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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 11:49:43 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/23/t1485168641wwk7sgw6gjlvr4d.htm/, Retrieved Wed, 15 May 2024 01:12:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304900, Retrieved Wed, 15 May 2024 01:12:35 +0000

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-       [Multiple Regression] [multiple regression] [2017-01-23 10:49:43] [e3cd721010e920ddac8a34a44b82c047] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304900&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
SKEOUSUM[t] = -3.18335e-14 -4.09919e-16Bevr_Leeftijd[t] + 1.12218e-16TVDC[t] + 1SKEOU6[t] + 1SKEOU5[t] + 1SKEOU4[t] + 1SKEOU3[t] + 1SKEOU1[t] + 1SKEOU2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SKEOUSUM[t] =  -3.18335e-14 -4.09919e-16Bevr_Leeftijd[t] +  1.12218e-16TVDC[t] +  1SKEOU6[t] +  1SKEOU5[t] +  1SKEOU4[t] +  1SKEOU3[t] +  1SKEOU1[t] +  1SKEOU2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SKEOUSUM[t] =  -3.18335e-14 -4.09919e-16Bevr_Leeftijd[t] +  1.12218e-16TVDC[t] +  1SKEOU6[t] +  1SKEOU5[t] +  1SKEOU4[t] +  1SKEOU3[t] +  1SKEOU1[t] +  1SKEOU2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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
SKEOUSUM[t] = -3.18335e-14 -4.09919e-16Bevr_Leeftijd[t] + 1.12218e-16TVDC[t] + 1SKEOU6[t] + 1SKEOU5[t] + 1SKEOU4[t] + 1SKEOU3[t] + 1SKEOU1[t] + 1SKEOU2[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)	-3.183e-14	3.124e-14	-1.0190e+00	0.3109	0.1554
Bevr_Leeftijd	-4.099e-16	1.024e-15	-4.0010e-01	0.69	0.345
TVDC	+1.122e-16	1.133e-15	+9.9010e-02	0.9213	0.4607
SKEOU6	+1	2.856e-15	+3.5010e+14	0	0
SKEOU5	+1	2.663e-15	+3.7550e+14	0	0
SKEOU4	+1	3.226e-15	+3.1000e+14	0	0
SKEOU3	+1	2.252e-15	+4.4400e+14	0	0
SKEOU1	+1	2.5e-15	+4.0000e+14	0	0
SKEOU2	+1	2.974e-15	+3.3620e+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) & -3.183e-14 &  3.124e-14 & -1.0190e+00 &  0.3109 &  0.1554 \tabularnewline
Bevr_Leeftijd & -4.099e-16 &  1.024e-15 & -4.0010e-01 &  0.69 &  0.345 \tabularnewline
TVDC & +1.122e-16 &  1.133e-15 & +9.9010e-02 &  0.9213 &  0.4607 \tabularnewline
SKEOU6 & +1 &  2.856e-15 & +3.5010e+14 &  0 &  0 \tabularnewline
SKEOU5 & +1 &  2.663e-15 & +3.7550e+14 &  0 &  0 \tabularnewline
SKEOU4 & +1 &  3.226e-15 & +3.1000e+14 &  0 &  0 \tabularnewline
SKEOU3 & +1 &  2.252e-15 & +4.4400e+14 &  0 &  0 \tabularnewline
SKEOU1 & +1 &  2.5e-15 & +4.0000e+14 &  0 &  0 \tabularnewline
SKEOU2 & +1 &  2.974e-15 & +3.3620e+14 &  0 &  0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&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]-3.183e-14[/C][C] 3.124e-14[/C][C]-1.0190e+00[/C][C] 0.3109[/C][C] 0.1554[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-4.099e-16[/C][C] 1.024e-15[/C][C]-4.0010e-01[/C][C] 0.69[/C][C] 0.345[/C][/ROW]
[ROW][C]TVDC[/C][C]+1.122e-16[/C][C] 1.133e-15[/C][C]+9.9010e-02[/C][C] 0.9213[/C][C] 0.4607[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+1[/C][C] 2.856e-15[/C][C]+3.5010e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+1[/C][C] 2.663e-15[/C][C]+3.7550e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+1[/C][C] 3.226e-15[/C][C]+3.1000e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+1[/C][C] 2.252e-15[/C][C]+4.4400e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+1[/C][C] 2.5e-15[/C][C]+4.0000e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1[/C][C] 2.974e-15[/C][C]+3.3620e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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)	-3.183e-14	3.124e-14	-1.0190e+00	0.3109	0.1554
Bevr_Leeftijd	-4.099e-16	1.024e-15	-4.0010e-01	0.69	0.345
TVDC	+1.122e-16	1.133e-15	+9.9010e-02	0.9213	0.4607
SKEOU6	+1	2.856e-15	+3.5010e+14	0	0
SKEOU5	+1	2.663e-15	+3.7550e+14	0	0
SKEOU4	+1	3.226e-15	+3.1000e+14	0	0
SKEOU3	+1	2.252e-15	+4.4400e+14	0	0
SKEOU1	+1	2.5e-15	+4.0000e+14	0	0
SKEOU2	+1	2.974e-15	+3.3620e+14	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	1.548e+29
F-TEST (DF numerator)	8
F-TEST (DF denominator)	93
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.686e-14
