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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 01 Dec 2010 13:24:40 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/01/t1291209857zhoh4m814790ltr.htm/, Retrieved Thu, 31 Oct 2024 23:17:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103973, Retrieved Thu, 31 Oct 2024 23:17:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact194
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [ws4] [2010-11-30 12:25:45] [a2638725f7f7c6bd63902ba17eba666b]
-         [Multiple Regression] [ws4] [2010-11-30 19:02:35] [df61ce38492c371f14c407a12b3bb2eb]
-   PD        [Multiple Regression] [ws 7] [2010-12-01 13:24:40] [76f6fcd790878de142f355e7238b5c71] [Current]
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Dataseries X:
2	5	2	3	3	4	4
2	4	2	4	3	4	4
4	4	2	4	2	5	4
2	4	2	2	2	2	4
3	2	2	2	3	2	4
4	5	1	3	2	4	5
3	5	1	2	1	4	4
3	4	3	3	3	4	3
3	3	2	3	2	4	4
2	4	1	3	2	2	4
4	4	4	3	3	3	4
4	2	2	4	2	4	4
3	3	3	2	2	3	4
3	3	2	2	2	4	2
4	4	1	1	3	4	3
4	5	1	1	1	4	4
3	4	2	3	3	4	3
3	2	2	2	2	2	2
3	4	2	2	3	4	4
4	4	2	3	4	4	3
2	4	1	4	2	4	3
5	4	2	4	3	3	4
4	4	4	3	5	2	3
2	4	2	2	2	4	3
3	5	2	3	2	2	4
4	4	2	4	3	3	4
4	4	2	3	2	4	4
3	4	2	2	2	3	4
4	4	3	1	2	4	4
4	4	2	3	2	4	4
1	4	1	2	3	4	5
4	4	4	4	4	4	4
5	2	1	4	1	4	4
2	4	2	5	3	4	4
4	4	2	2	3	4	3
3	5	2	4	2	5	4
2	5	2	4	1	4	3
4	4	2	2	1	2	4
5	3	2	4	2	4	4
4	4	2	4	2	4	3
4	5	2	2	2	5	5
4	4	2	3	1	4	4
3	4	2	2	2	2	3
4	5	2	4	1	4	3
2	4	2	3	2	4	3
2	5	1	1	2	4	4
4	4	2	2	4	2	4
2	4	1	5	2	5	4
4	4	2	2	2	4	4
4	3	1	4	2	4	4
1	4	1	4	1	4	4
4	4	2	2	2	4	4
2	4	2	2	2	4	5
1	2	1	2	1	3	3
4	3	5	4	5	5	3
3	5	2	3	2	4	5
2	4	2	4	2	4	5
4	4	1	2	2	4	4
3	5	1	3	1	4	4
2	3	2	2	3	2	3
2	5	2	2	1	4	4
3	4	1	3	1	4	4
2	5	1	2	2	4	5
1	4	2	3	3	4	4
3	4	1	2	2	3	4
2	5	1	4	2	4	5
3	4	2	2	2	2	4
3	4	1	5	4	4	3
3	5	1	1	1	4	4
2	4	2	3	2	4	4
3	3	1	2	2	4	4
2	4	1	2	2	4	4
4	5	3	3	2	4	4
4	5	3	4	2	3	4
4	5	2	4	1	4	4
2	4	2	2	2	4	3
3	4	1	3	2	4	4
4	5	3	4	2	4	3
3	5	2	2	2	4	5
4	4	2	2	1	4	4
2	5	2	4	4	4	5
3	3	2	2	2	2	5
3	4	1	4	3	3	4
4	4	4	2	2	5	4
2	4	1	3	1	3	4
4	4	1	4	2	3	4
2	4	1	3	2	4	4
2	5	1	1	1	4	5
4	4	4	3	2	4	4
3	4	2	2	1	4	3
4	4	2	2	2	4	4
2	5	1	1	1	3	3
2	3	1	3	2	4	4
3	3	1	2	2	4	4
3	5	3	3	3	4	4
5	5	4	5	4	5	4
2	4	4	3	1	4	4
3	4	3	4	3	4	3
4	4	2	2	1	2	3
3	4	2	2	1	3	3
4	4	3	3	2	3	3
3	4	1	2	1	3	3
3	4	3	2	3	4	2
2	4	2	2	2	4	3
3	5	2	3	2	2	5
2	2	2	5	1	3	2
3	4	2	2	2	3	2
2	2	4	3	2	4	3
4	4	3	3	1	4	3
2	5	1	1	2	2	3
4	3	1	1	2	3	4
4	4	2	3	4	4	4
1	3	1	4	3	4	3
5	4	3	5	2	5	2
2	4	2	3	5	3	3
3	4	2	3	1	3	4
4	2	2	3	2	4	2
1	1	1	2	1	3	4
5	4	3	3	2	3	4
3	3	1	2	1	2	2
3	4	1	3	1	4	3
3	3	2	2	2	3	3
3	3	3	4	2	4	3
2	5	2	2	2	5	4
2	4	1	2	3	4	4
4	3	2	4	2	3	4
4	4	1	4	1	3	3
3	4	2	3	2	3	4
3	4	1	3	2	3	4
3	4	2	3	3	4	4
4	3	3	4	2	4	2
3	4	2	2	2	3	4
4	4	1	1	2	2	5
4	4	1	3	1	3	4
2	4	2	2	2	2	4
4	4	2	3	2	4	4
2	3	1	2	2	4	3
4	4	2	2	3	4	1
3	4	3	3	1	4	4
3	2	4	2	3	4	3
2	2	2	4	4	4	3
2	4	4	4	2	5	3
5	2	5	2	5	3	1
2	4	1	2	1	4	4
4	3	3	3	2	4	5
3	4	2	4	2	4	4
3	3	2	4	2	5	3
3	2	2	4	2	3	4
3	2	1	1	3	2	3
4	4	4	4	2	4	4
4	3	2	4	1	3	4
4	4	2	3	2	4	4
4	4	3	1	1	5	5
4	2	1	2	2	3	2
5	5	4	2	3	3	3
3	4	2	2	2	3	3
3	4	2	3	2	5	4
4	4	4	3	2	4	4
4	3	4	3	4	2	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103973&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103973&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103973&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
standards[t] = + 2.48963932953509 + 0.0538450994962259organization[t] + 0.387877857721709punished[t] + 0.0566181025382133secondrate[t] -0.0508237544025616mistakes[t] -0.079317766161663competent[t] -0.0330405295816517neat[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
standards[t] =  +  2.48963932953509 +  0.0538450994962259organization[t] +  0.387877857721709punished[t] +  0.0566181025382133secondrate[t] -0.0508237544025616mistakes[t] -0.079317766161663competent[t] -0.0330405295816517neat[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103973&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]standards[t] =  +  2.48963932953509 +  0.0538450994962259organization[t] +  0.387877857721709punished[t] +  0.0566181025382133secondrate[t] -0.0508237544025616mistakes[t] -0.079317766161663competent[t] -0.0330405295816517neat[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103973&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103973&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
standards[t] = + 2.48963932953509 + 0.0538450994962259organization[t] + 0.387877857721709punished[t] + 0.0566181025382133secondrate[t] -0.0508237544025616mistakes[t] -0.079317766161663competent[t] -0.0330405295816517neat[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.489639329535090.5541114.4931.4e-057e-06
organization0.05384509949622590.0933580.57680.5649560.282478
punished0.3878778577217090.0857274.52461.2e-056e-06
secondrate0.05661810253821330.0774330.73120.465790.232895
mistakes-0.05082375440256160.087434-0.58130.561910.280955
competent-0.0793177661616630.096687-0.82040.4132980.206649
neat-0.03304052958165170.100069-0.33020.741720.37086

\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) & 2.48963932953509 & 0.554111 & 4.493 & 1.4e-05 & 7e-06 \tabularnewline
organization & 0.0538450994962259 & 0.093358 & 0.5768 & 0.564956 & 0.282478 \tabularnewline
punished & 0.387877857721709 & 0.085727 & 4.5246 & 1.2e-05 & 6e-06 \tabularnewline
secondrate & 0.0566181025382133 & 0.077433 & 0.7312 & 0.46579 & 0.232895 \tabularnewline
mistakes & -0.0508237544025616 & 0.087434 & -0.5813 & 0.56191 & 0.280955 \tabularnewline
competent & -0.079317766161663 & 0.096687 & -0.8204 & 0.413298 & 0.206649 \tabularnewline
neat & -0.0330405295816517 & 0.100069 & -0.3302 & 0.74172 & 0.37086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103973&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]2.48963932953509[/C][C]0.554111[/C][C]4.493[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]organization[/C][C]0.0538450994962259[/C][C]0.093358[/C][C]0.5768[/C][C]0.564956[/C][C]0.282478[/C][/ROW]
[ROW][C]punished[/C][C]0.387877857721709[/C][C]0.085727[/C][C]4.5246[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]secondrate[/C][C]0.0566181025382133[/C][C]0.077433[/C][C]0.7312[/C][C]0.46579[/C][C]0.232895[/C][/ROW]
[ROW][C]mistakes[/C][C]-0.0508237544025616[/C][C]0.087434[/C][C]-0.5813[/C][C]0.56191[/C][C]0.280955[/C][/ROW]
[ROW][C]competent[/C][C]-0.079317766161663[/C][C]0.096687[/C][C]-0.8204[/C][C]0.413298[/C][C]0.206649[/C][/ROW]
[ROW][C]neat[/C][C]-0.0330405295816517[/C][C]0.100069[/C][C]-0.3302[/C][C]0.74172[/C][C]0.37086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103973&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103973&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.489639329535090.5541114.4931.4e-057e-06
organization0.05384509949622590.0933580.57680.5649560.282478
punished0.3878778577217090.0857274.52461.2e-056e-06
secondrate0.05661810253821330.0774330.73120.465790.232895
mistakes-0.05082375440256160.087434-0.58130.561910.280955
competent-0.0793177661616630.096687-0.82040.4132980.206649
neat-0.03304052958165170.100069-0.33020.741720.37086







Multiple Linear Regression - Regression Statistics
Multiple R0.376060259714053
R-squared0.141421318936201
Adjusted R-squared0.107530055209998
F-TEST (value)4.1727956820584
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value0.000651696167687899
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.912727461888245
Sum Squared Residuals126.626855792114

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.376060259714053 \tabularnewline
R-squared & 0.141421318936201 \tabularnewline
Adjusted R-squared & 0.107530055209998 \tabularnewline
F-TEST (value) & 4.1727956820584 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0.000651696167687899 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.912727461888245 \tabularnewline
Sum Squared Residuals & 126.626855792114 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103973&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.376060259714053[/C][/ROW]
[ROW][C]R-squared[/C][C]0.141421318936201[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.107530055209998[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.1727956820584[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]0.000651696167687899[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.912727461888245[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]126.626855792114[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103973&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103973&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.376060259714053
R-squared0.141421318936201
Adjusted R-squared0.107530055209998
F-TEST (value)4.1727956820584
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value0.000651696167687899
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.912727461888245
Sum Squared Residuals126.626855792114







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
123.10257040389327-1.10257040389327
223.10534340693532-1.10534340693532
343.076849395176220.923150604823783
423.20156648858478-1.20156648858478
533.04305253518977-0.0430525351897662
642.732475770992531.26752422900747
732.759721952438530.240278047561468
833.46964369170047-0.469643691700466
933.04570395930344-0.0457039593034414
1022.87030673340128-0.870306733401284
1143.903798786002190.0962012139978136
1243.048476962345430.951523037654572
1333.4562814806486-0.4562814806486
1433.05516691592853-0.0551669159285313
1542.580651771180621.41934822881938
1642.703103849900321.29689615009968
1733.08176583397876-0.0817658339787574
1833.15995734875563-0.159957348755631
1932.992107201858890.0078927981411074
2043.030942079576200.969057920423804
2122.80132983319782-0.801329833197823
2253.184661173096981.81533882690302
2343.914509572940380.0854904270596224
2423.07597148584311-1.07597148584311
2533.31202969061922-0.312029690619219
2643.184661173096980.815338826903018
2743.099549058799670.900450941200333
2833.12224872242312-0.122248722423117
2943.374190711444950.62580928855505
3043.099549058799670.900450941200333
3112.57118881455553-1.57118881455553
3243.830275367976180.169724632023825
3352.711422859026282.28857714097372
3423.16196150947353-1.16196150947353
3543.025147731440540.974852268559456
3633.13069449467244-0.130694494672444
3723.29387654481832-1.29387654481832
3843.252390242987340.747609757012658
3953.102322061841651.89767793815835
4043.189207690919530.810792309080468
4142.984417760014371.01558223998563
4243.150372813202230.849627186797771
4333.23460701816643-0.234607018166432
4443.293876544818320.70612345518168
4523.13258958838132-1.13258958838132
4622.65228009549776-0.652280095497758
4743.099918979779660.900081020220343
4822.74558963999272-0.745589639992722
4943.042930956261450.957069043738545
5042.714444204119951.28555579588005
5112.81911305801873-1.81911305801873
5243.042930956261450.957069043738545
