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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 11:13:58 +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/t1485339269ydnbrhr55earn3f.htm/, Retrieved Tue, 14 May 2024 07:39:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306615, Retrieved Tue, 14 May 2024 07:39:33 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2017-01-25 10:13:58] [9372c1897bc6474dcced1b12eebd7354] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 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=306615&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]

9 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=306615&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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	9 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
SKEOU1[t] = + 1.52625e-14 -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[t] -1SKEOU6[t] -1.89029e-16ITHSUM[t] + 1SKEOUSUM[t] + 7.44588e-17Bevr_Leeftijd[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SKEOU1[t] =  +  1.52625e-14 -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[t] -1SKEOU6[t] -1.89029e-16ITHSUM[t] +  1SKEOUSUM[t] +  7.44588e-17Bevr_Leeftijd[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SKEOU1[t] =  +  1.52625e-14 -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[t] -1SKEOU6[t] -1.89029e-16ITHSUM[t] +  1SKEOUSUM[t] +  7.44588e-17Bevr_Leeftijd[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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
SKEOU1[t] = + 1.52625e-14 -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[t] -1SKEOU6[t] -1.89029e-16ITHSUM[t] + 1SKEOUSUM[t] + 7.44588e-17Bevr_Leeftijd[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+1.526e-14	6.352e-15	+2.4030e+00	0.01751	0.008755
SKEOU2	-1	9.019e-16	-1.1090e+15	0	0
SKEOU3	-1	6.548e-16	-1.5270e+15	0	0
SKEOU4	-1	8.144e-16	-1.2280e+15	0	0
SKEOU5	-1	8.008e-16	-1.2490e+15	0	0
SKEOU6	-1	7.751e-16	-1.2900e+15	0	0
ITHSUM	-1.89e-16	1.605e-16	-1.1780e+00	0.2407	0.1204
SKEOUSUM	+1	5.043e-16	+1.9830e+15	0	0
Bevr_Leeftijd	+7.446e-17	2.067e-16	+3.6030e-01	0.7192	0.3596

\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) & +1.526e-14 &  6.352e-15 & +2.4030e+00 &  0.01751 &  0.008755 \tabularnewline
SKEOU2 & -1 &  9.019e-16 & -1.1090e+15 &  0 &  0 \tabularnewline
SKEOU3 & -1 &  6.548e-16 & -1.5270e+15 &  0 &  0 \tabularnewline
SKEOU4 & -1 &  8.144e-16 & -1.2280e+15 &  0 &  0 \tabularnewline
SKEOU5 & -1 &  8.008e-16 & -1.2490e+15 &  0 &  0 \tabularnewline
SKEOU6 & -1 &  7.751e-16 & -1.2900e+15 &  0 &  0 \tabularnewline
ITHSUM & -1.89e-16 &  1.605e-16 & -1.1780e+00 &  0.2407 &  0.1204 \tabularnewline
SKEOUSUM & +1 &  5.043e-16 & +1.9830e+15 &  0 &  0 \tabularnewline
Bevr_Leeftijd & +7.446e-17 &  2.067e-16 & +3.6030e-01 &  0.7192 &  0.3596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&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]+1.526e-14[/C][C] 6.352e-15[/C][C]+2.4030e+00[/C][C] 0.01751[/C][C] 0.008755[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-1[/C][C] 9.019e-16[/C][C]-1.1090e+15[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-1[/C][C] 6.548e-16[/C][C]-1.5270e+15[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU4[/C][C]-1[/C][C] 8.144e-16[/C][C]-1.2280e+15[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU5[/C][C]-1[/C][C] 8.008e-16[/C][C]-1.2490e+15[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU6[/C][C]-1[/C][C] 7.751e-16[/C][C]-1.2900e+15[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-1.89e-16[/C][C] 1.605e-16[/C][C]-1.1780e+00[/C][C] 0.2407[/C][C] 0.1204[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+1[/C][C] 5.043e-16[/C][C]+1.9830e+15[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+7.446e-17[/C][C] 2.067e-16[/C][C]+3.6030e-01[/C][C] 0.7192[/C][C] 0.3596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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)	+1.526e-14	6.352e-15	+2.4030e+00	0.01751	0.008755
SKEOU2	-1	9.019e-16	-1.1090e+15	0	0
SKEOU3	-1	6.548e-16	-1.5270e+15	0	0
SKEOU4	-1	8.144e-16	-1.2280e+15	0	0
SKEOU5	-1	8.008e-16	-1.2490e+15	0	0
SKEOU6	-1	7.751e-16	-1.2900e+15	0	0
ITHSUM	-1.89e-16	1.605e-16	-1.1780e+00	0.2407	0.1204
SKEOUSUM	+1	5.043e-16	+1.9830e+15	0	0
Bevr_Leeftijd	+7.446e-17	2.067e-16	+3.6030e-01	0.7192	0.3596

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	5.417e+29
F-TEST (DF numerator)	8
F-TEST (DF denominator)	149
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.408e-15
Sum Squared Residuals	2.895e-27

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  1 \tabularnewline
R-squared &  1 \tabularnewline
Adjusted R-squared &  1 \tabularnewline
F-TEST (value) &  5.417e+29 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  4.408e-15 \tabularnewline
Sum Squared Residuals &  2.895e-27 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 1[/C][/ROW]
[ROW][C]R-squared[/C][C] 1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.417e+29[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 4.408e-15[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.895e-27[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	5.417e+29
F-TEST (DF numerator)	8
F-TEST (DF denominator)	149
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.408e-15
Sum Squared Residuals	2.895e-27

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	4	4.706e-14
2	5	5	-3.647e-15
3	4	4	-2.023e-14
4	3	3	-8.849e-16
5	4	4	-2.456e-15
6	3	3	-2.977e-15
7	3	3	-1.909e-17
8	3	3	-1.905e-15
9	4	4	1.471e-15
10	4	4	1.359e-15
11	4	4	6.329e-16
12	4	4	6.647e-16
13	3	3	-8.812e-16
14	4	4	6.765e-16
15	3	3	1.837e-16
16	3	3	1.281e-15
17	5	5	5.956e-16
18	4	4	-6.475e-16
19	3	3	1.441e-16
20	4	4	-1.586e-15
21	4	4	5.943e-16
22	4	4	3.573e-16
23	4	4	-1.308e-16
24	3	3	4.728e-16
25	3	3	1.694e-16
26	4	4	2.341e-16
27	2	2	1.56e-15
28	5	5	-4.46e-16
29	4	4	2.444e-15
30	5	5	4.037e-16
31	2	2	-4.685e-16
32	4	4	1.617e-15
33	3	3	4.113e-17
34	4	4	-2.275e-15
35	4	4	-1.303e-15
36	4	4	4.472e-18
37	5	5	-3.662e-16
38	4	4	1.817e-15
39	5	5	1.281e-15
40	5	5	-3.974e-16
41	4	4	-2.046e-15
42	4	4	-7.76e-16
43	3	3	1.741e-15
44	4	4	-7.22e-16
45	4	4	2.338e-15
46	5	5	1.161e-15
47	5	5	1.275e-15
48	4	4	-2.329e-15
49	4	4	-1.704e-15
50	4	4	-1.444e-16
51	3	3	1.46e-15
52	3	3	6.067e-17
53	4	4	-1.117e-15
54	4	4	-1.284e-15
55	5	5	3.441e-16
56	2	2	1.22e-15
57	4	4	-2.832e-16
58	3	3	2.049e-15
59	4	4	-1.608e-16
60	4	4	-3.988e-15
61	4	4	-1.87e-15
62	4	4	-1.341e-15
63	5	5	-9.358e-16
64	3	3	-3.452e-16
65	3	3	-4.985e-17
66	4	4	2.632e-15
67	4	4	-4.782e-16
68	4	4	1.41e-16
69	3	3	4.427e-16
70	4	4	-7.135e-17
71	3	3	-4.024e-17
72	3	3	-1.612e-15
73	4	4	-1.729e-15
74	4	4	6.688e-16
75	3	3	-1.486e-15
76	4	4	-1.02e-15
77	4	4	1.667e-16
