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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 03 Dec 2010 12:39:24 +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/03/t1291379982dtk7hu55vzq0oqq.htm/, Retrieved Tue, 07 May 2024 22:28:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104697, Retrieved Tue, 07 May 2024 22:28:31 +0000
QR Codes:

Original text written by user:Determistische Trend
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Workshop 7 Multip...] [2010-12-02 09:26:04] [82c18f3ebe9df70882495121eb816e07]
-   PD    [Multiple Regression] [Workshop 7 Multip...] [2010-12-03 12:39:24] [f6fdc0236f011c1845380977efc505f8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	5	2	2	1	1	1
1	4	1	3	1	4	1
1	7	3	3	1	5	1
1	7	2	2	1	2	2
2	5	2	1	1	1	2
2	5	1	1	1	1	2
1	4	1	3	1	2	2
2	4	3	3	2	1	1
1	6	1	1	1	1	1
2	5	1	1	1	1	2
1	1	1	1	1	3	2
2	5	1	1	1	1	1
1	4	2	1	2	1	1
2	6	3	1	1	1	1
2	7	2	2	1	2	1
2	7	3	3	1	4	2
1	2	2	1	1	1	2
1	6	1	1	1	1	1
1	3	1	1	1	2	1
2	6	1	1	1	3	2
2	6	1	3	1	1	1
1	5	1	1	1	1	2
2	6	3	2	1	1	1
2	4	1	3	2	1	2
2	3	3	1	2	2	1
2	4	1	1	1	1	1
2	5	1	1	2	1	1
2	6	1	1	2	1	2
1	6	3	3	2	1	2
1	4	1	1	1	1	2
2	6	1	3	1	1	1
1	6	1	1	1	1	2
2	5	1	3	1	1	1
2	6	3	1	1	1	1
2	4	1	1	1	1	2
1	6	1	1	1	1	1
2	7	1	3	1	1	2
1	5	2	3	1	1	1
1	6	1	3	1	1	2
2	6	1	1	2	1	1
1	5	2	2	2	4	2
2	7	2	3	1	1	1
2	6	2	2	1	1	1
1	3	1	1	1	4	2
1	4	1	1	1	2	1
2	5	2	3	1	2	1
2	4	2	3	2	1	1
1	3	1	1	1	1	2
2	5	3	1	1	2	1
2	5	1	1	1	1	2
1	4	1	2	1	1	2
1	5	1	1	1	1	1
2	1	2	2	1	1	1
2	2	1	1	2	1	1
2	3	3	3	1	1	2
1	4	2	2	1	2	1
1	3	3	3	1	1	2
1	7	1	1	1	1	2
1	2	1	1	1	1	1
1	4	3	1	1	2	1
1	2	1	1	1	1	2
2	5	1	1	1	2	1
2	6	1	2	1	4	2
2	6	1	2	1	1	2
2	6	1	1	1	1	1
1	6	2	2	1	1	1
2	6	3	3	1	2	1
2	6	1	1	1	3	1
1	6	1	3	1	1	2
1	4	2	2	1	1	1
1	4	2	2	1	1	2
2	5	3	3	1	1	1
1	6	2	3	1	1	1
1	6	1	1	1	1	1
1	7	3	2	1	1	2
1	6	2	3	1	1	1
2	6	1	3	2	1	1
1	6	1	2	1	2	1
2	3	2	1	1	1	1
2	5	2	3	1	1	1
2	6	2	3	1	1	1
2	4	2	3	1	1	2
1	5	3	3	1	1	1
2	6	3	2	1	1	1
2	6	2	2	1	1	1
1	3	1	3	1	1	2
2	6	2	3	1	2	1
2	5	1	1	1	1	1
1	6	1	1	1	1	2
1	4	1	1	1	1	2
2	7	2	1	1	1	2
2	5	2	2	1	1	1
2	6	2	2	1	2	1
1	6	2	2	1	1	2
2	6	2	3	1	5	1
1	7	1	1	2	1	1
2	6	2	3	1	1	1
1	6	2	2	1	1	2
1	6	3	3	1	2	1
2	6	1	1	1	1	1
2	2	1	1	1	3	1
1	4	1	1	1	1	2
2	4	3	3	1	1	2
2	6	2	3	1	1	1
1	5	3	3	1	3	1
1	6	3	1	1	1	1
1	6	1	2	1	1	1
1	2	1	1	2	1	2
2	7	2	2	1	1	1
1	1	1	2	1	1	2
1	4	1	1	1	1	1
1	1	2	3	1	2	2
1	6	2	2	1	2	1
2	6	2	2	1	4	1
1	6	2	3	1	4	1
2	7	2	2	1	1	1
1	6	1	3	1	1	1
2	4	1	1	1	1	1
2	4	3	3	1	1	1
1	6	1	1	1	4	1
1	5	3	1	2	1	1
2	7	1	1	1	1	1
2	4	1	1	1	1	2
1	4	2	2	1	1	1
2	6	2	2	1	3	2
2	7	3	1	1	1	2
2	5	1	1	1	1	2
2	6	3	2	1	1	2
1	6	2	2	2	4	2
2	6	3	3	1	4	1
2	5	2	3	1	1	1
2	7	1	1	1	2	1
2	4	3	3	1	1	2
1	6	2	1	1	2	1
1	6	3	3	1	1	1
2	7	2	2	1	3	1
2	6	3	3	1	2	2
2	6	3	3	1	2	1
2	5	1	2	1	1	1
1	5	2	1	1	1	2
2	5	1	1	1	2	2
2	6	2	3	1	1	1
2	6	3	1	2	2	1
1	7	1	3	2	2	1
1	4	1	2	1	1	2
2	6	2	2	1	1	2
2	6	2	2	1	2	2
2	7	1	1	1	1	1
1	6	1	1	1	2	1
2	7	1	2	1	1	1
2	4	1	2	2	1	1
2	6	1	2	1	1	1
1	4	3	3	1	1	2
1	4	1	1	1	1	2
2	7	3	1	1	1	1
1	4	1	2	1	1	1
2	7	3	1	2	1	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104697&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104697&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104697&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Member[t] = + 1.30465081631826 + 0.0640015744751596Provison[t] + 0.0595463954201537Mother[t] + 0.00440396071597927Father[t] + 0.0496845934749124Illness[t] -0.0440791315888167Tobacco[t] -0.130315178168314Gender[t] -3.27464775207176e-05t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Member[t] =  +  1.30465081631826 +  0.0640015744751596Provison[t] +  0.0595463954201537Mother[t] +  0.00440396071597927Father[t] +  0.0496845934749124Illness[t] -0.0440791315888167Tobacco[t] -0.130315178168314Gender[t] -3.27464775207176e-05t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104697&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Member[t] =  +  1.30465081631826 +  0.0640015744751596Provison[t] +  0.0595463954201537Mother[t] +  0.00440396071597927Father[t] +  0.0496845934749124Illness[t] -0.0440791315888167Tobacco[t] -0.130315178168314Gender[t] -3.27464775207176e-05t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104697&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104697&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Member[t] = + 1.30465081631826 + 0.0640015744751596Provison[t] + 0.0595463954201537Mother[t] + 0.00440396071597927Father[t] + 0.0496845934749124Illness[t] -0.0440791315888167Tobacco[t] -0.130315178168314Gender[t] -3.27464775207176e-05t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.304650816318260.28144.63638e-064e-06
Provison0.06400157447515960.0286252.23580.0268490.013425
Mother0.05954639542015370.0542351.09790.2740060.137003
Father0.004403960715979270.0498190.08840.9296790.464839
Illness0.04968459347491240.1180930.42070.6745610.337281
Tobacco-0.04407913158881670.042284-1.04250.2988870.149444
Gender-0.1303151781683140.08317-1.56680.1192710.059636
t-3.27464775207176e-050.00088-0.03720.970370.485185

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.30465081631826 & 0.2814 & 4.6363 & 8e-06 & 4e-06 \tabularnewline
Provison & 0.0640015744751596 & 0.028625 & 2.2358 & 0.026849 & 0.013425 \tabularnewline
Mother & 0.0595463954201537 & 0.054235 & 1.0979 & 0.274006 & 0.137003 \tabularnewline
Father & 0.00440396071597927 & 0.049819 & 0.0884 & 0.929679 & 0.464839 \tabularnewline
Illness & 0.0496845934749124 & 0.118093 & 0.4207 & 0.674561 & 0.337281 \tabularnewline
Tobacco & -0.0440791315888167 & 0.042284 & -1.0425 & 0.298887 & 0.149444 \tabularnewline
Gender & -0.130315178168314 & 0.08317 & -1.5668 & 0.119271 & 0.059636 \tabularnewline
t & -3.27464775207176e-05 & 0.00088 & -0.0372 & 0.97037 & 0.485185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104697&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.30465081631826[/C][C]0.2814[/C][C]4.6363[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Provison[/C][C]0.0640015744751596[/C][C]0.028625[/C][C]2.2358[/C][C]0.026849[/C][C]0.013425[/C][/ROW]
[ROW][C]Mother[/C][C]0.0595463954201537[/C][C]0.054235[/C][C]1.0979[/C][C]0.274006[/C][C]0.137003[/C][/ROW]
[ROW][C]Father[/C][C]0.00440396071597927[/C][C]0.049819[/C][C]0.0884[/C][C]0.929679[/C][C]0.464839[/C][/ROW]
[ROW][C]Illness[/C][C]0.0496845934749124[/C][C]0.118093[/C][C]0.4207[/C][C]0.674561[/C][C]0.337281[/C][/ROW]
[ROW][C]Tobacco[/C][C]-0.0440791315888167[/C][C]0.042284[/C][C]-1.0425[/C][C]0.298887[/C][C]0.149444[/C][/ROW]
[ROW][C]Gender[/C][C]-0.130315178168314[/C][C]0.08317[/C][C]-1.5668[/C][C]0.119271[/C][C]0.059636[/C][/ROW]
[ROW][C]t[/C][C]-3.27464775207176e-05[/C][C]0.00088[/C][C]-0.0372[/C][C]0.97037[/C][C]0.485185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104697&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104697&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.304650816318260.28144.63638e-064e-06
Provison0.06400157447515960.0286252.23580.0268490.013425
Mother0.05954639542015370.0542351.09790.2740060.137003
Father0.004403960715979270.0498190.08840.9296790.464839
Illness0.04968459347491240.1180930.42070.6745610.337281
Tobacco-0.04407913158881670.042284-1.04250.2988870.149444
Gender-0.1303151781683140.08317-1.56680.1192710.059636
t-3.27464775207176e-050.00088-0.03720.970370.485185






Multiple Linear Regression - Regression Statistics
Multiple R0.284283161782614
R-squared0.0808169160731198
Adjusted R-squared0.0376338181705147
F-TEST (value)1.87149417245131
F-TEST (DF numerator)7
F-TEST (DF denominator)149
p-value0.0780617191575274
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.489177752690995
Sum Squared Residuals35.654936185444

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.284283161782614 \tabularnewline
R-squared & 0.0808169160731198 \tabularnewline
Adjusted R-squared & 0.0376338181705147 \tabularnewline
F-TEST (value) & 1.87149417245131 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.0780617191575274 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.489177752690995 \tabularnewline
Sum Squared Residuals & 35.654936185444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104697&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.284283161782614[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0808169160731198[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0376338181705147[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.87149417245131[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.0780617191575274[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.489177752690995[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.654936185444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104697&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104697&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.284283161782614
R-squared0.0808169160731198
Adjusted R-squared0.0376338181705147
F-TEST (value)1.87149417245131
F-TEST (DF numerator)7
F-TEST (DF denominator)149
p-value0.0780617191575274
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.489177752690995
Sum Squared Residuals35.654936185444






