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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 08 Dec 2014 11:55:23 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/08/t14180397711edte820s5p6ku6.htm/, Retrieved Sun, 19 May 2024 10:48:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263948, Retrieved Sun, 19 May 2024 10:48:52 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [paper mr numeracy] [2014-12-08 11:51:34] [673773038936aef3a5778d7e6bda5c1e]
- R  D    [Multiple Regression] [paper mr numeracy] [2014-12-08 11:55:23] [ec1b40d1a9751af99658fe8fca4f9eca] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1 1.00 0.50 0.67 0.67 0.00 0.50
0 0.89 0.50 0.83 0.33 0.50 1.00
1 0.89 0.40 1.00 0.67 0.00 1.00
0 0.89 0.50 0.83 0.00 0.00 0.00
0 0.89 0.70 0.67 0.00 1.00 1.00
0 0.78 0.30 0.00 0.00 0.50 0.50
1 0.89 0.40 0.83 0.67 0.50 0.00
0 1.00 0.40 0.50 0.67 1.00 1.00
0 0.89 0.70 0.83 0.00 0.50 0.00
0 0.78 0.60 0.33 0.67 0.50 0.50
0 1.00 0.60 0.50 1.00 0.00 0.50
0 0.78 0.20 0.67 0.00 0.50 0.50
0 0.89 0.40 1.00 0.00 0.50 0.50
1 0.89 0.40 0.50 0.67 0.00 1.00
1 0.89 0.50 0.67 0.33 0.00 0.00
1 0.89 0.30 0.17 0.67 0.00 0.50
0 0.89 0.40 0.83 0.33 0.50 0.50
1 0.67 0.70 0.67 0.33 0.50 1.00
0 1.00 0.50 0.67 0.33 0.00 1.00
1 0.78 0.20 0.67 0.00 0.00 1.00
0 0.78 0.30 0.50 0.67 0.00 0.50
0 0.89 0.60 1.00 0.33 0.00 1.00
1 0.78 0.60 0.83 0.33 0.00 1.00
0 0.89 0.20 0.83 0.33 0.00 1.00
0 0.89 0.70 1.00 0.67 1.00 0.00
0 0.33 0.20 0.67 0.00 0.00 0.00
0 1.00 1.00 1.00 0.33 1.00 1.00
1 0.89 0.40 0.83 0.67 0.00 0.50
0 0.89 0.40 1.00 1.00 0.00 1.00
1 0.67 0.20 0.83 0.67 0.00 0.50
1 0.56 0.40 0.67 0.33 0.00 1.00
1 0.89 0.40 0.67 0.00 0.50 1.00
0 0.89 0.70 1.00 0.67 0.50 0.50
0 1.00 0.20 0.67 0.67 0.00 0.50
0 0.78 0.60 1.00 1.00 0.00 0.50
0 0.78 0.30 1.00 1.00 0.50 0.50
1 0.33 0.30 0.50 0.33 0.00 0.00
0 0.78 0.20 0.67 0.00 0.50 0.00
0 0.89 0.50 0.83 0.67 0.50 0.50
1 0.89 0.70 1.00 0.67 0.50 1.00
0 0.78 0.60 1.00 0.67 0.50 0.50
0 0.89 0.40 1.00 0.67 0.50 1.00
0 0.89 0.60 1.00 0.33 0.50 1.00
0 1.00 0.40 1.00 1.00 0.00 1.00
0 0.67 0.30 0.83 0.67 0.00 1.00
1 1.00 0.50 0.83 0.67 0.50 0.50
0 0.89 0.20 0.50 0.00 0.00 1.00
0 0.89 0.30 0.83 0.00 0.50 1.00
1 0.89 0.50 0.17 0.00 0.00 1.00
0 0.78 0.70 0.83 1.00 0.50 1.00
0 0.89 0.40 1.00 0.67 1.00 0.50
0 0.78 0.30 1.00 0.00 0.00 0.50
0 0.78 0.20 0.67 0.67 1.00 1.00
0 1.00 0.50 1.00 0.00 0.00 0.50
0 0.78 0.40 1.00 0.00 0.50 0.00
0 1.00 0.60 1.00 0.67 1.00 1.00
1 0.78 0.40 0.83 1.00 0.00 1.00
0 0.67 0.40 0.33 0.00 0.00 0.50
1 0.33 0.20 0.33 0.33 0.00 0.00
0 1.00 0.90 1.00 0.67 0.50 1.00
0 1.00 0.80 1.00 0.67 1.00 0.50
1 0.78 0.80 0.83 0.00 0.50 1.00
1 0.67 0.30 1.00 1.00 0.50 1.00
0 1.00 0.20 0.83 0.67 0.00 0.50
1 0.89 0.40 0.67 0.00 0.50 1.00
0 0.89 0.20 0.83 1.00 0.00 1.00
1 0.78 0.20 0.67 0.67 0.50 1.00
0 1.00 0.10 0.83 0.67 0.00 1.00
0 0.56 0.40 0.67 1.00 0.50 0.00
1 0.67 0.50 1.00 0.00 0.50 0.50
1 0.89 0.80 0.83 0.33 0.50 1.00
1 0.89 0.40 0.67 0.67 0.00 0.50
1 0.89 0.60 0.83 0.33 0.50 0.50
0 0.89 0.50 0.83 0.67 0.50 1.00
1 0.78 0.30 0.67 0.00 0.00 0.00
0 0.89 0.80 1.00 1.00 0.50 1.00
1 1.00 0.40 0.33 0.00 0.50 0.00
0 1.00 0.60 0.83 0.67 0.50 0.50
1 0.89 0.40 1.00 0.33 0.00 0.50
1 0.44 0.30 0.83 0.00 0.00 0.00
0 0.78 0.80 0.83 0.00 1.00 1.00
1 0.89 0.60 0.50 0.33 1.00 1.00
1 0.67 0.30 0.50 0.00 0.00 0.00
1 0.78 0.50 0.83 0.67 0.50 1.00
0 0.78 0.40 1.00 0.33 0.00 1.00
1 0.33 0.30 0.33 0.67 0.00 0.00
0 0.89 0.70 1.00 0.33 0.00 0.50
0 0.89 0.20 0.67 0.33 0.50 0.50
1 0.89 0.40 0.83 1.00 0.00 1.00
1 0.89 0.60 1.00 0.67 0.50 0.50
1 0.56 0.60 0.83 0.00 0.00 1.00
0 0.67 0.60 0.83 0.67 0.50 0.50
0 0.67 0.40 1.00 0.33 0.50 1.00
0 0.78 0.60 0.83 0.00 0.00 1.00
1 0.78 0.50 1.00 0.33 0.50 1.00
0 0.78 0.50 0.83 0.00 0.00 1.00
1 0.89 0.60 0.67 0.00 0.00 1.00
0 1.00 0.80 0.83 0.33 0.50 1.00
0 0.89 0.50 0.83 0.67 1.00 0.50
0 0.89 0.60 0.83 0.67 0.50 1.00
0 0.78 0.40 0.83 0.67 0.50 1.00
0 1.00 0.30 0.67 0.67 0.50 1.00
1 0.78 0.30 0.83 1.00 0.00 0.50
0 0.67 0.20 0.00 0.00 0.00 0.00
0 0.78 0.40 0.83 0.00 0.00 0.50
0 0.89 0.50 1.00 0.00 0.00 0.50
1 0.67 0.30 0.17 0.00 0.50 0.00
1 0.22 0.40 0.17 0.00 0.50 0.00
1 0.44 0.50 0.50 1.00 0.00 0.00
1 0.89 0.30 0.50 0.67 0.00 1.00
1 0.67 0.50 1.00 0.00 0.00 0.50
1 0.89 0.40 0.67 0.67 0.00 0.50
0 0.67 0.40 0.83 0.67 0.00 1.00

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263948&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263948&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263948&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
gendercode[t] = + 1.18316 -0.676694Calculation[t] + 0.347791Algebraic_Reasoning[t] -0.433844Graphical_Interpretation[t] + 0.041863Proportionality_and_Ratio[t] -0.320294Probability_and_Sampling[t] + 0.0517133Estimation[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
gendercode[t] =  +  1.18316 -0.676694Calculation[t] +  0.347791Algebraic_Reasoning[t] -0.433844Graphical_Interpretation[t] +  0.041863Proportionality_and_Ratio[t] -0.320294Probability_and_Sampling[t] +  0.0517133Estimation[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263948&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]gendercode[t] =  +  1.18316 -0.676694Calculation[t] +  0.347791Algebraic_Reasoning[t] -0.433844Graphical_Interpretation[t] +  0.041863Proportionality_and_Ratio[t] -0.320294Probability_and_Sampling[t] +  0.0517133Estimation[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263948&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263948&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
gendercode[t] = + 1.18316 -0.676694Calculation[t] + 0.347791Algebraic_Reasoning[t] -0.433844Graphical_Interpretation[t] + 0.041863Proportionality_and_Ratio[t] -0.320294Probability_and_Sampling[t] + 0.0517133Estimation[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.183160.2443884.8414.41156e-062.20578e-06
