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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 09:16:04 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t1356013031fz3rkj7x6awe4k8.htm/, Retrieved Sun, 28 Apr 2024 23:49:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202698, Retrieved Sun, 28 Apr 2024 23:49:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Arabica Price in ...] [2008-01-05 23:14:31] [74be16979710d4c4e7c6647856088456]
- RMPD  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Chi 3 - 4 variables] [2012-11-11 20:35:55] [b952eaabb01bdc8fc50f908b86502128]
- R P     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Chi 4 - factor 3 & 4] [2012-11-11 20:39:38] [b952eaabb01bdc8fc50f908b86502128]
- R PD      [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper statistiek ...] [2012-12-15 17:54:08] [9f87ad58f325f963ff5b3a15384d509e]
- RMPD          [Multiple Regression] [Paper statistiek ...] [2012-12-20 14:16:04] [fd3c35a156f52433b5d6e23e16a12a78] [Current]
- R  D            [Multiple Regression] [Paper statistiek ...] [2012-12-20 15:51:07] [b952eaabb01bdc8fc50f908b86502128]
- RM D            [Two-Way ANOVA] [Paper statistiek ...] [2012-12-20 16:31:08] [b952eaabb01bdc8fc50f908b86502128]
- RM D            [Two-Way ANOVA] [Paper statistiek ...] [2012-12-20 16:58:25] [b952eaabb01bdc8fc50f908b86502128]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'2'	'2'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
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'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
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'2'	'2'	'1'	'1'	'1'	'1'
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'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'2'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'2'
'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
'2'	'1'	'1'	'1'	'1'	'1'
'2'	'1'	'1'	'1'	'1'	'1'
'2'	'1'	'1'	'2'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
'2'	'1'	'1'	'2'	'1'	'2'
'2'	'1'	'2'	'2'	'1'	'2'
'1'	'1'	'2'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'2'	'2'	'1'
'1'	'1'	'2'	'2'	'1'	'1'
'2'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'2'
'1'	'1'	'1'	'1'	'1'	'2'
'1'	'1'	'2'	'1'	'1'	'1'
'1'	'1'	'2'	'2'	'1'	'1'
'1'	'1'	'2'	'1'	'1'	'1'
'2'	'1'	'1'	'1'	'1'	'1'
'1'	'1'	'1'	'1'	'1'	'2'
'1'	'1'	'1'	'1'	'1'	'1'
'2'	'1'	'1'	'2'	'2'	'1'
'2'	'1'	'1'	'2'	'2'	'2'
'2'	'1'	'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 time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202698&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202698&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202698&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 time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = + 0.693518932570807 + 0.000589806093213978UseLimit[t] + 0.136196246020742T20[t] -0.128440726388467T40[t] + 0.257119809675127Used[t] + 0.029577181961469Useful[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  +  0.693518932570807 +  0.000589806093213978UseLimit[t] +  0.136196246020742T20[t] -0.128440726388467T40[t] +  0.257119809675127Used[t] +  0.029577181961469Useful[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202698&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  +  0.693518932570807 +  0.000589806093213978UseLimit[t] +  0.136196246020742T20[t] -0.128440726388467T40[t] +  0.257119809675127Used[t] +  0.029577181961469Useful[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202698&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202698&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
CorrectAnalysis[t] = + 0.693518932570807 + 0.000589806093213978UseLimit[t] + 0.136196246020742T20[t] -0.128440726388467T40[t] + 0.257119809675127Used[t] + 0.029577181961469Useful[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6935189325708070.1175845.898100
UseLimit0.0005898060932139780.04090.01440.9885140.494257
T200.1361962460207420.0551392.47010.0146450.007322
T40-0.1284407263884670.063225-2.03150.0439950.021997
Used0.2571198096751270.0445315.77400
Useful0.0295771819614690.0448540.65940.5106580.255329

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.693518932570807 & 0.117584 & 5.8981 & 0 & 0 \tabularnewline
UseLimit & 0.000589806093213978 & 0.0409 & 0.0144 & 0.988514 & 0.494257 \tabularnewline
T20 & 0.136196246020742 & 0.055139 & 2.4701 & 0.014645 & 0.007322 \tabularnewline
T40 & -0.128440726388467 & 0.063225 & -2.0315 & 0.043995 & 0.021997 \tabularnewline
Used & 0.257119809675127 & 0.044531 & 5.774 & 0 & 0 \tabularnewline
Useful & 0.029577181961469 & 0.044854 & 0.6594 & 0.510658 & 0.255329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202698&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.693518932570807[/C][C]0.117584[/C][C]5.8981[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.000589806093213978[/C][C]0.0409[/C][C]0.0144[/C][C]0.988514[/C][C]0.494257[/C][/ROW]
[ROW][C]T20[/C][C]0.136196246020742[/C][C]0.055139[/C][C]2.4701[/C][C]0.014645[/C][C]0.007322[/C][/ROW]
[ROW][C]T40[/C][C]-0.128440726388467[/C][C]0.063225[/C][C]-2.0315[/C][C]0.043995[/C][C]0.021997[/C][/ROW]
[ROW][C]Used[/C][C]0.257119809675127[/C][C]0.044531[/C][C]5.774[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Useful[/C][C]0.029577181961469[/C][C]0.044854[/C][C]0.6594[/C][C]0.510658[/C][C]0.255329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202698&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202698&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6935189325708070.1175845.898100
UseLimit0.0005898060932139780.04090.01440.9885140.494257
T200.1361962460207420.0551392.47010.0146450.007322
T40-0.1284407263884670.063225-2.03150.0439950.021997
Used0.2571198096751270.0445315.77400
Useful0.0295771819614690.0448540.65940.5106580.255329






Multiple Linear Regression - Regression Statistics
Multiple R0.524181836397047
R-squared0.274766597608581
Adjusted R-squared0.250265469149411
F-TEST (value)11.2144466352426
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value3.52885420884519e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.232853342436111
Sum Squared Residuals8.02466050438298

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.524181836397047 \tabularnewline
R-squared & 0.274766597608581 \tabularnewline
Adjusted R-squared & 0.250265469149411 \tabularnewline
F-TEST (value) & 11.2144466352426 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 3.52885420884519e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.232853342436111 \tabularnewline
Sum Squared Residuals & 8.02466050438298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202698&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.524181836397047[/C][/ROW]
[ROW][C]R-squared[/C][C]0.274766597608581[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.250265469149411[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.2144466352426[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/C][/ROW]
[ROW][C]p-value[/C][C]3.52885420884519e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.232853342436111[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.02466050438298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202698&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202698&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 R0.524181836397047
R-squared0.274766597608581
Adjusted R-squared0.250265469149411
F-TEST (value)11.2144466352426
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value3.52885420884519e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.232853342436111
Sum Squared Residuals8.02466050438298






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.12534730204685-0.125347302046847
210.9885612499328910.0114387500671089
310.9885612499328910.0114387500671089
410.9885612499328910.0114387500671089
510.9885612499328910.0114387500671087
611.01872823798757-0.0187282379875742
710.9885612499328910.0114387500671088
811.12475749595363-0.124757495953633
910.9885612499328910.0114387500671088
1010.9891510560261050.0108489439738949
1111.12534730204685-0.125347302046847
1210.9885612499328910.0114387500671088
1311.27525824156949-0.275258241569487
1411.12534730204685-0.125347302046847
1511.27525824156949-0.275258241569487
1611.41145448759023-0.411454487590229
1721.412044293683440.587955706316557
1811.12534730204685-0.125347302046847
1910.9885612499328910.0114387500671088
2021.411454487590230.588545512409771
2111.01872823798757-0.0187282379875742
2211.2758480476627-0.275848047662701
2311.01813843189436-0.0181384318943602
2411.01872823798757-0.0187282379875742
2511.38187730562876-0.38187730562876
2611.27525824156949-0.275258241569487
2710.9891510560261050.0108489439738949
2811.24568105960802-0.245681059608018
2910.9885612499328910.0114387500671088
3011.01813843189436-0.0181384318943602
3110.9885612499328910.0114387500671088
3210.9891510560261050.0108489439738949
3311.01872823798757-0.0187282379875742
3411.12475749595363-0.124757495953633
3510.9885612499328910.0114387500671088
3610.9885612499328910.0114387500671088
3711.41204429368344-0.412044293683443
3811.24568105960802-0.245681059608018
3911.01813843189436-0.0181384318943602
4011.1543346779151-0.154334677915102
4121.275258241569490.724741758430513
4211.24568105960802-0.245681059608018
4311.01872823798757-0.0187282379875742
4411.12534730204685-0.125347302046847
4511.01813843189436-0.0181384318943602
4611.01813843189436-0.0181384318943602
4710.9885612499328910.0114387500671088
4810.9885612499328910.0114387500671088
4911.01813843189436-0.0181384318943602
