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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 16 Dec 2012 14:54:44 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t13556880846tvubvoymdu7qvo.htm/, Retrieved Sat, 27 Apr 2024 23:29:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200563, Retrieved Sat, 27 Apr 2024 23:29:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-01 13:37:53] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-16 19:15:53] [86dcce9422b96d4554cb918e531c1d5d]
- R  D      [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-16 19:54:44] [c63d55528b56cf8bb48e0b5d1a959d8e] [Current]
- R P         [Multiple Regression] [Paper Multiple Re...] [2012-12-19 10:59:45] [86dcce9422b96d4554cb918e531c1d5d]
- R P         [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-19 18:07:34] [74be16979710d4c4e7c6647856088456]
-   P           [Multiple Regression] [Multiple Regressi...] [2012-12-19 20:22:16] [f4325a5733446a4ce20d70c276c6a563]
-  MP             [Multiple Regression] [Paper 2012 (deel5.7)] [2012-12-22 02:04:24] [74be16979710d4c4e7c6647856088456]
- R P         [Multiple Regression] [Multiple Regressi...] [2012-12-19 19:43:52] [f4325a5733446a4ce20d70c276c6a563]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200563&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200563&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200563&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 time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Outcome [t] = + 0.409019844778356 -0.000189242265685719`Weeks*T`[t] -0.0950775989296687UseLimit[t] + 0.0936292750648398Used[t] -0.100058960911CorrectAnalysis[t] + 0.173486652736788Useful[t] -0.000614737681868545t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Outcome
[t] =  +  0.409019844778356 -0.000189242265685719`Weeks*T`[t] -0.0950775989296687UseLimit[t] +  0.0936292750648398Used[t] -0.100058960911CorrectAnalysis[t] +  0.173486652736788Useful[t] -0.000614737681868545t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200563&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Outcome
[t] =  +  0.409019844778356 -0.000189242265685719`Weeks*T`[t] -0.0950775989296687UseLimit[t] +  0.0936292750648398Used[t] -0.100058960911CorrectAnalysis[t] +  0.173486652736788Useful[t] -0.000614737681868545t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200563&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200563&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
Outcome [t] = + 0.409019844778356 -0.000189242265685719`Weeks*T`[t] -0.0950775989296687UseLimit[t] + 0.0936292750648398Used[t] -0.100058960911CorrectAnalysis[t] + 0.173486652736788Useful[t] -0.000614737681868545t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4090198447783560.095274.29333.2e-051.6e-05
`Weeks*T`-0.0001892422656857190.028968-0.00650.9947970.497398
UseLimit-0.09507759892966870.086031-1.10520.27090.13545
Used0.09362927506483980.100530.93140.3531980.176599
CorrectAnalysis-0.1000589609110.167461-0.59750.5510880.275544
Useful0.1734866527367880.0945471.83490.0685380.034269
t-0.0006147376818685450.000925-0.66450.5074330.253717

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.409019844778356 & 0.09527 & 4.2933 & 3.2e-05 & 1.6e-05 \tabularnewline
`Weeks*T` & -0.000189242265685719 & 0.028968 & -0.0065 & 0.994797 & 0.497398 \tabularnewline
UseLimit & -0.0950775989296687 & 0.086031 & -1.1052 & 0.2709 & 0.13545 \tabularnewline
Used & 0.0936292750648398 & 0.10053 & 0.9314 & 0.353198 & 0.176599 \tabularnewline
CorrectAnalysis & -0.100058960911 & 0.167461 & -0.5975 & 0.551088 & 0.275544 \tabularnewline
Useful & 0.173486652736788 & 0.094547 & 1.8349 & 0.068538 & 0.034269 \tabularnewline
t & -0.000614737681868545 & 0.000925 & -0.6645 & 0.507433 & 0.253717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200563&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.409019844778356[/C][C]0.09527[/C][C]4.2933[/C][C]3.2e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]`Weeks*T`[/C][C]-0.000189242265685719[/C][C]0.028968[/C][C]-0.0065[/C][C]0.994797[/C][C]0.497398[/C][/ROW]
[ROW][C]UseLimit[/C][C]-0.0950775989296687[/C][C]0.086031[/C][C]-1.1052[/C][C]0.2709[/C][C]0.13545[/C][/ROW]
[ROW][C]Used[/C][C]0.0936292750648398[/C][C]0.10053[/C][C]0.9314[/C][C]0.353198[/C][C]0.176599[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]-0.100058960911[/C][C]0.167461[/C][C]-0.5975[/C][C]0.551088[/C][C]0.275544[/C][/ROW]
[ROW][C]Useful[/C][C]0.173486652736788[/C][C]0.094547[/C][C]1.8349[/C][C]0.068538[/C][C]0.034269[/C][/ROW]
[ROW][C]t[/C][C]-0.000614737681868545[/C][C]0.000925[/C][C]-0.6645[/C][C]0.507433[/C][C]0.253717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200563&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200563&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4090198447783560.095274.29333.2e-051.6e-05
`Weeks*T`-0.0001892422656857190.028968-0.00650.9947970.497398
UseLimit-0.09507759892966870.086031-1.10520.27090.13545
Used0.09362927506483980.100530.93140.3531980.176599
CorrectAnalysis-0.1000589609110.167461-0.59750.5510880.275544
Useful0.1734866527367880.0945471.83490.0685380.034269
t-0.0006147376818685450.000925-0.66450.5074330.253717






Multiple Linear Regression - Regression Statistics
Multiple R0.215877452804126
R-squared0.0466030746291977
Adjusted R-squared0.00768891440998121
F-TEST (value)1.19758654347587
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.310788489800201
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.488792166861197
Sum Squared Residuals35.1209140105751

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.215877452804126 \tabularnewline
R-squared & 0.0466030746291977 \tabularnewline
Adjusted R-squared & 0.00768891440998121 \tabularnewline
F-TEST (value) & 1.19758654347587 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0.310788489800201 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.488792166861197 \tabularnewline
Sum Squared Residuals & 35.1209140105751 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200563&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.215877452804126[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0466030746291977[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00768891440998121[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.19758654347587[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0.310788489800201[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.488792166861197[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.1209140105751[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200563&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200563&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 R0.215877452804126
R-squared0.0466030746291977
Adjusted R-squared0.00768891440998121
F-TEST (value)1.19758654347587
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value0.310788489800201
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.488792166861197
Sum Squared Residuals35.1209140105751






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3125705391040760.687429460895924
200.407790369414619-0.407790369414619
300.40717563173275-0.40717563173275
400.406560894050882-0.406560894050882
500.405946156369013-0.405946156369013
610.4837404724942640.516259527505736
700.404716681005276-0.404716681005276
800.403344974260665-0.403344974260665
910.4034872056415390.596512794358461
1000.307794869030002-0.307794869030002
1100.30642316228539-0.30642316228539
1200.401642992595933-0.401642992595933
1300.668144182715693-0.668144182715693
1400.304578949239785-0.304578949239785
1510.6669147073519550.333085292648045
1610.6655430006073440.334456999392656
1700.469791703084807-0.469791703084807
1800.302119998512311-0.302119998512311
1910.3973398288228540.602660171177146
2010.5630250889688690.436974911031131
2100.474519407266235-0.474519407266235
2210.5675339446492070.432466055350793
2310.5683675308321670.431632469167833
2410.472675194220630.52732480577937
2510.4865237087337390.513476291266261
2600.660152592851401-0.660152592851401
2710.2973443284382370.702655671561763
2800.485436464750877-0.485436464750877
2910.3911924520041690.608807547995831
3000.564064367059087-0.564064367059087
3100.389962976640431-0.389962976640431
3200.294270640028894-0.294270640028894
3300.467142555083813-0.467142555083813
3410.3873617945320830.612638205467917
3500.387504025912957-0.387504025912957
3600.386889288231088-0.386889288231088
3700.557555910358436-0.557555910358436
3810.4792890879321910.520710912067809
3910.558531727922270.44146827207773
4000.557160021177659-0.557160021177659
4110.5508725667123730.449127433287627
4210.4768301372047170.523169862795283
4310.4609951782651280.539004821734872
4400.286136818783728-0.286136818783728
4500.554843301831059-0.554843301831059
4610.5542285641491910.445771435850809
4700.380127173730534-0.380127173730534
4810.3795124360486660.620487563951334
4910.5523843511035850.447615648896415
5000.378282960684929-0.378282960684929
5100.470540529005157-0.470540529005157
5200.448275884219407-0.448275884219407
5310.3764387476393230.623561252360677
5400.369394324111294-0.369394324111294
5500.375209272275586-0.375209272275586
5610.4674668405958140.532533159404186
5710.6410957247134760.358904275286524
5810.3733650592299810.626634940770019
5910.3727503215481120.627249678451888
6010.4433579827644590.556642017235541
6110.2756862781919630.724313721808037
6200.638022036304134-0.638022036304134
6300.370291370820638-0.370291370820638
6410.2738420651463580.726157934853642
6500.369061895456901-0.369061895456901
6600.368447157775032-0.368447157775032
6700.534132417921048-0.534132417921048
6800.272140083481626-0.272140083481626
6910.3666029447294270.633397055270573
7000.459617482112398-0.459617482112398
7100.365373469365689-0.365373469365689
7210.3647587316838210.635241268316179
7310.4577732690667920.542226730933208
7400.362080932455255-0.362080932455255
7510.3629145186382150.637085481361785
7610.5350294646303910.464970535369609
7710.3616850432744780.638314956725522
7810.6281862333942370.371813766605763
7910.3532689130018380.646731086998162
8000.532570513902917-0.532570513902917
8100.359226092547004-0.359226092547004
8210.3571630310003070.642836968999693
8300.357996617183267-0.357996617183267
8400.350952193655238-0.350952193655238
