FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSun, 21 Nov 2010 12:41:20 +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/2010/Nov/21/t1290343303xer0ydw8eku6b93.htm/, Retrieved Thu, 02 May 2024 03:46:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98342, Retrieved Thu, 02 May 2024 03:46:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact211

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [Poging 1] [2010-11-20 16:08:07] [26379b86c25fbf0febe6a7a428e65173]
-   P       [Multiple Regression] [Multiple regressi...] [2010-11-21 12:41:20] [bff44ea937c3f909b1dc9a8bfab919e2] [Current]
-             [Multiple Regression] [Multiple regressi...] [2010-11-21 12:49:51] [26379b86c25fbf0febe6a7a428e65173]
-   PD          [Multiple Regression] [Meervoudige regre...] [2010-11-29 20:14:20] [26379b86c25fbf0febe6a7a428e65173]
-   P             [Multiple Regression] [Meervoudige regre...] [2010-12-10 18:05:35] [26379b86c25fbf0febe6a7a428e65173]
-    D            [Multiple Regression] [Meervoudige regre...] [2010-12-11 18:22:44] [26379b86c25fbf0febe6a7a428e65173]
-    D              [Multiple Regression] [Meervoudige regre...] [2010-12-11 18:27:12] [26379b86c25fbf0febe6a7a428e65173]
-    D              [Multiple Regression] [MR (SWS=te verkla...] [2010-12-14 16:32:07] [2c7c841db524046f0462b1835d20d1ce]
-   PD                [Multiple Regression] [SWS (te verklaren...] [2010-12-14 16:43:37] [26379b86c25fbf0febe6a7a428e65173]
-    D                  [Multiple Regression] [MR: PS (te verkla...] [2010-12-14 17:03:27] [26379b86c25fbf0febe6a7a428e65173]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	4	4	4	4	4	4	4
4	3	4	4	5	4	4	4
5	5	5	4	5	4	4	4
3	4	4	3	3	2	3	4
2	2	4	3	4	2	3	4
5	4	5	4	5	4	3	4
4	3	4	4	4	3	3	4
2	2	4	2	4	2	4	4
4	4	5	3	4	2	4	4
4	2	2	3	4	4	3	2
4	4	4	4	4	3	4	4
2	2	3	2	4	3	4	3
5	5	5	4	5	4	5	5
3	3	4	4	4	3	3	4
4	4	4	4	4	4	4	3
4	4	5	4	4	4	4	4
3	3	3	3	3	3	3	4
4	4	4	4	5	3	4	4
2	2	4	2	3	2	2	3
4	3	4	4	4	2	4	4
3	4	4	3	4	3	4	4
3	2	3	4	4	4	4	4
4	4	4	4	4	4	4	4
4	4	4	4	4	4	4	4
4	4	4	4	4	4	4	4
4	3	4	3	5	3	4	4
5	4	4	4	5	4	4	4
	2	4	4	3	4	3	4
4	4	4	4	4	4	4	4
4	4	4	3	4	4	4	3
4	4	4	4	5	2	4	4
4	2	4	4	4	3	3	4
4	4	4	3	4	2	4	4
4	4	5	4	4	4	4	4
4	4	4	4	4	4	4	4
4	4	4	4	4	3	4	4
5	3	5	4	3	3	3	4
4	4	4	4	4	4	4	4
4	3	4	4	4	4	3	4
4	3	4	4	4	3	4	2
3	3	3	3	4	4	4	4
4	3	4	4	4	4	4	4
2	2	3	2	4	2	3	2
2	2	5	2	4	2	2	3
4	4	5	4	4	2	4	4
4	2	4	4	4	4	4	2
5	4	5	5	4	4	4	5
4	4	4	4	4	3	4	4
4	3	4	3	5	3	4	4
4	4	4	4	4	4	4	4
5	4	4	4	5	4	4	4
3	3	4	3	4	4	3	4
2	1	4	4	4	4	2	4
4	4	4	4	4	3	4	4
4	4	4	3	3	4	4	3
2	2	4	3	4	4	4	4
2	2	4	2	3	3	2	3
4	4	4	4	4	3	4	4
4	4	4	4	4	2	4	4
4	3	4	4	3	2	4	4
3	3	4	4	4	3	4	4
2	1	4	3	3	3	3	4
4	4	4	4	4	4	4	4
5	2	4	4	5	2	4	4
4	4	4	4	5	3	4	3
5	5	5	5	5	4	5	5
4	3	4	4	4	2	4	4
3	3	4	3	3	3	3	4
3	2	3	2	4	3	3	4
4	4	4	4	5	4	4	2
3	2	4	2	4	3	3	4
2	2	2	2	4	2	3	2
4	2	4	2	4	3	2	2
3	4	5	4	5	4	4	4
5	5	4	4	4	3	4	4
5	4	5	5	5	4	4	5
4	2	3	2	4	4	2	4
5	5	4	5	4	5	4	5
4	2	4	4	5	3	4	4
3	3	3	3	3	2	3	4
3	4	4	3	3	3	3	4
4	3	4	4	4	4	3	4
4	2	4	4	5	4	4	4
3	3	4	3	4	2	3	4
4	4	4	4	4	3	3	3
3	1	4	2	4	2	2	4
3	2	5	4	4	3	4	4
3	2	4	3	4	2	4	5
2	3	4	2	4	3	2	4
5	3	4	4	4	4	4	4
3	2	4	4	4	3	3	3
4	2	5	4	4	3	2	4
4	4	4	3	4	3	4	4
3	3	4	3	4	3	3	3
3	3	3	2	3	3	3	3
4	4	4	3	3	4	3	3
4	4	4	3	4	4	5	4
3	2	4	2	3	3	4	4
3	2	5	4	4	3	2	5
4	4	5	4	4	4	4	4
2	3	4	3	4	3	3	4
4	2	4	2	4	2	2	4
4	4	5	4	4	3	4	4
4	3	5	4	5	4	4	4
5	3	4	5	5	5	3	4
5	4	5	5	4	4	4	5
5	3	4	4	4	3	4	4
4	4	4	3	4	4	4	4
4	3	4	4	4	3	4	4
3	3	4	4	4	3	4	4
2	3	2	3	4	3	4	3
2	2	4	2	4	2	2	4
4	2	5	5	5	4	4	4
2	1	4	3	4	3	3	4
3	2	4	4	4	3	3	4
4	3	4	3	3	3	4	3
4	3	4	3	4	3	3	4
4	3	4	5	4	4	4	4
4	3	3	4	4	4	3	2
3	2	4	3	4	4	3	4
4	4	4	4	5	5	4	3
3	2	3	4	4	3	3	4
4	3	4	4	4	3	4	3
4	3	5	4	4	2	3	4
4	4	5	4	4	4	4	4
2	3	3	4	4	3	2	4
5	3	5	4	4	3	3	4
5	3	4	4	4	3	4	4
3	4	4	2	3	2	3	4
3	4	3	5	4	4	4	4
3	3	3	4	4	4	3	3
4	4	4	4	4	4	4	3
3	5	5	4	5	5	1	4
2	2	2	1	4	2	2	4
5	4	4	4	4	3	4	4
4	4	4	4	4	4	4	4
2	3	4	4	3	4	3	4
4	3	5	4	4	4	4	4
3	4	4	2	4	4	4	2
2	3	4	4	4	4	2	4
3	2	4	4	4	4	4	4
3	4	4	3	4	3	4	3
4	4	4	4	4	4	4	4
4	4	4	4	4	3	4	4
4	3	5	4	4	4	4	4
2	4	3	3	3	2	2	4
5	4	4	4	4	4	4	4
4	4	4	2	5	4	3	3
4	4	4	4	4	4	4	4
3	3	4	4	4	3	3	4
3	3	4	4	4	3	4	2
5	5	5	5	5	5	5	5

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98342&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98342&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98342&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'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Preferleader[t] = + 1.02830814409858 + 0.0842475032129696Leadership[t] + 0.207213675242005Jobcontrol[t] + 0.386522109686850Influenceothers[t] -0.134026305664132Destinycontrol[t] + 0.00410101084894554Handlingsituations[t] + 0.279829847242845Organization[t] -0.068409636934505Ability[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Preferleader[t] =  +  1.02830814409858 +  0.0842475032129696Leadership[t] +  0.207213675242005Jobcontrol[t] +  0.386522109686850Influenceothers[t] -0.134026305664132Destinycontrol[t] +  0.00410101084894554Handlingsituations[t] +  0.279829847242845Organization[t] -0.068409636934505Ability[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98342&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Preferleader[t] =  +  1.02830814409858 +  0.0842475032129696Leadership[t] +  0.207213675242005Jobcontrol[t] +  0.386522109686850Influenceothers[t] -0.134026305664132Destinycontrol[t] +  0.00410101084894554Handlingsituations[t] +  0.279829847242845Organization[t] -0.068409636934505Ability[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98342&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98342&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
Preferleader[t] = + 1.02830814409858 + 0.0842475032129696Leadership[t] + 0.207213675242005Jobcontrol[t] + 0.386522109686850Influenceothers[t] -0.134026305664132Destinycontrol[t] + 0.00410101084894554Handlingsituations[t] + 0.279829847242845Organization[t] -0.068409636934505Ability[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.028308144098580.4785982.14860.033340.01667
Leadership0.08424750321296960.0649631.29680.1967580.098379
Jobcontrol0.2072136752420050.074492.78180.0061310.003065
Influenceothers0.3865221096868500.0904064.27543.5e-051.7e-05
Destinycontrol-0.1340263056641320.071517-1.87410.0629490.031474
Handlingsituations0.004101010848945540.0788350.0520.9585850.479292
Organization0.2798298472428450.0794933.52020.0005780.000289
Ability-0.0684096369345050.062201-1.09980.2732510.136626

\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) & 1.02830814409858 & 0.478598 & 2.1486 & 0.03334 & 0.01667 \tabularnewline
Leadership & 0.0842475032129696 & 0.064963 & 1.2968 & 0.196758 & 0.098379 \tabularnewline
Jobcontrol & 0.207213675242005 & 0.07449 & 2.7818 & 0.006131 & 0.003065 \tabularnewline
Influenceothers & 0.386522109686850 & 0.090406 & 4.2754 & 3.5e-05 & 1.7e-05 \tabularnewline
Destinycontrol & -0.134026305664132 & 0.071517 & -1.8741 & 0.062949 & 0.031474 \tabularnewline
Handlingsituations & 0.00410101084894554 & 0.078835 & 0.052 & 0.958585 & 0.479292 \tabularnewline
Organization & 0.279829847242845 & 0.079493 & 3.5202 & 0.000578 & 0.000289 \tabularnewline
Ability & -0.068409636934505 & 0.062201 & -1.0998 & 0.273251 & 0.136626 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98342&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]1.02830814409858[/C][C]0.478598[/C][C]2.1486[/C][C]0.03334[/C][C]0.01667[/C][/ROW]
[ROW][C]Leadership[/C][C]0.0842475032129696[/C][C]0.064963[/C][C]1.2968[/C][C]0.196758[/C][C]0.098379[/C][/ROW]
[ROW][C]Jobcontrol[/C][C]0.207213675242005[/C][C]0.07449[/C][C]2.7818[/C][C]0.006131[/C][C]0.003065[/C][/ROW]
[ROW][C]Influenceothers[/C][C]0.386522109686850[/C][C]0.090406[/C][C]4.2754[/C][C]3.5e-05[/C][C]1.7e-05[/C][/ROW]
[ROW][C]Destinycontrol[/C][C]-0.134026305664132[/C][C]0.071517[/C][C]-1.8741[/C][C]0.062949[/C][C]0.031474[/C][/ROW]
[ROW][C]Handlingsituations[/C][C]0.00410101084894554[/C][C]0.078835[/C][C]0.052[/C][C]0.958585[/C][C]0.479292[/C][/ROW]
[ROW][C]Organization[/C][C]0.279829847242845[/C][C]0.079493[/C][C]3.5202[/C][C]0.000578[/C][C]0.000289[/C][/ROW]
[ROW][C]Ability[/C][C]-0.068409636934505[/C][C]0.062201[/C][C]-1.0998[/C][C]0.273251[/C][C]0.136626[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98342&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98342&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)1.028308144098580.4785982.14860.033340.01667
Leadership0.08424750321296960.0649631.29680.1967580.098379
Jobcontrol0.2072136752420050.074492.78180.0061310.003065
Influenceothers0.3865221096868500.0904064.27543.5e-051.7e-05
Destinycontrol-0.1340263056641320.071517-1.87410.0629490.031474
Handlingsituations0.004101010848945540.0788350.0520.9585850.479292
Organization0.2798298472428450.0794933.52020.0005780.000289
Ability-0.0684096369345050.062201-1.09980.2732510.136626






Multiple Linear Regression - Regression Statistics
Multiple R0.571118664491289
R-squared0.326176528930313
Adjusted R-squared0.29342122130887
F-TEST (value)9.95797483265836
F-TEST (DF numerator)7
F-TEST (DF denominator)144
p-value4.14612011390147e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.604931442781952
Sum Squared Residuals52.6956552671406

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.571118664491289 \tabularnewline
R-squared & 0.326176528930313 \tabularnewline
Adjusted R-squared & 0.29342122130887 \tabularnewline
F-TEST (value) & 9.95797483265836 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 144 \tabularnewline
p-value & 4.14612011390147e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.604931442781952 \tabularnewline
Sum Squared Residuals & 52.6956552671406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98342&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.571118664491289[/C][/ROW]
[ROW][C]R-squared[/C][C]0.326176528930313[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.29342122130887[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.95797483265836[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]144[/C][/ROW]
[ROW][C]p-value[/C][C]4.14612011390147e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.604931442781952[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]52.6956552671406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98342&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98342&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 R0.571118664491289
R-squared0.326176528930313
Adjusted R-squared0.29342122130887
F-TEST (value)9.95797483265836
F-TEST (DF numerator)7