5323.0098904266798-1.00989042667980
5412.71054494969317-1.71054494969317
5544.06720713521908-0.0672071352190847
5633.12035362871424-0.120353628714242
5723.12312663175623-1.12312663175623
5842.655053098539741.34494690146025
5932.816340054976750.183659945023254
6023.12993816426764-1.12993816426764
6123.14759981016024-1.14759981016024
6232.762494955480520.23750504451948
6322.67585766845432-0.67585766845432
6413.04872530439711-2.04872530439711
6532.734370864701410.265629135298592
6622.78909387353075-0.789093873530746
6733.20156648858478-0.20156648858478
6832.756300426930910.243699573069087
6932.703103849900320.29689615009968
7023.09954905879967-1.09954905879967
7132.601207999043520.398792000956481
7222.65505309853975-0.655053098539745
7343.54127201601760.458727983982398
7443.677207884717480.322792115282522
7543.260836015236670.739163984763332
7623.07597148584311-1.07597148584311
7732.711671201077960.288328798922041
7843.630930648137470.369069351862533
7933.06373552617603-0.0637355261760286
8043.093754710664020.906245289335984
8123.07532422244733-1.07532422244733
8233.1146808595069-0.114680859506902
8332.796783315375270.203216684624727
8443.739368905543210.260631094456791
8522.84181272164218-0.841812721642183
8642.847607069777831.15239293022217
8722.71167120107796-0.711671201077958
8822.67006332031867-0.670063320318668
8943.875304774243090.124695225756915
9033.12679524024567-0.126795240245667
9143.042930956261450.957069043738545
9222.81546214564363-0.815462145643634
9322.65782610158173-0.657826101581733
9432.601207999043520.398792000956481
9533.49044826161504-0.490448261615041
9653.861420803848951.13857919615105
9723.92612852864565-1.92612852864565
9833.52626179423868-0.526261794238679
9943.285430772568990.714569227431007
10033.20611300640733-0.206113006407330
10143.599785212264690.400214787735309
10232.818235148685620.181764851314379
10333.44606611874390-0.446066118743905
10423.07597148584311-1.07597148584311
10533.27898916103757-0.278989161037568
10623.30131764461117-1.30131764461117
10733.18832978158642-0.188329781586420
10823.80065510483228-1.80065510483228
10943.571291200505590.428708799494410
11022.84395615740274-0.843956157402736
11142.623907662666971.37609233733303
11242.997901549994541.00209845000546
11312.69666097929904-1.69666097929904
11453.587426414599441.41257358540056
11523.0594360913353-1.05943609133530
11633.22969057936389-0.229690579363892
11743.057939918970520.942060081029482
11812.62365932061529-1.62365932061529
11953.566744682683041.43325531731696
12032.876748344932710.12325165506729
12132.795535485062170.204464514937828
12233.10144415250854-0.101444152508543
12333.52324044914501-0.523240449145015
12423.01745828959602-1.01745828959602
12522.60422934413718-0.604229344137184
12643.181639828003320.818360171996682
12742.931471353762051.06852864623795
12833.17886682496133-0.178866824961330
12932.790988967239620.209011032760379
13033.04872530439711-0.0487253043971058
13143.556280978726670.443719021273333
13233.12224872242312-0.122248722423117
13342.724029998743211.27597000125679
13442.841812721642181.15818727835782
13523.20156648858478-1.20156648858478
13643.099549058799670.900450941200333
13722.63424852862517-0.634248528625171
13843.091228790603850.908771209396152
13933.53825067092394-0.538250670923938
14033.69321324789151-0.69321324789151
14122.97986998312196-0.979869983121957
14223.88564564020129-1.88564564020129
14354.124842422133060.875157577866938
14422.70587685294231-0.705876852942307
14543.40054128744350.599458712556501
14633.15616716133788-0.156167161337881
14733.05604482526164-0.0560448252616432
14833.12779472850709-0.127794728507091
14932.631597104511500.368402895488504
15043.93192287678130.0680771232187017
15143.232463582405880.767536417594121
15243.099549058799670.900450941200333
15343.31265617010420.687343829895803
15442.692761724872261.30723827512774
15553.934066312541851.06593368745815
15633.15528925200477-0.155289252004769
15733.02023129263800-0.0202312926380044
15843.875304774243090.124695225756915
15943.911488227846710.0885117721532867

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 3.10257040389327 & -1.10257040389327 \tabularnewline
2 & 2 & 3.10534340693532 & -1.10534340693532 \tabularnewline
3 & 4 & 3.07684939517622 & 0.923150604823783 \tabularnewline
4 & 2 & 3.20156648858478 & -1.20156648858478 \tabularnewline
5 & 3 & 3.04305253518977 & -0.0430525351897662 \tabularnewline
6 & 4 & 2.73247577099253 & 1.26752422900747 \tabularnewline
7 & 3 & 2.75972195243853 & 0.240278047561468 \tabularnewline
8 & 3 & 3.46964369170047 & -0.469643691700466 \tabularnewline
9 & 3 & 3.04570395930344 & -0.0457039593034414 \tabularnewline
10 & 2 & 2.87030673340128 & -0.870306733401284 \tabularnewline
11 & 4 & 3.90379878600219 & 0.0962012139978136 \tabularnewline
12 & 4 & 3.04847696234543 & 0.951523037654572 \tabularnewline
13 & 3 & 3.4562814806486 & -0.4562814806486 \tabularnewline
14 & 3 & 3.05516691592853 & -0.0551669159285313 \tabularnewline
15 & 4 & 2.58065177118062 & 1.41934822881938 \tabularnewline
16 & 4 & 2.70310384990032 & 1.29689615009968 \tabularnewline
17 & 3 & 3.08176583397876 & -0.0817658339787574 \tabularnewline
18 & 3 & 3.15995734875563 & -0.159957348755631 \tabularnewline
19 & 3 & 2.99210720185889 & 0.0078927981411074 \tabularnewline
20 & 4 & 3.03094207957620 & 0.969057920423804 \tabularnewline
21 & 2 & 2.80132983319782 & -0.801329833197823 \tabularnewline
22 & 5 & 3.18466117309698 & 1.81533882690302 \tabularnewline
23 & 4 & 3.91450957294038 & 0.0854904270596224 \tabularnewline
24 & 2 & 3.07597148584311 & -1.07597148584311 \tabularnewline
25 & 3 & 3.31202969061922 & -0.312029690619219 \tabularnewline
26 & 4 & 3.18466117309698 & 0.815338826903018 \tabularnewline
27 & 4 & 3.09954905879967 & 0.900450941200333 \tabularnewline
28 & 3 & 3.12224872242312 & -0.122248722423117 \tabularnewline
29 & 4 & 3.37419071144495 & 0.62580928855505 \tabularnewline
30 & 4 & 3.09954905879967 & 0.900450941200333 \tabularnewline
31 & 1 & 2.57118881455553 & -1.57118881455553 \tabularnewline
32 & 4 & 3.83027536797618 & 0.169724632023825 \tabularnewline
33 & 5 & 2.71142285902628 & 2.28857714097372 \tabularnewline
34 & 2 & 3.16196150947353 & -1.16196150947353 \tabularnewline
35 & 4 & 3.02514773144054 & 0.974852268559456 \tabularnewline
36 & 3 & 3.13069449467244 & -0.130694494672444 \tabularnewline
37 & 2 & 3.29387654481832 & -1.29387654481832 \tabularnewline
38 & 4 & 3.25239024298734 & 0.747609757012658 \tabularnewline
39 & 5 & 3.10232206184165 & 1.89767793815835 \tabularnewline
40 & 4 & 3.18920769091953 & 0.810792309080468 \tabularnewline
41 & 4 & 2.98441776001437 & 1.01558223998563 \tabularnewline
42 & 4 & 3.15037281320223 & 0.849627186797771 \tabularnewline
43 & 3 & 3.23460701816643 & -0.234607018166432 \tabularnewline
44 & 4 & 3.29387654481832 & 0.70612345518168 \tabularnewline
45 & 2 & 3.13258958838132 & -1.13258958838132 \tabularnewline
46 & 2 & 2.65228009549776 & -0.652280095497758 \tabularnewline
47 & 4 & 3.09991897977966 & 0.900081020220343 \tabularnewline
48 & 2 & 2.74558963999272 & -0.745589639992722 \tabularnewline
49 & 4 & 3.04293095626145 & 0.957069043738545 \tabularnewline
50 & 4 & 2.71444420411995 & 1.28555579588005 \tabularnewline
51 & 1 & 2.81911305801873 & -1.81911305801873 \tabularnewline
52 & 4 & 3.04293095626145 & 0.957069043738545 \tabularnewline
53 & 2 & 3.0098904266798 & -1.00989042667980 \tabularnewline
54 & 1 & 2.71054494969317 & -1.71054494969317 \tabularnewline
55 & 4 & 4.06720713521908 & -0.0672071352190847 \tabularnewline
56 & 3 & 3.12035362871424 & -0.120353628714242 \tabularnewline
57 & 2 & 3.12312663175623 & -1.12312663175623 \tabularnewline
58 & 4 & 2.65505309853974 & 1.34494690146025 \tabularnewline
59 & 3 & 2.81634005497675 & 0.183659945023254 \tabularnewline
60 & 2 & 3.12993816426764 & -1.12993816426764 \tabularnewline
61 & 2 & 3.14759981016024 & -1.14759981016024 \tabularnewline
62 & 3 & 2.76249495548052 & 0.23750504451948 \tabularnewline
63 & 2 & 2.67585766845432 & -0.67585766845432 \tabularnewline
64 & 1 & 3.04872530439711 & -2.04872530439711 \tabularnewline
65 & 3 & 2.73437086470141 & 0.265629135298592 \tabularnewline
66 & 2 & 2.78909387353075 & -0.789093873530746 \tabularnewline
67 & 3 & 3.20156648858478 & -0.20156648858478 \tabularnewline
68 & 3 & 2.75630042693091 & 0.243699573069087 \tabularnewline
69 & 3 & 2.70310384990032 & 0.29689615009968 \tabularnewline
70 & 2 & 3.09954905879967 & -1.09954905879967 \tabularnewline
71 & 3 & 2.60120799904352 & 0.398792000956481 \tabularnewline
72 & 2 & 2.65505309853975 & -0.655053098539745 \tabularnewline
73 & 4 & 3.5412720160176 & 0.458727983982398 \tabularnewline
74 & 4 & 3.67720788471748 & 0.322792115282522 \tabularnewline
75 & 4 & 3.26083601523667 & 0.739163984763332 \tabularnewline
76 & 2 & 3.07597148584311 & -1.07597148584311 \tabularnewline
77 & 3 & 2.71167120107796 & 0.288328798922041 \tabularnewline
78 & 4 & 3.63093064813747 & 0.369069351862533 \tabularnewline
79 & 3 & 3.06373552617603 & -0.0637355261760286 \tabularnewline
80 & 4 & 3.09375471066402 & 0.906245289335984 \tabularnewline
81 & 2 & 3.07532422244733 & -1.07532422244733 \tabularnewline
82 & 3 & 3.1146808595069 & -0.114680859506902 \tabularnewline
83 & 3 & 2.79678331537527 & 0.203216684624727 \tabularnewline
84 & 4 & 3.73936890554321 & 0.260631094456791 \tabularnewline
85 & 2 & 2.84181272164218 & -0.841812721642183 \tabularnewline
86 & 4 & 2.84760706977783 & 1.15239293022217 \tabularnewline
87 & 2 & 2.71167120107796 & -0.711671201077958 \tabularnewline
88 & 2 & 2.67006332031867 & -0.670063320318668 \tabularnewline
89 & 4 & 3.87530477424309 & 0.124695225756915 \tabularnewline
90 & 3 & 3.12679524024567 & -0.126795240245667 \tabularnewline
91 & 4 & 3.04293095626145 & 0.957069043738545 \tabularnewline
92 & 2 & 2.81546214564363 & -0.815462145643634 \tabularnewline
93 & 2 & 2.65782610158173 & -0.657826101581733 \tabularnewline
94 & 3 & 2.60120799904352 & 0.398792000956481 \tabularnewline
95 & 3 & 3.49044826161504 & -0.490448261615041 \tabularnewline
96 & 5 & 3.86142080384895 & 1.13857919615105 \tabularnewline
97 & 2 & 3.92612852864565 & -1.92612852864565 \tabularnewline
98 & 3 & 3.52626179423868 & -0.526261794238679 \tabularnewline
99 & 4 & 3.28543077256899 & 0.714569227431007 \tabularnewline
100 & 3 & 3.20611300640733 & -0.206113006407330 \tabularnewline
101 & 4 & 3.59978521226469 & 0.400214787735309 \tabularnewline
102 & 3 & 2.81823514868562 & 0.181764851314379 \tabularnewline
103 & 3 & 3.44606611874390 & -0.446066118743905 \tabularnewline
104 & 2 & 3.07597148584311 & -1.07597148584311 \tabularnewline
105 & 3 & 3.27898916103757 & -0.278989161037568 \tabularnewline
106 & 2 & 3.30131764461117 & -1.30131764461117 \tabularnewline
107 & 3 & 3.18832978158642 & -0.188329781586420 \tabularnewline