Calculation-0.6766940.311976-2.1690.03231310.0161566
Algebraic_Reasoning0.3477910.2866581.2130.2277270.113864
Graphical_Interpretation-0.4338440.211856-2.0480.04304970.0215248
Proportionality_and_Ratio0.0418630.1324380.31610.7525510.376276
Probability_and_Sampling-0.3202940.147876-2.1660.03255480.0162774
Estimation0.05171330.1313340.39380.6945540.347277

\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) & 1.18316 & 0.244388 & 4.841 & 4.41156e-06 & 2.20578e-06 \tabularnewline
Calculation & -0.676694 & 0.311976 & -2.169 & 0.0323131 & 0.0161566 \tabularnewline
Algebraic_Reasoning & 0.347791 & 0.286658 & 1.213 & 0.227727 & 0.113864 \tabularnewline
Graphical_Interpretation & -0.433844 & 0.211856 & -2.048 & 0.0430497 & 0.0215248 \tabularnewline
Proportionality_and_Ratio & 0.041863 & 0.132438 & 0.3161 & 0.752551 & 0.376276 \tabularnewline
Probability_and_Sampling & -0.320294 & 0.147876 & -2.166 & 0.0325548 & 0.0162774 \tabularnewline
Estimation & 0.0517133 & 0.131334 & 0.3938 & 0.694554 & 0.347277 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263948&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]1.18316[/C][C]0.244388[/C][C]4.841[/C][C]4.41156e-06[/C][C]2.20578e-06[/C][/ROW]
[ROW][C]Calculation[/C][C]-0.676694[/C][C]0.311976[/C][C]-2.169[/C][C]0.0323131[/C][C]0.0161566[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]0.347791[/C][C]0.286658[/C][C]1.213[/C][C]0.227727[/C][C]0.113864[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]-0.433844[/C][C]0.211856[/C][C]-2.048[/C][C]0.0430497[/C][C]0.0215248[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.041863[/C][C]0.132438[/C][C]0.3161[/C][C]0.752551[/C][C]0.376276[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]-0.320294[/C][C]0.147876[/C][C]-2.166[/C][C]0.0325548[/C][C]0.0162774[/C][/ROW]
[ROW][C]Estimation[/C][C]0.0517133[/C][C]0.131334[/C][C]0.3938[/C][C]0.694554[/C][C]0.347277[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263948&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263948&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)1.183160.2443884.8414.41156e-062.20578e-06
Calculation-0.6766940.311976-2.1690.03231310.0161566
Algebraic_Reasoning0.3477910.2866581.2130.2277270.113864
Graphical_Interpretation-0.4338440.211856-2.0480.04304970.0215248
Proportionality_and_Ratio0.0418630.1324380.31610.7525510.376276
Probability_and_Sampling-0.3202940.147876-2.1660.03255480.0162774
Estimation0.05171330.1313340.39380.6945540.347277






Multiple Linear Regression - Regression Statistics
Multiple R0.385177
R-squared0.148361
Adjusted R-squared0.100155
F-TEST (value)3.07765
F-TEST (DF numerator)6
F-TEST (DF denominator)106
p-value0.00811891
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.469631
Sum Squared Residuals23.3786

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.385177 \tabularnewline
R-squared & 0.148361 \tabularnewline
Adjusted R-squared & 0.100155 \tabularnewline
F-TEST (value) & 3.07765 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.00811891 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.469631 \tabularnewline
Sum Squared Residuals & 23.3786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263948&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.385177[/C][/ROW]
[ROW][C]R-squared[/C][C]0.148361[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.100155[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.07765[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.00811891[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.469631[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]23.3786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263948&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263948&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 R0.385177
R-squared0.148361
Adjusted R-squared0.100155
F-TEST (value)3.07765
F-TEST (DF numerator)6
F-TEST (DF denominator)106
p-value0.00811891
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.469631
Sum Squared Residuals23.3786






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.4435890.556411
200.300086-0.300086
310.3659340.634066
400.394705-0.394705
500.265098-0.265098
600.625384-0.625384
710.2278270.772173
800.188126-0.188126
900.304116-0.304116
1000.6146-0.6146
1100.565936-0.565936
1200.299929-0.299929
1300.151882-0.151882
1410.5828560.417144
1510.4779350.522065
1610.6653890.334611
1700.239451-0.239451
1810.5879320.412068
1900.455212-0.455212
2010.4859330.514067
2100.596657-0.596657
2200.421259-0.421259
2310.5694490.430551
2400.355896-0.355896
2500.098264-0.098264
2600.738732-0.738732
2700.165645-0.165645
2810.4138310.586169
2900.379749-0.379749
3010.4931460.506854
3110.7181780.281822
3210.3209070.679093
3300.284268-0.284268
3400.339251-0.339251
3500.497887-0.497887
3600.233403-0.233403
3710.8610790.138921
3800.274072-0.274072
3900.288463-0.288463
4010.3101240.689876
4100.323925-0.323925
4200.205787-0.205787
4300.261112-0.261112
4400.305313-0.305313
4500.553781-0.553781
4610.2140270.785973
4700.48525-0.48525
4800.216713-0.216713
4910.7327550.267245
5000.472129-0.472129
5100.0197835-0.0197835
5200.351687-0.351687
5300.193687-0.193687
5400.272372-0.272372
5500.200462-0.200462
5600.0407618-0.0407618
5710.5279390.472061
5800.751577-0.751577
5910.9000540.0999465
6000.305246-0.305246
6100.0844633-0.0844633
6210.4650450.534955
6310.3336960.666304
6400.269836-0.269836
6510.3209070.679093
6600.383944-0.383944
6710.3538340.646166
6800.260914-0.260914
6900.534366-0.534366
7010.3355340.664466
7110.4044230.595577
