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Author's title

Author

*The author of this computation has been verified*

R Software Module

Title produced by software

Multiple Regression

Date of computation

Mon, 05 Nov 2012 12:20:27 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352136047qmd0vg99llksmce.htm/, Retrieved Thu, 31 Oct 2024 23:08:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186166, Retrieved Thu, 31 Oct 2024 23:08:43 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

153

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- RM      [Multiple Regression] [] [2012-11-05 17:20:27] [e9a6d890a409316933ed6704aac2b42b] [Current]

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Dataseries X:

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Boxplots

1	1	1	41	41	38	38	13	12	12	14	14	12	12	53	53
1	2	2	39	39	32	32	16	11	11	18	18	11	11	83	83
1	3	3	30	30	35	35	19	15	15	11	11	14	14	66	66
1	4	4	31	31	33	33	15	6	6	12	12	12	12	67	67
1	5	5	34	34	37	37	14	13	13	16	16	21	21	76	76
1	6	6	35	35	29	29	13	10	10	18	18	12	12	78	78
1	7	7	39	39	31	31	19	12	12	14	14	22	22	53	53
1	8	8	34	34	36	36	15	14	14	14	14	11	11	80	80
1	9	9	36	36	35	35	14	12	12	15	15	10	10	74	74
1	10	10	37	37	38	38	15	9	9	15	15	13	13	76	76
1	11	11	38	38	31	31	16	10	10	17	17	10	10	79	79
1	12	12	36	36	34	34	16	12	12	19	19	8	8	54	54
1	13	13	38	38	35	35	16	12	12	10	10	15	15	67	67
1	14	14	39	39	38	38	16	11	11	16	16	14	14	54	54
1	15	15	33	33	37	37	17	15	15	18	18	10	10	87	87
1	16	16	32	32	33	33	15	12	12	14	14	14	14	58	58
1	17	17	36	36	32	32	15	10	10	14	14	14	14	75	75
1	18	18	38	38	38	38	20	12	12	17	17	11	11	88	88
1	19	19	39	39	38	38	18	11	11	14	14	10	10	64	64
1	20	20	32	32	32	32	16	12	12	16	16	13	13	57	57
1	21	21	32	32	33	33	16	11	11	18	18	9.5	9.5	66	66
1	22	22	31	31	31	31	16	12	12	11	11	14	14	68	68
1	23	23	39	39	38	38	19	13	13	14	14	12	12	54	54
1	24	24	37	37	39	39	16	11	11	12	12	14	14	56	56
1	25	25	39	39	32	32	17	12	12	17	17	11	11	86	86
1	26	26	41	41	32	32	17	13	13	9	9	9	9	80	80
1	27	27	36	36	35	35	16	10	10	16	16	11	11	76	76
1	28	28	33	33	37	37	15	14	14	14	14	15	15	69	69
1	29	29	33	33	33	33	16	12	12	15	15	14	14	78	78
1	30	30	34	34	33	33	14	10	10	11	11	13	13	67	67
1	31	31	31	31	31	31	15	12	12	16	16	9	9	80	80
1	32	32	27	27	32	32	12	8	8	13	13	15	15	54	54
1	33	33	37	37	31	31	14	10	10	17	17	10	10	71	71
1	34	34	34	34	37	37	16	12	12	15	15	11	11	84	84
1	35	35	34	34	30	30	14	12	12	14	14	13	13	74	74
1	36	36	32	32	33	33	10	7	7	16	16	8	8	71	71
1	37	37	29	29	31	31	10	9	9	9	9	20	20	63	63
1	38	38	36	36	33	33	14	12	12	15	15	12	12	71	71
1	39	39	29	29	31	31	16	10	10	17	17	10	10	76	76
1	40	40	35	35	33	33	16	10	10	13	13	10	10	69	69
1	41	41	37	37	32	32	16	10	10	15	15	9	9	74	74
1	42	42	34	34	33	33	14	12	12	16	16	14	14	75	75
1	43	43	38	38	32	32	20	15	15	16	16	8	8	54	54
1	44	44	35	35	33	33	14	10	10	12	12	14	14	52	52
1	45	45	38	38	28	28	14	10	10	15	15	11	11	69	69
1	46	46	37	37	35	35	11	12	12	11	11	13	13	68	68
1	47	47	38	38	39	39	14	13	13	15	15	9	9	65	65
1	48	48	33	33	34	34	15	11	11	15	15	11	11	75	75
1	49	49	36	36	38	38	16	11	11	17	17	15	15	74	74
1	50	50	38	38	32	32	14	12	12	13	13	11	11	75	75
1	51	51	32	32	38	38	16	14	14	16	16	10	10	72	72
1	52	52	32	32	30	30	14	10	10	14	14	14	14	67	67
1	53	53	32	32	33	33	12	12	12	11	11	18	18	63	63
1	54	54	34	34	38	38	16	13	13	12	12	14	14	62	62
1	55	55	32	32	32	32	9	5	5	12	12	11	11	63	63
1	56	56	37	37	35	35	14	6	6	15	15	14.5	14.5	76	76
1	57	57	39	39	34	34	16	12	12	16	16	13	13	74	74
1	58	58	29	29	34	34	16	12	12	15	15	9	9	67	67
1	59	59	37	37	36	36	15	11	11	12	12	10	10	73	73
1	60	60	35	35	34	34	16	10	10	12	12	15	15	70	70
1	61	61	30	30	28	28	12	7	7	8	8	20	20	53	53
1	62	62	38	38	34	34	16	12	12	13	13	12	12	77	77
1	63	63	34	34	35	35	16	14	14	11	11	12	12	80	80
1	64	64	31	31	35	35	14	11	11	14	14	14	14	52	52
1	65	65	34	34	31	31	16	12	12	15	15	13	13	54	54
1	66	66	35	35	37	37	17	13	13	10	10	11	11	80	80
1	67	67	36	36	35	35	18	14	14	11	11	17	17	66	66
1	68	68	30	30	27	27	18	11	11	12	12	12	12	73	73
1	69	69	39	39	40	40	12	12	12	15	15	13	13	63	63
1	70	70	35	35	37	37	16	12	12	15	15	14	14	69	69
1	71	71	38	38	36	36	10	8	8	14	14	13	13	67	67
1	72	72	31	31	38	38	14	11	11	16	16	15	15	54	54
1	73	73	34	34	39	39	18	14	14	15	15	13	13	81	81
1	74	74	38	38	41	41	18	14	14	15	15	10	10	69	69
1	75	75	34	34	27	27	16	12	12	13	13	11	11	84	84
1	76	76	39	39	30	30	17	9	9	12	12	19	19	80	80
1	77	77	37	37	37	37	16	13	13	17	17	13	13	70	70
1	78	78	34	34	31	31	16	11	11	13	13	17	17	69	69
1	79	79	28	28	31	31	13	12	12	15	15	13	13	77	77
1	80	80	37	37	27	27	16	12	12	13	13	9	9	54	54
1	81	81	33	33	36	36	16	12	12	15	15	11	11	79	79
1	82	82	35	35	37	37	16	12	12	15	15	9	9	71	71
1	83	83	37	37	33	33	15	12	12	16	16	12	12	73	73
1	84	84	32	32	34	34	15	11	11	15	15	12	12	72	72
1	85	85	33	33	31	31	16	10	10	14	14	13	13	77	77
1	86	86	38	38	39	39	14	9	9	15	15	13	13	75	75
1	87	87	33	33	34	34	16	12	12	14	14	12	12	69	69
1	88	88	29	29	32	32	16	12	12	13	13	15	15	54	54
1	89	89	33	33	33	33	15	12	12	7	7	22	22	70	70
1	90	90	31	31	36	36	12	9	9	17	17	13	13	73	73
1	91	91	36	36	32	32	17	15	15	13	13	15	15	54	54
1	92	92	35	35	41	41	16	12	12	15	15	13	13	77	77
1	93	93	32	32	28	28	15	12	12	14	14	15	15	82	82
1	94	94	29	29	30	30	13	12	12	13	13	12.5	12.5	80	80
1	95	95	39	39	36	36	16	10	10	16	16	11	11	80	80
1	96	96	37	37	35	35	16	13	13	12	12	16	16	69	69
1	97	97	35	35	31	31	16	9	9	14	14	11	11	78	78
1	98	98	37	37	34	34	16	12	12	17	17	11	11	81	81
1	99	99	32	32	36	36	14	10	10	15	15	10	10	76	76
1	100	100	38	38	36	36	16	14	14	17	17	10	10	76	76
1	101	101	37	37	35	35	16	11	11	12	12	16	16	73	73
1	102	102	36	36	37	37	20	15	15	16	16	12	12	85	85
1	103	103	32	32	28	28	15	11	11	11	11	11	11	66	66
1	104	104	33	33	39	39	16	11	11	15	15	16	16	79	79
1	105	105	40	40	32	32	13	12	12	9	9	19	19	68	68
1	106	106	38	38	35	35	17	12	12	16	16	11	11	76	76
1	107	107	41	41	39	39	16	12	12	15	15	16	16	71	71
1	108	108	36	36	35	35	16	11	11	10	10	15	15	54	54
1	109	109	43	43	42	42	12	7	7	10	10	24	24	46	46
1	110	110	30	30	34	34	16	12	12	15	15	14	14	85	85
1	111	111	31	31	33	33	16	14	14	11	11	15	15	74	74
1	112	112	32	32	41	41	17	11	11	13	13	11	11	88	88
1	113	113	32	32	33	33	13	11	11	14	14	15	15	38	38
1	114	114	37	37	34	34	12	10	10	18	18	12	12	76	76
1	115	115	37	37	32	32	18	13	13	16	16	10	10	86	86
1	116	116	33	33	40	40	14	13	13	14	14	14	14	54	54
1	117	117	34	34	40	40	14	8	8	14	14	13	13	67	67
1	118	118	33	33	35	35	13	11	11	14	14	9	9	69	69
1	119	119	38	38	36	36	16	12	12	14	14	15	15	90	90
1	120	120	33	33	37	37	13	11	11	12	12	15	15	54	54
1	121	121	31	31	27	27	16	13	13	14	14	14	14	76	76
1	122	122	38	38	39	39	13	12	12	15	15	11	11	89	89
1	123	123	37	37	38	38	16	14	14	15	15	8	8	76	76
1	124	124	36	36	31	31	15	13	13	15	15	11	11	73	73
1	125	125	31	31	33	33	16	15	15	13	13	11	11	79	79
1	126	126	39	39	32	32	15	10	10	17	17	8	8	90	90
1	127	127	44	44	39	39	17	11	11	17	17	10	10	74	74
1	128	128	33	33	36	36	15	9	9	19	19	11	11	81	81
1	129	129	35	35	33	33	12	11	11	15	15	13	13	72	72
1	130	130	32	32	33	33	16	10	10	13	13	11	11	71	71
1	131	131	28	28	32	32	10	11	11	9	9	20	20	66	66
1	132	132	40	40	37	37	16	8	8	15	15	10	10	77	77
1	133	133	27	27	30	30	12	11	11	15	15	15	15	65	65
1	134	134	37	37	38	38	14	12	12	15	15	12	12	74	74
1	135	135	32	32	29	29	15	12	12	16	16	14	14	85	85
1	136	136	28	28	22	22	13	9	9	11	11	23	23	54	54
1	137	137	34	34	35	35	15	11	11	14	14	14	14	63	63
1	138	138	30	30	35	35	11	10	10	11	11	16	16	54	54
1	139	139	35	35	34	34	12	8	8	15	15	11	11	64	64
1	140	140	31	31	35	35	11	9	9	13	13	12	12	69	69
1	141	141	32	32	34	34	16	8	8	15	15	10	10	54	54
1	142	142	30	30	37	37	15	9	9	16	16	14	14	84	84
1	143	143	30	30	35	35	17	15	15	14	14	12	12	86	86
1	144	144	31	31	23	23	16	11	11	15	15	12	12	77	77
1	145	145	40	40	31	31	10	8	8	16	16	11	11	89	89
1	146	146	32	32	27	27	18	13	13	16	16	12	12	76	76
1	147	147	36	36	36	36	13	12	12	11	11	13	13	60	60
1	148	148	32	32	31	31	16	12	12	12	12	11	11	75	75
1	149	149	35	35	32	32	13	9	9	9	9	19	19	73	73
1	150	150	38	38	39	39	10	7	7	16	16	12	12	85	85
1	151	151	42	42	37	37	15	13	13	13	13	17	17	79	79
1	152	152	34	34	38	38	16	9	9	16	16	9	9	71	71
1	153	153	35	35	39	39	16	6	6	12	12	12	12	72	72
1	154	154	38	38	34	34	14	8	8	9	9	19	19	69	69
1	155	155	33	33	31	31	10	8	8	13	13	18	18	78	78
1	156	156	36	36	32	32	17	15	15	13	13	15	15	54	54
1	157	157	32	32	37	37	13	6	6	14	14	14	14	69	69
1	158	158	33	33	36	36	15	9	9	19	19	11	11	81	81
1	159	159	34	34	32	32	16	11	11	13	13	9	9	84	84
1	160	160	32	32	38	38	12	8	8	12	12	18	18	84	84
1	161	161	34	34	36	36	13	8	8	13	13	16	16	69	69
0	162	0	27	0	26	0	13	10	0	10	0	24	0	66	0
0	163	0	31	0	26	0	12	8	0	14	0	14	0	81	0
0	164	0	38	0	33	0	17	14	0	16	0	20	0	82	0
0	165	0	34	0	39	0	15	10	0	10	0	18	0	72	0
0	166	0	24	0	30	0	10	8	0	11	0	23	0	54	0
0	167	0	30	0	33	0	14	11	0	14	0	12	0	78	0
0	168	0	26	0	25	0	11	12	0	12	0	14	0	74	0
0	169	0	34	0	38	0	13	12	0	9	0	16	0	82	0
0	170	0	27	0	37	0	16	12	0	9	0	18	0	73	0
0	171	0	37	0	31	0	12	5	0	11	0	20	0	55	0
0	172	0	36	0	37	0	16	12	0	16	0	12	0	72	0
0	173	0	41	0	35	0	12	10	0	9	0	12	0	78	0
0	174	0	29	0	25	0	9	7	0	13	0	17	0	59	0
0	175	0	36	0	28	0	12	12	0	16	0	13	0	72	0
0	176	0	32	0	35	0	15	11	0	13	0	9	0	78	0
0	177	0	37	0	33	0	12	8	0	9	0	16	0	68	0
0	178	0	30	0	30	0	12	9	0	12	0	18	0	69	0
0	179	0	31	0	31	0	14	10	0	16	0	10	0	67	0
0	180	0	38	0	37	0	12	9	0	11	0	14	0	74	0
0	181	0	36	0	36	0	16	12	0	14	0	11	0	54	0
0	182	0	35	0	30	0	11	6	0	13	0	9	0	67	0
0	183	0	31	0	36	0	19	15	0	15	0	11	0	70	0
0	184	0	38	0	32	0	15	12	0	14	0	10	0	80	0
0	185	0	22	0	28	0	8	12	0	16	0	11	0	89	0
0	186	0	32	0	36	0	16	12	0	13	0	19	0	76	0
0	187	0	36	0	34	0	17	11	0	14	0	14	0	74	0
0	188	0	39	0	31	0	12	7	0	15	0	12	0	87	0
0	189	0	28	0	28	0	11	7	0	13	0	14	0	54	0
0	190	0	32	0	36	0	11	5	0	11	0	21	0	61	0
0	191	0	32	0	36	0	14	12	0	11	0	13	0	38	0
0	192	0	38	0	40	0	16	12	0	14	0	10	0	75	0
0	193	0	32	0	33	0	12	3	0	15	0	15	0	69	0
0	194	0	35	0	37	0	16	11	0	11	0	16	0	62	0
0	195	0	32	0	32	0	13	10	0	15	0	14	0	72	0
0	196	0	37	0	38	0	15	12	0	12	0	12	0	70	0
0	197	0	34	0	31	0	16	9	0	14	0	19	0	79	0
0	198	0	33	0	37	0	16	12	0	14	0	15	0	87	0
0	199	0	33	0	33	0	14	9	0	8	0	19	0	62	0
0	200	0	26	0	32	0	16	12	0	13	0	13	0	77	0
0	201	0	30	0	30	0	16	12	0	9	0	17	0	69	0
0	202	0	24	0	30	0	14	10	0	15	0	12	0	69	0
0	203	0	34	0	31	0	11	9	0	17	0	11	0	75	0
0	204	0	34	0	32	0	12	12	0	13	0	14	0	54	0
0	205	0	33	0	34	0	15	8	0	15	0	11	0	72	0
0	206	0	34	0	36	0	15	11	0	15	0	13	0	74	0
0	207	0	35	0	37	0	16	11	0	14	0	12	0	85	0
0	208	0	35	0	36	0	16	12	0	16	0	15	0	52	0
0	209	0	36	0	33	0	11	10	0	13	0	14	0	70	0
0	210	0	34	0	33	0	15	10	0	16	0	12	0	84	0
0	211	0	34	0	33	0	12	12	0	9	0	17	0	64	0
0	212	0	41	0	44	0	12	12	0	16	0	11	0	84	0
0	213	0	32	0	39	0	15	11	0	11	0	18	0	87	0
0	214	0	30	0	32	0	15	8	0	10	0	13	0	79	0
0	215	0	35	0	35	0	16	12	0	11	0	17	0	67	0
0	216	0	28	0	25	0	14	10	0	15	0	13	0	65	0
0	217	0	33	0	35	0	17	11	0	17	0	11	0	85	0
0	218	0	39	0	34	0	14	10	0	14	0	12	0	83	0
0	219	0	36	0	35	0	13	8	0	8	0	22	0	61	0
0	220	0	36	0	39	0	15	12	0	15	0	14	0	82	0
0	221	0	35	0	33	0	13	12	0	11	0	12	0	76	0
0	222	0	38	0	36	0	14	10	0	16	0	12	0	58	0
0	223	0	33	0	32	0	15	12	0	10	0	17	0	72	0
0	224	0	31	0	32	0	12	9	0	15	0	9	0	72	0
0	225	0	34	0	36	0	13	9	0	9	0	21	0	38	0
0	226	0	32	0	36	0	8	6	0	16	0	10	0	78	0
0	227	0	31	0	32	0	14	10	0	19	0	11	0	54	0
0	228	0	33	0	34	0	14	9	0	12	0	12	0	63	0
0	229	0	34	0	33	0	11	9	0	8	0	23	0	66	0
0	230	0	34	0	35	0	12	9	0	11	0	13	0	70	0
0	231	0	34	0	30	0	13	6	0	14	0	12	0	71	0
0	232	0	33	0	38	0	10	10	0	9	0	16	0	67	0
0	233	0	32	0	34	0	16	6	0	15	0	9	0	58	0
0	234	0	41	0	33	0	18	14	0	13	0	17	0	72	0
0	235	0	34	0	32	0	13	10	0	16	0	9	0	72	0
0	236	0	36	0	31	0	11	10	0	11	0	14	0	70	0
0	237	0	37	0	30	0	4	6	0	12	0	17	0	76	0
0	238	0	36	0	27	0	13	12	0	13	0	13	0	50	0
0	239	0	29	0	31	0	16	12	0	10	0	11	0	72	0
0	240	0	37	0	30	0	10	7	0	11	0	12	0	72	0
0	241	0	27	0	32	0	12	8	0	12	0	10	0	88	0
0	242	0	35	0	35	0	12	11	0	8	0	19	0	53	0
0	243	0	28	0	28	0	10	3	0	12	0	16	0	58	0
0	244	0	35	0	33	0	13	6	0	12	0	16	0	66	0
0	245	0	37	0	31	0	15	10	0	15	0	14	0	82	0
0	246	0	29	0	35	0	12	8	0	11	0	20	0	69	0
0	247	0	32	0	35	0	14	9	0	13	0	15	0	68	0
0	248	0	36	0	32	0	10	9	0	14	0	23	0	44	0
0	249	0	19	0	21	0	12	8	0	10	0	20	0	56	0
0	250	0	21	0	20	0	12	9	0	12	0	16	0	53	0
0	251	0	31	0	34	0	11	7	0	15	0	14	0	70	0
0	252	0	33	0	32	0	10	7	0	13	0	17	0	78	0
0	253	0	36	0	34	0	12	6	0	13	0	11	0	71	0
0	254	0	33	0	32	0	16	9	0	13	0	13	0	72	0
0	255	0	37	0	33	0	12	10	0	12	0	17	0	68	0
0	256	0	34	0	33	0	14	11	0	12	0	15	0	67	0
0	257	0	35	0	37	0	16	12	0	9	0	21	0	75	0
0	258	0	31	0	32	0	14	8	0	9	0	18	0	62	0
0	259	0	37	0	34	0	13	11	0	15	0	15	0	67	0
0	260	0	35	0	30	0	4	3	0	10	0	8	0	83	0
0	261	0	27	0	30	0	15	11	0	14	0	12	0	64	0
0	262	0	34	0	38	0	11	12	0	15	0	12	0	68	0
0	263	0	40	0	36	0	11	7	0	7	0	22	0	62	0
0	264	0	29	0	32	0	14	9	0	14	0	12	0	72	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	17 seconds
R Server	'Sir Maurice George Kendall' @ kendall.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 & 17 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186166&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186166&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186166&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	17 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.02213192467022 + 2.51220871915759Pop[t] -0.00416326360064588t + 0.000172174548079316Pop_t[t] -0.0555568413104843Connected[t] + 0.142876235895835Connected_p[t] + 0.157004790836635Separate[t] -0.171050529312447Separate_p[t] + 0.561688749040416Software[t] -0.0423405001257778Software_p[t] + 0.129559018015456Happiness[t] -0.0990921264717638Happiness_p[t] + 0.0223866216094669Depression[t] -0.105904658670812Depression_p[t] -0.00941064469108985Belonging[t] + 0.0247479684327579Belonging_p[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  4.02213192467022 +  2.51220871915759Pop[t] -0.00416326360064588t +  0.000172174548079316Pop_t[t] -0.0555568413104843Connected[t] +  0.142876235895835Connected_p[t] +  0.157004790836635Separate[t] -0.171050529312447Separate_p[t] +  0.561688749040416Software[t] -0.0423405001257778Software_p[t] +  0.129559018015456Happiness[t] -0.0990921264717638Happiness_p[t] +  0.0223866216094669Depression[t] -0.105904658670812Depression_p[t] -0.00941064469108985Belonging[t] +  0.0247479684327579Belonging_p[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186166&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  4.02213192467022 +  2.51220871915759Pop[t] -0.00416326360064588t +  0.000172174548079316Pop_t[t] -0.0555568413104843Connected[t] +  0.142876235895835Connected_p[t] +  0.157004790836635Separate[t] -0.171050529312447Separate_p[t] +  0.561688749040416Software[t] -0.0423405001257778Software_p[t] +  0.129559018015456Happiness[t] -0.0990921264717638Happiness_p[t] +  0.0223866216094669Depression[t] -0.105904658670812Depression_p[t] -0.00941064469108985Belonging[t] +  0.0247479684327579Belonging_p[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186166&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186166&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
Learning[t] = + 4.02213192467022 + 2.51220871915759Pop[t] -0.00416326360064588t + 0.000172174548079316Pop_t[t] -0.0555568413104843Connected[t] + 0.142876235895835Connected_p[t] + 0.157004790836635Separate[t] -0.171050529312447Separate_p[t] + 0.561688749040416Software[t] -0.0423405001257778Software_p[t] + 0.129559018015456Happiness[t] -0.0990921264717638Happiness_p[t] + 0.0223866216094669Depression[t] -0.105904658670812Depression_p[t] -0.00941064469108985Belonging[t] + 0.0247479684327579Belonging_p[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	4.02213192467022	3.327578	1.2087	0.227919	0.11396
Pop	2.51220871915759	4.240155	0.5925	0.554069	0.277034
t	-0.00416326360064588	0.006372	-0.6534	0.514113	0.257057
Pop_t	0.000172174548079316	0.007181	0.024	0.980891	0.490446
Connected	-0.0555568413104843	0.052264	-1.063	0.288813	0.144406
Connected_p	0.142876235895835	0.07003	2.0402	0.042388	0.021194
Separate	0.157004790836635	0.059798	2.6256	0.009187	0.004594
Separate_p	-0.171050529312447	0.074474	-2.2968	0.022465	0.011232
Software	0.561688749040416	0.082232	6.8306	0	0
Software_p	-0.0423405001257778	0.109405	-0.387	0.699084	0.349542
Happiness	0.129559018015456	0.089382	1.4495	0.148462	0.074231
Happiness_p	-0.0990921264717638	0.117868	-0.8407	0.401322	0.200661
Depression	0.0223866216094669	0.062207	0.3599	0.719247	0.359624
Depression_p	-0.105904658670812	0.084312	-1.2561	0.210258	0.105129
Belonging	-0.00941064469108985	0.019054	-0.4939	0.621816	0.310908
Belonging_p	0.0247479684327579	0.024621	1.0052	0.315801	0.1579

