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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 28 Jan 2020 16:33:44 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Jan/28/t1580225919jncc2u2eblwjg1x.htm/, Retrieved Wed, 21 Apr 2021 09:01:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319055, Retrieved Wed, 21 Apr 2021 09:01:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact41
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Vraag 10 examen] [2020-01-28 15:33:44] [98f4fed75ec333f783274728d12cb650] [Current]
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Dataseries X:
13 4 2 3 3 2 3
16 5 3 4 2 1 2
17 4 4 4 3 2 3
NA 3 4 3 3 3 NA
NA 4 4 4 2 3 NA
16 3 4 4 4 2 3
NA 3 4 3 3 3 3
NA 3 4 4 2 3 NA
NA 4 5 4 4 3 NA
17 4 5 4 2 2 3
17 4 4 4 2 2 3
15 4 4 3 4 3 3
16 4 4 3 5 2 4
14 3 3 4 5 2 4
16 4 4 4 2 2 3
17 3 4 4 2 3 3
NA 3 4 4 1 2 NA
NA NA NA NA 5 NA NA
NA 5 5 3 2 3 1
NA 4 4 4 4 2 NA
16 3 4 3 4 2 3
NA 4 4 4 4 3 NA
16 4 4 4 4 2 4
NA 4 4 4 3 3 NA
NA 4 4 4 5 2 NA
NA 3 4 4 2 2 NA
16 3 4 3 5 2 3
15 4 4 4 2 2 3
16 2 4 4 3 3 5
16 5 4 4 1 2 NA
13 4 3 4 1 3 3
15 4 5 4 2 2 2
17 5 4 4 1 2 4
NA 4 3 4 4 2 NA
13 2 3 4 3 2 4
17 4 5 4 3 2 3
NA 3 4 4 2 3 NA
14 4 3 3 2 2 4
14 4 3 4 3 2 2
18 4 4 4 1 3 3
NA 5 4 4 3 2 NA
17 4 5 4 2 3 3
13 3 3 4 2 2 3
16 5 5 3 2 3 3
15 5 4 3 4 2 4
15 4 4 3 1 NA NA
NA 4 4 4 2 3 NA
15 3 5 3 5 3 4
13 4 4 4 4 2 3
NA 2 3 2 4 2 NA
17 4 5 4 4 3 4
NA 5 5 4 2 2 NA
NA 5 5 4 2 2 NA
11 4 3 4 2 1 1
14 4 3 3 2 2 NA
13 4 4 4 1 2 1
NA 3 4 3 5 3 NA
17 3 4 4 5 2 5
16 4 4 3 3 3 4
NA 4 4 4 2 4 NA
17 5 5 4 2 3 3
16 2 4 4 2 2 5
16 4 4 4 2 3 4
16 3 4 4 5 2 3
15 4 4 4 5 2 NA
12 4 2 4 3 2 NA
17 4 4 3 3 2 3
14 4 4 3 4 2 4
14 5 4 3 1 2 3
16 3 4 3 1 2 5
NA 3 4 3 1 2 NA
NA 4 5 5 5 2 NA
NA 4 4 4 4 2 NA
NA 4 4 4 1 NA NA
NA 4 4 5 2 1 NA
15 3 4 4 3 2 3
16 4 4 4 2 2 3
14 3 4 3 2 2 2
15 3 3 4 5 2 3
17 4 3 4 5 3 3
NA 4 4 4 2 3 NA
10 3 3 4 4 2 NA
NA 4 4 4 4 2 3
17 4 4 4 1 2 4
NA 4 4 4 1 3 NA
20 5 4 4 2 2 5
17 5 4 5 4 3 1
18 4 4 4 5 3 3
NA 3 4 4 2 2 NA
17 3 NA 4 2 2 4
14 4 2 3 3 3 2
NA 4 4 4 1 4 NA
17 4 4 4 4 3 3
NA 4 4 4 1 3 NA
17 4 5 4 5 3 3
NA 3 4 3 4 2 4
16 4 4 4 1 3 3
18 5 4 4 3 2 2
18 5 4 5 1 5 5
16 4 5 4 2 3 3
NA 3 4 4 1 4 NA
NA 5 3 4 1 2 4
15 4 4 4 3 2 NA
13 5 4 4 2 2 2
NA 3 4 3 2 2 NA
NA 5 4 5 2 2 NA
NA 4 4 3 5 3 NA
NA 4 4 3 3 2 NA
NA 4 4 4 2 2 NA
16 4 4 4 4 3 3
NA 3 4 4 3 3 NA
NA 4 4 4 2 3 NA
NA 4 4 3 2 2 NA
12 3 3 3 4 1 3
NA 4 4 3 1 2 NA
16 3 4 4 4 2 2
16 4 4 4 3 2 4
NA 5 4 1 5 2 NA
16 5 4 4 4 3 1
14 4 4 4 3 2 5
15 4 4 3 4 2 2
14 3 4 3 3 3 3
NA 4 4 4 5 3 NA
15 4 4 4 1 2 3
NA 4 5 4 2 3 NA
15 3 4 4 4 3 4
16 4 4 3 4 4 3
NA 4 4 4 5 2 3
NA 3 4 3 2 2 NA
NA 4 4 3 3 3 NA
11 3 2 2 4 2 2
NA 4 4 3 5 2 NA
18 5 4 3 5 3 3
NA 2 4 3 2 2 NA
11 3 3 4 4 2 3
NA 4 4 3 4 NA NA
18 5 5 4 5 2 4
NA NA NA NA 2 NA NA
15 4 5 4 4 2 NA
19 5 5 5 1 1 5
17 4 5 4 2 3 3
NA 4 4 3 2 2 3
14 3 4 4 1 3 3
NA 4 4 4 4 2 3
13 4 4 4 3 2 NA
17 4 4 4 4 2 4
14 4 4 4 1 2 3
19 5 4 3 4 2 5
14 4 3 4 2 2 NA
NA 4 4 4 3 2 NA
NA 3 3 3 3 2 NA
16 4 5 4 5 2 4
16 4 4 3 5 3 2
15 4 4 4 5 2 3
12 3 4 3 4 2 3
NA 4 4 4 2 3 NA
17 5 4 4 2 3 3
NA 4 4 4 1 2 NA
NA 2 3 4 2 4 NA
18 4 4 4 4 2 4
15 4 3 3 2 2 3
18 4 4 4 3 2 4
15 4 5 5 5 4 NA
NA 5 4 4 1 3 NA
NA 5 4 3 5 3 NA
NA 3 3 4 1 2 NA
16 4 4 4 3 2 4
NA 4 4 4 4 2 NA
16 2 3 5 3 3 2







Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319055&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319055&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 2.77125 + 0.792291`SK/EOU1`[t] + 0.817175`SK/EOU2`[t] + 0.809125`SK/EOU4`[t] + 0.208463EC2[t] + 0.447436GW1[t] + 0.583635GW2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM[t] =  +  2.77125 +  0.792291`SK/EOU1`[t] +  0.817175`SK/EOU2`[t] +  0.809125`SK/EOU4`[t] +  0.208463EC2[t] +  0.447436GW1[t] +  0.583635GW2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319055&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM[t] =  +  2.77125 +  0.792291`SK/EOU1`[t] +  0.817175`SK/EOU2`[t] +  0.809125`SK/EOU4`[t] +  0.208463EC2[t] +  0.447436GW1[t] +  0.583635GW2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319055&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&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
TVDCSUM[t] = + 2.77125 + 0.792291`SK/EOU1`[t] + 0.817175`SK/EOU2`[t] + 0.809125`SK/EOU4`[t] + 0.208463EC2[t] + 0.447436GW1[t] + 0.583635GW2[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+2.771 1.471+1.8840e+00 0.06303 0.03151
`SK/EOU1`+0.7923 0.1928+4.1080e+00 9.217e-05 4.608e-05
`SK/EOU2`+0.8172 0.2313+3.5330e+00 0.0006698 0.0003349
`SK/EOU4`+0.8091 0.265+3.0530e+00 0.003032 0.001516
EC2+0.2085 0.1088+1.9170e+00 0.0587 0.02935
GW1+0.4474 0.221+2.0250e+00 0.04608 0.02304
GW2+0.5836 0.1506+3.8750e+00 0.0002105 0.0001053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +2.771 &  1.471 & +1.8840e+00 &  0.06303 &  0.03151 \tabularnewline
`SK/EOU1` & +0.7923 &  0.1928 & +4.1080e+00 &  9.217e-05 &  4.608e-05 \tabularnewline
`SK/EOU2` & +0.8172 &  0.2313 & +3.5330e+00 &  0.0006698 &  0.0003349 \tabularnewline
`SK/EOU4` & +0.8091 &  0.265 & +3.0530e+00 &  0.003032 &  0.001516 \tabularnewline
EC2 & +0.2085 &  0.1088 & +1.9170e+00 &  0.0587 &  0.02935 \tabularnewline
GW1 & +0.4474 &  0.221 & +2.0250e+00 &  0.04608 &  0.02304 \tabularnewline
GW2 & +0.5836 &  0.1506 & +3.8750e+00 &  0.0002105 &  0.0001053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319055&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+2.771[/C][C] 1.471[/C][C]+1.8840e+00[/C][C] 0.06303[/C][C] 0.03151[/C][/ROW]
[ROW][C]`SK/EOU1`[/C][C]+0.7923[/C][C] 0.1928[/C][C]+4.1080e+00[/C][C] 9.217e-05[/C][C] 4.608e-05[/C][/ROW]
[ROW][C]`SK/EOU2`[/C][C]+0.8172[/C][C] 0.2313[/C][C]+3.5330e+00[/C][C] 0.0006698[/C][C] 0.0003349[/C][/ROW]
[ROW][C]`SK/EOU4`[/C][C]+0.8091[/C][C] 0.265[/C][C]+3.0530e+00[/C][C] 0.003032[/C][C] 0.001516[/C][/ROW]
[ROW][C]EC2[/C][C]+0.2085[/C][C] 0.1088[/C][C]+1.9170e+00[/C][C] 0.0587[/C][C] 0.02935[/C][/ROW]
[ROW][C]GW1[/C][C]+0.4474[/C][C] 0.221[/C][C]+2.0250e+00[/C][C] 0.04608[/C][C] 0.02304[/C][/ROW]
[ROW][C]GW2[/C][C]+0.5836[/C][C] 0.1506[/C][C]+3.8750e+00[/C][C] 0.0002105[/C][C] 0.0001053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319055&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+2.771 1.471+1.8840e+00 0.06303 0.03151
`SK/EOU1`+0.7923 0.1928+4.1080e+00 9.217e-05 4.608e-05
`SK/EOU2`+0.8172 0.2313+3.5330e+00 0.0006698 0.0003349
`SK/EOU4`+0.8091 0.265+3.0530e+00 0.003032 0.001516
EC2+0.2085 0.1088+1.9170e+00 0.0587 0.02935
GW1+0.4474 0.221+2.0250e+00 0.04608 0.02304
GW2+0.5836 0.1506+3.8750e+00 0.0002105 0.0001053







Multiple Linear Regression - Regression Statistics
Multiple R 0.7208
R-squared 0.5196
Adjusted R-squared 0.4852
F-TEST (value) 15.14
F-TEST (DF numerator)6
F-TEST (DF denominator)84
p-value 1.135e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.308
Sum Squared Residuals 143.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7208 \tabularnewline
R-squared &  0.5196 \tabularnewline
Adjusted R-squared &  0.4852 \tabularnewline
F-TEST (value) &  15.14 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value &  1.135e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.308 \tabularnewline
Sum Squared Residuals &  143.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319055&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7208[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5196[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.4852[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 15.14[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/C][/ROW]
[ROW][C]p-value[/C][C] 1.135e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.308[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 143.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319055&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&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 R 0.7208
R-squared 0.5196
Adjusted R-squared 0.4852
F-TEST (value) 15.14
F-TEST (DF numerator)6
F-TEST (DF denominator)84
p-value 1.135e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.308
Sum Squared Residuals 143.7







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319055&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319055&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&T=4

As an alternative you can also use a QR Code:  