108 & 2 & 3.80065510483228 & -1.80065510483228 \tabularnewline
109 & 4 & 3.57129120050559 & 0.428708799494410 \tabularnewline
110 & 2 & 2.84395615740274 & -0.843956157402736 \tabularnewline
111 & 4 & 2.62390766266697 & 1.37609233733303 \tabularnewline
112 & 4 & 2.99790154999454 & 1.00209845000546 \tabularnewline
113 & 1 & 2.69666097929904 & -1.69666097929904 \tabularnewline
114 & 5 & 3.58742641459944 & 1.41257358540056 \tabularnewline
115 & 2 & 3.0594360913353 & -1.05943609133530 \tabularnewline
116 & 3 & 3.22969057936389 & -0.229690579363892 \tabularnewline
117 & 4 & 3.05793991897052 & 0.942060081029482 \tabularnewline
118 & 1 & 2.62365932061529 & -1.62365932061529 \tabularnewline
119 & 5 & 3.56674468268304 & 1.43325531731696 \tabularnewline
120 & 3 & 2.87674834493271 & 0.12325165506729 \tabularnewline
121 & 3 & 2.79553548506217 & 0.204464514937828 \tabularnewline
122 & 3 & 3.10144415250854 & -0.101444152508543 \tabularnewline
123 & 3 & 3.52324044914501 & -0.523240449145015 \tabularnewline
124 & 2 & 3.01745828959602 & -1.01745828959602 \tabularnewline
125 & 2 & 2.60422934413718 & -0.604229344137184 \tabularnewline
126 & 4 & 3.18163982800332 & 0.818360171996682 \tabularnewline
127 & 4 & 2.93147135376205 & 1.06852864623795 \tabularnewline
128 & 3 & 3.17886682496133 & -0.178866824961330 \tabularnewline
129 & 3 & 2.79098896723962 & 0.209011032760379 \tabularnewline
130 & 3 & 3.04872530439711 & -0.0487253043971058 \tabularnewline
131 & 4 & 3.55628097872667 & 0.443719021273333 \tabularnewline
132 & 3 & 3.12224872242312 & -0.122248722423117 \tabularnewline
133 & 4 & 2.72402999874321 & 1.27597000125679 \tabularnewline
134 & 4 & 2.84181272164218 & 1.15818727835782 \tabularnewline
135 & 2 & 3.20156648858478 & -1.20156648858478 \tabularnewline
136 & 4 & 3.09954905879967 & 0.900450941200333 \tabularnewline
137 & 2 & 2.63424852862517 & -0.634248528625171 \tabularnewline
138 & 4 & 3.09122879060385 & 0.908771209396152 \tabularnewline
139 & 3 & 3.53825067092394 & -0.538250670923938 \tabularnewline
140 & 3 & 3.69321324789151 & -0.69321324789151 \tabularnewline
141 & 2 & 2.97986998312196 & -0.979869983121957 \tabularnewline
142 & 2 & 3.88564564020129 & -1.88564564020129 \tabularnewline
143 & 5 & 4.12484242213306 & 0.875157577866938 \tabularnewline
144 & 2 & 2.70587685294231 & -0.705876852942307 \tabularnewline
145 & 4 & 3.4005412874435 & 0.599458712556501 \tabularnewline
146 & 3 & 3.15616716133788 & -0.156167161337881 \tabularnewline
147 & 3 & 3.05604482526164 & -0.0560448252616432 \tabularnewline
148 & 3 & 3.12779472850709 & -0.127794728507091 \tabularnewline
149 & 3 & 2.63159710451150 & 0.368402895488504 \tabularnewline
150 & 4 & 3.9319228767813 & 0.0680771232187017 \tabularnewline
151 & 4 & 3.23246358240588 & 0.767536417594121 \tabularnewline
152 & 4 & 3.09954905879967 & 0.900450941200333 \tabularnewline
153 & 4 & 3.3126561701042 & 0.687343829895803 \tabularnewline
154 & 4 & 2.69276172487226 & 1.30723827512774 \tabularnewline
155 & 5 & 3.93406631254185 & 1.06593368745815 \tabularnewline
156 & 3 & 3.15528925200477 & -0.155289252004769 \tabularnewline
157 & 3 & 3.02023129263800 & -0.0202312926380044 \tabularnewline
158 & 4 & 3.87530477424309 & 0.124695225756915 \tabularnewline
159 & 4 & 3.91148822784671 & 0.0885117721532867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103973&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]2[/C][C]3.10257040389327[/C][C]-1.10257040389327[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]3.10534340693532[/C][C]-1.10534340693532[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.07684939517622[/C][C]0.923150604823783[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]3.20156648858478[/C][C]-1.20156648858478[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]3.04305253518977[/C][C]-0.0430525351897662[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]2.73247577099253[/C][C]1.26752422900747[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]2.75972195243853[/C][C]0.240278047561468[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]3.46964369170047[/C][C]-0.469643691700466[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]3.04570395930344[/C][C]-0.0457039593034414[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]2.87030673340128[/C][C]-0.870306733401284[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.90379878600219[/C][C]0.0962012139978136[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.04847696234543[/C][C]0.951523037654572[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]3.4562814806486[/C][C]-0.4562814806486[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]3.05516691592853[/C][C]-0.0551669159285313[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]2.58065177118062[/C][C]1.41934822881938[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]2.70310384990032[/C][C]1.29689615009968[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.08176583397876[/C][C]-0.0817658339787574[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]3.15995734875563[/C][C]-0.159957348755631[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]2.99210720185889[/C][C]0.0078927981411074[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.03094207957620[/C][C]0.969057920423804[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.80132983319782[/C][C]-0.801329833197823[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]3.18466117309698[/C][C]1.81533882690302[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]3.91450957294038[/C][C]0.0854904270596224[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]3.07597148584311[/C][C]-1.07597148584311[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.31202969061922[/C][C]-0.312029690619219[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.18466117309698[/C][C]0.815338826903018[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.09954905879967[/C][C]0.900450941200333[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]3.12224872242312[/C][C]-0.122248722423117[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]3.37419071144495[/C][C]0.62580928855505[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.09954905879967[/C][C]0.900450941200333[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]2.57118881455553[/C][C]-1.57118881455553[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.83027536797618[/C][C]0.169724632023825[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]2.71142285902628[/C][C]2.28857714097372[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]3.16196150947353[/C][C]-1.16196150947353[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.02514773144054[/C][C]0.974852268559456[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]3.13069449467244[/C][C]-0.130694494672444[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]3.29387654481832[/C][C]-1.29387654481832[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]3.25239024298734[/C][C]0.747609757012658[/C][/ROW]
[ROW][C]39[/C][C]5[/C][C]3.10232206184165[/C][C]1.89767793815835[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]3.18920769091953[/C][C]0.810792309080468[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]2.98441776001437[/C][C]1.01558223998563[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]3.15037281320223[/C][C]0.849627186797771[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.23460701816643[/C][C]-0.234607018166432[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]3.29387654481832[/C][C]0.70612345518168[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]3.13258958838132[/C][C]-1.13258958838132[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]2.65228009549776[/C][C]-0.652280095497758[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.09991897977966[/C][C]0.900081020220343[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]2.74558963999272[/C][C]-0.745589639992722[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]3.04293095626145[/C][C]0.957069043738545[/C][/ROW]
[ROW][C]50[/C][C]4[/C][C]2.71444420411995[/C][C]1.28555579588005[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]2.81911305801873[/C][C]-1.81911305801873[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.04293095626145[/C][C]0.957069043738545[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]3.0098904266798[/C][C]-1.00989042667980[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]2.71054494969317[/C][C]-1.71054494969317[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.06720713521908[/C][C]-0.0672071352190847[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]3.12035362871424[/C][C]-0.120353628714242[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]3.12312663175623[/C][C]-1.12312663175623[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]2.65505309853974[/C][C]1.34494690146025[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]2.81634005497675[/C][C]0.183659945023254[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]3.12993816426764[/C][C]-1.12993816426764[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]3.14759981016024[/C][C]-1.14759981016024[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]2.76249495548052[/C][C]0.23750504451948[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]2.67585766845432[/C][C]-0.67585766845432[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]3.04872530439711[/C][C]-2.04872530439711[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]2.73437086470141[/C][C]0.265629135298592[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]2.78909387353075[/C][C]-0.789093873530746[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]3.20156648858478[/C][C]-0.20156648858478[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]2.75630042693091[/C][C]0.243699573069087[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]2.70310384990032[/C][C]0.29689615009968[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]3.09954905879967[/C][C]-1.09954905879967[/C][/ROW]
[ROW][C]71[/C][C]3[/C][C]2.60120799904352[/C][C]0.398792000956481[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]2.65505309853975[/C][C]-0.655053098539745[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.5412720160176[/C][C]0.458727983982398[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]3.67720788471748[/C][C]0.322792115282522[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]3.26083601523667[/C][C]0.739163984763332[/C][/ROW]
[ROW][C]76[/C][C]2[/C][C]3.07597148584311[/C][C]-1.07597148584311[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.71167120107796[/C][C]0.288328798922041[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]3.63093064813747[/C][C]0.369069351862533[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]3.06373552617603[/C][C]-0.0637355261760286[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]3.09375471066402[/C][C]0.906245289335984[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]3.07532422244733[/C][C]-1.07532422244733[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]3.1146808595069[/C][C]-0.114680859506902[/C][/ROW]
