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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 10:49:43 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/25/t1485338022zlu063rnn06a35u.htm/, Retrieved Mon, 13 May 2024 21:40:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306432, Retrieved Mon, 13 May 2024 21:40:13 +0000

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-       [Multiple Regression] [vraag 11] [2017-01-25 09:49:43] [2c6d1bf778a41dbfbe416644f6498149] [Current]

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13 22 14 4 2 4 3 5 4
16 24 19 5 3 3 4 5 4
17 21 17 4 4 5 4 5 4
NA 21 17 3 4 3 3 4 4
NA 24 15 4 4 5 4 5 4
16 20 20 3 4 4 4 5 5
NA 22 15 3 4 4 3 3 4
NA 20 19 3 4 5 4 4 4
NA 19 15 4 5 4 4 5 5
17 23 15 4 5 5 4 5 5
17 21 19 4 4 2 4 5 4
15 19 NA 4 4 5 3 5 4
16 19 20 4 4 4 3 4 5
14 21 18 3 3 5 4 4 5
16 21 15 4 4 5 4 2 5
17 22 14 3 4 5 4 4 5
NA 22 20 3 4 5 4 4 5
NA 19 NA NA NA 5 NA 5 5
NA 21 16 5 5 4 3 4 4
NA 21 16 4 4 4 4 5 4
16 21 16 3 4 5 3 4 5
NA 20 10 4 4 4 4 5 5
16 22 19 4 4 5 4 4 5
NA 22 19 4 4 5 4 4 4
NA 24 16 4 4 5 4 4 5
NA 21 15 3 4 4 4 4 4
16 19 18 3 4 4 3 5 5
15 19 17 4 4 4 4 4 4
16 23 19 2 4 5 4 5 5
16 21 17 5 4 4 4 4 4
13 21 NA 4 3 5 4 4 4
15 19 19 4 5 5 4 5 5
17 21 20 5 4 5 4 4 5
NA 19 5 4 3 5 4 NA 5
13 21 19 2 3 5 4 5 4
17 21 16 4 5 2 4 4 4
NA 23 15 3 4 5 4 4 4
14 19 16 4 3 5 3 4 5
14 19 18 4 3 3 4 4 4
18 19 16 4 4 5 4 4 4
NA 18 15 5 4 4 4 4 4
17 22 17 4 5 5 4 5 5
13 18 NA 3 3 4 4 4 4
16 22 20 5 5 5 3 5 5
15 18 19 5 4 5 3 4 4
15 22 7 4 4 4 3 4 5
NA 22 13 4 4 4 4 4 4
15 19 16 3 5 5 3 3 4
13 22 16 4 4 4 4 5 4
NA 25 NA 2 3 4 2 NA 4
17 19 18 4 5 5 4 4 4
NA 19 18 5 5 2 4 5 4
NA 19 16 5 5 5 4 4 4
11 19 17 4 3 5 4 5 5
14 21 19 4 3 4 3 4 5
13 21 16 4 4 5 4 4 4
NA 20 19 3 4 4 3 3 4
17 19 13 3 4 4 4 4 3
16 19 16 4 4 4 3 5 4
NA 22 13 4 4 4 4 5 4
17 26 12 5 5 3 4 5 5
16 19 17 2 4 4 4 5 5
16 21 17 4 4 4 4 5 5
16 21 17 3 4 4 4 2 4
15 20 16 4 4 5 4 5 5
12 23 16 4 2 4 4 4 4
17 22 14 4 4 4 3 5 3
14 22 16 4 4 4 3 5 4
14 22 13 5 4 5 3 3 5
16 21 16 3 4 4 3 5 5
NA 21 14 3 4 4 3 4 5
NA 22 20 4 5 5 5 5 4
NA 23 12 4 4 3 4 NA 4
NA 18 13 4 4 4 4 4 4
NA 24 18 4 4 4 5 5 4
15 22 14 3 4 3 4 4 4
16 21 19 4 4 4 4 5 4
14 21 18 3 4 5 3 5 5
15 21 14 3 3 5 4 4 5
17 23 18 4 3 5 4 4 4
NA 21 19 4 4 5 4 4 5
10 23 15 3 3 3 4 4 4
NA 21 14 4 4 4 4 5 4
17 19 17 4 4 3 4 5 5
NA 21 19 4 4 4 4 5 5
20 21 13 5 4 4 4 4 4
17 21 19 5 4 3 5 4 5
18 23 18 4 4 5 4 5 5
NA 23 20 3 4 5 4 4 5
17 20 15 3 NA 4 4 4 4
14 20 15 4 2 3 3 4 4
NA 19 15 4 4 5 4 4 3
17 23 20 4 4 5 4 4 5
NA 22 15 4 4 4 4 5 4
17 19 19 4 5 4 4 5 3
NA 23 18 3 4 4 3 5 5
16 22 18 4 4 5 4 4 5
18 22 15 5 4 3 4 4 5
18 21 20 5 4 5 5 4 5
16 21 17 4 5 4 4 5 5
NA 21 12 3 4 5 4 4 5
NA 21 18 5 3 4 4 5 5
15 22 19 4 4 5 4 4 5
13 25 20 5 4 4 4 4 5
NA 21 NA 3 4 4 3 NA 4
NA 23 17 5 4 4 5 5 5
NA 19 15 4 4 5 3 NA 5
NA 22 16 4 4 3 3 4 3
NA 20 18 4 4 5 4 4 4
16 21 18 4 4 5 4 4 4
NA 25 14 3 4 5 4 5 3
NA 21 15 4 4 4 4 4 4
NA 19 12 4 4 4 3 4 5
12 23 17 3 3 4 3 5 5
NA 22 14 4 4 4 3 4 4
16 21 18 3 4 5 4 4 4
16 24 17 4 4 5 4 3 4
NA 21 17 5 4 5 1 5 5
16 19 20 5 4 5 4 5 5
14 18 16 4 4 4 4 4 3
15 19 14 4 4 5 3 4 4
14 20 15 3 4 4 3 4 5
NA 19 18 4 4 4 4 4 4
15 22 20 4 4 4 4 5 4
NA 21 17 4 5 3 4 4 4
15 22 17 3 4 4 4 4 4
16 24 17 4 4 4 3 4 4
NA 28 17 4 4 4 4 4 5
NA 19 15 3 4 3 3 4 4
NA 18 17 4 4 4 3 4 3
11 23 18 3 2 4 2 4 4
NA 19 17 4 4 4 3 5 4
18 23 20 5 4 4 3 5 4
NA 19 15 2 4 4 3 3 5
11 22 16 3 3 4 4 4 4
NA 21 15 4 4 4 3 4 4
18 19 18 5 5 4 4 5 4
NA 22 11 NA NA 2 NA NA NA
15 21 15 4 5 5 4 4 4
19 23 18 5 5 5 5 5 4
17 22 20 4 5 5 4 5 5
NA 19 19 4 4 4 3 4 5
14 19 14 3 4 5 4 5 4
NA 21 16 4 4 5 4 4 4
13 22 15 4 4 2 4 4 4
17 21 17 4 4 3 4 5 5
14 20 18 4 4 4 4 5 5
19 23 20 5 4 5 3 5 4
14 22 17 4 3 5 4 4 4
NA 23 18 4 4 5 4 4 4
NA 22 15 3 3 2 3 4 4
16 21 16 4 5 5 4 4 3
16 20 11 4 4 4 3 4 4
15 18 15 4 4 4 4 4 5
12 18 18 3 4 5 3 5 5
NA 20 17 4 4 5 4 4 5
17 19 16 5 4 5 4 5 4
NA 21 12 4 4 5 4 3 4
NA 24 19 2 3 5 4 4 4
18 19 18 4 4 4 4 4 5
15 20 15 4 3 4 3 5 5
18 19 17 4 4 4 4 4 3
15 23 19 4 5 5 5 4 4
NA 22 18 5 4 3 4 4 4
NA 21 19 5 4 4 3 4 4
NA 24 16 3 3 1 4 5 5
16 21 16 4 4 4 4 4 5
NA 21 16 4 4 4 4 5 4
16 22 14 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 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=306432&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]

8 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=306432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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	8 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
TVDC[t] = + 6.77021 -0.0179153Bevr_Leeftijd[t] -8.35579e-05ITHSUM[t] + 0.665054SKEOU1[t] + 1.14501SKEOU2[t] + 0.0149915SKEOU3[t] + 0.525774SKEOU4[t] + 0.156393SKEOU5[t] -0.10909SKEOU6[t] -0.00417335t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.77021 -0.0179153Bevr_Leeftijd[t] -8.35579e-05ITHSUM[t] +  0.665054SKEOU1[t] +  1.14501SKEOU2[t] +  0.0149915SKEOU3[t] +  0.525774SKEOU4[t] +  0.156393SKEOU5[t] -0.10909SKEOU6[t] -0.00417335t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.77021 -0.0179153Bevr_Leeftijd[t] -8.35579e-05ITHSUM[t] +  0.665054SKEOU1[t] +  1.14501SKEOU2[t] +  0.0149915SKEOU3[t] +  0.525774SKEOU4[t] +  0.156393SKEOU5[t] -0.10909SKEOU6[t] -0.00417335t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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