\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) & 4.02213192467022 & 3.327578 & 1.2087 & 0.227919 & 0.11396 \tabularnewline
Pop & 2.51220871915759 & 4.240155 & 0.5925 & 0.554069 & 0.277034 \tabularnewline
t & -0.00416326360064588 & 0.006372 & -0.6534 & 0.514113 & 0.257057 \tabularnewline
Pop_t & 0.000172174548079316 & 0.007181 & 0.024 & 0.980891 & 0.490446 \tabularnewline
Connected & -0.0555568413104843 & 0.052264 & -1.063 & 0.288813 & 0.144406 \tabularnewline
Connected_p & 0.142876235895835 & 0.07003 & 2.0402 & 0.042388 & 0.021194 \tabularnewline
Separate & 0.157004790836635 & 0.059798 & 2.6256 & 0.009187 & 0.004594 \tabularnewline
Separate_p & -0.171050529312447 & 0.074474 & -2.2968 & 0.022465 & 0.011232 \tabularnewline
Software & 0.561688749040416 & 0.082232 & 6.8306 & 0 & 0 \tabularnewline
Software_p & -0.0423405001257778 & 0.109405 & -0.387 & 0.699084 & 0.349542 \tabularnewline
Happiness & 0.129559018015456 & 0.089382 & 1.4495 & 0.148462 & 0.074231 \tabularnewline
Happiness_p & -0.0990921264717638 & 0.117868 & -0.8407 & 0.401322 & 0.200661 \tabularnewline
Depression & 0.0223866216094669 & 0.062207 & 0.3599 & 0.719247 & 0.359624 \tabularnewline
Depression_p & -0.105904658670812 & 0.084312 & -1.2561 & 0.210258 & 0.105129 \tabularnewline
Belonging & -0.00941064469108985 & 0.019054 & -0.4939 & 0.621816 & 0.310908 \tabularnewline
Belonging_p & 0.0247479684327579 & 0.024621 & 1.0052 & 0.315801 & 0.1579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186166&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]4.02213192467022[/C][C]3.327578[/C][C]1.2087[/C][C]0.227919[/C][C]0.11396[/C][/ROW]
[ROW][C]Pop[/C][C]2.51220871915759[/C][C]4.240155[/C][C]0.5925[/C][C]0.554069[/C][C]0.277034[/C][/ROW]
[ROW][C]t[/C][C]-0.00416326360064588[/C][C]0.006372[/C][C]-0.6534[/C][C]0.514113[/C][C]0.257057[/C][/ROW]
[ROW][C]Pop_t[/C][C]0.000172174548079316[/C][C]0.007181[/C][C]0.024[/C][C]0.980891[/C][C]0.490446[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0555568413104843[/C][C]0.052264[/C][C]-1.063[/C][C]0.288813[/C][C]0.144406[/C][/ROW]
[ROW][C]Connected_p[/C][C]0.142876235895835[/C][C]0.07003[/C][C]2.0402[/C][C]0.042388[/C][C]0.021194[/C][/ROW]
[ROW][C]Separate[/C][C]0.157004790836635[/C][C]0.059798[/C][C]2.6256[/C][C]0.009187[/C][C]0.004594[/C][/ROW]
[ROW][C]Separate_p[/C][C]-0.171050529312447[/C][C]0.074474[/C][C]-2.2968[/C][C]0.022465[/C][C]0.011232[/C][/ROW]
[ROW][C]Software[/C][C]0.561688749040416[/C][C]0.082232[/C][C]6.8306[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Software_p[/C][C]-0.0423405001257778[/C][C]0.109405[/C][C]-0.387[/C][C]0.699084[/C][C]0.349542[/C][/ROW]
[ROW][C]Happiness[/C][C]0.129559018015456[/C][C]0.089382[/C][C]1.4495[/C][C]0.148462[/C][C]0.074231[/C][/ROW]
[ROW][C]Happiness_p[/C][C]-0.0990921264717638[/C][C]0.117868[/C][C]-0.8407[/C][C]0.401322[/C][C]0.200661[/C][/ROW]
[ROW][C]Depression[/C][C]0.0223866216094669[/C][C]0.062207[/C][C]0.3599[/C][C]0.719247[/C][C]0.359624[/C][/ROW]
[ROW][C]Depression_p[/C][C]-0.105904658670812[/C][C]0.084312[/C][C]-1.2561[/C][C]0.210258[/C][C]0.105129[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.00941064469108985[/C][C]0.019054[/C][C]-0.4939[/C][C]0.621816[/C][C]0.310908[/C][/ROW]
[ROW][C]Belonging_p[/C][C]0.0247479684327579[/C][C]0.024621[/C][C]1.0052[/C][C]0.315801[/C][C]0.1579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186166&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186166&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	4.02213192467022	3.327578	1.2087	0.227919	0.11396
Pop	2.51220871915759	4.240155	0.5925	0.554069	0.277034
t	-0.00416326360064588	0.006372	-0.6534	0.514113	0.257057
Pop_t	0.000172174548079316	0.007181	0.024	0.980891	0.490446
Connected	-0.0555568413104843	0.052264	-1.063	0.288813	0.144406
Connected_p	0.142876235895835	0.07003	2.0402	0.042388	0.021194
Separate	0.157004790836635	0.059798	2.6256	0.009187	0.004594
Separate_p	-0.171050529312447	0.074474	-2.2968	0.022465	0.011232
Software	0.561688749040416	0.082232	6.8306	0	0
Software_p	-0.0423405001257778	0.109405	-0.387	0.699084	0.349542
Happiness	0.129559018015456	0.089382	1.4495	0.148462	0.074231
Happiness_p	-0.0990921264717638	0.117868	-0.8407	0.401322	0.200661
Depression	0.0223866216094669	0.062207	0.3599	0.719247	0.359624
Depression_p	-0.105904658670812	0.084312	-1.2561	0.210258	0.105129
Belonging	-0.00941064469108985	0.019054	-0.4939	0.621816	0.310908
Belonging_p	0.0247479684327579	0.024621	1.0052	0.315801	0.1579

Multiple Linear Regression - Regression Statistics
Multiple R	0.683693459674527
R-squared	0.467436746801725
Adjusted R-squared	0.435225259713119
F-TEST (value)	14.5114922982577
F-TEST (DF numerator)	15
F-TEST (DF denominator)	248
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.8457000801965
Sum Squared Residuals	844.838978937264

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.683693459674527 \tabularnewline
R-squared & 0.467436746801725 \tabularnewline
Adjusted R-squared & 0.435225259713119 \tabularnewline
F-TEST (value) & 14.5114922982577 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 248 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.8457000801965 \tabularnewline
Sum Squared Residuals & 844.838978937264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186166&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.683693459674527[/C][/ROW]
[ROW][C]R-squared[/C][C]0.467436746801725[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.435225259713119[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.5114922982577[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]248[/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]1.8457000801965[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]844.838978937264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186166&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186166&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 R	0.683693459674527
R-squared	0.467436746801725
Adjusted R-squared	0.435225259713119
F-TEST (value)	14.5114922982577
F-TEST (DF numerator)	15
F-TEST (DF denominator)	248
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.8457000801965
Sum Squared Residuals	844.838978937264

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	16.0460838528533	-3.04608385285332
2	16	16.0978854720565	-0.0978854720564726
3	19	16.6187187563686	2.38128124363138
4	15	12.2688445880293	2.73115541197066
5	14	15.6143076175297	-1.61430761752971
6	13	15.0952278482479	-2.09522784824791
7	19	15.1106383280845	3.88936167191553
8	15	16.9713242202552	-1.9713242202552
9	14	16.1392821471749	-2.13928214717491
10	15	14.4025490268356	0.597450973164367
11	16	15.4610456161102	0.53895438388983
12	16	16.1235117839571	-0.123511783957114
13	16	15.6206706709185	0.379329329081526
14	16	15.209447689791	0.790552310209006
15	17	17.6741165821685	-0.674116582168502
16	15	15.1802222027614	-0.180222202761399
17	15	14.7615924363051	0.238407563694873
18	20	16.4280021978545	3.57199780214554
19	18	15.6160038471028	2.38399615289716
20	16	15.307418081434	0.692581918565993
21	16	15.2613158314681	0.738684168531906
22	16	15.1590203135979	0.840979686402121
23	19	16.3183266771825	2.68167332281752
24	16	14.8896593529274	1.11034064707262
25	17	16.5409837524434	0.459016247556616
26	17	17.0622567007993	-0.0622567007993018
27	16	15.0063695483651	0.993630451634888
28	15	16.287354596739	-1.28735459673898
29	16	15.5528708060404	0.447129193959563
30	14	14.3904425234722	-0.390442523472173
31	15	15.87707304005	-0.877073040049984
32	12	12.4410863242391	-0.441086324239132
33	14	15.163223672435	-1.16322367243501
34	16	15.9066298550937	0.0933701449062521
35	14	15.6500827322888	-1.65008273228881
36	10	13.265086391234	-3.26508639123402
37	10	12.727741817731	-2.72774181773097
38	14	15.8385839962544	-1.8385839962544
39	16	14.5174086001451	1.48259139985485
40	16	14.7800135692866	1.21998643071339
41	16	15.1858454467376	0.814154553262369
42	14	15.5727609632611	-1.57276096326111
43	20	17.6691623615627	2.33083763843728
44	14	14.1387756696789	-0.138775669678917
45	14	15.0696607461849	-1.06966074618494
46	11	15.6144856270065	-4.61448562700648
47	14	16.5709069707458	-2.57090697074579
48	15	15.1481882666103	-0.148188266610253
49	16	15.0614967185108	0.938503281489179
50	14	16.0633090042108	-2.06330900421075
51	16	16.6187303550879	-0.6187303550879
52	14	14.1780196281422	-0.178019628142177
53	12	14.6837657036483	-2.68376570364832
54	16	15.6527346763494	0.347265323650568
55	9	11.6694846725896	-2.66948467258964
56	14	12.5777743435091	1.42222565649092
57	16	16.0036265752432	-0.00362657524322887
58	16	15.3226855308472	0.677314469152829
59	15	15.3869151033687	-0.38691510336873
60	16	14.2534262966507	1.74657370334928
61	12	11.5388756636925	0.46112433630749
62	16	15.9344810690503	0.0655189309496813
63	16	16.5909413491474	-0.590941349147434
64	14	14.2618668653366	-0.261866865336581
65	16	15.2400247389463	0.759975261053671
66	17	16.1718988962265	0.828101103773527
67	18	16.1173030644178	1.88269693558221
68	18	14.6991351119578	3.3008648880422
69	12	15.6722816230555	-3.67228162305551
70	16	15.3696560764776	0.630343923522355
71	10	13.5866524120327	-3.5866524120327
72	14	14.195891330998	-0.195891330997985
73	18	16.5485343575736	1.45146564242639
74	18	16.9322355961948	1.06776440380516
75	16	15.8225188056093	0.177481194390736
76	17	13.894982244311	3.10501775568903
77	16	16.1954946350854	-0.195494635085416
78	16	14.5038462571406	1.49615374285942
79	13	15.0129915707567	-2.01299157075666
80	16	15.7714379059751	0.228562094024869
81	16	15.5690883948052	0.430911605194758
82	16	15.7700278406369	0.229972159363091
83	15	15.8074459225013	-0.807445922501287
84	15	14.7876596578462	0.212340342153843
85	16	14.356478619995	1.64352138000496
86	14	14.1571625912084	-0.157162591208444
87	16	15.3058751714197	0.69412482858025
88	16	14.4696171221247	1.53038287787534
89	15	14.2788274441128	0.7211725558872
90	12	13.6023588239322	-1.60235882393221
91	17	16.6269243638083	0.373075636191673
92	16	15.4318857904126	0.568114209587381
93	15	15.2277147708315	-0.227714770831527
94	13	15.0813275746976	-2.08132757469762
95	16	15.0442372330375	0.955762766962505
96	16	15.7295295273941	0.270470472605888
97	16	14.1462494894847	1.85375051051533
98	16	15.9702173667754	0.0297826332246424
99	14	14.4087389652808	-0.408738965280757
100	16	17.0669910224862	-1.06699102248623
101	16	14.7322268792687	1.26777312073132
102	20	17.3302055136579	2.66979448634215
103	15	14.6657299251388	0.334270074861162
104	16	14.4982176969474	1.50178230305259
105	13	15.1210647666331	-2.12106476663309
106	17	15.9044088002123	1.09559119978767
107	16	15.5814492454538	0.418550754546195
108	16	14.3481449641976	1.65185503580237
109	12	11.9053155487678	0.0946844512321832
110	16	15.0609499367424	0.939050063257641
111	16	15.8229243141858	0.177075685814231
112	17	14.8455704288843	2.15442957111575
113	13	13.8834738038531	-0.883473803853097
114	12	14.737925679879	-2.73792567987902
115	18	16.579546342974	1.42045365702602
116	14	15.2281114767074	-1.22811147670737
117	14	12.99760178337	1.00239821663001
118	13	14.8993115345838	-1.89931153458377
119	16	15.6581955051037	0.34180449489626
120	13	14.0711360179465	-1.07113601794654
121	16	15.5535329647761	0.446467035223899
122	13	15.953286738566	-2.95328673856601
123	16	16.9658873937755	-0.96588739377553
124	15	16.1569827481446	-1.15698274814463
125	16	16.7580898664056	-0.758089866405581
126	15	15.4110906664556	-0.411090666455593
127	17	15.8522913759243	1.14770862407565
128	15	13.9760046762488	1.0239953237512
129	12	14.8005465356512	-2.80054653565118
130	16	14.1060139812216	1.89398601877844
131	10	13.3359227827828	-3.33592278278283
132	16	13.9381832706654	2.06181672933456
133	12	13.8537668978712	-1.85376689787118
134	14	15.5185421206393	-1.5185421206393
135	15	15.2365070835217	-0.236507083521652
136	13	12.0440600114522	0.95593998854782
137	15	14.4011861094136	0.598813890586378
138	11	13.1320965306762	-2.13209653067624
139	12	13.2328826440951	-1.23288264409513
140	11	13.3171512856996	-2.3171512856996
141	16	12.8930870818786	3.10691291812139
142	15	13.3481826926909	1.6518173073091
143	17	16.6251495125964	0.374850487403569
144	16	14.6920646620491	1.30793533795091
145	10	14.1015702832193	-4.10157028321932
146	18	15.7690449902574	2.23095500974264
147	13	14.9873219097027	-1.98732190970275
148	16	15.1318447564792	0.868155243520752
149	13	13.0275017473578	-0.0275017473578296
150	10	13.1303945600366	-3.13039456003663
151	15	16.0188472173771	-1.01884721737711
152	16	13.8617086186959	2.13829138130414
153	16	12.0158620853918	3.98413791460821
154	14	12.6607154650181	1.33928453498189
155	10	12.6056861353774	-2.60568613537736
156	17	16.3675035753915	0.632496424608499
157	13	11.6139167601168	1.38608323988322
158	15	13.8562720046718	1.14372799532819
159	16	15.0647264580227	0.93527354197734
160	12	12.4616481771048	-0.461648177104799
161	13	12.6278304637159	0.372169536284077
162	13	12.758427107309	0.241572892690999
163	12	11.5618691659864	0.438130834013608
164	17	16.0220011643079	0.977998835692081
165	15	14.2073181104066	0.792681889593419
166	10	12.6331863748026	-2.63318637480264
167	14	14.3683314267263	-0.36833142672632
168	11	13.7153437366673	-2.71534373666733
169	13	14.8885990551029	-1.88859905510291
170	16	15.2457979352778	0.754202064722239
171	12	10.285499153959	1.71450084604099
172	16	15.5194638774246	0.480536122575411
173	12	12.8367523332627	-0.836752333262689
174	9	11.0531284391401	-2.05312843914013
175	12	14.1163175907024	-2.11631759070241
176	15	14.337039070529	0.662960929471049
177	12	11.7885924976967	0.211407502303293
178	12	12.6880411523742	-0.688041152374177
179	14	13.7049789759084	0.295021024091634
180	12	13.0681347026367	-1.06813470263668
181	16	15.2128766609814	0.78712333901862
182	11	10.6554383572104	0.344561642789644
183	19	17.1463892904118	1.85361070958822
184	15	14.1941906406341	0.805809359365868
185	8	14.6477265300753	-6.64772653007527
186	16	15.2567874798764	0.743212520123609
187	17	14.1911457196704	2.80885428032958
188	12	11.2649899572791	0.73501004272089
189	11	11.4971440575779	-0.497144057577875
190	11	11.2351280597453	-0.235128059745265
191	14	15.2001378944469	-1.20013789444686
192	16	15.4639760819775	0.536023918022509
193	12	9.93687758322889	2.06312241677111
194	16	14.4575980137519	1.54240198624807
195	13	13.6527489527911	-0.652748952791145
196	15	15.0215787178556	-0.0215787178555945
197	16	12.7311147802861	3.26888521971392
198	16	15.2447717061704	0.755228293829609
199	14	12.4749815277243	1.52501847227566
200	16	14.7600932996358	1.23990670036417
201	16	13.8662886610247	2.13371133897526
202	14	13.7375099472516	0.262490052748435
203	11	12.9533618586172	-1.9533618586172
204	12	14.5378169642539	-2.5378169642539
205	15	12.6790316942382	2.32096830576176
206	15	14.6443393719584	0.355660628041621
207	16	14.486161326657	1.51383867334303
208	16	15.5235111969254	0.476488803074615
209	11	13.2889439413281	-2.28894394132807
210	15	13.6080491455006	1.39195085449943
211	12	14.1204962557417	-2.12049625574169
212	12	16.0388683048002	-4.03886830480023
213	15	14.6686832368861	0.331316763113927
214	15	11.8253269043946	3.17467309560537
215	16	14.5931820436595	1.40681795634046
216	14	12.7320021377912	1.26799786220876
217	17	14.6079232240351	2.39207677596489
218	14	13.2042562296398	0.79574377036021
219	13	12.0539370739323	0.946062926067691
220	15	15.4547445845594	-0.454744584559436
221	13	14.0575639701152	-1.05756397011525
222	14	14.0515537515291	-0.0515537515291178
223	15	14.0233630034945	0.976636996505471
224	12	12.9139492925951	-0.913949292595147
225	13	13.1823819391275	-0.182381939127509
226	8	11.888500611787	-3.88850061178705
227	14	14.195549170554	-0.195549170554001
228	14	12.8633707502467	1.13662924975329
229	11	12.346430686068	-1.34643068606798
230	12	12.7834452633279	-0.783445263327946
231	13	10.6660715861687	2.33392841383131
232	10	13.6996524618582	-3.69965246185822
233	16	11.5816154391061	4.41838456089387
234	18	15.2021717163674	2.79782828363262
235	13	13.3927306361125	-0.392730636112462
236	11	12.6034082064064	-1.60340820640645
237	4	10.2801833291943	-6.28018332919433
238	13	13.5153843221827	-0.515384322182692
239	16	13.8876536306327	2.1123463693673
240	10	10.6255327401344	-0.625532740134383
241	12	11.9868516800914	0.0131483199086499
242	12	13.7069303922495	-1.7069303922495
243	10	8.90314447342045	1.09685552657955
244	13	10.9048883644221	2.09511163557788
245	15	12.9156903284589	2.0843096715411
246	12	12.599045499187	-0.599045499186976
247	14	13.14649603337	0.85350396663004
248	10	12.9835984954948	-2.98359849549482
249	12	10.9368364127457	1.06316358725429
250	12	11.4240469083942	0.575953091605809
251	11	12.1229276563997	-1.12292765639966
252	10	11.4263977997735	-1.42639779977355
253	12	10.9394396280551	1.06056037194487
254	16	12.5083661523618	3.49163384763824
255	12	12.998299110583	-0.998299110582997
256	14	13.6871325214264	0.312867478573624
257	16	14.4874778469839	1.51252215301608
258	14	11.7289415144361	2.27105848556386
259	13	14.053654051576	-1.05365405157599
260	4	8.08400355852546	-4.08400355852546
261	15	13.8043898253624	1.19561017463759
262	11	15.320972187573	-4.32097218757297
263	11	11.1048724893516	-0.104872489351625
264	14	12.7961332780032	1.20386672199677

