ROW]
[ROW][C]239[/C][C]9[/C][C]8.549[/C][C]0.451005[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]8.05736[/C][C]1.94264[/C][/ROW]
[ROW][C]241[/C][C]7.5[/C][C]9.46269[/C][C]-1.96269[/C][/ROW]
[ROW][C]242[/C][C]4.5[/C][C]3.72649[/C][C]0.773508[/C][/ROW]
[ROW][C]243[/C][C]4.5[/C][C]8.89263[/C][C]-4.39263[/C][/ROW]
[ROW][C]244[/C][C]0.5[/C][C]-1.03038[/C][C]1.53038[/C][/ROW]
[ROW][C]245[/C][C]6.5[/C][C]8.84392[/C][C]-2.34392[/C][/ROW]
[ROW][C]246[/C][C]4.5[/C][C]4.29034[/C][C]0.209656[/C][/ROW]
[ROW][C]247[/C][C]5.5[/C][C]6.75197[/C][C]-1.25197[/C][/ROW]
[ROW][C]248[/C][C]5[/C][C]6.59751[/C][C]-1.59751[/C][/ROW]
[ROW][C]249[/C][C]6[/C][C]8.55298[/C][C]-2.55298[/C][/ROW]
[ROW][C]250[/C][C]4[/C][C]2.70837[/C][C]1.29163[/C][/ROW]
[ROW][C]251[/C][C]8[/C][C]6.77358[/C][C]1.22642[/C][/ROW]
[ROW][C]252[/C][C]10.5[/C][C]10.0297[/C][C]0.470266[/C][/ROW]
[ROW][C]253[/C][C]8.5[/C][C]7.18562[/C][C]1.31438[/C][/ROW]
[ROW][C]254[/C][C]6.5[/C][C]5.35513[/C][C]1.14487[/C][/ROW]
[ROW][C]255[/C][C]8[/C][C]7.75509[/C][C]0.244906[/C][/ROW]
[ROW][C]256[/C][C]8.5[/C][C]10.694[/C][C]-2.19397[/C][/ROW]
[ROW][C]257[/C][C]5.5[/C][C]6.56664[/C][C]-1.06664[/C][/ROW]
[ROW][C]258[/C][C]7[/C][C]8.18417[/C][C]-1.18417[/C][/ROW]
[ROW][C]259[/C][C]5[/C][C]7.03325[/C][C]-2.03325[/C][/ROW]
[ROW][C]260[/C][C]3.5[/C][C]4.86855[/C][C]-1.36855[/C][/ROW]
[ROW][C]261[/C][C]5[/C][C]3.11872[/C][C]1.88128[/C][/ROW]
[ROW][C]262[/C][C]9[/C][C]6.7429[/C][C]2.2571[/C][/ROW]
[ROW][C]263[/C][C]8.5[/C][C]10.2237[/C][C]-1.7237[/C][/ROW]
[ROW][C]264[/C][C]5[/C][C]4.1256[/C][C]0.874404[/C][/ROW]
[ROW][C]265[/C][C]9.5[/C][C]11.1376[/C][C]-1.63765[/C][/ROW]
[ROW][C]266[/C][C]3[/C][C]7.75358[/C][C]-4.75358[/C][/ROW]
[ROW][C]267[/C][C]1.5[/C][C]0.486562[/C][C]1.01344[/C][/ROW]
[ROW][C]268[/C][C]6[/C][C]11.2713[/C][C]-5.27132[/C][/ROW]
[ROW][C]269[/C][C]0.5[/C][C]-0.836816[/C][C]1.33682[/C][/ROW]
[ROW][C]270[/C][C]6.5[/C][C]5.07479[/C][C]1.42521[/C][/ROW]
[ROW][C]271[/C][C]7.5[/C][C]7.82215[/C][C]-0.322153[/C][/ROW]
[ROW][C]272[/C][C]4.5[/C][C]3.5159[/C][C]0.984102[/C][/ROW]
[ROW][C]273[/C][C]8[/C][C]8.03772[/C][C]-0.0377168[/C][/ROW]
[ROW][C]274[/C][C]9[/C][C]9.07323[/C][C]-0.0732305[/C][/ROW]
[ROW][C]275[/C][C]7.5[/C][C]7.17657[/C][C]0.323431[/C][/ROW]
[ROW][C]276[/C][C]8.5[/C][C]8.0008[/C][C]0.4992[/C][/ROW]
[ROW][C]277[/C][C]7[/C][C]4.92144[/C][C]2.07856[/C][/ROW]
[ROW][C]278[/C][C]9.5[/C][C]8.1801[/C][C]1.3199[/C][/ROW]
[ROW][C]279[/C][C]6.5[/C][C]2.29889[/C][C]4.20111[/C][/ROW]
[ROW][C]280[/C][C]9.5[/C][C]8.54872[/C][C]0.95128[/C][/ROW]
[ROW][C]281[/C][C]6[/C][C]3.98618[/C][C]2.01382[/C][/ROW]
[ROW][C]282[/C][C]8[/C][C]7.41347[/C][C]0.58653[/C][/ROW]
[ROW][C]283[/C][C]9.5[/C][C]9.24246[/C][C]0.257538[/C][/ROW]
[ROW][C]284[/C][C]8[/C][C]6.79944[/C][C]1.20056[/C][/ROW]
[ROW][C]285[/C][C]8[/C][C]6.74503[/C][C]1.25497[/C][/ROW]
[ROW][C]286[/C][C]9[/C][C]8.17547[/C][C]0.824528[/C][/ROW]
[ROW][C]287[/C][C]5[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252919&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252919&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.54.844712.65529
22.54.01618-1.51618
364.70521.2948
46.55.538460.961543
514.25417-3.25417
613.74303-2.74303
75.54.367391.13261
88.55.516792.98321
96.56.316970.183029
104.53.44961.0504
1126.07793-4.07793
1255.37811-0.37811
130.54.6791-4.1791
1453.114341.88566
1554.700630.299374
162.51.398271.10173
1756.10055-1.10055
185.54.811240.688764
193.53.79478-0.294783
2036.01173-3.01173
2143.731120.268882
220.53.76794-3.26794
236.54.463742.03626
244.54.018060.481944
257.54.057093.44291
265.55.101660.39834
2744.50679-0.506788
287.57.123550.376453
2976.873250.126752
3044.60382-0.603817
315.54.520260.979744
322.53.29646-0.79646
335.55.120670.379332
340.53.00351-2.50351
353.54.06321-0.563213
362.56.60067-4.10067
374.55.19597-0.695969
384.54.86088-0.360879
394.54.17060.329403
4065.989510.0104867
412.55.02221-2.52221
4256.10773-1.10773
4305.0988-5.0988
4455.56058-0.560581
456.54.345232.15477
4655.18737-0.187367
4765.796680.203323
484.56.05093-1.55093
495.54.575550.924445
5015.23684-4.23684
517.53.146254.35375
5265.419960.580041
5356.26395-1.26395
5415.8443-4.8443
5555.36461-0.364612
566.53.946712.55329
5775.356491.64351
584.55.61158-1.11158
5904.42923-4.42923
608.53.240835.25917
613.53.74172-0.241718
627.55.443472.05653
633.54.56358-1.06358
6464.004671.99533
651.54.97545-3.47545
6695.987513.01249
673.53.404140.0958579
683.55.68545-2.18545
6945.29423-1.29423
706.55.121991.37801
717.55.294852.20515
7264.868371.13163
7355.61564-0.615636
745.55.314620.185382
753.55.32059-1.82059
767.56.705640.794363
7714.42057-3.42057
786.54.307482.19252
79NANA1.38627
806.55.996010.503985
816.55.009131.49087
8278.91305-1.91305
833.56.23788-2.73788
841.53.54723-2.04723
8541.341472.65853
867.58.64457-1.14457
874.58.11709-3.61709
8802.05116-2.05116
893.53.58308-0.0830765
905.56.01669-0.516692
9156.60673-1.60673
924.57.86044-3.36044
932.5-1.405143.90514
947.55.966611.53339
95711.5335-4.53353
960-0.4704230.470423
974.56.45763-1.95763
9836.68269-3.68269
991.52.03712-0.537125
1003.55.81213-2.31213
1012.52.85362-0.353615
1025.52.903542.59646
103812.6096-4.60961
10411.63301-0.633012
10555.95518-0.955179
1064.55.70713-1.20713
10734.84718-1.84718
10830.2646082.73539
10989.99738-1.99738
1102.51.207341.29266
111711.6463-4.64629
11204.75655-4.75655
11312.58235-1.58235
1143.53.52009-0.0200893
1155.54.913650.58635
1165.58.41679-2.91679
1170.5-1.195081.69508
1187.55.582541.91746
11997.404821.59518
1209.510.2148-0.714808
1218.56.620811.87919
12278.98802-1.98802
12385.876362.12364
1241010.8441-0.844072
12573.147763.85224
1268.59.27204-0.772041
12794.175674.82433
1289.511.2867-1.78672
12943.03420.9658
13064.756551.24345
13188.79535-0.795349
1325.54.778730.721275
1339.58.509590.990409
1347.57.72312-0.223121
13577.84883-0.84883
1367.54.924012.57599
13787.226840.773159
13876.310010.689989
13977.41979-0.419791
14063.035752.96425
1411013.4093-3.40934
1422.52.71795-0.217952
14399.53181-0.531814
14487.847670.152334
14563.242592.75741
1468.57.985820.514177
14764.539951.46005
14897.913471.08653
14986.788531.21147
15088.06913-0.0691276
15199.00361-0.00360575
1525.56.79526-1.29526
15354.468130.531868
154710.8924-3.89237
1555.55.448940.051055
156913.5506-4.55057
15721.044370.955625
1588.57.657240.842764
15998.480380.519616
1608.56.56771.9323
16198.629670.370328
1627.57.124080.375924
163107.524742.47526
164910.2778-1.27783
1657.58.80969-1.30969
16663.929592.07041
16710.510.9004-0.400389
1688.59.59065-1.09065