TVDC[t] = + 6.77021 -0.0179153Bevr_Leeftijd[t] -8.35579e-05ITHSUM[t] + 0.665054SKEOU1[t] + 1.14501SKEOU2[t] + 0.0149915SKEOU3[t] + 0.525774SKEOU4[t] + 0.156393SKEOU5[t] -0.10909SKEOU6[t] -0.00417335t + 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)	+6.77	2.928	+2.3130e+00	0.02306	0.01153
Bevr_Leeftijd	-0.01792	0.09683	-1.8500e-01	0.8536	0.4268
ITHSUM	-8.356e-05	0.07451	-1.1220e-03	0.9991	0.4996
SKEOU1	+0.6651	0.2254	+2.9510e+00	0.004049	0.002025
SKEOU2	+1.145	0.2503	+4.5750e+00	1.535e-05	7.675e-06
SKEOU3	+0.01499	0.2155	+6.9580e-02	0.9447	0.4723
SKEOU4	+0.5258	0.3054	+1.7220e+00	0.08857	0.04428
SKEOU5	+0.1564	0.2544	+6.1480e-01	0.5402	0.2701
SKEOU6	-0.1091	0.2714	-4.0200e-01	0.6886	0.3443
t	-0.004173	0.005623	-7.4220e-01	0.4599	0.23

\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) & +6.77 &  2.928 & +2.3130e+00 &  0.02306 &  0.01153 \tabularnewline
Bevr_Leeftijd & -0.01792 &  0.09683 & -1.8500e-01 &  0.8536 &  0.4268 \tabularnewline
ITHSUM & -8.356e-05 &  0.07451 & -1.1220e-03 &  0.9991 &  0.4996 \tabularnewline
SKEOU1 & +0.6651 &  0.2254 & +2.9510e+00 &  0.004049 &  0.002025 \tabularnewline
SKEOU2 & +1.145 &  0.2503 & +4.5750e+00 &  1.535e-05 &  7.675e-06 \tabularnewline
SKEOU3 & +0.01499 &  0.2155 & +6.9580e-02 &  0.9447 &  0.4723 \tabularnewline
SKEOU4 & +0.5258 &  0.3054 & +1.7220e+00 &  0.08857 &  0.04428 \tabularnewline
SKEOU5 & +0.1564 &  0.2544 & +6.1480e-01 &  0.5402 &  0.2701 \tabularnewline
SKEOU6 & -0.1091 &  0.2714 & -4.0200e-01 &  0.6886 &  0.3443 \tabularnewline
t & -0.004173 &  0.005623 & -7.4220e-01 &  0.4599 &  0.23 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&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]+6.77[/C][C] 2.928[/C][C]+2.3130e+00[/C][C] 0.02306[/C][C] 0.01153[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.01792[/C][C] 0.09683[/C][C]-1.8500e-01[/C][C] 0.8536[/C][C] 0.4268[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-8.356e-05[/C][C] 0.07451[/C][C]-1.1220e-03[/C][C] 0.9991[/C][C] 0.4996[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.6651[/C][C] 0.2254[/C][C]+2.9510e+00[/C][C] 0.004049[/C][C] 0.002025[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1.145[/C][C] 0.2503[/C][C]+4.5750e+00[/C][C] 1.535e-05[/C][C] 7.675e-06[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.01499[/C][C] 0.2155[/C][C]+6.9580e-02[/C][C] 0.9447[/C][C] 0.4723[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.5258[/C][C] 0.3054[/C][C]+1.7220e+00[/C][C] 0.08857[/C][C] 0.04428[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.1564[/C][C] 0.2544[/C][C]+6.1480e-01[/C][C] 0.5402[/C][C] 0.2701[/C][/ROW]
[ROW][C]SKEOU6[/C][C]-0.1091[/C][C] 0.2714[/C][C]-4.0200e-01[/C][C] 0.6886[/C][C] 0.3443[/C][/ROW]
[ROW][C]t[/C][C]-0.004173[/C][C] 0.005623[/C][C]-7.4220e-01[/C][C] 0.4599[/C][C] 0.23[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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)	+6.77	2.928	+2.3130e+00	0.02306	0.01153
Bevr_Leeftijd	-0.01792	0.09683	-1.8500e-01	0.8536	0.4268
ITHSUM	-8.356e-05	0.07451	-1.1220e-03	0.9991	0.4996
SKEOU1	+0.6651	0.2254	+2.9510e+00	0.004049	0.002025
SKEOU2	+1.145	0.2503	+4.5750e+00	1.535e-05	7.675e-06
SKEOU3	+0.01499	0.2155	+6.9580e-02	0.9447	0.4723
SKEOU4	+0.5258	0.3054	+1.7220e+00	0.08857	0.04428
SKEOU5	+0.1564	0.2544	+6.1480e-01	0.5402	0.2701
SKEOU6	-0.1091	0.2714	-4.0200e-01	0.6886	0.3443
t	-0.004173	0.005623	-7.4220e-01	0.4599	0.23

Multiple Linear Regression - Regression Statistics
Multiple R	0.6073
R-squared	0.3689
Adjusted R-squared	0.3051
F-TEST (value)	5.78
F-TEST (DF numerator)	9
F-TEST (DF denominator)	89
p-value	2.668e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.559
Sum Squared Residuals	216.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6073 \tabularnewline
R-squared &  0.3689 \tabularnewline
Adjusted R-squared &  0.3051 \tabularnewline
F-TEST (value) &  5.78 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 89 \tabularnewline
p-value &  2.668e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.559 \tabularnewline
Sum Squared Residuals &  216.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6073[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3689[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3051[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.78[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]89[/C][/ROW]
[ROW][C]p-value[/C][C] 2.668e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.559[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 216.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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	0.6073
R-squared	0.3689
Adjusted R-squared	0.3051
F-TEST (value)	5.78
F-TEST (DF numerator)	9
F-TEST (DF denominator)	89
p-value	2.668e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.559
Sum Squared Residuals	216.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.3	-0.3039
2	16	15.58	0.4157
3	17	16.14	0.856
4	16	15.37	0.6317
5	17	17.14	-0.1359
