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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 19 Dec 2012 14:59:56 -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/t1355947617ou5wt7did10boyz.htm/, Retrieved Sat, 04 May 2024 10:45:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202367, Retrieved Sat, 04 May 2024 10:45:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 10 Multi...] [2010-12-14 15:33:34] [a9e130f95bad0a0597234e75c6380c5a]
- R       [Multiple Regression] [WS 10 - Multiple ...] [2011-12-13 14:06:27] [95a4a8598e82ac3272c4dca488d0ba38]
-    D        [Multiple Regression] [] [2012-12-19 19:59:56] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R PD          [Multiple Regression] [Teste] [2012-12-21 16:44:49] [0ee39c4db763367d76b81eeea0021391]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	1	3	8	9	0	0	6
4	0	5	8	9	0	0	7
4	0	5	8	9	0	0	7
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4	1	5	8	9	0	1	6
4	0	5	8	9	0	0	7
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4	0	5	8	9	0	0	6
4	1	5	8	9	0	0	7
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4	0	5	8	9	0	0	7
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4	1	3	8	9	0	0	7
4	0	5	8	10	0	1	6
4	0	3	8	10	0	1	6
4	1	3	8	10	1	1	7
4	1	3	8	9	0	0	7
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4	0	3	8	10	1	1	6
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4	0	5	8	9	0	1	7
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4	1	5	8	9	0	0	7
4	1	5	8	9	0	1	7
4	0	3	8	9	0	0	6
4	0	5	8	9	0	0	7
4	0	5	8	9	0	0	7
4	1	3	8	10	0	1	7
4	0	5	8	10	0	0	6
4	0	5	8	9	0	1	6
4	0	3	8	9	0	1	7
4	0	5	8	10	1	1	6
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4	0	5	8	9	0	0	7
4	0	3	8	10	0	0	7
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4	0	5	8	9	0	0	6
4	0	5	8	10	1	0	7
4	0	5	8	9	0	0	7
4	0	3	8	10	0	0	6
4	0	5	8	10	0	1	6
4	0	5	8	9	0	0	6
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4	1	3	8	9	0	0	6
4	0	5	8	10	0	1	7
4	0	5	8	9	0	0	7
4	1	3	8	9	0	0	6
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4	0	5	8	10	0	0	7
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4	0	5	8	9	0	0	6
4	0	5	8	10	0	1	6
4	0	3	8	10	1	0	6
4	0	3	8	9	0	1	7
4	0	5	8	9	0	0	7
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4	0	5	8	9	0	0	7
4	0	5	8	10	1	0	7
4	0	5	8	9	0	1	6
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2	1	5	3	10	1	0	7
2	1	5	3	10	1	1	7
2	1	5	3	10	0	0	7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202367&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]9 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=202367&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202367&T=0

As an alternative you can also use a QR Code:  
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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
UseLimit[t] = -0.213810071598671 + 0.132554010438934Weeks[t] -0.128607933450428T40[t] -0.0840656770554428T20[t] + 0.075387794529008Used[t] -0.0343701246133239CorrectAnalysis[t] + 0.065212162045852Useful[t] + 0.0745616972837747Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
UseLimit[t] =  -0.213810071598671 +  0.132554010438934Weeks[t] -0.128607933450428T40[t] -0.0840656770554428T20[t] +  0.075387794529008Used[t] -0.0343701246133239CorrectAnalysis[t] +  0.065212162045852Useful[t] +  0.0745616972837747Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202367&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]UseLimit[t] =  -0.213810071598671 +  0.132554010438934Weeks[t] -0.128607933450428T40[t] -0.0840656770554428T20[t] +  0.075387794529008Used[t] -0.0343701246133239CorrectAnalysis[t] +  0.065212162045852Useful[t] +  0.0745616972837747Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202367&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202367&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
UseLimit[t] = -0.213810071598671 + 0.132554010438934Weeks[t] -0.128607933450428T40[t] -0.0840656770554428T20[t] + 0.075387794529008Used[t] -0.0343701246133239CorrectAnalysis[t] + 0.065212162045852Useful[t] + 0.0745616972837747Outcome[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2138100715986711.112946-0.19210.8479220.423961
Weeks0.1325540104389340.3625270.36560.7151640.357582
T40-0.1286079334504280.058763-2.18860.0302150.015108
T20-0.08406567705544280.137493-0.61140.5418740.270937
Used0.0753877945290080.09920.760.4485070.224254
CorrectAnalysis-0.03437012461332390.165011-0.20830.8352930.417646
Useful0.0652121620458520.090940.71710.4744650.237233
Outcome0.07456169728377470.0788230.94590.3457450.172873

\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) & -0.213810071598671 & 1.112946 & -0.1921 & 0.847922 & 0.423961 \tabularnewline
Weeks & 0.132554010438934 & 0.362527 & 0.3656 & 0.715164 & 0.357582 \tabularnewline
T40 & -0.128607933450428 & 0.058763 & -2.1886 & 0.030215 & 0.015108 \tabularnewline
T20 & -0.0840656770554428 & 0.137493 & -0.6114 & 0.541874 & 0.270937 \tabularnewline
Used & 0.075387794529008 & 0.0992 & 0.76 & 0.448507 & 0.224254 \tabularnewline
CorrectAnalysis & -0.0343701246133239 & 0.165011 & -0.2083 & 0.835293 & 0.417646 \tabularnewline
Useful & 0.065212162045852 & 0.09094 & 0.7171 & 0.474465 & 0.237233 \tabularnewline
Outcome & 0.0745616972837747 & 0.078823 & 0.9459 & 0.345745 & 0.172873 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202367&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]-0.213810071598671[/C][C]1.112946[/C][C]-0.1921[/C][C]0.847922[/C][C]0.423961[/C][/ROW]
[ROW][C]Weeks[/C][C]0.132554010438934[/C][C]0.362527[/C][C]0.3656[/C][C]0.715164[/C][C]0.357582[/C][/ROW]
[ROW][C]T40[/C][C]-0.128607933450428[/C][C]0.058763[/C][C]-2.1886[/C][C]0.030215[/C][C]0.015108[/C][/ROW]
[ROW][C]T20[/C][C]-0.0840656770554428[/C][C]0.137493[/C][C]-0.6114[/C][C]0.541874[/C][C]0.270937[/C][/ROW]
[ROW][C]Used[/C][C]0.075387794529008[/C][C]0.0992[/C][C]0.76[/C][C]0.448507[/C][C]0.224254[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]-0.0343701246133239[/C][C]0.165011[/C][C]-0.2083[/C][C]0.835293[/C][C]0.417646[/C][/ROW]
[ROW][C]Useful[/C][C]0.065212162045852[/C][C]0.09094[/C][C]0.7171[/C][C]0.474465[/C][C]0.237233[/C][/ROW]
[ROW][C]Outcome[/C][C]0.0745616972837747[/C][C]0.078823[/C][C]0.9459[/C][C]0.345745[/C][C]0.172873[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202367&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202367&T=2

As an alternative you can also use a QR Code:  
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)-0.2138100715986711.112946-0.19210.8479220.423961
Weeks0.1325540104389340.3625270.36560.7151640.357582
T40-0.1286079334504280.058763-2.18860.0302150.015108
T20-0.08406567705544280.137493-0.61140.5418740.270937
Used0.0753877945290080.09920.760.4485070.224254
CorrectAnalysis-0.03437012461332390.165011-0.20830.8352930.417646
Useful0.0652121620458520.090940.71710.4744650.237233
Outcome0.07456169728377470.0788230.94590.3457450.172873






Multiple Linear Regression - Regression Statistics
Multiple R0.258868448639325
R-squared0.0670128737009306
Adjusted R-squared0.0222806142208383
F-TEST (value)1.49808828080223
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value0.172271071411246
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.464518172871729
Sum Squared Residuals31.503461407501

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.258868448639325 \tabularnewline
R-squared & 0.0670128737009306 \tabularnewline
Adjusted R-squared & 0.0222806142208383 \tabularnewline
F-TEST (value) & 1.49808828080223 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0.172271071411246 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.464518172871729 \tabularnewline
Sum Squared Residuals & 31.503461407501 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202367&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.258868448639325[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0670128737009306[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0222806142208383[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.49808828080223[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0.172271071411246[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.464518172871729[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]31.503461407501[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202367&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202367&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.258868448639325
R-squared0.0670128737009306
Adjusted R-squared0.0222806142208383
F-TEST (value)1.49808828080223
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value0.172271071411246
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.464518172871729
Sum Squared Residuals31.503461407501






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.383917087825960.61608291217404
200.201262918208877-0.201262918208877
300.201262918208877-0.201262918208877
400.201262918208877-0.201262918208877
500.201262918208877-0.201262918208877
610.1919133829709540.808086617029046
700.201262918208877-0.201262918208877
800.458478785109733-0.458478785109733
900.126701220925102-0.126701220925102
1010.2012629182088770.798737081791123
1110.4584787851097330.541521214890267
1200.201262918208877-0.201262918208877
1300.341862874783737-0.341862874783737
1410.4584787851097330.541521214890267
1500.267301177499962-0.267301177499962
1600.524517044400818-0.524517044400818
1710.5647086170712690.435291382928731
1810.4584787851097330.541521214890267
1900.126701220925102-0.126701220925102
2000.490146919787494-0.490146919787494
2110.2664750802547290.733524919745271
2210.2673011774999620.732698822500038
2300.191913382970955-0.191913382970955
2410.1919133829709540.808086617029046
2500.459304882354966-0.459304882354966
