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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 16:57:42 -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/21/t1356127255r1xxb4d1k8yakha.htm/, Retrieved Thu, 28 Mar 2024 20:40:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204332, Retrieved Thu, 28 Mar 2024 20:40:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact53
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multiple regressi...] [2012-12-21 21:57:42] [f49ded22e47bf433de387b207efbd53c] [Current]
- R  D    [Multiple Regression] [multiple regressi...] [2012-12-21 22:13:27] [9e2edbc475ba2b100cc6940e0b163d75]
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Dataseries X:
1	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	0	0	0	1	1
0	0	0	0	0	0
0	1	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
1	1	0	0	0	0
0	0	0	0	0	0
0	0	1	0	1	0
1	1	0	0	0	0
0	0	1	0	1	1
0	1	1	0	1	1
1	1	1	1	1	0
1	1	0	0	0	0
0	0	0	0	0	1
0	1	1	1	1	1
1	0	0	0	1	0
1	0	1	0	1	1
0	0	0	0	1	1
1	0	0	0	1	1
0	1	1	0	0	1
0	0	1	0	1	0
1	0	0	0	0	1
0	0	1	0	0	0
0	0	0	0	0	1
0	0	0	0	1	0
0	0	0	0	0	0
1	0	0	0	0	0
1	0	0	0	1	0
0	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
1	1	1	0	1	0
0	0	1	0	0	1
0	0	0	0	1	1
0	1	0	0	1	0
0	0	1	1	1	1
0	0	1	0	0	1
1	0	0	0	1	1
1	1	0	0	0	0
0	0	0	0	1	0
0	0	0	0	1	1
0	0	0	0	0	0
0	0	0	0	0	1
0	0	0	0	1	1
0	0	0	0	0	0
0	1	1	0	0	0
1	1	1	1	1	0
0	0	0	0	0	1
0	0	1	1	0	0
0	0	0	0	0	0
0	1	1	0	0	1
0	0	1	0	1	1
0	0	0	0	0	1
0	0	0	0	0	1
1	1	1	1	1	1
1	1	0	0	0	1
0	0	1	0	1	0
0	0	0	0	0	0
1	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	1	1	1	1	0
1	0	0	0	0	0
0	0	0	0	0	1
0	0	1	0	0	0
0	0	0	0	0	0
0	0	0	0	0	1
0	0	1	0	0	1
1	0	1	0	0	0
0	0	0	0	0	1
0	1	0	0	1	1
0	0	0	0	0	1
0	0	1	0	1	1
0	1	1	1	0	1
0	1	0	0	1	0
0	0	0	0	0	0
1	0	1	0	0	1
0	0	0	0	0	0
0	0	1	1	0	0
0	0	0	0	1	1
1	0	0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204332&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204332&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204332&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
UseLimit[t] = + 0.235134338868033 + 0.289167525349813T40[t] -0.110382655507169Used[t] -0.0208324295233631CorrectAnalysis[t] + 0.0963805213140549Useful[t] -0.0621403911301808Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
UseLimit[t] =  +  0.235134338868033 +  0.289167525349813T40[t] -0.110382655507169Used[t] -0.0208324295233631CorrectAnalysis[t] +  0.0963805213140549Useful[t] -0.0621403911301808Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204332&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]UseLimit[t] =  +  0.235134338868033 +  0.289167525349813T40[t] -0.110382655507169Used[t] -0.0208324295233631CorrectAnalysis[t] +  0.0963805213140549Useful[t] -0.0621403911301808Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204332&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204332&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
UseLimit[t] = + 0.235134338868033 + 0.289167525349813T40[t] -0.110382655507169Used[t] -0.0208324295233631CorrectAnalysis[t] + 0.0963805213140549Useful[t] -0.0621403911301808Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2351343388680330.0778693.01960.0033960.001698
T400.2891675253498130.1135732.54610.0128130.006407
Used-0.1103826555071690.119794-0.92140.3595920.179796
CorrectAnalysis-0.02083242952336310.186514-0.11170.9113460.455673
Useful0.09638052131405490.1057720.91120.3649240.182462
Outcome-0.06214039113018080.097318-0.63850.5249540.262477

\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) & 0.235134338868033 & 0.077869 & 3.0196 & 0.003396 & 0.001698 \tabularnewline
T40 & 0.289167525349813 & 0.113573 & 2.5461 & 0.012813 & 0.006407 \tabularnewline
Used & -0.110382655507169 & 0.119794 & -0.9214 & 0.359592 & 0.179796 \tabularnewline
CorrectAnalysis & -0.0208324295233631 & 0.186514 & -0.1117 & 0.911346 & 0.455673 \tabularnewline
Useful & 0.0963805213140549 & 0.105772 & 0.9112 & 0.364924 & 0.182462 \tabularnewline
Outcome & -0.0621403911301808 & 0.097318 & -0.6385 & 0.524954 & 0.262477 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204332&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]0.235134338868033[/C][C]0.077869[/C][C]3.0196[/C][C]0.003396[/C][C]0.001698[/C][/ROW]
[ROW][C]T40[/C][C]0.289167525349813[/C][C]0.113573[/C][C]2.5461[/C][C]0.012813[/C][C]0.006407[/C][/ROW]
[ROW][C]Used[/C][C]-0.110382655507169[/C][C]0.119794[/C][C]-0.9214[/C][C]0.359592[/C][C]0.179796[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]-0.0208324295233631[/C][C]0.186514[/C][C]-0.1117[/C][C]0.911346[/C][C]0.455673[/C][/ROW]
[ROW][C]Useful[/C][C]0.0963805213140549[/C][C]0.105772[/C][C]0.9112[/C][C]0.364924[/C][C]0.182462[/C][/ROW]
[ROW][C]Outcome[/C][C]-0.0621403911301808[/C][C]0.097318[/C][C]-0.6385[/C][C]0.524954[/C][C]0.262477[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204332&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204332&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2351343388680330.0778693.01960.0033960.001698
T400.2891675253498130.1135732.54610.0128130.006407
Used-0.1103826555071690.119794-0.92140.3595920.179796
CorrectAnalysis-0.02083242952336310.186514-0.11170.9113460.455673
Useful0.09638052131405490.1057720.91120.3649240.182462
Outcome-0.06214039113018080.097318-0.63850.5249540.262477







Multiple Linear Regression - Regression Statistics
Multiple R0.307272011961554
R-squared0.0944160893349011
Adjusted R-squared0.0378170949183324
F-TEST (value)1.66815842415854
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value0.151854778810414
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.44255942628647