[ROW][C]83[/C][C]3[/C][C]2.79678331537527[/C][C]0.203216684624727[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]3.73936890554321[/C][C]0.260631094456791[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]2.84181272164218[/C][C]-0.841812721642183[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]2.84760706977783[/C][C]1.15239293022217[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]2.71167120107796[/C][C]-0.711671201077958[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]2.67006332031867[/C][C]-0.670063320318668[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]3.87530477424309[/C][C]0.124695225756915[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]3.12679524024567[/C][C]-0.126795240245667[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]3.04293095626145[/C][C]0.957069043738545[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]2.81546214564363[/C][C]-0.815462145643634[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]2.65782610158173[/C][C]-0.657826101581733[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]2.60120799904352[/C][C]0.398792000956481[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]3.49044826161504[/C][C]-0.490448261615041[/C][/ROW]
[ROW][C]96[/C][C]5[/C][C]3.86142080384895[/C][C]1.13857919615105[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]3.92612852864565[/C][C]-1.92612852864565[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]3.52626179423868[/C][C]-0.526261794238679[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]3.28543077256899[/C][C]0.714569227431007[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]3.20611300640733[/C][C]-0.206113006407330[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]3.59978521226469[/C][C]0.400214787735309[/C][/ROW]
[ROW][C]102[/C][C]3[/C][C]2.81823514868562[/C][C]0.181764851314379[/C][/ROW]
[ROW][C]103[/C][C]3[/C][C]3.44606611874390[/C][C]-0.446066118743905[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]3.07597148584311[/C][C]-1.07597148584311[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]3.27898916103757[/C][C]-0.278989161037568[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]3.30131764461117[/C][C]-1.30131764461117[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]3.18832978158642[/C][C]-0.188329781586420[/C][/ROW]
[ROW][C]108[/C][C]2[/C][C]3.80065510483228[/C][C]-1.80065510483228[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]3.57129120050559[/C][C]0.428708799494410[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]2.84395615740274[/C][C]-0.843956157402736[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]2.62390766266697[/C][C]1.37609233733303[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]2.99790154999454[/C][C]1.00209845000546[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]2.69666097929904[/C][C]-1.69666097929904[/C][/ROW]
[ROW][C]114[/C][C]5[/C][C]3.58742641459944[/C][C]1.41257358540056[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]3.0594360913353[/C][C]-1.05943609133530[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]3.22969057936389[/C][C]-0.229690579363892[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]3.05793991897052[/C][C]0.942060081029482[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]2.62365932061529[/C][C]-1.62365932061529[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]3.56674468268304[/C][C]1.43325531731696[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]2.87674834493271[/C][C]0.12325165506729[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.79553548506217[/C][C]0.204464514937828[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]3.10144415250854[/C][C]-0.101444152508543[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]3.52324044914501[/C][C]-0.523240449145015[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]3.01745828959602[/C][C]-1.01745828959602[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]2.60422934413718[/C][C]-0.604229344137184[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.18163982800332[/C][C]0.818360171996682[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]2.93147135376205[/C][C]1.06852864623795[/C][/ROW]
[ROW][C]128[/C][C]3[/C][C]3.17886682496133[/C][C]-0.178866824961330[/C][/ROW]
[ROW][C]129[/C][C]3[/C][C]2.79098896723962[/C][C]0.209011032760379[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]3.04872530439711[/C][C]-0.0487253043971058[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]3.55628097872667[/C][C]0.443719021273333[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]3.12224872242312[/C][C]-0.122248722423117[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]2.72402999874321[/C][C]1.27597000125679[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]2.84181272164218[/C][C]1.15818727835782[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]3.20156648858478[/C][C]-1.20156648858478[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]3.09954905879967[/C][C]0.900450941200333[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]2.63424852862517[/C][C]-0.634248528625171[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]3.09122879060385[/C][C]0.908771209396152[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]3.53825067092394[/C][C]-0.538250670923938[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]3.69321324789151[/C][C]-0.69321324789151[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]2.97986998312196[/C][C]-0.979869983121957[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]3.88564564020129[/C][C]-1.88564564020129[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]4.12484242213306[/C][C]0.875157577866938[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]2.70587685294231[/C][C]-0.705876852942307[/C][/ROW]
[ROW][C]145[/C][C]4[/C][C]3.4005412874435[/C][C]0.599458712556501[/C][/ROW]
[ROW][C]146[/C][C]3[/C][C]3.15616716133788[/C][C]-0.156167161337881[/C][/ROW]
[ROW][C]147[/C][C]3[/C][C]3.05604482526164[/C][C]-0.0560448252616432[/C][/ROW]
[ROW][C]148[/C][C]3[/C][C]3.12779472850709[/C][C]-0.127794728507091[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]2.63159710451150[/C][C]0.368402895488504[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]3.9319228767813[/C][C]0.0680771232187017[/C][/ROW]
[ROW][C]151[/C][C]4[/C][C]3.23246358240588[/C][C]0.767536417594121[/C][/ROW]
[ROW][C]152[/C][C]4[/C][C]3.09954905879967[/C][C]0.900450941200333[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]3.3126561701042[/C][C]0.687343829895803[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]2.69276172487226[/C][C]1.30723827512774[/C][/ROW]
[ROW][C]155[/C][C]5[/C][C]3.93406631254185[/C][C]1.06593368745815[/C][/ROW]
[ROW][C]156[/C][C]3[/C][C]3.15528925200477[/C][C]-0.155289252004769[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]3.02023129263800[/C][C]-0.0202312926380044[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]3.87530477424309[/C][C]0.124695225756915[/C][/ROW]
[ROW][C]159[/C][C]4[/C][C]3.91148822784671[/C][C]0.0885117721532867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103973&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103973&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
123.10257040389327-1.10257040389327
223.10534340693532-1.10534340693532
343.076849395176220.923150604823783
423.20156648858478-1.20156648858478
533.04305253518977-0.0430525351897662
642.732475770992531.26752422900747
732.759721952438530.240278047561468
833.46964369170047-0.469643691700466
933.04570395930344-0.0457039593034414
1022.87030673340128-0.870306733401284
1143.903798786002190.0962012139978136
1243.048476962345430.951523037654572
1333.4562814806486-0.4562814806486
1433.05516691592853-0.0551669159285313
1542.580651771180621.41934822881938
1642.703103849900321.29689615009968
1733.08176583397876-0.0817658339787574
1833.15995734875563-0.159957348755631
1932.992107201858890.0078927981411074
2043.030942079576200.969057920423804
2122.80132983319782-0.801329833197823
2253.184661173096981.81533882690302
2343.914509572940380.0854904270596224
2423.07597148584311-1.07597148584311
2533.31202969061922-0.312029690619219
2643.184661173096980.815338826903018
2743.099549058799670.900450941200333
2833.12224872242312-0.122248722423117
2943.374190711444950.62580928855505
3043.099549058799670.900450941200333
3112.57118881455553-1.57118881455553
3243.830275367976180.169724632023825
3352.711422859026282.28857714097372
3423.16196150947353-1.16196150947353
3543.025147731440540.974852268559456
3633.13069449467244-0.130694494672444
3723.29387654481832-1.29387654481832
3843.252390242987340.747609757012658
3953.102322061841651.89767793815835
4043.189207690919530.810792309080468
4142.984417760014371.01558223998563
4243.150372813202230.849627186797771
4333.23460701816643-0.234607018166432
4443.293876544818320.70612345518168
4523.13258958838132-1.13258958838132
4622.65228009549776-0.652280095497758
4743.099918979779660.900081020220343
4822.74558963999272-0.745589639992722
4943.042930956261450.957069043738545
5042.714444204119951.28555579588005
5112.81911305801873-1.81911305801873
5243.042930956261450.957069043738545
5323.0098904266798-1.00989042667980
5412.71054494969317-1.71054494969317
5544.06720713521908-0.0672071352190847
5633.12035362871424-0.120353628714242
5723.12312663175623-1.12312663175623
5842.655053098539741.34494690146025
5932.816340054976750.183659945023254
6023.12993816426764-1.12993816426764
6123.14759981016024-1.14759981016024
6232.762494955480520.23750504451948
6322.67585766845432-0.67585766845432
6413.04872530439711-2.04872530439711
6532.734370864701410.265629135298592
6622.78909387353075-0.789093873530746
6733.20156648858478-0.20156648858478
6832.756300426930910.243699573069087
6932.703103849900320.29689615009968
7023.09954905879967-1.09954905879967
7132.601207999043520.398792000956481
7222.65505309853975-0.655053098539745
7343.54127201601760.458727983982398
7443.677207884717480.322792115282522
7543.260836015236670.739163984763332
7623.07597148584311-1.07597148584311
7732.711671201077960.288328798922041
7843.630930648137470.369069351862533
7933.06373552617603-0.0637355261760286
8043.093754710664020.906245289335984
8123.07532422244733-1.07532422244733
8233.1146808595069-0.114680859506902
8332.796783315375270.203216684624727
8443.739368905543210.260631094456791
8522.84181272164218-0.841812721642183
8642.847607069777831.15239293022217
8722.71167120107796-0.711671201077958
8822.67006332031867-0.670063320318668
