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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 01 Apr 2012 10:08:22 -0400

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Apr/01/t13332896125nrdspqmhk1uhut.htm/, Retrieved Wed, 01 May 2024 11:31:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=164243, Retrieved Wed, 01 May 2024 11:31:33 +0000

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No (this computation is public)

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Estimated Impact

235

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

-       [Multiple Regression] [LNDEFQPILS] [2012-04-01 14:08:22] [c897fb90cb9e1f725365d7e541ad7850] [Current]

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19.72	4.66	5.35	5.31	5.04	5.41	5.5	4.44	4.25	4.24	4.73	5.03	20.46	20.58
19.65	4.67	5.37	5.31	5.05	5.42	5.51	4.45	4.26	4.26	4.75	5.06	20.39	20.71
19.59	4.65	5.35	5.31	5.03	5.4	5.49	4.44	4.26	4.25	4.73	5.03	20.32	20.52
19.59	4.65	5.34	5.28	5.04	5.41	5.49	4.45	4.24	4.24	4.75	5.03	20.32	20.37
19.55	4.64	5.34	5.27	5.03	5.41	5.49	4.43	4.25	4.24	4.74	5.05	20.27	20.32
19.61	4.64	5.32	5.28	5.01	5.4	5.47	4.41	4.24	4.24	4.69	5.04	20.33	20.29
19.57	4.66	5.35	5.29	5.03	5.42	5.48	4.44	4.25	4.25	4.75	5.07	20.29	20.39
19.55	4.65	5.34	5.25	5.03	5.41	5.48	4.44	4.23	4.24	4.75	5.07	20.29	20.41
19.57	4.64	5.35	5.28	5.04	5.42	5.5	4.45	4.23	4.23	4.79	5.04	20.27	20.42
19.51	4.63	5.32	5.3	5.01	5.43	5.5	4.43	4.23	4.23	4.77	5.04	20.25	20.37
19.48	4.63	5.33	5.31	5.01	5.47	5.52	4.43	4.23	4.24	4.74	5.04	20.25	20.37
19.49	4.63	5.33	5.26	4.99	5.45	5.48	4.43	4.24	4.22	4.73	5.03	20.27	20.35
19.58	4.62	5.31	5.28	4.99	5.43	5.44	4.42	4.23	4.23	4.75	5.04	20.33	20.35
19.48	4.63	5.33	5.27	5.02	5.43	5.48	4.44	4.23	4.24	4.77	5.05	20.24	20.33
19.46	4.64	5.3	5.27	5.02	5.44	5.5	4.4	4.22	4.21	4.76	5.03	20.23	20.29
19.48	4.63	5.3	5.28	5.02	5.44	5.53	4.4	4.22	4.23	4.77	5.04	20.25	20.3
19.49	4.64	5.31	5.35	5.03	5.49	5.59	4.42	4.21	4.21	4.77	5.04	20.28	20.41
19.44	4.63	5.3	5.34	5.01	5.48	5.59	4.42	4.19	4.2	4.74	5.03	20.27	20.34
19.57	4.63	5.3	5.35	5.01	5.49	5.61	4.41	4.17	4.17	4.71	5.04	20.48	20.51
19.5	4.64	5.33	5.35	5.03	5.48	5.59	4.42	4.19	4.19	4.72	5.04	20.36	20.44
19.34	4.63	5.3	5.33	5.03	5.45	5.55	4.41	4.24	4.22	4.74	5.04	20.13	20.3
19.4	4.67	5.34	5.35	5.05	5.47	5.54	4.45	4.28	4.27	4.78	5.04	20.15	20.43
19.4	4.67	5.35	5.33	5.04	5.47	5.53	4.46	4.27	4.26	4.77	5.06	20.15	20.4
19.43	4.66	5.31	5.23	5.04	5.44	5.51	4.45	4.24	4.23	4.76	5.04	20.19	20.34
19.44	4.66	5.32	5.27	5.06	5.46	5.51	4.47	4.23	4.23	4.74	5.03	20.19	20.42
19.49	4.64	5.31	5.23	5.03	5.42	5.5	4.46	4.25	4.25	4.72	5.02	20.24	20.41
19.48	4.65	5.32	5.26	5.03	5.43	5.51	4.45	4.27	4.27	4.75	5.02	20.22	20.41
19.48	4.64	5.31	5.27	5.03	5.43	5.47	4.44	4.25	4.25	4.74	5.02	20.23	20.35
19.45	4.65	5.33	5.27	5.01	5.44	5.49	4.45	4.26	4.25	4.75	5.03	20.2	20.42
19.48	4.64	5.32	5.29	5.04	5.42	5.48	4.45	4.26	4.26	4.74	5.02	20.23	20.43
19.44	4.65	5.33	5.3	5.02	5.42	5.49	4.45	4.24	4.24	4.74	5.03	20.19	20.37
19.51	4.65	5.33	5.3	5.04	5.42	5.5	4.45	4.23	4.22	4.74	5.01	20.25	20.4
19.49	4.65	5.32	5.29	5.04	5.41	5.51	4.46	4.26	4.24	4.77	5.03	20.24	20.45
19.56	4.67	5.34	5.29	5.04	5.39	5.49	4.43	4.24	4.22	4.73	4.98	20.33	20.53
19.57	4.66	5.31	5.27	5.02	5.38	5.47	4.42	4.24	4.2	4.72	5	20.36	20.45
19.49	4.67	5.33	5.27	5.02	5.39	5.47	4.43	4.26	4.23	4.72	4.93	20.26	20.4
19.54	4.69	5.31	5.28	5	5.39	5.48	4.43	4.26	4.23	4.71	4.96	20.3	20.46
19.62	4.66	5.26	5.25	4.97	5.36	5.47	4.39	4.25	4.22	4.69	4.97	20.4	20.44
19.56	4.68	5.31	5.27	5	5.38	5.47	4.43	4.27	4.23	4.71	5.01	20.34	20.45
19.64	4.69	5.34	5.29	5.01	5.4	5.47	4.45	4.28	4.24	4.74	5.02	20.4	20.59
19.59	4.65	5.32	5.26	4.99	5.39	5.45	4.43	4.26	4.24	4.76	5	20.36	20.43
19.6	4.66	5.33	5.27	4.98	5.39	5.44	4.42	4.28	4.25	4.76	5.01	20.39	20.56
19.63	4.65	5.32	5.27	4.99	5.4	5.44	4.43	4.28	4.24	4.76	4.97	20.4	20.55
19.67	4.65	5.31	5.26	5	5.4	5.44	4.42	4.28	4.24	4.75	5.01	20.44	20.55
19.61	4.67	5.32	5.29	5	5.41	5.47	4.45	4.3	4.26	4.75	5.02	20.37	20.66
19.62	4.67	5.32	5.29	4.97	5.4	5.46	4.44	4.27	4.24	4.73	5	20.38	20.58
19.67	4.67	5.32	5.27	4.99	5.38	5.44	4.42	4.28	4.23	4.73	4.99	20.43	20.47
19.63	4.65	5.33	5.24	4.99	5.38	5.44	4.41	4.29	4.24	4.73	4.97	20.38	20.46
19.62	4.65	5.34	5.21	5.01	5.39	5.46	4.43	4.28	4.24	4.74	4.98	20.38	20.46
19.7	4.66	5.35	5.24	5.02	5.39	5.46	4.44	4.28	4.26	4.75	4.98	20.46	20.63
19.83	4.66	5.33	5.26	5	5.39	5.46	4.41	4.27	4.24	4.73	4.98	20.58	20.66
19.85	4.64	5.31	5.24	4.98	5.37	5.42	4.39	4.25	4.22	4.72	4.98	20.6	20.51
19.73	4.67	5.35	5.28	5.01	5.4	5.45	4.43	4.29	4.26	4.76	4.99	20.49	20.71
19.61	4.66	5.35	5.28	4.99	5.37	5.44	4.43	4.29	4.25	4.73	4.99	20.38	20.53
19.63	4.66	5.38	5.3	5.02	5.41	5.47	4.46	4.3	4.27	4.78	5.01	20.36	20.61
19.68	4.65	5.35	5.28	5.02	5.39	5.45	4.43	4.29	4.25	4.76	5	20.4	20.54
19.66	4.63	5.32	5.25	5.01	5.37	5.44	4.42	4.29	4.24	4.75	4.92	20.38	20.47
19.56	4.63	5.32	5.24	5	5.37	5.43	4.43	4.29	4.23	4.75	4.97	20.3	20.43
19.5	4.63	5.31	5.25	4.99	5.37	5.43	4.43	4.29	4.24	4.75	5.01	20.25	20.44
19.55	4.64	5.32	5.26	4.99	5.37	5.45	4.44	4.3	4.25	4.75	5.02	20.3	20.51
19.53	4.63	5.31	5.25	4.99	5.37	5.43	4.43	4.29	4.25	4.76	5.02	20.27	20.42
19.49	4.63	5.3	5.25	4.98	5.36	5.45	4.43	4.29	4.24	4.76	5.03	20.24	20.39
19.47	4.62	5.29	5.27	4.98	5.39	5.43	4.43	4.28	4.23	4.74	5.03	20.25	20.4
19.57	4.62	5.3	5.26	4.99	5.38	5.46	4.44	4.26	4.21	4.74	5.01	20.36	20.43
19.53	4.63	5.3	5.27	4.99	5.38	5.46	4.45	4.27	4.22	4.75	5.02	20.3	20.47
19.43	4.62	5.3	5.23	4.98	5.36	5.44	4.45	4.27	4.22	4.75	5.01	20.23	20.36
19.43	4.64	5.31	5.21	5.02	5.39	5.46	4.42	4.27	4.21	4.75	5.01	20.23	20.37
19.43	4.64	5.31	5.21	5.06	5.4	5.49	4.43	4.26	4.21	4.74	5.01	20.24	20.42
19.45	4.64	5.29	5.23	5.06	5.4	5.5	4.43	4.24	4.2	4.73	5.01	20.28	20.4
19.41	4.63	5.3	5.26	5.02	5.41	5.51	4.43	4.25	4.2	4.72	5.01	20.26	20.38
19.48	4.67	5.32	5.29	4.99	5.44	5.56	4.45	4.25	4.21	4.73	5.04	20.38	20.62
19.48	4.64	5.29	5.27	4.98	5.44	5.55	4.43	4.23	4.19	4.7	5.01	20.39	20.49
19.48	4.62	5.28	5.23	4.96	5.4	5.49	4.4	4.24	4.2	4.71	4.99	20.32	20.27
19.37	4.66	5.31	5.28	5.01	5.41	5.5	4.44	4.29	4.23	4.76	5.03	20.17	20.4
19.35	4.67	5.29	5.26	5	5.38	5.46	4.43	4.29	4.23	4.74	5.02	20.16	20.37
19.38	4.66	5.3	5.24	4.99	5.38	5.45	4.43	4.28	4.23	4.74	5.01	20.18	20.37
19.41	4.66	5.3	5.2	5	5.39	5.45	4.45	4.28	4.22	4.76	5.02	20.19	20.4
19.48	4.66	5.3	5.21	4.98	5.38	5.45	4.45	4.28	4.22	4.76	5.03	20.26	20.43
19.44	4.64	5.29	5.21	4.97	5.37	5.46	4.44	4.29	4.22	4.77	5.01	20.23	20.39
19.41	4.64	5.29	5.18	4.97	5.36	5.45	4.4	4.29	4.23	4.76	5.01	20.2	20.4
19.42	4.66	5.3	5.22	4.99	5.37	5.46	4.44	4.3	4.24	4.77	5	20.21	20.48
19.42	4.64	5.28	5.23	4.98	5.37	5.46	4.45	4.28	4.25	4.75	5.01	20.21	20.47
19.42	4.64	5.27	5.21	4.99	5.36	5.46	4.43	4.28	4.26	4.73	4.99	20.21	20.42
19.48	4.65	5.29	5.21	4.99	5.37	5.46	4.44	4.29	4.26	4.72	4.99	20.25	20.44
19.53	4.66	5.28	5.22	4.99	5.37	5.47	4.43	4.29	4.24	4.71	4.97	20.33	20.52
19.56	4.65	5.27	5.23	4.99	5.37	5.46	4.43	4.29	4.24	4.72	4.95	20.31	20.5
19.53	4.65	5.26	5.24	4.99	5.37	5.46	4.42	4.28	4.23	4.72	4.96	20.32	20.47
19.52	4.68	5.28	5.26	5.01	5.39	5.46	4.43	4.29	4.26	4.72	4.9	20.31	20.65
19.52	4.66	5.26	5.23	4.97	5.37	5.43	4.4	4.28	4.26	4.71	4.94	20.29	20.47
19.63	4.65	5.26	5.23	4.98	5.38	5.44	4.4	4.27	4.24	4.69	4.93	20.4	20.5
19.63	4.65	5.27	5.18	4.97	5.38	5.44	4.41	4.29	4.27	4.71	4.97	20.41	20.54
19.57	4.64	5.37	5.15	4.97	5.38	5.45	4.42	4.28	4.26	4.7	4.95	20.37	20.46
19.6	4.64	5.31	5.16	4.98	5.38	5.44	4.41	4.28	4.26	4.7	4.96	20.37	20.48
19.74	4.67	5.36	5.16	5.03	5.4	5.44	4.44	4.28	4.28	4.71	4.94	20.5	20.74
19.63	4.68	5.39	5.17	5.08	5.41	5.46	4.45	4.3	4.27	4.73	4.95	20.41	20.81
19.59	4.66	5.38	5.17	4.98	5.38	5.44	4.41	4.3	4.27	4.71	4.94	20.38	20.62
19.7	4.66	5.39	5.17	4.91	5.35	5.43	4.4	4.29	4.27	4.7	4.94	20.47	20.63
19.88	4.66	5.38	5.17	4.93	5.34	5.42	4.4	4.26	4.24	4.7	4.92	20.63	20.64
19.72	4.66	5.29	5.21	4.94	5.34	5.44	4.4	4.3	4.26	4.72	4.96	20.49	20.55
19.62	4.65	5.27	5.23	4.93	5.33	5.44	4.4	4.33	4.29	4.77	4.99	20.4	20.73
19.78	4.65	5.28	5.22	4.94	5.33	5.42	4.4	4.32	4.27	4.74	4.97	20.56	20.75
19.61	4.64	5.31	5.22	4.94	5.33	5.4	4.4	4.32	4.25	4.73	4.97	20.4	20.56
19.7	4.67	5.34	5.24	4.99	5.36	5.45	4.4	4.33	4.27	4.76	5.01	20.48	20.85
19.65	4.66	5.33	5.24	4.99	5.37	5.45	4.4	4.35	4.29	4.78	5	20.44	21
19.61	4.64	5.31	5.24	4.99	5.35	5.44	4.39	4.34	4.26	4.76	4.99	20.38	20.7
19.62	4.64	5.3	5.22	4.98	5.34	5.43	4.39	4.32	4.22	4.72	4.99	20.4	20.56
19.58	4.67	5.32	5.19	5.02	5.37	5.46	4.43	4.33	4.25	4.75	5	20.37	20.6
19.69	4.64	5.3	5.14	5	5.32	5.44	4.39	4.31	4.24	4.72	5	20.47	20.55
19.63	4.62	5.27	5.13	4.99	5.32	5.42	4.38	4.31	4.24	4.71	5	20.4	20.49
19.54	4.61	5.27	5.19	4.98	5.33	5.43	4.4	4.35	4.28	4.72	5.05	20.3	20.63
19.56	4.61	5.28	5.21	5.01	5.32	5.43	4.42	4.34	4.26	4.71	5.06	20.3	20.56
19.55	4.62	5.29	5.23	5.03	5.34	5.43	4.44	4.32	4.25	4.74	5.07	20.3	20.57
19.49	4.62	5.27	5.21	5.03	5.33	5.42	4.43	4.32	4.24	4.72	5.06	20.25	20.45
19.53	4.62	5.26	5.23	5.04	5.35	5.43	4.44	4.32	4.25	4.74	5.06	20.31	20.49
19.48	4.6	5.24	5.21	5.03	5.35	5.45	4.42	4.29	4.22	4.71	5.04	20.29	20.38
19.58	4.6	5.24	5.23	5.02	5.35	5.43	4.42	4.29	4.21	4.71	5.05	20.37	20.46
19.48	4.61	5.26	5.22	5.03	5.33	5.43	4.43	4.3	4.23	4.73	5.05	20.28	20.5
19.46	4.6	5.23	5.14	5.01	5.32	5.42	4.41	4.3	4.24	4.72	5.04	20.27	20.41
19.45	4.61	5.25	5.01	5.03	5.33	5.44	4.4	4.31	4.23	4.72	5.03	20.29	20.41
19.39	4.63	5.27	5.1	5.05	5.36	5.48	4.41	4.32	4.24	4.72	5.04	20.22	20.47
19.46	4.63	5.29	5.12	5.04	5.36	5.5	4.41	4.3	4.22	4.73	5.05	20.29	20.47
19.41	4.62	5.29	5.18	5.05	5.36	5.53	4.43	4.29	4.22	4.69	5.03	20.27	20.42
19.45	4.64	5.3	5.26	5.06	5.41	5.55	4.44	4.28	4.22	4.67	5.05	20.35	20.64
19.52	4.6	5.27	5.25	5.02	5.4	5.54	4.4	4.23	4.18	4.62	4.99	20.47	20.47
19.47	4.6	5.27	5.23	5.02	5.36	5.48	4.39	4.25	4.18	4.63	4.99	20.37	20.34
19.37	4.61	5.3	5.25	5.05	5.37	5.48	4.4	4.31	4.22	4.75	5.02	20.15	20.46
19.37	4.6	5.27	5.25	5.04	5.36	5.47	4.4	4.31	4.24	4.76	5.02	20.14	20.42
19.4	4.61	5.31	5.25	5.03	5.35	5.47	4.42	4.31	4.23	4.75	5.01	20.17	20.42
19.42	4.61	5.32	5.25	5.05	5.37	5.47	4.44	4.3	4.22	4.78	5.03	20.18	20.47
19.45	4.61	5.3	5.23	5.04	5.36	5.46	4.43	4.31	4.23	4.79	5.02	20.23	20.52