78	4	4	1.032e-16
79	5	5	-1.066e-15
80	5	5	8.775e-16
81	4	4	-1.856e-17
82	3	3	1.206e-15
83	4	4	-4.311e-15
84	4	4	-3.807e-16
85	4	4	6.888e-16
86	4	4	-9.013e-16
87	4	4	1.781e-15
88	3	3	-1.285e-16
89	4	4	4.115e-16
90	5	5	-6.3e-16
91	5	5	8.834e-16
92	4	4	1.698e-15
93	3	3	-2.185e-16
94	5	5	-2.322e-15
95	4	4	5.943e-16
96	5	5	1.665e-16
97	5	5	-1.366e-16
98	4	4	-5.923e-16
99	4	4	3.173e-16
100	4	4	2.429e-16
101	3	3	-1.091e-15
102	4	4	-2.943e-16
103	4	4	-1.075e-15
104	3	3	-1.904e-15
105	4	4	-1.128e-15
106	3	3	6.83e-16
107	4	4	5.731e-16
108	5	5	-2.189e-15
109	5	5	2.686e-18
110	4	4	-2.084e-16
111	4	4	-7.993e-16
112	3	3	1.519e-16
113	4	4	4.308e-16
114	4	4	9.242e-18
115	4	4	2.183e-15
116	3	3	5.419e-16
117	4	4	-6.318e-16
118	4	4	-1.018e-16
119	3	3	4.247e-16
120	4	4	8.106e-18
121	3	3	-4.162e-15
122	4	4	-9.222e-16
123	5	5	-1.021e-15
124	2	2	1.807e-15
125	3	3	-1.234e-15
126	4	4	-8.157e-16
127	5	5	1.04e-15
128	4	4	1.599e-15
129	5	5	1.161e-15
130	4	4	2.403e-15
131	4	4	4.819e-16
132	3	3	-3.382e-16
133	4	4	-1.444e-16
134	4	4	4.465e-16
135	4	4	1.776e-17
136	4	4	-1.378e-17
137	5	5	-1.192e-15
138	4	4	-1.837e-15
139	4	4	9.394e-17
140	3	3	-1.831e-15
141	4	4	1.739e-15
142	4	4	-1.472e-15
143	4	4	3.049e-16
144	3	3	1.831e-16
145	4	4	3.499e-16
146	5	5	-9.24e-16
147	4	4	-1.731e-16
148	2	2	-5.86e-16
149	4	4	7.511e-16
150	4	4	-2.855e-15
151	4	4	-1.001e-16
152	4	4	2.758e-15
153	5	5	-2.214e-16
154	5	5	-5.601e-16
155	3	3	-1.444e-15
156	4	4	2.088e-16
157	4	4	-6.475e-16
158	2	2	-1.305e-15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  4 &  4 &  4.706e-14 \tabularnewline
2 &  5 &  5 & -3.647e-15 \tabularnewline
3 &  4 &  4 & -2.023e-14 \tabularnewline
4 &  3 &  3 & -8.849e-16 \tabularnewline
5 &  4 &  4 & -2.456e-15 \tabularnewline
6 &  3 &  3 & -2.977e-15 \tabularnewline
7 &  3 &  3 & -1.909e-17 \tabularnewline
8 &  3 &  3 & -1.905e-15 \tabularnewline
9 &  4 &  4 &  1.471e-15 \tabularnewline
10 &  4 &  4 &  1.359e-15 \tabularnewline
11 &  4 &  4 &  6.329e-16 \tabularnewline
12 &  4 &  4 &  6.647e-16 \tabularnewline
13 &  3 &  3 & -8.812e-16 \tabularnewline
14 &  4 &  4 &  6.765e-16 \tabularnewline
15 &  3 &  3 &  1.837e-16 \tabularnewline
16 &  3 &  3 &  1.281e-15 \tabularnewline
17 &  5 &  5 &  5.956e-16 \tabularnewline
18 &  4 &  4 & -6.475e-16 \tabularnewline
19 &  3 &  3 &  1.441e-16 \tabularnewline
20 &  4 &  4 & -1.586e-15 \tabularnewline
21 &  4 &  4 &  5.943e-16 \tabularnewline
22 &  4 &  4 &  3.573e-16 \tabularnewline
23 &  4 &  4 & -1.308e-16 \tabularnewline
24 &  3 &  3 &  4.728e-16 \tabularnewline
25 &  3 &  3 &  1.694e-16 \tabularnewline
26 &  4 &  4 &  2.341e-16 \tabularnewline
27 &  2 &  2 &  1.56e-15 \tabularnewline
28 &  5 &  5 & -4.46e-16 \tabularnewline
29 &  4 &  4 &  2.444e-15 \tabularnewline
30 &  5 &  5 &  4.037e-16 \tabularnewline
31 &  2 &  2 & -4.685e-16 \tabularnewline
32 &  4 &  4 &  1.617e-15 \tabularnewline
33 &  3 &  3 &  4.113e-17 \tabularnewline
34 &  4 &  4 & -2.275e-15 \tabularnewline
35 &  4 &  4 & -1.303e-15 \tabularnewline
36 &  4 &  4 &  4.472e-18 \tabularnewline
37 &  5 &  5 & -3.662e-16 \tabularnewline
38 &  4 &  4 &  1.817e-15 \tabularnewline
39 &  5 &  5 &  1.281e-15 \tabularnewline
40 &  5 &  5 & -3.974e-16 \tabularnewline
41 &  4 &  4 & -2.046e-15 \tabularnewline
42 &  4 &  4 & -7.76e-16 \tabularnewline
43 &  3 &  3 &  1.741e-15 \tabularnewline
44 &  4 &  4 & -7.22e-16 \tabularnewline
45 &  4 &  4 &  2.338e-15 \tabularnewline
46 &  5 &  5 &  1.161e-15 \tabularnewline
47 &  5 &  5 &  1.275e-15 \tabularnewline
48 &  4 &  4 & -2.329e-15 \tabularnewline
49 &  4 &  4 & -1.704e-15 \tabularnewline
50 &  4 &  4 & -1.444e-16 \tabularnewline
51 &  3 &  3 &  1.46e-15 \tabularnewline
52 &  3 &  3 &  6.067e-17 \tabularnewline
53 &  4 &  4 & -1.117e-15 \tabularnewline
54 &  4 &  4 & -1.284e-15 \tabularnewline
55 &  5 &  5 &  3.441e-16 \tabularnewline
56 &  2 &  2 &  1.22e-15 \tabularnewline
57 &  4 &  4 & -2.832e-16 \tabularnewline
58 &  3 &  3 &  2.049e-15 \tabularnewline
59 &  4 &  4 & -1.608e-16 \tabularnewline
60 &  4 &  4 & -3.988e-15 \tabularnewline
61 &  4 &  4 & -1.87e-15 \tabularnewline
62 &  4 &  4 & -1.341e-15 \tabularnewline
63 &  5 &  5 & -9.358e-16 \tabularnewline
64 &  3 &  3 & -3.452e-16 \tabularnewline
65 &  3 &  3 & -4.985e-17 \tabularnewline
66 &  4 &  4 &  2.632e-15 \tabularnewline
67 &  4 &  4 & -4.782e-16 \tabularnewline
68 &  4 &  4 &  1.41e-16 \tabularnewline
69 &  3 &  3 &  4.427e-16 \tabularnewline
70 &  4 &  4 & -7.135e-17 \tabularnewline
71 &  3 &  3 & -4.024e-17 \tabularnewline
72 &  3 &  3 & -1.612e-15 \tabularnewline
73 &  4 &  4 & -1.729e-15 \tabularnewline
74 &  4 &  4 &  6.688e-16 \tabularnewline
75 &  3 &  3 & -1.486e-15 \tabularnewline
76 &  4 &  4 & -1.02e-15 \tabularnewline
77 &  4 &  4 &  1.667e-16 \tabularnewline
78 &  4 &  4 &  1.032e-16 \tabularnewline
79 &  5 &  5 & -1.066e-15 \tabularnewline
80 &  5 &  5 &  8.775e-16 \tabularnewline
81 &  4 &  4 & -1.856e-17 \tabularnewline
82 &  3 &  3 &  1.206e-15 \tabularnewline
83 &  4 &  4 & -4.311e-15 \tabularnewline
84 &  4 &  4 & -3.807e-16 \tabularnewline
85 &  4 &  4 &  6.888e-16 \tabularnewline
86 &  4 &  4 & -9.013e-16 \tabularnewline
87 &  4 &  4 &  1.781e-15 \tabularnewline
88 &  3 &  3 & -1.285e-16 \tabularnewline
89 &  4 &  4 &  4.115e-16 \tabularnewline
90 &  5 &  5 & -6.3e-16 \tabularnewline
91 &  5 &  5 &  8.834e-16 \tabularnewline
92 &  4 &  4 &  1.698e-15 \tabularnewline
93 &  3 &  3 & -2.185e-16 \tabularnewline
94 &  5 &  5 & -2.322e-15 \tabularnewline
95 &  4 &  4 &  5.943e-16 \tabularnewline
96 &  5 &  5 &  1.665e-16 \tabularnewline
97 &  5 &  5 & -1.366e-16 \tabularnewline
98 &  4 &  4 & -5.923e-16 \tabularnewline
99 &  4 &  4 &  3.173e-16 \tabularnewline
100 &  4 &  4 &  2.429e-16 \tabularnewline
101 &  3 &  3 & -1.091e-15 \tabularnewline
102 &  4 &  4 & -2.943e-16 \tabularnewline
103 &  4 &  4 & -1.075e-15 \tabularnewline
104 &  3 &  3 & -1.904e-15 \tabularnewline
105 &  4 &  4 & -1.128e-15 \tabularnewline
106 &  3 &  3 &  6.83e-16 \tabularnewline
107 &  4 &  4 &  5.731e-16 \tabularnewline
108 &  5 &  5 & -2.189e-15 \tabularnewline
109 &  5 &  5 &  2.686e-18 \tabularnewline
110 &  4 &  4 & -2.084e-16 \tabularnewline
111 &  4 &  4 & -7.993e-16 \tabularnewline
112 &  3 &  3 &  1.519e-16 \tabularnewline
113 &  4 &  4 &  4.308e-16 \tabularnewline
114 &  4 &  4 &  9.242e-18 \tabularnewline
115 &  4 &  4 &  2.183e-15 \tabularnewline
116 &  3 &  3 &  5.419e-16 \tabularnewline
117 &  4 &  4 & -6.318e-16 \tabularnewline
118 &  4 &  4 & -1.018e-16 \tabularnewline
119 &  3 &  3 &  4.247e-16 \tabularnewline
120 &  4 &  4 &  8.106e-18 \tabularnewline
121 &  3 &  3 & -4.162e-15 \tabularnewline
122 &  4 &  4 & -9.222e-16 \tabularnewline
123 &  5 &  5 & -1.021e-15 \tabularnewline