8510.5302537945563170.469746205443683
8600.261074805207992-0.261074805207992
8710.2604600675261240.739539932473876
8810.3530961203777240.646903879622276
8900.354308191092055-0.354308191092055
9010.3536934534101870.646306546589813
9100.526565368465106-0.526565368465106
9200.25700789458541-0.25700789458541
9300.4302582941717-0.4302582941717
9400.351234502682713-0.351234502682713
9500.350241280469473-0.350241280469473
9610.3500050273189760.649994972681024
9700.253934206176067-0.253934206176067
9800.348775551955239-0.348775551955239
9900.253083215343701-0.253083215343701
10010.3475460765915020.652453923408498
10110.2518537399799650.748146260020035
10200.346316601227764-0.346316601227764
10300.345701863545896-0.345701863545896
10400.345087125864027-0.345087125864027
10500.437723178715627-0.437723178715627
10600.34385765050029-0.34385765050029
10700.343242912818422-0.343242912818422
10800.340801366740353-0.340801366740353
10900.342013437454685-0.342013437454685
11000.246321100843147-0.246321100843147
11100.512443806431535-0.512443806431535
11200.339790739877708-0.339790739877708
11300.43318376179205-0.43318376179205
11400.337112940649142-0.337112940649142
11500.243247412433805-0.243247412433805
11600.337710273681605-0.337710273681605
11710.2420179370700680.757982062929932
11800.241403199388199-0.241403199388199
11900.335866060635999-0.335866060635999
12010.3352513229541310.664748677045869
12100.239558986342593-0.239558986342593
12200.334021847590394-0.334021847590394
12300.331580301512325-0.331580301512325
12410.5999083000282840.400091699971716
12510.3321776345447880.667822365455212
12600.331184412331548-0.331184412331548
12700.504434811917839-0.504434811917839
12810.3303334214991820.669666578500818
12900.329718683817314-0.329718683817314
13010.3291039461354450.670896053864555
13100.233411609523908-0.233411609523908
13210.232796871842040.76720312815796
13300.325811409225011-0.325811409225011
13400.326644995407971-0.326644995407971
13500.326030257726102-0.326030257726102
13600.325415520044234-0.325415520044234
13710.4968391112343240.503160888765676
13810.4958458890210840.504154110978916
13900.323192822467257-0.323192822467257
14000.32295656931676-0.32295656931676
14110.3159121457887310.684087854211269
14210.4149778844864910.585022115513509
14300.226034757341485-0.226034757341485
14410.4939842713260730.506015728673927
14500.493369533644205-0.493369533644205
14610.3188896586941770.681110341305823
14700.411904196077148-0.411904196077148
14800.31766018333044-0.31766018333044
14900.222346331250274-0.222346331250274
15010.4902958452348620.509704154765138
15110.3161944548162060.683805545183794
15200.214072432358508-0.214072432358508
15300.386944347413427-0.386944347413427
15400.312901917905771-0.312901917905771

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.312570539104076 & 0.687429460895924 \tabularnewline
2 & 0 & 0.407790369414619 & -0.407790369414619 \tabularnewline
3 & 0 & 0.40717563173275 & -0.40717563173275 \tabularnewline
4 & 0 & 0.406560894050882 & -0.406560894050882 \tabularnewline
5 & 0 & 0.405946156369013 & -0.405946156369013 \tabularnewline
6 & 1 & 0.483740472494264 & 0.516259527505736 \tabularnewline
7 & 0 & 0.404716681005276 & -0.404716681005276 \tabularnewline
8 & 0 & 0.403344974260665 & -0.403344974260665 \tabularnewline
9 & 1 & 0.403487205641539 & 0.596512794358461 \tabularnewline
10 & 0 & 0.307794869030002 & -0.307794869030002 \tabularnewline
11 & 0 & 0.30642316228539 & -0.30642316228539 \tabularnewline
12 & 0 & 0.401642992595933 & -0.401642992595933 \tabularnewline
13 & 0 & 0.668144182715693 & -0.668144182715693 \tabularnewline
14 & 0 & 0.304578949239785 & -0.304578949239785 \tabularnewline
15 & 1 & 0.666914707351955 & 0.333085292648045 \tabularnewline
16 & 1 & 0.665543000607344 & 0.334456999392656 \tabularnewline
17 & 0 & 0.469791703084807 & -0.469791703084807 \tabularnewline
18 & 0 & 0.302119998512311 & -0.302119998512311 \tabularnewline
19 & 1 & 0.397339828822854 & 0.602660171177146 \tabularnewline
20 & 1 & 0.563025088968869 & 0.436974911031131 \tabularnewline
21 & 0 & 0.474519407266235 & -0.474519407266235 \tabularnewline
22 & 1 & 0.567533944649207 & 0.432466055350793 \tabularnewline
23 & 1 & 0.568367530832167 & 0.431632469167833 \tabularnewline
24 & 1 & 0.47267519422063 & 0.52732480577937 \tabularnewline
25 & 1 & 0.486523708733739 & 0.513476291266261 \tabularnewline
26 & 0 & 0.660152592851401 & -0.660152592851401 \tabularnewline
27 & 1 & 0.297344328438237 & 0.702655671561763 \tabularnewline
28 & 0 & 0.485436464750877 & -0.485436464750877 \tabularnewline
29 & 1 & 0.391192452004169 & 0.608807547995831 \tabularnewline
30 & 0 & 0.564064367059087 & -0.564064367059087 \tabularnewline
31 & 0 & 0.389962976640431 & -0.389962976640431 \tabularnewline
32 & 0 & 0.294270640028894 & -0.294270640028894 \tabularnewline
33 & 0 & 0.467142555083813 & -0.467142555083813 \tabularnewline
34 & 1 & 0.387361794532083 & 0.612638205467917 \tabularnewline
35 & 0 & 0.387504025912957 & -0.387504025912957 \tabularnewline
36 & 0 & 0.386889288231088 & -0.386889288231088 \tabularnewline
37 & 0 & 0.557555910358436 & -0.557555910358436 \tabularnewline
38 & 1 & 0.479289087932191 & 0.520710912067809 \tabularnewline
39 & 1 & 0.55853172792227 & 0.44146827207773 \tabularnewline
40 & 0 & 0.557160021177659 & -0.557160021177659 \tabularnewline
41 & 1 & 0.550872566712373 & 0.449127433287627 \tabularnewline
42 & 1 & 0.476830137204717 & 0.523169862795283 \tabularnewline
43 & 1 & 0.460995178265128 & 0.539004821734872 \tabularnewline
44 & 0 & 0.286136818783728 & -0.286136818783728 \tabularnewline
45 & 0 & 0.554843301831059 & -0.554843301831059 \tabularnewline
46 & 1 & 0.554228564149191 & 0.445771435850809 \tabularnewline
47 & 0 & 0.380127173730534 & -0.380127173730534 \tabularnewline
48 & 1 & 0.379512436048666 & 0.620487563951334 \tabularnewline
49 & 1 & 0.552384351103585 & 0.447615648896415 \tabularnewline
50 & 0 & 0.378282960684929 & -0.378282960684929 \tabularnewline
51 & 0 & 0.470540529005157 & -0.470540529005157 \tabularnewline
52 & 0 & 0.448275884219407 & -0.448275884219407 \tabularnewline
53 & 1 & 0.376438747639323 & 0.623561252360677 \tabularnewline
54 & 0 & 0.369394324111294 & -0.369394324111294 \tabularnewline
55 & 0 & 0.375209272275586 & -0.375209272275586 \tabularnewline
56 & 1 & 0.467466840595814 & 0.532533159404186 \tabularnewline
57 & 1 & 0.641095724713476 & 0.358904275286524 \tabularnewline
58 & 1 & 0.373365059229981 & 0.626634940770019 \tabularnewline
59 & 1 & 0.372750321548112 & 0.627249678451888 \tabularnewline
60 & 1 & 0.443357982764459 & 0.556642017235541 \tabularnewline
61 & 1 & 0.275686278191963 & 0.724313721808037 \tabularnewline
62 & 0 & 0.638022036304134 & -0.638022036304134 \tabularnewline
63 & 0 & 0.370291370820638 & -0.370291370820638 \tabularnewline
64 & 1 & 0.273842065146358 & 0.726157934853642 \tabularnewline
65 & 0 & 0.369061895456901 & -0.369061895456901 \tabularnewline
66 & 0 & 0.368447157775032 & -0.368447157775032 \tabularnewline
67 & 0 & 0.534132417921048 & -0.534132417921048 \tabularnewline
68 & 0 & 0.272140083481626 & -0.272140083481626 \tabularnewline
69 & 1 & 0.366602944729427 & 0.633397055270573 \tabularnewline
70 & 0 & 0.459617482112398 & -0.459617482112398 \tabularnewline
71 & 0 & 0.365373469365689 & -0.365373469365689 \tabularnewline
72 & 1 & 0.364758731683821 & 0.635241268316179 \tabularnewline
73 & 1 & 0.457773269066792 & 0.542226730933208 \tabularnewline
74 & 0 & 0.362080932455255 & -0.362080932455255 \tabularnewline
75 & 1 & 0.362914518638215 & 0.637085481361785 \tabularnewline
76 & 1 & 0.535029464630391 & 0.464970535369609 \tabularnewline
77 & 1 & 0.361685043274478 & 0.638314956725522 \tabularnewline
78 & 1 & 0.628186233394237 & 0.371813766605763 \tabularnewline
79 & 1 & 0.353268913001838 & 0.646731086998162 \tabularnewline
80 & 0 & 0.532570513902917 & -0.532570513902917 \tabularnewline
81 & 0 & 0.359226092547004 & -0.359226092547004 \tabularnewline
82 & 1 & 0.357163031000307 & 0.642836968999693 \tabularnewline
83 & 0 & 0.357996617183267 & -0.357996617183267 \tabularnewline
84 & 0 & 0.350952193655238 & -0.350952193655238 \tabularnewline
85 & 1 & 0.530253794556317 & 0.469746205443683 \tabularnewline
86 & 0 & 0.261074805207992 & -0.261074805207992 \tabularnewline
87 & 1 & 0.260460067526124 & 0.739539932473876 \tabularnewline
88 & 1 & 0.353096120377724 & 0.646903879622276 \tabularnewline
89 & 0 & 0.354308191092055 & -0.354308191092055 \tabularnewline
90 & 1 & 0.353693453410187 & 0.646306546589813 \tabularnewline
91 & 0 & 0.526565368465106 & -0.526565368465106 \tabularnewline
92 & 0 & 0.25700789458541 & -0.25700789458541 \tabularnewline
93 & 0 & 0.4302582941717 & -0.4302582941717 \tabularnewline
94 & 0 & 0.351234502682713 & -0.351234502682713 \tabularnewline
95 & 0 & 0.350241280469473 & -0.350241280469473 \tabularnewline
96 & 1 & 0.350005027318976 & 0.649994972681024 \tabularnewline
97 & 0 & 0.253934206176067 & -0.253934206176067 \tabularnewline
98 & 0 & 0.348775551955239 & -0.348775551955239 \tabularnewline
99 & 0 & 0.253083215343701 & -0.253083215343701 \tabularnewline
100 & 1 & 0.347546076591502 & 0.652453923408498 \tabularnewline
101 & 1 & 0.251853739979965 & 0.748146260020035 \tabularnewline
102 & 0 & 0.346316601227764 & -0.346316601227764 \tabularnewline
103 & 0 & 0.345701863545896 & -0.345701863545896 \tabularnewline
104 & 0 & 0.345087125864027 & -0.345087125864027 \tabularnewline
105 & 0 & 0.437723178715627 & -0.437723178715627 \tabularnewline
106 & 0 & 0.34385765050029 & -0.34385765050029 \tabularnewline
107 & 0 & 0.343242912818422 & -0.343242912818422 \tabularnewline
108 & 0 & 0.340801366740353 & -0.340801366740353 \tabularnewline
109 & 0 & 0.342013437454685 & -0.342013437454685 \tabularnewline
110 & 0 & 0.246321100843147 & -0.246321100843147 \tabularnewline
111 & 0 & 0.512443806431535 & -0.512443806431535 \tabularnewline
112 & 0 & 0.339790739877708 & -0.339790739877708 \tabularnewline
113 & 0 & 0.43318376179205 & -0.43318376179205 \tabularnewline
114 & 0 & 0.337112940649142 & -0.337112940649142 \tabularnewline
115 & 0 & 0.243247412433805 & -0.243247412433805 \tabularnewline
116 & 0 & 0.337710273681605 & -0.337710273681605 \tabularnewline
117 & 1 & 0.242017937070068 & 0.757982062929932 \tabularnewline
118 & 0 & 0.241403199388199 & -0.241403199388199 \tabularnewline