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0460838528533 & -3.04608385285332 \tabularnewline
2 & 16 & 16.0978854720565 & -0.0978854720564726 \tabularnewline
3 & 19 & 16.6187187563686 & 2.38128124363138 \tabularnewline
4 & 15 & 12.2688445880293 & 2.73115541197066 \tabularnewline
5 & 14 & 15.6143076175297 & -1.61430761752971 \tabularnewline
6 & 13 & 15.0952278482479 & -2.09522784824791 \tabularnewline
7 & 19 & 15.1106383280845 & 3.88936167191553 \tabularnewline
8 & 15 & 16.9713242202552 & -1.9713242202552 \tabularnewline
9 & 14 & 16.1392821471749 & -2.13928214717491 \tabularnewline
10 & 15 & 14.4025490268356 & 0.597450973164367 \tabularnewline
11 & 16 & 15.4610456161102 & 0.53895438388983 \tabularnewline
12 & 16 & 16.1235117839571 & -0.123511783957114 \tabularnewline
13 & 16 & 15.6206706709185 & 0.379329329081526 \tabularnewline
14 & 16 & 15.209447689791 & 0.790552310209006 \tabularnewline
15 & 17 & 17.6741165821685 & -0.674116582168502 \tabularnewline
16 & 15 & 15.1802222027614 & -0.180222202761399 \tabularnewline
17 & 15 & 14.7615924363051 & 0.238407563694873 \tabularnewline
18 & 20 & 16.4280021978545 & 3.57199780214554 \tabularnewline
19 & 18 & 15.6160038471028 & 2.38399615289716 \tabularnewline
20 & 16 & 15.307418081434 & 0.692581918565993 \tabularnewline
21 & 16 & 15.2613158314681 & 0.738684168531906 \tabularnewline
22 & 16 & 15.1590203135979 & 0.840979686402121 \tabularnewline
23 & 19 & 16.3183266771825 & 2.68167332281752 \tabularnewline
24 & 16 & 14.8896593529274 & 1.11034064707262 \tabularnewline
25 & 17 & 16.5409837524434 & 0.459016247556616 \tabularnewline
26 & 17 & 17.0622567007993 & -0.0622567007993018 \tabularnewline
27 & 16 & 15.0063695483651 & 0.993630451634888 \tabularnewline
28 & 15 & 16.287354596739 & -1.28735459673898 \tabularnewline
29 & 16 & 15.5528708060404 & 0.447129193959563 \tabularnewline
30 & 14 & 14.3904425234722 & -0.390442523472173 \tabularnewline
31 & 15 & 15.87707304005 & -0.877073040049984 \tabularnewline
32 & 12 & 12.4410863242391 & -0.441086324239132 \tabularnewline
33 & 14 & 15.163223672435 & -1.16322367243501 \tabularnewline
34 & 16 & 15.9066298550937 & 0.0933701449062521 \tabularnewline
35 & 14 & 15.6500827322888 & -1.65008273228881 \tabularnewline
36 & 10 & 13.265086391234 & -3.26508639123402 \tabularnewline
37 & 10 & 12.727741817731 & -2.72774181773097 \tabularnewline
38 & 14 & 15.8385839962544 & -1.8385839962544 \tabularnewline
39 & 16 & 14.5174086001451 & 1.48259139985485 \tabularnewline
40 & 16 & 14.7800135692866 & 1.21998643071339 \tabularnewline
41 & 16 & 15.1858454467376 & 0.814154553262369 \tabularnewline
42 & 14 & 15.5727609632611 & -1.57276096326111 \tabularnewline
43 & 20 & 17.6691623615627 & 2.33083763843728 \tabularnewline
44 & 14 & 14.1387756696789 & -0.138775669678917 \tabularnewline
45 & 14 & 15.0696607461849 & -1.06966074618494 \tabularnewline
46 & 11 & 15.6144856270065 & -4.61448562700648 \tabularnewline
47 & 14 & 16.5709069707458 & -2.57090697074579 \tabularnewline
48 & 15 & 15.1481882666103 & -0.148188266610253 \tabularnewline
49 & 16 & 15.0614967185108 & 0.938503281489179 \tabularnewline
50 & 14 & 16.0633090042108 & -2.06330900421075 \tabularnewline
51 & 16 & 16.6187303550879 & -0.6187303550879 \tabularnewline
52 & 14 & 14.1780196281422 & -0.178019628142177 \tabularnewline
53 & 12 & 14.6837657036483 & -2.68376570364832 \tabularnewline
54 & 16 & 15.6527346763494 & 0.347265323650568 \tabularnewline
55 & 9 & 11.6694846725896 & -2.66948467258964 \tabularnewline
56 & 14 & 12.5777743435091 & 1.42222565649092 \tabularnewline
57 & 16 & 16.0036265752432 & -0.00362657524322887 \tabularnewline
58 & 16 & 15.3226855308472 & 0.677314469152829 \tabularnewline
59 & 15 & 15.3869151033687 & -0.38691510336873 \tabularnewline
60 & 16 & 14.2534262966507 & 1.74657370334928 \tabularnewline
61 & 12 & 11.5388756636925 & 0.46112433630749 \tabularnewline
62 & 16 & 15.9344810690503 & 0.0655189309496813 \tabularnewline
63 & 16 & 16.5909413491474 & -0.590941349147434 \tabularnewline
64 & 14 & 14.2618668653366 & -0.261866865336581 \tabularnewline
65 & 16 & 15.2400247389463 & 0.759975261053671 \tabularnewline
66 & 17 & 16.1718988962265 & 0.828101103773527 \tabularnewline
67 & 18 & 16.1173030644178 & 1.88269693558221 \tabularnewline
68 & 18 & 14.6991351119578 & 3.3008648880422 \tabularnewline
69 & 12 & 15.6722816230555 & -3.67228162305551 \tabularnewline
70 & 16 & 15.3696560764776 & 0.630343923522355 \tabularnewline
71 & 10 & 13.5866524120327 & -3.5866524120327 \tabularnewline
72 & 14 & 14.195891330998 & -0.195891330997985 \tabularnewline
73 & 18 & 16.5485343575736 & 1.45146564242639 \tabularnewline
74 & 18 & 16.9322355961948 & 1.06776440380516 \tabularnewline
75 & 16 & 15.8225188056093 & 0.177481194390736 \tabularnewline
76 & 17 & 13.894982244311 & 3.10501775568903 \tabularnewline
77 & 16 & 16.1954946350854 & -0.195494635085416 \tabularnewline
78 & 16 & 14.5038462571406 & 1.49615374285942 \tabularnewline
79 & 13 & 15.0129915707567 & -2.01299157075666 \tabularnewline
80 & 16 & 15.7714379059751 & 0.228562094024869 \tabularnewline
81 & 16 & 15.5690883948052 & 0.430911605194758 \tabularnewline
82 & 16 & 15.7700278406369 & 0.229972159363091 \tabularnewline
83 & 15 & 15.8074459225013 & -0.807445922501287 \tabularnewline
84 & 15 & 14.7876596578462 & 0.212340342153843 \tabularnewline
85 & 16 & 14.356478619995 & 1.64352138000496 \tabularnewline
86 & 14 & 14.1571625912084 & -0.157162591208444 \tabularnewline
87 & 16 & 15.3058751714197 & 0.69412482858025 \tabularnewline
88 & 16 & 14.4696171221247 & 1.53038287787534 \tabularnewline
89 & 15 & 14.2788274441128 & 0.7211725558872 \tabularnewline
90 & 12 & 13.6023588239322 & -1.60235882393221 \tabularnewline
91 & 17 & 16.6269243638083 & 0.373075636191673 \tabularnewline
92 & 16 & 15.4318857904126 & 0.568114209587381 \tabularnewline
93 & 15 & 15.2277147708315 & -0.227714770831527 \tabularnewline
94 & 13 & 15.0813275746976 & -2.08132757469762 \tabularnewline
95 & 16 & 15.0442372330375 & 0.955762766962505 \tabularnewline
96 & 16 & 15.7295295273941 & 0.270470472605888 \tabularnewline
97 & 16 & 14.1462494894847 & 1.85375051051533 \tabularnewline
98 & 16 & 15.9702173667754 & 0.0297826332246424 \tabularnewline
99 & 14 & 14.4087389652808 & -0.408738965280757 \tabularnewline
100 & 16 & 17.0669910224862 & -1.06699102248623 \tabularnewline
101 & 16 & 14.7322268792687 & 1.26777312073132 \tabularnewline
102 & 20 & 17.3302055136579 & 2.66979448634215 \tabularnewline
103 & 15 & 14.6657299251388 & 0.334270074861162 \tabularnewline
104 & 16 & 14.4982176969474 & 1.50178230305259 \tabularnewline
105 & 13 & 15.1210647666331 & -2.12106476663309 \tabularnewline
106 & 17 & 15.9044088002123 & 1.09559119978767 \tabularnewline
107 & 16 & 15.5814492454538 & 0.418550754546195 \tabularnewline
108 & 16 & 14.3481449641976 & 1.65185503580237 \tabularnewline
109 & 12 & 11.9053155487678 & 0.0946844512321832 \tabularnewline
110 & 16 & 15.0609499367424 & 0.939050063257641 \tabularnewline
111 & 16 & 15.8229243141858 & 0.177075685814231 \tabularnewline
112 & 17 & 14.8455704288843 & 2.15442957111575 \tabularnewline
113 & 13 & 13.8834738038531 & -0.883473803853097 \tabularnewline
114 & 12 & 14.737925679879 & -2.73792567987902 \tabularnewline
115 & 18 & 16.579546342974 & 1.42045365702602 \tabularnewline
116 & 14 & 15.2281114767074 & -1.22811147670737 \tabularnewline
117 & 14 & 12.99760178337 & 1.00239821663001 \tabularnewline
118 & 13 & 14.8993115345838 & -1.89931153458377 \tabularnewline
119 & 16 & 15.6581955051037 & 0.34180449489626 \tabularnewline
120 & 13 & 14.0711360179465 & -1.07113601794654 \tabularnewline
121 & 16 & 15.5535329647761 & 0.446467035223899 \tabularnewline
122 & 13 & 15.953286738566 & -2.95328673856601 \tabularnewline
123 & 16 & 16.9658873937755 & -0.96588739377553 \tabularnewline
124 & 15 & 16.1569827481446 & -1.15698274814463 \tabularnewline
125 & 16 & 16.7580898664056 & -0.758089866405581 \tabularnewline
126 & 15 & 15.4110906664556 & -0.411090666455593 \tabularnewline
127 & 17 & 15.8522913759243 & 1.14770862407565 \tabularnewline
128 & 15 & 13.9760046762488 & 1.0239953237512 \tabularnewline
129 & 12 & 14.8005465356512 & -2.80054653565118 \tabularnewline
130 & 16 & 14.1060139812216 & 1.89398601877844 \tabularnewline
131 & 10 & 13.3359227827828 & -3.33592278278283 \tabularnewline
132 & 16 & 13.9381832706654 & 2.06181672933456 \tabularnewline
133 & 12 & 13.8537668978712 & -1.85376689787118 \tabularnewline
134 & 14 & 15.5185421206393 & -1.5185421206393 \tabularnewline
135 & 15 & 15.2365070835217 & -0.236507083521652 \tabularnewline
136 & 13 & 12.0440600114522 & 0.95593998854782 \tabularnewline
137 & 15 & 14.4011861094136 & 0.598813890586378 \tabularnewline
138 & 11 & 13.1320965306762 & -2.13209653067624 \tabularnewline
139 & 12 & 13.2328826440951 & -1.23288264409513 \tabularnewline
140 & 11 & 13.3171512856996 & -2.3171512856996 \tabularnewline
141 & 16 & 12.8930870818786 & 3.10691291812139 \tabularnewline
142 & 15 & 13.3481826926909 & 1.6518173073091 \tabularnewline
143 & 17 & 16.6251495125964 & 0.374850487403569 \tabularnewline
144 & 16 & 14.6920646620491 & 1.30793533795091 \tabularnewline
145 & 10 & 14.1015702832193 & -4.10157028321932 \tabularnewline
146 & 18 & 15.7690449902574 & 2.23095500974264 \tabularnewline
147 & 13 & 14.9873219097027 & -1.98732190970275 \tabularnewline
148 & 16 & 15.1318447564792 & 0.868155243520752 \tabularnewline
149 & 13 & 13.0275017473578 & -0.0275017473578296 \tabularnewline
150 & 10 & 13.1303945600366 & -3.13039456003663 \tabularnewline
151 & 15 & 16.0188472173771 & -1.01884721737711 \tabularnewline
152 & 16 & 13.8617086186959 & 2.13829138130414 \tabularnewline
153 & 16 & 12.0158620853918 & 3.98413791460821 \tabularnewline
154 & 14 & 12.6607154650181 & 1.33928453498189 \tabularnewline
155 & 10 & 12.6056861353774 & -2.60568613537736 \tabularnewline
156 & 17 & 16.3675035753915 & 0.632496424608499 \tabularnewline
157 & 13 & 11.6139167601168 & 1.38608323988322 \tabularnewline
158 & 15 & 13.8562720046718 & 1.14372799532819 \tabularnewline
159 & 16 & 15.0647264580227 & 0.93527354197734 \tabularnewline
160 & 12 & 12.4616481771048 & -0.461648177104799 \tabularnewline
161 & 13 & 12.6278304637159 & 0.372169536284077 \tabularnewline
162 & 13 & 12.758427107309 & 0.241572892690999 \tabularnewline
163 & 12 & 11.5618691659864 & 0.438130834013608 \tabularnewline
164 & 17 & 16.0220011643079 & 0.977998835692081 \tabularnewline
165 & 15 & 14.2073181104066 & 0.792681889593419 \tabularnewline
166 & 10 & 12.6331863748026 & -2.63318637480264 \tabularnewline
167 & 14 & 14.3683314267263 & -0.36833142672632 \tabularnewline
168 & 11 & 13.7153437366673 & -2.71534373666733 \tabularnewline
169 & 13 & 14.8885990551029 & -1.88859905510291 \tabularnewline
170 & 16 & 15.2457979352778 & 0.754202064722239 \tabularnewline
171 & 12 & 10.285499153959 & 1.71450084604099 \tabularnewline
172 & 16 & 15.5194638774246 & 0.480536122575411 \tabularnewline
173 & 12 & 12.8367523332627 & -0.836752333262689 \tabularnewline
174 & 9 & 11.0531284391401 & -2.05312843914013 \tabularnewline
175 & 12 & 14.1163175907024 & -2.11631759070241 \tabularnewline
176 & 15 & 14.337039070529 & 0.662960929471049 \tabularnewline
177 & 12 & 11.7885924976967 & 0.211407502303293 \tabularnewline
178 & 12 & 12.6880411523742 & -0.688041152374177 \tabularnewline
179 & 14 & 13.7049789759084 & 0.295021024091634 \tabularnewline
180 & 12 & 13.0681347026367 & -1.06813470263668 \tabularnewline
181 & 16 & 15.2128766609814 & 0.78712333901862 \tabularnewline
182 & 11 & 10.6554383572104 & 0.344561642789644 \tabularnewline
183 & 19 & 17.1463892904118 & 1.85361070958822 \tabularnewline
184 & 15 & 14.1941906406341 & 0.805809359365868 \tabularnewline
185 & 8 & 14.6477265300753 & -6.64772653007527 \tabularnewline
186 & 16 & 15.2567874798764 & 0.743212520123609 \tabularnewline
187 & 17 & 14.1911457196704 & 2.80885428032958 \tabularnewline
188 & 12 & 11.2649899572791 & 0.73501004272089 \tabularnewline
189 & 11 & 11.4971440575779 & -0.497144057577875 \tabularnewline
190 & 11 & 11.2351280597453 & -0.235128059745265 \tabularnewline
191 & 14 & 15.2001378944469 & -1.20013789444686 \tabularnewline
192 & 16 & 15.4639760819775 & 0.536023918022509 \tabularnewline
193 & 12 & 9.93687758322889 & 2.06312241677111 \tabularnewline
194 & 16 & 14.4575980137519 & 1.54240198624807 \tabularnewline
195 & 13 & 13.6527489527911 & -0.652748952791145 \tabularnewline
196 & 15 & 15.0215787178556 & -0.0215787178555945 \tabularnewline
197 & 16 & 12.7311147802861 & 3.26888521971392 \tabularnewline
198 & 16 & 15.2447717061704 & 0.755228293829609 \tabularnewline
199 & 14 & 12.4749815277243 & 1.52501847227566 \tabularnewline
200 & 16 & 14.7600932996358 & 1.23990670036417 \tabularnewline
201 & 16 & 13.8662886610247 & 2.13371133897526 \tabularnewline
202 & 14 & 13.7375099472516 & 0.262490052748435 \tabularnewline
203 & 11 & 12.9533618586172 & -1.9533618586172 \tabularnewline
204 & 12 & 14.5378169642539 & -2.5378169642539 \tabularnewline
205 & 15 & 12.6790316942382 & 2.32096830576176 \tabularnewline
206 & 15 & 14.6443393719584 & 0.355660628041621 \tabularnewline
207 & 16 & 14.486161326657 & 1.51383867334303 \tabularnewline
208 & 16 & 15.5235111969254 & 0.476488803074615 \tabularnewline
209 & 11 & 13.2889439413281 & -2.28894394132807 \tabularnewline
210 & 15 & 13.6080491455006 & 1.39195085449943 \tabularnewline
211 & 12 & 14.1204962557417 & -2.12049625574169 \tabularnewline
212 & 12 & 16.0388683048002 & -4.03886830480023 \tabularnewline
213 & 15 & 14.6686832368861 & 0.331316763113927 \tabularnewline
214 & 15 & 11.8253269043946 & 3.17467309560537 \tabularnewline
215 & 16 & 14.5931820436595 & 1.40681795634046 \tabularnewline
216 & 14 & 12.7320021377912 & 1.26799786220876 \tabularnewline
217 & 17 & 14.6079232240351 & 2.39207677596489 \tabularnewline
218 & 14 & 13.2042562296398 & 0.79574377036021 \tabularnewline
219 & 13 & 12.0539370739323 & 0.946062926067691 \tabularnewline
220 & 15 & 15.4547445845594 & -0.454744584559436 \tabularnewline
221 & 13 & 14.0575639701152 & -1.05756397011525 \tabularnewline
222 & 14 & 14.0515537515291 & -0.0515537515291178 \tabularnewline
223 & 15 & 14.0233630034945 & 0.976636996505471 \tabularnewline
224 & 12 & 12.9139492925951 & -0.913949292595147 \tabularnewline
225 & 13 & 13.1823819391275 & -0.182381939127509 \tabularnewline
226 & 8 & 11.888500611787 & -3.88850061178705 \tabularnewline
227 & 14 & 14.195549170554 & -0.195549170554001 \tabularnewline
228 & 14 & 12.8633707502467 & 1.13662924975329 \tabularnewline
229 & 11 & 12.346430686068 & -1.34643068606798 \tabularnewline
230 & 12 & 12.7834452633279 & -0.783445263327946 \tabularnewline
231 & 13 & 10.6660715861687 & 2.33392841383131 \tabularnewline
232 & 10 & 13.6996524618582 & -3.69965246185822 \tabularnewline
233 & 16 & 11.5816154391061 & 4.41838456089387 \tabularnewline
234 & 18 & 15.2021717163674 & 2.79782828363262 \tabularnewline
235 & 13 & 13.3927306361125 & -0.392730636112462 \tabularnewline
236 & 11 & 12.6034082064064 & -1.60340820640645 \tabularnewline
237 & 4 & 10.2801833291943 & -6.28018332919433 \tabularnewline
238 & 13 & 13.5153843221827 & -0.515384322182692 \tabularnewline
239 & 16 & 13.8876536306327 & 2.1123463693673 \tabularnewline
240 & 10 & 10.6255327401344 & -0.625532740134383 \tabularnewline
241 & 12 & 11.9868516800914 & 0.0131483199086499 \tabularnewline
242 & 12 & 13.7069303922495 & -1.7069303922495 \tabularnewline
243 & 10 & 8.90314447342045 & 1.09685552657955 \tabularnewline
244 & 13 & 10.9048883644221 & 2.09511163557788 \tabularnewline
245 & 15 & 12.9156903284589 & 2.0843096715411 \tabularnewline
246 & 12 & 12.599045499187 & -0.599045499186976 \tabularnewline
247 & 14 & 13.14649603337 & 0.85350396663004 \tabularnewline
248 & 10 & 12.9835984954948 & -2.98359849549482 \tabularnewline
249 & 12 & 10.9368364127457 & 1.06316358725429 \tabularnewline
250 & 12 & 11.4240469083942 & 0.575953091605809 \tabularnewline
251 & 11 & 12.1229276563997 & -1.12292765639966 \tabularnewline
252 & 10 & 11.4263977997735 & -1.42639779977355 \tabularnewline
253 & 12 & 10.9394396280551 & 1.06056037194487 \tabularnewline
254 & 16 & 12.5083661523618 & 3.49163384763824 \tabularnewline
255 & 12 & 12.998299110583 & -0.998299110582997 \tabularnewline
256 & 14 & 13.6871325214264 & 0.312867478573624 \tabularnewline
257 & 16 & 14.4874778469839 & 1.51252215301608 \tabularnewline
258 & 14 & 11.7289415144361 & 2.27105848556386 \tabularnewline
259 & 13 & 14.053654051576 & -1.05365405157599 \tabularnewline
260 & 4 & 8.08400355852546 & -4.08400355852546 \tabularnewline
261 & 15 & 13.8043898253624 & 1.19561017463759 \tabularnewline
262 & 11 & 15.320972187573 & -4.32097218757297 \tabularnewline
263 & 11 & 11.1048724893516 & -0.104872489351625 \tabularnewline
264 & 14 & 12.7961332780032 & 1.20386672199677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186166&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]13[/C][C]16.0460838528533[/C][C]-3.04608385285332[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0978854720565[/C][C]-0.0978854720564726[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6187187563686[/C][C]2.38128124363138[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.2688445880293[/C][C]2.73115541197066[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.6143076175297[/C][C]-1.61430761752971[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0952278482479[/C][C]-2.09522784824791[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1106383280845[/C][C]3.88936167191553[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9713242202552[/C][C]-1.9713242202552[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1392821471749[/C][C]-2.13928214717491[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4025490268356[/C][C]0.597450973164367[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4610456161102[/C][C]0.53895438388983[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1235117839571[/C][C]-0.123511783957114[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6206706709185[/C][C]0.379329329081526[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.209447689791[/C][C]0.790552310209006[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6741165821685[/C][C]-0.674116582168502[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1802222027614[/C][C]-0.180222202761399[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7615924363051[/C][C]0.238407563694873[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4280021978545[/C][C]3.57199780214554[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6160038471028[/C][C]2.38399615289716[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.307418081434[/C][C]0.692581918565993[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2613158314681[/C][C]0.738684168531906[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1590203135979[/C][C]0.840979686402121[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3183266771825[/C][C]2.68167332281752[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8896593529274[/C][C]1.11034064707262[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.5409837524434[/C][C]0.459016247556616[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0622567007993[/C][C]-0.0622567007993018[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0063695483651[/C][C]0.993630451634888[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.287354596739[/C][C]-1.28735459673898[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5528708060404[/C][C]0.447129193959563[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3904425234722[/C][C]-0.390442523472173[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.87707304005[/C][C]-0.877073040049984[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4410863242391[/C][C]-0.441086324239132[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.163223672435[/C][C]-1.16322367243501[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.9066298550937[/C][C]0.0933701449062521[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6500827322888[/C][C]-1.65008273228881[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.265086391234[/C][C]-3.26508639123402[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.727741817731[/C][C]-2.72774181773097[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8385839962544[/C][C]-1.8385839962544[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5174086001451[/C][C]1.48259139985485[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7800135692866[/C][C]1.21998643071339[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1858454467376[/C][C]0.814154553262369[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5727609632611[/C][C]-1.57276096326111[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.6691623615627[/C][C]2.33083763843728[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1387756696789[/C][C]-0.138775669678917[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0696607461849[/C][C]-1.06966074618494[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6144856270065[/C][C]-4.61448562700648[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5709069707458[/C][C]-2.57090697074579[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1481882666103[/C][C]-0.148188266610253[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0614967185108[/C][C]0.938503281489179[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0633090042108[/C][C]-2.06330900421075[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6187303550879[/C][C]-0.6187303550879[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1780196281422[/C][C]-0.178019628142177[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6837657036483[/C][C]-2.68376570364832[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6527346763494[/C][C]0.347265323650568[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6694846725896[/C][C]-2.66948467258964[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5777743435091[/C][C]1.42222565649092[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0036265752432[/C][C]-0.00362657524322887[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.3226855308472[/C][C]0.677314469152829[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3869151033687[/C][C]-0.38691510336873[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2534262966507[/C][C]1.74657370334928[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5388756636925[/C][C]0.46112433630749[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9344810690503[/C][C]0.0655189309496813[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5909413491474[/C][C]-0.590941349147434[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.2618668653366[/C][C]-0.261866865336581[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2400247389463[/C][C]0.759975261053671[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.1718988962265[/C][C]0.828101103773527[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1173030644178[/C][C]1.88269693558221[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6991351119578[/C][C]3.3008648880422[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.6722816230555[/C][C]-3.67228162305551[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3696560764776[/C][C]0.630343923522355[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.5866524120327[/C][C]-3.5866524120327[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.195891330998[/C][C]-0.195891330997985[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5485343575736[/C][C]1.45146564242639[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.9322355961948[/C][C]1.06776440380516[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.8225188056093[/C][C]0.177481194390736[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.894982244311[/C][C]3.10501775568903[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.1954946350854[/C][C]-0.195494635085416[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5038462571406[/C][C]1.49615374285942[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0129915707567[/C][C]-2.01299157075666[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.7714379059751[/C][C]0.228562094024869[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5690883948052[/C][C]0.430911605194758[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.7700278406369[/C][C]0.229972159363091[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.8074459225013[/C][C]-0.807445922501287[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.7876596578462[/C][C]0.212340342153843[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.356478619995[/C][C]1.64352138000496[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1571625912084[/C][C]-0.157162591208444[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.3058751714197[/C][C]0.69412482858025[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.4696171221247[/C][C]1.53038287787534[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.2788274441128[/C][C]0.7211725558872[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6023588239322[/C][C]-1.60235882393221[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.6269243638083[/C][C]0.373075636191673[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.4318857904126[/C][C]0.568114209587381[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.2277147708315[/C][C]-0.227714770831527[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0813275746976[/C][C]-2.08132757469762[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.0442372330375[/C][C]0.955762766962505[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7295295273941[/C][C]0.270470472605888[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.1462494894847[/C][C]1.85375051051533[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.9702173667754[/C][C]0.0297826332246424[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.4087389652808[/C][C]-0.408738965280757[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.0669910224862[/C][C]-1.06699102248623[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.7322268792687[/C][C]1.26777312073132[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.3302055136579