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164243&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164243&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164243&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	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
LNDEFQPILS[t] = -4.91994815032968 + 0.178002134897566LNDEFPBEPIL[t] + 0.223446414184666LNDEFPBELUX[t] + 0.277900233886634LNDEFPBEABD[t] + 0.43352652473908LNDEFPBEWIT[t] -0.395586304850592LNDEFPBEZWB[t] -0.489088590001187LNDEFPBEREG[t] -0.116343477361705LNDEFPBETAF[t] -0.42192305910742LNDEFPSOORA[t] + 1.23160559784662LNDEFPSOLEM[t] + 0.501220960643375LNDEFPICET[t] -0.0132509861088801LNDEFPSPORT[t] + 1.08061028065186LNDEFBUDBEER[t] -0.172720011662728`LNDEFBUDSISSS `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LNDEFQPILS[t] =  -4.91994815032968 +  0.178002134897566LNDEFPBEPIL[t] +  0.223446414184666LNDEFPBELUX[t] +  0.277900233886634LNDEFPBEABD[t] +  0.43352652473908LNDEFPBEWIT[t] -0.395586304850592LNDEFPBEZWB[t] -0.489088590001187LNDEFPBEREG[t] -0.116343477361705LNDEFPBETAF[t] -0.42192305910742LNDEFPSOORA[t] +  1.23160559784662LNDEFPSOLEM[t] +  0.501220960643375LNDEFPICET[t] -0.0132509861088801LNDEFPSPORT[t] +  1.08061028065186LNDEFBUDBEER[t] -0.172720011662728`LNDEFBUDSISSS
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164243&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LNDEFQPILS[t] =  -4.91994815032968 +  0.178002134897566LNDEFPBEPIL[t] +  0.223446414184666LNDEFPBELUX[t] +  0.277900233886634LNDEFPBEABD[t] +  0.43352652473908LNDEFPBEWIT[t] -0.395586304850592LNDEFPBEZWB[t] -0.489088590001187LNDEFPBEREG[t] -0.116343477361705LNDEFPBETAF[t] -0.42192305910742LNDEFPSOORA[t] +  1.23160559784662LNDEFPSOLEM[t] +  0.501220960643375LNDEFPICET[t] -0.0132509861088801LNDEFPSPORT[t] +  1.08061028065186LNDEFBUDBEER[t] -0.172720011662728`LNDEFBUDSISSS
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164243&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164243&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
LNDEFQPILS[t] = -4.91994815032968 + 0.178002134897566LNDEFPBEPIL[t] + 0.223446414184666LNDEFPBELUX[t] + 0.277900233886634LNDEFPBEABD[t] + 0.43352652473908LNDEFPBEWIT[t] -0.395586304850592LNDEFPBEZWB[t] -0.489088590001187LNDEFPBEREG[t] -0.116343477361705LNDEFPBETAF[t] -0.42192305910742LNDEFPSOORA[t] + 1.23160559784662LNDEFPSOLEM[t] + 0.501220960643375LNDEFPICET[t] -0.0132509861088801LNDEFPSPORT[t] + 1.08061028065186LNDEFBUDBEER[t] -0.172720011662728`LNDEFBUDSISSS `[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)	-4.91994815032968	1.401569	-3.5103	0.000638	0.000319
LNDEFPBEPIL	0.178002134897566	0.121028	1.4708	0.144065	0.072033
LNDEFPBELUX	0.223446414184666	0.076675	2.9142	0.00428	0.00214
LNDEFPBEABD	0.277900233886634	0.047252	5.8813	0	0
LNDEFPBEWIT	0.43352652473908	0.076338	5.679	0	0
LNDEFPBEZWB	-0.395586304850592	0.126108	-3.1369	0.002164	0.001082
LNDEFPBEREG	-0.489088590001187	0.098559	-4.9624	2e-06	1e-06
LNDEFPBETAF	-0.116343477361705	0.127161	-0.9149	0.362127	0.181063
LNDEFPSOORA	-0.42192305910742	0.145445	-2.9009	0.004453	0.002226
LNDEFPSOLEM	1.23160559784662	0.125274	9.8313	0	0
LNDEFPICET	0.501220960643375	0.08636	5.8038	0	0
LNDEFPSPORT	-0.0132509861088801	0.06816	-0.1944	0.846194	0.423097
LNDEFBUDBEER	1.08061028065186	0.038231	28.2654	0	0
`LNDEFBUDSISSS `	-0.172720011662728	0.034468	-5.011	2e-06	1e-06

\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) & -4.91994815032968 & 1.401569 & -3.5103 & 0.000638 & 0.000319 \tabularnewline
LNDEFPBEPIL & 0.178002134897566 & 0.121028 & 1.4708 & 0.144065 & 0.072033 \tabularnewline
LNDEFPBELUX & 0.223446414184666 & 0.076675 & 2.9142 & 0.00428 & 0.00214 \tabularnewline
LNDEFPBEABD & 0.277900233886634 & 0.047252 & 5.8813 & 0 & 0 \tabularnewline
LNDEFPBEWIT & 0.43352652473908 & 0.076338 & 5.679 & 0 & 0 \tabularnewline
LNDEFPBEZWB & -0.395586304850592 & 0.126108 & -3.1369 & 0.002164 & 0.001082 \tabularnewline
LNDEFPBEREG & -0.489088590001187 & 0.098559 & -4.9624 & 2e-06 & 1e-06 \tabularnewline
LNDEFPBETAF & -0.116343477361705 & 0.127161 & -0.9149 & 0.362127 & 0.181063 \tabularnewline
LNDEFPSOORA & -0.42192305910742 & 0.145445 & -2.9009 & 0.004453 & 0.002226 \tabularnewline
LNDEFPSOLEM & 1.23160559784662 & 0.125274 & 9.8313 & 0 & 0 \tabularnewline
LNDEFPICET & 0.501220960643375 & 0.08636 & 5.8038 & 0 & 0 \tabularnewline
LNDEFPSPORT & -0.0132509861088801 & 0.06816 & -0.1944 & 0.846194 & 0.423097 \tabularnewline
LNDEFBUDBEER & 1.08061028065186 & 0.038231 & 28.2654 & 0 & 0 \tabularnewline
`LNDEFBUDSISSS
` & -0.172720011662728 & 0.034468 & -5.011 & 2e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164243&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]-4.91994815032968[/C][C]1.401569[/C][C]-3.5103[/C][C]0.000638[/C][C]0.000319[/C][/ROW]
[ROW][C]LNDEFPBEPIL[/C][C]0.178002134897566[/C][C]0.121028[/C][C]1.4708[/C][C]0.144065[/C][C]0.072033[/C][/ROW]
[ROW][C]LNDEFPBELUX[/C][C]0.223446414184666[/C][C]0.076675[/C][C]2.9142[/C][C]0.00428[/C][C]0.00214[/C][/ROW]
[ROW][C]LNDEFPBEABD[/C][C]0.277900233886634[/C][C]0.047252[/C][C]5.8813[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LNDEFPBEWIT[/C][C]0.43352652473908[/C][C]0.076338[/C][C]5.679[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LNDEFPBEZWB[/C][C]-0.395586304850592[/C][C]0.126108[/C][C]-3.1369[/C][C]0.002164[/C][C]0.001082[/C][/ROW]
[ROW][C]LNDEFPBEREG[/C][C]-0.489088590001187[/C][C]0.098559[/C][C]-4.9624[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]LNDEFPBETAF[/C][C]-0.116343477361705[/C][C]0.127161[/C][C]-0.9149[/C][C]0.362127[/C][C]0.181063[/C][/ROW]
[ROW][C]LNDEFPSOORA[/C][C]-0.42192305910742[/C][C]0.145445[/C][C]-2.9009[/C][C]0.004453[/C][C]0.002226[/C][/ROW]
[ROW][C]LNDEFPSOLEM[/C][C]1.23160559784662[/C][C]0.125274[/C][C]9.8313[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LNDEFPICET[/C][C]0.501220960643375[/C][C]0.08636[/C][C]5.8038[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LNDEFPSPORT[/C][C]-0.0132509861088801[/C][C]0.06816[/C][C]-0.1944[/C][C]0.846194[/C][C]0.423097[/C][/ROW]
[ROW][C]LNDEFBUDBEER[/C][C]1.08061028065186[/C][C]0.038231[/C][C]28.2654[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`LNDEFBUDSISSS
`[/C][C]-0.172720011662728[/C][C]0.034468[/C][C]-5.011[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164243&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164243&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)	-4.91994815032968	1.401569	-3.5103	0.000638	0.000319
LNDEFPBEPIL	0.178002134897566	0.121028	1.4708	0.144065	0.072033
LNDEFPBELUX	0.223446414184666	0.076675	2.9142	0.00428	0.00214
LNDEFPBEABD	0.277900233886634	0.047252	5.8813	0	0
LNDEFPBEWIT	0.43352652473908	0.076338	5.679	0	0
LNDEFPBEZWB	-0.395586304850592	0.126108	-3.1369	0.002164	0.001082
LNDEFPBEREG	-0.489088590001187	0.098559	-4.9624	2e-06	1e-06
LNDEFPBETAF	-0.116343477361705	0.127161	-0.9149	0.362127	0.181063
LNDEFPSOORA	-0.42192305910742	0.145445	-2.9009	0.004453	0.002226
LNDEFPSOLEM	1.23160559784662	0.125274	9.8313	0	0
LNDEFPICET	0.501220960643375	0.08636	5.8038	0	0
LNDEFPSPORT	-0.0132509861088801	0.06816	-0.1944	0.846194	0.423097
LNDEFBUDBEER	1.08061028065186	0.038231	28.2654	0	0
`LNDEFBUDSISSS `	-0.172720011662728	0.034468	-5.011	2e-06	1e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.98598255112275
R-squared	0.972161591118526
Adjusted R-squared	0.969041769433533
F-TEST (value)	311.608062664256
F-TEST (DF numerator)	13
F-TEST (DF denominator)	116
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0187328523825387
Sum Squared Residuals	0.0407066919727743

