FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 03 Dec 2010 12:20:20 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/03/t1291378745yqb5ixqdvvv6pxw.htm/, Retrieved Tue, 07 May 2024 20:51:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104674, Retrieved Tue, 07 May 2024 20:51:30 +0000
QR Codes:

Original text written by user:Mini-tutorial Gender
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

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]
-    D    [Multiple Regression] [Workshop 7 multip...] [2010-12-03 12:20:20] [f6fdc0236f011c1845380977efc505f8] [Current]
Feedback Forum

Post a new message

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 time10 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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104674&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104674&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104674&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Member[t] = + 1.30267905579266 + 0.0638572513593550Provison[t] + 0.0593542522425081Mother[t] + 0.00438665208409937Father[t] + 0.0498968809279368Illness[t] -0.0440151152769835Tobacco[t] -0.130198915314923Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Member[t] =  +  1.30267905579266 +  0.0638572513593550Provison[t] +  0.0593542522425081Mother[t] +  0.00438665208409937Father[t] +  0.0498968809279368Illness[t] -0.0440151152769835Tobacco[t] -0.130198915314923Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104674&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Member[t] =  +  1.30267905579266 +  0.0638572513593550Provison[t] +  0.0593542522425081Mother[t] +  0.00438665208409937Father[t] +  0.0498968809279368Illness[t] -0.0440151152769835Tobacco[t] -0.130198915314923Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104674&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104674&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] = + 1.30267905579266 + 0.0638572513593550Provison[t] + 0.0593542522425081Mother[t] + 0.00438665208409937Father[t] + 0.0498968809279368Illness[t] -0.0440151152769835Tobacco[t] -0.130198915314923Gender[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.302679055792660.2754434.72945e-063e-06
Provison0.06385725135935500.0282672.25910.0253190.01266
Mother0.05935425224250810.0538091.10310.2717680.135884
Father0.004386652084099370.0496510.08830.9297170.464858
Illness0.04989688092793680.1175610.42440.6718580.335929
Tobacco-0.04401511527698350.042108-1.04530.2975690.148784
Gender-0.1301989153149230.082834-1.57180.1181050.059052

\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) & 1.30267905579266 & 0.275443 & 4.7294 & 5e-06 & 3e-06 \tabularnewline
Provison & 0.0638572513593550 & 0.028267 & 2.2591 & 0.025319 & 0.01266 \tabularnewline
Mother & 0.0593542522425081 & 0.053809 & 1.1031 & 0.271768 & 0.135884 \tabularnewline
Father & 0.00438665208409937 & 0.049651 & 0.0883 & 0.929717 & 0.464858 \tabularnewline
Illness & 0.0498968809279368 & 0.117561 & 0.4244 & 0.671858 & 0.335929 \tabularnewline
Tobacco & -0.0440151152769835 & 0.042108 & -1.0453 & 0.297569 & 0.148784 \tabularnewline
Gender & -0.130198915314923 & 0.082834 & -1.5718 & 0.118105 & 0.059052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104674&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]1.30267905579266[/C][C]0.275443[/C][C]4.7294[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Provison[/C][C]0.0638572513593550[/C][C]0.028267[/C][C]2.2591[/C][C]0.025319[/C][C]0.01266[/C][/ROW]
[ROW][C]Mother[/C][C]0.0593542522425081[/C][C]0.053809[/C][C]1.1031[/C][C]0.271768[/C][C]0.135884[/C][/ROW]
[ROW][C]Father[/C][C]0.00438665208409937[/C][C]0.049651[/C][C]0.0883[/C][C]0.929717[/C][C]0.464858[/C][/ROW]
[ROW][C]Illness[/C][C]0.0498968809279368[/C][C]0.117561[/C][C]0.4244[/C][C]0.671858[/C][C]0.335929[/C][/ROW]
[ROW][C]Tobacco[/C][C]-0.0440151152769835[/C][C]0.042108[/C][C]-1.0453[/C][C]0.297569[/C][C]0.148784[/C][/ROW]
[ROW][C]Gender[/C][C]-0.130198915314923[/C][C]0.082834[/C][C]-1.5718[/C][C]0.118105[/C][C]0.059052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104674&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104674&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)1.302679055792660.2754434.72945e-063e-06
Provison0.06385725135935500.0282672.25910.0253190.01266
Mother0.05935425224250810.0538091.10310.2717680.135884
Father0.004386652084099370.0496510.08830.9297170.464858
Illness0.04989688092793680.1175610.42440.6718580.335929
Tobacco-0.04401511527698350.042108-1.04530.2975690.148784
Gender-0.1301989153149230.082834-1.57180.1181050.059052






Multiple Linear Regression - Regression Statistics
Multiple R0.284268140970020
R-squared0.0808083759705509
Adjusted R-squared0.0440407110093729
F-TEST (value)2.19781093131354
F-TEST (DF numerator)6
F-TEST (DF denominator)150
p-value0.0462273521497196
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.487546698306195
Sum Squared Residuals35.6552674543908

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.284268140970020 \tabularnewline
R-squared & 0.0808083759705509 \tabularnewline
Adjusted R-squared & 0.0440407110093729 \tabularnewline
F-TEST (value) & 2.19781093131354 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0.0462273521497196 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.487546698306195 \tabularnewline
Sum Squared Residuals & 35.6552674543908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104674&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.284268140970020[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0808083759705509[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0440407110093729[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.19781093131354[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0.0462273521497196[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.487546698306195[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.6552674543908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104674&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104674&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 R0.284268140970020
R-squared0.0808083759705509
Adjusted R-squared0.0440407110093729
F-TEST (value)2.19781093131354
F-TEST (DF numerator)6
F-TEST (DF denominator)150
p-value0.0462273521497196
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.487546698306195
Sum Squared Residuals35.6552674543908






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.625129971578640.374870028421361
211.37425977422996-0.374259774229960
311.64052491751606-0.64052491751606
411.57863044370548-0.578630443705482
521.490544404179660.509455595820344
621.431190151937150.568809848062853
711.33209108946901-0.332091089469008
821.674910505473870.325089494526134
911.62524631861143-0.625246318611425
1021.431190151937150.568809848062852
1111.08773091594576-0.0877309159457599
1221.561389067252070.43861093274793
1311.60678294906316-0.60678294906316
1421.743954823096440.256045176903559
1521.708829359020400.291170640979595
1621.554341117478120.445658882521878
1711.29897265010159-0.29897265010159
1811.62524631861143-0.625246318611425
1911.38965944925638-0.389659449256376
2021.407017172742540.592982827257464
2121.634019622779620.365980377220376
2211.43119015193715-0.431190151937147
2321.748341475180540.251658524819459
2421.426003085673930.573996914326072
2521.558264834669330.441735165330671
2621.497531815892710.502468184107285
2721.611285948180010.388714051819993
2821.544944284224440.455055715775560
2911.67242609287765-0.672426092877654
3011.36733290057779-0.367332900577792
3121.634019622779620.365980377220376
3211.49504740329650-0.495047403296503
3321.570162371420270.429837628579731
3421.743954823096440.256045176903559
3521.367332900577790.632667099422208
3611.62524631861143-0.625246318611425
3721.567677958824060.432322041175943
3811.62951662366278-0.629516623662777
3911.50382070746470-0.503820707464702
4021.675143199539360.324856800460638
4111.41278259136074-0.412782591360741
4221.757231126381490.242768873618513
4321.688987222938030.311012777061967
4411.17143030338749-0.171430303387487
4511.45351670061573-0.453516700615731
4621.585501508385790.414498491614207
4721.615556253231360.384443746768642
4811.30347564921844-0.303475649218437
4921.636082456460100.363917543539897
5021.431190151937150.568809848062852
5111.37171955266189-0.371719552661892
5211.56138906725207-0.56138906725207
5321.369700966141260.630299033858743
5421.419714194101940.580285805898058
5521.430957457871650.569042542128348
5611.51725760494234-0.517257604942339
5711.43095745787165-0.430957457871652
5811.55890465465586-0.558904654655858
5911.36981731317400-0.369817313174005
6011.57222520510075-0.572225205100748
6111.23961839785908-0.239618397859082
6221.517373951975090.482626048024913
6321.367388709549650.632611290450348
6421.499434055380600.500565944619398
6521.625246318611430.374753681388575
6611.68898722293803-0.688987222938033
6721.708713011987660.291286988012343
6821.537216088057460.462783911942542
6911.50382070746470-0.503820707464702
7011.56127272021932-0.561272720219322
7111.4310738049044-0.4310738049044
7221.688870875905280.311129124094715
7311.69337387502213-0.693373875022132
7411.62524631861143-0.625246318611425
7511.68199981122497-0.681999811224973
7611.69337387502213-0.693373875022132
7721.683916503707560.316083496292439
7811.58561785541854-0.585617855418541
7921.493028816775870.506971183224132
8021.629516623662780.370483376337223
8121.693373875022130.306626124977868
8221.43546045698850.564539543011501
8311.68887087590528-0.688870875905285
8421.748341475180540.251658524819459
8521.688987222938030.311012777061967
8611.31224895338664-0.312248953386636
8721.649358759745150.350641240254851
8821.561389067252070.43861093274793
8911.49504740329650-0.495047403296503
9011.36733290057779-0.367332900577792
9121.618258906898370.381741093101634
9221.625129971578680.374870028421322
9321.644972107661050.355027892338951
9411.55878830762311-0.55878830762311
9521.517313413914200.482686586085802
9611.73900045089872-0.739000450898717
9721.693373875022130.306626124977868
9811.55878830762311-0.55878830762311
9911.70871301198766-0.708713011987657
10021.625246318611430.374753681388575
10121.281787082620040.718212917379962
10211.36733290057779-0.367332900577792