16984.582993.41701
170108.707691.29231
17110.57.874512.62549
1726.57.06102-0.561018
1739.57.675731.82427
1748.510.0725-1.57248
1757.59.29795-1.79795
17655.77968-0.779679
17785.653162.34684
1781010.6155-0.615468
17976.658850.341146
1807.57.264570.235434
1817.54.978062.52194
1829.510.1459-0.645942
18364.132471.86753
1841010.5037-0.503652
18578.36583-1.36583
18634.73033-1.73033
18766.94726-0.947262
18875.881871.11813
1891010.838-0.837966
190711.8203-4.82029
1913.53.99402-0.49402
19285.283222.71678
1931011.9562-1.95623
1945.55.000440.499561
19564.642151.35785
1966.55.360741.13926
1976.54.544681.95532
1988.510.6868-2.18681
19942.280761.71924
2009.57.76511.7349
20187.690210.30979
2028.511.7764-3.27637
2035.54.878180.621822
20473.590023.40998
20598.073780.926218
20686.691021.30898
207108.420571.57943
20888.27967-0.279674
20966.48794-0.487939
21089.22981-1.22981
21152.783732.21627
21299.2107-0.210702
2134.51.366223.13378
2148.56.868451.63155
21576.450530.549472
2169.58.562850.937152
2178.55.857472.64253
2187.56.78930.710705
2197.58.90322-1.40322
22054.726820.273182
22177.15162-0.151623
22288.07034-0.0703445
2235.54.280941.21906
2248.58.7163-0.216302
2257.56.188181.31182
2269.58.274921.22508
22777.87248-0.872477
22886.694061.30594
2298.59.8446-1.3446
2303.53.065010.434993
2316.55.996640.503358
2326.53.719132.78087
23310.59.004251.49575
2348.58.130510.36949
23584.21563.7844
236107.975082.02492
237109.23280.767195
2389.57.091372.40863
23998.5490.451005
240108.057361.94264
2417.59.46269-1.96269
2424.53.726490.773508
2434.58.89263-4.39263
2440.5-1.030381.53038
2456.58.84392-2.34392
2464.54.290340.209656
2475.56.75197-1.25197
24856.59751-1.59751
24968.55298-2.55298
25042.708371.29163
25186.773581.22642
25210.510.02970.470266
2538.57.185621.31438
2546.55.355131.14487
25587.755090.244906
2568.510.694-2.19397
2575.56.56664-1.06664
25878.18417-1.18417
25957.03325-2.03325
2603.54.86855-1.36855
26153.118721.88128
26296.74292.2571
2638.510.2237-1.7237
26454.12560.874404
2659.511.1376-1.63765
26637.75358-4.75358
2671.50.4865621.01344
268611.2713-5.27132
2690.5-0.8368161.33682
2706.55.074791.42521
2717.57.82215-0.322153
2724.53.51590.984102
27388.03772-0.0377168
27499.07323-0.0732305
2757.57.176570.323431
2768.58.00080.4992
27774.921442.07856
2789.58.18011.3199
2796.52.298894.20111
2809.58.548720.95128
28163.986182.01382
28287.413470.58653
2839.59.242460.257538
28486.799441.20056
28586.745031.25497
28698.175470.824528
2875NANA







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
270.377430.7548610.62257
280.4805310.9610620.519469
290.3888830.7777670.611117
300.2998910.5997820.700109
310.2015770.4031540.798423
320.2623830.5247670.737617
330.2682450.5364890.731755
340.199090.3981790.80091
350.1406150.2812310.859385
360.2918050.583610.708195
370.2520530.5041050.747947
380.1965430.3930860.803457
390.1458380.2916760.854162
400.1054810.2109610.894519
410.4285580.8571160.571442
420.3678810.7357630.632119
430.5506970.8986050.449303
440.5133190.9733610.486681
450.574990.850020.42501
460.5124540.9750920.487546
470.4621020.9242050.537898
480.435040.870080.56496
490.3932170.7864330.606783
500.4910310.9820610.508969
510.624430.751140.37557
520.6547220.6905550.345278
530.6056150.788770.394385
540.609280.7814410.39072
550.5575130.8849740.442487
560.6577410.6845190.342259
570.7779770.4440450.222023
580.7408860.5182270.259114
590.8259480.3481040.174052
600.9095220.1809550.0904776
610.8941370.2117250.105863
620.9056320.1887360.0943679
630.8874420.2251160.112558
640.8815180.2369650.118482
650.9158330.1683340.0841668
660.9479210.1041580.0520789
670.9410140.1179720.0589859
680.9322320.1355370.0677685
690.9177780.1644440.0822222
700.9147820.1704350.0852176
710.9156140.1687730.0843863
720.8988460.2023070.101154
730.8878730.2242540.112127
740.8677040.2645920.132296
750.8465140.3069710.153486
760.8569970.2860070.143003
770.8779070.2441850.122093
780.8680330.2639350.131967
790.8529450.2941090.147055
800.8297150.3405690.170285
810.8190540.3618920.180946
820.8007340.3985320.199266
830.800910.3981790.19909
840.7984980.4030040.201502
850.8114220.3771570.188578
860.7867530.4264950.213247
870.7846220.4307560.215378
880.7770250.4459510.222975
890.7468430.5063150.253157
900.7157970.5684060.284203
910.6954390.6091210.304561
920.717520.564960.28248
930.795820.4083590.20418
940.805090.389820.19491
950.8635930.2728130.136407
960.844530.3109410.15547
970.8352360.3295280.164764
980.8733150.2533710.126685
990.8599480.2801040.140052
1000.8554370.2891270.144563
1010.8402030.3195940.159797
1020.8924810.2150380.107519
1030.9417610.1164790.0582393
1040.9340380.1319240.065962
1050.9251010.1497990.0748994
1060.9232040.1535930.0767964
1070.9310470.1379060.0689528
1080.9357360.1285270.0642637
1090.9368830.1262340.0631169
1100.9349790.1300420.0650212
1110.965210.06958080.0347904
1120.9850570.02988580.0149429
1130.9856120.0287770.0143885
1140.983670.03265910.0163295
1150.9828290.03434140.0171707
1160.9848270.03034550.0151728
1170.9818160.03636730.0181837
1180.9919860.01602750.00801376
1190.9925490.01490220.00745109
1200.9911890.01762160.00881079
1210.9912820.01743610.00871807
1220.9907750.01845060.00922531
1230.9891310.02173870.0108694
1240.9872380.02552350.0127618
1250.9957710.008458060.00422903
1260.9949550.01009010.00504503
1270.9982410.003518060.00175903
1280.9981930.003614810.0018074
1290.9977940.004412950.00220648
1300.9974360.005127280.00256364
1310.9969540.006091280.00304564
1320.9962130.007573360.00378668
1330.9958170.008366890.00418345
1340.99470.01059920.0052996
1350.9935810.01283790.00641897
1360.9939690.01206270.00603135
1370.9927230.01455470.00727734
1380.9909690.01806260.00903128
1390.9885060.02298840.0114942
1400.98940.02120020.0106001
1410.9959220.008155260.00407763
1420.9950040.009991170.00499558
1430.9935520.01289620.00644808
1440.9925480.01490310.00745153
1450.9911920.01761520.0088076
1460.9894930.0210130.0105065
1470.9890570.02188690.0109434
1480.9896120.02077560.0103878
1490.9876310.0247370.0123685
1500.9852020.02959570.0147978
1510.9821090.03578230.0178911
1520.9795730.04085420.0204271
1530.9746820.05063650.0253183
1540.9821890.03562280.0178114
1550.9777150.044570.022285
1560.9902610.01947810.00973906
1570.9889350.0221310.0110655
1580.9861550.02769060.0138453
1590.9831590.03368260.0168413
1600.985450.02909930.0145496
1610.9818710.0362580.018129
1620.9783190.04336180.0216809
1630.98220.03560080.0178004
1640.9801920.03961630.0198081
1650.9816090.03678240.0183912
1660.9809030.03819330.0190967
1670.9771520.04569580.0228479
