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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 computationWed, 19 Dec 2012 10:50:45 -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/2012/Dec/19/t1355932277ogrgc398cg0lje3.htm/, Retrieved Thu, 31 Oct 2024 23:09:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202101, Retrieved Thu, 31 Oct 2024 23:09:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-12-19 15:50:45] [24c042819fd2b1ab385eb96782c689cf] [Current]
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Dataseries X:
1	1	1	1	4
0	1	1	1	4
0	1	1	1	4
0	1	1	1	4
0	1	1	1	4
1	1	1	0	4
0	1	1	1	4
0	1	1	1	4
1	1	1	1	4
0	1	1	1	4
0	1	1	1	4
0	1	1	1	4
0	0	1	0	4
0	1	1	1	4
1	0	1	0	4
1	0	1	0	4
0	0	0	0	4
0	1	1	1	4
1	1	1	1	4
1	0	0	0	4
0	1	1	0	4
1	0	1	0	4
1	1	1	0	4
1	1	1	0	4
1	0	1	1	4
0	0	1	0	4
1	1	1	1	4
0	0	1	1	4
1	1	1	1	4
0	1	1	0	4
0	1	1	1	4
0	1	1	1	4
0	1	1	0	4
1	1	1	1	4
0	1	1	1	4
0	1	1	1	4
0	0	1	0	4
1	0	1	1	4
1	1	1	0	4
0	1	1	0	4
1	0	0	0	4
1	0	1	1	4
1	1	1	0	4
0	1	1	1	4
0	1	1	0	4
1	1	1	0	4
0	1	1	1	4
1	1	1	1	4
1	1	1	0	4
0	1	1	1	4
0	0	1	1	4
0	0	0	0	4
1	1	1	1	4
0	0	0	1	4
0	1	1	1	4
1	0	1	1	4
1	0	1	0	4
1	1	1	1	4
1	1	1	1	4
1	0	0	0	4
1	1	1	1	4
0	0	1	0	4
0	1	1	1	4
1	1	1	1	4
0	1	1	1	4
0	1	1	1	4
0	0	0	0	4
0	1	1	1	4
1	1	1	1	4
0	0	1	1	4
0	1	1	1	4
1	1	1	1	4
1	0	1	1	4
0	0	1	1	4
1	1	1	1	4
1	1	1	0	4
1	1	1	1	4
1	0	1	0	4
1	0	0	1	4
0	1	1	0	4
0	1	1	1	4
1	0	1	1	4
0	1	1	1	4
0	0	0	1	4
1	1	1	0	4
0	1	1	1	4
1	1	1	1	2
1	0	1	1	2
0	1	1	1	2
1	1	1	1	2
0	1	1	0	2
0	1	1	1	2
0	1	1	0	2
0	1	1	1	2
0	1	1	1	2
1	1	1	1	2
0	1	1	1	2
0	1	1	1	2
0	1	1	1	2
1	1	1	1	2
1	1	1	1	2
0	1	1	1	2
0	1	1	1	2
0	1	1	1	2
0	0	1	1	2
0	1	1	1	2
0	1	1	1	2
0	0	1	1	2
0	1	1	1	2
0	1	1	1	2
0	0	1	0	2
0	1	1	1	2
0	0	1	1	2
0	0	1	1	2
0	1	1	1	2
0	1	1	1	2
1	1	1	1	2
0	1	1	1	2
0	1	1	1	2
1	1	1	1	2
0	1	1	1	2
0	1	1	1	2
0	0	1	1	2
1	0	1	0	2
1	1	1	1	2
0	1	1	1	2
0	1	1	0	2
1	1	1	1	2
0	1	1	1	2
1	1	1	1	2
0	1	1	1	2
1	1	1	1	2
0	0	1	1	2
0	1	1	1	2
0	1	1	1	2
0	1	1	1	2
1	0	1	0	2
1	0	1	0	2
0	1	1	1	2
0	1	1	1	2
1	0	0	1	2
1	0	1	1	2
0	1	1	1	2
1	1	1	0	2
0	1	1	0	2
1	1	1	1	2
0	0	1	1	2
0	1	1	1	2
0	1	1	1	2
1	1	1	0	2
1	1	1	1	2
0	0	0	1	2
0	0	0	0	2
0	0	1	1	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.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 & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202101&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202101&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202101&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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Outcome[t] = + 0.256251270127548 -0.0811414467212272Used[t] + 0.119858055515931Correctanalysis[t] -0.154643563195883Useful[t] + 0.064242918439258Weeks[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Outcome[t] =  +  0.256251270127548 -0.0811414467212272Used[t] +  0.119858055515931Correctanalysis[t] -0.154643563195883Useful[t] +  0.064242918439258Weeks[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202101&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Outcome[t] =  +  0.256251270127548 -0.0811414467212272Used[t] +  0.119858055515931Correctanalysis[t] -0.154643563195883Useful[t] +  0.064242918439258Weeks[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202101&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202101&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
Outcome[t] = + 0.256251270127548 -0.0811414467212272Used[t] + 0.119858055515931Correctanalysis[t] -0.154643563195883Useful[t] + 0.064242918439258Weeks[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2562512701275480.2074811.23510.2187530.109377
Used-0.08114144672122720.098001-0.8280.4090130.204506
Correctanalysis0.1198580555159310.1645520.72840.4675180.233759
Useful-0.1546435631958830.093634-1.65160.1007290.050364
Weeks0.0642429184392580.0403011.59410.1130390.05652

\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) & 0.256251270127548 & 0.207481 & 1.2351 & 0.218753 & 0.109377 \tabularnewline
Used & -0.0811414467212272 & 0.098001 & -0.828 & 0.409013 & 0.204506 \tabularnewline
Correctanalysis & 0.119858055515931 & 0.164552 & 0.7284 & 0.467518 & 0.233759 \tabularnewline
Useful & -0.154643563195883 & 0.093634 & -1.6516 & 0.100729 & 0.050364 \tabularnewline
Weeks & 0.064242918439258 & 0.040301 & 1.5941 & 0.113039 & 0.05652 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202101&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]0.256251270127548[/C][C]0.207481[/C][C]1.2351[/C][C]0.218753[/C][C]0.109377[/C][/ROW]
[ROW][C]Used[/C][C]-0.0811414467212272[/C][C]0.098001[/C][C]-0.828[/C][C]0.409013[/C][C]0.204506[/C][/ROW]
[ROW][C]Correctanalysis[/C][C]0.119858055515931[/C][C]0.164552[/C][C]0.7284[/C][C]0.467518[/C][C]0.233759[/C][/ROW]
[ROW][C]Useful[/C][C]-0.154643563195883[/C][C]0.093634[/C][C]-1.6516[/C][C]0.100729[/C][C]0.050364[/C][/ROW]
[ROW][C]Weeks[/C][C]0.064242918439258[/C][C]0.040301[/C][C]1.5941[/C][C]0.113039[/C][C]0.05652[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202101&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202101&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)0.2562512701275480.2074811.23510.2187530.109377
Used-0.08114144672122720.098001-0.8280.4090130.204506
Correctanalysis0.1198580555159310.1645520.72840.4675180.233759
Useful-0.1546435631958830.093634-1.65160.1007290.050364
Weeks0.0642429184392580.0403011.59410.1130390.05652







Multiple Linear Regression - Regression Statistics
Multiple R0.225989980574527
R-squared0.0510714713200751
Adjusted R-squared0.0255968799461175
F-TEST (value)2.00480041349298
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0.0967327535979984
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.484361537290013
Sum Squared Residuals34.9563087220858

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.225989980574527 \tabularnewline
R-squared & 0.0510714713200751 \tabularnewline
Adjusted R-squared & 0.0255968799461175 \tabularnewline
F-TEST (value) & 2.00480041349298 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.0967327535979984 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.484361537290013 \tabularnewline
Sum Squared Residuals & 34.9563087220858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202101&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.225989980574527[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0510714713200751[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0255968799461175[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.00480041349298[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.0967327535979984[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.484361537290013[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.9563087220858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202101&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202101&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 R0.225989980574527
R-squared0.0510714713200751
Adjusted R-squared0.0255968799461175
F-TEST (value)2.00480041349298
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0.0967327535979984
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.484361537290013
Sum Squared Residuals34.9563087220858







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3972959894834020.602704010516598
200.397295989483402-0.397295989483402
300.397295989483402-0.397295989483402
400.397295989483402-0.397295989483402
500.397295989483402-0.397295989483402
610.5519395526792840.448060447320716
700.397295989483402-0.397295989483402
800.397295989483402-0.397295989483402
910.3972959894834020.602704010516598
1000.397295989483402-0.397295989483402
1100.397295989483402-0.397295989483402
1200.397295989483402-0.397295989483402
1300.633080999400511-0.633080999400511
1400.397295989483402-0.397295989483402
1510.6330809994005110.366919000599489
1610.6330809994005110.366919000599489
1700.51322294388458-0.51322294388458
1800.397295989483402-0.397295989483402
1910.3972959894834020.602704010516598
2010.513222943884580.48677705611542
2100.551939552679284-0.551939552679284
2210.6330809994005110.366919000599489
2310.5519395526792840.448060447320716
2410.5519395526792840.448060447320716
2510.4784374362046290.521562563795371
2600.633080999400511-0.633080999400511
2710.3972959894834020.602704010516598
2800.478437436204629-0.478437436204629
2910.3972959894834020.602704010516598
3000.551939552679284-0.551939552679284
3100.397295989483402-0.397295989483402
3200.397295989483402-0.397295989483402
3300.551939552679284-0.551939552679284