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.98598255112275 \tabularnewline
R-squared & 0.972161591118526 \tabularnewline
Adjusted R-squared & 0.969041769433533 \tabularnewline
F-TEST (value) & 311.608062664256 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 116 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0187328523825387 \tabularnewline
Sum Squared Residuals & 0.0407066919727743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164243&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.98598255112275[/C][/ROW]
[ROW][C]R-squared[/C][C]0.972161591118526[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.969041769433533[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]311.608062664256[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]116[/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]0.0187328523825387[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0407066919727743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164243&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164243&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	0.98598255112275
R-squared	0.972161591118526
Adjusted R-squared	0.969041769433533
F-TEST (value)	311.608062664256
F-TEST (DF numerator)	13
F-TEST (DF denominator)	116
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0187328523825387
Sum Squared Residuals	0.0407066919727743

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	19.72	19.7065957665665	0.0134042334335293
2	19.65	19.6391132475581	0.0108867524419381
3	19.59	19.5765018157145	0.013498184285492
4	19.59	19.5972011381473	-0.00720113814728906
5	19.55	19.5357427453901	0.0142572546098533
6	19.61	19.5907557029553	0.01924429704471
7	19.57	19.5734518956449	-0.00345189564493437
8	19.55	19.5549452688176	-0.0049452688175801
9	19.57	19.5379618205532	0.0320381794468341
10	19.51	19.4974009979702	0.0125990020298304
11	19.48	19.474088667616	0.00591133238399862
12	19.49	19.4703339586769	0.0196660413231116
13	19.58	19.5895455309531	-0.00954553095305885
14	19.48	19.5125383011466	-0.0325383011465818
15	19.46	19.4571576047474	0.00284239525263243
16	19.48	19.4928807452359	-0.0128807452359101
17	19.49	19.4622382379272	0.0277617620728468
18	19.44	19.4332326669065	0.00676733309347315
19	19.57	19.5773243806911	-0.00732438069106853
20	19.5	19.5106755525547	-0.0106755525546637
21	19.34	19.3307455728973	0.00925442710265383
22	19.4	19.4172682707923	-0.0172682707923337
23	19.4	19.4051444617693	-0.00514446176933068
24	19.43	19.4139993020629	0.0160006979371281
25	19.44	19.406291430717	0.033708569283005
26	19.49	19.4603123472194	0.029687652780563
27	19.48	19.474598599533	0.00540140046699587
28	19.48	19.4940535378798	-0.0140535378797789
29	19.45	19.4328826473138	0.0171173526861571
30	19.48	19.498362038776	-0.0183620387759996
31	19.44	19.4424067390487	-0.00240673904871145
32	19.51	19.485693538489	0.0243064615109711
33	19.49	19.4858855402744	0.00411445972562681
34	19.56	19.5629556979045	-0.00295569790447757
35	19.57	19.5714713866466	-0.00147138664662472
36	19.49	19.502613286756	-0.0126132867559977
37	19.54	19.5183734584509	0.0216265415490627
38	19.62	19.5951921589131	0.0248078410869093
39	19.56	19.5627310151578	-0.0027310151577964
40	19.64	19.6345258627651	0.00547413723488013
41	19.59	19.6251325067271	-0.0351325067270646
42	19.6	19.6473548418216	-0.0473548418216207
43	19.63	19.6433036101451	-0.0133036101451011
44	19.67	19.6814710058607	-0.0114710058606815
45	19.61	19.5949029146329	0.0150970853671472
46	19.62	19.594797186678	0.0252028133220504
47	19.67	19.6745570185463	-0.00455701854630984
48	19.63	19.6221163989408	0.00788360105917136
49	19.62	19.6177188125015	0.00228118749848887
50	19.7	19.7199728775156	-0.0199728775155605
51	19.83	19.8099360604853	0.0200639395147155
52	19.85	19.8437950405566	0.00620495944335334
53	19.73	19.7498932772385	-0.0198932772385377
54	19.61	19.6410709868701	-0.0310709868701315
55	19.63	19.6421308686305	-0.0121308686304605
56	19.68	19.6742832737209	0.00571672627907384
57	19.66	19.6395236216113	0.0204763783887497
58	19.56	19.5436181778818	0.016381822118158
59	19.5	19.4958557532164	0.00414424678364119
60	19.55	19.5416084632149	0.00839153678508849
61	19.53	19.5381181147865	-0.00811811478645969
62	19.49	19.4860372027365	0.00396279726352277
63	19.47	19.4764525626675	-0.00645256266752785
64	19.57	19.566119959762	0.00388004023803424
65	19.53	19.5107466565033	0.0192533434966928
66	19.43	19.4546978499469	-0.0246978499468706
67	19.43	19.4400731003787	-0.0100731003787152
68	19.43	19.4389993290541	-0.00899932905413983
69	19.45	19.4729865266047	-0.0229865266047493
70	19.41	19.4282009208988	-0.0182009208988469
71	19.48	19.5016234245409	-0.0216234245408573
72	19.48	19.4893314079024	-0.00933140790236955
73	19.48	19.4881391707634	-0.00813917076344308
74	19.37	19.3798713784271	-0.00987137842709404
75	19.35	19.384367357278	-0.0343673572780176
76	19.38	19.405783362111	-0.0257833621109792
77	19.41	19.3959202412371	0.0140797587628862
78	19.48	19.4643131855644	0.0156868144356361
79	19.44	19.4299603161838	0.0100396838162259
80	19.41	19.408282135046	0.00171786495398865
81	19.42	19.4305927404215	-0.0105927404214851
82	19.42	19.432168859961	-0.0121688599609848
83	19.42	19.4461870460554	-0.0261870460554481
84	19.48	19.4778552686613	0.00214473133869359
85	19.53	19.5197042967588	0.0102957032411525
86	19.56	19.5104791234557	0.0495208765442652
87	19.53	19.5199454643343	0.0100545356657092
88	19.52	19.5265361225718	-0.006536122571843
89	19.52	19.5270581497285	-0.00705814972847433
90	19.63	19.6041473844826	0.0258526155174415
91	19.63	19.6288895257708	0.00111047422916233
92	19.57	19.5928119925854	-0.0228119925853963
93	19.6	19.5889868858999	0.011013114100142
94	19.74	19.7412550412131	-0.00125504121308813
95	19.63	19.6390847800574	-0.00908478005738216
96	19.59	19.6067473052283	-0.0167473052282672
97	19.7	19.6912915685842	0.00870843141579338
98	19.88	19.8537193722333	0.0262806277666678
99	19.72	19.7207876288791	-0.000787628879076384
100	19.62	19.6403267489784	-0.0203267489783727
101	19.78	19.7881580020368	-0.00815800203682914
102	19.61	19.6331379806617	-0.0231379806616738
103	19.7	19.707373239214	-0.00737323921401315
104	19.65	19.6566210575479	-0.00662105754794452
105	19.61	19.6069166733005	0.00308332669945932
106	19.62	19.5885540942705	0.0314459057295456
107	19.58	19.5844791020505	-0.00447910205049416
108	19.69	19.6841021987005	0.00589780129951973
109	19.63	19.6073779740121	0.0226220259879248
110	19.54	19.5112582151057	0.0287417848942532
111	19.56	19.5202182731518	0.0397817268482197
112	19.55	19.5375220222161	0.0124779777838633
113	19.49	19.4819931950135	0.00800680498646357
114	19.53	19.5559542455901	-0.025954245590105
115	19.48	19.4889020128411	-0.00890201284111112
116	19.58	19.5600891797511	0.0199108202489435
117	19.48	19.5009162584692	-0.0209162584691928
118	19.46	19.484878968461	-0.0248789684609617
119	19.45	19.4563066470128	-0.00630664701282448
120	19.39	19.3873809972008	0.00261900279922772
121	19.46	19.447619661731	0.0123803382690245
122	19.41	19.4213085992729	-0.0113085992729353
123	19.45	19.4653260797973	-0.0153260797972649
124	19.52	19.5514846025271	-0.0314846025271332
125	19.47	19.5032251220924	-0.0332251220923694
126	19.37	19.3503902013831	0.019609798616916
127	19.37	19.3721652905331	-0.00216529053305454
128	19.4	19.395399449399	0.00460055060095283
129	19.42	19.4049107343245	0.0150892656754626
130	19.45	19.4591947781422	-0.00919477814223051