1680.9761870.04762660.0238133
1690.9892620.0214760.010738
1700.98750.02500010.0125001
1710.9917660.01646820.00823412
1720.9893750.02124940.0106247
1730.9886770.02264520.0113226
1740.987430.02514080.0125704
1750.9893470.02130670.0106533
1760.9862950.02741080.0137054
1770.9865340.02693150.0134658
1780.9828690.03426110.0171306
1790.9782840.04343190.0217159
1800.9727480.05450430.0272521
1810.9726670.05466640.0273332
1820.9662760.06744840.0337242
1830.9690.06199950.0309997
1840.9621160.07576760.0378838
1850.9554440.08911140.0445557
1860.9617720.07645620.0382281
1870.9548650.09026970.0451349
1880.9459140.1081710.0540857
1890.9370380.1259240.0629618
1900.9825160.03496760.0174838
1910.9802390.03952280.0197614
1920.9811560.03768790.018844
1930.9868390.02632240.0131612
1940.9855510.0288970.0144485
1950.9829440.03411240.0170562
1960.9799890.04002180.0200109
1970.978530.04294040.0214702
1980.9928850.01423060.00711528
1990.9910790.01784260.0089213
2000.9891780.0216440.010822
2010.9862160.02756880.0137844
2020.9864160.02716890.0135844
2030.9829110.03417820.0170891
2040.9848230.03035350.0151767
2050.980380.03923970.0196199
2060.9750440.04991170.0249559
2070.9766880.04662460.0233123
2080.9701480.05970430.0298522
2090.9639970.07200650.0360033
2100.9555980.08880410.0444021
2110.9521420.09571540.0478577
2120.9486050.102790.0513949
2130.961230.07754070.0387704
2140.9554420.08911640.0445582
2150.95090.09820050.0491002
2160.9380060.1239890.0619944
2170.9356480.1287030.0643517
2180.9202220.1595550.0797775
2190.90420.1915990.0957996
2200.8816740.2366530.118326
2210.8550530.2898940.144947
2220.8292980.3414040.170702
2230.8091090.3817810.190891
2240.7850080.4299840.214992
2250.7478490.5043030.252151
2260.7173740.5652510.282626
2270.6955750.6088490.304425
2280.7307730.5384540.269227
2290.706170.587660.29383
2300.6730940.6538120.326906
2310.6817260.6365490.318274
2320.6741480.6517040.325852
2330.6431110.7137790.356889
2340.5985830.8028350.401417
2350.6868590.6262810.313141
2360.7935840.4128320.206416
2370.7583660.4832680.241634
2380.7136910.5726180.286309
2390.6987240.6025520.301276
2400.7042150.591570.295785
2410.6529890.6940220.347011
2420.5943980.8112040.405602
2430.65960.68080.3404
2440.6110410.7779190.388959
2450.5580590.8838820.441941
2460.6067320.7865360.393268
2470.5350380.9299230.464962
2480.4905610.9811220.509439
2490.4208690.8417390.579131
2500.3668550.7337090.633145
2510.2944110.5888210.705589
2520.3732550.7465090.626745
2530.5203810.9592380.479619
2540.4532710.9065420.546729
2550.406450.8128990.59355
2560.5477220.9045550.452278
2570.9249860.1500280.0750142
2580.8872040.2255920.112796
2590.9076320.1847360.0923682
2600.7714910.4570180.228509

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
27 & 0.37743 & 0.754861 & 0.62257 \tabularnewline
28 & 0.480531 & 0.961062 & 0.519469 \tabularnewline
29 & 0.388883 & 0.777767 & 0.611117 \tabularnewline
30 & 0.299891 & 0.599782 & 0.700109 \tabularnewline
31 & 0.201577 & 0.403154 & 0.798423 \tabularnewline
32 & 0.262383 & 0.524767 & 0.737617 \tabularnewline
33 & 0.268245 & 0.536489 & 0.731755 \tabularnewline
34 & 0.19909 & 0.398179 & 0.80091 \tabularnewline
35 & 0.140615 & 0.281231 & 0.859385 \tabularnewline
36 & 0.291805 & 0.58361 & 0.708195 \tabularnewline
37 & 0.252053 & 0.504105 & 0.747947 \tabularnewline
38 & 0.196543 & 0.393086 & 0.803457 \tabularnewline
39 & 0.145838 & 0.291676 & 0.854162 \tabularnewline
40 & 0.105481 & 0.210961 & 0.894519 \tabularnewline
41 & 0.428558 & 0.857116 & 0.571442 \tabularnewline
42 & 0.367881 & 0.735763 & 0.632119 \tabularnewline
43 & 0.550697 & 0.898605 & 0.449303 \tabularnewline
44 & 0.513319 & 0.973361 & 0.486681 \tabularnewline
45 & 0.57499 & 0.85002 & 0.42501 \tabularnewline
46 & 0.512454 & 0.975092 & 0.487546 \tabularnewline
47 & 0.462102 & 0.924205 & 0.537898 \tabularnewline
48 & 0.43504 & 0.87008 & 0.56496 \tabularnewline
49 & 0.393217 & 0.786433 & 0.606783 \tabularnewline
50 & 0.491031 & 0.982061 & 0.508969 \tabularnewline
51 & 0.62443 & 0.75114 & 0.37557 \tabularnewline
52 & 0.654722 & 0.690555 & 0.345278 \tabularnewline
53 & 0.605615 & 0.78877 & 0.394385 \tabularnewline
54 & 0.60928 & 0.781441 & 0.39072 \tabularnewline
55 & 0.557513 & 0.884974 & 0.442487 \tabularnewline
56 & 0.657741 & 0.684519 & 0.342259 \tabularnewline
57 & 0.777977 & 0.444045 & 0.222023 \tabularnewline
58 & 0.740886 & 0.518227 & 0.259114 \tabularnewline
59 & 0.825948 & 0.348104 & 0.174052 \tabularnewline
60 & 0.909522 & 0.180955 & 0.0904776 \tabularnewline
61 & 0.894137 & 0.211725 & 0.105863 \tabularnewline
62 & 0.905632 & 0.188736 & 0.0943679 \tabularnewline
63 & 0.887442 & 0.225116 & 0.112558 \tabularnewline
64 & 0.881518 & 0.236965 & 0.118482 \tabularnewline
65 & 0.915833 & 0.168334 & 0.0841668 \tabularnewline
66 & 0.947921 & 0.104158 & 0.0520789 \tabularnewline
67 & 0.941014 & 0.117972 & 0.0589859 \tabularnewline
68 & 0.932232 & 0.135537 & 0.0677685 \tabularnewline
69 & 0.917778 & 0.164444 & 0.0822222 \tabularnewline
70 & 0.914782 & 0.170435 & 0.0852176 \tabularnewline
71 & 0.915614 & 0.168773 & 0.0843863 \tabularnewline
72 & 0.898846 & 0.202307 & 0.101154 \tabularnewline
73 & 0.887873 & 0.224254 & 0.112127 \tabularnewline
74 & 0.867704 & 0.264592 & 0.132296 \tabularnewline
75 & 0.846514 & 0.306971 & 0.153486 \tabularnewline
76 & 0.856997 & 0.286007 & 0.143003 \tabularnewline
77 & 0.877907 & 0.244185 & 0.122093 \tabularnewline
78 & 0.868033 & 0.263935 & 0.131967 \tabularnewline
79 & 0.852945 & 0.294109 & 0.147055 \tabularnewline
80 & 0.829715 & 0.340569 & 0.170285 \tabularnewline
81 & 0.819054 & 0.361892 & 0.180946 \tabularnewline
82 & 0.800734 & 0.398532 & 0.199266 \tabularnewline
83 & 0.80091 & 0.398179 & 0.19909 \tabularnewline
84 & 0.798498 & 0.403004 & 0.201502 \tabularnewline
85 & 0.811422 & 0.377157 & 0.188578 \tabularnewline
86 & 0.786753 & 0.426495 & 0.213247 \tabularnewline
87 & 0.784622 & 0.430756 & 0.215378 \tabularnewline
88 & 0.777025 & 0.445951 & 0.222975 \tabularnewline
89 & 0.746843 & 0.506315 & 0.253157 \tabularnewline
90 & 0.715797 & 0.568406 & 0.284203 \tabularnewline
91 & 0.695439 & 0.609121 & 0.304561 \tabularnewline
92 & 0.71752 & 0.56496 & 0.28248 \tabularnewline
93 & 0.79582 & 0.408359 & 0.20418 \tabularnewline
94 & 0.80509 & 0.38982 & 0.19491 \tabularnewline
95 & 0.863593 & 0.272813 & 0.136407 \tabularnewline
96 & 0.84453 & 0.310941 & 0.15547 \tabularnewline
97 & 0.835236 & 0.329528 & 0.164764 \tabularnewline
98 & 0.873315 & 0.253371 & 0.126685 \tabularnewline