119 & 0 & 0.335866060635999 & -0.335866060635999 \tabularnewline
120 & 1 & 0.335251322954131 & 0.664748677045869 \tabularnewline
121 & 0 & 0.239558986342593 & -0.239558986342593 \tabularnewline
122 & 0 & 0.334021847590394 & -0.334021847590394 \tabularnewline
123 & 0 & 0.331580301512325 & -0.331580301512325 \tabularnewline
124 & 1 & 0.599908300028284 & 0.400091699971716 \tabularnewline
125 & 1 & 0.332177634544788 & 0.667822365455212 \tabularnewline
126 & 0 & 0.331184412331548 & -0.331184412331548 \tabularnewline
127 & 0 & 0.504434811917839 & -0.504434811917839 \tabularnewline
128 & 1 & 0.330333421499182 & 0.669666578500818 \tabularnewline
129 & 0 & 0.329718683817314 & -0.329718683817314 \tabularnewline
130 & 1 & 0.329103946135445 & 0.670896053864555 \tabularnewline
131 & 0 & 0.233411609523908 & -0.233411609523908 \tabularnewline
132 & 1 & 0.23279687184204 & 0.76720312815796 \tabularnewline
133 & 0 & 0.325811409225011 & -0.325811409225011 \tabularnewline
134 & 0 & 0.326644995407971 & -0.326644995407971 \tabularnewline
135 & 0 & 0.326030257726102 & -0.326030257726102 \tabularnewline
136 & 0 & 0.325415520044234 & -0.325415520044234 \tabularnewline
137 & 1 & 0.496839111234324 & 0.503160888765676 \tabularnewline
138 & 1 & 0.495845889021084 & 0.504154110978916 \tabularnewline
139 & 0 & 0.323192822467257 & -0.323192822467257 \tabularnewline
140 & 0 & 0.32295656931676 & -0.32295656931676 \tabularnewline
141 & 1 & 0.315912145788731 & 0.684087854211269 \tabularnewline
142 & 1 & 0.414977884486491 & 0.585022115513509 \tabularnewline
143 & 0 & 0.226034757341485 & -0.226034757341485 \tabularnewline
144 & 1 & 0.493984271326073 & 0.506015728673927 \tabularnewline
145 & 0 & 0.493369533644205 & -0.493369533644205 \tabularnewline
146 & 1 & 0.318889658694177 & 0.681110341305823 \tabularnewline
147 & 0 & 0.411904196077148 & -0.411904196077148 \tabularnewline
148 & 0 & 0.31766018333044 & -0.31766018333044 \tabularnewline
149 & 0 & 0.222346331250274 & -0.222346331250274 \tabularnewline
150 & 1 & 0.490295845234862 & 0.509704154765138 \tabularnewline
151 & 1 & 0.316194454816206 & 0.683805545183794 \tabularnewline
152 & 0 & 0.214072432358508 & -0.214072432358508 \tabularnewline
153 & 0 & 0.386944347413427 & -0.386944347413427 \tabularnewline
154 & 0 & 0.312901917905771 & -0.312901917905771 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200563&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]0.312570539104076[/C][C]0.687429460895924[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.407790369414619[/C][C]-0.407790369414619[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.40717563173275[/C][C]-0.40717563173275[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.406560894050882[/C][C]-0.406560894050882[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.405946156369013[/C][C]-0.405946156369013[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.483740472494264[/C][C]0.516259527505736[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.404716681005276[/C][C]-0.404716681005276[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.403344974260665[/C][C]-0.403344974260665[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.403487205641539[/C][C]0.596512794358461[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.307794869030002[/C][C]-0.307794869030002[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.30642316228539[/C][C]-0.30642316228539[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.401642992595933[/C][C]-0.401642992595933[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.668144182715693[/C][C]-0.668144182715693[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.304578949239785[/C][C]-0.304578949239785[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.666914707351955[/C][C]0.333085292648045[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.665543000607344[/C][C]0.334456999392656[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.469791703084807[/C][C]-0.469791703084807[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.302119998512311[/C][C]-0.302119998512311[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.397339828822854[/C][C]0.602660171177146[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.563025088968869[/C][C]0.436974911031131[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.474519407266235[/C][C]-0.474519407266235[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.567533944649207[/C][C]0.432466055350793[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.568367530832167[/C][C]0.431632469167833[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.47267519422063[/C][C]0.52732480577937[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.486523708733739[/C][C]0.513476291266261[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.660152592851401[/C][C]-0.660152592851401[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.297344328438237[/C][C]0.702655671561763[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.485436464750877[/C][C]-0.485436464750877[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.391192452004169[/C][C]0.608807547995831[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.564064367059087[/C][C]-0.564064367059087[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.389962976640431[/C][C]-0.389962976640431[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.294270640028894[/C][C]-0.294270640028894[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.467142555083813[/C][C]-0.467142555083813[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.387361794532083[/C][C]0.612638205467917[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.387504025912957[/C][C]-0.387504025912957[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.386889288231088[/C][C]-0.386889288231088[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.557555910358436[/C][C]-0.557555910358436[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.479289087932191[/C][C]0.520710912067809[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.55853172792227[/C][C]0.44146827207773[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.557160021177659[/C][C]-0.557160021177659[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.550872566712373[/C][C]0.449127433287627[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.476830137204717[/C][C]0.523169862795283[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.460995178265128[/C][C]0.539004821734872[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.286136818783728[/C][C]-0.286136818783728[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.554843301831059[/C][C]-0.554843301831059[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.554228564149191[/C][C]0.445771435850809[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.380127173730534[/C][C]-0.380127173730534[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.379512436048666[/C][C]0.620487563951334[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.552384351103585[/C][C]0.447615648896415[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.378282960684929[/C][C]-0.378282960684929[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.470540529005157[/C][C]-0.470540529005157[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.448275884219407[/C][C]-0.448275884219407[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.376438747639323[/C][C]0.623561252360677[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.369394324111294[/C][C]-0.369394324111294[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.375209272275586[/C][C]-0.375209272275586[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.467466840595814[/C][C]0.532533159404186[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.641095724713476[/C][C]0.358904275286524[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.373365059229981[/C][C]0.626634940770019[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.372750321548112[/C][C]0.627249678451888[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.443357982764459[/C][C]0.556642017235541[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.275686278191963[/C][C]0.724313721808037[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.638022036304134[/C][C]-0.638022036304134[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.370291370820638[/C][C]-0.370291370820638[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.273842065146358[/C][C]0.726157934853642[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.369061895456901[/C][C]-0.369061895456901[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.368447157775032[/C][C]-0.368447157775032[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.534132417921048[/C][C]-0.534132417921048[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.272140083481626[/C][C]-0.272140083481626[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.366602944729427[/C][C]0.633397055270573[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.459617482112398[/C][C]-0.459617482112398[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.365373469365689[/C][C]-0.365373469365689[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.364758731683821[/C][C]0.635241268316179[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.457773269066792[/C][C]0.542226730933208[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.362080932455255[/C][C]-0.362080932455255[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.362914518638215[/C][C]0.637085481361785[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.535029464630391[/C][C]0.464970535369609[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.361685043274478[/C][C]0.638314956725522[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.628186233394237[/C][C]0.371813766605763[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.353268913001838[/C][C]0.646731086998162[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.532570513902917[/C][C]-0.532570513902917[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.359226092547004[/C][C]-0.359226092547004[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.357163031000307[/C][C]0.642836968999693[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.357996617183267[/C][C]-0.357996617183267[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.350952193655238[/C][C]