124 &  2 &  2 &  1.807e-15 \tabularnewline
125 &  3 &  3 & -1.234e-15 \tabularnewline
126 &  4 &  4 & -8.157e-16 \tabularnewline
127 &  5 &  5 &  1.04e-15 \tabularnewline
128 &  4 &  4 &  1.599e-15 \tabularnewline
129 &  5 &  5 &  1.161e-15 \tabularnewline
130 &  4 &  4 &  2.403e-15 \tabularnewline
131 &  4 &  4 &  4.819e-16 \tabularnewline
132 &  3 &  3 & -3.382e-16 \tabularnewline
133 &  4 &  4 & -1.444e-16 \tabularnewline
134 &  4 &  4 &  4.465e-16 \tabularnewline
135 &  4 &  4 &  1.776e-17 \tabularnewline
136 &  4 &  4 & -1.378e-17 \tabularnewline
137 &  5 &  5 & -1.192e-15 \tabularnewline
138 &  4 &  4 & -1.837e-15 \tabularnewline
139 &  4 &  4 &  9.394e-17 \tabularnewline
140 &  3 &  3 & -1.831e-15 \tabularnewline
141 &  4 &  4 &  1.739e-15 \tabularnewline
142 &  4 &  4 & -1.472e-15 \tabularnewline
143 &  4 &  4 &  3.049e-16 \tabularnewline
144 &  3 &  3 &  1.831e-16 \tabularnewline
145 &  4 &  4 &  3.499e-16 \tabularnewline
146 &  5 &  5 & -9.24e-16 \tabularnewline
147 &  4 &  4 & -1.731e-16 \tabularnewline
148 &  2 &  2 & -5.86e-16 \tabularnewline
149 &  4 &  4 &  7.511e-16 \tabularnewline
150 &  4 &  4 & -2.855e-15 \tabularnewline
151 &  4 &  4 & -1.001e-16 \tabularnewline
152 &  4 &  4 &  2.758e-15 \tabularnewline
153 &  5 &  5 & -2.214e-16 \tabularnewline
154 &  5 &  5 & -5.601e-16 \tabularnewline
155 &  3 &  3 & -1.444e-15 \tabularnewline
156 &  4 &  4 &  2.088e-16 \tabularnewline
157 &  4 &  4 & -6.475e-16 \tabularnewline
158 &  2 &  2 & -1.305e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&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] 4[/C][C] 4[/C][C] 4.706e-14[/C][/ROW]
[ROW][C]2[/C][C] 5[/C][C] 5[/C][C]-3.647e-15[/C][/ROW]
[ROW][C]3[/C][C] 4[/C][C] 4[/C][C]-2.023e-14[/C][/ROW]
[ROW][C]4[/C][C] 3[/C][C] 3[/C][C]-8.849e-16[/C][/ROW]
[ROW][C]5[/C][C] 4[/C][C] 4[/C][C]-2.456e-15[/C][/ROW]
[ROW][C]6[/C][C] 3[/C][C] 3[/C][C]-2.977e-15[/C][/ROW]
[ROW][C]7[/C][C] 3[/C][C] 3[/C][C]-1.909e-17[/C][/ROW]
[ROW][C]8[/C][C] 3[/C][C] 3[/C][C]-1.905e-15[/C][/ROW]
[ROW][C]9[/C][C] 4[/C][C] 4[/C][C] 1.471e-15[/C][/ROW]
[ROW][C]10[/C][C] 4[/C][C] 4[/C][C] 1.359e-15[/C][/ROW]
[ROW][C]11[/C][C] 4[/C][C] 4[/C][C] 6.329e-16[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 4[/C][C] 6.647e-16[/C][/ROW]
[ROW][C]13[/C][C] 3[/C][C] 3[/C][C]-8.812e-16[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 4[/C][C] 6.765e-16[/C][/ROW]
[ROW][C]15[/C][C] 3[/C][C] 3[/C][C] 1.837e-16[/C][/ROW]
[ROW][C]16[/C][C] 3[/C][C] 3[/C][C] 1.281e-15[/C][/ROW]
[ROW][C]17[/C][C] 5[/C][C] 5[/C][C] 5.956e-16[/C][/ROW]
[ROW][C]18[/C][C] 4[/C][C] 4[/C][C]-6.475e-16[/C][/ROW]
[ROW][C]19[/C][C] 3[/C][C] 3[/C][C] 1.441e-16[/C][/ROW]
[ROW][C]20[/C][C] 4[/C][C] 4[/C][C]-1.586e-15[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 4[/C][C] 5.943e-16[/C][/ROW]
[ROW][C]22[/C][C] 4[/C][C] 4[/C][C] 3.573e-16[/C][/ROW]
[ROW][C]23[/C][C] 4[/C][C] 4[/C][C]-1.308e-16[/C][/ROW]
[ROW][C]24[/C][C] 3[/C][C] 3[/C][C] 4.728e-16[/C][/ROW]
[ROW][C]25[/C][C] 3[/C][C] 3[/C][C] 1.694e-16[/C][/ROW]
[ROW][C]26[/C][C] 4[/C][C] 4[/C][C] 2.341e-16[/C][/ROW]
[ROW][C]27[/C][C] 2[/C][C] 2[/C][C] 1.56e-15[/C][/ROW]
[ROW][C]28[/C][C] 5[/C][C] 5[/C][C]-4.46e-16[/C][/ROW]
[ROW][C]29[/C][C] 4[/C][C] 4[/C][C] 2.444e-15[/C][/ROW]
[ROW][C]30[/C][C] 5[/C][C] 5[/C][C] 4.037e-16[/C][/ROW]
[ROW][C]31[/C][C] 2[/C][C] 2[/C][C]-4.685e-16[/C][/ROW]
[ROW][C]32[/C][C] 4[/C][C] 4[/C][C] 1.617e-15[/C][/ROW]
[ROW][C]33[/C][C] 3[/C][C] 3[/C][C] 4.113e-17[/C][/ROW]
[ROW][C]34[/C][C] 4[/C][C] 4[/C][C]-2.275e-15[/C][/ROW]
[ROW][C]35[/C][C] 4[/C][C] 4[/C][C]-1.303e-15[/C][/ROW]
[ROW][C]36[/C][C] 4[/C][C] 4[/C][C] 4.472e-18[/C][/ROW]
[ROW][C]37[/C][C] 5[/C][C] 5[/C][C]-3.662e-16[/C][/ROW]
[ROW][C]38[/C][C] 4[/C][C] 4[/C][C] 1.817e-15[/C][/ROW]
[ROW][C]39[/C][C] 5[/C][C] 5[/C][C] 1.281e-15[/C][/ROW]
[ROW][C]40[/C][C] 5[/C][C] 5[/C][C]-3.974e-16[/C][/ROW]
[ROW][C]41[/C][C] 4[/C][C] 4[/C][C]-2.046e-15[/C][/ROW]
[ROW][C]42[/C][C] 4[/C][C] 4[/C][C]-7.76e-16[/C][/ROW]
[ROW][C]43[/C][C] 3[/C][C] 3[/C][C] 1.741e-15[/C][/ROW]
[ROW][C]44[/C][C] 4[/C][C] 4[/C][C]-7.22e-16[/C][/ROW]
[ROW][C]45[/C][C] 4[/C][C] 4[/C][C] 2.338e-15[/C][/ROW]
[ROW][C]46[/C][C] 5[/C][C] 5[/C][C] 1.161e-15[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 5[/C][C] 1.275e-15[/C][/ROW]
[ROW][C]48[/C][C] 4[/C][C] 4[/C][C]-2.329e-15[/C][/ROW]
[ROW][C]49[/C][C] 4[/C][C] 4[/C][C]-1.704e-15[/C][/ROW]
[ROW][C]50[/C][C] 4[/C][C] 4[/C][C]-1.444e-16[/C][/ROW]
[ROW][C]51[/C][C] 3[/C][C] 3[/C][C] 1.46e-15[/C][/ROW]
[ROW][C]52[/C][C] 3[/C][C] 3[/C][C] 6.067e-17[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 4[/C][C]-1.117e-15[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 4[/C][C]-1.284e-15[/C][/ROW]
[ROW][C]55[/C][C] 5[/C][C] 5[/C][C] 3.441e-16[/C][/ROW]
[ROW][C]56[/C][C] 2[/C][C] 2[/C][C] 1.22e-15[/C][/ROW]
[ROW][C]57[/C][C] 4[/C][C] 4[/C][C]-2.832e-16[/C][/ROW]
[ROW][C]58[/C][C] 3[/C][C] 3[/C][C] 2.049e-15[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 4[/C][C]-1.608e-16[/C][/ROW]
[ROW][C]60[/C][C] 4[/C][C] 4[/C][C]-3.988e-15[/C][/ROW]
[ROW][C]61[/C][C] 4[/C][C] 4[/C][C]-1.87e-15[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 4[/C][C]-1.341e-15[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 5[/C][C]-9.358e-16[/C][/ROW]
[ROW][C]64[/C][C] 3[/C][C] 3[/C][C]-3.452e-16[/C][/ROW]
[ROW][C]65[/C][C] 3[/C][C] 3[/C][C]-4.985e-17[/C][/ROW]
[ROW][C]66[/C][C] 4[/C][C] 4[/C][C] 2.632e-15[/C][/ROW]
[ROW][C]67[/C][C] 4[/C][C] 4[/C][C]-4.782e-16[/C][/ROW]
[ROW][C]68[/C][C] 4[/C][C] 4[/C][C] 1.41e-16[/C][/ROW]
[ROW][C]69[/C][C] 3[/C][C] 3[/C][C] 4.427e-16[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 4[/C][C]-7.135e-17[/C][/ROW]
[ROW][C]71[/C][C] 3[/C][C] 3[/C][C]-4.024e-17[/C][/ROW]
[ROW][C]72[/C][C] 3[/C][C] 3[/C][C]-1.612e-15[/C][/ROW]
[ROW][C]73[/C][C] 4[/C][C] 4[/C][C]-1.729e-15[/C][/ROW]
[ROW][C]74[/C][C] 4[/C][C] 4[/C][C] 6.688e-16[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 3[/C][C]-1.486e-15[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 4[/C][C]-1.02e-15[/C][/ROW]
[ROW][C]77[/C][C] 4[/C][C] 4[/C][C] 1.667e-16[/C][/ROW]
[ROW][C]78[/C][C] 4[/C][C] 4[/C][C] 1.032e-16[/C][/ROW]
[ROW][C]79[/C][C] 5[/C][C] 5[/C][C]-1.066e-15[/C][/ROW]
[ROW][C]80[/C][C] 5[/C][C] 5[/C][C] 8.775e-16[/C][/ROW]
[ROW][C]81[/C][C] 4[/C][C] 4[/C][C]-1.856e-17[/C][/ROW]
[ROW][C]82[/C][C] 3[/C][C] 3[/C][C] 1.206e-15[/C][/ROW]
[ROW][C]83[/C][C] 4[/C][C] 4[/C][C]-4.311e-15[/C][/ROW]
[ROW][C]84[/C][C] 4[/C][C] 4[/C][C]-3.807e-16[/C][/ROW]
[ROW][C]85[/C][C] 4[/C][C] 4[/C][C] 6.888e-16[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 4[/C][C]-9.013e-16[/C][/ROW]
[ROW][C]87[/C][C] 4[/C][C] 4[/C][C] 1.781e-15[/C][/ROW]
[ROW][C]88[/C][C] 3[/C][C] 3[/C][C]-1.285e-16[/C][/ROW]
