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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 11:45:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321893949nucpjun7l83a6hv.htm/, Retrieved Thu, 31 Oct 2024 23:25:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145815, Retrieved Thu, 31 Oct 2024 23:25:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact185
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 16:40:16] [9d4f280afcb4ecc352d7c6f913a0a151]
- R  D    [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 16:45:28] [2a6d487209befbc7c5ce02a41ecac161] [Current]
-    D      [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 16:59:15] [9d4f280afcb4ecc352d7c6f913a0a151]
- R PD        [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 18:01:29] [9d4f280afcb4ecc352d7c6f913a0a151]
- R PD        [Multiple Regression] [WS7 Tutorial Acco...] [2011-11-21 18:09:29] [9d4f280afcb4ecc352d7c6f913a0a151]
-             [Multiple Regression] [Paper Multiple Li...] [2011-12-13 12:55:13] [9d4f280afcb4ecc352d7c6f913a0a151]
- RMP           [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper Chi-Squared...] [2011-12-18 14:41:58] [9d4f280afcb4ecc352d7c6f913a0a151]
- R  D            [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper Chi-Squared...] [2011-12-18 14:47:43] [9d4f280afcb4ecc352d7c6f913a0a151]
- R  D            [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper Chi-Squared...] [2011-12-18 14:53:55] [9d4f280afcb4ecc352d7c6f913a0a151]
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Dataseries X:
15	1	14	3	1	1
10	1	8	3	0	1
14	0	12	6	1	1
10	1	7	2	0	1
10	0	10	1	1	0
7	0	7	2	0	0
16	1	16	8	1	1
9	1	11	1	1	0
12	0	14	4	1	1
6	0	6	0	0	0
13	0	16	4	1	0
12	1	11	2	0	1
15	0	16	1	1	1
8	1	12	2	1	1
11	0	7	3	0	0
11	0	13	1	1	0
10	1	11	2	1	1
14	1	15	6	1	0
9	1	7	0	0	1
6	1	9	1	0	1
9	0	7	3	0	1
15	1	14	5	1	1
11	1	15	0	1	1
10	1	7	1	0	1
14	1	15	3	1	1
15	1	17	6	1	1
9	1	15	5	1	0
13	1	14	4	1	0
13	0	14	4	0	0
11	1	8	4	1	1
8	0	8	0	0	1
12	1	14	3	1	0
14	1	14	5	1	1
11	0	8	3	0	0
9	1	11	1	1	1
17	1	16	5	1	1
12	1	10	5	1	1
10	1	8	0	0	1
13	1	14	3	1	1
16	1	16	6	1	0
14	0	13	3	1	1
12	1	5	1	0	0
6	1	8	2	0	1
8	1	10	2	0	0
8	0	8	2	0	1
16	1	13	4	1	1
17	1	15	4	1	1
9	0	6	0	0	1
9	0	12	3	1	1
14	1	16	6	0	1
6	1	5	3	1	0
8	0	15	1	1	1
12	0	12	4	1	0
8	0	8	3	0	1
14	0	13	3	1	1
12	1	14	3	1	1
11	0	12	2	1	1
17	0	16	6	1	1
8	1	10	5	1	1
15	0	15	5	1	0
7	0	8	2	0	1
16	1	16	4	1	1
17	0	19	2	1	1
16	0	14	5	1	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145815&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145815&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145815&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Loon[t] = + 3.50066291082927 -0.110923670252448Change[t] + 0.554021747580255Size[t] + 0.512457650061833Complex[t] -0.235593372225139Big4[t] + 0.353091681649952Product[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Loon[t] =  +  3.50066291082927 -0.110923670252448Change[t] +  0.554021747580255Size[t] +  0.512457650061833Complex[t] -0.235593372225139Big4[t] +  0.353091681649952Product[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145815&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Loon[t] =  +  3.50066291082927 -0.110923670252448Change[t] +  0.554021747580255Size[t] +  0.512457650061833Complex[t] -0.235593372225139Big4[t] +  0.353091681649952Product[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145815&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145815&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
Loon[t] = + 3.50066291082927 -0.110923670252448Change[t] + 0.554021747580255Size[t] + 0.512457650061833Complex[t] -0.235593372225139Big4[t] + 0.353091681649952Product[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.500662910829271.0058113.48040.0009570.000479
Change-0.1109236702524480.527526-0.21030.8341930.417096
Size0.5540217475802550.1082755.11684e-062e-06
Complex0.5124576500618330.1639423.12580.0027690.001385
Big4-0.2355933722251390.748233-0.31490.7539930.376997
Product0.3530916816499520.559190.63140.5302380.265119

\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) & 3.50066291082927 & 1.005811 & 3.4804 & 0.000957 & 0.000479 \tabularnewline
Change & -0.110923670252448 & 0.527526 & -0.2103 & 0.834193 & 0.417096 \tabularnewline
Size & 0.554021747580255 & 0.108275 & 5.1168 & 4e-06 & 2e-06 \tabularnewline
Complex & 0.512457650061833 & 0.163942 & 3.1258 & 0.002769 & 0.001385 \tabularnewline
Big4 & -0.235593372225139 & 0.748233 & -0.3149 & 0.753993 & 0.376997 \tabularnewline
