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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 09:41:02 +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/23/t1485160886fu0jaghi7dad17k.htm/, Retrieved Thu, 16 May 2024 01:39:03 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Thu, 16 May 2024 01:39:03 +0200

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

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

Multiple Linear Regression - Estimated Regression Equation
SKEOU6[t] = + 4.77212e-14 + 4.68232e-16Bevr_Leeftijd[t] + 2.82742e-16TVDC[t] + 1SKEOUSUM[t] -1SKEOU1[t] -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SKEOU6[t] =  +  4.77212e-14 +  4.68232e-16Bevr_Leeftijd[t] +  2.82742e-16TVDC[t] +  1SKEOUSUM[t] -1SKEOU1[t] -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SKEOU6[t] =  +  4.77212e-14 +  4.68232e-16Bevr_Leeftijd[t] +  2.82742e-16TVDC[t] +  1SKEOUSUM[t] -1SKEOU1[t] -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
SKEOU6[t] = + 4.77212e-14 + 4.68232e-16Bevr_Leeftijd[t] + 2.82742e-16TVDC[t] + 1SKEOUSUM[t] -1SKEOU1[t] -1SKEOU2[t] -1SKEOU3[t] -1SKEOU4[t] -1SKEOU5[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)	+4.772e-14	3.19e-14	+1.4960e+00	0.138	0.06902
Bevr_Leeftijd	+4.682e-16	1.046e-15	+4.4760e-01	0.6554	0.3277
TVDC	+2.827e-16	1.157e-15	+2.4430e-01	0.8075	0.4038
SKEOUSUM	+1	2.916e-15	+3.4290e+14	0	0
SKEOU1	-1	3.857e-15	-2.5930e+14	0	0
SKEOU2	-1	4.275e-15	-2.3390e+14	0	0
SKEOU3	-1	3.973e-15	-2.5170e+14	0	0
SKEOU4	-1	4.298e-15	-2.3270e+14	0	0
SKEOU5	-1	4.208e-15	-2.3770e+14	0	0

\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) & +4.772e-14 &  3.19e-14 & +1.4960e+00 &  0.138 &  0.06902 \tabularnewline
Bevr_Leeftijd & +4.682e-16 &  1.046e-15 & +4.4760e-01 &  0.6554 &  0.3277 \tabularnewline
TVDC & +2.827e-16 &  1.157e-15 & +2.4430e-01 &  0.8075 &  0.4038 \tabularnewline
SKEOUSUM & +1 &  2.916e-15 & +3.4290e+14 &  0 &  0 \tabularnewline
SKEOU1 & -1 &  3.857e-15 & -2.5930e+14 &  0 &  0 \tabularnewline
SKEOU2 & -1 &  4.275e-15 & -2.3390e+14 &  0 &  0 \tabularnewline
SKEOU3 & -1 &  3.973e-15 & -2.5170e+14 &  0 &  0 \tabularnewline
SKEOU4 & -1 &  4.298e-15 & -2.3270e+14 &  0 &  0 \tabularnewline
SKEOU5 & -1 &  4.208e-15 & -2.3770e+14 &  0 &  0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+4.772e-14[/C][C] 3.19e-14[/C][C]+1.4960e+00[/C][C] 0.138[/C][C] 0.06902[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+4.682e-16[/C][C] 1.046e-15[/C][C]+4.4760e-01[/C][C] 0.6554[/C][C] 0.3277[/C][/ROW]
[ROW][C]TVDC[/C][C]+2.827e-16[/C][C] 1.157e-15[/C][C]+2.4430e-01[/C][C] 0.8075[/C][C] 0.4038[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+1[/C][C] 2.916e-15[/C][C]+3.4290e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU1[/C][C]-1[/C][C] 3.857e-15[/C][C]-2.5930e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-1[/C][C] 4.275e-15[/C][C]-2.3390e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-1[/C][C] 3.973e-15[/C][C]-2.5170e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU4[/C][C]-1[/C][C] 4.298e-15[/C][C]-2.3270e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU5[/C][C]-1[/C][C] 4.208e-15[/C][C]-2.3770e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	+4.772e-14	3.19e-14	+1.4960e+00	0.138	0.06902
Bevr_Leeftijd	+4.682e-16	1.046e-15	+4.4760e-01	0.6554	0.3277
TVDC	+2.827e-16	1.157e-15	+2.4430e-01	0.8075	0.4038
SKEOUSUM	+1	2.916e-15	+3.4290e+14	0	0
SKEOU1	-1	3.857e-15	-2.5930e+14	0	0
SKEOU2	-1	4.275e-15	-2.3390e+14	0	0
SKEOU3	-1	3.973e-15	-2.5170e+14	0	0
SKEOU4	-1	4.298e-15	-2.3270e+14	0	0
SKEOU5	-1	4.208e-15	-2.3770e+14	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	1.541e+28
F-TEST (DF numerator)	8
F-TEST (DF denominator)	93
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.721e-14
Sum Squared Residuals	2.754e-26

\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) &  1.541e+28 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 93 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.721e-14 \tabularnewline
Sum Squared Residuals &  2.754e-26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 1.541e+28[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]93[/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] 1.721e-14[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.754e-26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	1.541e+28
F-TEST (DF numerator)	8
F-TEST (DF denominator)	93
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.721e-14
Sum Squared Residuals	2.754e-26