Sum Squared Residuals15.6687076636008

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.307272011961554 \tabularnewline
R-squared & 0.0944160893349011 \tabularnewline
Adjusted R-squared & 0.0378170949183324 \tabularnewline
F-TEST (value) & 1.66815842415854 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 0.151854778810414 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.44255942628647 \tabularnewline
Sum Squared Residuals & 15.6687076636008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204332&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.307272011961554[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0944160893349011[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0378170949183324[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.66815842415854[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]0.151854778810414[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.44255942628647[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]15.6687076636008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204332&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204332&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.307272011961554
R-squared0.0944160893349011
Adjusted R-squared0.0378170949183324
F-TEST (value)1.66815842415854
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value0.151854778810414
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.44255942628647
Sum Squared Residuals15.6687076636008







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.4621614730876660.537838526912334
200.235134338868033-0.235134338868033
300.235134338868033-0.235134338868033
400.235134338868033-0.235134338868033
500.235134338868033-0.235134338868033
610.2693744690519080.730625530948092
700.235134338868033-0.235134338868033
800.524301864217847-0.524301864217847
900.172993947737853-0.172993947737853
1010.2351343388680330.764865661131967
1110.5243018642178470.475698135782153
1200.235134338868033-0.235134338868033
1300.22113220467492-0.22113220467492
1410.5243018642178470.475698135782153
1500.158991813544739-0.158991813544739
1600.448159338894552-0.448159338894552
1710.489467300501370.51053269949863
1810.5243018642178470.475698135782153
1900.172993947737853-0.172993947737853
2000.427326909371189-0.427326909371189
2110.3315148601820880.668485139817912
2210.1589918135447390.841008186455261
2300.269374469051907-0.269374469051907
2410.2693744690519080.730625530948092
2500.351778817580497-0.351778817580497
2600.22113220467492-0.22113220467492
2710.1729939477378530.827006052262147
2800.124751683360865-0.124751683360865
2900.172993947737853-0.172993947737853
3000.331514860182088-0.331514860182088
3100.235134338868033-0.235134338868033
3210.2351343388680330.764865661131967
3310.3315148601820880.668485139817912
3400.462161473087666-0.462161473087666
3500.235134338868033-0.235134338868033
3600.235134338868033-0.235134338868033
3710.5102997300247330.489700269975267
3800.0626112922306838-0.0626112922306838
3900.269374469051907-0.269374469051907
4000.620682385531902-0.620682385531902
4100.138159384021376-0.138159384021376
4200.0626112922306838-0.0626112922306838
4310.2693744690519080.730625530948092
4410.5243018642178470.475698135782153
4500.331514860182088-0.331514860182088
4600.269374469051907-0.269374469051907
4700.235134338868033-0.235134338868033
4800.172993947737853-0.172993947737853
4900.269374469051907-0.269374469051907
5000.235134338868033-0.235134338868033
5100.413919208710678-0.413919208710678
5210.489467300501370.51053269949863
5300.172993947737853-0.172993947737853
5400.103919253837501-0.103919253837501
5500.235134338868033-0.235134338868033
5600.351778817580497-0.351778817580497
5700.158991813544739-0.158991813544739
5800.172993947737853-0.172993947737853
5900.172993947737853-0.172993947737853
6010.4273269093711890.572673090628811
6110.4621614730876660.537838526912334
6200.22113220467492-0.22113220467492
6300.235134338868033-0.235134338868033
6410.4621614730876660.537838526912334
6500.235134338868033-0.235134338868033
6600.235134338868033-0.235134338868033
6700.48946730050137-0.48946730050137
6810.2351343388680330.764865661131967
6900.172993947737853-0.172993947737853
7000.124751683360865-0.124751683360865
7100.235134338868033-0.235134338868033
7200.172993947737853-0.172993947737853
7300.0626112922306838-0.0626112922306838
7410.1247516833608650.875248316639135
7500.172993947737853-0.172993947737853
7600.558541994401721-0.558541994401721
7700.172993947737853-0.172993947737853
7800.158991813544739-0.158991813544739
7900.330946388057134-0.330946388057134
8000.620682385531902-0.620682385531902
8100.235134338868033-0.235134338868033
8210.06261129223068380.937388707769316
8300.235134338868033-0.235134338868033
8400.103919253837501-0.103919253837501
8500.269374469051907-0.269374469051907
8610.2351343388680330.764865661131967

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.462161473087666 & 0.537838526912334 \tabularnewline
2 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
3 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
4 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
5 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
6 & 1 & 0.269374469051908 & 0.730625530948092 \tabularnewline
7 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
8 & 0 & 0.524301864217847 & -0.524301864217847 \tabularnewline
9 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
10 & 1 & 0.235134338868033 & 0.764865661131967 \tabularnewline
11 & 1 & 0.524301864217847 & 0.475698135782153 \tabularnewline
12 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