[ROW][C]89[/C][C] 4[/C][C] 4[/C][C] 4.115e-16[/C][/ROW]
[ROW][C]90[/C][C] 5[/C][C] 5[/C][C]-6.3e-16[/C][/ROW]
[ROW][C]91[/C][C] 5[/C][C] 5[/C][C] 8.834e-16[/C][/ROW]
[ROW][C]92[/C][C] 4[/C][C] 4[/C][C] 1.698e-15[/C][/ROW]
[ROW][C]93[/C][C] 3[/C][C] 3[/C][C]-2.185e-16[/C][/ROW]
[ROW][C]94[/C][C] 5[/C][C] 5[/C][C]-2.322e-15[/C][/ROW]
[ROW][C]95[/C][C] 4[/C][C] 4[/C][C] 5.943e-16[/C][/ROW]
[ROW][C]96[/C][C] 5[/C][C] 5[/C][C] 1.665e-16[/C][/ROW]
[ROW][C]97[/C][C] 5[/C][C] 5[/C][C]-1.366e-16[/C][/ROW]
[ROW][C]98[/C][C] 4[/C][C] 4[/C][C]-5.923e-16[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 4[/C][C] 3.173e-16[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 4[/C][C] 2.429e-16[/C][/ROW]
[ROW][C]101[/C][C] 3[/C][C] 3[/C][C]-1.091e-15[/C][/ROW]
[ROW][C]102[/C][C] 4[/C][C] 4[/C][C]-2.943e-16[/C][/ROW]
[ROW][C]103[/C][C] 4[/C][C] 4[/C][C]-1.075e-15[/C][/ROW]
[ROW][C]104[/C][C] 3[/C][C] 3[/C][C]-1.904e-15[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 4[/C][C]-1.128e-15[/C][/ROW]
[ROW][C]106[/C][C] 3[/C][C] 3[/C][C] 6.83e-16[/C][/ROW]
[ROW][C]107[/C][C] 4[/C][C] 4[/C][C] 5.731e-16[/C][/ROW]
[ROW][C]108[/C][C] 5[/C][C] 5[/C][C]-2.189e-15[/C][/ROW]
[ROW][C]109[/C][C] 5[/C][C] 5[/C][C] 2.686e-18[/C][/ROW]
[ROW][C]110[/C][C] 4[/C][C] 4[/C][C]-2.084e-16[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 4[/C][C]-7.993e-16[/C][/ROW]
[ROW][C]112[/C][C] 3[/C][C] 3[/C][C] 1.519e-16[/C][/ROW]
[ROW][C]113[/C][C] 4[/C][C] 4[/C][C] 4.308e-16[/C][/ROW]
[ROW][C]114[/C][C] 4[/C][C] 4[/C][C] 9.242e-18[/C][/ROW]
[ROW][C]115[/C][C] 4[/C][C] 4[/C][C] 2.183e-15[/C][/ROW]
[ROW][C]116[/C][C] 3[/C][C] 3[/C][C] 5.419e-16[/C][/ROW]
[ROW][C]117[/C][C] 4[/C][C] 4[/C][C]-6.318e-16[/C][/ROW]
[ROW][C]118[/C][C] 4[/C][C] 4[/C][C]-1.018e-16[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 3[/C][C] 4.247e-16[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C] 4[/C][C] 8.106e-18[/C][/ROW]
[ROW][C]121[/C][C] 3[/C][C] 3[/C][C]-4.162e-15[/C][/ROW]
[ROW][C]122[/C][C] 4[/C][C] 4[/C][C]-9.222e-16[/C][/ROW]
[ROW][C]123[/C][C] 5[/C][C] 5[/C][C]-1.021e-15[/C][/ROW]
[ROW][C]124[/C][C] 2[/C][C] 2[/C][C] 1.807e-15[/C][/ROW]
[ROW][C]125[/C][C] 3[/C][C] 3[/C][C]-1.234e-15[/C][/ROW]
[ROW][C]126[/C][C] 4[/C][C] 4[/C][C]-8.157e-16[/C][/ROW]
[ROW][C]127[/C][C] 5[/C][C] 5[/C][C] 1.04e-15[/C][/ROW]
[ROW][C]128[/C][C] 4[/C][C] 4[/C][C] 1.599e-15[/C][/ROW]
[ROW][C]129[/C][C] 5[/C][C] 5[/C][C] 1.161e-15[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 4[/C][C] 2.403e-15[/C][/ROW]
[ROW][C]131[/C][C] 4[/C][C] 4[/C][C] 4.819e-16[/C][/ROW]
[ROW][C]132[/C][C] 3[/C][C] 3[/C][C]-3.382e-16[/C][/ROW]
[ROW][C]133[/C][C] 4[/C][C] 4[/C][C]-1.444e-16[/C][/ROW]
[ROW][C]134[/C][C] 4[/C][C] 4[/C][C] 4.465e-16[/C][/ROW]
[ROW][C]135[/C][C] 4[/C][C] 4[/C][C] 1.776e-17[/C][/ROW]
[ROW][C]136[/C][C] 4[/C][C] 4[/C][C]-1.378e-17[/C][/ROW]
[ROW][C]137[/C][C] 5[/C][C] 5[/C][C]-1.192e-15[/C][/ROW]
[ROW][C]138[/C][C] 4[/C][C] 4[/C][C]-1.837e-15[/C][/ROW]
[ROW][C]139[/C][C] 4[/C][C] 4[/C][C] 9.394e-17[/C][/ROW]
[ROW][C]140[/C][C] 3[/C][C] 3[/C][C]-1.831e-15[/C][/ROW]
[ROW][C]141[/C][C] 4[/C][C] 4[/C][C] 1.739e-15[/C][/ROW]
[ROW][C]142[/C][C] 4[/C][C] 4[/C][C]-1.472e-15[/C][/ROW]
[ROW][C]143[/C][C] 4[/C][C] 4[/C][C] 3.049e-16[/C][/ROW]
[ROW][C]144[/C][C] 3[/C][C] 3[/C][C] 1.831e-16[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 4[/C][C] 3.499e-16[/C][/ROW]
[ROW][C]146[/C][C] 5[/C][C] 5[/C][C]-9.24e-16[/C][/ROW]
[ROW][C]147[/C][C] 4[/C][C] 4[/C][C]-1.731e-16[/C][/ROW]
[ROW][C]148[/C][C] 2[/C][C] 2[/C][C]-5.86e-16[/C][/ROW]
[ROW][C]149[/C][C] 4[/C][C] 4[/C][C] 7.511e-16[/C][/ROW]
[ROW][C]150[/C][C] 4[/C][C] 4[/C][C]-2.855e-15[/C][/ROW]
[ROW][C]151[/C][C] 4[/C][C] 4[/C][C]-1.001e-16[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 4[/C][C] 2.758e-15[/C][/ROW]
[ROW][C]153[/C][C] 5[/C][C] 5[/C][C]-2.214e-16[/C][/ROW]
[ROW][C]154[/C][C] 5[/C][C] 5[/C][C]-5.601e-16[/C][/ROW]
[ROW][C]155[/C][C] 3[/C][C] 3[/C][C]-1.444e-15[/C][/ROW]
[ROW][C]156[/C][C] 4[/C][C] 4[/C][C] 2.088e-16[/C][/ROW]
[ROW][C]157[/C][C] 4[/C][C] 4[/C][C]-6.475e-16[/C][/ROW]
[ROW][C]158[/C][C] 2[/C][C] 2[/C][C]-1.305e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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	4	4	4.706e-14
2	5	5	-3.647e-15
3	4	4	-2.023e-14
4	3	3	-8.849e-16
5	4	4	-2.456e-15
6	3	3	-2.977e-15
7	3	3	-1.909e-17
8	3	3	-1.905e-15
9	4	4	1.471e-15
10	4	4	1.359e-15
11	4	4	6.329e-16
12	4	4	6.647e-16
13	3	3	-8.812e-16
14	4	4	6.765e-16
15	3	3	1.837e-16
16	3	3	1.281e-15
17	5	5	5.956e-16
18	4	4	-6.475e-16
19	3	3	1.441e-16
20	4	4	-1.586e-15
21	4	4	5.943e-16
22	4	4	3.573e-16
23	4	4	-1.308e-16
24	3	3	4.728e-16
25	3	3	1.694e-16
26	4	4	2.341e-16
27	2	2	1.56e-15
28	5	5	-4.46e-16
29	4	4	2.444e-15
30	5	5	4.037e-16
31	2	2	-4.685e-16
32	4	4	1.617e-15
33	3	3	4.113e-17
34	4	4	-2.275e-15
35	4	4	-1.303e-15
36	4	4	4.472e-18
37	5	5	-3.662e-16
38	4	4	1.817e-15
39	5	5	1.281e-15
40	5	5	-3.974e-16
41	4	4	-2.046e-15
42	4	4	-7.76e-16
43	3	3	1.741e-15
44	4	4	-7.22e-16
45	4	4	2.338e-15
46	5	5	1.161e-15
47	5	5	1.275e-15
48	4	4	-2.329e-15
49	4	4	-1.704e-15
50	4	4	-1.444e-16
51	3	3	1.46e-15
52	3	3	6.067e-17
53	4	4	-1.117e-15
54	4	4	-1.284e-15
55	5	5	3.441e-16
56	2	2	1.22e-15
57	4	4	-2.832e-16
58	3	3	2.049e-15
59	4	4	-1.608e-16
60	4	4	-3.988e-15
61	4	4	-1.87e-15
62	4	4	-1.341e-15
63	5	5	-9.358e-16
64	3	3	-3.452e-16
65	3	3	-4.985e-17
66	4	4	2.632e-15
67	4	4	-4.782e-16
68	4	4	1.41e-16
69	3	3	4.427e-16
70	4	4	-7.135e-17
71	3	3	-4.024e-17
72	3	3	-1.612e-15
73	4	4	-1.729e-15
74	4	4	6.688e-16
75	3	3	-1.486e-15
76	4	4	-1.02e-15
77	4	4	1.667e-16
78	4	4	1.032e-16
79	5	5	-1.066e-15
80	5	5	8.775e-16
81	4	4	-1.856e-17
82	3	3	1.206e-15
83	4	4	-4.311e-15
84	4	4	-3.807e-16
85	4	4	6.888e-16
86	4	4	-9.013e-16
87	4	4	1.781e-15
88	3	3	-1.285e-16
89	4	4	4.115e-16
90	5	5	-6.3e-16
91	5	5	8.834e-16
92	4	4	1.698e-15
93	3	3	-2.185e-16
94	5	5	-2.322e-15
95	4	4	5.943e-16
96	5	5	1.665e-16
97	5	5	-1.366e-16
98	4	4	-5.923e-16
99	4	4	3.173e-16
100	4	4	2.429e-16
101	3	3	-1.091e-15
102	4	4	-2.943e-16
103	4	4	-1.075e-15
104	3	3	-1.904e-15
105	4	4	-1.128e-15
106	3	3	6.83e-16
107	4	4	5.731e-16
108	5	5	-2.189e-15
109	5	5	2.686e-18
110	4	4	-2.084e-16
111	4	4	-7.993e-16
112	3	3	1.519e-16
113	4	4	4.308e-16
114	4	4	9.242e-18
115	4	4	2.183e-15
116	3	3	5.419e-16
117	4	4	-6.318e-16
118	4	4	-1.018e-16
119	3	3	4.247e-16
120	4	4	8.106e-18
121	3	3	-4.162e-15
122	4	4	-9.222e-16
123	5	5	-1.021e-15
124	2	2	1.807e-15
125	3	3	-1.234e-15
126	4	4	-8.157e-16
127	5	5	1.04e-15
128	4	4	1.599e-15
129	5	5	1.161e-15
130	4	4	2.403e-15
131	4	4	4.819e-16
132	3	3	-3.382e-16
133	4	4	-1.444e-16
134	4	4	4.465e-16
135	4	4	1.776e-17
136	4	4	-1.378e-17
137	5	5	-1.192e-15
138	4	4	-1.837e-15
139	4	4	9.394e-17
140	3	3	-1.831e-15