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	4	1.551e-13
2	4	4	-1.662e-14
3	4	4	-4.944e-15
4	5	5	-8.204e-15
5	5	5	6.396e-15
6	4	4	-3.675e-15
7	4	4	-7.488e-15
8	5	5	1.603e-15
9	5	5	-4.808e-15
10	5	5	8.766e-15
11	5	5	3.594e-15
12	5	5	1.098e-15
13	5	5	7.644e-16
14	5	5	1.312e-16
15	4	4	9.299e-16
16	5	5	1.735e-15
17	4	4	-2.289e-15
18	4	4	-8.117e-15
19	5	5	7.038e-15
20	5	5	-6.76e-16
21	4	4	-7.333e-15
22	4	4	8.78e-15
23	5	5	-7.155e-15
24	4	4	-5.312e-15
25	4	4	3.727e-16
26	5	5	4.608e-15
27	4	4	-3.379e-15
28	5	5	-6.285e-16
29	4	4	-3.935e-15
30	5	5	-1.053e-15
31	4	4	1.124e-14
32	4	4	-4.198e-15
33	4	4	7.503e-15
34	5	5	-8.124e-15
35	5	5	-7.606e-15
36	4	4	-1.031e-15
37	3	3	1.016e-15
38	4	4	-5.08e-15
39	5	5	1.908e-15
40	5	5	4.528e-15
41	5	5	-8.793e-16
42	4	4	8.692e-15
43	5	5	-1.341e-15
44	4	4	-1.569e-14
45	3	3	-9.256e-15
46	4	4	-6.897e-15
47	5	5	-8.952e-16
48	5	5	-1.596e-15
49	4	4	2.341e-15
50	4	4	-3.538e-15
51	5	5	-2.432e-15
52	5	5	-3.282e-15
53	4	4	-9.127e-15
54	4	4	-5.186e-15
55	5	5	4.24e-16
56	4	4	-1.81e-15
57	5	5	3.965e-15
58	5	5	-2.597e-15
59	4	4	-1.555e-14
60	5	5	2.369e-16
61	3	3	2.57e-15
62	5	5	7.644e-16
63	5	5	6.359e-16
64	5	5	2.228e-15
65	5	5	6.402e-15
66	5	5	8.841e-16
67	5	5	-1.963e-15
68	4	4	-1.05e-15
69	5	5	-9.834e-15
70	4	4	1.303e-15
71	4	4	4.689e-16
72	5	5	-3.028e-15
73	3	3	-7.785e-16
74	4	4	-2.63e-15
75	5	5	2.387e-15
76	4	4	-4.437e-15
77	4	4	1.629e-15
78	4	4	-4.349e-15
79	4	4	-1.933e-14
80	4	4	-9.568e-15
81	4	4	-5.763e-15
82	4	4	3e-15
83	4	4	6.344e-15
84	4	4	2.52e-15
85	5	5	4.608e-15
86	4	4	-1.009e-15
87	4	4	4.394e-16
88	5	5	-4.748e-16
89	5	5	-8.219e-16
90	4	4	-1.038e-14
91	4	4	-8.749e-15
92	3	3	3.697e-15
93	4	4	-2.26e-15
94	5	5	4.268e-15
95	5	5	-1.024e-15
96	4	4	-5.102e-15
97	5	5	3.612e-15
98	5	5	-1.067e-14
99	3	3	-1.61e-15
100	4	4	7.941e-15
101	5	5	2.147e-15
102	4	4	-4.046e-15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  4 &  4 &  1.551e-13 \tabularnewline
2 &  4 &  4 & -1.662e-14 \tabularnewline
3 &  4 &  4 & -4.944e-15 \tabularnewline
4 &  5 &  5 & -8.204e-15 \tabularnewline
5 &  5 &  5 &  6.396e-15 \tabularnewline
6 &  4 &  4 & -3.675e-15 \tabularnewline
7 &  4 &  4 & -7.488e-15 \tabularnewline
8 &  5 &  5 &  1.603e-15 \tabularnewline
9 &  5 &  5 & -4.808e-15 \tabularnewline
10 &  5 &  5 &  8.766e-15 \tabularnewline
11 &  5 &  5 &  3.594e-15 \tabularnewline
12 &  5 &  5 &  1.098e-15 \tabularnewline
13 &  5 &  5 &  7.644e-16 \tabularnewline
14 &  5 &  5 &  1.312e-16 \tabularnewline
15 &  4 &  4 &  9.299e-16 \tabularnewline
16 &  5 &  5 &  1.735e-15 \tabularnewline
17 &  4 &  4 & -2.289e-15 \tabularnewline
18 &  4 &  4 & -8.117e-15 \tabularnewline
19 &  5 &  5 &  7.038e-15 \tabularnewline
20 &  5 &  5 & -6.76e-16 \tabularnewline
21 &  4 &  4 & -7.333e-15 \tabularnewline
22 &  4 &  4 &  8.78e-15 \tabularnewline
23 &  5 &  5 & -7.155e-15 \tabularnewline
24 &  4 &  4 & -5.312e-15 \tabularnewline
25 &  4 &  4 &  3.727e-16 \tabularnewline
26 &  5 &  5 &  4.608e-15 \tabularnewline
27 &  4 &  4 & -3.379e-15 \tabularnewline
28 &  5 &  5 & -6.285e-16 \tabularnewline
29 &  4 &  4 & -3.935e-15 \tabularnewline
30 &  5 &  5 & -1.053e-15 \tabularnewline
31 &  4 &  4 &  1.124e-14 \tabularnewline
32 &  4 &  4 & -4.198e-15 \tabularnewline
33 &  4 &  4 &  7.503e-15 \tabularnewline
34 &  5 &  5 & -8.124e-15 \tabularnewline
35 &  5 &  5 & -7.606e-15 \tabularnewline
36 &  4 &  4 & -1.031e-15 \tabularnewline
37 &  3 &  3 &  1.016e-15 \tabularnewline
38 &  4 &  4 & -5.08e-15 \tabularnewline
39 &  5 &  5 &  1.908e-15 \tabularnewline
40 &  5 &  5 &  4.528e-15 \tabularnewline
41 &  5 &  5 & -8.793e-16 \tabularnewline
42 &  4 &  4 &  8.692e-15 \tabularnewline
43 &  5 &  5 & -1.341e-15 \tabularnewline
44 &  4 &  4 & -1.569e-14 \tabularnewline
45 &  3 &  3 & -9.256e-15 \tabularnewline
46 &  4 &  4 & -6.897e-15 \tabularnewline
47 &  5 &  5 & -8.952e-16 \tabularnewline
48 &  5 &  5 & -1.596e-15 \tabularnewline
49 &  4 &  4 &  2.341e-15 \tabularnewline
50 &  4 &  4 & -3.538e-15 \tabularnewline
51 &  5 &  5 & -2.432e-15 \tabularnewline
52 &  5 &  5 & -3.282e-15 \tabularnewline
53 &  4 &  4 & -9.127e-15 \tabularnewline
54 &  4 &  4 & -5.186e-15 \tabularnewline
55 &  5 &  5 &  4.24e-16 \tabularnewline
56 &  4 &  4 & -1.81e-15 \tabularnewline
57 &  5 &  5 &  3.965e-15 \tabularnewline
58 &  5 &  5 & -2.597e-15 \tabularnewline
59 &  4 &  4 & -1.555e-14 \tabularnewline
60 &  5 &  5 &  2.369e-16 \tabularnewline
61 &  3 &  3 &  2.57e-15 \tabularnewline
62 &  5 &  5 &  7.644e-16 \tabularnewline
63 &  5 &  5 &  6.359e-16 \tabularnewline
64 &  5 &  5 &  2.228e-15 \tabularnewline
65 &  5 &  5 &  6.402e-15 \tabularnewline
66 &  5 &  5 &  8.841e-16 \tabularnewline