10321.494814709231010.505185290768993
10421.693373875022130.306626124977868
10511.60084064535132-0.600840645351318
10611.74395482309644-0.743954823096441
10711.62963297069552-0.629632970695525
10811.28951527878702-0.289515278787019
10921.752844474297390.247155525702612
11011.18014779858383-0.180147798583826
11111.49753181589271-0.497531815892715
11211.19987358763345-0.19987358763345
11311.64497210766105-0.644972107661049
11421.556941877107080.443058122892918
11511.56132852919118-0.561328529191182
11621.752844474297390.247155525702612
11711.63401962277962-0.634019622779624
11821.497531815892710.502468184107285
11921.625013624545930.374986375454070
12011.49320097278047-0.493200972780475
12111.72999445266502-0.729994452665023
12221.689103569970780.310896430029219
12321.367332900577790.632667099422208
12411.56127272021932-0.561272720219322
12521.470758077069140.529241922930857
12621.677613159140870.322386840859126
12721.431190151937150.568809848062852
12821.618142559865620.381857440134382
12911.47663984272010-0.476639842720097
13021.620682781433690.379317218566310
13121.629516623662780.370483376337223
13221.645088454693800.354911545306203
13321.494814709231010.505185290768993
13411.64058545557695-0.64058545557695
13511.75272812726464-0.75272812726464
13621.664814243743420.335185756256579
13721.578514096672730.421485903327266
13821.708713011987660.291286988012343
13921.565775719336170.434224280663831
14011.49054440417966-0.490544404179655
14121.387175036660160.612824963339836
14221.693373875022130.306626124977868
14321.749836588747390.250163411252605
14411.70375863978993-0.703758639789933
14511.37171955266189-0.371719552661892
14621.558788307623110.44121169237689
14721.514773192346130.485226807653873
14821.689103569970780.310896430029219
14911.58123120333444-0.581231203334442
15021.693490222054880.30650977794512
15121.551815348904750.448184651095249
15221.629632970695520.370367029304475
15311.49481470923101-0.494814709231007
15411.36733290057779-0.367332900577792
15521.807812074455800.192187925544203
15611.50191846797681-0.501918467976814
15721.857708955383730.142291044616267

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.62512997157864 & 0.374870028421361 \tabularnewline
2 & 1 & 1.37425977422996 & -0.374259774229960 \tabularnewline
3 & 1 & 1.64052491751606 & -0.64052491751606 \tabularnewline
4 & 1 & 1.57863044370548 & -0.578630443705482 \tabularnewline
5 & 2 & 1.49054440417966 & 0.509455595820344 \tabularnewline
6 & 2 & 1.43119015193715 & 0.568809848062853 \tabularnewline
7 & 1 & 1.33209108946901 & -0.332091089469008 \tabularnewline
8 & 2 & 1.67491050547387 & 0.325089494526134 \tabularnewline
9 & 1 & 1.62524631861143 & -0.625246318611425 \tabularnewline
10 & 2 & 1.43119015193715 & 0.568809848062852 \tabularnewline
11 & 1 & 1.08773091594576 & -0.0877309159457599 \tabularnewline
12 & 2 & 1.56138906725207 & 0.43861093274793 \tabularnewline
13 & 1 & 1.60678294906316 & -0.60678294906316 \tabularnewline
14 & 2 & 1.74395482309644 & 0.256045176903559 \tabularnewline
15 & 2 & 1.70882935902040 & 0.291170640979595 \tabularnewline
16 & 2 & 1.55434111747812 & 0.445658882521878 \tabularnewline
17 & 1 & 1.29897265010159 & -0.29897265010159 \tabularnewline
18 & 1 & 1.62524631861143 & -0.625246318611425 \tabularnewline
19 & 1 & 1.38965944925638 & -0.389659449256376 \tabularnewline
20 & 2 & 1.40701717274254 & 0.592982827257464 \tabularnewline
21 & 2 & 1.63401962277962 & 0.365980377220376 \tabularnewline
22 & 1 & 1.43119015193715 & -0.431190151937147 \tabularnewline
23 & 2 & 1.74834147518054 & 0.251658524819459 \tabularnewline
24 & 2 & 1.42600308567393 & 0.573996914326072 \tabularnewline
25 & 2 & 1.55826483466933 & 0.441735165330671 \tabularnewline
26 & 2 & 1.49753181589271 & 0.502468184107285 \tabularnewline
27 & 2 & 1.61128594818001 & 0.388714051819993 \tabularnewline
28 & 2 & 1.54494428422444 & 0.455055715775560 \tabularnewline
29 & 1 & 1.67242609287765 & -0.672426092877654 \tabularnewline
30 & 1 & 1.36733290057779 & -0.367332900577792 \tabularnewline
31 & 2 & 1.63401962277962 & 0.365980377220376 \tabularnewline
32 & 1 & 1.49504740329650 & -0.495047403296503 \tabularnewline
33 & 2 & 1.57016237142027 & 0.429837628579731 \tabularnewline
34 & 2 & 1.74395482309644 & 0.256045176903559 \tabularnewline
35 & 2 & 1.36733290057779 & 0.632667099422208 \tabularnewline
36 & 1 & 1.62524631861143 & -0.625246318611425 \tabularnewline
37 & 2 & 1.56767795882406 & 0.432322041175943 \tabularnewline
38 & 1 & 1.62951662366278 & -0.629516623662777 \tabularnewline
39 & 1 & 1.50382070746470 & -0.503820707464702 \tabularnewline
40 & 2 & 1.67514319953936 & 0.324856800460638 \tabularnewline
41 & 1 & 1.41278259136074 & -0.412782591360741 \tabularnewline
42 & 2 & 1.75723112638149 & 0.242768873618513 \tabularnewline
43 & 2 & 1.68898722293803 & 0.311012777061967 \tabularnewline
44 & 1 & 1.17143030338749 & -0.171430303387487 \tabularnewline
45 & 1 & 1.45351670061573 & -0.453516700615731 \tabularnewline
46 & 2 & 1.58550150838579 & 0.414498491614207 \tabularnewline
47 & 2 & 1.61555625323136 & 0.384443746768642 \tabularnewline
48 & 1 & 1.30347564921844 & -0.303475649218437 \tabularnewline
49 & 2 & 1.63608245646010 & 0.363917543539897 \tabularnewline
50 & 2 & 1.43119015193715 & 0.568809848062852 \tabularnewline
51 & 1 & 1.37171955266189 & -0.371719552661892 \tabularnewline
52 & 1 & 1.56138906725207 & -0.56138906725207 \tabularnewline
53 & 2 & 1.36970096614126 & 0.630299033858743 \tabularnewline
54 & 2 & 1.41971419410194 & 0.580285805898058 \tabularnewline
55 & 2 & 1.43095745787165 & 0.569042542128348 \tabularnewline
56 & 1 & 1.51725760494234 & -0.517257604942339 \tabularnewline
57 & 1 & 1.43095745787165 & -0.430957457871652 \tabularnewline
58 & 1 & 1.55890465465586 & -0.558904654655858 \tabularnewline
59 & 1 & 1.36981731317400 & -0.369817313174005 \tabularnewline
60 & 1 & 1.57222520510075 & -0.572225205100748 \tabularnewline
61 & 1 & 1.23961839785908 & -0.239618397859082 \tabularnewline
62 & 2 & 1.51737395197509 & 0.482626048024913 \tabularnewline
63 & 2 & 1.36738870954965 & 0.632611290450348 \tabularnewline
64 & 2 & 1.49943405538060 & 0.500565944619398 \tabularnewline
65 & 2 & 1.62524631861143 & 0.374753681388575 \tabularnewline
66 & 1 & 1.68898722293803 & -0.688987222938033 \tabularnewline
67 & 2 & 1.70871301198766 & 0.291286988012343 \tabularnewline
68 & 2 & 1.53721608805746 & 0.462783911942542 \tabularnewline
69 & 1 & 1.50382070746470 & -0.503820707464702 \tabularnewline
70 & 1 & 1.56127272021932 & -0.561272720219322 \tabularnewline
71 & 1 & 1.4310738049044 & -0.4310738049044 \tabularnewline
72 & 2 & 1.68887087590528 & 0.311129124094715 \tabularnewline
73 & 1 & 1.69337387502213 & -0.693373875022132 \tabularnewline
74 & 1 & 1.62524631861143 & -0.625246318611425 \tabularnewline
75 & 1 & 1.68199981122497 & -0.681999811224973 \tabularnewline
76 & 1 & 1.69337387502213 & -0.693373875022132 \tabularnewline
77 & 2 & 1.68391650370756 & 0.316083496292439 \tabularnewline
78 & 1 & 1.58561785541854 & -0.585617855418541 \tabularnewline
79 & 2 & 1.49302881677587 & 0.506971183224132 \tabularnewline
80 & 2 & 1.62951662366278 & 0.370483376337223 \tabularnewline
81 & 2 & 1.69337387502213 & 0.306626124977868 \tabularnewline
82 & 2 & 1.4354604569885 & 0.564539543011501 \tabularnewline
83 & 1 & 1.68887087590528 & -0.688870875905285 \tabularnewline
84 & 2 & 1.74834147518054 & 0.251658524819459 \tabularnewline
85 & 2 & 1.68898722293803 & 0.311012777061967 \tabularnewline
86 & 1 & 1.31224895338664 & -0.312248953386636 \tabularnewline
87 & 2 & 1.64935875974515 & 0.350641240254851 \tabularnewline
88 & 2 & 1.56138906725207 & 0.43861093274793 \tabularnewline
89 & 1 & 1.49504740329650 & -0.495047403296503 \tabularnewline
90 & 1 & 1.36733290057779 & -0.367332900577792 \tabularnewline
91 & 2 & 1.61825890689837 & 0.381741093101634 \tabularnewline
92 & 2 & 1.62512997157868 & 0.374870028421322 \tabularnewline
93 & 2 & 1.64497210766105 & 0.355027892338951 \tabularnewline
94 & 1 & 1.55878830762311 & -0.55878830762311 \tabularnewline
95 & 2 & 1.51731341391420 & 0.482686586085802 \tabularnewline
96 & 1 & 1.73900045089872 & -0.739000450898717 \tabularnewline
97 & 2 & 1.69337387502213 & 0.306626124977868 \tabularnewline
98 & 1 & 1.55878830762311 & -0.55878830762311 \tabularnewline
99 & 1 & 1.70871301198766 & -0.708713011987657 \tabularnewline
100 & 2 & 1.62524631861143 & 0.374753681388575 \tabularnewline
101 & 2 & 1.28178708262004 & 0.718212917379962 \tabularnewline
102 & 1 & 1.36733290057779 & -0.367332900577792 \tabularnewline
103 & 2 & 1.49481470923101 & 0.505185290768993 \tabularnewline
104 & 2 & 1.69337387502213 & 0.306626124977868 \tabularnewline
105 & 1 & 1.60084064535132 & -0.600840645351318 \tabularnewline
106 & 1 & 1.74395482309644 & -0.743954823096441 \tabularnewline
107 & 1 & 1.62963297069552 & -0.629632970695525 \tabularnewline
108 & 1 & 1.28951527878702 & -0.289515278787019 \tabularnewline
109 & 2 & 1.75284447429739 & 0.247155525702612 \tabularnewline
110 & 1 & 1.18014779858383 & -0.180147798583826 \tabularnewline
111 & 1 & 1.49753181589271 & -0.497531815892715 \tabularnewline
112 & 1 & 1.19987358763345 & -0.19987358763345 \tabularnewline
113 & 1 & 1.64497210766105 & -0.644972107661049 \tabularnewline
114 & 2 & 1.55694187710708 & 0.443058122892918 \tabularnewline
115 & 1 & 1.56132852919118 & -0.561328529191182 \tabularnewline
116 & 2 & 1.75284447429739 & 0.247155525702612 \tabularnewline
117 & 1 & 1.63401962277962 & -0.634019622779624 \tabularnewline
118 & 2 & 1.49753181589271 & 0.502468184107285 \tabularnewline
119 & 2 & 1.62501362454593 & 0.374986375454070 \tabularnewline
120 & 1 & 1.49320097278047 & -0.493200972780475 \tabularnewline
121 & 1 & 1.72999445266502 & -0.729994452665023 \tabularnewline
122 & 2 & 1.68910356997078 & 0.310896430029219 \tabularnewline
123 & 2 & 1.36733290057779 & 0.632667099422208 \tabularnewline
124 & 1 & 1.56127272021932 & -0.561272720219322 \tabularnewline
125 & 2 & 1.47075807706914 & 0.529241922930857 \tabularnewline
126 & 2 & 1.67761315914087 & 0.322386840859126 \tabularnewline
127 & 2 & 1.43119015193715 & 0.568809848062852 \tabularnewline
128 & 2 & 1.61814255986562 & 0.381857440134382 \tabularnewline
129 & 1 & 1.47663984272010 & -0.476639842720097 \tabularnewline
130 & 2 & 1.62068278143369 & 0.379317218566310 \tabularnewline