7210.4832460.516754
7310.3090090.690991
7400.31432-0.31432
7510.4689980.531002
7600.358718-0.358718
7710.3422650.657735
7800.248806-0.248806
7910.3258440.674156
8010.6296590.370341
8100.304898-0.304898
8210.3178870.682113
8310.6171880.382812
8410.3887560.611244
8500.426137-0.426137
8610.9490660.050934
8700.430181-0.430181
8800.239307-0.239307
8910.4535020.546498
9010.2494890.750511
9110.7045070.295493
9200.472115-0.472115
9300.340426-0.340426
9400.555634-0.555634
9510.3007690.699231
9600.520855-0.520855
9710.5506130.449387
9800.329987-0.329987
9900.128316-0.128316
10000.349099-0.349099
10100.353977-0.353977
10200.23974-0.23974
10310.4673030.532697
10400.799331-0.799331
10500.460219-0.460219
10600.346808-0.346808
10710.600210.39979
10810.9395010.0604987
10910.8842490.115751
11010.5480770.451923
11110.4956810.504319
11210.4832460.516754
11300.58856-0.58856

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.443589 & 0.556411 \tabularnewline
2 & 0 & 0.300086 & -0.300086 \tabularnewline
3 & 1 & 0.365934 & 0.634066 \tabularnewline
4 & 0 & 0.394705 & -0.394705 \tabularnewline
5 & 0 & 0.265098 & -0.265098 \tabularnewline
6 & 0 & 0.625384 & -0.625384 \tabularnewline
7 & 1 & 0.227827 & 0.772173 \tabularnewline
8 & 0 & 0.188126 & -0.188126 \tabularnewline
9 & 0 & 0.304116 & -0.304116 \tabularnewline
10 & 0 & 0.6146 & -0.6146 \tabularnewline
11 & 0 & 0.565936 & -0.565936 \tabularnewline
12 & 0 & 0.299929 & -0.299929 \tabularnewline
13 & 0 & 0.151882 & -0.151882 \tabularnewline
14 & 1 & 0.582856 & 0.417144 \tabularnewline
15 & 1 & 0.477935 & 0.522065 \tabularnewline
16 & 1 & 0.665389 & 0.334611 \tabularnewline
17 & 0 & 0.239451 & -0.239451 \tabularnewline
18 & 1 & 0.587932 & 0.412068 \tabularnewline
19 & 0 & 0.455212 & -0.455212 \tabularnewline
20 & 1 & 0.485933 & 0.514067 \tabularnewline
21 & 0 & 0.596657 & -0.596657 \tabularnewline
22 & 0 & 0.421259 & -0.421259 \tabularnewline
23 & 1 & 0.569449 & 0.430551 \tabularnewline
24 & 0 & 0.355896 & -0.355896 \tabularnewline
25 & 0 & 0.098264 & -0.098264 \tabularnewline
26 & 0 & 0.738732 & -0.738732 \tabularnewline
27 & 0 & 0.165645 & -0.165645 \tabularnewline
28 & 1 & 0.413831 & 0.586169 \tabularnewline
29 & 0 & 0.379749 & -0.379749 \tabularnewline
30 & 1 & 0.493146 & 0.506854 \tabularnewline
31 & 1 & 0.718178 & 0.281822 \tabularnewline
32 & 1 & 0.320907 & 0.679093 \tabularnewline
33 & 0 & 0.284268 & -0.284268 \tabularnewline
34 & 0 & 0.339251 & -0.339251 \tabularnewline
35 & 0 & 0.497887 & -0.497887 \tabularnewline
36 & 0 & 0.233403 & -0.233403 \tabularnewline
37 & 1 & 0.861079 & 0.138921 \tabularnewline
38 & 0 & 0.274072 & -0.274072 \tabularnewline
39 & 0 & 0.288463 & -0.288463 \tabularnewline
40 & 1 & 0.310124 & 0.689876 \tabularnewline
41 & 0 & 0.323925 & -0.323925 \tabularnewline
42 & 0 & 0.205787 & -0.205787 \tabularnewline
43 & 0 & 0.261112 & -0.261112 \tabularnewline
44 & 0 & 0.305313 & -0.305313 \tabularnewline
45 & 0 & 0.553781 & -0.553781 \tabularnewline
46 & 1 & 0.214027 & 0.785973 \tabularnewline
47 & 0 & 0.48525 & -0.48525 \tabularnewline
48 & 0 & 0.216713 & -0.216713 \tabularnewline
49 & 1 & 0.732755 & 0.267245 \tabularnewline
50 & 0 & 0.472129 & -0.472129 \tabularnewline
51 & 0 & 0.0197835 & -0.0197835 \tabularnewline
52 & 0 & 0.351687 & -0.351687 \tabularnewline
53 & 0 & 0.193687 & -0.193687 \tabularnewline
54 & 0 & 0.272372 & -0.272372 \tabularnewline
55 & 0 & 0.200462 & -0.200462 \tabularnewline
56 & 0 & 0.0407618 & -0.0407618 \tabularnewline
57 & 1 & 0.527939 & 0.472061 \tabularnewline
58 & 0 & 0.751577 & -0.751577 \tabularnewline
59 & 1 & 0.900054 & 0.0999465 \tabularnewline
60 & 0 & 0.305246 & -0.305246 \tabularnewline
61 & 0 & 0.0844633 & -0.0844633 \tabularnewline
62 & 1 & 0.465045 & 0.534955 \tabularnewline
63 & 1 & 0.333696 & 0.666304 \tabularnewline
64 & 0 & 0.269836 & -0.269836 \tabularnewline
65 & 1 & 0.320907 & 0.679093 \tabularnewline
66 & 0 & 0.383944 & -0.383944 \tabularnewline
67 & 1 & 0.353834 & 0.646166 \tabularnewline
68 & 0 & 0.260914 & -0.260914 \tabularnewline
69 & 0 & 0.534366 & -0.534366 \tabularnewline
70 & 1 & 0.335534 & 0.664466 \tabularnewline
71 & 1 & 0.404423 & 0.595577 \tabularnewline
72 & 1 & 0.483246 & 0.516754 \tabularnewline
73 & 1 & 0.309009 & 0.690991 \tabularnewline
74 & 0 & 0.31432 & -0.31432 \tabularnewline
75 & 1 & 0.468998 & 0.531002 \tabularnewline
76 & 0 & 0.358718 & -0.358718 \tabularnewline
77 & 1 & 0.342265 & 0.657735 \tabularnewline
78 & 0 & 0.248806 & -0.248806 \tabularnewline
79 & 1 & 0.325844 & 0.674156 \tabularnewline
80 & 1 & 0.629659 & 0.370341 \tabularnewline
81 & 0 & 0.304898 & -0.304898 \tabularnewline
82 & 1 & 0.317887 & 0.682113 \tabularnewline
83 & 1 & 0.617188 & 0.382812 \tabularnewline
84 & 1 & 0.388756 & 0.611244 \tabularnewline
85 & 0 & 0.426137 & -0.426137 \tabularnewline
86 & 1 & 0.949066 & 0.050934 \tabularnewline
87 & 0 & 0.430181 & -0.430181 \tabularnewline
88 & 0 & 0.239307 & -0.239307 \tabularnewline
89 & 1 & 0.453502 & 0.546498 \tabularnewline
90 & 1 & 0.249489 & 0.750511 \tabularnewline
91 & 1 & 0.704507 & 0.295493 \tabularnewline
92 & 0 & 0.472115 & -0.472115 \tabularnewline
93 & 0 & 0.340426 & -0.340426 \tabularnewline
94 & 0 & 0.555634 & -0.555634 \tabularnewline
95 & 1 & 0.300769 & 0.699231 \tabularnewline
96 & 0 & 0.520855 & -0.520855 \tabularnewline
97 & 1 & 0.550613 & 0.449387 \tabularnewline
98 & 0 & 0.329987 & -0.329987 \tabularnewline
99 & 0 & 0.128316 & -0.128316 \tabularnewline
100 & 0 & 0.349099 & -0.349099 \tabularnewline
101 & 0 & 0.353977 & -0.353977 \tabularnewline
102 & 0 & 0.23974 & -0.23974 \tabularnewline
103 & 1 & 0.467303 & 0.532697 \tabularnewline
104 & 0 & 0.799331 & -0.799331 \tabularnewline