[/C][C]2.66979448634215[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.6657299251388[/C][C]0.334270074861162[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.4982176969474[/C][C]1.50178230305259[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.1210647666331[/C][C]-2.12106476663309[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.9044088002123[/C][C]1.09559119978767[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.5814492454538[/C][C]0.418550754546195[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.3481449641976[/C][C]1.65185503580237[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]11.9053155487678[/C][C]0.0946844512321832[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.0609499367424[/C][C]0.939050063257641[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.8229243141858[/C][C]0.177075685814231[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.8455704288843[/C][C]2.15442957111575[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.8834738038531[/C][C]-0.883473803853097[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.737925679879[/C][C]-2.73792567987902[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.579546342974[/C][C]1.42045365702602[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.2281114767074[/C][C]-1.22811147670737[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.99760178337[/C][C]1.00239821663001[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.8993115345838[/C][C]-1.89931153458377[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.6581955051037[/C][C]0.34180449489626[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.0711360179465[/C][C]-1.07113601794654[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.5535329647761[/C][C]0.446467035223899[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.953286738566[/C][C]-2.95328673856601[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.9658873937755[/C][C]-0.96588739377553[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1569827481446[/C][C]-1.15698274814463[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7580898664056[/C][C]-0.758089866405581[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]15.4110906664556[/C][C]-0.411090666455593[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.8522913759243[/C][C]1.14770862407565[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.9760046762488[/C][C]1.0239953237512[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.8005465356512[/C][C]-2.80054653565118[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]14.1060139812216[/C][C]1.89398601877844[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.3359227827828[/C][C]-3.33592278278283[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.9381832706654[/C][C]2.06181672933456[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.8537668978712[/C][C]-1.85376689787118[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5185421206393[/C][C]-1.5185421206393[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.2365070835217[/C][C]-0.236507083521652[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0440600114522[/C][C]0.95593998854782[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.4011861094136[/C][C]0.598813890586378[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.1320965306762[/C][C]-2.13209653067624[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.2328826440951[/C][C]-1.23288264409513[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.3171512856996[/C][C]-2.3171512856996[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8930870818786[/C][C]3.10691291812139[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.3481826926909[/C][C]1.6518173073091[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.6251495125964[/C][C]0.374850487403569[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.6920646620491[/C][C]1.30793533795091[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]14.1015702832193[/C][C]-4.10157028321932[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.7690449902574[/C][C]2.23095500974264[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.9873219097027[/C][C]-1.98732190970275[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]15.1318447564792[/C][C]0.868155243520752[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.0275017473578[/C][C]-0.0275017473578296[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]13.1303945600366[/C][C]-3.13039456003663[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.0188472173771[/C][C]-1.01884721737711[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.8617086186959[/C][C]2.13829138130414[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]12.0158620853918[/C][C]3.98413791460821[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.6607154650181[/C][C]1.33928453498189[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.6056861353774[/C][C]-2.60568613537736[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.3675035753915[/C][C]0.632496424608499[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.6139167601168[/C][C]1.38608323988322[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.8562720046718[/C][C]1.14372799532819[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]15.0647264580227[/C][C]0.93527354197734[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.4616481771048[/C][C]-0.461648177104799[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6278304637159[/C][C]0.372169536284077[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.758427107309[/C][C]0.241572892690999[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]11.5618691659864[/C][C]0.438130834013608[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.0220011643079[/C][C]0.977998835692081[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]14.2073181104066[/C][C]0.792681889593419[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]12.6331863748026[/C][C]-2.63318637480264[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.3683314267263[/C][C]-0.36833142672632[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]13.7153437366673[/C][C]-2.71534373666733[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.8885990551029[/C][C]-1.88859905510291[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]15.2457979352778[/C][C]0.754202064722239[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.285499153959[/C][C]1.71450084604099[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.5194638774246[/C][C]0.480536122575411[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]12.8367523332627[/C][C]-0.836752333262689[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.0531284391401[/C][C]-2.05312843914013[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.1163175907024[/C][C]-2.11631759070241[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.337039070529[/C][C]0.662960929471049[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]11.7885924976967[/C][C]0.211407502303293[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.6880411523742[/C][C]-0.688041152374177[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.7049789759084[/C][C]0.295021024091634[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.0681347026367[/C][C]-1.06813470263668[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.2128766609814[/C][C]0.78712333901862[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]10.6554383572104[/C][C]0.344561642789644[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]17.1463892904118[/C][C]1.85361070958822[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]14.1941906406341[/C][C]0.805809359365868[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.6477265300753[/C][C]-6.64772653007527[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]15.2567874798764[/C][C]0.743212520123609[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.1911457196704[/C][C]2.80885428032958[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]11.2649899572791[/C][C]0.73501004272089[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.4971440575779[/C][C]-0.497144057577875[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]11.2351280597453[/C][C]-0.235128059745265[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]15.2001378944469[/C][C]-1.20013789444686[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.4639760819775[/C][C]0.536023918022509[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.93687758322889[/C][C]2.06312241677111[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.4575980137519[/C][C]1.54240198624807[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.6527489527911[/C][C]-0.652748952791145[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.0215787178556[/C][C]-0.0215787178555945[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]12.7311147802861[/C][C]3.26888521971392[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.2447717061704[/C][C]0.755228293829609[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4749815277243[/C][C]1.52501847227566[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.7600932996358[/C][C]1.23990670036417[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]13.8662886610247[/C][C]2.13371133897526[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.7375099472516[/C][C]0.262490052748435[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]12.9533618586172[/C][C]-1.9533618586172[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.5378169642539[/C][C]-2.5378169642539[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.6790316942382[/C][C]2.32096830576176[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.6443393719584[/C][C]0.355660628041621[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.486161326657[/C][C]1.51383867334303[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]15.5235111969254[/C][C]0.476488803074615[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.2889439413281[/C][C]-2.28894394132807[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.6080491455006[/C][C]1.39195085449943[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.1204962557417[/C][C]-2.12049625574169[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]16.0388683048002[/C][C]-4.03886830480023[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.6686832368861[/C][C]0.331316763113927[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]11.8253269043946[/C][C]3.17467309560537[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.5931820436595[/C][C]1.40681795634046[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]12.7320021377912[/C][C]1.26799786220876[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.6079232240351[/C][C]2.39207677596489[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.2042562296398[/C][C]0.79574377036021[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]12.0539370739323[/C][C]0.946062926067691[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.4547445845594[/C][C]-0.454744584559436[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.0575639701152[/C][C]-1.05756397011525[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]14.0515537515291[/C][C]-0.0515537515291178[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.0233630034945[/C][C]0.976636996505471[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]12.9139492925951[/C][C]-0.913949292595147[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]13.1823819391275[/C][C]-0.182381939127509[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.888500611787[/C][C]-3.88850061178705[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]14.195549170554[/C][C]-0.195549170554001[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.8633707502467[/C][C]1.13662924975329[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.346430686068[/C][C]-1.34643068606798[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.7834452633279[/C][C]-0.783445263327946[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]10.6660715861687[/C][C]2.33392841383131[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.6996524618582[/C][C]-3.69965246185822[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.5816154391061[/C][C]4.41838456089387[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.2021717163674[/C][C]2.79782828363262[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.3927306361125[/C][C]-0.392730636112462[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]12.6034082064064[/C][C]-1.60340820640645[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]10.2801833291943[/C][C]-6.28018332919433[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]13.5153843221827[/C][C]-0.515384322182692[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]13.8876536306327[/C][C]2.1123463693673[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]10.6255327401344[/C][C]-0.625532740134383[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]11.9868516800914[/C][C]0.0131483199086499[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.7069303922495[/C][C]-1.7069303922495[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.90314447342045[/C][C]1.09685552657955[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]10.9048883644221[/C][C]2.09511163557788[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]12.9156903284589[/C][C]2.0843096715411[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]12.599045499187[/C][C]-0.599045499186976[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]13.14649603337[/C][C]0.85350396663004[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.9835984954948[/C][C]-2.98359849549482[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.9368364127457[/C][C]1.06316358725429[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.4240469083942[/C][C]0.575953091605809[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]12.1229276563997[/C][C]-1.12292765639966[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.4263977997735[/C][C]-1.42639779977355[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]10.9394396280551[/C][C]1.06056037194487[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.5083661523618[/C][C]3.49163384763824[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]12.998299110583[/C][C]-0.998299110582997[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.6871325214264[/C][C]0.312867478573624[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.4874778469839[/C][C]1.51252215301608[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.7289415144361[/C][C]2.27105848556386[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.053654051576[/C][C]-1.05365405157599[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]8.08400355852546[/C][C]-4.08400355852546[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.8043898253624[/C][C]1.19561017463759[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]15.320972187573[/C][C]-4.32097218757297[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.1048724893516[/C][C]-0.104872489351625[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.7961332780032[/C][C]1.20386672199677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186166&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186166&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	16.0460838528533	-3.04608385285332
2	16	16.0978854720565	-0.0978854720564726
3	19	16.6187187563686	2.38128124363138
4	15	12.2688445880293	2.73115541197066
5	14	15.6143076175297	-1.61430761752971
6	13	15.0952278482479	-2.09522784824791
7	19	15.1106383280845	3.88936167191553
8	15	16.9713242202552	-1.9713242202552
9	14	16.1392821471749	-2.13928214717491
10	15	14.4025490268356	0.597450973164367
11	16	15.4610456161102	0.53895438388983
12	16	16.1235117839571	-0.123511783957114
13	16	15.6206706709185	0.379329329081526
14	16	15.209447689791	0.790552310209006
15	17	17.6741165821685	-0.674116582168502
16	15	15.1802222027614	-0.180222202761399
17	15	14.7615924363051	0.238407563694873
18	20	16.4280021978545	3.57199780214554
19	18	15.6160038471028	2.38399615289716
20	16	15.307418081434	0.692581918565993
21	16	15.2613158314681	0.738684168531906
22	16	15.1590203135979	0.840979686402121
23	19	16.3183266771825	2.68167332281752
24	16	14.8896593529274	1.11034064707262
25	17	16.5409837524434	0.459016247556616
26	17	17.0622567007993	-0.0622567007993018
27	16	15.0063695483651	0.993630451634888
28	15	16.287354596739	-1.28735459673898
29	16	15.5528708060404	0.447129193959563
30	14	14.3904425234722	-0.390442523472173
31	15	15.87707304005	-0.877073040049984
32	12	12.4410863242391	-0.441086324239132
33	14	15.163223672435	-1.16322367243501
34	16	15.9066298550937	0.0933701449062521
35	14	15.6500827322888	-1.65008273228881
36	10	13.265086391234	-3.26508639123402
37	10	12.727741817731	-2.72774181773097
38	14	15.8385839962544	-1.8385839962544
39	16	14.5174086001451	1.48259139985485
40	16	14.7800135692866	1.21998643071339
41	16	15.1858454467376	0.814154553262369
42	14	15.5727609632611	-1.57276096326111
43	20	17.6691623615627	2.33083763843728
44	14	14.1387756696789	-0.138775669678917
45	14	15.0696607461849	-1.06966074618494
46	11	15.6144856270065	-4.61448562700648
47	14	16.5709069707458	-2.57090697074579
48	15	15.1481882666103	-0.148188266610253
49	16	15.0614967185108	0.938503281489179
50	14	16.0633090042108	-2.06330900421075
51	16	16.6187303550879	-0.6187303550879
52	14	14.1780196281422	-0.178019628142177
53	12	14.6837657036483	-2.68376570364832
54	16	15.6527346763494	0.347265323650568
55	9	11.6694846725896	-2.66948467258964
56	14	12.5777743435091	1.42222565649092
57	16	16.0036265752432	-0.00362657524322887
58	16	15.3226855308472	0.677314469152829
59	15	15.3869151033687	-0.38691510336873
60	16	14.2534262966507	1.74657370334928
61	12	11.5388756636925	0.46112433630749
62	16	15.9344810690503	0.0655189309496813
63	16	16.5909413491474	-0.590941349147434
64	14	14.2618668653366	-0.261866865336581
65	16	15.2400247389463	0.759975261053671
66	17	16.1718988962265	0.828101103773527
67	18	16.1173030644178	1.88269693558221
68	18	14.6991351119578	3.3008648880422
69	12	15.6722816230555	-3.67228162305551
70	16	15.3696560764776	0.630343923522355
71	10	13.5866524120327	-3.5866524120327
72	14	14.195891330998	-0.195891330997985
73	18	16.5485343575736	1.45146564242639
74	18	16.9322355961948	1.06776440380516
75	16	15.8225188056093	0.177481194390736
76	17	13.894982244311	3.10501775568903
77	16	16.1954946350854	-0.195494635085416
78	16	14.5038462571406	1.49615374285942
79	13	15.0129915707567	-2.01299157075666
80	16	15.7714379059751	0.228562094024869
81	16	15.5690883948052	0.430911605194758
82	16	15.7700278406369	0.229972159363091
83	15	15.8074459225013	-0.807445922501287
84	15	14.7876596578462	0.212340342153843
85	16	14.356478619995	1.64352138000496
86	14	14.1571625912084	-0.157162591208444
87	16	15.3058751714197	0.69412482858025
88	16	14.4696171221247	1.53038287787534
89	15	14.2788274441128	0.7211725558872
90	12	13.6023588239322	-1.60235882393221
91	17	16.6269243638083	0.373075636191673
92	16	15.4318857904126	0.568114209587381
93	15	15.2277147708315	-0.227714770831527
94	13	15.0813275746976	-2.08132757469762
95	16	15.0442372330375	0.955762766962505
96	16	15.7295295273941	0.270470472605888
97	16	14.1462494894847	1.85375051051533
98	16	15.9702173667754	0.0297826332246424
99	14	14.4087389652808	-0.408738965280757
100	16	17.0669910224862	-1.06699102248623
101	16	14.7322268792687	1.26777312073132
102	20	17.3302055136579	2.66979448634215
103	15	14.6657299251388	0.334270074861162
104	16	14.4982176969474	1.50178230305259
105	13	15.1210647666331	-2.12106476663309
106	17	15.9044088002123	1.09559119978767
107	16	15.5814492454538	0.418550754546195
108	16	14.3481449641976	1.65185503580237
109	12	11.9053155487678	0.0946844512321832
110	16	15.0609499367424	0.939050063257641
111	16	15.8229243141858	0.177075685814231
112	17	14.8455704288843	2.15442957111575
113	13	13.8834738038531	-0.883473803853097
114	12	14.737925679879	-2.73792567987902
115	18	16.579546342974	1.42045365702602
116	14	15.2281114767074	-1.22811147670737
117	14	12.99760178337	1.00239821663001
118	13	14.8993115345838	-1.89931153458377
119	16	15.6581955051037	0.34180449489626
120	13	14.0711360179465	-1.07113601794654
121	16	15.5535329647761	0.446467035223899
122	13	15.953286738566	-2.95328673856601
123	16	16.9658873937755	-0.96588739377553
124	15	16.1569827481446	-1.15698274814463
125	16	16.7580898664056	-0.758089866405581
126	15	15.4110906664556	-0.411090666455593
127	17	15.8522913759243	1.14770862407565
128	15	13.9760046762488	1.0239953237512
129	12	14.8005465356512	-2.80054653565118
130	16	14.1060139812216	1.89398601877844
131	10	13.3359227827828	-3.33592278278283
132	16	13.9381832706654	2.06181672933456
133	12	13.8537668978712	-1.85376689787118
134	14	15.5185421206393	-1.5185421206393
135	15	15.2365070835217	-0.236507083521652
136	13	12.0440600114522	0.95593998854782
137	15	14.4011861094136	0.598813890586378
138	11	13.1320965306762	-2.13209653067624
139	12	13.2328826440951	-1.23288264409513
140	11	13.3171512856996	-2.3171512856996
141	16	12.8930870818786	3.10691291812139
142	15	13.3481826926909	1.6518173073091
143	17	16.6251495125964	0.374850487403569
144	16	14.6920646620491	1.30793533795091
145	10	14.1015702832193	-4.10157028321932
146	18	15.7690449902574	2.23095500974264
147	13	14.9873219097027	-1.98732190970275
148	16	15.1318447564792	0.868155243520752
149	13	13.0275017473578	-0.0275017473578296
150	10	13.1303945600366	-3.13039456003663
151	15	16.0188472173771	-1.01884721737711
152	16	13.8617086186959	2.13829138130414
153	16	12.0158620853918	3.98413791460821
154	14	12.6607154650181	1.33928453498189
155	10	12.6056861353774	-2.60568613537736
156	17	16.3675035753915	0.632496424608499
157	13	11.6139167601168	1.38608323988322
158	15	13.8562720046718	1.14372799532819
159	16	15.0647264580227	0.93527354197734
160	12	12.4616481771048	-0.461648177104799
161	13	12.6278304637159	0.372169536284077
162	13	12.758427107309	0.241572892690999
163	12	11.5618691659864	0.438130834013608
164	17	16.0220011643079	0.977998835692081
165	15	14.2073181104066	0.792681889593419
166	10	12.6331863748026	-2.63318637480264
167	14	14.3683314267263	-0.36833142672632
168	11	13.7153437366673	-2.71534373666733
169	13	14.8885990551029	-1.88859905510291
170	16	15.2457979352778	0.754202064722239
171	12	10.285499153959	1.71450084604099
172	16	15.5194638774246	0.480536122575411
173	12	12.8367523332627	-0.836752333262689
174	9	11.0531284391401	-2.05312843914013
175	12	14.1163175907024	-2.11631759070241
176	15	14.337039070529	0.662960929471049
177	12	11.7885924976967	0.211407502303293
178	12	12.6880411523742	-0.688041152374177
179	14	13.7049789759084	0.295021024091634
180	12	13.0681347026367	-1.06813470263668
181	16	15.2128766609814	0.78712333901862
182	11	10.6554383572104	0.344561642789644
183	19	17.1463892904118	1.85361070958822
184	15	14.1941906406341	0.805809359365868
185	8	14.6477265300753	-6.64772653007527
186	16	15.2567874798764	0.743212520123609
187	17	14.1911457196704	2.80885428032958
188	12	11.2649899572791	0.73501004272089
189	11	11.4971440575779	-0.497144057577875
190	11	11.2351280597453	-0.235128059745265
191	14	15.2001378944469	-1.20013789444686
192	16	15.4639760819775	0.536023918022509
193	12	9.93687758322889	2.06312241677111
194	16	14.4575980137519	1.54240198624807
195	13	13.6527489527911	-0.652748952791145
196	15	15.0215787178556	-0.0215787178555945
197	16	12.7311147802861	3.26888521971392
198	16	15.2447717061704	0.755228293829609
199	14	12.4749815277243	1.52501847227566
200	16	14.7600932996358	1.23990670036417
201	16	13.8662886610247	2.13371133897526
202	14	13.7375099472516	0.262490052748435
203	11	12.9533618586172	-1.9533618586172
204	12	14.5378169642539	-2.5378169642539
205	15	12.6790316942382	2.32096830576176
206	15	14.6443393719584	0.355660628041621
207	16	14.486161326657	1.51383867334303
208	16	15.5235111969254	0.476488803074615
209	11	13.2889439413281	-2.28894394132807
210	15	13.6080491455006	1.39195085449943
211	12	14.1204962557417	-2.12049625574169
212	12	16.0388683048002	-4.03886830480023
213	15	14.6686832368861	0.331316763113927
214	15	11.8253269043946	3.17467309560537
215	16	14.5931820436595	1.40681795634046
216	14	12.7320021377912	1.26799786220876
217	17	14.6079232240351	2.39207677596489
218	14	13.2042562296398	0.79574377036021
219	13	12.0539370739323	0.946062926067691
220	15	15.4547445845594	-0.454744584559436
221	13	14.0575639701152	-1.05756397011525
222	14	14.0515537515291	-0.0515537515291178
223	15	14.0233630034945	0.976636996505471
224	12	12.9139492925951	-0.913949292595147
225	13	13.1823819391275	-0.182381939127509
226	8	11.888500611787	-3.88850061178705
227	14	14.195549170554	-0.195549170554001
228	14	12.8633707502467	1.13662924975329
229	11	12.346430686068	-1.34643068606798
230	12	12.7834452633279	-0.783445263327946
231	13	10.6660715861687	2.33392841383131
232	10	13.6996524618582	-3.69965246185822
233	16	11.5816154391061	4.41838456089387
234	18	15.2021717163674	2.79782828363262
235	13	13.3927306361125	-0.392730636112462
236	11	12.6034082064064	-1.60340820640645
237	4	10.2801833291943	-6.28018332919433
238	13	13.5153843221827	-0.515384322182692
239	16	13.8876536306327	2.1123463693673
240	10	10.6255327401344	-0.625532740134383
241	12	11.9868516800914	0.0131483199086499
242	12	13.7069303922495	-1.7069303922495
243	10	8.90314447342045	1.09685552657955
244	13	10.9048883644221	2.09511163557788
245	15	12.9156903284589	2.0843096715411
246	12	12.599045499187	-0.599045499186976
247	14	13.14649603337	0.85350396663004
248	10	12.9835984954948	-2.98359849549482
249	12	10.9368364127457	1.06316358725429
250	12	11.4240469083942	0.575953091605809
251	11	12.1229276563997	-1.12292765639966
252	10	11.4263977997735	-1.42639779977355
253	12	10.9394396280551	1.06056037194487
254	16	12.5083661523618	3.49163384763824
255	12	12.998299110583	-0.998299110582997
256	14	13.6871325214264	0.312867478573624
257	16	14.4874778469839	1.51252215301608
258	14	11.7289415144361	2.27105848556386
259	13	14.053654051576	-1.05365405157599
260	4	8.08400355852546	-4.08400355852546
261	15	13.8043898253624	1.19561017463759
262	11	15.320972187573	-4.32097218757297
263	11	11.1048724893516	-0.104872489351625
264	14	12.7961332780032	1.20386672199677