131 & 2 & 1.62951662366278 & 0.370483376337223 \tabularnewline
132 & 2 & 1.64508845469380 & 0.354911545306203 \tabularnewline
133 & 2 & 1.49481470923101 & 0.505185290768993 \tabularnewline
134 & 1 & 1.64058545557695 & -0.64058545557695 \tabularnewline
135 & 1 & 1.75272812726464 & -0.75272812726464 \tabularnewline
136 & 2 & 1.66481424374342 & 0.335185756256579 \tabularnewline
137 & 2 & 1.57851409667273 & 0.421485903327266 \tabularnewline
138 & 2 & 1.70871301198766 & 0.291286988012343 \tabularnewline
139 & 2 & 1.56577571933617 & 0.434224280663831 \tabularnewline
140 & 1 & 1.49054440417966 & -0.490544404179655 \tabularnewline
141 & 2 & 1.38717503666016 & 0.612824963339836 \tabularnewline
142 & 2 & 1.69337387502213 & 0.306626124977868 \tabularnewline
143 & 2 & 1.74983658874739 & 0.250163411252605 \tabularnewline
144 & 1 & 1.70375863978993 & -0.703758639789933 \tabularnewline
145 & 1 & 1.37171955266189 & -0.371719552661892 \tabularnewline
146 & 2 & 1.55878830762311 & 0.44121169237689 \tabularnewline
147 & 2 & 1.51477319234613 & 0.485226807653873 \tabularnewline
148 & 2 & 1.68910356997078 & 0.310896430029219 \tabularnewline
149 & 1 & 1.58123120333444 & -0.581231203334442 \tabularnewline
150 & 2 & 1.69349022205488 & 0.30650977794512 \tabularnewline
151 & 2 & 1.55181534890475 & 0.448184651095249 \tabularnewline
152 & 2 & 1.62963297069552 & 0.370367029304475 \tabularnewline
153 & 1 & 1.49481470923101 & -0.494814709231007 \tabularnewline
154 & 1 & 1.36733290057779 & -0.367332900577792 \tabularnewline
155 & 2 & 1.80781207445580 & 0.192187925544203 \tabularnewline
156 & 1 & 1.50191846797681 & -0.501918467976814 \tabularnewline
157 & 2 & 1.85770895538373 & 0.142291044616267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104674&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]1.62512997157864[/C][C]0.374870028421361[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.37425977422996[/C][C]-0.374259774229960[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.64052491751606[/C][C]-0.64052491751606[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.57863044370548[/C][C]-0.578630443705482[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.49054440417966[/C][C]0.509455595820344[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.43119015193715[/C][C]0.568809848062853[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]1.33209108946901[/C][C]-0.332091089469008[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.67491050547387[/C][C]0.325089494526134[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.62524631861143[/C][C]-0.625246318611425[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.43119015193715[/C][C]0.568809848062852[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.08773091594576[/C][C]-0.0877309159457599[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.56138906725207[/C][C]0.43861093274793[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.60678294906316[/C][C]-0.60678294906316[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.74395482309644[/C][C]0.256045176903559[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.70882935902040[/C][C]0.291170640979595[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]1.55434111747812[/C][C]0.445658882521878[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.29897265010159[/C][C]-0.29897265010159[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.62524631861143[/C][C]-0.625246318611425[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.38965944925638[/C][C]-0.389659449256376[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.40701717274254[/C][C]0.592982827257464[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.63401962277962[/C][C]0.365980377220376[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.43119015193715[/C][C]-0.431190151937147[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.74834147518054[/C][C]0.251658524819459[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.42600308567393[/C][C]0.573996914326072[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]1.55826483466933[/C][C]0.441735165330671[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]1.49753181589271[/C][C]0.502468184107285[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.61128594818001[/C][C]0.388714051819993[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.54494428422444[/C][C]0.455055715775560[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.67242609287765[/C][C]-0.672426092877654[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.36733290057779[/C][C]-0.367332900577792[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.63401962277962[/C][C]0.365980377220376[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.49504740329650[/C][C]-0.495047403296503[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]1.57016237142027[/C][C]0.429837628579731[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.74395482309644[/C][C]0.256045176903559[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.36733290057779[/C][C]0.632667099422208[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.62524631861143[/C][C]-0.625246318611425[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]1.56767795882406[/C][C]0.432322041175943[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.62951662366278[/C][C]-0.629516623662777[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.50382070746470[/C][C]-0.503820707464702[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]1.67514319953936[/C][C]0.324856800460638[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.41278259136074[/C][C]-0.412782591360741[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.75723112638149[/C][C]0.242768873618513[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.68898722293803[/C][C]0.311012777061967[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.17143030338749[/C][C]-0.171430303387487[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.45351670061573[/C][C]-0.453516700615731[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.58550150838579[/C][C]0.414498491614207[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.61555625323136[/C][C]0.384443746768642[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]1.30347564921844[/C][C]-0.303475649218437[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.63608245646010[/C][C]0.363917543539897[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.43119015193715[/C][C]0.568809848062852[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.37171955266189[/C][C]-0.371719552661892[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.56138906725207[/C][C]-0.56138906725207[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]1.36970096614126[/C][C]0.630299033858743[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.41971419410194[/C][C]0.580285805898058[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.43095745787165[/C][C]0.569042542128348[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.51725760494234[/C][C]-0.517257604942339[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.43095745787165[/C][C]-0.430957457871652[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.55890465465586[/C][C]-0.558904654655858[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.36981731317400[/C][C]-0.369817313174005[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.57222520510075[/C][C]-0.572225205100748[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.23961839785908[/C][C]-0.239618397859082[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]1.51737395197509[/C][C]0.482626048024913[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]1.36738870954965[/C][C]0.632611290450348[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.49943405538060[/C][C]0.500565944619398[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.62524631861143[/C][C]0.374753681388575[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.68898722293803[/C][C]-0.688987222938033[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.70871301198766[/C][C]0.291286988012343[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.53721608805746[/C][C]0.462783911942542[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.50382070746470[/C][C]-0.503820707464702[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.56127272021932[/C][C]-0.561272720219322[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.4310738049044[/C][C]-0.4310738049044[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.68887087590528[/C][C]0.311129124094715[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.69337387502213[/C][C]-0.693373875022132[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.62524631861143[/C][C]-0.625246318611425[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.68199981122497[/C][C]-0.681999811224973[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.69337387502213[/C][C]-0.693373875022132[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.68391650370756[/C][C]0.316083496292439[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.58561785541854[/C][C]-0.585617855418541[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.49302881677587[/C][C]0.506971183224132[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.62951662366278[/C][C]0.370483376337223[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.69337387502213[/C][C]0.306626124977868[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]1.4354604569885[/C][C]0.564539543011501[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.68887087590528[/C][C]-0.688870875905285[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]1.74834147518054[/C][C]0.251658524819459[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.68898722293803[/C][C]0.311012777061967[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.31224895338664[/C][C]-0.312248953386636[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.64935875974515[/C][C]0.350641240254851[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.56138906725207[/C][C]0.43861093274793[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.49504740329650[/C][C]-0.495047403296503[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.36733290057779[/C][C]-0.367332900577792[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]1.61825890689837[/C][C]0.381741093101634[