Sum Squared Residuals	2.642e-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) &  1.548e+29 \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 &  1.686e-14 \tabularnewline
Sum Squared Residuals &  2.642e-26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&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] 1.548e+29[/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] 1.686e-14[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.642e-26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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)	1.548e+29
F-TEST (DF numerator)	8
F-TEST (DF denominator)	93
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.686e-14
Sum Squared Residuals	2.642e-26

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	22	-1.52e-13
2	24	24	1.5e-14
3	26	26	6.237e-15
4	25	25	-6.738e-15
5	28	28	-2.987e-15
6	23	23	-3.092e-16
7	25	25	-1.878e-16
8	24	24	-2.393e-16
9	24	24	3.563e-15
10	24	24	-9.447e-15
11	25	25	-3.159e-15
12	24	24	-6.095e-16
13	26	26	-7.675e-16
14	24	24	1.025e-15
15	24	24	-5.225e-16
16	25	25	-8.628e-16
17	25	25	2.38e-15
18	24	24	7.631e-15
19	28	28	-6.38e-15
20	27	27	2.464e-16
21	23	23	8.363e-15
22	23	23	-7.532e-15
23	24	24	7.292e-15
24	22	22	5.663e-15
25	25	25	1.736e-16
26	28	28	-4.865e-15
27	22	22	4.182e-15
28	28	28	5.958e-16
29	25	25	3.824e-15
30	24	24	7.875e-16
31	23	23	-1.083e-14
32	25	25	4.176e-15
33	26	26	-7.471e-15
34	26	26	8.49e-15
35	23	23	7.649e-15
36	25	25	7.223e-16
37	22	22	-1.926e-16
38	24	24	5.675e-15
39	27	27	-1.843e-15
40	24	24	-3.187e-15
41	26	26	1.789e-15
42	21	21	-9.511e-15
43	27	27	2.116e-15
44	22	22	1.544e-14
45	23	23	9.839e-15
46	24	24	7.134e-15
47	25	25	3.831e-16
48	24	24	2.7e-15
49	22	22	-2.109e-15
50	25	25	4.141e-15
51	25	25	2.658e-15
52	24	24	3.674e-15
53	24	24	9.142e-15
54	21	21	5.482e-15
55	25	25	1.955e-16
56	25	25	2.424e-15
57	26	26	-3.531e-15
58	27	27	3.035e-15
59	20	20	1.544e-14
60	26	26	-5.26e-16
61	25	25	-1.818e-15
62	26	26	-7.675e-16
63	25	25	-1.24e-16
64	28	28	-2.295e-15
65	27	27	-5.787e-15
66	26	26	-1.115e-15
67	26	26	1.277e-15
68	25	25	9.737e-16
69	23	23	1.017e-14
70	24	24	-1.294e-15
71	24	24	-1.267e-15
72	28	28	3.74e-15
73	23	23	8.296e-16
74	24	24	2.519e-15
75	23	23	-1.824e-15
76	25	25	4.593e-15
77	23	23	-1.179e-15
78	23	23	4.154e-15
79	19	19	1.883e-14
80	25	25	9.381e-15
81	22	22	5.7e-15
82	27	27	-2.559e-15
83	26	26	-6.435e-15
84	29	29	-2.984e-15
85	28	28	-4.865e-15
86	25	25	1.27e-15
87	22	22	2.398e-16
88	25	25	1.295e-15
89	26	26	1.532e-15
90	26	26	1.009e-14
91	24	24	8.455e-15
92	25	25	-4.355e-15
93	23	23	2.302e-15
94	25	25	-3.62e-15
95	25	25	1.31e-15
96	27	27	5.432e-15
97	25	25	-2.878e-15
98	24	24	1.087e-14
99	23	23	1.864e-15
100	27	27	-8.203e-15
101	25	25	-1.814e-15
102	23	23	4.842e-15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  22 &  22 & -1.52e-13 \tabularnewline
2 &  24 &  24 &  1.5e-14 \tabularnewline
3 &  26 &  26 &  6.237e-15 \tabularnewline
4 &  25 &  25 & -6.738e-15 \tabularnewline
5 &  28 &  28 & -2.987e-15 \tabularnewline
6 &  23 &  23 & -3.092e-16 \tabularnewline
7 &  25 &  25 & -1.878e-16 \tabularnewline
8 &  24 &  24 & -2.393e-16 \tabularnewline
9 &  24 &  24 &  3.563e-15 \tabularnewline
10 &  24 &  24 & -9.447e-15 \tabularnewline
11 &  25 &  25 & -3.159e-15 \tabularnewline
12 &  24 &  24 & -6.095e-16 \tabularnewline
13 &  26 &  26 & -7.675e-16 \tabularnewline
14 &  24 &  24 &  1.025e-15 \tabularnewline
15 &  24 &  24 & -5.225e-16 \tabularnewline
16 &  25 &  25 & -8.628e-16 \tabularnewline
17 &  25 &  25 &  2.38e-15 \tabularnewline
18 &  24 &  24 &  7.631e-15 \tabularnewline
19 &  28 &  28 & -6.38e-15 \tabularnewline
20 &  27 &  27 &  2.464e-16 \tabularnewline
21 &  23 &  23 &  8.363e-15 \tabularnewline
22 &  23 &  23 & -7.532e-15 \tabularnewline
23 &  24 &  24 &  7.292e-15 \tabularnewline
24 &  22 &  22 &  5.663e-15 \tabularnewline
25 &  25 &  25 &  1.736e-16 \tabularnewline
26 &  28 &  28 & -4.865e-15 \tabularnewline
27 &  22 &  22 &  4.182e-15 \tabularnewline
28 &  28 &  28 &  5.958e-16 \tabularnewline
29 &  25 &  25 &  3.824e-15 \tabularnewline
30 &  24 &  24 &  7.875e-16 \tabularnewline