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

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 13.27-0.2733
2 16 14.45 1.548
3 17 15.72 1.283
4 16 15.13 0.867
5 17 16.33 0.6745
6 17 15.51 1.492
7 15 15.56-0.5636
8 16 15.91 0.09178
9 14 15.11-1.108
10 16 15.51 0.4917
11 17 15.16 1.837
12 16 14.32 1.676
13 16 16.51-0.5089
14 16 14.53 1.468
15 15 15.51-0.5083
16 16 15.75 0.2531
17 13 14.93-1.93
18 15 15.74-0.7419
19 17 16.68 0.3242
20 13 13.9-0.8987
21 17 16.53 0.466
22 14 14.47-0.4657
23 14 14.32-0.316
24 18 15.75 2.253
25 17 16.77 0.2271
26 13 13.9-0.8989
27 16 16.76-0.7561
28 15 16.49-1.492
29 15 16.38-1.381
30 13 15.93-2.925
31 17 17.77-0.7735
32 11 13.08-2.076
33 13 14.13-1.133
34 17 16.51 0.4913
35 16 15.94 0.06127
36 17 17.57-0.5652
37 16 15.09 0.909
38 16 16.54-0.5394
39 16 15.34 0.6586
40 17 14.91 2.092
41 14 15.7-1.7
42 14 15.28-1.283
43 16 14.87 1.134
44 15 14.92 0.07551
45 16 15.51 0.4917
46 14 13.32 0.6767
47 15 14.52 0.4758
48 17 15.76 1.236
49 17 15.88 1.117
50 20 17.47 2.532
51 17 16.81 0.1932
52 18 16.58 1.419
53 14 13.14 0.8629
54 17 16.37 0.6273
55 17 17.4-0.3983
56 16 15.75 0.2527
57 18 15.93 2.075
58 18 19.41-1.411
59 16 16.77-0.7729
60 13 15.72-2.717
61 16 16.37-0.3727
62 12 13.06-1.059
63 16 14.55 1.451
64 16 16.3-0.3004
65 16 16 0.0023
66 14 16.88-2.884
67 15 14.53 0.4675
68 14 14.56-0.5628
69 15 15.3-0.2999
70 15 16.16-1.164
71 16 16.01-0.01099
72 11 11.3-0.2967
73 18 16.56 1.436
74 11 14.32-3.316
75 18 18.33-0.3268
76 19 18.44 0.5617
77 17 16.77 0.2271
78 14 14.96-0.955
79 17 16.51 0.4911
80 14 15.3-1.3
81 19 17.08 1.924
82 16 17.53-1.535
83 16 15.19 0.8116
84 15 16.13-1.134
85 12 14.32-2.324
86 17 16.75 0.252
87 18 16.51 1.491
88 15 13.88 1.118
89 18 16.3 1.7
90 16 16.3-0.3004
91 16 13.99 2.012

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.27 & -0.2733 \tabularnewline
2 &  16 &  14.45 &  1.548 \tabularnewline
3 &  17 &  15.72 &  1.283 \tabularnewline
4 &  16 &  15.13 &  0.867 \tabularnewline
5 &  17 &  16.33 &  0.6745 \tabularnewline
6 &  17 &  15.51 &  1.492 \tabularnewline
7 &  15 &  15.56 & -0.5636 \tabularnewline
8 &  16 &  15.91 &  0.09178 \tabularnewline
9 &  14 &  15.11 & -1.108 \tabularnewline
10 &  16 &  15.51 &  0.4917 \tabularnewline
11 &  17 &  15.16 &  1.837 \tabularnewline
12 &  16 &  14.32 &  1.676 \tabularnewline
13 &  16 &  16.51 & -0.5089 \tabularnewline
14 &  16 &  14.53 &  1.468 \tabularnewline
15 &  15 &  15.51 & -0.5083 \tabularnewline
16 &  16 &  15.75 &  0.2531 \tabularnewline
17 &  13 &  14.93 & -1.93 \tabularnewline
18 &  15 &  15.74 & -0.7419 \tabularnewline
19 &  17 &  16.68 &  0.3242 \tabularnewline
20 &  13 &  13.9 & -0.8987 \tabularnewline
21 &  17 &  16.53 &  0.466 \tabularnewline
22 &  14 &  14.47 & -0.4657 \tabularnewline
23 &  14 &  14.32 & -0.316 \tabularnewline
24 &  18 &  15.75 &  2.253 \tabularnewline
25 &  17 &  16.77 &  0.2271 \tabularnewline
26 &  13 &  13.9 & -0.8989 \tabularnewline
27 &  16 &  16.76 & -0.7561 \tabularnewline
28 &  15 &  16.49 & -1.492 \tabularnewline
29 &  15 &  16.38 & -1.381 \tabularnewline
30 &  13 &  15.93 & -2.925 \tabularnewline
31 &  17 &  17.77 & -0.7735 \tabularnewline
32 &  11 &  13.08 & -2.076 \tabularnewline
33 &  13 &  14.13 & -1.133 \tabularnewline
34 &  17 &  16.51 &  0.4913 \tabularnewline
35 &  16 &  15.94 &  0.06127 \tabularnewline
36 &  17 &  17.57 & -0.5652 \tabularnewline
37 &  16 &  15.09 &  0.909 \tabularnewline
38 &  16 &  16.54 & -0.5394 \tabularnewline
39 &  16 &  15.34 &  0.6586 \tabularnewline
40 &  17 &  14.91 &  2.092 \tabularnewline
41 &  14 &  15.7 & -1.7 \tabularnewline
42 &  14 &  15.28 & -1.283 \tabularnewline
43 &  16 &  14.87 &  1.134 \tabularnewline
44 &  15 &  14.92 &  0.07551 \tabularnewline
45 &  16 &  15.51 &  0.4917 \tabularnewline
46 &  14 &  13.32 &  0.6767 \tabularnewline
47 &  15 &  14.52 &  0.4758 \tabularnewline
48 &  17 &  15.76 &  1.236 \tabularnewline
49 &  17 &  15.88 &  1.117 \tabularnewline
50 &  20 &  17.47 &  2.532 \tabularnewline
51 &  17 &  16.81 &  0.1932 \tabularnewline
52 &  18 &  16.58 &  1.419 \tabularnewline
53 &  14 &  13.14 &  0.8629 \tabularnewline
54 &  17 &  16.37 &  0.6273 \tabularnewline
55 &  17 &  17.4 & -0.3983 \tabularnewline
56 &  16 &  15.75 &  0.2527 \tabularnewline
57 &  18 &  15.93 &  2.075 \tabularnewline
58 &  18 &  19.41 & -1.411 \tabularnewline
59 &  16 &  16.77 & -0.7729 \tabularnewline
60 &  13 &  15.72 & -2.717 \tabularnewline
61 &  16 &  16.37 & -0.3727 \tabularnewline
62 &  12 &  13.06 & -1.059 \tabularnewline
63 &  16 &  14.55 &  1.451 \tabularnewline
64 &  16 &  16.3 & -0.3004 \tabularnewline
65 &  16 &  16 &  0.0023 \tabularnewline
66 &  14 &  16.88 & -2.884 \tabularnewline
67 &  15 &  14.53 &  0.4675 \tabularnewline
68 &  14 &  14.56 & -0.5628 \tabularnewline
69 &  15 &  15.3 & -0.2999 \tabularnewline
70 &  15 &  16.16 & -1.164 \tabularnewline
71 &  16 &  16.01 & -0.01099 \tabularnewline
72 &  11 &  11.3 & -0.2967 \tabularnewline
73 &  18 &  16.56 &  1.436 \tabularnewline
74 &  11 &  14.32 & -3.316 \tabularnewline
75 &  18 &  18.33 & -0.3268 \tabularnewline
76 &  19 &  18.44 &  0.5617 \tabularnewline
77 &  17 &  16.77 &  0.2271 \tabularnewline
78 &  14 &  14.96 & -0.955 \tabularnewline
79 &  17 &  16.51 &  0.4911 \tabularnewline
80 &  14 &  15.3 & -1.3 \tabularnewline
81 &  19 &  17.08 &  1.924 \tabularnewline
82 &  16 &  17.53 & -1.535 \tabularnewline
83 &  16 &  15.19 &  0.8116 \tabularnewline
84 &  15 &  16.13 & -1.134 \tabularnewline
85 &  12 &  14.32 & -2.324 \tabularnewline
86 &  17 &  16.75 &  0.252 \tabularnewline
87 &  18 &  16.51 &  1.491 \tabularnewline
88 &  15 &  13.88 &  1.118 \tabularnewline
89 &  18 &  16.3 &  1.7 \tabularnewline
90 &  16 &  16.3 & -0.3004 \tabularnewline
91 &  16 &  13.99 &  2.012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319055&T=5