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 19.72 & 19.7065957665665 & 0.0134042334335293 \tabularnewline
2 & 19.65 & 19.6391132475581 & 0.0108867524419381 \tabularnewline
3 & 19.59 & 19.5765018157145 & 0.013498184285492 \tabularnewline
4 & 19.59 & 19.5972011381473 & -0.00720113814728906 \tabularnewline
5 & 19.55 & 19.5357427453901 & 0.0142572546098533 \tabularnewline
6 & 19.61 & 19.5907557029553 & 0.01924429704471 \tabularnewline
7 & 19.57 & 19.5734518956449 & -0.00345189564493437 \tabularnewline
8 & 19.55 & 19.5549452688176 & -0.0049452688175801 \tabularnewline
9 & 19.57 & 19.5379618205532 & 0.0320381794468341 \tabularnewline
10 & 19.51 & 19.4974009979702 & 0.0125990020298304 \tabularnewline
11 & 19.48 & 19.474088667616 & 0.00591133238399862 \tabularnewline
12 & 19.49 & 19.4703339586769 & 0.0196660413231116 \tabularnewline
13 & 19.58 & 19.5895455309531 & -0.00954553095305885 \tabularnewline
14 & 19.48 & 19.5125383011466 & -0.0325383011465818 \tabularnewline
15 & 19.46 & 19.4571576047474 & 0.00284239525263243 \tabularnewline
16 & 19.48 & 19.4928807452359 & -0.0128807452359101 \tabularnewline
17 & 19.49 & 19.4622382379272 & 0.0277617620728468 \tabularnewline
18 & 19.44 & 19.4332326669065 & 0.00676733309347315 \tabularnewline
19 & 19.57 & 19.5773243806911 & -0.00732438069106853 \tabularnewline
20 & 19.5 & 19.5106755525547 & -0.0106755525546637 \tabularnewline
21 & 19.34 & 19.3307455728973 & 0.00925442710265383 \tabularnewline
22 & 19.4 & 19.4172682707923 & -0.0172682707923337 \tabularnewline
23 & 19.4 & 19.4051444617693 & -0.00514446176933068 \tabularnewline
24 & 19.43 & 19.4139993020629 & 0.0160006979371281 \tabularnewline
25 & 19.44 & 19.406291430717 & 0.033708569283005 \tabularnewline
26 & 19.49 & 19.4603123472194 & 0.029687652780563 \tabularnewline
27 & 19.48 & 19.474598599533 & 0.00540140046699587 \tabularnewline
28 & 19.48 & 19.4940535378798 & -0.0140535378797789 \tabularnewline
29 & 19.45 & 19.4328826473138 & 0.0171173526861571 \tabularnewline
30 & 19.48 & 19.498362038776 & -0.0183620387759996 \tabularnewline
31 & 19.44 & 19.4424067390487 & -0.00240673904871145 \tabularnewline
32 & 19.51 & 19.485693538489 & 0.0243064615109711 \tabularnewline
33 & 19.49 & 19.4858855402744 & 0.00411445972562681 \tabularnewline
34 & 19.56 & 19.5629556979045 & -0.00295569790447757 \tabularnewline
35 & 19.57 & 19.5714713866466 & -0.00147138664662472 \tabularnewline
36 & 19.49 & 19.502613286756 & -0.0126132867559977 \tabularnewline
37 & 19.54 & 19.5183734584509 & 0.0216265415490627 \tabularnewline
38 & 19.62 & 19.5951921589131 & 0.0248078410869093 \tabularnewline
39 & 19.56 & 19.5627310151578 & -0.0027310151577964 \tabularnewline
40 & 19.64 & 19.6345258627651 & 0.00547413723488013 \tabularnewline
41 & 19.59 & 19.6251325067271 & -0.0351325067270646 \tabularnewline
42 & 19.6 & 19.6473548418216 & -0.0473548418216207 \tabularnewline
43 & 19.63 & 19.6433036101451 & -0.0133036101451011 \tabularnewline
44 & 19.67 & 19.6814710058607 & -0.0114710058606815 \tabularnewline
45 & 19.61 & 19.5949029146329 & 0.0150970853671472 \tabularnewline
46 & 19.62 & 19.594797186678 & 0.0252028133220504 \tabularnewline
47 & 19.67 & 19.6745570185463 & -0.00455701854630984 \tabularnewline
48 & 19.63 & 19.6221163989408 & 0.00788360105917136 \tabularnewline
49 & 19.62 & 19.6177188125015 & 0.00228118749848887 \tabularnewline
50 & 19.7 & 19.7199728775156 & -0.0199728775155605 \tabularnewline
51 & 19.83 & 19.8099360604853 & 0.0200639395147155 \tabularnewline
52 & 19.85 & 19.8437950405566 & 0.00620495944335334 \tabularnewline
53 & 19.73 & 19.7498932772385 & -0.0198932772385377 \tabularnewline
54 & 19.61 & 19.6410709868701 & -0.0310709868701315 \tabularnewline
55 & 19.63 & 19.6421308686305 & -0.0121308686304605 \tabularnewline
56 & 19.68 & 19.6742832737209 & 0.00571672627907384 \tabularnewline
57 & 19.66 & 19.6395236216113 & 0.0204763783887497 \tabularnewline
58 & 19.56 & 19.5436181778818 & 0.016381822118158 \tabularnewline
59 & 19.5 & 19.4958557532164 & 0.00414424678364119 \tabularnewline
60 & 19.55 & 19.5416084632149 & 0.00839153678508849 \tabularnewline
61 & 19.53 & 19.5381181147865 & -0.00811811478645969 \tabularnewline
62 & 19.49 & 19.4860372027365 & 0.00396279726352277 \tabularnewline
63 & 19.47 & 19.4764525626675 & -0.00645256266752785 \tabularnewline
64 & 19.57 & 19.566119959762 & 0.00388004023803424 \tabularnewline
65 & 19.53 & 19.5107466565033 & 0.0192533434966928 \tabularnewline
66 & 19.43 & 19.4546978499469 & -0.0246978499468706 \tabularnewline
67 & 19.43 & 19.4400731003787 & -0.0100731003787152 \tabularnewline
68 & 19.43 & 19.4389993290541 & -0.00899932905413983 \tabularnewline
69 & 19.45 & 19.4729865266047 & -0.0229865266047493 \tabularnewline
70 & 19.41 & 19.4282009208988 & -0.0182009208988469 \tabularnewline
71 & 19.48 & 19.5016234245409 & -0.0216234245408573 \tabularnewline
72 & 19.48 & 19.4893314079024 & -0.00933140790236955 \tabularnewline
73 & 19.48 & 19.4881391707634 & -0.00813917076344308 \tabularnewline
74 & 19.37 & 19.3798713784271 & -0.00987137842709404 \tabularnewline
75 & 19.35 & 19.384367357278 & -0.0343673572780176 \tabularnewline
76 & 19.38 & 19.405783362111 & -0.0257833621109792 \tabularnewline
77 & 19.41 & 19.3959202412371 & 0.0140797587628862 \tabularnewline
78 & 19.48 & 19.4643131855644 & 0.0156868144356361 \tabularnewline
79 & 19.44 & 19.4299603161838 & 0.0100396838162259 \tabularnewline
80 & 19.41 & 19.408282135046 & 0.00171786495398865 \tabularnewline
81 & 19.42 & 19.4305927404215 & -0.0105927404214851 \tabularnewline
82 & 19.42 & 19.432168859961 & -0.0121688599609848 \tabularnewline
83 & 19.42 & 19.4461870460554 & -0.0261870460554481 \tabularnewline
84 & 19.48 & 19.4778552686613 & 0.00214473133869359 \tabularnewline
85 & 19.53 & 19.5197042967588 & 0.0102957032411525 \tabularnewline
86 & 19.56 & 19.5104791234557 & 0.0495208765442652 \tabularnewline
87 & 19.53 & 19.5199454643343 & 0.0100545356657092 \tabularnewline
88 & 19.52 & 19.5265361225718 & -0.006536122571843 \tabularnewline
89 & 19.52 & 19.5270581497285 & -0.00705814972847433 \tabularnewline
90 & 19.63 & 19.6041473844826 & 0.0258526155174415 \tabularnewline
91 & 19.63 & 19.6288895257708 & 0.00111047422916233 \tabularnewline
92 & 19.57 & 19.5928119925854 & -0.0228119925853963 \tabularnewline
93 & 19.6 & 19.5889868858999 & 0.011013114100142 \tabularnewline
94 & 19.74 & 19.7412550412131 & -0.00125504121308813 \tabularnewline
95 & 19.63 & 19.6390847800574 & -0.00908478005738216 \tabularnewline
96 & 19.59 & 19.6067473052283 & -0.0167473052282672 \tabularnewline
97 & 19.7 & 19.6912915685842 & 0.00870843141579338 \tabularnewline
98 & 19.88 & 19.8537193722333 & 0.0262806277666678 \tabularnewline
99 & 19.72 & 19.7207876288791 & -0.000787628879076384 \tabularnewline
100 & 19.62 & 19.6403267489784 & -0.0203267489783727 \tabularnewline
101 & 19.78 & 19.7881580020368 & -0.00815800203682914 \tabularnewline
102 & 19.61 & 19.6331379806617 & -0.0231379806616738 \tabularnewline
103 & 19.7 & 19.707373239214 & -0.00737323921401315 \tabularnewline
104 & 19.65 & 19.6566210575479 & -0.00662105754794452 \tabularnewline
105 & 19.61 & 19.6069166733005 & 0.00308332669945932 \tabularnewline
106 & 19.62 & 19.5885540942705 & 0.0314459057295456 \tabularnewline
107 & 19.58 & 19.5844791020505 & -0.00447910205049416 \tabularnewline
108 & 19.69 & 19.6841021987005 & 0.00589780129951973 \tabularnewline
109 & 19.63 & 19.6073779740121 & 0.0226220259879248 \tabularnewline
110 & 19.54 & 19.5112582151057 & 0.0287417848942532 \tabularnewline
111 & 19.56 & 19.5202182731518 & 0.0397817268482197 \tabularnewline
112 & 19.55 & 19.5375220222161 & 0.0124779777838633 \tabularnewline
113 & 19.49 & 19.4819931950135 & 0.00800680498646357 \tabularnewline
114 & 19.53 & 19.5559542455901 & -0.025954245590105 \tabularnewline
115 & 19.48 & 19.4889020128411 & -0.00890201284111112 \tabularnewline
116 & 19.58 & 19.5600891797511 & 0.0199108202489435 \tabularnewline
117 & 19.48 & 19.5009162584692 & -0.0209162584691928 \tabularnewline
118 & 19.46 & 19.484878968461 & -0.0248789684609617 \tabularnewline
119 & 19.45 & 19.4563066470128 & -0.00630664701282448 \tabularnewline
120 & 19.39 & 19.3873809972008 & 0.00261900279922772 \tabularnewline
121 & 19.46 & 19.447619661731 & 0.0123803382690245 \tabularnewline
122 & 19.41 & 19.4213085992729 & -0.0113085992729353 \tabularnewline
123 & 19.45 & 19.4653260797973 & -0.0153260797972649 \tabularnewline
124 & 19.52 & 19.5514846025271 & -0.0314846025271332 \tabularnewline
125 & 19.47 & 19.5032251220924 & -0.0332251220923694 \tabularnewline
126 & 19.37 & 19.3503902013831 & 0.019609798616916 \tabularnewline
127 & 19.37 & 19.3721652905331 & -0.00216529053305454 \tabularnewline
128 & 19.4 & 19.395399449399 & 0.00460055060095283 \tabularnewline
129 & 19.42 & 19.4049107343245 & 0.0150892656754626 \tabularnewline
130 & 19.45 & 19.4591947781422 & -0.00919477814223051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164243&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]19.72[/C][C]19.7065957665665[/C][C]0.0134042334335293[/C][/ROW]
[ROW][C]2[/C][C]19.65[/C][C]19.6391132475581[/C][C]0.0108867524419381[/C][/ROW]
[ROW][C]3[/C][C]19.59[/C][C]19.5765018157145[/C][C]0.013498184285492[/C][/ROW]
[ROW][C]4[/C][C]19.59[/C][C]19.5972011381473[/C][C]-0.00720113814728906[/C][/ROW]
[ROW][C]5[/C][C]19.55[/C][C]19.5357427453901[/C][C]0.0142572546098533[/C][/ROW]
[ROW][C]6[/C][C]19.61[/C][C]19.5907557029553[/C][C]0.01924429704471[/C][/ROW]
[ROW][C]7[/C][C]19.57[/C][C]19.5734518956449[/C][C]-0.00345189564493437[/C][/ROW]
[ROW][C]8[/C][C]19.55[/C][C]19.5549452688176[/C][C]-0.0049452688175801[/C][/ROW]
[ROW][C]9[/C][C]19.57[/C][C]19.5379618205532[/C][C]0.0320381794468341[/C][/ROW]
[ROW][C]10[/C][C]19.51[/C][C]19.4974009979702[/C][C]0.0125990020298304[/C][/ROW]
[ROW][C]11[/C][C]19.48[/C][C]19.474088667616[/C][C]0.00591133238399862[/C][/ROW]
[ROW][C]12[/C][C]19.49[/C][C]19.4703339586769[/C][C]0.0196660413231116[/C][/ROW]
[ROW][C]13[/C][C]19.58[/C][C]19.5895455309531[/C][C]-0.00954553095305885[/C][/ROW]
[ROW][C]14[/C][C]19.48[/C][C]19.5125383011466[/C][C]-0.0325383011465818[/C][/ROW]
[ROW][C]15[/C][C]19.46[/C][C]19.4571576047474[/C][C]0.00284239525263243[/C][/ROW]
[ROW][C]16[/C][C]19.48[/C][C]19.4928807452359[/C][C]-0.0128807452359101[/C][/ROW]
[ROW][C]17[/C][C]19.49[/C][C]19.4622382379272[/C][C]0.0277617620728468[/C][/ROW]
[ROW][C]18[/C][C]19.44[/C][C]19.4332326669065[/C][C]0.00676733309347315[/C][/ROW]
[ROW][C]19[/C][C]19.57[/C][C]19.5773243806911[/C][C]-0.00732438069106853[/C][/ROW]
[ROW][C]20[/C][C]19.5[/C][C]19.5106755525547[/C][C]-0.0106755525546637[/C][/ROW]
[ROW][C]21[/C][C]19.34[/C][C]19.3307455728973[/C][C]0.00925442710265383[/C][/ROW]
[ROW][C]22[/C][C]19.4[/C][C]19.4172682707923[/C][C]-0.0172682707923337[/C][/ROW]
[ROW][C]23[/C][C]19.4[/C][C]19.4051444617693[/C][C]-0.00514446176933068[/C][/ROW]
[ROW][C]24[/C][C]19.43[/C][C]19.4139993020629[/C][C]0.0160006979371281[/C][/ROW]
[ROW][C]25[/C][C]19.44[/C][C]19.406291430717[/C][C]0.033708569283005[/C][/ROW]
[ROW][C]26[/C][C]19.49[/C][C]19.4603123472194[/C][C]0.029687652780563[/C][/ROW]
[ROW][C]27[/C][C]19.48[/C][C]19.474598599533[/C][C]0.00540140046699587[/C][/ROW]
[ROW][C]28[/C][C]19.48[/C][C]19.4940535378798[/C][C]-0.0140535378797789[/C][/ROW]
[ROW][C]29[/C][C]19.45[/C][C]19.4328826473138[/C][C]0.0171173526861571[/C][/ROW]
[ROW][C]30[/C][C]19.48[/C][C]19.498362038776[/C][C]-0.0183620387759996[/C][/ROW]
[ROW][C]31[/C][C]19.44[/C][C]19.4424067390487[/C][C]-0.00240673904871145[/C][/ROW]
[ROW][C]32[/C][C]19.51[/C][C]19.485693538489[/C][C]0.0243064615109711[/C][/ROW]
[ROW][C]33[/C][C]19.49[/C][C]19.4858855402744[/C][C]0.00411445972562681[/C][/ROW]
[ROW][C]34[/C][C]19.56[/C][C]19.5629556979045[/C][C]-0.00295569790447757[/C][/ROW]
[ROW][C]35[/C][C]19.57[/C][C]19.5714713866466[/C][C]-0.00147138664662472[/C][/ROW]
[ROW][C]36[/C][C]19.49[/C][C]19.502613286756[/C][C]-0.0126132867559977[/C][/ROW]
[ROW][C]37[/C][C]19.54[/C][C]19.5183734584509[/C][C]0.0216265415490627[/C][/ROW]
[ROW][C]38[/C][C]19.62[/C][C]19.5951921589131[/C][C]0.0248078410869093[/C][/ROW]
[ROW][C]39[/C][C]19.56[/C][C]19.5627310151578[/C][C]-0.0027310151577964[/C][/ROW]
[ROW][C]40[/C][C]19.64[/C][C]19.6345258627651[/C][C]0.00547413723488013[/C][/ROW]
[ROW][C]41[/C][C]19.59[/C][C]19.6251325067271[/C][C]-0.0351325067270646[/C][/ROW]
[ROW][C]42[/C][C]19.6[/C][C]19.6473548418216[/C][C]-0.0473548418216207[/C][/ROW]
[ROW][C]43[/C][C]19.63[/C][C]19.6433036101451[/C][C]-0.0133036101451011[/C][/ROW]
[ROW][C]44[/C][C]19.67[/C][C]19.6814710058607[/C][C]-0.0114710058606815[/C][/ROW]
[ROW][C]45[/C][C]19.61[/C][C]19.5949029146329[/C][C]0.0150970853671472[/C][/ROW]
[ROW][C]46[/C][C]19.62[/C][C]19.594797186678[/C][C]0.0252028133220504[/C][/ROW]
[ROW][C]47[/C][C]19.67[/C][C]19.6745570185463[/C][C]-0.00455701854630984[/C][/ROW]
[ROW][C]48[/C][C]19.63[/C][C]19.6221163989408[/C][C]0.00788360105917136[/C][/ROW]