99 & 0.859948 & 0.280104 & 0.140052 \tabularnewline
100 & 0.855437 & 0.289127 & 0.144563 \tabularnewline
101 & 0.840203 & 0.319594 & 0.159797 \tabularnewline
102 & 0.892481 & 0.215038 & 0.107519 \tabularnewline
103 & 0.941761 & 0.116479 & 0.0582393 \tabularnewline
104 & 0.934038 & 0.131924 & 0.065962 \tabularnewline
105 & 0.925101 & 0.149799 & 0.0748994 \tabularnewline
106 & 0.923204 & 0.153593 & 0.0767964 \tabularnewline
107 & 0.931047 & 0.137906 & 0.0689528 \tabularnewline
108 & 0.935736 & 0.128527 & 0.0642637 \tabularnewline
109 & 0.936883 & 0.126234 & 0.0631169 \tabularnewline
110 & 0.934979 & 0.130042 & 0.0650212 \tabularnewline
111 & 0.96521 & 0.0695808 & 0.0347904 \tabularnewline
112 & 0.985057 & 0.0298858 & 0.0149429 \tabularnewline
113 & 0.985612 & 0.028777 & 0.0143885 \tabularnewline
114 & 0.98367 & 0.0326591 & 0.0163295 \tabularnewline
115 & 0.982829 & 0.0343414 & 0.0171707 \tabularnewline
116 & 0.984827 & 0.0303455 & 0.0151728 \tabularnewline
117 & 0.981816 & 0.0363673 & 0.0181837 \tabularnewline
118 & 0.991986 & 0.0160275 & 0.00801376 \tabularnewline
119 & 0.992549 & 0.0149022 & 0.00745109 \tabularnewline
120 & 0.991189 & 0.0176216 & 0.00881079 \tabularnewline
121 & 0.991282 & 0.0174361 & 0.00871807 \tabularnewline
122 & 0.990775 & 0.0184506 & 0.00922531 \tabularnewline
123 & 0.989131 & 0.0217387 & 0.0108694 \tabularnewline
124 & 0.987238 & 0.0255235 & 0.0127618 \tabularnewline
125 & 0.995771 & 0.00845806 & 0.00422903 \tabularnewline
126 & 0.994955 & 0.0100901 & 0.00504503 \tabularnewline
127 & 0.998241 & 0.00351806 & 0.00175903 \tabularnewline
128 & 0.998193 & 0.00361481 & 0.0018074 \tabularnewline
129 & 0.997794 & 0.00441295 & 0.00220648 \tabularnewline
130 & 0.997436 & 0.00512728 & 0.00256364 \tabularnewline
131 & 0.996954 & 0.00609128 & 0.00304564 \tabularnewline
132 & 0.996213 & 0.00757336 & 0.00378668 \tabularnewline
133 & 0.995817 & 0.00836689 & 0.00418345 \tabularnewline
134 & 0.9947 & 0.0105992 & 0.0052996 \tabularnewline
135 & 0.993581 & 0.0128379 & 0.00641897 \tabularnewline
136 & 0.993969 & 0.0120627 & 0.00603135 \tabularnewline
137 & 0.992723 & 0.0145547 & 0.00727734 \tabularnewline
138 & 0.990969 & 0.0180626 & 0.00903128 \tabularnewline
139 & 0.988506 & 0.0229884 & 0.0114942 \tabularnewline
140 & 0.9894 & 0.0212002 & 0.0106001 \tabularnewline
141 & 0.995922 & 0.00815526 & 0.00407763 \tabularnewline
142 & 0.995004 & 0.00999117 & 0.00499558 \tabularnewline
143 & 0.993552 & 0.0128962 & 0.00644808 \tabularnewline
144 & 0.992548 & 0.0149031 & 0.00745153 \tabularnewline
145 & 0.991192 & 0.0176152 & 0.0088076 \tabularnewline
146 & 0.989493 & 0.021013 & 0.0105065 \tabularnewline
147 & 0.989057 & 0.0218869 & 0.0109434 \tabularnewline
148 & 0.989612 & 0.0207756 & 0.0103878 \tabularnewline
149 & 0.987631 & 0.024737 & 0.0123685 \tabularnewline
150 & 0.985202 & 0.0295957 & 0.0147978 \tabularnewline
151 & 0.982109 & 0.0357823 & 0.0178911 \tabularnewline
152 & 0.979573 & 0.0408542 & 0.0204271 \tabularnewline
153 & 0.974682 & 0.0506365 & 0.0253183 \tabularnewline
154 & 0.982189 & 0.0356228 & 0.0178114 \tabularnewline
155 & 0.977715 & 0.04457 & 0.022285 \tabularnewline
156 & 0.990261 & 0.0194781 & 0.00973906 \tabularnewline
157 & 0.988935 & 0.022131 & 0.0110655 \tabularnewline
158 & 0.986155 & 0.0276906 & 0.0138453 \tabularnewline
159 & 0.983159 & 0.0336826 & 0.0168413 \tabularnewline
160 & 0.98545 & 0.0290993 & 0.0145496 \tabularnewline
161 & 0.981871 & 0.036258 & 0.018129 \tabularnewline
162 & 0.978319 & 0.0433618 & 0.0216809 \tabularnewline
163 & 0.9822 & 0.0356008 & 0.0178004 \tabularnewline
164 & 0.980192 & 0.0396163 & 0.0198081 \tabularnewline
165 & 0.981609 & 0.0367824 & 0.0183912 \tabularnewline
166 & 0.980903 & 0.0381933 & 0.0190967 \tabularnewline
167 & 0.977152 & 0.0456958 & 0.0228479 \tabularnewline
168 & 0.976187 & 0.0476266 & 0.0238133 \tabularnewline
169 & 0.989262 & 0.021476 & 0.010738 \tabularnewline
170 & 0.9875 & 0.0250001 & 0.0125001 \tabularnewline
171 & 0.991766 & 0.0164682 & 0.00823412 \tabularnewline
172 & 0.989375 & 0.0212494 & 0.0106247 \tabularnewline
173 & 0.988677 & 0.0226452 & 0.0113226 \tabularnewline
174 & 0.98743 & 0.0251408 & 0.0125704 \tabularnewline
175 & 0.989347 & 0.0213067 & 0.0106533 \tabularnewline
176 & 0.986295 & 0.0274108 & 0.0137054 \tabularnewline
177 & 0.986534 & 0.0269315 & 0.0134658 \tabularnewline
178 & 0.982869 & 0.0342611 & 0.0171306 \tabularnewline
179 & 0.978284 & 0.0434319 & 0.0217159 \tabularnewline
180 & 0.972748 & 0.0545043 & 0.0272521 \tabularnewline
181 & 0.972667 & 0.0546664 & 0.0273332 \tabularnewline
182 & 0.966276 & 0.0674484 & 0.0337242 \tabularnewline
183 & 0.969 & 0.0619995 & 0.0309997 \tabularnewline
184 & 0.962116 & 0.0757676 & 0.0378838 \tabularnewline
185 & 0.955444 & 0.0891114 & 0.0445557 \tabularnewline
186 & 0.961772 & 0.0764562 & 0.0382281 \tabularnewline
187 & 0.954865 & 0.0902697 & 0.0451349 \tabularnewline
188 & 0.945914 & 0.108171 & 0.0540857 \tabularnewline
189 & 0.937038 & 0.125924 & 0.0629618 \tabularnewline
190 & 0.982516 & 0.0349676 & 0.0174838 \tabularnewline
191 & 0.980239 & 0.0395228 & 0.0197614 \tabularnewline
192 & 0.981156 & 0.0376879 & 0.018844 \tabularnewline
193 & 0.986839 & 0.0263224 & 0.0131612 \tabularnewline
194 & 0.985551 & 0.028897 & 0.0144485 \tabularnewline
195 & 0.982944 & 0.0341124 & 0.0170562 \tabularnewline
196 & 0.979989 & 0.0400218 & 0.0200109 \tabularnewline
197 & 0.97853 & 0.0429404 & 0.0214702 \tabularnewline
198 & 0.992885 & 0.0142306 & 0.00711528 \tabularnewline
199 & 0.991079 & 0.0178426 & 0.0089213 \tabularnewline
200 & 0.989178 & 0.021644 & 0.010822 \tabularnewline
201 & 0.986216 & 0.0275688 & 0.0137844 \tabularnewline
202 & 0.986416 & 0.0271689 & 0.0135844 \tabularnewline
203 & 0.982911 & 0.0341782 & 0.0170891 \tabularnewline
204 & 0.984823 & 0.0303535 & 0.0151767 \tabularnewline
205 & 0.98038 & 0.0392397 & 0.0196199 \tabularnewline
206 & 0.975044 & 0.0499117 & 0.0249559 \tabularnewline
207 & 0.976688 & 0.0466246 & 0.0233123 \tabularnewline
208 & 0.970148 & 0.0597043 & 0.0298522 \tabularnewline
209 & 0.963997 & 0.0720065 & 0.0360033 \tabularnewline
210 & 0.955598 & 0.0888041 & 0.0444021 \tabularnewline
211 & 0.952142 & 0.0957154 & 0.0478577 \tabularnewline
212 & 0.948605 & 0.10279 & 0.0513949 \tabularnewline
213 & 0.96123 & 0.0775407 & 0.0387704 \tabularnewline
214 & 0.955442 & 0.0891164 & 0.0445582 \tabularnewline
215 & 0.9509 & 0.0982005 & 0.0491002 \tabularnewline
216 & 0.938006 & 0.123989 & 0.0619944 \tabularnewline
217 & 0.935648 & 0.128703 & 0.0643517 \tabularnewline
