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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 09:56:02 -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/21/t1356101783v5mjhbstjqhhst7.htm/, Retrieved Fri, 29 Mar 2024 13:05:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203754, Retrieved Fri, 29 Mar 2024 13:05:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regressi...] [2012-12-21 14:56:02] [a641906195a0eb35087b0121beaccdc9] [Current]
- R P     [Multiple Regression] [Multiple Regressi...] [2012-12-21 14:57:49] [e35bad265b4f882bb08312c911aaf8f2]
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Dataseries X:
4	1	1	0	0	1	0
4	0	0	0	0	0	0
4	0	0	0	0	0	0
4	0	0	0	0	0	0
4	0	0	0	0	0	0
4	1	0	0	1	1	0
4	0	0	0	0	0	0
4	0	1	0	0	0	0
4	0	0	0	0	1	0
4	1	0	0	0	0	0
4	1	1	0	0	0	0
4	0	0	0	0	0	0
4	0	0	1	1	0	0
4	1	1	0	0	0	0
4	0	0	1	1	1	0
4	0	1	1	1	1	0
4	1	1	1	1	0	1
4	1	1	0	0	0	0
4	0	0	0	0	1	0
4	0	1	1	1	1	1
4	1	0	0	1	0	0
4	1	0	1	1	1	0
4	0	0	0	1	1	0
4	1	0	0	1	1	0
4	0	1	1	0	1	0
4	0	0	1	1	0	0
4	1	0	0	0	1	0
4	0	0	1	0	0	0
4	0	0	0	0	1	0
4	0	0	0	1	0	0
4	0	0	0	0	0	0
4	1	0	0	0	0	0
4	1	0	0	1	0	0
4	0	1	0	0	1	0
4	0	0	0	0	0	0
4	0	0	0	0	0	0
4	1	1	1	1	0	0
4	0	0	1	0	1	0
4	0	0	0	1	1	0
4	0	1	0	1	0	0
4	0	0	1	1	1	1
4	0	0	1	0	1	0
4	1	0	0	1	1	0
4	1	1	0	0	0	0
4	0	0	0	1	0	0
4	0	0	0	1	1	0
4	0	0	0	0	0	0
4	0	0	0	0	1	0
4	0	0	0	1	1	0
4	0	0	0	0	0	0
4	0	1	1	0	0	0
4	1	1	1	1	0	1
4	0	0	0	0	1	0
4	0	0	1	0	0	1
4	0	0	0	0	0	0
4	0	1	1	0	1	0
4	0	0	1	1	1	0
4	0	0	0	0	1	0
4	0	0	0	0	1	0
4	1	1	1	1	1	1
4	1	1	0	0	1	0
4	0	0	1	1	0	0
4	0	0	0	0	0	0
4	1	1	0	0	1	0
4	0	0	0	0	0	0
4	0	0	0	0	0	0
4	0	1	1	1	0	1
4	1	0	0	0	0	0
4	0	0	0	0	1	0
4	0	0	1	0	0	0
4	0	0	0	0	0	0
4	0	0	0	0	1	0
4	0	0	1	0	1	0
4	1	0	1	0	0	0
4	0	0	0	0	1	0
4	0	1	0	1	1	0
4	0	0	0	0	1	0
4	0	0	1	1	1	0
4	0	1	1	0	1	1
4	0	1	0	1	0	0
4	0	0	0	0	0	0
4	1	0	1	0	1	0
4	0	0	0	0	0	0
4	0	0	1	0	0	1
4	0	0	0	1	1	0
4	1	0	0	0	0	0
2	1	0	0	0	1	0
2	1	1	1	0	1	0
2	0	0	0	0	0	0
2	0	0	0	0	1	0
2	0	0	0	1	0	0
2	1	1	0	0	0	0
2	1	0	0	1	0	0
2	0	0	0	0	0	0
2	0	1	0	0	0	0
2	0	0	0	0	1	0
2	1	1	0	0	0	0
2	0	0	0	0	0	0
2	1	0	0	0	0	0
2	0	0	0	0	1	0
2	1	0	0	0	1	0
2	0	0	0	0	0	0
2	0	0	0	0	0	0
2	0	0	0	0	0	0
2	0	1	1	0	0	0
2	0	0	0	0	0	0
2	0	0	0	0	0	0
2	1	1	1	0	0	0
2	0	0	0	0	0	0
2	1	0	0	0	0	0
2	1	1	1	1	0	0
2	0	1	0	0	0	0
2	0	0	1	0	0	0
2	1	1	1	0	0	0
2	1	0	0	0	0	0
2	0	0	0	0	0	0
2	1	0	0	0	1	0
2	1	0	0	0	0	0
2	0	0	0	0	0	0
2	0	0	0	0	1	0
2	1	0	0	0	0	0
2	0	0	0	0	0	0
2	1	1	1	0	0	0
2	0	0	1	1	1	0
2	0	0	0	0	1	0
2	0	1	0	0	0	0
2	0	0	0	1	0	0
2	0	0	0	0	1	0
2	0	0	0	0	0	0
2	0	0	0	0	1	0
2	1	0	0	0	0	0
2	1	0	0	0	1	0
2	1	0	1	0	0	0
2	0	0	0	0	0	0
2	0	0	0	0	0	0
2	0	0	0	0	0	0
2	1	0	1	1	1	0
2	1	1	1	1	1	0
2	0	1	0	0	0	0
2	0	0	0	0	0	0
2	0	0	1	0	1	1
2	0	1	1	0	1	0
2	1	0	0	0	0	0
2	0	0	0	1	1	0
2	0	0	0	1	0	0
2	0	1	0	0	1	0
2	0	1	1	0	0	0
2	0	1	0	0	0	0
2	1	0	0	0	0	0
2	0	0	0	1	1	0
2	0	0	0	0	1	0
2	1	0	1	0	0	1
2	1	0	1	1	0	1
2	1	0	1	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203754&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203754&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203754&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 time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Useful[t] = + 0.183709865137359 + 0.0595520799693369Weeks[t] -0.0762411190828498UseLimit[t] -0.0392985464077028T40enT20[t] + 0.0980562791952251Used[t] + 0.157662701384507CorrectAnalysis[t] -0.11408526054452Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Useful[t] =  +  0.183709865137359 +  0.0595520799693369Weeks[t] -0.0762411190828498UseLimit[t] -0.0392985464077028T40enT20[t] +  0.0980562791952251Used[t] +  0.157662701384507CorrectAnalysis[t] -0.11408526054452Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203754&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Useful[t] =  +  0.183709865137359 +  0.0595520799693369Weeks[t] -0.0762411190828498UseLimit[t] -0.0392985464077028T40enT20[t] +  0.0980562791952251Used[t] +  0.157662701384507CorrectAnalysis[t] -0.11408526054452Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203754&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203754&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
Useful[t] = + 0.183709865137359 + 0.0595520799693369Weeks[t] -0.0762411190828498UseLimit[t] -0.0392985464077028T40enT20[t] + 0.0980562791952251Used[t] + 0.157662701384507CorrectAnalysis[t] -0.11408526054452Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1837098651373590.1367561.34330.1812320.090616
Weeks0.05955207996933690.0407651.46090.1461870.073093
UseLimit-0.07624111908284980.086055-0.8860.3770890.188544
T40enT20-0.03929854640770280.094264-0.41690.6773590.33868
Used0.09805627919522510.1010190.97070.3333080.166654
CorrectAnalysis0.1576627013845070.0941071.67540.0959890.047995
Outcome-0.114085260544520.165223-0.69050.4909740.245487

\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) & 0.183709865137359 & 0.136756 & 1.3433 & 0.181232 & 0.090616 \tabularnewline
Weeks & 0.0595520799693369 & 0.040765 & 1.4609 & 0.146187 & 0.073093 \tabularnewline
UseLimit & -0.0762411190828498 & 0.086055 & -0.886 & 0.377089 & 0.188544 \tabularnewline
T40enT20 & -0.0392985464077028 & 0.094264 & -0.4169 & 0.677359 & 0.33868 \tabularnewline
Used & 0.0980562791952251 & 0.101019 & 0.9707 & 0.333308 & 0.166654 \tabularnewline
CorrectAnalysis & 0.157662701384507 & 0.094107 & 1.6754 & 0.095989 & 0.047995 \tabularnewline
Outcome & -0.11408526054452 & 0.165223 & -0.6905 & 0.490974 & 0.245487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203754&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]0.183709865137359[/C][C]0.136756[/C][C]1.3433[/C][C]0.181232[/C][C]0.090616[/C][/ROW]
[ROW][C]Weeks[/C][C]0.0595520799693369[/C][C]0.040765[/C][C]1.4609[/C][C]0.146187[/C][C]0.073093[/C][/ROW]
[ROW][C]UseLimit[/C][C]-0.0762411190828498[/C][C]0.086055[/C][C]-0.886[/C][C]0.377089[/C][C]0.188544[/C][/ROW]
[ROW][C]T40enT20[/C][C]-0.0392985464077028[/C][C]0.094264[/C][C]-0.4169[/C][C]0.677359[/C][C]0.33868[/C][/ROW]
[ROW][C]Used[/C][C]0.0980562791952251[/C][C]0.101019[/C][C]0.9707[/C][C]0.333308[/C][C]0.166654[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]0.157662701384507[/C][C]0.094107[/C][C]1.6754[/C][C]0.095989[/C][C]0.047995[/C][/ROW]
[ROW][C]Outcome[/C][C]-0.11408526054452[/C][C]0.165223[/C][C]-0.6905[/C][C]0.490974[/C][C]0.245487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203754&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203754&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)0.1837098651373590.1367561.34330.1812320.090616
Weeks0.05955207996933690.0407651.46090.1461870.073093
UseLimit-0.07624111908284980.086055-0.8860.3770890.188544
T40enT20-0.03929854640770280.094264-0.41690.6773590.33868
Used0.09805627919522510.1010190.97070.3333080.166654
CorrectAnalysis0.1576627013845070.0941071.67540.0959890.047995
Outcome-0.114085260544520.165223-0.69050.4909740.245487







Multiple Linear Regression - Regression Statistics
Multiple R0.241319333815921
R-squared0.05823502087336
Adjusted R-squared0.0197956339702318
F-TEST (value)1.51498308284987
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.177049706162501
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.485801254836481
Sum Squared Residuals34.6924203025028

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.241319333815921 \tabularnewline
R-squared & 0.05823502087336 \tabularnewline
Adjusted R-squared & 0.0197956339702318 \tabularnewline
F-TEST (value) & 1.51498308284987 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0.177049706162501 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.485801254836481 \tabularnewline
Sum Squared Residuals & 34.6924203025028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203754&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.241319333815921[/C][/ROW]
[ROW][C]R-squared[/C][C]0.05823502087336[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0197956339702318[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.51498308284987[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0.177049706162501[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.485801254836481[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.6924203025028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203754&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203754&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 R0.241319333815921
R-squared0.05823502087336
Adjusted R-squared0.0197956339702318
F-TEST (value)1.51498308284987
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.177049706162501
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.485801254836481
Sum Squared Residuals34.6924203025028







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3063785195241540.693621480475846
200.421918185014706-0.421918185014706
300.421918185014706-0.421918185014706
400.421918185014706-0.421918185014706
500.421918185014706-0.421918185014706
610.5033397673163630.496660232683637
700.421918185014706-0.421918185014706
