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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 09:37:20 +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/t148533368558afn5oviw04d85.htm/, Retrieved Mon, 13 May 2024 22:42:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305696, Retrieved Mon, 13 May 2024 22:42:26 +0000

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-       [Multiple Regression] [] [2017-01-25 08:37:20] [e72637ad2c4fb1a07d34fcbb26863f53] [Current]

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13 4 2 4 22 3 5 14 22
16 5 3 3 24 4 5 19 24
17 4 4 5 26 4 5 17 21
NA 3 4 3 21 3 4 17 21
NA 4 4 5 26 4 5 15 24
16 3 4 4 25 4 5 20 20
NA 3 4 4 21 3 3 15 22
NA 3 4 5 24 4 4 19 20
NA 4 5 4 27 4 5 15 19
17 4 5 5 28 4 5 15 23
17 4 4 2 23 4 5 19 21
15 4 4 5 25 3 5 NA 19
16 4 4 4 24 3 4 20 19
14 3 3 5 24 4 4 18 21
16 4 4 5 24 4 2 15 21
17 3 4 5 25 4 4 14 22
NA 3 4 5 25 4 4 20 22
NA NA NA 5 NA NA 5 NA 19
NA 5 5 4 25 3 4 16 21
NA 4 4 4 25 4 5 16 21
16 3 4 5 24 3 4 16 21
NA 4 4 4 26 4 5 10 20
16 4 4 5 26 4 4 19 22
NA 4 4 5 25 4 4 19 22
NA 4 4 5 26 4 4 16 24
NA 3 4 4 23 4 4 15 21
16 3 4 4 24 3 5 18 19
15 4 4 4 24 4 4 17 19
16 2 4 5 25 4 5 19 23
16 5 4 4 25 4 4 17 21
13 4 3 5 24 4 4 NA 21
15 4 5 5 28 4 5 19 19
17 5 4 5 27 4 4 20 21
NA 4 3 5 NA 4 NA 5 19
13 2 3 5 23 4 5 19 21
17 4 5 2 23 4 4 16 21
NA 3 4 5 24 4 4 15 23
14 4 3 5 24 3 4 16 19
14 4 3 3 22 4 4 18 19
18 4 4 5 25 4 4 16 19
NA 5 4 4 25 4 4 15 18
17 4 5 5 28 4 5 17 22
13 3 3 4 22 4 4 NA 18
16 5 5 5 28 3 5 20 22
15 5 4 5 25 3 4 19 18
15 4 4 4 24 3 4 7 22
NA 4 4 4 24 4 4 13 22
15 3 5 5 23 3 3 16 19
13 4 4 4 25 4 5 16 22
NA 2 3 4 NA 2 NA NA 25
17 4 5 5 26 4 4 18 19
NA 5 5 2 25 4 5 18 19
NA 5 5 5 27 4 4 16 19
11 4 3 5 26 4 5 17 19
14 4 3 4 23 3 4 19 21
13 4 4 5 25 4 4 16 21
NA 3 4 4 21 3 3 19 20
17 3 4 4 22 4 4 13 19
16 4 4 4 24 3 5 16 19
NA 4 4 4 25 4 5 13 22
17 5 5 3 27 4 5 12 26
16 2 4 4 24 4 5 17 19
16 4 4 4 26 4 5 17 21
16 3 4 4 21 4 2 17 21
15 4 4 5 27 4 5 16 20
12 4 2 4 22 4 4 16 23
17 4 4 4 23 3 5 14 22
14 4 4 4 24 3 5 16 22
14 5 4 5 25 3 3 13 22
16 3 4 4 24 3 5 16 21
NA 3 4 4 23 3 4 14 21
NA 4 5 5 28 5 5 20 22
NA 4 4 3 NA 4 NA 12 23
NA 4 4 4 24 4 4 13 18
NA 4 4 4 26 5 5 18 24
15 3 4 3 22 4 4 14 22
16 4 4 4 25 4 5 19 21
14 3 4 5 25 3 5 18 21
15 3 3 5 24 4 4 14 21
17 4 3 5 24 4 4 18 23
NA 4 4 5 26 4 4 19 21
10 3 3 3 21 4 4 15 23
NA 4 4 4 25 4 5 14 21
17 4 4 3 25 4 5 17 19
NA 4 4 4 26 4 5 19 21
20 5 4 4 25 4 4 13 21
17 5 4 3 26 5 4 19 21
18 4 4 5 27 4 5 18 23
NA 3 4 5 25 4 4 20 23
17 3 NA 4 NA 4 4 15 20
14 4 2 3 20 3 4 15 20
NA 4 4 5 24 4 4 15 19
17 4 4 5 26 4 4 20 23
NA 4 4 4 25 4 5 15 22
17 4 5 4 25 4 5 19 19
NA 3 4 4 24 3 5 18 23
16 4 4 5 26 4 4 18 22
18 5 4 3 25 4 4 15 22
18 5 4 5 28 5 4 20 21
16 4 5 4 27 4 5 17 21
NA 3 4 5 25 4 4 12 21
NA 5 3 4 26 4 5 18 21
15 4 4 5 26 4 4 19 22
13 5 4 4 26 4 4 20 25
NA 3 4 4 NA 3 NA NA 21
NA 5 4 4 28 5 5 17 23
NA 4 4 5 NA 3 NA 15 19
NA 4 4 3 21 3 4 16 22
NA 4 4 5 25 4 4 18 20
16 4 4 5 25 4 4 18 21
NA 3 4 5 24 4 5 14 25
NA 4 4 4 24 4 4 15 21
NA 4 4 4 24 3 4 12 19
12 3 3 4 23 3 5 17 23
NA 4 4 4 23 3 4 14 22
16 3 4 5 24 4 4 18 21
16 4 4 5 24 4 3 17 24
NA 5 4 5 25 1 5 17 21
16 5 4 5 28 4 5 20 19
14 4 4 4 23 4 4 16 18
15 4 4 5 24 3 4 14 19
14 3 4 4 23 3 4 15 20
NA 4 4 4 24 4 4 18 19
15 4 4 4 25 4 5 20 22
NA 4 5 3 24 4 4 17 21
15 3 4 4 23 4 4 17 22
16 4 4 4 23 3 4 17 24
NA 4 4 4 25 4 4 17 28
NA 3 4 3 21 3 4 15 19
NA 4 4 4 22 3 4 17 18
11 3 2 4 19 2 4 18 23
NA 4 4 4 24 3 5 17 19
18 5 4 4 25 3 5 20 23
NA 2 4 4 21 3 3 15 19
11 3 3 4 22 4 4 16 22
NA 4 4 4 23 3 4 15 21
18 5 5 4 27 4 5 18 19
NA NA NA 2 NA NA NA 11 22
15 4 5 5 26 4 4 15 21
19 5 5 5 29 5 5 18 23
17 4 5 5 28 4 5 20 22
NA 4 4 4 24 3 4 19 19
14 3 4 5 25 4 5 14 19
NA 4 4 5 25 4 4 16 21
13 4 4 2 22 4 4 15 22
17 4 4 3 25 4 5 17 21
14 4 4 4 26 4 5 18 20
19 5 4 5 26 3 5 20 23
14 4 3 5 24 4 4 17 22
NA 4 4 5 25 4 4 18 23
NA 3 3 2 19 3 4 15 22
16 4 5 5 25 4 4 16 21
16 4 4 4 23 3 4 11 20
15 4 4 4 25 4 4 15 18
12 3 4 5 25 3 5 18 18
NA 4 4 5 26 4 4 17 20
17 5 4 5 27 4 5 16 19
NA 4 4 5 24 4 3 12 21
NA 2 3 5 22 4 4 19 24
18 4 4 4 25 4 4 18 19
15 4 3 4 24 3 5 15 20
18 4 4 4 23 4 4 17 19
15 4 5 5 27 5 4 19 23
NA 5 4 3 24 4 4 18 22
NA 5 4 4 24 3 4 19 21
NA 3 3 1 21 4 5 16 24
16 4 4 4 25 4 4 16 21
NA 4 4 4 25 4 5 16 21
16 2 3 4 23 5 5 14 22