5010.9885612499328910.0114387500671088
5111.38187730562876-0.38187730562876
5221.412044293683440.587955706316557
5310.9885612499328910.0114387500671088
5421.245681059608020.754318940391982
5510.9885612499328910.0114387500671088
5611.38187730562876-0.38187730562876
5711.27525824156949-0.275258241569487
5810.9885612499328910.0114387500671088
5910.9885612499328910.0114387500671088
6021.412044293683440.587955706316557
6111.12534730204685-0.125347302046847
6211.27525824156949-0.275258241569487
6310.9885612499328910.0114387500671088
6411.12534730204685-0.125347302046847
6510.9885612499328910.0114387500671088
6610.9885612499328910.0114387500671088
6721.411454487590230.588545512409771
6810.9891510560261050.0108489439738949
6910.9885612499328910.0114387500671088
7011.24568105960802-0.245681059608018
7110.9885612499328910.0114387500671088
7210.9885612499328910.0114387500671088
7311.24568105960802-0.245681059608018
7411.24627086570123-0.246270865701232
7510.9885612499328910.0114387500671088
7611.1543346779151-0.154334677915102
7710.9885612499328910.0114387500671088
7811.27525824156949-0.275258241569487
7921.381877305628760.61812269437124
8011.1543346779151-0.154334677915102
8110.9885612499328910.0114387500671088
8211.24627086570123-0.246270865701232
8310.9885612499328910.0114387500671088
8421.245681059608020.754318940391982
8511.01813843189436-0.0181384318943602
8610.9891510560261050.0108489439738949
8710.9891510560261050.0108489439738949
8811.11783013931276-0.117830139312765
8910.9885612499328910.0114387500671088
9010.9885612499328910.0114387500671088
9111.01813843189436-0.0181384318943602
9210.8607103296376380.139289670362362
9311.01872823798757-0.0187282379875742
9410.9885612499328910.0114387500671088
9510.8601205235444240.139879476455576
9610.9885612499328910.0114387500671088
9710.8607103296376380.139289670362362
9810.9885612499328910.0114387500671088
9910.9891510560261050.0108489439738949
10010.9885612499328910.0114387500671088
10110.9891510560261050.0108489439738949
10210.9885612499328910.0114387500671088
10310.9885612499328910.0114387500671088
10410.9885612499328910.0114387500671088
10511.11724033321955-0.117240333219551
10610.9885612499328910.0114387500671088
10710.9885612499328910.0114387500671088
10811.11783013931276-0.117830139312765
10910.9885612499328910.0114387500671088
11010.9891510560261050.0108489439738949
11111.14740732127423-0.147407321274234
11210.8601205235444240.139879476455576
11311.24568105960802-0.245681059608018
11411.11783013931276-0.117830139312765
11510.9891510560261050.0108489439738949
11610.9885612499328910.0114387500671088
11710.9891510560261050.0108489439738949
11810.9891510560261050.0108489439738949
11910.9885612499328910.0114387500671088
12010.9885612499328910.0114387500671088
12110.9891510560261050.0108489439738949
12210.9885612499328910.0114387500671088
12311.11783013931276-0.117830139312765
12411.27525824156949-0.275258241569487
12510.9885612499328910.0114387500671088
12610.8601205235444240.139879476455576
12711.01813843189436-0.0181384318943602
12810.9885612499328910.0114387500671088
12910.9885612499328910.0114387500671088
13010.9885612499328910.0114387500671088
13110.9891510560261050.0108489439738949
13210.9891510560261050.0108489439738949
13311.24627086570123-0.246270865701232
13410.9885612499328910.0114387500671088
13510.9885612499328910.0114387500671088
13610.9885612499328910.0114387500671088
13711.2758480476627-0.275848047662701
13811.14740732127423-0.147407321274234
13910.8601205235444240.139879476455576
14010.9885612499328910.0114387500671088
14121.245681059608020.754318940391982
14211.11724033321955-0.117240333219551
14310.9891510560261050.0108489439738949
14411.01813843189436-0.0181384318943602
14511.01813843189436-0.0181384318943602
14610.8601205235444240.139879476455576
14711.11724033321955-0.117240333219551
14810.8601205235444240.139879476455576
14910.9891510560261050.0108489439738949
15011.01813843189436-0.0181384318943602
15110.9885612499328910.0114387500671088
15221.246270865701230.753729134298768
15321.27584804766270.724151952337299
15411.24627086570123-0.246270865701232

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.12534730204685 & -0.125347302046847 \tabularnewline
2 & 1 & 0.988561249932891 & 0.0114387500671089 \tabularnewline
3 & 1 & 0.988561249932891 & 0.0114387500671089 \tabularnewline
4 & 1 & 0.988561249932891 & 0.0114387500671089 \tabularnewline
5 & 1 & 0.988561249932891 & 0.0114387500671087 \tabularnewline
6 & 1 & 1.01872823798757 & -0.0187282379875742 \tabularnewline
7 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
8 & 1 & 1.12475749595363 & -0.124757495953633 \tabularnewline
9 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
10 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
11 & 1 & 1.12534730204685 & -0.125347302046847 \tabularnewline
12 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
13 & 1 & 1.27525824156949 & -0.275258241569487 \tabularnewline
14 & 1 & 1.12534730204685 & -0.125347302046847 \tabularnewline
15 & 1 & 1.27525824156949 & -0.275258241569487 \tabularnewline
16 & 1 & 1.41145448759023 & -0.411454487590229 \tabularnewline
17 & 2 & 1.41204429368344 & 0.587955706316557 \tabularnewline
18 & 1 & 1.12534730204685 & -0.125347302046847 \tabularnewline
19 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
20 & 2 & 1.41145448759023 & 0.588545512409771 \tabularnewline
21 & 1 & 1.01872823798757 & -0.0187282379875742 \tabularnewline
22 & 1 & 1.2758480476627 & -0.275848047662701 \tabularnewline
23 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
24 & 1 & 1.01872823798757 & -0.0187282379875742 \tabularnewline
25 & 1 & 1.38187730562876 & -0.38187730562876 \tabularnewline
26 & 1 & 1.27525824156949 & -0.275258241569487 \tabularnewline
27 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
28 & 1 & 1.24568105960802 & -0.245681059608018 \tabularnewline
29 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
30 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
31 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
32 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
33 & 1 & 1.01872823798757 & -0.0187282379875742 \tabularnewline
34 & 1 & 1.12475749595363 & -0.124757495953633 \tabularnewline
35 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
36 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
37 & 1 & 1.41204429368344 & -0.412044293683443 \tabularnewline
38 & 1 & 1.24568105960802 & -0.245681059608018 \tabularnewline
39 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
40 & 1 & 1.1543346779151 & -0.154334677915102 \tabularnewline
41 & 2 & 1.27525824156949 & 0.724741758430513 \tabularnewline
42 & 1 & 1.24568105960802 & -0.245681059608018 \tabularnewline
43 & 1 & 1.01872823798757 & -0.0187282379875742 \tabularnewline
44 & 1 & 1.12534730204685 & -0.125347302046847 \tabularnewline
45 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
46 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
47 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
48 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
49 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
50 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
51 & 1 & 1.38187730562876 & -0.38187730562876 \tabularnewline
52 & 2 & 1.41204429368344 & 0.587955706316557 \tabularnewline
53 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
54 & 2 & 1.24568105960802 & 0.754318940391982 \tabularnewline
55 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
56 & 1 & 1.38187730562876 & -0.38187730562876 \tabularnewline
57 & 1 & 1.27525824156949 & -0.275258241569487 \tabularnewline
58 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
59 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
60 & 2 & 1.41204429368344 & 0.587955706316557 \tabularnewline
61 & 1 & 1.12534730204685 & -0.125347302046847 \tabularnewline
62 & 1 & 1.27525824156949 & -0.275258241569487 \tabularnewline
63 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
64 & 1 & 1.12534730204685 & -0.125347302046847 \tabularnewline
65 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
66 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
67 & 2 & 1.41145448759023 & 0.588545512409771 \tabularnewline
68 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
69 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
70 & 1 & 1.24568105960802 & -0.245681059608018 \tabularnewline
71 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
72 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
73 & 1 & 1.24568105960802 & -0.245681059608018 \tabularnewline
74 & 1 & 1.24627086570123 & -0.246270865701232 \tabularnewline
75 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
76 & 1 & 1.1543346779151 & -0.154334677915102 \tabularnewline
77 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
78 & 1 & 1.27525824156949 & -0.275258241569487 \tabularnewline
79 & 2 & 1.38187730562876 & 0.61812269437124 \tabularnewline
80 & 1 & 1.1543346779151 & -0.154334677915102 \tabularnewline
81 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
82 & 1 & 1.24627086570123 & -0.246270865701232 \tabularnewline
83 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
84 & 2 & 1.24568105960802 & 0.754318940391982 \tabularnewline
85 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
86 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
87 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
88 & 1 & 1.11783013931276 & -0.117830139312765 \tabularnewline
89 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
90 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