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.62512997157868[/C][C]0.374870028421322[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.64497210766105[/C][C]0.355027892338951[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.55878830762311[/C][C]-0.55878830762311[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]1.51731341391420[/C][C]0.482686586085802[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.73900045089872[/C][C]-0.739000450898717[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.69337387502213[/C][C]0.306626124977868[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.55878830762311[/C][C]-0.55878830762311[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.70871301198766[/C][C]-0.708713011987657[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.62524631861143[/C][C]0.374753681388575[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.28178708262004[/C][C]0.718212917379962[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.36733290057779[/C][C]-0.367332900577792[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.49481470923101[/C][C]0.505185290768993[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.69337387502213[/C][C]0.306626124977868[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.60084064535132[/C][C]-0.600840645351318[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.74395482309644[/C][C]-0.743954823096441[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]1.62963297069552[/C][C]-0.629632970695525[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.28951527878702[/C][C]-0.289515278787019[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]1.75284447429739[/C][C]0.247155525702612[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.18014779858383[/C][C]-0.180147798583826[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.49753181589271[/C][C]-0.497531815892715[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.19987358763345[/C][C]-0.19987358763345[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.64497210766105[/C][C]-0.644972107661049[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]1.55694187710708[/C][C]0.443058122892918[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.56132852919118[/C][C]-0.561328529191182[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.75284447429739[/C][C]0.247155525702612[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.63401962277962[/C][C]-0.634019622779624[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.49753181589271[/C][C]0.502468184107285[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.62501362454593[/C][C]0.374986375454070[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.49320097278047[/C][C]-0.493200972780475[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.72999445266502[/C][C]-0.729994452665023[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.68910356997078[/C][C]0.310896430029219[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.36733290057779[/C][C]0.632667099422208[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.56127272021932[/C][C]-0.561272720219322[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.47075807706914[/C][C]0.529241922930857[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.67761315914087[/C][C]0.322386840859126[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]1.43119015193715[/C][C]0.568809848062852[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.61814255986562[/C][C]0.381857440134382[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.47663984272010[/C][C]-0.476639842720097[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.62068278143369[/C][C]0.379317218566310[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.62951662366278[/C][C]0.370483376337223[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.64508845469380[/C][C]0.354911545306203[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.49481470923101[/C][C]0.505185290768993[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.64058545557695[/C][C]-0.64058545557695[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.75272812726464[/C][C]-0.75272812726464[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.66481424374342[/C][C]0.335185756256579[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]1.57851409667273[/C][C]0.421485903327266[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.70871301198766[/C][C]0.291286988012343[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.56577571933617[/C][C]0.434224280663831[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.49054440417966[/C][C]-0.490544404179655[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]1.38717503666016[/C][C]0.612824963339836[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.69337387502213[/C][C]0.306626124977868[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]1.74983658874739[/C][C]0.250163411252605[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.70375863978993[/C][C]-0.703758639789933[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.37171955266189[/C][C]-0.371719552661892[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.55878830762311[/C][C]0.44121169237689[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.51477319234613[/C][C]0.485226807653873[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.68910356997078[/C][C]0.310896430029219[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.58123120333444[/C][C]-0.581231203334442[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.69349022205488[/C][C]0.30650977794512[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.55181534890475[/C][C]0.448184651095249[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.62963297069552[/C][C]0.370367029304475[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.49481470923101[/C][C]-0.494814709231007[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.36733290057779[/C][C]-0.367332900577792[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.80781207445580[/C][C]0.192187925544203[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]1.50191846797681[/C][C]-0.501918467976814[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.85770895538373[/C][C]0.142291044616267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104674&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104674&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
121.625129971578640.374870028421361
211.37425977422996-0.374259774229960
311.64052491751606-0.64052491751606
411.57863044370548-0.578630443705482
521.490544404179660.509455595820344
621.431190151937150.568809848062853
711.33209108946901-0.332091089469008
821.674910505473870.325089494526134
911.62524631861143-0.625246318611425
1021.431190151937150.568809848062852
1111.08773091594576-0.0877309159457599
1221.561389067252070.43861093274793
1311.60678294906316-0.60678294906316
1421.743954823096440.256045176903559
1521.708829359020400.291170640979595
1621.554341117478120.445658882521878
1711.29897265010159-0.29897265010159
1811.62524631861143-0.625246318611425
1911.38965944925638-0.389659449256376
2021.407017172742540.592982827257464
2121.634019622779620.365980377220376
2211.43119015193715-0.431190151937147
2321.748341475180540.251658524819459
2421.426003085673930.573996914326072
2521.558264834669330.441735165330671
2621.497531815892710.502468184107285
2721.611285948180010.388714051819993
2821.544944284224440.455055715775560
2911.67242609287765-0.672426092877654
3011.36733290057779-0.367332900577792
3121.634019622779620.365980377220376
3211.49504740329650-0.495047403296503
3321.570162371420270.429837628579731
3421.743954823096440.256045176903559
3521.367332900577790.632667099422208
3611.62524631861143-0.625246318611425
3721.567677958824060.432322041175943
3811.62951662366278-0.629516623662777
3911.50382070746470-0.503820707464702
4021.675143199539360.324856800460638
4111.41278259136074-0.412782591360741
4221.757231126381490.242768873618513
4321.688987222938030.311012777061967
4411.17143030338749-0.171430303387487
4511.45351670061573-0.453516700615731
4621.585501508385790.414498491614207
4721.615556253231360.384443746768642
4811.30347564921844-0.303475649218437
4921.636082456460100.363917543539897
5021.431190151937150.568809848062852
5111.37171955266189-0.371719552661892
5211.56138906725207-0.56138906725207
5321.369700966141260.630299033858743
5421.419714194101940.580285805898058
5521.430957457871650.569042542128348
5611.51725760494234-0.517257604942339
5711.43095745787165-0.430957457871652
5811.55890465465586-0.558904654655858
5911.36981731317400-0.369817313174005
6011.57222520510075-0.572225205100748
6111.23961839785908-0.239618397859082
6221.517373951975090.482626048024913
6321.367388709549650.632611290450348
6421.499434055380600.500565944619398
6521.625246318611430.374753681388575
6611.68898722293803-0.688987222938033
6721.708713011987660.291286988012343
6821.537216088057460.462783911942542
6911.50382070746470-0.503820707464702
7011.56127272021932-0.561272720219322
7111.4310738049044-0.4310738049044
7221.688870875905280.311129124094715
7311.69337387502213-0.693373875022132
7411.62524631861143-0.625246318611425
7511.68199981122497-0.681999811224973
7611.69337387502213-0.693373875022132
7721.683916503707560.316083496292439
7811.58561785541854-0.585617855418541
7921.493028816775870.506971183224132
8021.629516623662780.370483376337223
8121.693373875022130.306626124977868
8221.43546045698850.564539543011501
8311.68887087590528-0.688870875905285
8421.748341475180540.251658524819459
8521.688987222938030.311012777061967
8611.31224895338664-0.312248953386636
8721.649358759745150.350641240254851
8821.561389067252070.43861093274793
8911.49504740329650-0.495047403296503
9011.36733290057779-0.367332900577792
9121.618258906898370.381741093101634
9221.625129971578680.374870028421322
9321.644972107661050.355027892338951
9411.55878830762311-0.55878830762311
9521.517313413914200.482686586085802
9611.73900045089872-0.739000450898717
9721.693373875022130.306626124977868
9811.55878830762311-0.55878830762311
9911.70871301198766-0.708713011987657
10021.625246318611430.374753681388575
10121.281787082620040.718212917379962
10211.36733290057779-0.367332900577792
10321.494814709231010.505185290768993
10421.693373875022130.306626124977868
10511.60084064535132-0.600840645351318
10611.74395482309644-0.743954823096441