800.382619638607003-0.382619638607003
910.4219181850147060.578081814985294
1000.345677065931856-0.345677065931856
1100.306378519524153-0.306378519524153
1200.421918185014706-0.421918185014706
1300.677637165594438-0.677637165594438
1400.306378519524153-0.306378519524153
1510.6776371655944380.322362834405562
1610.6383386191867350.361661380813265
1700.448012239559366-0.448012239559366
1800.306378519524153-0.306378519524153
1910.4219181850147060.578081814985294
2010.5242533586422160.475746641357784
2100.503339767316363-0.503339767316363
2210.6013960465115880.398603953488412
2310.5795808863992130.420419113600787
2410.5033397673163630.496660232683637
2510.4806759178022290.519324082197771
2600.677637165594438-0.677637165594438
2710.3456770659318560.654322934068144
2800.519974464209931-0.519974464209931
2910.4219181850147060.578081814985294
3000.579580886399213-0.579580886399213
3100.421918185014706-0.421918185014706
3200.345677065931856-0.345677065931856
3300.503339767316363-0.503339767316363
3410.3826196386070040.617380361392996
3500.421918185014706-0.421918185014706
3600.421918185014706-0.421918185014706
3700.562097500103885-0.562097500103885
3810.5199744642099310.480025535790069
3910.5795808863992130.420419113600787
4000.54028233999151-0.54028233999151
4110.5635519050499180.436448094950082
4210.5199744642099310.480025535790069
4310.5033397673163630.496660232683637
4400.306378519524153-0.306378519524153
4500.579580886399213-0.579580886399213
4610.5795808863992130.420419113600787
4700.421918185014706-0.421918185014706
4810.4219181850147060.578081814985294
4910.5795808863992130.420419113600787
5000.421918185014706-0.421918185014706
5100.480675917802228-0.480675917802228
5200.448012239559366-0.448012239559366
5310.4219181850147060.578081814985294
5400.405889203665412-0.405889203665412
5500.421918185014706-0.421918185014706
5610.4806759178022290.519324082197771
5710.6776371655944380.322362834405562
5810.4219181850147060.578081814985294
5910.4219181850147060.578081814985294
6010.4480122395593660.551987760440634
6110.3063785195241540.693621480475846
6200.677637165594438-0.677637165594438
6300.421918185014706-0.421918185014706
6410.3063785195241540.693621480475846
6500.421918185014706-0.421918185014706
6600.421918185014706-0.421918185014706
6700.524253358642216-0.524253358642216
6800.345677065931856-0.345677065931856
6910.4219181850147060.578081814985294
7000.519974464209931-0.519974464209931
7100.421918185014706-0.421918185014706
7210.4219181850147060.578081814985294
7310.5199744642099310.480025535790069
7400.443733345127081-0.443733345127081
7510.4219181850147060.578081814985294
7610.540282339991510.45971766000849
7710.4219181850147060.578081814985294
7810.6776371655944380.322362834405562
7910.3665906572577090.633409342742291
8000.54028233999151-0.54028233999151
8100.421918185014706-0.421918185014706
8210.4437333451270820.556266654872918
8300.421918185014706-0.421918185014706
8400.405889203665412-0.405889203665412
8510.5795808863992130.420419113600787
8600.345677065931856-0.345677065931856
8710.2265729059931820.773427094006818
8810.2853306387807050.714669361219295
8900.302814025076032-0.302814025076032
9010.3028140250760320.697185974923968
9100.460476726460539-0.460476726460539
9200.18727435958548-0.18727435958548
9300.384235607377689-0.384235607377689
9400.302814025076032-0.302814025076032
9500.26351547866833-0.26351547866833
9610.3028140250760320.697185974923968
9700.18727435958548-0.18727435958548
9800.302814025076032-0.302814025076032
9900.226572905993183-0.226572905993183
10010.3028140250760320.697185974923968
10110.2265729059931820.773427094006818
10200.302814025076032-0.302814025076032
10300.302814025076032-0.302814025076032
10400.302814025076032-0.302814025076032
10500.361571757863555-0.361571757863555
10600.302814025076032-0.302814025076032
10700.302814025076032-0.302814025076032
10800.285330638780705-0.285330638780705
10900.302814025076032-0.302814025076032
11000.226572905993183-0.226572905993183
11100.442993340165212-0.442993340165212
11200.26351547866833-0.26351547866833
11300.400870304271258-0.400870304271258
11400.285330638780705-0.285330638780705
11500.226572905993183-0.226572905993183
11600.302814025076032-0.302814025076032
11710.2265729059931820.773427094006818
11800.226572905993183-0.226572905993183
11900.302814025076032-0.302814025076032
12010.3028140250760320.697185974923968
12100.226572905993183-0.226572905993183
12200.302814025076032-0.302814025076032
12300.285330638780705-0.285330638780705
12410.5585330056557640.441466994344236
12510.3028140250760320.697185974923968
12600.26351547866833-0.26351547866833
12700.460476726460539-0.460476726460539
12810.3028140250760320.697185974923968
12900.302814025076032-0.302814025076032
13010.3028140250760320.697185974923968
13100.226572905993183-0.226572905993183
13210.2265729059931820.773427094006818
13300.324629185188408-0.324629185188408
13400.302814025076032-0.302814025076032
13500.302814025076032-0.302814025076032
13600.302814025076032-0.302814025076032
13710.4822918865729140.517708113427086
13810.4429933401652120.557006659834788
13900.26351547866833-0.26351547866833
14000.302814025076032-0.302814025076032
14110.2867850437267380.713214956273262
14210.3615717578635550.638428242136445
14300.226572905993183-0.226572905993183
14410.4604767264605390.539523273539461
14500.460476726460539-0.460476726460539
14610.2635154786683290.736484521331671
14700.361571757863555-0.361571757863555
14800.26351547866833-0.26351547866833
14900.226572905993183-0.226572905993183
15010.4604767264605390.539523273539461
15110.3028140250760320.697185974923968
15200.210543924643888-0.210543924643888
15300.368206626028395-0.368206626028395
15400.324629185188408-0.324629185188408

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.306378519524154 & 0.693621480475846 \tabularnewline
2 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
3 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
4 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
5 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
6 & 1 & 0.503339767316363 & 0.496660232683637 \tabularnewline
7 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
8 & 0 & 0.382619638607003 & -0.382619638607003 \tabularnewline
9 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
10 & 0 & 0.345677065931856 & -0.345677065931856 \tabularnewline
11 & 0 & 0.306378519524153 & -0.306378519524153 \tabularnewline
12 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
13 & 0 & 0.677637165594438 & -0.677637165594438 \tabularnewline
14 & 0 & 0.306378519524153 & -0.306378519524153 \tabularnewline
15 & 1 & 0.677637165594438 & 0.322362834405562 \tabularnewline
16 & 1 & 0.638338619186735 & 0.361661380813265 \tabularnewline
17 & 0 & 0.448012239559366 & -0.448012239559366 \tabularnewline
18 & 0 & 0.306378519524153 & -0.306378519524153 \tabularnewline
19 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
20 & 1 & 0.524253358642216 & 0.475746641357784 \tabularnewline
21 & 0 & 0.503339767316363 & -0.503339767316363 \tabularnewline
22 & 1 & 0.601396046511588 & 0.398603953488412 \tabularnewline
23 & 1 & 0.579580886399213 & 0.420419113600787 \tabularnewline
24 & 1 & 0.503339767316363 & 0.496660232683637 \tabularnewline
25 & 1 & 0.480675917802229 & 0.519324082197771 \tabularnewline
26 & 0 & 0.677637165594438 & -0.677637165594438 \tabularnewline
27 & 1 & 0.345677065931856 & 0.654322934068144 \tabularnewline
28 & 0 & 0.519974464209931 & -0.519974464209931 \tabularnewline
29 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
30 & 0 & 0.579580886399213 & -0.579580886399213 \tabularnewline
31 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
32 & 0 & 0.345677065931856 & -0.345677065931856 \tabularnewline
33 & 0 & 0.503339767316363 & -0.503339767316363 \tabularnewline
34 & 1 & 0.382619638607004 & 0.617380361392996 \tabularnewline
35 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
36 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
37 & 0 & 0.562097500103885 & -0.562097500103885 \tabularnewline
38 & 1 & 0.519974464209931 & 0.480025535790069 \tabularnewline
39 & 1 & 0.579580886399213 & 0.420419113600787 \tabularnewline
40 & 0 & 0.54028233999151 & -0.54028233999151 \tabularnewline
41 & 1 & 0.563551905049918 & 0.436448094950082 \tabularnewline
42 & 1 & 0.519974464209931 & 0.480025535790069 \tabularnewline
43 & 1 & 0.503339767316363 & 0.496660232683637 \tabularnewline
44 & 0 & 0.306378519524153 & -0.306378519524153 \tabularnewline
45 & 0 & 0.579580886399213 & -0.579580886399213 \tabularnewline
46 & 1 & 0.579580886399213 & 0.420419113600787 \tabularnewline
47 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
48 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
49 & 1 & 0.579580886399213 & 0.420419113600787 \tabularnewline
50 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