3410.3972959894834020.602704010516598
3500.397295989483402-0.397295989483402
3600.397295989483402-0.397295989483402
3700.633080999400511-0.633080999400511
3810.4784374362046290.521562563795371
3910.5519395526792840.448060447320716
4000.551939552679284-0.551939552679284
4110.513222943884580.48677705611542
4210.4784374362046290.521562563795371
4310.5519395526792840.448060447320716
4400.397295989483402-0.397295989483402
4500.551939552679284-0.551939552679284
4610.5519395526792840.448060447320716
4700.397295989483402-0.397295989483402
4810.3972959894834020.602704010516598
4910.5519395526792840.448060447320716
5000.397295989483402-0.397295989483402
5100.478437436204629-0.478437436204629
5200.51322294388458-0.51322294388458
5310.3972959894834020.602704010516598
5400.358579380688698-0.358579380688698
5500.397295989483402-0.397295989483402
5610.4784374362046290.521562563795371
5710.6330809994005110.366919000599489
5810.3972959894834020.602704010516598
5910.3972959894834020.602704010516598
6010.513222943884580.48677705611542
6110.3972959894834020.602704010516598
6200.633080999400511-0.633080999400511
6300.397295989483402-0.397295989483402
6410.3972959894834020.602704010516598
6500.397295989483402-0.397295989483402
6600.397295989483402-0.397295989483402
6700.51322294388458-0.51322294388458
6800.397295989483402-0.397295989483402
6910.3972959894834020.602704010516598
7000.478437436204629-0.478437436204629
7100.397295989483402-0.397295989483402
7210.3972959894834020.602704010516598
7310.4784374362046290.521562563795371
7400.478437436204629-0.478437436204629
7510.3972959894834020.602704010516598
7610.5519395526792840.448060447320716
7710.3972959894834020.602704010516598
7810.6330809994005110.366919000599489
7910.3585793806886980.641420619311302
8000.551939552679284-0.551939552679284
8100.397295989483402-0.397295989483402
8210.4784374362046290.521562563795371
8300.397295989483402-0.397295989483402
8400.358579380688698-0.358579380688698
8510.5519395526792840.448060447320716
8600.397295989483402-0.397295989483402
8710.2688101526048860.731189847395114
8810.3499515993261130.650048400673887
8900.268810152604886-0.268810152604886
9010.2688101526048860.731189847395114
9100.423453715800768-0.423453715800768
9200.268810152604886-0.268810152604886
9300.423453715800768-0.423453715800768
9400.268810152604886-0.268810152604886
9500.268810152604886-0.268810152604886
9610.2688101526048860.731189847395114
9700.268810152604886-0.268810152604886
9800.268810152604886-0.268810152604886
9900.268810152604886-0.268810152604886
10010.2688101526048860.731189847395114
10110.2688101526048860.731189847395114
10200.268810152604886-0.268810152604886
10300.268810152604886-0.268810152604886
10400.268810152604886-0.268810152604886
10500.349951599326113-0.349951599326113
10600.268810152604886-0.268810152604886
10700.268810152604886-0.268810152604886
10800.349951599326113-0.349951599326113
10900.268810152604886-0.268810152604886
11000.268810152604886-0.268810152604886
11100.504595162521995-0.504595162521995
11200.268810152604886-0.268810152604886
11300.349951599326113-0.349951599326113
11400.349951599326113-0.349951599326113
11500.268810152604886-0.268810152604886
11600.268810152604886-0.268810152604886
11710.2688101526048860.731189847395114
11800.268810152604886-0.268810152604886
11900.268810152604886-0.268810152604886
12010.2688101526048860.731189847395114
12100.268810152604886-0.268810152604886
12200.268810152604886-0.268810152604886
12300.349951599326113-0.349951599326113
12410.5045951625219950.495404837478005
12510.2688101526048860.731189847395114
12600.268810152604886-0.268810152604886
12700.423453715800768-0.423453715800768
12810.2688101526048860.731189847395114
12900.268810152604886-0.268810152604886
13010.2688101526048860.731189847395114
13100.268810152604886-0.268810152604886
13210.2688101526048860.731189847395114
13300.349951599326113-0.349951599326113
13400.268810152604886-0.268810152604886
13500.268810152604886-0.268810152604886
13600.268810152604886-0.268810152604886
13710.5045951625219950.495404837478005
13810.5045951625219950.495404837478005
13900.268810152604886-0.268810152604886
14000.268810152604886-0.268810152604886
14110.2300935438101810.769906456189819
14210.3499515993261130.650048400673887
14300.268810152604886-0.268810152604886
14410.4234537158007680.576546284199232
14500.423453715800768-0.423453715800768
14610.2688101526048860.731189847395114
14700.349951599326113-0.349951599326113
14800.268810152604886-0.268810152604886
14900.268810152604886-0.268810152604886
15010.4234537158007680.576546284199232
15110.2688101526048860.731189847395114
15200.230093543810181-0.230093543810181
15300.384737107006064-0.384737107006064
15400.349951599326113-0.349951599326113

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
2 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
3 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
4 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
5 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
6 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
7 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
8 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
9 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
10 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
11 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
12 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
13 & 0 & 0.633080999400511 & -0.633080999400511 \tabularnewline
14 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
15 & 1 & 0.633080999400511 & 0.366919000599489 \tabularnewline
16 & 1 & 0.633080999400511 & 0.366919000599489 \tabularnewline
17 & 0 & 0.51322294388458 & -0.51322294388458 \tabularnewline
18 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
19 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
20 & 1 & 0.51322294388458 & 0.48677705611542 \tabularnewline
21 & 0 & 0.551939552679284 & -0.551939552679284 \tabularnewline
22 & 1 & 0.633080999400511 & 0.366919000599489 \tabularnewline
23 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
24 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
25 & 1 & 0.478437436204629 & 0.521562563795371 \tabularnewline
26 & 0 & 0.633080999400511 & -0.633080999400511 \tabularnewline
27 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
28 & 0 & 0.478437436204629 & -0.478437436204629 \tabularnewline
29 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
30 & 0 & 0.551939552679284 & -0.551939552679284 \tabularnewline
31 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
32 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
33 & 0 & 0.551939552679284 & -0.551939552679284 \tabularnewline
34 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
35 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
36 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
37 & 0 & 0.633080999400511 & -0.633080999400511 \tabularnewline
38 & 1 & 0.478437436204629 & 0.521562563795371 \tabularnewline
39 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
40 & 0 & 0.551939552679284 & -0.551939552679284 \tabularnewline
41 & 1 & 0.51322294388458 & 0.48677705611542 \tabularnewline
42 & 1 & 0.478437436204629 & 0.521562563795371 \tabularnewline
43 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
44 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
45 & 0 & 0.551939552679284 & -0.551939552679284 \tabularnewline
46 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
47 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
48 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
49 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
50 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
51 & 0 & 0.478437436204629 & -0.478437436204629 \tabularnewline
52 & 0 & 0.51322294388458 & -0.51322294388458 \tabularnewline
53 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
54 & 0 & 0.358579380688698 & -0.358579380688698 \tabularnewline
55 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
56 & 1 & 0.478437436204629 & 0.521562563795371 \tabularnewline
57 & 1 & 0.633080999400511 & 0.366919000599489 \tabularnewline
58 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
59 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
60 & 1 & 0.51322294388458 & 0.48677705611542 \tabularnewline
61 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
62 & 0 & 0.633080999400511 & -0.633080999400511 \tabularnewline
