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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 computationMon, 23 Jan 2017 12:38:17 +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/2017/Jan/23/t1485171530l2i74wgfu3ynf3v.htm/, Retrieved Thu, 31 Oct 2024 23:10:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305090, Retrieved Thu, 31 Oct 2024 23:10:40 +0000
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IsPrivate?No (this computation is public)
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Estimated Impact80
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Examen 0652924 7.0] [2017-01-23 11:38:17] [070714f07871aeb0c40d04255feda5cb] [Current]
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Dataseries X:
14 22 13 22 4 2 4 3 5
19 24 16 24 5 3 3 4 5
17 21 17 26 4 4 5 4 5
17 21 NA 21 3 4 3 3 4
15 24 NA 26 4 4 5 4 5
20 20 16 25 3 4 4 4 5
15 22 NA 21 3 4 4 3 3
19 20 NA 24 3 4 5 4 4
15 19 NA 27 4 5 4 4 5
15 23 17 28 4 5 5 4 5
19 21 17 23 4 4 2 4 5
NA 19 15 25 4 4 5 3 5
20 19 16 24 4 4 4 3 4
18 21 14 24 3 3 5 4 4
15 21 16 24 4 4 5 4 2
14 22 17 25 3 4 5 4 4
20 22 NA 25 3 4 5 4 4
NA 19 NA NA NA NA 5 NA 5
16 21 NA 25 5 5 4 3 4
16 21 NA 25 4 4 4 4 5
16 21 16 24 3 4 5 3 4
10 20 NA 26 4 4 4 4 5
19 22 16 26 4 4 5 4 4
19 22 NA 25 4 4 5 4 4
16 24 NA 26 4 4 5 4 4
15 21 NA 23 3 4 4 4 4
18 19 16 24 3 4 4 3 5
17 19 15 24 4 4 4 4 4
19 23 16 25 2 4 5 4 5
17 21 16 25 5 4 4 4 4
NA 21 13 24 4 3 5 4 4
19 19 15 28 4 5 5 4 5
20 21 17 27 5 4 5 4 4
5 19 NA NA 4 3 5 4 NA
19 21 13 23 2 3 5 4 5
16 21 17 23 4 5 2 4 4
15 23 NA 24 3 4 5 4 4
16 19 14 24 4 3 5 3 4
18 19 14 22 4 3 3 4 4
16 19 18 25 4 4 5 4 4
15 18 NA 25 5 4 4 4 4
17 22 17 28 4 5 5 4 5
NA 18 13 22 3 3 4 4 4
20 22 16 28 5 5 5 3 5
19 18 15 25 5 4 5 3 4
7 22 15 24 4 4 4 3 4
13 22 NA 24 4 4 4 4 4
16 19 15 23 3 5 5 3 3
16 22 13 25 4 4 4 4 5
NA 25 NA NA 2 3 4 2 NA
18 19 17 26 4 5 5 4 4
18 19 NA 25 5 5 2 4 5
16 19 NA 27 5 5 5 4 4
17 19 11 26 4 3 5 4 5
19 21 14 23 4 3 4 3 4
16 21 13 25 4 4 5 4 4
19 20 NA 21 3 4 4 3 3
13 19 17 22 3 4 4 4 4
16 19 16 24 4 4 4 3 5
13 22 NA 25 4 4 4 4 5
12 26 17 27 5 5 3 4 5
17 19 16 24 2 4 4 4 5
17 21 16 26 4 4 4 4 5
17 21 16 21 3 4 4 4 2
16 20 15 27 4 4 5 4 5
16 23 12 22 4 2 4 4 4
14 22 17 23 4 4 4 3 5
16 22 14 24 4 4 4 3 5
13 22 14 25 5 4 5 3 3
16 21 16 24 3 4 4 3 5
14 21 NA 23 3 4 4 3 4
20 22 NA 28 4 5 5 5 5
12 23 NA NA 4 4 3 4 NA
13 18 NA 24 4 4 4 4 4
18 24 NA 26 4 4 4 5 5
14 22 15 22 3 4 3 4 4
19 21 16 25 4 4 4 4 5
18 21 14 25 3 4 5 3 5
14 21 15 24 3 3 5 4 4
18 23 17 24 4 3 5 4 4
19 21 NA 26 4 4 5 4 4
15 23 10 21 3 3 3 4 4
14 21 NA 25 4 4 4 4 5
17 19 17 25 4 4 3 4 5
19 21 NA 26 4 4 4 4 5
13 21 20 25 5 4 4 4 4
19 21 17 26 5 4 3 5 4
18 23 18 27 4 4 5 4 5
20 23 NA 25 3 4 5 4 4
15 20 17 NA 3 NA 4 4 4
15 20 14 20 4 2 3 3 4
15 19 NA 24 4 4 5 4 4
20 23 17 26 4 4 5 4 4
15 22 NA 25 4 4 4 4 5
19 19 17 25 4 5 4 4 5
18 23 NA 24 3 4 4 3 5
18 22 16 26 4 4 5 4 4
15 22 18 25 5 4 3 4 4
20 21 18 28 5 4 5 5 4
17 21 16 27 4 5 4 4 5
12 21 NA 25 3 4 5 4 4
18 21 NA 26 5 3 4 4 5
19 22 15 26 4 4 5 4 4
20 25 13 26 5 4 4 4 4
NA 21 NA NA 3 4 4 3 NA
17 23 NA 28 5 4 4 5 5
15 19 NA NA 4 4 5 3 NA
16 22 NA 21 4 4 3 3 4
18 20 NA 25 4 4 5 4 4
18 21 16 25 4 4 5 4 4
14 25 NA 24 3 4 5 4 5
15 21 NA 24 4 4 4 4 4
12 19 NA 24 4 4 4 3 4
17 23 12 23 3 3 4 3 5
14 22 NA 23 4 4 4 3 4
18 21 16 24 3 4 5 4 4
17 24 16 24 4 4 5 4 3
17 21 NA 25 5 4 5 1 5
20 19 16 28 5 4 5 4 5
16 18 14 23 4 4 4 4 4
14 19 15 24 4 4 5 3 4
15 20 14 23 3 4 4 3 4
18 19 NA 24 4 4 4 4 4
20 22 15 25 4 4 4 4 5
17 21 NA 24 4 5 3 4 4
17 22 15 23 3 4 4 4 4
17 24 16 23 4 4 4 3 4
17 28 NA 25 4 4 4 4 4
15 19 NA 21 3 4 3 3 4
17 18 NA 22 4 4 4 3 4
18 23 11 19 3 2 4 2 4
17 19 NA 24 4 4 4 3 5
20 23 18 25 5 4 4 3 5
15 19 NA 21 2 4 4 3 3
16 22 11 22 3 3 4 4 4
15 21 NA 23 4 4 4 3 4
18 19 18 27 5 5 4 4 5
11 22 NA NA NA NA 2 NA NA
15 21 15 26 4 5 5 4 4
18 23 19 29 5 5 5 5 5
20 22 17 28 4 5 5 4 5
19 19 NA 24 4 4 4 3 4
14 19 14 25 3 4 5 4 5
16 21 NA 25 4 4 5 4 4
15 22 13 22 4 4 2 4 4
17 21 17 25 4 4 3 4 5
18 20 14 26 4 4 4 4 5
20 23 19 26 5 4 5 3 5
17 22 14 24 4 3 5 4 4
18 23 NA 25 4 4 5 4 4
15 22 NA 19 3 3 2 3 4
16 21 16 25 4 5 5 4 4
11 20 16 23 4 4 4 3 4
15 18 15 25 4 4 4 4 4
18 18 12 25 3 4 5 3 5
17 20 NA 26 4 4 5 4 4
16 19 17 27 5 4 5 4 5
12 21 NA 24 4 4 5 4 3
19 24 NA 22 2 3 5 4 4
18 19 18 25 4 4 4 4 4
15 20 15 24 4 3 4 3 5
17 19 18 23 4 4 4 4 4
19 23 15 27 4 5 5 5 4
18 22 NA 24 5 4 3 4 4
19 21 NA 24 5 4 4 3 4
16 24 NA 21 3 3 1 4 5
16 21 16 25 4 4 4 4 4
16 21 NA 25 4 4 4 4 5
14 22 16 23 2 3 4 5 5




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R ServerBig Analytics Cloud Computing Center