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=305696&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=305696&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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] = + 6.57802 + 0.728295SKEOU1[t] + 1.2179SKEOU2[t] + 0.0852337SKEOU3[t] -0.0750223SKEOUSUM[t] + 0.579673SKEOU4[t] + 0.220847SKEOU5[t] + 0.00293692ITHSUM[t] -0.0187393Bevr_Leeftijd[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.57802 +  0.728295SKEOU1[t] +  1.2179SKEOU2[t] +  0.0852337SKEOU3[t] -0.0750223SKEOUSUM[t] +  0.579673SKEOU4[t] +  0.220847SKEOU5[t] +  0.00293692ITHSUM[t] -0.0187393Bevr_Leeftijd[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.57802 +  0.728295SKEOU1[t] +  1.2179SKEOU2[t] +  0.0852337SKEOU3[t] -0.0750223SKEOUSUM[t] +  0.579673SKEOU4[t] +  0.220847SKEOU5[t] +  0.00293692ITHSUM[t] -0.0187393Bevr_Leeftijd[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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] = + 6.57802 + 0.728295SKEOU1[t] + 1.2179SKEOU2[t] + 0.0852337SKEOU3[t] -0.0750223SKEOUSUM[t] + 0.579673SKEOU4[t] + 0.220847SKEOU5[t] + 0.00293692ITHSUM[t] -0.0187393Bevr_Leeftijd[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.578	2.909	+2.2610e+00	0.02615	0.01307
SKEOU1	+0.7283	0.3442	+2.1160e+00	0.03709	0.01855
SKEOU2	+1.218	0.3677	+3.3120e+00	0.001333	0.0006665
SKEOU3	+0.08523	0.365	+2.3350e-01	0.8159	0.408
SKEOUSUM	-0.07502	0.2668	-2.8120e-01	0.7792	0.3896
SKEOU4	+0.5797	0.3899	+1.4870e+00	0.1406	0.0703
SKEOU5	+0.2208	0.3845	+5.7440e-01	0.5671	0.2835
ITHSUM	+0.002937	0.07421	+3.9580e-02	0.9685	0.4843
Bevr_Leeftijd	-0.01874	0.09659	-1.9400e-01	0.8466	0.4233

\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) & +6.578 &  2.909 & +2.2610e+00 &  0.02615 &  0.01307 \tabularnewline
SKEOU1 & +0.7283 &  0.3442 & +2.1160e+00 &  0.03709 &  0.01855 \tabularnewline
SKEOU2 & +1.218 &  0.3677 & +3.3120e+00 &  0.001333 &  0.0006665 \tabularnewline
SKEOU3 & +0.08523 &  0.365 & +2.3350e-01 &  0.8159 &  0.408 \tabularnewline
SKEOUSUM & -0.07502 &  0.2668 & -2.8120e-01 &  0.7792 &  0.3896 \tabularnewline
SKEOU4 & +0.5797 &  0.3899 & +1.4870e+00 &  0.1406 &  0.0703 \tabularnewline
SKEOU5 & +0.2208 &  0.3845 & +5.7440e-01 &  0.5671 &  0.2835 \tabularnewline
ITHSUM & +0.002937 &  0.07421 & +3.9580e-02 &  0.9685 &  0.4843 \tabularnewline
Bevr_Leeftijd & -0.01874 &  0.09659 & -1.9400e-01 &  0.8466 &  0.4233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&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]+6.578[/C][C] 2.909[/C][C]+2.2610e+00[/C][C] 0.02615[/C][C] 0.01307[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.7283[/C][C] 0.3442[/C][C]+2.1160e+00[/C][C] 0.03709[/C][C] 0.01855[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1.218[/C][C] 0.3677[/C][C]+3.3120e+00[/C][C] 0.001333[/C][C] 0.0006665[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.08523[/C][C] 0.365[/C][C]+2.3350e-01[/C][C] 0.8159[/C][C] 0.408[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]-0.07502[/C][C] 0.2668[/C][C]-2.8120e-01[/C][C] 0.7792[/C][C] 0.3896[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.5797[/C][C] 0.3899[/C][C]+1.4870e+00[/C][C] 0.1406[/C][C] 0.0703[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.2208[/C][C] 0.3845[/C][C]+5.7440e-01[/C][C] 0.5671[/C][C] 0.2835[/C][/ROW]
[ROW][C]ITHSUM[/C][C]+0.002937[/C][C] 0.07421[/C][C]+3.9580e-02[/C][C] 0.9685[/C][C] 0.4843[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.01874[/C][C] 0.09659[/C][C]-1.9400e-01[/C][C] 0.8466[/C][C] 0.4233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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)	+6.578	2.909	+2.2610e+00	0.02615	0.01307
SKEOU1	+0.7283	0.3442	+2.1160e+00	0.03709	0.01855
SKEOU2	+1.218	0.3677	+3.3120e+00	0.001333	0.0006665
SKEOU3	+0.08523	0.365	+2.3350e-01	0.8159	0.408
SKEOUSUM	-0.07502	0.2668	-2.8120e-01	0.7792	0.3896
SKEOU4	+0.5797	0.3899	+1.4870e+00	0.1406	0.0703
SKEOU5	+0.2208	0.3845	+5.7440e-01	0.5671	0.2835
ITHSUM	+0.002937	0.07421	+3.9580e-02	0.9685	0.4843
Bevr_Leeftijd	-0.01874	0.09659	-1.9400e-01	0.8466	0.4233