2600.341862874783737-0.341862874783737
2710.1267012209251020.873298779074898
2800.276650712737885-0.276650712737885
2900.126701220925102-0.126701220925102
3000.266475080254729-0.266475080254729
3100.201262918208877-0.201262918208877
3210.2012629182088770.798737081791123
3310.2664750802547290.733524919745271
3400.383917087825958-0.383917087825958
3500.201262918208877-0.201262918208877
3600.201262918208877-0.201262918208877
3710.5990787416845930.400921258315407
3800.20208901545411-0.20208901545411
3900.191913382970955-0.191913382970955
4000.523690947155585-0.523690947155585
4100.232931052886639-0.232931052886639
4200.20208901545411-0.20208901545411
4310.1919133829709540.808086617029046
4410.4584787851097330.541521214890267
4500.266475080254729-0.266475080254729
4600.191913382970955-0.191913382970955
4700.201262918208877-0.201262918208877
4800.126701220925102-0.126701220925102
4900.191913382970955-0.191913382970955
5000.201262918208877-0.201262918208877
5100.533866579638741-0.533866579638741
5210.5647086170712690.435291382928731
5300.126701220925102-0.126701220925102
5400.242280588124561-0.242280588124561
5500.201262918208877-0.201262918208877
5600.459304882354966-0.459304882354966
5700.267301177499962-0.267301177499962
5800.126701220925102-0.126701220925102
5900.126701220925102-0.126701220925102
6010.4901469197874940.509853080212506
6110.3839170878259580.616082912174042
6200.341862874783737-0.341862874783737
6300.201262918208877-0.201262918208877
6410.3839170878259580.616082912174042
6500.201262918208877-0.201262918208877
6600.201262918208877-0.201262918208877
6700.564708617071269-0.564708617071269
6810.2012629182088770.798737081791123
6900.126701220925102-0.126701220925102
7000.276650712737885-0.276650712737885
7100.201262918208877-0.201262918208877
7200.126701220925102-0.126701220925102
7300.20208901545411-0.20208901545411
7410.2766507127378850.723349287262115
7500.126701220925102-0.126701220925102
7600.44912924987181-0.44912924987181
7700.126701220925102-0.126701220925102
7800.267301177499962-0.267301177499962
7900.424934757741642-0.424934757741642
8000.523690947155585-0.523690947155585
8100.201262918208877-0.201262918208877
8210.202089015454110.79791098454589
8300.201262918208877-0.201262918208877
8400.242280588124561-0.242280588124561
8500.191913382970955-0.191913382970955
8610.2012629182088770.798737081791123
8710.2819215853244490.718078414675551
8810.44137505690890.5586249430911
8900.356483282608224-0.356483282608224
9000.281921585324449-0.281921585324449
9100.421695444654076-0.421695444654076
9210.4405489596636660.559451040336334
9310.4216954446540750.578304555345925
9400.356483282608224-0.356483282608224
9500.440548959663666-0.440548959663666
9600.281921585324449-0.281921585324449
9710.4405489596636660.559451040336334
9800.356483282608224-0.356483282608224
9910.3564832826082230.643516717391777
10000.281921585324449-0.281921585324449
10110.2819215853244490.718078414675551
10200.356483282608224-0.356483282608224
10300.356483282608224-0.356483282608224
10400.356483282608224-0.356483282608224
10500.515936754192674-0.515936754192674
10600.356483282608224-0.356483282608224
10700.356483282608224-0.356483282608224
10810.5159367541926740.484063245807326
10900.356483282608224-0.356483282608224
11010.3564832826082230.643516717391777
11110.5811489162385260.418851083761474
11200.440548959663666-0.440548959663666
11300.431871077137232-0.431871077137232
11410.5159367541926740.484063245807326
11510.3564832826082230.643516717391777
11600.356483282608224-0.356483282608224
11710.2819215853244490.718078414675551
11810.3564832826082230.643516717391777
11900.356483282608224-0.356483282608224
12000.281921585324449-0.281921585324449
12110.3564832826082230.643516717391777
12200.356483282608224-0.356483282608224
12310.5159367541926740.484063245807326
12400.422521541899309-0.422521541899309
12500.281921585324449-0.281921585324449
12600.440548959663666-0.440548959663666
12700.421695444654076-0.421695444654076
12800.281921585324449-0.281921585324449
12900.356483282608224-0.356483282608224
13000.281921585324449-0.281921585324449
13110.3564832826082230.643516717391777
13210.2819215853244490.718078414675551
13310.4318710771372320.568128922862768
13400.356483282608224-0.356483282608224
13500.356483282608224-0.356483282608224
13600.356483282608224-0.356483282608224
13710.4225215418993090.577478458100691
13810.5065872189547520.493412781045248
13900.440548959663666-0.440548959663666
14000.356483282608224-0.356483282608224
14100.322939255240133-0.322939255240133
14200.441375056908899-0.441375056908899
14310.3564832826082230.643516717391777
14400.347133747370301-0.347133747370301
14500.421695444654076-0.421695444654076
14600.365987262379892-0.365987262379892
14700.515936754192674-0.515936754192674
14800.440548959663666-0.440548959663666
14910.3564832826082230.643516717391777
15000.347133747370301-0.347133747370301
15100.281921585324449-0.281921585324449
15210.3975009525239080.602499047476092
15310.462713114569760.53728688543024
15410.4318710771372320.568128922862768

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.38391708782596 & 0.61608291217404 \tabularnewline
2 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
3 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
4 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
5 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
6 & 1 & 0.191913382970954 & 0.808086617029046 \tabularnewline
7 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
8 & 0 & 0.458478785109733 & -0.458478785109733 \tabularnewline
9 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
10 & 1 & 0.201262918208877 & 0.798737081791123 \tabularnewline
11 & 1 & 0.458478785109733 & 0.541521214890267 \tabularnewline
12 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
13 & 0 & 0.341862874783737 & -0.341862874783737 \tabularnewline
14 & 1 & 0.458478785109733 & 0.541521214890267 \tabularnewline
15 & 0 & 0.267301177499962 & -0.267301177499962 \tabularnewline
16 & 0 & 0.524517044400818 & -0.524517044400818 \tabularnewline
17 & 1 & 0.564708617071269 & 0.435291382928731 \tabularnewline
18 & 1 & 0.458478785109733 & 0.541521214890267 \tabularnewline
19 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
20 & 0 & 0.490146919787494 & -0.490146919787494 \tabularnewline
21 & 1 & 0.266475080254729 & 0.733524919745271 \tabularnewline
22 & 1 & 0.267301177499962 & 0.732698822500038 \tabularnewline
23 & 0 & 0.191913382970955 & -0.191913382970955 \tabularnewline
24 & 1 & 0.191913382970954 & 0.808086617029046 \tabularnewline
25 & 0 & 0.459304882354966 & -0.459304882354966 \tabularnewline
26 & 0 & 0.341862874783737 & -0.341862874783737 \tabularnewline
27 & 1 & 0.126701220925102 & 0.873298779074898 \tabularnewline
28 & 0 & 0.276650712737885 & -0.276650712737885 \tabularnewline
29 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
30 & 0 & 0.266475080254729 & -0.266475080254729 \tabularnewline
31 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
32 & 1 & 0.201262918208877 & 0.798737081791123 \tabularnewline
33 & 1 & 0.266475080254729 & 0.733524919745271 \tabularnewline
34 & 0 & 0.383917087825958 & -0.383917087825958 \tabularnewline
35 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
36 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
37 & 1 & 0.599078741684593 & 0.400921258315407 \tabularnewline
38 & 0 & 0.20208901545411 & -0.20208901545411 \tabularnewline
39 & 0 & 0.191913382970955 & -0.191913382970955 \tabularnewline
40 & 0 & 0.523690947155585 & -0.523690947155585 \tabularnewline
41 & 0 & 0.232931052886639 & -0.232931052886639 \tabularnewline
42 & 0 & 0.20208901545411 & -0.20208901545411 \tabularnewline
43 & 1 & 0.191913382970954 & 0.808086617029046 \tabularnewline
44 & 1 & 0.458478785109733 & 0.541521214890267 \tabularnewline
45 & 0 & 0.266475080254729 & -0.266475080254729 \tabularnewline
46 & 0 & 0.191913382970955 & -0.191913382970955 \tabularnewline
47 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
48 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
49 & 0 & 0.191913382970955 & -0.191913382970955 \tabularnewline
50 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
51 & 0 & 0.533866579638741 & -0.533866579638741 \tabularnewline
52 & 1 & 0.564708617071269 & 0.435291382928731 \tabularnewline
53 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
54 & 0 & 0.242280588124561 & -0.242280588124561 \tabularnewline
55 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
56 & 0 & 0.459304882354966 & -0.459304882354966 \tabularnewline
57 & 0 & 0.267301177499962 & -0.267301177499962 \tabularnewline
58 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
59 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
60 & 1 & 0.490146919787494 & 0.509853080212506 \tabularnewline
61 & 1 & 0.383917087825958 & 0.616082912174042 \tabularnewline
62 & 0 & 0.341862874783737 & -0.341862874783737 \tabularnewline
63 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
64 & 1 & 0.383917087825958 & 0.616082912174042 \tabularnewline
65 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
66 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
67 & 0 & 0.564708617071269 & -0.564708617071269 \tabularnewline
68 & 1 & 0.201262918208877 & 0.798737081791123 \tabularnewline
69 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
70 & 0 & 0.276650712737885 & -0.276650712737885 \tabularnewline
71 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
72 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
73 & 0 & 0.20208901545411 & -0.20208901545411 \tabularnewline
74 & 1 & 0.276650712737885 & 0.723349287262115 \tabularnewline
75 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