51 & 0 & 0.480675917802228 & -0.480675917802228 \tabularnewline
52 & 0 & 0.448012239559366 & -0.448012239559366 \tabularnewline
53 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
54 & 0 & 0.405889203665412 & -0.405889203665412 \tabularnewline
55 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
56 & 1 & 0.480675917802229 & 0.519324082197771 \tabularnewline
57 & 1 & 0.677637165594438 & 0.322362834405562 \tabularnewline
58 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
59 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
60 & 1 & 0.448012239559366 & 0.551987760440634 \tabularnewline
61 & 1 & 0.306378519524154 & 0.693621480475846 \tabularnewline
62 & 0 & 0.677637165594438 & -0.677637165594438 \tabularnewline
63 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
64 & 1 & 0.306378519524154 & 0.693621480475846 \tabularnewline
65 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
66 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
67 & 0 & 0.524253358642216 & -0.524253358642216 \tabularnewline
68 & 0 & 0.345677065931856 & -0.345677065931856 \tabularnewline
69 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
70 & 0 & 0.519974464209931 & -0.519974464209931 \tabularnewline
71 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
72 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
73 & 1 & 0.519974464209931 & 0.480025535790069 \tabularnewline
74 & 0 & 0.443733345127081 & -0.443733345127081 \tabularnewline
75 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
76 & 1 & 0.54028233999151 & 0.45971766000849 \tabularnewline
77 & 1 & 0.421918185014706 & 0.578081814985294 \tabularnewline
78 & 1 & 0.677637165594438 & 0.322362834405562 \tabularnewline
79 & 1 & 0.366590657257709 & 0.633409342742291 \tabularnewline
80 & 0 & 0.54028233999151 & -0.54028233999151 \tabularnewline
81 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
82 & 1 & 0.443733345127082 & 0.556266654872918 \tabularnewline
83 & 0 & 0.421918185014706 & -0.421918185014706 \tabularnewline
84 & 0 & 0.405889203665412 & -0.405889203665412 \tabularnewline
85 & 1 & 0.579580886399213 & 0.420419113600787 \tabularnewline
86 & 0 & 0.345677065931856 & -0.345677065931856 \tabularnewline
87 & 1 & 0.226572905993182 & 0.773427094006818 \tabularnewline
88 & 1 & 0.285330638780705 & 0.714669361219295 \tabularnewline
89 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
90 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
91 & 0 & 0.460476726460539 & -0.460476726460539 \tabularnewline
92 & 0 & 0.18727435958548 & -0.18727435958548 \tabularnewline
93 & 0 & 0.384235607377689 & -0.384235607377689 \tabularnewline
94 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
95 & 0 & 0.26351547866833 & -0.26351547866833 \tabularnewline
96 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
97 & 0 & 0.18727435958548 & -0.18727435958548 \tabularnewline
98 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
99 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
100 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
101 & 1 & 0.226572905993182 & 0.773427094006818 \tabularnewline
102 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
103 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
104 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
105 & 0 & 0.361571757863555 & -0.361571757863555 \tabularnewline
106 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
107 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
108 & 0 & 0.285330638780705 & -0.285330638780705 \tabularnewline
109 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
110 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
111 & 0 & 0.442993340165212 & -0.442993340165212 \tabularnewline
112 & 0 & 0.26351547866833 & -0.26351547866833 \tabularnewline
113 & 0 & 0.400870304271258 & -0.400870304271258 \tabularnewline
114 & 0 & 0.285330638780705 & -0.285330638780705 \tabularnewline
115 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
116 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
117 & 1 & 0.226572905993182 & 0.773427094006818 \tabularnewline
118 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
119 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
120 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
121 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
122 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
123 & 0 & 0.285330638780705 & -0.285330638780705 \tabularnewline
124 & 1 & 0.558533005655764 & 0.441466994344236 \tabularnewline
125 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
126 & 0 & 0.26351547866833 & -0.26351547866833 \tabularnewline
127 & 0 & 0.460476726460539 & -0.460476726460539 \tabularnewline
128 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
129 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
130 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
131 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
132 & 1 & 0.226572905993182 & 0.773427094006818 \tabularnewline
133 & 0 & 0.324629185188408 & -0.324629185188408 \tabularnewline
134 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
135 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
136 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
137 & 1 & 0.482291886572914 & 0.517708113427086 \tabularnewline
138 & 1 & 0.442993340165212 & 0.557006659834788 \tabularnewline
139 & 0 & 0.26351547866833 & -0.26351547866833 \tabularnewline
140 & 0 & 0.302814025076032 & -0.302814025076032 \tabularnewline
141 & 1 & 0.286785043726738 & 0.713214956273262 \tabularnewline
142 & 1 & 0.361571757863555 & 0.638428242136445 \tabularnewline
143 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
144 & 1 & 0.460476726460539 & 0.539523273539461 \tabularnewline
145 & 0 & 0.460476726460539 & -0.460476726460539 \tabularnewline
146 & 1 & 0.263515478668329 & 0.736484521331671 \tabularnewline
147 & 0 & 0.361571757863555 & -0.361571757863555 \tabularnewline
148 & 0 & 0.26351547866833 & -0.26351547866833 \tabularnewline
149 & 0 & 0.226572905993183 & -0.226572905993183 \tabularnewline
150 & 1 & 0.460476726460539 & 0.539523273539461 \tabularnewline
151 & 1 & 0.302814025076032 & 0.697185974923968 \tabularnewline
152 & 0 & 0.210543924643888 & -0.210543924643888 \tabularnewline
153 & 0 & 0.368206626028395 & -0.368206626028395 \tabularnewline
154 & 0 & 0.324629185188408 & -0.324629185188408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203754&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]0.306378519524154[/C][C]0.693621480475846[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.503339767316363[/C][C]0.496660232683637[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.382619638607003[/C][C]-0.382619638607003[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.345677065931856[/C][C]-0.345677065931856[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.306378519524153[/C][C]-0.306378519524153[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.677637165594438[/C][C]-0.677637165594438[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.306378519524153[/C][C]-0.306378519524153[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.677637165594438[/C][C]0.322362834405562[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.638338619186735[/C][C]0.361661380813265[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.448012239559366[/C][C]-0.448012239559366[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.306378519524153[/C][C]-0.306378519524153[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.524253358642216[/C][C]0.475746641357784[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.503339767316363[/C][C]-0.503339767316363[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.601396046511588[/C][C]0.398603953488412[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.579580886399213[/C][C]0.420419113600787[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.503339767316363[/C][C]0.496660232683637[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.480675917802229[/C][C]0.519324082197771[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.677637165594438[/C][C]-0.677637165594438[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.345677065931856[/C][C]0.654322934068144[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.519974464209931[/C][C]-0.519974464209931[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.579580886399213[/C][C]-0.579580886399213[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.345677065931856[/C][C]-0.345677065931856[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.503339767316363[/C][C]-0.503339767316363[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.382619638607004[/C][C]0.617380361392996[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.562097500103885[/C][C]-0.562097500103885[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.519974464209931[/C][C]0