F-TEST (DF denominator)144
p-value4.14612011390147e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.604931442781952
Sum Squared Residuals52.6956552671406






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
144.06622095863846-0.0662209586384641
233.93219465297436-0.932194652974364
354.223655831429340.776344168570663
443.441445782462070.55855421753793
523.22317197358497-1.22317197358497
643.943825984186490.0561740158135078
733.7822901005467-0.782290100546702
823.11647971114096-1.11647971114096
943.878710502495760.121289497504241
1023.1222609250938-1.12226092509380
1144.06211994778955-0.0621199477895489
1222.98177668368241-0.98177668368241
1354.435076041737680.564923958262323
1433.69804259733373-0.698042597333733
1544.134630595573-0.134630595573000
1644.2734346338805-0.273434633880500
1733.23833311806901-0.238333118069011
1843.928093642125420.0719063578745837
1922.75925595925391-0.75925595925391
2034.0580189369406-1.05801893694060
2143.591350334889730.408649665110271
2223.77475978018352-1.77475978018352
2344.06622095863849-0.0662209586384942
2444.06622095863849-0.0662209586384942
2544.06622095863849-0.0662209586384942
2633.54157153243857-0.541571532438566
2744.01644215618733-0.0164421561873315
2843.507102831676760.492897168323241
2944.06622095863849-0.0662209586384942
3043.579177436153640.420822563846357
3144.72079567965361-0.72079567965361
3244.02765124702774-0.0276512470277418
3344.12705989472475-0.127059894724754
3454.066220958638490.933779041361506
3544.06622095863849-0.0662209586384942
3644.13183762736812-0.131837627368122
3753.725376640553861.27462335944614
3844.06622095863849-0.0662209586384942
3943.977872444576580.022127555423421
4043.624749703538470.375250296461529
4133.77475978018352-0.774759780183519
4244.11879272929453-0.118792729294535
4333.32440978159118-0.324409781591183
4453.463319344116071.53668065588393
4554.334273569966760.66572643003324
4643.269656620792360.730343379207642
4754.553264481123350.446735518876655
4844.20024726430263-0.200247264302627
4944.2953081955345-0.295308195534501
5043.997811321703990.00218867829601101
5144.52115270525985-0.52115270525985
5243.907478043203580.092521956796416
5343.805276427301690.194723572698306
5444.20024726430263-0.200247264302627
5543.329474600335800.670525399664196
5643.827331550839560.172668449160441
5742.942770928765091.05722907123491
5844.20024726430263-0.200247264302627
5944.33427356996676-0.33427356996676
6043.931913594001450.0680864059985549
6144.25281903495867-0.252819034958668
6243.349667958885920.650332041114084
6343.997811321703990.00218867829601101
6444.55230067322767-0.55230067322767
6544.23852988981213-0.238529889812126
6655.02813510487211-0.0281351048721104
6744.31843570368830-0.318435703688295
6843.586572602246360.413427397753639
6933.61322389654373-0.61322389654373
7043.961493010774160.0385069892258418
7143.750043170412740.249956829587259
7223.18759050772217-1.18759050772217
7343.11787282814360.8821271718564
7454.384333431390840.615666568609161
7544.21608513058109-0.216085130581092
7654.93978659081020.0602134091898048
7733.40668694309615-0.406686943096148
7844.50348567867218-0.503485678672182
7944.48668400449804-0.486684004498042
8033.72059890791049-0.720598907910494
8143.602410468524830.397589531475174
8243.977872444576580.022127555423421
8344.35265769883391-0.352657698833910
8444.03871138066284-0.0387113806628388
8543.984726043145340.0152739568546587
8643.727311325080450.272688674919547
8754.100161894811190.899838105188808
8844.37521400941067-0.37521400941067
8943.624960751973250.375039248026750
9044.05038309236003-0.0503830923600296
9143.74782139978490.252178600215104
9254.02355023617880.976449763821204
9344.06144322599513-0.0614432259951267
9443.693264864690370.306735135309634
9533.03111944282701-0.0311194428270053
9643.188554315617850.811445684382152
9743.931517931179940.0684820688200606
9843.299212434640330.700787565359668
9954.303380083421640.696619916578358
10054.203040232507500.796959767492496
10143.904685074998710.0953149250012938
10243.743149191358920.256850808641082
10354.200247264302630.799752735697373
10454.300085928177870.699914071822131
10544.3691722869068-0.369172286906796
10654.484854844188840.51514515581116
10744.11599976108966-0.115999761089657
10843.859007283396490.140992716603511
10944.18440939802416-0.184409398024162
11044.25281903495867-0.252819034958668
11123.76577551247382-1.76577551247382
11243.743149191358920.256850808641082
11354.628281011010420.37171898898958
11443.804599705507270.195400294492728
11544.02765124702774-0.0276512470277418
11643.242434128917960.757565871082044
11743.904685074998710.0953149250012938
11844.18918713066753-0.189187130667530
11933.48662238702539-0.486622387025393
12043.68641126612160.313588733878396
12144.10729655235287-0.107296552352871
12234.02765124702774-1.02765124702774
12343.836169913846810.163830086153189
12454.245925055904840.754074944095156
12554.203040232507500.796959767492496
12634.03938810245726-1.03938810245726
12754.043489113306210.956510886693793
12844.18440939802416-0.184409398024162
12943.597632735881460.402367264118541
13034.341844270815-1.34184427081501
13133.69804259733373-0.698042597333733
13243.854800748330150.145199251669846
13354.527480507196360.472519492803644
13423.46752587918241-1.46752587918241
13544.20024726430263-0.200247264302627
13644.20304023250750-0.203040232507505
13743.591350334889730.408649665110271
13854.050383092360030.94961690763997
13943.228953187537800.771046812462197
14044.04218107066214-0.0421810706621389
14143.966135589147060.0338644108529403
14243.713203741817780.286796258182224
14344.06622095863849-0.0662209586384942
14444.20024726430263-0.200247264302627
14554.118792729294530.881207270705465
14633.66392612640551-0.663926126405508
14744.06622095863849-0.0662209586384942
14843.754384859749540.245615140250458
14944.134630595573-0.134630595573000
15044.18030838717522-0.180308387175217
15143.487930429669460.512069570330539
15254.894108799207980.105891200792022

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 4.06622095863846 & -0.0662209586384641 \tabularnewline
2 & 3 & 3.93219465297436 & -0.932194652974364 \tabularnewline
3 & 5 & 4.22365583142934 & 0.776344168570663 \tabularnewline
4 & 4 & 3.44144578246207 & 0.55855421753793 \tabularnewline
5 & 2 & 3.22317197358497 & -1.22317197358497 \tabularnewline
6 & 4 & 3.94382598418649 & 0.0561740158135078 \tabularnewline
7 & 3 & 3.7822901005467 & -0.782290100546702 \tabularnewline
8 & 2 & 3.11647971114096 & -1.11647971114096 \tabularnewline
9 & 4 & 3.87871050249576 & 0.121289497504241 \tabularnewline
10 & 2 & 3.1222609250938 & -1.12226092509380 \tabularnewline
11 & 4 & 4.06211994778955 & -0.0621199477895489 \tabularnewline
12 & 2 & 2.98177668368241 & -0.98177668368241 \tabularnewline
13 & 5 & 4.43507604173768 & 0.564923958262323 \tabularnewline
14 & 3 & 3.69804259733373 & -0.698042597333733 \tabularnewline
15 & 4 & 4.134630595573 & -0.134630595573000 \tabularnewline
16 & 4 & 4.2734346338805 & -0.273434633880500 \tabularnewline
17 & 3 & 3.23833311806901 & -0.238333118069011 \tabularnewline
18 & 4 & 3.92809364212542 & 0.0719063578745837 \tabularnewline
19 & 2 & 2.75925595925391 & -0.75925595925391 \tabularnewline
20 & 3 & 4.0580189369406 & -1.05801893694060 \tabularnewline
21 & 4 & 3.59135033488973 & 0.408649665110271 \tabularnewline
22 & 2 & 3.77475978018352 & -1.77475978018352 \tabularnewline
23 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
24 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
25 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
26 & 3 & 3.54157153243857 & -0.541571532438566 \tabularnewline
27 & 4 & 4.01644215618733 & -0.0164421561873315 \tabularnewline
28 & 4 & 3.50710283167676 & 0.492897168323241 \tabularnewline
29 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
30 & 4 & 3.57917743615364 & 0.420822563846357 \tabularnewline
31 & 4 & 4.72079567965361 & -0.72079567965361 \tabularnewline
32 & 4 & 4.02765124702774 & -0.0276512470277418 \tabularnewline
33 & 4 & 4.12705989472475 & -0.127059894724754 \tabularnewline
34 & 5 & 4.06622095863849 & 0.933779041361506 \tabularnewline
35 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
36 & 4 & 4.13183762736812 & -0.131837627368122 \tabularnewline
37 & 5 & 3.72537664055386 & 1.27462335944614 \tabularnewline
38 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
39 & 4 & 3.97787244457658 & 0.022127555423421 \tabularnewline
40 & 4 & 3.62474970353847 & 0.375250296461529 \tabularnewline
41 & 3 & 3.77475978018352 & -0.774759780183519 \tabularnewline
42 & 4 & 4.11879272929453 & -0.118792729294535 \tabularnewline
43 & 3 & 3.32440978159118 & -0.324409781591183 \tabularnewline
44 & 5 & 3.46331934411607 & 1.53668065588393 \tabularnewline
45 & 5 & 4.33427356996676 & 0.66572643003324 \tabularnewline
46 & 4 & 3.26965662079236 & 0.730343379207642 \tabularnewline
47 & 5 & 4.55326448112335 & 0.446735518876655 \tabularnewline
48 & 4 & 4.20024726430263 & -0.200247264302627 \tabularnewline
49 & 4 & 4.2953081955345 & -0.295308195534501 \tabularnewline
50 & 4 & 3.99781132170399 & 0.00218867829601101 \tabularnewline
51 & 4 & 4.52115270525985 & -0.52115270525985 \tabularnewline
52 & 4 & 3.90747804320358 & 0.092521956796416 \tabularnewline
53 & 4 & 3.80527642730169 & 0.194723572698306 \tabularnewline
54 & 4 & 4.20024726430263 & -0.200247264302627 \tabularnewline
55 & 4 & 3.32947460033580 & 0.670525399664196 \tabularnewline
56 & 4 & 3.82733155083956 & 0.172668449160441 \tabularnewline
57 & 4 & 2.94277092876509 & 1.05722907123491 \tabularnewline
58 & 4 & 4.20024726430263 & -0.200247264302627 \tabularnewline
59 & 4 & 4.33427356996676 & -0.33427356996676 \tabularnewline
60 & 4 & 3.93191359400145 & 0.0680864059985549 \tabularnewline
61 & 4 & 4.25281903495867 & -0.252819034958668 \tabularnewline
62 & 4 & 3.34966795888592 & 0.650332041114084 \tabularnewline
63 & 4 & 3.99781132170399 & 0.00218867829601101 \tabularnewline
64 & 4 & 4.55230067322767 & -0.55230067322767 \tabularnewline
65 & 4 & 4.23852988981213 & -0.238529889812126 \tabularnewline
66 & 5 & 5.02813510487211 & -0.0281351048721104 \tabularnewline
67 & 4 & 4.31843570368830 & -0.318435703688295 \tabularnewline
68 & 4 & 3.58657260224636 & 0.413427397753639 \tabularnewline
69 & 3 & 3.61322389654373 & -0.61322389654373 \tabularnewline
70 & 4 & 3.96149301077416 & 0.0385069892258418 \tabularnewline
71 & 4 & 3.75004317041274 & 0.249956829587259 \tabularnewline