91 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
92 & 1 & 0.860710329637638 & 0.139289670362362 \tabularnewline
93 & 1 & 1.01872823798757 & -0.0187282379875742 \tabularnewline
94 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
95 & 1 & 0.860120523544424 & 0.139879476455576 \tabularnewline
96 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
97 & 1 & 0.860710329637638 & 0.139289670362362 \tabularnewline
98 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
99 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
100 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
101 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
102 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
103 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
104 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
105 & 1 & 1.11724033321955 & -0.117240333219551 \tabularnewline
106 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
107 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
108 & 1 & 1.11783013931276 & -0.117830139312765 \tabularnewline
109 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
110 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
111 & 1 & 1.14740732127423 & -0.147407321274234 \tabularnewline
112 & 1 & 0.860120523544424 & 0.139879476455576 \tabularnewline
113 & 1 & 1.24568105960802 & -0.245681059608018 \tabularnewline
114 & 1 & 1.11783013931276 & -0.117830139312765 \tabularnewline
115 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
116 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
117 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
118 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
119 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
120 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
121 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
122 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
123 & 1 & 1.11783013931276 & -0.117830139312765 \tabularnewline
124 & 1 & 1.27525824156949 & -0.275258241569487 \tabularnewline
125 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
126 & 1 & 0.860120523544424 & 0.139879476455576 \tabularnewline
127 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
128 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
129 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
130 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
131 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
132 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
133 & 1 & 1.24627086570123 & -0.246270865701232 \tabularnewline
134 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
135 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
136 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
137 & 1 & 1.2758480476627 & -0.275848047662701 \tabularnewline
138 & 1 & 1.14740732127423 & -0.147407321274234 \tabularnewline
139 & 1 & 0.860120523544424 & 0.139879476455576 \tabularnewline
140 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
141 & 2 & 1.24568105960802 & 0.754318940391982 \tabularnewline
142 & 1 & 1.11724033321955 & -0.117240333219551 \tabularnewline
143 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
144 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
145 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
146 & 1 & 0.860120523544424 & 0.139879476455576 \tabularnewline
147 & 1 & 1.11724033321955 & -0.117240333219551 \tabularnewline
148 & 1 & 0.860120523544424 & 0.139879476455576 \tabularnewline
149 & 1 & 0.989151056026105 & 0.0108489439738949 \tabularnewline
150 & 1 & 1.01813843189436 & -0.0181384318943602 \tabularnewline
151 & 1 & 0.988561249932891 & 0.0114387500671088 \tabularnewline
152 & 2 & 1.24627086570123 & 0.753729134298768 \tabularnewline
153 & 2 & 1.2758480476627 & 0.724151952337299 \tabularnewline
154 & 1 & 1.24627086570123 & -0.246270865701232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202698&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]1.12534730204685[/C][C]-0.125347302046847[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671089[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671089[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671089[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671087[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.01872823798757[/C][C]-0.0187282379875742[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]1.12475749595363[/C][C]-0.124757495953633[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.12534730204685[/C][C]-0.125347302046847[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.27525824156949[/C][C]-0.275258241569487[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.12534730204685[/C][C]-0.125347302046847[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.27525824156949[/C][C]-0.275258241569487[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.41145448759023[/C][C]-0.411454487590229[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]1.41204429368344[/C][C]0.587955706316557[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.12534730204685[/C][C]-0.125347302046847[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.41145448759023[/C][C]0.588545512409771[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.01872823798757[/C][C]-0.0187282379875742[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.2758480476627[/C][C]-0.275848047662701[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]1.01872823798757[/C][C]-0.0187282379875742[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.38187730562876[/C][C]-0.38187730562876[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.27525824156949[/C][C]-0.275258241569487[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]1.24568105960802[/C][C]-0.245681059608018[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.01872823798757[/C][C]-0.0187282379875742[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]1.12475749595363[/C][C]-0.124757495953633[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.41204429368344[/C][C]-0.412044293683443[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.24568105960802[/C][C]-0.245681059608018[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.1543346779151[/C][C]-0.154334677915102[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]1.27525824156949[/C][C]0.724741758430513[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.24568105960802[/C][C]-0.245681059608018[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.01872823798757[/C][C]-0.0187282379875742[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.12534730204685[/C][C]-0.125347302046847[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.38187730562876[/C][C]-0.38187730562876[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]1.41204429368344[/C][C]0.587955706316557[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.24568105960802[/C][C]0.754318940391982[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.38187730562876[/C][C]-0.38187730562876[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.27525824156949[/C][C]-0.275258241569487[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.41204429368344[/C][C]0.587955706316557[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.12534730204685[/C][C]-0.125347302046847[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.27525824156949[/C][C]-0.275258241569487[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]1.12534730204685[/C][C]-0.125347302046847[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.41145448759023[/C][C]0.588545512409771[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.24568105960802[/C][C]-0.245681059608018[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.24568105960802[/C][C]-0.245681059608018[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.24627086570123[/C][C]-0.246270865701232[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.1543346779151[/C][C]-0.154334677915102[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.27525824156949[/C][C]-0.275258241569487[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.38187730562876[/C][C]0.61812269437124[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.1543346779151[/C][C]-0.154334677915102[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.24627086570123[/C][C]-0.246270865701232[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]1.24568105960802[/C][C]0.754318940391982[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.11783013931276[/C][C]-0.117830139312765[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0.860710329637638[/C][C]0.139289670362362[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.01872823798757[/C][C]-0.0187282379875742[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0.860120523544424[/C][C]0.139879476455576[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0.860710329637638[/C][C]0.139289670362362[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.11724033321955[/C][C]-0.117240333219551[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.11783013931276[/C][C]-0.117830139312765[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.14740732127423[/C][C]-0.147407321274234[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0.860120523544424[/C][C]0.139879476455576[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.24568105960802[/C][C]-0.245681059608018[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.11783013931276[/C][C]-0.117830139312765[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.11783013931276[/C][C]-0.117830139