.480025535790069[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.579580886399213[/C][C]0.420419113600787[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.54028233999151[/C][C]-0.54028233999151[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.563551905049918[/C][C]0.436448094950082[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.519974464209931[/C][C]0.480025535790069[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.503339767316363[/C][C]0.496660232683637[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.306378519524153[/C][C]-0.306378519524153[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.579580886399213[/C][C]-0.579580886399213[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.579580886399213[/C][C]0.420419113600787[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.579580886399213[/C][C]0.420419113600787[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.480675917802228[/C][C]-0.480675917802228[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.448012239559366[/C][C]-0.448012239559366[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.405889203665412[/C][C]-0.405889203665412[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.480675917802229[/C][C]0.519324082197771[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.677637165594438[/C][C]0.322362834405562[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.448012239559366[/C][C]0.551987760440634[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.306378519524154[/C][C]0.693621480475846[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.677637165594438[/C][C]-0.677637165594438[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.306378519524154[/C][C]0.693621480475846[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.524253358642216[/C][C]-0.524253358642216[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.345677065931856[/C][C]-0.345677065931856[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.519974464209931[/C][C]-0.519974464209931[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.519974464209931[/C][C]0.480025535790069[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.443733345127081[/C][C]-0.443733345127081[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.54028233999151[/C][C]0.45971766000849[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.421918185014706[/C][C]0.578081814985294[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.677637165594438[/C][C]0.322362834405562[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.366590657257709[/C][C]0.633409342742291[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.54028233999151[/C][C]-0.54028233999151[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.443733345127082[/C][C]0.556266654872918[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.421918185014706[/C][C]-0.421918185014706[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.405889203665412[/C][C]-0.405889203665412[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.579580886399213[/C][C]0.420419113600787[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.345677065931856[/C][C]-0.345677065931856[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.226572905993182[/C][C]0.773427094006818[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.285330638780705[/C][C]0.714669361219295[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.460476726460539[/C][C]-0.460476726460539[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.18727435958548[/C][C]-0.18727435958548[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.384235607377689[/C][C]-0.384235607377689[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.26351547866833[/C][C]-0.26351547866833[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.18727435958548[/C][C]-0.18727435958548[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.226572905993182[/C][C]0.773427094006818[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.361571757863555[/C][C]-0.361571757863555[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.285330638780705[/C][C]-0.285330638780705[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.442993340165212[/C][C]-0.442993340165212[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.26351547866833[/C][C]-0.26351547866833[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.400870304271258[/C][C]-0.400870304271258[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.285330638780705[/C][C]-0.285330638780705[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.226572905993182[/C][C]0.773427094006818[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.285330638780705[/C][C]-0.285330638780705[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.558533005655764[/C][C]0.441466994344236[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.26351547866833[/C][C]-0.26351547866833[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.460476726460539[/C][C]-0.460476726460539[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.226572905993182[/C][C]0.773427094006818[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.324629185188408[/C][C]-0.324629185188408[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.482291886572914[/C][C]0.517708113427086[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.442993340165212[/C][C]0.557006659834788[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.26351547866833[/C][C]-0.26351547866833[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.302814025076032[/C][C]-0.302814025076032[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.286785043726738[/C][C]0.713214956273262[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0.361571757863555[/C][C]0.638428242136445[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0.460476726460539[/C][C]0.539523273539461[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.460476726460539[/C][C]-0.460476726460539[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.263515478668329[/C][C]0.736484521331671[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.361571757863555[/C][C]-0.361571757863555[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.26351547866833[/C][C]-0.26351547866833[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.226572905993183[/C][C]-0.226572905993183[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0.460476726460539[/C][C]0.539523273539461[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0.302814025076032[/C][C]0.697185974923968[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.210543924643888[/C][C]-0.210543924643888[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.368206626028395[/C][C]-0.368206626028395[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.324629185188408[/C][C]-0.324629185188408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203754&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203754&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
110.3063785195241540.693621480475846
200.421918185014706-0.421918185014706
300.421918185014706-0.421918185014706
400.421918185014706-0.421918185014706
500.421918185014706-0.421918185014706
610.5033397673163630.496660232683637
700.421918185014706-0.421918185014706
800.382619638607003-0.382619638607003
910.4219181850147060.578081814985294
1000.345677065931856-0.345677065931856
1100.306378519524153-0.306378519524153
1200.421918185014706-0.421918185014706
1300.677637165594438-0.677637165594438
1400.306378519524153-0.306378519524153
1510.6776371655944380.322362834405562
1610.6383386191867350.361661380813265
1700.448012239559366-0.448012239559366
1800.306378519524153-0.306378519524153
1910.4219181850147060.578081814985294
2010.5242533586422160.475746641357784
2100.503339767316363-0.503339767316363
2210.6013960465115880.398603953488412
2310.5795808863992130.420419113600787
2410.5033397673163630.496660232683637
2510.4806759178022290.519324082197771
2600.677637165594438-0.677637165594438
2710.3456770659318560.654322934068144
2800.519974464209931-0.519974464209931
2910.4219181850147060.578081814985294
3000.579580886399213-0.579580886399213
3100.421918185014706-0.421918185014706
3200.345677065931856-0.345677065931856
3300.503339767316363-0.503339767316363
3410.3826196386070040.617380361392996
3500.421918185014706-0.421918185014706
3600.421918185014706-0.421918185014706
3700.562097500103885-0.562097500103885
3810.5199744642099310.480025535790069
3910.5795808863992130.420419113600787
4000.54028233999151-0.54028233999151
4110.5635519050499180.436448094950082
4210.5199744642099310.480025535790069
4310.5033397673163630.496660232683637
4400.306378519524153-0.306378519524153
4500.579580886399213-0.579580886399213
4610.5795808863992130.420419113600787
4700.421918185014706-0.421918185014706
4810.4219181850147060.578081814985294
4910.5795808863992130.420419113600787
5000.421918185014706-0.421918185014706
5100.480675917802228-0.480675917802228