[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] 13[/C][C] 13.27[/C][C]-0.2733[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 14.45[/C][C] 1.548[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.72[/C][C] 1.283[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.13[/C][C] 0.867[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 16.33[/C][C] 0.6745[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.51[/C][C] 1.492[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.56[/C][C]-0.5636[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.91[/C][C] 0.09178[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 15.11[/C][C]-1.108[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.51[/C][C] 0.4917[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.16[/C][C] 1.837[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.32[/C][C] 1.676[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 16.51[/C][C]-0.5089[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.53[/C][C] 1.468[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.51[/C][C]-0.5083[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 15.75[/C][C] 0.2531[/C][/ROW]
[ROW][C]17[/C][C] 13[/C][C] 14.93[/C][C]-1.93[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 15.74[/C][C]-0.7419[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 16.68[/C][C] 0.3242[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 13.9[/C][C]-0.8987[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 16.53[/C][C] 0.466[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 14.47[/C][C]-0.4657[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 14.32[/C][C]-0.316[/C][/ROW]
[ROW][C]24[/C][C] 18[/C][C] 15.75[/C][C] 2.253[/C][/ROW]
[ROW][C]25[/C][C] 17[/C][C] 16.77[/C][C] 0.2271[/C][/ROW]
[ROW][C]26[/C][C] 13[/C][C] 13.9[/C][C]-0.8989[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 16.76[/C][C]-0.7561[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 16.49[/C][C]-1.492[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 16.38[/C][C]-1.381[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 15.93[/C][C]-2.925[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 17.77[/C][C]-0.7735[/C][/ROW]
[ROW][C]32[/C][C] 11[/C][C] 13.08[/C][C]-2.076[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 14.13[/C][C]-1.133[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 16.51[/C][C] 0.4913[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.94[/C][C] 0.06127[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.57[/C][C]-0.5652[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 15.09[/C][C] 0.909[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 16.54[/C][C]-0.5394[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 15.34[/C][C] 0.6586[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 14.91[/C][C] 2.092[/C][/ROW]
[ROW][C]41[/C][C] 14[/C][C] 15.7[/C][C]-1.7[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 15.28[/C][C]-1.283[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 14.87[/C][C] 1.134[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 14.92[/C][C] 0.07551[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 15.51[/C][C] 0.4917[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 13.32[/C][C] 0.6767[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 14.52[/C][C] 0.4758[/C][/ROW]
[ROW][C]48[/C][C] 17[/C][C] 15.76[/C][C] 1.236[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 15.88[/C][C] 1.117[/C][/ROW]
[ROW][C]50[/C][C] 20[/C][C] 17.47[/C][C] 2.532[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 16.81[/C][C] 0.1932[/C][/ROW]
[ROW][C]52[/C][C] 18[/C][C] 16.58[/C][C] 1.419[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 13.14[/C][C] 0.8629[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.37[/C][C] 0.6273[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 17.4[/C][C]-0.3983[/C][/ROW]
[ROW][C]56[/C][C] 16[/C][C] 15.75[/C][C] 0.2527[/C][/ROW]
[ROW][C]57[/C][C] 18[/C][C] 15.93[/C][C] 2.075[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 19.41[/C][C]-1.411[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 16.77[/C][C]-0.7729[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 15.72[/C][C]-2.717[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 16.37[/C][C]-0.3727[/C][/ROW]
[ROW][C]62[/C][C] 12[/C][C] 13.06[/C][C]-1.059[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 14.55[/C][C] 1.451[/C][/ROW]
[ROW][C]64[/C][C] 16[/C][C] 16.3[/C][C]-0.3004[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 16[/C][C] 0.0023[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 16.88[/C][C]-2.884[/C][/ROW]
[ROW][C]67[/C][C] 15[/C][C] 14.53[/C][C] 0.4675[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 14.56[/C][C]-0.5628[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 15.3[/C][C]-0.2999[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 16.16[/C][C]-1.164[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 16.01[/C][C]-0.01099[/C][/ROW]
[ROW][C]72[/C][C] 11[/C][C] 11.3[/C][C]-0.2967[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 16.56[/C][C] 1.436[/C][/ROW]
[ROW][C]74[/C][C] 11[/C][C] 14.32[/C][C]-3.316[/C][/ROW]
[ROW][C]75[/C][C] 18[/C][C] 18.33[/C][C]-0.3268[/C][/ROW]
[ROW][C]76[/C][C] 19[/C][C] 18.44[/C][C] 0.5617[/C][/ROW]
[ROW][C]77[/C][C] 17[/C][C] 16.77[/C][C] 0.2271[/C][/ROW]
[ROW][C]78[/C][C] 14[/C][C] 14.96[/C][C]-0.955[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 16.51[/C][C] 0.4911[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 15.3[/C][C]-1.3[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 17.08[/C][C] 1.924[/C][/ROW]
[ROW][C]82[/C][C] 16[/C][C] 17.53[/C][C]-1.535[/C][/ROW]
[ROW][C]83[/C][C] 16[/C][C] 15.19[/C][C] 0.8116[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 16.13[/C][C]-1.134[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 14.32[/C][C]-2.324[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 16.75[/C][C] 0.252[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 16.51[/C][C] 1.491[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 13.88[/C][C] 1.118[/C][/ROW]
[ROW][C]89[/C][C] 18[/C][C] 16.3[/C][C] 1.7[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 16.3[/C][C]-0.3004[/C][/ROW]
[ROW][C]91[/C][C] 16[/C][C] 13.99[/C][C] 2.012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319055&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&T=5