312765[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.27525824156949[/C][C]-0.275258241569487[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0.860120523544424[/C][C]0.139879476455576[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.24627086570123[/C][C]-0.246270865701232[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.2758480476627[/C][C]-0.275848047662701[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.14740732127423[/C][C]-0.147407321274234[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]0.860120523544424[/C][C]0.139879476455576[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]1.24568105960802[/C][C]0.754318940391982[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.11724033321955[/C][C]-0.117240333219551[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.860120523544424[/C][C]0.139879476455576[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]1.11724033321955[/C][C]-0.117240333219551[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]0.860120523544424[/C][C]0.139879476455576[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]0.989151056026105[/C][C]0.0108489439738949[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]1.01813843189436[/C][C]-0.0181384318943602[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0.988561249932891[/C][C]0.0114387500671088[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.24627086570123[/C][C]0.753729134298768[/C][/ROW]
[ROW][C]153[/C][C]2[/C][C]1.2758480476627[/C][C]0.724151952337299[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.24627086570123[/C][C]-0.246270865701232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202698&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202698&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
111.12534730204685-0.125347302046847
210.9885612499328910.0114387500671089
310.9885612499328910.0114387500671089
410.9885612499328910.0114387500671089
510.9885612499328910.0114387500671087
611.01872823798757-0.0187282379875742
710.9885612499328910.0114387500671088
811.12475749595363-0.124757495953633
910.9885612499328910.0114387500671088
1010.9891510560261050.0108489439738949
1111.12534730204685-0.125347302046847
1210.9885612499328910.0114387500671088
1311.27525824156949-0.275258241569487
1411.12534730204685-0.125347302046847
1511.27525824156949-0.275258241569487
1611.41145448759023-0.411454487590229
1721.412044293683440.587955706316557
1811.12534730204685-0.125347302046847
1910.9885612499328910.0114387500671088
2021.411454487590230.588545512409771
2111.01872823798757-0.0187282379875742
2211.2758480476627-0.275848047662701
2311.01813843189436-0.0181384318943602
2411.01872823798757-0.0187282379875742
2511.38187730562876-0.38187730562876
2611.27525824156949-0.275258241569487
2710.9891510560261050.0108489439738949
2811.24568105960802-0.245681059608018
2910.9885612499328910.0114387500671088
3011.01813843189436-0.0181384318943602
3110.9885612499328910.0114387500671088
3210.9891510560261050.0108489439738949
3311.01872823798757-0.0187282379875742
3411.12475749595363-0.124757495953633
3510.9885612499328910.0114387500671088
3610.9885612499328910.0114387500671088
3711.41204429368344-0.412044293683443
3811.24568105960802-0.245681059608018
3911.01813843189436-0.0181384318943602
4011.1543346779151-0.154334677915102
4121.275258241569490.724741758430513
4211.24568105960802-0.245681059608018
4311.01872823798757-0.0187282379875742
4411.12534730204685-0.125347302046847
4511.01813843189436-0.0181384318943602
4611.01813843189436-0.0181384318943602
4710.9885612499328910.0114387500671088
4810.9885612499328910.0114387500671088
4911.01813843189436-0.0181384318943602
5010.9885612499328910.0114387500671088
5111.38187730562876-0.38187730562876
5221.412044293683440.587955706316557
5310.9885612499328910.0114387500671088
5421.245681059608020.754318940391982
5510.9885612499328910.0114387500671088
5611.38187730562876-0.38187730562876
5711.27525824156949-0.275258241569487
5810.9885612499328910.0114387500671088
5910.9885612499328910.0114387500671088
6021.412044293683440.587955706316557
6111.12534730204685-0.125347302046847
6211.27525824156949-0.275258241569487
6310.9885612499328910.0114387500671088
6411.12534730204685-0.125347302046847
6510.9885612499328910.0114387500671088
6610.9885612499328910.0114387500671088
6721.411454487590230.588545512409771
6810.9891510560261050.0108489439738949
6910.9885612499328910.0114387500671088
7011.24568105960802-0.245681059608018
7110.9885612499328910.0114387500671088
7210.9885612499328910.0114387500671088
7311.24568105960802-0.245681059608018
7411.24627086570123-0.246270865701232
7510.9885612499328910.0114387500671088
7611.1543346779151-0.154334677915102
7710.9885612499328910.0114387500671088
7811.27525824156949-0.275258241569487
7921.381877305628760.61812269437124
8011.1543346779151-0.154334677915102
8110.9885612499328910.0114387500671088
8211.24627086570123-0.246270865701232
8310.9885612499328910.0114387500671088
8421.245681059608020.754318940391982
8511.01813843189436-0.0181384318943602
8610.9891510560261050.0108489439738949
8710.9891510560261050.0108489439738949
8811.11783013931276-0.117830139312765
8910.9885612499328910.0114387500671088
9010.9885612499328910.0114387500671088
9111.01813843189436-0.0181384318943602
9210.8607103296376380.139289670362362
9311.01872823798757-0.0187282379875742
9410.9885612499328910.0114387500671088
9510.8601205235444240.139879476455576
9610.9885612499328910.0114387500671088
9710.8607103296376380.139289670362362
9810.9885612499328910.0114387500671088
9910.9891510560261050.0108489439738949
10010.9885612499328910.0114387500671088
10110.9891510560261050.0108489439738949
10210.9885612499328910.0114387500671088
10310.9885612499328910.0114387500671088
10410.9885612499328910.0114387500671088
10511.11724033321955-0.117240333219551
10610.9885612499328910.0114387500671088
10710.9885612499328910.0114387500671088
10811.11783013931276-0.117830139312765
10910.9885612499328910.0114387500671088
11010.9891510560261050.0108489439738949
11111.14740732127423-0.147407321274234
11210.8601205235444240.139879476455576
11311.24568105960802-0.245681059608018
11411.11783013931276-0.117830139312765
11510.9891510560261050.0108489439738949
11610.9885612499328910.0114387500671088
11710.9891510560261050.0108489439738949
11810.9891510560261050.0108489439738949
11910.9885612499328910.0114387500671088
12010.9885612499328910.0114387500671088
12110.9891510560261050.0108489439738949
12210.9885612499328910.0114387500671088
12311.11783013931276-0.117830139312765
12411.27525824156949-0.275258241569487
12510.9885612499328910.0114387500671088
12610.8601205235444240.139879476455576
12711.01813843189436-0.0181384318943602
12810.9885612499328910.0114387500671088
12910.9885612499328910.0114387500671088
13010.9885612499328910.0114387500671088
13110.9891510560261050.0108489439738949
13210.9891510560261050.0108489439738949
13311.24627086570123-0.246270865701232
13410.9885612499328910.0114387500671088
13510.9885612499328910.0114387500671088
13610.9885612499328910.0114387500671088
13711.2758480476627-0.275848047662701
13811.14740732127423-0.147407321274234
13910.8601205235444240.139879476455576
14010.9885612499328910.0114387500671088
14121.245681059608020.754318940391982
14211.11724033321955-0.117240333219551
14310.9891510560261050.0108489439738949
14411.01813843189436-0.0181384318943602
14511.01813843189436-0.0181384318943602
14610.8601205235444240.139879476455576
14711.11724033321955-0.117240333219551
14810.8601205235444240.139879476455576
14910.9891510560261050.0108489439738949
15011.01813843189436-0.0181384318943602
15110.9885612499328910.0114387500671088
15221.246270865701230.753729134298768
15321.27584804766270.724151952337299
15411.24627086570123-0.246270865701232






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
94.72037660182768e-949.44075320365536e-941
103.41843939435462e-636.83687878870924e-631
116.83002339770546e-821.36600467954109e-811
129.5314090320936e-921.90628180641872e-911
131.812880520043e-1213.625761040086e-1211
145.44614806566947e-1211.08922961313389e-1201
153.31622069659942e-1366.63244139319885e-1361
16001
170.511712094621460.9765758107570790.48828790537854
180.4511033323969760.9022066647939520.548896667603024
190.3735317217744470.7470634435488940.626468278225553
200.8303258997528440.3393482004943110.169674100247156
210.7773284723110580.4453430553778850.222671527688942
220.7821451571788450.4357096856423090.217854842821155
230.7252843812233020.5494312375533950.274715618776698
240.6624378381682780.6751243236634440.337562161831722
250.7023032620521390.5953934758957210.297696737947861
260.6819557958933860.6360884082132290.318044204106614
270.6298213256877020.7403573486245950.370178674312298
280.5796148804809580.8407702390380840.420385119519042
290.5211159416349540.9577681167300920.478884058365046
300.4605700026491390.9211400052982780.539429997350861
310.4034920531506180.8069841063012360.596507946849382
320.3492513497863660.6985026995727320.650748650213634
330.2953550483193930.5907100966387860.704644951680607
340.2593806913307870.5187613826615740.740619308669213
350.2162950705118780.4325901410237560.783704929488122
360.1775893138719950.3551786277439890.822410686128005
370.2370002631106140.4740005262212280.762999736889386
380.2074245311979450.4148490623958890.792575468802055
390.1698722513062450.3397445026124890.830127748693755
400.160547393259320.3210947865186410.83945260674068
410.6872534400640670.6254931198718670.312746559935933
420.66550064183080.66899871633840.3344993581692