Multiple Linear Regression - Regression Statistics
Multiple R	0.6041
R-squared	0.365
Adjusted R-squared	0.3085
F-TEST (value)	6.466
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	1.261e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.555
Sum Squared Residuals	217.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6041 \tabularnewline
R-squared &  0.365 \tabularnewline
Adjusted R-squared &  0.3085 \tabularnewline
F-TEST (value) &  6.466 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 90 \tabularnewline
p-value &  1.261e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.555 \tabularnewline
Sum Squared Residuals &  217.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6041[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.365[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3085[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 6.466[/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] 1.261e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.555[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 217.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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	0.6041
R-squared	0.365
Adjusted R-squared	0.3085
F-TEST (value)	6.466
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	1.261e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.555
Sum Squared Residuals	217.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.09	-0.08956
2	16	15.36	0.6426
3	17	15.92	1.082
4	16	15.21	0.7932
5	17	16.94	0.05776
6	17	15.89	1.107
7	16	15.23	0.7717
8	14	13.9	0.09633
9	16	15.4	0.6006
10	17	15.02	1.984
11	16	14.54	1.464
12	16	15.68	0.316
13	16	14.71	1.285
14	15	15.8	-0.7992
15	16	14.5	1.495
16	16	16.41	-0.415
17	15	17.03	-2.029
18	17	16.36	0.641
19	13	13.47	-0.4742
20	17	16.88	0.1188
21	14	14.08	-0.08389
22	14	14.65	-0.649
23	18	15.81	2.194
24	17	16.97	0.03315
25	16	17.12	-1.124
26	15	15.98	-0.9826
27	15	15.13	-0.1339
28	15	15.65	-0.6456
29	13	15.89	-2.886
30	17	16.96	0.0448
31	11	14.74	-3.737
32	14	14.04	-0.04501
33	13	15.77	-2.769
34	17	15.21	1.791
35	16	15.44	0.5626
36	17	17.51	-0.5101
37	16	14.56	1.437
38	16	15.83	0.1675
39	16	14.82	1.183
40	15	15.86	-0.8585
41	12	13.44	-1.436
42	17	15.45	1.55
43	14	15.38	-1.381
44	14	15.67	-1.669
45	16	14.67	1.328
46	15	15.07	-0.07066
47	16	15.91	0.08661
48	14	14.69	-0.6877
49	15	13.89	1.108
50	17	14.59	2.406
51	10	13.91	-3.912
52	17	15.86	1.14
53	20	16.4	3.597
54	17	16.84	0.1597
55	18	15.81	2.192
56	14	12.97	1.026
57	17	15.67	1.332
58	17	17.17	-0.1688
59	16	15.68	0.3189
60	18	16.31	1.695
61	18	16.86	1.136
62	16	16.98	-0.9754
63	15	15.68	-0.684
64	13	16.27	-3.274
65	16	15.77	0.2252
66	12	13.49	-1.494
67	16	15.12	0.8784
68	16	15.57	0.4301
69	16	16.54	-0.5423
70	14	15.89	-1.89
71	15	15.3	-0.2959
72	14	14.54	-0.5416
73	15	15.9	-0.8976
74	15	15.09	-0.08968
75	16	15.2	0.7992
76	11	11.78	-0.7788
77	18	16.03	1.973
78	11	13.94	-2.944
79	18	17.74	0.2559
80	15	16.91	-1.909
81	19	18.18	0.816
82	17	16.98	0.02434
83	14	15.29	-1.293
84	13	15.72	-2.717
85	17	15.82	1.178
86	14	15.85	-1.854
87	19	16.04	2.962
88	14	14.61	-0.6103
89	16	16.99	-0.9869
90	16	15.26	0.7418
91	15	15.74	-0.737
92	12	14.74	-2.744
93	17	16.61	0.3945
94	18	15.73	2.273
95	15	14.2	0.8022
96	18	15.87	2.126
97	15	17.39	-2.388
98	16	15.68	0.3163
99	16	13.94	2.065