[ROW][C]49[/C][C]19.62[/C][C]19.6177188125015[/C][C]0.00228118749848887[/C][/ROW]
[ROW][C]50[/C][C]19.7[/C][C]19.7199728775156[/C][C]-0.0199728775155605[/C][/ROW]
[ROW][C]51[/C][C]19.83[/C][C]19.8099360604853[/C][C]0.0200639395147155[/C][/ROW]
[ROW][C]52[/C][C]19.85[/C][C]19.8437950405566[/C][C]0.00620495944335334[/C][/ROW]
[ROW][C]53[/C][C]19.73[/C][C]19.7498932772385[/C][C]-0.0198932772385377[/C][/ROW]
[ROW][C]54[/C][C]19.61[/C][C]19.6410709868701[/C][C]-0.0310709868701315[/C][/ROW]
[ROW][C]55[/C][C]19.63[/C][C]19.6421308686305[/C][C]-0.0121308686304605[/C][/ROW]
[ROW][C]56[/C][C]19.68[/C][C]19.6742832737209[/C][C]0.00571672627907384[/C][/ROW]
[ROW][C]57[/C][C]19.66[/C][C]19.6395236216113[/C][C]0.0204763783887497[/C][/ROW]
[ROW][C]58[/C][C]19.56[/C][C]19.5436181778818[/C][C]0.016381822118158[/C][/ROW]
[ROW][C]59[/C][C]19.5[/C][C]19.4958557532164[/C][C]0.00414424678364119[/C][/ROW]
[ROW][C]60[/C][C]19.55[/C][C]19.5416084632149[/C][C]0.00839153678508849[/C][/ROW]
[ROW][C]61[/C][C]19.53[/C][C]19.5381181147865[/C][C]-0.00811811478645969[/C][/ROW]
[ROW][C]62[/C][C]19.49[/C][C]19.4860372027365[/C][C]0.00396279726352277[/C][/ROW]
[ROW][C]63[/C][C]19.47[/C][C]19.4764525626675[/C][C]-0.00645256266752785[/C][/ROW]
[ROW][C]64[/C][C]19.57[/C][C]19.566119959762[/C][C]0.00388004023803424[/C][/ROW]
[ROW][C]65[/C][C]19.53[/C][C]19.5107466565033[/C][C]0.0192533434966928[/C][/ROW]
[ROW][C]66[/C][C]19.43[/C][C]19.4546978499469[/C][C]-0.0246978499468706[/C][/ROW]
[ROW][C]67[/C][C]19.43[/C][C]19.4400731003787[/C][C]-0.0100731003787152[/C][/ROW]
[ROW][C]68[/C][C]19.43[/C][C]19.4389993290541[/C][C]-0.00899932905413983[/C][/ROW]
[ROW][C]69[/C][C]19.45[/C][C]19.4729865266047[/C][C]-0.0229865266047493[/C][/ROW]
[ROW][C]70[/C][C]19.41[/C][C]19.4282009208988[/C][C]-0.0182009208988469[/C][/ROW]
[ROW][C]71[/C][C]19.48[/C][C]19.5016234245409[/C][C]-0.0216234245408573[/C][/ROW]
[ROW][C]72[/C][C]19.48[/C][C]19.4893314079024[/C][C]-0.00933140790236955[/C][/ROW]
[ROW][C]73[/C][C]19.48[/C][C]19.4881391707634[/C][C]-0.00813917076344308[/C][/ROW]
[ROW][C]74[/C][C]19.37[/C][C]19.3798713784271[/C][C]-0.00987137842709404[/C][/ROW]
[ROW][C]75[/C][C]19.35[/C][C]19.384367357278[/C][C]-0.0343673572780176[/C][/ROW]
[ROW][C]76[/C][C]19.38[/C][C]19.405783362111[/C][C]-0.0257833621109792[/C][/ROW]
[ROW][C]77[/C][C]19.41[/C][C]19.3959202412371[/C][C]0.0140797587628862[/C][/ROW]
[ROW][C]78[/C][C]19.48[/C][C]19.4643131855644[/C][C]0.0156868144356361[/C][/ROW]
[ROW][C]79[/C][C]19.44[/C][C]19.4299603161838[/C][C]0.0100396838162259[/C][/ROW]
[ROW][C]80[/C][C]19.41[/C][C]19.408282135046[/C][C]0.00171786495398865[/C][/ROW]
[ROW][C]81[/C][C]19.42[/C][C]19.4305927404215[/C][C]-0.0105927404214851[/C][/ROW]
[ROW][C]82[/C][C]19.42[/C][C]19.432168859961[/C][C]-0.0121688599609848[/C][/ROW]
[ROW][C]83[/C][C]19.42[/C][C]19.4461870460554[/C][C]-0.0261870460554481[/C][/ROW]
[ROW][C]84[/C][C]19.48[/C][C]19.4778552686613[/C][C]0.00214473133869359[/C][/ROW]
[ROW][C]85[/C][C]19.53[/C][C]19.5197042967588[/C][C]0.0102957032411525[/C][/ROW]
[ROW][C]86[/C][C]19.56[/C][C]19.5104791234557[/C][C]0.0495208765442652[/C][/ROW]
[ROW][C]87[/C][C]19.53[/C][C]19.5199454643343[/C][C]0.0100545356657092[/C][/ROW]
[ROW][C]88[/C][C]19.52[/C][C]19.5265361225718[/C][C]-0.006536122571843[/C][/ROW]
[ROW][C]89[/C][C]19.52[/C][C]19.5270581497285[/C][C]-0.00705814972847433[/C][/ROW]
[ROW][C]90[/C][C]19.63[/C][C]19.6041473844826[/C][C]0.0258526155174415[/C][/ROW]
[ROW][C]91[/C][C]19.63[/C][C]19.6288895257708[/C][C]0.00111047422916233[/C][/ROW]
[ROW][C]92[/C][C]19.57[/C][C]19.5928119925854[/C][C]-0.0228119925853963[/C][/ROW]
[ROW][C]93[/C][C]19.6[/C][C]19.5889868858999[/C][C]0.011013114100142[/C][/ROW]
[ROW][C]94[/C][C]19.74[/C][C]19.7412550412131[/C][C]-0.00125504121308813[/C][/ROW]
[ROW][C]95[/C][C]19.63[/C][C]19.6390847800574[/C][C]-0.00908478005738216[/C][/ROW]
[ROW][C]96[/C][C]19.59[/C][C]19.6067473052283[/C][C]-0.0167473052282672[/C][/ROW]
[ROW][C]97[/C][C]19.7[/C][C]19.6912915685842[/C][C]0.00870843141579338[/C][/ROW]
[ROW][C]98[/C][C]19.88[/C][C]19.8537193722333[/C][C]0.0262806277666678[/C][/ROW]
[ROW][C]99[/C][C]19.72[/C][C]19.7207876288791[/C][C]-0.000787628879076384[/C][/ROW]
[ROW][C]100[/C][C]19.62[/C][C]19.6403267489784[/C][C]-0.0203267489783727[/C][/ROW]
[ROW][C]101[/C][C]19.78[/C][C]19.7881580020368[/C][C]-0.00815800203682914[/C][/ROW]
[ROW][C]102[/C][C]19.61[/C][C]19.6331379806617[/C][C]-0.0231379806616738[/C][/ROW]
[ROW][C]103[/C][C]19.7[/C][C]19.707373239214[/C][C]-0.00737323921401315[/C][/ROW]
[ROW][C]104[/C][C]19.65[/C][C]19.6566210575479[/C][C]-0.00662105754794452[/C][/ROW]
[ROW][C]105[/C][C]19.61[/C][C]19.6069166733005[/C][C]0.00308332669945932[/C][/ROW]
[ROW][C]106[/C][C]19.62[/C][C]19.5885540942705[/C][C]0.0314459057295456[/C][/ROW]
[ROW][C]107[/C][C]19.58[/C][C]19.5844791020505[/C][C]-0.00447910205049416[/C][/ROW]
[ROW][C]108[/C][C]19.69[/C][C]19.6841021987005[/C][C]0.00589780129951973[/C][/ROW]
[ROW][C]109[/C][C]19.63[/C][C]19.6073779740121[/C][C]0.0226220259879248[/C][/ROW]
[ROW][C]110[/C][C]19.54[/C][C]19.5112582151057[/C][C]0.0287417848942532[/C][/ROW]
[ROW][C]111[/C][C]19.56[/C][C]19.5202182731518[/C][C]0.0397817268482197[/C][/ROW]
[ROW][C]112[/C][C]19.55[/C][C]19.5375220222161[/C][C]0.0124779777838633[/C][/ROW]
[ROW][C]113[/C][C]19.49[/C][C]19.4819931950135[/C][C]0.00800680498646357[/C][/ROW]
[ROW][C]114[/C][C]19.53[/C][C]19.5559542455901[/C][C]-0.025954245590105[/C][/ROW]
[ROW][C]115[/C][C]19.48[/C][C]19.4889020128411[/C][C]-0.00890201284111112[/C][/ROW]
[ROW][C]116[/C][C]19.58[/C][C]19.5600891797511[/C][C]0.0199108202489435[/C][/ROW]
[ROW][C]117[/C][C]19.48[/C][C]19.5009162584692[/C][C]-0.0209162584691928[/C][/ROW]
[ROW][C]118[/C][C]19.46[/C][C]19.484878968461[/C][C]-0.0248789684609617[/C][/ROW]
[ROW][C]119[/C][C]19.45[/C][C]19.4563066470128[/C][C]-0.00630664701282448[/C][/ROW]
[ROW][C]120[/C][C]19.39[/C][C]19.3873809972008[/C][C]0.00261900279922772[/C][/ROW]
[ROW][C]121[/C][C]19.46[/C][C]19.447619661731[/C][C]0.0123803382690245[/C][/ROW]
[ROW][C]122[/C][C]19.41[/C][C]19.4213085992729[/C][C]-0.0113085992729353[/C][/ROW]
[ROW][C]123[/C][C]19.45[/C][C]19.4653260797973[/C][C]-0.0153260797972649[/C][/ROW]
[ROW][C]124[/C][C]19.52[/C][C]19.5514846025271[/C][C]-0.0314846025271332[/C][/ROW]
[ROW][C]125[/C][C]19.47[/C][C]19.5032251220924[/C][C]-0.0332251220923694[/C][/ROW]
[ROW][C]126[/C][C]19.37[/C][C]19.3503902013831[/C][C]0.019609798616916[/C][/ROW]
[ROW][C]127[/C][C]19.37[/C][C]19.3721652905331[/C][C]-0.00216529053305454[/C][/ROW]
[ROW][C]128[/C][C]19.4[/C][C]19.395399449399[/C][C]0.00460055060095283[/C][/ROW]
[ROW][C]129[/C][C]19.42[/C][C]19.4049107343245[/C][C]0.0150892656754626[/C][/ROW]
[ROW][C]130[/C][C]19.45[/C][C]19.4591947781422[/C][C]-0.00919477814223051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164243&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164243&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	19.72	19.7065957665665	0.0134042334335293
2	19.65	19.6391132475581	0.0108867524419381
3	19.59	19.5765018157145	0.013498184285492
4	19.59	19.5972011381473	-0.00720113814728906
5	19.55	19.5357427453901	0.0142572546098533
6	19.61	19.5907557029553	0.01924429704471
7	19.57	19.5734518956449	-0.00345189564493437
8	19.55	19.5549452688176	-0.0049452688175801
9	19.57	19.5379618205532	0.0320381794468341
10	19.51	19.4974009979702	0.0125990020298304
11	19.48	19.474088667616	0.00591133238399862
12	19.49	19.4703339586769	0.0196660413231116
13	19.58	19.5895455309531	-0.00954553095305885
14	19.48	19.5125383011466	-0.0325383011465818
15	19.46	19.4571576047474	0.00284239525263243
16	19.48	19.4928807452359	-0.0128807452359101
17	19.49	19.4622382379272	0.0277617620728468
18	19.44	19.4332326669065	0.00676733309347315
19	19.57	19.5773243806911	-0.00732438069106853
20	19.5	19.5106755525547	-0.0106755525546637
21	19.34	19.3307455728973	0.00925442710265383
22	19.4	19.4172682707923	-0.0172682707923337
23	19.4	19.4051444617693	-0.00514446176933068
24	19.43	19.4139993020629	0.0160006979371281
25	19.44	19.406291430717	0.033708569283005
26	19.49	19.4603123472194	0.029687652780563
27	19.48	19.474598599533	0.00540140046699587
28	19.48	19.4940535378798	-0.0140535378797789
29	19.45	19.4328826473138	0.0171173526861571
30	19.48	19.498362038776	-0.0183620387759996
31	19.44	19.4424067390487	-0.00240673904871145
32	19.51	19.485693538489	0.0243064615109711
33	19.49	19.4858855402744	0.00411445972562681
34	19.56	19.5629556979045	-0.00295569790447757
35	19.57	19.5714713866466	-0.00147138664662472
36	19.49	19.502613286756	-0.0126132867559977
37	19.54	19.5183734584509	0.0216265415490627
38	19.62	19.5951921589131	0.0248078410869093
39	19.56	19.5627310151578	-0.0027310151577964
40	19.64	19.6345258627651	0.00547413723488013
41	19.59	19.6251325067271	-0.0351325067270646
42	19.6	19.6473548418216	-0.0473548418216207
43	19.63	19.6433036101451	-0.0133036101451011
44	19.67	19.6814710058607	-0.0114710058606815
45	19.61	19.5949029146329	0.0150970853671472
46	19.62	19.594797186678	0.0252028133220504
47	19.67	19.6745570185463	-0.00455701854630984
48	19.63	19.6221163989408	0.00788360105917136
49	19.62	19.6177188125015	0.00228118749848887
50	19.7	19.7199728775156	-0.0199728775155605
51	19.83	19.8099360604853	0.0200639395147155
52	19.85	19.8437950405566	0.00620495944335334
53	19.73	19.7498932772385	-0.0198932772385377
54	19.61	19.6410709868701	-0.0310709868701315
55	19.63	19.6421308686305	-0.0121308686304605
56	19.68	19.6742832737209	0.00571672627907384
57	19.66	19.6395236216113	0.0204763783887497
58	19.56	19.5436181778818	0.016381822118158
59	19.5	19.4958557532164	0.00414424678364119
60	19.55	19.5416084632149	0.00839153678508849
61	19.53	19.5381181147865	-0.00811811478645969
62	19.49	19.4860372027365	0.00396279726352277
63	19.47	19.4764525626675	-0.00645256266752785
64	19.57	19.566119959762	0.00388004023803424
65	19.53	19.5107466565033	0.0192533434966928
66	19.43	19.4546978499469	-0.0246978499468706
67	19.43	19.4400731003787	-0.0100731003787152
68	19.43	19.4389993290541	-0.00899932905413983
69	19.45	19.4729865266047	-0.0229865266047493
70	19.41	19.4282009208988	-0.0182009208988469
71	19.48	19.5016234245409	-0.0216234245408573
72	19.48	19.4893314079024	-0.00933140790236955
73	19.48	19.4881391707634	-0.00813917076344308
74	19.37	19.3798713784271	-0.00987137842709404
75	19.35	19.384367357278	-0.0343673572780176
76	19.38	19.405783362111	-0.0257833621109792
77	19.41	19.3959202412371	0.0140797587628862
78	19.48	19.4643131855644	0.0156868144356361
79	19.44	19.4299603161838	0.0100396838162259
80	19.41	19.408282135046	0.00171786495398865
81	19.42	19.4305927404215	-0.0105927404214851
82	19.42	19.432168859961	-0.0121688599609848
83	19.42	19.4461870460554	-0.0261870460554481
84	19.48	19.4778552686613	0.00214473133869359
85	19.53	19.5197042967588	0.0102957032411525
86	19.56	19.5104791234557	0.0495208765442652
87	19.53	19.5199454643343	0.0100545356657092
88	19.52	19.5265361225718	-0.006536122571843
89	19.52	19.5270581497285	-0.00705814972847433
90	19.63	19.6041473844826	0.0258526155174415
91	19.63	19.6288895257708	0.00111047422916233
92	19.57	19.5928119925854	-0.0228119925853963
93	19.6	19.5889868858999	0.011013114100142
94	19.74	19.7412550412131	-0.00125504121308813
95	19.63	19.6390847800574	-0.00908478005738216
96	19.59	19.6067473052283	-0.0167473052282672
97	19.7	19.6912915685842	0.00870843141579338
98	19.88	19.8537193722333	0.0262806277666678
99	19.72	19.7207876288791	-0.000787628879076384
100	19.62	19.6403267489784	-0.0203267489783727
101	19.78	19.7881580020368	-0.00815800203682914
102	19.61	19.6331379806617	-0.0231379806616738
103	19.7	19.707373239214	-0.00737323921401315
104	19.65	19.6566210575479	-0.00662105754794452
105	19.61	19.6069166733005	0.00308332669945932
106	19.62	19.5885540942705	0.0314459057295456
107	19.58	19.5844791020505	-0.00447910205049416
108	19.69	19.6841021987005	0.00589780129951973
109	19.63	19.6073779740121	0.0226220259879248
110	19.54	19.5112582151057	0.0287417848942532
111	19.56	19.5202182731518	0.0397817268482197
112	19.55	19.5375220222161	0.0124779777838633
113	19.49	19.4819931950135	0.00800680498646357
114	19.53	19.5559542455901	-0.025954245590105
115	19.48	19.4889020128411	-0.00890201284111112
116	19.58	19.5600891797511	0.0199108202489435
117	19.48	19.5009162584692	-0.0209162584691928
118	19.46	19.484878968461	-0.0248789684609617
119	19.45	19.4563066470128	-0.00630664701282448
120	19.39	19.3873809972008	0.00261900279922772
121	19.46	19.447619661731	0.0123803382690245
122	19.41	19.4213085992729	-0.0113085992729353
123	19.45	19.4653260797973	-0.0153260797972649
124	19.52	19.5514846025271	-0.0314846025271332
125	19.47	19.5032251220924	-0.0332251220923694
126	19.37	19.3503902013831	0.019609798616916
127	19.37	19.3721652905331	-0.00216529053305454
128	19.4	19.395399449399	0.00460055060095283
129	19.42	19.4049107343245	0.0150892656754626
130	19.45	19.4591947781422	-0.00919477814223051