\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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305090&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]7 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305090&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&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 time7 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 5.76873 -0.0237248Bevr_Leeftijd[t] + 0.00592575TVDC[t] + 0.36521SKEOUSUM[t] + 0.0992317SKEOU1[t] -0.493906SKEOU2[t] + 0.200782SKEOU3[t] + 0.379908SKEOU4[t] + 0.397164SKEOU5[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  5.76873 -0.0237248Bevr_Leeftijd[t] +  0.00592575TVDC[t] +  0.36521SKEOUSUM[t] +  0.0992317SKEOU1[t] -0.493906SKEOU2[t] +  0.200782SKEOU3[t] +  0.379908SKEOU4[t] +  0.397164SKEOU5[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305090&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  5.76873 -0.0237248Bevr_Leeftijd[t] +  0.00592575TVDC[t] +  0.36521SKEOUSUM[t] +  0.0992317SKEOU1[t] -0.493906SKEOU2[t] +  0.200782SKEOU3[t] +  0.379908SKEOU4[t] +  0.397164SKEOU5[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305090&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&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
ITHSUM[t] = + 5.76873 -0.0237248Bevr_Leeftijd[t] + 0.00592575TVDC[t] + 0.36521SKEOUSUM[t] + 0.0992317SKEOU1[t] -0.493906SKEOU2[t] + 0.200782SKEOU3[t] + 0.379908SKEOU4[t] + 0.397164SKEOU5[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+5.769 4.204+1.3720e+00 0.1734 0.0867
Bevr_Leeftijd-0.02372 0.1372-1.7290e-01 0.8631 0.4316
TVDC+0.005926 0.1497+3.9580e-02 0.9685 0.4843
SKEOUSUM+0.3652 0.3771+9.6830e-01 0.3355 0.1677
SKEOU1+0.09923 0.5008+1.9820e-01 0.8434 0.4217
SKEOU2-0.4939 0.5507-8.9680e-01 0.3722 0.1861
SKEOU3+0.2008 0.5182+3.8740e-01 0.6993 0.3497
SKEOU4+0.3799 0.5592+6.7940e-01 0.4986 0.2493
SKEOU5+0.3972 0.5455+7.2810e-01 0.4684 0.2342

\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) & +5.769 &  4.204 & +1.3720e+00 &  0.1734 &  0.0867 \tabularnewline
Bevr_Leeftijd & -0.02372 &  0.1372 & -1.7290e-01 &  0.8631 &  0.4316 \tabularnewline
TVDC & +0.005926 &  0.1497 & +3.9580e-02 &  0.9685 &  0.4843 \tabularnewline
SKEOUSUM & +0.3652 &  0.3771 & +9.6830e-01 &  0.3355 &  0.1677 \tabularnewline
SKEOU1 & +0.09923 &  0.5008 & +1.9820e-01 &  0.8434 &  0.4217 \tabularnewline
SKEOU2 & -0.4939 &  0.5507 & -8.9680e-01 &  0.3722 &  0.1861 \tabularnewline
SKEOU3 & +0.2008 &  0.5182 & +3.8740e-01 &  0.6993 &  0.3497 \tabularnewline
SKEOU4 & +0.3799 &  0.5592 & +6.7940e-01 &  0.4986 &  0.2493 \tabularnewline
SKEOU5 & +0.3972 &  0.5455 & +7.2810e-01 &  0.4684 &  0.2342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305090&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]+5.769[/C][C] 4.204[/C][C]+1.3720e+00[/C][C] 0.1734[/C][C] 0.0867[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.02372[/C][C] 0.1372[/C][C]-1.7290e-01[/C][C] 0.8631[/C][C] 0.4316[/C][/ROW]
[ROW][C]TVDC[/C][C]+0.005926[/C][C] 0.1497[/C][C]+3.9580e-02[/C][C] 0.9685[/C][C] 0.4843[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.3652[/C][C] 0.3771[/C][C]+9.6830e-01[/C][C] 0.3355[/C][C] 0.1677[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.09923[/C][C] 0.5008[/C][C]+1.9820e-01[/C][C] 0.8434[/C][C] 0.4217[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.4939[/C][C] 0.5507[/C][C]-8.9680e-01[/C][C] 0.3722[/C][C] 0.1861[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.2008[/C][C] 0.5182[/C][C]+3.8740e-01[/C][C] 0.6993[/C][C] 0.3497[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.3799[/C][C] 0.5592[/C][C]+6.7940e-01[/C][C] 0.4986[/C][C] 0.2493[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.3972[/C][C] 0.5455[/C][C]+7.2810e-01[/C][C] 0.4684[/C][C] 0.2342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305090&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&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)+5.769 4.204+1.3720e+00 0.1734 0.0867
Bevr_Leeftijd-0.02372 0.1372-1.7290e-01 0.8631 0.4316
TVDC+0.005926 0.1497+3.9580e-02 0.9685 0.4843
SKEOUSUM+0.3652 0.3771+9.6830e-01 0.3355 0.1677
SKEOU1+0.09923 0.5008+1.9820e-01 0.8434 0.4217
SKEOU2-0.4939 0.5507-8.9680e-01 0.3722 0.1861
SKEOU3+0.2008 0.5182+3.8740e-01 0.6993 0.3497
SKEOU4+0.3799 0.5592+6.7940e-01 0.4986 0.2493
SKEOU5+0.3972 0.5455+7.2810e-01 0.4684 0.2342







Multiple Linear Regression - Regression Statistics
Multiple R 0.3759
R-squared 0.1413
Adjusted R-squared 0.06501
F-TEST (value) 1.852
F-TEST (DF numerator)8
F-TEST (DF denominator)90
p-value 0.07766
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.209
Sum Squared Residuals 439.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3759 \tabularnewline
R-squared &  0.1413 \tabularnewline
Adjusted R-squared &  0.06501 \tabularnewline
F-TEST (value) &  1.852 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 90 \tabularnewline
p-value &  0.07766 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.209 \tabularnewline
Sum Squared Residuals &  439.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305090&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3759[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1413[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.06501[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.852[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]90[/C][/ROW]
[ROW][C]p-value[/C][C] 0.07766[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.209[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 439.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305090&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&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 0.3759
R-squared 0.1413
