## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 06:00:51 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t13519370393mdwlkedubd8vn9.htm/, Retrieved Sun, 03 Jul 2022 14:05:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185702, Retrieved Sun, 03 Jul 2022 14:05:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111
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]
- R  D    [Multiple Regression] [] [2012-11-03 10:00:51] [7338cd26db379c04f0557b08db763c32] [Current]
-  M        [Multiple Regression] [] [2012-12-20 10:07:43] [391561951b5d7f721cfaa4f5575ab127]
-  M        [Multiple Regression] [] [2012-12-20 10:08:47] [391561951b5d7f721cfaa4f5575ab127]
-  M        [Multiple Regression] [] [2012-12-20 10:10:50] [391561951b5d7f721cfaa4f5575ab127]
-  M        [Multiple Regression] [] [2012-12-20 10:12:46] [391561951b5d7f721cfaa4f5575ab127]
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Dataseries X:
1	1	1	41	41	38	38	13	12	12	14	14	53	53
1	2	2	39	39	32	32	16	11	11	18	18	83	83
1	3	3	30	30	35	35	19	15	15	11	11	66	66
1	4	4	31	31	33	33	15	6	6	12	12	67	67
1	5	5	34	34	37	37	14	13	13	16	16	76	76
1	6	6	35	35	29	29	13	10	10	18	18	78	78
1	7	7	39	39	31	31	19	12	12	14	14	53	53
1	8	8	34	34	36	36	15	14	14	14	14	80	80
1	9	9	36	36	35	35	14	12	12	15	15	74	74
1	10	10	37	37	38	38	15	9	9	15	15	76	76
1	11	11	38	38	31	31	16	10	10	17	17	79	79
1	12	12	36	36	34	34	16	12	12	19	19	54	54
1	13	13	38	38	35	35	16	12	12	10	10	67	67
1	14	14	39	39	38	38	16	11	11	16	16	54	54
1	15	15	33	33	37	37	17	15	15	18	18	87	87
1	16	16	32	32	33	33	15	12	12	14	14	58	58
1	17	17	36	36	32	32	15	10	10	14	14	75	75
1	18	18	38	38	38	38	20	12	12	17	17	88	88
1	19	19	39	39	38	38	18	11	11	14	14	64	64
1	20	20	32	32	32	32	16	12	12	16	16	57	57
1	21	21	32	32	33	33	16	11	11	18	18	66	66
1	22	22	31	31	31	31	16	12	12	11	11	68	68
1	23	23	39	39	38	38	19	13	13	14	14	54	54
1	24	24	37	37	39	39	16	11	11	12	12	56	56
1	25	25	39	39	32	32	17	12	12	17	17	86	86
1	26	26	41	41	32	32	17	13	13	9	9	80	80
1	27	27	36	36	35	35	16	10	10	16	16	76	76
1	28	28	33	33	37	37	15	14	14	14	14	69	69
1	29	29	33	33	33	33	16	12	12	15	15	78	78
1	30	30	34	34	33	33	14	10	10	11	11	67	67
1	31	31	31	31	31	31	15	12	12	16	16	80	80
1	32	32	27	27	32	32	12	8	8	13	13	54	54
1	33	33	37	37	31	31	14	10	10	17	17	71	71
1	34	34	34	34	37	37	16	12	12	15	15	84	84
1	35	35	34	34	30	30	14	12	12	14	14	74	74
1	36	36	32	32	33	33	10	7	7	16	16	71	71
1	37	37	29	29	31	31	10	9	9	9	9	63	63
1	38	38	36	36	33	33	14	12	12	15	15	71	71
1	39	39	29	29	31	31	16	10	10	17	17	76	76
1	40	40	35	35	33	33	16	10	10	13	13	69	69
1	41	41	37	37	32	32	16	10	10	15	15	74	74
1	42	42	34	34	33	33	14	12	12	16	16	75	75
1	43	43	38	38	32	32	20	15	15	16	16	54	54
1	44	44	35	35	33	33	14	10	10	12	12	52	52
1	45	45	38	38	28	28	14	10	10	15	15	69	69
1	46	46	37	37	35	35	11	12	12	11	11	68	68
1	47	47	38	38	39	39	14	13	13	15	15	65	65
1	48	48	33	33	34	34	15	11	11	15	15	75	75
1	49	49	36	36	38	38	16	11	11	17	17	74	74
1	50	50	38	38	32	32	14	12	12	13	13	75	75
1	51	51	32	32	38	38	16	14	14	16	16	72	72
1	52	52	32	32	30	30	14	10	10	14	14	67	67
1	53	53	32	32	33	33	12	12	12	11	11	63	63
1	54	54	34	34	38	38	16	13	13	12	12	62	62
1	55	55	32	32	32	32	9	5	5	12	12	63	63
1	56	56	37	37	35	35	14	6	6	15	15	76	76
1	57	57	39	39	34	34	16	12	12	16	16	74	74
1	58	58	29	29	34	34	16	12	12	15	15	67	67
1	59	59	37	37	36	36	15	11	11	12	12	73	73
1	60	60	35	35	34	34	16	10	10	12	12	70	70
1	61	61	30	30	28	28	12	7	7	8	8	53	53
1	62	62	38	38	34	34	16	12	12	13	13	77	77
1	63	63	34	34	35	35	16	14	14	11	11	80	80
1	64	64	31	31	35	35	14	11	11	14	14	52	52
1	65	65	34	34	31	31	16	12	12	15	15	54	54
1	66	66	35	35	37	37	17	13	13	10	10	80	80
1	67	67	36	36	35	35	18	14	14	11	11	66	66
1	68	68	30	30	27	27	18	11	11	12	12	73	73
1	69	69	39	39	40	40	12	12	12	15	15	63	63
1	70	70	35	35	37	37	16	12	12	15	15	69	69
1	71	71	38	38	36	36	10	8	8	14	14	67	67
1	72	72	31	31	38	38	14	11	11	16	16	54	54
1	73	73	34	34	39	39	18	14	14	15	15	81	81
1	74	74	38	38	41	41	18	14	14	15	15	69	69
1	75	75	34	34	27	27	16	12	12	13	13	84	84
1	76	76	39	39	30	30	17	9	9	12	12	80	80
1	77	77	37	37	37	37	16	13	13	17	17	70	70
1	78	78	34	34	31	31	16	11	11	13	13	69	69
1	79	79	28	28	31	31	13	12	12	15	15	77	77
1	80	80	37	37	27	27	16	12	12	13	13	54	54
1	81	81	33	33	36	36	16	12	12	15	15	79	79
1	82	82	35	35	37	37	16	12	12	15	15	71	71
1	83	83	37	37	33	33	15	12	12	16	16	73	73
1	84	84	32	32	34	34	15	11	11	15	15	72	72
1	85	85	33	33	31	31	16	10	10	14	14	77	77
1	86	86	38	38	39	39	14	9	9	15	15	75	75
1	87	87	33	33	34	34	16	12	12	14	14	69	69
1	88	88	29	29	32	32	16	12	12	13	13	54	54
1	89	89	33	33	33	33	15	12	12	7	7	70	70
1	90	90	31	31	36	36	12	9	9	17	17	73	73
1	91	91	36	36	32	32	17	15	15	13	13	54	54
1	92	92	35	35	41	41	16	12	12	15	15	77	77
1	93	93	32	32	28	28	15	12	12	14	14	82	82
1	94	94	29	29	30	30	13	12	12	13	13	80	80
1	95	95	39	39	36	36	16	10	10	16	16	80	80
1	96	96	37	37	35	35	16	13	13	12	12	69	69
1	97	97	35	35	31	31	16	9	9	14	14	78	78
1	98	98	37	37	34	34	16	12	12	17	17	81	81
1	99	99	32	32	36	36	14	10	10	15	15	76	76
1	100	100	38	38	36	36	16	14	14	17	17	76	76
1	101	101	37	37	35	35	16	11	11	12	12	73	73
1	102	102	36	36	37	37	20	15	15	16	16	85	85
1	103	103	32	32	28	28	15	11	11	11	11	66	66
1	104	104	33	33	39	39	16	11	11	15	15	79	79
1	105	105	40	40	32	32	13	12	12	9	9	68	68
1	106	106	38	38	35	35	17	12	12	16	16	76	76
1	107	107	41	41	39	39	16	12	12	15	15	71	71
1	108	108	36	36	35	35	16	11	11	10	10	54	54
1	109	109	43	43	42	42	12	7	7	10	10	46	46
1	110	110	30	30	34	34	16	12	12	15	15	85	85
1	111	111	31	31	33	33	16	14	14	11	11	74	74
1	112	112	32	32	41	41	17	11	11	13	13	88	88
1	113	113	32	32	33	33	13	11	11	14	14	38	38
1	114	114	37	37	34	34	12	10	10	18	18	76	76
1	115	115	37	37	32	32	18	13	13	16	16	86	86
1	116	116	33	33	40	40	14	13	13	14	14	54	54
1	117	117	34	34	40	40	14	8	8	14	14	67	67
1	118	118	33	33	35	35	13	11	11	14	14	69	69
1	119	119	38	38	36	36	16	12	12	14	14	90	90
1	120	120	33	33	37	37	13	11	11	12	12	54	54
1	121	121	31	31	27	27	16	13	13	14	14	76	76
1	122	122	38	38	39	39	13	12	12	15	15	89	89
1	123	123	37	37	38	38	16	14	14	15	15	76	76
1	124	124	36	36	31	31	15	13	13	15	15	73	73
1	125	125	31	31	33	33	16	15	15	13	13	79	79
1	126	126	39	39	32	32	15	10	10	17	17	90	90
1	127	127	44	44	39	39	17	11	11	17	17	74	74
1	128	128	33	33	36	36	15	9	9	19	19	81	81
1	129	129	35	35	33	33	12	11	11	15	15	72	72
1	130	130	32	32	33	33	16	10	10	13	13	71	71
1	131	131	28	28	32	32	10	11	11	9	9	66	66
1	132	132	40	40	37	37	16	8	8	15	15	77	77
1	133	133	27	27	30	30	12	11	11	15	15	65	65
1	134	134	37	37	38	38	14	12	12	15	15	74	74
1	135	135	32	32	29	29	15	12	12	16	16	85	85
1	136	136	28	28	22	22	13	9	9	11	11	54	54
1	137	137	34	34	35	35	15	11	11	14	14	63	63
1	138	138	30	30	35	35	11	10	10	11	11	54	54
1	139	139	35	35	34	34	12	8	8	15	15	64	64
1	140	140	31	31	35	35	11	9	9	13	13	69	69
1	141	141	32	32	34	34	16	8	8	15	15	54	54
1	142	142	30	30	37	37	15	9	9	16	16	84	84
1	143	143	30	30	35	35	17	15	15	14	14	86	86
1	144	144	31	31	23	23	16	11	11	15	15	77	77
1	145	145	40	40	31	31	10	8	8	16	16	89	89
1	146	146	32	32	27	27	18	13	13	16	16	76	76
1	147	147	36	36	36	36	13	12	12	11	11	60	60
1	148	148	32	32	31	31	16	12	12	12	12	75	75
1	149	149	35	35	32	32	13	9	9	9	9	73	73
1	150	150	38	38	39	39	10	7	7	16	16	85	85
1	151	151	42	42	37	37	15	13	13	13	13	79	79
1	152	152	34	34	38	38	16	9	9	16	16	71	71
1	153	153	35	35	39	39	16	6	6	12	12	72	72
1	154	154	38	38	34	34	14	8	8	9	9	69	69
1	155	155	33	33	31	31	10	8	8	13	13	78	78
1	156	156	36	36	32	32	17	15	15	13	13	54	54
1	157	157	32	32	37	37	13	6	6	14	14	69	69
1	158	158	33	33	36	36	15	9	9	19	19	81	81
1	159	159	34	34	32	32	16	11	11	13	13	84	84
1	160	160	32	32	38	38	12	8	8	12	12	84	84
1	161	161	34	34	36	36	13	8	8	13	13	69	69
0	162	0	27	0	26	0	13	10	0	10	0	66	0
0	163	0	31	0	26	0	12	8	0	14	0	81	0
0	164	0	38	0	33	0	17	14	0	16	0	82	0
0	165	0	34	0	39	0	15	10	0	10	0	72	0
0	166	0	24	0	30	0	10	8	0	11	0	54	0
0	167	0	30	0	33	0	14	11	0	14	0	78	0
0	168	0	26	0	25	0	11	12	0	12	0	74	0
0	169	0	34	0	38	0	13	12	0	9	0	82	0
0	170	0	27	0	37	0	16	12	0	9	0	73	0
0	171	0	37	0	31	0	12	5	0	11	0	55	0
0	172	0	36	0	37	0	16	12	0	16	0	72	0
0	173	0	41	0	35	0	12	10	0	9	0	78	0
0	174	0	29	0	25	0	9	7	0	13	0	59	0
0	175	0	36	0	28	0	12	12	0	16	0	72	0
0	176	0	32	0	35	0	15	11	0	13	0	78	0
0	177	0	37	0	33	0	12	8	0	9	0	68	0
0	178	0	30	0	30	0	12	9	0	12	0	69	0
0	179	0	31	0	31	0	14	10	0	16	0	67	0
0	180	0	38	0	37	0	12	9	0	11	0	74	0
0	181	0	36	0	36	0	16	12	0	14	0	54	0
0	182	0	35	0	30	0	11	6	0	13	0	67	0
0	183	0	31	0	36	0	19	15	0	15	0	70	0
0	184	0	38	0	32	0	15	12	0	14	0	80	0
0	185	0	22	0	28	0	8	12	0	16	0	89	0
0	186	0	32	0	36	0	16	12	0	13	0	76	0
0	187	0	36	0	34	0	17	11	0	14	0	74	0
0	188	0	39	0	31	0	12	7	0	15	0	87	0
0	189	0	28	0	28	0	11	7	0	13	0	54	0
0	190	0	32	0	36	0	11	5	0	11	0	61	0
0	191	0	32	0	36	0	14	12	0	11	0	38	0
0	192	0	38	0	40	0	16	12	0	14	0	75	0
0	193	0	32	0	33	0	12	3	0	15	0	69	0
0	194	0	35	0	37	0	16	11	0	11	0	62	0
0	195	0	32	0	32	0	13	10	0	15	0	72	0
0	196	0	37	0	38	0	15	12	0	12	0	70	0
0	197	0	34	0	31	0	16	9	0	14	0	79	0
0	198	0	33	0	37	0	16	12	0	14	0	87	0
0	199	0	33	0	33	0	14	9	0	8	0	62	0
0	200	0	26	0	32	0	16	12	0	13	0	77	0
0	201	0	30	0	30	0	16	12	0	9	0	69	0
0	202	0	24	0	30	0	14	10	0	15	0	69	0
0	203	0	34	0	31	0	11	9	0	17	0	75	0
0	204	0	34	0	32	0	12	12	0	13	0	54	0
0	205	0	33	0	34	0	15	8	0	15	0	72	0
0	206	0	34	0	36	0	15	11	0	15	0	74	0
0	207	0	35	0	37	0	16	11	0	14	0	85	0
0	208	0	35	0	36	0	16	12	0	16	0	52	0
0	209	0	36	0	33	0	11	10	0	13	0	70	0
0	210	0	34	0	33	0	15	10	0	16	0	84	0
0	211	0	34	0	33	0	12	12	0	9	0	64	0
0	212	0	41	0	44	0	12	12	0	16	0	84	0
0	213	0	32	0	39	0	15	11	0	11	0	87	0
0	214	0	30	0	32	0	15	8	0	10	0	79	0
0	215	0	35	0	35	0	16	12	0	11	0	67	0
0	216	0	28	0	25	0	14	10	0	15	0	65	0
0	217	0	33	0	35	0	17	11	0	17	0	85	0
0	218	0	39	0	34	0	14	10	0	14	0	83	0
0	219	0	36	0	35	0	13	8	0	8	0	61	0
0	220	0	36	0	39	0	15	12	0	15	0	82	0
0	221	0	35	0	33	0	13	12	0	11	0	76	0
0	222	0	38	0	36	0	14	10	0	16	0	58	0
0	223	0	33	0	32	0	15	12	0	10	0	72	0
0	224	0	31	0	32	0	12	9	0	15	0	72	0
0	225	0	34	0	36	0	13	9	0	9	0	38	0
0	226	0	32	0	36	0	8	6	0	16	0	78	0
0	227	0	31	0	32	0	14	10	0	19	0	54	0
0	228	0	33	0	34	0	14	9	0	12	0	63	0
0	229	0	34	0	33	0	11	9	0	8	0	66	0
0	230	0	34	0	35	0	12	9	0	11	0	70	0
0	231	0	34	0	30	0	13	6	0	14	0	71	0
0	232	0	33	0	38	0	10	10	0	9	0	67	0
0	233	0	32	0	34	0	16	6	0	15	0	58	0
0	234	0	41	0	33	0	18	14	0	13	0	72	0
0	235	0	34	0	32	0	13	10	0	16	0	72	0
0	236	0	36	0	31	0	11	10	0	11	0	70	0
0	237	0	37	0	30	0	4	6	0	12	0	76	0
0	238	0	36	0	27	0	13	12	0	13	0	50	0
0	239	0	29	0	31	0	16	12	0	10	0	72	0
0	240	0	37	0	30	0	10	7	0	11	0	72	0
0	241	0	27	0	32	0	12	8	0	12	0	88	0
0	242	0	35	0	35	0	12	11	0	8	0	53	0
0	243	0	28	0	28	0	10	3	0	12	0	58	0
0	244	0	35	0	33	0	13	6	0	12	0	66	0
0	245	0	37	0	31	0	15	10	0	15	0	82	0
0	246	0	29	0	35	0	12	8	0	11	0	69	0
0	247	0	32	0	35	0	14	9	0	13	0	68	0
0	248	0	36	0	32	0	10	9	0	14	0	44	0
0	249	0	19	0	21	0	12	8	0	10	0	56	0
0	250	0	21	0	20	0	12	9	0	12	0	53	0
0	251	0	31	0	34	0	11	7	0	15	0	70	0
0	252	0	33	0	32	0	10	7	0	13	0	78	0
0	253	0	36	0	34	0	12	6	0	13	0	71	0
0	254	0	33	0	32	0	16	9	0	13	0	72	0
0	255	0	37	0	33	0	12	10	0	12	0	68	0
0	256	0	34	0	33	0	14	11	0	12	0	67	0
0	257	0	35	0	37	0	16	12	0	9	0	75	0
0	258	0	31	0	32	0	14	8	0	9	0	62	0
0	259	0	37	0	34	0	13	11	0	15	0	67	0
0	260	0	35	0	30	0	4	3	0	10	0	83	0
0	261	0	27	0	30	0	15	11	0	14	0	64	0
0	262	0	34	0	38	0	11	12	0	15	0	68	0
0	263	0	40	0	36	0	11	7	0	7	0	62	0
0	264	0	29	0	32	0	14	9	0	14	0	72	0

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 15 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 & 15 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185702&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]15 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=185702&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185702&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 15 seconds R Server 'Sir Maurice George Kendall' @ kendall.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Learning[t] = + 4.76348289506874 -0.485576603308534Pop[t] -0.00437931852270916t -5.10504111858711e-05Pop_t[t] -0.0558670662591829Connected[t] + 0.145224903907176Connected_p[t] + 0.156566737435064Separate[t] -0.170900646235258Separate_p[t] + 0.562361449228819Software[t] -0.0335162245982637Software_p[t] + 0.112073294456415Happiness[t] -0.0279623279275028Happiness_p[t] -0.011262554231335Belonging[t] + 0.0306569232903473Belonging_p[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  4.76348289506874 -0.485576603308534Pop[t] -0.00437931852270916t -5.10504111858711e-05Pop_t[t] -0.0558670662591829Connected[t] +  0.145224903907176Connected_p[t] +  0.156566737435064Separate[t] -0.170900646235258Separate_p[t] +  0.562361449228819Software[t] -0.0335162245982637Software_p[t] +  0.112073294456415Happiness[t] -0.0279623279275028Happiness_p[t] -0.011262554231335Belonging[t] +  0.0306569232903473Belonging_p[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185702&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  4.76348289506874 -0.485576603308534Pop[t] -0.00437931852270916t -5.10504111858711e-05Pop_t[t] -0.0558670662591829Connected[t] +  0.145224903907176Connected_p[t] +  0.156566737435064Separate[t] -0.170900646235258Separate_p[t] +  0.562361449228819Software[t] -0.0335162245982637Software_p[t] +  0.112073294456415Happiness[t] -0.0279623279275028Happiness_p[t] -0.011262554231335Belonging[t] +  0.0306569232903473Belonging_p[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185702&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185702&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] = + 4.76348289506874 -0.485576603308534Pop[t] -0.00437931852270916t -5.10504111858711e-05Pop_t[t] -0.0558670662591829Connected[t] + 0.145224903907176Connected_p[t] + 0.156566737435064Separate[t] -0.170900646235258Separate_p[t] + 0.562361449228819Software[t] -0.0335162245982637Software_p[t] + 0.112073294456415Happiness[t] -0.0279623279275028Happiness_p[t] -0.011262554231335Belonging[t] + 0.0306569232903473Belonging_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) 4.76348289506874 2.61473 1.8218 0.069682 0.034841 Pop -0.485576603308534 3.374018 -0.1439 0.885682 0.442841 t -0.00437931852270916 0.006347 -0.69 0.490851 0.245425 Pop_t -5.10504111858711e-05 0.007154 -0.0071 0.994312 0.497156 Connected -0.0558670662591829 0.052286 -1.0685 0.286333 0.143167 Connected_p 0.145224903907176 0.07005 2.0731 0.039183 0.019591 Separate 0.156566737435064 0.059819 2.6173 0.009402 0.004701 Separate_p -0.170900646235258 0.074506 -2.2938 0.022632 0.011316 Software 0.562361449228819 0.082257 6.8366 0 0 Software_p -0.0335162245982637 0.10926 -0.3068 0.759283 0.379642 Happiness 0.112073294456415 0.075065 1.493 0.136691 0.068346 Happiness_p -0.0279623279275028 0.101031 -0.2768 0.782186 0.391093 Belonging -0.011262554231335 0.018356 -0.6136 0.540067 0.270033 Belonging_p 0.0306569232903473 0.023932 1.281 0.201371 0.100685

\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) & 4.76348289506874 & 2.61473 & 1.8218 & 0.069682 & 0.034841 \tabularnewline
Pop & -0.485576603308534 & 3.374018 & -0.1439 & 0.885682 & 0.442841 \tabularnewline
t & -0.00437931852270916 & 0.006347 & -0.69 & 0.490851 & 0.245425 \tabularnewline
Pop_t & -5.10504111858711e-05 & 0.007154 & -0.0071 & 0.994312 & 0.497156 \tabularnewline
Connected & -0.0558670662591829 & 0.052286 & -1.0685 & 0.286333 & 0.143167 \tabularnewline
Connected_p & 0.145224903907176 & 0.07005 & 2.0731 & 0.039183 & 0.019591 \tabularnewline
Separate & 0.156566737435064 & 0.059819 & 2.6173 & 0.009402 & 0.004701 \tabularnewline
Separate_p & -0.170900646235258 & 0.074506 & -2.2938 & 0.022632 & 0.011316 \tabularnewline
Software & 0.562361449228819 & 0.082257 & 6.8366 & 0 & 0 \tabularnewline
Software_p & -0.0335162245982637 & 0.10926 & -0.3068 & 0.759283 & 0.379642 \tabularnewline
Happiness & 0.112073294456415 & 0.075065 & 1.493 & 0.136691 & 0.068346 \tabularnewline
Happiness_p & -0.0279623279275028 & 0.101031 & -0.2768 & 0.782186 & 0.391093 \tabularnewline
Belonging & -0.011262554231335 & 0.018356 & -0.6136 & 0.540067 & 0.270033 \tabularnewline
Belonging_p & 0.0306569232903473 & 0.023932 & 1.281 & 0.201371 & 0.100685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185702&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]4.76348289506874[/C][C]2.61473[/C][C]1.8218[/C][C]0.069682[/C][C]0.034841[/C][/ROW]
[ROW][C]Pop[/C][C]-0.485576603308534[/C][C]3.374018[/C][C]-0.1439[/C][C]0.885682[/C][C]0.442841[/C][/ROW]
[ROW][C]t[/C][C]-0.00437931852270916[/C][C]0.006347[/C][C]-0.69[/C][C]0.490851[/C][C]0.245425[/C][/ROW]
[ROW][C]Pop_t[/C][C]-5.10504111858711e-05[/C][C]0.007154[/C][C]-0.0071[/C][C]0.994312[/C][C]0.497156[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0558670662591829[/C][C]0.052286[/C][C]-1.0685[/C][C]0.286333[/C][C]0.143167[/C][/ROW]
[ROW][C]Connected_p[/C][C]0.145224903907176[/C][C]0.07005[/C][C]2.0731[/C][C]0.039183[/C][C]0.019591[/C][/ROW]
[ROW][C]Separate[/C][C]0.156566737435064[/C][C]0.059819[/C][C]2.6173[/C][C]0.009402[/C][C]0.004701[/C][/ROW]
[ROW][C]Separate_p[/C][C]-0.170900646235258[/C][C]0.074506[/C][C]-2.2938[/C][C]0.022632[/C][C]0.011316[/C][/ROW]