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.254293158552024	0.508586317104048	0.745706841447976
18	0.425736247053807	0.851472494107614	0.574263752946193
19	0.589314894802262	0.821370210395476	0.410685105197738
20	0.674458387918835	0.65108322416233	0.325541612081165
21	0.575413848070156	0.849172303859689	0.424586151929844
22	0.590330941104984	0.819338117790032	0.409669058895016
23	0.486347496494325	0.97269499298865	0.513652503505675
24	0.503082073575625	0.99383585284875	0.496917926424375
25	0.601271471604629	0.797457056790743	0.398728528395371
26	0.555393731039002	0.889212537921996	0.444606268960998
27	0.493867094708413	0.987734189416826	0.506132905291587
28	0.491689029371156	0.983378058742312	0.508310970628844
29	0.445864895334479	0.891729790668959	0.554135104665521
30	0.459934935404419	0.919869870808837	0.540065064595581
31	0.427860109545524	0.855720219091048	0.572139890454476
32	0.426116644526204	0.852233289052408	0.573883355473796
33	0.398470689517852	0.796941379035703	0.601529310482148
34	0.461585246990828	0.923170493981656	0.538414753009172
35	0.421304609972191	0.842609219944382	0.578695390027809
36	0.383494730263658	0.766989460527316	0.616505269736342
37	0.456673460321569	0.913346920643137	0.543326539678431
38	0.489530476941699	0.979060953883398	0.510469523058301
39	0.491229673008167	0.982459346016333	0.508770326991833
40	0.442056075853237	0.884112151706475	0.557943924146763
41	0.527738189961518	0.944523620076963	0.472261810038482
42	0.670930949986782	0.658138100026436	0.329069050013218
43	0.648899350733909	0.702201298532181	0.351100649266091
44	0.601139203915427	0.797721592169147	0.398860796084573
45	0.590720171538846	0.818559656922308	0.409279828461154
46	0.671079804042844	0.657840391914312	0.328920195957156
47	0.619976339629282	0.760047320741435	0.380023660370718
48	0.581625836165341	0.836748327669318	0.418374163834659
49	0.525497616875084	0.949004766249832	0.474502383124916
50	0.489453416762792	0.978906833525585	0.510546583237208
51	0.614440157412288	0.771119685175424	0.385559842587712
52	0.616502443835546	0.766995112328907	0.383497556164454
53	0.572363246222345	0.85527350755531	0.427636753777655
54	0.62908342250839	0.741833154983221	0.37091657749161
55	0.579680095741604	0.840639808516793	0.420319904258396
56	0.562732652330521	0.874534695338959	0.437267347669479
57	0.568642047696694	0.862715904606612	0.431357952303306
58	0.516441697222478	0.967116605555045	0.483558302777522
59	0.466361859120417	0.932723718240834	0.533638140879583
60	0.420132695186358	0.840265390372715	0.579867304813642
61	0.374008921568992	0.748017843137984	0.625991078431008
62	0.334595587182837	0.669191174365675	0.665404412817163
63	0.304341852557771	0.608683705115542	0.695658147442229
64	0.267052611767291	0.534105223534583	0.732947388232709
65	0.265538616486953	0.531077232973907	0.734461383513047
66	0.363103064172053	0.726206128344106	0.636896935827947
67	0.35685769868324	0.713715397366481	0.64314230131676
68	0.364322556316227	0.728645112632453	0.635677443683773
69	0.402205120485427	0.804410240970854	0.597794879514573
70	0.40688272694827	0.81376545389654	0.59311727305173
71	0.416370282320709	0.832740564641418	0.583629717679291
72	0.381833381481677	0.763666762963355	0.618166618518323
73	0.333978526625659	0.667957053251318	0.666021473374341
74	0.285044472140626	0.570088944281253	0.714955527859374
75	0.339805941448252	0.679611882896504	0.660194058551748
76	0.366584794766488	0.733169589532976	0.633415205233512
77	0.353688602066373	0.707377204132745	0.646311397933627
78	0.34136071737448	0.682721434748961	0.65863928262552
79	0.304904831093523	0.609809662187046	0.695095168906477
80	0.262892574007868	0.525785148015736	0.737107425992132
81	0.235798049885636	0.471596099771272	0.764201950114364
82	0.207395128928885	0.41479025785777	0.792604871071115
83	0.216980724816139	0.433961449632277	0.783019275183861
84	0.175917870082749	0.351835740165499	0.824082129917251
85	0.144277946358051	0.288555892716102	0.855722053641949
86	0.390376009959128	0.780752019918256	0.609623990040872
87	0.364746114112376	0.729492228224752	0.635253885887624
88	0.329272430052365	0.65854486010473	0.670727569947635
89	0.276982779863355	0.55396555972671	0.723017220136645
90	0.472012543466295	0.94402508693259	0.527987456533705
91	0.482383138978233	0.964766277956466	0.517616861021767
92	0.591222892901685	0.817554214196631	0.408777107098315
93	0.611565631256919	0.776868737486163	0.388434368743081
94	0.641418441857605	0.717163116284789	0.358581558142395
95	0.730629914100687	0.538740171798626	0.269370085899313
96	0.668082797238348	0.663834405523303	0.331917202761652
97	0.666443903355741	0.667112193288518	0.333556096644259
98	0.860626035716783	0.278747928566434	0.139373964283217
99	0.95000651646395	0.0999869670720994	0.0499934835360497
100	0.926351906829362	0.147296186341275	0.0736480931706376
101	0.9532743822688	0.0934512354623993	0.0467256177311996
102	0.950148928484341	0.0997021430313181	0.0498510715156591
103	0.939895984514296	0.120208030971409	0.0601040154857045
104	0.908459885795193	0.183080228409614	0.0915401142048071
105	0.914181104735656	0.171637790528688	0.0858188952643441
106	0.895079197394374	0.209841605211252	0.104920802605626
107	0.837454249137015	0.32509150172597	0.162545750862985
108	0.765162840639712	0.469674318720577	0.234837159360288
109	0.994362753389074	0.0112744932218517	0.00563724661092583
110	0.997903056440228	0.00419388711954489	0.00209694355977245
111	0.996191215685608	0.0076175686287848	0.0038087843143924
112	0.986388212120826	0.0272235757583471	0.0136117878791736
113	0.959740537903688	0.0805189241926247	0.0402594620963123