Product & 0.353091681649952 & 0.55919 & 0.6314 & 0.530238 & 0.265119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145815&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]3.50066291082927[/C][C]1.005811[/C][C]3.4804[/C][C]0.000957[/C][C]0.000479[/C][/ROW]
[ROW][C]Change[/C][C]-0.110923670252448[/C][C]0.527526[/C][C]-0.2103[/C][C]0.834193[/C][C]0.417096[/C][/ROW]
[ROW][C]Size[/C][C]0.554021747580255[/C][C]0.108275[/C][C]5.1168[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Complex[/C][C]0.512457650061833[/C][C]0.163942[/C][C]3.1258[/C][C]0.002769[/C][C]0.001385[/C][/ROW]
[ROW][C]Big4[/C][C]-0.235593372225139[/C][C]0.748233[/C][C]-0.3149[/C][C]0.753993[/C][C]0.376997[/C][/ROW]
[ROW][C]Product[/C][C]0.353091681649952[/C][C]0.55919[/C][C]0.6314[/C][C]0.530238[/C][C]0.265119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145815&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145815&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.500662910829271.0058113.48040.0009570.000479
Change-0.1109236702524480.527526-0.21030.8341930.417096
Size0.5540217475802550.1082755.11684e-062e-06
Complex0.5124576500618330.1639423.12580.0027690.001385
Big4-0.2355933722251390.748233-0.31490.7539930.376997
Product0.3530916816499520.559190.63140.5302380.265119







Multiple Linear Regression - Regression Statistics
Multiple R0.795891501198665
R-squared0.633443281680265
Adjusted R-squared0.601843564583736
F-TEST (value)20.0458529342292
F-TEST (DF numerator)5
F-TEST (DF denominator)58
p-value1.4878875909119e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.0170432757326
Sum Squared Residuals235.970887418329

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.795891501198665 \tabularnewline
R-squared & 0.633443281680265 \tabularnewline
Adjusted R-squared & 0.601843564583736 \tabularnewline
F-TEST (value) & 20.0458529342292 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.4878875909119e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.0170432757326 \tabularnewline
Sum Squared Residuals & 235.970887418329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145815&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.795891501198665[/C][/ROW]
[ROW][C]R-squared[/C][C]0.633443281680265[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.601843564583736[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.0458529342292[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]1.4878875909119e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.0170432757326[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]235.970887418329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145815&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145815&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.795891501198665
R-squared0.633443281680265
Adjusted R-squared0.601843564583736
F-TEST (value)20.0458529342292
F-TEST (DF numerator)5
F-TEST (DF denominator)58
p-value1.4878875909119e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.0170432757326
Sum Squared Residuals235.970887418329







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11512.80091496631072.1990850336893
2109.712377853054320.287622146945684
31413.34116809158810.658831908411854
4108.645898455412231.35410154458777
5109.317744664468520.682255335531483
678.40373044401472-1.40373044401472
71616.4712467117804-0.471246711780384
899.76084274179632-0.760842741796324
91213.424296286625-1.42429628662499
1066.8247933963108-0.824793396310802
111314.1792481001355-1.17924810013555
121210.86198544573321.13801455426675
131512.99496683162.0050331684
14811.1804138210884-3.18041382108836
15118.916188094076562.08381190592344
161110.97980990720930.0201900927907174
171010.6263920735081-0.626392073508109
181414.5392179824265-0.539217982426511
1997.620983155288561.37901684471144
2069.2414843005109-3.2414843005109
2199.26927977572651-0.269279775726509
221513.82583026643441.17416973356563
231111.8175637637055-0.817563763705463
24108.133440805350391.86655919464961
251413.3549367138910.645063286109037
261516.000353159237-1.00035315923697
27914.0267603323647-5.02676033236468
281312.96028093472260.0397190652774104
291313.3067979772002-0.306797977200177
30119.989242130891011.01075786910899
3188.28592857312126-0.285928573121264
321212.4478232846608-0.447823284660756
331413.82583026643440.174169733565625
34119.470209841656811.52979015834319
35910.1139344234463-1.11393442344628
361714.93387376159492.06612623840512
371211.60974327611340.390256723886646
38108.175004902868821.82499509713118
391312.80091496631070.199085033689292
401615.09323973000680.906760269993234
411412.35781688898291.6421831110171
42126.672305628539935.32769437146007
4369.19992020299248-3.19992020299248
4489.95487201650304-1.95487201650304
4589.31084387324493-1.31084387324493