Adjusted R-squared 0.06501
F-TEST (value) 1.852
F-TEST (DF numerator)8
F-TEST (DF denominator)90
p-value 0.07766
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.209
Sum Squared Residuals 439.1







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 14 16.7-2.696
2 19 17.18 1.819
3 17 17.8-0.7974
4 20 17.15 2.85
5 15 17.99-2.986
6 19 16.1 2.901
7 20 16.13 3.869
8 18 17.05 0.9533
9 15 15.87-0.8695
10 14 16.91-2.912
11 16 16.18-0.1847
12 19 17.37 1.629
13 18 16.43 1.571
14 17 16.5 0.4954
15 19 17.18 1.82
16 17 16.93 0.07248
17 19 18.07 0.9305
18 20 17.86 2.135
19 19 16.97 2.027
20 16 15.21 0.7917
21 16 16.81-0.8135
22 18 16.06 1.939
23 16 17.09-1.088
24 17 18.01-1.01
25 20 17.72 2.276
26 19 16.81 2.186
27 7 16.05-9.054
28 16 14.97 1.03
29 16 17.18-1.184
30 18 16.95 1.046
31 17 18.3-1.303
32 19 16.2 2.8
33 16 17.01-1.011
34 13 15.69-2.687
35 16 16.53-0.5278
36 12 17.25-5.248
37 17 16.71 0.2908
38 17 17.59-0.5907
39 17 14.47 2.526
40 16 18.17-2.174
41 16 16.65-0.6493
42 14 16.1-2.097
43 16 16.44-0.4448
44 13 16.32-3.316
45 16 16.38-0.3811
46 14 15.4-1.403
47 19 17.23 1.775
48 18 16.94 1.065
49 14 17.05-3.053
50 18 17.12 0.8838
51 15 15.48-0.4783
52 17 17.08-0.07805
53 13 16.95-3.951
54 19 17.48 1.522
55 18 18.12-0.1211
56 15 15.42-0.4212
57 20 17.35 2.647
58 19 16.78 2.215
59 18 17.37 0.6294
60 15 16.71-1.715
61 20 18.62 1.384
62 17 17.46-0.462
63 19 17.36 1.635
64 20 17.18 2.82
65 18 17.03 0.9709
66 17 16.44 0.5614
67 18 16.56 1.435
68 17 16.2 0.8045
69 20 18.67 1.331
70 16 16.16-0.1572
71 14 16.33-2.325
72 15 15.63-0.6306
73 20 17.2 2.804
74 17 15.97 1.031
75 17 15.65 1.353
76 18 14.69 3.311
77 20 16.91 3.091
78 16 16.07-0.07398
79 18 17.62 0.3795
80 15 16.89-1.894
81 18 18.84-0.8426
82 20 18.01 1.99
83 14 17.36-3.363
84 15 15.29-0.2896
85 17 17.03-0.0306
86 18 17.6 0.3975
87 20 17.48 2.519
88 17 17.12-0.1222
89 16 16.54-0.5352
90 11 15.74-4.742
91 15 16.89-1.894
92 18 16.99 1.005
93 16 18.31-2.309
94 18 16.89 1.112
95 15 16.99-1.992
96 17 16.16 0.8428
97 19 17.59 1.408
98 16 16.83-0.8283
99 14 17.15-3.147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.7 & -2.696 \tabularnewline
2 &  19 &  17.18 &  1.819 \tabularnewline
3 &  17 &  17.8 & -0.7974 \tabularnewline
4 &  20 &  17.15 &  2.85 \tabularnewline
5 &  15 &  17.99 & -2.986 \tabularnewline
6 &  19 &  16.1 &  2.901 \tabularnewline
7 &  20 &  16.13 &  3.869 \tabularnewline
8 &  18 &  17.05 &  0.9533 \tabularnewline
9 &  15 &  15.87 & -0.8695 \tabularnewline
10 &  14 &  16.91 & -2.912 \tabularnewline
11 &  16 &  16.18 & -0.1847 \tabularnewline
12 &  19 &  17.37 &  1.629 \tabularnewline
13 &  18 &  16.43 &  1.571 \tabularnewline
14 &  17 &  16.5 &  0.4954 \tabularnewline
15 &  19 &  17.18 &  1.82 \tabularnewline
16 &  17 &  16.93 &  0.07248 \tabularnewline
17 &  19 &  18.07 &  0.9305 \tabularnewline
18 &  20 &  17.86 &  2.135 \tabularnewline
19 &  19 &  16.97 &  2.027 \tabularnewline
20 &  16 &  15.21 &  0.7917 \tabularnewline
21 &  16 &  16.81 & -0.8135 \tabularnewline
22 &  18 &  16.06 &  1.939 \tabularnewline
23 &  16 &  17.09 & -1.088 \tabularnewline
24 &  17 &  18.01 & -1.01 \tabularnewline
25 &  20 &  17.72 &  2.276 \tabularnewline
26 &  19 &  16.81 &  2.186 \tabularnewline
27 &  7 &  16.05 & -9.054 \tabularnewline
28 &  16 &  14.97 &  1.03 \tabularnewline
29 &  16 &  17.18 & -1.184 \tabularnewline
30 &  18 &  16.95 &  1.046 \tabularnewline
31 &  17 &  18.3 & -1.303 \tabularnewline
32 &  19 &  16.2 &  2.8 \tabularnewline
33 &  16 &  17.01 & -1.011 \tabularnewline
34 &  13 &  15.69 & -2.687 \tabularnewline
35 &  16 &  16.53 & -0.5278 \tabularnewline
36 &  12 &  17.25 & -5.248 \tabularnewline
37 &  17 &  16.71 &  0.2908 \tabularnewline
38 &  17 &  17.59 & -0.5907 \tabularnewline
39 &  17 &  14.47 &  2.526 \tabularnewline
40 &  16 &  18.17 & -2.174 \tabularnewline
41 &  16 &  16.65 & -0.6493 \tabularnewline
42 &  14 &  16.1 & -2.097 \tabularnewline
43 &  16 &  16.44 & -0.4448 \tabularnewline
44 &  13 &  16.32 & -3.316 \tabularnewline
45 &  16 &  16.38 & -0.3811 \tabularnewline
46 &  14 &  15.4 & -1.403 \tabularnewline
47 &  19 &  17.23 &  1.775 \tabularnewline
48 &  18 &  16.94 &  1.065 \tabularnewline
49 &  14 &  17.05 & -3.053 \tabularnewline
50 &  18 &  17.12 &  0.8838 \tabularnewline
51 &  15 &  15.48 & -0.4783 \tabularnewline
52 &  17 &  17.08 & -0.07805 \tabularnewline
53 &  13 &  16.95 & -3.951 \tabularnewline
54 &  19 &  17.48 &  1.522 \tabularnewline
55 &  18 &  18.12 & -0.1211 \tabularnewline
56 &  15 &  15.42 & -0.4212 \tabularnewline
57 &  20 &  17.35 &  2.647 \tabularnewline
58 &  19 &  16.78 &  2.215 \tabularnewline
59 &  18 &  17.37 &  0.6294 \tabularnewline
60 &  15 &  16.71 & -1.715 \tabularnewline
61 &  20 &  18.62 &  1.384 \tabularnewline
62 &  17 &  17.46 & -0.462 \tabularnewline
63 &  19 &  17.36 &  1.635 \tabularnewline
64 &  20 &  17.18 &  2.82 \tabularnewline
65 &  18 &  17.03 &  0.9709 \tabularnewline
66 &  17 &  16.44 &  0.5614 \tabularnewline
67 &  18 &  16.56 &  1.435 \tabularnewline
68 &  17 &  16.2 &  0.8045 \tabularnewline
69 &  20 &  18.67 &  1.331 \tabularnewline
70 &  16 &  16.16 & -0.1572 \tabularnewline
71 &  14 &  16.33 & -2.325 \tabularnewline