76 & 0 & 0.44912924987181 & -0.44912924987181 \tabularnewline
77 & 0 & 0.126701220925102 & -0.126701220925102 \tabularnewline
78 & 0 & 0.267301177499962 & -0.267301177499962 \tabularnewline
79 & 0 & 0.424934757741642 & -0.424934757741642 \tabularnewline
80 & 0 & 0.523690947155585 & -0.523690947155585 \tabularnewline
81 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
82 & 1 & 0.20208901545411 & 0.79791098454589 \tabularnewline
83 & 0 & 0.201262918208877 & -0.201262918208877 \tabularnewline
84 & 0 & 0.242280588124561 & -0.242280588124561 \tabularnewline
85 & 0 & 0.191913382970955 & -0.191913382970955 \tabularnewline
86 & 1 & 0.201262918208877 & 0.798737081791123 \tabularnewline
87 & 1 & 0.281921585324449 & 0.718078414675551 \tabularnewline
88 & 1 & 0.4413750569089 & 0.5586249430911 \tabularnewline
89 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
90 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
91 & 0 & 0.421695444654076 & -0.421695444654076 \tabularnewline
92 & 1 & 0.440548959663666 & 0.559451040336334 \tabularnewline
93 & 1 & 0.421695444654075 & 0.578304555345925 \tabularnewline
94 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
95 & 0 & 0.440548959663666 & -0.440548959663666 \tabularnewline
96 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
97 & 1 & 0.440548959663666 & 0.559451040336334 \tabularnewline
98 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
99 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
100 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
101 & 1 & 0.281921585324449 & 0.718078414675551 \tabularnewline
102 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
103 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
104 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
105 & 0 & 0.515936754192674 & -0.515936754192674 \tabularnewline
106 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
107 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
108 & 1 & 0.515936754192674 & 0.484063245807326 \tabularnewline
109 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
110 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
111 & 1 & 0.581148916238526 & 0.418851083761474 \tabularnewline
112 & 0 & 0.440548959663666 & -0.440548959663666 \tabularnewline
113 & 0 & 0.431871077137232 & -0.431871077137232 \tabularnewline
114 & 1 & 0.515936754192674 & 0.484063245807326 \tabularnewline
115 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
116 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
117 & 1 & 0.281921585324449 & 0.718078414675551 \tabularnewline
118 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
119 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
120 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
121 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
122 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
123 & 1 & 0.515936754192674 & 0.484063245807326 \tabularnewline
124 & 0 & 0.422521541899309 & -0.422521541899309 \tabularnewline
125 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
126 & 0 & 0.440548959663666 & -0.440548959663666 \tabularnewline
127 & 0 & 0.421695444654076 & -0.421695444654076 \tabularnewline
128 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
129 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
130 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
131 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
132 & 1 & 0.281921585324449 & 0.718078414675551 \tabularnewline
133 & 1 & 0.431871077137232 & 0.568128922862768 \tabularnewline
134 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
135 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
136 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
137 & 1 & 0.422521541899309 & 0.577478458100691 \tabularnewline
138 & 1 & 0.506587218954752 & 0.493412781045248 \tabularnewline
139 & 0 & 0.440548959663666 & -0.440548959663666 \tabularnewline
140 & 0 & 0.356483282608224 & -0.356483282608224 \tabularnewline
141 & 0 & 0.322939255240133 & -0.322939255240133 \tabularnewline
142 & 0 & 0.441375056908899 & -0.441375056908899 \tabularnewline
143 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
144 & 0 & 0.347133747370301 & -0.347133747370301 \tabularnewline
145 & 0 & 0.421695444654076 & -0.421695444654076 \tabularnewline
146 & 0 & 0.365987262379892 & -0.365987262379892 \tabularnewline
147 & 0 & 0.515936754192674 & -0.515936754192674 \tabularnewline
148 & 0 & 0.440548959663666 & -0.440548959663666 \tabularnewline
149 & 1 & 0.356483282608223 & 0.643516717391777 \tabularnewline
150 & 0 & 0.347133747370301 & -0.347133747370301 \tabularnewline
151 & 0 & 0.281921585324449 & -0.281921585324449 \tabularnewline
152 & 1 & 0.397500952523908 & 0.602499047476092 \tabularnewline
153 & 1 & 0.46271311456976 & 0.53728688543024 \tabularnewline
154 & 1 & 0.431871077137232 & 0.568128922862768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202367&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]0.38391708782596[/C][C]0.61608291217404[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.191913382970954[/C][C]0.808086617029046[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.458478785109733[/C][C]-0.458478785109733[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.201262918208877[/C][C]0.798737081791123[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.458478785109733[/C][C]0.541521214890267[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.341862874783737[/C][C]-0.341862874783737[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.458478785109733[/C][C]0.541521214890267[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.267301177499962[/C][C]-0.267301177499962[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.524517044400818[/C][C]-0.524517044400818[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.564708617071269[/C][C]0.435291382928731[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.458478785109733[/C][C]0.541521214890267[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.490146919787494[/C][C]-0.490146919787494[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.266475080254729[/C][C]0.733524919745271[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.267301177499962[/C][C]0.732698822500038[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.191913382970955[/C][C]-0.191913382970955[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.191913382970954[/C][C]0.808086617029046[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.459304882354966[/C][C]-0.459304882354966[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.341862874783737[/C][C]-0.341862874783737[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.126701220925102[/C][C]0.873298779074898[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.276650712737885[/C][C]-0.276650712737885[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.266475080254729[/C][C]-0.266475080254729[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.201262918208877[/C][C]0.798737081791123[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.266475080254729[/C][C]0.733524919745271[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.383917087825958[/C][C]-0.383917087825958[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.599078741684593[/C][C]0.400921258315407[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.20208901545411[/C][C]-0.20208901545411[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.191913382970955[/C][C]-0.191913382970955[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.523690947155585[/C][C]-0.523690947155585[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.232931052886639[/C][C]-0.232931052886639[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.20208901545411[/C][C]-0.20208901545411[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.191913382970954[/C][C]0.808086617029046[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.458478785109733[/C][C]0.541521214890267[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.266475080254729[/C][C]-0.266475080254729[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.191913382970955[/C][C]-0.191913382970955[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.191913382970955[/C][C]-0.191913382970955[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.533866579638741[/C][C]-0.533866579638741[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.564708617071269[/C][C]0.435291382928731[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.242280588124561[/C][C]-0.242280588124561[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.459304882354966[/C][C]-0.459304882354966[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.267301177499962[/C][C]-0.267301177499962[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.490146919787494[/C][C]0.509853080212506[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.383917087825958[/C][C]0.616082912174042[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.341862874783737[/C][C]-0.341862874783737[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.383917087825958[/C][C]0.616082912174042[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.564708617071269[/C][C]-0.564708617071269[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.201262918208877[/C][C]0.798737081791123[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.276650712737885[/C][C]-0.276650712737885[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.20208901545411[/C][C]-0.20208901545411[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.276650712737885[/C][C]0.723349287262115[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.44912924987181[/C][C]-0.44912924987181[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.126701220925102