8943.875304774243090.124695225756915
9033.12679524024567-0.126795240245667
9143.042930956261450.957069043738545
9222.81546214564363-0.815462145643634
9322.65782610158173-0.657826101581733
9432.601207999043520.398792000956481
9533.49044826161504-0.490448261615041
9653.861420803848951.13857919615105
9723.92612852864565-1.92612852864565
9833.52626179423868-0.526261794238679
9943.285430772568990.714569227431007
10033.20611300640733-0.206113006407330
10143.599785212264690.400214787735309
10232.818235148685620.181764851314379
10333.44606611874390-0.446066118743905
10423.07597148584311-1.07597148584311
10533.27898916103757-0.278989161037568
10623.30131764461117-1.30131764461117
10733.18832978158642-0.188329781586420
10823.80065510483228-1.80065510483228
10943.571291200505590.428708799494410
11022.84395615740274-0.843956157402736
11142.623907662666971.37609233733303
11242.997901549994541.00209845000546
11312.69666097929904-1.69666097929904
11453.587426414599441.41257358540056
11523.0594360913353-1.05943609133530
11633.22969057936389-0.229690579363892
11743.057939918970520.942060081029482
11812.62365932061529-1.62365932061529
11953.566744682683041.43325531731696
12032.876748344932710.12325165506729
12132.795535485062170.204464514937828
12233.10144415250854-0.101444152508543
12333.52324044914501-0.523240449145015
12423.01745828959602-1.01745828959602
12522.60422934413718-0.604229344137184
12643.181639828003320.818360171996682
12742.931471353762051.06852864623795
12833.17886682496133-0.178866824961330
12932.790988967239620.209011032760379
13033.04872530439711-0.0487253043971058
13143.556280978726670.443719021273333
13233.12224872242312-0.122248722423117
13342.724029998743211.27597000125679
13442.841812721642181.15818727835782
13523.20156648858478-1.20156648858478
13643.099549058799670.900450941200333
13722.63424852862517-0.634248528625171
13843.091228790603850.908771209396152
13933.53825067092394-0.538250670923938
14033.69321324789151-0.69321324789151
14122.97986998312196-0.979869983121957
14223.88564564020129-1.88564564020129
14354.124842422133060.875157577866938
14422.70587685294231-0.705876852942307
14543.40054128744350.599458712556501
14633.15616716133788-0.156167161337881
14733.05604482526164-0.0560448252616432
14833.12779472850709-0.127794728507091
14932.631597104511500.368402895488504
15043.93192287678130.0680771232187017
15143.232463582405880.767536417594121
15243.099549058799670.900450941200333
15343.31265617010420.687343829895803
15442.692761724872261.30723827512774
15553.934066312541851.06593368745815
15633.15528925200477-0.155289252004769
15733.02023129263800-0.0202312926380044
15843.875304774243090.124695225756915
15943.911488227846710.0885117721532867







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.557257169793390.8854856604132210.442742830206611
110.4285395074434990.8570790148869980.571460492556501
120.2934549518879480.5869099037758970.706545048112052
130.2974693167578750.594938633515750.702530683242125
140.2680493421265010.5360986842530020.731950657873499
150.3255494906975840.6510989813951680.674450509302416
160.2509681543537440.5019363087074890.749031845646256
170.1825876241255410.3651752482510830.817412375874458
180.1705312554065620.3410625108131250.829468744593438
190.1373836330375380.2747672660750770.862616366962462
200.1824868117426590.3649736234853180.817513188257341
210.1495820625826160.2991641251652310.850417937417384
220.5766638864345350.846672227130930.423336113565465
230.5464785973812140.9070428052375730.453521402618786
240.574692537669480.850614924661040.42530746233052
250.5169043413471380.9661913173057240.483095658652862
260.495595430308460.991190860616920.50440456969154
270.4728215541780430.9456431083560860.527178445821957
280.4063779087352620.8127558174705250.593622091264738
290.3579320586547790.7158641173095570.642067941345221
300.3334402724396050.666880544879210.666559727560395
310.6205325966451710.7589348067096580.379467403354829
320.560400694935450.87919861012910.43959930506455
330.7106415827279180.5787168345441650.289358417272082
340.7708291638356120.4583416723287770.229170836164388
350.7654756096832490.4690487806335020.234524390316751
360.7265687967424540.5468624065150920.273431203257546
370.7472245505553080.5055508988893850.252775449444692
380.7418912109079070.5162175781841860.258108789092093
390.817945863454860.3641082730902790.182054136545140
400.8059307605511310.3881384788977380.194069239448869
410.792290416046730.4154191679065410.207709583953270
420.7699168831259920.4601662337480160.230083116874008
430.7295472586228420.5409054827543160.270452741377158
440.71215872404890.5756825519021990.287841275951100
450.7496007050357050.5007985899285900.250399294964295
460.730825755895840.5383484882083210.269174244104160
470.7459371816760970.5081256366478060.254062818323903
480.7529882692479490.4940234615041030.247011730752051
490.7406411512096130.5187176975807750.259358848790387
500.7556495260233720.4887009479532570.244350473976629
510.8689902835990950.262019432801810.131009716400905
520.8622931984353980.2754136031292030.137706801564602
530.8892157229581220.2215685540837550.110784277041878
540.9450833550670760.1098332898658490.0549166449329244
550.9346567330454190.1306865339091620.065343266954581
560.9188237666476450.162352466704710.081176233352355
570.9304831609907020.1390336780185960.0695168390092978
580.944064099146460.1118718017070800.0559359008535402
590.9298565343967610.1402869312064780.0701434656032388
600.9368877185514880.1262245628970240.0631122814485118
610.9439517443109530.1120965113780940.056048255689047
620.9302107626046520.1395784747906960.0697892373953481
630.9231850770685780.1536298458628440.076814922931422
640.9710221656097140.0579556687805720.028977834390286
650.9630544999652370.07389100006952690.0369455000347634
660.9592968307600540.08140633847989170.0407031692399458
670.9487499739076550.1025000521846900.0512500260923448
680.9365615585437930.1268768829124150.0634384414562074
690.9223428665420980.1553142669158040.0776571334579022
700.9294031868790610.1411936262418770.0705968131209386
710.9163618998105710.1672762003788580.083638100189429
720.9074124117945520.1851751764108960.0925875882054482
730.8923259079188990.2153481841622030.107674092081101
740.8740029046548930.2519941906902140.125997095345107
750.8652410860794960.2695178278410080.134758913920504
760.8740266961013040.2519466077973930.125973303898696
770.8511744567668120.2976510864663770.148825543233188
780.8269957163072760.3460085673854470.173004283692724
790.7952668925824180.4094662148351650.204733107417582
800.7935877741931950.4128244516136090.206412225806805
810.8048195158952520.3903609682094960.195180484104748
820.7715932840118940.4568134319762130.228406715988106
830.7380391899005950.5239216201988090.261960810099405
840.7052993694933880.5894012610132240.294700630506612
850.6997201444100080.6005597111799850.300279855589992
860.7192430672038920.5615138655922150.280756932796108
870.7020435580838360.5959128838323290.297956441916164
880.6801747986986250.639650402602750.319825201301375
890.6376579047732310.7246841904535380.362342095226769
900.5932308159625990.8135383680748020.406769184037401
910.5974713426177490.8050573147645010.402528657382251
920.5910380033896770.8179239932206450.408961996610323
930.5682940225943850.863411954811230.431705977405615
940.5320660104910160.9358679790179670.467933989508984
950.5013426734023760.9973146531952490.498657326597624
960.5242679541786410.9514640916427170.475732045821359
970.6888410869847980.6223178260304040.311158913015202
980.6588836138805410.6822327722389180.341116386119459
990.6355391408098130.7289217183803740.364460859190187
1000.5950101070201760.8099797859596490.404989892979824
1010.5526261283159320.8947477433681350.447373871684068
1020.5047003890907370.9905992218185270.495299610909263
1030.4697994471698280.9395988943396550.530200552830172
1040.4951868464699150.990373692939830.504813153530085
1050.4614992371058850.922998474211770.538500762894115
1060.5156312261268050.968737547746390.484368773873194
1070.4787981242418610.9575962484837230.521201875758139
1080.6198808188855270.7602383622289470.380119181114473
1090.5763605622357830.8472788755284330.423639437764216
1100.6283817591914620.7432364816170760.371618240808538
1110.6909585587073850.618082882585230.309041441292615
1120.742615851424380.514768297151240.25738414857562
1130.813590500684640.3728189986307190.186409499315359
1140.8503429642644720.2993140714710560.149657035735528
1150.8688267164504530.2623465670990950.131173283549547
1160.848157001861240.3036859962775190.151842998138760
1170.8637941629428350.272411674114330.136205837057165
1180.9186861307893870.1626277384212270.0813138692106135
1190.9386878278601260.1226243442797480.0613121721398739
1200.9339882618231180.1320234763537640.066011738176882
1210.9127898201281580.1744203597436840.0872101798718422
1220.895818247304440.2083635053911190.104181752695560
1230.8788470802904840.2423058394190320.121152919709516
1240.8754991794202250.2490016411595490.124500820579775
1250.8553810120388720.2892379759222570.144618987961128
1260.8445349824855110.3109300350289780.155465017514489
1270.8361081633945070.3277836732109870.163891836605493
1280.80057807587320.39884384825360.1994219241268
1290.7516325749100530.4967348501798950.248367425089947
1300.695372090973260.6092558180534790.304627909026739
1310.6536205311366430.6927589377267140.346379468863357
1320.609244887600750.78151022479850.39075511239925
1330.5828298067196930.8343403865606140.417170193280307
1340.5965438477459020.8069123045081950.403456152254098
1350.787163544541360.4256729109172810.212836455458641
1360.7839557574322980.4320884851354040.216044242567702
1370.7778586255427180.4442827489145640.222141374457282
1380.7574132436638450.485173512672310.242586756336155
1390.7289542674762860.5420914650474290.271045732523714
1400.7470004177091150.5059991645817690.252999582290885
1410.7151942017975440.5696115964049120.284805798202456
1420.945280085818270.1094398283634590.0547199141817295
1430.9129331352508570.1741337294982850.0870668647491426
1440.9538275915074680.0923448169850640.046172408492532
1450.9385896112786070.1228207774427860.061410388721393
1460.8891525361641590.2216949276716820.110847463835841
1470.8168434815011020.3663130369977960.183156518498898
1480.7067936279380160.5864127441239670.293206372061984
1490.5556013911663210.8887972176673590.444398608833679

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.55725716979339 & 0.885485660413221 & 0.442742830206611 \tabularnewline
11 & 0.428539507443499 & 0.857079014886998 & 0.571460492556501 \tabularnewline
12 & 0.293454951887948 & 0.586909903775897 & 0.706545048112052 \tabularnewline
13 & 0.297469316757875 & 0.59493863351575 & 0.702530683242125 \tabularnewline