13 & 0 & 0.22113220467492 & -0.22113220467492 \tabularnewline
14 & 1 & 0.524301864217847 & 0.475698135782153 \tabularnewline
15 & 0 & 0.158991813544739 & -0.158991813544739 \tabularnewline
16 & 0 & 0.448159338894552 & -0.448159338894552 \tabularnewline
17 & 1 & 0.48946730050137 & 0.51053269949863 \tabularnewline
18 & 1 & 0.524301864217847 & 0.475698135782153 \tabularnewline
19 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
20 & 0 & 0.427326909371189 & -0.427326909371189 \tabularnewline
21 & 1 & 0.331514860182088 & 0.668485139817912 \tabularnewline
22 & 1 & 0.158991813544739 & 0.841008186455261 \tabularnewline
23 & 0 & 0.269374469051907 & -0.269374469051907 \tabularnewline
24 & 1 & 0.269374469051908 & 0.730625530948092 \tabularnewline
25 & 0 & 0.351778817580497 & -0.351778817580497 \tabularnewline
26 & 0 & 0.22113220467492 & -0.22113220467492 \tabularnewline
27 & 1 & 0.172993947737853 & 0.827006052262147 \tabularnewline
28 & 0 & 0.124751683360865 & -0.124751683360865 \tabularnewline
29 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
30 & 0 & 0.331514860182088 & -0.331514860182088 \tabularnewline
31 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
32 & 1 & 0.235134338868033 & 0.764865661131967 \tabularnewline
33 & 1 & 0.331514860182088 & 0.668485139817912 \tabularnewline
34 & 0 & 0.462161473087666 & -0.462161473087666 \tabularnewline
35 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
36 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
37 & 1 & 0.510299730024733 & 0.489700269975267 \tabularnewline
38 & 0 & 0.0626112922306838 & -0.0626112922306838 \tabularnewline
39 & 0 & 0.269374469051907 & -0.269374469051907 \tabularnewline
40 & 0 & 0.620682385531902 & -0.620682385531902 \tabularnewline
41 & 0 & 0.138159384021376 & -0.138159384021376 \tabularnewline
42 & 0 & 0.0626112922306838 & -0.0626112922306838 \tabularnewline
43 & 1 & 0.269374469051908 & 0.730625530948092 \tabularnewline
44 & 1 & 0.524301864217847 & 0.475698135782153 \tabularnewline
45 & 0 & 0.331514860182088 & -0.331514860182088 \tabularnewline
46 & 0 & 0.269374469051907 & -0.269374469051907 \tabularnewline
47 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
48 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
49 & 0 & 0.269374469051907 & -0.269374469051907 \tabularnewline
50 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
51 & 0 & 0.413919208710678 & -0.413919208710678 \tabularnewline
52 & 1 & 0.48946730050137 & 0.51053269949863 \tabularnewline
53 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
54 & 0 & 0.103919253837501 & -0.103919253837501 \tabularnewline
55 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
56 & 0 & 0.351778817580497 & -0.351778817580497 \tabularnewline
57 & 0 & 0.158991813544739 & -0.158991813544739 \tabularnewline
58 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
59 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
60 & 1 & 0.427326909371189 & 0.572673090628811 \tabularnewline
61 & 1 & 0.462161473087666 & 0.537838526912334 \tabularnewline
62 & 0 & 0.22113220467492 & -0.22113220467492 \tabularnewline
63 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
64 & 1 & 0.462161473087666 & 0.537838526912334 \tabularnewline
65 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
66 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
67 & 0 & 0.48946730050137 & -0.48946730050137 \tabularnewline
68 & 1 & 0.235134338868033 & 0.764865661131967 \tabularnewline
69 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
70 & 0 & 0.124751683360865 & -0.124751683360865 \tabularnewline
71 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
72 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
73 & 0 & 0.0626112922306838 & -0.0626112922306838 \tabularnewline
74 & 1 & 0.124751683360865 & 0.875248316639135 \tabularnewline
75 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
76 & 0 & 0.558541994401721 & -0.558541994401721 \tabularnewline
77 & 0 & 0.172993947737853 & -0.172993947737853 \tabularnewline
78 & 0 & 0.158991813544739 & -0.158991813544739 \tabularnewline
79 & 0 & 0.330946388057134 & -0.330946388057134 \tabularnewline
80 & 0 & 0.620682385531902 & -0.620682385531902 \tabularnewline
81 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
82 & 1 & 0.0626112922306838 & 0.937388707769316 \tabularnewline
83 & 0 & 0.235134338868033 & -0.235134338868033 \tabularnewline
84 & 0 & 0.103919253837501 & -0.103919253837501 \tabularnewline
85 & 0 & 0.269374469051907 & -0.269374469051907 \tabularnewline
86 & 1 & 0.235134338868033 & 0.764865661131967 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204332&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]0.462161473087666[/C][C]0.537838526912334[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.269374469051908[/C][C]0.730625530948092[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.524301864217847[/C][C]-0.524301864217847[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.235134338868033[/C][C]0.764865661131967[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.524301864217847[/C][C]0.475698135782153[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.22113220467492[/C][C]-0.22113220467492[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.524301864217847[/C][C]0.475698135782153[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.158991813544739[/C][C]-0.158991813544739[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.448159338894552[/C][C]-0.448159338894552[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.48946730050137[/C][C]0.51053269949863[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.524301