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.09 & -0.08956 \tabularnewline
2 &  16 &  15.36 &  0.6426 \tabularnewline
3 &  17 &  15.92 &  1.082 \tabularnewline
4 &  16 &  15.21 &  0.7932 \tabularnewline
5 &  17 &  16.94 &  0.05776 \tabularnewline
6 &  17 &  15.89 &  1.107 \tabularnewline
7 &  16 &  15.23 &  0.7717 \tabularnewline
8 &  14 &  13.9 &  0.09633 \tabularnewline
9 &  16 &  15.4 &  0.6006 \tabularnewline
10 &  17 &  15.02 &  1.984 \tabularnewline
11 &  16 &  14.54 &  1.464 \tabularnewline
12 &  16 &  15.68 &  0.316 \tabularnewline
13 &  16 &  14.71 &  1.285 \tabularnewline
14 &  15 &  15.8 & -0.7992 \tabularnewline
15 &  16 &  14.5 &  1.495 \tabularnewline
16 &  16 &  16.41 & -0.415 \tabularnewline
17 &  15 &  17.03 & -2.029 \tabularnewline
18 &  17 &  16.36 &  0.641 \tabularnewline
19 &  13 &  13.47 & -0.4742 \tabularnewline
20 &  17 &  16.88 &  0.1188 \tabularnewline
21 &  14 &  14.08 & -0.08389 \tabularnewline
22 &  14 &  14.65 & -0.649 \tabularnewline
23 &  18 &  15.81 &  2.194 \tabularnewline
24 &  17 &  16.97 &  0.03315 \tabularnewline
25 &  16 &  17.12 & -1.124 \tabularnewline
26 &  15 &  15.98 & -0.9826 \tabularnewline
27 &  15 &  15.13 & -0.1339 \tabularnewline
28 &  15 &  15.65 & -0.6456 \tabularnewline
29 &  13 &  15.89 & -2.886 \tabularnewline
30 &  17 &  16.96 &  0.0448 \tabularnewline
31 &  11 &  14.74 & -3.737 \tabularnewline
32 &  14 &  14.04 & -0.04501 \tabularnewline
33 &  13 &  15.77 & -2.769 \tabularnewline
34 &  17 &  15.21 &  1.791 \tabularnewline
35 &  16 &  15.44 &  0.5626 \tabularnewline
36 &  17 &  17.51 & -0.5101 \tabularnewline
37 &  16 &  14.56 &  1.437 \tabularnewline
38 &  16 &  15.83 &  0.1675 \tabularnewline
39 &  16 &  14.82 &  1.183 \tabularnewline
40 &  15 &  15.86 & -0.8585 \tabularnewline
41 &  12 &  13.44 & -1.436 \tabularnewline
42 &  17 &  15.45 &  1.55 \tabularnewline
43 &  14 &  15.38 & -1.381 \tabularnewline
44 &  14 &  15.67 & -1.669 \tabularnewline
45 &  16 &  14.67 &  1.328 \tabularnewline
46 &  15 &  15.07 & -0.07066 \tabularnewline
47 &  16 &  15.91 &  0.08661 \tabularnewline
48 &  14 &  14.69 & -0.6877 \tabularnewline
49 &  15 &  13.89 &  1.108 \tabularnewline
50 &  17 &  14.59 &  2.406 \tabularnewline
51 &  10 &  13.91 & -3.912 \tabularnewline
52 &  17 &  15.86 &  1.14 \tabularnewline
53 &  20 &  16.4 &  3.597 \tabularnewline
54 &  17 &  16.84 &  0.1597 \tabularnewline
55 &  18 &  15.81 &  2.192 \tabularnewline
56 &  14 &  12.97 &  1.026 \tabularnewline
57 &  17 &  15.67 &  1.332 \tabularnewline
58 &  17 &  17.17 & -0.1688 \tabularnewline
59 &  16 &  15.68 &  0.3189 \tabularnewline
60 &  18 &  16.31 &  1.695 \tabularnewline
61 &  18 &  16.86 &  1.136 \tabularnewline
62 &  16 &  16.98 & -0.9754 \tabularnewline
63 &  15 &  15.68 & -0.684 \tabularnewline
64 &  13 &  16.27 & -3.274 \tabularnewline
65 &  16 &  15.77 &  0.2252 \tabularnewline
66 &  12 &  13.49 & -1.494 \tabularnewline
67 &  16 &  15.12 &  0.8784 \tabularnewline
68 &  16 &  15.57 &  0.4301 \tabularnewline
69 &  16 &  16.54 & -0.5423 \tabularnewline
70 &  14 &  15.89 & -1.89 \tabularnewline
71 &  15 &  15.3 & -0.2959 \tabularnewline
72 &  14 &  14.54 & -0.5416 \tabularnewline
73 &  15 &  15.9 & -0.8976 \tabularnewline
74 &  15 &  15.09 & -0.08968 \tabularnewline
75 &  16 &  15.2 &  0.7992 \tabularnewline
76 &  11 &  11.78 & -0.7788 \tabularnewline
77 &  18 &  16.03 &  1.973 \tabularnewline
78 &  11 &  13.94 & -2.944 \tabularnewline
79 &  18 &  17.74 &  0.2559 \tabularnewline
80 &  15 &  16.91 & -1.909 \tabularnewline
81 &  19 &  18.18 &  0.816 \tabularnewline
82 &  17 &  16.98 &  0.02434 \tabularnewline
83 &  14 &  15.29 & -1.293 \tabularnewline
84 &  13 &  15.72 & -2.717 \tabularnewline
85 &  17 &  15.82 &  1.178 \tabularnewline
86 &  14 &  15.85 & -1.854 \tabularnewline
87 &  19 &  16.04 &  2.962 \tabularnewline
88 &  14 &  14.61 & -0.6103 \tabularnewline
89 &  16 &  16.99 & -0.9869 \tabularnewline
90 &  16 &  15.26 &  0.7418 \tabularnewline
91 &  15 &  15.74 & -0.737 \tabularnewline
92 &  12 &  14.74 & -2.744 \tabularnewline
93 &  17 &  16.61 &  0.3945 \tabularnewline
94 &  18 &  15.73 &  2.273 \tabularnewline
95 &  15 &  14.2 &  0.8022 \tabularnewline
96 &  18 &  15.87 &  2.126 \tabularnewline
97 &  15 &  17.39 & -2.388 \tabularnewline
98 &  16 &  15.68 &  0.3163 \tabularnewline
99 &  16 &  13.94 &  2.065 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&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] 13.09[/C][C]-0.08956[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.36[/C][C] 0.6426[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.92[/C][C] 1.082[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.21[/C][C] 0.7932[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 16.94[/C][C] 0.05776[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.89[/C][C] 1.107[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.23[/C][C] 0.7717[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 13.9[/C][C] 0.09633[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 15.4[/C][C] 0.6006[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.02[/C][C] 1.984[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 14.54[/C][C] 1.464[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.68[/C][C] 0.316[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.71[/C][C] 1.285[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.8[/C][C]-0.7992[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 14.5[/C][C] 1.495[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 16.41[/C][C]-0.415[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 17.03[/C][C]-2.029[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.36[/C][C] 0.641[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 13.47[/C][C]-0.4742[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.88[/C][C] 0.1188[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 14.08[/C][C]-0.08389[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 14.65[/C][C]-0.649[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 15.81[/C][C] 2.194[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 16.97[/C][C] 0.03315[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 17.12[/C][C]-1.124[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 15.98[/C][C]-0.9826[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.13[/C][C]-0.1339[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 15.65[/C][C]-0.6456[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 15.89[/C][C]-2.886[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 16.96[/C][C] 0.0448[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 14.74[/C][C]-3.737[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 14.04[/C][C]-0.04501[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 15.77[/C][C]-2.769[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 15.21[/C][C] 1.791[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.44[/C][C] 0.5626[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.51[/C][C]-0.5101[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 14.56[/C][C] 1.437[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.83[/C][C] 0.1675[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 14.82[/C][C] 1.183[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.86[/C][C]-0.8585[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 13.44[/C][C]-1.436[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 15.45[/C][C] 1.55[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.38[/C][C]-1.381[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.67[/C][C]-1.669[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 14.67[/C][C] 1.328[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.07[/C][C]-0.07066[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 15.91[/C][C] 0.08661[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 14.69[/C][C]-0.6877[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 13.89[/C][C] 1.108[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.59[/C][C] 2.406[/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] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]53[/C][C] 20[/C][C] 16.4[/C][C] 3.597[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.84[/C][C] 0.1597[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 15.81[/C][C] 2.192[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 12.97[/C][C] 1.026[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.67[/C][C] 1.332[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 17.17[/C][C]-0.1688[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 15.68[/C][C] 0.3189[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 16.31[/C][C] 1.695[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 16.86[/C][C] 1.136[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.98[/C][C]-0.9754[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.68[/C][C]-0.684[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 16.27[/C][C]-3.274[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 15.77[/C][C] 0.2252[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 13.49[/C][C]-1.494[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.12[/C][C] 0.8784[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.57[/C][C] 0.4301[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 16.54[/C][C]-0.5423[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 15.89[/C][C]-1.89[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 15.3[/C][C]-0.2959[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.54[/C][C]-0.5416[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.9[/C][C]-0.8976[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.09[/C][C]-0.08968[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.2[/C][C] 0.7992[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.78[/C][C]-0.7788[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 16.03[/C][C] 1.973[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 13.94[/C][C]-2.944[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.74[/C][C] 0.2559[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.91[/C][C]-1.909[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 18.18[/C][C] 0.816[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 16.98[/C][C] 0.02434[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 15.29[/C][C]-1.293[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 15.72[/C][C]-2.717[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 15.82[/C][C] 1.178[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.85[/C][C]-1.854[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 16.04[/C][C] 2.962[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 14.61[/C][C]-0.6103[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.99[/C][C]-0.9869[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.26[/C][C] 0.7418[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.74[/C][C]-0.737[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 14.74[/C][C]-2.744[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.61[/C][C] 0.3945[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.73[/C][C] 2.273[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 14.2[/C][C] 0.8022[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 15.87[/C][C] 2.126[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 17.39[/C][C]-2.388[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.68[/C][C] 0.3163[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 13.94[/C][C] 2.065[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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	13.09	-0.08956
2	16	15.36	0.6426
3	17	15.92	1.082
4	16	15.21	0.7932
5	17	16.94	0.05776
6	17	15.89	1.107
7	16	15.23	0.7717
8	14	13.9	0.09633
9	16	15.4	0.6006
10	17	15.02	1.984
11	16	14.54	1.464
12	16	15.68	0.316
13	16	14.71	1.285
14	15	15.8	-0.7992
15	16	14.5	1.495
16	16	16.41	-0.415
17	15	17.03	-2.029
18	17	16.36	0.641
19	13	13.47	-0.4742
20	17	16.88	0.1188
21	14	14.08	-0.08389
22	14	14.65	-0.649
23	18	15.81	2.194
24	17	16.97	0.03315
25	16	17.12	-1.124
26	15	15.98	-0.9826
27	15	15.13	-0.1339
28	15	15.65	-0.6456
29	13	15.89	-2.886
30	17	16.96	0.0448
31	11	14.74	-3.737
32	14	14.04	-0.04501
33	13	15.77	-2.769
34	17	15.21	1.791
35	16	15.44	0.5626
36	17	17.51	-0.5101
37	16	14.56	1.437
38	16	15.83	0.1675
39	16	14.82	1.183
40	15	15.86	-0.8585
41	12	13.44	-1.436
42	17	15.45	1.55
43	14	15.38	-1.381
44	14	15.67	-1.669
45	16	14.67	1.328
46	15	15.07	-0.07066
47	16	15.91	0.08661
48	14	14.69	-0.6877
49	15	13.89	1.108
50	17	14.59	2.406
51	10	13.91	-3.912
52	17	15.86	1.14
53	20	16.4	3.597
54	17	16.84	0.1597
55	18	15.81	2.192
56	14	12.97	1.026
57	17	15.67	1.332
58	17	17.17	-0.1688
59	16	15.68	0.3189
60	18	16.31	1.695
61	18	16.86	1.136
62	16	16.98	-0.9754
63	15	15.68	-0.684
64	13	16.27	-3.274
65	16	15.77	0.2252
66	12	13.49	-1.494
67	16	15.12	0.8784
68	16	15.57	0.4301
69	16	16.54	-0.5423
70	14	15.89	-1.89
71	15	15.3	-0.2959
72	14	14.54	-0.5416
73	15	15.9	-0.8976
74	15	15.09	-0.08968
75	16	15.2	0.7992
76	11	11.78	-0.7788
77	18	16.03	1.973
78	11	13.94	-2.944
79	18	17.74	0.2559
80	15	16.91	-1.909
81	19	18.18	0.816
82	17	16.98	0.02434
83	14	15.29	-1.293
84	13	15.72	-2.717
85	17	15.82	1.178
86	14	15.85	-1.854
87	19	16.04	2.962
88	14	14.61	-0.6103
89	16	16.99	-0.9869
90	16	15.26	0.7418
91	15	15.74	-0.737
92	12	14.74	-2.744
93	17	16.61	0.3945
94	18	15.73	2.273
95	15	14.2	0.8022
96	18	15.87	2.126
97	15	17.39	-2.388
98	16	15.68	0.3163
99	16	13.94	2.065