-0.350952193655238[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.530253794556317[/C][C]0.469746205443683[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.261074805207992[/C][C]-0.261074805207992[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.260460067526124[/C][C]0.739539932473876[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.353096120377724[/C][C]0.646903879622276[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.354308191092055[/C][C]-0.354308191092055[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.353693453410187[/C][C]0.646306546589813[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.526565368465106[/C][C]-0.526565368465106[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.25700789458541[/C][C]-0.25700789458541[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.4302582941717[/C][C]-0.4302582941717[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.351234502682713[/C][C]-0.351234502682713[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.350241280469473[/C][C]-0.350241280469473[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.350005027318976[/C][C]0.649994972681024[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.253934206176067[/C][C]-0.253934206176067[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.348775551955239[/C][C]-0.348775551955239[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.253083215343701[/C][C]-0.253083215343701[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0.347546076591502[/C][C]0.652453923408498[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.251853739979965[/C][C]0.748146260020035[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.346316601227764[/C][C]-0.346316601227764[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.345701863545896[/C][C]-0.345701863545896[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.345087125864027[/C][C]-0.345087125864027[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.437723178715627[/C][C]-0.437723178715627[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.34385765050029[/C][C]-0.34385765050029[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.343242912818422[/C][C]-0.343242912818422[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.340801366740353[/C][C]-0.340801366740353[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.342013437454685[/C][C]-0.342013437454685[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.246321100843147[/C][C]-0.246321100843147[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.512443806431535[/C][C]-0.512443806431535[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.339790739877708[/C][C]-0.339790739877708[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.43318376179205[/C][C]-0.43318376179205[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.337112940649142[/C][C]-0.337112940649142[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.243247412433805[/C][C]-0.243247412433805[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.337710273681605[/C][C]-0.337710273681605[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.242017937070068[/C][C]0.757982062929932[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.241403199388199[/C][C]-0.241403199388199[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.335866060635999[/C][C]-0.335866060635999[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.335251322954131[/C][C]0.664748677045869[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.239558986342593[/C][C]-0.239558986342593[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.334021847590394[/C][C]-0.334021847590394[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.331580301512325[/C][C]-0.331580301512325[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.599908300028284[/C][C]0.400091699971716[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.332177634544788[/C][C]0.667822365455212[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.331184412331548[/C][C]-0.331184412331548[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.504434811917839[/C][C]-0.504434811917839[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.330333421499182[/C][C]0.669666578500818[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.329718683817314[/C][C]-0.329718683817314[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.329103946135445[/C][C]0.670896053864555[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.233411609523908[/C][C]-0.233411609523908[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.23279687184204[/C][C]0.76720312815796[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.325811409225011[/C][C]-0.325811409225011[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.326644995407971[/C][C]-0.326644995407971[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.326030257726102[/C][C]-0.326030257726102[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.325415520044234[/C][C]-0.325415520044234[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.496839111234324[/C][C]0.503160888765676[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.495845889021084[/C][C]0.504154110978916[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.323192822467257[/C][C]-0.323192822467257[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.32295656931676[/C][C]-0.32295656931676[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.315912145788731[/C][C]0.684087854211269[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0.414977884486491[/C][C]0.585022115513509[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.226034757341485[/C][C]-0.226034757341485[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0.493984271326073[/C][C]0.506015728673927[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.493369533644205[/C][C]-0.493369533644205[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.318889658694177[/C][C]0.681110341305823[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.411904196077148[/C][C]-0.411904196077148[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.31766018333044[/C][C]-0.31766018333044[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.222346331250274[/C][C]-0.222346331250274[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0.490295845234862[/C][C]0.509704154765138[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0.316194454816206[/C][C]0.683805545183794[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.214072432358508[/C][C]-0.214072432358508[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.386944347413427[/C][C]-0.386944347413427[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.312901917905771[/C][C]-0.312901917905771[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200563&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200563&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3125705391040760.687429460895924
200.407790369414619-0.407790369414619
300.40717563173275-0.40717563173275
400.406560894050882-0.406560894050882
500.405946156369013-0.405946156369013
610.4837404724942640.516259527505736
700.404716681005276-0.404716681005276
800.403344974260665-0.403344974260665
910.4034872056415390.596512794358461
1000.307794869030002-0.307794869030002
1100.30642316228539-0.30642316228539
1200.401642992595933-0.401642992595933
1300.668144182715693-0.668144182715693
1400.304578949239785-0.304578949239785
1510.6669147073519550.333085292648045
1610.6655430006073440.334456999392656
1700.469791703084807-0.469791703084807
1800.302119998512311-0.302119998512311
1910.3973398288228540.602660171177146
2010.5630250889688690.436974911031131
2100.474519407266235-0.474519407266235
2210.5675339446492070.432466055350793
2310.5683675308321670.431632469167833
2410.472675194220630.52732480577937
2510.4865237087337390.513476291266261
2600.660152592851401-0.660152592851401
2710.2973443284382370.702655671561763
2800.485436464750877-0.485436464750877
2910.3911924520041690.608807547995831
3000.564064367059087-0.564064367059087
3100.389962976640431-0.389962976640431
3200.294270640028894-0.294270640028894
3300.467142555083813-0.467142555083813
3410.3873617945320830.612638205467917
3500.387504025912957-0.387504025912957
3600.386889288231088-0.386889288231088
3700.557555910358436-0.557555910358436
3810.4792890879321910.520710912067809
3910.558531727922270.44146827207773
4000.557160021177659-0.557160021177659
4110.5508725667123730.449127433287627
4210.4768301372047170.523169862795283
4310.4609951782651280.539004821734872
4400.286136818783728-0.286136818783728
4500.554843301831059-0.554843301831059
4610.5542285641491910.445771435850809
4700.380127173730534-0.380127173730534
4810.3795124360486660.620487563951334
4910.5523843511035850.447615648896415
5000.378282960684929-0.378282960684929
5100.470540529005157-0.470540529005157
5200.448275884219407-0.448275884219407
5310.3764387476393230.623561252360677
5400.369394324111294-0.369394324111294
5500.375209272275586-0.375209272275586
5610.4674668405958140.532533159404186
5710.6410957247134760.358904275286524
5810.3733650592299810.626634940770019
5910.3727503215481120.627249678451888
6010.4433579827644590.556642017235541
6110.2756862781919630.724313721808037
6200.638022036304134-0.638022036304134
6300.370291370820638-0.370291370820638
6410.2738420651463580.726157934853642
6500.369061895456901-0.369061895456901
6600.368447157775032-0.368447157775032
6700.534132417921048-0.534132417921048
6800.272140083481626-0.272140083481626
6910.3666029447294270.633397055270573
7000.459617482112398-0.459617482112398
7100.365373469365689-0.365373469365689
7210.3647587316838210.635241268316179
7310.4577732690667920.542226730933208
7400.362080932455255-0.362080932455255
7510.3629145186382150.637085481361785
7610.5350294646303910.464970535369609
7710.3616850432744780.638314956725522
7810.6281862333942370.371813766605763
7910.3532689130018380.646731086998162
8000.532570513902917-0.532570513902917
8100.359226092547004-0.359226092547004
8210.3571630310003070.642836968999693
8300.357996617183267-0.357996617183267
8400.350952193655238-0.350952193655238
8510.5302537945563170.469746205443683
8600.261074805207992-0.261074805207992
8710.2604600675261240.739539932473876
8810.3530961203777240.646903879622276
8900.354308191092055-0.354308191092055
9010.3536934534101870.646306546589813
9100.526565368465106-0.526565368465106