72 & 2 & 3.18759050772217 & -1.18759050772217 \tabularnewline
73 & 4 & 3.1178728281436 & 0.8821271718564 \tabularnewline
74 & 5 & 4.38433343139084 & 0.615666568609161 \tabularnewline
75 & 4 & 4.21608513058109 & -0.216085130581092 \tabularnewline
76 & 5 & 4.9397865908102 & 0.0602134091898048 \tabularnewline
77 & 3 & 3.40668694309615 & -0.406686943096148 \tabularnewline
78 & 4 & 4.50348567867218 & -0.503485678672182 \tabularnewline
79 & 4 & 4.48668400449804 & -0.486684004498042 \tabularnewline
80 & 3 & 3.72059890791049 & -0.720598907910494 \tabularnewline
81 & 4 & 3.60241046852483 & 0.397589531475174 \tabularnewline
82 & 4 & 3.97787244457658 & 0.022127555423421 \tabularnewline
83 & 4 & 4.35265769883391 & -0.352657698833910 \tabularnewline
84 & 4 & 4.03871138066284 & -0.0387113806628388 \tabularnewline
85 & 4 & 3.98472604314534 & 0.0152739568546587 \tabularnewline
86 & 4 & 3.72731132508045 & 0.272688674919547 \tabularnewline
87 & 5 & 4.10016189481119 & 0.899838105188808 \tabularnewline
88 & 4 & 4.37521400941067 & -0.37521400941067 \tabularnewline
89 & 4 & 3.62496075197325 & 0.375039248026750 \tabularnewline
90 & 4 & 4.05038309236003 & -0.0503830923600296 \tabularnewline
91 & 4 & 3.7478213997849 & 0.252178600215104 \tabularnewline
92 & 5 & 4.0235502361788 & 0.976449763821204 \tabularnewline
93 & 4 & 4.06144322599513 & -0.0614432259951267 \tabularnewline
94 & 4 & 3.69326486469037 & 0.306735135309634 \tabularnewline
95 & 3 & 3.03111944282701 & -0.0311194428270053 \tabularnewline
96 & 4 & 3.18855431561785 & 0.811445684382152 \tabularnewline
97 & 4 & 3.93151793117994 & 0.0684820688200606 \tabularnewline
98 & 4 & 3.29921243464033 & 0.700787565359668 \tabularnewline
99 & 5 & 4.30338008342164 & 0.696619916578358 \tabularnewline
100 & 5 & 4.20304023250750 & 0.796959767492496 \tabularnewline
101 & 4 & 3.90468507499871 & 0.0953149250012938 \tabularnewline
102 & 4 & 3.74314919135892 & 0.256850808641082 \tabularnewline
103 & 5 & 4.20024726430263 & 0.799752735697373 \tabularnewline
104 & 5 & 4.30008592817787 & 0.699914071822131 \tabularnewline
105 & 4 & 4.3691722869068 & -0.369172286906796 \tabularnewline
106 & 5 & 4.48485484418884 & 0.51514515581116 \tabularnewline
107 & 4 & 4.11599976108966 & -0.115999761089657 \tabularnewline
108 & 4 & 3.85900728339649 & 0.140992716603511 \tabularnewline
109 & 4 & 4.18440939802416 & -0.184409398024162 \tabularnewline
110 & 4 & 4.25281903495867 & -0.252819034958668 \tabularnewline
111 & 2 & 3.76577551247382 & -1.76577551247382 \tabularnewline
112 & 4 & 3.74314919135892 & 0.256850808641082 \tabularnewline
113 & 5 & 4.62828101101042 & 0.37171898898958 \tabularnewline
114 & 4 & 3.80459970550727 & 0.195400294492728 \tabularnewline
115 & 4 & 4.02765124702774 & -0.0276512470277418 \tabularnewline
116 & 4 & 3.24243412891796 & 0.757565871082044 \tabularnewline
117 & 4 & 3.90468507499871 & 0.0953149250012938 \tabularnewline
118 & 4 & 4.18918713066753 & -0.189187130667530 \tabularnewline
119 & 3 & 3.48662238702539 & -0.486622387025393 \tabularnewline
120 & 4 & 3.6864112661216 & 0.313588733878396 \tabularnewline
121 & 4 & 4.10729655235287 & -0.107296552352871 \tabularnewline
122 & 3 & 4.02765124702774 & -1.02765124702774 \tabularnewline
123 & 4 & 3.83616991384681 & 0.163830086153189 \tabularnewline
124 & 5 & 4.24592505590484 & 0.754074944095156 \tabularnewline
125 & 5 & 4.20304023250750 & 0.796959767492496 \tabularnewline
126 & 3 & 4.03938810245726 & -1.03938810245726 \tabularnewline
127 & 5 & 4.04348911330621 & 0.956510886693793 \tabularnewline
128 & 4 & 4.18440939802416 & -0.184409398024162 \tabularnewline
129 & 4 & 3.59763273588146 & 0.402367264118541 \tabularnewline
130 & 3 & 4.341844270815 & -1.34184427081501 \tabularnewline
131 & 3 & 3.69804259733373 & -0.698042597333733 \tabularnewline
132 & 4 & 3.85480074833015 & 0.145199251669846 \tabularnewline
133 & 5 & 4.52748050719636 & 0.472519492803644 \tabularnewline
134 & 2 & 3.46752587918241 & -1.46752587918241 \tabularnewline
135 & 4 & 4.20024726430263 & -0.200247264302627 \tabularnewline
136 & 4 & 4.20304023250750 & -0.203040232507505 \tabularnewline
137 & 4 & 3.59135033488973 & 0.408649665110271 \tabularnewline
138 & 5 & 4.05038309236003 & 0.94961690763997 \tabularnewline
139 & 4 & 3.22895318753780 & 0.771046812462197 \tabularnewline
140 & 4 & 4.04218107066214 & -0.0421810706621389 \tabularnewline
141 & 4 & 3.96613558914706 & 0.0338644108529403 \tabularnewline
142 & 4 & 3.71320374181778 & 0.286796258182224 \tabularnewline
143 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
144 & 4 & 4.20024726430263 & -0.200247264302627 \tabularnewline
145 & 5 & 4.11879272929453 & 0.881207270705465 \tabularnewline
146 & 3 & 3.66392612640551 & -0.663926126405508 \tabularnewline
147 & 4 & 4.06622095863849 & -0.0662209586384942 \tabularnewline
148 & 4 & 3.75438485974954 & 0.245615140250458 \tabularnewline
149 & 4 & 4.134630595573 & -0.134630595573000 \tabularnewline
150 & 4 & 4.18030838717522 & -0.180308387175217 \tabularnewline
151 & 4 & 3.48793042966946 & 0.512069570330539 \tabularnewline
152 & 5 & 4.89410879920798 & 0.105891200792022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98342&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]4[/C][C]4.06622095863846[/C][C]-0.0662209586384641[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]3.93219465297436[/C][C]-0.932194652974364[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]4.22365583142934[/C][C]0.776344168570663[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.44144578246207[/C][C]0.55855421753793[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]3.22317197358497[/C][C]-1.22317197358497[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]3.94382598418649[/C][C]0.0561740158135078[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.7822901005467[/C][C]-0.782290100546702[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]3.11647971114096[/C][C]-1.11647971114096[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.87871050249576[/C][C]0.121289497504241[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]3.1222609250938[/C][C]-1.12226092509380[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]4.06211994778955[/C][C]-0.0621199477895489[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]2.98177668368241[/C][C]-0.98177668368241[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]4.43507604173768[/C][C]0.564923958262323[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]3.69804259733373[/C][C]-0.698042597333733[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]4.134630595573[/C][C]-0.134630595573000[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]4.2734346338805[/C][C]-0.273434633880500[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.23833311806901[/C][C]-0.238333118069011[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.92809364212542[/C][C]0.0719063578745837[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]2.75925595925391[/C][C]-0.75925595925391[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]4.0580189369406[/C][C]-1.05801893694060[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.59135033488973[/C][C]0.408649665110271[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]3.77475978018352[/C][C]-1.77475978018352[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]3.54157153243857[/C][C]-0.541571532438566[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]4.01644215618733[/C][C]-0.0164421561873315[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.50710283167676[/C][C]0.492897168323241[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.57917743615364[/C][C]0.420822563846357[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.72079567965361[/C][C]-0.72079567965361[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]4.02765124702774[/C][C]-0.0276512470277418[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]4.12705989472475[/C][C]-0.127059894724754[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]4.06622095863849[/C][C]0.933779041361506[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.13183762736812[/C][C]-0.131837627368122[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]3.72537664055386[/C][C]1.27462335944614[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.97787244457658[/C][C]0.022127555423421[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]3.62474970353847[/C][C]0.375250296461529[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]3.77475978018352[/C][C]-0.774759780183519[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.11879272929453[/C][C]-0.118792729294535[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.32440978159118[/C][C]-0.324409781591183[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]3.46331934411607[/C][C]1.53668065588393[/C][/ROW]
[ROW][C]45[/C][C]5[/C][C]4.33427356996676[/C][C]0.66572643003324[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]3.26965662079236[/C][C]0.730343379207642[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]4.55326448112335[/C][C]0.446735518876655[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]4.20024726430263[/C][C]-0.200247264302627[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]4.2953081955345[/C][C]-0.295308195534501[/C][/ROW]
[ROW][C]50[/C][C]4[/C][C]3.99781132170399[/C][C]0.00218867829601101[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]4.52115270525985[/C][C]-0.52115270525985[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.90747804320358[/C][C]0.092521956796416[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.80527642730169[/C][C]0.194723572698306[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]4.20024726430263[/C][C]-0.200247264302627[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.32947460033580[/C][C]0.670525399664196[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]3.82733155083956[/C][C]0.172668449160441[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]2.94277092876509[/C][C]1.05722907123491[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.20024726430263[/C][C]-0.200247264302627[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.33427356996676[/C][C]-0.33427356996676[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.93191359400145[/C][C]0.0680864059985549[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]4.25281903495867[/C][C]-0.252819034958668[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.34966795888592[/C][C]0.650332041114084[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.99781132170399[/C][C]0.00218867829601101[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]4.55230067322767[/C][C]-0.55230067322767[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]4.23852988981213[/C][C]-0.238529889812126[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.02813510487211[/C][C]-0.0281351048721104[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]4.31843570368830[/C][C]-0.318435703688295[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]3.58657260224636[/C][C]0.413427397753639[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]3.61322389654373[/C][C]-0.61322389654373[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]3.96149301077416[/C][C]0.0385069892258418[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]3.75004317041274[/C][C]0.249956829587259[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]3.18759050772217[/C][C]-1.18759050772217[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.1178728281436[/C][C]0.8821271718564[/C][/ROW]