[ROW][C]Software[/C][C]0.562361449228819[/C][C]0.082257[/C][C]6.8366[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Software_p[/C][C]-0.0335162245982637[/C][C]0.10926[/C][C]-0.3068[/C][C]0.759283[/C][C]0.379642[/C][/ROW]
[ROW][C]Happiness[/C][C]0.112073294456415[/C][C]0.075065[/C][C]1.493[/C][C]0.136691[/C][C]0.068346[/C][/ROW]
[ROW][C]Happiness_p[/C][C]-0.0279623279275028[/C][C]0.101031[/C][C]-0.2768[/C][C]0.782186[/C][C]0.391093[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.011262554231335[/C][C]0.018356[/C][C]-0.6136[/C][C]0.540067[/C][C]0.270033[/C][/ROW]
[ROW][C]Belonging_p[/C][C]0.0306569232903473[/C][C]0.023932[/C][C]1.281[/C][C]0.201371[/C][C]0.100685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185702&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185702&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) 4.76348289506874 2.61473 1.8218 0.069682 0.034841 Pop -0.485576603308534 3.374018 -0.1439 0.885682 0.442841 t -0.00437931852270916 0.006347 -0.69 0.490851 0.245425 Pop_t -5.10504111858711e-05 0.007154 -0.0071 0.994312 0.497156 Connected -0.0558670662591829 0.052286 -1.0685 0.286333 0.143167 Connected_p 0.145224903907176 0.07005 2.0731 0.039183 0.019591 Separate 0.156566737435064 0.059819 2.6173 0.009402 0.004701 Separate_p -0.170900646235258 0.074506 -2.2938 0.022632 0.011316 Software 0.562361449228819 0.082257 6.8366 0 0 Software_p -0.0335162245982637 0.10926 -0.3068 0.759283 0.379642 Happiness 0.112073294456415 0.075065 1.493 0.136691 0.068346 Happiness_p -0.0279623279275028 0.101031 -0.2768 0.782186 0.391093 Belonging -0.011262554231335 0.018356 -0.6136 0.540067 0.270033 Belonging_p 0.0306569232903473 0.023932 1.281 0.201371 0.100685

 Multiple Linear Regression - Regression Statistics Multiple R 0.680098352063563 R-squared 0.462533768479574 Adjusted R-squared 0.434585524440512 F-TEST (value) 16.5496539901795 F-TEST (DF numerator) 13 F-TEST (DF denominator) 250 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.84674512097084 Sum Squared Residuals 852.616885457403

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.680098352063563 \tabularnewline
R-squared & 0.462533768479574 \tabularnewline
Adjusted R-squared & 0.434585524440512 \tabularnewline
F-TEST (value) & 16.5496539901795 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 250 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84674512097084 \tabularnewline
Sum Squared Residuals & 852.616885457403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185702&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.680098352063563[/C][/ROW]
[ROW][C]R-squared[/C][C]0.462533768479574[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.5496539901795[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]250[/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]1.84674512097084[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]852.616885457403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185702&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185702&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 0.680098352063563 R-squared 0.462533768479574 Adjusted R-squared 0.434585524440512 F-TEST (value) 16.5496539901795 F-TEST (DF numerator) 13 F-TEST (DF denominator) 250 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.84674512097084 Sum Squared Residuals 852.616885457403

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 13 15.9440565190857 -2.94405651908571 2 16 16.2363436409125 -0.23634364091247 3 19 16.5815908655627 2.41840913443732 4 15 12.0390844657901 2.9609155342099 5 14 16.45830173466 -2.45830173466005 6 13 15.2783754710599 -2.27837547105988 7 19 15.8390959917877 3.16090400821228 8 15 16.8975453044673 -1.89754530446734 9 14 15.9962188225434 -1.99621882254335 10 15 14.4903976290832 0.509602370916777 11 16 15.4309127242641 0.569087275735903 12 16 15.9458181094773 0.0541818905227417 13 16 15.6008976060461 0.399102393953892 14 16 15.3665171251354 0.633482874864622 15 17 17.7638906496412 -0.763890649641175 16 15 15.2420218355414 -0.242021835541392 17 15 14.8813705507418 0.118629449258239 18 20 16.5318025509177 3.4681974490823 19 18 15.3700870379982 2.6299129620018 20 16 15.3874618326048 0.612538167395183 21 16 15.1826235848291 0.817376415170891 22 16 15.0963603928938 0.903639607106195 23 19 16.2161123209336 2.78388767906639 24 16 14.8315087237026 1.16849127629737 25 17 16.6373625207116 0.362637479288438 26 17 16.5512391051188 0.448760894881163 27 16 14.9816814371191 1.01831856288093 28 15 16.4919081196921 -1.49190811969211 29 16 15.7457832247579 0.254216775242091 30 14 14.2232383184461 -0.223238318446112 31 15 15.7097743338415 -0.709774333841459 32 12 12.4616113118721 -0.461611311872121 33 14 15.0889318175983 -1.08893181759831 34 16 15.8720197968897 0.127980203110276 35 14 15.6898721324382 -1.68987213243815 36 10 12.9295370645357 -2.9295370645357 37 10 12.9994597313448 -2.99945973134484 38 14 15.8382228338837 -1.83822283388374 39 16 14.4444587481061 1.55554125189393 40 16 14.475303137931 1.524696862069 41 16 14.9291161314462 1.07088386855383 42 14 15.8034741256171 -1.80347412561714 43 20 17.3500629397278 2.64993706027219 44 14 14.0437664216633 -0.0437664216633002 45 14 14.9611162832643 -0.9611162832643 46 11 15.4688429291675 -4.4688429291675 47 14 16.30354074625 -2.30354074624999 48 15 15.0602439744061 -0.0602439744061165 49 16 15.4153790472142 0.584620952785766 50 14 15.8874635339514 -1.88746353395141 51 16 16.5127229279992 -0.512722927999202 52 14 14.2423891525918 -0.242389152591754 53 12 14.9227371306956 -2.9227371306956 54 16 15.6189147151572 0.381085284842825 55 9 11.310404695743 -2.31040469574303 56 14 12.743066710633 1.25693328936703 57 16 16.1500795019895 -0.150079501989472 58 16 15.0321992066337 0.967800793366348 59 15 15.0491518114201 -0.0491518114200926 60 16 14.307645252983 1.69235474701699 61 12 11.6897453345998 0.310254665400209 62 16 15.8444200272623 0.155579972737695 63 16 16.4158760223166 -0.415876022316569 64 14 14.2661270324814 -0.26612703248142 65 16 15.2388507409698 0.761149259030229 66 17 15.850318744403 1.14968125559698 67 18 16.3053490550508 1.6946509449492 68 18 14.5127788066808 3.48722119331916 69 12 15.7134625958035 -3.71346259580352 70 16 15.5109688170323 0.489031182967691 71 10 13.5506652666734 -3.55066526667343 72 14 14.3946930257855 -0.394693025785526 73 18 16.6700749329515 1.3299250670485 74 18 16.761675668301 1.23832433169896 75 16 15.6654918255441 0.334508174455855 76 17 14.316624801793 2.683375198207 77 16 16.3751334365384 -0.375133436538421 78 16 14.7751043230259 1.22489567697406 79 13 15.0867490383646 -2.08674903836457 80 16 15.3295824220483 0.670417577951724 81 16 15.4917966828538 0.508203317146203 82 16 15.4965931279436 0.503406872056406 83 15 15.8511137741534 -0.851113774153397 84 15 14.7532097479609 0.246790252039134 85 16 14.3651545972111 1.63484540278886 86 14 14.209319149896 -0.209319149895987 87 16 15.2158276297318 0.784172370268221 88 16 14.5076072253922 1.4923927746078 89 15 14.6519184040208 0.348081595979188 90 12 13.7385277319648 -1.73852773196484 91 17 16.7063566560181 0.293643343981867 92 16 15.5113200177579 0.488679982242059 93 15 15.4380178290487 -0.438017829048742 94 13 15.0139464249235 -2.01394642492355 95 16 15.011733429994 0.988266570005964 96 16 15.8796750426912 0.120324957308769 97 16 13.9812549897288 2.01874501027116 98 16 16.0095902503458 -0.00959025034578832 99 14 14.2068186479575 -0.206818647957546 100 16 17.0221381364917 -1.02213813649165 101 16 14.8774102249967 1.1225897750033 102 20 17.4395113941604 2.56048860583956 103 15 14.3022261105483 0.697773889451698 104 16 14.8180512463431 1.18194875365693 105 13 15.3503044683544 -2.35030446835443 106 17 15.8680884158985 1.13191158410155 107 16 15.8933131128838 0.106686887116215 108 16 14.2203248596324 1.77967514036762 109 12 12.4705261416388 -0.470526141638752 110 16 15.2402765027813 0.759723497218674 111 16 15.8474464037919 0.152553596208056 112 17 14.6709100280968 2.32908997190318 113 13 13.8955434431428 -0.895543443142771 114 12 14.8681530193762 -2.86815301937621 115 18 16.5046478994667 1.49535210053333 116 14 15.239273166593 -1.23927316659303 117 14 12.9321013099215 1.06789869007849 118 13 14.5353070593503 -1.53530705935028 119 16 15.899458944726 0.100541055274027 120 13 14.0386410349391 -1.0386410349391 121 16 15.6514225803284 0.348577419671636 122 13 15.9078827089936 -2.9078827089936 123 16 16.6339920627059 -0.633992062705861 124 15 16.0535128859177 -1.05351288591774 125 16 16.5794602417009 -0.579460241700854 126 15 15.2097822853631 -0.209782285363102 127 17 15.7703390627542 1.22966093724583 128 15 14.0722662733027 0.927733726697263 129 12 14.8362505676798 -2.83625056767976 130 16 13.8472851590545 2.1527148409455 131 10 13.5951868615487 -3.59518686154867 132 16 13.7228491853207 2.27715081467934 133 12 14.0109075337477 -2.01090753374774 134 14 15.4887788170539 -1.48877881705388 135 15 15.4640134652598 -0.464013465259818 136 13 12.5941731599697 0.405826840030297 137 15 14.4241216729002 0.575878327099804 138 11 13.1065325076259 -2.10653250762593 139 12 13.0359223431769 -1.03592234317685 140 11 13.1173218517186 -2.11732185171859 141 16 12.565044401775 3.43495559822504 142 15 13.5336838940743 1.46631610592566 143 17 16.6015594955844 0.398440504415638 144 16 14.6526746163764 1.34732538362363 145 10 14.0681012372182 -4.06810123721825 146 18 15.7982431276868 2.2017568723132 147 13 14.7625289679238 -1.76252896792382 148 16 14.847363294813 1.15263670518698 149 13 13.2190152184265 -0.219015218426485 150 10 13.1461397459846 -3.14613974598463 151 15 16.3321807790856 -1.33218077908561 152 16 13.58035084876 2.41964915124 153 16 11.7473592377256 4.2526407622744 154 14 12.829846368234 1.17015363176601 155 10 12.9326217251075 -2.93262172510748 156 17 16.418382675315 0.581617324685043 157 13 11.6002708925272 1.39972910747278 158 15 13.9393552052859 1.06064479471411 159 16 14.6928260664654 1.30717393353456 160 12 12.7530299290138 -0.753029929013813 161 13 12.74917848362 0.250821516379981 162 13 12.6173765362878 0.38262346371218 163 12 11.5441609186264 0.455839081373624 164 17 15.8317320283892 1.16826797161076 165 15 14.1809613781732 0.819038621826762 166 10 12.5162284574896 -2.51622845748959 167 14 14.3993498832206 -0.399349883220634 168 11 13.7491700074955 -2.74917000749547 169 13 14.9069014283352 -1.90690142833521 170 16 15.2383878242737 0.761612175726268 171 12 10.2262798390239 1.77372016097606 172 16 15.5226012063219 0.477398793678094 173 12 12.9489417965926 -0.948941796592602 174 9 11.024497259364 -2.02449725936401 175 12 14.1003626138382 -2.1003626138382 176 15 14.4492620644116 0.550737935588401 177 12 11.8296619565241 0.17033804347592 178 12 12.6339706678772 -0.633970667877187 179 14 13.7634707560475 0.236529243952493 180 12 13.1058565971907 -1.10585659719067 181 16 15.3051999894337 0.694800010566342 182 11 10.7846321177231 0.215367882276936 183 19 17.1947334581257 1.80526654187433 184 15 14.2612345415922 0.738765458407801 185 8 14.647244934307 -6.64724493430698 186 16 15.1469221743111 0.85307782568894 187 17 14.1781780695718 2.82182193042824 188 12 11.2527116325001 0.74728836749991 189 11 11.5406875312444 -0.540687531244425 190 11 11.1376664801757 -0.137666480175688 191 14 15.3288560535754 -1.32885605357541 192 16 15.5350466640477 0.464953335952285 193 12 9.88829815781971 2.11170184218029 194 16 14.4720208858839 1.52797911411606 195 13 13.625715265247 -0.625715265246953 196 15 15.0924291635898 -0.092429163589778 197 16 12.5953831349435 3.40461686505647 198 16 15.1832552211262 0.816744778873838 199 14 12.4746486942216 1.52535130577838 200 16 14.7832846085766 1.21671539142336 201 16 13.8841108061721 2.11588919382791 202 14 13.7626507534853 0.237349246514671 203 11 12.9503773241019 -1.95037732410186 204 12 14.577869551733 -2.57786955173304 205 15 12.7144655901732 2.28553440982683 206 15 14.6319119194852 0.368088080514808 207 16 14.4922708811373 1.50772911886274 208 16 15.4894971529552 0.510502847044806 209 11 13.2958817978772 -2.2958817978772 210 15 13.5817807360034 1.41821926399659 211 12 14.1428623393701 -2.14286233937013 212 12 16.0289096453871 -4.02890964538706 213 15 14.5879846518168 0.412015348183227 214 15 11.8903150954748 3.10968490452521 215 16 14.5729704001091 1.42702959989093 216 14 12.7400885588807 1.25991144111931 217 17 14.5832982369277 2.41670176307235 218 14 13.2110935592794 0.788906440720609 219 13 11.9814957048625 1.01850429513746 220 15 15.4008285553322 -0.40082855533223 221 13 14.1321980260207 -1.13219802602067 222 14 14.0682872710141 -0.068287271014069 223 15 14.0115837065275 0.988416293472523 224 12 12.9922206451188 -0.992220645118751 225 13 13.1569941546857 -0.15699415468565 226 8 11.9112755129364 -3.91127551293636 227 14 14.1924632927691 -0.192463292769131 228 14 12.9412458180924 1.05875418190755 229 11 12.2423518553558 -1.24235185535583 230 12 12.8422756781471 -0.842275678147151 231 13 10.6929356539006 2.30706434609943 232 10 13.7310868426761 -3.7310868426761 233 16 11.6806645985775 4.31933540142245 234 18 15.1339841919662 2.86601580803384 235 13 13.4508816862766 -0.450881686276635 236 11 12.6403601339811 -1.64036013398109 237 4 10.2185991839173 -6.21859918391726 238 13 13.5794551191926 -0.579455119192583 239 16 14.0084161377658 1.9915838622342 240 10 10.7007996000469 -0.700799600046881 241 12 12.06245829497 -0.0624582949700043 242 12 13.7138232266365 -1.71382322663655 243 10 8.8976350227211 1.1023649772789 244 13 10.8820038413952 2.11799615860479 245 15 12.8582217280672 2.14177827193283 246 12 12.5004430180822 -0.500443018082235 247 14 13.126233093155 0.87376690684504 248 10 12.8110598932988 -2.81105989329878 249 12 10.888381311566 1.11161868843402 250 12 11.4359968239255 0.564003176074509 251 11 12.0849147298808 -1.08491472988076 252 10 11.3414207812061 -1.34142078120605 253 12 10.9990501691664 1.00094983083355 254 16 12.5249603680063 3.47503963199372 255 12 12.9490178935796 -0.949017893579647 256 14 13.6858637772946 0.314136222705359 257 16 14.3879254742619 1.6120745257381 258 14 11.7211481416927 2.27885185830732 259 13 13.9979112437533 -0.997911243753274 260 4 8.23954017419469 -4.23954017419469 261 15 13.843270687797 1.15672931220298 262 11 15.3297403317004 -4.32974033170044 263 11 11.0362068643451 -0.0362068643450975 264 14 12.8167087422723 1.18329125772766

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.9440565190857 & -2.94405651908571 \tabularnewline
2 & 16 & 16.2363436409125 & -0.23634364091247 \tabularnewline
3 & 19 & 16.5815908655627 & 2.41840913443732 \tabularnewline
4 & 15 & 12.0390844657901 & 2.9609155342099 \tabularnewline
5 & 14 & 16.45830173466 & -2.45830173466005 \tabularnewline
6 & 13 & 15.2783754710599 & -2.27837547105988 \tabularnewline
7 & 19 & 15.8390959917877 & 3.16090400821228 \tabularnewline
8 & 15 & 16.8975453044673 & -1.89754530446734 \tabularnewline
9 & 14 & 15.9962188225434 & -1.99621882254335 \tabularnewline
10 & 15 & 14.4903976290832 & 0.509602370916777 \tabularnewline
11 & 16 & 15.4309127242641 & 0.569087275735903 \tabularnewline
12 & 16 & 15.9458181094773 & 0.0541818905227417 \tabularnewline
13 & 16 & 15.6008976060461 & 0.399102393953892 \tabularnewline
14 & 16 & 15.3665171251354 & 0.633482874864622 \tabularnewline
15 & 17 & 17.7638906496412 & -0.763890649641175 \tabularnewline
16 & 15 & 15.2420218355414 & -0.242021835541392 \tabularnewline
17 & 15 & 14.8813705507418 & 0.118629449258239 \tabularnewline
18 & 20 & 16.5318025509177 & 3.4681974490823 \tabularnewline
19 & 18 & 15.3700870379982 & 2.6299129620018 \tabularnewline
20 & 16 & 15.3874618326048 & 0.612538167395183 \tabularnewline
21 & 16 & 15.1826235848291 & 0.817376415170891 \tabularnewline
22 & 16 & 15.0963603928938 & 0.903639607106195 \tabularnewline
23 & 19 & 16.2161123209336 & 2.78388767906639 \tabularnewline
24 & 16 & 14.8315087237026 & 1.16849127629737 \tabularnewline
25 & 17 & 16.6373625207116 & 0.362637479288438 \tabularnewline
26 & 17 & 16.5512391051188 & 0.448760894881163 \tabularnewline
27 & 16 & 14.9816814371191 & 1.01831856288093 \tabularnewline
28 & 15 & 16.4919081196921 & -1.49190811969211 \tabularnewline
29 & 16 & 15.7457832247579 & 0.254216775242091 \tabularnewline
30 & 14 & 14.2232383184461 & -0.223238318446112 \tabularnewline
31 & 15 & 15.7097743338415 & -0.709774333841459 \tabularnewline
32 & 12 & 12.4616113118721 & -0.461611311872121 \tabularnewline
33 & 14 & 15.0889318175983 & -1.08893181759831 \tabularnewline
34 & 16 & 15.8720197968897 & 0.127980203110276 \tabularnewline
35 & 14 & 15.6898721324382 & -1.68987213243815 \tabularnewline
36 & 10 & 12.9295370645357 & -2.9295370645357 \tabularnewline
37 & 10 & 12.9994597313448 & -2.99945973134484 \tabularnewline
38 & 14 & 15.8382228338837 & -1.83822283388374 \tabularnewline
39 & 16 & 14.4444587481061 & 1.55554125189393 \tabularnewline
40 & 16 & 14.475303137931 & 1.524696862069 \tabularnewline
41 & 16 & 14.9291161314462 & 1.07088386855383 \tabularnewline