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.1136	0.2272	0.8864
13	0.04751	0.09502	0.9525
14	0.05477	0.1095	0.9452
15	0.04012	0.08024	0.9599
16	0.01842	0.03684	0.9816
17	0.02403	0.04806	0.976
18	0.03105	0.06211	0.9689
19	0.02392	0.04783	0.9761
20	0.01782	0.03563	0.9822
21	0.009075	0.01815	0.9909
22	0.004469	0.008938	0.9955
23	0.03715	0.0743	0.9628
24	0.02304	0.04607	0.977
25	0.02255	0.04509	0.9775
26	0.01406	0.02812	0.9859
27	0.01055	0.0211	0.9895
28	0.007987	0.01597	0.992
29	0.03952	0.07903	0.9605
30	0.02721	0.05441	0.9728
31	0.1173	0.2347	0.8827
32	0.09005	0.1801	0.91
33	0.1628	0.3257	0.8372
34	0.1912	0.3824	0.8088
35	0.1611	0.3221	0.8389
36	0.1269	0.2537	0.8731
37	0.117	0.2341	0.883
38	0.08894	0.1779	0.9111
39	0.08729	0.1746	0.9127
40	0.06932	0.1386	0.9307
41	0.07467	0.1493	0.9253
42	0.06821	0.1364	0.9318
43	0.07533	0.1507	0.9247
44	0.08173	0.1635	0.9183
45	0.07507	0.1501	0.9249
46	0.06688	0.1338	0.9331
47	0.04879	0.09758	0.9512
48	0.04046	0.08092	0.9595
49	0.03411	0.06823	0.9659
50	0.05506	0.1101	0.9449
51	0.2799	0.5598	0.7201
52	0.2714	0.5428	0.7286
53	0.4838	0.9677	0.5162
54	0.422	0.844	0.578
55	0.4699	0.9398	0.5301
56	0.4255	0.851	0.5745
57	0.421	0.842	0.579
58	0.3611	0.7221	0.6389
59	0.3095	0.6191	0.6905
60	0.3127	0.6255	0.6873
61	0.2901	0.5801	0.7099
62	0.2498	0.4995	0.7502
63	0.2064	0.4128	0.7936
64	0.3825	0.765	0.6175
65	0.3227	0.6454	0.6773
66	0.3158	0.6317	0.6842
67	0.324	0.6481	0.676
68	0.281	0.5619	0.719
69	0.2405	0.481	0.7595
70	0.2427	0.4854	0.7573
71	0.1899	0.3798	0.8101
72	0.1573	0.3146	0.8427
73	0.1381	0.2762	0.8619
74	0.1145	0.2291	0.8855
75	0.1009	0.2019	0.8991
76	0.073	0.146	0.927
77	0.06367	0.1273	0.9363
78	0.1273	0.2545	0.8727
79	0.08754	0.1751	0.9125
80	0.06685	0.1337	0.9331
81	0.04423	0.08846	0.9558
82	0.0342	0.06841	0.9658
83	0.02238	0.04477	0.9776
84	0.2399	0.4797	0.7601
85	0.1893	0.3785	0.8107
86	0.5007	0.9986	0.4993
87	0.5174	0.9652	0.4826