10711.62963297069552-0.629632970695525
10811.28951527878702-0.289515278787019
10921.752844474297390.247155525702612
11011.18014779858383-0.180147798583826
11111.49753181589271-0.497531815892715
11211.19987358763345-0.19987358763345
11311.64497210766105-0.644972107661049
11421.556941877107080.443058122892918
11511.56132852919118-0.561328529191182
11621.752844474297390.247155525702612
11711.63401962277962-0.634019622779624
11821.497531815892710.502468184107285
11921.625013624545930.374986375454070
12011.49320097278047-0.493200972780475
12111.72999445266502-0.729994452665023
12221.689103569970780.310896430029219
12321.367332900577790.632667099422208
12411.56127272021932-0.561272720219322
12521.470758077069140.529241922930857
12621.677613159140870.322386840859126
12721.431190151937150.568809848062852
12821.618142559865620.381857440134382
12911.47663984272010-0.476639842720097
13021.620682781433690.379317218566310
13121.629516623662780.370483376337223
13221.645088454693800.354911545306203
13321.494814709231010.505185290768993
13411.64058545557695-0.64058545557695
13511.75272812726464-0.75272812726464
13621.664814243743420.335185756256579
13721.578514096672730.421485903327266
13821.708713011987660.291286988012343
13921.565775719336170.434224280663831
14011.49054440417966-0.490544404179655
14121.387175036660160.612824963339836
14221.693373875022130.306626124977868
14321.749836588747390.250163411252605
14411.70375863978993-0.703758639789933
14511.37171955266189-0.371719552661892
14621.558788307623110.44121169237689
14721.514773192346130.485226807653873
14821.689103569970780.310896430029219
14911.58123120333444-0.581231203334442
15021.693490222054880.30650977794512
15121.551815348904750.448184651095249
15221.629632970695520.370367029304475
15311.49481470923101-0.494814709231007
15411.36733290057779-0.367332900577792
15521.807812074455800.192187925544203
15611.50191846797681-0.501918467976814
15721.857708955383730.142291044616267






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3516309155804570.7032618311609150.648369084419543
110.6670882037524050.6658235924951910.332911796247595
120.5883234142940630.8233531714118740.411676585705937
130.5731243316703360.8537513366593280.426875668329664
140.4901694029945100.9803388059890210.509830597005489
150.4759606658366790.9519213316733570.524039334163321
160.5393737227088470.9212525545823050.460626277291153
170.6498747982623850.700250403475230.350125201737615
180.666626880346390.666746239307220.33337311965361
190.591028103738950.81794379252210.40897189626105
200.664066334498840.671867331002320.33593366550116
210.6246710514906840.7506578970186330.375328948509316
220.6534015862766980.6931968274466040.346598413723302
230.5834193378189240.8331613243621520.416580662181076
240.5604620304865620.8790759390268760.439537969513438
250.5696274522116140.860745095576770.430372547788385
260.5962246638344910.8075506723310170.403775336165509
270.5539562906683990.8920874186632030.446043709331601
280.5002520783692690.9994958432614620.499747921630731
290.6710926496322640.6578147007354730.328907350367736
300.660789416684710.6784211666305810.339210583315290
310.624394278547670.7512114429046610.375605721452330
320.6423177583416670.7153644833166660.357682241658333
330.6109831204175590.7780337591648830.389016879582441
340.5614365498166510.8771269003666980.438563450183349
350.5812079577829570.8375840844340860.418792042217043
360.6273126565688120.7453746868623760.372687343431188
370.5940647355420470.8118705289159070.405935264457953
380.645694472462980.708611055074040.35430552753702
390.6597535974245770.6804928051508460.340246402575423
400.6206016275328650.758796744934270.379398372467135
410.5971025929858150.805794814028370.402897407014185
420.5527823135870320.8944353728259360.447217686412968
430.5144065977510340.9711868044979320.485593402248966
440.4647194657362880.9294389314725760.535280534263712
450.4513206403937050.902641280787410.548679359606295
460.438073377976660.876146755953320.56192662202334
470.4088882227877130.8177764455754250.591111777212287
480.38040517923150.7608103584630.6195948207685
490.3564500201938450.712900040387690.643549979806155
500.3661615063869940.7323230127739890.633838493613006
510.3501116424368670.7002232848737350.649888357563133
520.3702551334250640.7405102668501290.629744866574936
530.3896819243429690.7793638486859380.610318075657031
540.4059325107374180.8118650214748370.594067489262582
550.3994865546247220.7989731092494450.600513445375278
560.4098409673652370.8196819347304750.590159032634763
570.4231646067360360.8463292134720710.576835393263964
580.4390246372969770.8780492745939540.560975362703023
590.4208665748842760.8417331497685520.579133425115724
600.4371645125931890.8743290251863790.56283548740681
610.4009381570263480.8018763140526950.599061842973652
620.4062922958058720.8125845916117430.593707704194128
630.4541796842483240.9083593684966480.545820315751676
640.4523019076676360.9046038153352710.547698092332364
650.4295619652422080.8591239304844160.570438034757792
660.4803520032550160.9607040065100320.519647996744984
670.448337749471980.896675498943960.55166225052802
680.4424106534299940.8848213068599880.557589346570006
690.4465737694455210.8931475388910420.553426230554479
700.4598526180563180.9197052361126360.540147381943682
710.4458747256117380.8917494512234760.554125274388262
720.4182523430582830.8365046861165670.581747656941717
730.4636016095397410.9272032190794810.536398390460259
740.494308328096170.988616656192340.50569167190383
750.534502110363110.930995779273780.46549788963689
760.5772480014251210.8455039971497580.422751998574879
770.5647250905056170.8705498189887660.435274909494383
780.5844434854206420.8311130291587170.415556514579358
790.589986163402970.820027673194060.41001383659703
800.573315949099130.853368101801740.42668405090087
810.5465292487278020.9069415025443960.453470751272198
820.5649337027489980.8701325945020040.435066297251002
830.6039808365106560.7920383269786870.396019163489344
840.5705311374784240.8589377250431520.429468862521576
850.5425438627038310.9149122745923390.457456137296169
860.5113600828752960.9772798342494080.488639917124704
870.4884762049306750.976952409861350.511523795069325
880.4786707643067470.9573415286134940.521329235693253
890.4853774845395690.9707549690791370.514622515460431
900.4675751557284040.9351503114568080.532424844271596
910.4432101310886260.8864202621772520.556789868911374
920.4255285130536730.8510570261073460.574471486946327
930.4022001318395540.8044002636791090.597799868160446
940.4271015553966670.8542031107933350.572898444603333
950.4231923323617660.8463846647235330.576807667638234
960.4712057968253650.942411593650730.528794203174635
970.4432147940617690.8864295881235370.556785205938231
980.4798097990069380.9596195980138760.520190200993062
990.5259312808615630.9481374382768740.474068719138437
1000.5030572358430780.9938855283138450.496942764156922
1010.6387619223020390.7224761553959220.361238077697961
1020.6269011987558760.7461976024882470.373098801244124
1030.6210923145188020.7578153709623960.378907685481198
1040.589906696974680.820186606050640.41009330302532
1050.5921918870111840.8156162259776320.407808112988816
1060.6604806143780830.6790387712438340.339519385621917
1070.69843728699750.60312542600500.3015627130025
1080.6604080872229840.6791838255540330.339591912777016
1090.6177227546735070.7645544906529860.382277245326493
1100.5689004300152620.8621991399694750.431099569984737
1110.558404059352640.8831918812947210.441595940647361
1120.5078001016379220.9843997967241570.492199898362078
1130.554182282711550.8916354345769010.445817717288451
1140.5591168946001780.8817662107996440.440883105399822
1150.5587708033690880.8824583932618230.441229196630912
1160.50924380332140.98151239335720.4907561966786
1170.5816981508822820.8366036982354360.418301849117718
1180.6104623644026590.7790752711946820.389537635597341
1190.6126922814110210.7746154371779590.387307718588979
1200.6040536961232340.7918926077535320.395946303876766
1210.6072671940591670.7854656118816650.392732805940833
1220.5563211106313150.8873577787373710.443678889368685
1230.5918893945457560.8162212109084870.408110605454244
1240.5828060454959140.8343879090081720.417193954504086
1250.5640274575525350.871945084894930.435972542447465
1260.5087858356105430.9824283287789140.491214164389457
1270.5160536201526340.9678927596947330.483946379847366
1280.4663543812974760.9327087625949520.533645618702524
1290.4743388757476110.9486777514952220.525661124252389
1300.4252937666672730.8505875333345460.574706233332727
1310.403640030449980.807280060899960.59635996955002
1320.3547288415144210.7094576830288430.645271158485579
1330.3725327431932740.7450654863865480.627467256806726
1340.436018925922370.872037851844740.56398107407763
1350.5601921295896060.8796157408207870.439807870410394
1360.4857064024856180.9714128049712350.514293597514382
1370.4344561305199830.8689122610399660.565543869480017
1380.3747053658011950.749410731602390.625294634198805
1390.3663048331892770.7326096663785530.633695166810723
1400.4131243933952390.8262487867904790.586875606604761
1410.5106371522571310.9787256954857380.489362847742869
1420.4545025555675420.9090051111350850.545497444432458
1430.4182844638785690.8365689277571380.581715536121431
1440.806041608145670.387916783708660.19395839185433
1450.7473596354019710.5052807291960570.252640364598029
1460.6126911508317590.7746176983364830.387308849168241
1470.7539803762304380.4920392475391230.246019623769562

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.351630915580457 & 0.703261831160915 & 0.648369084419543 \tabularnewline
11 & 0.667088203752405 & 0.665823592495191 & 0.332911796247595 \tabularnewline
12 & 0.588323414294063 & 0.823353171411874 & 0.411676585705937 \tabularnewline
13 & 0.573124331670336 & 0.853751336659328 & 0.426875668329664 \tabularnewline
14 & 0.490169402994510 & 0.980338805989021 & 0.509830597005489 \tabularnewline
15 & 0.475960665836679 & 0.951921331673357 & 0.524039334163321 \tabularnewline
16 & 0.539373722708847 & 0.921252554582305 & 0.460626277291153 \tabularnewline
17 & 0.649874798262385 & 0.70025040347523 & 0.350125201737615 \tabularnewline
18 & 0.66662688034639 & 0.66674623930722 & 0.33337311965361 \tabularnewline
19 & 0.59102810373895 & 0.8179437925221 & 0.40897189626105 \tabularnewline