461612.75935086879233.24064913120771
471713.86739436395283.1326056360472
4897.177885077960751.82211492203925
49911.8037951414026-2.80379514140265
501415.6819247838819-1.68192478388186
5167.46162755643846-1.46162755643846
52812.4409450840197-4.44094508401974
531211.96316110981450.0368388901854729
5489.82330152330676-1.82330152330676
551412.35781688898291.6421831110171
561212.8009149663107-0.800914966310708
571111.2913374913408-0.291337491340812
581715.55725508190921.44274491809083
59811.6097432761134-3.60974327611335
601514.13768400261710.862315997382874
6179.31084387324493-2.31084387324493
621614.42141611153311.57858388846695
631715.16948972440261.8305102755974
641613.58366225503692.41633774496313

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 12.8009149663107 & 2.1990850336893 \tabularnewline
2 & 10 & 9.71237785305432 & 0.287622146945684 \tabularnewline
3 & 14 & 13.3411680915881 & 0.658831908411854 \tabularnewline
4 & 10 & 8.64589845541223 & 1.35410154458777 \tabularnewline
5 & 10 & 9.31774466446852 & 0.682255335531483 \tabularnewline
6 & 7 & 8.40373044401472 & -1.40373044401472 \tabularnewline
7 & 16 & 16.4712467117804 & -0.471246711780384 \tabularnewline
8 & 9 & 9.76084274179632 & -0.760842741796324 \tabularnewline
9 & 12 & 13.424296286625 & -1.42429628662499 \tabularnewline
10 & 6 & 6.8247933963108 & -0.824793396310802 \tabularnewline
11 & 13 & 14.1792481001355 & -1.17924810013555 \tabularnewline
12 & 12 & 10.8619854457332 & 1.13801455426675 \tabularnewline
13 & 15 & 12.9949668316 & 2.0050331684 \tabularnewline
14 & 8 & 11.1804138210884 & -3.18041382108836 \tabularnewline
15 & 11 & 8.91618809407656 & 2.08381190592344 \tabularnewline
16 & 11 & 10.9798099072093 & 0.0201900927907174 \tabularnewline
17 & 10 & 10.6263920735081 & -0.626392073508109 \tabularnewline
18 & 14 & 14.5392179824265 & -0.539217982426511 \tabularnewline
19 & 9 & 7.62098315528856 & 1.37901684471144 \tabularnewline
20 & 6 & 9.2414843005109 & -3.2414843005109 \tabularnewline
21 & 9 & 9.26927977572651 & -0.269279775726509 \tabularnewline
22 & 15 & 13.8258302664344 & 1.17416973356563 \tabularnewline
23 & 11 & 11.8175637637055 & -0.817563763705463 \tabularnewline
24 & 10 & 8.13344080535039 & 1.86655919464961 \tabularnewline
25 & 14 & 13.354936713891 & 0.645063286109037 \tabularnewline
26 & 15 & 16.000353159237 & -1.00035315923697 \tabularnewline
27 & 9 & 14.0267603323647 & -5.02676033236468 \tabularnewline
28 & 13 & 12.9602809347226 & 0.0397190652774104 \tabularnewline
29 & 13 & 13.3067979772002 & -0.306797977200177 \tabularnewline
30 & 11 & 9.98924213089101 & 1.01075786910899 \tabularnewline
31 & 8 & 8.28592857312126 & -0.285928573121264 \tabularnewline
32 & 12 & 12.4478232846608 & -0.447823284660756 \tabularnewline
33 & 14 & 13.8258302664344 & 0.174169733565625 \tabularnewline
34 & 11 & 9.47020984165681 & 1.52979015834319 \tabularnewline
35 & 9 & 10.1139344234463 & -1.11393442344628 \tabularnewline
36 & 17 & 14.9338737615949 & 2.06612623840512 \tabularnewline
37 & 12 & 11.6097432761134 & 0.390256723886646 \tabularnewline
38 & 10 & 8.17500490286882 & 1.82499509713118 \tabularnewline
39 & 13 & 12.8009149663107 & 0.199085033689292 \tabularnewline
40 & 16 & 15.0932397300068 & 0.906760269993234 \tabularnewline
41 & 14 & 12.3578168889829 & 1.6421831110171 \tabularnewline
42 & 12 & 6.67230562853993 & 5.32769437146007 \tabularnewline
43 & 6 & 9.19992020299248 & -3.19992020299248 \tabularnewline
44 & 8 & 9.95487201650304 & -1.95487201650304 \tabularnewline
45 & 8 & 9.31084387324493 & -1.31084387324493 \tabularnewline
46 & 16 & 12.7593508687923 & 3.24064913120771 \tabularnewline
47 & 17 & 13.8673943639528 & 3.1326056360472 \tabularnewline
48 & 9 & 7.17788507796075 & 1.82211492203925 \tabularnewline
49 & 9 & 11.8037951414026 & -2.80379514140265 \tabularnewline
50 & 14 & 15.6819247838819 & -1.68192478388186 \tabularnewline
51 & 6 & 7.46162755643846 & -1.46162755643846 \tabularnewline
52 & 8 & 12.4409450840197 & -4.44094508401974 \tabularnewline
53 & 12 & 11.9631611098145 & 0.0368388901854729 \tabularnewline
54 & 8 & 9.82330152330676 & -1.82330152330676 \tabularnewline
55 & 14 & 12.3578168889829 & 1.6421831110171 \tabularnewline
56 & 12 & 12.8009149663107 & -0.800914966310708 \tabularnewline
57 & 11 & 11.2913374913408 & -0.291337491340812 \tabularnewline
58 & 17 & 15.5572550819092 & 1.44274491809083 \tabularnewline
59 & 8 & 11.6097432761134 & -3.60974327611335 \tabularnewline
60 & 15 & 14.1376840026171 & 0.862315997382874 \tabularnewline
61 & 7 & 9.31084387324493 & -2.31084387324493 \tabularnewline
62 & 16 & 14.4214161115331 & 1.57858388846695 \tabularnewline
63 & 17 & 15.1694897244026 & 1.8305102755974 \tabularnewline