6	17	16.09	0.9137
7	16	15.36	0.6434
8	14	14.05	-0.04746
9	16	15.54	0.4592
10	17	15.17	1.833
11	16	14.65	1.346
12	16	15.82	0.1772
13	16	14.82	1.177
14	15	15.96	-0.9625
15	16	14.62	1.381
16	16	16.58	-0.5834
17	15	17.16	-2.157
18	17	16.48	0.5193
19	13	13.6	-0.6019
20	17	17.02	-0.01673
21	14	14.17	-0.1685
22	14	14.77	-0.769
23	18	15.94	2.06
24	17	17.07	-0.07432
25	16	17.21	-1.209
26	15	16.08	-1.084
27	15	15.22	-0.2205
28	15	15.72	-0.7169
29	13	16	-3.003
30	17	17.06	-0.05563
31	11	14.81	-3.809
32	14	14.07	-0.0715
33	13	15.86	-2.862
34	17	15.32	1.677
35	16	15.51	0.4944
36	17	17.59	-0.5881
37	16	14.58	1.416
38	16	15.87	0.1262
39	16	14.84	1.156
40	15	15.9	-0.8984
41	12	13.49	-1.488
42	17	15.53	1.468
43	14	15.42	-1.418
44	14	15.67	-1.673
45	16	14.65	1.346
46	15	15.1	-0.09541
47	16	15.95	0.05483
48	14	14.66	-0.6562
49	15	13.88	1.123
50	17	14.61	2.39
51	10	13.91	-3.912
52	17	15.84	1.164
53	20	16.43	3.571
54	17	16.83	0.1737
55	18	15.78	2.218
56	14	12.94	1.061
57	17	15.62	1.383
58	17	17.19	-0.1892
59	16	15.63	0.3732
60	18	16.26	1.742
61	18	16.83	1.173
62	16	16.92	-0.9187
63	15	15.61	-0.61
64	13	16.2	-3.202
65	16	15.73	0.2713
66	12	13.39	-1.385
67	16	15.06	0.9447
68	16	15.51	0.4938
69	16	16.46	-0.4601
70	14	15.86	-1.856
71	15	15.21	-0.2141
72	14	14.4	-0.4028
73	15	15.82	-0.8187
74	15	14.99	0.006708
75	16	15.09	0.9074
76	11	11.63	-0.6254
77	18	15.92	2.077
78	11	13.83	-2.832
79	18	17.66	0.3424
80	15	16.81	-1.811
81	19	18.12	0.8816
82	17	16.83	0.168
83	14	15.18	-1.181
84	13	15.59	-2.587
85	17	15.66	1.337
86	14	15.69	-1.691
87	19	15.9	3.103
88	14	14.47	-0.4699
89	16	16.88	-0.8828
90	16	15.1	0.8979
91	15	15.55	-0.5501
92	12	14.53	-2.526
93	17	16.47	0.5307
94	18	15.52	2.481
95	15	13.98	1.017
96	18	15.73	2.271
97	15	17.23	-2.23
98	16	15.47	0.5329
99	16	13.76	2.239

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.3 & -0.3039 \tabularnewline
2 &  16 &  15.58 &  0.4157 \tabularnewline
3 &  17 &  16.14 &  0.856 \tabularnewline
4 &  16 &  15.37 &  0.6317 \tabularnewline
5 &  17 &  17.14 & -0.1359 \tabularnewline
6 &  17 &  16.09 &  0.9137 \tabularnewline
7 &  16 &  15.36 &  0.6434 \tabularnewline
8 &  14 &  14.05 & -0.04746 \tabularnewline
9 &  16 &  15.54 &  0.4592 \tabularnewline
10 &  17 &  15.17 &  1.833 \tabularnewline
11 &  16 &  14.65 &  1.346 \tabularnewline
12 &  16 &  15.82 &  0.1772 \tabularnewline
13 &  16 &  14.82 &  1.177 \tabularnewline
14 &  15 &  15.96 & -0.9625 \tabularnewline
15 &  16 &  14.62 &  1.381 \tabularnewline
16 &  16 &  16.58 & -0.5834 \tabularnewline
17 &  15 &  17.16 & -2.157 \tabularnewline
18 &  17 &  16.48 &  0.5193 \tabularnewline
19 &  13 &  13.6 & -0.6019 \tabularnewline
20 &  17 &  17.02 & -0.01673 \tabularnewline
21 &  14 &  14.17 & -0.1685 \tabularnewline
22 &  14 &  14.77 & -0.769 \tabularnewline
23 &  18 &  15.94 &  2.06 \tabularnewline
24 &  17 &  17.07 & -0.07432 \tabularnewline
25 &  16 &  17.21 & -1.209 \tabularnewline
26 &  15 &  16.08 & -1.084 \tabularnewline
27 &  15 &  15.22 & -0.2205 \tabularnewline
28 &  15 &  15.72 & -0.7169 \tabularnewline
29 &  13 &  16 & -3.003 \tabularnewline
30 &  17 &  17.06 & -0.05563 \tabularnewline
31 &  11 &  14.81 & -3.809 \tabularnewline
32 &  14 &  14.07 & -0.0715 \tabularnewline
33 &  13 &  15.86 & -2.862 \tabularnewline
34 &  17 &  15.32 &  1.677 \tabularnewline
35 &  16 &  15.51 &  0.4944 \tabularnewline
36 &  17 &  17.59 & -0.5881 \tabularnewline
37 &  16 &  14.58 &  1.416 \tabularnewline
38 &  16 &  15.87 &  0.1262 \tabularnewline
39 &  16 &  14.84 &  1.156 \tabularnewline
40 &  15 &  15.9 & -0.8984 \tabularnewline
41 &  12 &  13.49 & -1.488 \tabularnewline
42 &  17 &  15.53 &  1.468 \tabularnewline
43 &  14 &  15.42 & -1.418 \tabularnewline
44 &  14 &  15.67 & -1.673 \tabularnewline
45 &  16 &  14.65 &  1.346 \tabularnewline
46 &  15 &  15.1 & -0.09541 \tabularnewline
47 &  16 &  15.95 &  0.05483 \tabularnewline
48 &  14 &  14.66 & -0.6562 \tabularnewline
49 &  15 &  13.88 &  1.123 \tabularnewline
50 &  17 &  14.61 &  2.39 \tabularnewline
51 &  10 &  13.91 & -3.912 \tabularnewline
52 &  17 &  15.84 &  1.164 \tabularnewline
53 &  20 &  16.43 &  3.571 \tabularnewline
54 &  17 &  16.83 &  0.1737 \tabularnewline
55 &  18 &  15.78 &  2.218 \tabularnewline
56 &  14 &  12.94 &  1.061 \tabularnewline
57 &  17 &  15.62 &  1.383 \tabularnewline
58 &  17 &  17.19 & -0.1892 \tabularnewline
59 &  16 &  15.63 &  0.3732 \tabularnewline
60 &  18 &  16.26 &  1.742 \tabularnewline
61 &  18 &  16.83 &  1.173 \tabularnewline
62 &  16 &  16.92 & -0.9187 \tabularnewline
63 &  15 &  15.61 & -0.61 \tabularnewline
64 &  13 &  16.2 & -3.202 \tabularnewline
65 &  16 &  15.73 &  0.2713 \tabularnewline
66 &  12 &  13.39 & -1.385 \tabularnewline
67 &  16 &  15.06 &  0.9447 \tabularnewline
68 &  16 &  15.51 &  0.4938 \tabularnewline
69 &  16 &  16.46 & -0.4601 \tabularnewline
70 &  14 &  15.86 & -1.856 \tabularnewline
71 &  15 &  15.21 & -0.2141 \tabularnewline
72 &  14 &  14.4 & -0.4028 \tabularnewline
73 &  15 &  15.82 & -0.8187 \tabularnewline
74 &  15 &  14.99 &  0.006708 \tabularnewline
75 &  16 &  15.09 &  0.9074 \tabularnewline
76 &  11 &  11.63 & -0.6254 \tabularnewline
77 &  18 &  15.92 &  2.077 \tabularnewline