864217847[/C][C]0.475698135782153[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.427326909371189[/C][C]-0.427326909371189[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.331514860182088[/C][C]0.668485139817912[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.158991813544739[/C][C]0.841008186455261[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.269374469051907[/C][C]-0.269374469051907[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.269374469051908[/C][C]0.730625530948092[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.351778817580497[/C][C]-0.351778817580497[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.22113220467492[/C][C]-0.22113220467492[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.172993947737853[/C][C]0.827006052262147[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.124751683360865[/C][C]-0.124751683360865[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.331514860182088[/C][C]-0.331514860182088[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.235134338868033[/C][C]0.764865661131967[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.331514860182088[/C][C]0.668485139817912[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.462161473087666[/C][C]-0.462161473087666[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.510299730024733[/C][C]0.489700269975267[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.0626112922306838[/C][C]-0.0626112922306838[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.269374469051907[/C][C]-0.269374469051907[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.620682385531902[/C][C]-0.620682385531902[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.138159384021376[/C][C]-0.138159384021376[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.0626112922306838[/C][C]-0.0626112922306838[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.269374469051908[/C][C]0.730625530948092[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.524301864217847[/C][C]0.475698135782153[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.331514860182088[/C][C]-0.331514860182088[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.269374469051907[/C][C]-0.269374469051907[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.269374469051907[/C][C]-0.269374469051907[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.413919208710678[/C][C]-0.413919208710678[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.48946730050137[/C][C]0.51053269949863[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.103919253837501[/C][C]-0.103919253837501[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.351778817580497[/C][C]-0.351778817580497[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.158991813544739[/C][C]-0.158991813544739[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.427326909371189[/C][C]0.572673090628811[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.462161473087666[/C][C]0.537838526912334[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.22113220467492[/C][C]-0.22113220467492[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.462161473087666[/C][C]0.537838526912334[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.48946730050137[/C][C]-0.48946730050137[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.235134338868033[/C][C]0.764865661131967[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.124751683360865[/C][C]-0.124751683360865[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.0626112922306838[/C][C]-0.0626112922306838[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.124751683360865[/C][C]0.875248316639135[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.558541994401721[/C][C]-0.558541994401721[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.172993947737853[/C][C]-0.172993947737853[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.158991813544739[/C][C]-0.158991813544739[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0.330946388057134[/C][C]-0.330946388057134[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.620682385531902[/C][C]-0.620682385531902[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.0626112922306838[/C][C]0.937388707769316[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.235134338868033[/C][C]-0.235134338868033[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.103919253837501[/C][C]-0.103919253837501[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.269374469051907[/C][C]-0.269374469051907[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.235134338868033[/C][C]0.764865661131967[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204332&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204332&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.4621614730876660.537838526912334
200.235134338868033-0.235134338868033
300.235134338868033-0.235134338868033
400.235134338868033-0.235134338868033
500.235134338868033-0.235134338868033
610.2693744690519080.730625530948092
700.235134338868033-0.235134338868033
800.524301864217847-0.524301864217847
900.172993947737853-0.172993947737853
1010.2351343388680330.764865661131967
1110.5243018642178470.475698135782153
1200.235134338868033-0.235134338868033
1300.22113220467492-0.22113220467492
1410.5243018642178470.475698135782153
1500.158991813544739-0.158991813544739
1600.448159338894552-0.448159338894552
1710.489467300501370.51053269949863
1810.5243018642178470.475698135782153
1900.172993947737853-0.172993947737853
2000.427326909371189-0.427326909371189
2110.3315148601820880.668485139817912
2210.1589918135447390.841008186455261
2300.269374469051907-0.269374469051907