64 & 16 & 13.5836622550369 & 2.41633774496313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145815&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]15[/C][C]12.8009149663107[/C][C]2.1990850336893[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]9.71237785305432[/C][C]0.287622146945684[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]13.3411680915881[/C][C]0.658831908411854[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]8.64589845541223[/C][C]1.35410154458777[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]9.31774466446852[/C][C]0.682255335531483[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]8.40373044401472[/C][C]-1.40373044401472[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]16.4712467117804[/C][C]-0.471246711780384[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]9.76084274179632[/C][C]-0.760842741796324[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]13.424296286625[/C][C]-1.42429628662499[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]6.8247933963108[/C][C]-0.824793396310802[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]14.1792481001355[/C][C]-1.17924810013555[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]10.8619854457332[/C][C]1.13801455426675[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.9949668316[/C][C]2.0050331684[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]11.1804138210884[/C][C]-3.18041382108836[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]8.91618809407656[/C][C]2.08381190592344[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.9798099072093[/C][C]0.0201900927907174[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.6263920735081[/C][C]-0.626392073508109[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.5392179824265[/C][C]-0.539217982426511[/C][/ROW]
[ROW][C]19[/C][C]9[/C][C]7.62098315528856[/C][C]1.37901684471144[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.2414843005109[/C][C]-3.2414843005109[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]9.26927977572651[/C][C]-0.269279775726509[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.8258302664344[/C][C]1.17416973356563[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.8175637637055[/C][C]-0.817563763705463[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]8.13344080535039[/C][C]1.86655919464961[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.354936713891[/C][C]0.645063286109037[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]16.000353159237[/C][C]-1.00035315923697[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.0267603323647[/C][C]-5.02676033236468[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.9602809347226[/C][C]0.0397190652774104[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.3067979772002[/C][C]-0.306797977200177[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]9.98924213089101[/C][C]1.01075786910899[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]8.28592857312126[/C][C]-0.285928573121264[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4478232846608[/C][C]-0.447823284660756[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.8258302664344[/C][C]0.174169733565625[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]9.47020984165681[/C][C]1.52979015834319[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.1139344234463[/C][C]-1.11393442344628[/C][/ROW]
[ROW][C]36[/C][C]17[/C][C]14.9338737615949[/C][C]2.06612623840512[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]11.6097432761134[/C][C]0.390256723886646[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]8.17500490286882[/C][C]1.82499509713118[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.8009149663107[/C][C]0.199085033689292[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.0932397300068[/C][C]0.906760269993234[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.3578168889829[/C][C]1.6421831110171[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]6.67230562853993[/C][C]5.32769437146007[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]9.19992020299248[/C][C]-3.19992020299248[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]9.95487201650304[/C][C]-1.95487201650304[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]9.31084387324493[/C][C]-1.31084387324493[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]12.7593508687923[/C][C]3.24064913120771[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.8673943639528[/C][C]3.1326056360472[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]7.17788507796075[/C][C]1.82211492203925[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.8037