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.627816938206540.372183061793461
211.37640278778327-0.37640278778327
311.64338842398272-0.64338842398272
411.58132753796720-0.581327537967204
521.49296681341220.507033186587799
621.433387671514530.566612328485474
711.33408214040499-0.334082140404989
821.677221087999820.322778912000182
911.62760618472544-0.627606184725438
1021.433256685604440.566743314395556
1111.08905937804865-0.0890593780486509
1221.563506370817720.436493629182283
1311.6087030387601-0.608703038760102
1421.746535243178140.253464756821858
1521.711282504882790.28871749511721
1621.556726673195450.443273326804545
1711.30056913225647-0.300569132256473
1811.62731146642775-0.627311466427752
1911.39119486493594-0.391194864935936
2021.408772532126760.591227467873237
2121.636021148427150.363978851572851
2211.43286372787420-0.432863727874195
2321.750644485596430.249355514403565
2421.427289175350860.572710824649135
2521.559775770386030.440224229613969
2621.499046345657270.500953654342733
2721.612699767129820.387300232870182
2821.546353416959140.453646583040857
2911.67422138275389-0.674221382753888
3011.36860018157887-0.36860018157887
3121.635693683651940.364306316348059
3211.49653783757415-0.496537837574148
3321.571626616221740.42837338377826
3421.745880313627730.254119686372272
3521.368436449191270.631563550808734
3611.62672202983238-0.626722029832379
3721.569183601093660.430816398906337
3811.63100927925429-0.63100927925429
3911.50511653366346-0.505116533663461
4021.676275637397210.323724362602791
4111.41363909964590-0.413639099645897
4221.758881442294530.241118557705473
4321.690443160625870.309556839374133
4411.17190276165197-0.17190276165197
4511.45434503099556-0.454345030995557
4621.586668175845310.413331824154692
4721.616397579956360.383602420043644
4811.30400917050834-0.304009170508337
4921.637308410400940.362691589599059
5021.431946826503620.568053173496385
5111.37231646626691-0.372316466266914
5211.56219651171689-0.562196511716888
5321.370107823474860.629892176525139
5421.419810888811280.58018911118872
5521.431680657437960.568319342562042
5611.51793517587896-0.517935175878962
5711.43161516448292-0.431615164482916
5811.55968800363377-0.559688003633769
5911.36996256294876-0.369962562948764
6011.57294662467305-0.572946624673053
6111.23958189182541-0.239581891825408
6221.517789915352860.482210084647136
6321.367689262720540.632310737279465
6421.499893911009460.500106088990536
6521.625772381984280.374227618015722
6611.68968999164289-0.68968999164289
6721.709528469712690.290471530287314
6821.537515879374080.462484120625917
6911.50413413933784-0.50413413933784
7011.56155585678249-0.561555856782488
7111.43120793213665-0.431207932136653
7221.689442294438740.310557705561261
7311.69386472701623-0.693864727016225
7411.62547766368659-0.625477663686592
7511.68262806507220-0.682628065072203
7611.69376648758366-0.693766487583663
7721.68387193916090.316128060839099
7811.58567150690367-0.585671506903672
7921.492855603293660.507144396706337
8021.629633927198420.37036607280158
8121.693602755196060.306397244803941
8221.435251681599900.564748318400095
8311.68908208318601-0.689082083186011
8421.748646950467670.251353049532329
8521.689067808570000.310932191430003
8611.31157272579451-0.311572725794508
8721.649327144742120.350672855257882
8821.561017638526140.438982361473858
8911.49467128835547-0.494671288355467
9011.36663539292763-0.366635392927627
9121.618153765295740.381846234704261
9221.624837008752190.375162991247808
9321.644726705161010.355273294838986
9411.55845791210400-0.558457912103996
9521.516827778155500.483172221844497
9611.73844340913121-0.738443409131208
9721.693078811555730.306921188444273
9811.55832692619391-0.558326926193913
9911.70848058243202-0.708480582432023
10021.624626255271050.375373744728947
10121.280428947715260.71957105228474
10211.36624243519738-0.366242435197378
10321.494110400992120.505889599007877
10421.692849586213080.307150413786918
10511.60020339750292-0.600203397502922
10611.74352256724624-0.743522567246236
10711.62880099064439-0.628800990644388
10811.28772740085685-0.287727400856847
10921.752283467584660.247716532415341
11011.17837970066771-0.178379700667712
11111.49626289506801-0.496262895068006
11211.19818543225999-0.198185432259987
11311.6440717756106-0.6440717756106
11421.555880765955450.444119234044554
11511.56025198019390-0.560251980193905
11621.752054242242010.247945757757986
11711.63287748658516-0.63287748658516
11821.496033669725360.503966330274639
11921.623901635520110.376098364479894
12011.49173393095419-0.491733930954189
12111.72871438908318-0.728714389083178
12221.687907407240760.312092592759243
12321.365554759169440.634445240830557
12411.55978754699637-0.559787546996369
12521.469284508123220.530715491876779
12621.676554034002670.323445965997333
12721.429425347734520.57057465226548
12821.616890927288450.383109072711555
12911.47475898409923-0.474758984099234
13021.619307178451250.380692821548753
13121.627963856844860.372036143155137
13221.643500810876730.356499189123267
13321.493128006666500.506871993333498
13411.63898013886669-0.638980138866686
13511.75138084083009-0.751380840830094
13621.663241049513970.336758950486033
13721.576921038117920.423078961882078
13821.707203469808710.292796530191285
13921.563751528888560.436248471111435
14011.48854603894690-0.488546038946904
14121.384887765460410.615112234539587
14221.691605220067300.308394779932705
14321.747916409464070.252083590535935
14411.70160036805336-0.701600368053356
14511.36923829737997-0.369238297379966
14621.556755095272920.443244904727081
14721.512643217206580.487356782793419
14821.687055998825220.312944001174781
14911.57894254628372-0.578942546283721
15021.691394466586160.308605533413844
15121.549041590158070.450958409841931
15221.627327399155960.372672600844045
15311.49247307711609-0.492473077116087
15411.3645396183663-0.364539618366301
15521.805919564322880.194080435677119
15611.49919326429555-0.499193264295553
15721.855538664842750.144461335157248