[/C][C]-0.126701220925102[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.267301177499962[/C][C]-0.267301177499962[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0.424934757741642[/C][C]-0.424934757741642[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.523690947155585[/C][C]-0.523690947155585[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.20208901545411[/C][C]0.79791098454589[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.201262918208877[/C][C]-0.201262918208877[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.242280588124561[/C][C]-0.242280588124561[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.191913382970955[/C][C]-0.191913382970955[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.201262918208877[/C][C]0.798737081791123[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.281921585324449[/C][C]0.718078414675551[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.4413750569089[/C][C]0.5586249430911[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.421695444654076[/C][C]-0.421695444654076[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0.440548959663666[/C][C]0.559451040336334[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0.421695444654075[/C][C]0.578304555345925[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.440548959663666[/C][C]-0.440548959663666[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0.440548959663666[/C][C]0.559451040336334[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.281921585324449[/C][C]0.718078414675551[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.515936754192674[/C][C]-0.515936754192674[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0.515936754192674[/C][C]0.484063245807326[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0.581148916238526[/C][C]0.418851083761474[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.440548959663666[/C][C]-0.440548959663666[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.431871077137232[/C][C]-0.431871077137232[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.515936754192674[/C][C]0.484063245807326[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.281921585324449[/C][C]0.718078414675551[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0.515936754192674[/C][C]0.484063245807326[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.422521541899309[/C][C]-0.422521541899309[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.440548959663666[/C][C]-0.440548959663666[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.421695444654076[/C][C]-0.421695444654076[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.281921585324449[/C][C]0.718078414675551[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0.431871077137232[/C][C]0.568128922862768[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.422521541899309[/C][C]0.577478458100691[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.506587218954752[/C][C]0.493412781045248[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.440548959663666[/C][C]-0.440548959663666[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.356483282608224[/C][C]-0.356483282608224[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]0.322939255240133[/C][C]-0.322939255240133[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.441375056908899[/C][C]-0.441375056908899[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.347133747370301[/C][C]-0.347133747370301[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.421695444654076[/C][C]-0.421695444654076[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.365987262379892[/C][C]-0.365987262379892[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.515936754192674[/C][C]-0.515936754192674[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.440548959663666[/C][C]-0.440548959663666[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]0.356483282608223[/C][C]0.643516717391777[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.347133747370301[/C][C]-0.347133747370301[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.281921585324449[/C][C]-0.281921585324449[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.397500952523908[/C][C]0.602499047476092[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.46271311456976[/C][C]0.53728688543024[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]0.431871077137232[/C][C]0.568128922862768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202367&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202367&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.383917087825960.61608291217404
200.201262918208877-0.201262918208877
300.201262918208877-0.201262918208877
400.201262918208877-0.201262918208877
500.201262918208877-0.201262918208877
610.1919133829709540.808086617029046
700.201262918208877-0.201262918208877
800.458478785109733-0.458478785109733
900.126701220925102-0.126701220925102
1010.2012629182088770.798737081791123
1110.4584787851097330.541521214890267
1200.201262918208877-0.201262918208877
1300.341862874783737-0.341862874783737
1410.4584787851097330.541521214890267
1500.267301177499962-0.267301177499962
1600.524517044400818-0.524517044400818
1710.5647086170712690.435291382928731
1810.4584787851097330.541521214890267
1900.126701220925102-0.126701220925102
2000.490146919787494-0.490146919787494
2110.2664750802547290.733524919745271
2210.2673011774999620.732698822500038
2300.191913382970955-0.191913382970955
2410.1919133829709540.808086617029046
2500.459304882354966-0.459304882354966
2600.341862874783737-0.341862874783737
2710.1267012209251020.873298779074898
2800.276650712737885-0.276650712737885
2900.126701220925102-0.126701220925102
3000.266475080254729-0.266475080254729
3100.201262918208877-0.201262918208877
3210.2012629182088770.798737081791123
3310.2664750802547290.733524919745271
3400.383917087825958-0.383917087825958
3500.201262918208877-0.201262918208877
3600.201262918208877-0.201262918208877
3710.5990787416845930.400921258315407
3800.20208901545411-0.20208901545411
3900.191913382970955-0.191913382970955
4000.523690947155585-0.523690947155585
4100.232931052886639-0.232931052886639
4200.20208901545411-0.20208901545411
4310.1919133829709540.808086617029046
4410.4584787851097330.541521214890267
4500.266475080254729-0.266475080254729
4600.191913382970955-0.191913382970955
4700.201262918208877-0.201262918208877
4800.126701220925102-0.126701220925102
4900.191913382970955-0.191913382970955
5000.201262918208877-0.201262918208877
5100.533866579638741-0.533866579638741
5210.5647086170712690.435291382928731
5300.126701220925102-0.126701220925102
5400.242280588124561-0.242280588124561
5500.201262918208877-0.201262918208877
5600.459304882354966-0.459304882354966
5700.267301177499962-0.267301177499962
5800.126701220925102-0.126701220925102
5900.126701220925102-0.126701220925102
6010.4901469197874940.509853080212506
6110.3839170878259580.616082912174042
6200.341862874783737-0.341862874783737
6300.201262918208877-0.201262918208877
6410.3839170878259580.616082912174042
6500.201262918208877-0.201262918208877
6600.201262918208877-0.201262918208877
6700.564708617071269-0.564708617071269
6810.2012629182088770.798737081791123
6900.126701220925102-0.126701220925102
7000.276650712737885-0.276650712737885
7100.201262918208877-0.201262918208877
7200.126701220925102-0.126701220925102
7300.20208901545411-0.20208901545411
7410.2766507127378850.723349287262115
7500.126701220925102-0.126701220925102
7600.44912924987181-0.44912924987181
7700.126701220925102-0.126701220925102
7800.267301177499962-0.267301177499962
7900.424934757741642-0.424934757741642
8000.523690947155585-0.523690947155585
8100.201262918208877-0.201262918208877
8210.202089015454110.79791098454589
8300.201262918208877-0.201262918208877
8400.242280588124561-0.242280588124561
8500.191913382970955-0.191913382970955
8610.2012629182088770.798737081791123
8710.2819215853244490.718078414675551
8810.44137505690890.5586249430911
8900.356483282608224-0.356483282608224
9000.281921585324449-0.281921585324449
9100.421695444654076-0.421695444654076
9210.4405489596636660.559451040336334
9310.4216954446540750.578304555345925
9400.356483282608224-0.356483282608224
9500.440548959663666-0.440548959663666
9600.281921585324449-0.281921585324449
9710.4405489596636660.559451040336334
9800.356483282608224-0.356483282608224
9910.3564832826082230.643516717391777
10000.281921585324449-0.281921585324449
10110.2819215853244490.718078414675551
10200.356483282608224-0.356483282608224
10300.356483282608224-0.356483282608224
10400.356483282608224-0.356483282608224
10500.515936754192674-0.515936754192674
10600.356483282608224-0.356483282608224
10700.356483282608224-0.356483282608224
10810.5159367541926740.484063245807326
10900.356483282608224-0.356483282608224
11010.3564832826082230.643516717391777
11110.5811489162385260.418851083761474
11200.440548959663666-0.440548959663666
11300.431871077137232-0.431871077137232
11410.5159367541926740.484063245807326
11510.3564832826082230.643516717391777
11600.356483282608224-0.356483282608224
11710.2819215853244490.718078414675551
11810.3564832826082230.643516717391777
11900.356483282608224-0.356483282608224
12000.281921585324449-0.281921585324449
12110.3564832826082230.643516717391777
12200.356483282608224-0.356483282608224
12310.5159367541926740.484063245807326
12400.422521541899309-0.422521541899309
12500.281921585324449-0.281921585324449
12600.440548959663666-0.440548959663666
12700.421695444654076-0.421695444654076
12800.281921585324449-0.281921585324449
12900.356483282608224-0.356483282608224
13000.281921585324449-0.281921585324449
13110.3564832826082230.643516717391777
13210.2819215853244490.718078414675551