78 &  11 &  13.83 & -2.832 \tabularnewline
79 &  18 &  17.66 &  0.3424 \tabularnewline
80 &  15 &  16.81 & -1.811 \tabularnewline
81 &  19 &  18.12 &  0.8816 \tabularnewline
82 &  17 &  16.83 &  0.168 \tabularnewline
83 &  14 &  15.18 & -1.181 \tabularnewline
84 &  13 &  15.59 & -2.587 \tabularnewline
85 &  17 &  15.66 &  1.337 \tabularnewline
86 &  14 &  15.69 & -1.691 \tabularnewline
87 &  19 &  15.9 &  3.103 \tabularnewline
88 &  14 &  14.47 & -0.4699 \tabularnewline
89 &  16 &  16.88 & -0.8828 \tabularnewline
90 &  16 &  15.1 &  0.8979 \tabularnewline
91 &  15 &  15.55 & -0.5501 \tabularnewline
92 &  12 &  14.53 & -2.526 \tabularnewline
93 &  17 &  16.47 &  0.5307 \tabularnewline
94 &  18 &  15.52 &  2.481 \tabularnewline
95 &  15 &  13.98 &  1.017 \tabularnewline
96 &  18 &  15.73 &  2.271 \tabularnewline
97 &  15 &  17.23 & -2.23 \tabularnewline
98 &  16 &  15.47 &  0.5329 \tabularnewline
99 &  16 &  13.76 &  2.239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&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] 13[/C][C] 13.3[/C][C]-0.3039[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.58[/C][C] 0.4157[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.14[/C][C] 0.856[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.37[/C][C] 0.6317[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.14[/C][C]-0.1359[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 16.09[/C][C] 0.9137[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.36[/C][C] 0.6434[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 14.05[/C][C]-0.04746[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 15.54[/C][C] 0.4592[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.17[/C][C] 1.833[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 14.65[/C][C] 1.346[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.82[/C][C] 0.1772[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.82[/C][C] 1.177[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.96[/C][C]-0.9625[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 14.62[/C][C] 1.381[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 16.58[/C][C]-0.5834[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 17.16[/C][C]-2.157[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.48[/C][C] 0.5193[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 13.6[/C][C]-0.6019[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 17.02[/C][C]-0.01673[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 14.17[/C][C]-0.1685[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 14.77[/C][C]-0.769[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 15.94[/C][C] 2.06[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 17.07[/C][C]-0.07432[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 17.21[/C][C]-1.209[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 16.08[/C][C]-1.084[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.22[/C][C]-0.2205[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 15.72[/C][C]-0.7169[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 16[/C][C]-3.003[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 17.06[/C][C]-0.05563[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 14.81[/C][C]-3.809[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 14.07[/C][C]-0.0715[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 15.86[/C][C]-2.862[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 15.32[/C][C] 1.677[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.51[/C][C] 0.4944[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.59[/C][C]-0.5881[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 14.58[/C][C] 1.416[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.87[/C][C] 0.1262[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 14.84[/C][C] 1.156[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.9[/C][C]-0.8984[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 13.49[/C][C]-1.488[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 15.53[/C][C] 1.468[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.42[/C][C]-1.418[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.67[/C][C]-1.673[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 14.65[/C][C] 1.346[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.1[/C][C]-0.09541[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 15.95[/C][C] 0.05483[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 14.66[/C][C]-0.6562[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 13.88[/C][C] 1.123[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.61[/C][C] 2.39[/C][/ROW]
[ROW][C]51[/C][C] 10[/C][C] 13.91[/C][C]-3.912[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 15.84[/C][C] 1.164[/C][/ROW]