42 & 14 & 15.8034741256171 & -1.80347412561714 \tabularnewline
43 & 20 & 17.3500629397278 & 2.64993706027219 \tabularnewline
44 & 14 & 14.0437664216633 & -0.0437664216633002 \tabularnewline
45 & 14 & 14.9611162832643 & -0.9611162832643 \tabularnewline
46 & 11 & 15.4688429291675 & -4.4688429291675 \tabularnewline
47 & 14 & 16.30354074625 & -2.30354074624999 \tabularnewline
48 & 15 & 15.0602439744061 & -0.0602439744061165 \tabularnewline
49 & 16 & 15.4153790472142 & 0.584620952785766 \tabularnewline
50 & 14 & 15.8874635339514 & -1.88746353395141 \tabularnewline
51 & 16 & 16.5127229279992 & -0.512722927999202 \tabularnewline
52 & 14 & 14.2423891525918 & -0.242389152591754 \tabularnewline
53 & 12 & 14.9227371306956 & -2.9227371306956 \tabularnewline
54 & 16 & 15.6189147151572 & 0.381085284842825 \tabularnewline
55 & 9 & 11.310404695743 & -2.31040469574303 \tabularnewline
56 & 14 & 12.743066710633 & 1.25693328936703 \tabularnewline
57 & 16 & 16.1500795019895 & -0.150079501989472 \tabularnewline
58 & 16 & 15.0321992066337 & 0.967800793366348 \tabularnewline
59 & 15 & 15.0491518114201 & -0.0491518114200926 \tabularnewline
60 & 16 & 14.307645252983 & 1.69235474701699 \tabularnewline
61 & 12 & 11.6897453345998 & 0.310254665400209 \tabularnewline
62 & 16 & 15.8444200272623 & 0.155579972737695 \tabularnewline
63 & 16 & 16.4158760223166 & -0.415876022316569 \tabularnewline
64 & 14 & 14.2661270324814 & -0.26612703248142 \tabularnewline
65 & 16 & 15.2388507409698 & 0.761149259030229 \tabularnewline
66 & 17 & 15.850318744403 & 1.14968125559698 \tabularnewline
67 & 18 & 16.3053490550508 & 1.6946509449492 \tabularnewline
68 & 18 & 14.5127788066808 & 3.48722119331916 \tabularnewline
69 & 12 & 15.7134625958035 & -3.71346259580352 \tabularnewline
70 & 16 & 15.5109688170323 & 0.489031182967691 \tabularnewline
71 & 10 & 13.5506652666734 & -3.55066526667343 \tabularnewline
72 & 14 & 14.3946930257855 & -0.394693025785526 \tabularnewline
73 & 18 & 16.6700749329515 & 1.3299250670485 \tabularnewline
74 & 18 & 16.761675668301 & 1.23832433169896 \tabularnewline
75 & 16 & 15.6654918255441 & 0.334508174455855 \tabularnewline
76 & 17 & 14.316624801793 & 2.683375198207 \tabularnewline
77 & 16 & 16.3751334365384 & -0.375133436538421 \tabularnewline
78 & 16 & 14.7751043230259 & 1.22489567697406 \tabularnewline
79 & 13 & 15.0867490383646 & -2.08674903836457 \tabularnewline
80 & 16 & 15.3295824220483 & 0.670417577951724 \tabularnewline
81 & 16 & 15.4917966828538 & 0.508203317146203 \tabularnewline
82 & 16 & 15.4965931279436 & 0.503406872056406 \tabularnewline
83 & 15 & 15.8511137741534 & -0.851113774153397 \tabularnewline
84 & 15 & 14.7532097479609 & 0.246790252039134 \tabularnewline
85 & 16 & 14.3651545972111 & 1.63484540278886 \tabularnewline
86 & 14 & 14.209319149896 & -0.209319149895987 \tabularnewline
87 & 16 & 15.2158276297318 & 0.784172370268221 \tabularnewline
88 & 16 & 14.5076072253922 & 1.4923927746078 \tabularnewline
89 & 15 & 14.6519184040208 & 0.348081595979188 \tabularnewline
90 & 12 & 13.7385277319648 & -1.73852773196484 \tabularnewline
91 & 17 & 16.7063566560181 & 0.293643343981867 \tabularnewline
92 & 16 & 15.5113200177579 & 0.488679982242059 \tabularnewline
93 & 15 & 15.4380178290487 & -0.438017829048742 \tabularnewline
94 & 13 & 15.0139464249235 & -2.01394642492355 \tabularnewline
95 & 16 & 15.011733429994 & 0.988266570005964 \tabularnewline
96 & 16 & 15.8796750426912 & 0.120324957308769 \tabularnewline
97 & 16 & 13.9812549897288 & 2.01874501027116 \tabularnewline
98 & 16 & 16.0095902503458 & -0.00959025034578832 \tabularnewline
99 & 14 & 14.2068186479575 & -0.206818647957546 \tabularnewline
100 & 16 & 17.0221381364917 & -1.02213813649165 \tabularnewline
101 & 16 & 14.8774102249967 & 1.1225897750033 \tabularnewline
102 & 20 & 17.4395113941604 & 2.56048860583956 \tabularnewline
103 & 15 & 14.3022261105483 & 0.697773889451698 \tabularnewline
104 & 16 & 14.8180512463431 & 1.18194875365693 \tabularnewline
105 & 13 & 15.3503044683544 & -2.35030446835443 \tabularnewline
106 & 17 & 15.8680884158985 & 1.13191158410155 \tabularnewline
107 & 16 & 15.8933131128838 & 0.106686887116215 \tabularnewline
108 & 16 & 14.2203248596324 & 1.77967514036762 \tabularnewline
109 & 12 & 12.4705261416388 & -0.470526141638752 \tabularnewline
110 & 16 & 15.2402765027813 & 0.759723497218674 \tabularnewline
111 & 16 & 15.8474464037919 & 0.152553596208056 \tabularnewline
112 & 17 & 14.6709100280968 & 2.32908997190318 \tabularnewline
113 & 13 & 13.8955434431428 & -0.895543443142771 \tabularnewline
114 & 12 & 14.8681530193762 & -2.86815301937621 \tabularnewline
115 & 18 & 16.5046478994667 & 1.49535210053333 \tabularnewline
116 & 14 & 15.239273166593 & -1.23927316659303 \tabularnewline
117 & 14 & 12.9321013099215 & 1.06789869007849 \tabularnewline
118 & 13 & 14.5353070593503 & -1.53530705935028 \tabularnewline
119 & 16 & 15.899458944726 & 0.100541055274027 \tabularnewline
120 & 13 & 14.0386410349391 & -1.0386410349391 \tabularnewline
121 & 16 & 15.6514225803284 & 0.348577419671636 \tabularnewline
122 & 13 & 15.9078827089936 & -2.9078827089936 \tabularnewline
123 & 16 & 16.6339920627059 & -0.633992062705861 \tabularnewline
124 & 15 & 16.0535128859177 & -1.05351288591774 \tabularnewline
125 & 16 & 16.5794602417009 & -0.579460241700854 \tabularnewline
126 & 15 & 15.2097822853631 & -0.209782285363102 \tabularnewline
127 & 17 & 15.7703390627542 & 1.22966093724583 \tabularnewline
128 & 15 & 14.0722662733027 & 0.927733726697263 \tabularnewline
129 & 12 & 14.8362505676798 & -2.83625056767976 \tabularnewline
130 & 16 & 13.8472851590545 & 2.1527148409455 \tabularnewline
131 & 10 & 13.5951868615487 & -3.59518686154867 \tabularnewline
132 & 16 & 13.7228491853207 & 2.27715081467934 \tabularnewline
133 & 12 & 14.0109075337477 & -2.01090753374774 \tabularnewline
134 & 14 & 15.4887788170539 & -1.48877881705388 \tabularnewline
135 & 15 & 15.4640134652598 & -0.464013465259818 \tabularnewline
136 & 13 & 12.5941731599697 & 0.405826840030297 \tabularnewline
137 & 15 & 14.4241216729002 & 0.575878327099804 \tabularnewline
138 & 11 & 13.1065325076259 & -2.10653250762593 \tabularnewline
139 & 12 & 13.0359223431769 & -1.03592234317685 \tabularnewline
140 & 11 & 13.1173218517186 & -2.11732185171859 \tabularnewline
141 & 16 & 12.565044401775 & 3.43495559822504 \tabularnewline
142 & 15 & 13.5336838940743 & 1.46631610592566 \tabularnewline
143 & 17 & 16.6015594955844 & 0.398440504415638 \tabularnewline
144 & 16 & 14.6526746163764 & 1.34732538362363 \tabularnewline
145 & 10 & 14.0681012372182 & -4.06810123721825 \tabularnewline
146 & 18 & 15.7982431276868 & 2.2017568723132 \tabularnewline
147 & 13 & 14.7625289679238 & -1.76252896792382 \tabularnewline
148 & 16 & 14.847363294813 & 1.15263670518698 \tabularnewline
149 & 13 & 13.2190152184265 & -0.219015218426485 \tabularnewline
150 & 10 & 13.1461397459846 & -3.14613974598463 \tabularnewline
151 & 15 & 16.3321807790856 & -1.33218077908561 \tabularnewline
152 & 16 & 13.58035084876 & 2.41964915124 \tabularnewline
153 & 16 & 11.7473592377256 & 4.2526407622744 \tabularnewline
154 & 14 & 12.829846368234 & 1.17015363176601 \tabularnewline
155 & 10 & 12.9326217251075 & -2.93262172510748 \tabularnewline
156 & 17 & 16.418382675315 & 0.581617324685043 \tabularnewline
157 & 13 & 11.6002708925272 & 1.39972910747278 \tabularnewline
158 & 15 & 13.9393552052859 & 1.06064479471411 \tabularnewline
159 & 16 & 14.6928260664654 & 1.30717393353456 \tabularnewline
160 & 12 & 12.7530299290138 & -0.753029929013813 \tabularnewline
161 & 13 & 12.74917848362 & 0.250821516379981 \tabularnewline
162 & 13 & 12.6173765362878 & 0.38262346371218 \tabularnewline
163 & 12 & 11.5441609186264 & 0.455839081373624 \tabularnewline
164 & 17 & 15.8317320283892 & 1.16826797161076 \tabularnewline
165 & 15 & 14.1809613781732 & 0.819038621826762 \tabularnewline
166 & 10 & 12.5162284574896 & -2.51622845748959 \tabularnewline
167 & 14 & 14.3993498832206 & -0.399349883220634 \tabularnewline
168 & 11 & 13.7491700074955 & -2.74917000749547 \tabularnewline
169 & 13 & 14.9069014283352 & -1.90690142833521 \tabularnewline
170 & 16 & 15.2383878242737 & 0.761612175726268 \tabularnewline
171 & 12 & 10.2262798390239 & 1.77372016097606 \tabularnewline
172 & 16 & 15.5226012063219 & 0.477398793678094 \tabularnewline
173 & 12 & 12.9489417965926 & -0.948941796592602 \tabularnewline
174 & 9 & 11.024497259364 & -2.02449725936401 \tabularnewline
175 & 12 & 14.1003626138382 & -2.1003626138382 \tabularnewline
176 & 15 & 14.4492620644116 & 0.550737935588401 \tabularnewline
177 & 12 & 11.8296619565241 & 0.17033804347592 \tabularnewline
178 & 12 & 12.6339706678772 & -0.633970667877187 \tabularnewline
179 & 14 & 13.7634707560475 & 0.236529243952493 \tabularnewline
180 & 12 & 13.1058565971907 & -1.10585659719067 \tabularnewline
181 & 16 & 15.3051999894337 & 0.694800010566342 \tabularnewline
182 & 11 & 10.7846321177231 & 0.215367882276936 \tabularnewline
183 & 19 & 17.1947334581257 & 1.80526654187433 \tabularnewline
184 & 15 & 14.2612345415922 & 0.738765458407801 \tabularnewline
185 & 8 & 14.647244934307 & -6.64724493430698 \tabularnewline
186 & 16 & 15.1469221743111 & 0.85307782568894 \tabularnewline
187 & 17 & 14.1781780695718 & 2.82182193042824 \tabularnewline
188 & 12 & 11.2527116325001 & 0.74728836749991 \tabularnewline
189 & 11 & 11.5406875312444 & -0.540687531244425 \tabularnewline
190 & 11 & 11.1376664801757 & -0.137666480175688 \tabularnewline
191 & 14 & 15.3288560535754 & -1.32885605357541 \tabularnewline
192 & 16 & 15.5350466640477 & 0.464953335952285 \tabularnewline
193 & 12 & 9.88829815781971 & 2.11170184218029 \tabularnewline
194 & 16 & 14.4720208858839 & 1.52797911411606 \tabularnewline
195 & 13 & 13.625715265247 & -0.625715265246953 \tabularnewline
196 & 15 & 15.0924291635898 & -0.092429163589778 \tabularnewline
197 & 16 & 12.5953831349435 & 3.40461686505647 \tabularnewline
198 & 16 & 15.1832552211262 & 0.816744778873838 \tabularnewline
199 & 14 & 12.4746486942216 & 1.52535130577838 \tabularnewline
200 & 16 & 14.7832846085766 & 1.21671539142336 \tabularnewline
201 & 16 & 13.8841108061721 & 2.11588919382791 \tabularnewline
202 & 14 & 13.7626507534853 & 0.237349246514671 \tabularnewline
203 & 11 & 12.9503773241019 & -1.95037732410186 \tabularnewline
204 & 12 & 14.577869551733 & -2.57786955173304 \tabularnewline
205 & 15 & 12.7144655901732 & 2.28553440982683 \tabularnewline
206 & 15 & 14.6319119194852 & 0.368088080514808 \tabularnewline
207 & 16 & 14.4922708811373 & 1.50772911886274 \tabularnewline
208 & 16 & 15.4894971529552 & 0.510502847044806 \tabularnewline
209 & 11 & 13.2958817978772 & -2.2958817978772 \tabularnewline
210 & 15 & 13.5817807360034 & 1.41821926399659 \tabularnewline
211 & 12 & 14.1428623393701 & -2.14286233937013 \tabularnewline
212 & 12 & 16.0289096453871 & -4.02890964538706 \tabularnewline
213 & 15 & 14.5879846518168 & 0.412015348183227 \tabularnewline
214 & 15 & 11.8903150954748 & 3.10968490452521 \tabularnewline
215 & 16 & 14.5729704001091 & 1.42702959989093 \tabularnewline
216 & 14 & 12.7400885588807 & 1.25991144111931 \tabularnewline
217 & 17 & 14.5832982369277 & 2.41670176307235 \tabularnewline
218 & 14 & 13.2110935592794 & 0.788906440720609 \tabularnewline
219 & 13 & 11.9814957048625 & 1.01850429513746 \tabularnewline
220 & 15 & 15.4008285553322 & -0.40082855533223 \tabularnewline
221 & 13 & 14.1321980260207 & -1.13219802602067 \tabularnewline
222 & 14 & 14.0682872710141 & -0.068287271014069 \tabularnewline
223 & 15 & 14.0115837065275 & 0.988416293472523 \tabularnewline
224 & 12 & 12.9922206451188 & -0.992220645118751 \tabularnewline
225 & 13 & 13.1569941546857 & -0.15699415468565 \tabularnewline
226 & 8 & 11.9112755129364 & -3.91127551293636 \tabularnewline
227 & 14 & 14.1924632927691 & -0.192463292769131 \tabularnewline
228 & 14 & 12.9412458180924 & 1.05875418190755 \tabularnewline
229 & 11 & 12.2423518553558 & -1.24235185535583 \tabularnewline
230 & 12 & 12.8422756781471 & -0.842275678147151 \tabularnewline
231 & 13 & 10.6929356539006 & 2.30706434609943 \tabularnewline
232 & 10 & 13.7310868426761 & -3.7310868426761 \tabularnewline
233 & 16 & 11.6806645985775 & 4.31933540142245 \tabularnewline
234 & 18 & 15.1339841919662 & 2.86601580803384 \tabularnewline
235 & 13 & 13.4508816862766 & -0.450881686276635 \tabularnewline
236 & 11 & 12.6403601339811 & -1.64036013398109 \tabularnewline
237 & 4 & 10.2185991839173 & -6.21859918391726 \tabularnewline
238 & 13 & 13.5794551191926 & -0.579455119192583 \tabularnewline
239 & 16 & 14.0084161377658 & 1.9915838622342 \tabularnewline
240 & 10 & 10.7007996000469 & -0.700799600046881 \tabularnewline
241 & 12 & 12.06245829497 & -0.0624582949700043 \tabularnewline
242 & 12 & 13.7138232266365 & -1.71382322663655 \tabularnewline
243 & 10 & 8.8976350227211 & 1.1023649772789 \tabularnewline
244 & 13 & 10.8820038413952 & 2.11799615860479 \tabularnewline
245 & 15 & 12.8582217280672 & 2.14177827193283 \tabularnewline
246 & 12 & 12.5004430180822 & -0.500443018082235 \tabularnewline
247 & 14 & 13.126233093155 & 0.87376690684504 \tabularnewline
248 & 10 & 12.8110598932988 & -2.81105989329878 \tabularnewline
249 & 12 & 10.888381311566 & 1.11161868843402 \tabularnewline
250 & 12 & 11.4359968239255 & 0.564003176074509 \tabularnewline
251 & 11 & 12.0849147298808 & -1.08491472988076 \tabularnewline
252 & 10 & 11.3414207812061 & -1.34142078120605 \tabularnewline
253 & 12 & 10.9990501691664 & 1.00094983083355 \tabularnewline
254 & 16 & 12.5249603680063 & 3.47503963199372 \tabularnewline
255 & 12 & 12.9490178935796 & -0.949017893579647 \tabularnewline
256 & 14 & 13.6858637772946 & 0.314136222705359 \tabularnewline
257 & 16 & 14.3879254742619 & 1.6120745257381 \tabularnewline
258 & 14 & 11.7211481416927 & 2.27885185830732 \tabularnewline
259 & 13 & 13.9979112437533 & -0.997911243753274 \tabularnewline
260 & 4 & 8.23954017419469 & -4.23954017419469 \tabularnewline
261 & 15 & 13.843270687797 & 1.15672931220298 \tabularnewline
262 & 11 & 15.3297403317004 & -4.32974033170044 \tabularnewline
263 & 11 & 11.0362068643451 & -0.0362068643450975 \tabularnewline
264 & 14 & 12.8167087422723 & 1.18329125772766 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185702&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]15.9440565190857[/C][C]-2.94405651908571[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.2363436409125[/C][C]-0.23634364091247[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5815908655627[/C][C]2.41840913443732[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.0390844657901[/C][C]2.9609155342099[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.45830173466[/C][C]-2.45830173466005[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2783754710599[/C][C]-2.27837547105988[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.8390959917877[/C][C]3.16090400821228[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.8975453044673[/C][C]-1.89754530446734[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9962188225434[/C][C]-1.99621882254335[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4903976290832[/C][C]0.509602370916777[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4309127242641[/C][C]0.569087275735903[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.9458181094773[/C][C]0.0541818905227417[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6008976060461[/C][C]0.399102393953892[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3665171251354[/C][C]0.633482874864622[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.7638906496412[/C][C]-0.763890649641175[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2420218355414[/C][C]-0.242021835541392[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8813705507418[/C][C]0.118629449258239[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.5318025509177[/C][C]3.4681974490823[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.3700870379982[/C][C]2.6299129620018[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3874618326048[/C][C]0.612538167395183[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.1826235848291[/C][C]0.817376415170891[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.0963603928938[/C][C]0.903639607106195[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.2161123209336[/C][C]2.78388767906639[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8315087237026[/C][C]1.16849127629737[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.6373625207116[/C][C]0.362637479288438[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.5512391051188[/C][C]0.448760894881163[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9816814371191[/C][C]1.01831856288093[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.4919081196921[/C][C]-1.49190811969211[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.7457832247579[/C][C]0.254216775242091[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.2232383184461[/C][C]-0.223238