67 &  5 &  5 & -1.963e-15 \tabularnewline
68 &  4 &  4 & -1.05e-15 \tabularnewline
69 &  5 &  5 & -9.834e-15 \tabularnewline
70 &  4 &  4 &  1.303e-15 \tabularnewline
71 &  4 &  4 &  4.689e-16 \tabularnewline
72 &  5 &  5 & -3.028e-15 \tabularnewline
73 &  3 &  3 & -7.785e-16 \tabularnewline
74 &  4 &  4 & -2.63e-15 \tabularnewline
75 &  5 &  5 &  2.387e-15 \tabularnewline
76 &  4 &  4 & -4.437e-15 \tabularnewline
77 &  4 &  4 &  1.629e-15 \tabularnewline
78 &  4 &  4 & -4.349e-15 \tabularnewline
79 &  4 &  4 & -1.933e-14 \tabularnewline
80 &  4 &  4 & -9.568e-15 \tabularnewline
81 &  4 &  4 & -5.763e-15 \tabularnewline
82 &  4 &  4 &  3e-15 \tabularnewline
83 &  4 &  4 &  6.344e-15 \tabularnewline
84 &  4 &  4 &  2.52e-15 \tabularnewline
85 &  5 &  5 &  4.608e-15 \tabularnewline
86 &  4 &  4 & -1.009e-15 \tabularnewline
87 &  4 &  4 &  4.394e-16 \tabularnewline
88 &  5 &  5 & -4.748e-16 \tabularnewline
89 &  5 &  5 & -8.219e-16 \tabularnewline
90 &  4 &  4 & -1.038e-14 \tabularnewline
91 &  4 &  4 & -8.749e-15 \tabularnewline
92 &  3 &  3 &  3.697e-15 \tabularnewline
93 &  4 &  4 & -2.26e-15 \tabularnewline
94 &  5 &  5 &  4.268e-15 \tabularnewline
95 &  5 &  5 & -1.024e-15 \tabularnewline
96 &  4 &  4 & -5.102e-15 \tabularnewline
97 &  5 &  5 &  3.612e-15 \tabularnewline
98 &  5 &  5 & -1.067e-14 \tabularnewline
99 &  3 &  3 & -1.61e-15 \tabularnewline
100 &  4 &  4 &  7.941e-15 \tabularnewline
101 &  5 &  5 &  2.147e-15 \tabularnewline
102 &  4 &  4 & -4.046e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 1.551e-13[/C][/ROW]
[ROW][C]2[/C][C] 4[/C][C] 4[/C][C]-1.662e-14[/C][/ROW]
[ROW][C]3[/C][C] 4[/C][C] 4[/C][C]-4.944e-15[/C][/ROW]
[ROW][C]4[/C][C] 5[/C][C] 5[/C][C]-8.204e-15[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 5[/C][C] 6.396e-15[/C][/ROW]
[ROW][C]6[/C][C] 4[/C][C] 4[/C][C]-3.675e-15[/C][/ROW]
[ROW][C]7[/C][C] 4[/C][C] 4[/C][C]-7.488e-15[/C][/ROW]
[ROW][C]8[/C][C] 5[/C][C] 5[/C][C] 1.603e-15[/C][/ROW]
[ROW][C]9[/C][C] 5[/C][C] 5[/C][C]-4.808e-15[/C][/ROW]
[ROW][C]10[/C][C] 5[/C][C] 5[/C][C] 8.766e-15[/C][/ROW]
[ROW][C]11[/C][C] 5[/C][C] 5[/C][C] 3.594e-15[/C][/ROW]
[ROW][C]12[/C][C] 5[/C][C] 5[/C][C] 1.098e-15[/C][/ROW]
[ROW][C]13[/C][C] 5[/C][C] 5[/C][C] 7.644e-16[/C][/ROW]
[ROW][C]14[/C][C] 5[/C][C] 5[/C][C] 1.312e-16[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 4[/C][C] 9.299e-16[/C][/ROW]
[ROW][C]16[/C][C] 5[/C][C] 5[/C][C] 1.735e-15[/C][/ROW]
[ROW][C]17[/C][C] 4[/C][C] 4[/C][C]-2.289e-15[/C][/ROW]
[ROW][C]18[/C][C] 4[/C][C] 4[/C][C]-8.117e-15[/C][/ROW]
[ROW][C]19[/C][C] 5[/C][C] 5[/C][C] 7.038e-15[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 5[/C][C]-6.76e-16[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 4[/C][C]-7.333e-15[/C][/ROW]
[ROW][C]22[/C][C] 4[/C][C] 4[/C][C] 8.78e-15[/C][/ROW]
[ROW][C]23[/C][C] 5[/C][C] 5[/C][C]-7.155e-15[/C][/ROW]
[ROW][C]24[/C][C] 4[/C][C] 4[/C][C]-5.312e-15[/C][/ROW]
[ROW][C]25[/C][C] 4[/C][C] 4[/C][C] 3.727e-16[/C][/ROW]
[ROW][C]26[/C][C] 5[/C][C] 5[/C][C] 4.608e-15[/C][/ROW]
[ROW][C]27[/C][C] 4[/C][C] 4[/C][C]-3.379e-15[/C][/ROW]
[ROW][C]28[/C][C] 5[/C][C] 5[/C][C]-6.285e-16[/C][/ROW]
[ROW][C]29[/C][C] 4[/C][C] 4[/C][C]-3.935e-15[/C][/ROW]
[ROW][C]30[/C][C] 5[/C][C] 5[/C][C]-1.053e-15[/C][/ROW]
[ROW][C]31[/C][C] 4[/C][C] 4[/C][C] 1.124e-14[/C][/ROW]
[ROW][C]32[/C][C] 4[/C][C] 4[/C][C]-4.198e-15[/C][/ROW]
[ROW][C]33[/C][C] 4[/C][C] 4[/C][C] 7.503e-15[/C][/ROW]
[ROW][C]34[/C][C] 5[/C][C] 5[/C][C]-8.124e-15[/C][/ROW]
[ROW][C]35[/C][C] 5[/C][C] 5[/C][C]-7.606e-15[/C][/ROW]
[ROW][C]36[/C][C] 4[/C][C] 4[/C][C]-1.031e-15[/C][/ROW]
[ROW][C]37[/C][C] 3[/C][C] 3[/C][C] 1.016e-15[/C][/ROW]
[ROW][C]38[/C][C] 4[/C][C] 4[/C][C]-5.08e-15[/C][/ROW]
[ROW][C]39[/C][C] 5[/C][C] 5[/C][C] 1.908e-15[/C][/ROW]
[ROW][C]40[/C][C] 5[/C][C] 5[/C][C] 4.528e-15[/C][/ROW]
[ROW][C]41[/C][C] 5[/C][C] 5[/C][C]-8.793e-16[/C][/ROW]
[ROW][C]42[/C][C] 4[/C][C] 4[/C][C] 8.692e-15[/C][/ROW]
[ROW][C]43[/C][C] 5[/C][C] 5[/C][C]-1.341e-15[/C][/ROW]
[ROW][C]44[/C][C] 4[/C][C] 4[/C][C]-1.569e-14[/C][/ROW]
[ROW][C]45[/C][C] 3[/C][C] 3[/C][C]-9.256e-15[/C][/ROW]
[ROW][C]46[/C][C] 4[/C][C] 4[/C][C]-6.897e-15[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 5[/C][C]-8.952e-16[/C][/ROW]
[ROW][C]48[/C][C] 5[/C][C] 5[/C][C]-1.596e-15[/C][/ROW]
[ROW][C]49[/C][C] 4[/C][C] 4[/C][C] 2.341e-15[/C][/ROW]
[ROW][C]50[/C][C] 4[/C][C] 4[/C][C]-3.538e-15[/C][/ROW]
[ROW][C]51[/C][C] 5[/C][C] 5[/C][C]-2.432e-15[/C][/ROW]
[ROW][C]52[/C][C] 5[/C][C] 5[/C][C]-3.282e-15[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 4[/C][C]-9.127e-15[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 4[/C][C]-5.186e-15[/C][/ROW]