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.1136 &  0.2272 &  0.8864 \tabularnewline
13 &  0.04751 &  0.09502 &  0.9525 \tabularnewline
14 &  0.05477 &  0.1095 &  0.9452 \tabularnewline
15 &  0.04012 &  0.08024 &  0.9599 \tabularnewline
16 &  0.01842 &  0.03684 &  0.9816 \tabularnewline
17 &  0.02403 &  0.04806 &  0.976 \tabularnewline
18 &  0.03105 &  0.06211 &  0.9689 \tabularnewline
19 &  0.02392 &  0.04783 &  0.9761 \tabularnewline
20 &  0.01782 &  0.03563 &  0.9822 \tabularnewline
21 &  0.009075 &  0.01815 &  0.9909 \tabularnewline
22 &  0.004469 &  0.008938 &  0.9955 \tabularnewline
23 &  0.03715 &  0.0743 &  0.9628 \tabularnewline
24 &  0.02304 &  0.04607 &  0.977 \tabularnewline
25 &  0.02255 &  0.04509 &  0.9775 \tabularnewline
26 &  0.01406 &  0.02812 &  0.9859 \tabularnewline
27 &  0.01055 &  0.0211 &  0.9895 \tabularnewline
28 &  0.007987 &  0.01597 &  0.992 \tabularnewline
29 &  0.03952 &  0.07903 &  0.9605 \tabularnewline
30 &  0.02721 &  0.05441 &  0.9728 \tabularnewline
31 &  0.1173 &  0.2347 &  0.8827 \tabularnewline
32 &  0.09005 &  0.1801 &  0.91 \tabularnewline
33 &  0.1628 &  0.3257 &  0.8372 \tabularnewline
34 &  0.1912 &  0.3824 &  0.8088 \tabularnewline
35 &  0.1611 &  0.3221 &  0.8389 \tabularnewline
36 &  0.1269 &  0.2537 &  0.8731 \tabularnewline
37 &  0.117 &  0.2341 &  0.883 \tabularnewline
38 &  0.08894 &  0.1779 &  0.9111 \tabularnewline
39 &  0.08729 &  0.1746 &  0.9127 \tabularnewline
40 &  0.06932 &  0.1386 &  0.9307 \tabularnewline
41 &  0.07467 &  0.1493 &  0.9253 \tabularnewline
42 &  0.06821 &  0.1364 &  0.9318 \tabularnewline
43 &  0.07533 &  0.1507 &  0.9247 \tabularnewline
44 &  0.08173 &  0.1635 &  0.9183 \tabularnewline
45 &  0.07507 &  0.1501 &  0.9249 \tabularnewline
46 &  0.06688 &  0.1338 &  0.9331 \tabularnewline
47 &  0.04879 &  0.09758 &  0.9512 \tabularnewline
48 &  0.04046 &  0.08092 &  0.9595 \tabularnewline
49 &  0.03411 &  0.06823 &  0.9659 \tabularnewline
50 &  0.05506 &  0.1101 &  0.9449 \tabularnewline
51 &  0.2799 &  0.5598 &  0.7201 \tabularnewline
52 &  0.2714 &  0.5428 &  0.7286 \tabularnewline
53 &  0.4838 &  0.9677 &  0.5162 \tabularnewline
54 &  0.422 &  0.844 &  0.578 \tabularnewline
55 &  0.4699 &  0.9398 &  0.5301 \tabularnewline
56 &  0.4255 &  0.851 &  0.5745 \tabularnewline
57 &  0.421 &  0.842 &  0.579 \tabularnewline
58 &  0.3611 &  0.7221 &  0.6389 \tabularnewline
59 &  0.3095 &  0.6191 &  0.6905 \tabularnewline
60 &  0.3127 &  0.6255 &  0.6873 \tabularnewline
61 &  0.2901 &  0.5801 &  0.7099 \tabularnewline
62 &  0.2498 &  0.4995 &  0.7502 \tabularnewline
63 &  0.2064 &  0.4128 &  0.7936 \tabularnewline
64 &  0.3825 &  0.765 &  0.6175 \tabularnewline
65 &  0.3227 &  0.6454 &  0.6773 \tabularnewline
66 &  0.3158 &  0.6317 &  0.6842 \tabularnewline
67 &  0.324 &  0.6481 &  0.676 \tabularnewline
68 &  0.281 &  0.5619 &  0.719 \tabularnewline
69 &  0.2405 &  0.481 &  0.7595 \tabularnewline
70 &  0.2427 &  0.4854 &  0.7573 \tabularnewline
71 &  0.1899 &  0.3798 &  0.8101 \tabularnewline
72 &  0.1573 &  0.3146 &  0.8427 \tabularnewline
73 &  0.1381 &  0.2762 &  0.8619 \tabularnewline
74 &  0.1145 &  0.2291 &  0.8855 \tabularnewline
75 &  0.1009 &  0.2019 &  0.8991 \tabularnewline
76 &  0.073 &  0.146 &  0.927 \tabularnewline
77 &  0.06367 &  0.1273 &  0.9363 \tabularnewline
78 &  0.1273 &  0.2545 &  0.8727 \tabularnewline
79 &  0.08754 &  0.1751 &  0.9125 \tabularnewline
80 &  0.06685 &  0.1337 &  0.9331 \tabularnewline
81 &  0.04423 &  0.08846 &  0.9558 \tabularnewline
82 &  0.0342 &  0.06841 &  0.9658 \tabularnewline
83 &  0.02238 &  0.04477 &  0.9776 \tabularnewline
84 &  0.2399 &  0.4797 &  0.7601 \tabularnewline
85 &  0.1893 &  0.3785 &  0.8107 \tabularnewline
86 &  0.5007 &  0.9986 &  0.4993 \tabularnewline
87 &  0.5174 &  0.9652 &  0.4826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&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] 0.1136[/C][C] 0.2272[/C][C] 0.8864[/C][/ROW]
[ROW][C]13[/C][C] 0.04751[/C][C] 0.09502[/C][C] 0.9525[/C][/ROW]
[ROW][C]14[/C][C] 0.05477[/C][C] 0.1095[/C][C] 0.9452[/C][/ROW]
[ROW][C]15[/C][C] 0.04012[/C][C] 0.08024[/C][C] 0.9599[/C][/ROW]
[ROW][C]16[/C][C] 0.01842[/C][C] 0.03684[/C][C] 0.9816[/C][/ROW]
[ROW][C]17[/C][C] 0.02403[/C][C] 0.04806[/C][C] 0.976[/C][/ROW]
[ROW][C]18[/C][C] 0.03105[/C][C] 0.06211[/C][C] 0.9689[/C][/ROW]
[ROW][C]19[/C][C] 0.02392[/C][C] 0.04783[/C][C] 0.9761[/C][/ROW]
[ROW][C]20[/C][C] 0.01782[/C][C] 0.03563[/C][C] 0.9822[/C][/ROW]
[ROW][C]21[/C][C] 0.009075[/C][C] 0.01815[/C][C] 0.9909[/C][/ROW]
[ROW][C]22[/C][C] 0.004469[/C][C] 0.008938[/C][C] 0.9955[/C][/ROW]
[ROW][C]23[/C][C] 0.03715[/C][C] 0.0743[/C][C] 0.9628[/C][/ROW]
[ROW][C]24[/C][C] 0.02304[/C][C] 0.04607[/C][C] 0.977[/C][/ROW]
[ROW][C]25[/C][C] 0.02255[/C][C] 0.04509[/C][C] 0.9775[/C][/ROW]
[ROW][C]26[/C][C] 0.01406[/C][C] 0.02812[/C][C] 0.9859[/C][/ROW]
[ROW][C]27[/C][C] 0.01055[/C][C] 0.0211[/C][C] 0.9895[/C][/ROW]
[ROW][C]28[/C][C] 0.007987[/C][C] 0.01597[/C][C] 0.992[/C][/ROW]
[ROW][C]29[/C][C] 0.03952[/C][C] 0.07903[/C][C] 0.9605[/C][/ROW]
[ROW][C]30[/C][C] 0.02721[/C][C] 0.05441[/C][C] 0.9728[/C][/ROW]
[ROW][C]31[/C][C] 0.1173[/C][C] 0.2347[/C][C] 0.8827[/C][/ROW]
[ROW][C]32[/C][C] 0.09005[/C][C] 0.1801[/C][C] 0.91[/C][/ROW]
[ROW][C]33[/C][C] 0.1628[/C][C] 0.3257[/C][C] 0.8372[/C][/ROW]
[ROW][C]34[/C][C] 0.1912[/C][C] 0.3824[/C][C] 0.8088[/C][/ROW]