9200.25700789458541-0.25700789458541
9300.4302582941717-0.4302582941717
9400.351234502682713-0.351234502682713
9500.350241280469473-0.350241280469473
9610.3500050273189760.649994972681024
9700.253934206176067-0.253934206176067
9800.348775551955239-0.348775551955239
9900.253083215343701-0.253083215343701
10010.3475460765915020.652453923408498
10110.2518537399799650.748146260020035
10200.346316601227764-0.346316601227764
10300.345701863545896-0.345701863545896
10400.345087125864027-0.345087125864027
10500.437723178715627-0.437723178715627
10600.34385765050029-0.34385765050029
10700.343242912818422-0.343242912818422
10800.340801366740353-0.340801366740353
10900.342013437454685-0.342013437454685
11000.246321100843147-0.246321100843147
11100.512443806431535-0.512443806431535
11200.339790739877708-0.339790739877708
11300.43318376179205-0.43318376179205
11400.337112940649142-0.337112940649142
11500.243247412433805-0.243247412433805
11600.337710273681605-0.337710273681605
11710.2420179370700680.757982062929932
11800.241403199388199-0.241403199388199
11900.335866060635999-0.335866060635999
12010.3352513229541310.664748677045869
12100.239558986342593-0.239558986342593
12200.334021847590394-0.334021847590394
12300.331580301512325-0.331580301512325
12410.5999083000282840.400091699971716
12510.3321776345447880.667822365455212
12600.331184412331548-0.331184412331548
12700.504434811917839-0.504434811917839
12810.3303334214991820.669666578500818
12900.329718683817314-0.329718683817314
13010.3291039461354450.670896053864555
13100.233411609523908-0.233411609523908
13210.232796871842040.76720312815796
13300.325811409225011-0.325811409225011
13400.326644995407971-0.326644995407971
13500.326030257726102-0.326030257726102
13600.325415520044234-0.325415520044234
13710.4968391112343240.503160888765676
13810.4958458890210840.504154110978916
13900.323192822467257-0.323192822467257
14000.32295656931676-0.32295656931676
14110.3159121457887310.684087854211269
14210.4149778844864910.585022115513509
14300.226034757341485-0.226034757341485
14410.4939842713260730.506015728673927
14500.493369533644205-0.493369533644205
14610.3188896586941770.681110341305823
14700.411904196077148-0.411904196077148
14800.31766018333044-0.31766018333044
14900.222346331250274-0.222346331250274
15010.4902958452348620.509704154765138
15110.3161944548162060.683805545183794
15200.214072432358508-0.214072432358508
15300.386944347413427-0.386944347413427
15400.312901917905771-0.312901917905771






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8410637309372020.3178725381255970.158936269062798
110.7888480220873720.4223039558252560.211151977912628
120.6808118441099810.6383763117800380.319188155890019
130.5702337173681150.859532565263770.429766282631885
140.4652722068755070.9305444137510140.534727793124493
150.5930098013649630.8139803972700730.406990198635037
160.5313480418562190.9373039162875620.468651958143781
170.4393499245993610.8786998491987220.560650075400639
180.3536612450802450.707322490160490.646338754919755
190.5558718316695360.8882563366609270.444128168330464
200.5566710296288910.8866579407422170.443328970371109
210.6491299843989470.7017400312021060.350870015601053
220.644712625078920.7105747498421590.35528737492108
230.5924172335245710.8151655329508590.40758276647543
240.5500730650809110.8998538698381780.449926934919089
250.5308722810914630.9382554378170730.469127718908536
260.6523482119767430.6953035760465150.347651788023258
270.675954533123030.648090933753940.32404546687697
280.668252252961580.663495494076840.33174774703842
290.6497169377675620.7005661244648750.350283062232438
300.7403132612347720.5193734775304550.259686738765228
310.728984089848630.542031820302740.27101591015137
320.7039299123742250.592140175251550.296070087625775
330.7114386814964160.5771226370071680.288561318503584
340.7088149229028690.5823701541942620.291185077097131
350.6874620765546140.6250758468907720.312537923445386
360.6609164840675210.6781670318649590.339083515932479
370.696312844769790.607374310460420.30368715523021
380.7112505572619650.5774988854760690.288749442738035
390.6949359695879740.6101280608240510.305064030412026
400.7168164485167140.5663671029665720.283183551483286
410.6928562263262230.6142875473475530.307143773673777
420.6783795204942490.6432409590115010.321620479505751
430.6740159647643770.6519680704712460.325984035235623
440.6514810954320190.6970378091359610.348518904567981
450.6640582566680780.6718834866638440.335941743331922
460.6521563712072610.6956872575854780.347843628792739
470.639621146999610.720757706000780.36037885300039
480.6507405131814860.6985189736370280.349259486818514
490.6316237472292350.736752505541530.368376252770765
500.6244181426981710.7511637146036570.375581857301829
510.6290035065114150.741992986977170.370996493488585
520.6365800733853110.7268398532293790.363419926614689
530.6496646178257680.7006707643484630.350335382174232
540.6355791457367010.7288417085265980.364420854263299
550.6215955408010790.7568089183978420.378404459198921
560.619578094941340.760843810117320.38042190505866
570.5860266882647420.8279466234705150.413973311735258
580.5971289006991040.8057421986017910.402871099300896
590.604790278088010.7904194438239810.39520972191199
600.6009242192157940.7981515615684110.399075780784206
610.6193933561891860.7612132876216290.380606643810814
620.6758846173136280.6482307653727450.324115382686372
630.6727465781548150.6545068436903690.327253421845185
640.7031440611473280.5937118777053450.296855938852672
650.6994778626302210.6010442747395580.300522137369779
660.6930455869315660.6139088261368690.306954413068434
670.7025698922814130.5948602154371750.297430107718587
680.6847597698135120.6304804603729750.315240230186488
690.6951118911623970.6097762176752060.304888108837603
700.7040200105804630.5919599788390740.295979989419537
710.6948894736743720.6102210526512560.305110526325628
720.7052801230542020.5894397538915970.294719876945798
730.6975055325135520.6049889349728960.302494467486448
740.6901062070060050.619787585987990.309893792993995
750.701090815833870.597818368332260.29890918416613
760.6944393377423970.6111213245152060.305560662257603
770.7085484771534350.582903045693130.291451522846565
780.6815640268310280.6368719463379440.318435973168972
790.7398752048590760.5202495902818490.260124795140924
800.7466380017395990.5067239965208010.253361998260401
810.7359121074371890.5281757851256230.264087892562811
820.7498863767529310.5002272464941390.250113623247069
830.7371932895090210.5256134209819590.262806710490979
840.7162084155523280.5675831688953430.283791584447672
850.7124208063147110.5751583873705790.287579193685289
860.6886004611992150.622799077601570.311399538800785
870.7398829774216820.5202340451566360.260117022578318
880.7952329614759110.4095340770481780.204767038524089
890.7798795793697390.4402408412605220.220120420630261
900.8184494233451960.3631011533096080.181550576654804
910.8204012026562810.3591975946874380.179598797343719
920.7999368650797450.400126269840510.200063134920255
930.7898300572610390.4203398854779220.210169942738961
940.769926188754170.460147622491660.23007381124583
950.7437931096992520.5124137806014960.256206890300748
960.787766865132560.424466269734880.21223313486744
970.7587407897615030.4825184204769950.241259210238497
980.7332295755549520.5335408488900960.266770424445048
990.6985467842915570.6029064314168860.301453215708443
1000.7550241354572890.4899517290854220.244975864542711
1010.8464158286343020.3071683427313960.153584171365698
1020.8231444677771680.3537110644456630.176855532222832
1030.797228912080060.4055421758398810.202771087919941
1040.7688665720643230.4622668558713540.231133427935677
1050.7453408811328810.5093182377342390.254659118867119
1060.7132409165122120.5735181669755750.286759083487788
1070.6801518253465580.6396963493068840.319848174653442
1080.6423672280604870.7152655438790270.357632771939513
1090.607729634507560.7845407309848790.39227036549244
1100.5599144832606860.8801710334786280.440085516739314
1110.5410121805053480.9179756389893030.458987819494651
1120.5007261004834330.9985477990331340.499273899516567
1130.4948347892240180.9896695784480360.505165210775982
1140.4561751762633340.9123503525266680.543824823736666
1150.4088217117711030.8176434235422070.591178288228897
1160.3901289542666170.7802579085332350.609871045733383
1170.487082875330760.9741657506615210.51291712466924
1180.4328019194953250.8656038389906490.567198080504675
1190.4126244398314790.8252488796629580.587375560168521
1200.4404712429288760.8809424858577530.559528757071124
1210.3849642897112570.7699285794225140.615035710288743
1220.362620930790050.7252418615800990.63737906920995
1230.3304676569222120.6609353138444240.669532343077788
1240.2852703534240810.5705407068481630.714729646575919
1250.3069347246170760.6138694492341520.693065275382924
1260.2845537534313530.5691075068627070.715446246568647
1270.3513363717918740.7026727435837490.648663628208126
1280.3650532664770870.7301065329541740.634946733522913
1290.3470076740056030.6940153480112060.652992325994397
1300.3696745081137090.7393490162274170.630325491886291
1310.3120202503195230.6240405006390460.687979749680477
1320.491133092184940.982266184369880.50886690781506
1330.4246366447792390.8492732895584790.575363355220761
1340.3691041296710610.7382082593421230.630895870328939
1350.3282465574678030.6564931149356060.671753442532197
1360.3194165709869150.638833141973830.680583429013085
1370.265056353806430.530112707612860.73494364619357
1380.3744310473725250.7488620947450510.625568952627475
1390.3188353099843950.637670619968790.681164690015605
1400.4298198217372160.8596396434744330.570180178262784
1410.3312375596250850.662475119250170.668762440374915
1420.4174214602238930.8348429204477860.582578539776107
1430.291147650263880.5822953005277610.70885234973612
1440.3231865028226240.6463730056452490.676813497177376

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.841063730937202 & 0.317872538125597 & 0.158936269062798 \tabularnewline
11 & 0.788848022087372 & 0.422303955825256 & 0.211151977912628 \tabularnewline
12 & 0.680811844109981 & 0.638376311780038 & 0.319188155890019 \tabularnewline
13 & 0.570233717368115 & 0.85953256526377 & 0.429766282631885 \tabularnewline
14 & 0.465272206875507 & 0.930544413751014 & 0.534727793124493 \tabularnewline