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.62781693820654 & 0.372183061793461 \tabularnewline
2 & 1 & 1.37640278778327 & -0.37640278778327 \tabularnewline
3 & 1 & 1.64338842398272 & -0.64338842398272 \tabularnewline
4 & 1 & 1.58132753796720 & -0.581327537967204 \tabularnewline
5 & 2 & 1.4929668134122 & 0.507033186587799 \tabularnewline
6 & 2 & 1.43338767151453 & 0.566612328485474 \tabularnewline
7 & 1 & 1.33408214040499 & -0.334082140404989 \tabularnewline
8 & 2 & 1.67722108799982 & 0.322778912000182 \tabularnewline
9 & 1 & 1.62760618472544 & -0.627606184725438 \tabularnewline
10 & 2 & 1.43325668560444 & 0.566743314395556 \tabularnewline
11 & 1 & 1.08905937804865 & -0.0890593780486509 \tabularnewline
12 & 2 & 1.56350637081772 & 0.436493629182283 \tabularnewline
13 & 1 & 1.6087030387601 & -0.608703038760102 \tabularnewline
14 & 2 & 1.74653524317814 & 0.253464756821858 \tabularnewline
15 & 2 & 1.71128250488279 & 0.28871749511721 \tabularnewline
16 & 2 & 1.55672667319545 & 0.443273326804545 \tabularnewline
17 & 1 & 1.30056913225647 & -0.300569132256473 \tabularnewline
18 & 1 & 1.62731146642775 & -0.627311466427752 \tabularnewline
19 & 1 & 1.39119486493594 & -0.391194864935936 \tabularnewline
20 & 2 & 1.40877253212676 & 0.591227467873237 \tabularnewline
21 & 2 & 1.63602114842715 & 0.363978851572851 \tabularnewline
22 & 1 & 1.43286372787420 & -0.432863727874195 \tabularnewline
23 & 2 & 1.75064448559643 & 0.249355514403565 \tabularnewline
24 & 2 & 1.42728917535086 & 0.572710824649135 \tabularnewline
25 & 2 & 1.55977577038603 & 0.440224229613969 \tabularnewline
26 & 2 & 1.49904634565727 & 0.500953654342733 \tabularnewline
27 & 2 & 1.61269976712982 & 0.387300232870182 \tabularnewline
28 & 2 & 1.54635341695914 & 0.453646583040857 \tabularnewline
29 & 1 & 1.67422138275389 & -0.674221382753888 \tabularnewline
30 & 1 & 1.36860018157887 & -0.36860018157887 \tabularnewline
31 & 2 & 1.63569368365194 & 0.364306316348059 \tabularnewline
32 & 1 & 1.49653783757415 & -0.496537837574148 \tabularnewline
33 & 2 & 1.57162661622174 & 0.42837338377826 \tabularnewline
34 & 2 & 1.74588031362773 & 0.254119686372272 \tabularnewline
35 & 2 & 1.36843644919127 & 0.631563550808734 \tabularnewline
36 & 1 & 1.62672202983238 & -0.626722029832379 \tabularnewline
37 & 2 & 1.56918360109366 & 0.430816398906337 \tabularnewline
38 & 1 & 1.63100927925429 & -0.63100927925429 \tabularnewline
39 & 1 & 1.50511653366346 & -0.505116533663461 \tabularnewline
40 & 2 & 1.67627563739721 & 0.323724362602791 \tabularnewline
41 & 1 & 1.41363909964590 & -0.413639099645897 \tabularnewline
42 & 2 & 1.75888144229453 & 0.241118557705473 \tabularnewline
43 & 2 & 1.69044316062587 & 0.309556839374133 \tabularnewline
44 & 1 & 1.17190276165197 & -0.17190276165197 \tabularnewline
45 & 1 & 1.45434503099556 & -0.454345030995557 \tabularnewline
46 & 2 & 1.58666817584531 & 0.413331824154692 \tabularnewline
47 & 2 & 1.61639757995636 & 0.383602420043644 \tabularnewline
48 & 1 & 1.30400917050834 & -0.304009170508337 \tabularnewline
49 & 2 & 1.63730841040094 & 0.362691589599059 \tabularnewline
50 & 2 & 1.43194682650362 & 0.568053173496385 \tabularnewline
51 & 1 & 1.37231646626691 & -0.372316466266914 \tabularnewline
52 & 1 & 1.56219651171689 & -0.562196511716888 \tabularnewline
53 & 2 & 1.37010782347486 & 0.629892176525139 \tabularnewline
54 & 2 & 1.41981088881128 & 0.58018911118872 \tabularnewline
55 & 2 & 1.43168065743796 & 0.568319342562042 \tabularnewline
56 & 1 & 1.51793517587896 & -0.517935175878962 \tabularnewline
57 & 1 & 1.43161516448292 & -0.431615164482916 \tabularnewline
58 & 1 & 1.55968800363377 & -0.559688003633769 \tabularnewline
59 & 1 & 1.36996256294876 & -0.369962562948764 \tabularnewline
60 & 1 & 1.57294662467305 & -0.572946624673053 \tabularnewline
61 & 1 & 1.23958189182541 & -0.239581891825408 \tabularnewline
62 & 2 & 1.51778991535286 & 0.482210084647136 \tabularnewline
63 & 2 & 1.36768926272054 & 0.632310737279465 \tabularnewline
64 & 2 & 1.49989391100946 & 0.500106088990536 \tabularnewline
65 & 2 & 1.62577238198428 & 0.374227618015722 \tabularnewline
66 & 1 & 1.68968999164289 & -0.68968999164289 \tabularnewline
67 & 2 & 1.70952846971269 & 0.290471530287314 \tabularnewline
68 & 2 & 1.53751587937408 & 0.462484120625917 \tabularnewline
69 & 1 & 1.50413413933784 & -0.50413413933784 \tabularnewline
70 & 1 & 1.56155585678249 & -0.561555856782488 \tabularnewline
71 & 1 & 1.43120793213665 & -0.431207932136653 \tabularnewline
72 & 2 & 1.68944229443874 & 0.310557705561261 \tabularnewline
73 & 1 & 1.69386472701623 & -0.693864727016225 \tabularnewline
74 & 1 & 1.62547766368659 & -0.625477663686592 \tabularnewline
75 & 1 & 1.68262806507220 & -0.682628065072203 \tabularnewline
76 & 1 & 1.69376648758366 & -0.693766487583663 \tabularnewline
77 & 2 & 1.6838719391609 & 0.316128060839099 \tabularnewline
78 & 1 & 1.58567150690367 & -0.585671506903672 \tabularnewline
79 & 2 & 1.49285560329366 & 0.507144396706337 \tabularnewline
80 & 2 & 1.62963392719842 & 0.37036607280158 \tabularnewline
81 & 2 & 1.69360275519606 & 0.306397244803941 \tabularnewline
82 & 2 & 1.43525168159990 & 0.564748318400095 \tabularnewline
83 & 1 & 1.68908208318601 & -0.689082083186011 \tabularnewline
84 & 2 & 1.74864695046767 & 0.251353049532329 \tabularnewline
85 & 2 & 1.68906780857000 & 0.310932191430003 \tabularnewline
86 & 1 & 1.31157272579451 & -0.311572725794508 \tabularnewline
87 & 2 & 1.64932714474212 & 0.350672855257882 \tabularnewline
88 & 2 & 1.56101763852614 & 0.438982361473858 \tabularnewline
89 & 1 & 1.49467128835547 & -0.494671288355467 \tabularnewline
90 & 1 & 1.36663539292763 & -0.366635392927627 \tabularnewline
91 & 2 & 1.61815376529574 & 0.381846234704261 \tabularnewline
92 & 2 & 1.62483700875219 & 0.375162991247808 \tabularnewline
93 & 2 & 1.64472670516101 & 0.355273294838986 \tabularnewline
94 & 1 & 1.55845791210400 & -0.558457912103996 \tabularnewline
95 & 2 & 1.51682777815550 & 0.483172221844497 \tabularnewline
96 & 1 & 1.73844340913121 & -0.738443409131208 \tabularnewline
97 & 2 & 1.69307881155573 & 0.306921188444273 \tabularnewline
98 & 1 & 1.55832692619391 & -0.558326926193913 \tabularnewline
99 & 1 & 1.70848058243202 & -0.708480582432023 \tabularnewline
100 & 2 & 1.62462625527105 & 0.375373744728947 \tabularnewline
101 & 2 & 1.28042894771526 & 0.71957105228474 \tabularnewline
102 & 1 & 1.36624243519738 & -0.366242435197378 \tabularnewline
103 & 2 & 1.49411040099212 & 0.505889599007877 \tabularnewline
104 & 2 & 1.69284958621308 & 0.307150413786918 \tabularnewline
105 & 1 & 1.60020339750292 & -0.600203397502922 \tabularnewline
106 & 1 & 1.74352256724624 & -0.743522567246236 \tabularnewline
107 & 1 & 1.62880099064439 & -0.628800990644388 \tabularnewline
108 & 1 & 1.28772740085685 & -0.287727400856847 \tabularnewline
109 & 2 & 1.75228346758466 & 0.247716532415341 \tabularnewline
110 & 1 & 1.17837970066771 & -0.178379700667712 \tabularnewline
111 & 1 & 1.49626289506801 & -0.496262895068006 \tabularnewline
112 & 1 & 1.19818543225999 & -0.198185432259987 \tabularnewline
113 & 1 & 1.6440717756106 & -0.6440717756106 \tabularnewline
114 & 2 & 1.55588076595545 & 0.444119234044554 \tabularnewline
115 & 1 & 1.56025198019390 & -0.560251980193905 \tabularnewline
116 & 2 & 1.75205424224201 & 0.247945757757986 \tabularnewline
117 & 1 & 1.63287748658516 & -0.63287748658516 \tabularnewline
118 & 2 & 1.49603366972536 & 0.503966330274639 \tabularnewline
119 & 2 & 1.62390163552011 & 0.376098364479894 \tabularnewline
120 & 1 & 1.49173393095419 & -0.491733930954189 \tabularnewline
121 & 1 & 1.72871438908318 & -0.728714389083178 \tabularnewline
122 & 2 & 1.68790740724076 & 0.312092592759243 \tabularnewline
123 & 2 & 1.36555475916944 & 0.634445240830557 \tabularnewline
124 & 1 & 1.55978754699637 & -0.559787546996369 \tabularnewline
125 & 2 & 1.46928450812322 & 0.530715491876779 \tabularnewline
126 & 2 & 1.67655403400267 & 0.323445965997333 \tabularnewline
127 & 2 & 1.42942534773452 & 0.57057465226548 \tabularnewline
128 & 2 & 1.61689092728845 & 0.383109072711555 \tabularnewline
129 & 1 & 1.47475898409923 & -0.474758984099234 \tabularnewline
130 & 2 & 1.61930717845125 & 0.380692821548753 \tabularnewline
131 & 2 & 1.62796385684486 & 0.372036143155137 \tabularnewline
132 & 2 & 1.64350081087673 & 0.356499189123267 \tabularnewline
133 & 2 & 1.49312800666650 & 0.506871993333498 \tabularnewline
134 & 1 & 1.63898013886669 & -0.638980138866686 \tabularnewline
135 & 1 & 1.75138084083009 & -0.751380840830094 \tabularnewline
136 & 2 & 1.66324104951397 & 0.336758950486033 \tabularnewline
137 & 2 & 1.57692103811792 & 0.423078961882078 \tabularnewline
138 & 2 & 1.70720346980871 & 0.292796530191285 \tabularnewline
139 & 2 & 1.56375152888856 & 0.436248471111435 \tabularnewline
140 & 1 & 1.48854603894690 & -0.488546038946904 \tabularnewline
141 & 2 & 1.38488776546041 & 0.615112234539587 \tabularnewline
142 & 2 & 1.69160522006730 & 0.308394779932705 \tabularnewline
143 & 2 & 1.74791640946407 & 0.252083590535935 \tabularnewline
144 & 1 & 1.70160036805336 & -0.701600368053356 \tabularnewline
145 & 1 & 1.36923829737997 & -0.369238297379966 \tabularnewline
146 & 2 & 1.55675509527292 & 0.443244904727081 \tabularnewline
147 & 2 & 1.51264321720658 & 0.487356782793419 \tabularnewline
148 & 2 & 1.68705599882522 & 0.312944001174781 \tabularnewline
149 & 1 & 1.57894254628372 & -0.578942546283721 \tabularnewline
150 & 2 & 1.69139446658616 & 0.308605533413844 \tabularnewline
151 & 2 & 1.54904159015807 & 0.450958409841931 \tabularnewline
152 & 2 & 1.62732739915596 & 0.372672600844045 \tabularnewline
153 & 1 & 1.49247307711609 & -0.492473077116087 \tabularnewline
154 & 1 & 1.3645396183663 & -0.364539618366301 \tabularnewline
155 & 2 & 1.80591956432288 & 0.194080435677119 \tabularnewline
156 & 1 & 1.49919326429555 & -0.499193264295553 \tabularnewline
157 & 2 & 1.85553866484275 & 0.144461335157248 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104697&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]1.62781693820654[/C][C]0.372183061793461[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.37640278778327[/C][C]-0.37640278778327[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.64338842398272[/C][C]-0.64338842398272[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.58132753796720[/C][C]-0.581327537967204[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.4929668134122[/C][C]0.507033186587799[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.43338767151453[/C][C]0.566612328485474[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]1.33408214040499[/C][C]-0.334082140404989[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.67722108799982[/C][C]0.322778912000182[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.62760618472544[/C][C]-0.627606184725438[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.43325668560444[/C][C]0.566743314395556[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.08905937804865[/C][C]-0.0890593780486509[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.56350637081772[/C][C]0.436493629182283[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.6087030387601[/C][C]-0.608703038760102[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.74653524317814[/C][C]0.253464756821858[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.71128250488279[/C][C]0.28871749511721[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]1.55672667319545[/C][C]0.443273326804545[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.30056913225647[/C][C]-0.300569132256473[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.62731146642775[/C][C]-0.627311466427752[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.39119486493594[/C][C]-0.391194864935936[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.40877253212676[/C][C]0.591227467873237[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.63602114842715[/C][C]0.363978851572851[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.43286372787420[/C][C]-0.432863727874195[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.75064448559643[/C][C]0.249355514403565[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.42728917535086[/C][C]0.572710824649135[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]1.55977577038603[/C][C]0.440224229613969[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]1.49904634565727[/C][C]0.500953654342733[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.61269976712982[/C][C]0.387300232870182[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.54635341695914[/C][C]0.453646583040857[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.67422138275389[/C][C]-0.674221382753888[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.36860018157887[/C][C]-0.36860018157887[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.63569368365194[/C][C]0.364306316348059[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.49653783757415[/C][C]-0.496537837574148[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]1.57162661622174[/C][C]0.42837338377826[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.74588031362773[/C][C]0.254119686372272[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.36843644919127[/C][C]0.631563550808734[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.62672202983238[/C][C]-0.626722029832379[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]1.56918360109366[/C][C]0.430816398906337[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.63100927925429[/C][C]-0.63100927925429[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.50511653366346[/C][C]-0.505116533663461[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]1.67627563739721[/C][C]0.323724362602791[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.41363909964590[/C][C]-0.413639099645897[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.75888144229453[/C][C]0.241118557705473[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.69044316062587[/C][C]0.309556839374133[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.17190276165197[/C][C]-0.17190276165197[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.45434503099556[/C][C]-0.454345030995557[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.58666817584531[/C][C]0.413331824154692[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.61639757995636[/C][C]0.383602420043644[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]1.30400917050834[/C][C]-0.304009170508337[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.63730841040094[/C][C]0.362691589599059[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.43194682650362[/C][C]0.568053173496385[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.37231646626691[/C][C]-0.372316466266914[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.56219651171689[/C][C]-0.562196511716888[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]1.37010782347486[/C][C]0.629892176525139[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.41981088881128[/C][C]0.58018911118872[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.43168065743796[/C][C]0.568319342562042[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.51793517587896[/C][C]-0.517935175878962[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.43161516448292[/C][C]-0.431615164482916[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.55968800363377[/C][C]-0.559688003633769[