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.99834241093483	0.00331517813033993	0.00165758906516997
20	0.995906117462993	0.00818776507401333	0.00409388253700666
21	0.990826282065174	0.0183474358696529	0.00917371793482643
22	0.98492508770394	0.0301498245921196	0.0150749122960598
23	0.977370010363987	0.0452599792720256	0.0226299896360128
24	0.968584787741185	0.0628304245176305	0.0314152122588153
25	0.95049422143068	0.0990115571386407	0.0495057785693203
26	0.930080053838236	0.139839892323527	0.0699199461617637
27	0.901301656325487	0.197396687349027	0.0986983436745133
28	0.912931732035044	0.174136535929912	0.0870682679649558
29	0.880683701505506	0.238632596988989	0.119316298494494
30	0.877212884686704	0.245574230626593	0.122787115313296
31	0.843656349020962	0.312687301958076	0.156343650979038
32	0.832330458063171	0.335339083873657	0.167669541936829
33	0.812231119340063	0.375537761319875	0.187768880659937
34	0.762488577442032	0.475022845115936	0.237511422557968
35	0.7444201901771	0.511159619645799	0.2555798098229
36	0.813288143244134	0.373423713511732	0.186711856755866
37	0.853115244027232	0.293769511945536	0.146884755972768
38	0.834344304551199	0.331311390897602	0.165655695448801
39	0.856005333760674	0.287989332478653	0.143994666239326
40	0.839466318647912	0.321067362704176	0.160533681352088
41	0.811148905162919	0.377702189674163	0.188851094837081
42	0.784908337256706	0.430183325486588	0.215091662743294
43	0.791269150689062	0.417461698621876	0.208730849310938
44	0.751193118881263	0.497613762237474	0.248806881118737
45	0.714645531181703	0.570708937636595	0.285354468818297
46	0.866035019490006	0.267929961019988	0.133964980509994
47	0.871818655186595	0.25636268962681	0.128181344813405
48	0.847774180009192	0.304451639981617	0.152225819990808
49	0.838064578707547	0.323870842584907	0.161935421292453
50	0.821797487932716	0.356405024134568	0.178202512067284
51	0.78760719930518	0.42478560138964	0.21239280069482
52	0.753308948662059	0.493382102675882	0.246691051337941
53	0.752081272011155	0.495837455977691	0.247918727988845
54	0.724345685967268	0.551308628065464	0.275654314032732
55	0.729881422899992	0.540237154200017	0.270118577100008
56	0.737144611966565	0.52571077606687	0.262855388033435
57	0.699266131063394	0.601467737873212	0.300733868936606
58	0.682461628147075	0.635076743705851	0.317538371852925
59	0.643453297771857	0.713093404456286	0.356546702228143
60	0.664082556873778	0.671834886252444	0.335917443126222
61	0.634917726962516	0.730164546074968	0.365082273037484
62	0.595636192086631	0.808727615826737	0.404363807913369
63	0.555721993483098	0.888556013033804	0.444278006516902
64	0.510392917347123	0.979214165305755	0.489607082652877
65	0.475841561544729	0.951683123089458	0.524158438455271
66	0.457616585657374	0.915233171314748	0.542383414342626
67	0.460321356256558	0.920642712513116	0.539678643743442
68	0.582781918395867	0.834436163208266	0.417218081604133
69	0.684565287143085	0.630869425713829	0.315434712856915
70	0.653138503969568	0.693722992060864	0.346861496030432
71	0.726477721917596	0.547044556164808	0.273522278082404
72	0.690325454942763	0.619349090114474	0.309674545057237
73	0.684313635514898	0.631372728970204	0.315686364485102
74	0.663290392654084	0.673419214691831	0.336709607345916
75	0.624681729167363	0.750636541665275	0.375318270832637
76	0.681478995429305	0.637042009141389	0.318521004570695
77	0.643364684096825	0.713270631806349	0.356635315903175
78	0.621899098945926	0.756201802108148	0.378100901054074
79	0.626463793264194	0.747072413471612	0.373536206735806
80	0.586556325421876	0.826887349156247	0.413443674578124
81	0.550754761816187	0.898490476367626	0.449245238183813
82	0.513169598208466	0.973660803583068	0.486830401791534
83	0.480834321515982	0.961668643031964	0.519165678484018
84	0.442012844470168	0.884025688940337	0.557987155529832
85	0.430425397720146	0.860850795440291	0.569574602279854
86	0.391470277525738	0.782940555051477	0.608529722474262
87	0.357646116924776	0.715292233849553	0.642353883075224
88	0.339886649257135	0.679773298514271	0.660113350742865
89	0.307332046176806	0.614664092353612	0.692667953823194
90	0.294578909078641	0.589157818157282	0.705421090921359
91	0.263240262740977	0.526480525481954	0.736759737259023
92	0.235974293926126	0.471948587852252	0.764025706073874
93	0.208768275507393	0.417536551014785	0.791231724492607
94	0.218197058168412	0.436394116336824	0.781802941831588
95	0.197603918939357	0.395207837878715	0.802396081060643
96	0.171976484261225	0.34395296852245	0.828023515738775
97	0.170697824463511	0.341395648927022	0.829302175536489
98	0.146897344180304	0.293794688360607	0.853102655819696
99	0.127308325102842	0.254616650205683	0.872691674897158
100	0.114909435547072	0.229818871094145	0.885090564452928
101	0.103514727279105	0.20702945455821	0.896485272720895
102	0.122156313391891	0.244312626783781	0.877843686608109
103	0.103742443691259	0.207484887382519	0.89625755630874
104	0.0981479885136099	0.19629597702722	0.90185201148639
105	0.111871343524145	0.223742687048291	0.888128656475855
106	0.100214763723445	0.200429527446889	0.899785236276555
107	0.0876906513353272	0.175381302670654	0.912309348664673
108	0.0843840951136196	0.168768190227239	0.91561590488638
109	0.0770797105229302	0.15415942104586	0.92292028947707
110	0.068319054736474	0.136638109472948	0.931680945263526
111	0.0581785809991711	0.116357161998342	0.941821419000829
112	0.066784725908587	0.133569451817174	0.933215274091413
113	0.0574081567541782	0.114816313508356	0.942591843245822
114	0.0691680949402302	0.13833618988046	0.93083190505977
115	0.0669146542744664	0.133829308548933	0.933085345725534
116	0.0591111191038087	0.118222238207617	0.940888880896191
117	0.054589505859749	0.109179011719498	0.945410494140251
118	0.0534005612170456	0.106801122434091	0.946599438782954
119	0.0518845574637998	0.1037691149276	0.9481154425362
120	0.0443377002697415	0.088675400539483	0.955662299730259
121	0.0384679182011819	0.0769358364023637	0.961532081798818
122	0.0462813555623264	0.0925627111246528	0.953718644437674
123	0.0389256393686864	0.0778512787373728	0.961074360631314
124	0.0334891430201009	0.0669782860402018	0.966510856979899
125	0.0277446614031485	0.055489322806297	0.972255338596852
126	0.0222956117433738	0.0445912234867477	0.977704388256626
127	0.0216849028989381	0.0433698057978763	0.978315097101062
128	0.0201508697043367	0.0403017394086734	0.979849130295663
129	0.0234295980753794	0.0468591961507588	0.976570401924621
130	0.025105485853533	0.0502109717070661	0.974894514146467
131	0.0341493871030383	0.0682987742060766	0.965850612896962
132	0.045527719462717	0.0910554389254339	0.954472280537283
133	0.0455418666578374	0.0910837333156748	0.954458133342163
134	0.0401102137148492	0.0802204274296985	0.959889786285151
135	0.0333855393462406	0.0667710786924813	0.966614460653759
136	0.0291114393918267	0.0582228787836534	0.970888560608173
137	0.0255955956612382	0.0511911913224764	0.974404404338762
138	0.0278357397364552	0.0556714794729104	0.972164260263545
139	0.0239276578752558	0.0478553157505117	0.976072342124744
140	0.0318433037693262	0.0636866075386523	0.968156696230674
141	0.0406557182287072	0.0813114364574143	0.959344281771293
142	0.0388354923595057	0.0776709847190113	0.961164507640494
143	0.0319513006387704	0.0639026012775409	0.96804869936123
144	0.028266723951582	0.056533447903164	0.971733276048418
145	0.0455787472975125	0.0911574945950251	0.954421252702487
146	0.0550820014967776	0.110164002993555	0.944917998503222
147	0.0683606260914233	0.136721252182847	0.931639373908577
148	0.0587190839758405	0.117438167951681	0.94128091602416
149	0.0493224508732781	0.0986449017465562	0.950677549126722
150	0.0659503884737645	0.131900776947529	0.934049611526236
151	0.0592835602159287	0.118567120431857	0.940716439784071
152	0.0607344049057952	0.12146880981159	0.939265595094205
153	0.0799299081102157	0.159859816220431	0.920070091889784
154	0.0697281116955254	0.139456223391051	0.930271888304475
155	0.0678997073161252	0.13579941463225	0.932100292683875
156	0.0569817198556673	0.113963439711335	0.943018280144333
157	0.0496549996959829	0.0993099993919658	0.950345000304017
158	0.0421839837097659	0.0843679674195317	0.957816016290234
159	0.0351699360270701	0.0703398720541403	0.96483006397293
160	0.028584231618283	0.0571684632365659	0.971415768381717
161	0.023005623820991	0.046011247641982	0.976994376179009
162	0.0183327574581663	0.0366655149163326	0.981667242541834
163	0.0145715584894567	0.0291431169789135	0.985428441510543
164	0.0119419208509944	0.0238838417019888	0.988058079149006
165	0.0095307382721275	0.019061476544255	0.990469261727873
166	0.00965105037803897	0.0193021007560779	0.990348949621961
167	0.00748160713850012	0.0149632142770002	0.9925183928615
168	0.00802810469021809	0.0160562093804362	0.991971895309782
169	0.00734008202786129	0.0146801640557226	0.992659917972139
170	0.00595684872788804	0.0119136974557761	0.994043151272112
171	0.00697948697363136	0.0139589739472627	0.993020513026369
172	0.00540801289874016	0.0108160257974803	0.99459198710126
173	0.00428839569225053	0.00857679138450106	0.99571160430775
174	0.00423645009044183	0.00847290018088365	0.995763549909558
175	0.00423120916071292	0.00846241832142584	0.995768790839287
176	0.0035021759515334	0.00700435190306679	0.996497824048467
177	0.00261941166355747	0.00523882332711494	0.997380588336442
178	0.00210580888372582	0.00421161776745164	0.997894191116274
179	0.00161645289068689	0.00323290578137379	0.998383547109313
180	0.00139131934826092	0.00278263869652184	0.998608680651739
181	0.00109417617304527	0.00218835234609055	0.998905823826955
182	0.000793506734611876	0.00158701346922375	0.999206493265388
183	0.000784705473104853	0.00156941094620971	0.999215294526895
184	0.000579880710362991	0.00115976142072598	0.999420119289637
185	0.0168208641147177	0.0336417282294353	0.983179135885282
186	0.0143774382629088	0.0287548765258175	0.985622561737091
187	0.0169893865571043	0.0339787731142087	0.983010613442896
188	0.0133777597333176	0.0267555194666352	0.986622240266682
189	0.0113163515161694	0.0226327030323388	0.988683648483831
190	0.00896456676155344	0.0179291335231069	0.991035433238447
191	0.0079047966450284	0.0158095932900568	0.992095203354972
192	0.00610213021328697	0.0122042604265739	0.993897869786713
193	0.00560855236725542	0.0112171047345108	0.994391447632745
194	0.00487378805682457	0.00974757611364914	0.995126211943175
195	0.00396489668982123	0.00792979337964246	0.996035103310179
196	0.00289780420419282	0.00579560840838564	0.997102195795807
197	0.00397174245770266	0.00794348491540531	0.996028257542297
198	0.00289820385376601	0.00579640770753203	0.997101796146234
199	0.00244658860185033	0.00489317720370067	0.99755341139815
200	0.00205378438038511	0.00410756876077021	0.997946215619615
201	0.0018114920461491	0.0036229840922982	0.998188507953851
202	0.00138944110369551	0.00277888220739102	0.998610558896304
203	0.00166339588968988	0.00332679177937976	0.99833660411031
204	0.0026458115906453	0.00529162318129061	0.997354188409355
205	0.00264013650507319	0.00528027301014639	0.997359863494927
206	0.00188058387268832	0.00376116774537664	0.998119416127312
207	0.00152157636537545	0.00304315273075089	0.998478423634625
208	0.00107857181716888	0.00215714363433776	0.998921428182831
209	0.00137886143694701	0.00275772287389403	0.998621138563053
210	0.00103622428384543	0.00207244856769087	0.998963775716155
211	0.00132384287287792	0.00264768574575585	0.998676157127122
212	0.00403249752394389	0.00806499504788777	0.995967502476056
213	0.00280390683500934	0.00560781367001868	0.997196093164991
214	0.00370430424015279	0.00740860848030558	0.996295695759847
215	0.00282967053901741	0.00565934107803482	0.997170329460983
216	0.00203020200574762	0.00406040401149524	0.997969797994252
217	0.00216560328630247	0.00433120657260494	0.997834396713698
218	0.00167963705990032	0.00335927411980064	0.9983203629401
219	0.00146345920615216	0.00292691841230431	0.998536540793848
220	0.00100758808543027	0.00201517617086054	0.99899241191457
221	0.000737995161715393	0.00147599032343079	0.999262004838285
222	0.000474872377526651	0.000949744755053302	0.999525127622473
223	0.000323486909106705	0.000646973818213409	0.999676513090893
224	0.000218725612292476	0.000437451224584953	0.999781274387707
225	0.000132904327453964	0.000265808654907928	0.999867095672546
226	0.00034500131670398	0.000690002633407961	0.999654998683296
227	0.000240104335768236	0.000480208671536472	0.999759895664232
228	0.000149924611790226	0.000299849223580452	0.99985007538821
229	9.76700348352576e-05	0.000195340069670515	0.999902329965165
230	5.8442096056655e-05	0.00011688419211331	0.999941557903943
231	6.14139037689169e-05	0.000122827807537834	0.999938586096231
232	0.000281736732812232	0.000563473465624463	0.999718263267188
233	0.0014008611783211	0.00280172235664219	0.998599138821679
234	0.00228974094929837	0.00457948189859675	0.997710259050702
235	0.00133784764113805	0.0026756952822761	0.998662152358862
236	0.000873597338241585	0.00174719467648317	0.999126402661758
237	0.019180914012955	0.0383618280259099	0.980819085987045
238	0.0116537111785716	0.0233074223571433	0.988346288821428
239	0.00794413414283288	0.0158882682856658	0.992055865857167
240	0.00453614482226409	0.00907228964452817	0.995463855177736
241	0.00312583339726638	0.00625166679453277	0.996874166602734
242	0.00511790268749582	0.0102358053749916	0.994882097312504
243	0.00293062144810694	0.00586124289621389	0.997069378551893
244	0.00212476806185606	0.00424953612371212	0.997875231938144
245	0.00111381482442249	0.00222762964884497	0.998886185175577