[ROW][C]74[/C][C]5[/C][C]4.38433343139084[/C][C]0.615666568609161[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]4.21608513058109[/C][C]-0.216085130581092[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]4.9397865908102[/C][C]0.0602134091898048[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]3.40668694309615[/C][C]-0.406686943096148[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]4.50348567867218[/C][C]-0.503485678672182[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]4.48668400449804[/C][C]-0.486684004498042[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]3.72059890791049[/C][C]-0.720598907910494[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]3.60241046852483[/C][C]0.397589531475174[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]3.97787244457658[/C][C]0.022127555423421[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]4.35265769883391[/C][C]-0.352657698833910[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]4.03871138066284[/C][C]-0.0387113806628388[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]3.98472604314534[/C][C]0.0152739568546587[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]3.72731132508045[/C][C]0.272688674919547[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]4.10016189481119[/C][C]0.899838105188808[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.37521400941067[/C][C]-0.37521400941067[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]3.62496075197325[/C][C]0.375039248026750[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]4.05038309236003[/C][C]-0.0503830923600296[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]3.7478213997849[/C][C]0.252178600215104[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]4.0235502361788[/C][C]0.976449763821204[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]4.06144322599513[/C][C]-0.0614432259951267[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]3.69326486469037[/C][C]0.306735135309634[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]3.03111944282701[/C][C]-0.0311194428270053[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.18855431561785[/C][C]0.811445684382152[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]3.93151793117994[/C][C]0.0684820688200606[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]3.29921243464033[/C][C]0.700787565359668[/C][/ROW]
[ROW][C]99[/C][C]5[/C][C]4.30338008342164[/C][C]0.696619916578358[/C][/ROW]
[ROW][C]100[/C][C]5[/C][C]4.20304023250750[/C][C]0.796959767492496[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]3.90468507499871[/C][C]0.0953149250012938[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.74314919135892[/C][C]0.256850808641082[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]4.20024726430263[/C][C]0.799752735697373[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]4.30008592817787[/C][C]0.699914071822131[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]4.3691722869068[/C][C]-0.369172286906796[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]4.48485484418884[/C][C]0.51514515581116[/C][/ROW]
[ROW][C]107[/C][C]4[/C][C]4.11599976108966[/C][C]-0.115999761089657[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]3.85900728339649[/C][C]0.140992716603511[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]4.18440939802416[/C][C]-0.184409398024162[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]4.25281903495867[/C][C]-0.252819034958668[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]3.76577551247382[/C][C]-1.76577551247382[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.74314919135892[/C][C]0.256850808641082[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]4.62828101101042[/C][C]0.37171898898958[/C][/ROW]
[ROW][C]114[/C][C]4[/C][C]3.80459970550727[/C][C]0.195400294492728[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]4.02765124702774[/C][C]-0.0276512470277418[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]3.24243412891796[/C][C]0.757565871082044[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]3.90468507499871[/C][C]0.0953149250012938[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]4.18918713066753[/C][C]-0.189187130667530[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]3.48662238702539[/C][C]-0.486622387025393[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]3.6864112661216[/C][C]0.313588733878396[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]4.10729655235287[/C][C]-0.107296552352871[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]4.02765124702774[/C][C]-1.02765124702774[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]3.83616991384681[/C][C]0.163830086153189[/C][/ROW]
[ROW][C]124[/C][C]5[/C][C]4.24592505590484[/C][C]0.754074944095156[/C][/ROW]
[ROW][C]125[/C][C]5[/C][C]4.20304023250750[/C][C]0.796959767492496[/C][/ROW]
[ROW][C]126[/C][C]3[/C][C]4.03938810245726[/C][C]-1.03938810245726[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]4.04348911330621[/C][C]0.956510886693793[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]4.18440939802416[/C][C]-0.184409398024162[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]3.59763273588146[/C][C]0.402367264118541[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]4.341844270815[/C][C]-1.34184427081501[/C][/ROW]
[ROW][C]131[/C][C]3[/C][C]3.69804259733373[/C][C]-0.698042597333733[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]3.85480074833015[/C][C]0.145199251669846[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]4.52748050719636[/C][C]0.472519492803644[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]3.46752587918241[/C][C]-1.46752587918241[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]4.20024726430263[/C][C]-0.200247264302627[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]4.20304023250750[/C][C]-0.203040232507505[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]3.59135033488973[/C][C]0.408649665110271[/C][/ROW]
[ROW][C]138[/C][C]5[/C][C]4.05038309236003[/C][C]0.94961690763997[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]3.22895318753780[/C][C]0.771046812462197[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]4.04218107066214[/C][C]-0.0421810706621389[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]3.96613558914706[/C][C]0.0338644108529403[/C][/ROW]
[ROW][C]142[/C][C]4[/C][C]3.71320374181778[/C][C]0.286796258182224[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]4.20024726430263[/C][C]-0.200247264302627[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]4.11879272929453[/C][C]0.881207270705465[/C][/ROW]
[ROW][C]146[/C][C]3[/C][C]3.66392612640551[/C][C]-0.663926126405508[/C][/ROW]
[ROW][C]147[/C][C]4[/C][C]4.06622095863849[/C][C]-0.0662209586384942[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]3.75438485974954[/C][C]0.245615140250458[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]4.134630595573[/C][C]-0.134630595573000[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]4.18030838717522[/C][C]-0.180308387175217[/C][/ROW]
[ROW][C]151[/C][C]4[/C][C]3.48793042966946[/C][C]0.512069570330539[/C][/ROW]
[ROW][C]152[/C][C]5[/C][C]4.89410879920798[/C][C]0.105891200792022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98342&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98342&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
144.06622095863846-0.0662209586384641
233.93219465297436-0.932194652974364
354.223655831429340.776344168570663
443.441445782462070.55855421753793
523.22317197358497-1.22317197358497
643.943825984186490.0561740158135078
733.7822901005467-0.782290100546702
823.11647971114096-1.11647971114096
943.878710502495760.121289497504241
1023.1222609250938-1.12226092509380
1144.06211994778955-0.0621199477895489
1222.98177668368241-0.98177668368241
1354.435076041737680.564923958262323
1433.69804259733373-0.698042597333733
1544.134630595573-0.134630595573000
1644.2734346338805-0.273434633880500
1733.23833311806901-0.238333118069011
1843.928093642125420.0719063578745837
1922.75925595925391-0.75925595925391
2034.0580189369406-1.05801893694060
2143.591350334889730.408649665110271
2223.77475978018352-1.77475978018352
2344.06622095863849-0.0662209586384942
2444.06622095863849-0.0662209586384942
2544.06622095863849-0.0662209586384942
2633.54157153243857-0.541571532438566
2744.01644215618733-0.0164421561873315
2843.507102831676760.492897168323241
2944.06622095863849-0.0662209586384942
3043.579177436153640.420822563846357
3144.72079567965361-0.72079567965361
3244.02765124702774-0.0276512470277418
3344.12705989472475-0.127059894724754
3454.066220958638490.933779041361506
3544.06622095863849-0.0662209586384942
3644.13183762736812-0.131837627368122
3753.725376640553861.27462335944614
3844.06622095863849-0.0662209586384942
3943.977872444576580.022127555423421
4043.624749703538470.375250296461529
4133.77475978018352-0.774759780183519
4244.11879272929453-0.118792729294535
4333.32440978159118-0.324409781591183
4453.463319344116071.53668065588393
4554.334273569966760.66572643003324
4643.269656620792360.730343379207642
4754.553264481123350.446735518876655
4844.20024726430263-0.200247264302627
4944.2953081955345-0.295308195534501
5043.997811321703990.00218867829601101
5144.52115270525985-0.52115270525985
5243.907478043203580.092521956796416
5343.805276427301690.194723572698306
5444.20024726430263-0.200247264302627
5543.329474600335800.670525399664196