72 &  15 &  15.63 & -0.6306 \tabularnewline
73 &  20 &  17.2 &  2.804 \tabularnewline
74 &  17 &  15.97 &  1.031 \tabularnewline
75 &  17 &  15.65 &  1.353 \tabularnewline
76 &  18 &  14.69 &  3.311 \tabularnewline
77 &  20 &  16.91 &  3.091 \tabularnewline
78 &  16 &  16.07 & -0.07398 \tabularnewline
79 &  18 &  17.62 &  0.3795 \tabularnewline
80 &  15 &  16.89 & -1.894 \tabularnewline
81 &  18 &  18.84 & -0.8426 \tabularnewline
82 &  20 &  18.01 &  1.99 \tabularnewline
83 &  14 &  17.36 & -3.363 \tabularnewline
84 &  15 &  15.29 & -0.2896 \tabularnewline
85 &  17 &  17.03 & -0.0306 \tabularnewline
86 &  18 &  17.6 &  0.3975 \tabularnewline
87 &  20 &  17.48 &  2.519 \tabularnewline
88 &  17 &  17.12 & -0.1222 \tabularnewline
89 &  16 &  16.54 & -0.5352 \tabularnewline
90 &  11 &  15.74 & -4.742 \tabularnewline
91 &  15 &  16.89 & -1.894 \tabularnewline
92 &  18 &  16.99 &  1.005 \tabularnewline
93 &  16 &  18.31 & -2.309 \tabularnewline
94 &  18 &  16.89 &  1.112 \tabularnewline
95 &  15 &  16.99 & -1.992 \tabularnewline
96 &  17 &  16.16 &  0.8428 \tabularnewline
97 &  19 &  17.59 &  1.408 \tabularnewline
98 &  16 &  16.83 & -0.8283 \tabularnewline
99 &  14 &  17.15 & -3.147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305090&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 14[/C][C] 16.7[/C][C]-2.696[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 17.18[/C][C] 1.819[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.8[/C][C]-0.7974[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 17.15[/C][C] 2.85[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.99[/C][C]-2.986[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 16.1[/C][C] 2.901[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 16.13[/C][C] 3.869[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 17.05[/C][C] 0.9533[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 15.87[/C][C]-0.8695[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 16.91[/C][C]-2.912[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.18[/C][C]-0.1847[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 17.37[/C][C] 1.629[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.43[/C][C] 1.571[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 16.5[/C][C] 0.4954[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 17.18[/C][C] 1.82[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 16.93[/C][C] 0.07248[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 18.07[/C][C] 0.9305[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 17.86[/C][C] 2.135[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 16.97[/C][C] 2.027[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 15.21[/C][C] 0.7917[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 16.81[/C][C]-0.8135[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 16.06[/C][C] 1.939[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 17.09[/C][C]-1.088[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 18.01[/C][C]-1.01[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 17.72[/C][C] 2.276[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 16.81[/C][C] 2.186[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 16.05[/C][C]-9.054[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.97[/C][C] 1.03[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 17.18[/C][C]-1.184[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 16.95[/C][C] 1.046[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 18.3[/C][C]-1.303[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.2[/C][C] 2.8[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 17.01[/C][C]-1.011[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.69[/C][C]-2.687[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.53[/C][C]-0.5278[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 17.25[/C][C]-5.248[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 16.71[/C][C] 0.2908[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.59[/C][C]-0.5907[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 14.47[/C][C] 2.526[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 18.17[/C][C]-2.174[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 16.65[/C][C]-0.6493[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 16.1[/C][C]-2.097[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.44[/C][C]-0.4448[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 16.32[/C][C]-3.316[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.38[/C][C]-0.3811[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.4[/C][C]-1.403[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 17.23[/C][C] 1.775[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 16.94[/C][C] 1.065[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 17.05[/C][C]-3.053[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17.12[/C][C] 0.8838[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 15.48[/C][C]-0.4783[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 17.08[/C][C]-0.07805[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 16.95[/C][C]-3.951[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 17.48[/C][C] 1.522[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 18.12[/C][C]-0.1211[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 15.42[/C][C]-0.4212[/C][/ROW]