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.99834241093483 & 0.00331517813033993 & 0.00165758906516997 \tabularnewline
20 & 0.995906117462993 & 0.00818776507401333 & 0.00409388253700666 \tabularnewline
21 & 0.990826282065174 & 0.0183474358696529 & 0.00917371793482643 \tabularnewline
22 & 0.98492508770394 & 0.0301498245921196 & 0.0150749122960598 \tabularnewline
23 & 0.977370010363987 & 0.0452599792720256 & 0.0226299896360128 \tabularnewline
24 & 0.968584787741185 & 0.0628304245176305 & 0.0314152122588153 \tabularnewline
25 & 0.95049422143068 & 0.0990115571386407 & 0.0495057785693203 \tabularnewline
26 & 0.930080053838236 & 0.139839892323527 & 0.0699199461617637 \tabularnewline
27 & 0.901301656325487 & 0.197396687349027 & 0.0986983436745133 \tabularnewline
28 & 0.912931732035044 & 0.174136535929912 & 0.0870682679649558 \tabularnewline
29 & 0.880683701505506 & 0.238632596988989 & 0.119316298494494 \tabularnewline
30 & 0.877212884686704 & 0.245574230626593 & 0.122787115313296 \tabularnewline
31 & 0.843656349020962 & 0.312687301958076 & 0.156343650979038 \tabularnewline
32 & 0.832330458063171 & 0.335339083873657 & 0.167669541936829 \tabularnewline
33 & 0.812231119340063 & 0.375537761319875 & 0.187768880659937 \tabularnewline
34 & 0.762488577442032 & 0.475022845115936 & 0.237511422557968 \tabularnewline
35 & 0.7444201901771 & 0.511159619645799 & 0.2555798098229 \tabularnewline
36 & 0.813288143244134 & 0.373423713511732 & 0.186711856755866 \tabularnewline
37 & 0.853115244027232 & 0.293769511945536 & 0.146884755972768 \tabularnewline
38 & 0.834344304551199 & 0.331311390897602 & 0.165655695448801 \tabularnewline
39 & 0.856005333760674 & 0.287989332478653 & 0.143994666239326 \tabularnewline
40 & 0.839466318647912 & 0.321067362704176 & 0.160533681352088 \tabularnewline
41 & 0.811148905162919 & 0.377702189674163 & 0.188851094837081 \tabularnewline
42 & 0.784908337256706 & 0.430183325486588 & 0.215091662743294 \tabularnewline
43 & 0.791269150689062 & 0.417461698621876 & 0.208730849310938 \tabularnewline
44 & 0.751193118881263 & 0.497613762237474 & 0.248806881118737 \tabularnewline
45 & 0.714645531181703 & 0.570708937636595 & 0.285354468818297 \tabularnewline
46 & 0.866035019490006 & 0.267929961019988 & 0.133964980509994 \tabularnewline
47 & 0.871818655186595 & 0.25636268962681 & 0.128181344813405 \tabularnewline
48 & 0.847774180009192 & 0.304451639981617 & 0.152225819990808 \tabularnewline
49 & 0.838064578707547 & 0.323870842584907 & 0.161935421292453 \tabularnewline
50 & 0.821797487932716 & 0.356405024134568 & 0.178202512067284 \tabularnewline
51 & 0.78760719930518 & 0.42478560138964 & 0.21239280069482 \tabularnewline
52 & 0.753308948662059 & 0.493382102675882 & 0.246691051337941 \tabularnewline
53 & 0.752081272011155 & 0.495837455977691 & 0.247918727988845 \tabularnewline
54 & 0.724345685967268 & 0.551308628065464 & 0.275654314032732 \tabularnewline
55 & 0.729881422899992 & 0.540237154200017 & 0.270118577100008 \tabularnewline
56 & 0.737144611966565 & 0.52571077606687 & 0.262855388033435 \tabularnewline
57 & 0.699266131063394 & 0.601467737873212 & 0.300733868936606 \tabularnewline
58 & 0.682461628147075 & 0.635076743705851 & 0.317538371852925 \tabularnewline
59 & 0.643453297771857 & 0.713093404456286 & 0.356546702228143 \tabularnewline
60 & 0.664082556873778 & 0.671834886252444 & 0.335917443126222 \tabularnewline
61 & 0.634917726962516 & 0.730164546074968 & 0.365082273037484 \tabularnewline
62 & 0.595636192086631 & 0.808727615826737 & 0.404363807913369 \tabularnewline
63 & 0.555721993483098 & 0.888556013033804 & 0.444278006516902 \tabularnewline
64 & 0.510392917347123 & 0.979214165305755 & 0.489607082652877 \tabularnewline
65 & 0.475841561544729 & 0.951683123089458 & 0.524158438455271 \tabularnewline
66 & 0.457616585657374 & 0.915233171314748 & 0.542383414342626 \tabularnewline
67 & 0.460321356256558 & 0.920642712513116 & 0.539678643743442 \tabularnewline
68 & 0.582781918395867 & 0.834436163208266 & 0.417218081604133 \tabularnewline
69 & 0.684565287143085 & 0.630869425713829 & 0.315434712856915 \tabularnewline
70 & 0.653138503969568 & 0.693722992060864 & 0.346861496030432 \tabularnewline
71 & 0.726477721917596 & 0.547044556164808 & 0.273522278082404 \tabularnewline
72 & 0.690325454942763 & 0.619349090114474 & 0.309674545057237 \tabularnewline
73 & 0.684313635514898 & 0.631372728970204 & 0.315686364485102 \tabularnewline
74 & 0.663290392654084 & 0.673419214691831 & 0.336709607345916 \tabularnewline
75 & 0.624681729167363 & 0.750636541665275 & 0.375318270832637 \tabularnewline
76 & 0.681478995429305 & 0.637042009141389 & 0.318521004570695 \tabularnewline
77 & 0.643364684096825 & 0.713270631806349 & 0.356635315903175 \tabularnewline
78 & 0.621899098945926 & 0.756201802108148 & 0.378100901054074 \tabularnewline
79 & 0.626463793264194 & 0.747072413471612 & 0.373536206735806 \tabularnewline
80 & 0.586556325421876 & 0.826887349156247 & 0.413443674578124 \tabularnewline
81 & 0.550754761816187 & 0.898490476367626 & 0.449245238183813 \tabularnewline
82 & 0.513169598208466 & 0.973660803583068 & 0.486830401791534 \tabularnewline
83 & 0.480834321515982 & 0.961668643031964 & 0.519165678484018 \tabularnewline
84 & 0.442012844470168 & 0.884025688940337 & 0.557987155529832 \tabularnewline
85 & 0.430425397720146 & 0.860850795440291 & 0.569574602279854 \tabularnewline
86 & 0.391470277525738 & 0.782940555051477 & 0.608529722474262 \tabularnewline
87 & 0.357646116924776 & 0.715292233849553 & 0.642353883075224 \tabularnewline
88 & 0.339886649257135 & 0.679773298514271 & 0.660113350742865 \tabularnewline
89 & 0.307332046176806 & 0.614664092353612 & 0.692667953823194 \tabularnewline
90 & 0.294578909078641 & 0.589157818157282 & 0.705421090921359 \tabularnewline
91 & 0.263240262740977 & 0.526480525481954 & 0.736759737259023 \tabularnewline
92 & 0.235974293926126 & 0.471948587852252 & 0.764025706073874 \tabularnewline
93 & 0.208768275507393 & 0.417536551014785 & 0.791231724492607 \tabularnewline
94 & 0.218197058168412 & 0.436394116336824 & 0.781802941831588 \tabularnewline
95 & 0.197603918939357 & 0.395207837878715 & 0.802396081060643 \tabularnewline
96 & 0.171976484261225 & 0.34395296852245 & 0.828023515738775 \tabularnewline
97 & 0.170697824463511 & 0.341395648927022 & 0.829302175536489 \tabularnewline
98 & 0.146897344180304 & 0.293794688360607 & 0.853102655819696 \tabularnewline
99 & 0.127308325102842 & 0.254616650205683 & 0.872691674897158 \tabularnewline
100 & 0.114909435547072 & 0.229818871094145 & 0.885090564452928 \tabularnewline
101 & 0.103514727279105 & 0.20702945455821 & 0.896485272720895 \tabularnewline
102 & 0.122156313391891 & 0.244312626783781 & 0.877843686608109 \tabularnewline
103 & 0.103742443691259 & 0.207484887382519 & 0.89625755630874 \tabularnewline
104 & 0.0981479885136099 & 0.19629597702722 & 0.90185201148639 \tabularnewline
105 & 0.111871343524145 & 0.223742687048291 & 0.888128656475855 \tabularnewline
106 & 0.100214763723445 & 0.200429527446889 & 0.899785236276555 \tabularnewline
107 & 0.0876906513353272 & 0.175381302670654 & 0.912309348664673 \tabularnewline
108 & 0.0843840951136196 & 0.168768190227239 & 0.91561590488638 \tabularnewline
109 & 0.0770797105229302 & 0.15415942104586 & 0.92292028947707 \tabularnewline
110 & 0.068319054736474 & 0.136638109472948 & 0.931680945263526 \tabularnewline
111 & 0.0581785809991711 & 0.116357161998342 & 0.941821419000829 \tabularnewline
112 & 0.066784725908587 & 0.133569451817174 & 0.933215274091413 \tabularnewline
113 & 0.0574081567541782 & 0.114816313508356 & 0.942591843245822 \tabularnewline
114 & 0.0691680949402302 & 0.13833618988046 & 0.93083190505977 \tabularnewline
115 & 0.0669146542744664 & 0.133829308548933 & 0.933085345725534 \tabularnewline
116 & 0.0591111191038087 & 0.118222238207617 & 0.940888880896191 \tabularnewline
117 & 0.054589505859749 & 0.109179011719498 & 0.945410494140251 \tabularnewline
118 & 0.0534005612170456 & 0.106801122434091 & 0.946599438782954 \tabularnewline
119 & 0.0518845574637998 & 0.1037691149276 & 0.9481154425362 \tabularnewline
120 & 0.0443377002697415 & 0.088675400539483 & 0.955662299730259 \tabularnewline
121 & 0.0384679182011819 & 0.0769358364023637 & 0.961532081798818 \tabularnewline
122 & 0.0462813555623264 & 0.0925627111246528 & 0.953718644437674 \tabularnewline
123 & 0.0389256393686864 & 0.0778512787373728 & 0.961074360631314 \tabularnewline
124 & 0.0334891430201009 & 0.0669782860402018 & 0.966510856979899 \tabularnewline
125 & 0.0277446614031485 & 0.055489322806297 & 0.972255338596852 \tabularnewline
126 & 0.0222956117433738 & 0.0445912234867477 & 0.977704388256626 \tabularnewline
127 & 0.0216849028989381 & 0.0433698057978763 & 0.978315097101062 \tabularnewline
128 & 0.0201508697043367 & 0.0403017394086734 & 0.979849130295663 \tabularnewline
129 & 0.0234295980753794 & 0.0468591961507588 & 0.976570401924621 \tabularnewline
130 & 0.025105485853533 & 0.0502109717070661 & 0.974894514146467 \tabularnewline
131 & 0.0341493871030383 & 0.0682987742060766 & 0.965850612896962 \tabularnewline
132 & 0.045527719462717 & 0.0910554389254339 & 0.954472280537283 \tabularnewline
133 & 0.0455418666578374 & 0.0910837333156748 & 0.954458133342163 \tabularnewline
134 & 0.0401102137148492 & 0.0802204274296985 & 0.959889786285151 \tabularnewline
135 & 0.0333855393462406 & 0.0667710786924813 & 0.966614460653759 \tabularnewline
136 & 0.0291114393918267 & 0.0582228787836534 & 0.970888560608173 \tabularnewline
137 & 0.0255955956612382 & 0.0511911913224764 & 0.974404404338762 \tabularnewline
138 & 0.0278357397364552 & 0.0556714794729104 & 0.972164260263545 \tabularnewline
139 & 0.0239276578752558 & 0.0478553157505117 & 0.976072342124744 \tabularnewline
140 & 0.0318433037693262 & 0.0636866075386523 & 0.968156696230674 \tabularnewline
141 & 0.0406557182287072 & 0.0813114364574143 & 0.959344281771293 \tabularnewline
142 & 0.0388354923595057 & 0.0776709847190113 & 0.961164507640494 \tabularnewline
143 & 0.0319513006387704 & 0.0639026012775409 & 0.96804869936123 \tabularnewline
144 & 0.028266723951582 & 0.056533447903164 & 0.971733276048418 \tabularnewline
145 & 0.0455787472975125 & 0.0911574945950251 & 0.954421252702487 \tabularnewline
146 & 0.0550820014967776 & 0.110164002993555 & 0.944917998503222 \tabularnewline
147 & 0.0683606260914233 & 0.136721252182847 & 0.931639373908577 \tabularnewline
148 & 0.0587190839758405 & 0.117438167951681 & 0.94128091602416 \tabularnewline
149 & 0.0493224508732781 & 0.0986449017465562 & 0.950677549126722 \tabularnewline
150 & 0.0659503884737645 & 0.131900776947529 & 0.934049611526236 \tabularnewline
151 & 0.0592835602159287 & 0.118567120431857 & 0.940716439784071 \tabularnewline
152 & 0.0607344049057952 & 0.12146880981159 & 0.939265595094205 \tabularnewline
153 & 0.0799299081102157 & 0.159859816220431 & 0.920070091889784 \tabularnewline
154 & 0.0697281116955254 & 0.139456223391051 & 0.930271888304475 \tabularnewline
155 & 0.0678997073161252 & 0.13579941463225 & 0.932100292683875 \tabularnewline
156 & 0.0569817198556673 & 0.113963439711335 & 0.943018280144333 \tabularnewline
157 & 0.0496549996959829 & 0.0993099993919658 & 0.950345000304017 \tabularnewline
158 & 0.0421839837097659 & 0.0843679674195317 & 0.957816016290234 \tabularnewline
159 & 0.0351699360270701 & 0.0703398720541403 & 0.96483006397293 \tabularnewline
160 & 0.028584231618283 & 0.0571684632365659 & 0.971415768381717 \tabularnewline
161 & 0.023005623820991 & 0.046011247641982 & 0.976994376179009 \tabularnewline
162 & 0.0183327574581663 & 0.0366655149163326 & 0.981667242541834 \tabularnewline
163 & 0.0145715584894567 & 0.0291431169789135 & 0.985428441510543 \tabularnewline
164 & 0.0119419208509944 & 0.0238838417019888 & 0.988058079149006 \tabularnewline
165 & 0.0095307382721275 & 0.019061476544255 & 0.990469261727873 \tabularnewline
166 & 0.00965105037803897 & 0.0193021007560779 & 0.990348949621961 \tabularnewline
167 & 0.00748160713850012 & 0.0149632142770002 & 0.9925183928615 \tabularnewline
168 & 0.00802810469021809 & 0.0160562093804362 & 0.991971895309782 \tabularnewline
169 & 0.00734008202786129 & 0.0146801640557226 & 0.992659917972139 \tabularnewline
170 & 0.00595684872788804 & 0.0119136974557761 & 0.994043151272112 \tabularnewline
171 & 0.00697948697363136 & 0.0139589739472627 & 0.993020513026369 \tabularnewline
172 & 0.00540801289874016 & 0.0108160257974803 & 0.99459198710126 \tabularnewline
173 & 0.00428839569225053 & 0.00857679138450106 & 0.99571160430775 \tabularnewline
174 & 0.00423645009044183 & 0.00847290018088365 & 0.995763549909558 \tabularnewline
175 & 0.00423120916071292 & 0.00846241832142584 & 0.995768790839287 \tabularnewline
176 & 0.0035021759515334 & 0.00700435190306679 & 0.996497824048467 \tabularnewline
177 & 0.00261941166355747 & 0.00523882332711494 & 0.997380588336442 \tabularnewline
178 & 0.00210580888372582 & 0.00421161776745164 & 0.997894191116274 \tabularnewline
179 & 0.00161645289068689 & 0.00323290578137379 & 0.998383547109313 \tabularnewline
180 & 0.00139131934826092 & 0.00278263869652184 & 0.998608680651739 \tabularnewline
181 & 0.00109417617304527 & 0.00218835234609055 & 0.998905823826955 \tabularnewline
182 & 0.000793506734611876 & 0.00158701346922375 & 0.999206493265388 \tabularnewline
183 & 0.000784705473104853 & 0.00156941094620971 & 0.999215294526895 \tabularnewline
184 & 0.000579880710362991 & 0.00115976142072598 & 0.999420119289637 \tabularnewline
185 & 0.0168208641147177 & 0.0336417282294353 & 0.983179135885282 \tabularnewline
186 & 0.0143774382629088 & 0.0287548765258175 & 0.985622561737091 \tabularnewline
187 & 0.0169893865571043 & 0.0339787731142087 & 0.983010613442896 \tabularnewline
188 & 0.0133777597333176 & 0.0267555194666352 & 0.986622240266682 \tabularnewline
189 & 0.0113163515161694 & 0.0226327030323388 & 0.988683648483831 \tabularnewline
190 & 0.00896456676155344 & 0.0179291335231069 & 0.991035433238447 \tabularnewline
191 & 0.0079047966450284 & 0.0158095932900568 & 0.992095203354972 \tabularnewline
192 & 0.00610213021328697 & 0.0122042604265739 & 0.993897869786713 \tabularnewline
193 & 0.00560855236725542 & 0.0112171047345108 & 0.994391447632745 \tabularnewline