14 & 0.268049342126501 & 0.536098684253002 & 0.731950657873499 \tabularnewline
15 & 0.325549490697584 & 0.651098981395168 & 0.674450509302416 \tabularnewline
16 & 0.250968154353744 & 0.501936308707489 & 0.749031845646256 \tabularnewline
17 & 0.182587624125541 & 0.365175248251083 & 0.817412375874458 \tabularnewline
18 & 0.170531255406562 & 0.341062510813125 & 0.829468744593438 \tabularnewline
19 & 0.137383633037538 & 0.274767266075077 & 0.862616366962462 \tabularnewline
20 & 0.182486811742659 & 0.364973623485318 & 0.817513188257341 \tabularnewline
21 & 0.149582062582616 & 0.299164125165231 & 0.850417937417384 \tabularnewline
22 & 0.576663886434535 & 0.84667222713093 & 0.423336113565465 \tabularnewline
23 & 0.546478597381214 & 0.907042805237573 & 0.453521402618786 \tabularnewline
24 & 0.57469253766948 & 0.85061492466104 & 0.42530746233052 \tabularnewline
25 & 0.516904341347138 & 0.966191317305724 & 0.483095658652862 \tabularnewline
26 & 0.49559543030846 & 0.99119086061692 & 0.50440456969154 \tabularnewline
27 & 0.472821554178043 & 0.945643108356086 & 0.527178445821957 \tabularnewline
28 & 0.406377908735262 & 0.812755817470525 & 0.593622091264738 \tabularnewline
29 & 0.357932058654779 & 0.715864117309557 & 0.642067941345221 \tabularnewline
30 & 0.333440272439605 & 0.66688054487921 & 0.666559727560395 \tabularnewline
31 & 0.620532596645171 & 0.758934806709658 & 0.379467403354829 \tabularnewline
32 & 0.56040069493545 & 0.8791986101291 & 0.43959930506455 \tabularnewline
33 & 0.710641582727918 & 0.578716834544165 & 0.289358417272082 \tabularnewline
34 & 0.770829163835612 & 0.458341672328777 & 0.229170836164388 \tabularnewline
35 & 0.765475609683249 & 0.469048780633502 & 0.234524390316751 \tabularnewline
36 & 0.726568796742454 & 0.546862406515092 & 0.273431203257546 \tabularnewline
37 & 0.747224550555308 & 0.505550898889385 & 0.252775449444692 \tabularnewline
38 & 0.741891210907907 & 0.516217578184186 & 0.258108789092093 \tabularnewline
39 & 0.81794586345486 & 0.364108273090279 & 0.182054136545140 \tabularnewline
40 & 0.805930760551131 & 0.388138478897738 & 0.194069239448869 \tabularnewline
41 & 0.79229041604673 & 0.415419167906541 & 0.207709583953270 \tabularnewline
42 & 0.769916883125992 & 0.460166233748016 & 0.230083116874008 \tabularnewline
43 & 0.729547258622842 & 0.540905482754316 & 0.270452741377158 \tabularnewline
44 & 0.7121587240489 & 0.575682551902199 & 0.287841275951100 \tabularnewline
45 & 0.749600705035705 & 0.500798589928590 & 0.250399294964295 \tabularnewline
46 & 0.73082575589584 & 0.538348488208321 & 0.269174244104160 \tabularnewline
47 & 0.745937181676097 & 0.508125636647806 & 0.254062818323903 \tabularnewline
48 & 0.752988269247949 & 0.494023461504103 & 0.247011730752051 \tabularnewline
49 & 0.740641151209613 & 0.518717697580775 & 0.259358848790387 \tabularnewline
50 & 0.755649526023372 & 0.488700947953257 & 0.244350473976629 \tabularnewline
51 & 0.868990283599095 & 0.26201943280181 & 0.131009716400905 \tabularnewline
52 & 0.862293198435398 & 0.275413603129203 & 0.137706801564602 \tabularnewline
53 & 0.889215722958122 & 0.221568554083755 & 0.110784277041878 \tabularnewline
54 & 0.945083355067076 & 0.109833289865849 & 0.0549166449329244 \tabularnewline
55 & 0.934656733045419 & 0.130686533909162 & 0.065343266954581 \tabularnewline
56 & 0.918823766647645 & 0.16235246670471 & 0.081176233352355 \tabularnewline
57 & 0.930483160990702 & 0.139033678018596 & 0.0695168390092978 \tabularnewline
58 & 0.94406409914646 & 0.111871801707080 & 0.0559359008535402 \tabularnewline
59 & 0.929856534396761 & 0.140286931206478 & 0.0701434656032388 \tabularnewline
60 & 0.936887718551488 & 0.126224562897024 & 0.0631122814485118 \tabularnewline
61 & 0.943951744310953 & 0.112096511378094 & 0.056048255689047 \tabularnewline
62 & 0.930210762604652 & 0.139578474790696 & 0.0697892373953481 \tabularnewline
63 & 0.923185077068578 & 0.153629845862844 & 0.076814922931422 \tabularnewline
64 & 0.971022165609714 & 0.057955668780572 & 0.028977834390286 \tabularnewline
65 & 0.963054499965237 & 0.0738910000695269 & 0.0369455000347634 \tabularnewline
66 & 0.959296830760054 & 0.0814063384798917 & 0.0407031692399458 \tabularnewline
67 & 0.948749973907655 & 0.102500052184690 & 0.0512500260923448 \tabularnewline
68 & 0.936561558543793 & 0.126876882912415 & 0.0634384414562074 \tabularnewline
69 & 0.922342866542098 & 0.155314266915804 & 0.0776571334579022 \tabularnewline
70 & 0.929403186879061 & 0.141193626241877 & 0.0705968131209386 \tabularnewline
71 & 0.916361899810571 & 0.167276200378858 & 0.083638100189429 \tabularnewline
72 & 0.907412411794552 & 0.185175176410896 & 0.0925875882054482 \tabularnewline
73 & 0.892325907918899 & 0.215348184162203 & 0.107674092081101 \tabularnewline
74 & 0.874002904654893 & 0.251994190690214 & 0.125997095345107 \tabularnewline
75 & 0.865241086079496 & 0.269517827841008 & 0.134758913920504 \tabularnewline
76 & 0.874026696101304 & 0.251946607797393 & 0.125973303898696 \tabularnewline
77 & 0.851174456766812 & 0.297651086466377 & 0.148825543233188 \tabularnewline
78 & 0.826995716307276 & 0.346008567385447 & 0.173004283692724 \tabularnewline
79 & 0.795266892582418 & 0.409466214835165 & 0.204733107417582 \tabularnewline
80 & 0.793587774193195 & 0.412824451613609 & 0.206412225806805 \tabularnewline
81 & 0.804819515895252 & 0.390360968209496 & 0.195180484104748 \tabularnewline
82 & 0.771593284011894 & 0.456813431976213 & 0.228406715988106 \tabularnewline
83 & 0.738039189900595 & 0.523921620198809 & 0.261960810099405 \tabularnewline
84 & 0.705299369493388 & 0.589401261013224 & 0.294700630506612 \tabularnewline
85 & 0.699720144410008 & 0.600559711179985 & 0.300279855589992 \tabularnewline
86 & 0.719243067203892 & 0.561513865592215 & 0.280756932796108 \tabularnewline
87 & 0.702043558083836 & 0.595912883832329 & 0.297956441916164 \tabularnewline
88 & 0.680174798698625 & 0.63965040260275 & 0.319825201301375 \tabularnewline
89 & 0.637657904773231 & 0.724684190453538 & 0.362342095226769 \tabularnewline
90 & 0.593230815962599 & 0.813538368074802 & 0.406769184037401 \tabularnewline
91 & 0.597471342617749 & 0.805057314764501 & 0.402528657382251 \tabularnewline
92 & 0.591038003389677 & 0.817923993220645 & 0.408961996610323 \tabularnewline
93 & 0.568294022594385 & 0.86341195481123 & 0.431705977405615 \tabularnewline
94 & 0.532066010491016 & 0.935867979017967 & 0.467933989508984 \tabularnewline
95 & 0.501342673402376 & 0.997314653195249 & 0.498657326597624 \tabularnewline
96 & 0.524267954178641 & 0.951464091642717 & 0.475732045821359 \tabularnewline
97 & 0.688841086984798 & 0.622317826030404 & 0.311158913015202 \tabularnewline
98 & 0.658883613880541 & 0.682232772238918 & 0.341116386119459 \tabularnewline
99 & 0.635539140809813 & 0.728921718380374 & 0.364460859190187 \tabularnewline
100 & 0.595010107020176 & 0.809979785959649 & 0.404989892979824 \tabularnewline
101 & 0.552626128315932 & 0.894747743368135 & 0.447373871684068 \tabularnewline
102 & 0.504700389090737 & 0.990599221818527 & 0.495299610909263 \tabularnewline
103 & 0.469799447169828 & 0.939598894339655 & 0.530200552830172 \tabularnewline
104 & 0.495186846469915 & 0.99037369293983 & 0.504813153530085 \tabularnewline
105 & 0.461499237105885 & 0.92299847421177 & 0.538500762894115 \tabularnewline
106 & 0.515631226126805 & 0.96873754774639 & 0.484368773873194 \tabularnewline
107 & 0.478798124241861 & 0.957596248483723 & 0.521201875758139 \tabularnewline
108 & 0.619880818885527 & 0.760238362228947 & 0.380119181114473 \tabularnewline
109 & 0.576360562235783 & 0.847278875528433 & 0.423639437764216 \tabularnewline
110 & 0.628381759191462 & 0.743236481617076 & 0.371618240808538 \tabularnewline
111 & 0.690958558707385 & 0.61808288258523 & 0.309041441292615 \tabularnewline
112 & 0.74261585142438 & 0.51476829715124 & 0.25738414857562 \tabularnewline
113 & 0.81359050068464 & 0.372818998630719 & 0.186409499315359 \tabularnewline
114 & 0.850342964264472 & 0.299314071471056 & 0.149657035735528 \tabularnewline
115 & 0.868826716450453 & 0.262346567099095 & 0.131173283549547 \tabularnewline
116 & 0.84815700186124 & 0.303685996277519 & 0.151842998138760 \tabularnewline
117 & 0.863794162942835 & 0.27241167411433 & 0.136205837057165 \tabularnewline
118 & 0.918686130789387 & 0.162627738421227 & 0.0813138692106135 \tabularnewline
119 & 0.938687827860126 & 0.122624344279748 & 0.0613121721398739 \tabularnewline
120 & 0.933988261823118 & 0.132023476353764 & 0.066011738176882 \tabularnewline
121 & 0.912789820128158 & 0.174420359743684 & 0.0872101798718422 \tabularnewline
122 & 0.89581824730444 & 0.208363505391119 & 0.104181752695560 \tabularnewline
123 & 0.878847080290484 & 0.242305839419032 & 0.121152919709516 \tabularnewline
124 & 0.875499179420225 & 0.249001641159549 & 0.124500820579775 \tabularnewline
125 & 0.855381012038872 & 0.289237975922257 & 0.144618987961128 \tabularnewline
126 & 0.844534982485511 & 0.310930035028978 & 0.155465017514489 \tabularnewline
127 & 0.836108163394507 & 0.327783673210987 & 0.163891836605493 \tabularnewline
128 & 0.8005780758732 & 0.3988438482536 & 0.1994219241268 \tabularnewline
129 & 0.751632574910053 & 0.496734850179895 & 0.248367425089947 \tabularnewline
130 & 0.69537209097326 & 0.609255818053479 & 0.304627909026739 \tabularnewline
131 & 0.653620531136643 & 0.692758937726714 & 0.346379468863357 \tabularnewline
132 & 0.60924488760075 & 0.7815102247985 & 0.39075511239925 \tabularnewline
133 & 0.582829806719693 & 0.834340386560614 & 0.417170193280307 \tabularnewline
134 & 0.596543847745902 & 0.806912304508195 & 0.403456152254098 \tabularnewline
135 & 0.78716354454136 & 0.425672910917281 & 0.212836455458641 \tabularnewline
136 & 0.783955757432298 & 0.432088485135404 & 0.216044242567702 \tabularnewline
137 & 0.777858625542718 & 0.444282748914564 & 0.222141374457282 \tabularnewline
138 & 0.757413243663845 & 0.48517351267231 & 0.242586756336155 \tabularnewline
139 & 0.728954267476286 & 0.542091465047429 & 0.271045732523714 \tabularnewline
140 & 0.747000417709115 & 0.505999164581769 & 0.252999582290885 \tabularnewline
141 & 0.715194201797544 & 0.569611596404912 & 0.284805798202456 \tabularnewline
142 & 0.94528008581827 & 0.109439828363459 & 0.0547199141817295 \tabularnewline
143 & 0.912933135250857 & 0.174133729498285 & 0.0870668647491426 \tabularnewline
144 & 0.953827591507468 & 0.092344816985064 & 0.046172408492532 \tabularnewline
145 & 0.938589611278607 & 0.122820777442786 & 0.061410388721393 \tabularnewline
146 & 0.889152536164159 & 0.221694927671682 & 0.110847463835841 \tabularnewline
147 & 0.816843481501102 & 0.366313036997796 & 0.183156518498898 \tabularnewline
148 & 0.706793627938016 & 0.586412744123967 & 0.293206372061984 \tabularnewline