63 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
64 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
65 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
66 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
67 & 0 & 0.51322294388458 & -0.51322294388458 \tabularnewline
68 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
69 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
70 & 0 & 0.478437436204629 & -0.478437436204629 \tabularnewline
71 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
72 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
73 & 1 & 0.478437436204629 & 0.521562563795371 \tabularnewline
74 & 0 & 0.478437436204629 & -0.478437436204629 \tabularnewline
75 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
76 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
77 & 1 & 0.397295989483402 & 0.602704010516598 \tabularnewline
78 & 1 & 0.633080999400511 & 0.366919000599489 \tabularnewline
79 & 1 & 0.358579380688698 & 0.641420619311302 \tabularnewline
80 & 0 & 0.551939552679284 & -0.551939552679284 \tabularnewline
81 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
82 & 1 & 0.478437436204629 & 0.521562563795371 \tabularnewline
83 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
84 & 0 & 0.358579380688698 & -0.358579380688698 \tabularnewline
85 & 1 & 0.551939552679284 & 0.448060447320716 \tabularnewline
86 & 0 & 0.397295989483402 & -0.397295989483402 \tabularnewline
87 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
88 & 1 & 0.349951599326113 & 0.650048400673887 \tabularnewline
89 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
90 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
91 & 0 & 0.423453715800768 & -0.423453715800768 \tabularnewline
92 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
93 & 0 & 0.423453715800768 & -0.423453715800768 \tabularnewline
94 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
95 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
96 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
97 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
98 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
99 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
100 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
101 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
102 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
103 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
104 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
105 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
106 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
107 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
108 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
109 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
110 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
111 & 0 & 0.504595162521995 & -0.504595162521995 \tabularnewline
112 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
113 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
114 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
115 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
116 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
117 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
118 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
119 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
120 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
121 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
122 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
123 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
124 & 1 & 0.504595162521995 & 0.495404837478005 \tabularnewline
125 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
126 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
127 & 0 & 0.423453715800768 & -0.423453715800768 \tabularnewline
128 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
129 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
130 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
131 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
132 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
133 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
134 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
135 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
136 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
137 & 1 & 0.504595162521995 & 0.495404837478005 \tabularnewline
138 & 1 & 0.504595162521995 & 0.495404837478005 \tabularnewline
139 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
140 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
141 & 1 & 0.230093543810181 & 0.769906456189819 \tabularnewline
142 & 1 & 0.349951599326113 & 0.650048400673887 \tabularnewline
143 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
144 & 1 & 0.423453715800768 & 0.576546284199232 \tabularnewline
145 & 0 & 0.423453715800768 & -0.423453715800768 \tabularnewline
146 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
147 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
148 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
149 & 0 & 0.268810152604886 & -0.268810152604886 \tabularnewline
150 & 1 & 0.423453715800768 & 0.576546284199232 \tabularnewline
151 & 1 & 0.268810152604886 & 0.731189847395114 \tabularnewline
152 & 0 & 0.230093543810181 & -0.230093543810181 \tabularnewline
153 & 0 & 0.384737107006064 & -0.384737107006064 \tabularnewline
154 & 0 & 0.349951599326113 & -0.349951599326113 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202101&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]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.633080999400511[/C][C]-0.633080999400511[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.633080999400511[/C][C]0.366919000599489[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.633080999400511[/C][C]0.366919000599489[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.51322294388458[/C][C]-0.51322294388458[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.51322294388458[/C][C]0.48677705611542[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.551939552679284[/C][C]-0.551939552679284[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.633080999400511[/C][C]0.366919000599489[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.478437436204629[/C][C]0.521562563795371[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.633080999400511[/C][C]-0.633080999400511[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.478437436204629[/C][C]-0.478437436204629[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.551939552679284[/C][C]-0.551939552679284[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.551939552679284[/C][C]-0.551939552679284[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.633080999400511[/C][C]-0.633080999400511[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.478437436204629[/C][C]0.521562563795371[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.551939552679284[/C][C]-0.551939552679284[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.51322294388458[/C][C]0.48677705611542[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.478437436204629[/C][C]0.521562563795371[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.551939552679284[/C][C]-0.551939552679284[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.478437436204629[/C][C]-0.478437436204629[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.51322294388458[/C][C]-0.51322294388458[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.358579380688698[/C][C]-0.358579380688698[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.478437436204629[/C][C]0.521562563795371[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.633080999400511[/C][C]0.366919000599489[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.51322294388458[/C][C]0.48677705611542[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.633080999400511[/C][C]-0.633080999400511[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.51322294388458[/C][C]-0.51322294388458[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.478437436204629[/C][C]-0.478437436204629[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.478437436204629[/C][C]0.521562563795371[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.478437436204629[/C][C]-0.478437436204629[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.397295989483402[/C][C]0.602704010516598[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