218 & 0.920222 & 0.159555 & 0.0797775 \tabularnewline
219 & 0.9042 & 0.191599 & 0.0957996 \tabularnewline
220 & 0.881674 & 0.236653 & 0.118326 \tabularnewline
221 & 0.855053 & 0.289894 & 0.144947 \tabularnewline
222 & 0.829298 & 0.341404 & 0.170702 \tabularnewline
223 & 0.809109 & 0.381781 & 0.190891 \tabularnewline
224 & 0.785008 & 0.429984 & 0.214992 \tabularnewline
225 & 0.747849 & 0.504303 & 0.252151 \tabularnewline
226 & 0.717374 & 0.565251 & 0.282626 \tabularnewline
227 & 0.695575 & 0.608849 & 0.304425 \tabularnewline
228 & 0.730773 & 0.538454 & 0.269227 \tabularnewline
229 & 0.70617 & 0.58766 & 0.29383 \tabularnewline
230 & 0.673094 & 0.653812 & 0.326906 \tabularnewline
231 & 0.681726 & 0.636549 & 0.318274 \tabularnewline
232 & 0.674148 & 0.651704 & 0.325852 \tabularnewline
233 & 0.643111 & 0.713779 & 0.356889 \tabularnewline
234 & 0.598583 & 0.802835 & 0.401417 \tabularnewline
235 & 0.686859 & 0.626281 & 0.313141 \tabularnewline
236 & 0.793584 & 0.412832 & 0.206416 \tabularnewline
237 & 0.758366 & 0.483268 & 0.241634 \tabularnewline
238 & 0.713691 & 0.572618 & 0.286309 \tabularnewline
239 & 0.698724 & 0.602552 & 0.301276 \tabularnewline
240 & 0.704215 & 0.59157 & 0.295785 \tabularnewline
241 & 0.652989 & 0.694022 & 0.347011 \tabularnewline
242 & 0.594398 & 0.811204 & 0.405602 \tabularnewline
243 & 0.6596 & 0.6808 & 0.3404 \tabularnewline
244 & 0.611041 & 0.777919 & 0.388959 \tabularnewline
245 & 0.558059 & 0.883882 & 0.441941 \tabularnewline
246 & 0.606732 & 0.786536 & 0.393268 \tabularnewline
247 & 0.535038 & 0.929923 & 0.464962 \tabularnewline
248 & 0.490561 & 0.981122 & 0.509439 \tabularnewline
249 & 0.420869 & 0.841739 & 0.579131 \tabularnewline
250 & 0.366855 & 0.733709 & 0.633145 \tabularnewline
251 & 0.294411 & 0.588821 & 0.705589 \tabularnewline
252 & 0.373255 & 0.746509 & 0.626745 \tabularnewline
253 & 0.520381 & 0.959238 & 0.479619 \tabularnewline
254 & 0.453271 & 0.906542 & 0.546729 \tabularnewline
255 & 0.40645 & 0.812899 & 0.59355 \tabularnewline
256 & 0.547722 & 0.904555 & 0.452278 \tabularnewline
257 & 0.924986 & 0.150028 & 0.0750142 \tabularnewline
258 & 0.887204 & 0.225592 & 0.112796 \tabularnewline
259 & 0.907632 & 0.184736 & 0.0923682 \tabularnewline
260 & 0.771491 & 0.457018 & 0.228509 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252919&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]27[/C][C]0.37743[/C][C]0.754861[/C][C]0.62257[/C][/ROW]
[ROW][C]28[/C][C]0.480531[/C][C]0.961062[/C][C]0.519469[/C][/ROW]
[ROW][C]29[/C][C]0.388883[/C][C]0.777767[/C][C]0.611117[/C][/ROW]
[ROW][C]30[/C][C]0.299891[/C][C]0.599782[/C][C]0.700109[/C][/ROW]
[ROW][C]31[/C][C]0.201577[/C][C]0.403154[/C][C]0.798423[/C][/ROW]
[ROW][C]32[/C][C]0.262383[/C][C]0.524767[/C][C]0.737617[/C][/ROW]
[ROW][C]33[/C][C]0.268245[/C][C]0.536489[/C][C]0.731755[/C][/ROW]
[ROW][C]34[/C][C]0.19909[/C][C]0.398179[/C][C]0.80091[/C][/ROW]
[ROW][C]35[/C][C]0.140615[/C][C]0.281231[/C][C]0.859385[/C][/ROW]
[ROW][C]36[/C][C]0.291805[/C][C]0.58361[/C][C]0.708195[/C][/ROW]
[ROW][C]37[/C][C]0.252053[/C][C]0.504105[/C][C]0.747947[/C][/ROW]
[ROW][C]38[/C][C]0.196543[/C][C]0.393086[/C][C]0.803457[/C][/ROW]
[ROW][C]39[/C][C]0.145838[/C][C]0.291676[/C][C]0.854162[/C][/ROW]
[ROW][C]40[/C][C]0.105481[/C][C]0.210961[/C][C]0.894519[/C][/ROW]
[ROW][C]41[/C][C]0.428558[/C][C]0.857116[/C][C]0.571442[/C][/ROW]
[ROW][C]42[/C][C]0.367881[/C][C]0.735763[/C][C]0.632119[/C][/ROW]
[ROW][C]43[/C][C]0.550697[/C][C]0.898605[/C][C]0.449303[/C][/ROW]
[ROW][C]44[/C][C]0.513319[/C][C]0.973361[/C][C]0.486681[/C][/ROW]
[ROW][C]45[/C][C]0.57499[/C][C]0.85002[/C][C]0.42501[/C][/ROW]
[ROW][C]46[/C][C]0.512454[/C][C]0.975092[/C][C]0.487546[/C][/ROW]
[ROW][C]47[/C][C]0.462102[/C][C]0.924205[/C][C]0.537898[/C][/ROW]
[ROW][C]48[/C][C]0.43504[/C][C]0.87008[/C][C]0.56496[/C][/ROW]
[ROW][C]49[/C][C]0.393217[/C][C]0.786433[/C][C]0.606783[/C][/ROW]
[ROW][C]50[/C][C]0.491031[/C][C]0.982061[/C][C]0.508969[/C][/ROW]
[ROW][C]51[/C][C]0.62443[/C][C]0.75114[/C][C]0.37557[/C][/ROW]
[ROW][C]52[/C][C]0.654722[/C][C]0.690555[/C][C]0.345278[/C][/ROW]
[ROW][C]53[/C][C]0.605615[/C][C]0.78877[/C][C]0.394385[/C][/ROW]
[ROW][C]54[/C][C]0.60928[/C][C]0.781441[/C][C]0.39072[/C][/ROW]
[ROW][C]55[/C][C]0.557513[/C][C]0.884974[/C][C]0.442487[/C][/ROW]
[ROW][C]56[/C][C]0.657741[/C][C]0.684519[/C][C]0.342259[/C][/ROW]
[ROW][C]57[/C][C]0.777977[/C][C]0.444045[/C][C]0.222023[/C][/ROW]
[ROW][C]58[/C][C]0.740886[/C][C]0.518227[/C][C]0.259114[/C][/ROW]
[ROW][C]59[/C][C]0.825948[/C][C]0.348104[/C][C]0.174052[/C][/ROW]
[ROW][C]60[/C][C]0.909522[/C][C]0.180955[/C][C]0.0904776[/C][/ROW]
[ROW][C]61[/C][C]0.894137[/C][C]0.211725[/C][C]0.105863[/C][/ROW]
[ROW][C]62[/C][C]0.905632[/C][C]0.188736[/C][C]0.0943679[/C][/ROW]
[ROW][C]63[/C][C]0.887442[/C][C]0.225116[/C][C]0.112558[/C][/ROW]
[ROW][C]64[/C][C]0.881518[/C][C]0.236965[/C][C]0.118482[/C][/ROW]
[ROW][C]65[/C][C]0.915833[/C][C]0.168334[/C][C]0.0841668[/C][/ROW]
[ROW][C]66[/C][C]0.947921[/C][C]0.104158[/C][C]0.0520789[/C][/ROW]
[ROW][C]67[/C][C]0.941014[/C][C]0.117972[/C][C]0.0589859[/C][/ROW]
[ROW][C]68[/C][C]0.932232[/C][C]0.135537[/C][C]0.0677685[/C][/ROW]
[ROW][C]69[/C][C]0.917778[/C][C]0.164444[/C][C]0.0822222[/C][/ROW]
[ROW][C]70[/C][C]0.914782[/C][C]0.170435[/C][C]0.0852176[/C][/ROW]
[ROW][C]71[/C][C]0.915614[/C][C]0.168773[/C][C]0.0843863[/C][/ROW]
[ROW][C]72[/C][C]0.898846[/C][C]0.202307[/C][C]0.101154[/C][/ROW]
[ROW][C]73[/C][C]0.887873[/C][C]0.224254[/C][C]0.112127[/C][/ROW]
[ROW][C]74[/C][C]0.867704[/C][C]0.264592[/C][C]0.132296[/C][/ROW]
[ROW][C]75[/C][C]0.846514[/C][C]0.306971[/C][C]0.153486[/C][/ROW]
[ROW][C]76[/C][C]0.856997[/C][C]0.286007[/C][C]0.143003[/C][/ROW]
[ROW][C]77[/C][C]0.877907[/C][C]0.244185[/C][C]0.122093[/C][/ROW]
[ROW][C]78[/C][C]0.868033[/C][C]0.263935[/C][C]0.131967[/C][/ROW]
[ROW][C]79[/C][C]0.852945[/C][C]0.294109[/C][C]0.147055[/C][/ROW]
[ROW][C]80[/C][C]0.829715[/C][C]0.340569[/C][C]0.170285[/C][/ROW]
[ROW][C]81[/C][C]0.819054[/C][C]0.361892[/C][C]0.180946[/C][/ROW]
[ROW][C]82[/C][C]0.800734[/C][C]0.398532[/C][C]0.199266[/C][/ROW]
[ROW][C]83[/C][C]0.80091[/C][C]0.398179[/C][C]0.19909[/C][/ROW]
[ROW][C]84[/C][C]0.798498[/C][C]0.403004[/C][C]0.201502[/C][/ROW]
[ROW][C]85[/C][C]0.811422[/C][C]0.377157[/C][C]0.188578[/C][/ROW]
[ROW][C]86[/C][C]0.786753[/C][C]0.426495[/C][C]0.213247[/C][/ROW]
[ROW][C]87[/C][C]0.784622[/C][C]0.430756[/C][C]0.215378[/C][/ROW]
[ROW][C]88[/C][C]0.777025[/C][C]0.445951[/C][C]0.222975[/C][/ROW]