31 &  23 &  23 & -1.083e-14 \tabularnewline
32 &  25 &  25 &  4.176e-15 \tabularnewline
33 &  26 &  26 & -7.471e-15 \tabularnewline
34 &  26 &  26 &  8.49e-15 \tabularnewline
35 &  23 &  23 &  7.649e-15 \tabularnewline
36 &  25 &  25 &  7.223e-16 \tabularnewline
37 &  22 &  22 & -1.926e-16 \tabularnewline
38 &  24 &  24 &  5.675e-15 \tabularnewline
39 &  27 &  27 & -1.843e-15 \tabularnewline
40 &  24 &  24 & -3.187e-15 \tabularnewline
41 &  26 &  26 &  1.789e-15 \tabularnewline
42 &  21 &  21 & -9.511e-15 \tabularnewline
43 &  27 &  27 &  2.116e-15 \tabularnewline
44 &  22 &  22 &  1.544e-14 \tabularnewline
45 &  23 &  23 &  9.839e-15 \tabularnewline
46 &  24 &  24 &  7.134e-15 \tabularnewline
47 &  25 &  25 &  3.831e-16 \tabularnewline
48 &  24 &  24 &  2.7e-15 \tabularnewline
49 &  22 &  22 & -2.109e-15 \tabularnewline
50 &  25 &  25 &  4.141e-15 \tabularnewline
51 &  25 &  25 &  2.658e-15 \tabularnewline
52 &  24 &  24 &  3.674e-15 \tabularnewline
53 &  24 &  24 &  9.142e-15 \tabularnewline
54 &  21 &  21 &  5.482e-15 \tabularnewline
55 &  25 &  25 &  1.955e-16 \tabularnewline
56 &  25 &  25 &  2.424e-15 \tabularnewline
57 &  26 &  26 & -3.531e-15 \tabularnewline
58 &  27 &  27 &  3.035e-15 \tabularnewline
59 &  20 &  20 &  1.544e-14 \tabularnewline
60 &  26 &  26 & -5.26e-16 \tabularnewline
61 &  25 &  25 & -1.818e-15 \tabularnewline
62 &  26 &  26 & -7.675e-16 \tabularnewline
63 &  25 &  25 & -1.24e-16 \tabularnewline
64 &  28 &  28 & -2.295e-15 \tabularnewline
65 &  27 &  27 & -5.787e-15 \tabularnewline
66 &  26 &  26 & -1.115e-15 \tabularnewline
67 &  26 &  26 &  1.277e-15 \tabularnewline
68 &  25 &  25 &  9.737e-16 \tabularnewline
69 &  23 &  23 &  1.017e-14 \tabularnewline
70 &  24 &  24 & -1.294e-15 \tabularnewline
71 &  24 &  24 & -1.267e-15 \tabularnewline
72 &  28 &  28 &  3.74e-15 \tabularnewline
73 &  23 &  23 &  8.296e-16 \tabularnewline
74 &  24 &  24 &  2.519e-15 \tabularnewline
75 &  23 &  23 & -1.824e-15 \tabularnewline
76 &  25 &  25 &  4.593e-15 \tabularnewline
77 &  23 &  23 & -1.179e-15 \tabularnewline
78 &  23 &  23 &  4.154e-15 \tabularnewline
79 &  19 &  19 &  1.883e-14 \tabularnewline
80 &  25 &  25 &  9.381e-15 \tabularnewline
81 &  22 &  22 &  5.7e-15 \tabularnewline
82 &  27 &  27 & -2.559e-15 \tabularnewline
83 &  26 &  26 & -6.435e-15 \tabularnewline
84 &  29 &  29 & -2.984e-15 \tabularnewline
85 &  28 &  28 & -4.865e-15 \tabularnewline
86 &  25 &  25 &  1.27e-15 \tabularnewline
87 &  22 &  22 &  2.398e-16 \tabularnewline
88 &  25 &  25 &  1.295e-15 \tabularnewline
89 &  26 &  26 &  1.532e-15 \tabularnewline
90 &  26 &  26 &  1.009e-14 \tabularnewline
91 &  24 &  24 &  8.455e-15 \tabularnewline
92 &  25 &  25 & -4.355e-15 \tabularnewline
93 &  23 &  23 &  2.302e-15 \tabularnewline
94 &  25 &  25 & -3.62e-15 \tabularnewline
95 &  25 &  25 &  1.31e-15 \tabularnewline
96 &  27 &  27 &  5.432e-15 \tabularnewline
97 &  25 &  25 & -2.878e-15 \tabularnewline
98 &  24 &  24 &  1.087e-14 \tabularnewline
99 &  23 &  23 &  1.864e-15 \tabularnewline
100 &  27 &  27 & -8.203e-15 \tabularnewline
101 &  25 &  25 & -1.814e-15 \tabularnewline
102 &  23 &  23 &  4.842e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&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] 22[/C][C] 22[/C][C]-1.52e-13[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 24[/C][C] 1.5e-14[/C][/ROW]
[ROW][C]3[/C][C] 26[/C][C] 26[/C][C] 6.237e-15[/C][/ROW]
[ROW][C]4[/C][C] 25[/C][C] 25[/C][C]-6.738e-15[/C][/ROW]
[ROW][C]5[/C][C] 28[/C][C] 28[/C][C]-2.987e-15[/C][/ROW]
[ROW][C]6[/C][C] 23[/C][C] 23[/C][C]-3.092e-16[/C][/ROW]
[ROW][C]7[/C][C] 25[/C][C] 25[/C][C]-1.878e-16[/C][/ROW]
[ROW][C]8[/C][C] 24[/C][C] 24[/C][C]-2.393e-16[/C][/ROW]
[ROW][C]9[/C][C] 24[/C][C] 24[/C][C] 3.563e-15[/C][/ROW]
[ROW][C]10[/C][C] 24[/C][C] 24[/C][C]-9.447e-15[/C][/ROW]
[ROW][C]11[/C][C] 25[/C][C] 25[/C][C]-3.159e-15[/C][/ROW]
[ROW][C]12[/C][C] 24[/C][C] 24[/C][C]-6.095e-16[/C][/ROW]
[ROW][C]13[/C][C] 26[/C][C] 26[/C][C]-7.675e-16[/C][/ROW]
[ROW][C]14[/C][C] 24[/C][C] 24[/C][C] 1.025e-15[/C][/ROW]
[ROW][C]15[/C][C] 24[/C][C] 24[/C][C]-5.225e-16[/C][/ROW]