13310.4318710771372320.568128922862768
13400.356483282608224-0.356483282608224
13500.356483282608224-0.356483282608224
13600.356483282608224-0.356483282608224
13710.4225215418993090.577478458100691
13810.5065872189547520.493412781045248
13900.440548959663666-0.440548959663666
14000.356483282608224-0.356483282608224
14100.322939255240133-0.322939255240133
14200.441375056908899-0.441375056908899
14310.3564832826082230.643516717391777
14400.347133747370301-0.347133747370301
14500.421695444654076-0.421695444654076
14600.365987262379892-0.365987262379892
14700.515936754192674-0.515936754192674
14800.440548959663666-0.440548959663666
14910.3564832826082230.643516717391777
15000.347133747370301-0.347133747370301
15100.281921585324449-0.281921585324449
15210.3975009525239080.602499047476092
15310.462713114569760.53728688543024
15410.4318710771372320.568128922862768






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9222071246319540.1555857507360920.0777928753680461
120.8568541518737080.2862916962525840.143145848126292
130.7695678542398210.4608642915203580.230432145760179
140.7130135886000270.5739728227999470.286986411399973
150.6101080960249930.7797838079500140.389891903975007
160.6026012563868260.7947974872263480.397398743613174
170.5068842636959570.9862314726080850.493115736304043
180.4586301784376910.9172603568753820.541369821562309
190.3716744561134360.7433489122268720.628325543886564
200.4353021775212760.8706043550425530.564697822478724
210.3760229756662640.7520459513325290.623977024333736
220.687562717872840.624874564254320.31243728212716
230.7605843850420530.4788312299158940.239415614957947
240.7503923133199570.4992153733600860.249607686680043
250.6970705519683460.6058588960633090.302929448031655
260.646678725092310.7066425498153790.35332127490769
270.7719198114331950.456160377133610.228080188566805
280.7434109974649650.5131780050700710.256589002535035
290.6995764028636830.6008471942726350.300423597136317
300.7316789053906380.5366421892187250.268321094609362
310.688176960096750.62364607980650.31182303990325
320.7700927185002180.4598145629995640.229907281499782
330.776732768348920.4465344633021590.22326723165108
340.7893594084152790.4212811831694420.210640591584721
350.7574330654547120.4851338690905760.242566934545288
360.721622294581240.556755410837520.27837770541876
370.7081966709137360.5836066581725280.291803329086264
380.6708842218310830.6582315563378330.329115778168917
390.672930043006350.65413991398730.32706995699365
400.7555254710456650.4889490579086690.244474528954335
410.7151322848796850.569735430240630.284867715120315
420.6709239100034970.6581521799930060.329076089996503
430.7206043204973770.5587913590052470.279395679502623
440.7271721254133780.5456557491732440.272827874586622
450.721759522238030.556480955523940.27824047776197
460.7043159266158270.5913681467683450.295684073384173
470.6672220932440.6655558135120010.332777906756
480.623885089634020.752229820731960.37611491036598
490.5977171580178310.8045656839643370.402282841982169
500.5561528912971770.8876942174056460.443847108702823
510.550736219778340.8985275604433210.44926378022166
520.5515009369972380.8969981260055230.448499063002762
530.5038552538802460.9922894922395070.496144746119754
540.4580113273353960.9160226546707930.541988672664603
550.4163266460315260.8326532920630520.583673353968474
560.4079081450462510.8158162900925030.592091854953749
570.3670142651511530.7340285303023060.632985734848847
580.3230804559425240.6461609118850490.676919544057476
590.2814000412739050.562800082547810.718599958726095
600.290657484382040.581314968764080.70934251561796
610.3178567155307590.6357134310615190.682143284469241
620.2892673427819230.5785346855638450.710732657218078
630.254726071230010.509452142460020.74527392876999
640.308378432474160.616756864948320.69162156752584
650.2729357184518150.5458714369036290.727064281548185
660.2397554021118350.479510804223670.760244597888165
670.2519483111401630.5038966222803270.748051688859837
680.3508786840513930.7017573681027860.649121315948607
690.3107327749958220.6214655499916440.689267225004178
700.2928368780664240.5856737561328480.707163121933576
710.2593273247103590.5186546494207180.740672675289641
720.2246628479735540.4493256959471090.775337152026446
730.2081884767340610.4163769534681230.791811523265939
740.3002469014292960.6004938028585930.699753098570704
750.2627602313882330.5255204627764660.737239768611767
760.2745776615520370.5491553231040740.725422338447963
770.2385335232243930.4770670464487870.761466476775607
780.2239148825044470.4478297650088950.776085117495553
790.1982441021327330.3964882042654650.801755897867267
800.2067059593816220.4134119187632450.793294040618378
810.1833566400539770.3667132801079530.816643359946023
820.2421183629130210.4842367258260420.757881637086979
830.2148508142072950.429701628414590.785149185792705
840.2184154509243610.4368309018487220.781584549075639
850.2213567711235590.4427135422471170.778643228876441
860.233586101467610.467172202935220.76641389853239
870.2532512379100680.5065024758201350.746748762089932
880.2378247439649010.4756494879298020.762175256035099
890.2545877274135990.5091754548271990.745412272586401
900.231153732982080.4623074659641590.76884626701792
910.2203674919622850.440734983924570.779632508037715
920.2362772760932180.4725545521864360.763722723906782
930.265446112145680.530892224291360.73455388785432
940.2463176348689320.4926352697378640.753682365131068
950.2686727684755740.5373455369511490.731327231524426
960.2377061468650530.4754122937301070.762293853134947
970.2738375861105060.5476751722210130.726162413889494
980.2503584752502220.5007169505004430.749641524749778
990.2999909966947250.5999819933894510.700009003305275
1000.2674417796409750.5348835592819510.732558220359025
1010.3479038163675160.6958076327350320.652096183632484
1020.3237179483930160.6474358967860310.676282051606984
1030.2997463545112690.5994927090225370.700253645488731
1040.2766158768458810.5532317536917620.723384123154119
1050.3115295083717810.6230590167435620.688470491628219
1060.288516366944390.5770327338887810.71148363305561
1070.2672413598871880.5344827197743760.732758640112812
1080.2517740646410190.5035481292820380.748225935358981
1090.2329427811732270.4658855623464540.767057218826773
1100.2752747156563380.5505494313126750.724725284343662
1110.2612703114190670.5225406228381350.738729688580933
1120.2481984848040640.4963969696081280.751801515195936
1130.3236451861856030.6472903723712070.676354813814397
1140.3058650406094270.6117300812188540.694134959390573
1150.3515105463208850.703021092641770.648489453679115
1160.3304305941276770.6608611882553530.669569405872323
1170.4478495033505160.8956990067010320.552150496649484
1180.5079504938850440.9840990122299110.492049506114956
1190.4815991437941110.9631982875882220.518400856205889
1200.4290425778672920.8580851557345850.570957422132708
1210.4954831217162160.9909662434324320.504516878283784
1220.4644577143232220.9289154286464430.535542285676778
1230.4522286799219990.9044573598439980.547771320078001
1240.515683019367080.968633961265840.48431698063292
1250.4596912753259530.9193825506519070.540308724674047
1260.4167961735931640.8335923471863270.583203826406836
1270.3961737702306450.7923475404612910.603826229769355
1280.3398164732424470.6796329464848940.660183526757553
1290.3119207112228770.6238414224457540.688079288777123
1300.2627231932107080.5254463864214170.737276806789292
1310.3159766999453060.6319533998906120.684023300054694
1320.5137147484360450.972570503127910.486285251563955
1330.4545860527638810.9091721055277620.545413947236119
1340.4011823550914560.8023647101829120.598817644908544
1350.3549799407258930.7099598814517860.645020059274107
1360.3205309587918140.6410619175836280.679469041208186
1370.2750799762613650.5501599525227310.724920023738635
1380.5424736897100110.9150526205799790.457526310289989
1390.4467899874301880.8935799748603770.553210012569812
1400.576595642760510.846808714478980.42340435723949
1410.6940736763575980.6118526472848040.305926323642402
1420.5737980951375760.8524038097248490.426201904862424
1430.4716748838953480.9433497677906960.528325116104652

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.922207124631954 & 0.155585750736092 & 0.0777928753680461 \tabularnewline
12 & 0.856854151873708 & 0.286291696252584 & 0.143145848126292 \tabularnewline
13 & 0.769567854239821 & 0.460864291520358 & 0.230432145760179 \tabularnewline
14 & 0.713013588600027 & 0.573972822799947 & 0.286986411399973 \tabularnewline
15 & 0.610108096024993 & 0.779783807950014 & 0.389891903975007 \tabularnewline
16 & 0.602601256386826 & 0.794797487226348 & 0.397398743613174 \tabularnewline
17 & 0.506884263695957 & 0.986231472608085 & 0.493115736304043 \tabularnewline
18 & 0.458630178437691 & 0.917260356875382 & 0.541369821562309 \tabularnewline
19 & 0.371674456113436 & 0.743348912226872 & 0.628325543886564 \tabularnewline
20 & 0.435302177521276 & 0.870604355042553 & 0.564697822478724 \tabularnewline
21 & 0.376022975666264 & 0.752045951332529 & 0.623977024333736 \tabularnewline
22 & 0.68756271787284 & 0.62487456425432 & 0.31243728212716 \tabularnewline
23 & 0.760584385042053 & 0.478831229915894 & 0.239415614957947 \tabularnewline
24 & 0.750392313319957 & 0.499215373360086 & 0.249607686680043 \tabularnewline
25 & 0.697070551968346 & 0.605858896063309 & 0.302929448031655 \tabularnewline
26 & 0.64667872509231 & 0.706642549815379 & 0.35332127490769 \tabularnewline
27 & 0.771919811433195 & 0.45616037713361 & 0.228080188566805 \tabularnewline
28 & 0.743410997464965 & 0.513178005070071 & 0.256589002535035 \tabularnewline
29 & 0.699576402863683 & 0.600847194272635 & 0.300423597136317 \tabularnewline
30 & 0.731678905390638 & 0.536642189218725 & 0.268321094609362 \tabularnewline