194 & 0.00487378805682457 & 0.00974757611364914 & 0.995126211943175 \tabularnewline
195 & 0.00396489668982123 & 0.00792979337964246 & 0.996035103310179 \tabularnewline
196 & 0.00289780420419282 & 0.00579560840838564 & 0.997102195795807 \tabularnewline
197 & 0.00397174245770266 & 0.00794348491540531 & 0.996028257542297 \tabularnewline
198 & 0.00289820385376601 & 0.00579640770753203 & 0.997101796146234 \tabularnewline
199 & 0.00244658860185033 & 0.00489317720370067 & 0.99755341139815 \tabularnewline
200 & 0.00205378438038511 & 0.00410756876077021 & 0.997946215619615 \tabularnewline
201 & 0.0018114920461491 & 0.0036229840922982 & 0.998188507953851 \tabularnewline
202 & 0.00138944110369551 & 0.00277888220739102 & 0.998610558896304 \tabularnewline
203 & 0.00166339588968988 & 0.00332679177937976 & 0.99833660411031 \tabularnewline
204 & 0.0026458115906453 & 0.00529162318129061 & 0.997354188409355 \tabularnewline
205 & 0.00264013650507319 & 0.00528027301014639 & 0.997359863494927 \tabularnewline
206 & 0.00188058387268832 & 0.00376116774537664 & 0.998119416127312 \tabularnewline
207 & 0.00152157636537545 & 0.00304315273075089 & 0.998478423634625 \tabularnewline
208 & 0.00107857181716888 & 0.00215714363433776 & 0.998921428182831 \tabularnewline
209 & 0.00137886143694701 & 0.00275772287389403 & 0.998621138563053 \tabularnewline
210 & 0.00103622428384543 & 0.00207244856769087 & 0.998963775716155 \tabularnewline
211 & 0.00132384287287792 & 0.00264768574575585 & 0.998676157127122 \tabularnewline
212 & 0.00403249752394389 & 0.00806499504788777 & 0.995967502476056 \tabularnewline
213 & 0.00280390683500934 & 0.00560781367001868 & 0.997196093164991 \tabularnewline
214 & 0.00370430424015279 & 0.00740860848030558 & 0.996295695759847 \tabularnewline
215 & 0.00282967053901741 & 0.00565934107803482 & 0.997170329460983 \tabularnewline
216 & 0.00203020200574762 & 0.00406040401149524 & 0.997969797994252 \tabularnewline
217 & 0.00216560328630247 & 0.00433120657260494 & 0.997834396713698 \tabularnewline
218 & 0.00167963705990032 & 0.00335927411980064 & 0.9983203629401 \tabularnewline
219 & 0.00146345920615216 & 0.00292691841230431 & 0.998536540793848 \tabularnewline
220 & 0.00100758808543027 & 0.00201517617086054 & 0.99899241191457 \tabularnewline
221 & 0.000737995161715393 & 0.00147599032343079 & 0.999262004838285 \tabularnewline
222 & 0.000474872377526651 & 0.000949744755053302 & 0.999525127622473 \tabularnewline
223 & 0.000323486909106705 & 0.000646973818213409 & 0.999676513090893 \tabularnewline
224 & 0.000218725612292476 & 0.000437451224584953 & 0.999781274387707 \tabularnewline
225 & 0.000132904327453964 & 0.000265808654907928 & 0.999867095672546 \tabularnewline
226 & 0.00034500131670398 & 0.000690002633407961 & 0.999654998683296 \tabularnewline
227 & 0.000240104335768236 & 0.000480208671536472 & 0.999759895664232 \tabularnewline
228 & 0.000149924611790226 & 0.000299849223580452 & 0.99985007538821 \tabularnewline
229 & 9.76700348352576e-05 & 0.000195340069670515 & 0.999902329965165 \tabularnewline
230 & 5.8442096056655e-05 & 0.00011688419211331 & 0.999941557903943 \tabularnewline
231 & 6.14139037689169e-05 & 0.000122827807537834 & 0.999938586096231 \tabularnewline
232 & 0.000281736732812232 & 0.000563473465624463 & 0.999718263267188 \tabularnewline
233 & 0.0014008611783211 & 0.00280172235664219 & 0.998599138821679 \tabularnewline
234 & 0.00228974094929837 & 0.00457948189859675 & 0.997710259050702 \tabularnewline
235 & 0.00133784764113805 & 0.0026756952822761 & 0.998662152358862 \tabularnewline
236 & 0.000873597338241585 & 0.00174719467648317 & 0.999126402661758 \tabularnewline
237 & 0.019180914012955 & 0.0383618280259099 & 0.980819085987045 \tabularnewline
238 & 0.0116537111785716 & 0.0233074223571433 & 0.988346288821428 \tabularnewline
239 & 0.00794413414283288 & 0.0158882682856658 & 0.992055865857167 \tabularnewline
240 & 0.00453614482226409 & 0.00907228964452817 & 0.995463855177736 \tabularnewline
241 & 0.00312583339726638 & 0.00625166679453277 & 0.996874166602734 \tabularnewline
242 & 0.00511790268749582 & 0.0102358053749916 & 0.994882097312504 \tabularnewline
243 & 0.00293062144810694 & 0.00586124289621389 & 0.997069378551893 \tabularnewline
244 & 0.00212476806185606 & 0.00424953612371212 & 0.997875231938144 \tabularnewline
245 & 0.00111381482442249 & 0.00222762964884497 & 0.998886185175577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186166&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]19[/C][C]0.99834241093483[/C][C]0.00331517813033993[/C][C]0.00165758906516997[/C][/ROW]
[ROW][C]20[/C][C]0.995906117462993[/C][C]0.00818776507401333[/C][C]0.00409388253700666[/C][/ROW]
[ROW][C]21[/C][C]0.990826282065174[/C][C]0.0183474358696529[/C][C]0.00917371793482643[/C][/ROW]
[ROW][C]22[/C][C]0.98492508770394[/C][C]0.0301498245921196[/C][C]0.0150749122960598[/C][/ROW]
[ROW][C]23[/C][C]0.977370010363987[/C][C]0.0452599792720256[/C][C]0.0226299896360128[/C][/ROW]
[ROW][C]24[/C][C]0.968584787741185[/C][C]0.0628304245176305[/C][C]0.0314152122588153[/C][/ROW]
[ROW][C]25[/C][C]0.95049422143068[/C][C]0.0990115571386407[/C][C]0.0495057785693203[/C][/ROW]
[ROW][C]26[/C][C]0.930080053838236[/C][C]0.139839892323527[/C][C]0.0699199461617637[/C][/ROW]
[ROW][C]27[/C][C]0.901301656325487[/C][C]0.197396687349027[/C][C]0.0986983436745133[/C][/ROW]
[ROW][C]28[/C][C]0.912931732035044[/C][C]0.174136535929912[/C][C]0.0870682679649558[/C][/ROW]
[ROW][C]29[/C][C]0.880683701505506[/C][C]0.238632596988989[/C][C]0.119316298494494[/C][/ROW]
[ROW][C]30[/C][C]0.877212884686704[/C][C]0.245574230626593[/C][C]0.122787115313296[/C][/ROW]
[ROW][C]31[/C][C]0.843656349020962[/C][C]0.312687301958076[/C][C]0.156343650979038[/C][/ROW]
[ROW][C]32[/C][C]0.832330458063171[/C][C]0.335339083873657[/C][C]0.167669541936829[/C][/ROW]
[ROW][C]33[/C][C]0.812231119340063[/C][C]0.375537761319875[/C][C]0.187768880659937[/C][/ROW]
[ROW][C]34[/C][C]0.762488577442032[/C][C]0.475022845115936[/C][C]0.237511422557968[/C][/ROW]
[ROW][C]35[/C][C]0.7444201901771[/C][C]0.511159619645799[/C][C]0.2555798098229[/C][/ROW]
[ROW][C]36[/C][C]0.813288143244134[/C][C]0.373423713511732[/C][C]0.186711856755866[/C][/ROW]
[ROW][C]37[/C][C]0.853115244027232[/C][C]0.293769511945536[/C][C]0.146884755972768[/C][/ROW]
[ROW][C]38[/C][C]0.834344304551199[/C][C]0.331311390897602[/C][C]0.165655695448801[/C][/ROW]
[ROW][C]39[/C][C]0.856005333760674[/C][C]0.287989332478653[/C][C]0.143994666239326[/C][/ROW]
[ROW][C]40[/C][C]0.839466318647912[/C][C]0.321067362704176[/C][C]0.160533681352088[/C][/ROW]
[ROW][C]41[/C][C]0.811148905162919[/C][C]0.377702189674163[/C][C]0.188851094837081[/C][/ROW]
[ROW][C]42[/C][C]0.784908337256706[/C][C]0.430183325486588[/C][C]0.215091662743294[/C][/ROW]
[ROW][C]43[/C][C]0.791269150689062[/C][C]0.417461698621876[/C][C]0.208730849310938[/C][/ROW]
[ROW][C]44[/C][C]0.751193118881263[/C][C]0.497613762237474[/C][C]0.248806881118737[/C][/ROW]
[ROW][C]45[/C][C]0.714645531181703[/C][C]0.570708937636595[/C][C]0.285354468818297[/C][/ROW]
[ROW][C]46[/C][C]0.866035019490006[/C][C]0.267929961019988[/C][C]0.133964980509994[/C][/ROW]
[ROW][C]47[/C][C]0.871818655186595[/C][C]0.25636268962681[/C][C]0.128181344813405[/C][/ROW]
[ROW][C]48[/C][C]0.847774180009192[/C][C]0.304451639981617[/C][C]0.152225819990808[/C][/ROW]
[ROW][C]49[/C][C]0.838064578707547[/C][C]0.323870842584907[/C][C]0.161935421292453[/C][/ROW]
[ROW][C]50[/C][C]0.821797487932716[/C][C]0.356405024134568[/C][C]0.178202512067284[/C][/ROW]
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[ROW][C]187[/C][C]0.0169893865571043[/C][C]0.0339787731142087[/C][C]0.983010613442896[/C][/ROW]
[ROW][C]188[/C][C]0.0133777597333176[/C][C]0.0267555194666352[/C][C]0.986622240266682[/C][/ROW]
[ROW][C]189[/C][C]0.0113163515161694[/C][C]0.0226327030323388[/C][C]0.988683648483831[/C][/ROW]
[ROW][C]190[/C][C]0.00896456676155344[/C][C]0.0179291335231069[/C][C]0.991035433238447[/C][/ROW]
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[ROW][C]192[/C][C]0.00610213021328697[/C][C]0.0122042604265739[/C][C]0.993897869786713[/C][/ROW]
[ROW][C]193[/C][C]0.00560855236725542[/C][C]0.0112171047345108[/C][C]0.994391447632745[/C][/ROW]
[ROW][C]194[/C][C]0.00487378805682457[/C][C]0.00974757611364914[/C][C]0.995126211943175[/C][/ROW]
[ROW][C]195[/C][C]0.00396489668982123[/C][C]0.00792979337964246[/C][C]0.996035103310179[/C][/ROW]
[ROW][C]196[/C][C]0.00289780420419282[/C][C]0.00579560840838564[/C][C]0.997102195795807[/C][/ROW]
[ROW][C]197[/C][C]0.00397174245770266[/C][C]0.00794348491540531[/C][C]0.996028257542297[/C][/ROW]
[ROW][C]198[/C][C]0.00289820385376601[/C][C]0.00579640770753203[/C][C]0.997101796146234[/C][/ROW]
[ROW][C]199[/C][C]0.00244658860185033[/C][C]0.00489317720370067[/C][C]0.99755341139815[/C][/ROW]
[ROW][C]200[/C][C]0.00205378438038511[/C][C]0.00410756876077021[/C][C]0.997946215619615[/C][/ROW]
[ROW][C]201[/C][C]0.0018114920461491[/C][C]0.0036229840922982[/C][C]0.998188507953851[/C][/ROW]
[ROW][C]202[/C][C]0.00138944110369551[/C][C]0.00277888220739102[/C][C]0.998610558896304[/C][/ROW]
[ROW][C]203[/C][C]0.00166339588968988[/C][C]0.00332679177937976[/C][C]0.99833660411031[/C][/ROW]
[ROW][C]204[/C][C]0.0026458115906453[/C][C]0.00529162318129061[/C][C]0.997354188409355[/C][/ROW]
[ROW][C]205[/C][C]0.00264013650507319[/C][C]0.00528027301014639[/C][C]0.997359863494927[/C][/ROW]
[ROW][C]206[/C][C]0.00188058387268832[/C][C]0.00376116774537664[/C][C]0.998119416127312[/C][/ROW]
[ROW][C]207[/C][C]0.00152157636537545[/C][C]0.00304315273075089[/C][C]0.998478423634625[/C][/ROW]
[ROW][C]208[/C][C]0.00107857181716888[/C][C]0.00215714363433776[/C][C]0.998921428182831[/C][/ROW]
[ROW][C]209[/C][C]0.00137886143694701[/C][C]0.00275772287389403[/C][C]0.998621138563053[/C][/ROW]
[ROW][C]210[/C][C]0.00103622428384543[/C][C]0.00207244856769087[/C][C]0.998963775716155[/C][/ROW]
[ROW][C]211[/C][C]0.00132384287287792[/C][C]0.00264768574575585[/C][C]0.998676157127122[/C][/ROW]
[ROW][C]212[/C][C]0.00403249752394389[/C][C]0.00806499504788777[/C][C]0.995967502476056[/C][/ROW]
[ROW][C]213[/C][C]0.00280390683500934[/C][C]0.00560781367001868[/C][C]0.997196093164991[/C][/ROW]
[ROW][C]214[/C][C]0.00370430424015279[/C][C]0.00740860848030558[/C][C]0.996295695759847[/C][/ROW]
[ROW][C]215[/C][C]0.00282967053901741[/C][C]0.00565934107803482[/C][C]0.997170329460983[/C][/ROW]
[ROW][C]216[/C][C]0.00203020200574762[/C][C]0.00406040401149524[/C][C]0.997969797994252[/C][/ROW]
[ROW][C]217[/C][C]0.00216560328630247[/C][C]0.00433120657260494[/C][C]0.997834396713698[/C][/ROW]
[ROW][C]218[/C][C]0.00167963705990032[/C][C]0.00335927411980064[/C][C]0.9983203629401[/C][/ROW]
[ROW][C]219[/C][C]0.00146345920615216[/C][C]0.00292691841230431[/C][C]0.998536540793848[/C][/ROW]
[ROW][C]220[/C][C]0.00100758808543027[/C][C]0.00201517617086054[/C][C]0.99899241191457[/C][/ROW]
[ROW][C]221[/C][C]0.000737995161715393[/C][C]0.00147599032343079[/C][C]0.999262004838285[/C][/ROW]
[ROW][C]222[/C][C]0.000474872377526651[/C][C]0.000949744755053302[/C][C]0.999525127622473[/C][/ROW]
[ROW][C]223[/C][C]0.000323486909106705[/C][C]0.000646973818213409[/C][C]0.999676513090893[/C][/ROW]
[ROW][C]224[/C][C]0.000218725612292476[/C][C]0.000437451224584953[/C][C]0.999781274387707[/C][/ROW]
[ROW][C]225[/C][C]0.000132904327453964[/C][C]0.000265808654907928[/C][C]0.999867095672546[/C][/ROW]
[ROW][C]226[/C][C]0.00034500131670398[/C][C]0.000690002633407961[/C][C]0.999654998683296[/C][/ROW]
[ROW][C]227[/C][C]0.000240104335768236[/C][C]0.000480208671536472[/C][C]0.999759895664232[/C][/ROW]
[ROW][C]228[/C][C]0.000149924611790226[/C][C]0.000299849223580452[/C][C]0.99985007538821[/C][/ROW]
[ROW][C]229[/C][C]9.76700348352576e-05[/C][C]0.000195340069670515[/C][C]0.999902329965165[/C][/ROW]
[ROW][C]230[/C][C]5.8442096056655e-05[/C][C]0.00011688419211331[/C][C]0.999941557903943[/C][/ROW]
[ROW][C]231[/C][C]6.14139037689169e-05[/C][C]0.000122827807537834[/C][C]0.999938586096231[/C][/ROW]
[ROW][C]232[/C][C]0.000281736732812232[/C][C]0.000563473465624463[/C][C]0.999718263267188[/C][/ROW]
[ROW][C]233[/C][C]0.0014008611783211[/C][C]0.00280172235664219[/C][C]0.998599138821679[/C][/ROW]
[ROW][C]234[/C][C]0.00228974094929837[/C][C]0.00457948189859675[/C][C]0.997710259050702[/C][/ROW]
[ROW][C]235[/C][C]0.00133784764113805[/C][C]0.0026756952822761[/C][C]0.998662152358862[/C][/ROW]
[ROW][C]236[/C][C]0.000873597338241585[/C][C]0.00174719467648317[/C][C]0.999126402661758[/C][/ROW]
[ROW][C]237[/C][C]0.019180914012955[/C][C]0.0383618280259099[/C][C]0.980819085987045[/C][/ROW]
[ROW][C]238[/C][C]0.0116537111785716[/C][C]0.0233074223571433[/C][C]0.988346288821428[/C][/ROW]
[ROW][C]239[/C][C]0.00794413414283288[/C][C]0.0158882682856658[/C][C]0.992055865857167[/C][/ROW]
[ROW][C]240[/C][C]0.00453614482226409[/C][C]0.00907228964452817[/C][C]0.995463855177736[/C][/ROW]
[ROW][C]241[/C][C]0.00312583339726638[/C][C]0.00625166679453277[/C][C]0.996874166602734[/C][/ROW]
[ROW][C]242[/C][C]0.00511790268749582[/C][C]0.0102358053749916[/C][C]0.994882097312504[/C][/ROW]
[ROW][C]243[/C][C]0.00293062144810694[/C][C]0.00586124289621389[/C][C]0.997069378551893[/C][/ROW]
[ROW][C]244[/C][C]0.00212476806185606[/C][C]0.00424953612371212[/C][C]0.997875231938144[/C][/ROW]
[ROW][C]245[/C][C]0.00111381482442249[/C][C]0.00222762964884497[/C][C]0.998886185175577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186166&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186166&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.99834241093483	0.00331517813033993	0.00165758906516997
20	0.995906117462993	0.00818776507401333	0.00409388253700666
21	0.990826282065174	0.0183474358696529	0.00917371793482643
22	0.98492508770394	0.0301498245921196	0.0150749122960598
23	0.977370010363987	0.0452599792720256	0.0226299896360128
24	0.968584787741185	0.0628304245176305	0.0314152122588153
25	0.95049422143068	0.0990115571386407	0.0495057785693203
26	0.930080053838236	0.139839892323527	0.0699199461617637
27	0.901301656325487	0.197396687349027	0.0986983436745133
28	0.912931732035044	0.174136535929912	0.0870682679649558
29	0.880683701505506	0.238632596988989	0.119316298494494
30	0.877212884686704	0.245574230626593	0.122787115313296