[ROW][C]53[/C][C] 20[/C][C] 16.43[/C][C] 3.571[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.83[/C][C] 0.1737[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 15.78[/C][C] 2.218[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 12.94[/C][C] 1.061[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.62[/C][C] 1.383[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 17.19[/C][C]-0.1892[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 15.63[/C][C] 0.3732[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 16.26[/C][C] 1.742[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 16.83[/C][C] 1.173[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.92[/C][C]-0.9187[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.61[/C][C]-0.61[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 16.2[/C][C]-3.202[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 15.73[/C][C] 0.2713[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 13.39[/C][C]-1.385[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.06[/C][C] 0.9447[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.51[/C][C] 0.4938[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 16.46[/C][C]-0.4601[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 15.86[/C][C]-1.856[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 15.21[/C][C]-0.2141[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.4[/C][C]-0.4028[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.82[/C][C]-0.8187[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 14.99[/C][C] 0.006708[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.09[/C][C] 0.9074[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.63[/C][C]-0.6254[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 15.92[/C][C] 2.077[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 13.83[/C][C]-2.832[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.66[/C][C] 0.3424[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.81[/C][C]-1.811[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 18.12[/C][C] 0.8816[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 16.83[/C][C] 0.168[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 15.18[/C][C]-1.181[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 15.59[/C][C]-2.587[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 15.66[/C][C] 1.337[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.69[/C][C]-1.691[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 15.9[/C][C] 3.103[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 14.47[/C][C]-0.4699[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.88[/C][C]-0.8828[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.1[/C][C] 0.8979[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.55[/C][C]-0.5501[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 14.53[/C][C]-2.526[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.47[/C][C] 0.5307[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.52[/C][C] 2.481[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 13.98[/C][C] 1.017[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 15.73[/C][C] 2.271[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 17.23[/C][C]-2.23[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.47[/C][C] 0.5329[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 13.76[/C][C] 2.239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.3	-0.3039
2	16	15.58	0.4157
3	17	16.14	0.856
4	16	15.37	0.6317
5	17	17.14	-0.1359
6	17	16.09	0.9137
7	16	15.36	0.6434
8	14	14.05	-0.04746
9	16	15.54	0.4592
10	17	15.17	1.833
11	16	14.65	1.346
12	16	15.82	0.1772
13	16	14.82	1.177
14	15	15.96	-0.9625
15	16	14.62	1.381
16	16	16.58	-0.5834
17	15	17.16	-2.157
18	17	16.48	0.5193
19	13	13.6	-0.6019
20	17	17.02	-0.01673
21	14	14.17	-0.1685
22	14	14.77	-0.769
23	18	15.94	2.06
24	17	17.07	-0.07432
25	16	17.21	-1.209
26	15	16.08	-1.084
27	15	15.22	-0.2205
28	15	15.72	-0.7169
29	13	16	-3.003
30	17	17.06	-0.05563
31	11	14.81	-3.809
32	14	14.07	-0.0715
33	13	15.86	-2.862
34	17	15.32	1.677
35	16	15.51	0.4944
36	17	17.59	-0.5881
37	16	14.58	1.416
38	16	15.87	0.1262
39	16	14.84	1.156
40	15	15.9	-0.8984
41	12	13.49	-1.488
42	17	15.53	1.468
43	14	15.42	-1.418
44	14	15.67	-1.673
45	16	14.65	1.346
46	15	15.1	-0.09541
47	16	15.95	0.05483
48	14	14.66	-0.6562
49	15	13.88	1.123
50	17	14.61	2.39
51	10	13.91	-3.912
52	17	15.84	1.164
53	20	16.43	3.571
54	17	16.83	0.1737
55	18	15.78	2.218
56	14	12.94	1.061
57	17	15.62	1.383
58	17	17.19	-0.1892
59	16	15.63	0.3732
60	18	16.26	1.742
61	18	16.83	1.173
62	16	16.92	-0.9187
63	15	15.61	-0.61
64	13	16.2	-3.202
65	16	15.73	0.2713
66	12	13.39	-1.385
67	16	15.06	0.9447
68	16	15.51	0.4938
69	16	16.46	-0.4601
70	14	15.86	-1.856
71	15	15.21	-0.2141
72	14	14.4	-0.4028
73	15	15.82	-0.8187
74	15	14.99	0.006708
75	16	15.09	0.9074
76	11	11.63	-0.6254
77	18	15.92	2.077
78	11	13.83	-2.832
79	18	17.66	0.3424
80	15	16.81	-1.811
81	19	18.12	0.8816
82	17	16.83	0.168
83	14	15.18	-1.181
84	13	15.59	-2.587
85	17	15.66	1.337
86	14	15.69	-1.691
87	19	15.9	3.103
88	14	14.47	-0.4699
89	16	16.88	-0.8828
90	16	15.1	0.8979
91	15	15.55	-0.5501
92	12	14.53	-2.526
93	17	16.47	0.5307
94	18	15.52	2.481
95	15	13.98	1.017
96	18	15.73	2.271
97	15	17.23	-2.23
98	16	15.47	0.5329
99	16	13.76	2.239

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.1151	0.2303	0.8849
14	0.09808	0.1962	0.9019
15	0.06172	0.1234	0.9383
16	0.02742	0.05483	0.9726
17	0.01961	0.03923	0.9804
18	0.04911	0.09822	0.9509
19	0.03244	0.06487	0.9676