15 & 0.593009801364963 & 0.813980397270073 & 0.406990198635037 \tabularnewline
16 & 0.531348041856219 & 0.937303916287562 & 0.468651958143781 \tabularnewline
17 & 0.439349924599361 & 0.878699849198722 & 0.560650075400639 \tabularnewline
18 & 0.353661245080245 & 0.70732249016049 & 0.646338754919755 \tabularnewline
19 & 0.555871831669536 & 0.888256336660927 & 0.444128168330464 \tabularnewline
20 & 0.556671029628891 & 0.886657940742217 & 0.443328970371109 \tabularnewline
21 & 0.649129984398947 & 0.701740031202106 & 0.350870015601053 \tabularnewline
22 & 0.64471262507892 & 0.710574749842159 & 0.35528737492108 \tabularnewline
23 & 0.592417233524571 & 0.815165532950859 & 0.40758276647543 \tabularnewline
24 & 0.550073065080911 & 0.899853869838178 & 0.449926934919089 \tabularnewline
25 & 0.530872281091463 & 0.938255437817073 & 0.469127718908536 \tabularnewline
26 & 0.652348211976743 & 0.695303576046515 & 0.347651788023258 \tabularnewline
27 & 0.67595453312303 & 0.64809093375394 & 0.32404546687697 \tabularnewline
28 & 0.66825225296158 & 0.66349549407684 & 0.33174774703842 \tabularnewline
29 & 0.649716937767562 & 0.700566124464875 & 0.350283062232438 \tabularnewline
30 & 0.740313261234772 & 0.519373477530455 & 0.259686738765228 \tabularnewline
31 & 0.72898408984863 & 0.54203182030274 & 0.27101591015137 \tabularnewline
32 & 0.703929912374225 & 0.59214017525155 & 0.296070087625775 \tabularnewline
33 & 0.711438681496416 & 0.577122637007168 & 0.288561318503584 \tabularnewline
34 & 0.708814922902869 & 0.582370154194262 & 0.291185077097131 \tabularnewline
35 & 0.687462076554614 & 0.625075846890772 & 0.312537923445386 \tabularnewline
36 & 0.660916484067521 & 0.678167031864959 & 0.339083515932479 \tabularnewline
37 & 0.69631284476979 & 0.60737431046042 & 0.30368715523021 \tabularnewline
38 & 0.711250557261965 & 0.577498885476069 & 0.288749442738035 \tabularnewline
39 & 0.694935969587974 & 0.610128060824051 & 0.305064030412026 \tabularnewline
40 & 0.716816448516714 & 0.566367102966572 & 0.283183551483286 \tabularnewline
41 & 0.692856226326223 & 0.614287547347553 & 0.307143773673777 \tabularnewline
42 & 0.678379520494249 & 0.643240959011501 & 0.321620479505751 \tabularnewline
43 & 0.674015964764377 & 0.651968070471246 & 0.325984035235623 \tabularnewline
44 & 0.651481095432019 & 0.697037809135961 & 0.348518904567981 \tabularnewline
45 & 0.664058256668078 & 0.671883486663844 & 0.335941743331922 \tabularnewline
46 & 0.652156371207261 & 0.695687257585478 & 0.347843628792739 \tabularnewline
47 & 0.63962114699961 & 0.72075770600078 & 0.36037885300039 \tabularnewline
48 & 0.650740513181486 & 0.698518973637028 & 0.349259486818514 \tabularnewline
49 & 0.631623747229235 & 0.73675250554153 & 0.368376252770765 \tabularnewline
50 & 0.624418142698171 & 0.751163714603657 & 0.375581857301829 \tabularnewline
51 & 0.629003506511415 & 0.74199298697717 & 0.370996493488585 \tabularnewline
52 & 0.636580073385311 & 0.726839853229379 & 0.363419926614689 \tabularnewline
53 & 0.649664617825768 & 0.700670764348463 & 0.350335382174232 \tabularnewline
54 & 0.635579145736701 & 0.728841708526598 & 0.364420854263299 \tabularnewline
55 & 0.621595540801079 & 0.756808918397842 & 0.378404459198921 \tabularnewline
56 & 0.61957809494134 & 0.76084381011732 & 0.38042190505866 \tabularnewline
57 & 0.586026688264742 & 0.827946623470515 & 0.413973311735258 \tabularnewline
58 & 0.597128900699104 & 0.805742198601791 & 0.402871099300896 \tabularnewline
59 & 0.60479027808801 & 0.790419443823981 & 0.39520972191199 \tabularnewline
60 & 0.600924219215794 & 0.798151561568411 & 0.399075780784206 \tabularnewline
61 & 0.619393356189186 & 0.761213287621629 & 0.380606643810814 \tabularnewline
62 & 0.675884617313628 & 0.648230765372745 & 0.324115382686372 \tabularnewline
63 & 0.672746578154815 & 0.654506843690369 & 0.327253421845185 \tabularnewline
64 & 0.703144061147328 & 0.593711877705345 & 0.296855938852672 \tabularnewline
65 & 0.699477862630221 & 0.601044274739558 & 0.300522137369779 \tabularnewline
66 & 0.693045586931566 & 0.613908826136869 & 0.306954413068434 \tabularnewline
67 & 0.702569892281413 & 0.594860215437175 & 0.297430107718587 \tabularnewline
68 & 0.684759769813512 & 0.630480460372975 & 0.315240230186488 \tabularnewline
69 & 0.695111891162397 & 0.609776217675206 & 0.304888108837603 \tabularnewline
70 & 0.704020010580463 & 0.591959978839074 & 0.295979989419537 \tabularnewline
71 & 0.694889473674372 & 0.610221052651256 & 0.305110526325628 \tabularnewline
72 & 0.705280123054202 & 0.589439753891597 & 0.294719876945798 \tabularnewline
73 & 0.697505532513552 & 0.604988934972896 & 0.302494467486448 \tabularnewline
74 & 0.690106207006005 & 0.61978758598799 & 0.309893792993995 \tabularnewline
75 & 0.70109081583387 & 0.59781836833226 & 0.29890918416613 \tabularnewline
76 & 0.694439337742397 & 0.611121324515206 & 0.305560662257603 \tabularnewline
77 & 0.708548477153435 & 0.58290304569313 & 0.291451522846565 \tabularnewline
78 & 0.681564026831028 & 0.636871946337944 & 0.318435973168972 \tabularnewline
79 & 0.739875204859076 & 0.520249590281849 & 0.260124795140924 \tabularnewline
80 & 0.746638001739599 & 0.506723996520801 & 0.253361998260401 \tabularnewline
81 & 0.735912107437189 & 0.528175785125623 & 0.264087892562811 \tabularnewline
82 & 0.749886376752931 & 0.500227246494139 & 0.250113623247069 \tabularnewline
83 & 0.737193289509021 & 0.525613420981959 & 0.262806710490979 \tabularnewline
84 & 0.716208415552328 & 0.567583168895343 & 0.283791584447672 \tabularnewline
85 & 0.712420806314711 & 0.575158387370579 & 0.287579193685289 \tabularnewline
86 & 0.688600461199215 & 0.62279907760157 & 0.311399538800785 \tabularnewline
87 & 0.739882977421682 & 0.520234045156636 & 0.260117022578318 \tabularnewline
88 & 0.795232961475911 & 0.409534077048178 & 0.204767038524089 \tabularnewline
89 & 0.779879579369739 & 0.440240841260522 & 0.220120420630261 \tabularnewline
90 & 0.818449423345196 & 0.363101153309608 & 0.181550576654804 \tabularnewline
91 & 0.820401202656281 & 0.359197594687438 & 0.179598797343719 \tabularnewline
92 & 0.799936865079745 & 0.40012626984051 & 0.200063134920255 \tabularnewline
93 & 0.789830057261039 & 0.420339885477922 & 0.210169942738961 \tabularnewline
94 & 0.76992618875417 & 0.46014762249166 & 0.23007381124583 \tabularnewline
95 & 0.743793109699252 & 0.512413780601496 & 0.256206890300748 \tabularnewline
96 & 0.78776686513256 & 0.42446626973488 & 0.21223313486744 \tabularnewline
97 & 0.758740789761503 & 0.482518420476995 & 0.241259210238497 \tabularnewline
98 & 0.733229575554952 & 0.533540848890096 & 0.266770424445048 \tabularnewline
99 & 0.698546784291557 & 0.602906431416886 & 0.301453215708443 \tabularnewline
100 & 0.755024135457289 & 0.489951729085422 & 0.244975864542711 \tabularnewline
101 & 0.846415828634302 & 0.307168342731396 & 0.153584171365698 \tabularnewline
102 & 0.823144467777168 & 0.353711064445663 & 0.176855532222832 \tabularnewline
103 & 0.79722891208006 & 0.405542175839881 & 0.202771087919941 \tabularnewline
104 & 0.768866572064323 & 0.462266855871354 & 0.231133427935677 \tabularnewline
105 & 0.745340881132881 & 0.509318237734239 & 0.254659118867119 \tabularnewline
106 & 0.713240916512212 & 0.573518166975575 & 0.286759083487788 \tabularnewline
107 & 0.680151825346558 & 0.639696349306884 & 0.319848174653442 \tabularnewline
108 & 0.642367228060487 & 0.715265543879027 & 0.357632771939513 \tabularnewline
109 & 0.60772963450756 & 0.784540730984879 & 0.39227036549244 \tabularnewline
110 & 0.559914483260686 & 0.880171033478628 & 0.440085516739314 \tabularnewline
111 & 0.541012180505348 & 0.917975638989303 & 0.458987819494651 \tabularnewline
112 & 0.500726100483433 & 0.998547799033134 & 0.499273899516567 \tabularnewline
113 & 0.494834789224018 & 0.989669578448036 & 0.505165210775982 \tabularnewline
114 & 0.456175176263334 & 0.912350352526668 & 0.543824823736666 \tabularnewline
115 & 0.408821711771103 & 0.817643423542207 & 0.591178288228897 \tabularnewline
116 & 0.390128954266617 & 0.780257908533235 & 0.609871045733383 \tabularnewline
117 & 0.48708287533076 & 0.974165750661521 & 0.51291712466924 \tabularnewline
118 & 0.432801919495325 & 0.865603838990649 & 0.567198080504675 \tabularnewline
119 & 0.412624439831479 & 0.825248879662958 & 0.587375560168521 \tabularnewline
120 & 0.440471242928876 & 0.880942485857753 & 0.559528757071124 \tabularnewline
121 & 0.384964289711257 & 0.769928579422514 & 0.615035710288743 \tabularnewline
122 & 0.36262093079005 & 0.725241861580099 & 0.63737906920995 \tabularnewline
123 & 0.330467656922212 & 0.660935313844424 & 0.669532343077788 \tabularnewline
124 & 0.285270353424081 & 0.570540706848163 & 0.714729646575919 \tabularnewline
125 & 0.306934724617076 & 0.613869449234152 & 0.693065275382924 \tabularnewline
126 & 0.284553753431353 & 0.569107506862707 & 0.715446246568647 \tabularnewline
127 & 0.351336371791874 & 0.702672743583749 & 0.648663628208126 \tabularnewline
128 & 0.365053266477087 & 0.730106532954174 & 0.634946733522913 \tabularnewline
129 & 0.347007674005603 & 0.694015348011206 & 0.652992325994397 \tabularnewline
130 & 0.369674508113709 & 0.739349016227417 & 0.630325491886291 \tabularnewline
131 & 0.312020250319523 & 0.624040500639046 & 0.687979749680477 \tabularnewline
132 & 0.49113309218494 & 0.98226618436988 & 0.50886690781506 \tabularnewline
133 & 0.424636644779239 & 0.849273289558479 & 0.575363355220761 \tabularnewline
134 & 0.369104129671061 & 0.738208259342123 & 0.630895870328939 \tabularnewline
135 & 0.328246557467803 & 0.656493114935606 & 0.671753442532197 \tabularnewline
136 & 0.319416570986915 & 0.63883314197383 & 0.680583429013085 \tabularnewline
137 & 0.26505635380643 & 0.53011270761286 & 0.73494364619357 \tabularnewline
138 & 0.374431047372525 & 0.748862094745051 & 0.625568952627475 \tabularnewline
139 & 0.318835309984395 & 0.63767061996879 & 0.681164690015605 \tabularnewline
140 & 0.429819821737216 & 0.859639643474433 & 0.570180178262784 \tabularnewline
141 & 0.331237559625085 & 0.66247511925017 & 0.668762440374915 \tabularnewline
142 & 0.417421460223893 & 0.834842920447786 & 0.582578539776107 \tabularnewline
143 & 0.29114765026388 & 0.582295300527761 & 0.70885234973612 \tabularnewline
144 & 0.323186502822624 & 0.646373005645249 & 0.676813497177376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200563&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.841063730937202[/C][C]0.317872538125597[/C][C]0.158936269062798[/C][/ROW]