[ROW][C]57[/C][C] 20[/C][C] 17.35[/C][C] 2.647[/C][/ROW]
[ROW][C]58[/C][C] 19[/C][C] 16.78[/C][C] 2.215[/C][/ROW]
[ROW][C]59[/C][C] 18[/C][C] 17.37[/C][C] 0.6294[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 16.71[/C][C]-1.715[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 18.62[/C][C] 1.384[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 17.46[/C][C]-0.462[/C][/ROW]
[ROW][C]63[/C][C] 19[/C][C] 17.36[/C][C] 1.635[/C][/ROW]
[ROW][C]64[/C][C] 20[/C][C] 17.18[/C][C] 2.82[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 17.03[/C][C] 0.9709[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 16.44[/C][C] 0.5614[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 16.56[/C][C] 1.435[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 16.2[/C][C] 0.8045[/C][/ROW]
[ROW][C]69[/C][C] 20[/C][C] 18.67[/C][C] 1.331[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 16.16[/C][C]-0.1572[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 16.33[/C][C]-2.325[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 15.63[/C][C]-0.6306[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 17.2[/C][C] 2.804[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 15.97[/C][C] 1.031[/C][/ROW]
[ROW][C]75[/C][C] 17[/C][C] 15.65[/C][C] 1.353[/C][/ROW]
[ROW][C]76[/C][C] 18[/C][C] 14.69[/C][C] 3.311[/C][/ROW]
[ROW][C]77[/C][C] 20[/C][C] 16.91[/C][C] 3.091[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 16.07[/C][C]-0.07398[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.62[/C][C] 0.3795[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.89[/C][C]-1.894[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 18.84[/C][C]-0.8426[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 18.01[/C][C] 1.99[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 17.36[/C][C]-3.363[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.29[/C][C]-0.2896[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 17.03[/C][C]-0.0306[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 17.6[/C][C] 0.3975[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 17.48[/C][C] 2.519[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 17.12[/C][C]-0.1222[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.54[/C][C]-0.5352[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 15.74[/C][C]-4.742[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 16.89[/C][C]-1.894[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 16.99[/C][C] 1.005[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 18.31[/C][C]-2.309[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.89[/C][C] 1.112[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.99[/C][C]-1.992[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.16[/C][C] 0.8428[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 17.59[/C][C] 1.408[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.83[/C][C]-0.8283[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 17.15[/C][C]-3.147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305090&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 14 16.7-2.696
2 19 17.18 1.819
3 17 17.8-0.7974
4 20 17.15 2.85
5 15 17.99-2.986
6 19 16.1 2.901
7 20 16.13 3.869
8 18 17.05 0.9533
9 15 15.87-0.8695
10 14 16.91-2.912
11 16 16.18-0.1847
12 19 17.37 1.629
13 18 16.43 1.571
14 17 16.5 0.4954
15 19 17.18 1.82
16 17 16.93 0.07248
17 19 18.07 0.9305
18 20 17.86 2.135
19 19 16.97 2.027
20 16 15.21 0.7917
21 16 16.81-0.8135
22 18 16.06 1.939
23 16 17.09-1.088
24 17 18.01-1.01
25 20 17.72 2.276
26 19 16.81 2.186
27 7 16.05-9.054
28 16 14.97 1.03
29 16 17.18-1.184
30 18 16.95 1.046
31 17 18.3-1.303
32 19 16.2 2.8
33 16 17.01-1.011
34 13 15.69-2.687
35 16 16.53-0.5278
36 12 17.25-5.248
37 17 16.71 0.2908
38 17 17.59-0.5907
39 17 14.47 2.526
40 16 18.17-2.174
41 16 16.65-0.6493
42 14 16.1-2.097
43 16 16.44-0.4448
44 13 16.32-3.316
45 16 16.38-0.3811
46 14 15.4-1.403
47 19 17.23 1.775
48 18 16.94 1.065
49 14 17.05-3.053
50 18 17.12 0.8838
51 15 15.48-0.4783
52 17 17.08-0.07805
53 13 16.95-3.951
54 19 17.48 1.522
55 18 18.12-0.1211
56 15 15.42-0.4212
57 20 17.35 2.647
58 19 16.78 2.215
59 18 17.37 0.6294
60 15 16.71-1.715
61 20 18.62 1.384
62 17 17.46-0.462
63 19 17.36 1.635
64 20 17.18 2.82
65 18 17.03 0.9709
66 17 16.44 0.5614
67 18 16.56 1.435
68 17 16.2 0.8045
69 20 18.67 1.331
70 16 16.16-0.1572
71 14 16.33-2.325
72 15 15.63-0.6306
73 20 17.2 2.804
74 17 15.97 1.031
75 17 15.65 1.353
76 18 14.69 3.311
77 20 16.91 3.091
78 16 16.07-0.07398
79 18 17.62 0.3795
80 15 16.89-1.894
81 18 18.84-0.8426
82 20 18.01 1.99
83 14 17.36-3.363
84 15 15.29-0.2896
85 17 17.03-0.0306
86 18 17.6 0.3975
87 20 17.48 2.519
88 17 17.12-0.1222
89 16 16.54-0.5352
90 11 15.74-4.742
91 15 16.89-1.894
92 18 16.99 1.005
93 16 18.31-2.309
94 18 16.89 1.112
95 15 16.99-1.992
96 17 16.16 0.8428
97 19 17.59 1.408
98 16 16.83-0.8283
99 14 17.15-3.147







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.595 0.81 0.405
13 0.4739 0.9478 0.5261
14 0.3274 0.6547 0.6726