318446112[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7097743338415[/C][C]-0.709774333841459[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4616113118721[/C][C]-0.461611311872121[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.0889318175983[/C][C]-1.08893181759831[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.8720197968897[/C][C]0.127980203110276[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6898721324382[/C][C]-1.68987213243815[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.9295370645357[/C][C]-2.9295370645357[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.9994597313448[/C][C]-2.99945973134484[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8382228338837[/C][C]-1.83822283388374[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4444587481061[/C][C]1.55554125189393[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.475303137931[/C][C]1.524696862069[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.9291161314462[/C][C]1.07088386855383[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.8034741256171[/C][C]-1.80347412561714[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.3500629397278[/C][C]2.64993706027219[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.0437664216633[/C][C]-0.0437664216633002[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9611162832643[/C][C]-0.9611162832643[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4688429291675[/C][C]-4.4688429291675[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.30354074625[/C][C]-2.30354074624999[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0602439744061[/C][C]-0.0602439744061165[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.4153790472142[/C][C]0.584620952785766[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.8874635339514[/C][C]-1.88746353395141[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5127229279992[/C][C]-0.512722927999202[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2423891525918[/C][C]-0.242389152591754[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.9227371306956[/C][C]-2.9227371306956[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6189147151572[/C][C]0.381085284842825[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.310404695743[/C][C]-2.31040469574303[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.743066710633[/C][C]1.25693328936703[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1500795019895[/C][C]-0.150079501989472[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0321992066337[/C][C]0.967800793366348[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.0491518114201[/C][C]-0.0491518114200926[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.307645252983[/C][C]1.69235474701699[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.6897453345998[/C][C]0.310254665400209[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8444200272623[/C][C]0.155579972737695[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4158760223166[/C][C]-0.415876022316569[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.2661270324814[/C][C]-0.26612703248142[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2388507409698[/C][C]0.761149259030229[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.850318744403[/C][C]1.14968125559698[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3053490550508[/C][C]1.6946509449492[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5127788066808[/C][C]3.48722119331916[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7134625958035[/C][C]-3.71346259580352[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.5109688170323[/C][C]0.489031182967691[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.5506652666734[/C][C]-3.55066526667343[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3946930257855[/C][C]-0.394693025785526[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.6700749329515[/C][C]1.3299250670485[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.761675668301[/C][C]1.23832433169896[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6654918255441[/C][C]0.334508174455855[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.316624801793[/C][C]2.683375198207[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3751334365384[/C][C]-0.375133436538421[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.7751043230259[/C][C]1.22489567697406[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0867490383646[/C][C]-2.08674903836457[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.3295824220483[/C][C]0.670417577951724[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4917966828538[/C][C]0.508203317146203[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.4965931279436[/C][C]0.503406872056406[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.8511137741534[/C][C]-0.851113774153397[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.7532097479609[/C][C]0.246790252039134[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.3651545972111[/C][C]1.63484540278886[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.209319149896[/C][C]-0.209319149895987[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.2158276297318[/C][C]0.784172370268221[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.5076072253922[/C][C]1.4923927746078[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.6519184040208[/C][C]0.348081595979188[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.7385277319648[/C][C]-1.73852773196484[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.7063566560181[/C][C]0.293643343981867[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.5113200177579[/C][C]0.488679982242059[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.4380178290487[/C][C]-0.438017829048742[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0139464249235[/C][C]-2.01394642492355[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.011733429994[/C][C]0.988266570005964[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.8796750426912[/C][C]0.120324957308769[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.9812549897288[/C][C]2.01874501027116[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]16.0095902503458[/C][C]-0.00959025034578832[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.2068186479575[/C][C]-0.206818647957546[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.0221381364917[/C][C]-1.02213813649165[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.8774102249967[/C][C]1.1225897750033[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.4395113941604[/C][C]2.56048860583956[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.3022261105483[/C][C]0.697773889451698[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.8180512463431[/C][C]1.18194875365693[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.3503044683544[/C][C]-2.35030446835443[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.8680884158985[/C][C]1.13191158410155[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.8933131128838[/C][C]0.106686887116215[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.2203248596324[/C][C]1.77967514036762[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.4705261416388[/C][C]-0.470526141638752[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.2402765027813[/C][C]0.759723497218674[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.8474464037919[/C][C]0.152553596208056[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.6709100280968[/C][C]2.32908997190318[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.8955434431428[/C][C]-0.895543443142771[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.8681530193762[/C][C]-2.86815301937621[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.5046478994667[/C][C]1.49535210053333[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.239273166593[/C][C]-1.23927316659303[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.9321013099215[/C][C]1.06789869007849[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.5353070593503[/C][C]-1.53530705935028[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.899458944726[/C][C]0.100541055274027[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.0386410349391[/C][C]-1.0386410349391[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.6514225803284[/C][C]0.348577419671636[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.9078827089936[/C][C]-2.9078827089936[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.6339920627059[/C][C]-0.633992062705861[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.0535128859177[/C][C]-1.05351288591774[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.5794602417009[/C][C]-0.579460241700854[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]15.2097822853631[/C][C]-0.209782285363102[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.7703390627542[/C][C]1.22966093724583[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.0722662733027[/C][C]0.927733726697263[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.8362505676798[/C][C]-2.83625056767976[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.8472851590545[/C][C]2.1527148409455[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.5951868615487[/C][C]-3.59518686154867[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.7228491853207[/C][C]2.27715081467934[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.0109075337477[/C][C]-2.01090753374774[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.4887788170539[/C][C]-1.48877881705388[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.4640134652598[/C][C]-0.464013465259818[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.5941731599697[/C][C]0.405826840030297[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.4241216729002[/C][C]0.575878327099804[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.1065325076259[/C][C]-2.10653250762593[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.0359223431769[/C][C]-1.03592234317685[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.1173218517186[/C][C]-2.11732185171859[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.565044401775[/C][C]3.43495559822504[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.5336838940743[/C][C]1.46631610592566[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.6015594955844[/C][C]0.398440504415638[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.6526746163764[/C][C]1.34732538362363[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]14.0681012372182[/C][C]-4.06810123721825[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.7982431276868[/C][C]2.2017568723132[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.7625289679238[/C][C]-1.76252896792382[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.847363294813[/C][C]1.15263670518698[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.2190152184265[/C][C]-0.219015218426485[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]13.1461397459846[/C][C]-3.14613974598463[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.3321807790856[/C][C]-1.33218077908561[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.58035084876[/C][C]2.41964915124[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.7473592377256[/C][C]4.2526407622744[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.829846368234[/C][C]1.17015363176601[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.9326217251075[/C][C]-2.93262172510748[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.418382675315[/C][C]0.581617324685043[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.6002708925272[/C][C]1.39972910747278[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.9393552052859[/C][C]1.06064479471411[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.6928260664654[/C][C]1.30717393353456[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.7530299290138[/C][C]-0.753029929013813[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.74917848362[/C][C]0.250821516379981[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6173765362878[/C][C]0.38262346371218[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]11.5441609186264[/C][C]0.455839081373624[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]15.8317320283892[/C][C]1.16826797161076[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]14.1809613781732[/C][C]0.819038621826762[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]12.5162284574896[/C][C]-2.51622845748959[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.3993498832206[/C][C]-0.399349883220634[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]13.7491700074955[/C][C]-2.74917000749547[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.9069014283352[/C][C]-1.90690142833521[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]15.2383878242737[/C][C]0.761612175726268[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.2262798390239[/C][C]1.77372016097606[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.5226012063219[/C][C]0.477398793678094[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]12.9489417965926[/C][C]-0.948941796592602[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.024497259364[/C][C]-2.02449725936401[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.1003626138382[/C][C]-2.1003626138382[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.4492620644116[/C][C]0.550737935588401[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]11.8296619565241[/C][C]0.17033804347592[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.6339706678772[/C][C]-0.633970667877187[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.7634707560475[/C][C]0.236529243952493[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.1058565971907[/C][C]-1.10585659719067[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.3051999894337[/C][C]0.694800010566342[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]10.7846321177231[/C][C]0.215367882276936[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]17.1947334581257[/C][C]1.80526654187433[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]14.2612345415922[/C][C]0.738765458407801[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.647244934307[/C][C]-6.64724493430698[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]15.1469221743111[/C][C]0.85307782568894[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.1781780695718[/C][C]2.82182193042824[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]11.2527116325001[/C][C]0.74728836749991[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.5406875312444[/C][C]-0.540687531244425[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]11.1376664801757[/C][C]-0.137666480175688[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]15.3288560535754[/C][C]-1.32885605357541[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.5350466640477[/C][C]0.464953335952285[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.88829815781971[/C][C]2.11170184218029[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.4720208858839[/C][C]1.52797911411606[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.625715265247[/C][C]-0.625715265246953[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.0924291635898[/C][C]-0.092429163589778[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]12.5953831349435[/C][C]3.40461686505647[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.1832552211262[/C][C]0.816744778873838[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4746486942216[/C][C]1.52535130577838[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.7832846085766[/C][C]1.21671539142336[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]13.8841108061721[/C][C]2.11588919382791[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.7626507534853[/C][C]0.237349246514671[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]12.9503773241019[/C][C]-1.95037732410186[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.577869551733[/C][C]-2.57786955173304[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.7144655901732[/C][C]2.28553440982683[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.6319119194852[/C][C]0.368088080514808[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.4922708811373[/C][C]1.50772911886274[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]15.4894971529552[/C][C]0.510502847044806[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.2958817978772[/C][C]-2.2958817978772[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.5817807360034[/C][C]1.41821926399659[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.1428623393701[/C][C]-2.14286233937013[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]16.0289096453871[/C][C]-4.02890964538706[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.5879846518168[/C][C]0.412015348183227[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]11.8903150954748[/