[ROW][C]16[/C][C] 25[/C][C] 25[/C][C]-8.628e-16[/C][/ROW]
[ROW][C]17[/C][C] 25[/C][C] 25[/C][C] 2.38e-15[/C][/ROW]
[ROW][C]18[/C][C] 24[/C][C] 24[/C][C] 7.631e-15[/C][/ROW]
[ROW][C]19[/C][C] 28[/C][C] 28[/C][C]-6.38e-15[/C][/ROW]
[ROW][C]20[/C][C] 27[/C][C] 27[/C][C] 2.464e-16[/C][/ROW]
[ROW][C]21[/C][C] 23[/C][C] 23[/C][C] 8.363e-15[/C][/ROW]
[ROW][C]22[/C][C] 23[/C][C] 23[/C][C]-7.532e-15[/C][/ROW]
[ROW][C]23[/C][C] 24[/C][C] 24[/C][C] 7.292e-15[/C][/ROW]
[ROW][C]24[/C][C] 22[/C][C] 22[/C][C] 5.663e-15[/C][/ROW]
[ROW][C]25[/C][C] 25[/C][C] 25[/C][C] 1.736e-16[/C][/ROW]
[ROW][C]26[/C][C] 28[/C][C] 28[/C][C]-4.865e-15[/C][/ROW]
[ROW][C]27[/C][C] 22[/C][C] 22[/C][C] 4.182e-15[/C][/ROW]
[ROW][C]28[/C][C] 28[/C][C] 28[/C][C] 5.958e-16[/C][/ROW]
[ROW][C]29[/C][C] 25[/C][C] 25[/C][C] 3.824e-15[/C][/ROW]
[ROW][C]30[/C][C] 24[/C][C] 24[/C][C] 7.875e-16[/C][/ROW]
[ROW][C]31[/C][C] 23[/C][C] 23[/C][C]-1.083e-14[/C][/ROW]
[ROW][C]32[/C][C] 25[/C][C] 25[/C][C] 4.176e-15[/C][/ROW]
[ROW][C]33[/C][C] 26[/C][C] 26[/C][C]-7.471e-15[/C][/ROW]
[ROW][C]34[/C][C] 26[/C][C] 26[/C][C] 8.49e-15[/C][/ROW]
[ROW][C]35[/C][C] 23[/C][C] 23[/C][C] 7.649e-15[/C][/ROW]
[ROW][C]36[/C][C] 25[/C][C] 25[/C][C] 7.223e-16[/C][/ROW]
[ROW][C]37[/C][C] 22[/C][C] 22[/C][C]-1.926e-16[/C][/ROW]
[ROW][C]38[/C][C] 24[/C][C] 24[/C][C] 5.675e-15[/C][/ROW]
[ROW][C]39[/C][C] 27[/C][C] 27[/C][C]-1.843e-15[/C][/ROW]
[ROW][C]40[/C][C] 24[/C][C] 24[/C][C]-3.187e-15[/C][/ROW]
[ROW][C]41[/C][C] 26[/C][C] 26[/C][C] 1.789e-15[/C][/ROW]
[ROW][C]42[/C][C] 21[/C][C] 21[/C][C]-9.511e-15[/C][/ROW]
[ROW][C]43[/C][C] 27[/C][C] 27[/C][C] 2.116e-15[/C][/ROW]
[ROW][C]44[/C][C] 22[/C][C] 22[/C][C] 1.544e-14[/C][/ROW]
[ROW][C]45[/C][C] 23[/C][C] 23[/C][C] 9.839e-15[/C][/ROW]
[ROW][C]46[/C][C] 24[/C][C] 24[/C][C] 7.134e-15[/C][/ROW]
[ROW][C]47[/C][C] 25[/C][C] 25[/C][C] 3.831e-16[/C][/ROW]
[ROW][C]48[/C][C] 24[/C][C] 24[/C][C] 2.7e-15[/C][/ROW]
[ROW][C]49[/C][C] 22[/C][C] 22[/C][C]-2.109e-15[/C][/ROW]
[ROW][C]50[/C][C] 25[/C][C] 25[/C][C] 4.141e-15[/C][/ROW]
[ROW][C]51[/C][C] 25[/C][C] 25[/C][C] 2.658e-15[/C][/ROW]
[ROW][C]52[/C][C] 24[/C][C] 24[/C][C] 3.674e-15[/C][/ROW]
[ROW][C]53[/C][C] 24[/C][C] 24[/C][C] 9.142e-15[/C][/ROW]
[ROW][C]54[/C][C] 21[/C][C] 21[/C][C] 5.482e-15[/C][/ROW]
[ROW][C]55[/C][C] 25[/C][C] 25[/C][C] 1.955e-16[/C][/ROW]
[ROW][C]56[/C][C] 25[/C][C] 25[/C][C] 2.424e-15[/C][/ROW]
[ROW][C]57[/C][C] 26[/C][C] 26[/C][C]-3.531e-15[/C][/ROW]
[ROW][C]58[/C][C] 27[/C][C] 27[/C][C] 3.035e-15[/C][/ROW]
[ROW][C]59[/C][C] 20[/C][C] 20[/C][C] 1.544e-14[/C][/ROW]
[ROW][C]60[/C][C] 26[/C][C] 26[/C][C]-5.26e-16[/C][/ROW]
[ROW][C]61[/C][C] 25[/C][C] 25[/C][C]-1.818e-15[/C][/ROW]
[ROW][C]62[/C][C] 26[/C][C] 26[/C][C]-7.675e-16[/C][/ROW]
[ROW][C]63[/C][C] 25[/C][C] 25[/C][C]-1.24e-16[/C][/ROW]
[ROW][C]64[/C][C] 28[/C][C] 28[/C][C]-2.295e-15[/C][/ROW]
[ROW][C]65[/C][C] 27[/C][C] 27[/C][C]-5.787e-15[/C][/ROW]
[ROW][C]66[/C][C] 26[/C][C] 26[/C][C]-1.115e-15[/C][/ROW]
[ROW][C]67[/C][C] 26[/C][C] 26[/C][C] 1.277e-15[/C][/ROW]
[ROW][C]68[/C][C] 25[/C][C] 25[/C][C] 9.737e-16[/C][/ROW]
[ROW][C]69[/C][C] 23[/C][C] 23[/C][C] 1.017e-14[/C][/ROW]
[ROW][C]70[/C][C] 24[/C][C] 24[/C][C]-1.294e-15[/C][/ROW]
[ROW][C]71[/C][C] 24[/C][C] 24[/C][C]-1.267e-15[/C][/ROW]
[ROW][C]72[/C][C] 28[/C][C] 28[/C][C] 3.74e-15[/C][/ROW]
[ROW][C]73[/C][C] 23[/C][C] 23[/C][C] 8.296e-16[/C][/ROW]
[ROW][C]74[/C][C] 24[/C][C] 24[/C][C] 2.519e-15[/C][/ROW]
[ROW][C]75[/C][C] 23[/C][C] 23[/C][C]-1.824e-15[/C][/ROW]
[ROW][C]76[/C][C] 25[/C][C] 25[/C][C] 4.593e-15[/C][/ROW]
[ROW][C]77[/C][C] 23[/C][C] 23[/C][C]-1.179e-15[/C][/ROW]
[ROW][C]78[/C][C] 23[/C][C] 23[/C][C] 4.154e-15[/C][/ROW]
[ROW][C]79[/C][C] 19[/C][C] 19[/C][C] 1.883e-14[/C][/ROW]
[ROW][C]80[/C][C] 25[/C][C] 25[/C][C] 9.381e-15[/C][/ROW]
[ROW][C]81[/C][C] 22[/C][C] 22[/C][C] 5.7e-15[/C][/ROW]
[ROW][C]82[/C][C] 27[/C][C] 27[/C][C]-2.559e-15[/C][/ROW]
[ROW][C]83[/C][C] 26[/C][C] 26[/C][C]-6.435e-15[/C][/ROW]
[ROW][C]84[/C][C] 29[/C][C] 29[/C][C]-2.984e-15[/C][/ROW]
[ROW][C]85[/C][C] 28[/C][C] 28[/C][C]-4.865e-15[/C][/ROW]