105 & 0 & 0.460219 & -0.460219 \tabularnewline
106 & 0 & 0.346808 & -0.346808 \tabularnewline
107 & 1 & 0.60021 & 0.39979 \tabularnewline
108 & 1 & 0.939501 & 0.0604987 \tabularnewline
109 & 1 & 0.884249 & 0.115751 \tabularnewline
110 & 1 & 0.548077 & 0.451923 \tabularnewline
111 & 1 & 0.495681 & 0.504319 \tabularnewline
112 & 1 & 0.483246 & 0.516754 \tabularnewline
113 & 0 & 0.58856 & -0.58856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263948&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]0.443589[/C][C]0.556411[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.300086[/C][C]-0.300086[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.365934[/C][C]0.634066[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.394705[/C][C]-0.394705[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.265098[/C][C]-0.265098[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.625384[/C][C]-0.625384[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.227827[/C][C]0.772173[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.188126[/C][C]-0.188126[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.304116[/C][C]-0.304116[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.6146[/C][C]-0.6146[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.565936[/C][C]-0.565936[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.299929[/C][C]-0.299929[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.151882[/C][C]-0.151882[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.582856[/C][C]0.417144[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.477935[/C][C]0.522065[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.665389[/C][C]0.334611[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.239451[/C][C]-0.239451[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.587932[/C][C]0.412068[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.455212[/C][C]-0.455212[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.485933[/C][C]0.514067[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.596657[/C][C]-0.596657[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.421259[/C][C]-0.421259[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.569449[/C][C]0.430551[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.355896[/C][C]-0.355896[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.098264[/C][C]-0.098264[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.738732[/C][C]-0.738732[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.165645[/C][C]-0.165645[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.413831[/C][C]0.586169[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.379749[/C][C]-0.379749[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.493146[/C][C]0.506854[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.718178[/C][C]0.281822[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.320907[/C][C]0.679093[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.284268[/C][C]-0.284268[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.339251[/C][C]-0.339251[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.497887[/C][C]-0.497887[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.233403[/C][C]-0.233403[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.861079[/C][C]0.138921[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.274072[/C][C]-0.274072[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.288463[/C][C]-0.288463[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.310124[/C][C]0.689876[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.323925[/C][C]-0.323925[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.205787[/C][C]-0.205787[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.261112[/C][C]-0.261112[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.305313[/C][C]-0.305313[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.553781[/C][C]-0.553781[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.214027[/C][C]0.785973[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.48525[/C][C]-0.48525[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.216713[/C][C]-0.216713[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.732755[/C][C]0.267245[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.472129[/C][C]-0.472129[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.0197835[/C][C]-0.0197835[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.351687[/C][C]-0.351687[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.193687[/C][C]-0.193687[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.272372[/C][C]-0.272372[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.200462[/C][C]-0.200462[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.0407618[/C][C]-0.0407618[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.527939[/C][C]0.472061[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.751577[/C][C]-0.751577[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.900054[/C][C]0.0999465[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0.305246[/C][C]-0.305246[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.0844633[/C][C]-0.0844633[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.465045[/C][C]0.534955[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.333696[/C][C]0.666304[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.269836[/C][C]-0.269836[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0.320907[/C][C]0.679093[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.383944[/C][C]-0.383944[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.353834[/C][C]0.646166[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.260914[/C][C]-0.260914[