[ROW][C]55[/C][C] 5[/C][C] 5[/C][C] 4.24e-16[/C][/ROW]
[ROW][C]56[/C][C] 4[/C][C] 4[/C][C]-1.81e-15[/C][/ROW]
[ROW][C]57[/C][C] 5[/C][C] 5[/C][C] 3.965e-15[/C][/ROW]
[ROW][C]58[/C][C] 5[/C][C] 5[/C][C]-2.597e-15[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 4[/C][C]-1.555e-14[/C][/ROW]
[ROW][C]60[/C][C] 5[/C][C] 5[/C][C] 2.369e-16[/C][/ROW]
[ROW][C]61[/C][C] 3[/C][C] 3[/C][C] 2.57e-15[/C][/ROW]
[ROW][C]62[/C][C] 5[/C][C] 5[/C][C] 7.644e-16[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 5[/C][C] 6.359e-16[/C][/ROW]
[ROW][C]64[/C][C] 5[/C][C] 5[/C][C] 2.228e-15[/C][/ROW]
[ROW][C]65[/C][C] 5[/C][C] 5[/C][C] 6.402e-15[/C][/ROW]
[ROW][C]66[/C][C] 5[/C][C] 5[/C][C] 8.841e-16[/C][/ROW]
[ROW][C]67[/C][C] 5[/C][C] 5[/C][C]-1.963e-15[/C][/ROW]
[ROW][C]68[/C][C] 4[/C][C] 4[/C][C]-1.05e-15[/C][/ROW]
[ROW][C]69[/C][C] 5[/C][C] 5[/C][C]-9.834e-15[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 4[/C][C] 1.303e-15[/C][/ROW]
[ROW][C]71[/C][C] 4[/C][C] 4[/C][C] 4.689e-16[/C][/ROW]
[ROW][C]72[/C][C] 5[/C][C] 5[/C][C]-3.028e-15[/C][/ROW]
[ROW][C]73[/C][C] 3[/C][C] 3[/C][C]-7.785e-16[/C][/ROW]
[ROW][C]74[/C][C] 4[/C][C] 4[/C][C]-2.63e-15[/C][/ROW]
[ROW][C]75[/C][C] 5[/C][C] 5[/C][C] 2.387e-15[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 4[/C][C]-4.437e-15[/C][/ROW]
[ROW][C]77[/C][C] 4[/C][C] 4[/C][C] 1.629e-15[/C][/ROW]
[ROW][C]78[/C][C] 4[/C][C] 4[/C][C]-4.349e-15[/C][/ROW]
[ROW][C]79[/C][C] 4[/C][C] 4[/C][C]-1.933e-14[/C][/ROW]
[ROW][C]80[/C][C] 4[/C][C] 4[/C][C]-9.568e-15[/C][/ROW]
[ROW][C]81[/C][C] 4[/C][C] 4[/C][C]-5.763e-15[/C][/ROW]
[ROW][C]82[/C][C] 4[/C][C] 4[/C][C] 3e-15[/C][/ROW]
[ROW][C]83[/C][C] 4[/C][C] 4[/C][C] 6.344e-15[/C][/ROW]
[ROW][C]84[/C][C] 4[/C][C] 4[/C][C] 2.52e-15[/C][/ROW]
[ROW][C]85[/C][C] 5[/C][C] 5[/C][C] 4.608e-15[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 4[/C][C]-1.009e-15[/C][/ROW]
[ROW][C]87[/C][C] 4[/C][C] 4[/C][C] 4.394e-16[/C][/ROW]
[ROW][C]88[/C][C] 5[/C][C] 5[/C][C]-4.748e-16[/C][/ROW]
[ROW][C]89[/C][C] 5[/C][C] 5[/C][C]-8.219e-16[/C][/ROW]
[ROW][C]90[/C][C] 4[/C][C] 4[/C][C]-1.038e-14[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 4[/C][C]-8.749e-15[/C][/ROW]
[ROW][C]92[/C][C] 3[/C][C] 3[/C][C] 3.697e-15[/C][/ROW]
[ROW][C]93[/C][C] 4[/C][C] 4[/C][C]-2.26e-15[/C][/ROW]
[ROW][C]94[/C][C] 5[/C][C] 5[/C][C] 4.268e-15[/C][/ROW]
[ROW][C]95[/C][C] 5[/C][C] 5[/C][C]-1.024e-15[/C][/ROW]
[ROW][C]96[/C][C] 4[/C][C] 4[/C][C]-5.102e-15[/C][/ROW]
[ROW][C]97[/C][C] 5[/C][C] 5[/C][C] 3.612e-15[/C][/ROW]
[ROW][C]98[/C][C] 5[/C][C] 5[/C][C]-1.067e-14[/C][/ROW]
[ROW][C]99[/C][C] 3[/C][C] 3[/C][C]-1.61e-15[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 4[/C][C] 7.941e-15[/C][/ROW]
[ROW][C]101[/C][C] 5[/C][C] 5[/C][C] 2.147e-15[/C][/ROW]
[ROW][C]102[/C][C] 4[/C][C] 4[/C][C]-4.046e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	1.551e-13
2	4	4	-1.662e-14
3	4	4	-4.944e-15
4	5	5	-8.204e-15
5	5	5	6.396e-15
6	4	4	-3.675e-15
7	4	4	-7.488e-15
8	5	5	1.603e-15
9	5	5	-4.808e-15
10	5	5	8.766e-15
11	5	5	3.594e-15
12	5	5	1.098e-15
13	5	5	7.644e-16
14	5	5	1.312e-16
15	4	4	9.299e-16
16	5	5	1.735e-15
17	4	4	-2.289e-15
18	4	4	-8.117e-15
19	5	5	7.038e-15
20	5	5	-6.76e-16
21	4	4	-7.333e-15
22	4	4	8.78e-15
23	5	5	-7.155e-15
24	4	4	-5.312e-15
25	4	4	3.727e-16
26	5	5	4.608e-15
27	4	4	-3.379e-15
28	5	5	-6.285e-16
29	4	4	-3.935e-15
30	5	5	-1.053e-15
31	4	4	1.124e-14
32	4	4	-4.198e-15
33	4	4	7.503e-15
34	5	5	-8.124e-15
35	5	5	-7.606e-15
36	4	4	-1.031e-15
37	3	3	1.016e-15
38	4	4	-5.08e-15
39	5	5	1.908e-15
40	5	5	4.528e-15
41	5	5	-8.793e-16
42	4	4	8.692e-15
43	5	5	-1.341e-15
44	4	4	-1.569e-14
45	3	3	-9.256e-15
46	4	4	-6.897e-15
47	5	5	-8.952e-16
48	5	5	-1.596e-15
49	4	4	2.341e-15
50	4	4	-3.538e-15
51	5	5	-2.432e-15
52	5	5	-3.282e-15
53	4	4	-9.127e-15
54	4	4	-5.186e-15
55	5	5	4.24e-16
56	4	4	-1.81e-15
57	5	5	3.965e-15
58	5	5	-2.597e-15
59	4	4	-1.555e-14
60	5	5	2.369e-16
61	3	3	2.57e-15
62	5	5	7.644e-16
63	5	5	6.359e-16
64	5	5	2.228e-15
65	5	5	6.402e-15
66	5	5	8.841e-16
67	5	5	-1.963e-15
68	4	4	-1.05e-15
69	5	5	-9.834e-15
70	4	4	1.303e-15
71	4	4	4.689e-16
72	5	5	-3.028e-15
73	3	3	-7.785e-16
74	4	4	-2.63e-15
75	5	5	2.387e-15
76	4	4	-4.437e-15
77	4	4	1.629e-15
78	4	4	-4.349e-15
79	4	4	-1.933e-14
80	4	4	-9.568e-15
81	4	4	-5.763e-15
82	4	4	3e-15
83	4	4	6.344e-15
84	4	4	2.52e-15
85	5	5	4.608e-15
86	4	4	-1.009e-15
87	4	4	4.394e-16
88	5	5	-4.748e-16
89	5	5	-8.219e-16
90	4	4	-1.038e-14
91	4	4	-8.749e-15
92	3	3	3.697e-15
93	4	4	-2.26e-15
94	5	5	4.268e-15
95	5	5	-1.024e-15
96	4	4	-5.102e-15
97	5	5	3.612e-15
98	5	5	-1.067e-14
99	3	3	-1.61e-15
100	4	4	7.941e-15
101	5	5	2.147e-15
102	4	4	-4.046e-15