5643.827331550839560.172668449160441
5742.942770928765091.05722907123491
5844.20024726430263-0.200247264302627
5944.33427356996676-0.33427356996676
6043.931913594001450.0680864059985549
6144.25281903495867-0.252819034958668
6243.349667958885920.650332041114084
6343.997811321703990.00218867829601101
6444.55230067322767-0.55230067322767
6544.23852988981213-0.238529889812126
6655.02813510487211-0.0281351048721104
6744.31843570368830-0.318435703688295
6843.586572602246360.413427397753639
6933.61322389654373-0.61322389654373
7043.961493010774160.0385069892258418
7143.750043170412740.249956829587259
7223.18759050772217-1.18759050772217
7343.11787282814360.8821271718564
7454.384333431390840.615666568609161
7544.21608513058109-0.216085130581092
7654.93978659081020.0602134091898048
7733.40668694309615-0.406686943096148
7844.50348567867218-0.503485678672182
7944.48668400449804-0.486684004498042
8033.72059890791049-0.720598907910494
8143.602410468524830.397589531475174
8243.977872444576580.022127555423421
8344.35265769883391-0.352657698833910
8444.03871138066284-0.0387113806628388
8543.984726043145340.0152739568546587
8643.727311325080450.272688674919547
8754.100161894811190.899838105188808
8844.37521400941067-0.37521400941067
8943.624960751973250.375039248026750
9044.05038309236003-0.0503830923600296
9143.74782139978490.252178600215104
9254.02355023617880.976449763821204
9344.06144322599513-0.0614432259951267
9443.693264864690370.306735135309634
9533.03111944282701-0.0311194428270053
9643.188554315617850.811445684382152
9743.931517931179940.0684820688200606
9843.299212434640330.700787565359668
9954.303380083421640.696619916578358
10054.203040232507500.796959767492496
10143.904685074998710.0953149250012938
10243.743149191358920.256850808641082
10354.200247264302630.799752735697373
10454.300085928177870.699914071822131
10544.3691722869068-0.369172286906796
10654.484854844188840.51514515581116
10744.11599976108966-0.115999761089657
10843.859007283396490.140992716603511
10944.18440939802416-0.184409398024162
11044.25281903495867-0.252819034958668
11123.76577551247382-1.76577551247382
11243.743149191358920.256850808641082
11354.628281011010420.37171898898958
11443.804599705507270.195400294492728
11544.02765124702774-0.0276512470277418
11643.242434128917960.757565871082044
11743.904685074998710.0953149250012938
11844.18918713066753-0.189187130667530
11933.48662238702539-0.486622387025393
12043.68641126612160.313588733878396
12144.10729655235287-0.107296552352871
12234.02765124702774-1.02765124702774
12343.836169913846810.163830086153189
12454.245925055904840.754074944095156
12554.203040232507500.796959767492496
12634.03938810245726-1.03938810245726
12754.043489113306210.956510886693793
12844.18440939802416-0.184409398024162
12943.597632735881460.402367264118541
13034.341844270815-1.34184427081501
13133.69804259733373-0.698042597333733
13243.854800748330150.145199251669846
13354.527480507196360.472519492803644
13423.46752587918241-1.46752587918241
13544.20024726430263-0.200247264302627
13644.20304023250750-0.203040232507505
13743.591350334889730.408649665110271
13854.050383092360030.94961690763997
13943.228953187537800.771046812462197
14044.04218107066214-0.0421810706621389
14143.966135589147060.0338644108529403
14243.713203741817780.286796258182224
14344.06622095863849-0.0662209586384942
14444.20024726430263-0.200247264302627
14554.118792729294530.881207270705465
14633.66392612640551-0.663926126405508
14744.06622095863849-0.0662209586384942
14843.754384859749540.245615140250458
14944.134630595573-0.134630595573000
15044.18030838717522-0.180308387175217
15143.487930429669460.512069570330539
15254.894108799207980.105891200792022






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4438858518421510.8877717036843010.55611414815785
120.2945416903563560.5890833807127120.705458309643644
130.2236507074052170.4473014148104330.776349292594783
140.1359277994820620.2718555989641250.864072200517938
150.1016495445676280.2032990891352570.898350455432372
160.2061075498216150.4122150996432300.793892450178385
170.1371487967287260.2742975934574520.862851203271274
180.1820074928062440.3640149856124880.817992507193756
190.1431358609890470.2862717219780950.856864139010953
200.2806062192196710.5612124384393430.719393780780329
210.3552123322125930.7104246644251850.644787667787407
220.5134872802859130.9730254394281730.486512719714087
230.4365142857328030.8730285714656050.563485714267197
240.3628665391675810.7257330783351620.637133460832419
250.2949514858233660.5899029716467320.705048514176634
260.2705564734090920.5411129468181840.729443526590908
270.2144614417647970.4289228835295950.785538558235203
280.4790368251019920.9580736502039850.520963174898007
290.4118442967437740.8236885934875480.588155703256226
300.5163266361011260.9673467277977480.483673363898874
310.5505074104910330.8989851790179330.449492589508966
320.5860767745924150.827846450815170.413923225407585
330.5244050701473370.9511898597053270.475594929852663
340.6464587501050740.7070824997898530.353541249894926
350.5890093154181370.8219813691637260.410990684581863
360.5732827344797990.8534345310404020.426717265520201
370.7519537664061590.4960924671876830.248046233593841
380.7044259943840850.591148011231830.295574005615915
390.6900987597565010.6198024804869980.309901240243499
400.685728567212460.628542865575080.31427143278754
410.6702078939359980.6595842121280040.329792106064002
420.632380488536720.735239022926560.36761951146328
430.6587227381961230.6825545236077540.341277261803877
440.9611307023580030.07773859528399330.0388692976419966
450.9596918285307020.08061634293859670.0403081714692983
460.9594748332312290.08105033353754240.0405251667687712
470.9513620184103350.09727596317933020.0486379815896651
480.9416177011081180.1167645977837630.0583822988918816
490.9267045423424970.1465909153150060.0732954576575028
500.9080406566134970.1839186867730050.0919593433865027
510.892294790792790.2154104184144220.107705209207211
520.8999872644928460.2000254710143070.100012735507154
530.881858001723330.2362839965533400.118141998276670
540.8605314009061330.2789371981877340.139468599093867
550.895553127925350.2088937441493020.104446872074651
560.8841943809931180.2316112380137650.115805619006882
570.9317582471852550.1364835056294910.0682417528147453
580.9167124928319010.1665750143361970.0832875071680986
590.9052351409863060.1895297180273880.094764859013694
600.8826816046402190.2346367907195620.117318395359781
610.8593820926363870.2812358147272250.140617907363613
620.8548086214135790.2903827571728430.145191378586421
630.8252048496444860.3495903007110270.174795150355514
640.8198648013468320.3602703973063360.180135198653168
650.792383275627780.4152334487444390.207616724372220
660.7557647981760160.4884704036479670.244235201823984
670.7226123291527950.554775341694410.277387670847205
680.6985503362651280.6028993274697450.301449663734872
690.6988284330054130.6023431339891750.301171566994587
700.6578745877389910.6842508245220180.342125412261009
710.6292055663548510.7415888672902980.370794433645149
720.7380544853526620.5238910292946750.261945514647337
730.7818470643321340.4363058713357310.218152935667866
740.7849720439050730.4300559121898540.215027956094927
750.7519651614913380.4960696770173250.248034838508662
760.7130043925547240.5739912148905520.286995607445276
770.7014702173309020.5970595653381970.298529782669098
780.7023857535415490.5952284929169010.297614246458451
790.6759793822578670.6480412354842660.324020617742133
800.6956362767323220.6087274465353560.304363723267678
810.6649780930517520.6700438138964960.335021906948248
820.6213662621488560.7572674757022880.378633737851144
830.5887261214072630.8225477571854740.411273878592737
840.540578142855680.918843714288640.45942185714432
850.4923584884451760.9847169768903520.507641511554824
860.4550755449451990.9101510898903980.544924455054801
870.5156865102686140.9686269794627730.484313489731386
880.4807632906780060.9615265813560130.519236709321994
890.4448941351175210.8897882702350410.555105864882479
900.40212372897380.80424745794760.5978762710262
910.3643029110802570.7286058221605140.635697088919743
920.440052807635180.880105615270360.55994719236482
930.3947440674136760.7894881348273520.605255932586324
940.3628606548428020.7257213096856050.637139345157198
950.3209934220277290.6419868440554590.67900657797227
960.3357344181334930.6714688362669870.664265581866507
970.3052793857624850.610558771524970.694720614237515
980.2990063216634170.5980126433268340.700993678336583
990.3191537231798320.6383074463596640.680846276820168
1000.3378751659429010.6757503318858020.662124834057099
1010.2925502958930070.5851005917860140.707449704106993
1020.266172001843450.53234400368690.73382799815655
1030.2924897459786330.5849794919572650.707510254021367
1040.3051690228169400.6103380456338790.69483097718306
1050.2732461724739220.5464923449478450.726753827526078
1060.2538781724729720.5077563449459440.746121827527028
1070.2133495806664930.4266991613329870.786650419333507
1080.1772101215824700.3544202431649400.82278987841753
1090.1456831860075880.2913663720151770.854316813992412
1100.1205839266000670.2411678532001340.879416073399933
1110.5282290355946690.9435419288106620.471770964405331
1120.493839207298550.98767841459710.50616079270145
1130.4525184743626070.9050369487252140.547481525637393
1140.4014294517521580.8028589035043160.598570548247842
1150.3529744106087070.7059488212174150.647025589391293
1160.3534853838577800.7069707677155590.64651461614222
1170.3054713544861970.6109427089723940.694528645513803
1180.2560082050161150.5120164100322290.743991794983885
1190.2585054958179300.5170109916358590.74149450418207
1200.2451950000489790.4903900000979580.754804999951021
1210.2154225532299230.4308451064598460.784577446770077
1220.2580644390054150.516128878010830.741935560994585
1230.2086843443629560.4173686887259110.791315655637044
1240.2914103681097510.5828207362195010.708589631890249
1250.2844668693457180.5689337386914360.715533130654282
1260.2953655572957760.5907311145915510.704634442704224
1270.5842298549141770.8315402901716450.415770145085823
1280.5093767279165950.981246544166810.490623272083405
1290.5021888117431210.9956223765137580.497811188256879
1300.8905896418694660.2188207162610690.109410358130534
1310.9748859472890660.05022810542186860.0251140527109343
1320.976096088619650.04780782276070030.0239039113803501
1330.9611108539030480.07777829219390310.0388891460969515
1340.9742326805883130.05153463882337490.0257673194116875