31 & 0.68817696009675 & 0.6236460798065 & 0.31182303990325 \tabularnewline
32 & 0.770092718500218 & 0.459814562999564 & 0.229907281499782 \tabularnewline
33 & 0.77673276834892 & 0.446534463302159 & 0.22326723165108 \tabularnewline
34 & 0.789359408415279 & 0.421281183169442 & 0.210640591584721 \tabularnewline
35 & 0.757433065454712 & 0.485133869090576 & 0.242566934545288 \tabularnewline
36 & 0.72162229458124 & 0.55675541083752 & 0.27837770541876 \tabularnewline
37 & 0.708196670913736 & 0.583606658172528 & 0.291803329086264 \tabularnewline
38 & 0.670884221831083 & 0.658231556337833 & 0.329115778168917 \tabularnewline
39 & 0.67293004300635 & 0.6541399139873 & 0.32706995699365 \tabularnewline
40 & 0.755525471045665 & 0.488949057908669 & 0.244474528954335 \tabularnewline
41 & 0.715132284879685 & 0.56973543024063 & 0.284867715120315 \tabularnewline
42 & 0.670923910003497 & 0.658152179993006 & 0.329076089996503 \tabularnewline
43 & 0.720604320497377 & 0.558791359005247 & 0.279395679502623 \tabularnewline
44 & 0.727172125413378 & 0.545655749173244 & 0.272827874586622 \tabularnewline
45 & 0.72175952223803 & 0.55648095552394 & 0.27824047776197 \tabularnewline
46 & 0.704315926615827 & 0.591368146768345 & 0.295684073384173 \tabularnewline
47 & 0.667222093244 & 0.665555813512001 & 0.332777906756 \tabularnewline
48 & 0.62388508963402 & 0.75222982073196 & 0.37611491036598 \tabularnewline
49 & 0.597717158017831 & 0.804565683964337 & 0.402282841982169 \tabularnewline
50 & 0.556152891297177 & 0.887694217405646 & 0.443847108702823 \tabularnewline
51 & 0.55073621977834 & 0.898527560443321 & 0.44926378022166 \tabularnewline
52 & 0.551500936997238 & 0.896998126005523 & 0.448499063002762 \tabularnewline
53 & 0.503855253880246 & 0.992289492239507 & 0.496144746119754 \tabularnewline
54 & 0.458011327335396 & 0.916022654670793 & 0.541988672664603 \tabularnewline
55 & 0.416326646031526 & 0.832653292063052 & 0.583673353968474 \tabularnewline
56 & 0.407908145046251 & 0.815816290092503 & 0.592091854953749 \tabularnewline
57 & 0.367014265151153 & 0.734028530302306 & 0.632985734848847 \tabularnewline
58 & 0.323080455942524 & 0.646160911885049 & 0.676919544057476 \tabularnewline
59 & 0.281400041273905 & 0.56280008254781 & 0.718599958726095 \tabularnewline
60 & 0.29065748438204 & 0.58131496876408 & 0.70934251561796 \tabularnewline
61 & 0.317856715530759 & 0.635713431061519 & 0.682143284469241 \tabularnewline
62 & 0.289267342781923 & 0.578534685563845 & 0.710732657218078 \tabularnewline
63 & 0.25472607123001 & 0.50945214246002 & 0.74527392876999 \tabularnewline
64 & 0.30837843247416 & 0.61675686494832 & 0.69162156752584 \tabularnewline
65 & 0.272935718451815 & 0.545871436903629 & 0.727064281548185 \tabularnewline
66 & 0.239755402111835 & 0.47951080422367 & 0.760244597888165 \tabularnewline
67 & 0.251948311140163 & 0.503896622280327 & 0.748051688859837 \tabularnewline
68 & 0.350878684051393 & 0.701757368102786 & 0.649121315948607 \tabularnewline
69 & 0.310732774995822 & 0.621465549991644 & 0.689267225004178 \tabularnewline
70 & 0.292836878066424 & 0.585673756132848 & 0.707163121933576 \tabularnewline
71 & 0.259327324710359 & 0.518654649420718 & 0.740672675289641 \tabularnewline
72 & 0.224662847973554 & 0.449325695947109 & 0.775337152026446 \tabularnewline
73 & 0.208188476734061 & 0.416376953468123 & 0.791811523265939 \tabularnewline
74 & 0.300246901429296 & 0.600493802858593 & 0.699753098570704 \tabularnewline
75 & 0.262760231388233 & 0.525520462776466 & 0.737239768611767 \tabularnewline
76 & 0.274577661552037 & 0.549155323104074 & 0.725422338447963 \tabularnewline
77 & 0.238533523224393 & 0.477067046448787 & 0.761466476775607 \tabularnewline
78 & 0.223914882504447 & 0.447829765008895 & 0.776085117495553 \tabularnewline
79 & 0.198244102132733 & 0.396488204265465 & 0.801755897867267 \tabularnewline
80 & 0.206705959381622 & 0.413411918763245 & 0.793294040618378 \tabularnewline
81 & 0.183356640053977 & 0.366713280107953 & 0.816643359946023 \tabularnewline
82 & 0.242118362913021 & 0.484236725826042 & 0.757881637086979 \tabularnewline
83 & 0.214850814207295 & 0.42970162841459 & 0.785149185792705 \tabularnewline
84 & 0.218415450924361 & 0.436830901848722 & 0.781584549075639 \tabularnewline
85 & 0.221356771123559 & 0.442713542247117 & 0.778643228876441 \tabularnewline
86 & 0.23358610146761 & 0.46717220293522 & 0.76641389853239 \tabularnewline
87 & 0.253251237910068 & 0.506502475820135 & 0.746748762089932 \tabularnewline
88 & 0.237824743964901 & 0.475649487929802 & 0.762175256035099 \tabularnewline
89 & 0.254587727413599 & 0.509175454827199 & 0.745412272586401 \tabularnewline
90 & 0.23115373298208 & 0.462307465964159 & 0.76884626701792 \tabularnewline
91 & 0.220367491962285 & 0.44073498392457 & 0.779632508037715 \tabularnewline
92 & 0.236277276093218 & 0.472554552186436 & 0.763722723906782 \tabularnewline
93 & 0.26544611214568 & 0.53089222429136 & 0.73455388785432 \tabularnewline
94 & 0.246317634868932 & 0.492635269737864 & 0.753682365131068 \tabularnewline
95 & 0.268672768475574 & 0.537345536951149 & 0.731327231524426 \tabularnewline
96 & 0.237706146865053 & 0.475412293730107 & 0.762293853134947 \tabularnewline
97 & 0.273837586110506 & 0.547675172221013 & 0.726162413889494 \tabularnewline
98 & 0.250358475250222 & 0.500716950500443 & 0.749641524749778 \tabularnewline
99 & 0.299990996694725 & 0.599981993389451 & 0.700009003305275 \tabularnewline
100 & 0.267441779640975 & 0.534883559281951 & 0.732558220359025 \tabularnewline
101 & 0.347903816367516 & 0.695807632735032 & 0.652096183632484 \tabularnewline
102 & 0.323717948393016 & 0.647435896786031 & 0.676282051606984 \tabularnewline
103 & 0.299746354511269 & 0.599492709022537 & 0.700253645488731 \tabularnewline
104 & 0.276615876845881 & 0.553231753691762 & 0.723384123154119 \tabularnewline
105 & 0.311529508371781 & 0.623059016743562 & 0.688470491628219 \tabularnewline
106 & 0.28851636694439 & 0.577032733888781 & 0.71148363305561 \tabularnewline
107 & 0.267241359887188 & 0.534482719774376 & 0.732758640112812 \tabularnewline
108 & 0.251774064641019 & 0.503548129282038 & 0.748225935358981 \tabularnewline
109 & 0.232942781173227 & 0.465885562346454 & 0.767057218826773 \tabularnewline
110 & 0.275274715656338 & 0.550549431312675 & 0.724725284343662 \tabularnewline
111 & 0.261270311419067 & 0.522540622838135 & 0.738729688580933 \tabularnewline
112 & 0.248198484804064 & 0.496396969608128 & 0.751801515195936 \tabularnewline
113 & 0.323645186185603 & 0.647290372371207 & 0.676354813814397 \tabularnewline
114 & 0.305865040609427 & 0.611730081218854 & 0.694134959390573 \tabularnewline
115 & 0.351510546320885 & 0.70302109264177 & 0.648489453679115 \tabularnewline
116 & 0.330430594127677 & 0.660861188255353 & 0.669569405872323 \tabularnewline
117 & 0.447849503350516 & 0.895699006701032 & 0.552150496649484 \tabularnewline
118 & 0.507950493885044 & 0.984099012229911 & 0.492049506114956 \tabularnewline
119 & 0.481599143794111 & 0.963198287588222 & 0.518400856205889 \tabularnewline
120 & 0.429042577867292 & 0.858085155734585 & 0.570957422132708 \tabularnewline
121 & 0.495483121716216 & 0.990966243432432 & 0.504516878283784 \tabularnewline
122 & 0.464457714323222 & 0.928915428646443 & 0.535542285676778 \tabularnewline
123 & 0.452228679921999 & 0.904457359843998 & 0.547771320078001 \tabularnewline
124 & 0.51568301936708 & 0.96863396126584 & 0.48431698063292 \tabularnewline
125 & 0.459691275325953 & 0.919382550651907 & 0.540308724674047 \tabularnewline
126 & 0.416796173593164 & 0.833592347186327 & 0.583203826406836 \tabularnewline
127 & 0.396173770230645 & 0.792347540461291 & 0.603826229769355 \tabularnewline
128 & 0.339816473242447 & 0.679632946484894 & 0.660183526757553 \tabularnewline
129 & 0.311920711222877 & 0.623841422445754 & 0.688079288777123 \tabularnewline
130 & 0.262723193210708 & 0.525446386421417 & 0.737276806789292 \tabularnewline
131 & 0.315976699945306 & 0.631953399890612 & 0.684023300054694 \tabularnewline
132 & 0.513714748436045 & 0.97257050312791 & 0.486285251563955 \tabularnewline
133 & 0.454586052763881 & 0.909172105527762 & 0.545413947236119 \tabularnewline
134 & 0.401182355091456 & 0.802364710182912 & 0.598817644908544 \tabularnewline
135 & 0.354979940725893 & 0.709959881451786 & 0.645020059274107 \tabularnewline
136 & 0.320530958791814 & 0.641061917583628 & 0.679469041208186 \tabularnewline
137 & 0.275079976261365 & 0.550159952522731 & 0.724920023738635 \tabularnewline
138 & 0.542473689710011 & 0.915052620579979 & 0.457526310289989 \tabularnewline
139 & 0.446789987430188 & 0.893579974860377 & 0.553210012569812 \tabularnewline
140 & 0.57659564276051 & 0.84680871447898 & 0.42340435723949 \tabularnewline
141 & 0.694073676357598 & 0.611852647284804 & 0.305926323642402 \tabularnewline
142 & 0.573798095137576 & 0.852403809724849 & 0.426201904862424 \tabularnewline
143 & 0.471674883895348 & 0.943349767790696 & 0.528325116104652 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202367&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]11[/C][C]0.922207124631954[/C][C]0.155585750736092[/C][C]0.0777928753680461[/C][/ROW]
[ROW][C]12[/C][C]0.856854151873708[/C][C]0.286291696252584[/C][C]0.143145848126292[/C][/ROW]
[ROW][C]13[/C][C]0.769567854239821[/C][C]0.460864291520358[/C][C]0.230432145760179[/C][/ROW]
[ROW][C]14[/C][C]0.713013588600027[/C][C]0.573972822799947[/C][C]0.286986411399973[/C][/ROW]
[ROW][C]15[/C][C]0.610108096024993[/C][C]0.779783807950014[/C][C]0.389891903975007[/C][/ROW]
[ROW][C]16[/C][C]0.602601256386826[/C][C]0.794797487226348[/C][C]0.397398743613174[/C][/ROW]
[ROW][C]17[/C][C]0.506884263695957[/C][C]0.986231472608085[/C][C]0.493115736304043[/C][/ROW]