141	4	4	1.739e-15
142	4	4	-1.472e-15
143	4	4	3.049e-16
144	3	3	1.831e-16
145	4	4	3.499e-16
146	5	5	-9.24e-16
147	4	4	-1.731e-16
148	2	2	-5.86e-16
149	4	4	7.511e-16
150	4	4	-2.855e-15
151	4	4	-1.001e-16
152	4	4	2.758e-15
153	5	5	-2.214e-16
154	5	5	-5.601e-16
155	3	3	-1.444e-15
156	4	4	2.088e-16
157	4	4	-6.475e-16
158	2	2	-1.305e-15

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	8.068e-05	0.0001614	0.9999
13	0.0003363	0.0006725	0.9997
14	2.742e-05	5.483e-05	1
15	6.117e-10	1.223e-09	1
16	1.493e-13	2.985e-13	1
17	1.25e-10	2.5e-10	1
18	8.106e-10	1.621e-09	1
19	0.3272	0.6545	0.6728
20	2.138e-08	4.276e-08	1
21	1.968e-23	3.936e-23	1
22	0.002597	0.005193	0.9974
23	4.858e-14	9.717e-14	1
24	9.63e-17	1.926e-16	1
25	0.006112	0.01222	0.9939
26	1.836e-16	3.672e-16	1
27	0.9999	0.0002959	0.0001479
28	1.592e-13	3.184e-13	1
29	1.071e-18	2.143e-18	1
30	1.502e-08	3.004e-08	1
31	0.0267	0.05341	0.9733
32	0.3249	0.6498	0.6751
33	9.294e-17	1.859e-16	1
34	1.184e-24	2.369e-24	1
35	0.1169	0.2337	0.8831
36	7.332e-18	1.466e-17	1
37	4.334e-21	8.669e-21	1
38	1.088e-14	2.176e-14	1
39	1.331e-33	2.662e-33	1
40	8.523e-11	1.705e-10	1
41	7.9e-08	1.58e-07	1
42	0.9668	0.06636	0.03318
43	6.181e-28	1.236e-27	1
44	0.9996	0.0008206	0.0004103
45	1.129e-05	2.257e-05	1
46	1.721e-06	3.443e-06	1
47	4.709e-07	9.419e-07	1
48	0.1998	0.3995	0.8002
49	0.02668	0.05335	0.9733
50	1	8.62e-68	4.31e-68
51	6.846e-11	1.369e-10	1
52	1	1.725e-37	8.624e-38
53	3.079e-42	6.158e-42	1
54	3.11e-26	6.22e-26	1
55	1.717e-14	3.433e-14	1
56	5.091e-22	1.018e-21	1
57	1	6.064e-13	3.032e-13
58	8.999e-32	1.8e-31	1
59	1.591e-14	3.181e-14	1
60	5.516e-23	1.103e-22	1
61	1	5.188e-13	2.594e-13
62	2.734e-42	5.469e-42	1
63	5.305e-30	1.061e-29	1
64	3.27e-49	6.54e-49	1
65	1.068e-19	2.136e-19	1
66	1	2.509e-07	1.255e-07
67	0.00194	0.003881	0.9981
68	5.325e-50	1.065e-49	1
69	0.4765	0.9531	0.5235
70	2.246e-54	4.493e-54	1
71	1	3.389e-09	1.695e-09
72	0.9999	0.0001234	6.17e-05
73	1.054e-07	2.108e-07	1
74	1	3.934e-26	1.967e-26
75	1	4.898e-78	2.449e-78
76	3.819e-10	7.637e-10	1
77	0.3194	0.6388	0.6806
78	0.8044	0.3913	0.1956
79	1	4.965e-14	2.482e-14
80	0.7315	0.537	0.2685
81	0.1334	0.2668	0.8666
82	1	3.349e-35	1.675e-35
83	3.298e-41	6.596e-41	1
84	1.505e-09	3.01e-09	1
85	1	9.219e-16	4.609e-16
86	5.687e-20	1.137e-19	1
87	1.666e-05	3.332e-05	1
88	1	1.808e-09	9.038e-10
89	0.000295	0.0005899	0.9997
90	1	3.656e-30	1.828e-30
91	1	1.024e-05	5.12e-06
92	1.356e-08	2.712e-08	1
93	1	4.667e-22	2.334e-22
94	1	1.565e-57	7.827e-58
95	1	8.442e-20	4.221e-20
96	1	4.4e-14	2.2e-14
97	0.005257	0.01051	0.9947
98	0.01078	0.02155	0.9892
99	0.9997	0.0006858	0.0003429
100	1	5.73e-28	2.865e-28
101	0.5038	0.9925	0.4963
102	1	9.556e-23	4.778e-23
103	1	5.169e-06	2.585e-06
104	1	1.606e-46	8.03e-47
105	1	2.06e-35	1.03e-35
106	2.555e-29	5.11e-29	1
107	0.9363	0.1274	0.06372
108	1.153e-30	2.305e-30	1
109	2.494e-23	4.989e-23	1
110	0.9888	0.02238	0.01119
111	0.9999	0.000121	6.05e-05
112	1	1.387e-10	6.936e-11
113	0.4604	0.9208	0.5396
114	1.181e-21	2.362e-21	1
115	1	1.014e-36	5.068e-37
116	4.986e-26	9.972e-26	1
117	1	6.162e-09	3.081e-09
118	0.02855	0.0571	0.9715
119	1	2.826e-17	1.413e-17
120	1	1.405e-19	7.024e-20
121	1	4.912e-09	2.456e-09
122	1	2.681e-19	1.341e-19
123	4.172e-18	8.345e-18	1
124	1	6.066e-15	3.033e-15
125	1	1.999e-11	9.994e-12
126	0.9993	0.001326	0.0006631
127	0.9999	0.0002966	0.0001483
128	0.9996	0.0007912	0.0003956
129	1	7.889e-08	3.945e-08
130	0.993	0.014	0.006998
131	0.01775	0.0355	0.9823
132	1	1.442e-07	7.21e-08
133	0.9846	0.03072	0.01536
134	1	2.006e-07	1.003e-07
135	1	5.741e-13	2.87e-13
136	0.4693	0.9386	0.5307
137	1	2.043e-19	1.021e-19
138	1	3.019e-05	1.51e-05
139	0.4367	0.8734	0.5633
140	1	1.558e-08	7.792e-09
141	1	8.606e-09	4.303e-09
142	1	1.288e-14	6.44e-15
143	1	7.518e-07	3.759e-07
144	0.9999	0.000114	5.701e-05
145	0.9876	0.02476	0.01238
146	1	8.575e-05	4.287e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  8.068e-05 &  0.0001614 &  0.9999 \tabularnewline
13 &  0.0003363 &  0.0006725 &  0.9997 \tabularnewline
14 &  2.742e-05 &  5.483e-05 &  1 \tabularnewline
15 &  6.117e-10 &  1.223e-09 &  1 \tabularnewline
16 &  1.493e-13 &  2.985e-13 &  1 \tabularnewline
17 &  1.25e-10 &  2.5e-10 &  1 \tabularnewline
18 &  8.106e-10 &  1.621e-09 &  1 \tabularnewline
19 &  0.3272 &  0.6545 &  0.6728 \tabularnewline
20 &  2.138e-08 &  4.276e-08 &  1 \tabularnewline
21 &  1.968e-23 &  3.936e-23 &  1 \tabularnewline
22 &  0.002597 &  0.005193 &  0.9974 \tabularnewline
23 &  4.858e-14 &  9.717e-14 &  1 \tabularnewline
24 &  9.63e-17 &  1.926e-16 &  1 \tabularnewline
25 &  0.006112 &  0.01222 &  0.9939 \tabularnewline
26 &  1.836e-16 &  3.672e-16 &  1 \tabularnewline
27 &  0.9999 &  0.0002959 &  0.0001479 \tabularnewline
28 &  1.592e-13 &  3.184e-13 &  1 \tabularnewline
29 &  1.071e-18 &  2.143e-18 &  1 \tabularnewline
30 &  1.502e-08 &  3.004e-08 &  1 \tabularnewline
31 &  0.0267 &  0.05341 &  0.9733 \tabularnewline
32 &  0.3249 &  0.6498 &  0.6751 \tabularnewline
33 &  9.294e-17 &  1.859e-16 &  1 \tabularnewline
34 &  1.184e-24 &  2.369e-24 &  1 \tabularnewline
35 &  0.1169 &  0.2337 &  0.8831 \tabularnewline
36 &  7.332e-18 &  1.466e-17 &  1 \tabularnewline
37 &  4.334e-21 &  8.669e-21 &  1 \tabularnewline
38 &  1.088e-14 &  2.176e-14 &  1 \tabularnewline
39 &  1.331e-33 &  2.662e-33 &  1 \tabularnewline
40 &  8.523e-11 &  1.705e-10 &  1 \tabularnewline
41 &  7.9e-08 &  1.58e-07 &  1 \tabularnewline
42 &  0.9668 &  0.06636 &  0.03318 \tabularnewline
43 &  6.181e-28 &  1.236e-27 &  1 \tabularnewline
44 &  0.9996 &  0.0008206 &  0.0004103 \tabularnewline
45 &  1.129e-05 &  2.257e-05 &  1 \tabularnewline
46 &  1.721e-06 &  3.443e-06 &  1 \tabularnewline
47 &  4.709e-07 &  9.419e-07 &  1 \tabularnewline
48 &  0.1998 &  0.3995 &  0.8002 \tabularnewline
49 &  0.02668 &  0.05335 &  0.9733 \tabularnewline
50 &  1 &  8.62e-68 &  4.31e-68 \tabularnewline
51 &  6.846e-11 &  1.369e-10 &  1 \tabularnewline
52 &  1 &  1.725e-37 &  8.624e-38 \tabularnewline
53 &  3.079e-42 &  6.158e-42 &  1 \tabularnewline
54 &  3.11e-26 &  6.22e-26 &  1 \tabularnewline
55 &  1.717e-14 &  3.433e-14 &  1 \tabularnewline
56 &  5.091e-22 &  1.018e-21 &  1 \tabularnewline
57 &  1 &  6.064e-13 &  3.032e-13 \tabularnewline
58 &  8.999e-32 &  1.8e-31 &  1 \tabularnewline
59 &  1.591e-14 &  3.181e-14 &  1 \tabularnewline
60 &  5.516e-23 &  1.103e-22 &  1 \tabularnewline
61 &  1 &  5.188e-13 &  2.594e-13 \tabularnewline
62 &  2.734e-42 &  5.469e-42 &  1 \tabularnewline
63 &  5.305e-30 &  1.061e-29 &  1 \tabularnewline
64 &  3.27e-49 &  6.54e-49 &  1 \tabularnewline
65 &  1.068e-19 &  2.136e-19 &  1 \tabularnewline
66 &  1 &  2.509e-07 &  1.255e-07 \tabularnewline
67 &  0.00194 &  0.003881 &  0.9981 \tabularnewline