1350.9520226673451290.09595466530974260.0479773326548713
1360.9455540631748340.1088918736503310.0544459368251655
1370.9209129000164720.1581741999670570.0790870999835284
1380.9730348915438530.05393021691229370.0269651084561468
1390.9512107366611530.09757852667769480.0487892633388474
1400.8884867480208830.2230265039582340.111513251979117
1410.8573047848086740.2853904303826530.142695215191326

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.443885851842151 & 0.887771703684301 & 0.55611414815785 \tabularnewline
12 & 0.294541690356356 & 0.589083380712712 & 0.705458309643644 \tabularnewline
13 & 0.223650707405217 & 0.447301414810433 & 0.776349292594783 \tabularnewline
14 & 0.135927799482062 & 0.271855598964125 & 0.864072200517938 \tabularnewline
15 & 0.101649544567628 & 0.203299089135257 & 0.898350455432372 \tabularnewline
16 & 0.206107549821615 & 0.412215099643230 & 0.793892450178385 \tabularnewline
17 & 0.137148796728726 & 0.274297593457452 & 0.862851203271274 \tabularnewline
18 & 0.182007492806244 & 0.364014985612488 & 0.817992507193756 \tabularnewline
19 & 0.143135860989047 & 0.286271721978095 & 0.856864139010953 \tabularnewline
20 & 0.280606219219671 & 0.561212438439343 & 0.719393780780329 \tabularnewline
21 & 0.355212332212593 & 0.710424664425185 & 0.644787667787407 \tabularnewline
22 & 0.513487280285913 & 0.973025439428173 & 0.486512719714087 \tabularnewline
23 & 0.436514285732803 & 0.873028571465605 & 0.563485714267197 \tabularnewline
24 & 0.362866539167581 & 0.725733078335162 & 0.637133460832419 \tabularnewline
25 & 0.294951485823366 & 0.589902971646732 & 0.705048514176634 \tabularnewline
26 & 0.270556473409092 & 0.541112946818184 & 0.729443526590908 \tabularnewline
27 & 0.214461441764797 & 0.428922883529595 & 0.785538558235203 \tabularnewline
28 & 0.479036825101992 & 0.958073650203985 & 0.520963174898007 \tabularnewline
29 & 0.411844296743774 & 0.823688593487548 & 0.588155703256226 \tabularnewline
30 & 0.516326636101126 & 0.967346727797748 & 0.483673363898874 \tabularnewline
31 & 0.550507410491033 & 0.898985179017933 & 0.449492589508966 \tabularnewline
32 & 0.586076774592415 & 0.82784645081517 & 0.413923225407585 \tabularnewline
33 & 0.524405070147337 & 0.951189859705327 & 0.475594929852663 \tabularnewline
34 & 0.646458750105074 & 0.707082499789853 & 0.353541249894926 \tabularnewline
35 & 0.589009315418137 & 0.821981369163726 & 0.410990684581863 \tabularnewline
36 & 0.573282734479799 & 0.853434531040402 & 0.426717265520201 \tabularnewline
37 & 0.751953766406159 & 0.496092467187683 & 0.248046233593841 \tabularnewline
38 & 0.704425994384085 & 0.59114801123183 & 0.295574005615915 \tabularnewline
39 & 0.690098759756501 & 0.619802480486998 & 0.309901240243499 \tabularnewline
40 & 0.68572856721246 & 0.62854286557508 & 0.31427143278754 \tabularnewline
41 & 0.670207893935998 & 0.659584212128004 & 0.329792106064002 \tabularnewline
42 & 0.63238048853672 & 0.73523902292656 & 0.36761951146328 \tabularnewline
43 & 0.658722738196123 & 0.682554523607754 & 0.341277261803877 \tabularnewline
44 & 0.961130702358003 & 0.0777385952839933 & 0.0388692976419966 \tabularnewline
45 & 0.959691828530702 & 0.0806163429385967 & 0.0403081714692983 \tabularnewline
46 & 0.959474833231229 & 0.0810503335375424 & 0.0405251667687712 \tabularnewline
47 & 0.951362018410335 & 0.0972759631793302 & 0.0486379815896651 \tabularnewline
48 & 0.941617701108118 & 0.116764597783763 & 0.0583822988918816 \tabularnewline
49 & 0.926704542342497 & 0.146590915315006 & 0.0732954576575028 \tabularnewline
50 & 0.908040656613497 & 0.183918686773005 & 0.0919593433865027 \tabularnewline
51 & 0.89229479079279 & 0.215410418414422 & 0.107705209207211 \tabularnewline
52 & 0.899987264492846 & 0.200025471014307 & 0.100012735507154 \tabularnewline
53 & 0.88185800172333 & 0.236283996553340 & 0.118141998276670 \tabularnewline
54 & 0.860531400906133 & 0.278937198187734 & 0.139468599093867 \tabularnewline
55 & 0.89555312792535 & 0.208893744149302 & 0.104446872074651 \tabularnewline
56 & 0.884194380993118 & 0.231611238013765 & 0.115805619006882 \tabularnewline
57 & 0.931758247185255 & 0.136483505629491 & 0.0682417528147453 \tabularnewline
58 & 0.916712492831901 & 0.166575014336197 & 0.0832875071680986 \tabularnewline
59 & 0.905235140986306 & 0.189529718027388 & 0.094764859013694 \tabularnewline
60 & 0.882681604640219 & 0.234636790719562 & 0.117318395359781 \tabularnewline
61 & 0.859382092636387 & 0.281235814727225 & 0.140617907363613 \tabularnewline
62 & 0.854808621413579 & 0.290382757172843 & 0.145191378586421 \tabularnewline
63 & 0.825204849644486 & 0.349590300711027 & 0.174795150355514 \tabularnewline
64 & 0.819864801346832 & 0.360270397306336 & 0.180135198653168 \tabularnewline
65 & 0.79238327562778 & 0.415233448744439 & 0.207616724372220 \tabularnewline
66 & 0.755764798176016 & 0.488470403647967 & 0.244235201823984 \tabularnewline
67 & 0.722612329152795 & 0.55477534169441 & 0.277387670847205 \tabularnewline
68 & 0.698550336265128 & 0.602899327469745 & 0.301449663734872 \tabularnewline
69 & 0.698828433005413 & 0.602343133989175 & 0.301171566994587 \tabularnewline
70 & 0.657874587738991 & 0.684250824522018 & 0.342125412261009 \tabularnewline
71 & 0.629205566354851 & 0.741588867290298 & 0.370794433645149 \tabularnewline
72 & 0.738054485352662 & 0.523891029294675 & 0.261945514647337 \tabularnewline
73 & 0.781847064332134 & 0.436305871335731 & 0.218152935667866 \tabularnewline
74 & 0.784972043905073 & 0.430055912189854 & 0.215027956094927 \tabularnewline
75 & 0.751965161491338 & 0.496069677017325 & 0.248034838508662 \tabularnewline
76 & 0.713004392554724 & 0.573991214890552 & 0.286995607445276 \tabularnewline
77 & 0.701470217330902 & 0.597059565338197 & 0.298529782669098 \tabularnewline
78 & 0.702385753541549 & 0.595228492916901 & 0.297614246458451 \tabularnewline
79 & 0.675979382257867 & 0.648041235484266 & 0.324020617742133 \tabularnewline
80 & 0.695636276732322 & 0.608727446535356 & 0.304363723267678 \tabularnewline
81 & 0.664978093051752 & 0.670043813896496 & 0.335021906948248 \tabularnewline
82 & 0.621366262148856 & 0.757267475702288 & 0.378633737851144 \tabularnewline
83 & 0.588726121407263 & 0.822547757185474 & 0.411273878592737 \tabularnewline
84 & 0.54057814285568 & 0.91884371428864 & 0.45942185714432 \tabularnewline
85 & 0.492358488445176 & 0.984716976890352 & 0.507641511554824 \tabularnewline
86 & 0.455075544945199 & 0.910151089890398 & 0.544924455054801 \tabularnewline
87 & 0.515686510268614 & 0.968626979462773 & 0.484313489731386 \tabularnewline
88 & 0.480763290678006 & 0.961526581356013 & 0.519236709321994 \tabularnewline
89 & 0.444894135117521 & 0.889788270235041 & 0.555105864882479 \tabularnewline
90 & 0.4021237289738 & 0.8042474579476 & 0.5978762710262 \tabularnewline
91 & 0.364302911080257 & 0.728605822160514 & 0.635697088919743 \tabularnewline
92 & 0.44005280763518 & 0.88010561527036 & 0.55994719236482 \tabularnewline
93 & 0.394744067413676 & 0.789488134827352 & 0.605255932586324 \tabularnewline
94 & 0.362860654842802 & 0.725721309685605 & 0.637139345157198 \tabularnewline
95 & 0.320993422027729 & 0.641986844055459 & 0.67900657797227 \tabularnewline
96 & 0.335734418133493 & 0.671468836266987 & 0.664265581866507 \tabularnewline
97 & 0.305279385762485 & 0.61055877152497 & 0.694720614237515 \tabularnewline
98 & 0.299006321663417 & 0.598012643326834 & 0.700993678336583 \tabularnewline
99 & 0.319153723179832 & 0.638307446359664 & 0.680846276820168 \tabularnewline
100 & 0.337875165942901 & 0.675750331885802 & 0.662124834057099 \tabularnewline
101 & 0.292550295893007 & 0.585100591786014 & 0.707449704106993 \tabularnewline
102 & 0.26617200184345 & 0.5323440036869 & 0.73382799815655 \tabularnewline
103 & 0.292489745978633 & 0.584979491957265 & 0.707510254021367 \tabularnewline
104 & 0.305169022816940 & 0.610338045633879 & 0.69483097718306 \tabularnewline
105 & 0.273246172473922 & 0.546492344947845 & 0.726753827526078 \tabularnewline
106 & 0.253878172472972 & 0.507756344945944 & 0.746121827527028 \tabularnewline
107 & 0.213349580666493 & 0.426699161332987 & 0.786650419333507 \tabularnewline
108 & 0.177210121582470 & 0.354420243164940 & 0.82278987841753 \tabularnewline
109 & 0.145683186007588 & 0.291366372015177 & 0.854316813992412 \tabularnewline
110 & 0.120583926600067 & 0.241167853200134 & 0.879416073399933 \tabularnewline
111 & 0.528229035594669 & 0.943541928810662 & 0.471770964405331 \tabularnewline
112 & 0.49383920729855 & 0.9876784145971 & 0.50616079270145 \tabularnewline
113 & 0.452518474362607 & 0.905036948725214 & 0.547481525637393 \tabularnewline
114 & 0.401429451752158 & 0.802858903504316 & 0.598570548247842 \tabularnewline
115 & 0.352974410608707 & 0.705948821217415 & 0.647025589391293 \tabularnewline
116 & 0.353485383857780 & 0.706970767715559 & 0.64651461614222 \tabularnewline
117 & 0.305471354486197 & 0.610942708972394 & 0.694528645513803 \tabularnewline
118 & 0.256008205016115 & 0.512016410032229 & 0.743991794983885 \tabularnewline
119 & 0.258505495817930 & 0.517010991635859 & 0.74149450418207 \tabularnewline
120 & 0.245195000048979 & 0.490390000097958 & 0.754804999951021 \tabularnewline
121 & 0.215422553229923 & 0.430845106459846 & 0.784577446770077 \tabularnewline
122 & 0.258064439005415 & 0.51612887801083 & 0.741935560994585 \tabularnewline
123 & 0.208684344362956 & 0.417368688725911 & 0.791315655637044 \tabularnewline
124 & 0.291410368109751 & 0.582820736219501 & 0.708589631890249 \tabularnewline
125 & 0.284466869345718 & 0.568933738691436 & 0.715533130654282 \tabularnewline
126 & 0.295365557295776 & 0.590731114591551 & 0.704634442704224 \tabularnewline
127 & 0.584229854914177 & 0.831540290171645 & 0.415770145085823 \tabularnewline
128 & 0.509376727916595 & 0.98124654416681 & 0.490623272083405 \tabularnewline
129 & 0.502188811743121 & 0.995622376513758 & 0.497811188256879 \tabularnewline
130 & 0.890589641869466 & 0.218820716261069 & 0.109410358130534 \tabularnewline
131 & 0.974885947289066 & 0.0502281054218686 & 0.0251140527109343 \tabularnewline
132 & 0.97609608861965 & 0.0478078227607003 & 0.0239039113803501 \tabularnewline
133 & 0.961110853903048 & 0.0777782921939031 & 0.0388891460969515 \tabularnewline
134 & 0.974232680588313 & 0.0515346388233749 & 0.0257673194116875 \tabularnewline
135 & 0.952022667345129 & 0.0959546653097426 & 0.0479773326548713 \tabularnewline
136 & 0.945554063174834 & 0.108891873650331 & 0.0544459368251655 \tabularnewline