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.534366[/C][C]-0.534366[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0.335534[/C][C]0.664466[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.404423[/C][C]0.595577[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.483246[/C][C]0.516754[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.309009[/C][C]0.690991[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.31432[/C][C]-0.31432[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.468998[/C][C]0.531002[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.358718[/C][C]-0.358718[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.342265[/C][C]0.657735[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.248806[/C][C]-0.248806[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.325844[/C][C]0.674156[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.629659[/C][C]0.370341[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.304898[/C][C]-0.304898[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.317887[/C][C]0.682113[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.617188[/C][C]0.382812[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.388756[/C][C]0.611244[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.426137[/C][C]-0.426137[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.949066[/C][C]0.050934[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.430181[/C][C]-0.430181[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.239307[/C][C]-0.239307[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0.453502[/C][C]0.546498[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.249489[/C][C]0.750511[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0.704507[/C][C]0.295493[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.472115[/C][C]-0.472115[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.340426[/C][C]-0.340426[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.555634[/C][C]-0.555634[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0.300769[/C][C]0.699231[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.520855[/C][C]-0.520855[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0.550613[/C][C]0.449387[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.329987[/C][C]-0.329987[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.128316[/C][C]-0.128316[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.349099[/C][C]-0.349099[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.353977[/C][C]-0.353977[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.23974[/C][C]-0.23974[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0.467303[/C][C]0.532697[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.799331[/C][C]-0.799331[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.460219[/C][C]-0.460219[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.346808[/C][C]-0.346808[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0.60021[/C][C]0.39979[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0.939501[/C][C]0.0604987[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0.884249[/C][C]0.115751[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0.548077[/C][C]0.451923[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0.495681[/C][C]0.504319[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0.483246[/C][C]0.516754[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.58856[/C][C]-0.58856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263948&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263948&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
110.4435890.556411
200.300086-0.300086
310.3659340.634066
400.394705-0.394705
500.265098-0.265098
600.625384-0.625384
710.2278270.772173
800.188126-0.188126
900.304116-0.304116
1000.6146-0.6146
1100.565936-0.565936
1200.299929-0.299929
1300.151882-0.151882
1410.5828560.417144
1510.4779350.522065
1610.6653890.334611
1700.239451-0.239451
1810.5879320.412068
1900.455212-0.455212
2010.4859330.514067
2100.596657-0.596657
2200.421259-0.421259
2310.5694490.430551
2400.355896-0.355896
2500.098264-0.098264
2600.738732-0.738732
2700.165645-0.165645
2810.4138310.586169
2900.379749-0.379749
3010.4931460.506854
3110.7181780.281822
3210.3209070.679093
3300.284268-0.284268
3400.339251-0.339251
3500.497887-0.497887
3600.233403-0.233403
3710.8610790.138921
3800.274072-0.274072
3900.288463-0.288463
4010.3101240.689876
4100.323925-0.323925
4200.205787-0.205787
4300.261112-0.261112
4400.305313-0.305313
4500.553781-0.553781
4610.2140270.785973
4700.48525-0.48525
4800.216713-0.216713
4910.7327550.267245
5000.472129-0.472129
5100.0197835-0.0197835
5200.351687-0.351687
5300.193687-0.193687
5400.272372-0.272372
5500.200462-0.200462
5600.0407618-0.0407618
5710.5279390.472061
5800.751577-0.751577
5910.9000540.0999465
6000.305246-0.305246
6100.0844633-0.0844633
6210.4650450.534955
6310.3336960.666304
6400.269836-0.269836
6510.3209070.679093
6600.383944-0.383944
6710.3538340.646166
6800.260914-0.260914
6900.534366-0.534366
7010.3355340.664466
7110.4044230.595577
7210.4832460.516754
7310.3090090.690991
7400.31432-0.31432
7510.4689980.531002
7600.358718-0.358718
7710.3422650.657735
7800.248806-0.248806
7910.3258440.674156
8010.6296590.370341
8100.304898-0.304898
8210.3178870.682113
8310.6171880.382812
8410.3887560.611244
8500.426137-0.426137
8610.9490660.050934
8700.430181-0.430181
8800.239307-0.239307
8910.4535020.546498
9010.2494890.750511
9110.7045070.295493
9200.472115-0.472115
9300.340426-0.340426
9400.555634-0.555634
9510.3007690.699231
9600.520855-0.520855
9710.5506130.449387
9800.329987-0.329987
9900.128316-0.128316
10000.349099-0.349099