C][C]3.10968490452521[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.5729704001091[/C][C]1.42702959989093[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]12.7400885588807[/C][C]1.25991144111931[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.5832982369277[/C][C]2.41670176307235[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.2110935592794[/C][C]0.788906440720609[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.9814957048625[/C][C]1.01850429513746[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.4008285553322[/C][C]-0.40082855533223[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.1321980260207[/C][C]-1.13219802602067[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]14.0682872710141[/C][C]-0.068287271014069[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.0115837065275[/C][C]0.988416293472523[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]12.9922206451188[/C][C]-0.992220645118751[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]13.1569941546857[/C][C]-0.15699415468565[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.9112755129364[/C][C]-3.91127551293636[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]14.1924632927691[/C][C]-0.192463292769131[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.9412458180924[/C][C]1.05875418190755[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.2423518553558[/C][C]-1.24235185535583[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.8422756781471[/C][C]-0.842275678147151[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]10.6929356539006[/C][C]2.30706434609943[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.7310868426761[/C][C]-3.7310868426761[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.6806645985775[/C][C]4.31933540142245[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.1339841919662[/C][C]2.86601580803384[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.4508816862766[/C][C]-0.450881686276635[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]12.6403601339811[/C][C]-1.64036013398109[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]10.2185991839173[/C][C]-6.21859918391726[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]13.5794551191926[/C][C]-0.579455119192583[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.0084161377658[/C][C]1.9915838622342[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]10.7007996000469[/C][C]-0.700799600046881[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.06245829497[/C][C]-0.0624582949700043[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.7138232266365[/C][C]-1.71382322663655[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.8976350227211[/C][C]1.1023649772789[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]10.8820038413952[/C][C]2.11799615860479[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]12.8582217280672[/C][C]2.14177827193283[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]12.5004430180822[/C][C]-0.500443018082235[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]13.126233093155[/C][C]0.87376690684504[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.8110598932988[/C][C]-2.81105989329878[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.888381311566[/C][C]1.11161868843402[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.4359968239255[/C][C]0.564003176074509[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]12.0849147298808[/C][C]-1.08491472988076[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.3414207812061[/C][C]-1.34142078120605[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]10.9990501691664[/C][C]1.00094983083355[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.5249603680063[/C][C]3.47503963199372[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]12.9490178935796[/C][C]-0.949017893579647[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.6858637772946[/C][C]0.314136222705359[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.3879254742619[/C][C]1.6120745257381[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.7211481416927[/C][C]2.27885185830732[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]13.9979112437533[/C][C]-0.997911243753274[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]8.23954017419469[/C][C]-4.23954017419469[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.843270687797[/C][C]1.15672931220298[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]15.3297403317004[/C][C]-4.32974033170044[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.0362068643451[/C][C]-0.0362068643450975[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.8167087422723[/C][C]1.18329125772766[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185702&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185702&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 15.9440565190857 -2.94405651908571 2 16 16.2363436409125 -0.23634364091247 3 19 16.5815908655627 2.41840913443732 4 15 12.0390844657901 2.9609155342099 5 14 16.45830173466 -2.45830173466005 6 13 15.2783754710599 -2.27837547105988 7 19 15.8390959917877 3.16090400821228 8 15 16.8975453044673 -1.89754530446734 9 14 15.9962188225434 -1.99621882254335 10 15 14.4903976290832 0.509602370916777 11 16 15.4309127242641 0.569087275735903 12 16 15.9458181094773 0.0541818905227417 13 16 15.6008976060461 0.399102393953892 14 16 15.3665171251354 0.633482874864622 15 17 17.7638906496412 -0.763890649641175 16 15 15.2420218355414 -0.242021835541392 17 15 14.8813705507418 0.118629449258239 18 20 16.5318025509177 3.4681974490823 19 18 15.3700870379982 2.6299129620018 20 16 15.3874618326048 0.612538167395183 21 16 15.1826235848291 0.817376415170891 22 16 15.0963603928938 0.903639607106195 23 19 16.2161123209336 2.78388767906639 24 16 14.8315087237026 1.16849127629737 25 17 16.6373625207116 0.362637479288438 26 17 16.5512391051188 0.448760894881163 27 16 14.9816814371191 1.01831856288093 28 15 16.4919081196921 -1.49190811969211 29 16 15.7457832247579 0.254216775242091 30 14 14.2232383184461 -0.223238318446112 31 15 15.7097743338415 -0.709774333841459 32 12 12.4616113118721 -0.461611311872121 33 14 15.0889318175983 -1.08893181759831 34 16 15.8720197968897 0.127980203110276 35 14 15.6898721324382 -1.68987213243815 36 10 12.9295370645357 -2.9295370645357 37 10 12.9994597313448 -2.99945973134484 38 14 15.8382228338837 -1.83822283388374 39 16 14.4444587481061 1.55554125189393 40 16 14.475303137931 1.524696862069 41 16 14.9291161314462 1.07088386855383 42 14 15.8034741256171 -1.80347412561714 43 20 17.3500629397278 2.64993706027219 44 14 14.0437664216633 -0.0437664216633002 45 14 14.9611162832643 -0.9611162832643 46 11 15.4688429291675 -4.4688429291675 47 14 16.30354074625 -2.30354074624999 48 15 15.0602439744061 -0.0602439744061165 49 16 15.4153790472142 0.584620952785766 50 14 15.8874635339514 -1.88746353395141 51 16 16.5127229279992 -0.512722927999202 52 14 14.2423891525918 -0.242389152591754 53 12 14.9227371306956 -2.9227371306956 54 16 15.6189147151572 0.381085284842825 55 9 11.310404695743 -2.31040469574303 56 14 12.743066710633 1.25693328936703 57 16 16.1500795019895 -0.150079501989472 58 16 15.0321992066337 0.967800793366348 59 15 15.0491518114201 -0.0491518114200926 60 16 14.307645252983 1.69235474701699 61 12 11.6897453345998 0.310254665400209 62 16 15.8444200272623 0.155579972737695 63 16 16.4158760223166 -0.415876022316569 64 14 14.2661270324814 -0.26612703248142 65 16 15.2388507409698 0.761149259030229 66 17 15.850318744403 1.14968125559698 67 18 16.3053490550508 1.6946509449492 68 18 14.5127788066808 3.48722119331916 69 12 15.7134625958035 -3.71346259580352 70 16 15.5109688170323 0.489031182967691 71 10 13.5506652666734 -3.55066526667343 72 14 14.3946930257855 -0.394693025785526 73 18 16.6700749329515 1.3299250670485 74 18 16.761675668301 1.23832433169896 75 16 15.6654918255441 0.334508174455855 76 17 14.316624801793 2.683375198207 77 16 16.3751334365384 -0.375133436538421 78 16 14.7751043230259 1.22489567697406 79 13 15.0867490383646 -2.08674903836457 80 16 15.3295824220483 0.670417577951724 81 16 15.4917966828538 0.508203317146203 82 16 15.4965931279436 0.503406872056406 83 15 15.8511137741534 -0.851113774153397 84 15 14.7532097479609 0.246790252039134 85 16 14.3651545972111 1.63484540278886 86 14 14.209319149896 -0.209319149895987 87 16 15.2158276297318 0.784172370268221 88 16 14.5076072253922 1.4923927746078 89 15 14.6519184040208 0.348081595979188 90 12 13.7385277319648 -1.73852773196484 91 17 16.7063566560181 0.293643343981867 92 16 15.5113200177579 0.488679982242059 93 15 15.4380178290487 -0.438017829048742 94 13 15.0139464249235 -2.01394642492355 95 16 15.011733429994 0.988266570005964 96 16 15.8796750426912 0.120324957308769 97 16 13.9812549897288 2.01874501027116 98 16 16.0095902503458 -0.00959025034578832 99 14 14.2068186479575 -0.206818647957546 100 16 17.0221381364917 -1.02213813649165 101 16 14.8774102249967 1.1225897750033 102 20 17.4395113941604 2.56048860583956 103 15 14.3022261105483 0.697773889451698 104 16 14.8180512463431 1.18194875365693 105 13 15.3503044683544 -2.35030446835443 106 17 15.8680884158985 1.13191158410155 107 16 15.8933131128838 0.106686887116215 108 16 14.2203248596324 1.77967514036762 109 12 12.4705261416388 -0.470526141638752 110 16 15.2402765027813 0.759723497218674 111 16 15.8474464037919 0.152553596208056 112 17 14.6709100280968 2.32908997190318 113 13 13.8955434431428 -0.895543443142771 114 12 14.8681530193762 -2.86815301937621 115 18 16.5046478994667 1.49535210053333 116 14 15.239273166593 -1.23927316659303 117 14 12.9321013099215 1.06789869007849 118 13 14.5353070593503 -1.53530705935028 119 16 15.899458944726 0.100541055274027 120 13 14.0386410349391 -1.0386410349391 121 16 15.6514225803284 0.348577419671636 122 13 15.9078827089936 -2.9078827089936 123 16 16.6339920627059 -0.633992062705861 124 15 16.0535128859177 -1.05351288591774 125 16 16.5794602417009 -0.579460241700854 126 15 15.2097822853631 -0.209782285363102 127 17 15.7703390627542 1.22966093724583 128 15 14.0722662733027 0.927733726697263 129 12 14.8362505676798 -2.83625056767976 130 16 13.8472851590545 2.1527148409455 131 10 13.5951868615487 -3.59518686154867 132 16 13.7228491853207 2.27715081467934 133 12 14.0109075337477 -2.01090753374774 134 14 15.4887788170539 -1.48877881705388 135 15 15.4640134652598 -0.464013465259818 136 13 12.5941731599697 0.405826840030297 137 15 14.4241216729002 0.575878327099804 138 11 13.1065325076259 -2.10653250762593 139 12 13.0359223431769 -1.03592234317685 140 11 13.1173218517186 -2.11732185171859 141 16 12.565044401775 3.43495559822504 142 15 13.5336838940743 1.46631610592566 143 17 16.6015594955844 0.398440504415638 144 16 14.6526746163764 1.34732538362363 145 10 14.0681012372182 -4.06810123721825 146 18 15.7982431276868 2.2017568723132 147 13 14.7625289679238 -1.76252896792382 148 16 14.847363294813 1.15263670518698 149 13 13.2190152184265 -0.219015218426485 150 10 13.1461397459846 -3.14613974598463 151 15 16.3321807790856 -1.33218077908561 152 16 13.58035084876 2.41964915124 153 16 11.7473592377256 4.2526407622744 154 14 12.829846368234 1.17015363176601 155 10 12.9326217251075 -2.93262172510748 156 17 16.418382675315 0.581617324685043 157 13 11.6002708925272 1.39972910747278 158 15 13.9393552052859 1.06064479471411 159 16 14.6928260664654 1.30717393353456 160 12 12.7530299290138 -0.753029929013813 161 13 12.74917848362 0.250821516379981 162 13 12.6173765362878 0.38262346371218 163 12 11.5441609186264 0.455839081373624 164 17 15.8317320283892 1.16826797161076 165 15 14.1809613781732 0.819038621826762 166 10 12.5162284574896 -2.51622845748959 167 14 14.3993498832206 -0.399349883220634 168 11 13.7491700074955 -2.74917000749547 169 13 14.9069014283352 -1.90690142833521 170 16 15.2383878242737 0.761612175726268 171 12 10.2262798390239 1.77372016097606 172 16 15.5226012063219 0.477398793678094 173 12 12.9489417965926 -0.948941796592602 174 9 11.024497259364 -2.02449725936401 175 12 14.1003626138382 -2.1003626138382 176 15 14.4492620644116 0.550737935588401 177 12 11.8296619565241 0.17033804347592 178 12 12.6339706678772 -0.633970667877187 179 14 13.7634707560475 0.236529243952493 180 12 13.1058565971907 -1.10585659719067 181 16 15.3051999894337 0.694800010566342 182 11 10.7846321177231 0.215367882276936 183 19 17.1947334581257 1.80526654187433 184 15 14.2612345415922 0.738765458407801 185 8 14.647244934307 -6.64724493430698 186 16 15.1469221743111 0.85307782568894 187 17 14.1781780695718 2.82182193042824 188 12 11.2527116325001 0.74728836749991 189 11 11.5406875312444 -0.540687531244425 190 11 11.1376664801757 -0.137666480175688 191 14 15.3288560535754 -1.32885605357541 192 16 15.5350466640477 0.464953335952285 193 12 9.88829815781971 2.11170184218029 194 16 14.4720208858839 1.52797911411606 195 13 13.625715265247 -0.625715265246953 196 15 15.0924291635898 -0.092429163589778 197 16 12.5953831349435 3.40461686505647 198 16 15.1832552211262 0.816744778873838 199 14 12.4746486942216 1.52535130577838 200 16 14.7832846085766 1.21671539142336 201 16 13.8841108061721 2.11588919382791 202 14 13.7626507534853 0.237349246514671 203 11 12.9503773241019 -1.95037732410186 204 12 14.577869551733 -2.57786955173304 205 15 12.7144655901732 2.28553440982683 206 15 14.6319119194852 0.368088080514808 207 16 14.4922708811373 1.50772911886274 208 16 15.4894971529552 0.510502847044806 209 11 13.2958817978772 -2.2958817978772 210 15 13.5817807360034 1.41821926399659 211 12 14.1428623393701 -2.14286233937013 212 12 16.0289096453871 -4.02890964538706 213 15 14.5879846518168 0.412015348183227 214 15 11.8903150954748 3.10968490452521 215 16 14.5729704001091 1.42702959989093 216 14 12.7400885588807 1.25991144111931 217 17 14.5832982369277 2.41670176307235 218 14 13.2110935592794 0.788906440720609 219 13 11.9814957048625 1.01850429513746 220 15 15.4008285553322 -0.40082855533223 221 13 14.1321980260207 -1.13219802602067 222 14 14.0682872710141 -0.068287271014069 223 15 14.0115837065275 0.988416293472523 224 12 12.9922206451188 -0.992220645118751 225 13 13.1569941546857 -0.15699415468565 226 8 11.9112755129364 -3.91127551293636 227 14 14.1924632927691 -0.192463292769131 228 14 12.9412458180924 1.05875418190755 229 11 12.2423518553558 -1.24235185535583 230 12 12.8422756781471 -0.842275678147151 231 13 10.6929356539006 2.30706434609943 232 10 13.7310868426761 -3.7310868426761 233 16 11.6806645985775 4.31933540142245 234 18 15.1339841919662 2.86601580803384 235 13 13.4508816862766 -0.450881686276635 236 11 12.6403601339811 -1.64036013398109 237 4 10.2185991839173 -6.21859918391726 238 13 13.5794551191926 -0.579455119192583 239 16 14.0084161377658 1.9915838622342 240 10 10.7007996000469 -0.700799600046881 241 12 12.06245829497 -0.0624582949700043 242 12 13.7138232266365 -1.71382322663655 243 10 8.8976350227211 1.1023649772789 244 13 10.8820038413952 2.11799615860479 245 15 12.8582217280672 2.14177827193283 246 12 12.5004430180822 -0.500443018082235 247 14 13.126233093155 0.87376690684504 248 10 12.8110598932988 -2.81105989329878 249 12 10.888381311566 1.11161868843402 250 12 11.4359968239255 0.564003176074509 251 11 12.0849147298808 -1.08491472988076 252 10 11.3414207812061 -1.34142078120605 253 12 10.9990501691664 1.00094983083355 254 16 12.5249603680063 3.47503963199372 255 12 12.9490178935796 -0.949017893579647 256 14 13.6858637772946 0.314136222705359 257 16 14.3879254742619 1.6120745257381 258 14 11.7211481416927 2.27885185830732 259 13 13.9979112437533 -0.997911243753274 260 4 8.23954017419469 -4.23954017419469 261 15 13.843270687797 1.15672931220298 262 11 15.3297403317004 -4.32974033170044 263 11 11.0362068643451 -0.0362068643450975 264 14 12.8167087422723 1.18329125772766