149 & 0.555601391166321 & 0.888797217667359 & 0.444398608833679 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103973&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]0.55725716979339[/C][C]0.885485660413221[/C][C]0.442742830206611[/C][/ROW]
[ROW][C]11[/C][C]0.428539507443499[/C][C]0.857079014886998[/C][C]0.571460492556501[/C][/ROW]
[ROW][C]12[/C][C]0.293454951887948[/C][C]0.586909903775897[/C][C]0.706545048112052[/C][/ROW]
[ROW][C]13[/C][C]0.297469316757875[/C][C]0.59493863351575[/C][C]0.702530683242125[/C][/ROW]
[ROW][C]14[/C][C]0.268049342126501[/C][C]0.536098684253002[/C][C]0.731950657873499[/C][/ROW]
[ROW][C]15[/C][C]0.325549490697584[/C][C]0.651098981395168[/C][C]0.674450509302416[/C][/ROW]
[ROW][C]16[/C][C]0.250968154353744[/C][C]0.501936308707489[/C][C]0.749031845646256[/C][/ROW]
[ROW][C]17[/C][C]0.182587624125541[/C][C]0.365175248251083[/C][C]0.817412375874458[/C][/ROW]
[ROW][C]18[/C][C]0.170531255406562[/C][C]0.341062510813125[/C][C]0.829468744593438[/C][/ROW]
[ROW][C]19[/C][C]0.137383633037538[/C][C]0.274767266075077[/C][C]0.862616366962462[/C][/ROW]
[ROW][C]20[/C][C]0.182486811742659[/C][C]0.364973623485318[/C][C]0.817513188257341[/C][/ROW]
[ROW][C]21[/C][C]0.149582062582616[/C][C]0.299164125165231[/C][C]0.850417937417384[/C][/ROW]
[ROW][C]22[/C][C]0.576663886434535[/C][C]0.84667222713093[/C][C]0.423336113565465[/C][/ROW]
[ROW][C]23[/C][C]0.546478597381214[/C][C]0.907042805237573[/C][C]0.453521402618786[/C][/ROW]
[ROW][C]24[/C][C]0.57469253766948[/C][C]0.85061492466104[/C][C]0.42530746233052[/C][/ROW]
[ROW][C]25[/C][C]0.516904341347138[/C][C]0.966191317305724[/C][C]0.483095658652862[/C][/ROW]
[ROW][C]26[/C][C]0.49559543030846[/C][C]0.99119086061692[/C][C]0.50440456969154[/C][/ROW]
[ROW][C]27[/C][C]0.472821554178043[/C][C]0.945643108356086[/C][C]0.527178445821957[/C][/ROW]
[ROW][C]28[/C][C]0.406377908735262[/C][C]0.812755817470525[/C][C]0.593622091264738[/C][/ROW]
[ROW][C]29[/C][C]0.357932058654779[/C][C]0.715864117309557[/C][C]0.642067941345221[/C][/ROW]
[ROW][C]30[/C][C]0.333440272439605[/C][C]0.66688054487921[/C][C]0.666559727560395[/C][/ROW]
[ROW][C]31[/C][C]0.620532596645171[/C][C]0.758934806709658[/C][C]0.379467403354829[/C][/ROW]
[ROW][C]32[/C][C]0.56040069493545[/C][C]0.8791986101291[/C][C]0.43959930506455[/C][/ROW]
[ROW][C]33[/C][C]0.710641582727918[/C][C]0.578716834544165[/C][C]0.289358417272082[/C][/ROW]
[ROW][C]34[/C][C]0.770829163835612[/C][C]0.458341672328777[/C][C]0.229170836164388[/C][/ROW]
[ROW][C]35[/C][C]0.765475609683249[/C][C]0.469048780633502[/C][C]0.234524390316751[/C][/ROW]
[ROW][C]36[/C][C]0.726568796742454[/C][C]0.546862406515092[/C][C]0.273431203257546[/C][/ROW]
[ROW][C]37[/C][C]0.747224550555308[/C][C]0.505550898889385[/C][C]0.252775449444692[/C][/ROW]
[ROW][C]38[/C][C]0.741891210907907[/C][C]0.516217578184186[/C][C]0.258108789092093[/C][/ROW]
[ROW][C]39[/C][C]0.81794586345486[/C][C]0.364108273090279[/C][C]0.182054136545140[/C][/ROW]
[ROW][C]40[/C][C]0.805930760551131[/C][C]0.388138478897738[/C][C]0.194069239448869[/C][/ROW]
[ROW][C]41[/C][C]0.79229041604673[/C][C]0.415419167906541[/C][C]0.207709583953270[/C][/ROW]
[ROW][C]42[/C][C]0.769916883125992[/C][C]0.460166233748016[/C][C]0.230083116874008[/C][/ROW]
[ROW][C]43[/C][C]0.729547258622842[/C][C]0.540905482754316[/C][C]0.270452741377158[/C][/ROW]
[ROW][C]44[/C][C]0.7121587240489[/C][C]0.575682551902199[/C][C]0.287841275951100[/C][/ROW]
[ROW][C]45[/C][C]0.749600705035705[/C][C]0.500798589928590[/C][C]0.250399294964295[/C][/ROW]
[ROW][C]46[/C][C]0.73082575589584[/C][C]0.538348488208321[/C][C]0.269174244104160[/C][/ROW]
[ROW][C]47[/C][C]0.745937181676097[/C][C]0.508125636647806[/C][C]0.254062818323903[/C][/ROW]
[ROW][C]48[/C][C]0.752988269247949[/C][C]0.494023461504103[/C][C]0.247011730752051[/C][/ROW]
[ROW][C]49[/C][C]0.740641151209613[/C][C]0.518717697580775[/C][C]0.259358848790387[/C][/ROW]
[ROW][C]50[/C][C]0.755649526023372[/C][C]0.488700947953257[/C][C]0.244350473976629[/C][/ROW]
[ROW][C]51[/C][C]0.868990283599095[/C][C]0.26201943280181[/C][C]0.131009716400905[/C][/ROW]
[ROW][C]52[/C][C]0.862293198435398[/C][C]0.275413603129203[/C][C]0.137706801564602[/C][/ROW]
[ROW][C]53[/C][C]0.889215722958122[/C][C]0.221568554083755[/C][C]0.110784277041878[/C][/ROW]
[ROW][C]54[/C][C]0.945083355067076[/C][C]0.109833289865849[/C][C]0.0549166449329244[/C][/ROW]
[ROW][C]55[/C][C]0.934656733045419[/C][C]0.130686533909162[/C][C]0.065343266954581[/C][/ROW]
[ROW][C]56[/C][C]0.918823766647645[/C][C]0.16235246670471[/C][C]0.081176233352355[/C][/ROW]
[ROW][C]57[/C][C]0.930483160990702[/C][C]0.139033678018596[/C][C]0.0695168390092978[/C][/ROW]
[ROW][C]58[/C][C]0.94406409914646[/C][C]0.111871801707080[/C][C]0.0559359008535402[/C][/ROW]
[ROW][C]59[/C][C]0.929856534396761[/C][C]0.140286931206478[/C][C]0.0701434656032388[/C][/ROW]
[ROW][C]60[/C][C]0.936887718551488[/C][C]0.126224562897024[/C][C]0.0631122814485118[/C][/ROW]
[ROW][C]61[/C][C]0.943951744310953[/C][C]0.112096511378094[/C][C]0.056048255689047[/C][/ROW]
[ROW][C]62[/C][C]0.930210762604652[/C][C]0.139578474790696[/C][C]0.0697892373953481[/C][/ROW]
[ROW][C]63[/C][C]0.923185077068578[/C][C]0.153629845862844[/C][C]0.076814922931422[/C][/ROW]
[ROW][C]64[/C][C]0.971022165609714[/C][C]0.057955668780572[/C][C]0.028977834390286[/C][/ROW]
[ROW][C]65[/C][C]0.963054499965237[/C][C]0.0738910000695269[/C][C]0.0369455000347634[/C][/ROW]
[ROW][C]66[/C][C]0.959296830760054[/C][C]0.0814063384798917[/C][C]0.0407031692399458[/C][/ROW]
[ROW][C]67[/C][C]0.948749973907655[/C][C]0.102500052184690[/C][C]0.0512500260923448[/C][/ROW]
[ROW][C]68[/C][C]0.936561558543793[/C][C]0.126876882912415[/C][C]0.0634384414562074[/C][/ROW]
[ROW][C]69[/C][C]0.922342866542098[/C][C]0.155314266915804[/C][C]0.0776571334579022[/C][/ROW]
[ROW][C]70[/C][C]0.929403186879061[/C][C]0.141193626241877[/C][C]0.0705968131209386[/C][/ROW]
[ROW][C]71[/C][C]0.916361899810571[/C][C]0.167276200378858[/C][C]0.083638100189429[/C][/ROW]
[ROW][C]72[/C][C]0.907412411794552[/C][C]0.185175176410896[/C][C]0.0925875882054482[/C][/ROW]
[ROW][C]73[/C][C]0.892325907918899[/C][C]0.215348184162203[/C][C]0.107674092081101[/C][/ROW]
[ROW][C]74[/C][C]0.874002904654893[/C][C]0.251994190690214[/C][C]0.125997095345107[/C][/ROW]
[ROW][C]75[/C][C]0.865241086079496[/C][C]0.269517827841008[/C][C]0.134758913920504[/C][/ROW]
[ROW][C]76[/C][C]0.874026696101304[/C][C]0.251946607797393[/C][C]0.125973303898696[/C][/ROW]
[ROW][C]77[/C][C]0.851174456766812[/C][C]0.297651086466377[/C][C]0.148825543233188[/C][/ROW]
[ROW][C]78[/C][C]0.826995716307276[/C][C]0.346008567385447[/C][C]0.173004283692724[/C][/ROW]
[ROW][C]79[/C][C]0.795266892582418[/C][C]0.409466214835165[/C][C]0.204733107417582[/C][/ROW]
[ROW][C]80[/C][C]0.793587774193195[/C][C]0.412824451613609[/C][C]0.206412225806805[/C][/ROW]
[ROW][C]81[/C][C]0.804819515895252[/C][C]0.390360968209496[/C][C]0.195180484104748[/C][/ROW]
[ROW][C]82[/C][C]0.771593284011894[/C][C]0.456813431976213[/C][C]0.228406715988106[/C][/ROW]
[ROW][C]83[/C][C]0.738039189900595[/C][C]0.523921620198809[/C][C]0.261960810099405[/C][/ROW]
[ROW][C]84[/C][C]0.705299369493388[/C][C]0.589401261013224[/C][C]0.294700630506612[/C][/ROW]
[ROW][C]85[/C][C]0.699720144410008[/C][C]0.600559711179985[/C][C]0.300279855589992[/C][/ROW]
[ROW][C]86[/C][C]0.719243067203892[/C][C]0.561513865592215[/C][C]0.280756932796108[/C][/ROW]
[ROW][C]87[/C][C]0.702043558083836[/C][C]0.595912883832329[/C][C]0.297956441916164[/C][/ROW]
[ROW][C]88[/C][C]0.680174798698625[/C][C]0.63965040260275[/C][C]0.319825201301375[/C][/ROW]
[ROW][C]89[/C][C]0.637657904773231[/C][C]0.724684190453538[/C][C]0.362342095226769[/C][/ROW]
[ROW][C]90[/C][C]0.593230815962599[/C][C]0.813538368074802[/C][C]0.406769184037401[/C][/ROW]
[ROW][C]91[/C][C]0.597471342617749[/C][C]0.805057314764501[/C][C]0.402528657382251[/C][/ROW]
[ROW][C]92[/C][C]0.591038003389677[/C][C]0.817923993220645[/C][C]0.408961996610323[/C][/ROW]
[ROW][C]93[/C][C]0.568294022594385[/C][C]0.86341195481123[/C][C]0.431705977405615[/C][/ROW]
[ROW][C]94[/C][C]0.532066010491016[/C][C]0.935867979017967[/C][C]0.467933989508984[/C][/ROW]
[ROW][C]95[/C][C]0.501342673402376[/C][C]0.997314653195249[/C][C]0.498657326597624[/C][/ROW]
[ROW][C]96[/C][C]0.524267954178641[/C][C]0.951464091642717[/C][C]0.475732045821359[/C][/ROW]
[ROW][C]97[/C][C]0.688841086984798[/C][C]0.622317826030404[/C][C]0.311158913015202[/C][/ROW]
[ROW][C]98[/C][C]0.658883613880541[/C][C]0.682232772238918[/C][C]0.341116386119459[/C][/ROW]
[ROW][C]99[/C][C]0.635539140809813[/C][C]0.728921718380374[/C][C]0.364460859190187[/C][/ROW]
[ROW][C]100[/C][C]0.595010107020176[/C][C]0.809979785959649[/C][C]0.404989892979824[/C][/ROW]
[ROW][C]101[/C][C]0.552626128315932[/C][C]0.894747743368135[/C][C]0.447373871684068[/C][/ROW]
[ROW][C]102[/C][C]0.504700389090737[/C][C]0.990599221818527[/C][C]0.495299610909263[/C][/ROW]
[ROW][C]103[/C][C]0.469799447169828[/C][C]0.939598894339655[/C][C]0.530200552830172[/C][/ROW]
[ROW][C]104[/C][C]0.495186846469915[/C][C]0.99037369293983[/C][C]0.504813153530085[/C][/ROW]
[ROW][C]105[/C][C]0.461499237105885[/C][C]0.92299847421177[/C][C]0.538500762894115[/C][/ROW]
[ROW][C]106[/C][C]0.515631226126805[/C][C]0.96873754774639[/C][C]0.484368773873194[/C][/ROW]
[ROW][C]107[/C][C]0.478798124241861[/C][C]0.957596248483723[/C][C]0.521201875758139[/C][/ROW]
[ROW][C]108[/C][C]0.619880818885527[/C][C]0.760238362228947[/C][C]0.380119181114473[/C][/ROW]
[ROW][C]109[/C][C]0.576360562235783[/C][C]0.847278875528433[/C][C]0.423639437764216[/C][/ROW]
[ROW][C]110[/C][C]0.628381759191462[/C][C]0.743236481617076[/C][C]0.371618240808538[/C][/ROW]
[ROW][C]111[/C][C]0.690958558707385[/C][C]0.61808288258523[/C][C]0.309041441292615[/C][/ROW]
[ROW][C]112[/C][C]0.74261585142438[/C][C]0.51476829715124[/C][C]0.25738414857562[/C][/ROW]
[ROW][C]113[/C][C]0.81359050068464[/C][C]0.372818998630719[/C][C]0.186409499315359[/C][/ROW]
[ROW][C]114[/C][C]0.850342964264472[/C][C]0.299314071471056[/C][C]0.149657035735528[/C][/ROW]
[ROW][C]115[/C][C]0.868826716450453[/C][C]0.262346567099095[/C][C]0.131173283549547[/C][/ROW]
[ROW][C]116[/C][C]0.84815700186124[/C][C]0.303685996277519[/C][C]0.151842998138760[/C][/ROW]
[ROW][C]117[/C][C]0.863794162942835[/C][C]0.27241167411433[/C][C]0.136205837057165[/C][/ROW]
[ROW][C]118[/C][C]0.918686130789387[/C][C]0.162627738421227[/C][C]0.0813138692106135[/C][/ROW]
[ROW][C]119[/C][C]0.938687827860126[/C][C]0.122624344279748[/C][C]0.0613121721398739[/C][/ROW]
[ROW][C]120[/C][C]0.933988261823118[/C][C]0.132023476353764[/C][C]0.066011738176882[/C][/ROW]
[ROW][C]121[/C][C]0.912789820128158[/C][C]0.174420359743684[/C][C]0.0872101798718422[/C][/ROW]
[ROW][C]122[/C][C]0.89581824730444[/C][C]0.208363505391119[/C][C]0.104181752695560[/C][/ROW]
[ROW][C]123[/C][C]0.878847080290484[/C][C]0.242305839419032[/C][C]0.121152919709516[/C][/ROW]