[ROW][C]89[/C][C]0.746843[/C][C]0.506315[/C][C]0.253157[/C][/ROW]
[ROW][C]90[/C][C]0.715797[/C][C]0.568406[/C][C]0.284203[/C][/ROW]
[ROW][C]91[/C][C]0.695439[/C][C]0.609121[/C][C]0.304561[/C][/ROW]
[ROW][C]92[/C][C]0.71752[/C][C]0.56496[/C][C]0.28248[/C][/ROW]
[ROW][C]93[/C][C]0.79582[/C][C]0.408359[/C][C]0.20418[/C][/ROW]
[ROW][C]94[/C][C]0.80509[/C][C]0.38982[/C][C]0.19491[/C][/ROW]
[ROW][C]95[/C][C]0.863593[/C][C]0.272813[/C][C]0.136407[/C][/ROW]
[ROW][C]96[/C][C]0.84453[/C][C]0.310941[/C][C]0.15547[/C][/ROW]
[ROW][C]97[/C][C]0.835236[/C][C]0.329528[/C][C]0.164764[/C][/ROW]
[ROW][C]98[/C][C]0.873315[/C][C]0.253371[/C][C]0.126685[/C][/ROW]
[ROW][C]99[/C][C]0.859948[/C][C]0.280104[/C][C]0.140052[/C][/ROW]
[ROW][C]100[/C][C]0.855437[/C][C]0.289127[/C][C]0.144563[/C][/ROW]
[ROW][C]101[/C][C]0.840203[/C][C]0.319594[/C][C]0.159797[/C][/ROW]
[ROW][C]102[/C][C]0.892481[/C][C]0.215038[/C][C]0.107519[/C][/ROW]
[ROW][C]103[/C][C]0.941761[/C][C]0.116479[/C][C]0.0582393[/C][/ROW]
[ROW][C]104[/C][C]0.934038[/C][C]0.131924[/C][C]0.065962[/C][/ROW]
[ROW][C]105[/C][C]0.925101[/C][C]0.149799[/C][C]0.0748994[/C][/ROW]
[ROW][C]106[/C][C]0.923204[/C][C]0.153593[/C][C]0.0767964[/C][/ROW]
[ROW][C]107[/C][C]0.931047[/C][C]0.137906[/C][C]0.0689528[/C][/ROW]
[ROW][C]108[/C][C]0.935736[/C][C]0.128527[/C][C]0.0642637[/C][/ROW]
[ROW][C]109[/C][C]0.936883[/C][C]0.126234[/C][C]0.0631169[/C][/ROW]
[ROW][C]110[/C][C]0.934979[/C][C]0.130042[/C][C]0.0650212[/C][/ROW]
[ROW][C]111[/C][C]0.96521[/C][C]0.0695808[/C][C]0.0347904[/C][/ROW]
[ROW][C]112[/C][C]0.985057[/C][C]0.0298858[/C][C]0.0149429[/C][/ROW]
[ROW][C]113[/C][C]0.985612[/C][C]0.028777[/C][C]0.0143885[/C][/ROW]
[ROW][C]114[/C][C]0.98367[/C][C]0.0326591[/C][C]0.0163295[/C][/ROW]
[ROW][C]115[/C][C]0.982829[/C][C]0.0343414[/C][C]0.0171707[/C][/ROW]
[ROW][C]116[/C][C]0.984827[/C][C]0.0303455[/C][C]0.0151728[/C][/ROW]
[ROW][C]117[/C][C]0.981816[/C][C]0.0363673[/C][C]0.0181837[/C][/ROW]
[ROW][C]118[/C][C]0.991986[/C][C]0.0160275[/C][C]0.00801376[/C][/ROW]
[ROW][C]119[/C][C]0.992549[/C][C]0.0149022[/C][C]0.00745109[/C][/ROW]
[ROW][C]120[/C][C]0.991189[/C][C]0.0176216[/C][C]0.00881079[/C][/ROW]
[ROW][C]121[/C][C]0.991282[/C][C]0.0174361[/C][C]0.00871807[/C][/ROW]
[ROW][C]122[/C][C]0.990775[/C][C]0.0184506[/C][C]0.00922531[/C][/ROW]
[ROW][C]123[/C][C]0.989131[/C][C]0.0217387[/C][C]0.0108694[/C][/ROW]
[ROW][C]124[/C][C]0.987238[/C][C]0.0255235[/C][C]0.0127618[/C][/ROW]
[ROW][C]125[/C][C]0.995771[/C][C]0.00845806[/C][C]0.00422903[/C][/ROW]
[ROW][C]126[/C][C]0.994955[/C][C]0.0100901[/C][C]0.00504503[/C][/ROW]
[ROW][C]127[/C][C]0.998241[/C][C]0.00351806[/C][C]0.00175903[/C][/ROW]
[ROW][C]128[/C][C]0.998193[/C][C]0.00361481[/C][C]0.0018074[/C][/ROW]
[ROW][C]129[/C][C]0.997794[/C][C]0.00441295[/C][C]0.00220648[/C][/ROW]
[ROW][C]130[/C][C]0.997436[/C][C]0.00512728[/C][C]0.00256364[/C][/ROW]
[ROW][C]131[/C][C]0.996954[/C][C]0.00609128[/C][C]0.00304564[/C][/ROW]
[ROW][C]132[/C][C]0.996213[/C][C]0.00757336[/C][C]0.00378668[/C][/ROW]
[ROW][C]133[/C][C]0.995817[/C][C]0.00836689[/C][C]0.00418345[/C][/ROW]
[ROW][C]134[/C][C]0.9947[/C][C]0.0105992[/C][C]0.0052996[/C][/ROW]
[ROW][C]135[/C][C]0.993581[/C][C]0.0128379[/C][C]0.00641897[/C][/ROW]
[ROW][C]136[/C][C]0.993969[/C][C]0.0120627[/C][C]0.00603135[/C][/ROW]
[ROW][C]137[/C][C]0.992723[/C][C]0.0145547[/C][C]0.00727734[/C][/ROW]
[ROW][C]138[/C][C]0.990969[/C][C]0.0180626[/C][C]0.00903128[/C][/ROW]
[ROW][C]139[/C][C]0.988506[/C][C]0.0229884[/C][C]0.0114942[/C][/ROW]
[ROW][C]140[/C][C]0.9894[/C][C]0.0212002[/C][C]0.0106001[/C][/ROW]
[ROW][C]141[/C][C]0.995922[/C][C]0.00815526[/C][C]0.00407763[/C][/ROW]
[ROW][C]142[/C][C]0.995004[/C][C]0.00999117[/C][C]0.00499558[/C][/ROW]
[ROW][C]143[/C][C]0.993552[/C][C]0.0128962[/C][C]0.00644808[/C][/ROW]
[ROW][C]144[/C][C]0.992548[/C][C]0.0149031[/C][C]0.00745153[/C][/ROW]
[ROW][C]145[/C][C]0.991192[/C][C]0.0176152[/C][C]0.0088076[/C][/ROW]
[ROW][C]146[/C][C]0.989493[/C][C]0.021013[/C][C]0.0105065[/C][/ROW]
[ROW][C]147[/C][C]0.989057[/C][C]0.0218869[/C][C]0.0109434[/C][/ROW]
[ROW][C]148[/C][C]0.989612[/C][C]0.0207756[/C][C]0.0103878[/C][/ROW]
[ROW][C]149[/C][C]0.987631[/C][C]0.024737[/C][C]0.0123685[/C][/ROW]
[ROW][C]150[/C][C]0.985202[/C][C]0.0295957[/C][C]0.0147978[/C][/ROW]
[ROW][C]151[/C][C]0.982109[/C][C]0.0357823[/C][C]0.0178911[/C][/ROW]
[ROW][C]152[/C][C]0.979573[/C][C]0.0408542[/C][C]0.0204271[/C][/ROW]
[ROW][C]153[/C][C]0.974682[/C][C]0.0506365[/C][C]0.0253183[/C][/ROW]
[ROW][C]154[/C][C]0.982189[/C][C]0.0356228[/C][C]0.0178114[/C][/ROW]
[ROW][C]155[/C][C]0.977715[/C][C]0.04457[/C][C]0.022285[/C][/ROW]
[ROW][C]156[/C][C]0.990261[/C][C]0.0194781[/C][C]0.00973906[/C][/ROW]
[ROW][C]157[/C][C]0.988935[/C][C]0.022131[/C][C]0.0110655[/C][/ROW]
[ROW][C]158[/C][C]0.986155[/C][C]0.0276906[/C][C]0.0138453[/C][/ROW]
[ROW][C]159[/C][C]0.983159[/C][C]0.0336826[/C][C]0.0168413[/C][/ROW]
[ROW][C]160[/C][C]0.98545[/C][C]0.0290993[/C][C]0.0145496[/C][/ROW]
[ROW][C]161[/C][C]0.981871[/C][C]0.036258[/C][C]0.018129[/C][/ROW]
[ROW][C]162[/C][C]0.978319[/C][C]0.0433618[/C][C]0.0216809[/C][/ROW]
[ROW][C]163[/C][C]0.9822[/C][C]0.0356008[/C][C]0.0178004[/C][/ROW]
[ROW][C]164[/C][C]0.980192[/C][C]0.0396163[/C][C]0.0198081[/C][/ROW]
[ROW][C]165[/C][C]0.981609[/C][C]0.0367824[/C][C]0.0183912[/C][/ROW]
[ROW][C]166[/C][C]0.980903[/C][C]0.0381933[/C][C]0.0190967[/C][/ROW]
[ROW][C]167[/C][C]0.977152[/C][C]0.0456958[/C][C]0.0228479[/C][/ROW]
[ROW][C]168[/C][C]0.976187[/C][C]0.0476266[/C][C]0.0238133[/C][/ROW]
[ROW][C]169[/C][C]0.989262[/C][C]0.021476[/C][C]0.010738[/C][/ROW]
[ROW][C]170[/C][C]0.9875[/C][C]0.0250001[/C][C]0.0125001[/C][/ROW]
[ROW][C]171[/C][C]0.991766[/C][C]0.0164682[/C][C]0.00823412[/C][/ROW]
[ROW][C]172[/C][C]0.989375[/C][C]0.0212494[/C][C]0.0106247[/C][/ROW]
[ROW][C]173[/C][C]0.988677[/C][C]0.0226452[/C][C]0.0113226[/C][/ROW]
[ROW][C]174[/C][C]0.98743[/C][C]0.0251408[/C][C]0.0125704[/C][/ROW]
[ROW][C]175[/C][C]0.989347[/C][C]0.0213067[/C][C]0.0106533[/C][/ROW]
[ROW][C]176[/C][C]0.986295[/C][C]0.0274108[/C][C]0.0137054[/C][/ROW]
[ROW][C]177[/C][C]0.986534[/C][C]0.0269315[/C][C]0.0134658[/C][/ROW]
[ROW][C]178[/C][C]0.982869[/C][C]0.0342611[/C][C]0.0171306[/C][/ROW]
[ROW][C]179[/C][C]0.978284[/C][C]0.0434319[/C][C]0.0217159[/C][/ROW]
[ROW][C]180[/C][C]0.972748[/C][C]0.0545043[/C][C]0.0272521[/C][/ROW]
[ROW][C]181[/C][C]0.972667[/C][C]0.0546664[/C][C]0.0273332[/C][/ROW]
[ROW][C]182[/C][C]0.966276[/C][C]0.0674484[/C][C]0.0337242[/C][/ROW]
[ROW][C]183[/C][C]0.969[/C][C]0.0619995[/C][C]0.0309997[/C][/ROW]