[ROW][C]86[/C][C] 25[/C][C] 25[/C][C] 1.27e-15[/C][/ROW]
[ROW][C]87[/C][C] 22[/C][C] 22[/C][C] 2.398e-16[/C][/ROW]
[ROW][C]88[/C][C] 25[/C][C] 25[/C][C] 1.295e-15[/C][/ROW]
[ROW][C]89[/C][C] 26[/C][C] 26[/C][C] 1.532e-15[/C][/ROW]
[ROW][C]90[/C][C] 26[/C][C] 26[/C][C] 1.009e-14[/C][/ROW]
[ROW][C]91[/C][C] 24[/C][C] 24[/C][C] 8.455e-15[/C][/ROW]
[ROW][C]92[/C][C] 25[/C][C] 25[/C][C]-4.355e-15[/C][/ROW]
[ROW][C]93[/C][C] 23[/C][C] 23[/C][C] 2.302e-15[/C][/ROW]
[ROW][C]94[/C][C] 25[/C][C] 25[/C][C]-3.62e-15[/C][/ROW]
[ROW][C]95[/C][C] 25[/C][C] 25[/C][C] 1.31e-15[/C][/ROW]
[ROW][C]96[/C][C] 27[/C][C] 27[/C][C] 5.432e-15[/C][/ROW]
[ROW][C]97[/C][C] 25[/C][C] 25[/C][C]-2.878e-15[/C][/ROW]
[ROW][C]98[/C][C] 24[/C][C] 24[/C][C] 1.087e-14[/C][/ROW]
[ROW][C]99[/C][C] 23[/C][C] 23[/C][C] 1.864e-15[/C][/ROW]
[ROW][C]100[/C][C] 27[/C][C] 27[/C][C]-8.203e-15[/C][/ROW]
[ROW][C]101[/C][C] 25[/C][C] 25[/C][C]-1.814e-15[/C][/ROW]
[ROW][C]102[/C][C] 23[/C][C] 23[/C][C] 4.842e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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	22	22	-1.52e-13
2	24	24	1.5e-14
3	26	26	6.237e-15
4	25	25	-6.738e-15
5	28	28	-2.987e-15
6	23	23	-3.092e-16
7	25	25	-1.878e-16
8	24	24	-2.393e-16
9	24	24	3.563e-15
10	24	24	-9.447e-15
11	25	25	-3.159e-15
12	24	24	-6.095e-16
13	26	26	-7.675e-16
14	24	24	1.025e-15
15	24	24	-5.225e-16
16	25	25	-8.628e-16
17	25	25	2.38e-15
18	24	24	7.631e-15
19	28	28	-6.38e-15
20	27	27	2.464e-16
21	23	23	8.363e-15
22	23	23	-7.532e-15
23	24	24	7.292e-15
24	22	22	5.663e-15
25	25	25	1.736e-16
26	28	28	-4.865e-15
27	22	22	4.182e-15
28	28	28	5.958e-16
29	25	25	3.824e-15
30	24	24	7.875e-16
31	23	23	-1.083e-14
32	25	25	4.176e-15
33	26	26	-7.471e-15
34	26	26	8.49e-15
35	23	23	7.649e-15
36	25	25	7.223e-16
37	22	22	-1.926e-16
38	24	24	5.675e-15
39	27	27	-1.843e-15
40	24	24	-3.187e-15
41	26	26	1.789e-15
42	21	21	-9.511e-15
43	27	27	2.116e-15
44	22	22	1.544e-14
45	23	23	9.839e-15
46	24	24	7.134e-15
47	25	25	3.831e-16
48	24	24	2.7e-15
49	22	22	-2.109e-15
50	25	25	4.141e-15
51	25	25	2.658e-15
52	24	24	3.674e-15
53	24	24	9.142e-15
54	21	21	5.482e-15
55	25	25	1.955e-16
56	25	25	2.424e-15
57	26	26	-3.531e-15
58	27	27	3.035e-15
59	20	20	1.544e-14
60	26	26	-5.26e-16
61	25	25	-1.818e-15
62	26	26	-7.675e-16
63	25	25	-1.24e-16
64	28	28	-2.295e-15
65	27	27	-5.787e-15
66	26	26	-1.115e-15
67	26	26	1.277e-15
68	25	25	9.737e-16
69	23	23	1.017e-14
70	24	24	-1.294e-15
71	24	24	-1.267e-15
72	28	28	3.74e-15
73	23	23	8.296e-16
74	24	24	2.519e-15
75	23	23	-1.824e-15
76	25	25	4.593e-15
77	23	23	-1.179e-15
78	23	23	4.154e-15
79	19	19	1.883e-14
80	25	25	9.381e-15
81	22	22	5.7e-15
82	27	27	-2.559e-15
83	26	26	-6.435e-15
84	29	29	-2.984e-15
85	28	28	-4.865e-15
86	25	25	1.27e-15
87	22	22	2.398e-16
88	25	25	1.295e-15
89	26	26	1.532e-15
90	26	26	1.009e-14
91	24	24	8.455e-15
92	25	25	-4.355e-15
93	23	23	2.302e-15
94	25	25	-3.62e-15
95	25	25	1.31e-15
96	27	27	5.432e-15
97	25	25	-2.878e-15
98	24	24	1.087e-14
99	23	23	1.864e-15
100	27	27	-8.203e-15
101	25	25	-1.814e-15
102	23	23	4.842e-15

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.02507	0.05013	0.9749
13	0.9555	0.08905	0.04453
14	0.523	0.954	0.477
15	0.002915	0.005829	0.9971
16	1.772e-10	3.544e-10	1
17	1.503e-05	3.005e-05	1
18	1	4.862e-05	2.431e-05
19	0.00325	0.0065	0.9968
20	1.749e-05	3.499e-05	1
21	0.0415	0.083	0.9585
22	0.9981	0.003888	0.001944
23	0.002644	0.005288	0.9974
24	0.1436	0.2871	0.8564
25	3.24e-13	6.48e-13	1
26	0.9903	0.01933	0.009665
27	2.519e-11	5.039e-11	1
28	1.797e-06	3.594e-06	1
29	1	2.156e-10	1.078e-10
30	0.05525	0.1105	0.9447
31	0.0002553	0.0005106	0.9997
32	1	1.534e-50	7.672e-51
33	0.4206	0.8411	0.5794
34	1.488e-13	2.975e-13	1
35	0.04868	0.09736	0.9513
36	1.609e-17	3.218e-17	1
37	1	9.096e-24	4.548e-24
38	1	2.292e-62	1.146e-62
39	0.00645	0.0129	0.9936
40	0.9999	0.0002202	0.0001101
41	0.1079	0.2157	0.8921
42	0.9976	0.004812	0.002406
43	0.9998	0.0003512	0.0001756
44	1	2.11e-06	1.055e-06
45	1	2.255e-31	1.128e-31
46	1	1.438e-21	7.189e-22
47	1	2.145e-16	1.073e-16
48	1	7.305e-17	3.652e-17
49	1	2.612e-20	1.306e-20
50	1	8.368e-15	4.184e-15
51	0.9588	0.08245	0.04123
52	2.735e-09	5.469e-09	1