68 &  5.325e-50 &  1.065e-49 &  1 \tabularnewline
69 &  0.4765 &  0.9531 &  0.5235 \tabularnewline
70 &  2.246e-54 &  4.493e-54 &  1 \tabularnewline
71 &  1 &  3.389e-09 &  1.695e-09 \tabularnewline
72 &  0.9999 &  0.0001234 &  6.17e-05 \tabularnewline
73 &  1.054e-07 &  2.108e-07 &  1 \tabularnewline
74 &  1 &  3.934e-26 &  1.967e-26 \tabularnewline
75 &  1 &  4.898e-78 &  2.449e-78 \tabularnewline
76 &  3.819e-10 &  7.637e-10 &  1 \tabularnewline
77 &  0.3194 &  0.6388 &  0.6806 \tabularnewline
78 &  0.8044 &  0.3913 &  0.1956 \tabularnewline
79 &  1 &  4.965e-14 &  2.482e-14 \tabularnewline
80 &  0.7315 &  0.537 &  0.2685 \tabularnewline
81 &  0.1334 &  0.2668 &  0.8666 \tabularnewline
82 &  1 &  3.349e-35 &  1.675e-35 \tabularnewline
83 &  3.298e-41 &  6.596e-41 &  1 \tabularnewline
84 &  1.505e-09 &  3.01e-09 &  1 \tabularnewline
85 &  1 &  9.219e-16 &  4.609e-16 \tabularnewline
86 &  5.687e-20 &  1.137e-19 &  1 \tabularnewline
87 &  1.666e-05 &  3.332e-05 &  1 \tabularnewline
88 &  1 &  1.808e-09 &  9.038e-10 \tabularnewline
89 &  0.000295 &  0.0005899 &  0.9997 \tabularnewline
90 &  1 &  3.656e-30 &  1.828e-30 \tabularnewline
91 &  1 &  1.024e-05 &  5.12e-06 \tabularnewline
92 &  1.356e-08 &  2.712e-08 &  1 \tabularnewline
93 &  1 &  4.667e-22 &  2.334e-22 \tabularnewline
94 &  1 &  1.565e-57 &  7.827e-58 \tabularnewline
95 &  1 &  8.442e-20 &  4.221e-20 \tabularnewline
96 &  1 &  4.4e-14 &  2.2e-14 \tabularnewline
97 &  0.005257 &  0.01051 &  0.9947 \tabularnewline
98 &  0.01078 &  0.02155 &  0.9892 \tabularnewline
99 &  0.9997 &  0.0006858 &  0.0003429 \tabularnewline
100 &  1 &  5.73e-28 &  2.865e-28 \tabularnewline
101 &  0.5038 &  0.9925 &  0.4963 \tabularnewline
102 &  1 &  9.556e-23 &  4.778e-23 \tabularnewline
103 &  1 &  5.169e-06 &  2.585e-06 \tabularnewline
104 &  1 &  1.606e-46 &  8.03e-47 \tabularnewline
105 &  1 &  2.06e-35 &  1.03e-35 \tabularnewline
106 &  2.555e-29 &  5.11e-29 &  1 \tabularnewline
107 &  0.9363 &  0.1274 &  0.06372 \tabularnewline
108 &  1.153e-30 &  2.305e-30 &  1 \tabularnewline
109 &  2.494e-23 &  4.989e-23 &  1 \tabularnewline
110 &  0.9888 &  0.02238 &  0.01119 \tabularnewline
111 &  0.9999 &  0.000121 &  6.05e-05 \tabularnewline
112 &  1 &  1.387e-10 &  6.936e-11 \tabularnewline
113 &  0.4604 &  0.9208 &  0.5396 \tabularnewline
114 &  1.181e-21 &  2.362e-21 &  1 \tabularnewline
115 &  1 &  1.014e-36 &  5.068e-37 \tabularnewline
116 &  4.986e-26 &  9.972e-26 &  1 \tabularnewline
117 &  1 &  6.162e-09 &  3.081e-09 \tabularnewline
118 &  0.02855 &  0.0571 &  0.9715 \tabularnewline
119 &  1 &  2.826e-17 &  1.413e-17 \tabularnewline
120 &  1 &  1.405e-19 &  7.024e-20 \tabularnewline
121 &  1 &  4.912e-09 &  2.456e-09 \tabularnewline
122 &  1 &  2.681e-19 &  1.341e-19 \tabularnewline
123 &  4.172e-18 &  8.345e-18 &  1 \tabularnewline
124 &  1 &  6.066e-15 &  3.033e-15 \tabularnewline
125 &  1 &  1.999e-11 &  9.994e-12 \tabularnewline
126 &  0.9993 &  0.001326 &  0.0006631 \tabularnewline
127 &  0.9999 &  0.0002966 &  0.0001483 \tabularnewline
128 &  0.9996 &  0.0007912 &  0.0003956 \tabularnewline
129 &  1 &  7.889e-08 &  3.945e-08 \tabularnewline
130 &  0.993 &  0.014 &  0.006998 \tabularnewline
131 &  0.01775 &  0.0355 &  0.9823 \tabularnewline
132 &  1 &  1.442e-07 &  7.21e-08 \tabularnewline
133 &  0.9846 &  0.03072 &  0.01536 \tabularnewline
134 &  1 &  2.006e-07 &  1.003e-07 \tabularnewline
135 &  1 &  5.741e-13 &  2.87e-13 \tabularnewline
136 &  0.4693 &  0.9386 &  0.5307 \tabularnewline
137 &  1 &  2.043e-19 &  1.021e-19 \tabularnewline
138 &  1 &  3.019e-05 &  1.51e-05 \tabularnewline
139 &  0.4367 &  0.8734 &  0.5633 \tabularnewline
140 &  1 &  1.558e-08 &  7.792e-09 \tabularnewline
141 &  1 &  8.606e-09 &  4.303e-09 \tabularnewline
142 &  1 &  1.288e-14 &  6.44e-15 \tabularnewline
143 &  1 &  7.518e-07 &  3.759e-07 \tabularnewline
144 &  0.9999 &  0.000114 &  5.701e-05 \tabularnewline
145 &  0.9876 &  0.02476 &  0.01238 \tabularnewline
146 &  1 &  8.575e-05 &  4.287e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&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] 8.068e-05[/C][C] 0.0001614[/C][C] 0.9999[/C][/ROW]
[ROW][C]13[/C][C] 0.0003363[/C][C] 0.0006725[/C][C] 0.9997[/C][/ROW]
[ROW][C]14[/C][C] 2.742e-05[/C][C] 5.483e-05[/C][C] 1[/C][/ROW]
[ROW][C]15[/C][C] 6.117e-10[/C][C] 1.223e-09[/C][C] 1[/C][/ROW]
[ROW][C]16[/C][C] 1.493e-13[/C][C] 2.985e-13[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 1.25e-10[/C][C] 2.5e-10[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 8.106e-10[/C][C] 1.621e-09[/C][C] 1[/C][/ROW]
[ROW][C]19[/C][C] 0.3272[/C][C] 0.6545[/C][C] 0.6728[/C][/ROW]
[ROW][C]20[/C][C] 2.138e-08[/C][C] 4.276e-08[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 1.968e-23[/C][C] 3.936e-23[/C][C] 1[/C][/ROW]
[ROW][C]22[/C][C] 0.002597[/C][C] 0.005193[/C][C] 0.9974[/C][/ROW]
[ROW][C]23[/C][C] 4.858e-14[/C][C] 9.717e-14[/C][C] 1[/C][/ROW]
[ROW][C]24[/C][C] 9.63e-17[/C][C] 1.926e-16[/C][C] 1[/C][/ROW]
[ROW][C]25[/C][C] 0.006112[/C][C] 0.01222[/C][C] 0.9939[/C][/ROW]
[ROW][C]26[/C][C] 1.836e-16[/C][C] 3.672e-16[/C][C] 1[/C][/ROW]
[ROW][C]27[/C][C] 0.9999[/C][C] 0.0002959[/C][C] 0.0001479[/C][/ROW]
[ROW][C]28[/C][C] 1.592e-13[/C][C] 3.184e-13[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 1.071e-18[/C][C] 2.143e-18[/C][C] 1[/C][/ROW]
[ROW][C]30[/C][C] 1.502e-08[/C][C] 3.004e-08[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 0.0267[/C][C] 0.05341[/C][C] 0.9733[/C][/ROW]
[ROW][C]32[/C][C] 0.3249[/C][C] 0.6498[/C][C] 0.6751[/C][/ROW]
[ROW][C]33[/C][C] 9.294e-17[/C][C] 1.859e-16[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 1.184e-24[/C][C] 2.369e-24[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 0.1169[/C][C] 0.2337[/C][C] 0.8831[/C][/ROW]
[ROW][C]36[/C][C] 7.332e-18[/C][C] 1.466e-17[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 4.334e-21[/C][C] 8.669e-21[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 1.088e-14[/C][C] 2.176e-14[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 1.331e-33[/C][C] 2.662e-33[/C][C] 1[/C][/ROW]
[ROW][C]40[/C][C] 8.523e-11[/C][C] 1.705e-10[/C][C] 1[/C][/ROW]
[ROW][C]41[/C][C] 7.9e-08[/C][C] 1.58e-07[/C][C] 1[/C][/ROW]
[ROW][C]42[/C][C] 0.9668[/C][C] 0.06636[/C][C] 0.03318[/C][/ROW]
[ROW][C]43[/C][C] 6.181e-28[/C][C] 1.236e-27[/C][C] 1[/C][/ROW]
[ROW][C]44[/C][C] 0.9996[/C][C] 0.0008206[/C][C] 0.0004103[/C][/ROW]
[ROW][C]45[/C][C] 1.129e-05[/C][C] 2.257e-05[/C][C] 1[/C][/ROW]
[ROW][C]46[/C][C] 1.721e-06[/C][C] 3.443e-06[/C][C] 1[/C][/ROW]
[ROW][C]47[/C][C] 4.709e-07[/C][C] 9.419e-07[/C][C] 1[/C][/ROW]
[ROW][C]48[/C][C] 0.1998[/C][C] 0.3995[/C][C] 0.8002[/C][/ROW]
[ROW][C]49[/C][C] 0.02668[/C][C] 0.05335[/C][C] 0.9733[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 8.62e-68[/C][C] 4.31e-68[/C][/ROW]