.633080999400511[/C][C]0.366919000599489[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.358579380688698[/C][C]0.641420619311302[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.551939552679284[/C][C]-0.551939552679284[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.478437436204629[/C][C]0.521562563795371[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.358579380688698[/C][C]-0.358579380688698[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.551939552679284[/C][C]0.448060447320716[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.397295989483402[/C][C]-0.397295989483402[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.349951599326113[/C][C]0.650048400673887[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.423453715800768[/C][C]-0.423453715800768[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.423453715800768[/C][C]-0.423453715800768[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.504595162521995[/C][C]-0.504595162521995[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.504595162521995[/C][C]0.495404837478005[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.423453715800768[/C][C]-0.423453715800768[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.504595162521995[/C][C]0.495404837478005[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.504595162521995[/C][C]0.495404837478005[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.230093543810181[/C][C]0.769906456189819[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0.349951599326113[/C][C]0.650048400673887[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0.423453715800768[/C][C]0.576546284199232[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.423453715800768[/C][C]-0.423453715800768[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.268810152604886[/C][C]-0.268810152604886[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0.423453715800768[/C][C]0.576546284199232[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0.268810152604886[/C][C]0.731189847395114[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.230093543810181[/C][C]-0.230093543810181[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.384737107006064[/C][C]-0.384737107006064[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.349951599326113[/C][C]-0.349951599326113[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202101&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202101&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
110.3972959894834020.602704010516598
200.397295989483402-0.397295989483402
300.397295989483402-0.397295989483402
400.397295989483402-0.397295989483402
500.397295989483402-0.397295989483402
610.5519395526792840.448060447320716
700.397295989483402-0.397295989483402
800.397295989483402-0.397295989483402
910.3972959894834020.602704010516598
1000.397295989483402-0.397295989483402
1100.397295989483402-0.397295989483402
1200.397295989483402-0.397295989483402
1300.633080999400511-0.633080999400511
1400.397295989483402-0.397295989483402
1510.6330809994005110.366919000599489
1610.6330809994005110.366919000599489
1700.51322294388458-0.51322294388458
1800.397295989483402-0.397295989483402
1910.3972959894834020.602704010516598
2010.513222943884580.48677705611542
2100.551939552679284-0.551939552679284
2210.6330809994005110.366919000599489
2310.5519395526792840.448060447320716
2410.5519395526792840.448060447320716
2510.4784374362046290.521562563795371
2600.633080999400511-0.633080999400511
2710.3972959894834020.602704010516598
2800.478437436204629-0.478437436204629
2910.3972959894834020.602704010516598
3000.551939552679284-0.551939552679284
3100.397295989483402-0.397295989483402
3200.397295989483402-0.397295989483402
3300.551939552679284-0.551939552679284
3410.3972959894834020.602704010516598
3500.397295989483402-0.397295989483402
3600.397295989483402-0.397295989483402
3700.633080999400511-0.633080999400511
3810.4784374362046290.521562563795371
3910.5519395526792840.448060447320716
4000.551939552679284-0.551939552679284
4110.513222943884580.48677705611542
4210.4784374362046290.521562563795371
4310.5519395526792840.448060447320716
4400.397295989483402-0.397295989483402
4500.551939552679284-0.551939552679284
4610.5519395526792840.448060447320716
4700.397295989483402-0.397295989483402
4810.3972959894834020.602704010516598
4910.5519395526792840.448060447320716
5000.397295989483402-0.397295989483402
5100.478437436204629-0.478437436204629
5200.51322294388458-0.51322294388458
5310.3972959894834020.602704010516598
5400.358579380688698-0.358579380688698
5500.397295989483402-0.397295989483402
5610.4784374362046290.521562563795371
5710.6330809994005110.366919000599489
5810.3972959894834020.602704010516598
5910.3972959894834020.602704010516598
6010.513222943884580.48677705611542
6110.3972959894834020.602704010516598
6200.633080999400511-0.633080999400511
6300.397295989483402-0.397295989483402
6410.3972959894834020.602704010516598
6500.397295989483402-0.397295989483402
6600.397295989483402-0.397295989483402
6700.51322294388458-0.51322294388458
6800.397295989483402-0.397295989483402
6910.3972959894834020.602704010516598
7000.478437436204629-0.478437436204629
7100.397295989483402-0.397295989483402
7210.3972959894834020.602704010516598
7310.4784374362046290.521562563795371
7400.478437436204629-0.478437436204629
7510.3972959894834020.602704010516598
7610.5519395526792840.448060447320716
7710.3972959894834020.602704010516598
7810.6330809994005110.366919000599489
7910.3585793806886980.641420619311302
8000.551939552679284-0.551939552679284
8100.397295989483402-0.397295989483402
8210.4784374362046290.521562563795371
8300.397295989483402-0.397295989483402
8400.358579380688698-0.358579380688698
8510.5519395526792840.448060447320716
8600.397295989483402-0.397295989483402
8710.2688101526048860.731189847395114
8810.3499515993261130.650048400673887
8900.268810152604886-0.268810152604886
9010.2688101526048860.731189847395114
9100.423453715800768-0.423453715800768
9200.268810152604886-0.268810152604886
9300.423453715800768-0.423453715800768
9400.268810152604886-0.268810152604886
9500.268810152604886-0.268810152604886
9610.2688101526048860.731189847395114
9700.268810152604886-0.268810152604886
9800.268810152604886-0.268810152604886
9900.268810152604886-0.268810152604886
10010.2688101526048860.731189847395114
10110.2688101526048860.731189847395114
10200.268810152604886-0.268810152604886
10300.268810152604886-0.268810152604886
10400.268810152604886-0.268810152604886
10500.349951599326113-0.349951599326113
10600.268810152604886-0.268810152604886
10700.268810152604886-0.268810152604886
10800.349951599326113-0.349951599326113
10900.268810152604886-0.268810152604886
11000.268810152604886-0.268810152604886
11100.504595162521995-0.504595162521995
11200.268810152604886-0.268810152604886
11300.349951599326113-0.349951599326113
11400.349951599326113-0.349951599326113
11500.268810152604886-0.268810152604886
11600.268810152604886-0.268810152604886
11710.2688101526048860.731189847395114
11800.268810152604886-0.268810152604886
11900.268810152604886-0.268810152604886
12010.2688101526048860.731189847395114
12100.268810152604886-0.268810152604886
12200.268810152604886-0.268810152604886
12300.349951599326113-0.349951599326113
12410.5045951625219950.495404837478005
12510.2688101526048860.731189847395114
12600.268810152604886-0.268810152604886
12700.423453715800768-0.423453715800768
12810.2688101526048860.731189847395114
12900.268810152604886-0.268810152604886
13010.2688101526048860.731189847395114
13100.268810152604886-0.268810152604886
13210.2688101526048860.731189847395114
13300.349951599326113-0.349951599326113
13400.268810152604886-0.268810152604886
13500.268810152604886-0.268810152604886
13600.268810152604886-0.268810152604886
13710.5045951625219950.495404837478005
13810.5045951625219950.495404837478005
13900.268810152604886-0.268810152604886
14000.268810152604886-0.268810152604886
14110.2300935438101810.769906456189819
14210.3499515993261130.650048400673887
14300.268810152604886-0.268810152604886
14410.4234537158007680.576546284199232
14500.423453715800768-0.423453715800768
14610.2688101526048860.731189847395114
14700.349951599326113-0.349951599326113
14800.268810152604886-0.268810152604886
14900.268810152604886-0.268810152604886
15010.4234537158007680.576546284199232
15110.2688101526048860.731189847395114
15200.230093543810181-0.230093543810181