[ROW][C]18[/C][C]0.458630178437691[/C][C]0.917260356875382[/C][C]0.541369821562309[/C][/ROW]
[ROW][C]19[/C][C]0.371674456113436[/C][C]0.743348912226872[/C][C]0.628325543886564[/C][/ROW]
[ROW][C]20[/C][C]0.435302177521276[/C][C]0.870604355042553[/C][C]0.564697822478724[/C][/ROW]
[ROW][C]21[/C][C]0.376022975666264[/C][C]0.752045951332529[/C][C]0.623977024333736[/C][/ROW]
[ROW][C]22[/C][C]0.68756271787284[/C][C]0.62487456425432[/C][C]0.31243728212716[/C][/ROW]
[ROW][C]23[/C][C]0.760584385042053[/C][C]0.478831229915894[/C][C]0.239415614957947[/C][/ROW]
[ROW][C]24[/C][C]0.750392313319957[/C][C]0.499215373360086[/C][C]0.249607686680043[/C][/ROW]
[ROW][C]25[/C][C]0.697070551968346[/C][C]0.605858896063309[/C][C]0.302929448031655[/C][/ROW]
[ROW][C]26[/C][C]0.64667872509231[/C][C]0.706642549815379[/C][C]0.35332127490769[/C][/ROW]
[ROW][C]27[/C][C]0.771919811433195[/C][C]0.45616037713361[/C][C]0.228080188566805[/C][/ROW]
[ROW][C]28[/C][C]0.743410997464965[/C][C]0.513178005070071[/C][C]0.256589002535035[/C][/ROW]
[ROW][C]29[/C][C]0.699576402863683[/C][C]0.600847194272635[/C][C]0.300423597136317[/C][/ROW]
[ROW][C]30[/C][C]0.731678905390638[/C][C]0.536642189218725[/C][C]0.268321094609362[/C][/ROW]
[ROW][C]31[/C][C]0.68817696009675[/C][C]0.6236460798065[/C][C]0.31182303990325[/C][/ROW]
[ROW][C]32[/C][C]0.770092718500218[/C][C]0.459814562999564[/C][C]0.229907281499782[/C][/ROW]
[ROW][C]33[/C][C]0.77673276834892[/C][C]0.446534463302159[/C][C]0.22326723165108[/C][/ROW]
[ROW][C]34[/C][C]0.789359408415279[/C][C]0.421281183169442[/C][C]0.210640591584721[/C][/ROW]
[ROW][C]35[/C][C]0.757433065454712[/C][C]0.485133869090576[/C][C]0.242566934545288[/C][/ROW]
[ROW][C]36[/C][C]0.72162229458124[/C][C]0.55675541083752[/C][C]0.27837770541876[/C][/ROW]
[ROW][C]37[/C][C]0.708196670913736[/C][C]0.583606658172528[/C][C]0.291803329086264[/C][/ROW]
[ROW][C]38[/C][C]0.670884221831083[/C][C]0.658231556337833[/C][C]0.329115778168917[/C][/ROW]
[ROW][C]39[/C][C]0.67293004300635[/C][C]0.6541399139873[/C][C]0.32706995699365[/C][/ROW]
[ROW][C]40[/C][C]0.755525471045665[/C][C]0.488949057908669[/C][C]0.244474528954335[/C][/ROW]
[ROW][C]41[/C][C]0.715132284879685[/C][C]0.56973543024063[/C][C]0.284867715120315[/C][/ROW]
[ROW][C]42[/C][C]0.670923910003497[/C][C]0.658152179993006[/C][C]0.329076089996503[/C][/ROW]
[ROW][C]43[/C][C]0.720604320497377[/C][C]0.558791359005247[/C][C]0.279395679502623[/C][/ROW]
[ROW][C]44[/C][C]0.727172125413378[/C][C]0.545655749173244[/C][C]0.272827874586622[/C][/ROW]
[ROW][C]45[/C][C]0.72175952223803[/C][C]0.55648095552394[/C][C]0.27824047776197[/C][/ROW]
[ROW][C]46[/C][C]0.704315926615827[/C][C]0.591368146768345[/C][C]0.295684073384173[/C][/ROW]
[ROW][C]47[/C][C]0.667222093244[/C][C]0.665555813512001[/C][C]0.332777906756[/C][/ROW]
[ROW][C]48[/C][C]0.62388508963402[/C][C]0.75222982073196[/C][C]0.37611491036598[/C][/ROW]
[ROW][C]49[/C][C]0.597717158017831[/C][C]0.804565683964337[/C][C]0.402282841982169[/C][/ROW]
[ROW][C]50[/C][C]0.556152891297177[/C][C]0.887694217405646[/C][C]0.443847108702823[/C][/ROW]
[ROW][C]51[/C][C]0.55073621977834[/C][C]0.898527560443321[/C][C]0.44926378022166[/C][/ROW]
[ROW][C]52[/C][C]0.551500936997238[/C][C]0.896998126005523[/C][C]0.448499063002762[/C][/ROW]
[ROW][C]53[/C][C]0.503855253880246[/C][C]0.992289492239507[/C][C]0.496144746119754[/C][/ROW]
[ROW][C]54[/C][C]0.458011327335396[/C][C]0.916022654670793[/C][C]0.541988672664603[/C][/ROW]
[ROW][C]55[/C][C]0.416326646031526[/C][C]0.832653292063052[/C][C]0.583673353968474[/C][/ROW]
[ROW][C]56[/C][C]0.407908145046251[/C][C]0.815816290092503[/C][C]0.592091854953749[/C][/ROW]
[ROW][C]57[/C][C]0.367014265151153[/C][C]0.734028530302306[/C][C]0.632985734848847[/C][/ROW]
[ROW][C]58[/C][C]0.323080455942524[/C][C]0.646160911885049[/C][C]0.676919544057476[/C][/ROW]
[ROW][C]59[/C][C]0.281400041273905[/C][C]0.56280008254781[/C][C]0.718599958726095[/C][/ROW]
[ROW][C]60[/C][C]0.29065748438204[/C][C]0.58131496876408[/C][C]0.70934251561796[/C][/ROW]
[ROW][C]61[/C][C]0.317856715530759[/C][C]0.635713431061519[/C][C]0.682143284469241[/C][/ROW]
[ROW][C]62[/C][C]0.289267342781923[/C][C]0.578534685563845[/C][C]0.710732657218078[/C][/ROW]
[ROW][C]63[/C][C]0.25472607123001[/C][C]0.50945214246002[/C][C]0.74527392876999[/C][/ROW]
[ROW][C]64[/C][C]0.30837843247416[/C][C]0.61675686494832[/C][C]0.69162156752584[/C][/ROW]
[ROW][C]65[/C][C]0.272935718451815[/C][C]0.545871436903629[/C][C]0.727064281548185[/C][/ROW]
[ROW][C]66[/C][C]0.239755402111835[/C][C]0.47951080422367[/C][C]0.760244597888165[/C][/ROW]
[ROW][C]67[/C][C]0.251948311140163[/C][C]0.503896622280327[/C][C]0.748051688859837[/C][/ROW]
[ROW][C]68[/C][C]0.350878684051393[/C][C]0.701757368102786[/C][C]0.649121315948607[/C][/ROW]
[ROW][C]69[/C][C]0.310732774995822[/C][C]0.621465549991644[/C][C]0.689267225004178[/C][/ROW]
[ROW][C]70[/C][C]0.292836878066424[/C][C]0.585673756132848[/C][C]0.707163121933576[/C][/ROW]
[ROW][C]71[/C][C]0.259327324710359[/C][C]0.518654649420718[/C][C]0.740672675289641[/C][/ROW]
[ROW][C]72[/C][C]0.224662847973554[/C][C]0.449325695947109[/C][C]0.775337152026446[/C][/ROW]
[ROW][C]73[/C][C]0.208188476734061[/C][C]0.416376953468123[/C][C]0.791811523265939[/C][/ROW]
[ROW][C]74[/C][C]0.300246901429296[/C][C]0.600493802858593[/C][C]0.699753098570704[/C][/ROW]
[ROW][C]75[/C][C]0.262760231388233[/C][C]0.525520462776466[/C][C]0.737239768611767[/C][/ROW]
[ROW][C]76[/C][C]0.274577661552037[/C][C]0.549155323104074[/C][C]0.725422338447963[/C][/ROW]
[ROW][C]77[/C][C]0.238533523224393[/C][C]0.477067046448787[/C][C]0.761466476775607[/C][/ROW]
[ROW][C]78[/C][C]0.223914882504447[/C][C]0.447829765008895[/C][C]0.776085117495553[/C][/ROW]
[ROW][C]79[/C][C]0.198244102132733[/C][C]0.396488204265465[/C][C]0.801755897867267[/C][/ROW]
[ROW][C]80[/C][C]0.206705959381622[/C][C]0.413411918763245[/C][C]0.793294040618378[/C][/ROW]
[ROW][C]81[/C][C]0.183356640053977[/C][C]0.366713280107953[/C][C]0.816643359946023[/C][/ROW]
[ROW][C]82[/C][C]0.242118362913021[/C][C]0.484236725826042[/C][C]0.757881637086979[/C][/ROW]
[ROW][C]83[/C][C]0.214850814207295[/C][C]0.42970162841459[/C][C]0.785149185792705[/C][/ROW]
[ROW][C]84[/C][C]0.218415450924361[/C][C]0.436830901848722[/C][C]0.781584549075639[/C][/ROW]
[ROW][C]85[/C][C]0.221356771123559[/C][C]0.442713542247117[/C][C]0.778643228876441[/C][/ROW]
[ROW][C]86[/C][C]0.23358610146761[/C][C]0.46717220293522[/C][C]0.76641389853239[/C][/ROW]
[ROW][C]87[/C][C]0.253251237910068[/C][C]0.506502475820135[/C][C]0.746748762089932[/C][/ROW]
[ROW][C]88[/C][C]0.237824743964901[/C][C]0.475649487929802[/C][C]0.762175256035099[/C][/ROW]
[ROW][C]89[/C][C]0.254587727413599[/C][C]0.509175454827199[/C][C]0.745412272586401[/C][/ROW]
[ROW][C]90[/C][C]0.23115373298208[/C][C]0.462307465964159[/C][C]0.76884626701792[/C][/ROW]
[ROW][C]91[/C][C]0.220367491962285[/C][C]0.44073498392457[/C][C]0.779632508037715[/C][/ROW]
[ROW][C]92[/C][C]0.236277276093218[/C][C]0.472554552186436[/C][C]0.763722723906782[/C][/ROW]
[ROW][C]93[/C][C]0.26544611214568[/C][C]0.53089222429136[/C][C]0.73455388785432[/C][/ROW]
[ROW][C]94[/C][C]0.246317634868932[/C][C]0.492635269737864[/C][C]0.753682365131068[/C][/ROW]
[ROW][C]95[/C][C]0.268672768475574[/C][C]0.537345536951149[/C][C]0.731327231524426[/C][/ROW]
[ROW][C]96[/C][C]0.237706146865053[/C][C]0.475412293730107[/C][C]0.762293853134947[/C][/ROW]
[ROW][C]97[/C][C]0.273837586110506[/C][C]0.547675172221013[/C][C]0.726162413889494[/C][/ROW]
[ROW][C]98[/C][C]0.250358475250222[/C][C]0.500716950500443[/C][C]0.749641524749778[/C][/ROW]
[ROW][C]99[/C][C]0.299990996694725[/C][C]0.599981993389451[/C][C]0.700009003305275[/C][/ROW]
[ROW][C]100[/C][C]0.267441779640975[/C][C]0.534883559281951[/C][C]0.732558220359025[/C][/ROW]
[ROW][C]101[/C][C]0.347903816367516[/C][C]0.695807632735032[/C][C]0.652096183632484[/C][/ROW]
[ROW][C]102[/C][C]0.323717948393016[/C][C]0.647435896786031[/C][C]0.676282051606984[/C][/ROW]
[ROW][C]103[/C][C]0.299746354511269[/C][C]0.599492709022537[/C][C]0.700253645488731[/C][/ROW]
[ROW][C]104[/C][C]0.276615876845881[/C][C]0.553231753691762[/C][C]0.723384123154119[/C][/ROW]
[ROW][C]105[/C][C]0.311529508371781[/C][C]0.623059016743562[/C][C]0.688470491628219[/C][/ROW]
[ROW][C]106[/C][C]0.28851636694439[/C][C]0.577032733888781[/C][C]0.71148363305561[/C][/ROW]
[ROW][C]107[/C][C]0.267241359887188[/C][C]0.534482719774376[/C][C]0.732758640112812[/C][/ROW]
[ROW][C]108[/C][C]0.251774064641019[/C][C]0.503548129282038[/C][C]0.748225935358981[/C][/ROW]
[ROW][C]109[/C][C]0.232942781173227[/C][C]0.465885562346454[/C][C]0.767057218826773[/C][/ROW]
[ROW][C]110[/C][C]0.275274715656338[/C][C]0.550549431312675[/C][C]0.724725284343662[/C][/ROW]
[ROW][C]111[/C][C]0.261270311419067[/C][C]0.522540622838135[/C][C]0.738729688580933[/C][/ROW]
[ROW][C]112[/C][C]0.248198484804064[/C][C]0.496396969608128[/C][C]0.751801515195936[/C][/ROW]
[ROW][C]113[/C][C]0.323645186185603[/C][C]0.647290372371207[/C][C]0.676354813814397[/C][/ROW]
[ROW][C]114[/C][C]0.305865040609427[/C][C]0.611730081218854[/C][C]0.694134959390573[/C][/ROW]
[ROW][C]115[/C][C]0.351510546320885[/C][C]0.70302109264177[/C][C]0.648489453679115[/C][/ROW]
[ROW][C]116[/C][C]0.330430594127677[/C][C]0.660861188255353[/C][C]0.669569405872323[/C][/ROW]
[ROW][C]117[/C][C]0.447849503350516[/C][C]0.895699006701032[/C][C]0.552150496649484[/C][/ROW]
[ROW][C]118[/C][C]0.507950493885044[/C][C]0.984099012229911[/C][C]0.492049506114956[/C][/ROW]
[ROW][C]119[/C][C]0.481599143794111[/C][C]0.963198287588222[/C][C]0.518400856205889[/C][/ROW]
[ROW][C]120[/C][C]0.429042577867292[/C][C]0.858085155734585[/C][C]0.570957422132708[/C][/ROW]
[ROW][C]121[/C][C]0.495483121716216[/C][C]0.990966243432432[/C][C]0.504516878283784[/C][/ROW]
[ROW][C]122[/C][C]0.464457714323222[/C][C]0.928915428646443[/C][C]0.535542285676778[/C][/ROW]