[ROW][C]51[/C][C] 6.846e-11[/C][C] 1.369e-10[/C][C] 1[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 1.725e-37[/C][C] 8.624e-38[/C][/ROW]
[ROW][C]53[/C][C] 3.079e-42[/C][C] 6.158e-42[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 3.11e-26[/C][C] 6.22e-26[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 1.717e-14[/C][C] 3.433e-14[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 5.091e-22[/C][C] 1.018e-21[/C][C] 1[/C][/ROW]
[ROW][C]57[/C][C] 1[/C][C] 6.064e-13[/C][C] 3.032e-13[/C][/ROW]
[ROW][C]58[/C][C] 8.999e-32[/C][C] 1.8e-31[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 1.591e-14[/C][C] 3.181e-14[/C][C] 1[/C][/ROW]
[ROW][C]60[/C][C] 5.516e-23[/C][C] 1.103e-22[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 1[/C][C] 5.188e-13[/C][C] 2.594e-13[/C][/ROW]
[ROW][C]62[/C][C] 2.734e-42[/C][C] 5.469e-42[/C][C] 1[/C][/ROW]
[ROW][C]63[/C][C] 5.305e-30[/C][C] 1.061e-29[/C][C] 1[/C][/ROW]
[ROW][C]64[/C][C] 3.27e-49[/C][C] 6.54e-49[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 1.068e-19[/C][C] 2.136e-19[/C][C] 1[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 2.509e-07[/C][C] 1.255e-07[/C][/ROW]
[ROW][C]67[/C][C] 0.00194[/C][C] 0.003881[/C][C] 0.9981[/C][/ROW]
[ROW][C]68[/C][C] 5.325e-50[/C][C] 1.065e-49[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 0.4765[/C][C] 0.9531[/C][C] 0.5235[/C][/ROW]
[ROW][C]70[/C][C] 2.246e-54[/C][C] 4.493e-54[/C][C] 1[/C][/ROW]
[ROW][C]71[/C][C] 1[/C][C] 3.389e-09[/C][C] 1.695e-09[/C][/ROW]
[ROW][C]72[/C][C] 0.9999[/C][C] 0.0001234[/C][C] 6.17e-05[/C][/ROW]
[ROW][C]73[/C][C] 1.054e-07[/C][C] 2.108e-07[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 1[/C][C] 3.934e-26[/C][C] 1.967e-26[/C][/ROW]
[ROW][C]75[/C][C] 1[/C][C] 4.898e-78[/C][C] 2.449e-78[/C][/ROW]
[ROW][C]76[/C][C] 3.819e-10[/C][C] 7.637e-10[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 0.3194[/C][C] 0.6388[/C][C] 0.6806[/C][/ROW]
[ROW][C]78[/C][C] 0.8044[/C][C] 0.3913[/C][C] 0.1956[/C][/ROW]
[ROW][C]79[/C][C] 1[/C][C] 4.965e-14[/C][C] 2.482e-14[/C][/ROW]
[ROW][C]80[/C][C] 0.7315[/C][C] 0.537[/C][C] 0.2685[/C][/ROW]
[ROW][C]81[/C][C] 0.1334[/C][C] 0.2668[/C][C] 0.8666[/C][/ROW]
[ROW][C]82[/C][C] 1[/C][C] 3.349e-35[/C][C] 1.675e-35[/C][/ROW]
[ROW][C]83[/C][C] 3.298e-41[/C][C] 6.596e-41[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 1.505e-09[/C][C] 3.01e-09[/C][C] 1[/C][/ROW]
[ROW][C]85[/C][C] 1[/C][C] 9.219e-16[/C][C] 4.609e-16[/C][/ROW]
[ROW][C]86[/C][C] 5.687e-20[/C][C] 1.137e-19[/C][C] 1[/C][/ROW]
[ROW][C]87[/C][C] 1.666e-05[/C][C] 3.332e-05[/C][C] 1[/C][/ROW]
[ROW][C]88[/C][C] 1[/C][C] 1.808e-09[/C][C] 9.038e-10[/C][/ROW]
[ROW][C]89[/C][C] 0.000295[/C][C] 0.0005899[/C][C] 0.9997[/C][/ROW]
[ROW][C]90[/C][C] 1[/C][C] 3.656e-30[/C][C] 1.828e-30[/C][/ROW]
[ROW][C]91[/C][C] 1[/C][C] 1.024e-05[/C][C] 5.12e-06[/C][/ROW]
[ROW][C]92[/C][C] 1.356e-08[/C][C] 2.712e-08[/C][C] 1[/C][/ROW]
[ROW][C]93[/C][C] 1[/C][C] 4.667e-22[/C][C] 2.334e-22[/C][/ROW]
[ROW][C]94[/C][C] 1[/C][C] 1.565e-57[/C][C] 7.827e-58[/C][/ROW]
[ROW][C]95[/C][C] 1[/C][C] 8.442e-20[/C][C] 4.221e-20[/C][/ROW]
[ROW][C]96[/C][C] 1[/C][C] 4.4e-14[/C][C] 2.2e-14[/C][/ROW]
[ROW][C]97[/C][C] 0.005257[/C][C] 0.01051[/C][C] 0.9947[/C][/ROW]
[ROW][C]98[/C][C] 0.01078[/C][C] 0.02155[/C][C] 0.9892[/C][/ROW]
[ROW][C]99[/C][C] 0.9997[/C][C] 0.0006858[/C][C] 0.0003429[/C][/ROW]
[ROW][C]100[/C][C] 1[/C][C] 5.73e-28[/C][C] 2.865e-28[/C][/ROW]
[ROW][C]101[/C][C] 0.5038[/C][C] 0.9925[/C][C] 0.4963[/C][/ROW]
[ROW][C]102[/C][C] 1[/C][C] 9.556e-23[/C][C] 4.778e-23[/C][/ROW]
[ROW][C]103[/C][C] 1[/C][C] 5.169e-06[/C][C] 2.585e-06[/C][/ROW]
[ROW][C]104[/C][C] 1[/C][C] 1.606e-46[/C][C] 8.03e-47[/C][/ROW]
[ROW][C]105[/C][C] 1[/C][C] 2.06e-35[/C][C] 1.03e-35[/C][/ROW]
[ROW][C]106[/C][C] 2.555e-29[/C][C] 5.11e-29[/C][C] 1[/C][/ROW]
[ROW][C]107[/C][C] 0.9363[/C][C] 0.1274[/C][C] 0.06372[/C][/ROW]
[ROW][C]108[/C][C] 1.153e-30[/C][C] 2.305e-30[/C][C] 1[/C][/ROW]
[ROW][C]109[/C][C] 2.494e-23[/C][C] 4.989e-23[/C][C] 1[/C][/ROW]
[ROW][C]110[/C][C] 0.9888[/C][C] 0.02238[/C][C] 0.01119[/C][/ROW]
[ROW][C]111[/C][C] 0.9999[/C][C] 0.000121[/C][C] 6.05e-05[/C][/ROW]
[ROW][C]112[/C][C] 1[/C][C] 1.387e-10[/C][C] 6.936e-11[/C][/ROW]
[ROW][C]113[/C][C] 0.4604[/C][C] 0.9208[/C][C] 0.5396[/C][/ROW]
[ROW][C]114[/C][C] 1.181e-21[/C][C] 2.362e-21[/C][C] 1[/C][/ROW]
[ROW][C]115[/C][C] 1[/C][C] 1.014e-36[/C][C] 5.068e-37[/C][/ROW]
[ROW][C]116[/C][C] 4.986e-26[/C][C] 9.972e-26[/C][C] 1[/C][/ROW]
[ROW][C]117[/C][C] 1[/C][C] 6.162e-09[/C][C] 3.081e-09[/C][/ROW]
[ROW][C]118[/C][C] 0.02855[/C][C] 0.0571[/C][C] 0.9715[/C][/ROW]
[ROW][C]119[/C][C] 1[/C][C] 2.826e-17[/C][C] 1.413e-17[/C][/ROW]
[ROW][C]120[/C][C] 1[/C][C] 1.405e-19[/C][C] 7.024e-20[/C][/ROW]
[ROW][C]121[/C][C] 1[/C][C] 4.912e-09[/C][C] 2.456e-09[/C][/ROW]
[ROW][C]122[/C][C] 1[/C][C] 2.681e-19[/C][C] 1.341e-19[/C][/ROW]
[ROW][C]123[/C][C] 4.172e-18[/C][C] 8.345e-18[/C][C] 1[/C][/ROW]
[ROW][C]124[/C][C] 1[/C][C] 6.066e-15[/C][C] 3.033e-15[/C][/ROW]
[ROW][C]125[/C][C] 1[/C][C] 1.999e-11[/C][C] 9.994e-12[/C][/ROW]
[ROW][C]126[/C][C] 0.9993[/C][C] 0.001326[/C][C] 0.0006631[/C][/ROW]
[ROW][C]127[/C][C] 0.9999[/C][C] 0.0002966[/C][C] 0.0001483[/C][/ROW]
[ROW][C]128[/C][C] 0.9996[/C][C] 0.0007912[/C][C] 0.0003956[/C][/ROW]
[ROW][C]129[/C][C] 1[/C][C] 7.889e-08[/C][C] 3.945e-08[/C][/ROW]
[ROW][C]130[/C][C] 0.993[/C][C] 0.014[/C][C] 0.006998[/C][/ROW]
[ROW][C]131[/C][C] 0.01775[/C][C] 0.0355[/C][C] 0.9823[/C][/ROW]
[ROW][C]132[/C][C] 1[/C][C] 1.442e-07[/C][C] 7.21e-08[/C][/ROW]
[ROW][C]133[/C][C] 0.9846[/C][C] 0.03072[/C][C] 0.01536[/C][/ROW]
[ROW][C]134[/C][C] 1[/C][C] 2.006e-07[/C][C] 1.003e-07[/C][/ROW]
[ROW][C]135[/C][C] 1[/C][C] 5.741e-13[/C][C] 2.87e-13[/C][/ROW]
[ROW][C]136[/C][C] 0.4693[/C][C] 0.9386[/C][C] 0.5307[/C][/ROW]
[ROW][C]137[/C][C] 1[/C][C] 2.043e-19[/C][C] 1.021e-19[/C][/ROW]
[ROW][C]138[/C][C] 1[/C][C] 3.019e-05[/C][C] 1.51e-05[/C][/ROW]
[ROW][C]139[/C][C] 0.4367[/C][C] 0.8734[/C][C] 0.5633[/C][/ROW]
[ROW][C]140[/C][C] 1[/C][C] 1.558e-08[/C][C] 7.792e-09[/C][/ROW]
[ROW][C]141[/C][C] 1[/C][C] 8.606e-09[/C][C] 4.303e-09[/C][/ROW]
[ROW][C]142[/C][C] 1[/C][C] 1.288e-14[/C][C] 6.44e-15[/C][/ROW]
[ROW][C]143[/C][C] 1[/C][C] 7.518e-07[/C][C] 3.759e-07[/C][/ROW]
[ROW][C]144[/C][C] 0.9999[/C][C] 0.000114[/C][C] 5.701e-05[/C][/ROW]
[ROW][C]145[/C][C] 0.9876[/C][C] 0.02476[/C][C] 0.01238[/C][/ROW]
[ROW][C]146[/C][C] 1[/C][C] 8.575e-05[/C][C] 4.287e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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	8.068e-05	0.0001614	0.9999
13	0.0003363	0.0006725	0.9997
14	2.742e-05	5.483e-05	1
15	6.117e-10	1.223e-09	1
16	1.493e-13	2.985e-13	1