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.977955087555212 0.0440898248895755 0.0220449124447877 18 0.995018686795154 0.00996262640969223 0.00498131320484611 19 0.991043466316251 0.017913067367498 0.00895653368374899 20 0.983004681419042 0.0339906371619158 0.0169953185809579 21 0.969239251412634 0.0615214971747324 0.0307607485873662 22 0.956415754307936 0.0871684913841286 0.0435842456920643 23 0.941419423594714 0.117161152810572 0.0585805764052862 24 0.925037568114176 0.149924863771649 0.0749624318858244 25 0.89327779709033 0.21344440581934 0.10672220290967 26 0.866278926204539 0.267442147590922 0.133721073795461 27 0.823971416773333 0.352057166453333 0.176028583226667 28 0.840523932242884 0.318952135514231 0.159476067757116 29 0.794757769577171 0.410484460845659 0.205242230422829 30 0.79339034255239 0.413219314895219 0.20660965744761 31 0.750445074219343 0.499109851561313 0.249554925780656 32 0.738323314592152 0.523353370815696 0.261676685407848 33 0.715434219505237 0.569131560989526 0.284565780494763 34 0.656740057917593 0.686519884164813 0.343259942082407 35 0.639060210941821 0.721879578116358 0.360939789058179 36 0.723591823873427 0.552816352253147 0.276408176126573 37 0.783867304760463 0.432265390479075 0.216132695239537 38 0.763096917718206 0.473806164563589 0.236903082281794 39 0.79437831495396 0.41124337009208 0.20562168504604 40 0.779966106854365 0.44006778629127 0.220033893145635 41 0.749897171273924 0.500205657452152 0.250102828726076 42 0.729612691249747 0.540774617500506 0.270387308750253 43 0.755486261864494 0.489027476271012 0.244513738135506 44 0.7150456588546 0.569908682290799 0.2849543411454 45 0.67904228192718 0.64191543614564 0.32095771807282 46 0.846351540153258 0.307296919693484 0.153648459846742 47 0.847780097900919 0.304439804198163 0.152219902099081 48 0.820731483290323 0.358537033419355 0.179268516709677 49 0.796967213148615 0.406065573702771 0.203032786851385 50 0.77738374396325 0.4452325120735 0.22261625603675 51 0.7397057962769 0.5205884074462 0.2602942037231 52 0.700541121038397 0.598917757923205 0.299458878961603 53 0.716883891246429 0.566232217507141 0.283116108753571 54 0.686467087992366 0.627065824015268 0.313532912007634 55 0.680940742733998 0.638118514532005 0.319059257266002 56 0.675313662864148 0.649372674271704 0.324686337135852 57 0.633175183840637 0.733649632318726 0.366824816159363 58 0.623923207562088 0.752153584875824 0.376076792437912 59 0.583146788522007 0.833706422955985 0.416853211477993 60 0.59719119515775 0.805617609684499 0.40280880484225 61 0.562502880985813 0.874994238028375 0.437497119014187 62 0.521804615161652 0.956390769676695 0.478195384838348 63 0.480025261953368 0.960050523906736 0.519974738046632 64 0.43540610791067 0.870812215821339 0.56459389208933 65 0.401604431318089 0.803208862636179 0.598395568681911 66 0.38865630865111 0.777312617302219 0.61134369134889 67 0.384250906786486 0.768501813572972 0.615749093213514 68 0.517720056094523 0.964559887810954 0.482279943905477 69 0.63061702649402 0.738765947011959 0.36938297350598 70 0.595416007907377 0.809167984185246 0.404583992092623 71 0.672371158460862 0.655257683078275 0.327628841539137 72 0.634854377732834 0.730291244534332 0.365145622267166 73 0.624670895867978 0.750658208264045 0.375329104132022 74 0.606648383557719 0.786703232884562 0.393351616442281 75 0.566499947146784 0.867000105706432 0.433500052853216 76 0.607592034967605 0.784815930064789 0.392407965032395 77 0.568317893268798 0.863364213462405 0.431682106731202 78 0.541558134188577 0.916883731622846 0.458441865811423 79 0.549296507902011 0.901406984195979 0.450703492097989 80 0.510567657429531 0.978864685140938 0.489432342570469 81 0.474951684987043 0.949903369974086 0.525048315012957 82 0.438389405829715 0.876778811659431 0.561610594170285 83 0.407430458324123 0.814860916648246 0.592569541675877 84 0.370254702832498 0.740509405664996 0.629745297167502 85 0.360518147094309 0.721036294188618 0.639481852905691 86 0.324033384284653 0.648066768569306 0.675966615715347 87 0.293887378851484 0.587774757702968 0.706112621148516 88 0.277730240627193 0.555460481254386 0.722269759372807 89 0.246430051612688 0.492860103225376 0.753569948387312 90 0.238740319112994 0.477480638225987 0.761259680887006 91 0.210816058874235 0.42163211774847 0.789183941125765 92 0.186765732028805 0.373531464057609 0.813234267971195 93 0.164024780701546 0.328049561403092 0.835975219298454 94 0.170322263651312 0.340644527302625 0.829677736348688 95 0.153305219397003 0.306610438794007 0.846694780602997 96 0.131547035812515 0.26309407162503 0.868452964187485 97 0.134152959198113 0.268305918396227 0.865847040801887 98 0.114190299606183 0.228380599212365 0.885809700393817 99 0.0969282590804472 0.193856518160894 0.903071740919553 100 0.086688083082024 0.173376166164048 0.913311916917976 101 0.0769268646977879 0.153853729395576 0.923073135302212 102 0.0892578246194909 0.178515649238982 0.910742175380509 103 0.0767647712400794 0.153529542480159 0.923235228759921 104 0.0693115660415813 0.138623132083163 0.930688433958419 105 0.0813770685522494 0.162754137104499 0.918622931447751 106 0.0727359957473762 0.145471991494752 0.927264004252624 107 0.0609281945985376 0.121856389197075 0.939071805401462 108 0.0615427561539167 0.123085512307833 0.938457243846083 109 0.0519477422715837 0.103895484543167 0.948052257728416 110 0.0441347828415425 0.0882695656830849 0.955865217158458 111 0.0369647949548216 0.0739295899096432 0.963035205045178 112 0.0454516805763338 0.0909033611526676 0.954548319423666 113 0.0388671559514368 0.0777343119028737 0.961132844048563 114 0.0509529948528077 0.101905989705615 0.949047005147192 115 0.0498435718105525 0.099687143621105 0.950156428189448 116 0.0442789109568897 0.0885578219137794 0.95572108904311 117 0.0403689939347767 0.0807379878695535 0.959631006065223 118 0.0369512286974033 0.0739024573948067 0.963048771302597 119 0.0325674222633728 0.0651348445267457 0.967432577736627 120 0.0275450005285695 0.0550900010571391 0.97245499947143 121 0.0232870247522251 0.0465740495044503 0.976712975247775 122 0.0288827609037482 0.0577655218074965 0.971117239096252 123 0.0237392429159829 0.0474784858319659 0.976260757084017 124 0.0200542515655416 0.0401085031310832 0.979945748434458 125 0.0166138209238604 0.0332276418477207 0.98338617907614 126 0.0133266653342354 0.0266533306684709 0.986673334665765 127 0.0124835918936114 0.0249671837872228 0.987516408106389 128 0.0107994528778388 0.0215989057556776 0.989200547122161 129 0.0132460082758826 0.0264920165517652 0.986753991724117 130 0.0166703451650448 0.0333406903300897 0.983329654834955 131 0.0249947968704932 0.0499895937409864 0.975005203129507 132 0.0375281032510634 0.0750562065021269 0.962471896748937 133 0.0398526373182284 0.0797052746364568 0.960147362681772 134 0.034908069722082 0.0698161394441641 0.965091930277918 135 0.0285660994191789 0.0571321988383577 0.971433900580821 136 0.0237453746428418 0.0474907492856837 0.976254625357158 137 0.0200038681208887 0.0400077362417774 0.979996131879111 138 0.0241396729448151 0.0482793458896303 0.975860327055185 139 0.0205801450456984 0.0411602900913968 0.979419854954302 140 0.0265135962856059 0.0530271925712119 0.973486403714394 141 0.0359695405551018 0.0719390811102036 0.964030459444898 142 0.032294168453362 0.0645883369067239 0.967705831546638 143 0.0267285224137314 0.0534570448274628 0.973271477586269 144 0.0234775668803732 0.0469551337607464 0.976522433119627 145 0.0351128413041757 0.0702256826083513 0.964887158695824 146 0.0381457465488872 0.0762914930977743 0.961854253451113 147 0.0453973783189103 0.0907947566378206 0.95460262168109 148 0.0393477893275986 0.0786955786551973 0.960652210672401 149 0.0321126205108259 0.0642252410216518 0.967887379489174 150 0.0426887770740969 0.0853775541481938 0.957311222925903 151 0.0502760726058603 0.100552145211721 0.94972392739414 152 0.0518655506799563 0.103731101359913 0.948134449320044 153 0.0760346520932306 0.152069304186461 0.923965347906769 154 0.080475530059051 0.160951060118102 0.919524469940949 155 0.0814825953957819 0.162965190791564 0.918517404604218 156 0.0689051157639046 0.137810231527809 0.931094884236095 157 0.060435110835844 0.120870221671688 0.939564889164156 158 0.0515058455842663 0.103011691168533 0.948494154415734 159 0.0441864062489805 0.0883728124979611 0.955813593751019 160 0.0366373857959031 0.0732747715918062 0.963362614204097 161 0.0297494802248474 0.0594989604496948 0.970250519775153 162 0.0240461981371726 0.0480923962743452 0.975953801862827 163 0.0193476025227718 0.0386952050455436 0.980652397477228 164 0.0161820082434436 0.0323640164868872 0.983817991756556 165 0.0130707973597127 0.0261415947194253 0.986929202640287 166 0.0130553701900596 0.0261107403801192 0.98694462980994 167 0.010246978944537 0.0204939578890739 0.989753021055463 168 0.0110148111191236 0.0220296222382472 0.988985188880876 169 0.0102006347356799 0.0204012694713599 0.98979936526432 170 0.0084874753427659 0.0169749506855318 0.991512524657234 171 0.0100754548803796 0.0201509097607592 0.98992454511962 172 0.00786146858895123 0.0157229371779025 0.992138531411049 173 0.00649448677693935 0.0129889735538787 0.993505513223061 174 0.0062439150417824 0.0124878300835648 0.993756084958218 175 0.00618090595575049 0.012361811911501 0.99381909404425 176 0.00523542140084943 0.0104708428016989 0.994764578599151 177 0.0040625882724304 0.00812517654486081 0.99593741172757 178 0.0033102813103204 0.00662056262064081 0.99668971868968 179 0.0025394158512275 0.005078831702455 0.997460584148772 180 0.00214100896045101 0.00428201792090202 0.997858991039549 181 0.00164917521877868 0.00329835043755736 0.998350824781221 182 0.00121617042110731 0.00243234084221462 0.998783829578893 183 0.00120568901473981 0.00241137802947963 0.99879431098526 184 0.000908438375735931 0.00181687675147186 0.999091561624264 185 0.0223564821736094 0.0447129643472188 0.977643517826391 186 0.0191915934265549 0.0383831868531099 0.980808406573445 187 0.0234753547328429 0.0469507094656858 0.976524645267157 188 0.0190876552168483 0.0381753104336965 0.980912344783152 189 0.016459752435779 0.0329195048715579 0.983540247564221 190 0.0128207742827646 0.0256415485655292 0.987179225717235 191 0.0121100814681219 0.0242201629362439 0.987889918531878 192 0.00939230882336032 0.0187846176467206 0.99060769117664 193 0.00901275553128828 0.0180255110625766 0.990987244468712 194 0.00804175862601702 0.016083517252034 0.991958241373983 195 0.00654256183595825 0.0130851236719165 0.993457438164042 196 0.00486791808910514 0.00973583617821029 0.995132081910895 197 0.00827315719269327 0.0165463143853865 0.991726842807307 198 0.00629874433611938 0.0125974886722388 0.993701255663881 199 0.00553150679118512 0.0110630135823702 0.994468493208815 200 0.00457784421607706 0.00915568843215412 0.995422155783923 201 0.00413196588969706 0.00826393177939413 0.995868034110303 202 0.0032049491383809 0.00640989827676181 0.996795050861619 203 0.00373869819170514 0.00747739638341028 0.996261301808295 204 0.00562630614121184 0.0112526122824237 0.994373693858788 205 0.00555604348301657 0.0111120869660331 0.994443956516983 206 0.0040429328686677 0.0080858657373354 0.995957067131332 207 0.00334172745065212 0.00668345490130424 0.996658272549348 208 0.00241568360781308 0.00483136721562615 0.997584316392187 209 0.00298596592716199 0.00597193185432398 0.997014034072838 210 0.00230985819649199 0.00461971639298398 0.997690141803508 211 0.00286357144241323 0.00572714288482647 0.997136428557587 212 0.00769719641816444 0.0153943928363289 0.992302803581836 213 0.0055198343700456 0.0110396687400912 0.994480165629954 214 0.00699468112872576 0.0139893622574515 0.993005318871274 215 0.00551646601893475 0.0110329320378695 0.994483533981065 216 0.00406498586949212 0.00812997173898424 0.995935014130508 217 0.00428416737999878 0.00856833475999757 0.995715832620001 218 0.00338062632749029 0.00676125265498058 0.99661937367251 219 0.00286114278938317 0.00572228557876633 0.997138857210617 220 0.00198603627629227 0.00397207255258454 0.998013963723708 221 0.00148773829442331 0.00297547658884663 0.998512261705577 222 0.000994023790745994 0.00198804758149199 0.999005976209254 223 0.00068762752033979 0.00137525504067958 0.99931237247966 224 0.00048167838196872 0.000963356763937439 0.999518321618031 225 0.000302235680795473 0.000604471361590945 0.999697764319205 226 0.000827624618855564 0.00165524923771113 0.999172375381144 227 0.00060393761180431 0.00120787522360862 0.999396062388196 228 0.000399444985623957 0.000798889971247915 0.999600555014376 229 0.00026740785399305 0.000534815707986101 0.999732592146007 230 0.000168169590524875 0.000336339181049751 0.999831830409475 231 0.000177259770253977 0.000354519540507954 0.999822740229746 232 0.00073477502799852 0.00146955005599704 0.999265224972001 233 0.00358082830930928 0.00716165661861855 0.996419171690691 234 0.00592406133672894 0.0118481226734579 0.994075938663271 235 0.00374402940767164 0.00748805881534327 0.996255970592328 236 0.00261534816696455 0.0052306963339291 0.997384651833035 237 0.037818387084205 0.07563677416841 0.962181612915795 238 0.0259026736009734 0.0518053472019469 0.974097326399027 239 0.0175292997370702 0.0350585994741404 0.98247070026293 240 0.0119394592826401 0.0238789185652801 0.98806054071736 241 0.00976945343009399 0.019538906860188 0.990230546569906 242 0.014045999210174 0.0280919984203481 0.985954000789826 243 0.00947556015193659 0.0189511203038732 0.990524439848063 244 0.00919401049465485 0.0183880209893097 0.990805989505345 245 0.0059719211681674 0.0119438423363348 0.994028078831833 246 0.00382792350694076 0.00765584701388152 0.996172076493059 247 0.00145093137205649 0.00290186274411298 0.998549068627943

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.977955087555212 & 0.0440898248895755 & 0.0220449124447877 \tabularnewline
18 & 0.995018686795154 & 0.00996262640969223 & 0.00498131320484611 \tabularnewline
19 & 0.991043466316251 & 0.017913067367498 & 0.00895653368374899 \tabularnewline
20 & 0.983004681419042 & 0.0339906371619158 & 0.0169953185809579 \tabularnewline
21 & 0.969239251412634 & 0.0615214971747324 & 0.0307607485873662 \tabularnewline
22 & 0.956415754307936 & 0.0871684913841286 & 0.0435842456920643 \tabularnewline
23 & 0.941419423594714 & 0.117161152810572 & 0.0585805764052862 \tabularnewline
24 & 0.925037568114176 & 0.149924863771649 & 0.0749624318858244 \tabularnewline
25 & 0.89327779709033 & 0.21344440581934 & 0.10672220290967 \tabularnewline
26 & 0.866278926204539 & 0.267442147590922 & 0.133721073795461 \tabularnewline
27 & 0.823971416773333 & 0.352057166453333 & 0.176028583226667 \tabularnewline
28 & 0.840523932242884 & 0.318952135514231 & 0.159476067757116 \tabularnewline
29 & 0.794757769577171 & 0.410484460845659 & 0.205242230422829 \tabularnewline
30 & 0.79339034255239 & 0.413219314895219 & 0.20660965744761 \tabularnewline
31 & 0.750445074219343 & 0.499109851561313 & 0.249554925780656 \tabularnewline
32 & 0.738323314592152 & 0.523353370815696 & 0.261676685407848 \tabularnewline
33 & 0.715434219505237 & 0.569131560989526 & 0.284565780494763 \tabularnewline
34 & 0.656740057917593 & 0.686519884164813 & 0.343259942082407 \tabularnewline
35 & 0.639060210941821 & 0.721879578116358 & 0.360939789058179 \tabularnewline
36 & 0.723591823873427 & 0.552816352253147 & 0.276408176126573 \tabularnewline
37 & 0.783867304760463 & 0.432265390479075 & 0.216132695239537 \tabularnewline
38 & 0.763096917718206 & 0.473806164563589 & 0.236903082281794 \tabularnewline
39 & 0.79437831495396 & 0.41124337009208 & 0.20562168504604 \tabularnewline
40 & 0.779966106854365 & 0.44006778629127 & 0.220033893145635 \tabularnewline
41 & 0.749897171273924 & 0.500205657452152 & 0.250102828726076 \tabularnewline
42 & 0.729612691249747 & 0.540774617500506 & 0.270387308750253 \tabularnewline
43 & 0.755486261864494 & 0.489027476271012 & 0.244513738135506 \tabularnewline
44 & 0.7150456588546 & 0.569908682290799 & 0.2849543411454 \tabularnewline
45 & 0.67904228192718 & 0.64191543614564 & 0.32095771807282 \tabularnewline
46 & 0.846351540153258 & 0.307296919693484 & 0.153648459846742 \tabularnewline
47 & 0.847780097900919 & 0.304439804198163 & 0.152219902099081 \tabularnewline
48 & 0.820731483290323 & 0.358537033419355 & 0.179268516709677 \tabularnewline
49 & 0.796967213148615 & 0.406065573702771 & 0.203032786851385 \tabularnewline
50 & 0.77738374396325 & 0.4452325120735 & 0.22261625603675 \tabularnewline
51 & 0.7397057962769 & 0.5205884074462 & 0.2602942037231 \tabularnewline
52 & 0.700541121038397 & 0.598917757923205 & 0.299458878961603 \tabularnewline
53 & 0.716883891246429 & 0.566232217507141 & 0.283116108753571 \tabularnewline
54 & 0.686467087992366 & 0.627065824015268 & 0.313532912007634 \tabularnewline
55 & 0.680940742733998 & 0.638118514532005 & 0.319059257266002 \tabularnewline
56 & 0.675313662864148 & 0.649372674271704 & 0.324686337135852 \tabularnewline
57 & 0.633175183840637 & 0.733649632318726 & 0.366824816159363 \tabularnewline
58 & 0.623923207562088 & 0.752153584875824 & 0.376076792437912 \tabularnewline
59 & 0.583146788522007 & 0.833706422955985 & 0.416853211477993 \tabularnewline
60 & 0.59719119515775 & 0.805617609684499 & 0.40280880484225 \tabularnewline
61 & 0.562502880985813 & 0.874994238028375 & 0.437497119014187 \tabularnewline
62 & 0.521804615161652 & 0.956390769676695 & 0.478195384838348 \tabularnewline
63 & 0.480025261953368 & 0.960050523906736 & 0.519974738046632 \tabularnewline
64 & 0.43540610791067 & 0.870812215821339 & 0.56459389208933 \tabularnewline
65 & 0.401604431318089 & 0.803208862636179 & 0.598395568681911 \tabularnewline
66 & 0.38865630865111 & 0.777312617302219 & 0.61134369134889 \tabularnewline
67 & 0.384250906786486 & 0.768501813572972 & 0.615749093213514 \tabularnewline
68 & 0.517720056094523 & 0.964559887810954 & 0.482279943905477 \tabularnewline
69 & 0.63061702649402 & 0.738765947011959 & 0.36938297350598 \tabularnewline
70 & 0.595416007907377 & 0.809167984185246 & 0.404583992092623 \tabularnewline
71 & 0.672371158460862 & 0.655257683078275 & 0.327628841539137 \tabularnewline