951414026[/C][C]-2.80379514140265[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.6819247838819[/C][C]-1.68192478388186[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]7.46162755643846[/C][C]-1.46162755643846[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]12.4409450840197[/C][C]-4.44094508401974[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.9631611098145[/C][C]0.0368388901854729[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]9.82330152330676[/C][C]-1.82330152330676[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.3578168889829[/C][C]1.6421831110171[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.8009149663107[/C][C]-0.800914966310708[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]11.2913374913408[/C][C]-0.291337491340812[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]15.5572550819092[/C][C]1.44274491809083[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]11.6097432761134[/C][C]-3.60974327611335[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.1376840026171[/C][C]0.862315997382874[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.31084387324493[/C][C]-2.31084387324493[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.4214161115331[/C][C]1.57858388846695[/C][/ROW]
[ROW][C]63[/C][C]17[/C][C]15.1694897244026[/C][C]1.8305102755974[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.5836622550369[/C][C]2.41633774496313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145815&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145815&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11512.80091496631072.1990850336893
2109.712377853054320.287622146945684
31413.34116809158810.658831908411854
4108.645898455412231.35410154458777
5109.317744664468520.682255335531483
678.40373044401472-1.40373044401472
71616.4712467117804-0.471246711780384
899.76084274179632-0.760842741796324
91213.424296286625-1.42429628662499
1066.8247933963108-0.824793396310802
111314.1792481001355-1.17924810013555
121210.86198544573321.13801455426675
131512.99496683162.0050331684
14811.1804138210884-3.18041382108836
15118.916188094076562.08381190592344
161110.97980990720930.0201900927907174
171010.6263920735081-0.626392073508109
181414.5392179824265-0.539217982426511
1997.620983155288561.37901684471144
2069.2414843005109-3.2414843005109
2199.26927977572651-0.269279775726509
221513.82583026643441.17416973356563
231111.8175637637055-0.817563763705463
24108.133440805350391.86655919464961
251413.3549367138910.645063286109037
261516.000353159237-1.00035315923697
27914.0267603323647-5.02676033236468
281312.96028093472260.0397190652774104
291313.3067979772002-0.306797977200177
30119.989242130891011.01075786910899
3188.28592857312126-0.285928573121264
321212.4478232846608-0.447823284660756
331413.82583026643440.174169733565625
34119.470209841656811.52979015834319
35910.1139344234463-1.11393442344628
361714.93387376159492.06612623840512
371211.60974327611340.390256723886646
38108.175004902868821.82499509713118
391312.80091496631070.199085033689292
401615.09323973000680.906760269993234
411412.35781688898291.6421831110171
42126.672305628539935.32769437146007
4369.19992020299248-3.19992020299248
4489.95487201650304-1.95487201650304
4589.31084387324493-1.31084387324493
461612.75935086879233.24064913120771
471713.86739436395283.1326056360472
4897.177885077960751.82211492203925
49911.8037951414026-2.80379514140265
501415.6819247838819-1.68192478388186
5167.46162755643846-1.46162755643846
52812.4409450840197-4.44094508401974
531211.96316110981450.0368388901854729
5489.82330152330676-1.82330152330676
551412.35781688898291.6421831110171
561212.8009149663107-0.800914966310708
571111.2913374913408-0.291337491340812
581715.55725508190921.44274491809083
59811.6097432761134-3.60974327611335
601514.13768400261710.862315997382874
6179.31084387324493-2.31084387324493
621614.42141611153311.57858388846695
631715.16948972440261.8305102755974
641613.58366225503692.41633774496313







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2516447798769690.5032895597539390.748355220123031
100.1253937781444090.2507875562888190.874606221855591
110.08272899764264980.16545799528530.91727100235735
120.0436440785528770.0872881571057540.956355921447123
130.02281743273767630.04563486547535270.977182567262324
140.2739739284553890.5479478569107790.726026071544611
150.3113749697469440.6227499394938870.688625030253056
160.2239384173305660.4478768346611330.776061582669434
170.1551954019613580.3103908039227150.844804598038642
180.103397843386340.206795686772680.89660215661366
190.07193912288004710.1438782457600940.928060877119953
200.1957142191787690.3914284383575390.804285780821231