31	0.843656349020962	0.312687301958076	0.156343650979038
32	0.832330458063171	0.335339083873657	0.167669541936829
33	0.812231119340063	0.375537761319875	0.187768880659937
34	0.762488577442032	0.475022845115936	0.237511422557968
35	0.7444201901771	0.511159619645799	0.2555798098229
36	0.813288143244134	0.373423713511732	0.186711856755866
37	0.853115244027232	0.293769511945536	0.146884755972768
38	0.834344304551199	0.331311390897602	0.165655695448801
39	0.856005333760674	0.287989332478653	0.143994666239326
40	0.839466318647912	0.321067362704176	0.160533681352088
41	0.811148905162919	0.377702189674163	0.188851094837081
42	0.784908337256706	0.430183325486588	0.215091662743294
43	0.791269150689062	0.417461698621876	0.208730849310938
44	0.751193118881263	0.497613762237474	0.248806881118737
45	0.714645531181703	0.570708937636595	0.285354468818297
46	0.866035019490006	0.267929961019988	0.133964980509994
47	0.871818655186595	0.25636268962681	0.128181344813405
48	0.847774180009192	0.304451639981617	0.152225819990808
49	0.838064578707547	0.323870842584907	0.161935421292453
50	0.821797487932716	0.356405024134568	0.178202512067284
51	0.78760719930518	0.42478560138964	0.21239280069482
52	0.753308948662059	0.493382102675882	0.246691051337941
53	0.752081272011155	0.495837455977691	0.247918727988845
54	0.724345685967268	0.551308628065464	0.275654314032732
55	0.729881422899992	0.540237154200017	0.270118577100008
56	0.737144611966565	0.52571077606687	0.262855388033435
57	0.699266131063394	0.601467737873212	0.300733868936606
58	0.682461628147075	0.635076743705851	0.317538371852925
59	0.643453297771857	0.713093404456286	0.356546702228143
60	0.664082556873778	0.671834886252444	0.335917443126222
61	0.634917726962516	0.730164546074968	0.365082273037484
62	0.595636192086631	0.808727615826737	0.404363807913369
63	0.555721993483098	0.888556013033804	0.444278006516902
64	0.510392917347123	0.979214165305755	0.489607082652877
65	0.475841561544729	0.951683123089458	0.524158438455271
66	0.457616585657374	0.915233171314748	0.542383414342626
67	0.460321356256558	0.920642712513116	0.539678643743442
68	0.582781918395867	0.834436163208266	0.417218081604133
69	0.684565287143085	0.630869425713829	0.315434712856915
70	0.653138503969568	0.693722992060864	0.346861496030432
71	0.726477721917596	0.547044556164808	0.273522278082404
72	0.690325454942763	0.619349090114474	0.309674545057237
73	0.684313635514898	0.631372728970204	0.315686364485102
74	0.663290392654084	0.673419214691831	0.336709607345916
75	0.624681729167363	0.750636541665275	0.375318270832637
76	0.681478995429305	0.637042009141389	0.318521004570695
77	0.643364684096825	0.713270631806349	0.356635315903175
78	0.621899098945926	0.756201802108148	0.378100901054074
79	0.626463793264194	0.747072413471612	0.373536206735806
80	0.586556325421876	0.826887349156247	0.413443674578124
81	0.550754761816187	0.898490476367626	0.449245238183813
82	0.513169598208466	0.973660803583068	0.486830401791534
83	0.480834321515982	0.961668643031964	0.519165678484018
84	0.442012844470168	0.884025688940337	0.557987155529832
85	0.430425397720146	0.860850795440291	0.569574602279854
86	0.391470277525738	0.782940555051477	0.608529722474262
87	0.357646116924776	0.715292233849553	0.642353883075224
88	0.339886649257135	0.679773298514271	0.660113350742865
89	0.307332046176806	0.614664092353612	0.692667953823194
90	0.294578909078641	0.589157818157282	0.705421090921359
91	0.263240262740977	0.526480525481954	0.736759737259023
92	0.235974293926126	0.471948587852252	0.764025706073874
93	0.208768275507393	0.417536551014785	0.791231724492607
94	0.218197058168412	0.436394116336824	0.781802941831588
95	0.197603918939357	0.395207837878715	0.802396081060643
96	0.171976484261225	0.34395296852245	0.828023515738775
97	0.170697824463511	0.341395648927022	0.829302175536489
98	0.146897344180304	0.293794688360607	0.853102655819696
99	0.127308325102842	0.254616650205683	0.872691674897158
100	0.114909435547072	0.229818871094145	0.885090564452928
101	0.103514727279105	0.20702945455821	0.896485272720895
102	0.122156313391891	0.244312626783781	0.877843686608109
103	0.103742443691259	0.207484887382519	0.89625755630874
104	0.0981479885136099	0.19629597702722	0.90185201148639
105	0.111871343524145	0.223742687048291	0.888128656475855
106	0.100214763723445	0.200429527446889	0.899785236276555
107	0.0876906513353272	0.175381302670654	0.912309348664673
108	0.0843840951136196	0.168768190227239	0.91561590488638
109	0.0770797105229302	0.15415942104586	0.92292028947707
110	0.068319054736474	0.136638109472948	0.931680945263526
111	0.0581785809991711	0.116357161998342	0.941821419000829
112	0.066784725908587	0.133569451817174	0.933215274091413
113	0.0574081567541782	0.114816313508356	0.942591843245822
114	0.0691680949402302	0.13833618988046	0.93083190505977
115	0.0669146542744664	0.133829308548933	0.933085345725534
116	0.0591111191038087	0.118222238207617	0.940888880896191
117	0.054589505859749	0.109179011719498	0.945410494140251
118	0.0534005612170456	0.106801122434091	0.946599438782954
119	0.0518845574637998	0.1037691149276	0.9481154425362
120	0.0443377002697415	0.088675400539483	0.955662299730259
121	0.0384679182011819	0.0769358364023637	0.961532081798818
122	0.0462813555623264	0.0925627111246528	0.953718644437674
123	0.0389256393686864	0.0778512787373728	0.961074360631314
124	0.0334891430201009	0.0669782860402018	0.966510856979899
125	0.0277446614031485	0.055489322806297	0.972255338596852
126	0.0222956117433738	0.0445912234867477	0.977704388256626
127	0.0216849028989381	0.0433698057978763	0.978315097101062
128	0.0201508697043367	0.0403017394086734	0.979849130295663
129	0.0234295980753794	0.0468591961507588	0.976570401924621
130	0.025105485853533	0.0502109717070661	0.974894514146467
131	0.0341493871030383	0.0682987742060766	0.965850612896962
132	0.045527719462717	0.0910554389254339	0.954472280537283
133	0.0455418666578374	0.0910837333156748	0.954458133342163
134	0.0401102137148492	0.0802204274296985	0.959889786285151
135	0.0333855393462406	0.0667710786924813	0.966614460653759
136	0.0291114393918267	0.0582228787836534	0.970888560608173
137	0.0255955956612382	0.0511911913224764	0.974404404338762
138	0.0278357397364552	0.0556714794729104	0.972164260263545
139	0.0239276578752558	0.0478553157505117	0.976072342124744
140	0.0318433037693262	0.0636866075386523	0.968156696230674
141	0.0406557182287072	0.0813114364574143	0.959344281771293
142	0.0388354923595057	0.0776709847190113	0.961164507640494
143	0.0319513006387704	0.0639026012775409	0.96804869936123
144	0.028266723951582	0.056533447903164	0.971733276048418
145	0.0455787472975125	0.0911574945950251	0.954421252702487
146	0.0550820014967776	0.110164002993555	0.944917998503222
147	0.0683606260914233	0.136721252182847	0.931639373908577
148	0.0587190839758405	0.117438167951681	0.94128091602416
149	0.0493224508732781	0.0986449017465562	0.950677549126722
150	0.0659503884737645	0.131900776947529	0.934049611526236
151	0.0592835602159287	0.118567120431857	0.940716439784071
152	0.0607344049057952	0.12146880981159	0.939265595094205
153	0.0799299081102157	0.159859816220431	0.920070091889784
154	0.0697281116955254	0.139456223391051	0.930271888304475
155	0.0678997073161252	0.13579941463225	0.932100292683875
156	0.0569817198556673	0.113963439711335	0.943018280144333
157	0.0496549996959829	0.0993099993919658	0.950345000304017
158	0.0421839837097659	0.0843679674195317	0.957816016290234
159	0.0351699360270701	0.0703398720541403	0.96483006397293
160	0.028584231618283	0.0571684632365659	0.971415768381717
161	0.023005623820991	0.046011247641982	0.976994376179009
162	0.0183327574581663	0.0366655149163326	0.981667242541834
163	0.0145715584894567	0.0291431169789135	0.985428441510543
164	0.0119419208509944	0.0238838417019888	0.988058079149006
165	0.0095307382721275	0.019061476544255	0.990469261727873
166	0.00965105037803897	0.0193021007560779	0.990348949621961
167	0.00748160713850012	0.0149632142770002	0.9925183928615
168	0.00802810469021809	0.0160562093804362	0.991971895309782
169	0.00734008202786129	0.0146801640557226	0.992659917972139
170	0.00595684872788804	0.0119136974557761	0.994043151272112
171	0.00697948697363136	0.0139589739472627	0.993020513026369
172	0.00540801289874016	0.0108160257974803	0.99459198710126
173	0.00428839569225053	0.00857679138450106	0.99571160430775
174	0.00423645009044183	0.00847290018088365	0.995763549909558
175	0.00423120916071292	0.00846241832142584	0.995768790839287
176	0.0035021759515334	0.00700435190306679	0.996497824048467
177	0.00261941166355747	0.00523882332711494	0.997380588336442
178	0.00210580888372582	0.00421161776745164	0.997894191116274
179	0.00161645289068689	0.00323290578137379	0.998383547109313
180	0.00139131934826092	0.00278263869652184	0.998608680651739
181	0.00109417617304527	0.00218835234609055	0.998905823826955
182	0.000793506734611876	0.00158701346922375	0.999206493265388
183	0.000784705473104853	0.00156941094620971	0.999215294526895
184	0.000579880710362991	0.00115976142072598	0.999420119289637
185	0.0168208641147177	0.0336417282294353	0.983179135885282
186	0.0143774382629088	0.0287548765258175	0.985622561737091
187	0.0169893865571043	0.0339787731142087	0.983010613442896
188	0.0133777597333176	0.0267555194666352	0.986622240266682
189	0.0113163515161694	0.0226327030323388	0.988683648483831
190	0.00896456676155344	0.0179291335231069	0.991035433238447
191	0.0079047966450284	0.0158095932900568	0.992095203354972
192	0.00610213021328697	0.0122042604265739	0.993897869786713
193	0.00560855236725542	0.0112171047345108	0.994391447632745
194	0.00487378805682457	0.00974757611364914	0.995126211943175
195	0.00396489668982123	0.00792979337964246	0.996035103310179
196	0.00289780420419282	0.00579560840838564	0.997102195795807
197	0.00397174245770266	0.00794348491540531	0.996028257542297
198	0.00289820385376601	0.00579640770753203	0.997101796146234
199	0.00244658860185033	0.00489317720370067	0.99755341139815
200	0.00205378438038511	0.00410756876077021	0.997946215619615
201	0.0018114920461491	0.0036229840922982	0.998188507953851
202	0.00138944110369551	0.00277888220739102	0.998610558896304
203	0.00166339588968988	0.00332679177937976	0.99833660411031
204	0.0026458115906453	0.00529162318129061	0.997354188409355
205	0.00264013650507319	0.00528027301014639	0.997359863494927
206	0.00188058387268832	0.00376116774537664	0.998119416127312
207	0.00152157636537545	0.00304315273075089	0.998478423634625
208	0.00107857181716888	0.00215714363433776	0.998921428182831
209	0.00137886143694701	0.00275772287389403	0.998621138563053
210	0.00103622428384543	0.00207244856769087	0.998963775716155
211	0.00132384287287792	0.00264768574575585	0.998676157127122
212	0.00403249752394389	0.00806499504788777	0.995967502476056
213	0.00280390683500934	0.00560781367001868	0.997196093164991
214	0.00370430424015279	0.00740860848030558	0.996295695759847
215	0.00282967053901741	0.00565934107803482	0.997170329460983
216	0.00203020200574762	0.00406040401149524	0.997969797994252
217	0.00216560328630247	0.00433120657260494	0.997834396713698
218	0.00167963705990032	0.00335927411980064	0.9983203629401
219	0.00146345920615216	0.00292691841230431	0.998536540793848
220	0.00100758808543027	0.00201517617086054	0.99899241191457
221	0.000737995161715393	0.00147599032343079	0.999262004838285
222	0.000474872377526651	0.000949744755053302	0.999525127622473
223	0.000323486909106705	0.000646973818213409	0.999676513090893
224	0.000218725612292476	0.000437451224584953	0.999781274387707
225	0.000132904327453964	0.000265808654907928	0.999867095672546
226	0.00034500131670398	0.000690002633407961	0.999654998683296
227	0.000240104335768236	0.000480208671536472	0.999759895664232
228	0.000149924611790226	0.000299849223580452	0.99985007538821
229	9.76700348352576e-05	0.000195340069670515	0.999902329965165
230	5.8442096056655e-05	0.00011688419211331	0.999941557903943
231	6.14139037689169e-05	0.000122827807537834	0.999938586096231
232	0.000281736732812232	0.000563473465624463	0.999718263267188
233	0.0014008611783211	0.00280172235664219	0.998599138821679
234	0.00228974094929837	0.00457948189859675	0.997710259050702
235	0.00133784764113805	0.0026756952822761	0.998662152358862
236	0.000873597338241585	0.00174719467648317	0.999126402661758
237	0.019180914012955	0.0383618280259099	0.980819085987045
238	0.0116537111785716	0.0233074223571433	0.988346288821428
239	0.00794413414283288	0.0158882682856658	0.992055865857167
240	0.00453614482226409	0.00907228964452817	0.995463855177736
241	0.00312583339726638	0.00625166679453277	0.996874166602734
242	0.00511790268749582	0.0102358053749916	0.994882097312504
243	0.00293062144810694	0.00586124289621389	0.997069378551893
244	0.00212476806185606	0.00424953612371212	0.997875231938144
245	0.00111381482442249	0.00222762964884497	0.998886185175577

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	62	0.273127753303965	NOK
5% type I error level	95	0.418502202643172	NOK
10% type I error level	123	0.541850220264317	NOK

\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 & 62 & 0.273127753303965 & NOK \tabularnewline
5% type I error level & 95 & 0.418502202643172 & NOK \tabularnewline
10% type I error level & 123 & 0.541850220264317 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186166&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]62[/C][C]0.273127753303965[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]95[/C][C]0.418502202643172[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]123[/C][C]0.541850220264317[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186166&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186166&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 tests	OK/NOK
1% type I error level	62	0.273127753303965	NOK
5% type I error level	95	0.418502202643172	NOK
10% type I error level	123	0.541850220264317	NOK

Figure 1

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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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 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;

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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code