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.0296	0.05919	0.9704
13	0.9525	0.09501	0.0475
14	0.5629	0.8743	0.4371
15	0.00199	0.003979	0.998
16	7.659e-10	1.532e-09	1
17	1.192e-05	2.383e-05	1
18	1	3.023e-05	1.512e-05
19	0.004147	0.008294	0.9959
20	3.474e-05	6.948e-05	1
21	0.04374	0.08747	0.9563
22	0.9978	0.004486	0.002243
23	0.004501	0.009002	0.9955
24	0.1628	0.3257	0.8372
25	6.687e-10	1.337e-09	1
26	0.99	0.01991	0.009953
27	1.416e-11	2.831e-11	1
28	3.225e-06	6.449e-06	1
29	1	4.686e-11	2.343e-11
30	0.07539	0.1508	0.9246
31	0.0002471	0.0004941	0.9998
32	1	1.311e-39	6.555e-40
33	0.428	0.8559	0.572
34	1.315e-13	2.629e-13	1
35	0.0544	0.1088	0.9456
36	9.985e-18	1.997e-17	1
37	1	1.638e-23	8.189e-24
38	1	2.191e-59	1.095e-59
39	0.009095	0.01819	0.9909
40	0.9999	0.0001297	6.487e-05
41	0.1103	0.2206	0.8897
42	0.9973	0.005334	0.002667
43	0.9997	0.0006006	0.0003003
44	1	1.56e-06	7.8e-07
45	1	1.148e-31	5.739e-32
46	1	1.508e-19	7.542e-20
47	1	6.795e-16	3.398e-16
48	1	1.375e-19	6.876e-20
49	1	9.365e-22	4.682e-22
50	1	6.928e-15	3.464e-15
51	0.953	0.09399	0.04699
52	2.189e-09	4.377e-09	1
53	6.274e-35	1.255e-34	1
54	1.572e-15	3.143e-15	1
55	7.197e-09	1.439e-08	1
56	0.0001213	0.0002426	0.9999
57	3.536e-24	7.071e-24	1
58	1.401e-09	2.803e-09	1
59	1	1.624e-12	8.119e-13
60	1.052e-13	2.103e-13	1
61	9.232e-13	1.846e-12	1
62	0.006522	0.01304	0.9935
63	0.01239	0.02477	0.9876
64	6.189e-30	1.238e-29	1
65	0.9925	0.01507	0.007534
66	1	3.138e-18	1.569e-18
67	1.791e-29	3.582e-29	1
68	0.0001758	0.0003515	0.9998
69	0.0189	0.0378	0.9811
70	0.8193	0.3613	0.1807
71	0.9999	0.0001033	5.167e-05
72	0.9871	0.02573	0.01286
73	0.006947	0.01389	0.9931
74	0.9887	0.02256	0.01128
75	0.999	0.001941	0.0009704
76	4.014e-18	8.028e-18	1
77	0.3127	0.6254	0.6873
78	1	3.449e-10	1.725e-10
79	3.336e-10	6.672e-10	1
80	8.741e-11	1.748e-10	1
81	3.212e-12	6.424e-12	1
82	1	2.127e-05	1.063e-05
83	0.9992	0.001563	0.0007817
84	0.6013	0.7975	0.3987
85	1	2.42e-08	1.21e-08
86	1	1.836e-08	9.181e-09
87	0.9992	0.001666	0.0008328
88	1	8.432e-06	4.216e-06
89	0.3818	0.7637	0.6182
90	1	2.08e-06	1.04e-06