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
1 13 13.27-0.2733
2 16 14.45 1.548
3 17 15.72 1.283
4 16 15.13 0.867
5 17 16.33 0.6745
6 17 15.51 1.492
7 15 15.56-0.5636
8 16 15.91 0.09178
9 14 15.11-1.108
10 16 15.51 0.4917
11 17 15.16 1.837
12 16 14.32 1.676
13 16 16.51-0.5089
14 16 14.53 1.468
15 15 15.51-0.5083
16 16 15.75 0.2531
17 13 14.93-1.93
18 15 15.74-0.7419
19 17 16.68 0.3242
20 13 13.9-0.8987
21 17 16.53 0.466
22 14 14.47-0.4657
23 14 14.32-0.316
24 18 15.75 2.253
25 17 16.77 0.2271
26 13 13.9-0.8989
27 16 16.76-0.7561
28 15 16.49-1.492
29 15 16.38-1.381
30 13 15.93-2.925
31 17 17.77-0.7735
32 11 13.08-2.076
33 13 14.13-1.133
34 17 16.51 0.4913
35 16 15.94 0.06127
36 17 17.57-0.5652
37 16 15.09 0.909
38 16 16.54-0.5394
39 16 15.34 0.6586
40 17 14.91 2.092
41 14 15.7-1.7
42 14 15.28-1.283
43 16 14.87 1.134
44 15 14.92 0.07551
45 16 15.51 0.4917
46 14 13.32 0.6767
47 15 14.52 0.4758
48 17 15.76 1.236
49 17 15.88 1.117
50 20 17.47 2.532
51 17 16.81 0.1932
52 18 16.58 1.419
53 14 13.14 0.8629
54 17 16.37 0.6273
55 17 17.4-0.3983
56 16 15.75 0.2527
57 18 15.93 2.075
58 18 19.41-1.411
59 16 16.77-0.7729
60 13 15.72-2.717
61 16 16.37-0.3727
62 12 13.06-1.059
63 16 14.55 1.451
64 16 16.3-0.3004
65 16 16 0.0023
66 14 16.88-2.884
67 15 14.53 0.4675
68 14 14.56-0.5628
69 15 15.3-0.2999
70 15 16.16-1.164
71 16 16.01-0.01099
72 11 11.3-0.2967
73 18 16.56 1.436
74 11 14.32-3.316
75 18 18.33-0.3268
76 19 18.44 0.5617
77 17 16.77 0.2271
78 14 14.96-0.955
79 17 16.51 0.4911
80 14 15.3-1.3
81 19 17.08 1.924
82 16 17.53-1.535
83 16 15.19 0.8116
84 15 16.13-1.134
85 12 14.32-2.324
86 17 16.75 0.252
87 18 16.51 1.491
88 15 13.88 1.118
89 18 16.3 1.7
90 16 16.3-0.3004
91 16 13.99 2.012







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
10 0.1974 0.3947 0.8026
11 0.2259 0.4519 0.7741
12 0.1365 0.2729 0.8635
13 0.07034 0.1407 0.9297
14 0.03793 0.07587 0.9621
15 0.06892 0.1378 0.9311
16 0.04164 0.08329 0.9584
17 0.06216 0.1243 0.9378
18 0.2086 0.4172 0.7914
19 0.1579 0.3158 0.8421
20 0.2088 0.4177 0.7912
21 0.155 0.3101 0.845
22 0.1222 0.2443 0.8778
23 0.08584 0.1717 0.9142
24 0.1946 0.3892 0.8054
25 0.1489 0.2979 0.8511
26 0.1367 0.2735 0.8633
27 0.1358 0.2715 0.8642
28 0.1325 0.2649 0.8675
29 0.1358 0.2716 0.8642
30 0.3253 0.6506 0.6747
31 0.2714 0.5428 0.7286
32 0.4519 0.9038 0.5481
33 0.45 0.9 0.55
34 0.3938 0.7877 0.6062
35 0.3308 0.6615 0.6692
36 0.2744 0.5487 0.7256
37 0.2409 0.4818 0.7591
38 0.1955 0.391 0.8045
39 0.173 0.346 0.827
40 0.2282 0.4564 0.7718
41 0.2693 0.5386 0.7307
42 0.2839 0.5677 0.7161
43 0.2613 0.5226 0.7387
44 0.2143 0.4287 0.7857
45 0.1754 0.3508 0.8246
46 0.1506 0.3013 0.8494
47 0.1267 0.2535 0.8733
48 0.1471 0.2943 0.8529
49 0.1389 0.2778 0.8611
50 0.2488 0.4975 0.7512
51 0.2159 0.4318 0.7841
52 0.2225 0.445 0.7775
53 0.1968 0.3937 0.8032
54 0.1625 0.325 0.8375
55 0.1275 0.255 0.8725
56 0.0992 0.1984 0.9008
57 0.1332 0.2663 0.8668
58 0.1486 0.2973 0.8514
59 0.1196 0.2391 0.8804
60 0.2916 0.5832 0.7084
61 0.2424 0.4849 0.7576
62 0.2082 0.4163 0.7918
63 0.2678 0.5355 0.7322
64 0.2132 0.4264 0.7868
65 0.2089 0.4177 0.7911
66 0.4156 0.8312 0.5844
67 0.4008 0.8016 0.5992
68 0.3399 0.6798 0.6601
69 0.2708 0.5416 0.7292
70 0.2578 0.5157 0.7422
71 0.2637 0.5273 0.7363
72 0.2116 0.4232 0.7884
73 0.1688 0.3376 0.8312
74 0.6903 0.6194 0.3097
75 0.5949 0.8101 0.4051
76 0.6445 0.7111 0.3555
77 0.7948 0.4104 0.2052
78 0.7281 0.5437 0.2719
79 0.6085 0.7829 0.3915
80 0.5096 0.9808 0.4904
81 0.6382 0.7235 0.3618