53	2.147e-33	4.295e-33	1
54	2.245e-15	4.49e-15	1
55	9.54e-09	1.908e-08	1
56	0.0001459	0.0002918	0.9999
57	1.378e-24	2.756e-24	1
58	4.192e-09	8.384e-09	1
59	1	1.31e-13	6.549e-14
60	1.033e-13	2.065e-13	1
61	2.869e-13	5.738e-13	1
62	0.007175	0.01435	0.9928
63	0.007585	0.01517	0.9924
64	9.569e-40	1.914e-39	1
65	0.9943	0.01143	0.005716
66	1	1.118e-18	5.59e-19
67	2.654e-29	5.308e-29	1
68	0.0001858	0.0003717	0.9998
69	0.02362	0.04723	0.9764
70	0.8247	0.3506	0.1753
71	1	7.974e-05	3.987e-05
72	0.9883	0.02333	0.01166
73	0.006507	0.01301	0.9935
74	0.9872	0.02568	0.01284
75	0.9991	0.001778	0.0008892
76	2.92e-19	5.839e-19	1
77	0.4437	0.8874	0.5563
78	1	3.391e-10	1.696e-10
79	2.859e-10	5.719e-10	1
80	7.831e-12	1.566e-11	1
81	5.9e-08	1.18e-07	1
82	1	2.226e-05	1.113e-05
83	0.9989	0.002103	0.001051
84	0.6949	0.6102	0.3051
85	1	3.988e-08	1.994e-08
86	1	2.435e-07	1.217e-07
87	0.9993	0.001302	0.000651
88	1	2.04e-05	1.02e-05
89	0.3136	0.6273	0.6864
90	1	2.459e-06	1.23e-06

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.02507 &  0.05013 &  0.9749 \tabularnewline
13 &  0.9555 &  0.08905 &  0.04453 \tabularnewline
14 &  0.523 &  0.954 &  0.477 \tabularnewline
15 &  0.002915 &  0.005829 &  0.9971 \tabularnewline
16 &  1.772e-10 &  3.544e-10 &  1 \tabularnewline
17 &  1.503e-05 &  3.005e-05 &  1 \tabularnewline
18 &  1 &  4.862e-05 &  2.431e-05 \tabularnewline
19 &  0.00325 &  0.0065 &  0.9968 \tabularnewline
20 &  1.749e-05 &  3.499e-05 &  1 \tabularnewline
21 &  0.0415 &  0.083 &  0.9585 \tabularnewline
22 &  0.9981 &  0.003888 &  0.001944 \tabularnewline
23 &  0.002644 &  0.005288 &  0.9974 \tabularnewline
24 &  0.1436 &  0.2871 &  0.8564 \tabularnewline
25 &  3.24e-13 &  6.48e-13 &  1 \tabularnewline
26 &  0.9903 &  0.01933 &  0.009665 \tabularnewline
27 &  2.519e-11 &  5.039e-11 &  1 \tabularnewline
28 &  1.797e-06 &  3.594e-06 &  1 \tabularnewline
29 &  1 &  2.156e-10 &  1.078e-10 \tabularnewline
30 &  0.05525 &  0.1105 &  0.9447 \tabularnewline
31 &  0.0002553 &  0.0005106 &  0.9997 \tabularnewline
32 &  1 &  1.534e-50 &  7.672e-51 \tabularnewline
33 &  0.4206 &  0.8411 &  0.5794 \tabularnewline
34 &  1.488e-13 &  2.975e-13 &  1 \tabularnewline
35 &  0.04868 &  0.09736 &  0.9513 \tabularnewline
36 &  1.609e-17 &  3.218e-17 &  1 \tabularnewline
37 &  1 &  9.096e-24 &  4.548e-24 \tabularnewline
38 &  1 &  2.292e-62 &  1.146e-62 \tabularnewline
39 &  0.00645 &  0.0129 &  0.9936 \tabularnewline
40 &  0.9999 &  0.0002202 &  0.0001101 \tabularnewline
41 &  0.1079 &  0.2157 &  0.8921 \tabularnewline
42 &  0.9976 &  0.004812 &  0.002406 \tabularnewline
43 &  0.9998 &  0.0003512 &  0.0001756 \tabularnewline
44 &  1 &  2.11e-06 &  1.055e-06 \tabularnewline
45 &  1 &  2.255e-31 &  1.128e-31 \tabularnewline
46 &  1 &  1.438e-21 &  7.189e-22 \tabularnewline
47 &  1 &  2.145e-16 &  1.073e-16 \tabularnewline
48 &  1 &  7.305e-17 &  3.652e-17 \tabularnewline
49 &  1 &  2.612e-20 &  1.306e-20 \tabularnewline
50 &  1 &  8.368e-15 &  4.184e-15 \tabularnewline
51 &  0.9588 &  0.08245 &  0.04123 \tabularnewline
52 &  2.735e-09 &  5.469e-09 &  1 \tabularnewline
53 &  2.147e-33 &  4.295e-33 &  1 \tabularnewline
54 &  2.245e-15 &  4.49e-15 &  1 \tabularnewline
55 &  9.54e-09 &  1.908e-08 &  1 \tabularnewline
56 &  0.0001459 &  0.0002918 &  0.9999 \tabularnewline
57 &  1.378e-24 &  2.756e-24 &  1 \tabularnewline
58 &  4.192e-09 &  8.384e-09 &  1 \tabularnewline
59 &  1 &  1.31e-13 &  6.549e-14 \tabularnewline
60 &  1.033e-13 &  2.065e-13 &  1 \tabularnewline
61 &  2.869e-13 &  5.738e-13 &  1 \tabularnewline
62 &  0.007175 &  0.01435 &  0.9928 \tabularnewline
63 &  0.007585 &  0.01517 &  0.9924 \tabularnewline
64 &  9.569e-40 &  1.914e-39 &  1 \tabularnewline
65 &  0.9943 &  0.01143 &  0.005716 \tabularnewline
66 &  1 &  1.118e-18 &  5.59e-19 \tabularnewline
67 &  2.654e-29 &  5.308e-29 &  1 \tabularnewline
68 &  0.0001858 &  0.0003717 &  0.9998 \tabularnewline
69 &  0.02362 &  0.04723 &  0.9764 \tabularnewline
70 &  0.8247 &  0.3506 &  0.1753 \tabularnewline
71 &  1 &  7.974e-05 &  3.987e-05 \tabularnewline
72 &  0.9883 &  0.02333 &  0.01166 \tabularnewline