[ROW][C]123[/C][C]0.452228679921999[/C][C]0.904457359843998[/C][C]0.547771320078001[/C][/ROW]
[ROW][C]124[/C][C]0.51568301936708[/C][C]0.96863396126584[/C][C]0.48431698063292[/C][/ROW]
[ROW][C]125[/C][C]0.459691275325953[/C][C]0.919382550651907[/C][C]0.540308724674047[/C][/ROW]
[ROW][C]126[/C][C]0.416796173593164[/C][C]0.833592347186327[/C][C]0.583203826406836[/C][/ROW]
[ROW][C]127[/C][C]0.396173770230645[/C][C]0.792347540461291[/C][C]0.603826229769355[/C][/ROW]
[ROW][C]128[/C][C]0.339816473242447[/C][C]0.679632946484894[/C][C]0.660183526757553[/C][/ROW]
[ROW][C]129[/C][C]0.311920711222877[/C][C]0.623841422445754[/C][C]0.688079288777123[/C][/ROW]
[ROW][C]130[/C][C]0.262723193210708[/C][C]0.525446386421417[/C][C]0.737276806789292[/C][/ROW]
[ROW][C]131[/C][C]0.315976699945306[/C][C]0.631953399890612[/C][C]0.684023300054694[/C][/ROW]
[ROW][C]132[/C][C]0.513714748436045[/C][C]0.97257050312791[/C][C]0.486285251563955[/C][/ROW]
[ROW][C]133[/C][C]0.454586052763881[/C][C]0.909172105527762[/C][C]0.545413947236119[/C][/ROW]
[ROW][C]134[/C][C]0.401182355091456[/C][C]0.802364710182912[/C][C]0.598817644908544[/C][/ROW]
[ROW][C]135[/C][C]0.354979940725893[/C][C]0.709959881451786[/C][C]0.645020059274107[/C][/ROW]
[ROW][C]136[/C][C]0.320530958791814[/C][C]0.641061917583628[/C][C]0.679469041208186[/C][/ROW]
[ROW][C]137[/C][C]0.275079976261365[/C][C]0.550159952522731[/C][C]0.724920023738635[/C][/ROW]
[ROW][C]138[/C][C]0.542473689710011[/C][C]0.915052620579979[/C][C]0.457526310289989[/C][/ROW]
[ROW][C]139[/C][C]0.446789987430188[/C][C]0.893579974860377[/C][C]0.553210012569812[/C][/ROW]
[ROW][C]140[/C][C]0.57659564276051[/C][C]0.84680871447898[/C][C]0.42340435723949[/C][/ROW]
[ROW][C]141[/C][C]0.694073676357598[/C][C]0.611852647284804[/C][C]0.305926323642402[/C][/ROW]
[ROW][C]142[/C][C]0.573798095137576[/C][C]0.852403809724849[/C][C]0.426201904862424[/C][/ROW]
[ROW][C]143[/C][C]0.471674883895348[/C][C]0.943349767790696[/C][C]0.528325116104652[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202367&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202367&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9222071246319540.1555857507360920.0777928753680461
120.8568541518737080.2862916962525840.143145848126292
130.7695678542398210.4608642915203580.230432145760179
140.7130135886000270.5739728227999470.286986411399973
150.6101080960249930.7797838079500140.389891903975007
160.6026012563868260.7947974872263480.397398743613174
170.5068842636959570.9862314726080850.493115736304043
180.4586301784376910.9172603568753820.541369821562309
190.3716744561134360.7433489122268720.628325543886564
200.4353021775212760.8706043550425530.564697822478724
210.3760229756662640.7520459513325290.623977024333736
220.687562717872840.624874564254320.31243728212716
230.7605843850420530.4788312299158940.239415614957947
240.7503923133199570.4992153733600860.249607686680043
250.6970705519683460.6058588960633090.302929448031655
260.646678725092310.7066425498153790.35332127490769
270.7719198114331950.456160377133610.228080188566805
280.7434109974649650.5131780050700710.256589002535035
290.6995764028636830.6008471942726350.300423597136317
300.7316789053906380.5366421892187250.268321094609362
310.688176960096750.62364607980650.31182303990325
320.7700927185002180.4598145629995640.229907281499782
330.776732768348920.4465344633021590.22326723165108
340.7893594084152790.4212811831694420.210640591584721
350.7574330654547120.4851338690905760.242566934545288
360.721622294581240.556755410837520.27837770541876
370.7081966709137360.5836066581725280.291803329086264
380.6708842218310830.6582315563378330.329115778168917
390.672930043006350.65413991398730.32706995699365
400.7555254710456650.4889490579086690.244474528954335
410.7151322848796850.569735430240630.284867715120315
420.6709239100034970.6581521799930060.329076089996503
430.7206043204973770.5587913590052470.279395679502623
440.7271721254133780.5456557491732440.272827874586622
450.721759522238030.556480955523940.27824047776197
460.7043159266158270.5913681467683450.295684073384173
470.6672220932440.6655558135120010.332777906756
480.623885089634020.752229820731960.37611491036598
490.5977171580178310.8045656839643370.402282841982169
500.5561528912971770.8876942174056460.443847108702823
510.550736219778340.8985275604433210.44926378022166
520.5515009369972380.8969981260055230.448499063002762
530.5038552538802460.9922894922395070.496144746119754
540.4580113273353960.9160226546707930.541988672664603
550.4163266460315260.8326532920630520.583673353968474
560.4079081450462510.8158162900925030.592091854953749
570.3670142651511530.7340285303023060.632985734848847
580.3230804559425240.6461609118850490.676919544057476
590.2814000412739050.562800082547810.718599958726095
600.290657484382040.581314968764080.70934251561796
610.3178567155307590.6357134310615190.682143284469241
620.2892673427819230.5785346855638450.710732657218078
630.254726071230010.509452142460020.74527392876999
640.308378432474160.616756864948320.69162156752584
650.2729357184518150.5458714369036290.727064281548185
660.2397554021118350.479510804223670.760244597888165
670.2519483111401630.5038966222803270.748051688859837
680.3508786840513930.7017573681027860.649121315948607
690.3107327749958220.6214655499916440.689267225004178
700.2928368780664240.5856737561328480.707163121933576
710.2593273247103590.5186546494207180.740672675289641
720.2246628479735540.4493256959471090.775337152026446
730.2081884767340610.4163769534681230.791811523265939
740.3002469014292960.6004938028585930.699753098570704
750.2627602313882330.5255204627764660.737239768611767
760.2745776615520370.5491553231040740.725422338447963
770.2385335232243930.4770670464487870.761466476775607
780.2239148825044470.4478297650088950.776085117495553
790.1982441021327330.3964882042654650.801755897867267
800.2067059593816220.4134119187632450.793294040618378
810.1833566400539770.3667132801079530.816643359946023
820.2421183629130210.4842367258260420.757881637086979
830.2148508142072950.429701628414590.785149185792705
840.2184154509243610.4368309018487220.781584549075639
850.2213567711235590.4427135422471170.778643228876441
860.233586101467610.467172202935220.76641389853239
870.2532512379100680.5065024758201350.746748762089932
880.2378247439649010.4756494879298020.762175256035099
890.2545877274135990.5091754548271990.745412272586401
900.231153732982080.4623074659641590.76884626701792
910.2203674919622850.440734983924570.779632508037715
920.2362772760932180.4725545521864360.763722723906782
930.265446112145680.530892224291360.73455388785432
940.2463176348689320.4926352697378640.753682365131068
950.2686727684755740.5373455369511490.731327231524426
960.2377061468650530.4754122937301070.762293853134947
970.2738375861105060.5476751722210130.726162413889494
980.2503584752502220.5007169505004430.749641524749778
990.2999909966947250.5999819933894510.700009003305275
1000.2674417796409750.5348835592819510.732558220359025
1010.3479038163675160.6958076327350320.652096183632484
1020.3237179483930160.6474358967860310.676282051606984
1030.2997463545112690.5994927090225370.700253645488731
1040.2766158768458810.5532317536917620.723384123154119
1050.3115295083717810.6230590167435620.688470491628219
1060.288516366944390.5770327338887810.71148363305561
1070.2672413598871880.5344827197743760.732758640112812
1080.2517740646410190.5035481292820380.748225935358981
1090.2329427811732270.4658855623464540.767057218826773
1100.2752747156563380.5505494313126750.724725284343662
1110.2612703114190670.5225406228381350.738729688580933
1120.2481984848040640.4963969696081280.751801515195936
1130.3236451861856030.6472903723712070.676354813814397
1140.3058650406094270.6117300812188540.694134959390573
1150.3515105463208850.703021092641770.648489453679115
1160.3304305941276770.6608611882553530.669569405872323
1170.4478495033505160.8956990067010320.552150496649484
1180.5079504938850440.9840990122299110.492049506114956
1190.4815991437941110.9631982875882220.518400856205889
1200.4290425778672920.8580851557345850.570957422132708
1210.4954831217162160.9909662434324320.504516878283784
1220.4644577143232220.9289154286464430.535542285676778
1230.4522286799219990.9044573598439980.547771320078001
1240.515683019367080.968633961265840.48431698063292
1250.4596912753259530.9193825506519070.540308724674047
1260.4167961735931640.8335923471863270.583203826406836
1270.3961737702306450.7923475404612910.603826229769355
1280.3398164732424470.6796329464848940.660183526757553
1290.3119207112228770.6238414224457540.688079288777123
1300.2627231932107080.5254463864214170.737276806789292
1310.3159766999453060.6319533998906120.684023300054694
1320.5137147484360450.972570503127910.486285251563955
1330.4545860527638810.9091721055277620.545413947236119
1340.4011823550914560.8023647101829120.598817644908544
1350.3549799407258930.7099598814517860.645020059274107
1360.3205309587918140.6410619175836280.679469041208186
1370.2750799762613650.5501599525227310.724920023738635
1380.5424736897100110.9150526205799790.457526310289989
1390.4467899874301880.8935799748603770.553210012569812
1400.576595642760510.846808714478980.42340435723949
1410.6940736763575980.6118526472848040.305926323642402
1420.5737980951375760.8524038097248490.426201904862424
1430.4716748838953480.9433497677906960.528325116104652






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=202367&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=202367&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202367&T=6

As an alternative you can also use a QR Code:  
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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}