## Free Statistics

of Irreproducible Research!

Author's title
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:
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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 Output view raw output of R engine Computing time 3 seconds R Server Big 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 Output view raw output of R engine Computing time 3 seconds R Server Big 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 + 0.792291SK/EOU1[t] + 0.817175SK/EOU2[t] + 0.809125SK/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 +  0.792291SK/EOU1[t] +  0.817175SK/EOU2[t] +  0.809125SK/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 +  0.792291SK/EOU1[t] +  0.817175SK/EOU2[t] +  0.809125SK/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 + 0.792291SK/EOU1[t] + 0.817175SK/EOU2[t] + 0.809125SK/EOU4[t] + 0.208463EC2[t] + 0.447436GW1[t] + 0.583635GW2[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-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
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]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 Description Link Histogram Compute Central Tendency Compute QQ Plot Compute Kernel Density Plot Compute Skewness/Kurtosis Test Compute Skewness-Kurtosis Plot Compute Harrell-Davis Plot Compute Bootstrap Plot -- Central Tendency Compute Blocked Bootstrap Plot -- Central Tendency Compute (Partial) Autocorrelation Plot Compute Spectral Analysis Compute Tukey lambda PPCC Plot Compute Box-Cox Normality Plot Compute Summary Statistics Compute

\begin{tabular}{lllllllll}
\hline
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]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 Description Link Histogram Compute Central Tendency Compute QQ Plot Compute Kernel Density Plot Compute Skewness/Kurtosis Test Compute Skewness-Kurtosis Plot Compute Harrell-Davis Plot Compute Bootstrap Plot -- Central Tendency Compute Blocked Bootstrap Plot -- Central Tendency Compute (Partial) Autocorrelation Plot Compute Spectral Analysis Compute Tukey lambda PPCC Plot Compute Box-Cox Normality Plot Compute Summary Statistics Compute

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction 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 Index Actuals InterpolationForecast ResidualsPrediction 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-values Alternative Hypothesis breakpoint index greater 2-sided less 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-values Alternative Hypothesis breakpoint index greater 2-sided less 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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.0277778 OK

\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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.0277778 OK

 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 

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 testmywarning <- ''par6 <- as.numeric(par6)if(is.na(par6)) {par6 <- 12mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'}par1 <- as.numeric(par1)if(is.na(par1)) {par1 <- 1mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'}if (par4=='') par4 <- 0par4 <- as.numeric(par4)if (!is.numeric(par4)) par4 <- 0if (par5=='') par5 <- 0par5 <- as.numeric(par5)if (!is.numeric(par5)) par5 <- 0x <- 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 <- x1if (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 + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)print(z)plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')lines(lowess(z))abline(lm(z))grid()dev.off()bitmap(file='test6.png')acf(mysum$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-STATH0: 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$resid)myra <-table.start()a <- table.row.start(a)a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <- table.element(a,'Description',1,TRUE)a <- table.element(a,'Link',1,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Histogram',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$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$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a<-table.end(a)table.save(a,file='mytable7.tab')if(n < 200) {a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))a<-table.element(a,formatC(signif(x[i]-mysum$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')