20 & 0.66406633449884 & 0.67186733100232 & 0.33593366550116 \tabularnewline
21 & 0.624671051490684 & 0.750657897018633 & 0.375328948509316 \tabularnewline
22 & 0.653401586276698 & 0.693196827446604 & 0.346598413723302 \tabularnewline
23 & 0.583419337818924 & 0.833161324362152 & 0.416580662181076 \tabularnewline
24 & 0.560462030486562 & 0.879075939026876 & 0.439537969513438 \tabularnewline
25 & 0.569627452211614 & 0.86074509557677 & 0.430372547788385 \tabularnewline
26 & 0.596224663834491 & 0.807550672331017 & 0.403775336165509 \tabularnewline
27 & 0.553956290668399 & 0.892087418663203 & 0.446043709331601 \tabularnewline
28 & 0.500252078369269 & 0.999495843261462 & 0.499747921630731 \tabularnewline
29 & 0.671092649632264 & 0.657814700735473 & 0.328907350367736 \tabularnewline
30 & 0.66078941668471 & 0.678421166630581 & 0.339210583315290 \tabularnewline
31 & 0.62439427854767 & 0.751211442904661 & 0.375605721452330 \tabularnewline
32 & 0.642317758341667 & 0.715364483316666 & 0.357682241658333 \tabularnewline
33 & 0.610983120417559 & 0.778033759164883 & 0.389016879582441 \tabularnewline
34 & 0.561436549816651 & 0.877126900366698 & 0.438563450183349 \tabularnewline
35 & 0.581207957782957 & 0.837584084434086 & 0.418792042217043 \tabularnewline
36 & 0.627312656568812 & 0.745374686862376 & 0.372687343431188 \tabularnewline
37 & 0.594064735542047 & 0.811870528915907 & 0.405935264457953 \tabularnewline
38 & 0.64569447246298 & 0.70861105507404 & 0.35430552753702 \tabularnewline
39 & 0.659753597424577 & 0.680492805150846 & 0.340246402575423 \tabularnewline
40 & 0.620601627532865 & 0.75879674493427 & 0.379398372467135 \tabularnewline
41 & 0.597102592985815 & 0.80579481402837 & 0.402897407014185 \tabularnewline
42 & 0.552782313587032 & 0.894435372825936 & 0.447217686412968 \tabularnewline
43 & 0.514406597751034 & 0.971186804497932 & 0.485593402248966 \tabularnewline
44 & 0.464719465736288 & 0.929438931472576 & 0.535280534263712 \tabularnewline
45 & 0.451320640393705 & 0.90264128078741 & 0.548679359606295 \tabularnewline
46 & 0.43807337797666 & 0.87614675595332 & 0.56192662202334 \tabularnewline
47 & 0.408888222787713 & 0.817776445575425 & 0.591111777212287 \tabularnewline
48 & 0.3804051792315 & 0.760810358463 & 0.6195948207685 \tabularnewline
49 & 0.356450020193845 & 0.71290004038769 & 0.643549979806155 \tabularnewline
50 & 0.366161506386994 & 0.732323012773989 & 0.633838493613006 \tabularnewline
51 & 0.350111642436867 & 0.700223284873735 & 0.649888357563133 \tabularnewline
52 & 0.370255133425064 & 0.740510266850129 & 0.629744866574936 \tabularnewline
53 & 0.389681924342969 & 0.779363848685938 & 0.610318075657031 \tabularnewline
54 & 0.405932510737418 & 0.811865021474837 & 0.594067489262582 \tabularnewline
55 & 0.399486554624722 & 0.798973109249445 & 0.600513445375278 \tabularnewline
56 & 0.409840967365237 & 0.819681934730475 & 0.590159032634763 \tabularnewline
57 & 0.423164606736036 & 0.846329213472071 & 0.576835393263964 \tabularnewline
58 & 0.439024637296977 & 0.878049274593954 & 0.560975362703023 \tabularnewline
59 & 0.420866574884276 & 0.841733149768552 & 0.579133425115724 \tabularnewline
60 & 0.437164512593189 & 0.874329025186379 & 0.56283548740681 \tabularnewline
61 & 0.400938157026348 & 0.801876314052695 & 0.599061842973652 \tabularnewline
62 & 0.406292295805872 & 0.812584591611743 & 0.593707704194128 \tabularnewline
63 & 0.454179684248324 & 0.908359368496648 & 0.545820315751676 \tabularnewline
64 & 0.452301907667636 & 0.904603815335271 & 0.547698092332364 \tabularnewline
65 & 0.429561965242208 & 0.859123930484416 & 0.570438034757792 \tabularnewline
66 & 0.480352003255016 & 0.960704006510032 & 0.519647996744984 \tabularnewline
67 & 0.44833774947198 & 0.89667549894396 & 0.55166225052802 \tabularnewline
68 & 0.442410653429994 & 0.884821306859988 & 0.557589346570006 \tabularnewline
69 & 0.446573769445521 & 0.893147538891042 & 0.553426230554479 \tabularnewline
70 & 0.459852618056318 & 0.919705236112636 & 0.540147381943682 \tabularnewline
71 & 0.445874725611738 & 0.891749451223476 & 0.554125274388262 \tabularnewline
72 & 0.418252343058283 & 0.836504686116567 & 0.581747656941717 \tabularnewline
73 & 0.463601609539741 & 0.927203219079481 & 0.536398390460259 \tabularnewline
74 & 0.49430832809617 & 0.98861665619234 & 0.50569167190383 \tabularnewline
75 & 0.53450211036311 & 0.93099577927378 & 0.46549788963689 \tabularnewline
76 & 0.577248001425121 & 0.845503997149758 & 0.422751998574879 \tabularnewline
77 & 0.564725090505617 & 0.870549818988766 & 0.435274909494383 \tabularnewline
78 & 0.584443485420642 & 0.831113029158717 & 0.415556514579358 \tabularnewline
79 & 0.58998616340297 & 0.82002767319406 & 0.41001383659703 \tabularnewline
80 & 0.57331594909913 & 0.85336810180174 & 0.42668405090087 \tabularnewline
81 & 0.546529248727802 & 0.906941502544396 & 0.453470751272198 \tabularnewline
82 & 0.564933702748998 & 0.870132594502004 & 0.435066297251002 \tabularnewline
83 & 0.603980836510656 & 0.792038326978687 & 0.396019163489344 \tabularnewline
84 & 0.570531137478424 & 0.858937725043152 & 0.429468862521576 \tabularnewline
85 & 0.542543862703831 & 0.914912274592339 & 0.457456137296169 \tabularnewline
86 & 0.511360082875296 & 0.977279834249408 & 0.488639917124704 \tabularnewline
87 & 0.488476204930675 & 0.97695240986135 & 0.511523795069325 \tabularnewline
88 & 0.478670764306747 & 0.957341528613494 & 0.521329235693253 \tabularnewline
89 & 0.485377484539569 & 0.970754969079137 & 0.514622515460431 \tabularnewline
90 & 0.467575155728404 & 0.935150311456808 & 0.532424844271596 \tabularnewline
91 & 0.443210131088626 & 0.886420262177252 & 0.556789868911374 \tabularnewline
92 & 0.425528513053673 & 0.851057026107346 & 0.574471486946327 \tabularnewline
93 & 0.402200131839554 & 0.804400263679109 & 0.597799868160446 \tabularnewline
94 & 0.427101555396667 & 0.854203110793335 & 0.572898444603333 \tabularnewline
95 & 0.423192332361766 & 0.846384664723533 & 0.576807667638234 \tabularnewline
96 & 0.471205796825365 & 0.94241159365073 & 0.528794203174635 \tabularnewline
97 & 0.443214794061769 & 0.886429588123537 & 0.556785205938231 \tabularnewline
98 & 0.479809799006938 & 0.959619598013876 & 0.520190200993062 \tabularnewline
99 & 0.525931280861563 & 0.948137438276874 & 0.474068719138437 \tabularnewline
100 & 0.503057235843078 & 0.993885528313845 & 0.496942764156922 \tabularnewline
101 & 0.638761922302039 & 0.722476155395922 & 0.361238077697961 \tabularnewline
102 & 0.626901198755876 & 0.746197602488247 & 0.373098801244124 \tabularnewline
103 & 0.621092314518802 & 0.757815370962396 & 0.378907685481198 \tabularnewline
104 & 0.58990669697468 & 0.82018660605064 & 0.41009330302532 \tabularnewline
105 & 0.592191887011184 & 0.815616225977632 & 0.407808112988816 \tabularnewline
106 & 0.660480614378083 & 0.679038771243834 & 0.339519385621917 \tabularnewline
107 & 0.6984372869975 & 0.6031254260050 & 0.3015627130025 \tabularnewline
108 & 0.660408087222984 & 0.679183825554033 & 0.339591912777016 \tabularnewline
109 & 0.617722754673507 & 0.764554490652986 & 0.382277245326493 \tabularnewline
110 & 0.568900430015262 & 0.862199139969475 & 0.431099569984737 \tabularnewline
111 & 0.55840405935264 & 0.883191881294721 & 0.441595940647361 \tabularnewline
112 & 0.507800101637922 & 0.984399796724157 & 0.492199898362078 \tabularnewline
113 & 0.55418228271155 & 0.891635434576901 & 0.445817717288451 \tabularnewline
114 & 0.559116894600178 & 0.881766210799644 & 0.440883105399822 \tabularnewline
115 & 0.558770803369088 & 0.882458393261823 & 0.441229196630912 \tabularnewline
116 & 0.5092438033214 & 0.9815123933572 & 0.4907561966786 \tabularnewline
117 & 0.581698150882282 & 0.836603698235436 & 0.418301849117718 \tabularnewline
118 & 0.610462364402659 & 0.779075271194682 & 0.389537635597341 \tabularnewline
119 & 0.612692281411021 & 0.774615437177959 & 0.387307718588979 \tabularnewline
120 & 0.604053696123234 & 0.791892607753532 & 0.395946303876766 \tabularnewline
121 & 0.607267194059167 & 0.785465611881665 & 0.392732805940833 \tabularnewline
122 & 0.556321110631315 & 0.887357778737371 & 0.443678889368685 \tabularnewline
123 & 0.591889394545756 & 0.816221210908487 & 0.408110605454244 \tabularnewline
124 & 0.582806045495914 & 0.834387909008172 & 0.417193954504086 \tabularnewline
125 & 0.564027457552535 & 0.87194508489493 & 0.435972542447465 \tabularnewline
126 & 0.508785835610543 & 0.982428328778914 & 0.491214164389457 \tabularnewline
127 & 0.516053620152634 & 0.967892759694733 & 0.483946379847366 \tabularnewline
128 & 0.466354381297476 & 0.932708762594952 & 0.533645618702524 \tabularnewline
129 & 0.474338875747611 & 0.948677751495222 & 0.525661124252389 \tabularnewline
130 & 0.425293766667273 & 0.850587533334546 & 0.574706233332727 \tabularnewline
131 & 0.40364003044998 & 0.80728006089996 & 0.59635996955002 \tabularnewline
132 & 0.354728841514421 & 0.709457683028843 & 0.645271158485579 \tabularnewline
133 & 0.372532743193274 & 0.745065486386548 & 0.627467256806726 \tabularnewline
134 & 0.43601892592237 & 0.87203785184474 & 0.56398107407763 \tabularnewline
135 & 0.560192129589606 & 0.879615740820787 & 0.439807870410394 \tabularnewline
136 & 0.485706402485618 & 0.971412804971235 & 0.514293597514382 \tabularnewline
137 & 0.434456130519983 & 0.868912261039966 & 0.565543869480017 \tabularnewline
138 & 0.374705365801195 & 0.74941073160239 & 0.625294634198805 \tabularnewline
139 & 0.366304833189277 & 0.732609666378553 & 0.633695166810723 \tabularnewline
140 & 0.413124393395239 & 0.826248786790479 & 0.586875606604761 \tabularnewline
141 & 0.510637152257131 & 0.978725695485738 & 0.489362847742869 \tabularnewline
142 & 0.454502555567542 & 0.909005111135085 & 0.545497444432458 \tabularnewline
143 & 0.418284463878569 & 0.836568927757138 & 0.581715536121431 \tabularnewline
144 & 0.80604160814567 & 0.38791678370866 & 0.19395839185433 \tabularnewline
145 & 0.747359635401971 & 0.505280729196057 & 0.252640364598029 \tabularnewline
146 & 0.612691150831759 & 0.774617698336483 & 0.387308849168241 \tabularnewline
147 & 0.753980376230438 & 0.492039247539123 & 0.246019623769562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104674&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.351630915580457[/C][C]0.703261831160915[/C][C]0.648369084419543[/C][/ROW]