[ROW][C]35[/C][C] 0.1611[/C][C] 0.3221[/C][C] 0.8389[/C][/ROW]
[ROW][C]36[/C][C] 0.1269[/C][C] 0.2537[/C][C] 0.8731[/C][/ROW]
[ROW][C]37[/C][C] 0.117[/C][C] 0.2341[/C][C] 0.883[/C][/ROW]
[ROW][C]38[/C][C] 0.08894[/C][C] 0.1779[/C][C] 0.9111[/C][/ROW]
[ROW][C]39[/C][C] 0.08729[/C][C] 0.1746[/C][C] 0.9127[/C][/ROW]
[ROW][C]40[/C][C] 0.06932[/C][C] 0.1386[/C][C] 0.9307[/C][/ROW]
[ROW][C]41[/C][C] 0.07467[/C][C] 0.1493[/C][C] 0.9253[/C][/ROW]
[ROW][C]42[/C][C] 0.06821[/C][C] 0.1364[/C][C] 0.9318[/C][/ROW]
[ROW][C]43[/C][C] 0.07533[/C][C] 0.1507[/C][C] 0.9247[/C][/ROW]
[ROW][C]44[/C][C] 0.08173[/C][C] 0.1635[/C][C] 0.9183[/C][/ROW]
[ROW][C]45[/C][C] 0.07507[/C][C] 0.1501[/C][C] 0.9249[/C][/ROW]
[ROW][C]46[/C][C] 0.06688[/C][C] 0.1338[/C][C] 0.9331[/C][/ROW]
[ROW][C]47[/C][C] 0.04879[/C][C] 0.09758[/C][C] 0.9512[/C][/ROW]
[ROW][C]48[/C][C] 0.04046[/C][C] 0.08092[/C][C] 0.9595[/C][/ROW]
[ROW][C]49[/C][C] 0.03411[/C][C] 0.06823[/C][C] 0.9659[/C][/ROW]
[ROW][C]50[/C][C] 0.05506[/C][C] 0.1101[/C][C] 0.9449[/C][/ROW]
[ROW][C]51[/C][C] 0.2799[/C][C] 0.5598[/C][C] 0.7201[/C][/ROW]
[ROW][C]52[/C][C] 0.2714[/C][C] 0.5428[/C][C] 0.7286[/C][/ROW]
[ROW][C]53[/C][C] 0.4838[/C][C] 0.9677[/C][C] 0.5162[/C][/ROW]
[ROW][C]54[/C][C] 0.422[/C][C] 0.844[/C][C] 0.578[/C][/ROW]
[ROW][C]55[/C][C] 0.4699[/C][C] 0.9398[/C][C] 0.5301[/C][/ROW]
[ROW][C]56[/C][C] 0.4255[/C][C] 0.851[/C][C] 0.5745[/C][/ROW]
[ROW][C]57[/C][C] 0.421[/C][C] 0.842[/C][C] 0.579[/C][/ROW]
[ROW][C]58[/C][C] 0.3611[/C][C] 0.7221[/C][C] 0.6389[/C][/ROW]
[ROW][C]59[/C][C] 0.3095[/C][C] 0.6191[/C][C] 0.6905[/C][/ROW]
[ROW][C]60[/C][C] 0.3127[/C][C] 0.6255[/C][C] 0.6873[/C][/ROW]
[ROW][C]61[/C][C] 0.2901[/C][C] 0.5801[/C][C] 0.7099[/C][/ROW]
[ROW][C]62[/C][C] 0.2498[/C][C] 0.4995[/C][C] 0.7502[/C][/ROW]
[ROW][C]63[/C][C] 0.2064[/C][C] 0.4128[/C][C] 0.7936[/C][/ROW]
[ROW][C]64[/C][C] 0.3825[/C][C] 0.765[/C][C] 0.6175[/C][/ROW]
[ROW][C]65[/C][C] 0.3227[/C][C] 0.6454[/C][C] 0.6773[/C][/ROW]
[ROW][C]66[/C][C] 0.3158[/C][C] 0.6317[/C][C] 0.6842[/C][/ROW]
[ROW][C]67[/C][C] 0.324[/C][C] 0.6481[/C][C] 0.676[/C][/ROW]
[ROW][C]68[/C][C] 0.281[/C][C] 0.5619[/C][C] 0.719[/C][/ROW]
[ROW][C]69[/C][C] 0.2405[/C][C] 0.481[/C][C] 0.7595[/C][/ROW]
[ROW][C]70[/C][C] 0.2427[/C][C] 0.4854[/C][C] 0.7573[/C][/ROW]
[ROW][C]71[/C][C] 0.1899[/C][C] 0.3798[/C][C] 0.8101[/C][/ROW]
[ROW][C]72[/C][C] 0.1573[/C][C] 0.3146[/C][C] 0.8427[/C][/ROW]
[ROW][C]73[/C][C] 0.1381[/C][C] 0.2762[/C][C] 0.8619[/C][/ROW]
[ROW][C]74[/C][C] 0.1145[/C][C] 0.2291[/C][C] 0.8855[/C][/ROW]
[ROW][C]75[/C][C] 0.1009[/C][C] 0.2019[/C][C] 0.8991[/C][/ROW]
[ROW][C]76[/C][C] 0.073[/C][C] 0.146[/C][C] 0.927[/C][/ROW]
[ROW][C]77[/C][C] 0.06367[/C][C] 0.1273[/C][C] 0.9363[/C][/ROW]
[ROW][C]78[/C][C] 0.1273[/C][C] 0.2545[/C][C] 0.8727[/C][/ROW]
[ROW][C]79[/C][C] 0.08754[/C][C] 0.1751[/C][C] 0.9125[/C][/ROW]
[ROW][C]80[/C][C] 0.06685[/C][C] 0.1337[/C][C] 0.9331[/C][/ROW]
[ROW][C]81[/C][C] 0.04423[/C][C] 0.08846[/C][C] 0.9558[/C][/ROW]
[ROW][C]82[/C][C] 0.0342[/C][C] 0.06841[/C][C] 0.9658[/C][/ROW]
[ROW][C]83[/C][C] 0.02238[/C][C] 0.04477[/C][C] 0.9776[/C][/ROW]
[ROW][C]84[/C][C] 0.2399[/C][C] 0.4797[/C][C] 0.7601[/C][/ROW]
[ROW][C]85[/C][C] 0.1893[/C][C] 0.3785[/C][C] 0.8107[/C][/ROW]
[ROW][C]86[/C][C] 0.5007[/C][C] 0.9986[/C][C] 0.4993[/C][/ROW]
[ROW][C]87[/C][C] 0.5174[/C][C] 0.9652[/C][C] 0.4826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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
12	0.1136	0.2272	0.8864
13	0.04751	0.09502	0.9525
14	0.05477	0.1095	0.9452
15	0.04012	0.08024	0.9599
16	0.01842	0.03684	0.9816
17	0.02403	0.04806	0.976
18	0.03105	0.06211	0.9689
19	0.02392	0.04783	0.9761
20	0.01782	0.03563	0.9822
21	0.009075	0.01815	0.9909
22	0.004469	0.008938	0.9955
23	0.03715	0.0743	0.9628
24	0.02304	0.04607	0.977
25	0.02255	0.04509	0.9775
26	0.01406	0.02812	0.9859
27	0.01055	0.0211	0.9895
28	0.007987	0.01597	0.992
29	0.03952	0.07903	0.9605
30	0.02721	0.05441	0.9728
31	0.1173	0.2347	0.8827
32	0.09005	0.1801	0.91
33	0.1628	0.3257	0.8372
34	0.1912	0.3824	0.8088
35	0.1611	0.3221	0.8389
36	0.1269	0.2537	0.8731
37	0.117	0.2341	0.883
38	0.08894	0.1779	0.9111
39	0.08729	0.1746	0.9127
40	0.06932	0.1386	0.9307
41	0.07467	0.1493	0.9253
42	0.06821	0.1364	0.9318
43	0.07533	0.1507	0.9247
44	0.08173	0.1635	0.9183
45	0.07507	0.1501	0.9249
46	0.06688	0.1338	0.9331
47	0.04879	0.09758	0.9512
48	0.04046	0.08092	0.9595
49	0.03411	0.06823	0.9659
50	0.05506	0.1101	0.9449
51	0.2799	0.5598	0.7201
52	0.2714	0.5428	0.7286
53	0.4838	0.9677	0.5162
54	0.422	0.844	0.578
55	0.4699	0.9398	0.5301
56	0.4255	0.851	0.5745
57	0.421	0.842	0.579
58	0.3611	0.7221	0.6389
59	0.3095	0.6191	0.6905
60	0.3127	0.6255	0.6873
61	0.2901	0.5801	0.7099
62	0.2498	0.4995	0.7502
63	0.2064	0.4128	0.7936
64	0.3825	0.765	0.6175
65	0.3227	0.6454	0.6773
66	0.3158	0.6317	0.6842
67	0.324	0.6481	0.676
68	0.281	0.5619	0.719
69	0.2405	0.481	0.7595
70	0.2427	0.4854	0.7573
71	0.1899	0.3798	0.8101
72	0.1573	0.3146	0.8427
73	0.1381	0.2762	0.8619
74	0.1145	0.2291	0.8855
75	0.1009	0.2019	0.8991
76	0.073	0.146	0.927
77	0.06367	0.1273	0.9363
78	0.1273	0.2545	0.8727
79	0.08754	0.1751	0.9125
80	0.06685	0.1337	0.9331
81	0.04423	0.08846	0.9558
82	0.0342	0.06841	0.9658
83	0.02238	0.04477	0.9776
84	0.2399	0.4797	0.7601
85	0.1893	0.3785	0.8107
86	0.5007	0.9986	0.4993
87	0.5174	0.9652	0.4826