5200.448012239559366-0.448012239559366
5310.4219181850147060.578081814985294
5400.405889203665412-0.405889203665412
5500.421918185014706-0.421918185014706
5610.4806759178022290.519324082197771
5710.6776371655944380.322362834405562
5810.4219181850147060.578081814985294
5910.4219181850147060.578081814985294
6010.4480122395593660.551987760440634
6110.3063785195241540.693621480475846
6200.677637165594438-0.677637165594438
6300.421918185014706-0.421918185014706
6410.3063785195241540.693621480475846
6500.421918185014706-0.421918185014706
6600.421918185014706-0.421918185014706
6700.524253358642216-0.524253358642216
6800.345677065931856-0.345677065931856
6910.4219181850147060.578081814985294
7000.519974464209931-0.519974464209931
7100.421918185014706-0.421918185014706
7210.4219181850147060.578081814985294
7310.5199744642099310.480025535790069
7400.443733345127081-0.443733345127081
7510.4219181850147060.578081814985294
7610.540282339991510.45971766000849
7710.4219181850147060.578081814985294
7810.6776371655944380.322362834405562
7910.3665906572577090.633409342742291
8000.54028233999151-0.54028233999151
8100.421918185014706-0.421918185014706
8210.4437333451270820.556266654872918
8300.421918185014706-0.421918185014706
8400.405889203665412-0.405889203665412
8510.5795808863992130.420419113600787
8600.345677065931856-0.345677065931856
8710.2265729059931820.773427094006818
8810.2853306387807050.714669361219295
8900.302814025076032-0.302814025076032
9010.3028140250760320.697185974923968
9100.460476726460539-0.460476726460539
9200.18727435958548-0.18727435958548
9300.384235607377689-0.384235607377689
9400.302814025076032-0.302814025076032
9500.26351547866833-0.26351547866833
9610.3028140250760320.697185974923968
9700.18727435958548-0.18727435958548
9800.302814025076032-0.302814025076032
9900.226572905993183-0.226572905993183
10010.3028140250760320.697185974923968
10110.2265729059931820.773427094006818
10200.302814025076032-0.302814025076032
10300.302814025076032-0.302814025076032
10400.302814025076032-0.302814025076032
10500.361571757863555-0.361571757863555
10600.302814025076032-0.302814025076032
10700.302814025076032-0.302814025076032
10800.285330638780705-0.285330638780705
10900.302814025076032-0.302814025076032
11000.226572905993183-0.226572905993183
11100.442993340165212-0.442993340165212
11200.26351547866833-0.26351547866833
11300.400870304271258-0.400870304271258
11400.285330638780705-0.285330638780705
11500.226572905993183-0.226572905993183
11600.302814025076032-0.302814025076032
11710.2265729059931820.773427094006818
11800.226572905993183-0.226572905993183
11900.302814025076032-0.302814025076032
12010.3028140250760320.697185974923968
12100.226572905993183-0.226572905993183
12200.302814025076032-0.302814025076032
12300.285330638780705-0.285330638780705
12410.5585330056557640.441466994344236
12510.3028140250760320.697185974923968
12600.26351547866833-0.26351547866833
12700.460476726460539-0.460476726460539
12810.3028140250760320.697185974923968
12900.302814025076032-0.302814025076032
13010.3028140250760320.697185974923968
13100.226572905993183-0.226572905993183
13210.2265729059931820.773427094006818
13300.324629185188408-0.324629185188408
13400.302814025076032-0.302814025076032
13500.302814025076032-0.302814025076032
13600.302814025076032-0.302814025076032
13710.4822918865729140.517708113427086
13810.4429933401652120.557006659834788
13900.26351547866833-0.26351547866833
14000.302814025076032-0.302814025076032
14110.2867850437267380.713214956273262
14210.3615717578635550.638428242136445
14300.226572905993183-0.226572905993183
14410.4604767264605390.539523273539461
14500.460476726460539-0.460476726460539
14610.2635154786683290.736484521331671
14700.361571757863555-0.361571757863555
14800.26351547866833-0.26351547866833
14900.226572905993183-0.226572905993183
15010.4604767264605390.539523273539461
15110.3028140250760320.697185974923968
15200.210543924643888-0.210543924643888
15300.368206626028395-0.368206626028395
15400.324629185188408-0.324629185188408







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8463342428433430.3073315143133130.153665757156657
110.8166965029557340.3666069940885310.183303497044266
120.721320223931740.5573595521365210.27867977606826
130.6165084470830230.7669831058339550.383491552916977
140.5478264899584450.9043470200831110.452173510041555
150.6406809306653370.7186381386693250.359319069334663
160.5759022767078250.848195446584350.424097723292175
170.4835595251116830.9671190502233660.516440474888317
180.4197120972082180.8394241944164370.580287902791782
190.5807712538950450.838457492209910.419228746104955
200.5976415266848180.8047169466303640.402358473315182
210.6633100532921540.6733798934156920.336689946707846
220.6589660494819270.6820679010361450.341033950518073
230.6130290506421790.7739418987156420.386970949357821
240.5780586289996060.8438827420007880.421941371000394
250.6044527981442850.7910944037114290.395547201855715
260.6858721371773260.6282557256453470.314127862822674
270.7619782202852410.4760435594295190.238021779714759
280.7339594452149930.5320811095700140.266040554785007
290.7723618222929680.4552763554140640.227638177707032
300.7920176540565020.4159646918869960.207982345943498
310.76399164977210.4720167004557990.2360083502279
320.7312841691310330.5374316617379350.268715830868967
330.7316727964405990.5366544071188020.268327203559401
340.7434107091906050.5131785816187910.256589290809395
350.7149358048246080.5701283903507840.285064195175392
360.6847396920978120.6305206158043760.315260307902188
370.7173482552178730.5653034895642530.282651744782127
380.7337729905191840.5324540189616330.266227009480816
390.7249312708928660.5501374582142680.275068729107134
400.7380471062062790.5239057875874430.261952893793721
410.7199657578167350.560068484366530.280034242183265
420.7125591483807050.574881703238590.287440851619295
430.7202193874518810.5595612250962390.279780612548119
440.6888549690915940.6222900618168130.311145030908406
450.6941582926221820.6116834147556360.305841707377818
460.6895481332377550.620903733524490.310451866762245
470.671777591761570.6564448164768610.32822240823843
480.6928170071941740.6143659856116520.307182992805826
490.6817785670078670.6364428659842670.318221432992133
500.6663348423495440.6673303153009120.333665157650456
510.6593521171029810.6812957657940390.340647882897019
520.6581883376883180.6836233246233640.341811662311682
530.6740089526024260.6519820947951480.325991047397574
540.6581063079515050.6837873840969910.341893692048495
550.6449067669708510.7101864660582970.355093233029149
560.6505071594440390.6989856811119220.349492840555961
570.6188980659576550.762203868084690.381101934042345
580.6359285411022610.7281429177954770.364071458897738
590.6508024697397560.6983950605204870.349197530260244
600.6627191048642420.6745617902715170.337280895135758
610.7048070880522630.5903858238954740.295192911947737
620.7386641212581550.5226717574836910.261335878741845
630.7270808295790740.5458383408418520.272919170420926
640.7630946775915950.473810644816810.236905322408405
650.7512642947463330.4974714105073340.248735705253667
660.7401402017916890.5197195964166220.259859798208311
670.7439891747531110.5120216504937780.256010825246889
680.7255846797651820.5488306404696350.274415320234818
690.7374056373383550.5251887253232890.262594362661645
700.746671529994320.506656940011360.25332847000568
710.7415386784054430.5169226431891140.258461321594557
720.749595736677320.5008085266453610.25040426332268
730.7420669890504420.5158660218991150.257933010949558
740.7412580209304230.5174839581391530.258741979069577
750.7481216129017430.5037567741965140.251878387098257
760.7395958001689160.5208083996621690.260404199831084
770.7539038343477320.4921923313045360.246096165652268
780.7315703833743110.5368592332513780.268429616625689
790.7758365744430090.4483268511139820.224163425556991
800.7717196319817540.4565607360364910.228280368018246
810.7567426227951750.486514754409650.243257377204825
820.773634719974990.4527305600500190.22636528002501
830.7542890883968520.4914218232062970.245710911603148
840.7366070143988140.5267859712023720.263392985601186
850.7361281625886260.5277436748227490.263871837411374
860.7028354401923440.5943291196153130.297164559807656
870.725106459935390.5497870801292190.27489354006461
880.7547172232641440.4905655534717110.245282776735856
890.7667902367386780.4664195265226430.233209763261322
900.7803841804546540.4392316390906920.219615819545346
910.8038346292607740.3923307414784520.196165370739226
920.7816506507464350.436698698507130.218349349253565
930.7776508132611590.4446983734776810.222349186738841
940.7581824672931450.483635065413710.241817532706855
950.7289279993339310.5421440013321390.271072000666069
960.7645929180620390.4708141638759220.235407081937961
970.7305787816652530.5388424366694950.269421218334747
980.7061705379533880.5876589240932240.293829462046612