[ROW][C]184[/C][C]0.962116[/C][C]0.0757676[/C][C]0.0378838[/C][/ROW]
[ROW][C]185[/C][C]0.955444[/C][C]0.0891114[/C][C]0.0445557[/C][/ROW]
[ROW][C]186[/C][C]0.961772[/C][C]0.0764562[/C][C]0.0382281[/C][/ROW]
[ROW][C]187[/C][C]0.954865[/C][C]0.0902697[/C][C]0.0451349[/C][/ROW]
[ROW][C]188[/C][C]0.945914[/C][C]0.108171[/C][C]0.0540857[/C][/ROW]
[ROW][C]189[/C][C]0.937038[/C][C]0.125924[/C][C]0.0629618[/C][/ROW]
[ROW][C]190[/C][C]0.982516[/C][C]0.0349676[/C][C]0.0174838[/C][/ROW]
[ROW][C]191[/C][C]0.980239[/C][C]0.0395228[/C][C]0.0197614[/C][/ROW]
[ROW][C]192[/C][C]0.981156[/C][C]0.0376879[/C][C]0.018844[/C][/ROW]
[ROW][C]193[/C][C]0.986839[/C][C]0.0263224[/C][C]0.0131612[/C][/ROW]
[ROW][C]194[/C][C]0.985551[/C][C]0.028897[/C][C]0.0144485[/C][/ROW]
[ROW][C]195[/C][C]0.982944[/C][C]0.0341124[/C][C]0.0170562[/C][/ROW]
[ROW][C]196[/C][C]0.979989[/C][C]0.0400218[/C][C]0.0200109[/C][/ROW]
[ROW][C]197[/C][C]0.97853[/C][C]0.0429404[/C][C]0.0214702[/C][/ROW]
[ROW][C]198[/C][C]0.992885[/C][C]0.0142306[/C][C]0.00711528[/C][/ROW]
[ROW][C]199[/C][C]0.991079[/C][C]0.0178426[/C][C]0.0089213[/C][/ROW]
[ROW][C]200[/C][C]0.989178[/C][C]0.021644[/C][C]0.010822[/C][/ROW]
[ROW][C]201[/C][C]0.986216[/C][C]0.0275688[/C][C]0.0137844[/C][/ROW]
[ROW][C]202[/C][C]0.986416[/C][C]0.0271689[/C][C]0.0135844[/C][/ROW]
[ROW][C]203[/C][C]0.982911[/C][C]0.0341782[/C][C]0.0170891[/C][/ROW]
[ROW][C]204[/C][C]0.984823[/C][C]0.0303535[/C][C]0.0151767[/C][/ROW]
[ROW][C]205[/C][C]0.98038[/C][C]0.0392397[/C][C]0.0196199[/C][/ROW]
[ROW][C]206[/C][C]0.975044[/C][C]0.0499117[/C][C]0.0249559[/C][/ROW]
[ROW][C]207[/C][C]0.976688[/C][C]0.0466246[/C][C]0.0233123[/C][/ROW]
[ROW][C]208[/C][C]0.970148[/C][C]0.0597043[/C][C]0.0298522[/C][/ROW]
[ROW][C]209[/C][C]0.963997[/C][C]0.0720065[/C][C]0.0360033[/C][/ROW]
[ROW][C]210[/C][C]0.955598[/C][C]0.0888041[/C][C]0.0444021[/C][/ROW]
[ROW][C]211[/C][C]0.952142[/C][C]0.0957154[/C][C]0.0478577[/C][/ROW]
[ROW][C]212[/C][C]0.948605[/C][C]0.10279[/C][C]0.0513949[/C][/ROW]
[ROW][C]213[/C][C]0.96123[/C][C]0.0775407[/C][C]0.0387704[/C][/ROW]
[ROW][C]214[/C][C]0.955442[/C][C]0.0891164[/C][C]0.0445582[/C][/ROW]
[ROW][C]215[/C][C]0.9509[/C][C]0.0982005[/C][C]0.0491002[/C][/ROW]
[ROW][C]216[/C][C]0.938006[/C][C]0.123989[/C][C]0.0619944[/C][/ROW]
[ROW][C]217[/C][C]0.935648[/C][C]0.128703[/C][C]0.0643517[/C][/ROW]
[ROW][C]218[/C][C]0.920222[/C][C]0.159555[/C][C]0.0797775[/C][/ROW]
[ROW][C]219[/C][C]0.9042[/C][C]0.191599[/C][C]0.0957996[/C][/ROW]
[ROW][C]220[/C][C]0.881674[/C][C]0.236653[/C][C]0.118326[/C][/ROW]
[ROW][C]221[/C][C]0.855053[/C][C]0.289894[/C][C]0.144947[/C][/ROW]
[ROW][C]222[/C][C]0.829298[/C][C]0.341404[/C][C]0.170702[/C][/ROW]
[ROW][C]223[/C][C]0.809109[/C][C]0.381781[/C][C]0.190891[/C][/ROW]
[ROW][C]224[/C][C]0.785008[/C][C]0.429984[/C][C]0.214992[/C][/ROW]
[ROW][C]225[/C][C]0.747849[/C][C]0.504303[/C][C]0.252151[/C][/ROW]
[ROW][C]226[/C][C]0.717374[/C][C]0.565251[/C][C]0.282626[/C][/ROW]
[ROW][C]227[/C][C]0.695575[/C][C]0.608849[/C][C]0.304425[/C][/ROW]
[ROW][C]228[/C][C]0.730773[/C][C]0.538454[/C][C]0.269227[/C][/ROW]
[ROW][C]229[/C][C]0.70617[/C][C]0.58766[/C][C]0.29383[/C][/ROW]
[ROW][C]230[/C][C]0.673094[/C][C]0.653812[/C][C]0.326906[/C][/ROW]
[ROW][C]231[/C][C]0.681726[/C][C]0.636549[/C][C]0.318274[/C][/ROW]
[ROW][C]232[/C][C]0.674148[/C][C]0.651704[/C][C]0.325852[/C][/ROW]
[ROW][C]233[/C][C]0.643111[/C][C]0.713779[/C][C]0.356889[/C][/ROW]
[ROW][C]234[/C][C]0.598583[/C][C]0.802835[/C][C]0.401417[/C][/ROW]
[ROW][C]235[/C][C]0.686859[/C][C]0.626281[/C][C]0.313141[/C][/ROW]
[ROW][C]236[/C][C]0.793584[/C][C]0.412832[/C][C]0.206416[/C][/ROW]
[ROW][C]237[/C][C]0.758366[/C][C]0.483268[/C][C]0.241634[/C][/ROW]
[ROW][C]238[/C][C]0.713691[/C][C]0.572618[/C][C]0.286309[/C][/ROW]
[ROW][C]239[/C][C]0.698724[/C][C]0.602552[/C][C]0.301276[/C][/ROW]
[ROW][C]240[/C][C]0.704215[/C][C]0.59157[/C][C]0.295785[/C][/ROW]
[ROW][C]241[/C][C]0.652989[/C][C]0.694022[/C][C]0.347011[/C][/ROW]
[ROW][C]242[/C][C]0.594398[/C][C]0.811204[/C][C]0.405602[/C][/ROW]
[ROW][C]243[/C][C]0.6596[/C][C]0.6808[/C][C]0.3404[/C][/ROW]
[ROW][C]244[/C][C]0.611041[/C][C]0.777919[/C][C]0.388959[/C][/ROW]
[ROW][C]245[/C][C]0.558059[/C][C]0.883882[/C][C]0.441941[/C][/ROW]
[ROW][C]246[/C][C]0.606732[/C][C]0.786536[/C][C]0.393268[/C][/ROW]
[ROW][C]247[/C][C]0.535038[/C][C]0.929923[/C][C]0.464962[/C][/ROW]
[ROW][C]248[/C][C]0.490561[/C][C]0.981122[/C][C]0.509439[/C][/ROW]
[ROW][C]249[/C][C]0.420869[/C][C]0.841739[/C][C]0.579131[/C][/ROW]
[ROW][C]250[/C][C]0.366855[/C][C]0.733709[/C][C]0.633145[/C][/ROW]
[ROW][C]251[/C][C]0.294411[/C][C]0.588821[/C][C]0.705589[/C][/ROW]
[ROW][C]252[/C][C]0.373255[/C][C]0.746509[/C][C]0.626745[/C][/ROW]
[ROW][C]253[/C][C]0.520381[/C][C]0.959238[/C][C]0.479619[/C][/ROW]
[ROW][C]254[/C][C]0.453271[/C][C]0.906542[/C][C]0.546729[/C][/ROW]
[ROW][C]255[/C][C]0.40645[/C][C]0.812899[/C][C]0.59355[/C][/ROW]
[ROW][C]256[/C][C]0.547722[/C][C]0.904555[/C][C]0.452278[/C][/ROW]
[ROW][C]257[/C][C]0.924986[/C][C]0.150028[/C][C]0.0750142[/C][/ROW]
[ROW][C]258[/C][C]0.887204[/C][C]0.225592[/C][C]0.112796[/C][/ROW]
[ROW][C]259[/C][C]0.907632[/C][C]0.184736[/C][C]0.0923682[/C][/ROW]
[ROW][C]260[/C][C]0.771491[/C][C]0.457018[/C][C]0.228509[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252919&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252919&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
270.377430.7548610.62257
280.4805310.9610620.519469
290.3888830.7777670.611117
300.2998910.5997820.700109
310.2015770.4031540.798423
320.2623830.5247670.737617
330.2682450.5364890.731755
340.199090.3981790.80091
350.1406150.2812310.859385
360.2918050.583610.708195
370.2520530.5041050.747947
380.1965430.3930860.803457
390.1458380.2916760.854162
400.1054810.2109610.894519
410.4285580.8571160.571442
420.3678810.7357630.632119
430.5506970.8986050.449303
440.5133190.9733610.486681
450.574990.850020.42501
460.5124540.9750920.487546
470.4621020.9242050.537898
480.435040.870080.56496
490.3932170.7864330.606783
500.4910310.9820610.508969
510.624430.751140.37557
520.6547220.6905550.345278
530.6056150.788770.394385
540.609280.7814410.39072
550.5575130.8849740.442487
560.6577410.6845190.342259
570.7779770.4440450.222023
580.7408860.5182270.259114
590.8259480.3481040.174052
600.9095220.1809550.0904776
610.8941370.2117250.105863
620.9056320.1887360.0943679
630.8874420.2251160.112558
640.8815180.2369650.118482
650.9158330.1683340.0841668
660.9479210.1041580.0520789
670.9410140.1179720.0589859
680.9322320.1355370.0677685