20	0.01805	0.0361	0.9819
21	0.009809	0.01962	0.9902
22	0.004728	0.009456	0.9953
23	0.05128	0.1026	0.9487
24	0.03095	0.06189	0.9691
25	0.02441	0.04881	0.9756
26	0.01483	0.02967	0.9852
27	0.008677	0.01735	0.9913
28	0.006484	0.01297	0.9935
29	0.01522	0.03043	0.9848
30	0.01097	0.02195	0.989
31	0.02621	0.05242	0.9738
32	0.02042	0.04084	0.9796
33	0.02895	0.05789	0.9711
34	0.06093	0.1219	0.9391
35	0.06224	0.1245	0.9378
36	0.04731	0.09462	0.9527
37	0.05709	0.1142	0.9429
38	0.05	0.09999	0.95
39	0.04751	0.09502	0.9525
40	0.03803	0.07606	0.962
41	0.03501	0.07003	0.965
42	0.03946	0.07893	0.9605
43	0.0375	0.075	0.9625
44	0.03913	0.07826	0.9609
45	0.03949	0.07899	0.9605
46	0.02965	0.0593	0.9704
47	0.02368	0.04736	0.9763
48	0.01642	0.03283	0.9836
49	0.01772	0.03544	0.9823
50	0.04333	0.08667	0.9567
51	0.2141	0.4282	0.7859
52	0.2285	0.4569	0.7715
53	0.4599	0.9197	0.5401
54	0.4014	0.8027	0.5986
55	0.4634	0.9268	0.5366
56	0.423	0.8459	0.577
57	0.4249	0.8498	0.5751
58	0.3697	0.7393	0.6303
59	0.3229	0.6457	0.6771
60	0.3508	0.7015	0.6492
61	0.3537	0.7074	0.6463
62	0.3122	0.6245	0.6878
63	0.2688	0.5376	0.7312
64	0.4129	0.8258	0.5871
65	0.3638	0.7275	0.6362
66	0.3286	0.6573	0.6714
67	0.3818	0.7635	0.6182
68	0.3734	0.7468	0.6266
69	0.3074	0.6148	0.6926
70	0.2763	0.5525	0.7237
71	0.2313	0.4627	0.7687
72	0.2292	0.4583	0.7708
73	0.1816	0.3632	0.8184
74	0.2046	0.4092	0.7954
75	0.2119	0.4238	0.7881
76	0.1587	0.3174	0.8413
77	0.1556	0.3111	0.8444
78	0.1443	0.2886	0.8557
79	0.1018	0.2036	0.8982
80	0.07306	0.1461	0.9269
81	0.05322	0.1064	0.9468
82	0.06005	0.1201	0.9399
83	0.04028	0.08056	0.9597
84	0.2643	0.5287	0.7357
85	0.1731	0.3463	0.8269
86	0.5025	0.995	0.4975

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 &  0.1151 &  0.2303 &  0.8849 \tabularnewline
14 &  0.09808 &  0.1962 &  0.9019 \tabularnewline
15 &  0.06172 &  0.1234 &  0.9383 \tabularnewline
16 &  0.02742 &  0.05483 &  0.9726 \tabularnewline
17 &  0.01961 &  0.03923 &  0.9804 \tabularnewline
18 &  0.04911 &  0.09822 &  0.9509 \tabularnewline
19 &  0.03244 &  0.06487 &  0.9676 \tabularnewline
20 &  0.01805 &  0.0361 &  0.9819 \tabularnewline
21 &  0.009809 &  0.01962 &  0.9902 \tabularnewline
22 &  0.004728 &  0.009456 &  0.9953 \tabularnewline
23 &  0.05128 &  0.1026 &  0.9487 \tabularnewline
24 &  0.03095 &  0.06189 &  0.9691 \tabularnewline
25 &  0.02441 &  0.04881 &  0.9756 \tabularnewline
26 &  0.01483 &  0.02967 &  0.9852 \tabularnewline
27 &  0.008677 &  0.01735 &  0.9913 \tabularnewline
28 &  0.006484 &  0.01297 &  0.9935 \tabularnewline
29 &  0.01522 &  0.03043 &  0.9848 \tabularnewline
30 &  0.01097 &  0.02195 &  0.989 \tabularnewline
31 &  0.02621 &  0.05242 &  0.9738 \tabularnewline
32 &  0.02042 &  0.04084 &  0.9796 \tabularnewline
33 &  0.02895 &  0.05789 &  0.9711 \tabularnewline
34 &  0.06093 &  0.1219 &  0.9391 \tabularnewline
35 &  0.06224 &  0.1245 &  0.9378 \tabularnewline
36 &  0.04731 &  0.09462 &  0.9527 \tabularnewline
37 &  0.05709 &  0.1142 &  0.9429 \tabularnewline
38 &  0.05 &  0.09999 &  0.95 \tabularnewline
39 &  0.04751 &  0.09502 &  0.9525 \tabularnewline
40 &  0.03803 &  0.07606 &  0.962 \tabularnewline
41 &  0.03501 &  0.07003 &  0.965 \tabularnewline
42 &  0.03946 &  0.07893 &  0.9605 \tabularnewline
43 &  0.0375 &  0.075 &  0.9625 \tabularnewline
44 &  0.03913 &  0.07826 &  0.9609 \tabularnewline
45 &  0.03949 &  0.07899 &  0.9605 \tabularnewline
46 &  0.02965 &  0.0593 &  0.9704 \tabularnewline
47 &  0.02368 &  0.04736 &  0.9763 \tabularnewline
48 &  0.01642 &  0.03283 &  0.9836 \tabularnewline
49 &  0.01772 &  0.03544 &  0.9823 \tabularnewline
50 &  0.04333 &  0.08667 &  0.9567 \tabularnewline
51 &  0.2141 &  0.4282 &  0.7859 \tabularnewline
52 &  0.2285 &  0.4569 &  0.7715 \tabularnewline
53 &  0.4599 &  0.9197 &  0.5401 \tabularnewline
54 &  0.4014 &  0.8027 &  0.5986 \tabularnewline
55 &  0.4634 &  0.9268 &  0.5366 \tabularnewline
56 &  0.423 &  0.8459 &  0.577 \tabularnewline
57 &  0.4249 &  0.8498 &  0.5751 \tabularnewline
58 &  0.3697 &  0.7393 &  0.6303 \tabularnewline
59 &  0.3229 &  0.6457 &  0.6771 \tabularnewline
60 &  0.3508 &  0.7015 &  0.6492 \tabularnewline
61 &  0.3537 &  0.7074 &  0.6463 \tabularnewline
62 &  0.3122 &  0.6245 &  0.6878 \tabularnewline
63 &  0.2688 &  0.5376 &  0.7312 \tabularnewline
64 &  0.4129 &  0.8258 &  0.5871 \tabularnewline
65 &  0.3638 &  0.7275 &  0.6362 \tabularnewline
66 &  0.3286 &  0.6573 &  0.6714 \tabularnewline
67 &  0.3818 &  0.7635 &  0.6182 \tabularnewline
68 &  0.3734 &  0.7468 &  0.6266 \tabularnewline
69 &  0.3074 &  0.6148 &  0.6926 \tabularnewline
70 &  0.2763 &  0.5525 &  0.7237 \tabularnewline
71 &  0.2313 &  0.4627 &  0.7687 \tabularnewline
72 &  0.2292 &  0.4583 &  0.7708 \tabularnewline
73 &  0.1816 &  0.3632 &  0.8184 \tabularnewline
74 &  0.2046 &  0.4092 &  0.7954 \tabularnewline
75 &  0.2119 &  0.4238 &  0.7881 \tabularnewline
76 &  0.1587 &  0.3174 &  0.8413 \tabularnewline
77 &  0.1556 &  0.3111 &  0.8444 \tabularnewline
78 &  0.1443 &  0.2886 &  0.8557 \tabularnewline
79 &  0.1018 &  0.2036 &  0.8982 \tabularnewline
80 &  0.07306 &  0.1461 &  0.9269 \tabularnewline