72 & 0.634854377732834 & 0.730291244534332 & 0.365145622267166 \tabularnewline
73 & 0.624670895867978 & 0.750658208264045 & 0.375329104132022 \tabularnewline
74 & 0.606648383557719 & 0.786703232884562 & 0.393351616442281 \tabularnewline
75 & 0.566499947146784 & 0.867000105706432 & 0.433500052853216 \tabularnewline
76 & 0.607592034967605 & 0.784815930064789 & 0.392407965032395 \tabularnewline
77 & 0.568317893268798 & 0.863364213462405 & 0.431682106731202 \tabularnewline
78 & 0.541558134188577 & 0.916883731622846 & 0.458441865811423 \tabularnewline
79 & 0.549296507902011 & 0.901406984195979 & 0.450703492097989 \tabularnewline
80 & 0.510567657429531 & 0.978864685140938 & 0.489432342570469 \tabularnewline
81 & 0.474951684987043 & 0.949903369974086 & 0.525048315012957 \tabularnewline
82 & 0.438389405829715 & 0.876778811659431 & 0.561610594170285 \tabularnewline
83 & 0.407430458324123 & 0.814860916648246 & 0.592569541675877 \tabularnewline
84 & 0.370254702832498 & 0.740509405664996 & 0.629745297167502 \tabularnewline
85 & 0.360518147094309 & 0.721036294188618 & 0.639481852905691 \tabularnewline
86 & 0.324033384284653 & 0.648066768569306 & 0.675966615715347 \tabularnewline
87 & 0.293887378851484 & 0.587774757702968 & 0.706112621148516 \tabularnewline
88 & 0.277730240627193 & 0.555460481254386 & 0.722269759372807 \tabularnewline
89 & 0.246430051612688 & 0.492860103225376 & 0.753569948387312 \tabularnewline
90 & 0.238740319112994 & 0.477480638225987 & 0.761259680887006 \tabularnewline
91 & 0.210816058874235 & 0.42163211774847 & 0.789183941125765 \tabularnewline
92 & 0.186765732028805 & 0.373531464057609 & 0.813234267971195 \tabularnewline
93 & 0.164024780701546 & 0.328049561403092 & 0.835975219298454 \tabularnewline
94 & 0.170322263651312 & 0.340644527302625 & 0.829677736348688 \tabularnewline
95 & 0.153305219397003 & 0.306610438794007 & 0.846694780602997 \tabularnewline
96 & 0.131547035812515 & 0.26309407162503 & 0.868452964187485 \tabularnewline
97 & 0.134152959198113 & 0.268305918396227 & 0.865847040801887 \tabularnewline
98 & 0.114190299606183 & 0.228380599212365 & 0.885809700393817 \tabularnewline
99 & 0.0969282590804472 & 0.193856518160894 & 0.903071740919553 \tabularnewline
100 & 0.086688083082024 & 0.173376166164048 & 0.913311916917976 \tabularnewline
101 & 0.0769268646977879 & 0.153853729395576 & 0.923073135302212 \tabularnewline
102 & 0.0892578246194909 & 0.178515649238982 & 0.910742175380509 \tabularnewline
103 & 0.0767647712400794 & 0.153529542480159 & 0.923235228759921 \tabularnewline
104 & 0.0693115660415813 & 0.138623132083163 & 0.930688433958419 \tabularnewline
105 & 0.0813770685522494 & 0.162754137104499 & 0.918622931447751 \tabularnewline
106 & 0.0727359957473762 & 0.145471991494752 & 0.927264004252624 \tabularnewline
107 & 0.0609281945985376 & 0.121856389197075 & 0.939071805401462 \tabularnewline
108 & 0.0615427561539167 & 0.123085512307833 & 0.938457243846083 \tabularnewline
109 & 0.0519477422715837 & 0.103895484543167 & 0.948052257728416 \tabularnewline
110 & 0.0441347828415425 & 0.0882695656830849 & 0.955865217158458 \tabularnewline
111 & 0.0369647949548216 & 0.0739295899096432 & 0.963035205045178 \tabularnewline
112 & 0.0454516805763338 & 0.0909033611526676 & 0.954548319423666 \tabularnewline
113 & 0.0388671559514368 & 0.0777343119028737 & 0.961132844048563 \tabularnewline
114 & 0.0509529948528077 & 0.101905989705615 & 0.949047005147192 \tabularnewline
115 & 0.0498435718105525 & 0.099687143621105 & 0.950156428189448 \tabularnewline
116 & 0.0442789109568897 & 0.0885578219137794 & 0.95572108904311 \tabularnewline
117 & 0.0403689939347767 & 0.0807379878695535 & 0.959631006065223 \tabularnewline
118 & 0.0369512286974033 & 0.0739024573948067 & 0.963048771302597 \tabularnewline
119 & 0.0325674222633728 & 0.0651348445267457 & 0.967432577736627 \tabularnewline
120 & 0.0275450005285695 & 0.0550900010571391 & 0.97245499947143 \tabularnewline
121 & 0.0232870247522251 & 0.0465740495044503 & 0.976712975247775 \tabularnewline
122 & 0.0288827609037482 & 0.0577655218074965 & 0.971117239096252 \tabularnewline
123 & 0.0237392429159829 & 0.0474784858319659 & 0.976260757084017 \tabularnewline
124 & 0.0200542515655416 & 0.0401085031310832 & 0.979945748434458 \tabularnewline
125 & 0.0166138209238604 & 0.0332276418477207 & 0.98338617907614 \tabularnewline
126 & 0.0133266653342354 & 0.0266533306684709 & 0.986673334665765 \tabularnewline
127 & 0.0124835918936114 & 0.0249671837872228 & 0.987516408106389 \tabularnewline
128 & 0.0107994528778388 & 0.0215989057556776 & 0.989200547122161 \tabularnewline
129 & 0.0132460082758826 & 0.0264920165517652 & 0.986753991724117 \tabularnewline
130 & 0.0166703451650448 & 0.0333406903300897 & 0.983329654834955 \tabularnewline
131 & 0.0249947968704932 & 0.0499895937409864 & 0.975005203129507 \tabularnewline
132 & 0.0375281032510634 & 0.0750562065021269 & 0.962471896748937 \tabularnewline
133 & 0.0398526373182284 & 0.0797052746364568 & 0.960147362681772 \tabularnewline
134 & 0.034908069722082 & 0.0698161394441641 & 0.965091930277918 \tabularnewline
135 & 0.0285660994191789 & 0.0571321988383577 & 0.971433900580821 \tabularnewline
136 & 0.0237453746428418 & 0.0474907492856837 & 0.976254625357158 \tabularnewline
137 & 0.0200038681208887 & 0.0400077362417774 & 0.979996131879111 \tabularnewline
138 & 0.0241396729448151 & 0.0482793458896303 & 0.975860327055185 \tabularnewline
139 & 0.0205801450456984 & 0.0411602900913968 & 0.979419854954302 \tabularnewline
140 & 0.0265135962856059 & 0.0530271925712119 & 0.973486403714394 \tabularnewline
141 & 0.0359695405551018 & 0.0719390811102036 & 0.964030459444898 \tabularnewline
142 & 0.032294168453362 & 0.0645883369067239 & 0.967705831546638 \tabularnewline
143 & 0.0267285224137314 & 0.0534570448274628 & 0.973271477586269 \tabularnewline
144 & 0.0234775668803732 & 0.0469551337607464 & 0.976522433119627 \tabularnewline
145 & 0.0351128413041757 & 0.0702256826083513 & 0.964887158695824 \tabularnewline
146 & 0.0381457465488872 & 0.0762914930977743 & 0.961854253451113 \tabularnewline
147 & 0.0453973783189103 & 0.0907947566378206 & 0.95460262168109 \tabularnewline
148 & 0.0393477893275986 & 0.0786955786551973 & 0.960652210672401 \tabularnewline
149 & 0.0321126205108259 & 0.0642252410216518 & 0.967887379489174 \tabularnewline
150 & 0.0426887770740969 & 0.0853775541481938 & 0.957311222925903 \tabularnewline
151 & 0.0502760726058603 & 0.100552145211721 & 0.94972392739414 \tabularnewline
152 & 0.0518655506799563 & 0.103731101359913 & 0.948134449320044 \tabularnewline
153 & 0.0760346520932306 & 0.152069304186461 & 0.923965347906769 \tabularnewline
154 & 0.080475530059051 & 0.160951060118102 & 0.919524469940949 \tabularnewline
155 & 0.0814825953957819 & 0.162965190791564 & 0.918517404604218 \tabularnewline
156 & 0.0689051157639046 & 0.137810231527809 & 0.931094884236095 \tabularnewline
157 & 0.060435110835844 & 0.120870221671688 & 0.939564889164156 \tabularnewline
158 & 0.0515058455842663 & 0.103011691168533 & 0.948494154415734 \tabularnewline
159 & 0.0441864062489805 & 0.0883728124979611 & 0.955813593751019 \tabularnewline
160 & 0.0366373857959031 & 0.0732747715918062 & 0.963362614204097 \tabularnewline
161 & 0.0297494802248474 & 0.0594989604496948 & 0.970250519775153 \tabularnewline
162 & 0.0240461981371726 & 0.0480923962743452 & 0.975953801862827 \tabularnewline
163 & 0.0193476025227718 & 0.0386952050455436 & 0.980652397477228 \tabularnewline
164 & 0.0161820082434436 & 0.0323640164868872 & 0.983817991756556 \tabularnewline
165 & 0.0130707973597127 & 0.0261415947194253 & 0.986929202640287 \tabularnewline
166 & 0.0130553701900596 & 0.0261107403801192 & 0.98694462980994 \tabularnewline
167 & 0.010246978944537 & 0.0204939578890739 & 0.989753021055463 \tabularnewline
168 & 0.0110148111191236 & 0.0220296222382472 & 0.988985188880876 \tabularnewline
169 & 0.0102006347356799 & 0.0204012694713599 & 0.98979936526432 \tabularnewline
170 & 0.0084874753427659 & 0.0169749506855318 & 0.991512524657234 \tabularnewline
171 & 0.0100754548803796 & 0.0201509097607592 & 0.98992454511962 \tabularnewline
172 & 0.00786146858895123 & 0.0157229371779025 & 0.992138531411049 \tabularnewline
173 & 0.00649448677693935 & 0.0129889735538787 & 0.993505513223061 \tabularnewline
174 & 0.0062439150417824 & 0.0124878300835648 & 0.993756084958218 \tabularnewline
175 & 0.00618090595575049 & 0.012361811911501 & 0.99381909404425 \tabularnewline
176 & 0.00523542140084943 & 0.0104708428016989 & 0.994764578599151 \tabularnewline
177 & 0.0040625882724304 & 0.00812517654486081 & 0.99593741172757 \tabularnewline
178 & 0.0033102813103204 & 0.00662056262064081 & 0.99668971868968 \tabularnewline
179 & 0.0025394158512275 & 0.005078831702455 & 0.997460584148772 \tabularnewline
180 & 0.00214100896045101 & 0.00428201792090202 & 0.997858991039549 \tabularnewline
181 & 0.00164917521877868 & 0.00329835043755736 & 0.998350824781221 \tabularnewline
182 & 0.00121617042110731 & 0.00243234084221462 & 0.998783829578893 \tabularnewline
183 & 0.00120568901473981 & 0.00241137802947963 & 0.99879431098526 \tabularnewline
184 & 0.000908438375735931 & 0.00181687675147186 & 0.999091561624264 \tabularnewline
185 & 0.0223564821736094 & 0.0447129643472188 & 0.977643517826391 \tabularnewline
186 & 0.0191915934265549 & 0.0383831868531099 & 0.980808406573445 \tabularnewline
187 & 0.0234753547328429 & 0.0469507094656858 & 0.976524645267157 \tabularnewline
188 & 0.0190876552168483 & 0.0381753104336965 & 0.980912344783152 \tabularnewline
189 & 0.016459752435779 & 0.0329195048715579 & 0.983540247564221 \tabularnewline
190 & 0.0128207742827646 & 0.0256415485655292 & 0.987179225717235 \tabularnewline
191 & 0.0121100814681219 & 0.0242201629362439 & 0.987889918531878 \tabularnewline
192 & 0.00939230882336032 & 0.0187846176467206 & 0.99060769117664 \tabularnewline
193 & 0.00901275553128828 & 0.0180255110625766 & 0.990987244468712 \tabularnewline
194 & 0.00804175862601702 & 0.016083517252034 & 0.991958241373983 \tabularnewline
195 & 0.00654256183595825 & 0.0130851236719165 & 0.993457438164042 \tabularnewline
196 & 0.00486791808910514 & 0.00973583617821029 & 0.995132081910895 \tabularnewline
197 & 0.00827315719269327 & 0.0165463143853865 & 0.991726842807307 \tabularnewline
198 & 0.00629874433611938 & 0.0125974886722388 & 0.993701255663881 \tabularnewline
199 & 0.00553150679118512 & 0.0110630135823702 & 0.994468493208815 \tabularnewline
200 & 0.00457784421607706 & 0.00915568843215412 & 0.995422155783923 \tabularnewline
201 & 0.00413196588969706 & 0.00826393177939413 & 0.995868034110303 \tabularnewline
202 & 0.0032049491383809 & 0.00640989827676181 & 0.996795050861619 \tabularnewline
203 & 0.00373869819170514 & 0.00747739638341028 & 0.996261301808295 \tabularnewline
204 & 0.00562630614121184 & 0.0112526122824237 & 0.994373693858788 \tabularnewline
205 & 0.00555604348301657 & 0.0111120869660331 & 0.994443956516983 \tabularnewline
206 & 0.0040429328686677 & 0.0080858657373354 & 0.995957067131332 \tabularnewline
207 & 0.00334172745065212 & 0.00668345490130424 & 0.996658272549348 \tabularnewline
208 & 0.00241568360781308 & 0.00483136721562615 & 0.997584316392187 \tabularnewline
209 & 0.00298596592716199 & 0.00597193185432398 & 0.997014034072838 \tabularnewline
210 & 0.00230985819649199 & 0.00461971639298398 & 0.997690141803508 \tabularnewline
211 & 0.00286357144241323 & 0.00572714288482647 & 0.997136428557587 \tabularnewline
212 & 0.00769719641816444 & 0.0153943928363289 & 0.992302803581836 \tabularnewline
213 & 0.0055198343700456 & 0.0110396687400912 & 0.994480165629954 \tabularnewline
214 & 0.00699468112872576 & 0.0139893622574515 & 0.993005318871274 \tabularnewline
215 & 0.00551646601893475 & 0.0110329320378695 & 0.994483533981065 \tabularnewline
216 & 0.00406498586949212 & 0.00812997173898424 & 0.995935014130508 \tabularnewline
217 & 0.00428416737999878 & 0.00856833475999757 & 0.995715832620001 \tabularnewline
218 & 0.00338062632749029 & 0.00676125265498058 & 0.99661937367251 \tabularnewline
219 & 0.00286114278938317 & 0.00572228557876633 & 0.997138857210617 \tabularnewline
220 & 0.00198603627629227 & 0.00397207255258454 & 0.998013963723708 \tabularnewline
221 & 0.00148773829442331 & 0.00297547658884663 & 0.998512261705577 \tabularnewline
222 & 0.000994023790745994 & 0.00198804758149199 & 0.999005976209254 \tabularnewline
223 & 0.00068762752033979 & 0.00137525504067958 & 0.99931237247966 \tabularnewline
224 & 0.00048167838196872 & 0.000963356763937439 & 0.999518321618031 \tabularnewline
225 & 0.000302235680795473 & 0.000604471361590945 & 0.999697764319205 \tabularnewline
226 & 0.000827624618855564 & 0.00165524923771113 & 0.999172375381144 \tabularnewline
227 & 0.00060393761180431 & 0.00120787522360862 & 0.999396062388196 \tabularnewline
228 & 0.000399444985623957 & 0.000798889971247915 & 0.999600555014376 \tabularnewline
229 & 0.00026740785399305 & 0.000534815707986101 & 0.999732592146007 \tabularnewline
230 & 0.000168169590524875 & 0.000336339181049751 & 0.999831830409475 \tabularnewline
231 & 0.000177259770253977 & 0.000354519540507954 & 0.999822740229746 \tabularnewline
232 & 0.00073477502799852 & 0.00146955005599704 & 0.999265224972001 \tabularnewline
233 & 0.00358082830930928 & 0.00716165661861855 & 0.996419171690691 \tabularnewline
234 & 0.00592406133672894 & 0.0118481226734579 & 0.994075938663271 \tabularnewline
235 & 0.00374402940767164 & 0.00748805881534327 & 0.996255970592328 \tabularnewline
236 & 0.00261534816696455 & 0.0052306963339291 & 0.997384651833035 \tabularnewline
237 & 0.037818387084205 & 0.07563677416841 & 0.962181612915795 \tabularnewline
238 & 0.0259026736009734 & 0.0518053472019469 & 0.974097326399027 \tabularnewline
239 & 0.0175292997370702 & 0.0350585994741404 & 0.98247070026293 \tabularnewline
240 & 0.0119394592826401 & 0.0238789185652801 & 0.98806054071736 \tabularnewline
241 & 0.00976945343009399 & 0.019538906860188 & 0.990230546569906 \tabularnewline
242 & 0.014045999210174 & 0.0280919984203481 & 0.985954000789826 \tabularnewline
243 & 0.00947556015193659 & 0.0189511203038732 & 0.990524439848063 \tabularnewline
244 & 0.00919401049465485 & 0.0183880209893097 & 0.990805989505345 \tabularnewline
245 & 0.0059719211681674 & 0.0119438423363348 & 0.994028078831833 \tabularnewline
246 & 0.00382792350694076 & 0.00765584701388152 & 0.996172076493059 \tabularnewline
247 & 0.00145093137205649 & 0.00290186274411298 & 0.998549068627943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185702&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]17[/C][C]0.977955087555212[/C][C]0.0440898248895755[/C][C]0.0220449124447877[/C][/ROW]
[ROW][C]18[/C][C]0.995018686795154[/C][C]0.00996262640969223[/C][C]0.00498131320484611[/C][/ROW]
[ROW][C]19[/C][C]0.991043466316251[/C][C]0.017913067367498[/C][C]0.00895653368374899[/C][/ROW]
[ROW][C]20[/C][C]0.983004681419042[/C][C]0.0339906371619158[/C][C]0.0169953185809579[/C][/ROW]
[ROW][C]21[/C][C]0.969239251412634[/C][C]0.0615214971747324[/C][C]0.0307607485873662[/C][/ROW]
[ROW][C]22[/C][C]0.956415754307936[/C][C]0.0871684913841286[/C][C]0.0435842456920643[/C][/ROW]
[ROW][C]23[/C][C]0.941419423594714[/C][C]0.117161152810572[/C][C]0.0585805764052862[/C][/ROW]
[ROW][C]24[/C][C]0.925037568114176[/C][C]0.149924863771649[/C][C]0.0749624318858244[/C][/ROW]
[ROW][C]25[/C][C]0.89327779709033[/C][C]0.21344440581934[/C][C]0.10672220290967[/C][/ROW]
[ROW][C]26[/C][C]0.866278926204539[/C][C]0.267442147590922[/C][C]0.133721073795461[/C][/ROW]
[ROW][C]27[/C][C]0.823971416773333[/C][C]0.352057166453333[/C][C]0.176028583226667[/C][/ROW]
[ROW][C]28[/C][C]0.840523932242884[/C][C]0.318952135514231[/C][C]0.159476067757116[/C][/ROW]
[ROW][C]29[/C][C]0.794757769577171[/C][C]0.410484460845659[/C][C]0.205242230422829[/C][/ROW]
[ROW][C]30[/C][C]0.79339034255239[/C][C]0.413219314895219[/C][C]0.20660965744761[/C][/ROW]
[ROW][C]31[/C][C]0.750445074219343[/C][C]0.499109851561313[/C][C]0.249554925780656[/C][/ROW]
[ROW][C]32[/C][C]0.738323314592152[/C][C]0.523353370815696[/C][C]0.261676685407848[/C][/ROW]
[ROW][C]33[/C][C]0.715434219505237[/C][C]0.569131560989526[/C][C]0.284565780494763[/C][/ROW]
[ROW][C]34[/C][C]0.656740057917593[/C][C]0.686519884164813[/C][C]0.343259942082407[/C][/ROW]
[ROW][C]35[/C][C]0.639060210941821[/C][C]0.721879578116358[/C][C]0.360939789058179[/C][/ROW]
[ROW][C]36[/C][C]0.723591823873427[/C][C]0.552816352253147[/C][C]0.276408176126573[/C][/ROW]
[ROW][C]37[/C][C]0.783867304760463[/C][C]0.432265390479075[/C][C]0.216132695239537[/C][/ROW]
[ROW][C]38[/C][C]0.763096917718206[/C][C]0.473806164563589[/C][C]0.236903082281794[/C][/ROW]
[ROW][C]39[/C][C]0.79437831495396[/C][C]0.41124337009208[/C][C]0.20562168504604[/C][/ROW]
[ROW][C]40[/C][C]0.779966106854365[/C][C]0.44006778629127[/C][C]0.220033893145635[/C][/ROW]
[ROW][C]41[/C][C]0.749897171273924[/C][C]0.500205657452152[/C][C]0.250102828726076[/C][/ROW]
[ROW][C]42[/C][C]0.729612691249747[/C][C]0.540774617500506[/C][C]0.270387308750253[/C][/ROW]