15300.384737107006064-0.384737107006064
15400.349951599326113-0.349951599326113







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.6903112129373520.6193775741252960.309688787062648
90.8192447314099570.3615105371800850.180755268590043
100.7389746950595740.5220506098808510.261025304940426
110.6493969797606830.7012060404786350.350603020239317
120.5555450936382680.8889098127234630.444454906361732
130.4538220645303630.9076441290607260.546177935469637
140.3685421739585470.7370843479170950.631457826041453
150.4776843042828190.9553686085656370.522315695717181
160.450136699004710.900273398009420.54986330099529
170.3692975025401260.7385950050802530.630702497459874
180.3031219643306460.6062439286612920.696878035669354
190.4506209905942630.9012419811885250.549379009405737
200.5302004467337340.9395991065325320.469799553266266
210.6025772685301260.7948454629397470.397422731469874
220.556176649351580.8876467012968410.44382335064842
230.5341026653592920.9317946692814150.465897334640708
240.4920976102224160.9841952204448330.507902389777584
250.5313331347186690.9373337305626620.468666865281331
260.6238294564092680.7523410871814640.376170543590732
270.6791798977647880.6416402044704240.320820102235212
280.655152695337340.689694609325320.34484730466266
290.6979727432501830.6040545134996350.302027256749817
300.7194433605624710.5611132788750590.280556639437529
310.6906935185525960.6186129628948090.309306481447404
320.6601021506160240.6797956987679530.339897849383976
330.6667363313917590.6665273372164830.333263668608241
340.7063961045672210.5872077908655570.293603895432779
350.6799173134878290.6401653730243420.320082686512171
360.6522704581005370.6954590837989270.347729541899463
370.6772729730619670.6454540538760670.322727026938033
380.6859472891825280.6281054216349430.314052710817472
390.6887085363546460.6225829272907080.311291463645354
400.6900450391548550.619909921690290.309954960845145
410.6772615105707630.6454769788584750.322738489429237
420.6749894905601930.6500210188796140.325010509439807
430.6797586976892070.6404826046215860.320241302310793
440.6577892097544640.6844215804910720.342210790245536
450.6597179939981850.6805640120036290.340282006001815
460.6617180367662140.6765639264675720.338281963233786
470.640604088588250.71879182282350.35939591141175
480.671330185680330.6573396286393390.32866981431967
490.6674161398141110.6651677203717770.332583860185889
500.6481472044996520.7037055910006960.351852795500348
510.6463534843906870.7072930312186260.353646515609313
520.6585536765296210.6828926469407580.341446323470379
530.6845076016955810.6309847966088380.315492398304419
540.6599467519155910.6801064961688180.340053248084409
550.6432084776785190.7135830446429620.356791522321481
560.6466803364983040.7066393270033920.353319663501696
570.619945458463290.7601090830734210.38005454153671
580.6454535587542260.7090928824915470.354546441245774
590.6683738283909990.6632523432180020.331626171609001
600.6679191675716820.6641616648566350.332080832428318
610.6897685535140630.6204628929718740.310231446485937
620.7177420607220770.5645158785558470.282257939277923
630.703593891528820.5928122169423610.29640610847118
640.7218217526523230.5563564946953530.278178247347677
650.7079160146676580.5841679706646830.292083985332342
660.6947453920269070.6105092159461850.305254607973093
670.7028375602438390.5943248795123230.297162439756161
680.6924317670445190.6151364659109630.307568232955481
690.7073478262940830.5853043474118340.292652173705917
700.7095603713614610.5808792572770790.290439628638539
710.7023909443685070.5952181112629860.297609055631493
720.7140493058033540.5719013883932930.285950694196646
730.71359795541890.5728040891621990.2864020445811
740.7177988904044080.5644022191911830.282201109595592
750.7298907532181530.5402184935636940.270109246781847
760.7183084296039050.5633831407921910.281691570396095
770.7383235899801050.523352820039790.261676410019895
780.7234891386764510.5530217226470970.276510861323549
790.7590969956188780.4818060087622440.240903004381122
800.7595672765484110.4808654469031790.240432723451589
810.7427664358325570.5144671283348860.257233564167443
820.7597476659593130.4805046680813730.240252334040687
830.7381386385247290.5237227229505420.261861361475271
840.7164590983160750.5670818033678490.283540901683925
850.7233756969277050.553248606144590.276624303072295
860.693230189920280.613539620159440.30676981007972
870.7073018194613630.5853963610772730.292698180538637
880.7218140845931910.5563718308136170.278185915406809
890.727231557050010.5455368858999810.27276844294999
900.7527942133291560.4944115733416880.247205786670844
910.7720402509377340.4559194981245310.227959749062266
920.7552683145048890.4894633709902230.244731685495111
930.7635392996498380.4729214007003230.236460700350162
940.7393242516888450.5213514966223110.260675748311155
950.7123889808434150.5752220383131710.287611019156586
960.7594556226378990.4810887547242010.240544377362101
970.7339958747358790.5320082505282430.266004125264121
980.7062760995892330.5874478008215340.293723900410767
990.6766404477343420.6467191045313170.323359552265658
1000.7292793387828820.5414413224342370.270720661217118
1010.7791869521489790.4416260957020430.220813047851021
1020.7533433671622570.4933132656754870.246656632837743
1030.725332598856940.549334802286120.27466740114306
1040.6953824776586470.6092350446827060.304617522341353
1050.6670849888539240.6658300222921520.332915011146076
1060.6334143773628810.7331712452742380.366585622637119
1070.5986571401203140.8026857197593730.401342859879686
1080.5651259970115520.8697480059768950.434874002988448
1090.5285528867889440.9428942264221130.471447113211057
1100.4920560352881120.9841120705762240.507943964711888
1110.5058541123309220.9882917753381570.494145887669078
1120.4685508392927260.9371016785854530.531449160707274
1130.4362075845933340.8724151691866680.563792415406666
1140.4081476894865990.8162953789731970.591852310513401
1150.372362890653990.744725781307980.62763710934601
1160.3384104734920130.6768209469840270.661589526507987
1170.3962446727717990.7924893455435980.603755327228201
1180.3598017477899340.7196034955798680.640198252210066
1190.3254727397402460.6509454794804910.674527260259754
1200.3824662541953280.7649325083906560.617533745804672
1210.345072429977690.6901448599553810.65492757002231
1220.3101714154230670.6203428308461340.689828584576933
1230.290690869732290.5813817394645810.70930913026771
1240.2704706174809960.5409412349619930.729529382519004
1250.3198569671990450.6397139343980910.680143032800955
1260.2835075678263220.5670151356526450.716492432173678
1270.2976993322368170.5953986644736340.702300667763183
1280.3540434472630460.7080868945260930.645956552736954
1290.3130064735667820.6260129471335640.686993526433218
1300.380225606307470.760451212614940.61977439369253
1310.332595400561470.665190801122940.66740459943853
1320.4177041660336930.8354083320673860.582295833966307
1330.390826283019640.781652566039280.60917371698036
1340.3347664316606570.6695328633213130.665233568339343
1350.2832419807024420.5664839614048850.716758019297558
1360.2376164957903570.4752329915807150.762383504209643
1370.1969911874572080.3939823749144150.803008812542792
1380.1724762642810590.3449525285621180.827523735718941
1390.1435706330900870.2871412661801740.856429366909913
1400.1237356636267390.2474713272534790.876264336373261
1410.2170706612712010.4341413225424010.782929338728799
1420.3366271007469860.6732542014939720.663372899253014
1430.3138485476935170.6276970953870330.686151452306483
1440.2782739394529610.5565478789059220.721726060547039
1450.3373120412129080.6746240824258150.662687958787092
1460.3410615187802020.6821230375604030.658938481219798

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.690311212937352 & 0.619377574125296 & 0.309688787062648 \tabularnewline
9 & 0.819244731409957 & 0.361510537180085 & 0.180755268590043 \tabularnewline
10 & 0.738974695059574 & 0.522050609880851 & 0.261025304940426 \tabularnewline
11 & 0.649396979760683 & 0.701206040478635 & 0.350603020239317 \tabularnewline
12 & 0.555545093638268 & 0.888909812723463 & 0.444454906361732 \tabularnewline
13 & 0.453822064530363 & 0.907644129060726 & 0.546177935469637 \tabularnewline
14 & 0.368542173958547 & 0.737084347917095 & 0.631457826041453 \tabularnewline
15 & 0.477684304282819 & 0.955368608565637 & 0.522315695717181 \tabularnewline
16 & 0.45013669900471 & 0.90027339800942 & 0.54986330099529 \tabularnewline
17 & 0.369297502540126 & 0.738595005080253 & 0.630702497459874 \tabularnewline