430.6160020189043030.7679959621913950.383997981095697
440.5769857064075860.8460285871848270.423014293592414
450.5263346045167710.9473307909664570.473665395483229
460.4750660087406650.9501320174813290.524933991259335
470.4246070806171330.8492141612342660.575392919382867
480.3754123106205870.7508246212411750.624587689379413
490.3283253483350870.6566506966701750.671674651664913
500.2840161849929860.5680323699859730.715983815007014
510.3431085292752380.6862170585504760.656891470724762
520.6337275897800240.7325448204399510.366272410219976
530.5883286813666520.8233426372666960.411671318633348
540.9292051095381450.1415897809237110.0707948904618555
550.9112898743811140.1774202512377710.0887101256188856
560.9484331018318610.1031337963362780.051566898168139
570.9520864590572110.09582708188557840.0479135409427892
580.9389490369147840.1221019261704320.0610509630852158
590.9231610646625780.1536778706748440.076838935337422
600.9745645785297090.0508708429405820.025435421470291
610.9723594777882910.05528104442341820.0276405222117091
620.9742392550858190.0515214898283630.0257607449141815
630.9664653041289320.06706939174213580.0335346958710679
640.9674548682129270.06509026357414690.0325451317870734
650.9580861544432750.08382769111345050.0419138455567252
660.9466323472626950.1067353054746090.0533676527373047
670.9822239866360830.0355520267278350.0177760133639175
680.9763482111022370.04730357779552590.023651788897763
690.9691451569902870.0617096860194250.0308548430097125
700.9703698539666070.05926029206678560.0296301460333928
710.9617793225796020.07644135484079620.0382206774203981
720.9512438308212380.09751233835752470.0487561691787623
730.9547045271807980.09059094563840370.0452954728192018
740.9572801862476750.08543962750465080.0427198137523254
750.9458708053847190.1082583892305610.0541291946152806
760.9459106404016460.1081787191967090.0540893595983543
770.9322027569315370.1355944861369250.0677972430684625
780.938161059456780.123677881086440.06183894054322
790.9828322400682490.03433551986350180.0171677599317509
800.978574563626090.04285087274782010.02142543637391
810.9719037209183630.05619255816327490.0280962790816375
820.974474146823710.05105170635258020.0255258531762901
830.9667609460552390.06647810788952110.0332390539447605
840.9985529714156740.00289405716865250.00144702858432625
850.9978568816925080.004286236614984660.00214311830749233
860.9968917159924840.006216568015032440.00310828400751622
870.9955471792021140.00890564159577110.00445282079788555
880.9940777611988590.01184447760228240.00592223880114122
890.9916928506796030.0166142986407930.00830714932039651
900.9884934878926020.02301302421479610.011506512107398
910.9841806578884810.03163868422303710.0158193421115186
920.9810164639791840.03796707204163290.0189835360208165
930.9744239631902090.05115207361958140.0255760368097907
940.9661400966302510.06771980673949730.0338599033697486
950.9590511552339750.08189768953204950.0409488447660248
960.9468844745609920.1062310508780160.0531155254390078
970.9374895562401080.1250208875197830.0625104437598915
980.9205617158675990.1588765682648020.0794382841324009
990.9002267465420610.1995465069158780.0997732534579389
1000.8763180801576530.2473638396846940.123681919842347
1010.8484314101773060.3031371796453880.151568589822694
1020.8166597265438590.3666805469122830.183340273456141
1030.7808989427486220.4382021145027560.219101057251378
1040.7412797296168650.517440540766270.258720270383135
1050.7124150772316040.5751698455367910.287584922768396
1060.6669560355749780.6660879288500450.333043964425023
1070.6187172977309020.7625654045381960.381282702269098
1080.5805119017281470.8389761965437060.419488098271853
1090.5287762505299410.9424474989401170.471223749470059
1100.4756967276916710.9513934553833420.524303272308329
1110.4353308590889330.8706617181778670.564669140911067
1120.4000635909623060.8001271819246130.599936409037694
1130.4410482856051240.8820965712102480.558951714394876
1140.4030160376512070.8060320753024130.596983962348793
1150.350295486222590.7005909724451790.64970451377741
1160.3010200596902990.6020401193805980.698979940309701
1170.2538227141560580.5076454283121170.746177285843942
1180.2105176535443110.4210353070886220.789482346455689
1190.1724800748639010.3449601497278020.827519925136099
1200.1389910307445670.2779820614891340.861008969255433
1210.1092594494979530.2185188989959050.890740550502047
1220.08490318022237750.1698063604447550.915096819777622
1230.07117190555487510.142343811109750.928828094445125
1240.0937292348315440.1874584696630880.906270765168456
1250.07149476572446540.1429895314489310.928505234275535
1260.05995214159414850.1199042831882970.940047858405851
1270.04356498584194530.08712997168389070.956435014158055
1280.0312641540340220.0625283080680440.968735845965978
1290.02196408656898650.04392817313797290.978035913431014
1300.01511663545898870.03023327091797730.984883364541011
1310.009795857317195640.01959171463439130.990204142682804
1320.006144522133283120.01228904426656620.993855477866717
1330.01146252835208830.02292505670417650.988537471647912
1340.007513858172494530.01502771634498910.992486141827505
1350.00486286515445050.009725730308901010.995137134845549
1360.003146270554251750.006292541108503490.996853729445748
1370.007314424613481390.01462884922696280.992685575386519
1380.006621181392799610.01324236278559920.9933788186072
1390.00532320570252670.01064641140505340.994676794297473
1400.002825829242628290.005651658485256580.997174170757372
1410.03786449249933640.07572898499867270.962135507500664
1420.02753713760319530.05507427520639060.972462862396805
1430.01701755586069990.03403511172139990.9829824441393
1440.00829657728636380.01659315457272760.991703422713636
1450.003784182876406920.007568365752813840.996215817123593

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 4.72037660182768e-94 & 9.44075320365536e-94 & 1 \tabularnewline
10 & 3.41843939435462e-63 & 6.83687878870924e-63 & 1 \tabularnewline
11 & 6.83002339770546e-82 & 1.36600467954109e-81 & 1 \tabularnewline
12 & 9.5314090320936e-92 & 1.90628180641872e-91 & 1 \tabularnewline
13 & 1.812880520043e-121 & 3.625761040086e-121 & 1 \tabularnewline
14 & 5.44614806566947e-121 & 1.08922961313389e-120 & 1 \tabularnewline
15 & 3.31622069659942e-136 & 6.63244139319885e-136 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.51171209462146 & 0.976575810757079 & 0.48828790537854 \tabularnewline
18 & 0.451103332396976 & 0.902206664793952 & 0.548896667603024 \tabularnewline
19 & 0.373531721774447 & 0.747063443548894 & 0.626468278225553 \tabularnewline
20 & 0.830325899752844 & 0.339348200494311 & 0.169674100247156 \tabularnewline
21 & 0.777328472311058 & 0.445343055377885 & 0.222671527688942 \tabularnewline
22 & 0.782145157178845 & 0.435709685642309 & 0.217854842821155 \tabularnewline
23 & 0.725284381223302 & 0.549431237553395 & 0.274715618776698 \tabularnewline
24 & 0.662437838168278 & 0.675124323663444 & 0.337562161831722 \tabularnewline
25 & 0.702303262052139 & 0.595393475895721 & 0.297696737947861 \tabularnewline
26 & 0.681955795893386 & 0.636088408213229 & 0.318044204106614 \tabularnewline
27 & 0.629821325687702 & 0.740357348624595 & 0.370178674312298 \tabularnewline
28 & 0.579614880480958 & 0.840770239038084 & 0.420385119519042 \tabularnewline
29 & 0.521115941634954 & 0.957768116730092 & 0.478884058365046 \tabularnewline
30 & 0.460570002649139 & 0.921140005298278 & 0.539429997350861 \tabularnewline
31 & 0.403492053150618 & 0.806984106301236 & 0.596507946849382 \tabularnewline
32 & 0.349251349786366 & 0.698502699572732 & 0.650748650213634 \tabularnewline
33 & 0.295355048319393 & 0.590710096638786 & 0.704644951680607 \tabularnewline
34 & 0.259380691330787 & 0.518761382661574 & 0.740619308669213 \tabularnewline
35 & 0.216295070511878 & 0.432590141023756 & 0.783704929488122 \tabularnewline
36 & 0.177589313871995 & 0.355178627743989 & 0.822410686128005 \tabularnewline
37 & 0.237000263110614 & 0.474000526221228 & 0.762999736889386 \tabularnewline
38 & 0.207424531197945 & 0.414849062395889 & 0.792575468802055 \tabularnewline
39 & 0.169872251306245 & 0.339744502612489 & 0.830127748693755 \tabularnewline
40 & 0.16054739325932 & 0.321094786518641 & 0.83945260674068 \tabularnewline
41 & 0.687253440064067 & 0.625493119871867 & 0.312746559935933 \tabularnewline
42 & 0.6655006418308 & 0.6689987163384 & 0.3344993581692 \tabularnewline
43 & 0.616002018904303 & 0.767995962191395 & 0.383997981095697 \tabularnewline
44 & 0.576985706407586 & 0.846028587184827 & 0.423014293592414 \tabularnewline
45 & 0.526334604516771 & 0.947330790966457 & 0.473665395483229 \tabularnewline
46 & 0.475066008740665 & 0.950132017481329 & 0.524933991259335 \tabularnewline
47 & 0.424607080617133 & 0.849214161234266 & 0.575392919382867 \tabularnewline
48 & 0.375412310620587 & 0.750824621241175 & 0.624587689379413 \tabularnewline
49 & 0.328325348335087 & 0.656650696670175 & 0.671674651664913 \tabularnewline
50 & 0.284016184992986 & 0.568032369985973 & 0.715983815007014 \tabularnewline
51 & 0.343108529275238 & 0.686217058550476 & 0.656891470724762 \tabularnewline
52 & 0.633727589780024 & 0.732544820439951 & 0.366272410219976 \tabularnewline
53 & 0.588328681366652 & 0.823342637266696 & 0.411671318633348 \tabularnewline
54 & 0.929205109538145 & 0.141589780923711 & 0.0707948904618555 \tabularnewline
55 & 0.911289874381114 & 0.177420251237771 & 0.0887101256188856 \tabularnewline
56 & 0.948433101831861 & 0.103133796336278 & 0.051566898168139 \tabularnewline
57 & 0.952086459057211 & 0.0958270818855784 & 0.0479135409427892 \tabularnewline
58 & 0.938949036914784 & 0.122101926170432 & 0.0610509630852158 \tabularnewline