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.0296 &  0.05919 &  0.9704 \tabularnewline
13 &  0.9525 &  0.09501 &  0.0475 \tabularnewline
14 &  0.5629 &  0.8743 &  0.4371 \tabularnewline
15 &  0.00199 &  0.003979 &  0.998 \tabularnewline
16 &  7.659e-10 &  1.532e-09 &  1 \tabularnewline
17 &  1.192e-05 &  2.383e-05 &  1 \tabularnewline
18 &  1 &  3.023e-05 &  1.512e-05 \tabularnewline
19 &  0.004147 &  0.008294 &  0.9959 \tabularnewline
20 &  3.474e-05 &  6.948e-05 &  1 \tabularnewline
21 &  0.04374 &  0.08747 &  0.9563 \tabularnewline
22 &  0.9978 &  0.004486 &  0.002243 \tabularnewline
23 &  0.004501 &  0.009002 &  0.9955 \tabularnewline
24 &  0.1628 &  0.3257 &  0.8372 \tabularnewline
25 &  6.687e-10 &  1.337e-09 &  1 \tabularnewline
26 &  0.99 &  0.01991 &  0.009953 \tabularnewline
27 &  1.416e-11 &  2.831e-11 &  1 \tabularnewline
28 &  3.225e-06 &  6.449e-06 &  1 \tabularnewline
29 &  1 &  4.686e-11 &  2.343e-11 \tabularnewline
30 &  0.07539 &  0.1508 &  0.9246 \tabularnewline
31 &  0.0002471 &  0.0004941 &  0.9998 \tabularnewline
32 &  1 &  1.311e-39 &  6.555e-40 \tabularnewline
33 &  0.428 &  0.8559 &  0.572 \tabularnewline
34 &  1.315e-13 &  2.629e-13 &  1 \tabularnewline
35 &  0.0544 &  0.1088 &  0.9456 \tabularnewline
36 &  9.985e-18 &  1.997e-17 &  1 \tabularnewline
37 &  1 &  1.638e-23 &  8.189e-24 \tabularnewline
38 &  1 &  2.191e-59 &  1.095e-59 \tabularnewline
39 &  0.009095 &  0.01819 &  0.9909 \tabularnewline
40 &  0.9999 &  0.0001297 &  6.487e-05 \tabularnewline
41 &  0.1103 &  0.2206 &  0.8897 \tabularnewline
42 &  0.9973 &  0.005334 &  0.002667 \tabularnewline
43 &  0.9997 &  0.0006006 &  0.0003003 \tabularnewline
44 &  1 &  1.56e-06 &  7.8e-07 \tabularnewline
45 &  1 &  1.148e-31 &  5.739e-32 \tabularnewline
46 &  1 &  1.508e-19 &  7.542e-20 \tabularnewline
47 &  1 &  6.795e-16 &  3.398e-16 \tabularnewline
48 &  1 &  1.375e-19 &  6.876e-20 \tabularnewline
49 &  1 &  9.365e-22 &  4.682e-22 \tabularnewline
50 &  1 &  6.928e-15 &  3.464e-15 \tabularnewline
51 &  0.953 &  0.09399 &  0.04699 \tabularnewline
52 &  2.189e-09 &  4.377e-09 &  1 \tabularnewline
53 &  6.274e-35 &  1.255e-34 &  1 \tabularnewline
54 &  1.572e-15 &  3.143e-15 &  1 \tabularnewline
55 &  7.197e-09 &  1.439e-08 &  1 \tabularnewline
56 &  0.0001213 &  0.0002426 &  0.9999 \tabularnewline
57 &  3.536e-24 &  7.071e-24 &  1 \tabularnewline
58 &  1.401e-09 &  2.803e-09 &  1 \tabularnewline
59 &  1 &  1.624e-12 &  8.119e-13 \tabularnewline
60 &  1.052e-13 &  2.103e-13 &  1 \tabularnewline
61 &  9.232e-13 &  1.846e-12 &  1 \tabularnewline
62 &  0.006522 &  0.01304 &  0.9935 \tabularnewline
63 &  0.01239 &  0.02477 &  0.9876 \tabularnewline
64 &  6.189e-30 &  1.238e-29 &  1 \tabularnewline
65 &  0.9925 &  0.01507 &  0.007534 \tabularnewline
66 &  1 &  3.138e-18 &  1.569e-18 \tabularnewline
67 &  1.791e-29 &  3.582e-29 &  1 \tabularnewline
68 &  0.0001758 &  0.0003515 &  0.9998 \tabularnewline
69 &  0.0189 &  0.0378 &  0.9811 \tabularnewline
70 &  0.8193 &  0.3613 &  0.1807 \tabularnewline
71 &  0.9999 &  0.0001033 &  5.167e-05 \tabularnewline
72 &  0.9871 &  0.02573 &  0.01286 \tabularnewline
73 &  0.006947 &  0.01389 &  0.9931 \tabularnewline
74 &  0.9887 &  0.02256 &  0.01128 \tabularnewline
75 &  0.999 &  0.001941 &  0.0009704 \tabularnewline
76 &  4.014e-18 &  8.028e-18 &  1 \tabularnewline
77 &  0.3127 &  0.6254 &  0.6873 \tabularnewline
78 &  1 &  3.449e-10 &  1.725e-10 \tabularnewline
79 &  3.336e-10 &  6.672e-10 &  1 \tabularnewline
80 &  8.741e-11 &  1.748e-10 &  1 \tabularnewline
81 &  3.212e-12 &  6.424e-12 &  1 \tabularnewline
82 &  1 &  2.127e-05 &  1.063e-05 \tabularnewline
83 &  0.9992 &  0.001563 &  0.0007817 \tabularnewline
84 &  0.6013 &  0.7975 &  0.3987 \tabularnewline
85 &  1 &  2.42e-08 &  1.21e-08 \tabularnewline
86 &  1 &  1.836e-08 &  9.181e-09 \tabularnewline
87 &  0.9992 &  0.001666 &  0.0008328 \tabularnewline
88 &  1 &  8.432e-06 &  4.216e-06 \tabularnewline
89 &  0.3818 &  0.7637 &  0.6182 \tabularnewline
90 &  1 &  2.08e-06 &  1.04e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 0.0296[/C][C] 0.05919[/C][C] 0.9704[/C][/ROW]
[ROW][C]13[/C][C] 0.9525[/C][C] 0.09501[/C][C] 0.0475[/C][/ROW]
[ROW][C]14[/C][C] 0.5629[/C][C] 0.8743[/C][C] 0.4371[/C][/ROW]
[ROW][C]15[/C][C] 0.00199[/C][C] 0.003979[/C][C] 0.998[/C][/ROW]
[ROW][C]16[/C][C] 7.659e-10[/C][C] 1.532e-09[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 1.192e-05[/C][C] 2.383e-05[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 1[/C][C] 3.023e-05[/C][C] 1.512e-05[/C][/ROW]
[ROW][C]19[/C][C] 0.004147[/C][C] 0.008294[/C][C] 0.9959[/C][/ROW]
[ROW][C]20[/C][C] 3.474e-05[/C][C] 6.948e-05[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 0.04374[/C][C] 0.08747[/C][C] 0.9563[/C][/ROW]
[ROW][C]22[/C][C] 0.9978[/C][C] 0.004486[/C][C] 0.002243[/C][/ROW]
[ROW][C]23[/C][C] 0.004501[/C][C] 0.009002[/C][C] 0.9955[/C][/ROW]
[ROW][C]24[/C][C] 0.1628[/C][C] 0.3257[/C][C] 0.8372[/C][/ROW]
[ROW][C]25[/C][C] 6.687e-10[/C][C] 1.337e-09[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 0.99[/C][C] 0.01991[/C][C] 0.009953[/C][/ROW]
[ROW][C]27[/C][C] 1.416e-11[/C][C] 2.831e-11[/C][C] 1[/C][/ROW]
[ROW][C]28[/C][C] 3.225e-06[/C][C] 6.449e-06[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 1[/C][C] 4.686e-11[/C][C] 2.343e-11[/C][/ROW]
[ROW][C]30[/C][C] 0.07539[/C][C] 0.1508[/C][C] 0.9246[/C][/ROW]
[ROW][C]31[/C][C] 0.0002471[/C][C] 0.0004941[/C][C] 0.9998[/C][/ROW]
[ROW][C]32[/C][C] 1[/C][C] 1.311e-39[/C][C] 6.555e-40[/C][/ROW]
[ROW][C]33[/C][C] 0.428[/C][C] 0.8559[/C][C] 0.572[/C][/ROW]
[ROW][C]34[/C][C] 1.315e-13[/C][C] 2.629e-13[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 0.0544[/C][C] 0.1088[/C][C] 0.9456[/C][/ROW]
[ROW][C]36[/C][C] 9.985e-18[/C][C] 1.997e-17[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 1[/C][C] 1.638e-23[/C][C] 8.189e-24[/C][/ROW]