2410.2693744690519080.730625530948092
2500.351778817580497-0.351778817580497
2600.22113220467492-0.22113220467492
2710.1729939477378530.827006052262147
2800.124751683360865-0.124751683360865
2900.172993947737853-0.172993947737853
3000.331514860182088-0.331514860182088
3100.235134338868033-0.235134338868033
3210.2351343388680330.764865661131967
3310.3315148601820880.668485139817912
3400.462161473087666-0.462161473087666
3500.235134338868033-0.235134338868033
3600.235134338868033-0.235134338868033
3710.5102997300247330.489700269975267
3800.0626112922306838-0.0626112922306838
3900.269374469051907-0.269374469051907
4000.620682385531902-0.620682385531902
4100.138159384021376-0.138159384021376
4200.0626112922306838-0.0626112922306838
4310.2693744690519080.730625530948092
4410.5243018642178470.475698135782153
4500.331514860182088-0.331514860182088
4600.269374469051907-0.269374469051907
4700.235134338868033-0.235134338868033
4800.172993947737853-0.172993947737853
4900.269374469051907-0.269374469051907
5000.235134338868033-0.235134338868033
5100.413919208710678-0.413919208710678
5210.489467300501370.51053269949863
5300.172993947737853-0.172993947737853
5400.103919253837501-0.103919253837501
5500.235134338868033-0.235134338868033
5600.351778817580497-0.351778817580497
5700.158991813544739-0.158991813544739
5800.172993947737853-0.172993947737853
5900.172993947737853-0.172993947737853
6010.4273269093711890.572673090628811
6110.4621614730876660.537838526912334
6200.22113220467492-0.22113220467492
6300.235134338868033-0.235134338868033
6410.4621614730876660.537838526912334
6500.235134338868033-0.235134338868033
6600.235134338868033-0.235134338868033
6700.48946730050137-0.48946730050137
6810.2351343388680330.764865661131967
6900.172993947737853-0.172993947737853
7000.124751683360865-0.124751683360865
7100.235134338868033-0.235134338868033
7200.172993947737853-0.172993947737853
7300.0626112922306838-0.0626112922306838
7410.1247516833608650.875248316639135
7500.172993947737853-0.172993947737853
7600.558541994401721-0.558541994401721
7700.172993947737853-0.172993947737853
7800.158991813544739-0.158991813544739
7900.330946388057134-0.330946388057134
8000.620682385531902-0.620682385531902
8100.235134338868033-0.235134338868033
8210.06261129223068380.937388707769316
8300.235134338868033-0.235134338868033
8400.103919253837501-0.103919253837501
8500.269374469051907-0.269374469051907
8610.2351343388680330.764865661131967







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3326224654070970.6652449308141930.667377534592903
100.8224196666472530.3551606667054930.177580333352747
110.812948583816280.3741028323674410.18705141618372
120.7253215167992980.5493569664014050.274678483200702
130.6220098998551490.7559802002897020.377990100144851
140.5734536367595590.8530927264808830.426546363240441
150.4698225465895210.9396450931790410.530177453410479
160.476771953940630.953543907881260.52322804605937
170.3991962675147130.7983925350294260.600803732485287
180.3671058271466970.7342116542933940.632894172853303
190.2906988570908380.5813977141816750.709301142909162
200.356919948211210.713839896422420.64308005178879
210.3201079433595320.6402158867190630.679892056640468
220.6668594656638830.6662810686722350.333140534336117
230.7409561842666250.518087631466750.259043815733375
240.7561275523794230.4877448952411540.243872447620577
250.7066672313234950.5866655373530090.293332768676505
260.6529882664133670.6940234671732670.347011733586633
270.8027215478176340.3945569043647330.197278452182366
280.7726021553441870.4547956893116260.227397844655813
290.7279471870798730.5441056258402530.272052812920127
300.7524165434184590.4951669131630820.247583456581541
310.7089248155321570.5821503689356860.291075184467843
320.809212141520830.3815757169583390.19078785847917
330.846553240586040.306893518827920.15344675941396
340.8612831669948360.2774336660103270.138716833005164
350.8324614791442350.3350770417115290.167538520855765
360.7990039304765610.4019921390468790.200996069523439
370.8083006706895740.3833986586208520.191699329310426
380.7706310420772610.4587379158454770.229368957922739
390.7599358605991180.4801282788017630.240064139400882
400.8215334073789950.356933185242010.178466592621005
410.7783677499480820.4432645001038350.221632250051918
420.7297253589369230.5405492821261540.270274641063077
430.8333824696470140.3332350607059710.166617530352986
440.8430457203477530.3139085593044940.156954279652247
450.8252801224315050.349439755136990.174719877568495
460.7980389225520730.4039221548958550.201961077447927
470.7578562234150520.4842875531698950.242143776584948
480.7098855364007590.5802289271984820.290114463599241
490.6679899517489740.6640200965020530.332010048251026
500.616434530151930.767130939696140.38356546984807
510.635663925861050.72867214827790.36433607413895
520.6920831005851460.6158337988297090.307916899414854
530.635994328414720.7280113431705610.36400567158528
540.5720410288360260.8559179423279470.427958971163974
550.5168543762020110.9662912475959780.483145623797989
560.6049124570620740.7901750858758520.395087542937926
570.5366950748726380.9266098502547250.463304925127362
580.4720443241910120.9440886483820250.527955675808988
590.4087783581967730.8175567163935460.591221641803227
600.6381652425234840.7236695149530310.361834757476516
610.6325853003564950.734829399287010.367414699643505
620.5632169579936170.8735660840127660.436783042006383
630.5090942756451590.9818114487096830.490905724354841
640.5806492694184330.8387014611631340.419350730581567
650.5288722945152590.9422554109694820.471127705484741
660.4830757029425230.9661514058850450.516924297057477