[ROW][C]11[/C][C]0.667088203752405[/C][C]0.665823592495191[/C][C]0.332911796247595[/C][/ROW]
[ROW][C]12[/C][C]0.588323414294063[/C][C]0.823353171411874[/C][C]0.411676585705937[/C][/ROW]
[ROW][C]13[/C][C]0.573124331670336[/C][C]0.853751336659328[/C][C]0.426875668329664[/C][/ROW]
[ROW][C]14[/C][C]0.490169402994510[/C][C]0.980338805989021[/C][C]0.509830597005489[/C][/ROW]
[ROW][C]15[/C][C]0.475960665836679[/C][C]0.951921331673357[/C][C]0.524039334163321[/C][/ROW]
[ROW][C]16[/C][C]0.539373722708847[/C][C]0.921252554582305[/C][C]0.460626277291153[/C][/ROW]
[ROW][C]17[/C][C]0.649874798262385[/C][C]0.70025040347523[/C][C]0.350125201737615[/C][/ROW]
[ROW][C]18[/C][C]0.66662688034639[/C][C]0.66674623930722[/C][C]0.33337311965361[/C][/ROW]
[ROW][C]19[/C][C]0.59102810373895[/C][C]0.8179437925221[/C][C]0.40897189626105[/C][/ROW]
[ROW][C]20[/C][C]0.66406633449884[/C][C]0.67186733100232[/C][C]0.33593366550116[/C][/ROW]
[ROW][C]21[/C][C]0.624671051490684[/C][C]0.750657897018633[/C][C]0.375328948509316[/C][/ROW]
[ROW][C]22[/C][C]0.653401586276698[/C][C]0.693196827446604[/C][C]0.346598413723302[/C][/ROW]
[ROW][C]23[/C][C]0.583419337818924[/C][C]0.833161324362152[/C][C]0.416580662181076[/C][/ROW]
[ROW][C]24[/C][C]0.560462030486562[/C][C]0.879075939026876[/C][C]0.439537969513438[/C][/ROW]
[ROW][C]25[/C][C]0.569627452211614[/C][C]0.86074509557677[/C][C]0.430372547788385[/C][/ROW]
[ROW][C]26[/C][C]0.596224663834491[/C][C]0.807550672331017[/C][C]0.403775336165509[/C][/ROW]
[ROW][C]27[/C][C]0.553956290668399[/C][C]0.892087418663203[/C][C]0.446043709331601[/C][/ROW]
[ROW][C]28[/C][C]0.500252078369269[/C][C]0.999495843261462[/C][C]0.499747921630731[/C][/ROW]
[ROW][C]29[/C][C]0.671092649632264[/C][C]0.657814700735473[/C][C]0.328907350367736[/C][/ROW]
[ROW][C]30[/C][C]0.66078941668471[/C][C]0.678421166630581[/C][C]0.339210583315290[/C][/ROW]
[ROW][C]31[/C][C]0.62439427854767[/C][C]0.751211442904661[/C][C]0.375605721452330[/C][/ROW]
[ROW][C]32[/C][C]0.642317758341667[/C][C]0.715364483316666[/C][C]0.357682241658333[/C][/ROW]
[ROW][C]33[/C][C]0.610983120417559[/C][C]0.778033759164883[/C][C]0.389016879582441[/C][/ROW]
[ROW][C]34[/C][C]0.561436549816651[/C][C]0.877126900366698[/C][C]0.438563450183349[/C][/ROW]
[ROW][C]35[/C][C]0.581207957782957[/C][C]0.837584084434086[/C][C]0.418792042217043[/C][/ROW]
[ROW][C]36[/C][C]0.627312656568812[/C][C]0.745374686862376[/C][C]0.372687343431188[/C][/ROW]
[ROW][C]37[/C][C]0.594064735542047[/C][C]0.811870528915907[/C][C]0.405935264457953[/C][/ROW]
[ROW][C]38[/C][C]0.64569447246298[/C][C]0.70861105507404[/C][C]0.35430552753702[/C][/ROW]
[ROW][C]39[/C][C]0.659753597424577[/C][C]0.680492805150846[/C][C]0.340246402575423[/C][/ROW]
[ROW][C]40[/C][C]0.620601627532865[/C][C]0.75879674493427[/C][C]0.379398372467135[/C][/ROW]
[ROW][C]41[/C][C]0.597102592985815[/C][C]0.80579481402837[/C][C]0.402897407014185[/C][/ROW]
[ROW][C]42[/C][C]0.552782313587032[/C][C]0.894435372825936[/C][C]0.447217686412968[/C][/ROW]
[ROW][C]43[/C][C]0.514406597751034[/C][C]0.971186804497932[/C][C]0.485593402248966[/C][/ROW]
[ROW][C]44[/C][C]0.464719465736288[/C][C]0.929438931472576[/C][C]0.535280534263712[/C][/ROW]
[ROW][C]45[/C][C]0.451320640393705[/C][C]0.90264128078741[/C][C]0.548679359606295[/C][/ROW]
[ROW][C]46[/C][C]0.43807337797666[/C][C]0.87614675595332[/C][C]0.56192662202334[/C][/ROW]
[ROW][C]47[/C][C]0.408888222787713[/C][C]0.817776445575425[/C][C]0.591111777212287[/C][/ROW]
[ROW][C]48[/C][C]0.3804051792315[/C][C]0.760810358463[/C][C]0.6195948207685[/C][/ROW]
[ROW][C]49[/C][C]0.356450020193845[/C][C]0.71290004038769[/C][C]0.643549979806155[/C][/ROW]
[ROW][C]50[/C][C]0.366161506386994[/C][C]0.732323012773989[/C][C]0.633838493613006[/C][/ROW]
[ROW][C]51[/C][C]0.350111642436867[/C][C]0.700223284873735[/C][C]0.649888357563133[/C][/ROW]
[ROW][C]52[/C][C]0.370255133425064[/C][C]0.740510266850129[/C][C]0.629744866574936[/C][/ROW]
[ROW][C]53[/C][C]0.389681924342969[/C][C]0.779363848685938[/C][C]0.610318075657031[/C][/ROW]
[ROW][C]54[/C][C]0.405932510737418[/C][C]0.811865021474837[/C][C]0.594067489262582[/C][/ROW]
[ROW][C]55[/C][C]0.399486554624722[/C][C]0.798973109249445[/C][C]0.600513445375278[/C][/ROW]
[ROW][C]56[/C][C]0.409840967365237[/C][C]0.819681934730475[/C][C]0.590159032634763[/C][/ROW]
[ROW][C]57[/C][C]0.423164606736036[/C][C]0.846329213472071[/C][C]0.576835393263964[/C][/ROW]
[ROW][C]58[/C][C]0.439024637296977[/C][C]0.878049274593954[/C][C]0.560975362703023[/C][/ROW]
[ROW][C]59[/C][C]0.420866574884276[/C][C]0.841733149768552[/C][C]0.579133425115724[/C][/ROW]
[ROW][C]60[/C][C]0.437164512593189[/C][C]0.874329025186379[/C][C]0.56283548740681[/C][/ROW]
[ROW][C]61[/C][C]0.400938157026348[/C][C]0.801876314052695[/C][C]0.599061842973652[/C][/ROW]
[ROW][C]62[/C][C]0.406292295805872[/C][C]0.812584591611743[/C][C]0.593707704194128[/C][/ROW]
[ROW][C]63[/C][C]0.454179684248324[/C][C]0.908359368496648[/C][C]0.545820315751676[/C][/ROW]
[ROW][C]64[/C][C]0.452301907667636[/C][C]0.904603815335271[/C][C]0.547698092332364[/C][/ROW]
[ROW][C]65[/C][C]0.429561965242208[/C][C]0.859123930484416[/C][C]0.570438034757792[/C][/ROW]
[ROW][C]66[/C][C]0.480352003255016[/C][C]0.960704006510032[/C][C]0.519647996744984[/C][/ROW]
[ROW][C]67[/C][C]0.44833774947198[/C][C]0.89667549894396[/C][C]0.55166225052802[/C][/ROW]
[ROW][C]68[/C][C]0.442410653429994[/C][C]0.884821306859988[/C][C]0.557589346570006[/C][/ROW]
[ROW][C]69[/C][C]0.446573769445521[/C][C]0.893147538891042[/C][C]0.553426230554479[/C][/ROW]
[ROW][C]70[/C][C]0.459852618056318[/C][C]0.919705236112636[/C][C]0.540147381943682[/C][/ROW]
[ROW][C]71[/C][C]0.445874725611738[/C][C]0.891749451223476[/C][C]0.554125274388262[/C][/ROW]
[ROW][C]72[/C][C]0.418252343058283[/C][C]0.836504686116567[/C][C]0.581747656941717[/C][/ROW]
[ROW][C]73[/C][C]0.463601609539741[/C][C]0.927203219079481[/C][C]0.536398390460259[/C][/ROW]
[ROW][C]74[/C][C]0.49430832809617[/C][C]0.98861665619234[/C][C]0.50569167190383[/C][/ROW]
[ROW][C]75[/C][C]0.53450211036311[/C][C]0.93099577927378[/C][C]0.46549788963689[/C][/ROW]
[ROW][C]76[/C][C]0.577248001425121[/C][C]0.845503997149758[/C][C]0.422751998574879[/C][/ROW]
[ROW][C]77[/C][C]0.564725090505617[/C][C]0.870549818988766[/C][C]0.435274909494383[/C][/ROW]
[ROW][C]78[/C][C]0.584443485420642[/C][C]0.831113029158717[/C][C]0.415556514579358[/C][/ROW]
[ROW][C]79[/C][C]0.58998616340297[/C][C]0.82002767319406[/C][C]0.41001383659703[/C][/ROW]
[ROW][C]80[/C][C]0.57331594909913[/C][C]0.85336810180174[/C][C]0.42668405090087[/C][/ROW]
[ROW][C]81[/C][C]0.546529248727802[/C][C]0.906941502544396[/C][C]0.453470751272198[/C][/ROW]
[ROW][C]82[/C][C]0.564933702748998[/C][C]0.870132594502004[/C][C]0.435066297251002[/C][/ROW]
[ROW][C]83[/C][C]0.603980836510656[/C][C]0.792038326978687[/C][C]0.396019163489344[/C][/ROW]
[ROW][C]84[/C][C]0.570531137478424[/C][C]0.858937725043152[/C][C]0.429468862521576[/C][/ROW]
[ROW][C]85[/C][C]0.542543862703831[/C][C]0.914912274592339[/C][C]0.457456137296169[/C][/ROW]
[ROW][C]86[/C][C]0.511360082875296[/C][C]0.977279834249408[/C][C]0.488639917124704[/C][/ROW]
[ROW][C]87[/C][C]0.488476204930675[/C][C]0.97695240986135[/C][C]0.511523795069325[/C][/ROW]
[ROW][C]88[/C][C]0.478670764306747[/C][C]0.957341528613494[/C][C]0.521329235693253[/C][/ROW]
[ROW][C]89[/C][C]0.485377484539569[/C][C]0.970754969079137[/C][C]0.514622515460431[/C][/ROW]
[ROW][C]90[/C][C]0.467575155728404[/C][C]0.935150311456808[/C][C]0.532424844271596[/C][/ROW]
[ROW][C]91[/C][C]0.443210131088626[/C][C]0.886420262177252[/C][C]0.556789868911374[/C][/ROW]
[ROW][C]92[/C][C]0.425528513053673[/C][C]0.851057026107346[/C][C]0.574471486946327[/C][/ROW]
[ROW][C]93[/C][C]0.402200131839554[/C][C]0.804400263679109[/C][C]0.597799868160446[/C][/ROW]
[ROW][C]94[/C][C]0.427101555396667[/C][C]0.854203110793335[/C][C]0.572898444603333[/C][/ROW]
[ROW][C]95[/C][C]0.423192332361766[/C][C]0.846384664723533[/C][C]0.576807667638234[/C][/ROW]
[ROW][C]96[/C][C]0.471205796825365[/C][C]0.94241159365073[/C][C]0.528794203174635[/C][/ROW]
[ROW][C]97[/C][C]0.443214794061769[/C][C]0.886429588123537[/C][C]0.556785205938231[/C][/ROW]
[ROW][C]98[/C][C]0.479809799006938[/C][C]0.959619598013876[/C][C]0.520190200993062[/C][/ROW]
[ROW][C]99[/C][C]0.525931280861563[/C][C]0.948137438276874[/C][C]0.474068719138437[/C][/ROW]
[ROW][C]100[/C][C]0.503057235843078[/C][C]0.993885528313845[/C][C]0.496942764156922[/C][/ROW]
[ROW][C]101[/C][C]0.638761922302039[/C][C]0.722476155395922[/C][C]0.361238077697961[/C][/ROW]
[ROW][C]102[/C][C]0.626901198755876[/C][C]0.746197602488247[/C][C]0.373098801244124[/C][/ROW]
[ROW][C]103[/C][C]0.621092314518802[/C][C]0.757815370962396[/C][C]0.378907685481198[/C][/ROW]
[ROW][C]104[/C][C]0.58990669697468[/C][C]0.82018660605064[/C][C]0.41009330302532[/C][/ROW]
[ROW][C]105[/C][C]0.592191887011184[/C][C]0.815616225977632[/C][C]0.407808112988816[/C][/ROW]
[ROW][C]106[/C][C]0.660480614378083[/C][C]0.679038771243834[/C][C]0.339519385621917[/C][/ROW]
[ROW][C]107[/C][C]0.6984372869975[/C][C]0.6031254260050[/C][C]0.3015627130025[/C][/ROW]
[ROW][C]108[/C][C]0.660408087222984[/C][C]0.679183825554033[/C][C]0.339591912777016[/C][/ROW]
[ROW][C]109[/C][C]0.617722754673507[/C][C]0.764554490652986[/C][C]0.382277245326493[/C][/ROW]
[ROW][C]110[/C][C]0.568900430015262[/C][C]0.862199139969475[/C][C]0.431099569984737[/C][/ROW]
[ROW][C]111[/C][C]0.55840405935264[/C][C]0.883191881294721[/C][C]0.441595940647361[/C][/ROW]
[ROW][C]112[/C][C]0.507800101637922[/C][C]0.984399796724157[/C][C]0.492199898362078[/C][/ROW]
[ROW][C]113[/C][C]0.55418228271155[/C][C]0.891635434576901[/C][C]0.445817717288451[/C][/ROW]
[ROW][C]114[/C][C]0.559116894600178[/C][C]0.881766210799644[/C][C]0.440883105399822[/C][/ROW]
[ROW][C]115[/C][C]0.558770803369088[/C][C]0.882458393261823[/C][C]0.441229196630912[/C][/ROW]
[ROW][C]116[/C][C]0.5092438033214[/C][C]0.9815123933572[/C][C]0.4907561966786[/C][/ROW]
[ROW][C]117[/C][C]0.581698150882282[/C][C]0.836603698235436[/C][C]0.418301849117718[/C][/ROW]
[ROW][C]118[/C][C]0.610462364402659[/C][C]0.779075271194682[/C][C]0.389537635597341[/C][/ROW]
[ROW][C]119[/C][C]0.612692281411021[/C][C]0.774615437177959[/C][C]0.387307718588979[/C][/ROW]
[ROW][C]120[/C][C]0.604053696123234[/C][C]0.791892607753532[/C][C]0.395946303876766[/C][/ROW]
[ROW][C]121[/C][C]0.607267194059167[/C][C]0.785465611881665[/C][C]0.392732805940833[/C][/ROW]