10100.353977-0.353977
10200.23974-0.23974
10310.4673030.532697
10400.799331-0.799331
10500.460219-0.460219
10600.346808-0.346808
10710.600210.39979
10810.9395010.0604987
10910.8842490.115751
11010.5480770.451923
11110.4956810.504319
11210.4832460.516754
11300.58856-0.58856






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6290640.7418730.370936
110.6235410.7529180.376459
120.6000070.7999850.399993
130.5210440.9579130.478956
140.5218720.9562550.478128
150.5671020.8657960.432898
160.5139170.9721650.486083
170.4662180.9324360.533782
180.4977370.9954740.502263
190.4464070.8928130.553593
200.410020.8200410.58998
210.6380830.7238350.361917
220.6562930.6874140.343707
230.6214350.757130.378565
240.6275490.7449030.372451
250.5638570.8722860.436143
260.6516240.6967520.348376
270.5879530.8240950.412047
280.5816120.8367770.418388
290.6350790.7298430.364921
300.6304970.7390060.369503
310.596580.8068410.40342
320.6862740.6274510.313726
330.6491930.7016140.350807
340.6332660.7334670.366734
350.644290.711420.35571
360.5961610.8076770.403839
370.5553590.8892810.444641
380.507570.984860.49243
390.4639120.9278240.536088
400.5255250.9489490.474475
410.4903010.9806020.509699
420.4437330.8874670.556267
430.4037120.8074240.596288
440.3779870.7559740.622013
450.3957010.7914030.604299
460.5015030.9969930.498497
470.500930.9981390.49907
480.4542140.9084280.545786
490.4154740.8309470.584526
500.4049980.8099960.595002
510.355660.7113190.64434
520.331980.663960.66802
530.294960.589920.70504
540.2644940.5289890.735506
550.2355880.4711750.764412
560.197650.3953010.80235
570.2024890.4049780.797511
580.2524310.5048630.747569
590.2189430.4378860.781057
600.1923090.3846180.807691
610.1615610.3231220.838439
620.1758540.3517070.824146
630.2103160.4206320.789684
640.1891050.378210.810895
650.2268560.4537110.773144
660.2126650.425330.787335
670.2499210.4998410.750079
680.2209860.4419720.779014
690.2502460.5004920.749754
700.2834980.5669970.716502
710.3176750.6353510.682325
720.3154920.6309840.684508
730.3594060.7188120.640594
740.3291140.6582270.670886
750.3227310.6454610.677269
760.2998650.5997310.700135
770.3318650.6637310.668135
780.3032610.6065210.696739
790.3403650.680730.659635
800.323740.647480.67626
810.287350.57470.71265
820.3417850.6835690.658215
830.3303860.6607720.669614
840.3609430.7218860.639057
850.3392210.6784420.660779
860.2794290.5588580.720571
870.2816130.5632250.718387
880.2313460.4626920.768654
890.2226420.4452850.777358
900.2746550.5493090.725345
910.2604390.5208790.739561
920.2558620.5117240.744138
930.2098820.4197650.790118
940.1914290.3828580.808571
950.3030620.6061230.696938
960.2642910.5285830.735709
970.3281380.6562760.671862
980.2480230.4960470.751977
990.1889760.3779510.811024
1000.1417540.2835090.858246
1010.1191320.2382640.880868
1020.216270.4325390.78373
1030.158790.3175790.84121

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.629064 & 0.741873 & 0.370936 \tabularnewline
11 & 0.623541 & 0.752918 & 0.376459 \tabularnewline
12 & 0.600007 & 0.799985 & 0.399993 \tabularnewline
13 & 0.521044 & 0.957913 & 0.478956 \tabularnewline
14 & 0.521872 & 0.956255 & 0.478128 \tabularnewline
15 & 0.567102 & 0.865796 & 0.432898 \tabularnewline
16 & 0.513917 & 0.972165 & 0.486083 \tabularnewline
17 & 0.466218 & 0.932436 & 0.533782 \tabularnewline
18 & 0.497737 & 0.995474 & 0.502263 \tabularnewline
19 & 0.446407 & 0.892813 & 0.553593 \tabularnewline
20 & 0.41002 & 0.820041 & 0.58998 \tabularnewline
21 & 0.638083 & 0.723835 & 0.361917 \tabularnewline
22 & 0.656293 & 0.687414 & 0.343707 \tabularnewline
23 & 0.621435 & 0.75713 & 0.378565 \tabularnewline
24 & 0.627549 & 0.744903 & 0.372451 \tabularnewline
25 & 0.563857 & 0.872286 & 0.436143 \tabularnewline
26 & 0.651624 & 0.696752 & 0.348376 \tabularnewline
27 & 0.587953 & 0.824095 & 0.412047 \tabularnewline
28 & 0.581612 & 0.836777 & 0.418388 \tabularnewline
29 & 0.635079 & 0.729843 & 0.364921 \tabularnewline
30 & 0.630497 & 0.739006 & 0.369503 \tabularnewline
31 & 0.59658 & 0.806841 & 0.40342 \tabularnewline
32 & 0.686274 & 0.627451 & 0.313726 \tabularnewline
33 & 0.649193 & 0.701614 & 0.350807 \tabularnewline
34 & 0.633266 & 0.733467 & 0.366734 \tabularnewline
35 & 0.64429 & 0.71142 & 0.35571 \tabularnewline
36 & 0.596161 & 0.807677 & 0.403839 \tabularnewline
37 & 0.555359 & 0.889281 & 0.444641 \tabularnewline
38 & 0.50757 & 0.98486 & 0.49243 \tabularnewline
39 & 0.463912 & 0.927824 & 0.536088 \tabularnewline
40 & 0.525525 & 0.948949 & 0.474475 \tabularnewline
41 & 0.490301 & 0.980602 & 0.509699 \tabularnewline
42 & 0.443733 & 0.887467 & 0.556267 \tabularnewline
43 & 0.403712 & 0.807424 & 0.596288 \tabularnewline
44 & 0.377987 & 0.755974 & 0.622013 \tabularnewline
45 & 0.395701 & 0.791403 & 0.604299 \tabularnewline
46 & 0.501503 & 0.996993 & 0.498497 \tabularnewline
47 & 0.50093 & 0.998139 & 0.49907 \tabularnewline
48 & 0.454214 & 0.908428 & 0.545786 \tabularnewline
49 & 0.415474 & 0.830947 & 0.584526 \tabularnewline
50 & 0.404998 & 0.809996 & 0.595002 \tabularnewline
51 & 0.35566 & 0.711319 & 0.64434 \tabularnewline
52 & 0.33198 & 0.66396 & 0.66802 \tabularnewline
53 & 0.29496 & 0.58992 & 0.70504 \tabularnewline
54 & 0.264494 & 0.528989 & 0.735506 \tabularnewline
55 & 0.235588 & 0.471175 & 0.764412 \tabularnewline
56 & 0.19765 & 0.395301 & 0.80235 \tabularnewline
57 & 0.202489 & 0.404978 & 0.797511 \tabularnewline
58 & 0.252431 & 0.504863 & 0.747569 \tabularnewline
59 & 0.218943 & 0.437886 & 0.781057 \tabularnewline
60 & 0.192309 & 0.384618 & 0.807691 \tabularnewline
61 & 0.161561 & 0.323122 & 0.838439 \tabularnewline
62 & 0.175854 & 0.351707 & 0.824146 \tabularnewline
63 & 0.210316 & 0.420632 & 0.789684 \tabularnewline