670.435299102061530.8705982041230610.56470089793847
680.6166225439082730.7667549121834540.383377456091727
690.5295736505854880.9408526988290230.470426349414512
700.6217656092049410.7564687815901180.378234390795059
710.5714363770768580.8571272458462840.428563622923142
720.4689846724183950.9379693448367890.531015327581605
730.5912592279259880.8174815441480250.408740772074012
740.5311682076783970.9376635846432060.468831792321603
750.4266234124505710.8532468249011420.573376587549429
760.3152496415587490.6304992831174980.684750358441251
770.2435982255660830.4871964511321660.756401774433917

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.332622465407097 & 0.665244930814193 & 0.667377534592903 \tabularnewline
10 & 0.822419666647253 & 0.355160666705493 & 0.177580333352747 \tabularnewline
11 & 0.81294858381628 & 0.374102832367441 & 0.18705141618372 \tabularnewline
12 & 0.725321516799298 & 0.549356966401405 & 0.274678483200702 \tabularnewline
13 & 0.622009899855149 & 0.755980200289702 & 0.377990100144851 \tabularnewline
14 & 0.573453636759559 & 0.853092726480883 & 0.426546363240441 \tabularnewline
15 & 0.469822546589521 & 0.939645093179041 & 0.530177453410479 \tabularnewline
16 & 0.47677195394063 & 0.95354390788126 & 0.52322804605937 \tabularnewline
17 & 0.399196267514713 & 0.798392535029426 & 0.600803732485287 \tabularnewline
18 & 0.367105827146697 & 0.734211654293394 & 0.632894172853303 \tabularnewline
19 & 0.290698857090838 & 0.581397714181675 & 0.709301142909162 \tabularnewline
20 & 0.35691994821121 & 0.71383989642242 & 0.64308005178879 \tabularnewline
21 & 0.320107943359532 & 0.640215886719063 & 0.679892056640468 \tabularnewline
22 & 0.666859465663883 & 0.666281068672235 & 0.333140534336117 \tabularnewline
23 & 0.740956184266625 & 0.51808763146675 & 0.259043815733375 \tabularnewline
24 & 0.756127552379423 & 0.487744895241154 & 0.243872447620577 \tabularnewline
25 & 0.706667231323495 & 0.586665537353009 & 0.293332768676505 \tabularnewline
26 & 0.652988266413367 & 0.694023467173267 & 0.347011733586633 \tabularnewline
27 & 0.802721547817634 & 0.394556904364733 & 0.197278452182366 \tabularnewline
28 & 0.772602155344187 & 0.454795689311626 & 0.227397844655813 \tabularnewline
29 & 0.727947187079873 & 0.544105625840253 & 0.272052812920127 \tabularnewline
30 & 0.752416543418459 & 0.495166913163082 & 0.247583456581541 \tabularnewline
31 & 0.708924815532157 & 0.582150368935686 & 0.291075184467843 \tabularnewline
32 & 0.80921214152083 & 0.381575716958339 & 0.19078785847917 \tabularnewline
33 & 0.84655324058604 & 0.30689351882792 & 0.15344675941396 \tabularnewline
34 & 0.861283166994836 & 0.277433666010327 & 0.138716833005164 \tabularnewline
35 & 0.832461479144235 & 0.335077041711529 & 0.167538520855765 \tabularnewline
36 & 0.799003930476561 & 0.401992139046879 & 0.200996069523439 \tabularnewline
37 & 0.808300670689574 & 0.383398658620852 & 0.191699329310426 \tabularnewline
38 & 0.770631042077261 & 0.458737915845477 & 0.229368957922739 \tabularnewline
39 & 0.759935860599118 & 0.480128278801763 & 0.240064139400882 \tabularnewline
40 & 0.821533407378995 & 0.35693318524201 & 0.178466592621005 \tabularnewline
41 & 0.778367749948082 & 0.443264500103835 & 0.221632250051918 \tabularnewline
42 & 0.729725358936923 & 0.540549282126154 & 0.270274641063077 \tabularnewline
43 & 0.833382469647014 & 0.333235060705971 & 0.166617530352986 \tabularnewline
44 & 0.843045720347753 & 0.313908559304494 & 0.156954279652247 \tabularnewline
45 & 0.825280122431505 & 0.34943975513699 & 0.174719877568495 \tabularnewline
46 & 0.798038922552073 & 0.403922154895855 & 0.201961077447927 \tabularnewline
47 & 0.757856223415052 & 0.484287553169895 & 0.242143776584948 \tabularnewline
48 & 0.709885536400759 & 0.580228927198482 & 0.290114463599241 \tabularnewline
49 & 0.667989951748974 & 0.664020096502053 & 0.332010048251026 \tabularnewline
50 & 0.61643453015193 & 0.76713093969614 & 0.38356546984807 \tabularnewline
51 & 0.63566392586105 & 0.7286721482779 & 0.36433607413895 \tabularnewline
52 & 0.692083100585146 & 0.615833798829709 & 0.307916899414854 \tabularnewline
53 & 0.63599432841472 & 0.728011343170561 & 0.36400567158528 \tabularnewline
54 & 0.572041028836026 & 0.855917942327947 & 0.427958971163974 \tabularnewline
55 & 0.516854376202011 & 0.966291247595978 & 0.483145623797989 \tabularnewline
56 & 0.604912457062074 & 0.790175085875852 & 0.395087542937926 \tabularnewline
57 & 0.536695074872638 & 0.926609850254725 & 0.463304925127362 \tabularnewline
58 & 0.472044324191012 & 0.944088648382025 & 0.527955675808988 \tabularnewline
59 & 0.408778358196773 & 0.817556716393546 & 0.591221641803227 \tabularnewline
60 & 0.638165242523484 & 0.723669514953031 & 0.361834757476516 \tabularnewline
61 & 0.632585300356495 & 0.73482939928701 & 0.367414699643505 \tabularnewline
62 & 0.563216957993617 & 0.873566084012766 & 0.436783042006383 \tabularnewline
63 & 0.509094275645159 & 0.981811448709683 & 0.490905724354841 \tabularnewline
64 & 0.580649269418433 & 0.838701461163134 & 0.419350730581567 \tabularnewline
65 & 0.528872294515259 & 0.942255410969482 & 0.471127705484741 \tabularnewline
66 & 0.483075702942523 & 0.966151405885045 & 0.516924297057477 \tabularnewline
67 & 0.43529910206153 & 0.870598204123061 & 0.56470089793847 \tabularnewline
68 & 0.616622543908273 & 0.766754912183454 & 0.383377456091727 \tabularnewline
69 & 0.529573650585488 & 0.940852698829023 & 0.470426349414512 \tabularnewline
70 & 0.621765609204941 & 0.756468781590118 & 0.378234390795059 \tabularnewline
71 & 0.571436377076858 & 0.857127245846284 & 0.428563622923142 \tabularnewline