137 & 0.920912900016472 & 0.158174199967057 & 0.0790870999835284 \tabularnewline
138 & 0.973034891543853 & 0.0539302169122937 & 0.0269651084561468 \tabularnewline
139 & 0.951210736661153 & 0.0975785266776948 & 0.0487892633388474 \tabularnewline
140 & 0.888486748020883 & 0.223026503958234 & 0.111513251979117 \tabularnewline
141 & 0.857304784808674 & 0.285390430382653 & 0.142695215191326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98342&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]0.443885851842151[/C][C]0.887771703684301[/C][C]0.55611414815785[/C][/ROW]
[ROW][C]12[/C][C]0.294541690356356[/C][C]0.589083380712712[/C][C]0.705458309643644[/C][/ROW]
[ROW][C]13[/C][C]0.223650707405217[/C][C]0.447301414810433[/C][C]0.776349292594783[/C][/ROW]
[ROW][C]14[/C][C]0.135927799482062[/C][C]0.271855598964125[/C][C]0.864072200517938[/C][/ROW]
[ROW][C]15[/C][C]0.101649544567628[/C][C]0.203299089135257[/C][C]0.898350455432372[/C][/ROW]
[ROW][C]16[/C][C]0.206107549821615[/C][C]0.412215099643230[/C][C]0.793892450178385[/C][/ROW]
[ROW][C]17[/C][C]0.137148796728726[/C][C]0.274297593457452[/C][C]0.862851203271274[/C][/ROW]
[ROW][C]18[/C][C]0.182007492806244[/C][C]0.364014985612488[/C][C]0.817992507193756[/C][/ROW]
[ROW][C]19[/C][C]0.143135860989047[/C][C]0.286271721978095[/C][C]0.856864139010953[/C][/ROW]
[ROW][C]20[/C][C]0.280606219219671[/C][C]0.561212438439343[/C][C]0.719393780780329[/C][/ROW]
[ROW][C]21[/C][C]0.355212332212593[/C][C]0.710424664425185[/C][C]0.644787667787407[/C][/ROW]
[ROW][C]22[/C][C]0.513487280285913[/C][C]0.973025439428173[/C][C]0.486512719714087[/C][/ROW]
[ROW][C]23[/C][C]0.436514285732803[/C][C]0.873028571465605[/C][C]0.563485714267197[/C][/ROW]
[ROW][C]24[/C][C]0.362866539167581[/C][C]0.725733078335162[/C][C]0.637133460832419[/C][/ROW]
[ROW][C]25[/C][C]0.294951485823366[/C][C]0.589902971646732[/C][C]0.705048514176634[/C][/ROW]
[ROW][C]26[/C][C]0.270556473409092[/C][C]0.541112946818184[/C][C]0.729443526590908[/C][/ROW]
[ROW][C]27[/C][C]0.214461441764797[/C][C]0.428922883529595[/C][C]0.785538558235203[/C][/ROW]
[ROW][C]28[/C][C]0.479036825101992[/C][C]0.958073650203985[/C][C]0.520963174898007[/C][/ROW]
[ROW][C]29[/C][C]0.411844296743774[/C][C]0.823688593487548[/C][C]0.588155703256226[/C][/ROW]
[ROW][C]30[/C][C]0.516326636101126[/C][C]0.967346727797748[/C][C]0.483673363898874[/C][/ROW]
[ROW][C]31[/C][C]0.550507410491033[/C][C]0.898985179017933[/C][C]0.449492589508966[/C][/ROW]
[ROW][C]32[/C][C]0.586076774592415[/C][C]0.82784645081517[/C][C]0.413923225407585[/C][/ROW]
[ROW][C]33[/C][C]0.524405070147337[/C][C]0.951189859705327[/C][C]0.475594929852663[/C][/ROW]
[ROW][C]34[/C][C]0.646458750105074[/C][C]0.707082499789853[/C][C]0.353541249894926[/C][/ROW]
[ROW][C]35[/C][C]0.589009315418137[/C][C]0.821981369163726[/C][C]0.410990684581863[/C][/ROW]
[ROW][C]36[/C][C]0.573282734479799[/C][C]0.853434531040402[/C][C]0.426717265520201[/C][/ROW]
[ROW][C]37[/C][C]0.751953766406159[/C][C]0.496092467187683[/C][C]0.248046233593841[/C][/ROW]
[ROW][C]38[/C][C]0.704425994384085[/C][C]0.59114801123183[/C][C]0.295574005615915[/C][/ROW]
[ROW][C]39[/C][C]0.690098759756501[/C][C]0.619802480486998[/C][C]0.309901240243499[/C][/ROW]
[ROW][C]40[/C][C]0.68572856721246[/C][C]0.62854286557508[/C][C]0.31427143278754[/C][/ROW]
[ROW][C]41[/C][C]0.670207893935998[/C][C]0.659584212128004[/C][C]0.329792106064002[/C][/ROW]
[ROW][C]42[/C][C]0.63238048853672[/C][C]0.73523902292656[/C][C]0.36761951146328[/C][/ROW]
[ROW][C]43[/C][C]0.658722738196123[/C][C]0.682554523607754[/C][C]0.341277261803877[/C][/ROW]
[ROW][C]44[/C][C]0.961130702358003[/C][C]0.0777385952839933[/C][C]0.0388692976419966[/C][/ROW]
[ROW][C]45[/C][C]0.959691828530702[/C][C]0.0806163429385967[/C][C]0.0403081714692983[/C][/ROW]
[ROW][C]46[/C][C]0.959474833231229[/C][C]0.0810503335375424[/C][C]0.0405251667687712[/C][/ROW]
[ROW][C]47[/C][C]0.951362018410335[/C][C]0.0972759631793302[/C][C]0.0486379815896651[/C][/ROW]
[ROW][C]48[/C][C]0.941617701108118[/C][C]0.116764597783763[/C][C]0.0583822988918816[/C][/ROW]
[ROW][C]49[/C][C]0.926704542342497[/C][C]0.146590915315006[/C][C]0.0732954576575028[/C][/ROW]
[ROW][C]50[/C][C]0.908040656613497[/C][C]0.183918686773005[/C][C]0.0919593433865027[/C][/ROW]
[ROW][C]51[/C][C]0.89229479079279[/C][C]0.215410418414422[/C][C]0.107705209207211[/C][/ROW]
[ROW][C]52[/C][C]0.899987264492846[/C][C]0.200025471014307[/C][C]0.100012735507154[/C][/ROW]
[ROW][C]53[/C][C]0.88185800172333[/C][C]0.236283996553340[/C][C]0.118141998276670[/C][/ROW]
[ROW][C]54[/C][C]0.860531400906133[/C][C]0.278937198187734[/C][C]0.139468599093867[/C][/ROW]
[ROW][C]55[/C][C]0.89555312792535[/C][C]0.208893744149302[/C][C]0.104446872074651[/C][/ROW]
[ROW][C]56[/C][C]0.884194380993118[/C][C]0.231611238013765[/C][C]0.115805619006882[/C][/ROW]
[ROW][C]57[/C][C]0.931758247185255[/C][C]0.136483505629491[/C][C]0.0682417528147453[/C][/ROW]
[ROW][C]58[/C][C]0.916712492831901[/C][C]0.166575014336197[/C][C]0.0832875071680986[/C][/ROW]
[ROW][C]59[/C][C]0.905235140986306[/C][C]0.189529718027388[/C][C]0.094764859013694[/C][/ROW]
[ROW][C]60[/C][C]0.882681604640219[/C][C]0.234636790719562[/C][C]0.117318395359781[/C][/ROW]
[ROW][C]61[/C][C]0.859382092636387[/C][C]0.281235814727225[/C][C]0.140617907363613[/C][/ROW]
[ROW][C]62[/C][C]0.854808621413579[/C][C]0.290382757172843[/C][C]0.145191378586421[/C][/ROW]
[ROW][C]63[/C][C]0.825204849644486[/C][C]0.349590300711027[/C][C]0.174795150355514[/C][/ROW]
[ROW][C]64[/C][C]0.819864801346832[/C][C]0.360270397306336[/C][C]0.180135198653168[/C][/ROW]
[ROW][C]65[/C][C]0.79238327562778[/C][C]0.415233448744439[/C][C]0.207616724372220[/C][/ROW]
[ROW][C]66[/C][C]0.755764798176016[/C][C]0.488470403647967[/C][C]0.244235201823984[/C][/ROW]
[ROW][C]67[/C][C]0.722612329152795[/C][C]0.55477534169441[/C][C]0.277387670847205[/C][/ROW]
[ROW][C]68[/C][C]0.698550336265128[/C][C]0.602899327469745[/C][C]0.301449663734872[/C][/ROW]
[ROW][C]69[/C][C]0.698828433005413[/C][C]0.602343133989175[/C][C]0.301171566994587[/C][/ROW]
[ROW][C]70[/C][C]0.657874587738991[/C][C]0.684250824522018[/C][C]0.342125412261009[/C][/ROW]
[ROW][C]71[/C][C]0.629205566354851[/C][C]0.741588867290298[/C][C]0.370794433645149[/C][/ROW]
[ROW][C]72[/C][C]0.738054485352662[/C][C]0.523891029294675[/C][C]0.261945514647337[/C][/ROW]
[ROW][C]73[/C][C]0.781847064332134[/C][C]0.436305871335731[/C][C]0.218152935667866[/C][/ROW]
[ROW][C]74[/C][C]0.784972043905073[/C][C]0.430055912189854[/C][C]0.215027956094927[/C][/ROW]
[ROW][C]75[/C][C]0.751965161491338[/C][C]0.496069677017325[/C][C]0.248034838508662[/C][/ROW]
[ROW][C]76[/C][C]0.713004392554724[/C][C]0.573991214890552[/C][C]0.286995607445276[/C][/ROW]
[ROW][C]77[/C][C]0.701470217330902[/C][C]0.597059565338197[/C][C]0.298529782669098[/C][/ROW]
[ROW][C]78[/C][C]0.702385753541549[/C][C]0.595228492916901[/C][C]0.297614246458451[/C][/ROW]
[ROW][C]79[/C][C]0.675979382257867[/C][C]0.648041235484266[/C][C]0.324020617742133[/C][/ROW]
[ROW][C]80[/C][C]0.695636276732322[/C][C]0.608727446535356[/C][C]0.304363723267678[/C][/ROW]
[ROW][C]81[/C][C]0.664978093051752[/C][C]0.670043813896496[/C][C]0.335021906948248[/C][/ROW]
[ROW][C]82[/C][C]0.621366262148856[/C][C]0.757267475702288[/C][C]0.378633737851144[/C][/ROW]
[ROW][C]83[/C][C]0.588726121407263[/C][C]0.822547757185474[/C][C]0.411273878592737[/C][/ROW]
[ROW][C]84[/C][C]0.54057814285568[/C][C]0.91884371428864[/C][C]0.45942185714432[/C][/ROW]
[ROW][C]85[/C][C]0.492358488445176[/C][C]0.984716976890352[/C][C]0.507641511554824[/C][/ROW]
[ROW][C]86[/C][C]0.455075544945199[/C][C]0.910151089890398[/C][C]0.544924455054801[/C][/ROW]
[ROW][C]87[/C][C]0.515686510268614[/C][C]0.968626979462773[/C][C]0.484313489731386[/C][/ROW]
[ROW][C]88[/C][C]0.480763290678006[/C][C]0.961526581356013[/C][C]0.519236709321994[/C][/ROW]
[ROW][C]89[/C][C]0.444894135117521[/C][C]0.889788270235041[/C][C]0.555105864882479[/C][/ROW]
[ROW][C]90[/C][C]0.4021237289738[/C][C]0.8042474579476[/C][C]0.5978762710262[/C][/ROW]
[ROW][C]91[/C][C]0.364302911080257[/C][C]0.728605822160514[/C][C]0.635697088919743[/C][/ROW]
[ROW][C]92[/C][C]0.44005280763518[/C][C]0.88010561527036[/C][C]0.55994719236482[/C][/ROW]
[ROW][C]93[/C][C]0.394744067413676[/C][C]0.789488134827352[/C][C]0.605255932586324[/C][/ROW]
[ROW][C]94[/C][C]0.362860654842802[/C][C]0.725721309685605[/C][C]0.637139345157198[/C][/ROW]
[ROW][C]95[/C][C]0.320993422027729[/C][C]0.641986844055459[/C][C]0.67900657797227[/C][/ROW]
[ROW][C]96[/C][C]0.335734418133493[/C][C]0.671468836266987[/C][C]0.664265581866507[/C][/ROW]
[ROW][C]97[/C][C]0.305279385762485[/C][C]0.61055877152497[/C][C]0.694720614237515[/C][/ROW]
[ROW][C]98[/C][C]0.299006321663417[/C][C]0.598012643326834[/C][C]0.700993678336583[/C][/ROW]
[ROW][C]99[/C][C]0.319153723179832[/C][C]0.638307446359664[/C][C]0.680846276820168[/C][/ROW]
[ROW][C]100[/C][C]0.337875165942901[/C][C]0.675750331885802[/C][C]0.662124834057099[/C][/ROW]
[ROW][C]101[/C][C]0.292550295893007[/C][C]0.585100591786014[/C][C]0.707449704106993[/C][/ROW]
[ROW][C]102[/C][C]0.26617200184345[/C][C]0.5323440036869[/C][C]0.73382799815655[/C][/ROW]
[ROW][C]103[/C][C]0.292489745978633[/C][C]0.584979491957265[/C][C]0.707510254021367[/C][/ROW]
[ROW][C]104[/C][C]0.305169022816940[/C][C]0.610338045633879[/C][C]0.69483097718306[/C][/ROW]
[ROW][C]105[/C][C]0.273246172473922[/C][C]0.546492344947845[/C][C]0.726753827526078[/C][/ROW]
[ROW][C]106[/C][C]0.253878172472972[/C][C]0.507756344945944[/C][C]0.746121827527028[/C][/ROW]
[ROW][C]107[/C][C]0.213349580666493[/C][C]0.426699161332987[/C][C]0.786650419333507[/C][/ROW]
[ROW][C]108[/C][C]0.177210121582470[/C][C]0.354420243164940[/C][C]0.82278987841753[/C][/ROW]
[ROW][C]109[/C][C]0.145683186007588[/C][C]0.291366372015177[/C][C]0.854316813992412[/C][/ROW]
[ROW][C]110[/C][C]0.120583926600067[/C][C]0.241167853200134[/C][C]0.879416073399933[/C][/ROW]
[ROW][C]111[/C][C]0.528229035594669[/C][C]0.943541928810662[/C][C]0.471770964405331[/C][/ROW]
[ROW][C]112[/C][C]0.49383920729855[/C][C]0.9876784145971[/C][C]0.50616079270145[/C][/ROW]
[ROW][C]113[/C][C]0.452518474362607[/C][C]0.905036948725214[/C][C]0.547481525637393[/C][/ROW]
[ROW][C]114[/C][C]0.401429451752158[/C][C]0.802858903504316[/C][C]0.598570548247842[/C][/ROW]
[ROW][C]115[/C][C]0.352974410608707[/C][C]0.705948821217415[/C][C]0.647025589391293[/C][/ROW]
[ROW][C]116[/C][C]0.353485383857780[/C][C]0.706970767715559[/C][C]0.64651461614222[/C][/ROW]
[ROW][C]117[/C][C]0.305471354486197[/C][C]0.610942708972394[/C][C]0.694528645513803[/C][/ROW]