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1974 &  0.3947 &  0.8026 \tabularnewline
11 &  0.2259 &  0.4519 &  0.7741 \tabularnewline
12 &  0.1365 &  0.2729 &  0.8635 \tabularnewline
13 &  0.07034 &  0.1407 &  0.9297 \tabularnewline
14 &  0.03793 &  0.07587 &  0.9621 \tabularnewline
15 &  0.06892 &  0.1378 &  0.9311 \tabularnewline
16 &  0.04164 &  0.08329 &  0.9584 \tabularnewline
17 &  0.06216 &  0.1243 &  0.9378 \tabularnewline
18 &  0.2086 &  0.4172 &  0.7914 \tabularnewline
19 &  0.1579 &  0.3158 &  0.8421 \tabularnewline
20 &  0.2088 &  0.4177 &  0.7912 \tabularnewline
21 &  0.155 &  0.3101 &  0.845 \tabularnewline
22 &  0.1222 &  0.2443 &  0.8778 \tabularnewline
23 &  0.08584 &  0.1717 &  0.9142 \tabularnewline
24 &  0.1946 &  0.3892 &  0.8054 \tabularnewline
25 &  0.1489 &  0.2979 &  0.8511 \tabularnewline
26 &  0.1367 &  0.2735 &  0.8633 \tabularnewline
27 &  0.1358 &  0.2715 &  0.8642 \tabularnewline
28 &  0.1325 &  0.2649 &  0.8675 \tabularnewline
29 &  0.1358 &  0.2716 &  0.8642 \tabularnewline
30 &  0.3253 &  0.6506 &  0.6747 \tabularnewline
31 &  0.2714 &  0.5428 &  0.7286 \tabularnewline
32 &  0.4519 &  0.9038 &  0.5481 \tabularnewline
33 &  0.45 &  0.9 &  0.55 \tabularnewline
34 &  0.3938 &  0.7877 &  0.6062 \tabularnewline
35 &  0.3308 &  0.6615 &  0.6692 \tabularnewline
36 &  0.2744 &  0.5487 &  0.7256 \tabularnewline
37 &  0.2409 &  0.4818 &  0.7591 \tabularnewline
38 &  0.1955 &  0.391 &  0.8045 \tabularnewline
39 &  0.173 &  0.346 &  0.827 \tabularnewline
40 &  0.2282 &  0.4564 &  0.7718 \tabularnewline
41 &  0.2693 &  0.5386 &  0.7307 \tabularnewline
42 &  0.2839 &  0.5677 &  0.7161 \tabularnewline
43 &  0.2613 &  0.5226 &  0.7387 \tabularnewline
44 &  0.2143 &  0.4287 &  0.7857 \tabularnewline
45 &  0.1754 &  0.3508 &  0.8246 \tabularnewline
46 &  0.1506 &  0.3013 &  0.8494 \tabularnewline
47 &  0.1267 &  0.2535 &  0.8733 \tabularnewline
48 &  0.1471 &  0.2943 &  0.8529 \tabularnewline
49 &  0.1389 &  0.2778 &  0.8611 \tabularnewline
50 &  0.2488 &  0.4975 &  0.7512 \tabularnewline
51 &  0.2159 &  0.4318 &  0.7841 \tabularnewline
52 &  0.2225 &  0.445 &  0.7775 \tabularnewline
53 &  0.1968 &  0.3937 &  0.8032 \tabularnewline
54 &  0.1625 &  0.325 &  0.8375 \tabularnewline
55 &  0.1275 &  0.255 &  0.8725 \tabularnewline
56 &  0.0992 &  0.1984 &  0.9008 \tabularnewline
57 &  0.1332 &  0.2663 &  0.8668 \tabularnewline
58 &  0.1486 &  0.2973 &  0.8514 \tabularnewline
59 &  0.1196 &  0.2391 &  0.8804 \tabularnewline
60 &  0.2916 &  0.5832 &  0.7084 \tabularnewline
61 &  0.2424 &  0.4849 &  0.7576 \tabularnewline
62 &  0.2082 &  0.4163 &  0.7918 \tabularnewline
63 &  0.2678 &  0.5355 &  0.7322 \tabularnewline
64 &  0.2132 &  0.4264 &  0.7868 \tabularnewline
65 &  0.2089 &  0.4177 &  0.7911 \tabularnewline
66 &  0.4156 &  0.8312 &  0.5844 \tabularnewline
67 &  0.4008 &  0.8016 &  0.5992 \tabularnewline
68 &  0.3399 &  0.6798 &  0.6601 \tabularnewline
69 &  0.2708 &  0.5416 &  0.7292 \tabularnewline
70 &  0.2578 &  0.5157 &  0.7422 \tabularnewline
71 &  0.2637 &  0.5273 &  0.7363 \tabularnewline
72 &  0.2116 &  0.4232 &  0.7884 \tabularnewline
73 &  0.1688 &  0.3376 &  0.8312 \tabularnewline
74 &  0.6903 &  0.6194 &  0.3097 \tabularnewline
75 &  0.5949 &  0.8101 &  0.4051 \tabularnewline
76 &  0.6445 &  0.7111 &  0.3555 \tabularnewline
77 &  0.7948 &  0.4104 &  0.2052 \tabularnewline
78 &  0.7281 &  0.5437 &  0.2719 \tabularnewline
79 &  0.6085 &  0.7829 &  0.3915 \tabularnewline
80 &  0.5096 &  0.9808 &  0.4904 \tabularnewline
81 &  0.6382 &  0.7235 &  0.3618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319055&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.1974[/C][C] 0.3947[/C][C] 0.8026[/C][/ROW]
[ROW][C]11[/C][C] 0.2259[/C][C] 0.4519[/C][C] 0.7741[/C][/ROW]
[ROW][C]12[/C][C] 0.1365[/C][C] 0.2729[/C][C] 0.8635[/C][/ROW]
[ROW][C]13[/C][C] 0.07034[/C][C] 0.1407[/C][C] 0.9297[/C][/ROW]
[ROW][C]14[/C][C] 0.03793[/C][C] 0.07587[/C][C] 0.9621[/C][/ROW]
[ROW][C]15[/C][C] 0.06892[/C][C] 0.1378[/C][C] 0.9311[/C][/ROW]
[ROW][C]16[/C][C] 0.04164[/C][C] 0.08329[/C][C] 0.9584[/C][/ROW]
[ROW][C]17[/C][C] 0.06216[/C][C] 0.1243[/C][C] 0.9378[/C][/ROW]
[ROW][C]18[/C][C] 0.2086[/C][C] 0.4172[/C][C] 0.7914[/C][/ROW]
[ROW][C]19[/C][C] 0.1579[/C][C] 0.3158[/C][C] 0.8421[/C][/ROW]
[ROW][C]20[/C][C] 0.2088[/C][C] 0.4177[/C][C] 0.7912[/C][/ROW]
[ROW][C]21[/C][C] 0.155[/C][C] 0.3101[/C][C] 0.845[/C][/ROW]
[ROW][C]22[/C][C] 0.1222[/C][C] 0.2443[/C][C] 0.8778[/C][/ROW]
[ROW][C]23[/C][C] 0.08584[/C][C] 0.1717[/C][C] 0.9142[/C][/ROW]
[ROW][C]24[/C][C] 0.1946[/C][C] 0.3892[/C][C] 0.8054[/C][/ROW]
[ROW][C]25[/C][C] 0.1489[/C][C] 0.2979[/C][C] 0.8511[/C][/ROW]
[ROW][C]26[/C][C] 0.1367[/C][C] 0.2735[/C][C] 0.8633[/C][/ROW]
[ROW][C]27[/C][C] 0.1358[/C][C] 0.2715[/C][C] 0.8642[/C][/ROW]
[ROW][C]28[/C][C] 0.1325[/C][C] 0.2649[/C][C] 0.8675[/C][/ROW]
[ROW][C]29[/C][C] 0.1358[/C][C] 0.2716[/C][C] 0.8642[/C][/ROW]
[ROW][C]30[/C][C] 0.3253[/C][C] 0.6506[/C][C] 0.6747[/C][/ROW]
[ROW][C]31[/C][C] 0.2714[/C][C] 0.5428[/C][C] 0.7286[/C][/ROW]
[ROW][C]32[/C][C] 0.4519[/C][C] 0.9038[/C][C] 0.5481[/C][/ROW]
[ROW][C]33[/C][C] 0.45[/C][C] 0.9[/C][C] 0.55[/C][/ROW]
[ROW][C]34[/C][C] 0.3938[/C][C] 0.7877[/C][C] 0.6062[/C][/ROW]
[ROW][C]35[/C][C] 0.3308[/C][C] 0.6615[/C][C] 0.6692[/C][/ROW]
[ROW][C]36[/C][C] 0.2744[/C][C] 0.5487[/C][C] 0.7256[/C][/ROW]
[ROW][C]37[/C][C] 0.2409[/C][C] 0.4818[/C][C] 0.7591[/C][/ROW]
[ROW][C]38[/C][C] 0.1955[/C][C] 0.391[/C][C] 0.8045[/C][/ROW]
[ROW][C]39[/C][C] 0.173[/C][C] 0.346[/C][C] 0.827[/C][/ROW]
[ROW][C]40[/C][C] 0.2282[/C][C] 0.4564[/C][C] 0.7718[/C][/ROW]
[ROW][C]41[/C][C] 0.2693[/C][C] 0.5386[/C][C] 0.7307[/C][/ROW]
[ROW][C]42[/C][C] 0.2839[/C][C] 0.5677[/C][C] 0.7161[/C][/ROW]
[ROW][C]43[/C][C] 0.2613[/C][C] 0.5226[/C][C] 0.7387[/C][/ROW]
[ROW][C]44[/C][C] 0.2143[/C][C] 0.4287[/C][C] 0.7857[/C][/ROW]
[ROW][C]45[/C][C] 0.1754[/C][C] 0.3508[/C][C] 0.8246[/C][/ROW]
[ROW][C]46[/C][C] 0.1506[/C][C] 0.3013[/C][C] 0.8494[/C][/ROW]
[ROW][C]47[/C][C] 0.1267[/C][C] 0.2535[/C][C] 0.8733[/C][/ROW]
[ROW][C]48[/C][C] 0.1471[/C][C] 0.2943[/C][C] 0.8529[/C][/ROW]
[ROW][C]49[/C][C] 0.1389[/C][C] 0.2778[/C][C] 0.8611[/C][/ROW]
[ROW][C]50[/C][C] 0.2488[/C][C] 0.4975[/C][C] 0.7512[/C][/ROW]
[ROW][C]51[/C][C] 0.2159[/C][C] 0.4318[/C][C] 0.7841[/C][/ROW]
[ROW][C]52[/C][C] 0.2225[/C][C] 0.445[/C][C] 0.7775[/C][/ROW]
[ROW][C]53[/C][C] 0.1968[/C][C] 0.3937[/C][C] 0.8032[/C][/ROW]
[ROW][C]54[/C][C] 0.1625[/C][C] 0.325[/C][C] 0.8375[/C][/ROW]
[ROW][C]55[/C][C] 0.1275[/C][C] 0.255[/C][C] 0.8725[/C][/ROW]
[ROW][C]56[/C][C] 0.0992[/C][C] 0.1984[/C][C] 0.9008[/C][/ROW]
[ROW][C]57[/C][C] 0.1332[/C][C] 0.2663[/C][C] 0.8668[/C][/ROW]
[ROW][C]58[/C][C] 0.1486[/C][C] 0.2973[/C][C] 0.8514[/C][/ROW]
[ROW][C]59[/C][C] 0.1196[/C][C] 0.2391[/C][C] 0.8804[/C][/ROW]
[ROW][C]60[/C][C] 0.2916[/C][C] 0.5832[/C][C] 0.7084[/C][/ROW]
[ROW][C]61[/C][C] 0.2424[/C][C] 0.4849[/C][C] 0.7576[/C][/ROW]
[ROW][C]62[/C][C] 0.2082[/C][C] 0.4163[/C][C] 0.7918[/C][/ROW]
[ROW][C]63[/C][C] 0.2678[/C][C] 0.5355[/C][C] 0.7322[/C][/ROW]
[ROW][C]64[/C][C] 0.2132[/C][C] 0.4264[/C][C] 0.7868[/C][/ROW]
[ROW][C]65[/C][C] 0.2089[/C][C] 0.4177[/C][C] 0.7911[/C][/ROW]
[ROW][C]66[/C][C] 0.4156[/C][C] 0.8312[/C][C] 0.5844[/C][/ROW]
[ROW][C]67[/C][C] 0.4008[/C][C] 0.8016[/C][C] 0.5992[/C][/ROW]
[ROW][C]68[/C][C] 0.3399[/C][C] 0.6798[/C][C] 0.6601[/C][/ROW]
[ROW][C]69[/C][C] 0.2708[/C][C] 0.5416[/C][C] 0.7292[/C][/ROW]
[ROW][C]70[/C][C] 0.2578[/C][C] 0.5157[/C][C] 0.7422[/C][/ROW]
[ROW][C]71[/C][C] 0.2637[/C][C] 0.5273[/C][C] 0.7363[/C][/ROW]
[ROW][C]72[/C][C] 0.2116[/C][C] 0.4232[/C][C] 0.7884[/C][/ROW]
[ROW][C]73[/C][C] 0.1688[/C][C] 0.3376[/C][C] 0.8312[/C][/ROW]
[ROW][C]74[/C][C] 0.6903[/C][C] 0.6194[/C][C] 0.3097[/C][/ROW]
[ROW][C]75[/C][C] 0.5949[/C][C] 0.8101[/C][C] 0.4051[/C][/ROW]
[ROW][C]76[/C][C] 0.6445[/C][C] 0.7111[/C][C] 0.3555[/C][/ROW]
[ROW][C]77[/C][C] 0.7948[/C][C] 0.4104[/C][C] 0.2052[/C][/ROW]
[ROW][C]78[/C][C] 0.7281[/C][C] 0.5437[/C][C] 0.2719[/C][/ROW]
[ROW][C]79[/C][C] 0.6085[/C][C] 0.7829[/C][C] 0.3915[/C][/ROW]
[ROW][C]80[/C][C] 0.5096[/C][C] 0.9808[/C][C] 0.4904[/C][/ROW]
[ROW][C]81[/C][C] 0.6382[/C][C] 0.7235[/C][C] 0.3618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319055&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&T=6