59 & 0.923161064662578 & 0.153677870674844 & 0.076838935337422 \tabularnewline
60 & 0.974564578529709 & 0.050870842940582 & 0.025435421470291 \tabularnewline
61 & 0.972359477788291 & 0.0552810444234182 & 0.0276405222117091 \tabularnewline
62 & 0.974239255085819 & 0.051521489828363 & 0.0257607449141815 \tabularnewline
63 & 0.966465304128932 & 0.0670693917421358 & 0.0335346958710679 \tabularnewline
64 & 0.967454868212927 & 0.0650902635741469 & 0.0325451317870734 \tabularnewline
65 & 0.958086154443275 & 0.0838276911134505 & 0.0419138455567252 \tabularnewline
66 & 0.946632347262695 & 0.106735305474609 & 0.0533676527373047 \tabularnewline
67 & 0.982223986636083 & 0.035552026727835 & 0.0177760133639175 \tabularnewline
68 & 0.976348211102237 & 0.0473035777955259 & 0.023651788897763 \tabularnewline
69 & 0.969145156990287 & 0.061709686019425 & 0.0308548430097125 \tabularnewline
70 & 0.970369853966607 & 0.0592602920667856 & 0.0296301460333928 \tabularnewline
71 & 0.961779322579602 & 0.0764413548407962 & 0.0382206774203981 \tabularnewline
72 & 0.951243830821238 & 0.0975123383575247 & 0.0487561691787623 \tabularnewline
73 & 0.954704527180798 & 0.0905909456384037 & 0.0452954728192018 \tabularnewline
74 & 0.957280186247675 & 0.0854396275046508 & 0.0427198137523254 \tabularnewline
75 & 0.945870805384719 & 0.108258389230561 & 0.0541291946152806 \tabularnewline
76 & 0.945910640401646 & 0.108178719196709 & 0.0540893595983543 \tabularnewline
77 & 0.932202756931537 & 0.135594486136925 & 0.0677972430684625 \tabularnewline
78 & 0.93816105945678 & 0.12367788108644 & 0.06183894054322 \tabularnewline
79 & 0.982832240068249 & 0.0343355198635018 & 0.0171677599317509 \tabularnewline
80 & 0.97857456362609 & 0.0428508727478201 & 0.02142543637391 \tabularnewline
81 & 0.971903720918363 & 0.0561925581632749 & 0.0280962790816375 \tabularnewline
82 & 0.97447414682371 & 0.0510517063525802 & 0.0255258531762901 \tabularnewline
83 & 0.966760946055239 & 0.0664781078895211 & 0.0332390539447605 \tabularnewline
84 & 0.998552971415674 & 0.0028940571686525 & 0.00144702858432625 \tabularnewline
85 & 0.997856881692508 & 0.00428623661498466 & 0.00214311830749233 \tabularnewline
86 & 0.996891715992484 & 0.00621656801503244 & 0.00310828400751622 \tabularnewline
87 & 0.995547179202114 & 0.0089056415957711 & 0.00445282079788555 \tabularnewline
88 & 0.994077761198859 & 0.0118444776022824 & 0.00592223880114122 \tabularnewline
89 & 0.991692850679603 & 0.016614298640793 & 0.00830714932039651 \tabularnewline
90 & 0.988493487892602 & 0.0230130242147961 & 0.011506512107398 \tabularnewline
91 & 0.984180657888481 & 0.0316386842230371 & 0.0158193421115186 \tabularnewline
92 & 0.981016463979184 & 0.0379670720416329 & 0.0189835360208165 \tabularnewline
93 & 0.974423963190209 & 0.0511520736195814 & 0.0255760368097907 \tabularnewline
94 & 0.966140096630251 & 0.0677198067394973 & 0.0338599033697486 \tabularnewline
95 & 0.959051155233975 & 0.0818976895320495 & 0.0409488447660248 \tabularnewline
96 & 0.946884474560992 & 0.106231050878016 & 0.0531155254390078 \tabularnewline
97 & 0.937489556240108 & 0.125020887519783 & 0.0625104437598915 \tabularnewline
98 & 0.920561715867599 & 0.158876568264802 & 0.0794382841324009 \tabularnewline
99 & 0.900226746542061 & 0.199546506915878 & 0.0997732534579389 \tabularnewline
100 & 0.876318080157653 & 0.247363839684694 & 0.123681919842347 \tabularnewline
101 & 0.848431410177306 & 0.303137179645388 & 0.151568589822694 \tabularnewline
102 & 0.816659726543859 & 0.366680546912283 & 0.183340273456141 \tabularnewline
103 & 0.780898942748622 & 0.438202114502756 & 0.219101057251378 \tabularnewline
104 & 0.741279729616865 & 0.51744054076627 & 0.258720270383135 \tabularnewline
105 & 0.712415077231604 & 0.575169845536791 & 0.287584922768396 \tabularnewline
106 & 0.666956035574978 & 0.666087928850045 & 0.333043964425023 \tabularnewline
107 & 0.618717297730902 & 0.762565404538196 & 0.381282702269098 \tabularnewline
108 & 0.580511901728147 & 0.838976196543706 & 0.419488098271853 \tabularnewline
109 & 0.528776250529941 & 0.942447498940117 & 0.471223749470059 \tabularnewline
110 & 0.475696727691671 & 0.951393455383342 & 0.524303272308329 \tabularnewline
111 & 0.435330859088933 & 0.870661718177867 & 0.564669140911067 \tabularnewline
112 & 0.400063590962306 & 0.800127181924613 & 0.599936409037694 \tabularnewline
113 & 0.441048285605124 & 0.882096571210248 & 0.558951714394876 \tabularnewline
114 & 0.403016037651207 & 0.806032075302413 & 0.596983962348793 \tabularnewline
115 & 0.35029548622259 & 0.700590972445179 & 0.64970451377741 \tabularnewline
116 & 0.301020059690299 & 0.602040119380598 & 0.698979940309701 \tabularnewline
117 & 0.253822714156058 & 0.507645428312117 & 0.746177285843942 \tabularnewline
118 & 0.210517653544311 & 0.421035307088622 & 0.789482346455689 \tabularnewline
119 & 0.172480074863901 & 0.344960149727802 & 0.827519925136099 \tabularnewline
120 & 0.138991030744567 & 0.277982061489134 & 0.861008969255433 \tabularnewline
121 & 0.109259449497953 & 0.218518898995905 & 0.890740550502047 \tabularnewline
122 & 0.0849031802223775 & 0.169806360444755 & 0.915096819777622 \tabularnewline
123 & 0.0711719055548751 & 0.14234381110975 & 0.928828094445125 \tabularnewline
124 & 0.093729234831544 & 0.187458469663088 & 0.906270765168456 \tabularnewline
125 & 0.0714947657244654 & 0.142989531448931 & 0.928505234275535 \tabularnewline
126 & 0.0599521415941485 & 0.119904283188297 & 0.940047858405851 \tabularnewline
127 & 0.0435649858419453 & 0.0871299716838907 & 0.956435014158055 \tabularnewline
128 & 0.031264154034022 & 0.062528308068044 & 0.968735845965978 \tabularnewline
129 & 0.0219640865689865 & 0.0439281731379729 & 0.978035913431014 \tabularnewline
130 & 0.0151166354589887 & 0.0302332709179773 & 0.984883364541011 \tabularnewline
131 & 0.00979585731719564 & 0.0195917146343913 & 0.990204142682804 \tabularnewline
132 & 0.00614452213328312 & 0.0122890442665662 & 0.993855477866717 \tabularnewline
133 & 0.0114625283520883 & 0.0229250567041765 & 0.988537471647912 \tabularnewline
134 & 0.00751385817249453 & 0.0150277163449891 & 0.992486141827505 \tabularnewline
135 & 0.0048628651544505 & 0.00972573030890101 & 0.995137134845549 \tabularnewline
136 & 0.00314627055425175 & 0.00629254110850349 & 0.996853729445748 \tabularnewline
137 & 0.00731442461348139 & 0.0146288492269628 & 0.992685575386519 \tabularnewline
138 & 0.00662118139279961 & 0.0132423627855992 & 0.9933788186072 \tabularnewline
139 & 0.0053232057025267 & 0.0106464114050534 & 0.994676794297473 \tabularnewline
140 & 0.00282582924262829 & 0.00565165848525658 & 0.997174170757372 \tabularnewline
141 & 0.0378644924993364 & 0.0757289849986727 & 0.962135507500664 \tabularnewline
142 & 0.0275371376031953 & 0.0550742752063906 & 0.972462862396805 \tabularnewline
143 & 0.0170175558606999 & 0.0340351117213999 & 0.9829824441393 \tabularnewline
144 & 0.0082965772863638 & 0.0165931545727276 & 0.991703422713636 \tabularnewline
145 & 0.00378418287640692 & 0.00756836575281384 & 0.996215817123593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202698&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]9[/C][C]4.72037660182768e-94[/C][C]9.44075320365536e-94[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]3.41843939435462e-63[/C][C]6.83687878870924e-63[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]6.83002339770546e-82[/C][C]1.36600467954109e-81[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]9.5314090320936e-92[/C][C]1.90628180641872e-91[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]1.812880520043e-121[/C][C]3.625761040086e-121[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]5.44614806566947e-121[/C][C]1.08922961313389e-120[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]3.31622069659942e-136[/C][C]6.63244139319885e-136[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.51171209462146[/C][C]0.976575810757079[/C][C]0.48828790537854[/C][/ROW]
[ROW][C]18[/C][C]0.451103332396976[/C][C]0.902206664793952[/C][C]0.548896667603024[/C][/ROW]
[ROW][C]19[/C][C]0.373531721774447[/C][C]0.747063443548894[/C][C]0.626468278225553[/C][/ROW]
[ROW][C]20[/C][C]0.830325899752844[/C][C]0.339348200494311[/C][C]0.169674100247156[/C][/ROW]
[ROW][C]21[/C][C]0.777328472311058[/C][C]0.445343055377885[/C][C]0.222671527688942[/C][/ROW]
[ROW][C]22[/C][C]0.782145157178845[/C][C]0.435709685642309[/C][C]0.217854842821155[/C][/ROW]
[ROW][C]23[/C][C]0.725284381223302[/C][C]0.549431237553395[/C][C]0.274715618776698[/C][/ROW]
[ROW][C]24[/C][C]0.662437838168278[/C][C]0.675124323663444[/C][C]0.337562161831722[/C][/ROW]
[ROW][C]25[/C][C]0.702303262052139[/C][C]0.595393475895721[/C][C]0.297696737947861[/C][/ROW]
[ROW][C]26[/C][C]0.681955795893386[/C][C]0.636088408213229[/C][C]0.318044204106614[/C][/ROW]
[ROW][C]27[/C][C]0.629821325687702[/C][C]0.740357348624595[/C][C]0.370178674312298[/C][/ROW]
[ROW][C]28[/C][C]0.579614880480958[/C][C]0.840770239038084[/C][C]0.420385119519042[/C][/ROW]
[ROW][C]29[/C][C]0.521115941634954[/C][C]0.957768116730092[/C][C]0.478884058365046[/C][/ROW]
[ROW][C]30[/C][C]0.460570002649139[/C][C]0.921140005298278[/C][C]0.539429997350861[/C][/ROW]
[ROW][C]31[/C][C]0.403492053150618[/C][C]0.806984106301236[/C][C]0.596507946849382[/C][/ROW]
[ROW][C]32[/C][C]0.349251349786366[/C][C]0.698502699572732[/C][C]0.650748650213634[/C][/ROW]
[ROW][C]33[/C][C]0.295355048319393[/C][C]0.590710096638786[/C][C]0.704644951680607[/C][/ROW]
[ROW][C]34[/C][C]0.259380691330787[/C][C]0.518761382661574[/C][C]0.740619308669213[/C][/ROW]