210.1437285573872130.2874571147744270.856271442612787
220.1167859369456610.2335718738913220.883214063054339
230.08373736029609860.1674747205921970.916262639703901
240.07804609156314540.1560921831262910.921953908436855
250.05376771559183830.1075354311836770.946232284408162
260.03798495963674310.07596991927348630.962015040363257
270.1681765614687360.3363531229374710.831823438531264
280.1419749960796650.283949992159330.858025003920335
290.1037727668012880.2075455336025750.896227233198712
300.07964292310469580.1592858462093920.920357076895304
310.05890866009571790.1178173201914360.941091339904282
320.0461450651338920.0922901302677840.953854934866108
330.03004169207103760.06008338414207530.969958307928962
340.0247623421284980.0495246842569960.975237657871502
350.01887961529983590.03775923059967180.981120384700164
360.02013376418811720.04026752837623440.979866235811883
370.01285983569738860.02571967139477710.987140164302611
380.01123360197286020.02246720394572040.98876639802714
390.006539373333772970.01307874666754590.993460626666227
400.004805629314585870.009611258629171730.995194370685414
410.003751301913498740.007502603826997490.996248698086501
420.08202361405421650.1640472281084330.917976385945783
430.1063100016727950.212620003345590.893689998327205
440.09266623181576240.1853324636315250.907333768184238
450.06896223602629510.137924472052590.931037763973705
460.1239645249459130.2479290498918260.876035475054087
470.2251514093616470.4503028187232940.774848590638353
480.4346151356107650.869230271221530.565384864389235
490.4731038085896510.9462076171793020.526896191410349
500.4342487822797520.8684975645595030.565751217720248
510.4176831538325480.8353663076650950.582316846167452
520.9050423019117850.1899153961764290.0949576980882145
530.8301277697041730.3397444605916540.169872230295827
540.7204810653732310.5590378692535390.279518934626769
550.7060823534450380.5878352931099240.293917646554962

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.251644779876969 & 0.503289559753939 & 0.748355220123031 \tabularnewline
10 & 0.125393778144409 & 0.250787556288819 & 0.874606221855591 \tabularnewline
11 & 0.0827289976426498 & 0.1654579952853 & 0.91727100235735 \tabularnewline
12 & 0.043644078552877 & 0.087288157105754 & 0.956355921447123 \tabularnewline
13 & 0.0228174327376763 & 0.0456348654753527 & 0.977182567262324 \tabularnewline
14 & 0.273973928455389 & 0.547947856910779 & 0.726026071544611 \tabularnewline
15 & 0.311374969746944 & 0.622749939493887 & 0.688625030253056 \tabularnewline
16 & 0.223938417330566 & 0.447876834661133 & 0.776061582669434 \tabularnewline
17 & 0.155195401961358 & 0.310390803922715 & 0.844804598038642 \tabularnewline
18 & 0.10339784338634 & 0.20679568677268 & 0.89660215661366 \tabularnewline
19 & 0.0719391228800471 & 0.143878245760094 & 0.928060877119953 \tabularnewline
20 & 0.195714219178769 & 0.391428438357539 & 0.804285780821231 \tabularnewline
21 & 0.143728557387213 & 0.287457114774427 & 0.856271442612787 \tabularnewline
22 & 0.116785936945661 & 0.233571873891322 & 0.883214063054339 \tabularnewline
23 & 0.0837373602960986 & 0.167474720592197 & 0.916262639703901 \tabularnewline
24 & 0.0780460915631454 & 0.156092183126291 & 0.921953908436855 \tabularnewline
25 & 0.0537677155918383 & 0.107535431183677 & 0.946232284408162 \tabularnewline
26 & 0.0379849596367431 & 0.0759699192734863 & 0.962015040363257 \tabularnewline
27 & 0.168176561468736 & 0.336353122937471 & 0.831823438531264 \tabularnewline
28 & 0.141974996079665 & 0.28394999215933 & 0.858025003920335 \tabularnewline
29 & 0.103772766801288 & 0.207545533602575 & 0.896227233198712 \tabularnewline
30 & 0.0796429231046958 & 0.159285846209392 & 0.920357076895304 \tabularnewline
31 & 0.0589086600957179 & 0.117817320191436 & 0.941091339904282 \tabularnewline
32 & 0.046145065133892 & 0.092290130267784 & 0.953854934866108 \tabularnewline
33 & 0.0300416920710376 & 0.0600833841420753 & 0.969958307928962 \tabularnewline
34 & 0.024762342128498 & 0.049524684256996 & 0.975237657871502 \tabularnewline
35 & 0.0188796152998359 & 0.0377592305996718 & 0.981120384700164 \tabularnewline
36 & 0.0201337641881172 & 0.0402675283762344 & 0.979866235811883 \tabularnewline
37 & 0.0128598356973886 & 0.0257196713947771 & 0.987140164302611 \tabularnewline
38 & 0.0112336019728602 & 0.0224672039457204 & 0.98876639802714 \tabularnewline
39 & 0.00653937333377297 & 0.0130787466675459 & 0.993460626666227 \tabularnewline
40 & 0.00480562931458587 & 0.00961125862917173 & 0.995194370685414 \tabularnewline
41 & 0.00375130191349874 & 0.00750260382699749 & 0.996248698086501 \tabularnewline