15 0.6129 0.7742 0.3871
16 0.4996 0.9992 0.5004
17 0.3891 0.7782 0.6109
18 0.3077 0.6155 0.6923
19 0.3855 0.771 0.6145
20 0.303 0.6061 0.697
21 0.272 0.544 0.728
22 0.2255 0.4509 0.7745
23 0.1782 0.3564 0.8218
24 0.1337 0.2675 0.8663
25 0.2132 0.4264 0.7868
26 0.2236 0.4473 0.7764
27 0.9638 0.07244 0.03622
28 0.9621 0.07572 0.03786
29 0.9492 0.1017 0.05084
30 0.9312 0.1376 0.06878
31 0.9242 0.1516 0.07579
32 0.9445 0.1109 0.05547
33 0.9254 0.1492 0.07459
34 0.9437 0.1126 0.05632
35 0.9274 0.1452 0.07258
36 0.9818 0.03639 0.01819
37 0.9783 0.04347 0.02174
38 0.9707 0.0586 0.0293
39 0.9819 0.03617 0.01808
40 0.9834 0.03323 0.01661
41 0.9769 0.04625 0.02313
42 0.9765 0.04699 0.0235
43 0.9715 0.05696 0.02848
44 0.983 0.03404 0.01702
45 0.9752 0.04957 0.02478
46 0.9676 0.06489 0.03245
47 0.9632 0.07366 0.03683
48 0.9542 0.09158 0.04579
49 0.9635 0.07298 0.03649
50 0.9548 0.09035 0.04518
51 0.9426 0.1148 0.05742
52 0.9305 0.1389 0.06946
53 0.9686 0.06284 0.03142
54 0.9636 0.07276 0.03638
55 0.954 0.09201 0.046
56 0.9369 0.1261 0.06307
57 0.9462 0.1076 0.05382
58 0.9587 0.08257 0.04129
59 0.9433 0.1135 0.05673
60 0.9461 0.1079 0.05395
61 0.9308 0.1383 0.06917
62 0.9081 0.1837 0.09186
63 0.8907 0.2186 0.1093
64 0.89 0.22 0.11
65 0.864 0.2721 0.136
66 0.8304 0.3392 0.1696
67 0.8349 0.3302 0.1651
68 0.792 0.416 0.208
69 0.7682 0.4636 0.2318
70 0.7508 0.4985 0.2492
71 0.7272 0.5456 0.2728
72 0.6653 0.6694 0.3347
73 0.6982 0.6036 0.3018
74 0.6481 0.7037 0.3519
75 0.5997 0.8006 0.4003
76 0.6324 0.7353 0.3676
77 0.6354 0.7292 0.3646
78 0.5841 0.8318 0.4159
79 0.4998 0.9996 0.5002
80 0.4862 0.9724 0.5138
81 0.4678 0.9356 0.5322
82 0.3745 0.7489 0.6255
83 0.3555 0.711 0.6445
84 0.3025 0.6051 0.6975
85 0.2263 0.4527 0.7737
86 0.2625 0.5249 0.7375
87 0.2008 0.4016 0.7992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.595 &  0.81 &  0.405 \tabularnewline
13 &  0.4739 &  0.9478 &  0.5261 \tabularnewline
14 &  0.3274 &  0.6547 &  0.6726 \tabularnewline
15 &  0.6129 &  0.7742 &  0.3871 \tabularnewline
16 &  0.4996 &  0.9992 &  0.5004 \tabularnewline
17 &  0.3891 &  0.7782 &  0.6109 \tabularnewline
18 &  0.3077 &  0.6155 &  0.6923 \tabularnewline
19 &  0.3855 &  0.771 &  0.6145 \tabularnewline
20 &  0.303 &  0.6061 &  0.697 \tabularnewline
21 &  0.272 &  0.544 &  0.728 \tabularnewline
22 &  0.2255 &  0.4509 &  0.7745 \tabularnewline
23 &  0.1782 &  0.3564 &  0.8218 \tabularnewline
24 &  0.1337 &  0.2675 &  0.8663 \tabularnewline
25 &  0.2132 &  0.4264 &  0.7868 \tabularnewline
26 &  0.2236 &  0.4473 &  0.7764 \tabularnewline
27 &  0.9638 &  0.07244 &  0.03622 \tabularnewline
28 &  0.9621 &  0.07572 &  0.03786 \tabularnewline
29 &  0.9492 &  0.1017 &  0.05084 \tabularnewline
30 &  0.9312 &  0.1376 &  0.06878 \tabularnewline
31 &  0.9242 &  0.1516 &  0.07579 \tabularnewline
32 &  0.9445 &  0.1109 &  0.05547 \tabularnewline
33 &  0.9254 &  0.1492 &  0.07459 \tabularnewline
34 &  0.9437 &  0.1126 &  0.05632 \tabularnewline
35 &  0.9274 &  0.1452 &  0.07258 \tabularnewline
36 &  0.9818 &  0.03639 &  0.01819 \tabularnewline
37 &  0.9783 &  0.04347 &  0.02174 \tabularnewline
38 &  0.9707 &  0.0586 &  0.0293 \tabularnewline
39 &  0.9819 &  0.03617 &  0.01808 \tabularnewline
40 &  0.9834 &  0.03323 &  0.01661 \tabularnewline
41 &  0.9769 &  0.04625 &  0.02313 \tabularnewline
42 &  0.9765 &  0.04699 &  0.0235 \tabularnewline
43 &  0.9715 &  0.05696 &  0.02848 \tabularnewline
44 &  0.983 &  0.03404 &  0.01702 \tabularnewline
45 &  0.9752 &  0.04957 &  0.02478 \tabularnewline
46 &  0.9676 &  0.06489 &  0.03245 \tabularnewline
47 &  0.9632 &  0.07366 &  0.03683 \tabularnewline
48 &  0.9542 &  0.09158 &  0.04579 \tabularnewline
49 &  0.9635 &  0.07298 &  0.03649 \tabularnewline
50 &  0.9548 &  0.09035 &  0.04518 \tabularnewline
51 &  0.9426 &  0.1148 &  0.05742 \tabularnewline
52 &  0.9305 &  0.1389 &  0.06946 \tabularnewline
53 &  0.9686 &  0.06284 &  0.03142 \tabularnewline
54 &  0.9636 &  0.07276 &  0.03638 \tabularnewline
55 &  0.954 &  0.09201 &  0.046 \tabularnewline
56 &  0.9369 &  0.1261 &  0.06307 \tabularnewline
57 &  0.9462 &  0.1076 &  0.05382 \tabularnewline
58 &  0.9587 &  0.08257 &  0.04129 \tabularnewline
59 &  0.9433 &  0.1135 &  0.05673 \tabularnewline
60 &  0.9461 &  0.1079 &  0.05395 \tabularnewline
61 &  0.9308 &  0.1383 &  0.06917 \tabularnewline
62 &  0.9081 &  0.1837 &  0.09186 \tabularnewline
63 &  0.8907 &  0.2186 &  0.1093 \tabularnewline
64 &  0.89 &  0.22 &  0.11 \tabularnewline
65 &  0.864 &  0.2721 &  0.136 \tabularnewline
66 &  0.8304 &  0.3392 &  0.1696 \tabularnewline
67 &  0.8349 &  0.3302 &  0.1651 \tabularnewline
68 &  0.792 &  0.416 &  0.208 \tabularnewline
69 &  0.7682 &  0.4636 &  0.2318 \tabularnewline
70 &  0.7508 &  0.4985 &  0.2492 \tabularnewline
71 &  0.7272 &  0.5456 &  0.2728 \tabularnewline
72 &  0.6653 &  0.6694 &  0.3347 \tabularnewline
73 &  0.6982 &  0.6036 &  0.3018 \tabularnewline
74 &  0.6481 &  0.7037 &  0.3519 \tabularnewline
75 &  0.5997 &  0.8006 &  0.4003 \tabularnewline
76 &  0.6324 &  0.7353 &  0.3676 \tabularnewline
77 &  0.6354 &  0.7292 &  0.3646 \tabularnewline
78 &  0.5841 &  0.8318 &  0.4159 \tabularnewline