[ROW][C]124[/C][C]0.875499179420225[/C][C]0.249001641159549[/C][C]0.124500820579775[/C][/ROW]
[ROW][C]125[/C][C]0.855381012038872[/C][C]0.289237975922257[/C][C]0.144618987961128[/C][/ROW]
[ROW][C]126[/C][C]0.844534982485511[/C][C]0.310930035028978[/C][C]0.155465017514489[/C][/ROW]
[ROW][C]127[/C][C]0.836108163394507[/C][C]0.327783673210987[/C][C]0.163891836605493[/C][/ROW]
[ROW][C]128[/C][C]0.8005780758732[/C][C]0.3988438482536[/C][C]0.1994219241268[/C][/ROW]
[ROW][C]129[/C][C]0.751632574910053[/C][C]0.496734850179895[/C][C]0.248367425089947[/C][/ROW]
[ROW][C]130[/C][C]0.69537209097326[/C][C]0.609255818053479[/C][C]0.304627909026739[/C][/ROW]
[ROW][C]131[/C][C]0.653620531136643[/C][C]0.692758937726714[/C][C]0.346379468863357[/C][/ROW]
[ROW][C]132[/C][C]0.60924488760075[/C][C]0.7815102247985[/C][C]0.39075511239925[/C][/ROW]
[ROW][C]133[/C][C]0.582829806719693[/C][C]0.834340386560614[/C][C]0.417170193280307[/C][/ROW]
[ROW][C]134[/C][C]0.596543847745902[/C][C]0.806912304508195[/C][C]0.403456152254098[/C][/ROW]
[ROW][C]135[/C][C]0.78716354454136[/C][C]0.425672910917281[/C][C]0.212836455458641[/C][/ROW]
[ROW][C]136[/C][C]0.783955757432298[/C][C]0.432088485135404[/C][C]0.216044242567702[/C][/ROW]
[ROW][C]137[/C][C]0.777858625542718[/C][C]0.444282748914564[/C][C]0.222141374457282[/C][/ROW]
[ROW][C]138[/C][C]0.757413243663845[/C][C]0.48517351267231[/C][C]0.242586756336155[/C][/ROW]
[ROW][C]139[/C][C]0.728954267476286[/C][C]0.542091465047429[/C][C]0.271045732523714[/C][/ROW]
[ROW][C]140[/C][C]0.747000417709115[/C][C]0.505999164581769[/C][C]0.252999582290885[/C][/ROW]
[ROW][C]141[/C][C]0.715194201797544[/C][C]0.569611596404912[/C][C]0.284805798202456[/C][/ROW]
[ROW][C]142[/C][C]0.94528008581827[/C][C]0.109439828363459[/C][C]0.0547199141817295[/C][/ROW]
[ROW][C]143[/C][C]0.912933135250857[/C][C]0.174133729498285[/C][C]0.0870668647491426[/C][/ROW]
[ROW][C]144[/C][C]0.953827591507468[/C][C]0.092344816985064[/C][C]0.046172408492532[/C][/ROW]
[ROW][C]145[/C][C]0.938589611278607[/C][C]0.122820777442786[/C][C]0.061410388721393[/C][/ROW]
[ROW][C]146[/C][C]0.889152536164159[/C][C]0.221694927671682[/C][C]0.110847463835841[/C][/ROW]
[ROW][C]147[/C][C]0.816843481501102[/C][C]0.366313036997796[/C][C]0.183156518498898[/C][/ROW]
[ROW][C]148[/C][C]0.706793627938016[/C][C]0.586412744123967[/C][C]0.293206372061984[/C][/ROW]
[ROW][C]149[/C][C]0.555601391166321[/C][C]0.888797217667359[/C][C]0.444398608833679[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103973&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103973&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.557257169793390.8854856604132210.442742830206611
110.4285395074434990.8570790148869980.571460492556501
120.2934549518879480.5869099037758970.706545048112052
130.2974693167578750.594938633515750.702530683242125
140.2680493421265010.5360986842530020.731950657873499
150.3255494906975840.6510989813951680.674450509302416
160.2509681543537440.5019363087074890.749031845646256
170.1825876241255410.3651752482510830.817412375874458
180.1705312554065620.3410625108131250.829468744593438
190.1373836330375380.2747672660750770.862616366962462
200.1824868117426590.3649736234853180.817513188257341
210.1495820625826160.2991641251652310.850417937417384
220.5766638864345350.846672227130930.423336113565465
230.5464785973812140.9070428052375730.453521402618786
240.574692537669480.850614924661040.42530746233052
250.5169043413471380.9661913173057240.483095658652862
260.495595430308460.991190860616920.50440456969154
270.4728215541780430.9456431083560860.527178445821957
280.4063779087352620.8127558174705250.593622091264738
290.3579320586547790.7158641173095570.642067941345221
300.3334402724396050.666880544879210.666559727560395
310.6205325966451710.7589348067096580.379467403354829
320.560400694935450.87919861012910.43959930506455
330.7106415827279180.5787168345441650.289358417272082
340.7708291638356120.4583416723287770.229170836164388
350.7654756096832490.4690487806335020.234524390316751
360.7265687967424540.5468624065150920.273431203257546
370.7472245505553080.5055508988893850.252775449444692
380.7418912109079070.5162175781841860.258108789092093
390.817945863454860.3641082730902790.182054136545140
400.8059307605511310.3881384788977380.194069239448869
410.792290416046730.4154191679065410.207709583953270
420.7699168831259920.4601662337480160.230083116874008
430.7295472586228420.5409054827543160.270452741377158
440.71215872404890.5756825519021990.287841275951100
450.7496007050357050.5007985899285900.250399294964295
460.730825755895840.5383484882083210.269174244104160
470.7459371816760970.5081256366478060.254062818323903
480.7529882692479490.4940234615041030.247011730752051
490.7406411512096130.5187176975807750.259358848790387
500.7556495260233720.4887009479532570.244350473976629
510.8689902835990950.262019432801810.131009716400905
520.8622931984353980.2754136031292030.137706801564602
530.8892157229581220.2215685540837550.110784277041878
540.9450833550670760.1098332898658490.0549166449329244
550.9346567330454190.1306865339091620.065343266954581
560.9188237666476450.162352466704710.081176233352355
570.9304831609907020.1390336780185960.0695168390092978
580.944064099146460.1118718017070800.0559359008535402
590.9298565343967610.1402869312064780.0701434656032388
600.9368877185514880.1262245628970240.0631122814485118
610.9439517443109530.1120965113780940.056048255689047
620.9302107626046520.1395784747906960.0697892373953481
630.9231850770685780.1536298458628440.076814922931422
640.9710221656097140.0579556687805720.028977834390286
650.9630544999652370.07389100006952690.0369455000347634
660.9592968307600540.08140633847989170.0407031692399458
670.9487499739076550.1025000521846900.0512500260923448
680.9365615585437930.1268768829124150.0634384414562074
690.9223428665420980.1553142669158040.0776571334579022
700.9294031868790610.1411936262418770.0705968131209386
710.9163618998105710.1672762003788580.083638100189429
720.9074124117945520.1851751764108960.0925875882054482
730.8923259079188990.2153481841622030.107674092081101
740.8740029046548930.2519941906902140.125997095345107
750.8652410860794960.2695178278410080.134758913920504
760.8740266961013040.2519466077973930.125973303898696
770.8511744567668120.2976510864663770.148825543233188
780.8269957163072760.3460085673854470.173004283692724
790.7952668925824180.4094662148351650.204733107417582
800.7935877741931950.4128244516136090.206412225806805
810.8048195158952520.3903609682094960.195180484104748
820.7715932840118940.4568134319762130.228406715988106
830.7380391899005950.5239216201988090.261960810099405
840.7052993694933880.5894012610132240.294700630506612
850.6997201444100080.6005597111799850.300279855589992
860.7192430672038920.5615138655922150.280756932796108
870.7020435580838360.5959128838323290.297956441916164
880.6801747986986250.639650402602750.319825201301375
890.6376579047732310.7246841904535380.362342095226769
900.5932308159625990.8135383680748020.406769184037401
910.5974713426177490.8050573147645010.402528657382251
920.5910380033896770.8179239932206450.408961996610323
930.5682940225943850.863411954811230.431705977405615
940.5320660104910160.9358679790179670.467933989508984
950.5013426734023760.9973146531952490.498657326597624
960.5242679541786410.9514640916427170.475732045821359
970.6888410869847980.6223178260304040.311158913015202
980.6588836138805410.6822327722389180.341116386119459
990.6355391408098130.7289217183803740.364460859190187
1000.5950101070201760.8099797859596490.404989892979824
1010.5526261283159320.8947477433681350.447373871684068
1020.5047003890907370.9905992218185270.495299610909263
1030.4697994471698280.9395988943396550.530200552830172
1040.4951868464699150.990373692939830.504813153530085
1050.4614992371058850.922998474211770.538500762894115
1060.5156312261268050.968737547746390.484368773873194
1070.4787981242418610.9575962484837230.521201875758139
1080.6198808188855270.7602383622289470.380119181114473
1090.5763605622357830.8472788755284330.423639437764216
1100.6283817591914620.7432364816170760.371618240808538
1110.6909585587073850.618082882585230.309041441292615
1120.742615851424380.514768297151240.25738414857562
1130.813590500684640.3728189986307190.186409499315359
1140.8503429642644720.2993140714710560.149657035735528
1150.8688267164504530.2623465670990950.131173283549547
1160.848157001861240.3036859962775190.151842998138760
1170.8637941629428350.272411674114330.136205837057165
1180.9186861307893870.1626277384212270.0813138692106135
1190.9386878278601260.1226243442797480.0613121721398739
1200.9339882618231180.1320234763537640.066011738176882
1210.9127898201281580.1744203597436840.0872101798718422
1220.895818247304440.2083635053911190.104181752695560
1230.8788470802904840.2423058394190320.121152919709516
1240.8754991794202250.2490016411595490.124500820579775
1250.8553810120388720.2892379759222570.144618987961128
1260.8445349824855110.3109300350289780.155465017514489
1270.8361081633945070.3277836732109870.163891836605493
1280.80057807587320.39884384825360.1994219241268
1290.7516325749100530.4967348501798950.248367425089947
1300.695372090973260.6092558180534790.304627909026739
1310.6536205311366430.6927589377267140.346379468863357
1320.609244887600750.78151022479850.39075511239925
1330.5828298067196930.8343403865606140.417170193280307
1340.5965438477459020.8069123045081950.403456152254098
1350.787163544541360.4256729109172810.212836455458641
1360.7839557574322980.4320884851354040.216044242567702
1370.7778586255427180.4442827489145640.222141374457282
1380.7574132436638450.485173512672310.242586756336155
1390.7289542674762860.5420914650474290.271045732523714
1400.7470004177091150.5059991645817690.252999582290885
1410.7151942017975440.5696115964049120.284805798202456
1420.945280085818270.1094398283634590.0547199141817295
1430.9129331352508570.1741337294982850.0870668647491426
1440.9538275915074680.0923448169850640.046172408492532
1450.9385896112786070.1228207774427860.061410388721393
1460.8891525361641590.2216949276716820.110847463835841
1470.8168434815011020.3663130369977960.183156518498898
1480.7067936279380160.5864127441239670.293206372061984
1490.5556013911663210.8887972176673590.444398608833679







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 level40.0285714285714286OK

\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 & 4 & 0.0285714285714286 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103973&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]4[/C][C]0.0285714285714286[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103973&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103973&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0285714285714286OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
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')
}