[ROW][C]35[/C][C]0.216295070511878[/C][C]0.432590141023756[/C][C]0.783704929488122[/C][/ROW]
[ROW][C]36[/C][C]0.177589313871995[/C][C]0.355178627743989[/C][C]0.822410686128005[/C][/ROW]
[ROW][C]37[/C][C]0.237000263110614[/C][C]0.474000526221228[/C][C]0.762999736889386[/C][/ROW]
[ROW][C]38[/C][C]0.207424531197945[/C][C]0.414849062395889[/C][C]0.792575468802055[/C][/ROW]
[ROW][C]39[/C][C]0.169872251306245[/C][C]0.339744502612489[/C][C]0.830127748693755[/C][/ROW]
[ROW][C]40[/C][C]0.16054739325932[/C][C]0.321094786518641[/C][C]0.83945260674068[/C][/ROW]
[ROW][C]41[/C][C]0.687253440064067[/C][C]0.625493119871867[/C][C]0.312746559935933[/C][/ROW]
[ROW][C]42[/C][C]0.6655006418308[/C][C]0.6689987163384[/C][C]0.3344993581692[/C][/ROW]
[ROW][C]43[/C][C]0.616002018904303[/C][C]0.767995962191395[/C][C]0.383997981095697[/C][/ROW]
[ROW][C]44[/C][C]0.576985706407586[/C][C]0.846028587184827[/C][C]0.423014293592414[/C][/ROW]
[ROW][C]45[/C][C]0.526334604516771[/C][C]0.947330790966457[/C][C]0.473665395483229[/C][/ROW]
[ROW][C]46[/C][C]0.475066008740665[/C][C]0.950132017481329[/C][C]0.524933991259335[/C][/ROW]
[ROW][C]47[/C][C]0.424607080617133[/C][C]0.849214161234266[/C][C]0.575392919382867[/C][/ROW]
[ROW][C]48[/C][C]0.375412310620587[/C][C]0.750824621241175[/C][C]0.624587689379413[/C][/ROW]
[ROW][C]49[/C][C]0.328325348335087[/C][C]0.656650696670175[/C][C]0.671674651664913[/C][/ROW]
[ROW][C]50[/C][C]0.284016184992986[/C][C]0.568032369985973[/C][C]0.715983815007014[/C][/ROW]
[ROW][C]51[/C][C]0.343108529275238[/C][C]0.686217058550476[/C][C]0.656891470724762[/C][/ROW]
[ROW][C]52[/C][C]0.633727589780024[/C][C]0.732544820439951[/C][C]0.366272410219976[/C][/ROW]
[ROW][C]53[/C][C]0.588328681366652[/C][C]0.823342637266696[/C][C]0.411671318633348[/C][/ROW]
[ROW][C]54[/C][C]0.929205109538145[/C][C]0.141589780923711[/C][C]0.0707948904618555[/C][/ROW]
[ROW][C]55[/C][C]0.911289874381114[/C][C]0.177420251237771[/C][C]0.0887101256188856[/C][/ROW]
[ROW][C]56[/C][C]0.948433101831861[/C][C]0.103133796336278[/C][C]0.051566898168139[/C][/ROW]
[ROW][C]57[/C][C]0.952086459057211[/C][C]0.0958270818855784[/C][C]0.0479135409427892[/C][/ROW]
[ROW][C]58[/C][C]0.938949036914784[/C][C]0.122101926170432[/C][C]0.0610509630852158[/C][/ROW]
[ROW][C]59[/C][C]0.923161064662578[/C][C]0.153677870674844[/C][C]0.076838935337422[/C][/ROW]
[ROW][C]60[/C][C]0.974564578529709[/C][C]0.050870842940582[/C][C]0.025435421470291[/C][/ROW]
[ROW][C]61[/C][C]0.972359477788291[/C][C]0.0552810444234182[/C][C]0.0276405222117091[/C][/ROW]
[ROW][C]62[/C][C]0.974239255085819[/C][C]0.051521489828363[/C][C]0.0257607449141815[/C][/ROW]
[ROW][C]63[/C][C]0.966465304128932[/C][C]0.0670693917421358[/C][C]0.0335346958710679[/C][/ROW]
[ROW][C]64[/C][C]0.967454868212927[/C][C]0.0650902635741469[/C][C]0.0325451317870734[/C][/ROW]
[ROW][C]65[/C][C]0.958086154443275[/C][C]0.0838276911134505[/C][C]0.0419138455567252[/C][/ROW]
[ROW][C]66[/C][C]0.946632347262695[/C][C]0.106735305474609[/C][C]0.0533676527373047[/C][/ROW]
[ROW][C]67[/C][C]0.982223986636083[/C][C]0.035552026727835[/C][C]0.0177760133639175[/C][/ROW]
[ROW][C]68[/C][C]0.976348211102237[/C][C]0.0473035777955259[/C][C]0.023651788897763[/C][/ROW]
[ROW][C]69[/C][C]0.969145156990287[/C][C]0.061709686019425[/C][C]0.0308548430097125[/C][/ROW]
[ROW][C]70[/C][C]0.970369853966607[/C][C]0.0592602920667856[/C][C]0.0296301460333928[/C][/ROW]
[ROW][C]71[/C][C]0.961779322579602[/C][C]0.0764413548407962[/C][C]0.0382206774203981[/C][/ROW]
[ROW][C]72[/C][C]0.951243830821238[/C][C]0.0975123383575247[/C][C]0.0487561691787623[/C][/ROW]
[ROW][C]73[/C][C]0.954704527180798[/C][C]0.0905909456384037[/C][C]0.0452954728192018[/C][/ROW]
[ROW][C]74[/C][C]0.957280186247675[/C][C]0.0854396275046508[/C][C]0.0427198137523254[/C][/ROW]
[ROW][C]75[/C][C]0.945870805384719[/C][C]0.108258389230561[/C][C]0.0541291946152806[/C][/ROW]
[ROW][C]76[/C][C]0.945910640401646[/C][C]0.108178719196709[/C][C]0.0540893595983543[/C][/ROW]
[ROW][C]77[/C][C]0.932202756931537[/C][C]0.135594486136925[/C][C]0.0677972430684625[/C][/ROW]
[ROW][C]78[/C][C]0.93816105945678[/C][C]0.12367788108644[/C][C]0.06183894054322[/C][/ROW]
[ROW][C]79[/C][C]0.982832240068249[/C][C]0.0343355198635018[/C][C]0.0171677599317509[/C][/ROW]
[ROW][C]80[/C][C]0.97857456362609[/C][C]0.0428508727478201[/C][C]0.02142543637391[/C][/ROW]
[ROW][C]81[/C][C]0.971903720918363[/C][C]0.0561925581632749[/C][C]0.0280962790816375[/C][/ROW]
[ROW][C]82[/C][C]0.97447414682371[/C][C]0.0510517063525802[/C][C]0.0255258531762901[/C][/ROW]
[ROW][C]83[/C][C]0.966760946055239[/C][C]0.0664781078895211[/C][C]0.0332390539447605[/C][/ROW]
[ROW][C]84[/C][C]0.998552971415674[/C][C]0.0028940571686525[/C][C]0.00144702858432625[/C][/ROW]
[ROW][C]85[/C][C]0.997856881692508[/C][C]0.00428623661498466[/C][C]0.00214311830749233[/C][/ROW]
[ROW][C]86[/C][C]0.996891715992484[/C][C]0.00621656801503244[/C][C]0.00310828400751622[/C][/ROW]
[ROW][C]87[/C][C]0.995547179202114[/C][C]0.0089056415957711[/C][C]0.00445282079788555[/C][/ROW]
[ROW][C]88[/C][C]0.994077761198859[/C][C]0.0118444776022824[/C][C]0.00592223880114122[/C][/ROW]
[ROW][C]89[/C][C]0.991692850679603[/C][C]0.016614298640793[/C][C]0.00830714932039651[/C][/ROW]
[ROW][C]90[/C][C]0.988493487892602[/C][C]0.0230130242147961[/C][C]0.011506512107398[/C][/ROW]
[ROW][C]91[/C][C]0.984180657888481[/C][C]0.0316386842230371[/C][C]0.0158193421115186[/C][/ROW]
[ROW][C]92[/C][C]0.981016463979184[/C][C]0.0379670720416329[/C][C]0.0189835360208165[/C][/ROW]
[ROW][C]93[/C][C]0.974423963190209[/C][C]0.0511520736195814[/C][C]0.0255760368097907[/C][/ROW]
[ROW][C]94[/C][C]0.966140096630251[/C][C]0.0677198067394973[/C][C]0.0338599033697486[/C][/ROW]
[ROW][C]95[/C][C]0.959051155233975[/C][C]0.0818976895320495[/C][C]0.0409488447660248[/C][/ROW]
[ROW][C]96[/C][C]0.946884474560992[/C][C]0.106231050878016[/C][C]0.0531155254390078[/C][/ROW]
[ROW][C]97[/C][C]0.937489556240108[/C][C]0.125020887519783[/C][C]0.0625104437598915[/C][/ROW]
[ROW][C]98[/C][C]0.920561715867599[/C][C]0.158876568264802[/C][C]0.0794382841324009[/C][/ROW]
[ROW][C]99[/C][C]0.900226746542061[/C][C]0.199546506915878[/C][C]0.0997732534579389[/C][/ROW]
[ROW][C]100[/C][C]0.876318080157653[/C][C]0.247363839684694[/C][C]0.123681919842347[/C][/ROW]
[ROW][C]101[/C][C]0.848431410177306[/C][C]0.303137179645388[/C][C]0.151568589822694[/C][/ROW]
[ROW][C]102[/C][C]0.816659726543859[/C][C]0.366680546912283[/C][C]0.183340273456141[/C][/ROW]
[ROW][C]103[/C][C]0.780898942748622[/C][C]0.438202114502756[/C][C]0.219101057251378[/C][/ROW]
[ROW][C]104[/C][C]0.741279729616865[/C][C]0.51744054076627[/C][C]0.258720270383135[/C][/ROW]
[ROW][C]105[/C][C]0.712415077231604[/C][C]0.575169845536791[/C][C]0.287584922768396[/C][/ROW]
[ROW][C]106[/C][C]0.666956035574978[/C][C]0.666087928850045[/C][C]0.333043964425023[/C][/ROW]
[ROW][C]107[/C][C]0.618717297730902[/C][C]0.762565404538196[/C][C]0.381282702269098[/C][/ROW]
[ROW][C]108[/C][C]0.580511901728147[/C][C]0.838976196543706[/C][C]0.419488098271853[/C][/ROW]
[ROW][C]109[/C][C]0.528776250529941[/C][C]0.942447498940117[/C][C]0.471223749470059[/C][/ROW]
[ROW][C]110[/C][C]0.475696727691671[/C][C]0.951393455383342[/C][C]0.524303272308329[/C][/ROW]
[ROW][C]111[/C][C]0.435330859088933[/C][C]0.870661718177867[/C][C]0.564669140911067[/C][/ROW]
[ROW][C]112[/C][C]0.400063590962306[/C][C]0.800127181924613[/C][C]0.599936409037694[/C][/ROW]
[ROW][C]113[/C][C]0.441048285605124[/C][C]0.882096571210248[/C][C]0.558951714394876[/C][/ROW]
[ROW][C]114[/C][C]0.403016037651207[/C][C]0.806032075302413[/C][C]0.596983962348793[/C][/ROW]
[ROW][C]115[/C][C]0.35029548622259[/C][C]0.700590972445179[/C][C]0.64970451377741[/C][/ROW]
[ROW][C]116[/C][C]0.301020059690299[/C][C]0.602040119380598[/C][C]0.698979940309701[/C][/ROW]
[ROW][C]117[/C][C]0.253822714156058[/C][C]0.507645428312117[/C][C]0.746177285843942[/C][/ROW]
[ROW][C]118[/C][C]0.210517653544311[/C][C]0.421035307088622[/C][C]0.789482346455689[/C][/ROW]
[ROW][C]119[/C][C]0.172480074863901[/C][C]0.344960149727802[/C][C]0.827519925136099[/C][/ROW]
[ROW][C]120[/C][C]0.138991030744567[/C][C]0.277982061489134[/C][C]0.861008969255433[/C][/ROW]
[ROW][C]121[/C][C]0.109259449497953[/C][C]0.218518898995905[/C][C]0.890740550502047[/C][/ROW]
[ROW][C]122[/C][C]0.0849031802223775[/C][C]0.169806360444755[/C][C]0.915096819777622[/C][/ROW]
[ROW][C]123[/C][C]0.0711719055548751[/C][C]0.14234381110975[/C][C]0.928828094445125[/C][/ROW]
[ROW][C]124[/C][C]0.093729234831544[/C][C]0.187458469663088[/C][C]0.906270765168456[/C][/ROW]
[ROW][C]125[/C][C]0.0714947657244654[/C][C]0.142989531448931[/C][C]0.928505234275535[/C][/ROW]
[ROW][C]126[/C][C]0.0599521415941485[/C][C]0.119904283188297[/C][C]0.940047858405851[/C][/ROW]
[ROW][C]127[/C][C]0.0435649858419453[/C][C]0.0871299716838907[/C][C]0.956435014158055[/C][/ROW]
[ROW][C]128[/C][C]0.031264154034022[/C][C]0.062528308068044[/C][C]0.968735845965978[/C][/ROW]
[ROW][C]129[/C][C]0.0219640865689865[/C][C]0.0439281731379729[/C][C]0.978035913431014[/C][/ROW]
[ROW][C]130[/C][C]0.0151166354589887[/C][C]0.0302332709179773[/C][C]0.984883364541011[/C][/ROW]
[ROW][C]131[/C][C]0.00979585731719564[/C][C]0.0195917146343913[/C][C]0.990204142682804[/C][/ROW]
[ROW][C]132[/C][C]0.00614452213328312[/C][C]0.0122890442665662[/C][C]0.993855477866717[/C][/ROW]
[ROW][C]133[/C][C]0.0114625283520883[/C][C]0.0229250567041765[/C][C]0.988537471647912[/C][/ROW]
[ROW][C]134[/C][C]0.00751385817249453[/C][C]0.0150277163449891[/C][C]0.992486141827505[/C][/ROW]
[ROW][C]135[/C][C]0.0048628651544505[/C][C]0.00972573030890101[/C][C]0.995137134845549[/C][/ROW]