42 & 0.0820236140542165 & 0.164047228108433 & 0.917976385945783 \tabularnewline
43 & 0.106310001672795 & 0.21262000334559 & 0.893689998327205 \tabularnewline
44 & 0.0926662318157624 & 0.185332463631525 & 0.907333768184238 \tabularnewline
45 & 0.0689622360262951 & 0.13792447205259 & 0.931037763973705 \tabularnewline
46 & 0.123964524945913 & 0.247929049891826 & 0.876035475054087 \tabularnewline
47 & 0.225151409361647 & 0.450302818723294 & 0.774848590638353 \tabularnewline
48 & 0.434615135610765 & 0.86923027122153 & 0.565384864389235 \tabularnewline
49 & 0.473103808589651 & 0.946207617179302 & 0.526896191410349 \tabularnewline
50 & 0.434248782279752 & 0.868497564559503 & 0.565751217720248 \tabularnewline
51 & 0.417683153832548 & 0.835366307665095 & 0.582316846167452 \tabularnewline
52 & 0.905042301911785 & 0.189915396176429 & 0.0949576980882145 \tabularnewline
53 & 0.830127769704173 & 0.339744460591654 & 0.169872230295827 \tabularnewline
54 & 0.720481065373231 & 0.559037869253539 & 0.279518934626769 \tabularnewline
55 & 0.706082353445038 & 0.587835293109924 & 0.293917646554962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145815&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]9[/C][C]0.251644779876969[/C][C]0.503289559753939[/C][C]0.748355220123031[/C][/ROW]
[ROW][C]10[/C][C]0.125393778144409[/C][C]0.250787556288819[/C][C]0.874606221855591[/C][/ROW]
[ROW][C]11[/C][C]0.0827289976426498[/C][C]0.1654579952853[/C][C]0.91727100235735[/C][/ROW]
[ROW][C]12[/C][C]0.043644078552877[/C][C]0.087288157105754[/C][C]0.956355921447123[/C][/ROW]
[ROW][C]13[/C][C]0.0228174327376763[/C][C]0.0456348654753527[/C][C]0.977182567262324[/C][/ROW]
[ROW][C]14[/C][C]0.273973928455389[/C][C]0.547947856910779[/C][C]0.726026071544611[/C][/ROW]
[ROW][C]15[/C][C]0.311374969746944[/C][C]0.622749939493887[/C][C]0.688625030253056[/C][/ROW]
[ROW][C]16[/C][C]0.223938417330566[/C][C]0.447876834661133[/C][C]0.776061582669434[/C][/ROW]
[ROW][C]17[/C][C]0.155195401961358[/C][C]0.310390803922715[/C][C]0.844804598038642[/C][/ROW]
[ROW][C]18[/C][C]0.10339784338634[/C][C]0.20679568677268[/C][C]0.89660215661366[/C][/ROW]
[ROW][C]19[/C][C]0.0719391228800471[/C][C]0.143878245760094[/C][C]0.928060877119953[/C][/ROW]
[ROW][C]20[/C][C]0.195714219178769[/C][C]0.391428438357539[/C][C]0.804285780821231[/C][/ROW]
[ROW][C]21[/C][C]0.143728557387213[/C][C]0.287457114774427[/C][C]0.856271442612787[/C][/ROW]
[ROW][C]22[/C][C]0.116785936945661[/C][C]0.233571873891322[/C][C]0.883214063054339[/C][/ROW]
[ROW][C]23[/C][C]0.0837373602960986[/C][C]0.167474720592197[/C][C]0.916262639703901[/C][/ROW]
[ROW][C]24[/C][C]0.0780460915631454[/C][C]0.156092183126291[/C][C]0.921953908436855[/C][/ROW]
[ROW][C]25[/C][C]0.0537677155918383[/C][C]0.107535431183677[/C][C]0.946232284408162[/C][/ROW]
[ROW][C]26[/C][C]0.0379849596367431[/C][C]0.0759699192734863[/C][C]0.962015040363257[/C][/ROW]
[ROW][C]27[/C][C]0.168176561468736[/C][C]0.336353122937471[/C][C]0.831823438531264[/C][/ROW]
[ROW][C]28[/C][C]0.141974996079665[/C][C]0.28394999215933[/C][C]0.858025003920335[/C][/ROW]
[ROW][C]29[/C][C]0.103772766801288[/C][C]0.207545533602575[/C][C]0.896227233198712[/C][/ROW]
[ROW][C]30[/C][C]0.0796429231046958[/C][C]0.159285846209392[/C][C]0.920357076895304[/C][/ROW]
[ROW][C]31[/C][C]0.0589086600957179[/C][C]0.117817320191436[/C][C]0.941091339904282[/C][/ROW]
[ROW][C]32[/C][C]0.046145065133892[/C][C]0.092290130267784[/C][C]0.953854934866108[/C][/ROW]
[ROW][C]33[/C][C]0.0300416920710376[/C][C]0.0600833841420753[/C][C]0.969958307928962[/C][/ROW]
[ROW][C]34[/C][C]0.024762342128498[/C][C]0.049524684256996[/C][C]0.975237657871502[/C][/ROW]
[ROW][C]35[/C][C]0.0188796152998359[/C][C]0.0377592305996718[/C][C]0.981120384700164[/C][/ROW]
[ROW][C]36[/C][C]0.0201337641881172[/C][C]0.0402675283762344[/C][C]0.979866235811883[/C][/ROW]
[ROW][C]37[/C][C]0.0128598356973886[/C][C]0.0257196713947771[/C][C]0.987140164302611[/C][/ROW]
[ROW][C]38[/C][C]0.0112336019728602[/C][C]0.0224672039457204[/C][C]0.98876639802714[/C][/ROW]
[ROW][C]39[/C][C]0.00653937333377297[/C][C]0.0130787466675459[/C][C]0.993460626666227[/C][/ROW]
[ROW][C]40[/C][C]0.00480562931458587[/C][C]0.00961125862917173[/C][C]0.995194370685414[/C][/ROW]
[ROW][C]41[/C][C]0.00375130191349874[/C][C]0.00750260382699749[/C][C]0.996248698086501[/C][/ROW]
[ROW][C]42[/C][C]0.0820236140542165[/C][C]0.164047228108433[/C][C]0.917976385945783[/C][/ROW]
[ROW][C]43[/C][C]0.106310001672795[/C][C]0.21262000334559[/C][C]0.893689998327205[/C][/ROW]
[ROW][C]44[/C][C]0.0926662318157624[/C][C]0.185332463631525[/C][C]0.907333768184238[/C][/ROW]
[ROW][C]45[/C][C]0.0689622360262951[/C][C]0.13792447205259[/C][C]0.931037763973705[/C][/ROW]