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
10 0.1974 0.3947 0.8026
11 0.2259 0.4519 0.7741
12 0.1365 0.2729 0.8635
13 0.07034 0.1407 0.9297
14 0.03793 0.07587 0.9621
15 0.06892 0.1378 0.9311
16 0.04164 0.08329 0.9584
17 0.06216 0.1243 0.9378
18 0.2086 0.4172 0.7914
19 0.1579 0.3158 0.8421
20 0.2088 0.4177 0.7912
21 0.155 0.3101 0.845
22 0.1222 0.2443 0.8778
23 0.08584 0.1717 0.9142
24 0.1946 0.3892 0.8054
25 0.1489 0.2979 0.8511
26 0.1367 0.2735 0.8633
27 0.1358 0.2715 0.8642
28 0.1325 0.2649 0.8675
29 0.1358 0.2716 0.8642
30 0.3253 0.6506 0.6747
31 0.2714 0.5428 0.7286
32 0.4519 0.9038 0.5481
33 0.45 0.9 0.55
34 0.3938 0.7877 0.6062
35 0.3308 0.6615 0.6692
36 0.2744 0.5487 0.7256
37 0.2409 0.4818 0.7591
38 0.1955 0.391 0.8045
39 0.173 0.346 0.827
40 0.2282 0.4564 0.7718
41 0.2693 0.5386 0.7307
42 0.2839 0.5677 0.7161
43 0.2613 0.5226 0.7387
44 0.2143 0.4287 0.7857
45 0.1754 0.3508 0.8246
46 0.1506 0.3013 0.8494
47 0.1267 0.2535 0.8733
48 0.1471 0.2943 0.8529
49 0.1389 0.2778 0.8611
50 0.2488 0.4975 0.7512
51 0.2159 0.4318 0.7841
52 0.2225 0.445 0.7775
53 0.1968 0.3937 0.8032
54 0.1625 0.325 0.8375
55 0.1275 0.255 0.8725
56 0.0992 0.1984 0.9008
57 0.1332 0.2663 0.8668
58 0.1486 0.2973 0.8514
59 0.1196 0.2391 0.8804
60 0.2916 0.5832 0.7084
61 0.2424 0.4849 0.7576
62 0.2082 0.4163 0.7918
63 0.2678 0.5355 0.7322
64 0.2132 0.4264 0.7868
65 0.2089 0.4177 0.7911
66 0.4156 0.8312 0.5844
67 0.4008 0.8016 0.5992
68 0.3399 0.6798 0.6601
69 0.2708 0.5416 0.7292
70 0.2578 0.5157 0.7422
71 0.2637 0.5273 0.7363
72 0.2116 0.4232 0.7884
73 0.1688 0.3376 0.8312
74 0.6903 0.6194 0.3097
75 0.5949 0.8101 0.4051
76 0.6445 0.7111 0.3555
77 0.7948 0.4104 0.2052
78 0.7281 0.5437 0.2719
79 0.6085 0.7829 0.3915
80 0.5096 0.9808 0.4904
81 0.6382 0.7235 0.3618