[ROW][C]43[/C][C]0.755486261864494[/C][C]0.489027476271012[/C][C]0.244513738135506[/C][/ROW]
[ROW][C]44[/C][C]0.7150456588546[/C][C]0.569908682290799[/C][C]0.2849543411454[/C][/ROW]
[ROW][C]45[/C][C]0.67904228192718[/C][C]0.64191543614564[/C][C]0.32095771807282[/C][/ROW]
[ROW][C]46[/C][C]0.846351540153258[/C][C]0.307296919693484[/C][C]0.153648459846742[/C][/ROW]
[ROW][C]47[/C][C]0.847780097900919[/C][C]0.304439804198163[/C][C]0.152219902099081[/C][/ROW]
[ROW][C]48[/C][C]0.820731483290323[/C][C]0.358537033419355[/C][C]0.179268516709677[/C][/ROW]
[ROW][C]49[/C][C]0.796967213148615[/C][C]0.406065573702771[/C][C]0.203032786851385[/C][/ROW]
[ROW][C]50[/C][C]0.77738374396325[/C][C]0.4452325120735[/C][C]0.22261625603675[/C][/ROW]
[ROW][C]51[/C][C]0.7397057962769[/C][C]0.5205884074462[/C][C]0.2602942037231[/C][/ROW]
[ROW][C]52[/C][C]0.700541121038397[/C][C]0.598917757923205[/C][C]0.299458878961603[/C][/ROW]
[ROW][C]53[/C][C]0.716883891246429[/C][C]0.566232217507141[/C][C]0.283116108753571[/C][/ROW]
[ROW][C]54[/C][C]0.686467087992366[/C][C]0.627065824015268[/C][C]0.313532912007634[/C][/ROW]
[ROW][C]55[/C][C]0.680940742733998[/C][C]0.638118514532005[/C][C]0.319059257266002[/C][/ROW]
[ROW][C]56[/C][C]0.675313662864148[/C][C]0.649372674271704[/C][C]0.324686337135852[/C][/ROW]
[ROW][C]57[/C][C]0.633175183840637[/C][C]0.733649632318726[/C][C]0.366824816159363[/C][/ROW]
[ROW][C]58[/C][C]0.623923207562088[/C][C]0.752153584875824[/C][C]0.376076792437912[/C][/ROW]
[ROW][C]59[/C][C]0.583146788522007[/C][C]0.833706422955985[/C][C]0.416853211477993[/C][/ROW]
[ROW][C]60[/C][C]0.59719119515775[/C][C]0.805617609684499[/C][C]0.40280880484225[/C][/ROW]
[ROW][C]61[/C][C]0.562502880985813[/C][C]0.874994238028375[/C][C]0.437497119014187[/C][/ROW]
[ROW][C]62[/C][C]0.521804615161652[/C][C]0.956390769676695[/C][C]0.478195384838348[/C][/ROW]
[ROW][C]63[/C][C]0.480025261953368[/C][C]0.960050523906736[/C][C]0.519974738046632[/C][/ROW]
[ROW][C]64[/C][C]0.43540610791067[/C][C]0.870812215821339[/C][C]0.56459389208933[/C][/ROW]
[ROW][C]65[/C][C]0.401604431318089[/C][C]0.803208862636179[/C][C]0.598395568681911[/C][/ROW]
[ROW][C]66[/C][C]0.38865630865111[/C][C]0.777312617302219[/C][C]0.61134369134889[/C][/ROW]
[ROW][C]67[/C][C]0.384250906786486[/C][C]0.768501813572972[/C][C]0.615749093213514[/C][/ROW]
[ROW][C]68[/C][C]0.517720056094523[/C][C]0.964559887810954[/C][C]0.482279943905477[/C][/ROW]
[ROW][C]69[/C][C]0.63061702649402[/C][C]0.738765947011959[/C][C]0.36938297350598[/C][/ROW]
[ROW][C]70[/C][C]0.595416007907377[/C][C]0.809167984185246[/C][C]0.404583992092623[/C][/ROW]
[ROW][C]71[/C][C]0.672371158460862[/C][C]0.655257683078275[/C][C]0.327628841539137[/C][/ROW]
[ROW][C]72[/C][C]0.634854377732834[/C][C]0.730291244534332[/C][C]0.365145622267166[/C][/ROW]
[ROW][C]73[/C][C]0.624670895867978[/C][C]0.750658208264045[/C][C]0.375329104132022[/C][/ROW]
[ROW][C]74[/C][C]0.606648383557719[/C][C]0.786703232884562[/C][C]0.393351616442281[/C][/ROW]
[ROW][C]75[/C][C]0.566499947146784[/C][C]0.867000105706432[/C][C]0.433500052853216[/C][/ROW]
[ROW][C]76[/C][C]0.607592034967605[/C][C]0.784815930064789[/C][C]0.392407965032395[/C][/ROW]
[ROW][C]77[/C][C]0.568317893268798[/C][C]0.863364213462405[/C][C]0.431682106731202[/C][/ROW]
[ROW][C]78[/C][C]0.541558134188577[/C][C]0.916883731622846[/C][C]0.458441865811423[/C][/ROW]
[ROW][C]79[/C][C]0.549296507902011[/C][C]0.901406984195979[/C][C]0.450703492097989[/C][/ROW]
[ROW][C]80[/C][C]0.510567657429531[/C][C]0.978864685140938[/C][C]0.489432342570469[/C][/ROW]
[ROW][C]81[/C][C]0.474951684987043[/C][C]0.949903369974086[/C][C]0.525048315012957[/C][/ROW]
[ROW][C]82[/C][C]0.438389405829715[/C][C]0.876778811659431[/C][C]0.561610594170285[/C][/ROW]
[ROW][C]83[/C][C]0.407430458324123[/C][C]0.814860916648246[/C][C]0.592569541675877[/C][/ROW]
[ROW][C]84[/C][C]0.370254702832498[/C][C]0.740509405664996[/C][C]0.629745297167502[/C][/ROW]
[ROW][C]85[/C][C]0.360518147094309[/C][C]0.721036294188618[/C][C]0.639481852905691[/C][/ROW]
[ROW][C]86[/C][C]0.324033384284653[/C][C]0.648066768569306[/C][C]0.675966615715347[/C][/ROW]
[ROW][C]87[/C][C]0.293887378851484[/C][C]0.587774757702968[/C][C]0.706112621148516[/C][/ROW]
[ROW][C]88[/C][C]0.277730240627193[/C][C]0.555460481254386[/C][C]0.722269759372807[/C][/ROW]
[ROW][C]89[/C][C]0.246430051612688[/C][C]0.492860103225376[/C][C]0.753569948387312[/C][/ROW]
[ROW][C]90[/C][C]0.238740319112994[/C][C]0.477480638225987[/C][C]0.761259680887006[/C][/ROW]
[ROW][C]91[/C][C]0.210816058874235[/C][C]0.42163211774847[/C][C]0.789183941125765[/C][/ROW]
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[ROW][C]234[/C][C]0.00592406133672894[/C][C]0.0118481226734579[/C][C]0.994075938663271[/C][/ROW]
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[ROW][C]236[/C][C]0.00261534816696455[/C][C]0.0052306963339291[/C][C]0.997384651833035[/C][/ROW]
[ROW][C]237[/C][C]0.037818387084205[/C][C]0.07563677416841[/C][C]0.962181612915795[/C][/ROW]
[ROW][C]238[/C][C]0.0259026736009734[/C][C]0.0518053472019469[/C][C]0.974097326399027[/C][/ROW]
[ROW][C]239[/C][C]0.0175292997370702[/C][C]0.0350585994741404[/C][C]0.98247070026293[/C][/ROW]
[ROW][C]240[/C][C]0.0119394592826401[/C][C]0.0238789185652801[/C][C]0.98806054071736[/C][/ROW]
[ROW][C]241[/C][C]0.00976945343009399[/C][C]0.019538906860188[/C][C]0.990230546569906[/C][/ROW]
[ROW][C]242[/C][C]0.014045999210174[/C][C]0.0280919984203481[/C][C]0.985954000789826[/C][/ROW]
[ROW][C]243[/C][C]0.00947556015193659[/C][C]0.0189511203038732[/C][C]0.990524439848063[/C][/ROW]
[ROW][C]244[/C][C]0.00919401049465485[/C][C]0.0183880209893097[/C][C]0.990805989505345[/C][/ROW]
[ROW][C]245[/C][C]0.0059719211681674[/C][C]0.0119438423363348[/C][C]0.994028078831833[/C][/ROW]
[ROW][C]246[/C][C]0.00382792350694076[/C][C]0.00765584701388152[/C][C]0.996172076493059[/C][/ROW]
[ROW][C]247[/C][C]0.00145093137205649[/C][C]0.00290186274411298[/C][C]0.998549068627943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185702&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185702&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 17 0.977955087555212 0.0440898248895755 0.0220449124447877 18 0.995018686795154 0.00996262640969223 0.00498131320484611 19 0.991043466316251 0.017913067367498 0.00895653368374899 20 0.983004681419042 0.0339906371619158 0.0169953185809579 21 0.969239251412634 0.0615214971747324 0.0307607485873662 22 0.956415754307936 0.0871684913841286 0.0435842456920643 23 0.941419423594714 0.117161152810572 0.0585805764052862 24 0.925037568114176 0.149924863771649 0.0749624318858244 25 0.89327779709033 0.21344440581934 0.10672220290967 26 0.866278926204539 0.267442147590922 0.133721073795461 27 0.823971416773333 0.352057166453333 0.176028583226667 28 0.840523932242884 0.318952135514231 0.159476067757116 29 0.794757769577171 0.410484460845659 0.205242230422829 30 0.79339034255239 0.413219314895219 0.20660965744761 31 0.750445074219343 0.499109851561313 0.249554925780656 32 0.738323314592152 0.523353370815696 0.261676685407848 33 0.715434219505237 0.569131560989526 0.284565780494763 34 0.656740057917593 0.686519884164813 0.343259942082407 35 0.639060210941821 0.721879578116358 0.360939789058179 36 0.723591823873427 0.552816352253147 0.276408176126573 37 0.783867304760463 0.432265390479075 0.216132695239537 38 0.763096917718206 0.473806164563589 0.236903082281794 39 0.79437831495396 0.41124337009208 0.20562168504604 40 0.779966106854365 0.44006778629127 0.220033893145635 41 0.749897171273924 0.500205657452152 0.250102828726076 42 0.729612691249747 0.540774617500506 0.270387308750253 43 0.755486261864494 0.489027476271012 0.244513738135506 44 0.7150456588546 0.569908682290799 0.2849543411454 45 0.67904228192718 0.64191543614564 0.32095771807282 46 0.846351540153258 0.307296919693484 0.153648459846742 47 0.847780097900919 0.304439804198163 0.152219902099081 48 0.820731483290323 0.358537033419355 0.179268516709677 49 0.796967213148615 0.406065573702771 0.203032786851385 50 0.77738374396325 0.4452325120735 0.22261625603675 51 0.7397057962769 0.5205884074462 0.2602942037231 52 0.700541121038397 0.598917757923205 0.299458878961603 53 0.716883891246429 0.566232217507141 0.283116108753571 54 0.686467087992366 0.627065824015268 0.313532912007634 55 0.680940742733998 0.638118514532005 0.319059257266002 56 0.675313662864148 0.649372674271704 0.324686337135852 57 0.633175183840637 0.733649632318726 0.366824816159363 58 0.623923207562088 0.752153584875824 0.376076792437912 59 0.583146788522007 0.833706422955985 0.416853211477993 60 0.59719119515775 0.805617609684499 0.40280880484225 61 0.562502880985813 0.874994238028375 0.437497119014187 62 0.521804615161652 0.956390769676695 0.478195384838348 63 0.480025261953368 0.960050523906736 0.519974738046632 64 0.43540610791067 0.870812215821339 0.56459389208933 65 0.401604431318089 0.803208862636179 0.598395568681911 66 0.38865630865111 0.777312617302219 0.61134369134889 67 0.384250906786486 0.768501813572972 0.615749093213514 68 0.517720056094523 0.964559887810954 0.482279943905477 69 0.63061702649402 0.738765947011959 0.36938297350598 70 0.595416007907377 0.809167984185246 0.404583992092623 71 0.672371158460862 0.655257683078275 0.327628841539137 72 0.634854377732834 0.730291244534332 0.365145622267166 73 0.624670895867978 0.750658208264045 0.375329104132022 74 0.606648383557719 0.786703232884562 0.393351616442281 75 0.566499947146784 0.867000105706432 0.433500052853216 76 0.607592034967605 0.784815930064789 0.392407965032395 77 0.568317893268798 0.863364213462405 0.431682106731202 78 0.541558134188577 0.916883731622846 0.458441865811423 79 0.549296507902011 0.901406984195979 0.450703492097989 80 0.510567657429531 0.978864685140938 0.489432342570469 81 0.474951684987043 0.949903369974086 0.525048315012957 82 0.438389405829715 0.876778811659431 0.561610594170285 83 0.407430458324123 0.814860916648246 0.592569541675877 84 0.370254702832498 0.740509405664996 0.629745297167502 85 0.360518147094309 0.721036294188618 0.639481852905691 86 0.324033384284653 0.648066768569306 0.675966615715347 87 0.293887378851484 0.587774757702968 0.706112621148516 88 0.277730240627193 0.555460481254386 0.722269759372807 89 0.246430051612688 0.492860103225376 0.753569948387312 90 0.238740319112994 0.477480638225987 0.761259680887006 91 0.210816058874235 0.42163211774847 0.789183941125765 92 0.186765732028805 0.373531464057609 0.813234267971195 93 0.164024780701546 0.328049561403092 0.835975219298454 94 0.170322263651312 0.340644527302625 0.829677736348688 95 0.153305219397003 0.306610438794007 0.846694780602997 96 0.131547035812515 0.26309407162503 0.868452964187485 97 0.134152959198113 0.268305918396227 0.865847040801887 98 0.114190299606183 0.228380599212365 0.885809700393817 99 0.0969282590804472 0.193856518160894 0.903071740919553 100 0.086688083082024 0.173376166164048 0.913311916917976 101 0.0769268646977879 0.153853729395576 0.923073135302212 102 0.0892578246194909 0.178515649238982 0.910742175380509 103 0.0767647712400794 0.153529542480159 0.923235228759921 104 0.0693115660415813 0.138623132083163 0.930688433958419 105 0.0813770685522494 0.162754137104499 0.918622931447751 106 0.0727359957473762 0.145471991494752 0.927264004252624 107 0.0609281945985376 0.121856389197075 0.939071805401462 108 0.0615427561539167 0.123085512307833 0.938457243846083 109 0.0519477422715837 0.103895484543167 0.948052257728416 110 0.0441347828415425 0.0882695656830849 0.955865217158458 111 0.0369647949548216 0.0739295899096432 0.963035205045178 112 0.0454516805763338 0.0909033611526676 0.954548319423666 113 0.0388671559514368 0.0777343119028737 0.961132844048563 114 0.0509529948528077 0.101905989705615 0.949047005147192 115 0.0498435718105525 0.099687143621105 0.950156428189448 116 0.0442789109568897 0.0885578219137794 0.95572108904311 117 0.0403689939347767 0.0807379878695535 0.959631006065223 118 0.0369512286974033 0.0739024573948067 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0.988985188880876 169 0.0102006347356799 0.0204012694713599 0.98979936526432 170 0.0084874753427659 0.0169749506855318 0.991512524657234 171 0.0100754548803796 0.0201509097607592 0.98992454511962 172 0.00786146858895123 0.0157229371779025 0.992138531411049 173 0.00649448677693935 0.0129889735538787 0.993505513223061 174 0.0062439150417824 0.0124878300835648 0.993756084958218 175 0.00618090595575049 0.012361811911501 0.99381909404425 176 0.00523542140084943 0.0104708428016989 0.994764578599151 177 0.0040625882724304 0.00812517654486081 0.99593741172757 178 0.0033102813103204 0.00662056262064081 0.99668971868968 179 0.0025394158512275 0.005078831702455 0.997460584148772 180 0.00214100896045101 0.00428201792090202 0.997858991039549 181 0.00164917521877868 0.00329835043755736 0.998350824781221 182 0.00121617042110731 0.00243234084221462 0.998783829578893 183 0.00120568901473981 0.00241137802947963 0.99879431098526 184 0.000908438375735931 0.00181687675147186 0.999091561624264 185 0.0223564821736094 0.0447129643472188 0.977643517826391 186 0.0191915934265549 0.0383831868531099 0.980808406573445 187 0.0234753547328429 0.0469507094656858 0.976524645267157 188 0.0190876552168483 0.0381753104336965 0.980912344783152 189 0.016459752435779 0.0329195048715579 0.983540247564221 190 0.0128207742827646 0.0256415485655292 0.987179225717235 191 0.0121100814681219 0.0242201629362439 0.987889918531878 192 0.00939230882336032 0.0187846176467206 0.99060769117664 193 0.00901275553128828 0.0180255110625766 0.990987244468712 194 0.00804175862601702 0.016083517252034 0.991958241373983 195 0.00654256183595825 0.0130851236719165 0.993457438164042 196 0.00486791808910514 0.00973583617821029 0.995132081910895 197 0.00827315719269327 0.0165463143853865 0.991726842807307 198 0.00629874433611938 0.0125974886722388 0.993701255663881 199 0.00553150679118512 0.0110630135823702 0.994468493208815 200 0.00457784421607706 0.00915568843215412 0.995422155783923 201 0.00413196588969706 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0.00856833475999757 0.995715832620001 218 0.00338062632749029 0.00676125265498058 0.99661937367251 219 0.00286114278938317 0.00572228557876633 0.997138857210617 220 0.00198603627629227 0.00397207255258454 0.998013963723708 221 0.00148773829442331 0.00297547658884663 0.998512261705577 222 0.000994023790745994 0.00198804758149199 0.999005976209254 223 0.00068762752033979 0.00137525504067958 0.99931237247966 224 0.00048167838196872 0.000963356763937439 0.999518321618031 225 0.000302235680795473 0.000604471361590945 0.999697764319205 226 0.000827624618855564 0.00165524923771113 0.999172375381144 227 0.00060393761180431 0.00120787522360862 0.999396062388196 228 0.000399444985623957 0.000798889971247915 0.999600555014376 229 0.00026740785399305 0.000534815707986101 0.999732592146007 230 0.000168169590524875 0.000336339181049751 0.999831830409475 231 0.000177259770253977 0.000354519540507954 0.999822740229746 232 0.00073477502799852 0.00146955005599704 0.999265224972001 233 0.00358082830930928 0.00716165661861855 0.996419171690691 234 0.00592406133672894 0.0118481226734579 0.994075938663271 235 0.00374402940767164 0.00748805881534327 0.996255970592328 236 0.00261534816696455 0.0052306963339291 0.997384651833035 237 0.037818387084205 0.07563677416841 0.962181612915795 238 0.0259026736009734 0.0518053472019469 0.974097326399027 239 0.0175292997370702 0.0350585994741404 0.98247070026293 240 0.0119394592826401 0.0238789185652801 0.98806054071736 241 0.00976945343009399 0.019538906860188 0.990230546569906 242 0.014045999210174 0.0280919984203481 0.985954000789826 243 0.00947556015193659 0.0189511203038732 0.990524439848063 244 0.00919401049465485 0.0183880209893097 0.990805989505345 245 0.0059719211681674 0.0119438423363348 0.994028078831833 246 0.00382792350694076 0.00765584701388152 0.996172076493059 247 0.00145093137205649 0.00290186274411298 0.998549068627943

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 42 0.181818181818182 NOK 5% type I error level 103 0.445887445887446 NOK 10% type I error level 135 0.584415584415584 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 & 42 & 0.181818181818182 & NOK \tabularnewline
5% type I error level & 103 & 0.445887445887446 & NOK \tabularnewline
10% type I error level & 135 & 0.584415584415584 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185702&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]42[/C][C]0.181818181818182[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]103[/C][C]0.445887445887446[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]135[/C][C]0.584415584415584[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185702&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185702&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 42 0.181818181818182 NOK 5% type I error level 103 0.445887445887446 NOK 10% type I error level 135 0.584415584415584 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}