17	1.25e-10	2.5e-10	1
18	8.106e-10	1.621e-09	1
19	0.3272	0.6545	0.6728
20	2.138e-08	4.276e-08	1
21	1.968e-23	3.936e-23	1
22	0.002597	0.005193	0.9974
23	4.858e-14	9.717e-14	1
24	9.63e-17	1.926e-16	1
25	0.006112	0.01222	0.9939
26	1.836e-16	3.672e-16	1
27	0.9999	0.0002959	0.0001479
28	1.592e-13	3.184e-13	1
29	1.071e-18	2.143e-18	1
30	1.502e-08	3.004e-08	1
31	0.0267	0.05341	0.9733
32	0.3249	0.6498	0.6751
33	9.294e-17	1.859e-16	1
34	1.184e-24	2.369e-24	1
35	0.1169	0.2337	0.8831
36	7.332e-18	1.466e-17	1
37	4.334e-21	8.669e-21	1
38	1.088e-14	2.176e-14	1
39	1.331e-33	2.662e-33	1
40	8.523e-11	1.705e-10	1
41	7.9e-08	1.58e-07	1
42	0.9668	0.06636	0.03318
43	6.181e-28	1.236e-27	1
44	0.9996	0.0008206	0.0004103
45	1.129e-05	2.257e-05	1
46	1.721e-06	3.443e-06	1
47	4.709e-07	9.419e-07	1
48	0.1998	0.3995	0.8002
49	0.02668	0.05335	0.9733
50	1	8.62e-68	4.31e-68
51	6.846e-11	1.369e-10	1
52	1	1.725e-37	8.624e-38
53	3.079e-42	6.158e-42	1
54	3.11e-26	6.22e-26	1
55	1.717e-14	3.433e-14	1
56	5.091e-22	1.018e-21	1
57	1	6.064e-13	3.032e-13
58	8.999e-32	1.8e-31	1
59	1.591e-14	3.181e-14	1
60	5.516e-23	1.103e-22	1
61	1	5.188e-13	2.594e-13
62	2.734e-42	5.469e-42	1
63	5.305e-30	1.061e-29	1
64	3.27e-49	6.54e-49	1
65	1.068e-19	2.136e-19	1
66	1	2.509e-07	1.255e-07
67	0.00194	0.003881	0.9981
68	5.325e-50	1.065e-49	1
69	0.4765	0.9531	0.5235
70	2.246e-54	4.493e-54	1
71	1	3.389e-09	1.695e-09
72	0.9999	0.0001234	6.17e-05
73	1.054e-07	2.108e-07	1
74	1	3.934e-26	1.967e-26
75	1	4.898e-78	2.449e-78
76	3.819e-10	7.637e-10	1
77	0.3194	0.6388	0.6806
78	0.8044	0.3913	0.1956
79	1	4.965e-14	2.482e-14
80	0.7315	0.537	0.2685
81	0.1334	0.2668	0.8666
82	1	3.349e-35	1.675e-35
83	3.298e-41	6.596e-41	1
84	1.505e-09	3.01e-09	1
85	1	9.219e-16	4.609e-16
86	5.687e-20	1.137e-19	1
87	1.666e-05	3.332e-05	1
88	1	1.808e-09	9.038e-10
89	0.000295	0.0005899	0.9997
90	1	3.656e-30	1.828e-30
91	1	1.024e-05	5.12e-06
92	1.356e-08	2.712e-08	1
93	1	4.667e-22	2.334e-22
94	1	1.565e-57	7.827e-58
95	1	8.442e-20	4.221e-20
96	1	4.4e-14	2.2e-14
97	0.005257	0.01051	0.9947
98	0.01078	0.02155	0.9892
99	0.9997	0.0006858	0.0003429
100	1	5.73e-28	2.865e-28
101	0.5038	0.9925	0.4963
102	1	9.556e-23	4.778e-23
103	1	5.169e-06	2.585e-06
104	1	1.606e-46	8.03e-47
105	1	2.06e-35	1.03e-35
106	2.555e-29	5.11e-29	1
107	0.9363	0.1274	0.06372
108	1.153e-30	2.305e-30	1
109	2.494e-23	4.989e-23	1
110	0.9888	0.02238	0.01119
111	0.9999	0.000121	6.05e-05
112	1	1.387e-10	6.936e-11
113	0.4604	0.9208	0.5396
114	1.181e-21	2.362e-21	1
115	1	1.014e-36	5.068e-37
116	4.986e-26	9.972e-26	1
117	1	6.162e-09	3.081e-09
118	0.02855	0.0571	0.9715
119	1	2.826e-17	1.413e-17
120	1	1.405e-19	7.024e-20
121	1	4.912e-09	2.456e-09
122	1	2.681e-19	1.341e-19
123	4.172e-18	8.345e-18	1
124	1	6.066e-15	3.033e-15
125	1	1.999e-11	9.994e-12
126	0.9993	0.001326	0.0006631
127	0.9999	0.0002966	0.0001483
128	0.9996	0.0007912	0.0003956
129	1	7.889e-08	3.945e-08
130	0.993	0.014	0.006998
131	0.01775	0.0355	0.9823
132	1	1.442e-07	7.21e-08
133	0.9846	0.03072	0.01536
134	1	2.006e-07	1.003e-07
135	1	5.741e-13	2.87e-13
136	0.4693	0.9386	0.5307
137	1	2.043e-19	1.021e-19
138	1	3.019e-05	1.51e-05
139	0.4367	0.8734	0.5633
140	1	1.558e-08	7.792e-09
141	1	8.606e-09	4.303e-09
142	1	1.288e-14	6.44e-15
143	1	7.518e-07	3.759e-07
144	0.9999	0.000114	5.701e-05
145	0.9876	0.02476	0.01238
146	1	8.575e-05	4.287e-05

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	109	0.8074	NOK
5% type I error level	117	0.866667	NOK
10% type I error level	121	0.896296	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 & 109 &  0.8074 & NOK \tabularnewline
5% type I error level & 117 & 0.866667 & NOK \tabularnewline
10% type I error level & 121 & 0.896296 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&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]109[/C][C] 0.8074[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]117[/C][C]0.866667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]121[/C][C]0.896296[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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	109	0.8074	NOK
5% type I error level	117	0.866667	NOK
10% type I error level	121	0.896296	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.18051, df1 = 2, df2 = 147, p-value = 0.835
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.933, df1 = 16, df2 = 133, p-value = 0.0003642
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.137, df1 = 2, df2 = 147, p-value = 0.3236

\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 = 0.18051, df1 = 2, df2 = 147, p-value = 0.835
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.933, df1 = 16, df2 = 133, p-value = 0.0003642
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.137, df1 = 2, df2 = 147, p-value = 0.3236
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&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 = 0.18051, df1 = 2, df2 = 147, p-value = 0.835
[/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 = 2.933, df1 = 16, df2 = 133, p-value = 0.0003642
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.137, df1 = 2, df2 = 147, p-value = 0.3236
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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 = 0.18051, df1 = 2, df2 = 147, p-value = 0.835
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.933, df1 = 16, df2 = 133, p-value = 0.0003642
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.137, df1 = 2, df2 = 147, p-value = 0.3236

Variance Inflation Factors (Multicollinearity)
> vif SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 2.379854 2.213996 1.839023 1.998239 1.736424 ITHSUM SKEOUSUM Bevr_Leeftijd 1.092119 7.165353 1.063134

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       SKEOU2        SKEOU3        SKEOU4        SKEOU5        SKEOU6 
     2.379854      2.213996      1.839023      1.998239      1.736424 
       ITHSUM      SKEOUSUM Bevr_Leeftijd 
     1.092119      7.165353      1.063134 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306615&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       SKEOU2        SKEOU3        SKEOU4        SKEOU5        SKEOU6 
     2.379854      2.213996      1.839023      1.998239      1.736424 
       ITHSUM      SKEOUSUM Bevr_Leeftijd 
     1.092119      7.165353      1.063134 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306615&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306615&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 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 2.379854 2.213996 1.839023 1.998239 1.736424 ITHSUM SKEOUSUM Bevr_Leeftijd 1.092119 7.165353 1.063134

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 = ; 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