72 & 0.468984672418395 & 0.937969344836789 & 0.531015327581605 \tabularnewline
73 & 0.591259227925988 & 0.817481544148025 & 0.408740772074012 \tabularnewline
74 & 0.531168207678397 & 0.937663584643206 & 0.468831792321603 \tabularnewline
75 & 0.426623412450571 & 0.853246824901142 & 0.573376587549429 \tabularnewline
76 & 0.315249641558749 & 0.630499283117498 & 0.684750358441251 \tabularnewline
77 & 0.243598225566083 & 0.487196451132166 & 0.756401774433917 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204332&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]0.332622465407097[/C][C]0.665244930814193[/C][C]0.667377534592903[/C][/ROW]
[ROW][C]10[/C][C]0.822419666647253[/C][C]0.355160666705493[/C][C]0.177580333352747[/C][/ROW]
[ROW][C]11[/C][C]0.81294858381628[/C][C]0.374102832367441[/C][C]0.18705141618372[/C][/ROW]
[ROW][C]12[/C][C]0.725321516799298[/C][C]0.549356966401405[/C][C]0.274678483200702[/C][/ROW]
[ROW][C]13[/C][C]0.622009899855149[/C][C]0.755980200289702[/C][C]0.377990100144851[/C][/ROW]
[ROW][C]14[/C][C]0.573453636759559[/C][C]0.853092726480883[/C][C]0.426546363240441[/C][/ROW]
[ROW][C]15[/C][C]0.469822546589521[/C][C]0.939645093179041[/C][C]0.530177453410479[/C][/ROW]
[ROW][C]16[/C][C]0.47677195394063[/C][C]0.95354390788126[/C][C]0.52322804605937[/C][/ROW]
[ROW][C]17[/C][C]0.399196267514713[/C][C]0.798392535029426[/C][C]0.600803732485287[/C][/ROW]
[ROW][C]18[/C][C]0.367105827146697[/C][C]0.734211654293394[/C][C]0.632894172853303[/C][/ROW]
[ROW][C]19[/C][C]0.290698857090838[/C][C]0.581397714181675[/C][C]0.709301142909162[/C][/ROW]
[ROW][C]20[/C][C]0.35691994821121[/C][C]0.71383989642242[/C][C]0.64308005178879[/C][/ROW]
[ROW][C]21[/C][C]0.320107943359532[/C][C]0.640215886719063[/C][C]0.679892056640468[/C][/ROW]
[ROW][C]22[/C][C]0.666859465663883[/C][C]0.666281068672235[/C][C]0.333140534336117[/C][/ROW]
[ROW][C]23[/C][C]0.740956184266625[/C][C]0.51808763146675[/C][C]0.259043815733375[/C][/ROW]
[ROW][C]24[/C][C]0.756127552379423[/C][C]0.487744895241154[/C][C]0.243872447620577[/C][/ROW]
[ROW][C]25[/C][C]0.706667231323495[/C][C]0.586665537353009[/C][C]0.293332768676505[/C][/ROW]
[ROW][C]26[/C][C]0.652988266413367[/C][C]0.694023467173267[/C][C]0.347011733586633[/C][/ROW]
[ROW][C]27[/C][C]0.802721547817634[/C][C]0.394556904364733[/C][C]0.197278452182366[/C][/ROW]
[ROW][C]28[/C][C]0.772602155344187[/C][C]0.454795689311626[/C][C]0.227397844655813[/C][/ROW]
[ROW][C]29[/C][C]0.727947187079873[/C][C]0.544105625840253[/C][C]0.272052812920127[/C][/ROW]
[ROW][C]30[/C][C]0.752416543418459[/C][C]0.495166913163082[/C][C]0.247583456581541[/C][/ROW]
[ROW][C]31[/C][C]0.708924815532157[/C][C]0.582150368935686[/C][C]0.291075184467843[/C][/ROW]
[ROW][C]32[/C][C]0.80921214152083[/C][C]0.381575716958339[/C][C]0.19078785847917[/C][/ROW]
[ROW][C]33[/C][C]0.84655324058604[/C][C]0.30689351882792[/C][C]0.15344675941396[/C][/ROW]
[ROW][C]34[/C][C]0.861283166994836[/C][C]0.277433666010327[/C][C]0.138716833005164[/C][/ROW]
[ROW][C]35[/C][C]0.832461479144235[/C][C]0.335077041711529[/C][C]0.167538520855765[/C][/ROW]
[ROW][C]36[/C][C]0.799003930476561[/C][C]0.401992139046879[/C][C]0.200996069523439[/C][/ROW]
[ROW][C]37[/C][C]0.808300670689574[/C][C]0.383398658620852[/C][C]0.191699329310426[/C][/ROW]
[ROW][C]38[/C][C]0.770631042077261[/C][C]0.458737915845477[/C][C]0.229368957922739[/C][/ROW]
[ROW][C]39[/C][C]0.759935860599118[/C][C]0.480128278801763[/C][C]0.240064139400882[/C][/ROW]
[ROW][C]40[/C][C]0.821533407378995[/C][C]0.35693318524201[/C][C]0.178466592621005[/C][/ROW]
[ROW][C]41[/C][C]0.778367749948082[/C][C]0.443264500103835[/C][C]0.221632250051918[/C][/ROW]
[ROW][C]42[/C][C]0.729725358936923[/C][C]0.540549282126154[/C][C]0.270274641063077[/C][/ROW]
[ROW][C]43[/C][C]0.833382469647014[/C][C]0.333235060705971[/C][C]0.166617530352986[/C][/ROW]
[ROW][C]44[/C][C]0.843045720347753[/C][C]0.313908559304494[/C][C]0.156954279652247[/C][/ROW]
[ROW][C]45[/C][C]0.825280122431505[/C][C]0.34943975513699[/C][C]0.174719877568495[/C][/ROW]
[ROW][C]46[/C][C]0.798038922552073[/C][C]0.403922154895855[/C][C]0.201961077447927[/C][/ROW]
[ROW][C]47[/C][C]0.757856223415052[/C][C]0.484287553169895[/C][C]0.242143776584948[/C][/ROW]
[ROW][C]48[/C][C]0.709885536400759[/C][C]0.580228927198482[/C][C]0.290114463599241[/C][/ROW]
[ROW][C]49[/C][C]0.667989951748974[/C][C]0.664020096502053[/C][C]0.332010048251026[/C][/ROW]
[ROW][C]50[/C][C]0.61643453015193[/C][C]0.76713093969614[/C][C]0.38356546984807[/C][/ROW]
[ROW][C]51[/C][C]0.63566392586105[/C][C]0.7286721482779[/C][C]0.36433607413895[/C][/ROW]
[ROW][C]52[/C][C]0.692083100585146[/C][C]0.615833798829709[/C][C]0.307916899414854[/C][/ROW]
[ROW][C]53[/C][C]0.63599432841472[/C][C]0.728011343170561[/C][C]0.36400567158528[/C][/ROW]
[ROW][C]54[/C][C]0.572041028836026[/C][C]0.855917942327947[/C][C]0.427958971163974[/C][/ROW]
[ROW][C]55[/C][C]0.516854376202011[/C][C]0.966291247595978[/C][C]0.483145623797989[/C][/ROW]
[ROW][C]56[/C][C]0.604912457062074[/C][C]0.790175085875852[/C][C]0.395087542937926[/C][/ROW]
[ROW][C]57[/C][C]0.536695074872638[/C][C]0.926609850254725[/C][C]0.463304925127362[/C][/ROW]
[ROW][C]58[/C][C]0.472044324191012[/C][C]0.944088648382025[/C][C]0.527955675808988[/C][/ROW]
[ROW][C]59[/C][C]0.408778358196773[/C][C]0.817556716393546[/C][C]0.591221641803227[/C][/ROW]
[ROW][C]60[/C][C]0.638165242523484[/C][C]0.723669514953031[/C][C]0.361834757476516[/C][/ROW]
[ROW][C]61[/C][C]0.632585300356495[/C][C]0.73482939928701[/C][C]0.367414699643505[/C][/ROW]
[ROW][C]62[/C][C]0.563216957993617[/C][C]0.873566084012766[/C][C]0.436783042006383[/C][/ROW]
[ROW][C]63[/C][C]0.509094275645159[/C][C]0.981811448709683[/C][C]0.490905724354841[/C][/ROW]