990.6701804529203690.6596390941592630.329819547079631
1000.7127006004113030.5745987991773940.287299399588697
1010.780100962066110.4397980758677810.21989903793389
1020.7577913268411750.4844173463176510.242208673158825
1030.7339631650775530.5320736698448950.266036834922447
1040.7090083835601680.5819832328796650.290991616439832
1050.6863455592999450.627308881400110.313654440700055
1060.6593510945325630.6812978109348740.340648905467437
1070.6324409850203670.7351180299592660.367559014979633
1080.5907660208985190.8184679582029620.409233979101481
1090.5628714306138560.8742571387722880.437128569386144
1100.5156255887367830.9687488225264340.484374411263217
1110.5026594007319970.9946811985360050.497340599268003
1120.4639398743275190.9278797486550370.536060125672481
1130.457375268898630.914750537797260.54262473110137
1140.4156021542925250.8312043085850510.584397845707475
1150.3672311755435970.7344623510871940.632768824456403
1160.3413733097087570.6827466194175140.658626690291243
1170.4500025222907170.9000050445814340.549997477709283
1180.3958067880660450.791613576132090.604193211933955
1190.3711201599426710.7422403198853410.628879840057329
1200.4082201902121130.8164403804242250.591779809787887
1210.3535625031289360.7071250062578720.646437496871064
1220.327556739294990.655113478589980.67244326070501
1230.2872473397242330.5744946794484660.712752660275767
1240.2500931877136310.5001863754272630.749906812286369
1250.2824432274783610.5648864549567230.717556772521639
1260.2519317639173390.5038635278346780.748068236082661
1270.2780279443512070.5560558887024140.721972055648793
1280.3190290239141450.638058047828290.680970976085855
1290.2831459673415440.5662919346830880.716854032658456
1300.3343460463040670.6686920926081350.665653953695933
1310.2741008478866850.5482016957733710.725899152113315
1320.4671962110144090.9343924220288170.532803788985591
1330.4041402934969310.8082805869938630.595859706503069
1340.3482949003647490.6965898007294970.651705099635251
1350.2995365850718740.5990731701437480.700463414928126
1360.2613292179520280.5226584359040550.738670782047972
1370.235298979081370.4705979581627410.76470102091863
1380.33657063162990.67314126325980.6634293683701
1390.2993721200510950.5987442401021890.700627879948905
1400.4088931926810080.8177863853620160.591106807318992
1410.3104978337697460.6209956675394920.689502166230254
1420.3747590505118510.7495181010237010.625240949488149
1430.2569762106913980.5139524213827970.743023789308602
1440.1909270425981090.3818540851962180.809072957401891

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.846334242843343 & 0.307331514313313 & 0.153665757156657 \tabularnewline
11 & 0.816696502955734 & 0.366606994088531 & 0.183303497044266 \tabularnewline
12 & 0.72132022393174 & 0.557359552136521 & 0.27867977606826 \tabularnewline
13 & 0.616508447083023 & 0.766983105833955 & 0.383491552916977 \tabularnewline
14 & 0.547826489958445 & 0.904347020083111 & 0.452173510041555 \tabularnewline
15 & 0.640680930665337 & 0.718638138669325 & 0.359319069334663 \tabularnewline
16 & 0.575902276707825 & 0.84819544658435 & 0.424097723292175 \tabularnewline
17 & 0.483559525111683 & 0.967119050223366 & 0.516440474888317 \tabularnewline
18 & 0.419712097208218 & 0.839424194416437 & 0.580287902791782 \tabularnewline
19 & 0.580771253895045 & 0.83845749220991 & 0.419228746104955 \tabularnewline
20 & 0.597641526684818 & 0.804716946630364 & 0.402358473315182 \tabularnewline
21 & 0.663310053292154 & 0.673379893415692 & 0.336689946707846 \tabularnewline
22 & 0.658966049481927 & 0.682067901036145 & 0.341033950518073 \tabularnewline
23 & 0.613029050642179 & 0.773941898715642 & 0.386970949357821 \tabularnewline
24 & 0.578058628999606 & 0.843882742000788 & 0.421941371000394 \tabularnewline
25 & 0.604452798144285 & 0.791094403711429 & 0.395547201855715 \tabularnewline
26 & 0.685872137177326 & 0.628255725645347 & 0.314127862822674 \tabularnewline
27 & 0.761978220285241 & 0.476043559429519 & 0.238021779714759 \tabularnewline
28 & 0.733959445214993 & 0.532081109570014 & 0.266040554785007 \tabularnewline
29 & 0.772361822292968 & 0.455276355414064 & 0.227638177707032 \tabularnewline
30 & 0.792017654056502 & 0.415964691886996 & 0.207982345943498 \tabularnewline
31 & 0.7639916497721 & 0.472016700455799 & 0.2360083502279 \tabularnewline
32 & 0.731284169131033 & 0.537431661737935 & 0.268715830868967 \tabularnewline
33 & 0.731672796440599 & 0.536654407118802 & 0.268327203559401 \tabularnewline
34 & 0.743410709190605 & 0.513178581618791 & 0.256589290809395 \tabularnewline
35 & 0.714935804824608 & 0.570128390350784 & 0.285064195175392 \tabularnewline
36 & 0.684739692097812 & 0.630520615804376 & 0.315260307902188 \tabularnewline
37 & 0.717348255217873 & 0.565303489564253 & 0.282651744782127 \tabularnewline
38 & 0.733772990519184 & 0.532454018961633 & 0.266227009480816 \tabularnewline
39 & 0.724931270892866 & 0.550137458214268 & 0.275068729107134 \tabularnewline
40 & 0.738047106206279 & 0.523905787587443 & 0.261952893793721 \tabularnewline
41 & 0.719965757816735 & 0.56006848436653 & 0.280034242183265 \tabularnewline
42 & 0.712559148380705 & 0.57488170323859 & 0.287440851619295 \tabularnewline
43 & 0.720219387451881 & 0.559561225096239 & 0.279780612548119 \tabularnewline
44 & 0.688854969091594 & 0.622290061816813 & 0.311145030908406 \tabularnewline
45 & 0.694158292622182 & 0.611683414755636 & 0.305841707377818 \tabularnewline
46 & 0.689548133237755 & 0.62090373352449 & 0.310451866762245 \tabularnewline
47 & 0.67177759176157 & 0.656444816476861 & 0.32822240823843 \tabularnewline
48 & 0.692817007194174 & 0.614365985611652 & 0.307182992805826 \tabularnewline
49 & 0.681778567007867 & 0.636442865984267 & 0.318221432992133 \tabularnewline
50 & 0.666334842349544 & 0.667330315300912 & 0.333665157650456 \tabularnewline
51 & 0.659352117102981 & 0.681295765794039 & 0.340647882897019 \tabularnewline
52 & 0.658188337688318 & 0.683623324623364 & 0.341811662311682 \tabularnewline
53 & 0.674008952602426 & 0.651982094795148 & 0.325991047397574 \tabularnewline
54 & 0.658106307951505 & 0.683787384096991 & 0.341893692048495 \tabularnewline
55 & 0.644906766970851 & 0.710186466058297 & 0.355093233029149 \tabularnewline
56 & 0.650507159444039 & 0.698985681111922 & 0.349492840555961 \tabularnewline
57 & 0.618898065957655 & 0.76220386808469 & 0.381101934042345 \tabularnewline
58 & 0.635928541102261 & 0.728142917795477 & 0.364071458897738 \tabularnewline
59 & 0.650802469739756 & 0.698395060520487 & 0.349197530260244 \tabularnewline
60 & 0.662719104864242 & 0.674561790271517 & 0.337280895135758 \tabularnewline
61 & 0.704807088052263 & 0.590385823895474 & 0.295192911947737 \tabularnewline
62 & 0.738664121258155 & 0.522671757483691 & 0.261335878741845 \tabularnewline
63 & 0.727080829579074 & 0.545838340841852 & 0.272919170420926 \tabularnewline
64 & 0.763094677591595 & 0.47381064481681 & 0.236905322408405 \tabularnewline
65 & 0.751264294746333 & 0.497471410507334 & 0.248735705253667 \tabularnewline
66 & 0.740140201791689 & 0.519719596416622 & 0.259859798208311 \tabularnewline
67 & 0.743989174753111 & 0.512021650493778 & 0.256010825246889 \tabularnewline
68 & 0.725584679765182 & 0.548830640469635 & 0.274415320234818 \tabularnewline
69 & 0.737405637338355 & 0.525188725323289 & 0.262594362661645 \tabularnewline
70 & 0.74667152999432 & 0.50665694001136 & 0.25332847000568 \tabularnewline
71 & 0.741538678405443 & 0.516922643189114 & 0.258461321594557 \tabularnewline
72 & 0.74959573667732 & 0.500808526645361 & 0.25040426332268 \tabularnewline
73 & 0.742066989050442 & 0.515866021899115 & 0.257933010949558 \tabularnewline
74 & 0.741258020930423 & 0.517483958139153 & 0.258741979069577 \tabularnewline
75 & 0.748121612901743 & 0.503756774196514 & 0.251878387098257 \tabularnewline
76 & 0.739595800168916 & 0.520808399662169 & 0.260404199831084 \tabularnewline
77 & 0.753903834347732 & 0.492192331304536 & 0.246096165652268 \tabularnewline
78 & 0.731570383374311 & 0.536859233251378 & 0.268429616625689 \tabularnewline
79 & 0.775836574443009 & 0.448326851113982 & 0.224163425556991 \tabularnewline
80 & 0.771719631981754 & 0.456560736036491 & 0.228280368018246 \tabularnewline
81 & 0.756742622795175 & 0.48651475440965 & 0.243257377204825 \tabularnewline
82 & 0.77363471997499 & 0.452730560050019 & 0.22636528002501 \tabularnewline
83 & 0.754289088396852 & 0.491421823206297 & 0.245710911603148 \tabularnewline
84 & 0.736607014398814 & 0.526785971202372 & 0.263392985601186 \tabularnewline
85 & 0.736128162588626 & 0.527743674822749 & 0.263871837411374 \tabularnewline
86 & 0.702835440192344 & 0.594329119615313 & 0.297164559807656 \tabularnewline
87 & 0.72510645993539 & 0.549787080129219 & 0.27489354006461 \tabularnewline
88 & 0.754717223264144 & 0.490565553471711 & 0.245282776735856 \tabularnewline
89 & 0.766790236738678 & 0.466419526522643 & 0.233209763261322 \tabularnewline