81 &  0.05322 &  0.1064 &  0.9468 \tabularnewline
82 &  0.06005 &  0.1201 &  0.9399 \tabularnewline
83 &  0.04028 &  0.08056 &  0.9597 \tabularnewline
84 &  0.2643 &  0.5287 &  0.7357 \tabularnewline
85 &  0.1731 &  0.3463 &  0.8269 \tabularnewline
86 &  0.5025 &  0.995 &  0.4975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&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]13[/C][C] 0.1151[/C][C] 0.2303[/C][C] 0.8849[/C][/ROW]
[ROW][C]14[/C][C] 0.09808[/C][C] 0.1962[/C][C] 0.9019[/C][/ROW]
[ROW][C]15[/C][C] 0.06172[/C][C] 0.1234[/C][C] 0.9383[/C][/ROW]
[ROW][C]16[/C][C] 0.02742[/C][C] 0.05483[/C][C] 0.9726[/C][/ROW]
[ROW][C]17[/C][C] 0.01961[/C][C] 0.03923[/C][C] 0.9804[/C][/ROW]
[ROW][C]18[/C][C] 0.04911[/C][C] 0.09822[/C][C] 0.9509[/C][/ROW]
[ROW][C]19[/C][C] 0.03244[/C][C] 0.06487[/C][C] 0.9676[/C][/ROW]
[ROW][C]20[/C][C] 0.01805[/C][C] 0.0361[/C][C] 0.9819[/C][/ROW]
[ROW][C]21[/C][C] 0.009809[/C][C] 0.01962[/C][C] 0.9902[/C][/ROW]
[ROW][C]22[/C][C] 0.004728[/C][C] 0.009456[/C][C] 0.9953[/C][/ROW]
[ROW][C]23[/C][C] 0.05128[/C][C] 0.1026[/C][C] 0.9487[/C][/ROW]
[ROW][C]24[/C][C] 0.03095[/C][C] 0.06189[/C][C] 0.9691[/C][/ROW]
[ROW][C]25[/C][C] 0.02441[/C][C] 0.04881[/C][C] 0.9756[/C][/ROW]
[ROW][C]26[/C][C] 0.01483[/C][C] 0.02967[/C][C] 0.9852[/C][/ROW]
[ROW][C]27[/C][C] 0.008677[/C][C] 0.01735[/C][C] 0.9913[/C][/ROW]
[ROW][C]28[/C][C] 0.006484[/C][C] 0.01297[/C][C] 0.9935[/C][/ROW]
[ROW][C]29[/C][C] 0.01522[/C][C] 0.03043[/C][C] 0.9848[/C][/ROW]
[ROW][C]30[/C][C] 0.01097[/C][C] 0.02195[/C][C] 0.989[/C][/ROW]
[ROW][C]31[/C][C] 0.02621[/C][C] 0.05242[/C][C] 0.9738[/C][/ROW]
[ROW][C]32[/C][C] 0.02042[/C][C] 0.04084[/C][C] 0.9796[/C][/ROW]
[ROW][C]33[/C][C] 0.02895[/C][C] 0.05789[/C][C] 0.9711[/C][/ROW]
[ROW][C]34[/C][C] 0.06093[/C][C] 0.1219[/C][C] 0.9391[/C][/ROW]
[ROW][C]35[/C][C] 0.06224[/C][C] 0.1245[/C][C] 0.9378[/C][/ROW]
[ROW][C]36[/C][C] 0.04731[/C][C] 0.09462[/C][C] 0.9527[/C][/ROW]
[ROW][C]37[/C][C] 0.05709[/C][C] 0.1142[/C][C] 0.9429[/C][/ROW]
[ROW][C]38[/C][C] 0.05[/C][C] 0.09999[/C][C] 0.95[/C][/ROW]
[ROW][C]39[/C][C] 0.04751[/C][C] 0.09502[/C][C] 0.9525[/C][/ROW]
[ROW][C]40[/C][C] 0.03803[/C][C] 0.07606[/C][C] 0.962[/C][/ROW]
[ROW][C]41[/C][C] 0.03501[/C][C] 0.07003[/C][C] 0.965[/C][/ROW]
[ROW][C]42[/C][C] 0.03946[/C][C] 0.07893[/C][C] 0.9605[/C][/ROW]
[ROW][C]43[/C][C] 0.0375[/C][C] 0.075[/C][C] 0.9625[/C][/ROW]
[ROW][C]44[/C][C] 0.03913[/C][C] 0.07826[/C][C] 0.9609[/C][/ROW]
[ROW][C]45[/C][C] 0.03949[/C][C] 0.07899[/C][C] 0.9605[/C][/ROW]
[ROW][C]46[/C][C] 0.02965[/C][C] 0.0593[/C][C] 0.9704[/C][/ROW]
[ROW][C]47[/C][C] 0.02368[/C][C] 0.04736[/C][C] 0.9763[/C][/ROW]
[ROW][C]48[/C][C] 0.01642[/C][C] 0.03283[/C][C] 0.9836[/C][/ROW]
[ROW][C]49[/C][C] 0.01772[/C][C] 0.03544[/C][C] 0.9823[/C][/ROW]
[ROW][C]50[/C][C] 0.04333[/C][C] 0.08667[/C][C] 0.9567[/C][/ROW]
[ROW][C]51[/C][C] 0.2141[/C][C] 0.4282[/C][C] 0.7859[/C][/ROW]
[ROW][C]52[/C][C] 0.2285[/C][C] 0.4569[/C][C] 0.7715[/C][/ROW]
[ROW][C]53[/C][C] 0.4599[/C][C] 0.9197[/C][C] 0.5401[/C][/ROW]
[ROW][C]54[/C][C] 0.4014[/C][C] 0.8027[/C][C] 0.5986[/C][/ROW]
[ROW][C]55[/C][C] 0.4634[/C][C] 0.9268[/C][C] 0.5366[/C][/ROW]
[ROW][C]56[/C][C] 0.423[/C][C] 0.8459[/C][C] 0.577[/C][/ROW]
[ROW][C]57[/C][C] 0.4249[/C][C] 0.8498[/C][C] 0.5751[/C][/ROW]
[ROW][C]58[/C][C] 0.3697[/C][C] 0.7393[/C][C] 0.6303[/C][/ROW]
[ROW][C]59[/C][C] 0.3229[/C][C] 0.6457[/C][C] 0.6771[/C][/ROW]
[ROW][C]60[/C][C] 0.3508[/C][C] 0.7015[/C][C] 0.6492[/C][/ROW]
[ROW][C]61[/C][C] 0.3537[/C][C] 0.7074[/C][C] 0.6463[/C][/ROW]
[ROW][C]62[/C][C] 0.3122[/C][C] 0.6245[/C][C] 0.6878[/C][/ROW]
[ROW][C]63[/C][C] 0.2688[/C][C] 0.5376[/C][C] 0.7312[/C][/ROW]
[ROW][C]64[/C][C] 0.4129[/C][C] 0.8258[/C][C] 0.5871[/C][/ROW]
[ROW][C]65[/C][C] 0.3638[/C][C] 0.7275[/C][C] 0.6362[/C][/ROW]
[ROW][C]66[/C][C] 0.3286[/C][C] 0.6573[/C][C] 0.6714[/C][/ROW]
[ROW][C]67[/C][C] 0.3818[/C][C] 0.7635[/C][C] 0.6182[/C][/ROW]
[ROW][C]68[/C][C] 0.3734[/C][C] 0.7468[/C][C] 0.6266[/C][/ROW]
[ROW][C]69[/C][C] 0.3074[/C][C] 0.6148[/C][C] 0.6926[/C][/ROW]
[ROW][C]70[/C][C] 0.2763[/C][C] 0.5525[/C][C] 0.7237[/C][/ROW]
[ROW][C]71[/C][C] 0.2313[/C][C] 0.4627[/C][C] 0.7687[/C][/ROW]
[ROW][C]72[/C][C] 0.2292[/C][C] 0.4583[/C][C] 0.7708[/C][/ROW]
[ROW][C]73[/C][C] 0.1816[/C][C] 0.3632[/C][C] 0.8184[/C][/ROW]
[ROW][C]74[/C][C] 0.2046[/C][C] 0.4092[/C][C] 0.7954[/C][/ROW]
[ROW][C]75[/C][C] 0.2119[/C][C] 0.4238[/C][C] 0.7881[/C][/ROW]
[ROW][C]76[/C][C] 0.1587[/C][C] 0.3174[/C][C] 0.8413[/C][/ROW]
[ROW][C]77[/C][C] 0.1556[/C][C] 0.3111[/C][C] 0.8444[/C][/ROW]
[ROW][C]78[/C][C] 0.1443[/C][C] 0.2886[/C][C] 0.8557[/C][/ROW]
[ROW][C]79[/C][C] 0.1018[/C][C] 0.2036[/C][C] 0.8982[/C][/ROW]
[ROW][C]80[/C][C] 0.07306[/C][C] 0.1461[/C][C] 0.9269[/C][/ROW]
[ROW][C]81[/C][C] 0.05322[/C][C] 0.1064[/C][C] 0.9468[/C][/ROW]
[ROW][C]82[/C][C] 0.06005[/C][C] 0.1201[/C][C] 0.9399[/C][/ROW]
[ROW][C]83[/C][C] 0.04028[/C][C] 0.08056[/C][C] 0.9597[/C][/ROW]
[ROW][C]84[/C][C] 0.2643[/C][C] 0.5287[/C][C] 0.7357[/C][/ROW]
[ROW][C]85[/C][C] 0.1731[/C][C] 0.3463[/C][C] 0.8269[/C][/ROW]
[ROW][C]86[/C][C] 0.5025[/C][C] 0.995[/C][C] 0.4975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.1151	0.2303	0.8849