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.254293158552024 & 0.508586317104048 & 0.745706841447976 \tabularnewline
18 & 0.425736247053807 & 0.851472494107614 & 0.574263752946193 \tabularnewline
19 & 0.589314894802262 & 0.821370210395476 & 0.410685105197738 \tabularnewline
20 & 0.674458387918835 & 0.65108322416233 & 0.325541612081165 \tabularnewline
21 & 0.575413848070156 & 0.849172303859689 & 0.424586151929844 \tabularnewline
22 & 0.590330941104984 & 0.819338117790032 & 0.409669058895016 \tabularnewline
23 & 0.486347496494325 & 0.97269499298865 & 0.513652503505675 \tabularnewline
24 & 0.503082073575625 & 0.99383585284875 & 0.496917926424375 \tabularnewline
25 & 0.601271471604629 & 0.797457056790743 & 0.398728528395371 \tabularnewline
26 & 0.555393731039002 & 0.889212537921996 & 0.444606268960998 \tabularnewline
27 & 0.493867094708413 & 0.987734189416826 & 0.506132905291587 \tabularnewline
28 & 0.491689029371156 & 0.983378058742312 & 0.508310970628844 \tabularnewline
29 & 0.445864895334479 & 0.891729790668959 & 0.554135104665521 \tabularnewline
30 & 0.459934935404419 & 0.919869870808837 & 0.540065064595581 \tabularnewline
31 & 0.427860109545524 & 0.855720219091048 & 0.572139890454476 \tabularnewline
32 & 0.426116644526204 & 0.852233289052408 & 0.573883355473796 \tabularnewline
33 & 0.398470689517852 & 0.796941379035703 & 0.601529310482148 \tabularnewline
34 & 0.461585246990828 & 0.923170493981656 & 0.538414753009172 \tabularnewline
35 & 0.421304609972191 & 0.842609219944382 & 0.578695390027809 \tabularnewline
36 & 0.383494730263658 & 0.766989460527316 & 0.616505269736342 \tabularnewline
37 & 0.456673460321569 & 0.913346920643137 & 0.543326539678431 \tabularnewline
38 & 0.489530476941699 & 0.979060953883398 & 0.510469523058301 \tabularnewline
39 & 0.491229673008167 & 0.982459346016333 & 0.508770326991833 \tabularnewline
40 & 0.442056075853237 & 0.884112151706475 & 0.557943924146763 \tabularnewline
41 & 0.527738189961518 & 0.944523620076963 & 0.472261810038482 \tabularnewline
42 & 0.670930949986782 & 0.658138100026436 & 0.329069050013218 \tabularnewline
43 & 0.648899350733909 & 0.702201298532181 & 0.351100649266091 \tabularnewline
44 & 0.601139203915427 & 0.797721592169147 & 0.398860796084573 \tabularnewline
45 & 0.590720171538846 & 0.818559656922308 & 0.409279828461154 \tabularnewline
46 & 0.671079804042844 & 0.657840391914312 & 0.328920195957156 \tabularnewline
47 & 0.619976339629282 & 0.760047320741435 & 0.380023660370718 \tabularnewline
48 & 0.581625836165341 & 0.836748327669318 & 0.418374163834659 \tabularnewline
49 & 0.525497616875084 & 0.949004766249832 & 0.474502383124916 \tabularnewline
50 & 0.489453416762792 & 0.978906833525585 & 0.510546583237208 \tabularnewline
51 & 0.614440157412288 & 0.771119685175424 & 0.385559842587712 \tabularnewline
52 & 0.616502443835546 & 0.766995112328907 & 0.383497556164454 \tabularnewline
53 & 0.572363246222345 & 0.85527350755531 & 0.427636753777655 \tabularnewline
54 & 0.62908342250839 & 0.741833154983221 & 0.37091657749161 \tabularnewline
55 & 0.579680095741604 & 0.840639808516793 & 0.420319904258396 \tabularnewline
56 & 0.562732652330521 & 0.874534695338959 & 0.437267347669479 \tabularnewline
57 & 0.568642047696694 & 0.862715904606612 & 0.431357952303306 \tabularnewline
58 & 0.516441697222478 & 0.967116605555045 & 0.483558302777522 \tabularnewline
59 & 0.466361859120417 & 0.932723718240834 & 0.533638140879583 \tabularnewline
60 & 0.420132695186358 & 0.840265390372715 & 0.579867304813642 \tabularnewline
61 & 0.374008921568992 & 0.748017843137984 & 0.625991078431008 \tabularnewline
62 & 0.334595587182837 & 0.669191174365675 & 0.665404412817163 \tabularnewline
63 & 0.304341852557771 & 0.608683705115542 & 0.695658147442229 \tabularnewline
64 & 0.267052611767291 & 0.534105223534583 & 0.732947388232709 \tabularnewline
65 & 0.265538616486953 & 0.531077232973907 & 0.734461383513047 \tabularnewline
66 & 0.363103064172053 & 0.726206128344106 & 0.636896935827947 \tabularnewline
67 & 0.35685769868324 & 0.713715397366481 & 0.64314230131676 \tabularnewline
68 & 0.364322556316227 & 0.728645112632453 & 0.635677443683773 \tabularnewline
69 & 0.402205120485427 & 0.804410240970854 & 0.597794879514573 \tabularnewline
70 & 0.40688272694827 & 0.81376545389654 & 0.59311727305173 \tabularnewline
71 & 0.416370282320709 & 0.832740564641418 & 0.583629717679291 \tabularnewline
72 & 0.381833381481677 & 0.763666762963355 & 0.618166618518323 \tabularnewline
73 & 0.333978526625659 & 0.667957053251318 & 0.666021473374341 \tabularnewline
74 & 0.285044472140626 & 0.570088944281253 & 0.714955527859374 \tabularnewline
75 & 0.339805941448252 & 0.679611882896504 & 0.660194058551748 \tabularnewline
76 & 0.366584794766488 & 0.733169589532976 & 0.633415205233512 \tabularnewline
77 & 0.353688602066373 & 0.707377204132745 & 0.646311397933627 \tabularnewline
78 & 0.34136071737448 & 0.682721434748961 & 0.65863928262552 \tabularnewline
79 & 0.304904831093523 & 0.609809662187046 & 0.695095168906477 \tabularnewline
80 & 0.262892574007868 & 0.525785148015736 & 0.737107425992132 \tabularnewline
81 & 0.235798049885636 & 0.471596099771272 & 0.764201950114364 \tabularnewline
82 & 0.207395128928885 & 0.41479025785777 & 0.792604871071115 \tabularnewline
83 & 0.216980724816139 & 0.433961449632277 & 0.783019275183861 \tabularnewline
84 & 0.175917870082749 & 0.351835740165499 & 0.824082129917251 \tabularnewline
85 & 0.144277946358051 & 0.288555892716102 & 0.855722053641949 \tabularnewline
86 & 0.390376009959128 & 0.780752019918256 & 0.609623990040872 \tabularnewline
87 & 0.364746114112376 & 0.729492228224752 & 0.635253885887624 \tabularnewline
88 & 0.329272430052365 & 0.65854486010473 & 0.670727569947635 \tabularnewline
89 & 0.276982779863355 & 0.55396555972671 & 0.723017220136645 \tabularnewline
90 & 0.472012543466295 & 0.94402508693259 & 0.527987456533705 \tabularnewline
91 & 0.482383138978233 & 0.964766277956466 & 0.517616861021767 \tabularnewline
92 & 0.591222892901685 & 0.817554214196631 & 0.408777107098315 \tabularnewline
93 & 0.611565631256919 & 0.776868737486163 & 0.388434368743081 \tabularnewline
94 & 0.641418441857605 & 0.717163116284789 & 0.358581558142395 \tabularnewline
95 & 0.730629914100687 & 0.538740171798626 & 0.269370085899313 \tabularnewline
96 & 0.668082797238348 & 0.663834405523303 & 0.331917202761652 \tabularnewline
97 & 0.666443903355741 & 0.667112193288518 & 0.333556096644259 \tabularnewline
98 & 0.860626035716783 & 0.278747928566434 & 0.139373964283217 \tabularnewline
99 & 0.95000651646395 & 0.0999869670720994 & 0.0499934835360497 \tabularnewline
100 & 0.926351906829362 & 0.147296186341275 & 0.0736480931706376 \tabularnewline
101 & 0.9532743822688 & 0.0934512354623993 & 0.0467256177311996 \tabularnewline
102 & 0.950148928484341 & 0.0997021430313181 & 0.0498510715156591 \tabularnewline
103 & 0.939895984514296 & 0.120208030971409 & 0.0601040154857045 \tabularnewline
104 & 0.908459885795193 & 0.183080228409614 & 0.0915401142048071 \tabularnewline
105 & 0.914181104735656 & 0.171637790528688 & 0.0858188952643441 \tabularnewline
106 & 0.895079197394374 & 0.209841605211252 & 0.104920802605626 \tabularnewline
107 & 0.837454249137015 & 0.32509150172597 & 0.162545750862985 \tabularnewline
108 & 0.765162840639712 & 0.469674318720577 & 0.234837159360288 \tabularnewline
109 & 0.994362753389074 & 0.0112744932218517 & 0.00563724661092583 \tabularnewline
110 & 0.997903056440228 & 0.00419388711954489 & 0.00209694355977245 \tabularnewline
111 & 0.996191215685608 & 0.0076175686287848 & 0.0038087843143924 \tabularnewline
112 & 0.986388212120826 & 0.0272235757583471 & 0.0136117878791736 \tabularnewline
113 & 0.959740537903688 & 0.0805189241926247 & 0.0402594620963123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164243&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]0.254293158552024[/C][C]0.508586317104048[/C][C]0.745706841447976[/C][/ROW]
[ROW][C]18[/C][C]0.425736247053807[/C][C]0.851472494107614[/C][C]0.574263752946193[/C][/ROW]
[ROW][C]19[/C][C]0.589314894802262[/C][C]0.821370210395476[/C][C]0.410685105197738[/C][/ROW]
[ROW][C]20[/C][C]0.674458387918835[/C][C]0.65108322416233[/C][C]0.325541612081165[/C][/ROW]
[ROW][C]21[/C][C]0.575413848070156[/C][C]0.849172303859689[/C][C]0.424586151929844[/C][/ROW]
[ROW][C]22[/C][C]0.590330941104984[/C][C]0.819338117790032[/C][C]0.409669058895016[/C][/ROW]
[ROW][C]23[/C][C]0.486347496494325[/C][C]0.97269499298865[/C][C]0.513652503505675[/C][/ROW]
[ROW][C]24[/C][C]0.503082073575625[/C][C]0.99383585284875[/C][C]0.496917926424375[/C][/ROW]
[ROW][C]25[/C][C]0.601271471604629[/C][C]0.797457056790743[/C][C]0.398728528395371[/C][/ROW]
[ROW][C]26[/C][C]0.555393731039002[/C][C]0.889212537921996[/C][C]0.444606268960998[/C][/ROW]
[ROW][C]27[/C][C]0.493867094708413[/C][C]0.987734189416826[/C][C]0.506132905291587[/C][/ROW]
[ROW][C]28[/C][C]0.491689029371156[/C][C]0.983378058742312[/C][C]0.508310970628844[/C][/ROW]
[ROW][C]29[/C][C]0.445864895334479[/C][C]0.891729790668959[/C][C]0.554135104665521[/C][/ROW]
[ROW][C]30[/C][C]0.459934935404419[/C][C]0.919869870808837[/C][C]0.540065064595581[/C][/ROW]
[ROW][C]31[/C][C]0.427860109545524[/C][C]0.855720219091048[/C][C]0.572139890454476[/C][/ROW]
[ROW][C]32[/C][C]0.426116644526204[/C][C]0.852233289052408[/C][C]0.573883355473796[/C][/ROW]
[ROW][C]33[/C][C]0.398470689517852[/C][C]0.796941379035703[/C][C]0.601529310482148[/C][/ROW]
[ROW][C]34[/C][C]0.461585246990828[/C][C]0.923170493981656[/C][C]0.538414753009172[/C][/ROW]
[ROW][C]35[/C][C]0.421304609972191[/C][C]0.842609219944382[/C][C]0.578695390027809[/C][/ROW]
[ROW][C]36[/C][C]0.383494730263658[/C][C]0.766989460527316[/C][C]0.616505269736342[/C][/ROW]
[ROW][C]37[/C][C]0.456673460321569[/C][C]0.913346920643137[/C][C]0.543326539678431[/C][/ROW]
[ROW][C]38[/C][C]0.489530476941699[/C][C]0.979060953883398[/C][C]0.510469523058301[/C][/ROW]
[ROW][C]39[/C][C]0.491229673008167[/C][C]0.982459346016333[/C][C]0.508770326991833[/C][/ROW]
[ROW][C]40[/C][C]0.442056075853237[/C][C]0.884112151706475[/C][C]0.557943924146763[/C][/ROW]
[ROW][C]41[/C][C]0.527738189961518[/C][C]0.944523620076963[/C][C]0.472261810038482[/C][/ROW]
[ROW][C]42[/C][C]0.670930949986782[/C][C]0.658138100026436[/C][C]0.329069050013218[/C][/ROW]
[ROW][C]43[/C][C]0.648899350733909[/C][C]0.702201298532181[/C][C]0.351100649266091[/C][/ROW]
[ROW][C]44[/C][C]0.601139203915427[/C][C]0.797721592169147[/C][C]0.398860796084573[/C][/ROW]
[ROW][C]45[/C][C]0.590720171538846[/C][C]0.818559656922308[/C][C]0.409279828461154[/C][/ROW]
[ROW][C]46[/C][C]0.671079804042844[/C][C]0.657840391914312[/C][C]0.328920195957156[/C][/ROW]
[ROW][C]47[/C][C]0.619976339629282[/C][C]0.760047320741435[/C][C]0.380023660370718[/C][/ROW]
[ROW][C]48[/C][C]0.581625836165341[/C][C]0.836748327669318[/C][C]0.418374163834659[/C][/ROW]
[ROW][C]49[/C][C]0.525497616875084[/C][C]0.949004766249832[/C][C]0.474502383124916[/C][/ROW]
[ROW][C]50[/C][C]0.489453416762792[/C][C]0.978906833525585[/C][C]0.510546583237208[/C][/ROW]
[ROW][C]51[/C][C]0.614440157412288[/C][C]0.771119685175424[/C][C]0.385559842587712[/C][/ROW]
[ROW][C]52[/C][C]0.616502443835546[/C][C]0.766995112328907[/C][C]0.383497556164454[/C][/ROW]
[ROW][C]53[/C][C]0.572363246222345[/C][C]0.85527350755531[/C][C]0.427636753777655[/C][/ROW]
[ROW][C]54[/C][C]0.62908342250839[/C][C]0.741833154983221[/C][C]0.37091657749161[/C][/ROW]
[ROW][C]55[/C][C]0.579680095741604[/C][C]0.840639808516793[/C][C]0.420319904258396[/C][/ROW]
[ROW][C]56[/C][C]0.562732652330521[/C][C]0.874534695338959[/C][C]0.437267347669479[/C][/ROW]
[ROW][C]57[/C][C]0.568642047696694[/C][C]0.862715904606612[/C][C]0.431357952303306[/C][/ROW]
[ROW][C]58[/C][C]0.516441697222478[/C][C]0.967116605555045[/C][C]0.483558302777522[/C][/ROW]
[ROW][C]59[/C][C]0.466361859120417[/C][C]0.932723718240834[/C][C]0.533638140879583[/C][/ROW]