73 &  0.006507 &  0.01301 &  0.9935 \tabularnewline
74 &  0.9872 &  0.02568 &  0.01284 \tabularnewline
75 &  0.9991 &  0.001778 &  0.0008892 \tabularnewline
76 &  2.92e-19 &  5.839e-19 &  1 \tabularnewline
77 &  0.4437 &  0.8874 &  0.5563 \tabularnewline
78 &  1 &  3.391e-10 &  1.696e-10 \tabularnewline
79 &  2.859e-10 &  5.719e-10 &  1 \tabularnewline
80 &  7.831e-12 &  1.566e-11 &  1 \tabularnewline
81 &  5.9e-08 &  1.18e-07 &  1 \tabularnewline
82 &  1 &  2.226e-05 &  1.113e-05 \tabularnewline
83 &  0.9989 &  0.002103 &  0.001051 \tabularnewline
84 &  0.6949 &  0.6102 &  0.3051 \tabularnewline
85 &  1 &  3.988e-08 &  1.994e-08 \tabularnewline
86 &  1 &  2.435e-07 &  1.217e-07 \tabularnewline
87 &  0.9993 &  0.001302 &  0.000651 \tabularnewline
88 &  1 &  2.04e-05 &  1.02e-05 \tabularnewline
89 &  0.3136 &  0.6273 &  0.6864 \tabularnewline
90 &  1 &  2.459e-06 &  1.23e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&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] 0.02507[/C][C] 0.05013[/C][C] 0.9749[/C][/ROW]
[ROW][C]13[/C][C] 0.9555[/C][C] 0.08905[/C][C] 0.04453[/C][/ROW]
[ROW][C]14[/C][C] 0.523[/C][C] 0.954[/C][C] 0.477[/C][/ROW]
[ROW][C]15[/C][C] 0.002915[/C][C] 0.005829[/C][C] 0.9971[/C][/ROW]
[ROW][C]16[/C][C] 1.772e-10[/C][C] 3.544e-10[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 1.503e-05[/C][C] 3.005e-05[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 1[/C][C] 4.862e-05[/C][C] 2.431e-05[/C][/ROW]
[ROW][C]19[/C][C] 0.00325[/C][C] 0.0065[/C][C] 0.9968[/C][/ROW]
[ROW][C]20[/C][C] 1.749e-05[/C][C] 3.499e-05[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 0.0415[/C][C] 0.083[/C][C] 0.9585[/C][/ROW]
[ROW][C]22[/C][C] 0.9981[/C][C] 0.003888[/C][C] 0.001944[/C][/ROW]
[ROW][C]23[/C][C] 0.002644[/C][C] 0.005288[/C][C] 0.9974[/C][/ROW]
[ROW][C]24[/C][C] 0.1436[/C][C] 0.2871[/C][C] 0.8564[/C][/ROW]
[ROW][C]25[/C][C] 3.24e-13[/C][C] 6.48e-13[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 0.9903[/C][C] 0.01933[/C][C] 0.009665[/C][/ROW]
[ROW][C]27[/C][C] 2.519e-11[/C][C] 5.039e-11[/C][C] 1[/C][/ROW]
[ROW][C]28[/C][C] 1.797e-06[/C][C] 3.594e-06[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 1[/C][C] 2.156e-10[/C][C] 1.078e-10[/C][/ROW]
[ROW][C]30[/C][C] 0.05525[/C][C] 0.1105[/C][C] 0.9447[/C][/ROW]
[ROW][C]31[/C][C] 0.0002553[/C][C] 0.0005106[/C][C] 0.9997[/C][/ROW]
[ROW][C]32[/C][C] 1[/C][C] 1.534e-50[/C][C] 7.672e-51[/C][/ROW]
[ROW][C]33[/C][C] 0.4206[/C][C] 0.8411[/C][C] 0.5794[/C][/ROW]
[ROW][C]34[/C][C] 1.488e-13[/C][C] 2.975e-13[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 0.04868[/C][C] 0.09736[/C][C] 0.9513[/C][/ROW]
[ROW][C]36[/C][C] 1.609e-17[/C][C] 3.218e-17[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 1[/C][C] 9.096e-24[/C][C] 4.548e-24[/C][/ROW]
[ROW][C]38[/C][C] 1[/C][C] 2.292e-62[/C][C] 1.146e-62[/C][/ROW]
[ROW][C]39[/C][C] 0.00645[/C][C] 0.0129[/C][C] 0.9936[/C][/ROW]
[ROW][C]40[/C][C] 0.9999[/C][C] 0.0002202[/C][C] 0.0001101[/C][/ROW]
[ROW][C]41[/C][C] 0.1079[/C][C] 0.2157[/C][C] 0.8921[/C][/ROW]
[ROW][C]42[/C][C] 0.9976[/C][C] 0.004812[/C][C] 0.002406[/C][/ROW]
[ROW][C]43[/C][C] 0.9998[/C][C] 0.0003512[/C][C] 0.0001756[/C][/ROW]
[ROW][C]44[/C][C] 1[/C][C] 2.11e-06[/C][C] 1.055e-06[/C][/ROW]
[ROW][C]45[/C][C] 1[/C][C] 2.255e-31[/C][C] 1.128e-31[/C][/ROW]
[ROW][C]46[/C][C] 1[/C][C] 1.438e-21[/C][C] 7.189e-22[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 2.145e-16[/C][C] 1.073e-16[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 7.305e-17[/C][C] 3.652e-17[/C][/ROW]
[ROW][C]49[/C][C] 1[/C][C] 2.612e-20[/C][C] 1.306e-20[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 8.368e-15[/C][C] 4.184e-15[/C][/ROW]
[ROW][C]51[/C][C] 0.9588[/C][C] 0.08245[/C][C] 0.04123[/C][/ROW]
[ROW][C]52[/C][C] 2.735e-09[/C][C] 5.469e-09[/C][C] 1[/C][/ROW]
[ROW][C]53[/C][C] 2.147e-33[/C][C] 4.295e-33[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 2.245e-15[/C][C] 4.49e-15[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 9.54e-09[/C][C] 1.908e-08[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 0.0001459[/C][C] 0.0002918[/C][C] 0.9999[/C][/ROW]