18 & 0.303121964330646 & 0.606243928661292 & 0.696878035669354 \tabularnewline
19 & 0.450620990594263 & 0.901241981188525 & 0.549379009405737 \tabularnewline
20 & 0.530200446733734 & 0.939599106532532 & 0.469799553266266 \tabularnewline
21 & 0.602577268530126 & 0.794845462939747 & 0.397422731469874 \tabularnewline
22 & 0.55617664935158 & 0.887646701296841 & 0.44382335064842 \tabularnewline
23 & 0.534102665359292 & 0.931794669281415 & 0.465897334640708 \tabularnewline
24 & 0.492097610222416 & 0.984195220444833 & 0.507902389777584 \tabularnewline
25 & 0.531333134718669 & 0.937333730562662 & 0.468666865281331 \tabularnewline
26 & 0.623829456409268 & 0.752341087181464 & 0.376170543590732 \tabularnewline
27 & 0.679179897764788 & 0.641640204470424 & 0.320820102235212 \tabularnewline
28 & 0.65515269533734 & 0.68969460932532 & 0.34484730466266 \tabularnewline
29 & 0.697972743250183 & 0.604054513499635 & 0.302027256749817 \tabularnewline
30 & 0.719443360562471 & 0.561113278875059 & 0.280556639437529 \tabularnewline
31 & 0.690693518552596 & 0.618612962894809 & 0.309306481447404 \tabularnewline
32 & 0.660102150616024 & 0.679795698767953 & 0.339897849383976 \tabularnewline
33 & 0.666736331391759 & 0.666527337216483 & 0.333263668608241 \tabularnewline
34 & 0.706396104567221 & 0.587207790865557 & 0.293603895432779 \tabularnewline
35 & 0.679917313487829 & 0.640165373024342 & 0.320082686512171 \tabularnewline
36 & 0.652270458100537 & 0.695459083798927 & 0.347729541899463 \tabularnewline
37 & 0.677272973061967 & 0.645454053876067 & 0.322727026938033 \tabularnewline
38 & 0.685947289182528 & 0.628105421634943 & 0.314052710817472 \tabularnewline
39 & 0.688708536354646 & 0.622582927290708 & 0.311291463645354 \tabularnewline
40 & 0.690045039154855 & 0.61990992169029 & 0.309954960845145 \tabularnewline
41 & 0.677261510570763 & 0.645476978858475 & 0.322738489429237 \tabularnewline
42 & 0.674989490560193 & 0.650021018879614 & 0.325010509439807 \tabularnewline
43 & 0.679758697689207 & 0.640482604621586 & 0.320241302310793 \tabularnewline
44 & 0.657789209754464 & 0.684421580491072 & 0.342210790245536 \tabularnewline
45 & 0.659717993998185 & 0.680564012003629 & 0.340282006001815 \tabularnewline
46 & 0.661718036766214 & 0.676563926467572 & 0.338281963233786 \tabularnewline
47 & 0.64060408858825 & 0.7187918228235 & 0.35939591141175 \tabularnewline
48 & 0.67133018568033 & 0.657339628639339 & 0.32866981431967 \tabularnewline
49 & 0.667416139814111 & 0.665167720371777 & 0.332583860185889 \tabularnewline
50 & 0.648147204499652 & 0.703705591000696 & 0.351852795500348 \tabularnewline
51 & 0.646353484390687 & 0.707293031218626 & 0.353646515609313 \tabularnewline
52 & 0.658553676529621 & 0.682892646940758 & 0.341446323470379 \tabularnewline
53 & 0.684507601695581 & 0.630984796608838 & 0.315492398304419 \tabularnewline
54 & 0.659946751915591 & 0.680106496168818 & 0.340053248084409 \tabularnewline
55 & 0.643208477678519 & 0.713583044642962 & 0.356791522321481 \tabularnewline
56 & 0.646680336498304 & 0.706639327003392 & 0.353319663501696 \tabularnewline
57 & 0.61994545846329 & 0.760109083073421 & 0.38005454153671 \tabularnewline
58 & 0.645453558754226 & 0.709092882491547 & 0.354546441245774 \tabularnewline
59 & 0.668373828390999 & 0.663252343218002 & 0.331626171609001 \tabularnewline
60 & 0.667919167571682 & 0.664161664856635 & 0.332080832428318 \tabularnewline
61 & 0.689768553514063 & 0.620462892971874 & 0.310231446485937 \tabularnewline
62 & 0.717742060722077 & 0.564515878555847 & 0.282257939277923 \tabularnewline
63 & 0.70359389152882 & 0.592812216942361 & 0.29640610847118 \tabularnewline
64 & 0.721821752652323 & 0.556356494695353 & 0.278178247347677 \tabularnewline
65 & 0.707916014667658 & 0.584167970664683 & 0.292083985332342 \tabularnewline
66 & 0.694745392026907 & 0.610509215946185 & 0.305254607973093 \tabularnewline
67 & 0.702837560243839 & 0.594324879512323 & 0.297162439756161 \tabularnewline
68 & 0.692431767044519 & 0.615136465910963 & 0.307568232955481 \tabularnewline
69 & 0.707347826294083 & 0.585304347411834 & 0.292652173705917 \tabularnewline
70 & 0.709560371361461 & 0.580879257277079 & 0.290439628638539 \tabularnewline
71 & 0.702390944368507 & 0.595218111262986 & 0.297609055631493 \tabularnewline
72 & 0.714049305803354 & 0.571901388393293 & 0.285950694196646 \tabularnewline
73 & 0.7135979554189 & 0.572804089162199 & 0.2864020445811 \tabularnewline
74 & 0.717798890404408 & 0.564402219191183 & 0.282201109595592 \tabularnewline
75 & 0.729890753218153 & 0.540218493563694 & 0.270109246781847 \tabularnewline
76 & 0.718308429603905 & 0.563383140792191 & 0.281691570396095 \tabularnewline
77 & 0.738323589980105 & 0.52335282003979 & 0.261676410019895 \tabularnewline
78 & 0.723489138676451 & 0.553021722647097 & 0.276510861323549 \tabularnewline
79 & 0.759096995618878 & 0.481806008762244 & 0.240903004381122 \tabularnewline
80 & 0.759567276548411 & 0.480865446903179 & 0.240432723451589 \tabularnewline
81 & 0.742766435832557 & 0.514467128334886 & 0.257233564167443 \tabularnewline
82 & 0.759747665959313 & 0.480504668081373 & 0.240252334040687 \tabularnewline
83 & 0.738138638524729 & 0.523722722950542 & 0.261861361475271 \tabularnewline
84 & 0.716459098316075 & 0.567081803367849 & 0.283540901683925 \tabularnewline
85 & 0.723375696927705 & 0.55324860614459 & 0.276624303072295 \tabularnewline
86 & 0.69323018992028 & 0.61353962015944 & 0.30676981007972 \tabularnewline
87 & 0.707301819461363 & 0.585396361077273 & 0.292698180538637 \tabularnewline
88 & 0.721814084593191 & 0.556371830813617 & 0.278185915406809 \tabularnewline
89 & 0.72723155705001 & 0.545536885899981 & 0.27276844294999 \tabularnewline
90 & 0.752794213329156 & 0.494411573341688 & 0.247205786670844 \tabularnewline
91 & 0.772040250937734 & 0.455919498124531 & 0.227959749062266 \tabularnewline
92 & 0.755268314504889 & 0.489463370990223 & 0.244731685495111 \tabularnewline
93 & 0.763539299649838 & 0.472921400700323 & 0.236460700350162 \tabularnewline
94 & 0.739324251688845 & 0.521351496622311 & 0.260675748311155 \tabularnewline
95 & 0.712388980843415 & 0.575222038313171 & 0.287611019156586 \tabularnewline
96 & 0.759455622637899 & 0.481088754724201 & 0.240544377362101 \tabularnewline
97 & 0.733995874735879 & 0.532008250528243 & 0.266004125264121 \tabularnewline
98 & 0.706276099589233 & 0.587447800821534 & 0.293723900410767 \tabularnewline
99 & 0.676640447734342 & 0.646719104531317 & 0.323359552265658 \tabularnewline
100 & 0.729279338782882 & 0.541441322434237 & 0.270720661217118 \tabularnewline
101 & 0.779186952148979 & 0.441626095702043 & 0.220813047851021 \tabularnewline
102 & 0.753343367162257 & 0.493313265675487 & 0.246656632837743 \tabularnewline
103 & 0.72533259885694 & 0.54933480228612 & 0.27466740114306 \tabularnewline
104 & 0.695382477658647 & 0.609235044682706 & 0.304617522341353 \tabularnewline
105 & 0.667084988853924 & 0.665830022292152 & 0.332915011146076 \tabularnewline
106 & 0.633414377362881 & 0.733171245274238 & 0.366585622637119 \tabularnewline
107 & 0.598657140120314 & 0.802685719759373 & 0.401342859879686 \tabularnewline
108 & 0.565125997011552 & 0.869748005976895 & 0.434874002988448 \tabularnewline
109 & 0.528552886788944 & 0.942894226422113 & 0.471447113211057 \tabularnewline
110 & 0.492056035288112 & 0.984112070576224 & 0.507943964711888 \tabularnewline
111 & 0.505854112330922 & 0.988291775338157 & 0.494145887669078 \tabularnewline
112 & 0.468550839292726 & 0.937101678585453 & 0.531449160707274 \tabularnewline
113 & 0.436207584593334 & 0.872415169186668 & 0.563792415406666 \tabularnewline
114 & 0.408147689486599 & 0.816295378973197 & 0.591852310513401 \tabularnewline
115 & 0.37236289065399 & 0.74472578130798 & 0.62763710934601 \tabularnewline
116 & 0.338410473492013 & 0.676820946984027 & 0.661589526507987 \tabularnewline
117 & 0.396244672771799 & 0.792489345543598 & 0.603755327228201 \tabularnewline
118 & 0.359801747789934 & 0.719603495579868 & 0.640198252210066 \tabularnewline
119 & 0.325472739740246 & 0.650945479480491 & 0.674527260259754 \tabularnewline
120 & 0.382466254195328 & 0.764932508390656 & 0.617533745804672 \tabularnewline
121 & 0.34507242997769 & 0.690144859955381 & 0.65492757002231 \tabularnewline
122 & 0.310171415423067 & 0.620342830846134 & 0.689828584576933 \tabularnewline
123 & 0.29069086973229 & 0.581381739464581 & 0.70930913026771 \tabularnewline