90 & 0.780384180454654 & 0.439231639090692 & 0.219615819545346 \tabularnewline
91 & 0.803834629260774 & 0.392330741478452 & 0.196165370739226 \tabularnewline
92 & 0.781650650746435 & 0.43669869850713 & 0.218349349253565 \tabularnewline
93 & 0.777650813261159 & 0.444698373477681 & 0.222349186738841 \tabularnewline
94 & 0.758182467293145 & 0.48363506541371 & 0.241817532706855 \tabularnewline
95 & 0.728927999333931 & 0.542144001332139 & 0.271072000666069 \tabularnewline
96 & 0.764592918062039 & 0.470814163875922 & 0.235407081937961 \tabularnewline
97 & 0.730578781665253 & 0.538842436669495 & 0.269421218334747 \tabularnewline
98 & 0.706170537953388 & 0.587658924093224 & 0.293829462046612 \tabularnewline
99 & 0.670180452920369 & 0.659639094159263 & 0.329819547079631 \tabularnewline
100 & 0.712700600411303 & 0.574598799177394 & 0.287299399588697 \tabularnewline
101 & 0.78010096206611 & 0.439798075867781 & 0.21989903793389 \tabularnewline
102 & 0.757791326841175 & 0.484417346317651 & 0.242208673158825 \tabularnewline
103 & 0.733963165077553 & 0.532073669844895 & 0.266036834922447 \tabularnewline
104 & 0.709008383560168 & 0.581983232879665 & 0.290991616439832 \tabularnewline
105 & 0.686345559299945 & 0.62730888140011 & 0.313654440700055 \tabularnewline
106 & 0.659351094532563 & 0.681297810934874 & 0.340648905467437 \tabularnewline
107 & 0.632440985020367 & 0.735118029959266 & 0.367559014979633 \tabularnewline
108 & 0.590766020898519 & 0.818467958202962 & 0.409233979101481 \tabularnewline
109 & 0.562871430613856 & 0.874257138772288 & 0.437128569386144 \tabularnewline
110 & 0.515625588736783 & 0.968748822526434 & 0.484374411263217 \tabularnewline
111 & 0.502659400731997 & 0.994681198536005 & 0.497340599268003 \tabularnewline
112 & 0.463939874327519 & 0.927879748655037 & 0.536060125672481 \tabularnewline
113 & 0.45737526889863 & 0.91475053779726 & 0.54262473110137 \tabularnewline
114 & 0.415602154292525 & 0.831204308585051 & 0.584397845707475 \tabularnewline
115 & 0.367231175543597 & 0.734462351087194 & 0.632768824456403 \tabularnewline
116 & 0.341373309708757 & 0.682746619417514 & 0.658626690291243 \tabularnewline
117 & 0.450002522290717 & 0.900005044581434 & 0.549997477709283 \tabularnewline
118 & 0.395806788066045 & 0.79161357613209 & 0.604193211933955 \tabularnewline
119 & 0.371120159942671 & 0.742240319885341 & 0.628879840057329 \tabularnewline
120 & 0.408220190212113 & 0.816440380424225 & 0.591779809787887 \tabularnewline
121 & 0.353562503128936 & 0.707125006257872 & 0.646437496871064 \tabularnewline
122 & 0.32755673929499 & 0.65511347858998 & 0.67244326070501 \tabularnewline
123 & 0.287247339724233 & 0.574494679448466 & 0.712752660275767 \tabularnewline
124 & 0.250093187713631 & 0.500186375427263 & 0.749906812286369 \tabularnewline
125 & 0.282443227478361 & 0.564886454956723 & 0.717556772521639 \tabularnewline
126 & 0.251931763917339 & 0.503863527834678 & 0.748068236082661 \tabularnewline
127 & 0.278027944351207 & 0.556055888702414 & 0.721972055648793 \tabularnewline
128 & 0.319029023914145 & 0.63805804782829 & 0.680970976085855 \tabularnewline
129 & 0.283145967341544 & 0.566291934683088 & 0.716854032658456 \tabularnewline
130 & 0.334346046304067 & 0.668692092608135 & 0.665653953695933 \tabularnewline
131 & 0.274100847886685 & 0.548201695773371 & 0.725899152113315 \tabularnewline
132 & 0.467196211014409 & 0.934392422028817 & 0.532803788985591 \tabularnewline
133 & 0.404140293496931 & 0.808280586993863 & 0.595859706503069 \tabularnewline
134 & 0.348294900364749 & 0.696589800729497 & 0.651705099635251 \tabularnewline
135 & 0.299536585071874 & 0.599073170143748 & 0.700463414928126 \tabularnewline
136 & 0.261329217952028 & 0.522658435904055 & 0.738670782047972 \tabularnewline
137 & 0.23529897908137 & 0.470597958162741 & 0.76470102091863 \tabularnewline
138 & 0.3365706316299 & 0.6731412632598 & 0.6634293683701 \tabularnewline
139 & 0.299372120051095 & 0.598744240102189 & 0.700627879948905 \tabularnewline
140 & 0.408893192681008 & 0.817786385362016 & 0.591106807318992 \tabularnewline
141 & 0.310497833769746 & 0.620995667539492 & 0.689502166230254 \tabularnewline
142 & 0.374759050511851 & 0.749518101023701 & 0.625240949488149 \tabularnewline
143 & 0.256976210691398 & 0.513952421382797 & 0.743023789308602 \tabularnewline
144 & 0.190927042598109 & 0.381854085196218 & 0.809072957401891 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203754&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]10[/C][C]0.846334242843343[/C][C]0.307331514313313[/C][C]0.153665757156657[/C][/ROW]
[ROW][C]11[/C][C]0.816696502955734[/C][C]0.366606994088531[/C][C]0.183303497044266[/C][/ROW]
[ROW][C]12[/C][C]0.72132022393174[/C][C]0.557359552136521[/C][C]0.27867977606826[/C][/ROW]
[ROW][C]13[/C][C]0.616508447083023[/C][C]0.766983105833955[/C][C]0.383491552916977[/C][/ROW]
[ROW][C]14[/C][C]0.547826489958445[/C][C]0.904347020083111[/C][C]0.452173510041555[/C][/ROW]
[ROW][C]15[/C][C]0.640680930665337[/C][C]0.718638138669325[/C][C]0.359319069334663[/C][/ROW]
[ROW][C]16[/C][C]0.575902276707825[/C][C]0.84819544658435[/C][C]0.424097723292175[/C][/ROW]
[ROW][C]17[/C][C]0.483559525111683[/C][C]0.967119050223366[/C][C]0.516440474888317[/C][/ROW]
[ROW][C]18[/C][C]0.419712097208218[/C][C]0.839424194416437[/C][C]0.580287902791782[/C][/ROW]
[ROW][C]19[/C][C]0.580771253895045[/C][C]0.83845749220991[/C][C]0.419228746104955[/C][/ROW]
[ROW][C]20[/C][C]0.597641526684818[/C][C]0.804716946630364[/C][C]0.402358473315182[/C][/ROW]
[ROW][C]21[/C][C]0.663310053292154[/C][C]0.673379893415692[/C][C]0.336689946707846[/C][/ROW]
[ROW][C]22[/C][C]0.658966049481927[/C][C]0.682067901036145[/C][C]0.341033950518073[/C][/ROW]
[ROW][C]23[/C][C]0.613029050642179[/C][C]0.773941898715642[/C][C]0.386970949357821[/C][/ROW]
[ROW][C]24[/C][C]0.578058628999606[/C][C]0.843882742000788[/C][C]0.421941371000394[/C][/ROW]
[ROW][C]25[/C][C]0.604452798144285[/C][C]0.791094403711429[/C][C]0.395547201855715[/C][/ROW]
[ROW][C]26[/C][C]0.685872137177326[/C][C]0.628255725645347[/C][C]0.314127862822674[/C][/ROW]
[ROW][C]27[/C][C]0.761978220285241[/C][C]0.476043559429519[/C][C]0.238021779714759[/C][/ROW]
[ROW][C]28[/C][C]0.733959445214993[/C][C]0.532081109570014[/C][C]0.266040554785007[/C][/ROW]
[ROW][C]29[/C][C]0.772361822292968[/C][C]0.455276355414064[/C][C]0.227638177707032[/C][/ROW]
[ROW][C]30[/C][C]0.792017654056502[/C][C]0.415964691886996[/C][C]0.207982345943498[/C][/ROW]
[ROW][C]31[/C][C]0.7639916497721[/C][C]0.472016700455799[/C][C]0.2360083502279[/C][/ROW]
[ROW][C]32[/C][C]0.731284169131033[/C][C]0.537431661737935[/C][C]0.268715830868967[/C][/ROW]
[ROW][C]33[/C][C]0.731672796440599[/C][C]0.536654407118802[/C][C]0.268327203559401[/C][/ROW]
[ROW][C]34[/C][C]0.743410709190605[/C][C]0.513178581618791[/C][C]0.256589290809395[/C][/ROW]
[ROW][C]35[/C][C]0.714935804824608[/C][C]0.570128390350784[/C][C]0.285064195175392[/C][/ROW]
[ROW][C]36[/C][C]0.684739692097812[/C][C]0.630520615804376[/C][C]0.315260307902188[/C][/ROW]
[ROW][C]37[/C][C]0.717348255217873[/C][C]0.565303489564253[/C][C]0.282651744782127[/C][/ROW]
[ROW][C]38[/C][C]0.733772990519184[/C][C]0.532454018961633[/C][C]0.266227009480816[/C][/ROW]
[ROW][C]39[/C][C]0.724931270892866[/C][C]0.550137458214268[/C][C]0.275068729107134[/C][/ROW]
[ROW][C]40[/C][C]0.738047106206279[/C][C]0.523905787587443[/C][C]0.261952893793721[/C][/ROW]
[ROW][C]41[/C][C]0.719965757816735[/C][C]0.56006848436653[/C][C]0.280034242183265[/C][/ROW]
[ROW][C]42[/C][C]0.712559148380705[/C][C]0.57488170323859[/C][C]0.287440851619295[/C][/ROW]
[ROW][C]43[/C][C]0.720219387451881[/C][C]0.559561225096239[/C][C]0.279780612548119[/C][/ROW]
[ROW][C]44[/C][C]0.688854969091594[/C][C]0.622290061816813[/C][C]0.311145030908406[/C][/ROW]
[ROW][C]45[/C][C]0.694158292622182[/C][C]0.611683414755636[/C][C]0.305841707377818[/C][/ROW]
[ROW][C]46[/C][C]0.689548133237755[/C][C]0.62090373352449[/C][C]0.310451866762245[/C][/ROW]
[ROW][C]47[/C][C]0.67177759176157[/C][C]0.656444816476861[/C][C]0.32822240823843[/C][/ROW]
[ROW][C]48[/C][C]0.692817007194174[/C][C]0.614365985611652[/C][C]0.307182992805826[/C][/ROW]
[ROW][C]49[/C][C]0.681778567007867[/C][C]0.636442865984267[/C][C]0.318221432992133[/C][/ROW]
[ROW][C]50[/C][C]0.666334842349544[/C][C]0.667330315300912[/C][C]0.333665157650456[/C][/ROW]
[ROW][C]51[/C][C]0.659352117102981[/C][C]0.681295765794039[/C][C]0.340647882897019[/C][/ROW]
[ROW][C]52[/C][C]0.658188337688318[/C][C]0.683623324623364[/C][C]0.341811662311682[/C][/ROW]
[ROW][C]53[/C][C]0.674008952602426[/C][C]0.651982094795148[/C][C]0.325991047397574[/C][/ROW]
[ROW][C]54[/C][C]0.658106307951505[/C][C]0.683787384096991[/C][C]0.341893692048495[/C][/ROW]
[ROW][C]55[/C][C]0.644906766970851[/C][C]0.710186466058297[/C][C]0.355093233029149[/C][/ROW]
[ROW][C]56[/C][C]0.650507159444039[/C][C]0.698985681111922[/C][C]0.349492840555961[/C][/ROW]