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.36996256294876[/C][C]-0.369962562948764[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.57294662467305[/C][C]-0.572946624673053[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.23958189182541[/C][C]-0.239581891825408[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]1.51778991535286[/C][C]0.482210084647136[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]1.36768926272054[/C][C]0.632310737279465[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.49989391100946[/C][C]0.500106088990536[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.62577238198428[/C][C]0.374227618015722[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.68968999164289[/C][C]-0.68968999164289[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.70952846971269[/C][C]0.290471530287314[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.53751587937408[/C][C]0.462484120625917[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.50413413933784[/C][C]-0.50413413933784[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.56155585678249[/C][C]-0.561555856782488[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.43120793213665[/C][C]-0.431207932136653[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.68944229443874[/C][C]0.310557705561261[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.69386472701623[/C][C]-0.693864727016225[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.62547766368659[/C][C]-0.625477663686592[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.68262806507220[/C][C]-0.682628065072203[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.69376648758366[/C][C]-0.693766487583663[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.6838719391609[/C][C]0.316128060839099[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.58567150690367[/C][C]-0.585671506903672[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.49285560329366[/C][C]0.507144396706337[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.62963392719842[/C][C]0.37036607280158[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.69360275519606[/C][C]0.306397244803941[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]1.43525168159990[/C][C]0.564748318400095[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.68908208318601[/C][C]-0.689082083186011[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]1.74864695046767[/C][C]0.251353049532329[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.68906780857000[/C][C]0.310932191430003[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.31157272579451[/C][C]-0.311572725794508[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.64932714474212[/C][C]0.350672855257882[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.56101763852614[/C][C]0.438982361473858[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.49467128835547[/C][C]-0.494671288355467[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.36663539292763[/C][C]-0.366635392927627[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]1.61815376529574[/C][C]0.381846234704261[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.62483700875219[/C][C]0.375162991247808[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.64472670516101[/C][C]0.355273294838986[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.55845791210400[/C][C]-0.558457912103996[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]1.51682777815550[/C][C]0.483172221844497[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.73844340913121[/C][C]-0.738443409131208[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.69307881155573[/C][C]0.306921188444273[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.55832692619391[/C][C]-0.558326926193913[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.70848058243202[/C][C]-0.708480582432023[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.62462625527105[/C][C]0.375373744728947[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.28042894771526[/C][C]0.71957105228474[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.36624243519738[/C][C]-0.366242435197378[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.49411040099212[/C][C]0.505889599007877[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.69284958621308[/C][C]0.307150413786918[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.60020339750292[/C][C]-0.600203397502922[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.74352256724624[/C][C]-0.743522567246236[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]1.62880099064439[/C][C]-0.628800990644388[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.28772740085685[/C][C]-0.287727400856847[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]1.75228346758466[/C][C]0.247716532415341[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.17837970066771[/C][C]-0.178379700667712[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.49626289506801[/C][C]-0.496262895068006[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.19818543225999[/C][C]-0.198185432259987[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.6440717756106[/C][C]-0.6440717756106[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]1.55588076595545[/C][C]0.444119234044554[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.56025198019390[/C][C]-0.560251980193905[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.75205424224201[/C][C]0.247945757757986[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.63287748658516[/C][C]-0.63287748658516[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.49603366972536[/C][C]0.503966330274639[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.62390163552011[/C][C]0.376098364479894[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.49173393095419[/C][C]-0.491733930954189[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.72871438908318[/C][C]-0.728714389083178[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.68790740724076[/C][C]0.312092592759243[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.36555475916944[/C][C]0.634445240830557[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.55978754699637[/C][C]-0.559787546996369[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.46928450812322[/C][C]0.530715491876779[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.67655403400267[/C][C]0.323445965997333[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]1.42942534773452[/C][C]0.57057465226548[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.61689092728845[/C][C]0.383109072711555[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.47475898409923[/C][C]-0.474758984099234[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.61930717845125[/C][C]0.380692821548753[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.62796385684486[/C][C]0.372036143155137[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.64350081087673[/C][C]0.356499189123267[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.49312800666650[/C][C]0.506871993333498[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.63898013886669[/C][C]-0.638980138866686[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.75138084083009[/C][C]-0.751380840830094[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.66324104951397[/C][C]0.336758950486033[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]1.57692103811792[/C][C]0.423078961882078[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.70720346980871[/C][C]0.292796530191285[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.56375152888856[/C][C]0.436248471111435[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.48854603894690[/C][C]-0.488546038946904[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]1.38488776546041[/C][C]0.615112234539587[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.69160522006730[/C][C]0.308394779932705[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]1.74791640946407[/C][C]0.252083590535935[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.70160036805336[/C][C]-0.701600368053356[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.36923829737997[/C][C]-0.369238297379966[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.55675509527292[/C][C]0.443244904727081[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.51264321720658[/C][C]0.487356782793419[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.68705599882522[/C][C]0.312944001174781[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.57894254628372[/C][C]-0.578942546283721[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.69139446658616[/C][C]0.308605533413844[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.54904159015807[/C][C]0.450958409841931[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.62732739915596[/C][C]0.372672600844045[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.49247307711609[/C][C]-0.492473077116087[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.3645396183663[/C][C]-0.364539618366301[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.80591956432288[/C][C]0.194080435677119[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]1.49919326429555[/C][C]-0.499193264295553[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.85553866484275[/C][C]0.144461335157248[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104697&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104697&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.627816938206540.372183061793461
211.37640278778327-0.37640278778327
311.64338842398272-0.64338842398272
411.58132753796720-0.581327537967204
521.49296681341220.507033186587799
621.433387671514530.566612328485474
711.33408214040499-0.334082140404989
821.677221087999820.322778912000182
911.62760618472544-0.627606184725438
1021.433256685604440.566743314395556
1111.08905937804865-0.0890593780486509
1221.563506370817720.436493629182283
1311.6087030387601-0.608703038760102
1421.746535243178140.253464756821858
1521.711282504882790.28871749511721
1621.556726673195450.443273326804545
1711.30056913225647-0.300569132256473
1811.62731146642775-0.627311466427752
1911.39119486493594-0.391194864935936
2021.408772532126760.591227467873237
2121.636021148427150.363978851572851
2211.43286372787420-0.432863727874195
2321.750644485596430.249355514403565
2421.427289175350860.572710824649135
2521.559775770386030.440224229613969
2621.499046345657270.500953654342733
2721.612699767129820.387300232870182
2821.546353416959140.453646583040857
2911.67422138275389-0.674221382753888
3011.36860018157887-0.36860018157887
3121.635693683651940.364306316348059
3211.49653783757415-0.496537837574148
3321.571626616221740.42837338377826
3421.745880313627730.254119686372272
3521.368436449191270.631563550808734
3611.62672202983238-0.626722029832379
3721.569183601093660.430816398906337
3811.63100927925429-0.63100927925429
3911.50511653366346-0.505116533663461
4021.676275637397210.323724362602791
4111.41363909964590-0.413639099645897
4221.758881442294530.241118557705473
4321.690443160625870.309556839374133
4411.17190276165197-0.17190276165197
4511.45434503099556-0.454345030995557
4621.586668175845310.413331824154692
4721.616397579956360.383602420043644
4811.30400917050834-0.304009170508337
4921.637308410400940.362691589599059
5021.431946826503620.568053173496385
5111.37231646626691-0.372316466266914
5211.56219651171689-0.562196511716888
5321.370107823474860.629892176525139
5421.419810888811280.58018911118872
5521.431680657437960.568319342562042
5611.51793517587896-0.517935175878962
5711.43161516448292-0.431615164482916
5811.55968800363377-0.559688003633769
5911.36996256294876-0.369962562948764
6011.57294662467305-0.572946624673053
6111.23958189182541-0.239581891825408
6221.517789915352860.482210084647136
6321.367689262720540.632310737279465
6421.499893911009460.500106088990536
6521.625772381984280.374227618015722
6611.68968999164289-0.68968999164289
6721.709528469712690.290471530287314
6821.537515879374080.462484120625917
6911.50413413933784-0.50413413933784
7011.56155585678249-0.561555856782488
7111.43120793213665-0.431207932136653
7221.689442294438740.310557705561261
7311.69386472701623-0.693864727016225
7411.62547766368659-0.625477663686592
7511.68262806507220-0.682628065072203
7611.69376648758366-0.693766487583663
7721.68387193916090.316128060839099
7811.58567150690367-0.585671506903672
7921.492855603293660.507144396706337
8021.629633927198420.37036607280158
8121.693602755196060.306397244803941
8221.435251681599900.564748318400095
8311.68908208318601-0.689082083186011
8421.748646950467670.251353049532329
8521.689067808570000.310932191430003
8611.31157272579451-0.311572725794508
8721.649327144742120.350672855257882
8821.561017638526140.438982361473858
8911.49467128835547-0.494671288355467
9011.36663539292763-0.366635392927627
9121.618153765295740.381846234704261
9221.624837008752190.375162991247808
9321.644726705161010.355273294838986
9411.55845791210400-0.558457912103996
9521.516827778155500.483172221844497
9611.73844340913121-0.738443409131208
9721.693078811555730.306921188444273
9811.55832692619391-0.558326926193913
9911.70848058243202-0.708480582432023
10021.624626255271050.375373744728947
10121.280428947715260.71957105228474
10211.36624243519738-0.366242435197378
10321.494110400992120.505889599007877
10421.692849586213080.307150413786918
10511.60020339750292-0.600203397502922
10611.74352256724624-0.743522567246236
10711.62880099064439-0.628800990644388
10811.28772740085685-0.287727400856847
10921.752283467584660.247716532415341
11011.17837970066771-0.178379700667712
11111.49626289506801-0.496262895068006
11211.19818543225999-0.198185432259987
11311.6440717756106-0.6440717756106
11421.555880765955450.444119234044554
11511.56025198019390-0.560251980193905
11621.752054242242010.247945757757986
11711.63287748658516-0.63287748658516
11821.496033669725360.503966330274639
11921.623901635520110.376098364479894
12011.49173393095419-0.491733930954189
12111.72871438908318-0.728714389083178
12221.687907407240760.312092592759243
12321.365554759169440.634445240830557
12411.55978754699637-0.559787546996369
12521.469284508123220.530715491876779
12621.676554034002670.323445965997333
12721.429425347734520.57057465226548
12821.616890927288450.383109072711555
12911.47475898409923-0.474758984099234
13021.619307178451250.380692821548753
13121.627963856844860.372036143155137
13221.643500810876730.356499189123267
13321.493128006666500.506871993333498
13411.63898013886669-0.638980138866686
13511.75138084083009-0.751380840830094
13621.663241049513970.336758950486033
13721.576921038117920.423078961882078
13821.707203469808710.292796530191285
13921.563751528888560.436248471111435
14011.48854603894690-0.488546038946904
14121.384887765460410.615112234539587
14221.691605220067300.308394779932705
14321.747916409464070.252083590535935
14411.70160036805336-0.701600368053356
14511.36923829737997-0.369238297379966
14621.556755095272920.443244904727081
14721.512643217206580.487356782793419
14821.687055998825220.312944001174781
14911.57894254628372-0.578942546283721
15021.691394466586160.308605533413844
15121.549041590158070.450958409841931
15221.627327399155960.372672600844045
15311.49247307711609-0.492473077116087
15411.3645396183663-0.364539618366301
15521.805919564322880.194080435677119
15611.49919326429555-0.499193264295553
15721.855538664842750.144461335157248