64 & 0.189105 & 0.37821 & 0.810895 \tabularnewline
65 & 0.226856 & 0.453711 & 0.773144 \tabularnewline
66 & 0.212665 & 0.42533 & 0.787335 \tabularnewline
67 & 0.249921 & 0.499841 & 0.750079 \tabularnewline
68 & 0.220986 & 0.441972 & 0.779014 \tabularnewline
69 & 0.250246 & 0.500492 & 0.749754 \tabularnewline
70 & 0.283498 & 0.566997 & 0.716502 \tabularnewline
71 & 0.317675 & 0.635351 & 0.682325 \tabularnewline
72 & 0.315492 & 0.630984 & 0.684508 \tabularnewline
73 & 0.359406 & 0.718812 & 0.640594 \tabularnewline
74 & 0.329114 & 0.658227 & 0.670886 \tabularnewline
75 & 0.322731 & 0.645461 & 0.677269 \tabularnewline
76 & 0.299865 & 0.599731 & 0.700135 \tabularnewline
77 & 0.331865 & 0.663731 & 0.668135 \tabularnewline
78 & 0.303261 & 0.606521 & 0.696739 \tabularnewline
79 & 0.340365 & 0.68073 & 0.659635 \tabularnewline
80 & 0.32374 & 0.64748 & 0.67626 \tabularnewline
81 & 0.28735 & 0.5747 & 0.71265 \tabularnewline
82 & 0.341785 & 0.683569 & 0.658215 \tabularnewline
83 & 0.330386 & 0.660772 & 0.669614 \tabularnewline
84 & 0.360943 & 0.721886 & 0.639057 \tabularnewline
85 & 0.339221 & 0.678442 & 0.660779 \tabularnewline
86 & 0.279429 & 0.558858 & 0.720571 \tabularnewline
87 & 0.281613 & 0.563225 & 0.718387 \tabularnewline
88 & 0.231346 & 0.462692 & 0.768654 \tabularnewline
89 & 0.222642 & 0.445285 & 0.777358 \tabularnewline
90 & 0.274655 & 0.549309 & 0.725345 \tabularnewline
91 & 0.260439 & 0.520879 & 0.739561 \tabularnewline
92 & 0.255862 & 0.511724 & 0.744138 \tabularnewline
93 & 0.209882 & 0.419765 & 0.790118 \tabularnewline
94 & 0.191429 & 0.382858 & 0.808571 \tabularnewline
95 & 0.303062 & 0.606123 & 0.696938 \tabularnewline
96 & 0.264291 & 0.528583 & 0.735709 \tabularnewline
97 & 0.328138 & 0.656276 & 0.671862 \tabularnewline
98 & 0.248023 & 0.496047 & 0.751977 \tabularnewline
99 & 0.188976 & 0.377951 & 0.811024 \tabularnewline
100 & 0.141754 & 0.283509 & 0.858246 \tabularnewline
101 & 0.119132 & 0.238264 & 0.880868 \tabularnewline
102 & 0.21627 & 0.432539 & 0.78373 \tabularnewline
103 & 0.15879 & 0.317579 & 0.84121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263948&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.629064[/C][C]0.741873[/C][C]0.370936[/C][/ROW]
[ROW][C]11[/C][C]0.623541[/C][C]0.752918[/C][C]0.376459[/C][/ROW]
[ROW][C]12[/C][C]0.600007[/C][C]0.799985[/C][C]0.399993[/C][/ROW]
[ROW][C]13[/C][C]0.521044[/C][C]0.957913[/C][C]0.478956[/C][/ROW]
[ROW][C]14[/C][C]0.521872[/C][C]0.956255[/C][C]0.478128[/C][/ROW]
[ROW][C]15[/C][C]0.567102[/C][C]0.865796[/C][C]0.432898[/C][/ROW]
[ROW][C]16[/C][C]0.513917[/C][C]0.972165[/C][C]0.486083[/C][/ROW]
[ROW][C]17[/C][C]0.466218[/C][C]0.932436[/C][C]0.533782[/C][/ROW]
[ROW][C]18[/C][C]0.497737[/C][C]0.995474[/C][C]0.502263[/C][/ROW]
[ROW][C]19[/C][C]0.446407[/C][C]0.892813[/C][C]0.553593[/C][/ROW]
[ROW][C]20[/C][C]0.41002[/C][C]0.820041[/C][C]0.58998[/C][/ROW]
[ROW][C]21[/C][C]0.638083[/C][C]0.723835[/C][C]0.361917[/C][/ROW]
[ROW][C]22[/C][C]0.656293[/C][C]0.687414[/C][C]0.343707[/C][/ROW]
[ROW][C]23[/C][C]0.621435[/C][C]0.75713[/C][C]0.378565[/C][/ROW]
[ROW][C]24[/C][C]0.627549[/C][C]0.744903[/C][C]0.372451[/C][/ROW]
[ROW][C]25[/C][C]0.563857[/C][C]0.872286[/C][C]0.436143[/C][/ROW]
[ROW][C]26[/C][C]0.651624[/C][C]0.696752[/C][C]0.348376[/C][/ROW]
[ROW][C]27[/C][C]0.587953[/C][C]0.824095[/C][C]0.412047[/C][/ROW]
[ROW][C]28[/C][C]0.581612[/C][C]0.836777[/C][C]0.418388[/C][/ROW]
[ROW][C]29[/C][C]0.635079[/C][C]0.729843[/C][C]0.364921[/C][/ROW]
[ROW][C]30[/C][C]0.630497[/C][C]0.739006[/C][C]0.369503[/C][/ROW]
[ROW][C]31[/C][C]0.59658[/C][C]0.806841[/C][C]0.40342[/C][/ROW]
[ROW][C]32[/C][C]0.686274[/C][C]0.627451[/C][C]0.313726[/C][/ROW]
[ROW][C]33[/C][C]0.649193[/C][C]0.701614[/C][C]0.350807[/C][/ROW]
[ROW][C]34[/C][C]0.633266[/C][C]0.733467[/C][C]0.366734[/C][/ROW]
[ROW][C]35[/C][C]0.64429[/C][C]0.71142[/C][C]0.35571[/C][/ROW]
[ROW][C]36[/C][C]0.596161[/C][C]0.807677[/C][C]0.403839[/C][/ROW]
[ROW][C]37[/C][C]0.555359[/C][C]0.889281[/C][C]0.444641[/C][/ROW]
[ROW][C]38[/C][C]0.50757[/C][C]0.98486[/C][C]0.49243[/C][/ROW]
[ROW][C]39[/C][C]0.463912[/C][C]0.927824[/C][C]0.536088[/C][/ROW]
[ROW][C]40[/C][C]0.525525[/C][C]0.948949[/C][C]0.474475[/C][/ROW]
[ROW][C]41[/C][C]0.490301[/C][C]0.980602[/C][C]0.509699[/C][/ROW]
[ROW][C]42[/C][C]0.443733[/C][C]0.887467[/C][C]0.556267[/C][/ROW]
[ROW][C]43[/C][C]0.403712[/C][C]0.807424[/C][C]0.596288[/C][/ROW]
[ROW][C]44[/C][C]0.377987[/C][C]0.755974[/C][C]0.622013[/C][/ROW]
[ROW][C]45[/C][C]0.395701[/C][C]0.791403[/C][C]0.604299[/C][/ROW]
[ROW][C]46[/C][C]0.501503[/C][C]0.996993[/C][C]0.498497[/C][/ROW]
[ROW][C]47[/C][C]0.50093[/C][C]0.998139[/C][C]0.49907[/C][/ROW]
[ROW][C]48[/C][C]0.454214[/C][C]0.908428[/C][C]0.545786[/C][/ROW]
[ROW][C]49[/C][C]0.415474[/C][C]0.830947[/C][C]0.584526[/C][/ROW]
[ROW][C]50[/C][C]0.404998[/C][C]0.809996[/C][C]0.595002[/C][/ROW]
[ROW][C]51[/C][C]0.35566[/C][C]0.711319[/C][C]0.64434[/C][/ROW]
[ROW][C]52[/C][C]0.33198[/C][C]0.66396[/C][C]0.66802[/C][/ROW]
[ROW][C]53[/C][C]0.29496[/C][C]0.58992[/C][C]0.70504[/C][/ROW]
[ROW][C]54[/C][C]0.264494[/C][C]0.528989[/C][C]0.735506[/C][/ROW]
[ROW][C]55[/C][C]0.235588[/C][C]0.471175[/C][C]0.764412[/C][/ROW]
[ROW][C]56[/C][C]0.19765[/C][C]0.395301[/C][C]0.80235[/C][/ROW]
[ROW][C]57[/C][C]0.202489[/C][C]0.404978[/C][C]0.797511[/C][/ROW]
[ROW][C]58[/C][C]0.252431[/C][C]0.504863[/C][C]0.747569[/C][/ROW]
[ROW][C]59[/C][C]0.218943[/C][C]0.437886[/C][C]0.781057[/C][/ROW]
[ROW][C]60[/C][C]0.192309[/C][C]0.384618[/C][C]0.807691[/C][/ROW]
[ROW][C]61[/C][C]0.161561[/C][C]0.323122[/C][C]0.838439[/C][/ROW]
[ROW][C]62[/C][C]0.175854[/C][C]0.351707[/C][C]0.824146[/C][/ROW]
[ROW][C]63[/C][C]0.210316[/C][C]0.420632[/C][C]0.789684[/C][/ROW]