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.01316	NOK
5% type I error level	12	0.157895	NOK
10% type I error level	23	0.302632	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 &  0.01316 & NOK \tabularnewline
5% type I error level & 12 & 0.157895 & NOK \tabularnewline
10% type I error level & 23 & 0.302632 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&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] 0.01316[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.157895[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.302632[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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	0.01316	NOK
5% type I error level	12	0.157895	NOK
10% type I error level	23	0.302632	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.84344, df1 = 2, df2 = 88, p-value = 0.4337
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2019, df1 = 16, df2 = 74, p-value = 0.2872
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.042066, df1 = 2, df2 = 88, p-value = 0.9588

\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 = 0.84344, df1 = 2, df2 = 88, p-value = 0.4337
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2019, df1 = 16, df2 = 74, p-value = 0.2872
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.042066, df1 = 2, df2 = 88, p-value = 0.9588
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&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 = 0.84344, df1 = 2, df2 = 88, p-value = 0.4337
[/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 = 1.2019, df1 = 16, df2 = 74, p-value = 0.2872
[/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 = 0.042066, df1 = 2, df2 = 88, p-value = 0.9588
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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 = 0.84344, df1 = 2, df2 = 88, p-value = 0.4337
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2019, df1 = 16, df2 = 74, p-value = 0.2872
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.042066, df1 = 2, df2 = 88, p-value = 0.9588

Variance Inflation Factors (Multicollinearity)
> vif SKEOU1 SKEOU2 SKEOU3 SKEOUSUM SKEOU4 2.634020 2.599798 3.223565 10.133461 1.864480 SKEOU5 ITHSUM Bevr_Leeftijd 2.500328 1.164580 1.049105

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       SKEOU1        SKEOU2        SKEOU3      SKEOUSUM        SKEOU4 
     2.634020      2.599798      3.223565     10.133461      1.864480 
       SKEOU5        ITHSUM Bevr_Leeftijd 
     2.500328      1.164580      1.049105 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305696&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       SKEOU1        SKEOU2        SKEOU3      SKEOUSUM        SKEOU4 
     2.634020      2.599798      3.223565     10.133461      1.864480 
       SKEOU5        ITHSUM Bevr_Leeftijd 
     2.500328      1.164580      1.049105 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305696&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305696&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 SKEOU1 SKEOU2 SKEOU3 SKEOUSUM SKEOU4 2.634020 2.599798 3.223565 10.133461 1.864480 SKEOU5 ITHSUM Bevr_Leeftijd 2.500328 1.164580 1.049105

Figure 1

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Postscript link

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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

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ;

Parameters (R input):

par1 = 1 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code