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.679825307655190.6403493846896210.320174692344810
120.6918552617414940.6162894765170110.308144738258506
130.6822125406123850.6355749187752290.317787459387615
140.5726131113675610.8547737772648770.427386888632439
150.5701783141328120.8596433717343760.429821685867188
160.545171965857650.90965606828470.45482803414235
170.7144411840292440.5711176319415120.285558815970756
180.7308736679653020.5382526640693960.269126332034698
190.6570564337300410.6858871325399180.342943566269959
200.7211610542850580.5576778914298840.278838945714942
210.6645944631185190.6708110737629620.335405536881481
220.7073617944362150.5852764111275690.292638205563785
230.6389536035783010.7220927928433980.361046396421699
240.6051385029980640.7897229940038730.394861497001936
250.5929494557097060.8141010885805880.407050544290294
260.5813391290252350.837321741949530.418660870974765
270.529401929734650.94119614053070.47059807026535
280.4752634898675340.9505269797350690.524736510132465
290.6995264683367590.6009470633264820.300473531663241
300.6999868421576240.6000263156847520.300013157842376
310.6596019966409570.6807960067180850.340398003359043
320.6806330438729820.6387339122540370.319366956127018
330.648426772143710.703146455712580.35157322785629
340.5975653846845990.8048692306308030.402434615315401
350.6044997864537630.7910004270924740.395500213546237
360.6612315900779650.677536819844070.338768409922035
370.6279103568682580.7441792862634830.372089643131741
380.6794201604795280.6411596790409440.320579839520472
390.6864928097359020.6270143805281960.313507190264098
400.6518604786521780.6962790426956440.348139521347822
410.6187105749757450.762578850048510.381289425024255
420.5799542710674650.840091457865070.420045728932535
430.5474339590752720.9051320818494570.452566040924728
440.4965146811027560.9930293622055130.503485318897244
450.474554095309170.949108190618340.52544590469083
460.4686532877535090.9373065755070170.531346712246491
470.4437469527551210.8874939055102420.556253047244879
480.4103498690547260.8206997381094530.589650130945274
490.390463044509240.780926089018480.60953695549076
500.4038599913738330.8077199827476660.596140008626167
510.3860489693831530.7720979387663060.613951030616847
520.3999031247997020.7998062495994040.600096875200298
530.4244378344760180.8488756689520350.575562165523982
540.4513601513853840.9027203027707670.548639848614616
550.4530048437981060.9060096875962110.546995156201894
560.4593105099332420.9186210198664830.540689490066758
570.4667435356255790.9334870712511590.53325646437442
580.4665162394164570.9330324788329140.533483760583543
590.4400064204878650.880012840975730.559993579512135
600.4375578278704500.8751156557408990.562442172129550
610.3951743901064450.790348780212890.604825609893555
620.420818548666550.84163709733310.57918145133345
630.4880749648561360.9761499297122710.511925035143864
640.4963797366252340.9927594732504680.503620263374766
650.4812270044977570.9624540089955150.518772995502243
660.5220625667702660.9558748664594690.477937433229734
670.4960891578092470.9921783156184950.503910842190753
680.5012446358074740.9975107283850520.498755364192526
690.499052183622820.998104367245640.50094781637718
700.5022665027609860.9954669944780280.497733497239014
710.4782967875927640.9565935751855280.521703212407236
720.4577904600508030.9155809201016060.542209539949197
730.488727374547820.977454749095640.51127262545218
740.501584764400010.996830471199980.49841523559999
750.5214654530549060.9570690938901880.478534546945094
760.5489261519432950.9021476961134110.451073848056705
770.5458853437279910.9082293125440180.454114656272009
780.5491026839687620.9017946320624760.450897316031238
790.5703648998698580.8592702002602840.429635100130142
800.563549379739780.872901240520440.43645062026022
810.5455171842122830.9089656315754330.454482815787717
820.5807339565507150.838532086898570.419266043449285
830.6032629752564900.7934740494870190.396737024743510
840.5774621591818180.8450756816363630.422537840818182
850.5596566689133520.8806866621732950.440343331086648
860.5215985790386240.9568028419227520.478401420961376
870.510647082078090.978705835843820.48935291792191
880.5169672465275830.9660655069448350.483032753472417
890.5052348377691390.9895303244617220.494765162230861
900.4752785966977040.9505571933954080.524721403302296
910.4638094036272540.9276188072545080.536190596372746
920.4605212316852680.9210424633705370.539478768314732
930.4517567715217070.9035135430434140.548243228478293
940.4517703614670370.9035407229340750.548229638532963
950.4619886320826280.9239772641652560.538011367917372
960.4837889454209640.9675778908419270.516211054579036
970.4704247323557870.9408494647115740.529575267644213
980.4791521618564160.9583043237128310.520847838143584
990.5082143896453770.9835712207092470.491785610354623
1000.496750683288390.993501366576780.50324931671161
1010.6421580568310070.7156838863379860.357841943168993
1020.6181176596819280.7637646806361430.381882340318072
1030.6236396314136690.7527207371726620.376360368586331
1040.6058270935780870.7883458128438260.394172906421913
1050.59786860020260.8042627995947990.402131399797400
1060.6444962375142390.7110075249715220.355503762485761
1070.6702583295538320.6594833408923360.329741670446168
1080.6262480828436010.7475038343127970.373751917156399
1090.5855765913457660.8288468173084680.414423408654234
1100.5338437341154180.9323125317691640.466156265884582
1110.516440163063750.96711967387250.48355983693625
1120.4634471669624410.9268943339248820.536552833037559
1130.5070230082263570.9859539835472870.492976991773643
1140.5147696111738350.970460777652330.485230388826165
1150.5110728329378480.9778543341243050.488927167062153
1160.4636326320127310.9272652640254620.536367367987269
1170.5687530579571140.8624938840857730.431246942042886
1180.5807669524016190.8384660951967630.419233047598381
1190.5717982475352980.8564035049294040.428201752464702
1200.5637121081099740.8725757837800530.436287891890026
1210.583945520873130.832108958253740.41605447912687
1220.5362669554158610.9274660891682780.463733044584139
1230.5518836612509120.8962326774981760.448116338749088
1240.5736142145432650.852771570913470.426385785456735
1250.5517123920552190.8965752158895610.448287607944781
1260.5025900745441070.9948198509117870.497409925455893
1270.4801793144436060.9603586288872120.519820685556394
1280.4250428871702080.8500857743404150.574957112829792
1290.4395325948772360.8790651897544720.560467405122764
1300.3971600779086240.7943201558172480.602839922091376
1310.3598556093831330.7197112187662670.640144390616867
1320.3071882382659350.614376476531870.692811761734065
1330.3229923067083150.645984613416630.677007693291685
1340.382363718899310.764727437798620.61763628110069
1350.5861829583357720.8276340833284550.413817041664228
1360.5094164228024840.9811671543950310.490583577197516
1370.4454048221776740.8908096443553480.554595177822326
1380.3680574386319440.7361148772638880.631942561368056
1390.3123953518248330.6247907036496660.687604648175167
1400.519883702575570.960232594848860.48011629742443
1410.5329911823050240.9340176353899520.467008817694976
1420.4248334204721750.849666840944350.575166579527825
1430.3263726465908540.6527452931817070.673627353409146
1440.7028634065583440.5942731868833110.297136593441656
1450.6596910018090130.6806179963819750.340308998190988
1460.5008988622235950.998202275552810.499101137776405