[ROW][C]38[/C][C] 1[/C][C] 2.191e-59[/C][C] 1.095e-59[/C][/ROW]
[ROW][C]39[/C][C] 0.009095[/C][C] 0.01819[/C][C] 0.9909[/C][/ROW]
[ROW][C]40[/C][C] 0.9999[/C][C] 0.0001297[/C][C] 6.487e-05[/C][/ROW]
[ROW][C]41[/C][C] 0.1103[/C][C] 0.2206[/C][C] 0.8897[/C][/ROW]
[ROW][C]42[/C][C] 0.9973[/C][C] 0.005334[/C][C] 0.002667[/C][/ROW]
[ROW][C]43[/C][C] 0.9997[/C][C] 0.0006006[/C][C] 0.0003003[/C][/ROW]
[ROW][C]44[/C][C] 1[/C][C] 1.56e-06[/C][C] 7.8e-07[/C][/ROW]
[ROW][C]45[/C][C] 1[/C][C] 1.148e-31[/C][C] 5.739e-32[/C][/ROW]
[ROW][C]46[/C][C] 1[/C][C] 1.508e-19[/C][C] 7.542e-20[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 6.795e-16[/C][C] 3.398e-16[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 1.375e-19[/C][C] 6.876e-20[/C][/ROW]
[ROW][C]49[/C][C] 1[/C][C] 9.365e-22[/C][C] 4.682e-22[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 6.928e-15[/C][C] 3.464e-15[/C][/ROW]
[ROW][C]51[/C][C] 0.953[/C][C] 0.09399[/C][C] 0.04699[/C][/ROW]
[ROW][C]52[/C][C] 2.189e-09[/C][C] 4.377e-09[/C][C] 1[/C][/ROW]
[ROW][C]53[/C][C] 6.274e-35[/C][C] 1.255e-34[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 1.572e-15[/C][C] 3.143e-15[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 7.197e-09[/C][C] 1.439e-08[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 0.0001213[/C][C] 0.0002426[/C][C] 0.9999[/C][/ROW]
[ROW][C]57[/C][C] 3.536e-24[/C][C] 7.071e-24[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 1.401e-09[/C][C] 2.803e-09[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 1[/C][C] 1.624e-12[/C][C] 8.119e-13[/C][/ROW]
[ROW][C]60[/C][C] 1.052e-13[/C][C] 2.103e-13[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 9.232e-13[/C][C] 1.846e-12[/C][C] 1[/C][/ROW]
[ROW][C]62[/C][C] 0.006522[/C][C] 0.01304[/C][C] 0.9935[/C][/ROW]
[ROW][C]63[/C][C] 0.01239[/C][C] 0.02477[/C][C] 0.9876[/C][/ROW]
[ROW][C]64[/C][C] 6.189e-30[/C][C] 1.238e-29[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 0.9925[/C][C] 0.01507[/C][C] 0.007534[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 3.138e-18[/C][C] 1.569e-18[/C][/ROW]
[ROW][C]67[/C][C] 1.791e-29[/C][C] 3.582e-29[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 0.0001758[/C][C] 0.0003515[/C][C] 0.9998[/C][/ROW]
[ROW][C]69[/C][C] 0.0189[/C][C] 0.0378[/C][C] 0.9811[/C][/ROW]
[ROW][C]70[/C][C] 0.8193[/C][C] 0.3613[/C][C] 0.1807[/C][/ROW]
[ROW][C]71[/C][C] 0.9999[/C][C] 0.0001033[/C][C] 5.167e-05[/C][/ROW]
[ROW][C]72[/C][C] 0.9871[/C][C] 0.02573[/C][C] 0.01286[/C][/ROW]
[ROW][C]73[/C][C] 0.006947[/C][C] 0.01389[/C][C] 0.9931[/C][/ROW]
[ROW][C]74[/C][C] 0.9887[/C][C] 0.02256[/C][C] 0.01128[/C][/ROW]
[ROW][C]75[/C][C] 0.999[/C][C] 0.001941[/C][C] 0.0009704[/C][/ROW]
[ROW][C]76[/C][C] 4.014e-18[/C][C] 8.028e-18[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 0.3127[/C][C] 0.6254[/C][C] 0.6873[/C][/ROW]
[ROW][C]78[/C][C] 1[/C][C] 3.449e-10[/C][C] 1.725e-10[/C][/ROW]
[ROW][C]79[/C][C] 3.336e-10[/C][C] 6.672e-10[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 8.741e-11[/C][C] 1.748e-10[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 3.212e-12[/C][C] 6.424e-12[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 1[/C][C] 2.127e-05[/C][C] 1.063e-05[/C][/ROW]
[ROW][C]83[/C][C] 0.9992[/C][C] 0.001563[/C][C] 0.0007817[/C][/ROW]
[ROW][C]84[/C][C] 0.6013[/C][C] 0.7975[/C][C] 0.3987[/C][/ROW]
[ROW][C]85[/C][C] 1[/C][C] 2.42e-08[/C][C] 1.21e-08[/C][/ROW]
[ROW][C]86[/C][C] 1[/C][C] 1.836e-08[/C][C] 9.181e-09[/C][/ROW]
[ROW][C]87[/C][C] 0.9992[/C][C] 0.001666[/C][C] 0.0008328[/C][/ROW]
[ROW][C]88[/C][C] 1[/C][C] 8.432e-06[/C][C] 4.216e-06[/C][/ROW]
[ROW][C]89[/C][C] 0.3818[/C][C] 0.7637[/C][C] 0.6182[/C][/ROW]
[ROW][C]90[/C][C] 1[/C][C] 2.08e-06[/C][C] 1.04e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.0296	0.05919	0.9704
13	0.9525	0.09501	0.0475
14	0.5629	0.8743	0.4371
15	0.00199	0.003979	0.998
16	7.659e-10	1.532e-09	1
17	1.192e-05	2.383e-05	1
18	1	3.023e-05	1.512e-05
19	0.004147	0.008294	0.9959
20	3.474e-05	6.948e-05	1
21	0.04374	0.08747	0.9563
22	0.9978	0.004486	0.002243
23	0.004501	0.009002	0.9955
24	0.1628	0.3257	0.8372
25	6.687e-10	1.337e-09	1
26	0.99	0.01991	0.009953
27	1.416e-11	2.831e-11	1
28	3.225e-06	6.449e-06	1
29	1	4.686e-11	2.343e-11
30	0.07539	0.1508	0.9246
31	0.0002471	0.0004941	0.9998
32	1	1.311e-39	6.555e-40
33	0.428	0.8559	0.572
34	1.315e-13	2.629e-13	1
35	0.0544	0.1088	0.9456
36	9.985e-18	1.997e-17	1
37	1	1.638e-23	8.189e-24
38	1	2.191e-59	1.095e-59
39	0.009095	0.01819	0.9909
40	0.9999	0.0001297	6.487e-05
41	0.1103	0.2206	0.8897
42	0.9973	0.005334	0.002667
43	0.9997	0.0006006	0.0003003
44	1	1.56e-06	7.8e-07
45	1	1.148e-31	5.739e-32
46	1	1.508e-19	7.542e-20
47	1	6.795e-16	3.398e-16
48	1	1.375e-19	6.876e-20
49	1	9.365e-22	4.682e-22
50	1	6.928e-15	3.464e-15
51	0.953	0.09399	0.04699
52	2.189e-09	4.377e-09	1
53	6.274e-35	1.255e-34	1
54	1.572e-15	3.143e-15	1
55	7.197e-09	1.439e-08	1
56	0.0001213	0.0002426	0.9999
57	3.536e-24	7.071e-24	1
58	1.401e-09	2.803e-09	1
59	1	1.624e-12	8.119e-13
60	1.052e-13	2.103e-13	1
61	9.232e-13	1.846e-12	1
62	0.006522	0.01304	0.9935
63	0.01239	0.02477	0.9876
64	6.189e-30	1.238e-29	1
65	0.9925	0.01507	0.007534
66	1	3.138e-18	1.569e-18
67	1.791e-29	3.582e-29	1
68	0.0001758	0.0003515	0.9998
69	0.0189	0.0378	0.9811
70	0.8193	0.3613	0.1807
71	0.9999	0.0001033	5.167e-05
72	0.9871	0.02573	0.01286
73	0.006947	0.01389	0.9931
74	0.9887	0.02256	0.01128
75	0.999	0.001941	0.0009704
76	4.014e-18	8.028e-18	1
77	0.3127	0.6254	0.6873
78	1	3.449e-10	1.725e-10
79	3.336e-10	6.672e-10	1
80	8.741e-11	1.748e-10	1
81	3.212e-12	6.424e-12	1
82	1	2.127e-05	1.063e-05
83	0.9992	0.001563	0.0007817
84	0.6013	0.7975	0.3987
85	1	2.42e-08	1.21e-08
86	1	1.836e-08	9.181e-09
87	0.9992	0.001666	0.0008328
88	1	8.432e-06	4.216e-06
89	0.3818	0.7637	0.6182
90	1	2.08e-06	1.04e-06