[ROW][C]64[/C][C]0.189105[/C][C]0.37821[/C][C]0.810895[/C][/ROW]
[ROW][C]65[/C][C]0.226856[/C][C]0.453711[/C][C]0.773144[/C][/ROW]
[ROW][C]66[/C][C]0.212665[/C][C]0.42533[/C][C]0.787335[/C][/ROW]
[ROW][C]67[/C][C]0.249921[/C][C]0.499841[/C][C]0.750079[/C][/ROW]
[ROW][C]68[/C][C]0.220986[/C][C]0.441972[/C][C]0.779014[/C][/ROW]
[ROW][C]69[/C][C]0.250246[/C][C]0.500492[/C][C]0.749754[/C][/ROW]
[ROW][C]70[/C][C]0.283498[/C][C]0.566997[/C][C]0.716502[/C][/ROW]
[ROW][C]71[/C][C]0.317675[/C][C]0.635351[/C][C]0.682325[/C][/ROW]
[ROW][C]72[/C][C]0.315492[/C][C]0.630984[/C][C]0.684508[/C][/ROW]
[ROW][C]73[/C][C]0.359406[/C][C]0.718812[/C][C]0.640594[/C][/ROW]
[ROW][C]74[/C][C]0.329114[/C][C]0.658227[/C][C]0.670886[/C][/ROW]
[ROW][C]75[/C][C]0.322731[/C][C]0.645461[/C][C]0.677269[/C][/ROW]
[ROW][C]76[/C][C]0.299865[/C][C]0.599731[/C][C]0.700135[/C][/ROW]
[ROW][C]77[/C][C]0.331865[/C][C]0.663731[/C][C]0.668135[/C][/ROW]
[ROW][C]78[/C][C]0.303261[/C][C]0.606521[/C][C]0.696739[/C][/ROW]
[ROW][C]79[/C][C]0.340365[/C][C]0.68073[/C][C]0.659635[/C][/ROW]
[ROW][C]80[/C][C]0.32374[/C][C]0.64748[/C][C]0.67626[/C][/ROW]
[ROW][C]81[/C][C]0.28735[/C][C]0.5747[/C][C]0.71265[/C][/ROW]
[ROW][C]82[/C][C]0.341785[/C][C]0.683569[/C][C]0.658215[/C][/ROW]
[ROW][C]83[/C][C]0.330386[/C][C]0.660772[/C][C]0.669614[/C][/ROW]
[ROW][C]84[/C][C]0.360943[/C][C]0.721886[/C][C]0.639057[/C][/ROW]
[ROW][C]85[/C][C]0.339221[/C][C]0.678442[/C][C]0.660779[/C][/ROW]
[ROW][C]86[/C][C]0.279429[/C][C]0.558858[/C][C]0.720571[/C][/ROW]
[ROW][C]87[/C][C]0.281613[/C][C]0.563225[/C][C]0.718387[/C][/ROW]
[ROW][C]88[/C][C]0.231346[/C][C]0.462692[/C][C]0.768654[/C][/ROW]
[ROW][C]89[/C][C]0.222642[/C][C]0.445285[/C][C]0.777358[/C][/ROW]
[ROW][C]90[/C][C]0.274655[/C][C]0.549309[/C][C]0.725345[/C][/ROW]
[ROW][C]91[/C][C]0.260439[/C][C]0.520879[/C][C]0.739561[/C][/ROW]
[ROW][C]92[/C][C]0.255862[/C][C]0.511724[/C][C]0.744138[/C][/ROW]
[ROW][C]93[/C][C]0.209882[/C][C]0.419765[/C][C]0.790118[/C][/ROW]
[ROW][C]94[/C][C]0.191429[/C][C]0.382858[/C][C]0.808571[/C][/ROW]
[ROW][C]95[/C][C]0.303062[/C][C]0.606123[/C][C]0.696938[/C][/ROW]
[ROW][C]96[/C][C]0.264291[/C][C]0.528583[/C][C]0.735709[/C][/ROW]
[ROW][C]97[/C][C]0.328138[/C][C]0.656276[/C][C]0.671862[/C][/ROW]
[ROW][C]98[/C][C]0.248023[/C][C]0.496047[/C][C]0.751977[/C][/ROW]
[ROW][C]99[/C][C]0.188976[/C][C]0.377951[/C][C]0.811024[/C][/ROW]
[ROW][C]100[/C][C]0.141754[/C][C]0.283509[/C][C]0.858246[/C][/ROW]
[ROW][C]101[/C][C]0.119132[/C][C]0.238264[/C][C]0.880868[/C][/ROW]
[ROW][C]102[/C][C]0.21627[/C][C]0.432539[/C][C]0.78373[/C][/ROW]
[ROW][C]103[/C][C]0.15879[/C][C]0.317579[/C][C]0.84121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263948&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263948&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6290640.7418730.370936
110.6235410.7529180.376459
120.6000070.7999850.399993
130.5210440.9579130.478956
140.5218720.9562550.478128
150.5671020.8657960.432898
160.5139170.9721650.486083
170.4662180.9324360.533782
180.4977370.9954740.502263
190.4464070.8928130.553593
200.410020.8200410.58998
210.6380830.7238350.361917
220.6562930.6874140.343707
230.6214350.757130.378565
240.6275490.7449030.372451
250.5638570.8722860.436143
260.6516240.6967520.348376
270.5879530.8240950.412047
280.5816120.8367770.418388
290.6350790.7298430.364921
300.6304970.7390060.369503
310.596580.8068410.40342
320.6862740.6274510.313726
330.6491930.7016140.350807
340.6332660.7334670.366734
350.644290.711420.35571
360.5961610.8076770.403839
370.5553590.8892810.444641
380.507570.984860.49243
390.4639120.9278240.536088
400.5255250.9489490.474475
410.4903010.9806020.509699
420.4437330.8874670.556267
430.4037120.8074240.596288
440.3779870.7559740.622013
450.3957010.7914030.604299
460.5015030.9969930.498497
470.500930.9981390.49907
480.4542140.9084280.545786
490.4154740.8309470.584526
500.4049980.8099960.595002
510.355660.7113190.64434
520.331980.663960.66802
530.294960.589920.70504
540.2644940.5289890.735506
550.2355880.4711750.764412
560.197650.3953010.80235
570.2024890.4049780.797511
580.2524310.5048630.747569
590.2189430.4378860.781057
600.1923090.3846180.807691
610.1615610.3231220.838439
620.1758540.3517070.824146
630.2103160.4206320.789684
640.1891050.378210.810895
650.2268560.4537110.773144
660.2126650.425330.787335
670.2499210.4998410.750079
680.2209860.4419720.779014
690.2502460.5004920.749754
700.2834980.5669970.716502
710.3176750.6353510.682325
720.3154920.6309840.684508
730.3594060.7188120.640594
740.3291140.6582270.670886
750.3227310.6454610.677269
760.2998650.5997310.700135
770.3318650.6637310.668135
780.3032610.6065210.696739
790.3403650.680730.659635
800.323740.647480.67626
810.287350.57470.71265
820.3417850.6835690.658215
830.3303860.6607720.669614
840.3609430.7218860.639057
850.3392210.6784420.660779
860.2794290.5588580.720571
870.2816130.5632250.718387
880.2313460.4626920.768654
890.2226420.4452850.777358
900.2746550.5493090.725345
910.2604390.5208790.739561
920.2558620.5117240.744138
930.2098820.4197650.790118
940.1914290.3828580.808571
950.3030620.6061230.696938
960.2642910.5285830.735709
970.3281380.6562760.671862
980.2480230.4960470.751977
990.1889760.3779510.811024
1000.1417540.2835090.858246
1010.1191320.2382640.880868
1020.216270.4325390.78373
1030.158790.3175790.84121






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=263948&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=263948&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263948&T=6

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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)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}