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.67982530765519 & 0.640349384689621 & 0.320174692344810 \tabularnewline
12 & 0.691855261741494 & 0.616289476517011 & 0.308144738258506 \tabularnewline
13 & 0.682212540612385 & 0.635574918775229 & 0.317787459387615 \tabularnewline
14 & 0.572613111367561 & 0.854773777264877 & 0.427386888632439 \tabularnewline
15 & 0.570178314132812 & 0.859643371734376 & 0.429821685867188 \tabularnewline
16 & 0.54517196585765 & 0.9096560682847 & 0.45482803414235 \tabularnewline
17 & 0.714441184029244 & 0.571117631941512 & 0.285558815970756 \tabularnewline
18 & 0.730873667965302 & 0.538252664069396 & 0.269126332034698 \tabularnewline
19 & 0.657056433730041 & 0.685887132539918 & 0.342943566269959 \tabularnewline
20 & 0.721161054285058 & 0.557677891429884 & 0.278838945714942 \tabularnewline
21 & 0.664594463118519 & 0.670811073762962 & 0.335405536881481 \tabularnewline
22 & 0.707361794436215 & 0.585276411127569 & 0.292638205563785 \tabularnewline
23 & 0.638953603578301 & 0.722092792843398 & 0.361046396421699 \tabularnewline
24 & 0.605138502998064 & 0.789722994003873 & 0.394861497001936 \tabularnewline
25 & 0.592949455709706 & 0.814101088580588 & 0.407050544290294 \tabularnewline
26 & 0.581339129025235 & 0.83732174194953 & 0.418660870974765 \tabularnewline
27 & 0.52940192973465 & 0.9411961405307 & 0.47059807026535 \tabularnewline
28 & 0.475263489867534 & 0.950526979735069 & 0.524736510132465 \tabularnewline
29 & 0.699526468336759 & 0.600947063326482 & 0.300473531663241 \tabularnewline
30 & 0.699986842157624 & 0.600026315684752 & 0.300013157842376 \tabularnewline
31 & 0.659601996640957 & 0.680796006718085 & 0.340398003359043 \tabularnewline
32 & 0.680633043872982 & 0.638733912254037 & 0.319366956127018 \tabularnewline
33 & 0.64842677214371 & 0.70314645571258 & 0.35157322785629 \tabularnewline
34 & 0.597565384684599 & 0.804869230630803 & 0.402434615315401 \tabularnewline
35 & 0.604499786453763 & 0.791000427092474 & 0.395500213546237 \tabularnewline
36 & 0.661231590077965 & 0.67753681984407 & 0.338768409922035 \tabularnewline
37 & 0.627910356868258 & 0.744179286263483 & 0.372089643131741 \tabularnewline
38 & 0.679420160479528 & 0.641159679040944 & 0.320579839520472 \tabularnewline
39 & 0.686492809735902 & 0.627014380528196 & 0.313507190264098 \tabularnewline
40 & 0.651860478652178 & 0.696279042695644 & 0.348139521347822 \tabularnewline
41 & 0.618710574975745 & 0.76257885004851 & 0.381289425024255 \tabularnewline
42 & 0.579954271067465 & 0.84009145786507 & 0.420045728932535 \tabularnewline
43 & 0.547433959075272 & 0.905132081849457 & 0.452566040924728 \tabularnewline
44 & 0.496514681102756 & 0.993029362205513 & 0.503485318897244 \tabularnewline
45 & 0.47455409530917 & 0.94910819061834 & 0.52544590469083 \tabularnewline
46 & 0.468653287753509 & 0.937306575507017 & 0.531346712246491 \tabularnewline
47 & 0.443746952755121 & 0.887493905510242 & 0.556253047244879 \tabularnewline
48 & 0.410349869054726 & 0.820699738109453 & 0.589650130945274 \tabularnewline
49 & 0.39046304450924 & 0.78092608901848 & 0.60953695549076 \tabularnewline
50 & 0.403859991373833 & 0.807719982747666 & 0.596140008626167 \tabularnewline
51 & 0.386048969383153 & 0.772097938766306 & 0.613951030616847 \tabularnewline
52 & 0.399903124799702 & 0.799806249599404 & 0.600096875200298 \tabularnewline
53 & 0.424437834476018 & 0.848875668952035 & 0.575562165523982 \tabularnewline
54 & 0.451360151385384 & 0.902720302770767 & 0.548639848614616 \tabularnewline
55 & 0.453004843798106 & 0.906009687596211 & 0.546995156201894 \tabularnewline
56 & 0.459310509933242 & 0.918621019866483 & 0.540689490066758 \tabularnewline
57 & 0.466743535625579 & 0.933487071251159 & 0.53325646437442 \tabularnewline
58 & 0.466516239416457 & 0.933032478832914 & 0.533483760583543 \tabularnewline
59 & 0.440006420487865 & 0.88001284097573 & 0.559993579512135 \tabularnewline
60 & 0.437557827870450 & 0.875115655740899 & 0.562442172129550 \tabularnewline
61 & 0.395174390106445 & 0.79034878021289 & 0.604825609893555 \tabularnewline
62 & 0.42081854866655 & 0.8416370973331 & 0.57918145133345 \tabularnewline
63 & 0.488074964856136 & 0.976149929712271 & 0.511925035143864 \tabularnewline
64 & 0.496379736625234 & 0.992759473250468 & 0.503620263374766 \tabularnewline
65 & 0.481227004497757 & 0.962454008995515 & 0.518772995502243 \tabularnewline
66 & 0.522062566770266 & 0.955874866459469 & 0.477937433229734 \tabularnewline
67 & 0.496089157809247 & 0.992178315618495 & 0.503910842190753 \tabularnewline
68 & 0.501244635807474 & 0.997510728385052 & 0.498755364192526 \tabularnewline
69 & 0.49905218362282 & 0.99810436724564 & 0.50094781637718 \tabularnewline
70 & 0.502266502760986 & 0.995466994478028 & 0.497733497239014 \tabularnewline
71 & 0.478296787592764 & 0.956593575185528 & 0.521703212407236 \tabularnewline
72 & 0.457790460050803 & 0.915580920101606 & 0.542209539949197 \tabularnewline
73 & 0.48872737454782 & 0.97745474909564 & 0.51127262545218 \tabularnewline
74 & 0.50158476440001 & 0.99683047119998 & 0.49841523559999 \tabularnewline
75 & 0.521465453054906 & 0.957069093890188 & 0.478534546945094 \tabularnewline
76 & 0.548926151943295 & 0.902147696113411 & 0.451073848056705 \tabularnewline
77 & 0.545885343727991 & 0.908229312544018 & 0.454114656272009 \tabularnewline
78 & 0.549102683968762 & 0.901794632062476 & 0.450897316031238 \tabularnewline
79 & 0.570364899869858 & 0.859270200260284 & 0.429635100130142 \tabularnewline
80 & 0.56354937973978 & 0.87290124052044 & 0.43645062026022 \tabularnewline
81 & 0.545517184212283 & 0.908965631575433 & 0.454482815787717 \tabularnewline
82 & 0.580733956550715 & 0.83853208689857 & 0.419266043449285 \tabularnewline
83 & 0.603262975256490 & 0.793474049487019 & 0.396737024743510 \tabularnewline
84 & 0.577462159181818 & 0.845075681636363 & 0.422537840818182 \tabularnewline
85 & 0.559656668913352 & 0.880686662173295 & 0.440343331086648 \tabularnewline
86 & 0.521598579038624 & 0.956802841922752 & 0.478401420961376 \tabularnewline
87 & 0.51064708207809 & 0.97870583584382 & 0.48935291792191 \tabularnewline
88 & 0.516967246527583 & 0.966065506944835 & 0.483032753472417 \tabularnewline
89 & 0.505234837769139 & 0.989530324461722 & 0.494765162230861 \tabularnewline
90 & 0.475278596697704 & 0.950557193395408 & 0.524721403302296 \tabularnewline
91 & 0.463809403627254 & 0.927618807254508 & 0.536190596372746 \tabularnewline
92 & 0.460521231685268 & 0.921042463370537 & 0.539478768314732 \tabularnewline
93 & 0.451756771521707 & 0.903513543043414 & 0.548243228478293 \tabularnewline
94 & 0.451770361467037 & 0.903540722934075 & 0.548229638532963 \tabularnewline
95 & 0.461988632082628 & 0.923977264165256 & 0.538011367917372 \tabularnewline
96 & 0.483788945420964 & 0.967577890841927 & 0.516211054579036 \tabularnewline
97 & 0.470424732355787 & 0.940849464711574 & 0.529575267644213 \tabularnewline
98 & 0.479152161856416 & 0.958304323712831 & 0.520847838143584 \tabularnewline
99 & 0.508214389645377 & 0.983571220709247 & 0.491785610354623 \tabularnewline
100 & 0.49675068328839 & 0.99350136657678 & 0.50324931671161 \tabularnewline
101 & 0.642158056831007 & 0.715683886337986 & 0.357841943168993 \tabularnewline
102 & 0.618117659681928 & 0.763764680636143 & 0.381882340318072 \tabularnewline
103 & 0.623639631413669 & 0.752720737172662 & 0.376360368586331 \tabularnewline
104 & 0.605827093578087 & 0.788345812843826 & 0.394172906421913 \tabularnewline
105 & 0.5978686002026 & 0.804262799594799 & 0.402131399797400 \tabularnewline
106 & 0.644496237514239 & 0.711007524971522 & 0.355503762485761 \tabularnewline
107 & 0.670258329553832 & 0.659483340892336 & 0.329741670446168 \tabularnewline
108 & 0.626248082843601 & 0.747503834312797 & 0.373751917156399 \tabularnewline
109 & 0.585576591345766 & 0.828846817308468 & 0.414423408654234 \tabularnewline
110 & 0.533843734115418 & 0.932312531769164 & 0.466156265884582 \tabularnewline
111 & 0.51644016306375 & 0.9671196738725 & 0.48355983693625 \tabularnewline
112 & 0.463447166962441 & 0.926894333924882 & 0.536552833037559 \tabularnewline
113 & 0.507023008226357 & 0.985953983547287 & 0.492976991773643 \tabularnewline
114 & 0.514769611173835 & 0.97046077765233 & 0.485230388826165 \tabularnewline
115 & 0.511072832937848 & 0.977854334124305 & 0.488927167062153 \tabularnewline
116 & 0.463632632012731 & 0.927265264025462 & 0.536367367987269 \tabularnewline
117 & 0.568753057957114 & 0.862493884085773 & 0.431246942042886 \tabularnewline
118 & 0.580766952401619 & 0.838466095196763 & 0.419233047598381 \tabularnewline
119 & 0.571798247535298 & 0.856403504929404 & 0.428201752464702 \tabularnewline
120 & 0.563712108109974 & 0.872575783780053 & 0.436287891890026 \tabularnewline
121 & 0.58394552087313 & 0.83210895825374 & 0.41605447912687 \tabularnewline
122 & 0.536266955415861 & 0.927466089168278 & 0.463733044584139 \tabularnewline
123 & 0.551883661250912 & 0.896232677498176 & 0.448116338749088 \tabularnewline
124 & 0.573614214543265 & 0.85277157091347 & 0.426385785456735 \tabularnewline
125 & 0.551712392055219 & 0.896575215889561 & 0.448287607944781 \tabularnewline
126 & 0.502590074544107 & 0.994819850911787 & 0.497409925455893 \tabularnewline
127 & 0.480179314443606 & 0.960358628887212 & 0.519820685556394 \tabularnewline
128 & 0.425042887170208 & 0.850085774340415 & 0.574957112829792 \tabularnewline
129 & 0.439532594877236 & 0.879065189754472 & 0.560467405122764 \tabularnewline
130 & 0.397160077908624 & 0.794320155817248 & 0.602839922091376 \tabularnewline
131 & 0.359855609383133 & 0.719711218766267 & 0.640144390616867 \tabularnewline
132 & 0.307188238265935 & 0.61437647653187 & 0.692811761734065 \tabularnewline
133 & 0.322992306708315 & 0.64598461341663 & 0.677007693291685 \tabularnewline
134 & 0.38236371889931 & 0.76472743779862 & 0.61763628110069 \tabularnewline
135 & 0.586182958335772 & 0.827634083328455 & 0.413817041664228 \tabularnewline
136 & 0.509416422802484 & 0.981167154395031 & 0.490583577197516 \tabularnewline
137 & 0.445404822177674 & 0.890809644355348 & 0.554595177822326 \tabularnewline
138 & 0.368057438631944 & 0.736114877263888 & 0.631942561368056 \tabularnewline
139 & 0.312395351824833 & 0.624790703649666 & 0.687604648175167 \tabularnewline
140 & 0.51988370257557 & 0.96023259484886 & 0.48011629742443 \tabularnewline
141 & 0.532991182305024 & 0.934017635389952 & 0.467008817694976 \tabularnewline
142 & 0.424833420472175 & 0.84966684094435 & 0.575166579527825 \tabularnewline