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	56	0.7089	NOK
5% type I error level	65	0.822785	NOK
10% type I error level	69	0.873418	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 & 56 &  0.7089 & NOK \tabularnewline
5% type I error level & 65 & 0.822785 & NOK \tabularnewline
10% type I error level & 69 & 0.873418 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]56[/C][C] 0.7089[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]65[/C][C]0.822785[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]69[/C][C]0.873418[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	56	0.7089	NOK
5% type I error level	65	0.822785	NOK
10% type I error level	69	0.873418	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.006871, df1 = 2, df2 = 91, p-value = 0.9932
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.6028, df1 = 16, df2 = 77, p-value = 0.002756
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.77545, df1 = 2, df2 = 91, p-value = 0.4635

\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 = 0.006871, df1 = 2, df2 = 91, p-value = 0.9932
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.6028, df1 = 16, df2 = 77, p-value = 0.002756
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.77545, df1 = 2, df2 = 91, p-value = 0.4635
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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 = 0.006871, df1 = 2, df2 = 91, p-value = 0.9932
[/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 = 2.6028, df1 = 16, df2 = 77, p-value = 0.002756
[/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 = 0.77545, df1 = 2, df2 = 91, p-value = 0.4635
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 = 0.006871, df1 = 2, df2 = 91, p-value = 0.9932
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.6028, df1 = 16, df2 = 77, p-value = 0.002756
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.77545, df1 = 2, df2 = 91, p-value = 0.4635

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM SKEOU1 SKEOU2 1.052480 1.606652 10.101660 2.742214 2.974011 SKEOU3 SKEOU4 SKEOU5 3.175014 1.892758 2.485342

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     1.052480      1.606652     10.101660      2.742214      2.974011 
       SKEOU3        SKEOU4        SKEOU5 
     3.175014      1.892758      2.485342 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     1.052480      1.606652     10.101660      2.742214      2.974011 
       SKEOU3        SKEOU4        SKEOU5 
     3.175014      1.892758      2.485342 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=8

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

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM SKEOU1 SKEOU2 1.052480 1.606652 10.101660 2.742214 2.974011 SKEOU3 SKEOU4 SKEOU5 3.175014 1.892758 2.485342

Figure 1

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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):

par1 = 1ScoreScoreScore19 ; par2 = 2X1X1X12Do not include Seasonal DummiesDo not include Seasonal Dummies ; par3 = Exact Pearson Chi-Squared by SimulationGroepLabelGroepLabelGroepLabelExact Pearson Chi-Squared by SimulationNo Linear TrendNo Linear Trend ; par4 = TRUETRUETRUE ;

Parameters (R input):

par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

R code (references can be found in the software module):

par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- ''
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