690.9177780.1644440.0822222
700.9147820.1704350.0852176
710.9156140.1687730.0843863
720.8988460.2023070.101154
730.8878730.2242540.112127
740.8677040.2645920.132296
750.8465140.3069710.153486
760.8569970.2860070.143003
770.8779070.2441850.122093
780.8680330.2639350.131967
790.8529450.2941090.147055
800.8297150.3405690.170285
810.8190540.3618920.180946
820.8007340.3985320.199266
830.800910.3981790.19909
840.7984980.4030040.201502
850.8114220.3771570.188578
860.7867530.4264950.213247
870.7846220.4307560.215378
880.7770250.4459510.222975
890.7468430.5063150.253157
900.7157970.5684060.284203
910.6954390.6091210.304561
920.717520.564960.28248
930.795820.4083590.20418
940.805090.389820.19491
950.8635930.2728130.136407
960.844530.3109410.15547
970.8352360.3295280.164764
980.8733150.2533710.126685
990.8599480.2801040.140052
1000.8554370.2891270.144563
1010.8402030.3195940.159797
1020.8924810.2150380.107519
1030.9417610.1164790.0582393
1040.9340380.1319240.065962
1050.9251010.1497990.0748994
1060.9232040.1535930.0767964
1070.9310470.1379060.0689528
1080.9357360.1285270.0642637
1090.9368830.1262340.0631169
1100.9349790.1300420.0650212
1110.965210.06958080.0347904
1120.9850570.02988580.0149429
1130.9856120.0287770.0143885
1140.983670.03265910.0163295
1150.9828290.03434140.0171707
1160.9848270.03034550.0151728
1170.9818160.03636730.0181837
1180.9919860.01602750.00801376
1190.9925490.01490220.00745109
1200.9911890.01762160.00881079
1210.9912820.01743610.00871807
1220.9907750.01845060.00922531
1230.9891310.02173870.0108694
1240.9872380.02552350.0127618
1250.9957710.008458060.00422903
1260.9949550.01009010.00504503
1270.9982410.003518060.00175903
1280.9981930.003614810.0018074
1290.9977940.004412950.00220648
1300.9974360.005127280.00256364
1310.9969540.006091280.00304564
1320.9962130.007573360.00378668
1330.9958170.008366890.00418345
1340.99470.01059920.0052996
1350.9935810.01283790.00641897
1360.9939690.01206270.00603135
1370.9927230.01455470.00727734
1380.9909690.01806260.00903128
1390.9885060.02298840.0114942
1400.98940.02120020.0106001
1410.9959220.008155260.00407763
1420.9950040.009991170.00499558
1430.9935520.01289620.00644808
1440.9925480.01490310.00745153
1450.9911920.01761520.0088076
1460.9894930.0210130.0105065
1470.9890570.02188690.0109434
1480.9896120.02077560.0103878
1490.9876310.0247370.0123685
1500.9852020.02959570.0147978
1510.9821090.03578230.0178911
1520.9795730.04085420.0204271
1530.9746820.05063650.0253183
1540.9821890.03562280.0178114
1550.9777150.044570.022285
1560.9902610.01947810.00973906
1570.9889350.0221310.0110655
1580.9861550.02769060.0138453
1590.9831590.03368260.0168413
1600.985450.02909930.0145496
1610.9818710.0362580.018129
1620.9783190.04336180.0216809
1630.98220.03560080.0178004
1640.9801920.03961630.0198081
1650.9816090.03678240.0183912
1660.9809030.03819330.0190967
1670.9771520.04569580.0228479
1680.9761870.04762660.0238133
1690.9892620.0214760.010738
1700.98750.02500010.0125001
1710.9917660.01646820.00823412
1720.9893750.02124940.0106247
1730.9886770.02264520.0113226
1740.987430.02514080.0125704
1750.9893470.02130670.0106533
1760.9862950.02741080.0137054
1770.9865340.02693150.0134658
1780.9828690.03426110.0171306
1790.9782840.04343190.0217159
1800.9727480.05450430.0272521
1810.9726670.05466640.0273332
1820.9662760.06744840.0337242
1830.9690.06199950.0309997
1840.9621160.07576760.0378838
1850.9554440.08911140.0445557
1860.9617720.07645620.0382281
1870.9548650.09026970.0451349
1880.9459140.1081710.0540857
1890.9370380.1259240.0629618
1900.9825160.03496760.0174838
1910.9802390.03952280.0197614
1920.9811560.03768790.018844
1930.9868390.02632240.0131612
1940.9855510.0288970.0144485
1950.9829440.03411240.0170562
1960.9799890.04002180.0200109
1970.978530.04294040.0214702
1980.9928850.01423060.00711528
1990.9910790.01784260.0089213
2000.9891780.0216440.010822
2010.9862160.02756880.0137844
2020.9864160.02716890.0135844
2030.9829110.03417820.0170891
2040.9848230.03035350.0151767
2050.980380.03923970.0196199
2060.9750440.04991170.0249559
2070.9766880.04662460.0233123
2080.9701480.05970430.0298522
2090.9639970.07200650.0360033
2100.9555980.08880410.0444021
2110.9521420.09571540.0478577
2120.9486050.102790.0513949
2130.961230.07754070.0387704
2140.9554420.08911640.0445582
2150.95090.09820050.0491002
2160.9380060.1239890.0619944
2170.9356480.1287030.0643517
2180.9202220.1595550.0797775
2190.90420.1915990.0957996
2200.8816740.2366530.118326
2210.8550530.2898940.144947
2220.8292980.3414040.170702
2230.8091090.3817810.190891
2240.7850080.4299840.214992
2250.7478490.5043030.252151
2260.7173740.5652510.282626
2270.6955750.6088490.304425
2280.7307730.5384540.269227
2290.706170.587660.29383
2300.6730940.6538120.326906
2310.6817260.6365490.318274
2320.6741480.6517040.325852
2330.6431110.7137790.356889
2340.5985830.8028350.401417
2350.6868590.6262810.313141
2360.7935840.4128320.206416
2370.7583660.4832680.241634
2380.7136910.5726180.286309
2390.6987240.6025520.301276
2400.7042150.591570.295785
2410.6529890.6940220.347011
2420.5943980.8112040.405602
2430.65960.68080.3404
2440.6110410.7779190.388959
2450.5580590.8838820.441941
2460.6067320.7865360.393268
2470.5350380.9299230.464962
2480.4905610.9811220.509439
2490.4208690.8417390.579131
2500.3668550.7337090.633145
2510.2944110.5888210.705589
2520.3732550.7465090.626745
2530.5203810.9592380.479619
2540.4532710.9065420.546729
2550.406450.8128990.59355
2560.5477220.9045550.452278
2570.9249860.1500280.0750142
2580.8872040.2255920.112796
2590.9076320.1847360.0923682
2600.7714910.4570180.228509







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.042735NOK
5% type I error level850.363248NOK
10% type I error level1020.435897NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 10 & 0.042735 & NOK \tabularnewline
5% type I error level & 85 & 0.363248 & NOK \tabularnewline
10% type I error level & 102 & 0.435897 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252919&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]10[/C][C]0.042735[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]85[/C][C]0.363248[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]102[/C][C]0.435897[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252919&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252919&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.042735NOK
5% type I error level850.363248NOK
10% type I error level1020.435897NOK



Parameters (Session):
par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}