[ROW][C]64[/C][C]0.580649269418433[/C][C]0.838701461163134[/C][C]0.419350730581567[/C][/ROW]
[ROW][C]65[/C][C]0.528872294515259[/C][C]0.942255410969482[/C][C]0.471127705484741[/C][/ROW]
[ROW][C]66[/C][C]0.483075702942523[/C][C]0.966151405885045[/C][C]0.516924297057477[/C][/ROW]
[ROW][C]67[/C][C]0.43529910206153[/C][C]0.870598204123061[/C][C]0.56470089793847[/C][/ROW]
[ROW][C]68[/C][C]0.616622543908273[/C][C]0.766754912183454[/C][C]0.383377456091727[/C][/ROW]
[ROW][C]69[/C][C]0.529573650585488[/C][C]0.940852698829023[/C][C]0.470426349414512[/C][/ROW]
[ROW][C]70[/C][C]0.621765609204941[/C][C]0.756468781590118[/C][C]0.378234390795059[/C][/ROW]
[ROW][C]71[/C][C]0.571436377076858[/C][C]0.857127245846284[/C][C]0.428563622923142[/C][/ROW]
[ROW][C]72[/C][C]0.468984672418395[/C][C]0.937969344836789[/C][C]0.531015327581605[/C][/ROW]
[ROW][C]73[/C][C]0.591259227925988[/C][C]0.817481544148025[/C][C]0.408740772074012[/C][/ROW]
[ROW][C]74[/C][C]0.531168207678397[/C][C]0.937663584643206[/C][C]0.468831792321603[/C][/ROW]
[ROW][C]75[/C][C]0.426623412450571[/C][C]0.853246824901142[/C][C]0.573376587549429[/C][/ROW]
[ROW][C]76[/C][C]0.315249641558749[/C][C]0.630499283117498[/C][C]0.684750358441251[/C][/ROW]
[ROW][C]77[/C][C]0.243598225566083[/C][C]0.487196451132166[/C][C]0.756401774433917[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204332&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204332&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3326224654070970.6652449308141930.667377534592903
100.8224196666472530.3551606667054930.177580333352747
110.812948583816280.3741028323674410.18705141618372
120.7253215167992980.5493569664014050.274678483200702
130.6220098998551490.7559802002897020.377990100144851
140.5734536367595590.8530927264808830.426546363240441
150.4698225465895210.9396450931790410.530177453410479
160.476771953940630.953543907881260.52322804605937
170.3991962675147130.7983925350294260.600803732485287
180.3671058271466970.7342116542933940.632894172853303
190.2906988570908380.5813977141816750.709301142909162
200.356919948211210.713839896422420.64308005178879
210.3201079433595320.6402158867190630.679892056640468
220.6668594656638830.6662810686722350.333140534336117
230.7409561842666250.518087631466750.259043815733375
240.7561275523794230.4877448952411540.243872447620577
250.7066672313234950.5866655373530090.293332768676505
260.6529882664133670.6940234671732670.347011733586633
270.8027215478176340.3945569043647330.197278452182366
280.7726021553441870.4547956893116260.227397844655813
290.7279471870798730.5441056258402530.272052812920127
300.7524165434184590.4951669131630820.247583456581541
310.7089248155321570.5821503689356860.291075184467843
320.809212141520830.3815757169583390.19078785847917
330.846553240586040.306893518827920.15344675941396
340.8612831669948360.2774336660103270.138716833005164
350.8324614791442350.3350770417115290.167538520855765
360.7990039304765610.4019921390468790.200996069523439
370.8083006706895740.3833986586208520.191699329310426
380.7706310420772610.4587379158454770.229368957922739
390.7599358605991180.4801282788017630.240064139400882
400.8215334073789950.356933185242010.178466592621005
410.7783677499480820.4432645001038350.221632250051918
420.7297253589369230.5405492821261540.270274641063077
430.8333824696470140.3332350607059710.166617530352986
440.8430457203477530.3139085593044940.156954279652247
450.8252801224315050.349439755136990.174719877568495
460.7980389225520730.4039221548958550.201961077447927
470.7578562234150520.4842875531698950.242143776584948
480.7098855364007590.5802289271984820.290114463599241
490.6679899517489740.6640200965020530.332010048251026
500.616434530151930.767130939696140.38356546984807
510.635663925861050.72867214827790.36433607413895
520.6920831005851460.6158337988297090.307916899414854
530.635994328414720.7280113431705610.36400567158528
540.5720410288360260.8559179423279470.427958971163974
550.5168543762020110.9662912475959780.483145623797989
560.6049124570620740.7901750858758520.395087542937926
570.5366950748726380.9266098502547250.463304925127362
580.4720443241910120.9440886483820250.527955675808988
590.4087783581967730.8175567163935460.591221641803227
600.6381652425234840.7236695149530310.361834757476516
610.6325853003564950.734829399287010.367414699643505
620.5632169579936170.8735660840127660.436783042006383
630.5090942756451590.9818114487096830.490905724354841
640.5806492694184330.8387014611631340.419350730581567
650.5288722945152590.9422554109694820.471127705484741
660.4830757029425230.9661514058850450.516924297057477
670.435299102061530.8705982041230610.56470089793847
680.6166225439082730.7667549121834540.383377456091727
690.5295736505854880.9408526988290230.470426349414512
700.6217656092049410.7564687815901180.378234390795059
710.5714363770768580.8571272458462840.428563622923142
720.4689846724183950.9379693448367890.531015327581605
730.5912592279259880.8174815441480250.408740772074012
740.5311682076783970.9376635846432060.468831792321603
750.4266234124505710.8532468249011420.573376587549429
760.3152496415587490.6304992831174980.684750358441251
770.2435982255660830.4871964511321660.756401774433917







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204332&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204332&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204332&T=6

As an alternative you can also use a QR Code:  

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

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



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