79 &  0.4998 &  0.9996 &  0.5002 \tabularnewline
80 &  0.4862 &  0.9724 &  0.5138 \tabularnewline
81 &  0.4678 &  0.9356 &  0.5322 \tabularnewline
82 &  0.3745 &  0.7489 &  0.6255 \tabularnewline
83 &  0.3555 &  0.711 &  0.6445 \tabularnewline
84 &  0.3025 &  0.6051 &  0.6975 \tabularnewline
85 &  0.2263 &  0.4527 &  0.7737 \tabularnewline
86 &  0.2625 &  0.5249 &  0.7375 \tabularnewline
87 &  0.2008 &  0.4016 &  0.7992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305090&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C] 0.595[/C][C] 0.81[/C][C] 0.405[/C][/ROW]
[ROW][C]13[/C][C] 0.4739[/C][C] 0.9478[/C][C] 0.5261[/C][/ROW]
[ROW][C]14[/C][C] 0.3274[/C][C] 0.6547[/C][C] 0.6726[/C][/ROW]
[ROW][C]15[/C][C] 0.6129[/C][C] 0.7742[/C][C] 0.3871[/C][/ROW]
[ROW][C]16[/C][C] 0.4996[/C][C] 0.9992[/C][C] 0.5004[/C][/ROW]
[ROW][C]17[/C][C] 0.3891[/C][C] 0.7782[/C][C] 0.6109[/C][/ROW]
[ROW][C]18[/C][C] 0.3077[/C][C] 0.6155[/C][C] 0.6923[/C][/ROW]
[ROW][C]19[/C][C] 0.3855[/C][C] 0.771[/C][C] 0.6145[/C][/ROW]
[ROW][C]20[/C][C] 0.303[/C][C] 0.6061[/C][C] 0.697[/C][/ROW]
[ROW][C]21[/C][C] 0.272[/C][C] 0.544[/C][C] 0.728[/C][/ROW]
[ROW][C]22[/C][C] 0.2255[/C][C] 0.4509[/C][C] 0.7745[/C][/ROW]
[ROW][C]23[/C][C] 0.1782[/C][C] 0.3564[/C][C] 0.8218[/C][/ROW]
[ROW][C]24[/C][C] 0.1337[/C][C] 0.2675[/C][C] 0.8663[/C][/ROW]
[ROW][C]25[/C][C] 0.2132[/C][C] 0.4264[/C][C] 0.7868[/C][/ROW]
[ROW][C]26[/C][C] 0.2236[/C][C] 0.4473[/C][C] 0.7764[/C][/ROW]
[ROW][C]27[/C][C] 0.9638[/C][C] 0.07244[/C][C] 0.03622[/C][/ROW]
[ROW][C]28[/C][C] 0.9621[/C][C] 0.07572[/C][C] 0.03786[/C][/ROW]
[ROW][C]29[/C][C] 0.9492[/C][C] 0.1017[/C][C] 0.05084[/C][/ROW]
[ROW][C]30[/C][C] 0.9312[/C][C] 0.1376[/C][C] 0.06878[/C][/ROW]
[ROW][C]31[/C][C] 0.9242[/C][C] 0.1516[/C][C] 0.07579[/C][/ROW]
[ROW][C]32[/C][C] 0.9445[/C][C] 0.1109[/C][C] 0.05547[/C][/ROW]
[ROW][C]33[/C][C] 0.9254[/C][C] 0.1492[/C][C] 0.07459[/C][/ROW]
[ROW][C]34[/C][C] 0.9437[/C][C] 0.1126[/C][C] 0.05632[/C][/ROW]
[ROW][C]35[/C][C] 0.9274[/C][C] 0.1452[/C][C] 0.07258[/C][/ROW]
[ROW][C]36[/C][C] 0.9818[/C][C] 0.03639[/C][C] 0.01819[/C][/ROW]
[ROW][C]37[/C][C] 0.9783[/C][C] 0.04347[/C][C] 0.02174[/C][/ROW]
[ROW][C]38[/C][C] 0.9707[/C][C] 0.0586[/C][C] 0.0293[/C][/ROW]
[ROW][C]39[/C][C] 0.9819[/C][C] 0.03617[/C][C] 0.01808[/C][/ROW]
[ROW][C]40[/C][C] 0.9834[/C][C] 0.03323[/C][C] 0.01661[/C][/ROW]
[ROW][C]41[/C][C] 0.9769[/C][C] 0.04625[/C][C] 0.02313[/C][/ROW]
[ROW][C]42[/C][C] 0.9765[/C][C] 0.04699[/C][C] 0.0235[/C][/ROW]
[ROW][C]43[/C][C] 0.9715[/C][C] 0.05696[/C][C] 0.02848[/C][/ROW]
[ROW][C]44[/C][C] 0.983[/C][C] 0.03404[/C][C] 0.01702[/C][/ROW]
[ROW][C]45[/C][C] 0.9752[/C][C] 0.04957[/C][C] 0.02478[/C][/ROW]
[ROW][C]46[/C][C] 0.9676[/C][C] 0.06489[/C][C] 0.03245[/C][/ROW]
[ROW][C]47[/C][C] 0.9632[/C][C] 0.07366[/C][C] 0.03683[/C][/ROW]
[ROW][C]48[/C][C] 0.9542[/C][C] 0.09158[/C][C] 0.04579[/C][/ROW]
[ROW][C]49[/C][C] 0.9635[/C][C] 0.07298[/C][C] 0.03649[/C][/ROW]
[ROW][C]50[/C][C] 0.9548[/C][C] 0.09035[/C][C] 0.04518[/C][/ROW]
[ROW][C]51[/C][C] 0.9426[/C][C] 0.1148[/C][C] 0.05742[/C][/ROW]
[ROW][C]52[/C][C] 0.9305[/C][C] 0.1389[/C][C] 0.06946[/C][/ROW]
[ROW][C]53[/C][C] 0.9686[/C][C] 0.06284[/C][C] 0.03142[/C][/ROW]
[ROW][C]54[/C][C] 0.9636[/C][C] 0.07276[/C][C] 0.03638[/C][/ROW]
[ROW][C]55[/C][C] 0.954[/C][C] 0.09201[/C][C] 0.046[/C][/ROW]
[ROW][C]56[/C][C] 0.9369[/C][C] 0.1261[/C][C] 0.06307[/C][/ROW]
[ROW][C]57[/C][C] 0.9462[/C][C] 0.1076[/C][C] 0.05382[/C][/ROW]
[ROW][C]58[/C][C] 0.9587[/C][C] 0.08257[/C][C] 0.04129[/C][/ROW]
[ROW][C]59[/C][C] 0.9433[/C][C] 0.1135[/C][C] 0.05673[/C][/ROW]
[ROW][C]60[/C][C] 0.9461[/C][C] 0.1079[/C][C] 0.05395[/C][/ROW]
[ROW][C]61[/C][C] 0.9308[/C][C] 0.1383[/C][C] 0.06917[/C][/ROW]
[ROW][C]62[/C][C] 0.9081[/C][C] 0.1837[/C][C] 0.09186[/C][/ROW]
[ROW][C]63[/C][C] 0.8907[/C][C] 0.2186[/C][C] 0.1093[/C][/ROW]
[ROW][C]64[/C][C] 0.89[/C][C] 0.22[/C][C] 0.11[/C][/ROW]
[ROW][C]65[/C][C] 0.864[/C][C] 0.2721[/C][C] 0.136[/C][/ROW]
[ROW][C]66[/C][C] 0.8304[/C][C] 0.3392[/C][C] 0.1696[/C][/ROW]
[ROW][C]67[/C][C] 0.8349[/C][C] 0.3302[/C][C] 0.1651[/C][/ROW]
[ROW][C]68[/C][C] 0.792[/C][C] 0.416[/C][C] 0.208[/C][/ROW]
[ROW][C]69[/C][C] 0.7682[/C][C] 0.4636[/C][C] 0.2318[/C][/ROW]
[ROW][C]70[/C][C] 0.7508[/C][C] 0.4985[/C][C] 0.2492[/C][/ROW]
[ROW][C]71[/C][C] 0.7272[/C][C] 0.5456[/C][C] 0.2728[/C][/ROW]
[ROW][C]72[/C][C] 0.6653[/C][C] 0.6694[/C][C] 0.3347[/C][/ROW]
[ROW][C]73[/C][C] 0.6982[/C][C] 0.6036[/C][C] 0.3018[/C][/ROW]
[ROW][C]74[/C][C] 0.6481[/C][C] 0.7037[/C][C] 0.3519[/C][/ROW]
[ROW][C]75[/C][C] 0.5997[/C][C] 0.8006[/C][C] 0.4003[/C][/ROW]
[ROW][C]76[/C][C] 0.6324[/C][C] 0.7353[/C][C] 0.3676[/C][/ROW]
[ROW][C]77[/C][C] 0.6354[/C][C] 0.7292[/C][C] 0.3646[/C][/ROW]
[ROW][C]78[/C][C] 0.5841[/C][C] 0.8318[/C][C] 0.4159[/C][/ROW]
[ROW][C]79[/C][C] 0.4998[/C][C] 0.9996[/C][C] 0.5002[/C][/ROW]
[ROW][C]80[/C][C] 0.4862[/C][C] 0.9724[/C][C] 0.5138[/C][/ROW]
[ROW][C]81[/C][C] 0.4678[/C][C] 0.9356[/C][C] 0.5322[/C][/ROW]
[ROW][C]82[/C][C] 0.3745[/C][C] 0.7489[/C][C] 0.6255[/C][/ROW]
[ROW][C]83[/C][C] 0.3555[/C][C] 0.711[/C][C] 0.6445[/C][/ROW]
[ROW][C]84[/C][C] 0.3025[/C][C] 0.6051[/C][C] 0.6975[/C][/ROW]