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level20.0277778OK

\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 & 2 & 0.0277778 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319055&T=7

[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]2[/C][C]0.0277778[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319055&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&T=7

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 level0 0OK
5% type I error level00OK
10% type I error level20.0277778OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.78749, df1 = 2, df2 = 82, p-value = 0.4584
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6041, df1 = 12, df2 = 72, p-value = 0.1097
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1867, df1 = 2, df2 = 82, p-value = 0.3104

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.78749, df1 = 2, df2 = 82, p-value = 0.4584
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6041, df1 = 12, df2 = 72, p-value = 0.1097
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1867, df1 = 2, df2 = 82, p-value = 0.3104
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319055&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.78749, df1 = 2, df2 = 82, p-value = 0.4584
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW] [ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6041, df1 = 12, df2 = 72, p-value = 0.1097
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1867, df1 = 2, df2 = 82, p-value = 0.3104
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319055&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&T=8

As an alternative you can also use a QR Code:  

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.78749, df1 = 2, df2 = 82, p-value = 0.4584
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6041, df1 = 12, df2 = 72, p-value = 0.1097
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1867, df1 = 2, df2 = 82, p-value = 0.3104







Variance Inflation Factors (Multicollinearity)
> vif
`SK/EOU1` `SK/EOU2` `SK/EOU4`       EC2       GW1       GW2 
 1.139783  1.238548  1.116313  1.091735  1.040507  1.074693 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`SK/EOU1` `SK/EOU2` `SK/EOU4`       EC2       GW1       GW2 
 1.139783  1.238548  1.116313  1.091735  1.040507  1.074693 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319055&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
`SK/EOU1` `SK/EOU2` `SK/EOU4`       EC2       GW1       GW2 
 1.139783  1.238548  1.116313  1.091735  1.040507  1.074693 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319055&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319055&T=9

As an alternative you can also use a QR Code:  

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

Variance Inflation Factors (Multicollinearity)
> vif
`SK/EOU1` `SK/EOU2` `SK/EOU4`       EC2       GW1       GW2 
 1.139783  1.238548  1.116313  1.091735  1.040507  1.074693 



Parameters (Session):
par1 = 1 ; par2 = 7 ; par3 = 8 ; par4 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;
R code (references can be found in the software module):
par6 <- '12'
par5 <- ''
par4 <- 'TRUE'
par3 <- '8'
par2 <- '7'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')