143 & 0.326372646590854 & 0.652745293181707 & 0.673627353409146 \tabularnewline
144 & 0.702863406558344 & 0.594273186883311 & 0.297136593441656 \tabularnewline
145 & 0.659691001809013 & 0.680617996381975 & 0.340308998190988 \tabularnewline
146 & 0.500898862223595 & 0.99820227555281 & 0.499101137776405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104697&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.67982530765519[/C][C]0.640349384689621[/C][C]0.320174692344810[/C][/ROW]
[ROW][C]12[/C][C]0.691855261741494[/C][C]0.616289476517011[/C][C]0.308144738258506[/C][/ROW]
[ROW][C]13[/C][C]0.682212540612385[/C][C]0.635574918775229[/C][C]0.317787459387615[/C][/ROW]
[ROW][C]14[/C][C]0.572613111367561[/C][C]0.854773777264877[/C][C]0.427386888632439[/C][/ROW]
[ROW][C]15[/C][C]0.570178314132812[/C][C]0.859643371734376[/C][C]0.429821685867188[/C][/ROW]
[ROW][C]16[/C][C]0.54517196585765[/C][C]0.9096560682847[/C][C]0.45482803414235[/C][/ROW]
[ROW][C]17[/C][C]0.714441184029244[/C][C]0.571117631941512[/C][C]0.285558815970756[/C][/ROW]
[ROW][C]18[/C][C]0.730873667965302[/C][C]0.538252664069396[/C][C]0.269126332034698[/C][/ROW]
[ROW][C]19[/C][C]0.657056433730041[/C][C]0.685887132539918[/C][C]0.342943566269959[/C][/ROW]
[ROW][C]20[/C][C]0.721161054285058[/C][C]0.557677891429884[/C][C]0.278838945714942[/C][/ROW]
[ROW][C]21[/C][C]0.664594463118519[/C][C]0.670811073762962[/C][C]0.335405536881481[/C][/ROW]
[ROW][C]22[/C][C]0.707361794436215[/C][C]0.585276411127569[/C][C]0.292638205563785[/C][/ROW]
[ROW][C]23[/C][C]0.638953603578301[/C][C]0.722092792843398[/C][C]0.361046396421699[/C][/ROW]
[ROW][C]24[/C][C]0.605138502998064[/C][C]0.789722994003873[/C][C]0.394861497001936[/C][/ROW]
[ROW][C]25[/C][C]0.592949455709706[/C][C]0.814101088580588[/C][C]0.407050544290294[/C][/ROW]
[ROW][C]26[/C][C]0.581339129025235[/C][C]0.83732174194953[/C][C]0.418660870974765[/C][/ROW]
[ROW][C]27[/C][C]0.52940192973465[/C][C]0.9411961405307[/C][C]0.47059807026535[/C][/ROW]
[ROW][C]28[/C][C]0.475263489867534[/C][C]0.950526979735069[/C][C]0.524736510132465[/C][/ROW]
[ROW][C]29[/C][C]0.699526468336759[/C][C]0.600947063326482[/C][C]0.300473531663241[/C][/ROW]
[ROW][C]30[/C][C]0.699986842157624[/C][C]0.600026315684752[/C][C]0.300013157842376[/C][/ROW]
[ROW][C]31[/C][C]0.659601996640957[/C][C]0.680796006718085[/C][C]0.340398003359043[/C][/ROW]
[ROW][C]32[/C][C]0.680633043872982[/C][C]0.638733912254037[/C][C]0.319366956127018[/C][/ROW]
[ROW][C]33[/C][C]0.64842677214371[/C][C]0.70314645571258[/C][C]0.35157322785629[/C][/ROW]
[ROW][C]34[/C][C]0.597565384684599[/C][C]0.804869230630803[/C][C]0.402434615315401[/C][/ROW]
[ROW][C]35[/C][C]0.604499786453763[/C][C]0.791000427092474[/C][C]0.395500213546237[/C][/ROW]
[ROW][C]36[/C][C]0.661231590077965[/C][C]0.67753681984407[/C][C]0.338768409922035[/C][/ROW]
[ROW][C]37[/C][C]0.627910356868258[/C][C]0.744179286263483[/C][C]0.372089643131741[/C][/ROW]
[ROW][C]38[/C][C]0.679420160479528[/C][C]0.641159679040944[/C][C]0.320579839520472[/C][/ROW]
[ROW][C]39[/C][C]0.686492809735902[/C][C]0.627014380528196[/C][C]0.313507190264098[/C][/ROW]
[ROW][C]40[/C][C]0.651860478652178[/C][C]0.696279042695644[/C][C]0.348139521347822[/C][/ROW]
[ROW][C]41[/C][C]0.618710574975745[/C][C]0.76257885004851[/C][C]0.381289425024255[/C][/ROW]
[ROW][C]42[/C][C]0.579954271067465[/C][C]0.84009145786507[/C][C]0.420045728932535[/C][/ROW]
[ROW][C]43[/C][C]0.547433959075272[/C][C]0.905132081849457[/C][C]0.452566040924728[/C][/ROW]
[ROW][C]44[/C][C]0.496514681102756[/C][C]0.993029362205513[/C][C]0.503485318897244[/C][/ROW]
[ROW][C]45[/C][C]0.47455409530917[/C][C]0.94910819061834[/C][C]0.52544590469083[/C][/ROW]
[ROW][C]46[/C][C]0.468653287753509[/C][C]0.937306575507017[/C][C]0.531346712246491[/C][/ROW]
[ROW][C]47[/C][C]0.443746952755121[/C][C]0.887493905510242[/C][C]0.556253047244879[/C][/ROW]
[ROW][C]48[/C][C]0.410349869054726[/C][C]0.820699738109453[/C][C]0.589650130945274[/C][/ROW]
[ROW][C]49[/C][C]0.39046304450924[/C][C]0.78092608901848[/C][C]0.60953695549076[/C][/ROW]
[ROW][C]50[/C][C]0.403859991373833[/C][C]0.807719982747666[/C][C]0.596140008626167[/C][/ROW]
[ROW][C]51[/C][C]0.386048969383153[/C][C]0.772097938766306[/C][C]0.613951030616847[/C][/ROW]
[ROW][C]52[/C][C]0.399903124799702[/C][C]0.799806249599404[/C][C]0.600096875200298[/C][/ROW]
[ROW][C]53[/C][C]0.424437834476018[/C][C]0.848875668952035[/C][C]0.575562165523982[/C][/ROW]
[ROW][C]54[/C][C]0.451360151385384[/C][C]0.902720302770767[/C][C]0.548639848614616[/C][/ROW]
[ROW][C]55[/C][C]0.453004843798106[/C][C]0.906009687596211[/C][C]0.546995156201894[/C][/ROW]
[ROW][C]56[/C][C]0.459310509933242[/C][C]0.918621019866483[/C][C]0.540689490066758[/C][/ROW]
[ROW][C]57[/C][C]0.466743535625579[/C][C]0.933487071251159[/C][C]0.53325646437442[/C][/ROW]
[ROW][C]58[/C][C]0.466516239416457[/C][C]0.933032478832914[/C][C]0.533483760583543[/C][/ROW]
[ROW][C]59[/C][C]0.440006420487865[/C][C]0.88001284097573[/C][C]0.559993579512135[/C][/ROW]
[ROW][C]60[/C][C]0.437557827870450[/C][C]0.875115655740899[/C][C]0.562442172129550[/C][/ROW]
[ROW][C]61[/C][C]0.395174390106445[/C][C]0.79034878021289[/C][C]0.604825609893555[/C][/ROW]
[ROW][C]62[/C][C]0.42081854866655[/C][C]0.8416370973331[/C][C]0.57918145133345[/C][/ROW]
[ROW][C]63[/C][C]0.488074964856136[/C][C]0.976149929712271[/C][C]0.511925035143864[/C][/ROW]
[ROW][C]64[/C][C]0.496379736625234[/C][C]0.992759473250468[/C][C]0.503620263374766[/C][/ROW]
[ROW][C]65[/C][C]0.481227004497757[/C][C]0.962454008995515[/C][C]0.518772995502243[/C][/ROW]
[ROW][C]66[/C][C]0.522062566770266[/C][C]0.955874866459469[/C][C]0.477937433229734[/C][/ROW]
[ROW][C]67[/C][C]0.496089157809247[/C][C]0.992178315618495[/C][C]0.503910842190753[/C][/ROW]
[ROW][C]68[/C][C]0.501244635807474[/C][C]0.997510728385052[/C][C]0.498755364192526[/C][/ROW]
[ROW][C]69[/C][C]0.49905218362282[/C][C]0.99810436724564[/C][C]0.50094781637718[/C][/ROW]
[ROW][C]70[/C][C]0.502266502760986[/C][C]0.995466994478028[/C][C]0.497733497239014[/C][/ROW]
[ROW][C]71[/C][C]0.478296787592764[/C][C]0.956593575185528[/C][C]0.521703212407236[/C][/ROW]
[ROW][C]72[/C][C]0.457790460050803[/C][C]0.915580920101606[/C][C]0.542209539949197[/C][/ROW]
[ROW][C]73[/C][C]0.48872737454782[/C][C]0.97745474909564[/C][C]0.51127262545218[/C][/ROW]
[ROW][C]74[/C][C]0.50158476440001[/C][C]0.99683047119998[/C][C]0.49841523559999[/C][/ROW]
[ROW][C]75[/C][C]0.521465453054906[/C][C]0.957069093890188[/C][C]0.478534546945094[/C][/ROW]
[ROW][C]76[/C][C]0.548926151943295[/C][C]0.902147696113411[/C][C]0.451073848056705[/C][/ROW]
[ROW][C]77[/C][C]0.545885343727991[/C][C]0.908229312544018[/C][C]0.454114656272009[/C][/ROW]
[ROW][C]78[/C][C]0.549102683968762[/C][C]0.901794632062476[/C][C]0.450897316031238[/C][/ROW]
[ROW][C]79[/C][C]0.570364899869858[/C][C]0.859270200260284[/C][C]0.429635100130142[/C][/ROW]
[ROW][C]80[/C][C]0.56354937973978[/C][C]0.87290124052044[/C][C]0.43645062026022[/C][/ROW]
[ROW][C]81[/C][C]0.545517184212283[/C][C]0.908965631575433[/C][C]0.454482815787717[/C][/ROW]
[ROW][C]82[/C][C]0.580733956550715[/C][C]0.83853208689857[/C][C]0.419266043449285[/C][/ROW]
[ROW][C]83[/C][C]0.603262975256490[/C][C]0.793474049487019[/C][C]0.396737024743510[/C][/ROW]
[ROW][C]84[/C][C]0.577462159181818[/C][C]0.845075681636363[/C][C]0.422537840818182[/C][/ROW]
[ROW][C]85[/C][C]0.559656668913352[/C][C]0.880686662173295[/C][C]0.440343331086648[/C][/ROW]
[ROW][C]86[/C][C]0.521598579038624[/C][C]0.956802841922752[/C][C]0.478401420961376[/C][/ROW]
[ROW][C]87[/C][C]0.51064708207809[/C][C]0.97870583584382[/C][C]0.48935291792191[/C][/ROW]
[ROW][C]88[/C][C]0.516967246527583[/C][C]0.966065506944835[/C][C]0.483032753472417[/C][/ROW]
[ROW][C]89[/C][C]0.505234837769139[/C][C]0.989530324461722[/C][C]0.494765162230861[/C][/ROW]
[ROW][C]90[/C][C]0.475278596697704[/C][C]0.950557193395408[/C][C]0.524721403302296[/C][/ROW]
[ROW][C]91[/C][C]0.463809403627254[/C][C]0.927618807254508[/C][C]0.536190596372746[/C][/ROW]
[ROW][C]92[/C][C]0.460521231685268[/C][C]0.921042463370537[/C][C]0.539478768314732[/C][/ROW]
[ROW][C]93[/C][C]0.451756771521707[/C][C]0.903513543043414[/C][C]0.548243228478293[/C][/ROW]
[ROW][C]94[/C][C]0.451770361467037[/C][C]0.903540722934075[/C][C]0.548229638532963[/C][/ROW]
[ROW][C]95[/C][C]0.461988632082628[/C][C]0.923977264165256[/C][C]0.538011367917372[/C][/ROW]
[ROW][C]96[/C][C]0.483788945420964[/C][C]0.967577890841927[/C][C]0.516211054579036[/C][/ROW]
[ROW][C]97[/C][C]0.470424732355787[/C][C]0.940849464711574[/C][C]0.529575267644213[/C][/ROW]
[ROW][C]98[/C][C]0.479152161856416[/C][C]0.958304323712831[/C][C]0.520847838143584[/C][/ROW]
[ROW][C]99[/C][C]0.508214389645377[/C][C]0.983571220709247[/C][C]0.491785610354623[/C][/ROW]
[ROW][C]100[/C][C]0.49675068328839[/C][C]0.99350136657678[/C][C]0.50324931671161[/C][/ROW]
[ROW][C]101[/C][C]0.642158056831007[/C][C]0.715683886337986[/C][C]0.357841943168993[/C][/ROW]
[ROW][C]102[/C][C]0.618117659681928[/C][C]0.763764680636143[/C][C]0.381882340318072[/C][/ROW]
[ROW][C]103[/C][C]0.623639631413669[/C][C]0.752720737172662[/C][C]0.376360368586331[/C][/ROW]
[ROW][C]104[/C][C]0.605827093578087[/C][C]0.788345812843826[/C][C]0.394172906421913[/C][/ROW]
[ROW][C]105[/C][C]0.5978686002026[/C][C]0.804262799594799[/C][C]0.402131399797400[/C][/ROW]
[ROW][C]106[/C][C]0.644496237514239[/C][C]0.711007524971522[/C][C]0.355503762485761[/C][/ROW]
[ROW][C]107[/C][C]0.670258329553832[/C][C]0.659483340892336[/C][C]0.329741670446168[/C][/ROW]
[ROW][C]108[/C][C]0.626248082843601[/C][C]0.747503834312797[/C][C]0.373751917156399[/C][/ROW]
[ROW][C]109[/C][C]0.585576591345766[/C][C]0.828846817308468[/C][C]0.414423408654234[/C][/ROW]
[ROW][C]110[/C][C]0.533843734115418[/C][C]0.932312531769164[/C][C]0.466156265884582[/C][/ROW]
[ROW][C]111[/C][C]0.51644016306375[/C][C]0.9671196738725[/C][C]0.48355983693625[/C][/ROW]
[ROW][C]112[/C][C]0.463447166962441[/C][C]0.926894333924882[/C][C]0.536552833037559[/C][/ROW]
[ROW][C]113[/C][C]0.507023008226357[/C][C]0.985953983547287[/C][C]0.492976991773643[/C][/ROW]
[ROW][C]114[/C][C]0.514769611173835[/C][C]0.97046077765233[/C][C]0.485230388826165[/C][/ROW]
[ROW][C]115[/C][C]0.511072832937848[/C][C]0.977854334124305[/C][C]0.488927167062153[/C][/ROW]
[ROW][C]116[/C][C]0.463632632012731[/C][C]0.927265264025462[/C][C]0.536367367987269[/C][/ROW]
[ROW][C]117[/C][C]0.568753057957114[/C][C]0.862493884085773[/C][C]0.431246942042886[/C][/ROW]
[ROW][C]118[/C][C]0.580766952401619[/C][C]0.838466095196763[/C][C]0.419233047598381[/C][/ROW]
[ROW][C]119[/C][C]0.571798247535298[/C][C]0.856403504929404[/C][C]0.428201752464702[/C][/ROW]