[ROW][C]136[/C][C]0.00314627055425175[/C][C]0.00629254110850349[/C][C]0.996853729445748[/C][/ROW]
[ROW][C]137[/C][C]0.00731442461348139[/C][C]0.0146288492269628[/C][C]0.992685575386519[/C][/ROW]
[ROW][C]138[/C][C]0.00662118139279961[/C][C]0.0132423627855992[/C][C]0.9933788186072[/C][/ROW]
[ROW][C]139[/C][C]0.0053232057025267[/C][C]0.0106464114050534[/C][C]0.994676794297473[/C][/ROW]
[ROW][C]140[/C][C]0.00282582924262829[/C][C]0.00565165848525658[/C][C]0.997174170757372[/C][/ROW]
[ROW][C]141[/C][C]0.0378644924993364[/C][C]0.0757289849986727[/C][C]0.962135507500664[/C][/ROW]
[ROW][C]142[/C][C]0.0275371376031953[/C][C]0.0550742752063906[/C][C]0.972462862396805[/C][/ROW]
[ROW][C]143[/C][C]0.0170175558606999[/C][C]0.0340351117213999[/C][C]0.9829824441393[/C][/ROW]
[ROW][C]144[/C][C]0.0082965772863638[/C][C]0.0165931545727276[/C][C]0.991703422713636[/C][/ROW]
[ROW][C]145[/C][C]0.00378418287640692[/C][C]0.00756836575281384[/C][C]0.996215817123593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202698&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202698&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
94.72037660182768e-949.44075320365536e-941
103.41843939435462e-636.83687878870924e-631
116.83002339770546e-821.36600467954109e-811
129.5314090320936e-921.90628180641872e-911
131.812880520043e-1213.625761040086e-1211
145.44614806566947e-1211.08922961313389e-1201
153.31622069659942e-1366.63244139319885e-1361
16001
170.511712094621460.9765758107570790.48828790537854
180.4511033323969760.9022066647939520.548896667603024
190.3735317217744470.7470634435488940.626468278225553
200.8303258997528440.3393482004943110.169674100247156
210.7773284723110580.4453430553778850.222671527688942
220.7821451571788450.4357096856423090.217854842821155
230.7252843812233020.5494312375533950.274715618776698
240.6624378381682780.6751243236634440.337562161831722
250.7023032620521390.5953934758957210.297696737947861
260.6819557958933860.6360884082132290.318044204106614
270.6298213256877020.7403573486245950.370178674312298
280.5796148804809580.8407702390380840.420385119519042
290.5211159416349540.9577681167300920.478884058365046
300.4605700026491390.9211400052982780.539429997350861
310.4034920531506180.8069841063012360.596507946849382
320.3492513497863660.6985026995727320.650748650213634
330.2953550483193930.5907100966387860.704644951680607
340.2593806913307870.5187613826615740.740619308669213
350.2162950705118780.4325901410237560.783704929488122
360.1775893138719950.3551786277439890.822410686128005
370.2370002631106140.4740005262212280.762999736889386
380.2074245311979450.4148490623958890.792575468802055
390.1698722513062450.3397445026124890.830127748693755
400.160547393259320.3210947865186410.83945260674068
410.6872534400640670.6254931198718670.312746559935933
420.66550064183080.66899871633840.3344993581692
430.6160020189043030.7679959621913950.383997981095697
440.5769857064075860.8460285871848270.423014293592414
450.5263346045167710.9473307909664570.473665395483229
460.4750660087406650.9501320174813290.524933991259335
470.4246070806171330.8492141612342660.575392919382867
480.3754123106205870.7508246212411750.624587689379413
490.3283253483350870.6566506966701750.671674651664913
500.2840161849929860.5680323699859730.715983815007014
510.3431085292752380.6862170585504760.656891470724762
520.6337275897800240.7325448204399510.366272410219976
530.5883286813666520.8233426372666960.411671318633348
540.9292051095381450.1415897809237110.0707948904618555
550.9112898743811140.1774202512377710.0887101256188856
560.9484331018318610.1031337963362780.051566898168139
570.9520864590572110.09582708188557840.0479135409427892
580.9389490369147840.1221019261704320.0610509630852158
590.9231610646625780.1536778706748440.076838935337422
600.9745645785297090.0508708429405820.025435421470291
610.9723594777882910.05528104442341820.0276405222117091
620.9742392550858190.0515214898283630.0257607449141815
630.9664653041289320.06706939174213580.0335346958710679
640.9674548682129270.06509026357414690.0325451317870734
650.9580861544432750.08382769111345050.0419138455567252
660.9466323472626950.1067353054746090.0533676527373047
670.9822239866360830.0355520267278350.0177760133639175
680.9763482111022370.04730357779552590.023651788897763
690.9691451569902870.0617096860194250.0308548430097125
700.9703698539666070.05926029206678560.0296301460333928
710.9617793225796020.07644135484079620.0382206774203981
720.9512438308212380.09751233835752470.0487561691787623
730.9547045271807980.09059094563840370.0452954728192018
740.9572801862476750.08543962750465080.0427198137523254
750.9458708053847190.1082583892305610.0541291946152806
760.9459106404016460.1081787191967090.0540893595983543
770.9322027569315370.1355944861369250.0677972430684625
780.938161059456780.123677881086440.06183894054322
790.9828322400682490.03433551986350180.0171677599317509
800.978574563626090.04285087274782010.02142543637391
810.9719037209183630.05619255816327490.0280962790816375
820.974474146823710.05105170635258020.0255258531762901
830.9667609460552390.06647810788952110.0332390539447605
840.9985529714156740.00289405716865250.00144702858432625
850.9978568816925080.004286236614984660.00214311830749233
860.9968917159924840.006216568015032440.00310828400751622
870.9955471792021140.00890564159577110.00445282079788555
880.9940777611988590.01184447760228240.00592223880114122
890.9916928506796030.0166142986407930.00830714932039651
900.9884934878926020.02301302421479610.011506512107398
910.9841806578884810.03163868422303710.0158193421115186
920.9810164639791840.03796707204163290.0189835360208165
930.9744239631902090.05115207361958140.0255760368097907
940.9661400966302510.06771980673949730.0338599033697486
950.9590511552339750.08189768953204950.0409488447660248
960.9468844745609920.1062310508780160.0531155254390078
970.9374895562401080.1250208875197830.0625104437598915
980.9205617158675990.1588765682648020.0794382841324009
990.9002267465420610.1995465069158780.0997732534579389
1000.8763180801576530.2473638396846940.123681919842347
1010.8484314101773060.3031371796453880.151568589822694
1020.8166597265438590.3666805469122830.183340273456141
1030.7808989427486220.4382021145027560.219101057251378
1040.7412797296168650.517440540766270.258720270383135
1050.7124150772316040.5751698455367910.287584922768396
1060.6669560355749780.6660879288500450.333043964425023
1070.6187172977309020.7625654045381960.381282702269098
1080.5805119017281470.8389761965437060.419488098271853
1090.5287762505299410.9424474989401170.471223749470059
1100.4756967276916710.9513934553833420.524303272308329
1110.4353308590889330.8706617181778670.564669140911067
1120.4000635909623060.8001271819246130.599936409037694
1130.4410482856051240.8820965712102480.558951714394876
1140.4030160376512070.8060320753024130.596983962348793
1150.350295486222590.7005909724451790.64970451377741
1160.3010200596902990.6020401193805980.698979940309701
1170.2538227141560580.5076454283121170.746177285843942
1180.2105176535443110.4210353070886220.789482346455689
1190.1724800748639010.3449601497278020.827519925136099
1200.1389910307445670.2779820614891340.861008969255433
1210.1092594494979530.2185188989959050.890740550502047
1220.08490318022237750.1698063604447550.915096819777622
1230.07117190555487510.142343811109750.928828094445125
1240.0937292348315440.1874584696630880.906270765168456
1250.07149476572446540.1429895314489310.928505234275535
1260.05995214159414850.1199042831882970.940047858405851
1270.04356498584194530.08712997168389070.956435014158055
1280.0312641540340220.0625283080680440.968735845965978
1290.02196408656898650.04392817313797290.978035913431014
1300.01511663545898870.03023327091797730.984883364541011
1310.009795857317195640.01959171463439130.990204142682804
1320.006144522133283120.01228904426656620.993855477866717
1330.01146252835208830.02292505670417650.988537471647912
1340.007513858172494530.01502771634498910.992486141827505
1350.00486286515445050.009725730308901010.995137134845549
1360.003146270554251750.006292541108503490.996853729445748
1370.007314424613481390.01462884922696280.992685575386519
1380.006621181392799610.01324236278559920.9933788186072
1390.00532320570252670.01064641140505340.994676794297473
1400.002825829242628290.005651658485256580.997174170757372
1410.03786449249933640.07572898499867270.962135507500664
1420.02753713760319530.05507427520639060.972462862396805
1430.01701755586069990.03403511172139990.9829824441393
1440.00829657728636380.01659315457272760.991703422713636
1450.003784182876406920.007568365752813840.996215817123593






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.116788321167883NOK
5% type I error level360.262773722627737NOK
10% type I error level590.430656934306569NOK

\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 & 16 & 0.116788321167883 & NOK \tabularnewline
5% type I error level & 36 & 0.262773722627737 & NOK \tabularnewline
10% type I error level & 59 & 0.430656934306569 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202698&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]16[/C][C]0.116788321167883[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.262773722627737[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.430656934306569[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202698&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202698&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 level160.116788321167883NOK
5% type I error level360.262773722627737NOK
10% type I error level590.430656934306569NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}