[ROW][C]118[/C][C]0.256008205016115[/C][C]0.512016410032229[/C][C]0.743991794983885[/C][/ROW]
[ROW][C]119[/C][C]0.258505495817930[/C][C]0.517010991635859[/C][C]0.74149450418207[/C][/ROW]
[ROW][C]120[/C][C]0.245195000048979[/C][C]0.490390000097958[/C][C]0.754804999951021[/C][/ROW]
[ROW][C]121[/C][C]0.215422553229923[/C][C]0.430845106459846[/C][C]0.784577446770077[/C][/ROW]
[ROW][C]122[/C][C]0.258064439005415[/C][C]0.51612887801083[/C][C]0.741935560994585[/C][/ROW]
[ROW][C]123[/C][C]0.208684344362956[/C][C]0.417368688725911[/C][C]0.791315655637044[/C][/ROW]
[ROW][C]124[/C][C]0.291410368109751[/C][C]0.582820736219501[/C][C]0.708589631890249[/C][/ROW]
[ROW][C]125[/C][C]0.284466869345718[/C][C]0.568933738691436[/C][C]0.715533130654282[/C][/ROW]
[ROW][C]126[/C][C]0.295365557295776[/C][C]0.590731114591551[/C][C]0.704634442704224[/C][/ROW]
[ROW][C]127[/C][C]0.584229854914177[/C][C]0.831540290171645[/C][C]0.415770145085823[/C][/ROW]
[ROW][C]128[/C][C]0.509376727916595[/C][C]0.98124654416681[/C][C]0.490623272083405[/C][/ROW]
[ROW][C]129[/C][C]0.502188811743121[/C][C]0.995622376513758[/C][C]0.497811188256879[/C][/ROW]
[ROW][C]130[/C][C]0.890589641869466[/C][C]0.218820716261069[/C][C]0.109410358130534[/C][/ROW]
[ROW][C]131[/C][C]0.974885947289066[/C][C]0.0502281054218686[/C][C]0.0251140527109343[/C][/ROW]
[ROW][C]132[/C][C]0.97609608861965[/C][C]0.0478078227607003[/C][C]0.0239039113803501[/C][/ROW]
[ROW][C]133[/C][C]0.961110853903048[/C][C]0.0777782921939031[/C][C]0.0388891460969515[/C][/ROW]
[ROW][C]134[/C][C]0.974232680588313[/C][C]0.0515346388233749[/C][C]0.0257673194116875[/C][/ROW]
[ROW][C]135[/C][C]0.952022667345129[/C][C]0.0959546653097426[/C][C]0.0479773326548713[/C][/ROW]
[ROW][C]136[/C][C]0.945554063174834[/C][C]0.108891873650331[/C][C]0.0544459368251655[/C][/ROW]
[ROW][C]137[/C][C]0.920912900016472[/C][C]0.158174199967057[/C][C]0.0790870999835284[/C][/ROW]
[ROW][C]138[/C][C]0.973034891543853[/C][C]0.0539302169122937[/C][C]0.0269651084561468[/C][/ROW]
[ROW][C]139[/C][C]0.951210736661153[/C][C]0.0975785266776948[/C][C]0.0487892633388474[/C][/ROW]
[ROW][C]140[/C][C]0.888486748020883[/C][C]0.223026503958234[/C][C]0.111513251979117[/C][/ROW]
[ROW][C]141[/C][C]0.857304784808674[/C][C]0.285390430382653[/C][C]0.142695215191326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98342&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98342&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
110.4438858518421510.8877717036843010.55611414815785
120.2945416903563560.5890833807127120.705458309643644
130.2236507074052170.4473014148104330.776349292594783
140.1359277994820620.2718555989641250.864072200517938
150.1016495445676280.2032990891352570.898350455432372
160.2061075498216150.4122150996432300.793892450178385
170.1371487967287260.2742975934574520.862851203271274
180.1820074928062440.3640149856124880.817992507193756
190.1431358609890470.2862717219780950.856864139010953
200.2806062192196710.5612124384393430.719393780780329
210.3552123322125930.7104246644251850.644787667787407
220.5134872802859130.9730254394281730.486512719714087
230.4365142857328030.8730285714656050.563485714267197
240.3628665391675810.7257330783351620.637133460832419
250.2949514858233660.5899029716467320.705048514176634
260.2705564734090920.5411129468181840.729443526590908
270.2144614417647970.4289228835295950.785538558235203
280.4790368251019920.9580736502039850.520963174898007
290.4118442967437740.8236885934875480.588155703256226
300.5163266361011260.9673467277977480.483673363898874
310.5505074104910330.8989851790179330.449492589508966
320.5860767745924150.827846450815170.413923225407585
330.5244050701473370.9511898597053270.475594929852663
340.6464587501050740.7070824997898530.353541249894926
350.5890093154181370.8219813691637260.410990684581863
360.5732827344797990.8534345310404020.426717265520201
370.7519537664061590.4960924671876830.248046233593841
380.7044259943840850.591148011231830.295574005615915
390.6900987597565010.6198024804869980.309901240243499
400.685728567212460.628542865575080.31427143278754
410.6702078939359980.6595842121280040.329792106064002
420.632380488536720.735239022926560.36761951146328
430.6587227381961230.6825545236077540.341277261803877
440.9611307023580030.07773859528399330.0388692976419966
450.9596918285307020.08061634293859670.0403081714692983
460.9594748332312290.08105033353754240.0405251667687712
470.9513620184103350.09727596317933020.0486379815896651
480.9416177011081180.1167645977837630.0583822988918816
490.9267045423424970.1465909153150060.0732954576575028
500.9080406566134970.1839186867730050.0919593433865027
510.892294790792790.2154104184144220.107705209207211
520.8999872644928460.2000254710143070.100012735507154
530.881858001723330.2362839965533400.118141998276670
540.8605314009061330.2789371981877340.139468599093867
550.895553127925350.2088937441493020.104446872074651
560.8841943809931180.2316112380137650.115805619006882
570.9317582471852550.1364835056294910.0682417528147453
580.9167124928319010.1665750143361970.0832875071680986
590.9052351409863060.1895297180273880.094764859013694
600.8826816046402190.2346367907195620.117318395359781
610.8593820926363870.2812358147272250.140617907363613
620.8548086214135790.2903827571728430.145191378586421
630.8252048496444860.3495903007110270.174795150355514
640.8198648013468320.3602703973063360.180135198653168
650.792383275627780.4152334487444390.207616724372220
660.7557647981760160.4884704036479670.244235201823984
670.7226123291527950.554775341694410.277387670847205
680.6985503362651280.6028993274697450.301449663734872
690.6988284330054130.6023431339891750.301171566994587
700.6578745877389910.6842508245220180.342125412261009
710.6292055663548510.7415888672902980.370794433645149
720.7380544853526620.5238910292946750.261945514647337
730.7818470643321340.4363058713357310.218152935667866
740.7849720439050730.4300559121898540.215027956094927
750.7519651614913380.4960696770173250.248034838508662
760.7130043925547240.5739912148905520.286995607445276
770.7014702173309020.5970595653381970.298529782669098
780.7023857535415490.5952284929169010.297614246458451
790.6759793822578670.6480412354842660.324020617742133
800.6956362767323220.6087274465353560.304363723267678
810.6649780930517520.6700438138964960.335021906948248
820.6213662621488560.7572674757022880.378633737851144
830.5887261214072630.8225477571854740.411273878592737
840.540578142855680.918843714288640.45942185714432
850.4923584884451760.9847169768903520.507641511554824
860.4550755449451990.9101510898903980.544924455054801
870.5156865102686140.9686269794627730.484313489731386
880.4807632906780060.9615265813560130.519236709321994
890.4448941351175210.8897882702350410.555105864882479
900.40212372897380.80424745794760.5978762710262
910.3643029110802570.7286058221605140.635697088919743
920.440052807635180.880105615270360.55994719236482
930.3947440674136760.7894881348273520.605255932586324
940.3628606548428020.7257213096856050.637139345157198
950.3209934220277290.6419868440554590.67900657797227
960.3357344181334930.6714688362669870.664265581866507
970.3052793857624850.610558771524970.694720614237515
980.2990063216634170.5980126433268340.700993678336583
990.3191537231798320.6383074463596640.680846276820168
1000.3378751659429010.6757503318858020.662124834057099
1010.2925502958930070.5851005917860140.707449704106993
1020.266172001843450.53234400368690.73382799815655
1030.2924897459786330.5849794919572650.707510254021367
1040.3051690228169400.6103380456338790.69483097718306
1050.2732461724739220.5464923449478450.726753827526078
1060.2538781724729720.5077563449459440.746121827527028
1070.2133495806664930.4266991613329870.786650419333507
1080.1772101215824700.3544202431649400.82278987841753
1090.1456831860075880.2913663720151770.854316813992412
1100.1205839266000670.2411678532001340.879416073399933
1110.5282290355946690.9435419288106620.471770964405331
1120.493839207298550.98767841459710.50616079270145
1130.4525184743626070.9050369487252140.547481525637393
1140.4014294517521580.8028589035043160.598570548247842
1150.3529744106087070.7059488212174150.647025589391293
1160.3534853838577800.7069707677155590.64651461614222
1170.3054713544861970.6109427089723940.694528645513803
1180.2560082050161150.5120164100322290.743991794983885
1190.2585054958179300.5170109916358590.74149450418207
1200.2451950000489790.4903900000979580.754804999951021
1210.2154225532299230.4308451064598460.784577446770077
1220.2580644390054150.516128878010830.741935560994585
1230.2086843443629560.4173686887259110.791315655637044
1240.2914103681097510.5828207362195010.708589631890249
1250.2844668693457180.5689337386914360.715533130654282
1260.2953655572957760.5907311145915510.704634442704224
1270.5842298549141770.8315402901716450.415770145085823
1280.5093767279165950.981246544166810.490623272083405
1290.5021888117431210.9956223765137580.497811188256879
1300.8905896418694660.2188207162610690.109410358130534
1310.9748859472890660.05022810542186860.0251140527109343
1320.976096088619650.04780782276070030.0239039113803501
1330.9611108539030480.07777829219390310.0388891460969515
1340.9742326805883130.05153463882337490.0257673194116875
1350.9520226673451290.09595466530974260.0479773326548713
1360.9455540631748340.1088918736503310.0544459368251655
1370.9209129000164720.1581741999670570.0790870999835284
1380.9730348915438530.05393021691229370.0269651084561468
1390.9512107366611530.09757852667769480.0487892633388474
1400.8884867480208830.2230265039582340.111513251979117
1410.8573047848086740.2853904303826530.142695215191326






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00763358778625954OK
10% type I error level110.083969465648855OK

\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 & 1 & 0.00763358778625954 & OK \tabularnewline
10% type I error level & 11 & 0.083969465648855 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98342&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]1[/C][C]0.00763358778625954[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.083969465648855[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98342&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98342&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 level10.00763358778625954OK
10% type I error level110.083969465648855OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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')
}