[ROW][C]120[/C][C]0.563712108109974[/C][C]0.872575783780053[/C][C]0.436287891890026[/C][/ROW]
[ROW][C]121[/C][C]0.58394552087313[/C][C]0.83210895825374[/C][C]0.41605447912687[/C][/ROW]
[ROW][C]122[/C][C]0.536266955415861[/C][C]0.927466089168278[/C][C]0.463733044584139[/C][/ROW]
[ROW][C]123[/C][C]0.551883661250912[/C][C]0.896232677498176[/C][C]0.448116338749088[/C][/ROW]
[ROW][C]124[/C][C]0.573614214543265[/C][C]0.85277157091347[/C][C]0.426385785456735[/C][/ROW]
[ROW][C]125[/C][C]0.551712392055219[/C][C]0.896575215889561[/C][C]0.448287607944781[/C][/ROW]
[ROW][C]126[/C][C]0.502590074544107[/C][C]0.994819850911787[/C][C]0.497409925455893[/C][/ROW]
[ROW][C]127[/C][C]0.480179314443606[/C][C]0.960358628887212[/C][C]0.519820685556394[/C][/ROW]
[ROW][C]128[/C][C]0.425042887170208[/C][C]0.850085774340415[/C][C]0.574957112829792[/C][/ROW]
[ROW][C]129[/C][C]0.439532594877236[/C][C]0.879065189754472[/C][C]0.560467405122764[/C][/ROW]
[ROW][C]130[/C][C]0.397160077908624[/C][C]0.794320155817248[/C][C]0.602839922091376[/C][/ROW]
[ROW][C]131[/C][C]0.359855609383133[/C][C]0.719711218766267[/C][C]0.640144390616867[/C][/ROW]
[ROW][C]132[/C][C]0.307188238265935[/C][C]0.61437647653187[/C][C]0.692811761734065[/C][/ROW]
[ROW][C]133[/C][C]0.322992306708315[/C][C]0.64598461341663[/C][C]0.677007693291685[/C][/ROW]
[ROW][C]134[/C][C]0.38236371889931[/C][C]0.76472743779862[/C][C]0.61763628110069[/C][/ROW]
[ROW][C]135[/C][C]0.586182958335772[/C][C]0.827634083328455[/C][C]0.413817041664228[/C][/ROW]
[ROW][C]136[/C][C]0.509416422802484[/C][C]0.981167154395031[/C][C]0.490583577197516[/C][/ROW]
[ROW][C]137[/C][C]0.445404822177674[/C][C]0.890809644355348[/C][C]0.554595177822326[/C][/ROW]
[ROW][C]138[/C][C]0.368057438631944[/C][C]0.736114877263888[/C][C]0.631942561368056[/C][/ROW]
[ROW][C]139[/C][C]0.312395351824833[/C][C]0.624790703649666[/C][C]0.687604648175167[/C][/ROW]
[ROW][C]140[/C][C]0.51988370257557[/C][C]0.96023259484886[/C][C]0.48011629742443[/C][/ROW]
[ROW][C]141[/C][C]0.532991182305024[/C][C]0.934017635389952[/C][C]0.467008817694976[/C][/ROW]
[ROW][C]142[/C][C]0.424833420472175[/C][C]0.84966684094435[/C][C]0.575166579527825[/C][/ROW]
[ROW][C]143[/C][C]0.326372646590854[/C][C]0.652745293181707[/C][C]0.673627353409146[/C][/ROW]
[ROW][C]144[/C][C]0.702863406558344[/C][C]0.594273186883311[/C][C]0.297136593441656[/C][/ROW]
[ROW][C]145[/C][C]0.659691001809013[/C][C]0.680617996381975[/C][C]0.340308998190988[/C][/ROW]
[ROW][C]146[/C][C]0.500898862223595[/C][C]0.99820227555281[/C][C]0.499101137776405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104697&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104697&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.679825307655190.6403493846896210.320174692344810
120.6918552617414940.6162894765170110.308144738258506
130.6822125406123850.6355749187752290.317787459387615
140.5726131113675610.8547737772648770.427386888632439
150.5701783141328120.8596433717343760.429821685867188
160.545171965857650.90965606828470.45482803414235
170.7144411840292440.5711176319415120.285558815970756
180.7308736679653020.5382526640693960.269126332034698
190.6570564337300410.6858871325399180.342943566269959
200.7211610542850580.5576778914298840.278838945714942
210.6645944631185190.6708110737629620.335405536881481
220.7073617944362150.5852764111275690.292638205563785
230.6389536035783010.7220927928433980.361046396421699
240.6051385029980640.7897229940038730.394861497001936
250.5929494557097060.8141010885805880.407050544290294
260.5813391290252350.837321741949530.418660870974765
270.529401929734650.94119614053070.47059807026535
280.4752634898675340.9505269797350690.524736510132465
290.6995264683367590.6009470633264820.300473531663241
300.6999868421576240.6000263156847520.300013157842376
310.6596019966409570.6807960067180850.340398003359043
320.6806330438729820.6387339122540370.319366956127018
330.648426772143710.703146455712580.35157322785629
340.5975653846845990.8048692306308030.402434615315401
350.6044997864537630.7910004270924740.395500213546237
360.6612315900779650.677536819844070.338768409922035
370.6279103568682580.7441792862634830.372089643131741
380.6794201604795280.6411596790409440.320579839520472
390.6864928097359020.6270143805281960.313507190264098
400.6518604786521780.6962790426956440.348139521347822
410.6187105749757450.762578850048510.381289425024255
420.5799542710674650.840091457865070.420045728932535
430.5474339590752720.9051320818494570.452566040924728
440.4965146811027560.9930293622055130.503485318897244
450.474554095309170.949108190618340.52544590469083
460.4686532877535090.9373065755070170.531346712246491
470.4437469527551210.8874939055102420.556253047244879
480.4103498690547260.8206997381094530.589650130945274
490.390463044509240.780926089018480.60953695549076
500.4038599913738330.8077199827476660.596140008626167
510.3860489693831530.7720979387663060.613951030616847
520.3999031247997020.7998062495994040.600096875200298
530.4244378344760180.8488756689520350.575562165523982
540.4513601513853840.9027203027707670.548639848614616
550.4530048437981060.9060096875962110.546995156201894
560.4593105099332420.9186210198664830.540689490066758
570.4667435356255790.9334870712511590.53325646437442
580.4665162394164570.9330324788329140.533483760583543
590.4400064204878650.880012840975730.559993579512135
600.4375578278704500.8751156557408990.562442172129550
610.3951743901064450.790348780212890.604825609893555
620.420818548666550.84163709733310.57918145133345
630.4880749648561360.9761499297122710.511925035143864
640.4963797366252340.9927594732504680.503620263374766
650.4812270044977570.9624540089955150.518772995502243
660.5220625667702660.9558748664594690.477937433229734
670.4960891578092470.9921783156184950.503910842190753
680.5012446358074740.9975107283850520.498755364192526
690.499052183622820.998104367245640.50094781637718
700.5022665027609860.9954669944780280.497733497239014
710.4782967875927640.9565935751855280.521703212407236
720.4577904600508030.9155809201016060.542209539949197
730.488727374547820.977454749095640.51127262545218
740.501584764400010.996830471199980.49841523559999
750.5214654530549060.9570690938901880.478534546945094
760.5489261519432950.9021476961134110.451073848056705
770.5458853437279910.9082293125440180.454114656272009
780.5491026839687620.9017946320624760.450897316031238
790.5703648998698580.8592702002602840.429635100130142
800.563549379739780.872901240520440.43645062026022
810.5455171842122830.9089656315754330.454482815787717
820.5807339565507150.838532086898570.419266043449285
830.6032629752564900.7934740494870190.396737024743510
840.5774621591818180.8450756816363630.422537840818182
850.5596566689133520.8806866621732950.440343331086648
860.5215985790386240.9568028419227520.478401420961376
870.510647082078090.978705835843820.48935291792191
880.5169672465275830.9660655069448350.483032753472417
890.5052348377691390.9895303244617220.494765162230861
900.4752785966977040.9505571933954080.524721403302296
910.4638094036272540.9276188072545080.536190596372746
920.4605212316852680.9210424633705370.539478768314732
930.4517567715217070.9035135430434140.548243228478293
940.4517703614670370.9035407229340750.548229638532963
950.4619886320826280.9239772641652560.538011367917372
960.4837889454209640.9675778908419270.516211054579036
970.4704247323557870.9408494647115740.529575267644213
980.4791521618564160.9583043237128310.520847838143584
990.5082143896453770.9835712207092470.491785610354623
1000.496750683288390.993501366576780.50324931671161
1010.6421580568310070.7156838863379860.357841943168993
1020.6181176596819280.7637646806361430.381882340318072
1030.6236396314136690.7527207371726620.376360368586331
1040.6058270935780870.7883458128438260.394172906421913
1050.59786860020260.8042627995947990.402131399797400
1060.6444962375142390.7110075249715220.355503762485761
1070.6702583295538320.6594833408923360.329741670446168
1080.6262480828436010.7475038343127970.373751917156399
1090.5855765913457660.8288468173084680.414423408654234
1100.5338437341154180.9323125317691640.466156265884582
1110.516440163063750.96711967387250.48355983693625
1120.4634471669624410.9268943339248820.536552833037559
1130.5070230082263570.9859539835472870.492976991773643
1140.5147696111738350.970460777652330.485230388826165
1150.5110728329378480.9778543341243050.488927167062153
1160.4636326320127310.9272652640254620.536367367987269
1170.5687530579571140.8624938840857730.431246942042886
1180.5807669524016190.8384660951967630.419233047598381
1190.5717982475352980.8564035049294040.428201752464702
1200.5637121081099740.8725757837800530.436287891890026
1210.583945520873130.832108958253740.41605447912687
1220.5362669554158610.9274660891682780.463733044584139
1230.5518836612509120.8962326774981760.448116338749088
1240.5736142145432650.852771570913470.426385785456735
1250.5517123920552190.8965752158895610.448287607944781
1260.5025900745441070.9948198509117870.497409925455893
1270.4801793144436060.9603586288872120.519820685556394
1280.4250428871702080.8500857743404150.574957112829792
1290.4395325948772360.8790651897544720.560467405122764
1300.3971600779086240.7943201558172480.602839922091376
1310.3598556093831330.7197112187662670.640144390616867
1320.3071882382659350.614376476531870.692811761734065
1330.3229923067083150.645984613416630.677007693291685
1340.382363718899310.764727437798620.61763628110069
1350.5861829583357720.8276340833284550.413817041664228
1360.5094164228024840.9811671543950310.490583577197516
1370.4454048221776740.8908096443553480.554595177822326
1380.3680574386319440.7361148772638880.631942561368056
1390.3123953518248330.6247907036496660.687604648175167
1400.519883702575570.960232594848860.48011629742443
1410.5329911823050240.9340176353899520.467008817694976
1420.4248334204721750.849666840944350.575166579527825
1430.3263726465908540.6527452931817070.673627353409146
1440.7028634065583440.5942731868833110.297136593441656
1450.6596910018090130.6806179963819750.340308998190988
1460.5008988622235950.998202275552810.499101137776405






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104697&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104697&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104697&T=6

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


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

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


Figures (Output of Computation)

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

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}