[ROW][C]85[/C][C] 0.2263[/C][C] 0.4527[/C][C] 0.7737[/C][/ROW]
[ROW][C]86[/C][C] 0.2625[/C][C] 0.5249[/C][C] 0.7375[/C][/ROW]
[ROW][C]87[/C][C] 0.2008[/C][C] 0.4016[/C][C] 0.7992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305090&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.595 0.81 0.405
13 0.4739 0.9478 0.5261
14 0.3274 0.6547 0.6726
15 0.6129 0.7742 0.3871
16 0.4996 0.9992 0.5004
17 0.3891 0.7782 0.6109
18 0.3077 0.6155 0.6923
19 0.3855 0.771 0.6145
20 0.303 0.6061 0.697
21 0.272 0.544 0.728
22 0.2255 0.4509 0.7745
23 0.1782 0.3564 0.8218
24 0.1337 0.2675 0.8663
25 0.2132 0.4264 0.7868
26 0.2236 0.4473 0.7764
27 0.9638 0.07244 0.03622
28 0.9621 0.07572 0.03786
29 0.9492 0.1017 0.05084
30 0.9312 0.1376 0.06878
31 0.9242 0.1516 0.07579
32 0.9445 0.1109 0.05547
33 0.9254 0.1492 0.07459
34 0.9437 0.1126 0.05632
35 0.9274 0.1452 0.07258
36 0.9818 0.03639 0.01819
37 0.9783 0.04347 0.02174
38 0.9707 0.0586 0.0293
39 0.9819 0.03617 0.01808
40 0.9834 0.03323 0.01661
41 0.9769 0.04625 0.02313
42 0.9765 0.04699 0.0235
43 0.9715 0.05696 0.02848
44 0.983 0.03404 0.01702
45 0.9752 0.04957 0.02478
46 0.9676 0.06489 0.03245
47 0.9632 0.07366 0.03683
48 0.9542 0.09158 0.04579
49 0.9635 0.07298 0.03649
50 0.9548 0.09035 0.04518
51 0.9426 0.1148 0.05742
52 0.9305 0.1389 0.06946
53 0.9686 0.06284 0.03142
54 0.9636 0.07276 0.03638
55 0.954 0.09201 0.046
56 0.9369 0.1261 0.06307
57 0.9462 0.1076 0.05382
58 0.9587 0.08257 0.04129
59 0.9433 0.1135 0.05673
60 0.9461 0.1079 0.05395
61 0.9308 0.1383 0.06917
62 0.9081 0.1837 0.09186
63 0.8907 0.2186 0.1093
64 0.89 0.22 0.11
65 0.864 0.2721 0.136
66 0.8304 0.3392 0.1696
67 0.8349 0.3302 0.1651
68 0.792 0.416 0.208
69 0.7682 0.4636 0.2318
70 0.7508 0.4985 0.2492
71 0.7272 0.5456 0.2728
72 0.6653 0.6694 0.3347
73 0.6982 0.6036 0.3018
74 0.6481 0.7037 0.3519
75 0.5997 0.8006 0.4003
76 0.6324 0.7353 0.3676
77 0.6354 0.7292 0.3646
78 0.5841 0.8318 0.4159
79 0.4998 0.9996 0.5002
80 0.4862 0.9724 0.5138
81 0.4678 0.9356 0.5322
82 0.3745 0.7489 0.6255
83 0.3555 0.711 0.6445
84 0.3025 0.6051 0.6975
85 0.2263 0.4527 0.7737
86 0.2625 0.5249 0.7375
87 0.2008 0.4016 0.7992







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level80.105263NOK
10% type I error level210.276316NOK

\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 & 8 & 0.105263 & NOK \tabularnewline
10% type I error level & 21 & 0.276316 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305090&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.105263[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.276316[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305090&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level80.105263NOK
10% type I error level210.276316NOK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.81003, df1 = 16, df2 = 74, p-value = 0.6701
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3486, df1 = 2, df2 = 88, p-value = 0.2649

\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 = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.81003, df1 = 16, df2 = 74, p-value = 0.6701
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3486, df1 = 2, df2 = 88, p-value = 0.2649
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=305090&T=7

[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 = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
[/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 = 0.81003, df1 = 16, df2 = 74, p-value = 0.6701
[/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 = 1.3486, df1 = 2, df2 = 88, p-value = 0.2649
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305090&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&T=7

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 = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.81003, df1 = 16, df2 = 74, p-value = 0.6701
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3486, df1 = 2, df2 = 88, p-value = 0.2649







Variance Inflation Factors (Multicollinearity)
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     1.049195      1.574693     10.037784      2.763875      2.890906 
       SKEOU3        SKEOU4        SKEOU5 
     3.220147      1.900520      2.494801 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     1.049195      1.574693     10.037784      2.763875      2.890906 
       SKEOU3        SKEOU4        SKEOU5 
     3.220147      1.900520      2.494801 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=305090&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     1.049195      1.574693     10.037784      2.763875      2.890906 
       SKEOU3        SKEOU4        SKEOU5 
     3.220147      1.900520      2.494801 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305090&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305090&T=8

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
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     1.049195      1.574693     10.037784      2.763875      2.890906 
       SKEOU3        SKEOU4        SKEOU5 
     3.220147      1.900520      2.494801 



Parameters (Session):
Parameters (R input):
par1 = ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')