[ROW][C]11[/C][C]0.788848022087372[/C][C]0.422303955825256[/C][C]0.211151977912628[/C][/ROW]
[ROW][C]12[/C][C]0.680811844109981[/C][C]0.638376311780038[/C][C]0.319188155890019[/C][/ROW]
[ROW][C]13[/C][C]0.570233717368115[/C][C]0.85953256526377[/C][C]0.429766282631885[/C][/ROW]
[ROW][C]14[/C][C]0.465272206875507[/C][C]0.930544413751014[/C][C]0.534727793124493[/C][/ROW]
[ROW][C]15[/C][C]0.593009801364963[/C][C]0.813980397270073[/C][C]0.406990198635037[/C][/ROW]
[ROW][C]16[/C][C]0.531348041856219[/C][C]0.937303916287562[/C][C]0.468651958143781[/C][/ROW]
[ROW][C]17[/C][C]0.439349924599361[/C][C]0.878699849198722[/C][C]0.560650075400639[/C][/ROW]
[ROW][C]18[/C][C]0.353661245080245[/C][C]0.70732249016049[/C][C]0.646338754919755[/C][/ROW]
[ROW][C]19[/C][C]0.555871831669536[/C][C]0.888256336660927[/C][C]0.444128168330464[/C][/ROW]
[ROW][C]20[/C][C]0.556671029628891[/C][C]0.886657940742217[/C][C]0.443328970371109[/C][/ROW]
[ROW][C]21[/C][C]0.649129984398947[/C][C]0.701740031202106[/C][C]0.350870015601053[/C][/ROW]
[ROW][C]22[/C][C]0.64471262507892[/C][C]0.710574749842159[/C][C]0.35528737492108[/C][/ROW]
[ROW][C]23[/C][C]0.592417233524571[/C][C]0.815165532950859[/C][C]0.40758276647543[/C][/ROW]
[ROW][C]24[/C][C]0.550073065080911[/C][C]0.899853869838178[/C][C]0.449926934919089[/C][/ROW]
[ROW][C]25[/C][C]0.530872281091463[/C][C]0.938255437817073[/C][C]0.469127718908536[/C][/ROW]
[ROW][C]26[/C][C]0.652348211976743[/C][C]0.695303576046515[/C][C]0.347651788023258[/C][/ROW]
[ROW][C]27[/C][C]0.67595453312303[/C][C]0.64809093375394[/C][C]0.32404546687697[/C][/ROW]
[ROW][C]28[/C][C]0.66825225296158[/C][C]0.66349549407684[/C][C]0.33174774703842[/C][/ROW]
[ROW][C]29[/C][C]0.649716937767562[/C][C]0.700566124464875[/C][C]0.350283062232438[/C][/ROW]
[ROW][C]30[/C][C]0.740313261234772[/C][C]0.519373477530455[/C][C]0.259686738765228[/C][/ROW]
[ROW][C]31[/C][C]0.72898408984863[/C][C]0.54203182030274[/C][C]0.27101591015137[/C][/ROW]
[ROW][C]32[/C][C]0.703929912374225[/C][C]0.59214017525155[/C][C]0.296070087625775[/C][/ROW]
[ROW][C]33[/C][C]0.711438681496416[/C][C]0.577122637007168[/C][C]0.288561318503584[/C][/ROW]
[ROW][C]34[/C][C]0.708814922902869[/C][C]0.582370154194262[/C][C]0.291185077097131[/C][/ROW]
[ROW][C]35[/C][C]0.687462076554614[/C][C]0.625075846890772[/C][C]0.312537923445386[/C][/ROW]
[ROW][C]36[/C][C]0.660916484067521[/C][C]0.678167031864959[/C][C]0.339083515932479[/C][/ROW]
[ROW][C]37[/C][C]0.69631284476979[/C][C]0.60737431046042[/C][C]0.30368715523021[/C][/ROW]
[ROW][C]38[/C][C]0.711250557261965[/C][C]0.577498885476069[/C][C]0.288749442738035[/C][/ROW]
[ROW][C]39[/C][C]0.694935969587974[/C][C]0.610128060824051[/C][C]0.305064030412026[/C][/ROW]
[ROW][C]40[/C][C]0.716816448516714[/C][C]0.566367102966572[/C][C]0.283183551483286[/C][/ROW]
[ROW][C]41[/C][C]0.692856226326223[/C][C]0.614287547347553[/C][C]0.307143773673777[/C][/ROW]
[ROW][C]42[/C][C]0.678379520494249[/C][C]0.643240959011501[/C][C]0.321620479505751[/C][/ROW]
[ROW][C]43[/C][C]0.674015964764377[/C][C]0.651968070471246[/C][C]0.325984035235623[/C][/ROW]
[ROW][C]44[/C][C]0.651481095432019[/C][C]0.697037809135961[/C][C]0.348518904567981[/C][/ROW]
[ROW][C]45[/C][C]0.664058256668078[/C][C]0.671883486663844[/C][C]0.335941743331922[/C][/ROW]
[ROW][C]46[/C][C]0.652156371207261[/C][C]0.695687257585478[/C][C]0.347843628792739[/C][/ROW]
[ROW][C]47[/C][C]0.63962114699961[/C][C]0.72075770600078[/C][C]0.36037885300039[/C][/ROW]
[ROW][C]48[/C][C]0.650740513181486[/C][C]0.698518973637028[/C][C]0.349259486818514[/C][/ROW]
[ROW][C]49[/C][C]0.631623747229235[/C][C]0.73675250554153[/C][C]0.368376252770765[/C][/ROW]
[ROW][C]50[/C][C]0.624418142698171[/C][C]0.751163714603657[/C][C]0.375581857301829[/C][/ROW]
[ROW][C]51[/C][C]0.629003506511415[/C][C]0.74199298697717[/C][C]0.370996493488585[/C][/ROW]
[ROW][C]52[/C][C]0.636580073385311[/C][C]0.726839853229379[/C][C]0.363419926614689[/C][/ROW]
[ROW][C]53[/C][C]0.649664617825768[/C][C]0.700670764348463[/C][C]0.350335382174232[/C][/ROW]
[ROW][C]54[/C][C]0.635579145736701[/C][C]0.728841708526598[/C][C]0.364420854263299[/C][/ROW]
[ROW][C]55[/C][C]0.621595540801079[/C][C]0.756808918397842[/C][C]0.378404459198921[/C][/ROW]
[ROW][C]56[/C][C]0.61957809494134[/C][C]0.76084381011732[/C][C]0.38042190505866[/C][/ROW]
[ROW][C]57[/C][C]0.586026688264742[/C][C]0.827946623470515[/C][C]0.413973311735258[/C][/ROW]
[ROW][C]58[/C][C]0.597128900699104[/C][C]0.805742198601791[/C][C]0.402871099300896[/C][/ROW]
[ROW][C]59[/C][C]0.60479027808801[/C][C]0.790419443823981[/C][C]0.39520972191199[/C][/ROW]
[ROW][C]60[/C][C]0.600924219215794[/C][C]0.798151561568411[/C][C]0.399075780784206[/C][/ROW]
[ROW][C]61[/C][C]0.619393356189186[/C][C]0.761213287621629[/C][C]0.380606643810814[/C][/ROW]
[ROW][C]62[/C][C]0.675884617313628[/C][C]0.648230765372745[/C][C]0.324115382686372[/C][/ROW]
[ROW][C]63[/C][C]0.672746578154815[/C][C]0.654506843690369[/C][C]0.327253421845185[/C][/ROW]
[ROW][C]64[/C][C]0.703144061147328[/C][C]0.593711877705345[/C][C]0.296855938852672[/C][/ROW]
[ROW][C]65[/C][C]0.699477862630221[/C][C]0.601044274739558[/C][C]0.300522137369779[/C][/ROW]
[ROW][C]66[/C][C]0.693045586931566[/C][C]0.613908826136869[/C][C]0.306954413068434[/C][/ROW]
[ROW][C]67[/C][C]0.702569892281413[/C][C]0.594860215437175[/C][C]0.297430107718587[/C][/ROW]
[ROW][C]68[/C][C]0.684759769813512[/C][C]0.630480460372975[/C][C]0.315240230186488[/C][/ROW]
[ROW][C]69[/C][C]0.695111891162397[/C][C]0.609776217675206[/C][C]0.304888108837603[/C][/ROW]
[ROW][C]70[/C][C]0.704020010580463[/C][C]0.591959978839074[/C][C]0.295979989419537[/C][/ROW]
[ROW][C]71[/C][C]0.694889473674372[/C][C]0.610221052651256[/C][C]0.305110526325628[/C][/ROW]
[ROW][C]72[/C][C]0.705280123054202[/C][C]0.589439753891597[/C][C]0.294719876945798[/C][/ROW]
[ROW][C]73[/C][C]0.697505532513552[/C][C]0.604988934972896[/C][C]0.302494467486448[/C][/ROW]
[ROW][C]74[/C][C]0.690106207006005[/C][C]0.61978758598799[/C][C]0.309893792993995[/C][/ROW]
[ROW][C]75[/C][C]0.70109081583387[/C][C]0.59781836833226[/C][C]0.29890918416613[/C][/ROW]
[ROW][C]76[/C][C]0.694439337742397[/C][C]0.611121324515206[/C][C]0.305560662257603[/C][/ROW]
[ROW][C]77[/C][C]0.708548477153435[/C][C]0.58290304569313[/C][C]0.291451522846565[/C][/ROW]
[ROW][C]78[/C][C]0.681564026831028[/C][C]0.636871946337944[/C][C]0.318435973168972[/C][/ROW]
[ROW][C]79[/C][C]0.739875204859076[/C][C]0.520249590281849[/C][C]0.260124795140924[/C][/ROW]
[ROW][C]80[/C][C]0.746638001739599[/C][C]0.506723996520801[/C][C]0.253361998260401[/C][/ROW]
[ROW][C]81[/C][C]0.735912107437189[/C][C]0.528175785125623[/C][C]0.264087892562811[/C][/ROW]
[ROW][C]82[/C][C]0.749886376752931[/C][C]0.500227246494139[/C][C]0.250113623247069[/C][/ROW]
[ROW][C]83[/C][C]0.737193289509021[/C][C]0.525613420981959[/C][C]0.262806710490979[/C][/ROW]
[ROW][C]84[/C][C]0.716208415552328[/C][C]0.567583168895343[/C][C]0.283791584447672[/C][/ROW]
[ROW][C]85[/C][C]0.712420806314711[/C][C]0.575158387370579[/C][C]0.287579193685289[/C][/ROW]
[ROW][C]86[/C][C]0.688600461199215[/C][C]0.62279907760157[/C][C]0.311399538800785[/C][/ROW]
[ROW][C]87[/C][C]0.739882977421682[/C][C]0.520234045156636[/C][C]0.260117022578318[/C][/ROW]
[ROW][C]88[/C][C]0.795232961475911[/C][C]0.409534077048178[/C][C]0.204767038524089[/C][/ROW]
[ROW][C]89[/C][C]0.779879579369739[/C][C]0.440240841260522[/C][C]0.220120420630261[/C][/ROW]
[ROW][C]90[/C][C]0.818449423345196[/C][C]0.363101153309608[/C][C]0.181550576654804[/C][/ROW]
[ROW][C]91[/C][C]0.820401202656281[/C][C]0.359197594687438[/C][C]0.179598797343719[/C][/ROW]
[ROW][C]92[/C][C]0.799936865079745[/C][C]0.40012626984051[/C][C]0.200063134920255[/C][/ROW]
[ROW][C]93[/C][C]0.789830057261039[/C][C]0.420339885477922[/C][C]0.210169942738961[/C][/ROW]
[ROW][C]94[/C][C]0.76992618875417[/C][C]0.46014762249166[/C][C]0.23007381124583[/C][/ROW]
[ROW][C]95[/C][C]0.743793109699252[/C][C]0.512413780601496[/C][C]0.256206890300748[/C][/ROW]
[ROW][C]96[/C][C]0.78776686513256[/C][C]0.42446626973488[/C][C]0.21223313486744[/C][/ROW]
[ROW][C]97[/C][C]0.758740789761503[/C][C]0.482518420476995[/C][C]0.241259210238497[/C][/ROW]
[ROW][C]98[/C][C]0.733229575554952[/C][C]0.533540848890096[/C][C]0.266770424445048[/C][/ROW]
[ROW][C]99[/C][C]0.698546784291557[/C][C]0.602906431416886[/C][C]0.301453215708443[/C][/ROW]
[ROW][C]100[/C][C]0.755024135457289[/C][C]0.489951729085422[/C][C]0.244975864542711[/C][/ROW]
[ROW][C]101[/C][C]0.846415828634302[/C][C]0.307168342731396[/C][C]0.153584171365698[/C][/ROW]
[ROW][C]102[/C][C]0.823144467777168[/C][C]0.353711064445663[/C][C]0.176855532222832[/C][/ROW]
[ROW][C]103[/C][C]0.79722891208006[/C][C]0.405542175839881[/C][C]0.202771087919941[/C][/ROW]
[ROW][C]104[/C][C]0.768866572064323[/C][C]0.462266855871354[/C][C]0.231133427935677[/C][/ROW]
[ROW][C]105[/C][C]0.745340881132881[/C][C]0.509318237734239[/C][C]0.254659118867119[/C][/ROW]
[ROW][C]106[/C][C]0.713240916512212[/C][C]0.573518166975575[/C][C]0.286759083487788[/C][/ROW]
[ROW][C]107[/C][C]0.680151825346558[/C][C]0.639696349306884[/C][C]0.319848174653442[/C][/ROW]
[ROW][C]108[/C][C]0.642367228060487[/C][C]0.715265543879027[/C][C]0.357632771939513[/C][/ROW]
[ROW][C]109[/C][C]0.60772963450756[/C][C]0.784540730984879[/C][C]0.39227036549244[/C][/ROW]
[ROW][C]110[/C][C]0.559914483260686[/C][C]0.880171033478628[/C][C]0.440085516739314[/C][/ROW]
[ROW][C]111[/C][C]0.541012180505348[/C][C]0.917975638989303[/C][C]0.458987819494651[/C][/ROW]
[ROW][C]112[/C][C]0.500726100483433[/C][C]0.998547799033134[/C][C]0.499273899516567[/C][/ROW]
[ROW][C]113[/C][C]0.494834789224018[/C][C]0.989669578448036[/C][C]0.505165210775982[/C][/ROW]
[ROW][C]114[/C][C]0.456175176263334[/C][C]0.912350352526668[/C][C]0.543824823736666[/C][/ROW]
[ROW][C]115[/C][C]0.408821711771103[/C][C]0.817643423542207[/C][C]0.591178288228897[/C][/ROW]
[ROW][C]116[/C][C]0.390128954266617[/C][C]0.780257908533235[/C][C]0.609871045733383[/C][/ROW]
[ROW][C]117[/C][C]0.48708287533076[/C][C]0.974165750661521[/C][C]0.51291712466924[/C][/ROW]
[ROW][C]118[/C][C]0.432801919495325[/C][C]0.865603838990649[/C][C]0.567198080504675[/C][/ROW]
[ROW][C]119[/C][C]0.412624439831479[/C][C]0.825248879662958[/C][C]0.587375560168521[/C][/ROW]
[ROW][C]120[/C][C]0.440471242928876[/C][C]0.880942485857753[/C][C]0.559528757071124[/C][/ROW]