[ROW][C]60[/C][C]0.420132695186358[/C][C]0.840265390372715[/C][C]0.579867304813642[/C][/ROW]
[ROW][C]61[/C][C]0.374008921568992[/C][C]0.748017843137984[/C][C]0.625991078431008[/C][/ROW]
[ROW][C]62[/C][C]0.334595587182837[/C][C]0.669191174365675[/C][C]0.665404412817163[/C][/ROW]
[ROW][C]63[/C][C]0.304341852557771[/C][C]0.608683705115542[/C][C]0.695658147442229[/C][/ROW]
[ROW][C]64[/C][C]0.267052611767291[/C][C]0.534105223534583[/C][C]0.732947388232709[/C][/ROW]
[ROW][C]65[/C][C]0.265538616486953[/C][C]0.531077232973907[/C][C]0.734461383513047[/C][/ROW]
[ROW][C]66[/C][C]0.363103064172053[/C][C]0.726206128344106[/C][C]0.636896935827947[/C][/ROW]
[ROW][C]67[/C][C]0.35685769868324[/C][C]0.713715397366481[/C][C]0.64314230131676[/C][/ROW]
[ROW][C]68[/C][C]0.364322556316227[/C][C]0.728645112632453[/C][C]0.635677443683773[/C][/ROW]
[ROW][C]69[/C][C]0.402205120485427[/C][C]0.804410240970854[/C][C]0.597794879514573[/C][/ROW]
[ROW][C]70[/C][C]0.40688272694827[/C][C]0.81376545389654[/C][C]0.59311727305173[/C][/ROW]
[ROW][C]71[/C][C]0.416370282320709[/C][C]0.832740564641418[/C][C]0.583629717679291[/C][/ROW]
[ROW][C]72[/C][C]0.381833381481677[/C][C]0.763666762963355[/C][C]0.618166618518323[/C][/ROW]
[ROW][C]73[/C][C]0.333978526625659[/C][C]0.667957053251318[/C][C]0.666021473374341[/C][/ROW]
[ROW][C]74[/C][C]0.285044472140626[/C][C]0.570088944281253[/C][C]0.714955527859374[/C][/ROW]
[ROW][C]75[/C][C]0.339805941448252[/C][C]0.679611882896504[/C][C]0.660194058551748[/C][/ROW]
[ROW][C]76[/C][C]0.366584794766488[/C][C]0.733169589532976[/C][C]0.633415205233512[/C][/ROW]
[ROW][C]77[/C][C]0.353688602066373[/C][C]0.707377204132745[/C][C]0.646311397933627[/C][/ROW]
[ROW][C]78[/C][C]0.34136071737448[/C][C]0.682721434748961[/C][C]0.65863928262552[/C][/ROW]
[ROW][C]79[/C][C]0.304904831093523[/C][C]0.609809662187046[/C][C]0.695095168906477[/C][/ROW]
[ROW][C]80[/C][C]0.262892574007868[/C][C]0.525785148015736[/C][C]0.737107425992132[/C][/ROW]
[ROW][C]81[/C][C]0.235798049885636[/C][C]0.471596099771272[/C][C]0.764201950114364[/C][/ROW]
[ROW][C]82[/C][C]0.207395128928885[/C][C]0.41479025785777[/C][C]0.792604871071115[/C][/ROW]
[ROW][C]83[/C][C]0.216980724816139[/C][C]0.433961449632277[/C][C]0.783019275183861[/C][/ROW]
[ROW][C]84[/C][C]0.175917870082749[/C][C]0.351835740165499[/C][C]0.824082129917251[/C][/ROW]
[ROW][C]85[/C][C]0.144277946358051[/C][C]0.288555892716102[/C][C]0.855722053641949[/C][/ROW]
[ROW][C]86[/C][C]0.390376009959128[/C][C]0.780752019918256[/C][C]0.609623990040872[/C][/ROW]
[ROW][C]87[/C][C]0.364746114112376[/C][C]0.729492228224752[/C][C]0.635253885887624[/C][/ROW]
[ROW][C]88[/C][C]0.329272430052365[/C][C]0.65854486010473[/C][C]0.670727569947635[/C][/ROW]
[ROW][C]89[/C][C]0.276982779863355[/C][C]0.55396555972671[/C][C]0.723017220136645[/C][/ROW]
[ROW][C]90[/C][C]0.472012543466295[/C][C]0.94402508693259[/C][C]0.527987456533705[/C][/ROW]
[ROW][C]91[/C][C]0.482383138978233[/C][C]0.964766277956466[/C][C]0.517616861021767[/C][/ROW]
[ROW][C]92[/C][C]0.591222892901685[/C][C]0.817554214196631[/C][C]0.408777107098315[/C][/ROW]
[ROW][C]93[/C][C]0.611565631256919[/C][C]0.776868737486163[/C][C]0.388434368743081[/C][/ROW]
[ROW][C]94[/C][C]0.641418441857605[/C][C]0.717163116284789[/C][C]0.358581558142395[/C][/ROW]
[ROW][C]95[/C][C]0.730629914100687[/C][C]0.538740171798626[/C][C]0.269370085899313[/C][/ROW]
[ROW][C]96[/C][C]0.668082797238348[/C][C]0.663834405523303[/C][C]0.331917202761652[/C][/ROW]
[ROW][C]97[/C][C]0.666443903355741[/C][C]0.667112193288518[/C][C]0.333556096644259[/C][/ROW]
[ROW][C]98[/C][C]0.860626035716783[/C][C]0.278747928566434[/C][C]0.139373964283217[/C][/ROW]
[ROW][C]99[/C][C]0.95000651646395[/C][C]0.0999869670720994[/C][C]0.0499934835360497[/C][/ROW]
[ROW][C]100[/C][C]0.926351906829362[/C][C]0.147296186341275[/C][C]0.0736480931706376[/C][/ROW]
[ROW][C]101[/C][C]0.9532743822688[/C][C]0.0934512354623993[/C][C]0.0467256177311996[/C][/ROW]
[ROW][C]102[/C][C]0.950148928484341[/C][C]0.0997021430313181[/C][C]0.0498510715156591[/C][/ROW]
[ROW][C]103[/C][C]0.939895984514296[/C][C]0.120208030971409[/C][C]0.0601040154857045[/C][/ROW]
[ROW][C]104[/C][C]0.908459885795193[/C][C]0.183080228409614[/C][C]0.0915401142048071[/C][/ROW]
[ROW][C]105[/C][C]0.914181104735656[/C][C]0.171637790528688[/C][C]0.0858188952643441[/C][/ROW]
[ROW][C]106[/C][C]0.895079197394374[/C][C]0.209841605211252[/C][C]0.104920802605626[/C][/ROW]
[ROW][C]107[/C][C]0.837454249137015[/C][C]0.32509150172597[/C][C]0.162545750862985[/C][/ROW]
[ROW][C]108[/C][C]0.765162840639712[/C][C]0.469674318720577[/C][C]0.234837159360288[/C][/ROW]
[ROW][C]109[/C][C]0.994362753389074[/C][C]0.0112744932218517[/C][C]0.00563724661092583[/C][/ROW]
[ROW][C]110[/C][C]0.997903056440228[/C][C]0.00419388711954489[/C][C]0.00209694355977245[/C][/ROW]
[ROW][C]111[/C][C]0.996191215685608[/C][C]0.0076175686287848[/C][C]0.0038087843143924[/C][/ROW]
[ROW][C]112[/C][C]0.986388212120826[/C][C]0.0272235757583471[/C][C]0.0136117878791736[/C][/ROW]
[ROW][C]113[/C][C]0.959740537903688[/C][C]0.0805189241926247[/C][C]0.0402594620963123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164243&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164243&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	0.254293158552024	0.508586317104048	0.745706841447976
18	0.425736247053807	0.851472494107614	0.574263752946193
19	0.589314894802262	0.821370210395476	0.410685105197738
20	0.674458387918835	0.65108322416233	0.325541612081165
21	0.575413848070156	0.849172303859689	0.424586151929844
22	0.590330941104984	0.819338117790032	0.409669058895016
23	0.486347496494325	0.97269499298865	0.513652503505675
24	0.503082073575625	0.99383585284875	0.496917926424375
25	0.601271471604629	0.797457056790743	0.398728528395371
26	0.555393731039002	0.889212537921996	0.444606268960998
27	0.493867094708413	0.987734189416826	0.506132905291587
28	0.491689029371156	0.983378058742312	0.508310970628844
29	0.445864895334479	0.891729790668959	0.554135104665521
30	0.459934935404419	0.919869870808837	0.540065064595581
31	0.427860109545524	0.855720219091048	0.572139890454476
32	0.426116644526204	0.852233289052408	0.573883355473796
33	0.398470689517852	0.796941379035703	0.601529310482148
34	0.461585246990828	0.923170493981656	0.538414753009172
35	0.421304609972191	0.842609219944382	0.578695390027809
36	0.383494730263658	0.766989460527316	0.616505269736342
37	0.456673460321569	0.913346920643137	0.543326539678431
38	0.489530476941699	0.979060953883398	0.510469523058301
39	0.491229673008167	0.982459346016333	0.508770326991833
40	0.442056075853237	0.884112151706475	0.557943924146763
41	0.527738189961518	0.944523620076963	0.472261810038482
42	0.670930949986782	0.658138100026436	0.329069050013218
43	0.648899350733909	0.702201298532181	0.351100649266091
44	0.601139203915427	0.797721592169147	0.398860796084573
45	0.590720171538846	0.818559656922308	0.409279828461154
46	0.671079804042844	0.657840391914312	0.328920195957156
47	0.619976339629282	0.760047320741435	0.380023660370718
48	0.581625836165341	0.836748327669318	0.418374163834659
49	0.525497616875084	0.949004766249832	0.474502383124916
50	0.489453416762792	0.978906833525585	0.510546583237208
51	0.614440157412288	0.771119685175424	0.385559842587712
52	0.616502443835546	0.766995112328907	0.383497556164454
53	0.572363246222345	0.85527350755531	0.427636753777655
54	0.62908342250839	0.741833154983221	0.37091657749161
55	0.579680095741604	0.840639808516793	0.420319904258396
56	0.562732652330521	0.874534695338959	0.437267347669479
57	0.568642047696694	0.862715904606612	0.431357952303306
58	0.516441697222478	0.967116605555045	0.483558302777522
59	0.466361859120417	0.932723718240834	0.533638140879583
60	0.420132695186358	0.840265390372715	0.579867304813642
61	0.374008921568992	0.748017843137984	0.625991078431008
62	0.334595587182837	0.669191174365675	0.665404412817163
63	0.304341852557771	0.608683705115542	0.695658147442229
64	0.267052611767291	0.534105223534583	0.732947388232709
65	0.265538616486953	0.531077232973907	0.734461383513047
66	0.363103064172053	0.726206128344106	0.636896935827947
67	0.35685769868324	0.713715397366481	0.64314230131676
68	0.364322556316227	0.728645112632453	0.635677443683773
69	0.402205120485427	0.804410240970854	0.597794879514573
70	0.40688272694827	0.81376545389654	0.59311727305173
71	0.416370282320709	0.832740564641418	0.583629717679291
72	0.381833381481677	0.763666762963355	0.618166618518323
73	0.333978526625659	0.667957053251318	0.666021473374341
74	0.285044472140626	0.570088944281253	0.714955527859374
75	0.339805941448252	0.679611882896504	0.660194058551748
76	0.366584794766488	0.733169589532976	0.633415205233512
77	0.353688602066373	0.707377204132745	0.646311397933627
78	0.34136071737448	0.682721434748961	0.65863928262552
79	0.304904831093523	0.609809662187046	0.695095168906477
80	0.262892574007868	0.525785148015736	0.737107425992132
81	0.235798049885636	0.471596099771272	0.764201950114364
82	0.207395128928885	0.41479025785777	0.792604871071115
83	0.216980724816139	0.433961449632277	0.783019275183861
84	0.175917870082749	0.351835740165499	0.824082129917251
85	0.144277946358051	0.288555892716102	0.855722053641949
86	0.390376009959128	0.780752019918256	0.609623990040872
87	0.364746114112376	0.729492228224752	0.635253885887624
88	0.329272430052365	0.65854486010473	0.670727569947635
89	0.276982779863355	0.55396555972671	0.723017220136645
90	0.472012543466295	0.94402508693259	0.527987456533705
91	0.482383138978233	0.964766277956466	0.517616861021767
92	0.591222892901685	0.817554214196631	0.408777107098315
93	0.611565631256919	0.776868737486163	0.388434368743081
94	0.641418441857605	0.717163116284789	0.358581558142395
95	0.730629914100687	0.538740171798626	0.269370085899313
96	0.668082797238348	0.663834405523303	0.331917202761652
97	0.666443903355741	0.667112193288518	0.333556096644259
98	0.860626035716783	0.278747928566434	0.139373964283217
99	0.95000651646395	0.0999869670720994	0.0499934835360497
100	0.926351906829362	0.147296186341275	0.0736480931706376
101	0.9532743822688	0.0934512354623993	0.0467256177311996
102	0.950148928484341	0.0997021430313181	0.0498510715156591
103	0.939895984514296	0.120208030971409	0.0601040154857045
104	0.908459885795193	0.183080228409614	0.0915401142048071
105	0.914181104735656	0.171637790528688	0.0858188952643441
106	0.895079197394374	0.209841605211252	0.104920802605626
107	0.837454249137015	0.32509150172597	0.162545750862985
108	0.765162840639712	0.469674318720577	0.234837159360288
109	0.994362753389074	0.0112744932218517	0.00563724661092583
110	0.997903056440228	0.00419388711954489	0.00209694355977245
111	0.996191215685608	0.0076175686287848	0.0038087843143924
112	0.986388212120826	0.0272235757583471	0.0136117878791736
113	0.959740537903688	0.0805189241926247	0.0402594620963123

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0206185567010309	NOK
5% type I error level	4	0.0412371134020619	OK
10% type I error level	8	0.0824742268041237	OK

\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 & 2 & 0.0206185567010309 & NOK \tabularnewline
5% type I error level & 4 & 0.0412371134020619 & OK \tabularnewline
10% type I error level & 8 & 0.0824742268041237 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164243&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]2[/C][C]0.0206185567010309[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0412371134020619[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0824742268041237[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164243&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164243&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	2	0.0206185567010309	NOK
5% type I error level	4	0.0412371134020619	OK
10% type I error level	8	0.0824742268041237	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code