124 & 0.270470617480996 & 0.540941234961993 & 0.729529382519004 \tabularnewline
125 & 0.319856967199045 & 0.639713934398091 & 0.680143032800955 \tabularnewline
126 & 0.283507567826322 & 0.567015135652645 & 0.716492432173678 \tabularnewline
127 & 0.297699332236817 & 0.595398664473634 & 0.702300667763183 \tabularnewline
128 & 0.354043447263046 & 0.708086894526093 & 0.645956552736954 \tabularnewline
129 & 0.313006473566782 & 0.626012947133564 & 0.686993526433218 \tabularnewline
130 & 0.38022560630747 & 0.76045121261494 & 0.61977439369253 \tabularnewline
131 & 0.33259540056147 & 0.66519080112294 & 0.66740459943853 \tabularnewline
132 & 0.417704166033693 & 0.835408332067386 & 0.582295833966307 \tabularnewline
133 & 0.39082628301964 & 0.78165256603928 & 0.60917371698036 \tabularnewline
134 & 0.334766431660657 & 0.669532863321313 & 0.665233568339343 \tabularnewline
135 & 0.283241980702442 & 0.566483961404885 & 0.716758019297558 \tabularnewline
136 & 0.237616495790357 & 0.475232991580715 & 0.762383504209643 \tabularnewline
137 & 0.196991187457208 & 0.393982374914415 & 0.803008812542792 \tabularnewline
138 & 0.172476264281059 & 0.344952528562118 & 0.827523735718941 \tabularnewline
139 & 0.143570633090087 & 0.287141266180174 & 0.856429366909913 \tabularnewline
140 & 0.123735663626739 & 0.247471327253479 & 0.876264336373261 \tabularnewline
141 & 0.217070661271201 & 0.434141322542401 & 0.782929338728799 \tabularnewline
142 & 0.336627100746986 & 0.673254201493972 & 0.663372899253014 \tabularnewline
143 & 0.313848547693517 & 0.627697095387033 & 0.686151452306483 \tabularnewline
144 & 0.278273939452961 & 0.556547878905922 & 0.721726060547039 \tabularnewline
145 & 0.337312041212908 & 0.674624082425815 & 0.662687958787092 \tabularnewline
146 & 0.341061518780202 & 0.682123037560403 & 0.658938481219798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202101&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]8[/C][C]0.690311212937352[/C][C]0.619377574125296[/C][C]0.309688787062648[/C][/ROW]
[ROW][C]9[/C][C]0.819244731409957[/C][C]0.361510537180085[/C][C]0.180755268590043[/C][/ROW]
[ROW][C]10[/C][C]0.738974695059574[/C][C]0.522050609880851[/C][C]0.261025304940426[/C][/ROW]
[ROW][C]11[/C][C]0.649396979760683[/C][C]0.701206040478635[/C][C]0.350603020239317[/C][/ROW]
[ROW][C]12[/C][C]0.555545093638268[/C][C]0.888909812723463[/C][C]0.444454906361732[/C][/ROW]
[ROW][C]13[/C][C]0.453822064530363[/C][C]0.907644129060726[/C][C]0.546177935469637[/C][/ROW]
[ROW][C]14[/C][C]0.368542173958547[/C][C]0.737084347917095[/C][C]0.631457826041453[/C][/ROW]
[ROW][C]15[/C][C]0.477684304282819[/C][C]0.955368608565637[/C][C]0.522315695717181[/C][/ROW]
[ROW][C]16[/C][C]0.45013669900471[/C][C]0.90027339800942[/C][C]0.54986330099529[/C][/ROW]
[ROW][C]17[/C][C]0.369297502540126[/C][C]0.738595005080253[/C][C]0.630702497459874[/C][/ROW]
[ROW][C]18[/C][C]0.303121964330646[/C][C]0.606243928661292[/C][C]0.696878035669354[/C][/ROW]
[ROW][C]19[/C][C]0.450620990594263[/C][C]0.901241981188525[/C][C]0.549379009405737[/C][/ROW]
[ROW][C]20[/C][C]0.530200446733734[/C][C]0.939599106532532[/C][C]0.469799553266266[/C][/ROW]
[ROW][C]21[/C][C]0.602577268530126[/C][C]0.794845462939747[/C][C]0.397422731469874[/C][/ROW]
[ROW][C]22[/C][C]0.55617664935158[/C][C]0.887646701296841[/C][C]0.44382335064842[/C][/ROW]
[ROW][C]23[/C][C]0.534102665359292[/C][C]0.931794669281415[/C][C]0.465897334640708[/C][/ROW]
[ROW][C]24[/C][C]0.492097610222416[/C][C]0.984195220444833[/C][C]0.507902389777584[/C][/ROW]
[ROW][C]25[/C][C]0.531333134718669[/C][C]0.937333730562662[/C][C]0.468666865281331[/C][/ROW]
[ROW][C]26[/C][C]0.623829456409268[/C][C]0.752341087181464[/C][C]0.376170543590732[/C][/ROW]
[ROW][C]27[/C][C]0.679179897764788[/C][C]0.641640204470424[/C][C]0.320820102235212[/C][/ROW]
[ROW][C]28[/C][C]0.65515269533734[/C][C]0.68969460932532[/C][C]0.34484730466266[/C][/ROW]
[ROW][C]29[/C][C]0.697972743250183[/C][C]0.604054513499635[/C][C]0.302027256749817[/C][/ROW]
[ROW][C]30[/C][C]0.719443360562471[/C][C]0.561113278875059[/C][C]0.280556639437529[/C][/ROW]
[ROW][C]31[/C][C]0.690693518552596[/C][C]0.618612962894809[/C][C]0.309306481447404[/C][/ROW]
[ROW][C]32[/C][C]0.660102150616024[/C][C]0.679795698767953[/C][C]0.339897849383976[/C][/ROW]
[ROW][C]33[/C][C]0.666736331391759[/C][C]0.666527337216483[/C][C]0.333263668608241[/C][/ROW]
[ROW][C]34[/C][C]0.706396104567221[/C][C]0.587207790865557[/C][C]0.293603895432779[/C][/ROW]
[ROW][C]35[/C][C]0.679917313487829[/C][C]0.640165373024342[/C][C]0.320082686512171[/C][/ROW]
[ROW][C]36[/C][C]0.652270458100537[/C][C]0.695459083798927[/C][C]0.347729541899463[/C][/ROW]
[ROW][C]37[/C][C]0.677272973061967[/C][C]0.645454053876067[/C][C]0.322727026938033[/C][/ROW]
[ROW][C]38[/C][C]0.685947289182528[/C][C]0.628105421634943[/C][C]0.314052710817472[/C][/ROW]
[ROW][C]39[/C][C]0.688708536354646[/C][C]0.622582927290708[/C][C]0.311291463645354[/C][/ROW]
[ROW][C]40[/C][C]0.690045039154855[/C][C]0.61990992169029[/C][C]0.309954960845145[/C][/ROW]
[ROW][C]41[/C][C]0.677261510570763[/C][C]0.645476978858475[/C][C]0.322738489429237[/C][/ROW]
[ROW][C]42[/C][C]0.674989490560193[/C][C]0.650021018879614[/C][C]0.325010509439807[/C][/ROW]
[ROW][C]43[/C][C]0.679758697689207[/C][C]0.640482604621586[/C][C]0.320241302310793[/C][/ROW]
[ROW][C]44[/C][C]0.657789209754464[/C][C]0.684421580491072[/C][C]0.342210790245536[/C][/ROW]
[ROW][C]45[/C][C]0.659717993998185[/C][C]0.680564012003629[/C][C]0.340282006001815[/C][/ROW]
[ROW][C]46[/C][C]0.661718036766214[/C][C]0.676563926467572[/C][C]0.338281963233786[/C][/ROW]
[ROW][C]47[/C][C]0.64060408858825[/C][C]0.7187918228235[/C][C]0.35939591141175[/C][/ROW]
[ROW][C]48[/C][C]0.67133018568033[/C][C]0.657339628639339[/C][C]0.32866981431967[/C][/ROW]
[ROW][C]49[/C][C]0.667416139814111[/C][C]0.665167720371777[/C][C]0.332583860185889[/C][/ROW]
[ROW][C]50[/C][C]0.648147204499652[/C][C]0.703705591000696[/C][C]0.351852795500348[/C][/ROW]
[ROW][C]51[/C][C]0.646353484390687[/C][C]0.707293031218626[/C][C]0.353646515609313[/C][/ROW]
[ROW][C]52[/C][C]0.658553676529621[/C][C]0.682892646940758[/C][C]0.341446323470379[/C][/ROW]
[ROW][C]53[/C][C]0.684507601695581[/C][C]0.630984796608838[/C][C]0.315492398304419[/C][/ROW]
[ROW][C]54[/C][C]0.659946751915591[/C][C]0.680106496168818[/C][C]0.340053248084409[/C][/ROW]
[ROW][C]55[/C][C]0.643208477678519[/C][C]0.713583044642962[/C][C]0.356791522321481[/C][/ROW]
[ROW][C]56[/C][C]0.646680336498304[/C][C]0.706639327003392[/C][C]0.353319663501696[/C][/ROW]
[ROW][C]57[/C][C]0.61994545846329[/C][C]0.760109083073421[/C][C]0.38005454153671[/C][/ROW]
[ROW][C]58[/C][C]0.645453558754226[/C][C]0.709092882491547[/C][C]0.354546441245774[/C][/ROW]
[ROW][C]59[/C][C]0.668373828390999[/C][C]0.663252343218002[/C][C]0.331626171609001[/C][/ROW]
[ROW][C]60[/C][C]0.667919167571682[/C][C]0.664161664856635[/C][C]0.332080832428318[/C][/ROW]
[ROW][C]61[/C][C]0.689768553514063[/C][C]0.620462892971874[/C][C]0.310231446485937[/C][/ROW]
[ROW][C]62[/C][C]0.717742060722077[/C][C]0.564515878555847[/C][C]0.282257939277923[/C][/ROW]
[ROW][C]63[/C][C]0.70359389152882[/C][C]0.592812216942361[/C][C]0.29640610847118[/C][/ROW]
[ROW][C]64[/C][C]0.721821752652323[/C][C]0.556356494695353[/C][C]0.278178247347677[/C][/ROW]
[ROW][C]65[/C][C]0.707916014667658[/C][C]0.584167970664683[/C][C]0.292083985332342[/C][/ROW]
[ROW][C]66[/C][C]0.694745392026907[/C][C]0.610509215946185[/C][C]0.305254607973093[/C][/ROW]
[ROW][C]67[/C][C]0.702837560243839[/C][C]0.594324879512323[/C][C]0.297162439756161[/C][/ROW]
[ROW][C]68[/C][C]0.692431767044519[/C][C]0.615136465910963[/C][C]0.307568232955481[/C][/ROW]
[ROW][C]69[/C][C]0.707347826294083[/C][C]0.585304347411834[/C][C]0.292652173705917[/C][/ROW]
[ROW][C]70[/C][C]0.709560371361461[/C][C]0.580879257277079[/C][C]0.290439628638539[/C][/ROW]
[ROW][C]71[/C][C]0.702390944368507[/C][C]0.595218111262986[/C][C]0.297609055631493[/C][/ROW]
[ROW][C]72[/C][C]0.714049305803354[/C][C]0.571901388393293[/C][C]0.285950694196646[/C][/ROW]
[ROW][C]73[/C][C]0.7135979554189[/C][C]0.572804089162199[/C][C]0.2864020445811[/C][/ROW]
[ROW][C]74[/C][C]0.717798890404408[/C][C]0.564402219191183[/C][C]0.282201109595592[/C][/ROW]
[ROW][C]75[/C][C]0.729890753218153[/C][C]0.540218493563694[/C][C]0.270109246781847[/C][/ROW]
[ROW][C]76[/C][C]0.718308429603905[/C][C]0.563383140792191[/C][C]0.281691570396095[/C][/ROW]
[ROW][C]77[/C][C]0.738323589980105[/C][C]0.52335282003979[/C][C]0.261676410019895[/C][/ROW]