14	0.09808	0.1962	0.9019
15	0.06172	0.1234	0.9383
16	0.02742	0.05483	0.9726
17	0.01961	0.03923	0.9804
18	0.04911	0.09822	0.9509
19	0.03244	0.06487	0.9676
20	0.01805	0.0361	0.9819
21	0.009809	0.01962	0.9902
22	0.004728	0.009456	0.9953
23	0.05128	0.1026	0.9487
24	0.03095	0.06189	0.9691
25	0.02441	0.04881	0.9756
26	0.01483	0.02967	0.9852
27	0.008677	0.01735	0.9913
28	0.006484	0.01297	0.9935
29	0.01522	0.03043	0.9848
30	0.01097	0.02195	0.989
31	0.02621	0.05242	0.9738
32	0.02042	0.04084	0.9796
33	0.02895	0.05789	0.9711
34	0.06093	0.1219	0.9391
35	0.06224	0.1245	0.9378
36	0.04731	0.09462	0.9527
37	0.05709	0.1142	0.9429
38	0.05	0.09999	0.95
39	0.04751	0.09502	0.9525
40	0.03803	0.07606	0.962
41	0.03501	0.07003	0.965
42	0.03946	0.07893	0.9605
43	0.0375	0.075	0.9625
44	0.03913	0.07826	0.9609
45	0.03949	0.07899	0.9605
46	0.02965	0.0593	0.9704
47	0.02368	0.04736	0.9763
48	0.01642	0.03283	0.9836
49	0.01772	0.03544	0.9823
50	0.04333	0.08667	0.9567
51	0.2141	0.4282	0.7859
52	0.2285	0.4569	0.7715
53	0.4599	0.9197	0.5401
54	0.4014	0.8027	0.5986
55	0.4634	0.9268	0.5366
56	0.423	0.8459	0.577
57	0.4249	0.8498	0.5751
58	0.3697	0.7393	0.6303
59	0.3229	0.6457	0.6771
60	0.3508	0.7015	0.6492
61	0.3537	0.7074	0.6463
62	0.3122	0.6245	0.6878
63	0.2688	0.5376	0.7312
64	0.4129	0.8258	0.5871
65	0.3638	0.7275	0.6362
66	0.3286	0.6573	0.6714
67	0.3818	0.7635	0.6182
68	0.3734	0.7468	0.6266
69	0.3074	0.6148	0.6926
70	0.2763	0.5525	0.7237
71	0.2313	0.4627	0.7687
72	0.2292	0.4583	0.7708
73	0.1816	0.3632	0.8184
74	0.2046	0.4092	0.7954
75	0.2119	0.4238	0.7881
76	0.1587	0.3174	0.8413
77	0.1556	0.3111	0.8444
78	0.1443	0.2886	0.8557
79	0.1018	0.2036	0.8982
80	0.07306	0.1461	0.9269
81	0.05322	0.1064	0.9468
82	0.06005	0.1201	0.9399
83	0.04028	0.08056	0.9597
84	0.2643	0.5287	0.7357
85	0.1731	0.3463	0.8269
86	0.5025	0.995	0.4975

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.01351	NOK
5% type I error level	14	0.189189	NOK
10% type I error level	32	0.432432	NOK

\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 & 1 &  0.01351 & NOK \tabularnewline
5% type I error level & 14 & 0.189189 & NOK \tabularnewline
10% type I error level & 32 & 0.432432 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&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]1[/C][C] 0.01351[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.189189[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.432432[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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 tests	OK/NOK
1% type I error level	1	0.01351	NOK
5% type I error level	14	0.189189	NOK
10% type I error level	32	0.432432	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.82261, df1 = 2, df2 = 87, p-value = 0.4427
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0424, df1 = 18, df2 = 71, p-value = 0.4264
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.59716, df1 = 2, df2 = 87, p-value = 0.5526

\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 = 0.82261, df1 = 2, df2 = 87, p-value = 0.4427
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0424, df1 = 18, df2 = 71, p-value = 0.4264
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.59716, df1 = 2, df2 = 87, p-value = 0.5526
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&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 = 0.82261, df1 = 2, df2 = 87, p-value = 0.4427
[/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 = 1.0424, df1 = 18, df2 = 71, p-value = 0.4264
[/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 = 0.59716, df1 = 2, df2 = 87, p-value = 0.5526
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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 = 0.82261, df1 = 2, df2 = 87, p-value = 0.4427
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0424, df1 = 18, df2 = 71, p-value = 0.4264
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.59716, df1 = 2, df2 = 87, p-value = 0.5526

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd ITHSUM SKEOU1 SKEOU2 SKEOU3 1.049243 1.168065 1.123890 1.198626 1.117431 SKEOU4 SKEOU5 SKEOU6 t 1.137729 1.089155 1.091307 1.051924

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd        ITHSUM        SKEOU1        SKEOU2        SKEOU3 
     1.049243      1.168065      1.123890      1.198626      1.117431 
       SKEOU4        SKEOU5        SKEOU6             t 
     1.137729      1.089155      1.091307      1.051924 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306432&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd        ITHSUM        SKEOU1        SKEOU2        SKEOU3 
     1.049243      1.168065      1.123890      1.198626      1.117431 
       SKEOU4        SKEOU5        SKEOU6             t 
     1.137729      1.089155      1.091307      1.051924 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306432&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306432&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 ITHSUM SKEOU1 SKEOU2 SKEOU3 1.049243 1.168065 1.123890 1.198626 1.117431 SKEOU4 SKEOU5 SKEOU6 t 1.137729 1.089155 1.091307 1.051924

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code