[ROW][C]122[/C][C]0.556321110631315[/C][C]0.887357778737371[/C][C]0.443678889368685[/C][/ROW]
[ROW][C]123[/C][C]0.591889394545756[/C][C]0.816221210908487[/C][C]0.408110605454244[/C][/ROW]
[ROW][C]124[/C][C]0.582806045495914[/C][C]0.834387909008172[/C][C]0.417193954504086[/C][/ROW]
[ROW][C]125[/C][C]0.564027457552535[/C][C]0.87194508489493[/C][C]0.435972542447465[/C][/ROW]
[ROW][C]126[/C][C]0.508785835610543[/C][C]0.982428328778914[/C][C]0.491214164389457[/C][/ROW]
[ROW][C]127[/C][C]0.516053620152634[/C][C]0.967892759694733[/C][C]0.483946379847366[/C][/ROW]
[ROW][C]128[/C][C]0.466354381297476[/C][C]0.932708762594952[/C][C]0.533645618702524[/C][/ROW]
[ROW][C]129[/C][C]0.474338875747611[/C][C]0.948677751495222[/C][C]0.525661124252389[/C][/ROW]
[ROW][C]130[/C][C]0.425293766667273[/C][C]0.850587533334546[/C][C]0.574706233332727[/C][/ROW]
[ROW][C]131[/C][C]0.40364003044998[/C][C]0.80728006089996[/C][C]0.59635996955002[/C][/ROW]
[ROW][C]132[/C][C]0.354728841514421[/C][C]0.709457683028843[/C][C]0.645271158485579[/C][/ROW]
[ROW][C]133[/C][C]0.372532743193274[/C][C]0.745065486386548[/C][C]0.627467256806726[/C][/ROW]
[ROW][C]134[/C][C]0.43601892592237[/C][C]0.87203785184474[/C][C]0.56398107407763[/C][/ROW]
[ROW][C]135[/C][C]0.560192129589606[/C][C]0.879615740820787[/C][C]0.439807870410394[/C][/ROW]
[ROW][C]136[/C][C]0.485706402485618[/C][C]0.971412804971235[/C][C]0.514293597514382[/C][/ROW]
[ROW][C]137[/C][C]0.434456130519983[/C][C]0.868912261039966[/C][C]0.565543869480017[/C][/ROW]
[ROW][C]138[/C][C]0.374705365801195[/C][C]0.74941073160239[/C][C]0.625294634198805[/C][/ROW]
[ROW][C]139[/C][C]0.366304833189277[/C][C]0.732609666378553[/C][C]0.633695166810723[/C][/ROW]
[ROW][C]140[/C][C]0.413124393395239[/C][C]0.826248786790479[/C][C]0.586875606604761[/C][/ROW]
[ROW][C]141[/C][C]0.510637152257131[/C][C]0.978725695485738[/C][C]0.489362847742869[/C][/ROW]
[ROW][C]142[/C][C]0.454502555567542[/C][C]0.909005111135085[/C][C]0.545497444432458[/C][/ROW]
[ROW][C]143[/C][C]0.418284463878569[/C][C]0.836568927757138[/C][C]0.581715536121431[/C][/ROW]
[ROW][C]144[/C][C]0.80604160814567[/C][C]0.38791678370866[/C][C]0.19395839185433[/C][/ROW]
[ROW][C]145[/C][C]0.747359635401971[/C][C]0.505280729196057[/C][C]0.252640364598029[/C][/ROW]
[ROW][C]146[/C][C]0.612691150831759[/C][C]0.774617698336483[/C][C]0.387308849168241[/C][/ROW]
[ROW][C]147[/C][C]0.753980376230438[/C][C]0.492039247539123[/C][C]0.246019623769562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104674&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104674&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3516309155804570.7032618311609150.648369084419543
110.6670882037524050.6658235924951910.332911796247595
120.5883234142940630.8233531714118740.411676585705937
130.5731243316703360.8537513366593280.426875668329664
140.4901694029945100.9803388059890210.509830597005489
150.4759606658366790.9519213316733570.524039334163321
160.5393737227088470.9212525545823050.460626277291153
170.6498747982623850.700250403475230.350125201737615
180.666626880346390.666746239307220.33337311965361
190.591028103738950.81794379252210.40897189626105
200.664066334498840.671867331002320.33593366550116
210.6246710514906840.7506578970186330.375328948509316
220.6534015862766980.6931968274466040.346598413723302
230.5834193378189240.8331613243621520.416580662181076
240.5604620304865620.8790759390268760.439537969513438
250.5696274522116140.860745095576770.430372547788385
260.5962246638344910.8075506723310170.403775336165509
270.5539562906683990.8920874186632030.446043709331601
280.5002520783692690.9994958432614620.499747921630731
290.6710926496322640.6578147007354730.328907350367736
300.660789416684710.6784211666305810.339210583315290
310.624394278547670.7512114429046610.375605721452330
320.6423177583416670.7153644833166660.357682241658333
330.6109831204175590.7780337591648830.389016879582441
340.5614365498166510.8771269003666980.438563450183349
350.5812079577829570.8375840844340860.418792042217043
360.6273126565688120.7453746868623760.372687343431188
370.5940647355420470.8118705289159070.405935264457953
380.645694472462980.708611055074040.35430552753702
390.6597535974245770.6804928051508460.340246402575423
400.6206016275328650.758796744934270.379398372467135
410.5971025929858150.805794814028370.402897407014185
420.5527823135870320.8944353728259360.447217686412968
430.5144065977510340.9711868044979320.485593402248966
440.4647194657362880.9294389314725760.535280534263712
450.4513206403937050.902641280787410.548679359606295
460.438073377976660.876146755953320.56192662202334
470.4088882227877130.8177764455754250.591111777212287
480.38040517923150.7608103584630.6195948207685
490.3564500201938450.712900040387690.643549979806155
500.3661615063869940.7323230127739890.633838493613006
510.3501116424368670.7002232848737350.649888357563133
520.3702551334250640.7405102668501290.629744866574936
530.3896819243429690.7793638486859380.610318075657031
540.4059325107374180.8118650214748370.594067489262582
550.3994865546247220.7989731092494450.600513445375278
560.4098409673652370.8196819347304750.590159032634763
570.4231646067360360.8463292134720710.576835393263964
580.4390246372969770.8780492745939540.560975362703023
590.4208665748842760.8417331497685520.579133425115724
600.4371645125931890.8743290251863790.56283548740681
610.4009381570263480.8018763140526950.599061842973652
620.4062922958058720.8125845916117430.593707704194128
630.4541796842483240.9083593684966480.545820315751676
640.4523019076676360.9046038153352710.547698092332364
650.4295619652422080.8591239304844160.570438034757792
660.4803520032550160.9607040065100320.519647996744984
670.448337749471980.896675498943960.55166225052802
680.4424106534299940.8848213068599880.557589346570006
690.4465737694455210.8931475388910420.553426230554479
700.4598526180563180.9197052361126360.540147381943682
710.4458747256117380.8917494512234760.554125274388262
720.4182523430582830.8365046861165670.581747656941717
730.4636016095397410.9272032190794810.536398390460259
740.494308328096170.988616656192340.50569167190383
750.534502110363110.930995779273780.46549788963689
760.5772480014251210.8455039971497580.422751998574879
770.5647250905056170.8705498189887660.435274909494383
780.5844434854206420.8311130291587170.415556514579358
790.589986163402970.820027673194060.41001383659703
800.573315949099130.853368101801740.42668405090087
810.5465292487278020.9069415025443960.453470751272198
820.5649337027489980.8701325945020040.435066297251002
830.6039808365106560.7920383269786870.396019163489344
840.5705311374784240.8589377250431520.429468862521576
850.5425438627038310.9149122745923390.457456137296169
860.5113600828752960.9772798342494080.488639917124704
870.4884762049306750.976952409861350.511523795069325
880.4786707643067470.9573415286134940.521329235693253
890.4853774845395690.9707549690791370.514622515460431
900.4675751557284040.9351503114568080.532424844271596
910.4432101310886260.8864202621772520.556789868911374
920.4255285130536730.8510570261073460.574471486946327
930.4022001318395540.8044002636791090.597799868160446
940.4271015553966670.8542031107933350.572898444603333
950.4231923323617660.8463846647235330.576807667638234
960.4712057968253650.942411593650730.528794203174635
970.4432147940617690.8864295881235370.556785205938231
980.4798097990069380.9596195980138760.520190200993062
990.5259312808615630.9481374382768740.474068719138437
1000.5030572358430780.9938855283138450.496942764156922
1010.6387619223020390.7224761553959220.361238077697961
1020.6269011987558760.7461976024882470.373098801244124
1030.6210923145188020.7578153709623960.378907685481198
1040.589906696974680.820186606050640.41009330302532
1050.5921918870111840.8156162259776320.407808112988816
1060.6604806143780830.6790387712438340.339519385621917
1070.69843728699750.60312542600500.3015627130025
1080.6604080872229840.6791838255540330.339591912777016
1090.6177227546735070.7645544906529860.382277245326493
1100.5689004300152620.8621991399694750.431099569984737
1110.558404059352640.8831918812947210.441595940647361
1120.5078001016379220.9843997967241570.492199898362078
1130.554182282711550.8916354345769010.445817717288451
1140.5591168946001780.8817662107996440.440883105399822
1150.5587708033690880.8824583932618230.441229196630912
1160.50924380332140.98151239335720.4907561966786
1170.5816981508822820.8366036982354360.418301849117718
1180.6104623644026590.7790752711946820.389537635597341
1190.6126922814110210.7746154371779590.387307718588979
1200.6040536961232340.7918926077535320.395946303876766
1210.6072671940591670.7854656118816650.392732805940833
1220.5563211106313150.8873577787373710.443678889368685
1230.5918893945457560.8162212109084870.408110605454244
1240.5828060454959140.8343879090081720.417193954504086
1250.5640274575525350.871945084894930.435972542447465
1260.5087858356105430.9824283287789140.491214164389457
1270.5160536201526340.9678927596947330.483946379847366
1280.4663543812974760.9327087625949520.533645618702524
1290.4743388757476110.9486777514952220.525661124252389
1300.4252937666672730.8505875333345460.574706233332727
1310.403640030449980.807280060899960.59635996955002
1320.3547288415144210.7094576830288430.645271158485579
1330.3725327431932740.7450654863865480.627467256806726
1340.436018925922370.872037851844740.56398107407763
1350.5601921295896060.8796157408207870.439807870410394
1360.4857064024856180.9714128049712350.514293597514382
1370.4344561305199830.8689122610399660.565543869480017
1380.3747053658011950.749410731602390.625294634198805
1390.3663048331892770.7326096663785530.633695166810723
1400.4131243933952390.8262487867904790.586875606604761
1410.5106371522571310.9787256954857380.489362847742869
1420.4545025555675420.9090051111350850.545497444432458
1430.4182844638785690.8365689277571380.581715536121431
1440.806041608145670.387916783708660.19395839185433
1450.7473596354019710.5052807291960570.252640364598029
1460.6126911508317590.7746176983364830.387308849168241
1470.7539803762304380.4920392475391230.246019623769562






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=104674&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=104674&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104674&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
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}