[ROW][C]46[/C][C]0.123964524945913[/C][C]0.247929049891826[/C][C]0.876035475054087[/C][/ROW]
[ROW][C]47[/C][C]0.225151409361647[/C][C]0.450302818723294[/C][C]0.774848590638353[/C][/ROW]
[ROW][C]48[/C][C]0.434615135610765[/C][C]0.86923027122153[/C][C]0.565384864389235[/C][/ROW]
[ROW][C]49[/C][C]0.473103808589651[/C][C]0.946207617179302[/C][C]0.526896191410349[/C][/ROW]
[ROW][C]50[/C][C]0.434248782279752[/C][C]0.868497564559503[/C][C]0.565751217720248[/C][/ROW]
[ROW][C]51[/C][C]0.417683153832548[/C][C]0.835366307665095[/C][C]0.582316846167452[/C][/ROW]
[ROW][C]52[/C][C]0.905042301911785[/C][C]0.189915396176429[/C][C]0.0949576980882145[/C][/ROW]
[ROW][C]53[/C][C]0.830127769704173[/C][C]0.339744460591654[/C][C]0.169872230295827[/C][/ROW]
[ROW][C]54[/C][C]0.720481065373231[/C][C]0.559037869253539[/C][C]0.279518934626769[/C][/ROW]
[ROW][C]55[/C][C]0.706082353445038[/C][C]0.587835293109924[/C][C]0.293917646554962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145815&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145815&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2516447798769690.5032895597539390.748355220123031
100.1253937781444090.2507875562888190.874606221855591
110.08272899764264980.16545799528530.91727100235735
120.0436440785528770.0872881571057540.956355921447123
130.02281743273767630.04563486547535270.977182567262324
140.2739739284553890.5479478569107790.726026071544611
150.3113749697469440.6227499394938870.688625030253056
160.2239384173305660.4478768346611330.776061582669434
170.1551954019613580.3103908039227150.844804598038642
180.103397843386340.206795686772680.89660215661366
190.07193912288004710.1438782457600940.928060877119953
200.1957142191787690.3914284383575390.804285780821231
210.1437285573872130.2874571147744270.856271442612787
220.1167859369456610.2335718738913220.883214063054339
230.08373736029609860.1674747205921970.916262639703901
240.07804609156314540.1560921831262910.921953908436855
250.05376771559183830.1075354311836770.946232284408162
260.03798495963674310.07596991927348630.962015040363257
270.1681765614687360.3363531229374710.831823438531264
280.1419749960796650.283949992159330.858025003920335
290.1037727668012880.2075455336025750.896227233198712
300.07964292310469580.1592858462093920.920357076895304
310.05890866009571790.1178173201914360.941091339904282
320.0461450651338920.0922901302677840.953854934866108
330.03004169207103760.06008338414207530.969958307928962
340.0247623421284980.0495246842569960.975237657871502
350.01887961529983590.03775923059967180.981120384700164
360.02013376418811720.04026752837623440.979866235811883
370.01285983569738860.02571967139477710.987140164302611
380.01123360197286020.02246720394572040.98876639802714
390.006539373333772970.01307874666754590.993460626666227
400.004805629314585870.009611258629171730.995194370685414
410.003751301913498740.007502603826997490.996248698086501
420.08202361405421650.1640472281084330.917976385945783
430.1063100016727950.212620003345590.893689998327205
440.09266623181576240.1853324636315250.907333768184238
450.06896223602629510.137924472052590.931037763973705
460.1239645249459130.2479290498918260.876035475054087
470.2251514093616470.4503028187232940.774848590638353
480.4346151356107650.869230271221530.565384864389235
490.4731038085896510.9462076171793020.526896191410349
500.4342487822797520.8684975645595030.565751217720248
510.4176831538325480.8353663076650950.582316846167452
520.9050423019117850.1899153961764290.0949576980882145
530.8301277697041730.3397444605916540.169872230295827
540.7204810653732310.5590378692535390.279518934626769
550.7060823534450380.5878352931099240.293917646554962







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0425531914893617NOK
5% type I error level90.191489361702128NOK
10% type I error level130.276595744680851NOK

\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 & 0.0425531914893617 & NOK \tabularnewline
5% type I error level & 9 & 0.191489361702128 & NOK \tabularnewline
10% type I error level & 13 & 0.276595744680851 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145815&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]0.0425531914893617[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.191489361702128[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.276595744680851[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145815&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145815&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0425531914893617NOK
5% type I error level90.191489361702128NOK
10% type I error level130.276595744680851NOK



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')
}