[ROW][C]121[/C][C]0.384964289711257[/C][C]0.769928579422514[/C][C]0.615035710288743[/C][/ROW]
[ROW][C]122[/C][C]0.36262093079005[/C][C]0.725241861580099[/C][C]0.63737906920995[/C][/ROW]
[ROW][C]123[/C][C]0.330467656922212[/C][C]0.660935313844424[/C][C]0.669532343077788[/C][/ROW]
[ROW][C]124[/C][C]0.285270353424081[/C][C]0.570540706848163[/C][C]0.714729646575919[/C][/ROW]
[ROW][C]125[/C][C]0.306934724617076[/C][C]0.613869449234152[/C][C]0.693065275382924[/C][/ROW]
[ROW][C]126[/C][C]0.284553753431353[/C][C]0.569107506862707[/C][C]0.715446246568647[/C][/ROW]
[ROW][C]127[/C][C]0.351336371791874[/C][C]0.702672743583749[/C][C]0.648663628208126[/C][/ROW]
[ROW][C]128[/C][C]0.365053266477087[/C][C]0.730106532954174[/C][C]0.634946733522913[/C][/ROW]
[ROW][C]129[/C][C]0.347007674005603[/C][C]0.694015348011206[/C][C]0.652992325994397[/C][/ROW]
[ROW][C]130[/C][C]0.369674508113709[/C][C]0.739349016227417[/C][C]0.630325491886291[/C][/ROW]
[ROW][C]131[/C][C]0.312020250319523[/C][C]0.624040500639046[/C][C]0.687979749680477[/C][/ROW]
[ROW][C]132[/C][C]0.49113309218494[/C][C]0.98226618436988[/C][C]0.50886690781506[/C][/ROW]
[ROW][C]133[/C][C]0.424636644779239[/C][C]0.849273289558479[/C][C]0.575363355220761[/C][/ROW]
[ROW][C]134[/C][C]0.369104129671061[/C][C]0.738208259342123[/C][C]0.630895870328939[/C][/ROW]
[ROW][C]135[/C][C]0.328246557467803[/C][C]0.656493114935606[/C][C]0.671753442532197[/C][/ROW]
[ROW][C]136[/C][C]0.319416570986915[/C][C]0.63883314197383[/C][C]0.680583429013085[/C][/ROW]
[ROW][C]137[/C][C]0.26505635380643[/C][C]0.53011270761286[/C][C]0.73494364619357[/C][/ROW]
[ROW][C]138[/C][C]0.374431047372525[/C][C]0.748862094745051[/C][C]0.625568952627475[/C][/ROW]
[ROW][C]139[/C][C]0.318835309984395[/C][C]0.63767061996879[/C][C]0.681164690015605[/C][/ROW]
[ROW][C]140[/C][C]0.429819821737216[/C][C]0.859639643474433[/C][C]0.570180178262784[/C][/ROW]
[ROW][C]141[/C][C]0.331237559625085[/C][C]0.66247511925017[/C][C]0.668762440374915[/C][/ROW]
[ROW][C]142[/C][C]0.417421460223893[/C][C]0.834842920447786[/C][C]0.582578539776107[/C][/ROW]
[ROW][C]143[/C][C]0.29114765026388[/C][C]0.582295300527761[/C][C]0.70885234973612[/C][/ROW]
[ROW][C]144[/C][C]0.323186502822624[/C][C]0.646373005645249[/C][C]0.676813497177376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200563&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200563&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
100.8410637309372020.3178725381255970.158936269062798
110.7888480220873720.4223039558252560.211151977912628
120.6808118441099810.6383763117800380.319188155890019
130.5702337173681150.859532565263770.429766282631885
140.4652722068755070.9305444137510140.534727793124493
150.5930098013649630.8139803972700730.406990198635037
160.5313480418562190.9373039162875620.468651958143781
170.4393499245993610.8786998491987220.560650075400639
180.3536612450802450.707322490160490.646338754919755
190.5558718316695360.8882563366609270.444128168330464
200.5566710296288910.8866579407422170.443328970371109
210.6491299843989470.7017400312021060.350870015601053
220.644712625078920.7105747498421590.35528737492108
230.5924172335245710.8151655329508590.40758276647543
240.5500730650809110.8998538698381780.449926934919089
250.5308722810914630.9382554378170730.469127718908536
260.6523482119767430.6953035760465150.347651788023258
270.675954533123030.648090933753940.32404546687697
280.668252252961580.663495494076840.33174774703842
290.6497169377675620.7005661244648750.350283062232438
300.7403132612347720.5193734775304550.259686738765228
310.728984089848630.542031820302740.27101591015137
320.7039299123742250.592140175251550.296070087625775
330.7114386814964160.5771226370071680.288561318503584
340.7088149229028690.5823701541942620.291185077097131
350.6874620765546140.6250758468907720.312537923445386
360.6609164840675210.6781670318649590.339083515932479
370.696312844769790.607374310460420.30368715523021
380.7112505572619650.5774988854760690.288749442738035
390.6949359695879740.6101280608240510.305064030412026
400.7168164485167140.5663671029665720.283183551483286
410.6928562263262230.6142875473475530.307143773673777
420.6783795204942490.6432409590115010.321620479505751
430.6740159647643770.6519680704712460.325984035235623
440.6514810954320190.6970378091359610.348518904567981
450.6640582566680780.6718834866638440.335941743331922
460.6521563712072610.6956872575854780.347843628792739
470.639621146999610.720757706000780.36037885300039
480.6507405131814860.6985189736370280.349259486818514
490.6316237472292350.736752505541530.368376252770765
500.6244181426981710.7511637146036570.375581857301829
510.6290035065114150.741992986977170.370996493488585
520.6365800733853110.7268398532293790.363419926614689
530.6496646178257680.7006707643484630.350335382174232
540.6355791457367010.7288417085265980.364420854263299
550.6215955408010790.7568089183978420.378404459198921
560.619578094941340.760843810117320.38042190505866
570.5860266882647420.8279466234705150.413973311735258
580.5971289006991040.8057421986017910.402871099300896
590.604790278088010.7904194438239810.39520972191199
600.6009242192157940.7981515615684110.399075780784206
610.6193933561891860.7612132876216290.380606643810814
620.6758846173136280.6482307653727450.324115382686372
630.6727465781548150.6545068436903690.327253421845185
640.7031440611473280.5937118777053450.296855938852672
650.6994778626302210.6010442747395580.300522137369779
660.6930455869315660.6139088261368690.306954413068434
670.7025698922814130.5948602154371750.297430107718587
680.6847597698135120.6304804603729750.315240230186488
690.6951118911623970.6097762176752060.304888108837603
700.7040200105804630.5919599788390740.295979989419537
710.6948894736743720.6102210526512560.305110526325628
720.7052801230542020.5894397538915970.294719876945798
730.6975055325135520.6049889349728960.302494467486448
740.6901062070060050.619787585987990.309893792993995
750.701090815833870.597818368332260.29890918416613
760.6944393377423970.6111213245152060.305560662257603
770.7085484771534350.582903045693130.291451522846565
780.6815640268310280.6368719463379440.318435973168972
790.7398752048590760.5202495902818490.260124795140924
800.7466380017395990.5067239965208010.253361998260401
810.7359121074371890.5281757851256230.264087892562811
820.7498863767529310.5002272464941390.250113623247069
830.7371932895090210.5256134209819590.262806710490979
840.7162084155523280.5675831688953430.283791584447672
850.7124208063147110.5751583873705790.287579193685289
860.6886004611992150.622799077601570.311399538800785
870.7398829774216820.5202340451566360.260117022578318
880.7952329614759110.4095340770481780.204767038524089
890.7798795793697390.4402408412605220.220120420630261
900.8184494233451960.3631011533096080.181550576654804
910.8204012026562810.3591975946874380.179598797343719
920.7999368650797450.400126269840510.200063134920255
930.7898300572610390.4203398854779220.210169942738961
940.769926188754170.460147622491660.23007381124583
950.7437931096992520.5124137806014960.256206890300748
960.787766865132560.424466269734880.21223313486744
970.7587407897615030.4825184204769950.241259210238497
980.7332295755549520.5335408488900960.266770424445048
990.6985467842915570.6029064314168860.301453215708443
1000.7550241354572890.4899517290854220.244975864542711
1010.8464158286343020.3071683427313960.153584171365698
1020.8231444677771680.3537110644456630.176855532222832
1030.797228912080060.4055421758398810.202771087919941
1040.7688665720643230.4622668558713540.231133427935677
1050.7453408811328810.5093182377342390.254659118867119
1060.7132409165122120.5735181669755750.286759083487788
1070.6801518253465580.6396963493068840.319848174653442
1080.6423672280604870.7152655438790270.357632771939513
1090.607729634507560.7845407309848790.39227036549244
1100.5599144832606860.8801710334786280.440085516739314
1110.5410121805053480.9179756389893030.458987819494651
1120.5007261004834330.9985477990331340.499273899516567
1130.4948347892240180.9896695784480360.505165210775982
1140.4561751762633340.9123503525266680.543824823736666
1150.4088217117711030.8176434235422070.591178288228897
1160.3901289542666170.7802579085332350.609871045733383
1170.487082875330760.9741657506615210.51291712466924
1180.4328019194953250.8656038389906490.567198080504675
1190.4126244398314790.8252488796629580.587375560168521
1200.4404712429288760.8809424858577530.559528757071124
1210.3849642897112570.7699285794225140.615035710288743
1220.362620930790050.7252418615800990.63737906920995
1230.3304676569222120.6609353138444240.669532343077788
1240.2852703534240810.5705407068481630.714729646575919
1250.3069347246170760.6138694492341520.693065275382924
1260.2845537534313530.5691075068627070.715446246568647
1270.3513363717918740.7026727435837490.648663628208126
1280.3650532664770870.7301065329541740.634946733522913
1290.3470076740056030.6940153480112060.652992325994397
1300.3696745081137090.7393490162274170.630325491886291
1310.3120202503195230.6240405006390460.687979749680477
1320.491133092184940.982266184369880.50886690781506
1330.4246366447792390.8492732895584790.575363355220761
1340.3691041296710610.7382082593421230.630895870328939
1350.3282465574678030.6564931149356060.671753442532197
1360.3194165709869150.638833141973830.680583429013085
1370.265056353806430.530112707612860.73494364619357
1380.3744310473725250.7488620947450510.625568952627475
1390.3188353099843950.637670619968790.681164690015605
1400.4298198217372160.8596396434744330.570180178262784
1410.3312375596250850.662475119250170.668762440374915
1420.4174214602238930.8348429204477860.582578539776107
1430.291147650263880.5822953005277610.70885234973612
1440.3231865028226240.6463730056452490.676813497177376






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200563&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200563&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200563&T=6

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


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

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


Figures (Output of Computation)

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

Parameters (Session):
par1 = 3 ; par2 = 6 ; par3 = Pearson Chi-Squared ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}