[ROW][C]57[/C][C]0.618898065957655[/C][C]0.76220386808469[/C][C]0.381101934042345[/C][/ROW]
[ROW][C]58[/C][C]0.635928541102261[/C][C]0.728142917795477[/C][C]0.364071458897738[/C][/ROW]
[ROW][C]59[/C][C]0.650802469739756[/C][C]0.698395060520487[/C][C]0.349197530260244[/C][/ROW]
[ROW][C]60[/C][C]0.662719104864242[/C][C]0.674561790271517[/C][C]0.337280895135758[/C][/ROW]
[ROW][C]61[/C][C]0.704807088052263[/C][C]0.590385823895474[/C][C]0.295192911947737[/C][/ROW]
[ROW][C]62[/C][C]0.738664121258155[/C][C]0.522671757483691[/C][C]0.261335878741845[/C][/ROW]
[ROW][C]63[/C][C]0.727080829579074[/C][C]0.545838340841852[/C][C]0.272919170420926[/C][/ROW]
[ROW][C]64[/C][C]0.763094677591595[/C][C]0.47381064481681[/C][C]0.236905322408405[/C][/ROW]
[ROW][C]65[/C][C]0.751264294746333[/C][C]0.497471410507334[/C][C]0.248735705253667[/C][/ROW]
[ROW][C]66[/C][C]0.740140201791689[/C][C]0.519719596416622[/C][C]0.259859798208311[/C][/ROW]
[ROW][C]67[/C][C]0.743989174753111[/C][C]0.512021650493778[/C][C]0.256010825246889[/C][/ROW]
[ROW][C]68[/C][C]0.725584679765182[/C][C]0.548830640469635[/C][C]0.274415320234818[/C][/ROW]
[ROW][C]69[/C][C]0.737405637338355[/C][C]0.525188725323289[/C][C]0.262594362661645[/C][/ROW]
[ROW][C]70[/C][C]0.74667152999432[/C][C]0.50665694001136[/C][C]0.25332847000568[/C][/ROW]
[ROW][C]71[/C][C]0.741538678405443[/C][C]0.516922643189114[/C][C]0.258461321594557[/C][/ROW]
[ROW][C]72[/C][C]0.74959573667732[/C][C]0.500808526645361[/C][C]0.25040426332268[/C][/ROW]
[ROW][C]73[/C][C]0.742066989050442[/C][C]0.515866021899115[/C][C]0.257933010949558[/C][/ROW]
[ROW][C]74[/C][C]0.741258020930423[/C][C]0.517483958139153[/C][C]0.258741979069577[/C][/ROW]
[ROW][C]75[/C][C]0.748121612901743[/C][C]0.503756774196514[/C][C]0.251878387098257[/C][/ROW]
[ROW][C]76[/C][C]0.739595800168916[/C][C]0.520808399662169[/C][C]0.260404199831084[/C][/ROW]
[ROW][C]77[/C][C]0.753903834347732[/C][C]0.492192331304536[/C][C]0.246096165652268[/C][/ROW]
[ROW][C]78[/C][C]0.731570383374311[/C][C]0.536859233251378[/C][C]0.268429616625689[/C][/ROW]
[ROW][C]79[/C][C]0.775836574443009[/C][C]0.448326851113982[/C][C]0.224163425556991[/C][/ROW]
[ROW][C]80[/C][C]0.771719631981754[/C][C]0.456560736036491[/C][C]0.228280368018246[/C][/ROW]
[ROW][C]81[/C][C]0.756742622795175[/C][C]0.48651475440965[/C][C]0.243257377204825[/C][/ROW]
[ROW][C]82[/C][C]0.77363471997499[/C][C]0.452730560050019[/C][C]0.22636528002501[/C][/ROW]
[ROW][C]83[/C][C]0.754289088396852[/C][C]0.491421823206297[/C][C]0.245710911603148[/C][/ROW]
[ROW][C]84[/C][C]0.736607014398814[/C][C]0.526785971202372[/C][C]0.263392985601186[/C][/ROW]
[ROW][C]85[/C][C]0.736128162588626[/C][C]0.527743674822749[/C][C]0.263871837411374[/C][/ROW]
[ROW][C]86[/C][C]0.702835440192344[/C][C]0.594329119615313[/C][C]0.297164559807656[/C][/ROW]
[ROW][C]87[/C][C]0.72510645993539[/C][C]0.549787080129219[/C][C]0.27489354006461[/C][/ROW]
[ROW][C]88[/C][C]0.754717223264144[/C][C]0.490565553471711[/C][C]0.245282776735856[/C][/ROW]
[ROW][C]89[/C][C]0.766790236738678[/C][C]0.466419526522643[/C][C]0.233209763261322[/C][/ROW]
[ROW][C]90[/C][C]0.780384180454654[/C][C]0.439231639090692[/C][C]0.219615819545346[/C][/ROW]
[ROW][C]91[/C][C]0.803834629260774[/C][C]0.392330741478452[/C][C]0.196165370739226[/C][/ROW]
[ROW][C]92[/C][C]0.781650650746435[/C][C]0.43669869850713[/C][C]0.218349349253565[/C][/ROW]
[ROW][C]93[/C][C]0.777650813261159[/C][C]0.444698373477681[/C][C]0.222349186738841[/C][/ROW]
[ROW][C]94[/C][C]0.758182467293145[/C][C]0.48363506541371[/C][C]0.241817532706855[/C][/ROW]
[ROW][C]95[/C][C]0.728927999333931[/C][C]0.542144001332139[/C][C]0.271072000666069[/C][/ROW]
[ROW][C]96[/C][C]0.764592918062039[/C][C]0.470814163875922[/C][C]0.235407081937961[/C][/ROW]
[ROW][C]97[/C][C]0.730578781665253[/C][C]0.538842436669495[/C][C]0.269421218334747[/C][/ROW]
[ROW][C]98[/C][C]0.706170537953388[/C][C]0.587658924093224[/C][C]0.293829462046612[/C][/ROW]
[ROW][C]99[/C][C]0.670180452920369[/C][C]0.659639094159263[/C][C]0.329819547079631[/C][/ROW]
[ROW][C]100[/C][C]0.712700600411303[/C][C]0.574598799177394[/C][C]0.287299399588697[/C][/ROW]
[ROW][C]101[/C][C]0.78010096206611[/C][C]0.439798075867781[/C][C]0.21989903793389[/C][/ROW]
[ROW][C]102[/C][C]0.757791326841175[/C][C]0.484417346317651[/C][C]0.242208673158825[/C][/ROW]
[ROW][C]103[/C][C]0.733963165077553[/C][C]0.532073669844895[/C][C]0.266036834922447[/C][/ROW]
[ROW][C]104[/C][C]0.709008383560168[/C][C]0.581983232879665[/C][C]0.290991616439832[/C][/ROW]
[ROW][C]105[/C][C]0.686345559299945[/C][C]0.62730888140011[/C][C]0.313654440700055[/C][/ROW]
[ROW][C]106[/C][C]0.659351094532563[/C][C]0.681297810934874[/C][C]0.340648905467437[/C][/ROW]
[ROW][C]107[/C][C]0.632440985020367[/C][C]0.735118029959266[/C][C]0.367559014979633[/C][/ROW]
[ROW][C]108[/C][C]0.590766020898519[/C][C]0.818467958202962[/C][C]0.409233979101481[/C][/ROW]
[ROW][C]109[/C][C]0.562871430613856[/C][C]0.874257138772288[/C][C]0.437128569386144[/C][/ROW]
[ROW][C]110[/C][C]0.515625588736783[/C][C]0.968748822526434[/C][C]0.484374411263217[/C][/ROW]
[ROW][C]111[/C][C]0.502659400731997[/C][C]0.994681198536005[/C][C]0.497340599268003[/C][/ROW]
[ROW][C]112[/C][C]0.463939874327519[/C][C]0.927879748655037[/C][C]0.536060125672481[/C][/ROW]
[ROW][C]113[/C][C]0.45737526889863[/C][C]0.91475053779726[/C][C]0.54262473110137[/C][/ROW]
[ROW][C]114[/C][C]0.415602154292525[/C][C]0.831204308585051[/C][C]0.584397845707475[/C][/ROW]
[ROW][C]115[/C][C]0.367231175543597[/C][C]0.734462351087194[/C][C]0.632768824456403[/C][/ROW]
[ROW][C]116[/C][C]0.341373309708757[/C][C]0.682746619417514[/C][C]0.658626690291243[/C][/ROW]
[ROW][C]117[/C][C]0.450002522290717[/C][C]0.900005044581434[/C][C]0.549997477709283[/C][/ROW]
[ROW][C]118[/C][C]0.395806788066045[/C][C]0.79161357613209[/C][C]0.604193211933955[/C][/ROW]
[ROW][C]119[/C][C]0.371120159942671[/C][C]0.742240319885341[/C][C]0.628879840057329[/C][/ROW]
[ROW][C]120[/C][C]0.408220190212113[/C][C]0.816440380424225[/C][C]0.591779809787887[/C][/ROW]
[ROW][C]121[/C][C]0.353562503128936[/C][C]0.707125006257872[/C][C]0.646437496871064[/C][/ROW]
[ROW][C]122[/C][C]0.32755673929499[/C][C]0.65511347858998[/C][C]0.67244326070501[/C][/ROW]
[ROW][C]123[/C][C]0.287247339724233[/C][C]0.574494679448466[/C][C]0.712752660275767[/C][/ROW]
[ROW][C]124[/C][C]0.250093187713631[/C][C]0.500186375427263[/C][C]0.749906812286369[/C][/ROW]
[ROW][C]125[/C][C]0.282443227478361[/C][C]0.564886454956723[/C][C]0.717556772521639[/C][/ROW]
[ROW][C]126[/C][C]0.251931763917339[/C][C]0.503863527834678[/C][C]0.748068236082661[/C][/ROW]
[ROW][C]127[/C][C]0.278027944351207[/C][C]0.556055888702414[/C][C]0.721972055648793[/C][/ROW]
[ROW][C]128[/C][C]0.319029023914145[/C][C]0.63805804782829[/C][C]0.680970976085855[/C][/ROW]
[ROW][C]129[/C][C]0.283145967341544[/C][C]0.566291934683088[/C][C]0.716854032658456[/C][/ROW]
[ROW][C]130[/C][C]0.334346046304067[/C][C]0.668692092608135[/C][C]0.665653953695933[/C][/ROW]
[ROW][C]131[/C][C]0.274100847886685[/C][C]0.548201695773371[/C][C]0.725899152113315[/C][/ROW]
[ROW][C]132[/C][C]0.467196211014409[/C][C]0.934392422028817[/C][C]0.532803788985591[/C][/ROW]
[ROW][C]133[/C][C]0.404140293496931[/C][C]0.808280586993863[/C][C]0.595859706503069[/C][/ROW]
[ROW][C]134[/C][C]0.348294900364749[/C][C]0.696589800729497[/C][C]0.651705099635251[/C][/ROW]
[ROW][C]135[/C][C]0.299536585071874[/C][C]0.599073170143748[/C][C]0.700463414928126[/C][/ROW]
[ROW][C]136[/C][C]0.261329217952028[/C][C]0.522658435904055[/C][C]0.738670782047972[/C][/ROW]
[ROW][C]137[/C][C]0.23529897908137[/C][C]0.470597958162741[/C][C]0.76470102091863[/C][/ROW]
[ROW][C]138[/C][C]0.3365706316299[/C][C]0.6731412632598[/C][C]0.6634293683701[/C][/ROW]
[ROW][C]139[/C][C]0.299372120051095[/C][C]0.598744240102189[/C][C]0.700627879948905[/C][/ROW]
[ROW][C]140[/C][C]0.408893192681008[/C][C]0.817786385362016[/C][C]0.591106807318992[/C][/ROW]
[ROW][C]141[/C][C]0.310497833769746[/C][C]0.620995667539492[/C][C]0.689502166230254[/C][/ROW]
[ROW][C]142[/C][C]0.374759050511851[/C][C]0.749518101023701[/C][C]0.625240949488149[/C][/ROW]
[ROW][C]143[/C][C]0.256976210691398[/C][C]0.513952421382797[/C][C]0.743023789308602[/C][/ROW]
[ROW][C]144[/C][C]0.190927042598109[/C][C]0.381854085196218[/C][C]0.809072957401891[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203754&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203754&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
100.8463342428433430.3073315143133130.153665757156657
110.8166965029557340.3666069940885310.183303497044266
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1440.1909270425981090.3818540851962180.809072957401891







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=203754&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=203754&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203754&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 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}