[ROW][C]78[/C][C]0.723489138676451[/C][C]0.553021722647097[/C][C]0.276510861323549[/C][/ROW]
[ROW][C]79[/C][C]0.759096995618878[/C][C]0.481806008762244[/C][C]0.240903004381122[/C][/ROW]
[ROW][C]80[/C][C]0.759567276548411[/C][C]0.480865446903179[/C][C]0.240432723451589[/C][/ROW]
[ROW][C]81[/C][C]0.742766435832557[/C][C]0.514467128334886[/C][C]0.257233564167443[/C][/ROW]
[ROW][C]82[/C][C]0.759747665959313[/C][C]0.480504668081373[/C][C]0.240252334040687[/C][/ROW]
[ROW][C]83[/C][C]0.738138638524729[/C][C]0.523722722950542[/C][C]0.261861361475271[/C][/ROW]
[ROW][C]84[/C][C]0.716459098316075[/C][C]0.567081803367849[/C][C]0.283540901683925[/C][/ROW]
[ROW][C]85[/C][C]0.723375696927705[/C][C]0.55324860614459[/C][C]0.276624303072295[/C][/ROW]
[ROW][C]86[/C][C]0.69323018992028[/C][C]0.61353962015944[/C][C]0.30676981007972[/C][/ROW]
[ROW][C]87[/C][C]0.707301819461363[/C][C]0.585396361077273[/C][C]0.292698180538637[/C][/ROW]
[ROW][C]88[/C][C]0.721814084593191[/C][C]0.556371830813617[/C][C]0.278185915406809[/C][/ROW]
[ROW][C]89[/C][C]0.72723155705001[/C][C]0.545536885899981[/C][C]0.27276844294999[/C][/ROW]
[ROW][C]90[/C][C]0.752794213329156[/C][C]0.494411573341688[/C][C]0.247205786670844[/C][/ROW]
[ROW][C]91[/C][C]0.772040250937734[/C][C]0.455919498124531[/C][C]0.227959749062266[/C][/ROW]
[ROW][C]92[/C][C]0.755268314504889[/C][C]0.489463370990223[/C][C]0.244731685495111[/C][/ROW]
[ROW][C]93[/C][C]0.763539299649838[/C][C]0.472921400700323[/C][C]0.236460700350162[/C][/ROW]
[ROW][C]94[/C][C]0.739324251688845[/C][C]0.521351496622311[/C][C]0.260675748311155[/C][/ROW]
[ROW][C]95[/C][C]0.712388980843415[/C][C]0.575222038313171[/C][C]0.287611019156586[/C][/ROW]
[ROW][C]96[/C][C]0.759455622637899[/C][C]0.481088754724201[/C][C]0.240544377362101[/C][/ROW]
[ROW][C]97[/C][C]0.733995874735879[/C][C]0.532008250528243[/C][C]0.266004125264121[/C][/ROW]
[ROW][C]98[/C][C]0.706276099589233[/C][C]0.587447800821534[/C][C]0.293723900410767[/C][/ROW]
[ROW][C]99[/C][C]0.676640447734342[/C][C]0.646719104531317[/C][C]0.323359552265658[/C][/ROW]
[ROW][C]100[/C][C]0.729279338782882[/C][C]0.541441322434237[/C][C]0.270720661217118[/C][/ROW]
[ROW][C]101[/C][C]0.779186952148979[/C][C]0.441626095702043[/C][C]0.220813047851021[/C][/ROW]
[ROW][C]102[/C][C]0.753343367162257[/C][C]0.493313265675487[/C][C]0.246656632837743[/C][/ROW]
[ROW][C]103[/C][C]0.72533259885694[/C][C]0.54933480228612[/C][C]0.27466740114306[/C][/ROW]
[ROW][C]104[/C][C]0.695382477658647[/C][C]0.609235044682706[/C][C]0.304617522341353[/C][/ROW]
[ROW][C]105[/C][C]0.667084988853924[/C][C]0.665830022292152[/C][C]0.332915011146076[/C][/ROW]
[ROW][C]106[/C][C]0.633414377362881[/C][C]0.733171245274238[/C][C]0.366585622637119[/C][/ROW]
[ROW][C]107[/C][C]0.598657140120314[/C][C]0.802685719759373[/C][C]0.401342859879686[/C][/ROW]
[ROW][C]108[/C][C]0.565125997011552[/C][C]0.869748005976895[/C][C]0.434874002988448[/C][/ROW]
[ROW][C]109[/C][C]0.528552886788944[/C][C]0.942894226422113[/C][C]0.471447113211057[/C][/ROW]
[ROW][C]110[/C][C]0.492056035288112[/C][C]0.984112070576224[/C][C]0.507943964711888[/C][/ROW]
[ROW][C]111[/C][C]0.505854112330922[/C][C]0.988291775338157[/C][C]0.494145887669078[/C][/ROW]
[ROW][C]112[/C][C]0.468550839292726[/C][C]0.937101678585453[/C][C]0.531449160707274[/C][/ROW]
[ROW][C]113[/C][C]0.436207584593334[/C][C]0.872415169186668[/C][C]0.563792415406666[/C][/ROW]
[ROW][C]114[/C][C]0.408147689486599[/C][C]0.816295378973197[/C][C]0.591852310513401[/C][/ROW]
[ROW][C]115[/C][C]0.37236289065399[/C][C]0.74472578130798[/C][C]0.62763710934601[/C][/ROW]
[ROW][C]116[/C][C]0.338410473492013[/C][C]0.676820946984027[/C][C]0.661589526507987[/C][/ROW]
[ROW][C]117[/C][C]0.396244672771799[/C][C]0.792489345543598[/C][C]0.603755327228201[/C][/ROW]
[ROW][C]118[/C][C]0.359801747789934[/C][C]0.719603495579868[/C][C]0.640198252210066[/C][/ROW]
[ROW][C]119[/C][C]0.325472739740246[/C][C]0.650945479480491[/C][C]0.674527260259754[/C][/ROW]
[ROW][C]120[/C][C]0.382466254195328[/C][C]0.764932508390656[/C][C]0.617533745804672[/C][/ROW]
[ROW][C]121[/C][C]0.34507242997769[/C][C]0.690144859955381[/C][C]0.65492757002231[/C][/ROW]
[ROW][C]122[/C][C]0.310171415423067[/C][C]0.620342830846134[/C][C]0.689828584576933[/C][/ROW]
[ROW][C]123[/C][C]0.29069086973229[/C][C]0.581381739464581[/C][C]0.70930913026771[/C][/ROW]
[ROW][C]124[/C][C]0.270470617480996[/C][C]0.540941234961993[/C][C]0.729529382519004[/C][/ROW]
[ROW][C]125[/C][C]0.319856967199045[/C][C]0.639713934398091[/C][C]0.680143032800955[/C][/ROW]
[ROW][C]126[/C][C]0.283507567826322[/C][C]0.567015135652645[/C][C]0.716492432173678[/C][/ROW]
[ROW][C]127[/C][C]0.297699332236817[/C][C]0.595398664473634[/C][C]0.702300667763183[/C][/ROW]
[ROW][C]128[/C][C]0.354043447263046[/C][C]0.708086894526093[/C][C]0.645956552736954[/C][/ROW]
[ROW][C]129[/C][C]0.313006473566782[/C][C]0.626012947133564[/C][C]0.686993526433218[/C][/ROW]
[ROW][C]130[/C][C]0.38022560630747[/C][C]0.76045121261494[/C][C]0.61977439369253[/C][/ROW]
[ROW][C]131[/C][C]0.33259540056147[/C][C]0.66519080112294[/C][C]0.66740459943853[/C][/ROW]
[ROW][C]132[/C][C]0.417704166033693[/C][C]0.835408332067386[/C][C]0.582295833966307[/C][/ROW]
[ROW][C]133[/C][C]0.39082628301964[/C][C]0.78165256603928[/C][C]0.60917371698036[/C][/ROW]
[ROW][C]134[/C][C]0.334766431660657[/C][C]0.669532863321313[/C][C]0.665233568339343[/C][/ROW]
[ROW][C]135[/C][C]0.283241980702442[/C][C]0.566483961404885[/C][C]0.716758019297558[/C][/ROW]
[ROW][C]136[/C][C]0.237616495790357[/C][C]0.475232991580715[/C][C]0.762383504209643[/C][/ROW]
[ROW][C]137[/C][C]0.196991187457208[/C][C]0.393982374914415[/C][C]0.803008812542792[/C][/ROW]
[ROW][C]138[/C][C]0.172476264281059[/C][C]0.344952528562118[/C][C]0.827523735718941[/C][/ROW]
[ROW][C]139[/C][C]0.143570633090087[/C][C]0.287141266180174[/C][C]0.856429366909913[/C][/ROW]
[ROW][C]140[/C][C]0.123735663626739[/C][C]0.247471327253479[/C][C]0.876264336373261[/C][/ROW]
[ROW][C]141[/C][C]0.217070661271201[/C][C]0.434141322542401[/C][C]0.782929338728799[/C][/ROW]
[ROW][C]142[/C][C]0.336627100746986[/C][C]0.673254201493972[/C][C]0.663372899253014[/C][/ROW]
[ROW][C]143[/C][C]0.313848547693517[/C][C]0.627697095387033[/C][C]0.686151452306483[/C][/ROW]
[ROW][C]144[/C][C]0.278273939452961[/C][C]0.556547878905922[/C][C]0.721726060547039[/C][/ROW]
[ROW][C]145[/C][C]0.337312041212908[/C][C]0.674624082425815[/C][C]0.662687958787092[/C][/ROW]
[ROW][C]146[/C][C]0.341061518780202[/C][C]0.682123037560403[/C][C]0.658938481219798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202101&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202101&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
80.6903112129373520.6193775741252960.309688787062648
90.8192447314099570.3615105371800850.180755268590043
100.7389746950595740.5220506098808510.261025304940426
110.6493969797606830.7012060404786350.350603020239317
120.5555450936382680.8889098127234630.444454906361732
130.4538220645303630.9076441290607260.546177935469637
140.3685421739585470.7370843479170950.631457826041453
150.4776843042828190.9553686085656370.522315695717181
160.450136699004710.900273398009420.54986330099529
170.3692975025401260.7385950050802530.630702497459874
180.3031219643306460.6062439286612920.696878035669354
190.4506209905942630.9012419811885250.549379009405737
200.5302004467337340.9395991065325320.469799553266266
210.6025772685301260.7948454629397470.397422731469874
220.556176649351580.8876467012968410.44382335064842
230.5341026653592920.9317946692814150.465897334640708
240.4920976102224160.9841952204448330.507902389777584
250.5313331347186690.9373337305626620.468666865281331
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280.655152695337340.689694609325320.34484730466266
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300.7194433605624710.5611132788750590.280556639437529
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930.7635392996498380.4729214007003230.236460700350162
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1080.5651259970115520.8697480059768950.434874002988448
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1460.3410615187802020.6821230375604030.658938481219798







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202101&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202101&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202101&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 level00OK
5% type I error level00OK
10% type I error level00OK



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')
}