[ROW][C]57[/C][C] 1.378e-24[/C][C] 2.756e-24[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 4.192e-09[/C][C] 8.384e-09[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 1[/C][C] 1.31e-13[/C][C] 6.549e-14[/C][/ROW]
[ROW][C]60[/C][C] 1.033e-13[/C][C] 2.065e-13[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 2.869e-13[/C][C] 5.738e-13[/C][C] 1[/C][/ROW]
[ROW][C]62[/C][C] 0.007175[/C][C] 0.01435[/C][C] 0.9928[/C][/ROW]
[ROW][C]63[/C][C] 0.007585[/C][C] 0.01517[/C][C] 0.9924[/C][/ROW]
[ROW][C]64[/C][C] 9.569e-40[/C][C] 1.914e-39[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 0.9943[/C][C] 0.01143[/C][C] 0.005716[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 1.118e-18[/C][C] 5.59e-19[/C][/ROW]
[ROW][C]67[/C][C] 2.654e-29[/C][C] 5.308e-29[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 0.0001858[/C][C] 0.0003717[/C][C] 0.9998[/C][/ROW]
[ROW][C]69[/C][C] 0.02362[/C][C] 0.04723[/C][C] 0.9764[/C][/ROW]
[ROW][C]70[/C][C] 0.8247[/C][C] 0.3506[/C][C] 0.1753[/C][/ROW]
[ROW][C]71[/C][C] 1[/C][C] 7.974e-05[/C][C] 3.987e-05[/C][/ROW]
[ROW][C]72[/C][C] 0.9883[/C][C] 0.02333[/C][C] 0.01166[/C][/ROW]
[ROW][C]73[/C][C] 0.006507[/C][C] 0.01301[/C][C] 0.9935[/C][/ROW]
[ROW][C]74[/C][C] 0.9872[/C][C] 0.02568[/C][C] 0.01284[/C][/ROW]
[ROW][C]75[/C][C] 0.9991[/C][C] 0.001778[/C][C] 0.0008892[/C][/ROW]
[ROW][C]76[/C][C] 2.92e-19[/C][C] 5.839e-19[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 0.4437[/C][C] 0.8874[/C][C] 0.5563[/C][/ROW]
[ROW][C]78[/C][C] 1[/C][C] 3.391e-10[/C][C] 1.696e-10[/C][/ROW]
[ROW][C]79[/C][C] 2.859e-10[/C][C] 5.719e-10[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 7.831e-12[/C][C] 1.566e-11[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 5.9e-08[/C][C] 1.18e-07[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 1[/C][C] 2.226e-05[/C][C] 1.113e-05[/C][/ROW]
[ROW][C]83[/C][C] 0.9989[/C][C] 0.002103[/C][C] 0.001051[/C][/ROW]
[ROW][C]84[/C][C] 0.6949[/C][C] 0.6102[/C][C] 0.3051[/C][/ROW]
[ROW][C]85[/C][C] 1[/C][C] 3.988e-08[/C][C] 1.994e-08[/C][/ROW]
[ROW][C]86[/C][C] 1[/C][C] 2.435e-07[/C][C] 1.217e-07[/C][/ROW]
[ROW][C]87[/C][C] 0.9993[/C][C] 0.001302[/C][C] 0.000651[/C][/ROW]
[ROW][C]88[/C][C] 1[/C][C] 2.04e-05[/C][C] 1.02e-05[/C][/ROW]
[ROW][C]89[/C][C] 0.3136[/C][C] 0.6273[/C][C] 0.6864[/C][/ROW]
[ROW][C]90[/C][C] 1[/C][C] 2.459e-06[/C][C] 1.23e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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	0.02507	0.05013	0.9749
13	0.9555	0.08905	0.04453
14	0.523	0.954	0.477
15	0.002915	0.005829	0.9971
16	1.772e-10	3.544e-10	1
17	1.503e-05	3.005e-05	1
18	1	4.862e-05	2.431e-05
19	0.00325	0.0065	0.9968
20	1.749e-05	3.499e-05	1
21	0.0415	0.083	0.9585
22	0.9981	0.003888	0.001944
23	0.002644	0.005288	0.9974
24	0.1436	0.2871	0.8564
25	3.24e-13	6.48e-13	1
26	0.9903	0.01933	0.009665
27	2.519e-11	5.039e-11	1
28	1.797e-06	3.594e-06	1
29	1	2.156e-10	1.078e-10
30	0.05525	0.1105	0.9447
31	0.0002553	0.0005106	0.9997
32	1	1.534e-50	7.672e-51
33	0.4206	0.8411	0.5794
34	1.488e-13	2.975e-13	1
35	0.04868	0.09736	0.9513
36	1.609e-17	3.218e-17	1
37	1	9.096e-24	4.548e-24
38	1	2.292e-62	1.146e-62
39	0.00645	0.0129	0.9936
40	0.9999	0.0002202	0.0001101
41	0.1079	0.2157	0.8921
42	0.9976	0.004812	0.002406
43	0.9998	0.0003512	0.0001756
44	1	2.11e-06	1.055e-06
45	1	2.255e-31	1.128e-31
46	1	1.438e-21	7.189e-22
47	1	2.145e-16	1.073e-16
48	1	7.305e-17	3.652e-17
49	1	2.612e-20	1.306e-20
50	1	8.368e-15	4.184e-15
51	0.9588	0.08245	0.04123
52	2.735e-09	5.469e-09	1
53	2.147e-33	4.295e-33	1
54	2.245e-15	4.49e-15	1
55	9.54e-09	1.908e-08	1
56	0.0001459	0.0002918	0.9999
57	1.378e-24	2.756e-24	1
58	4.192e-09	8.384e-09	1
59	1	1.31e-13	6.549e-14
60	1.033e-13	2.065e-13	1
61	2.869e-13	5.738e-13	1
62	0.007175	0.01435	0.9928
63	0.007585	0.01517	0.9924
64	9.569e-40	1.914e-39	1
65	0.9943	0.01143	0.005716
66	1	1.118e-18	5.59e-19
67	2.654e-29	5.308e-29	1
68	0.0001858	0.0003717	0.9998
69	0.02362	0.04723	0.9764
70	0.8247	0.3506	0.1753
71	1	7.974e-05	3.987e-05
72	0.9883	0.02333	0.01166
73	0.006507	0.01301	0.9935
74	0.9872	0.02568	0.01284
75	0.9991	0.001778	0.0008892
76	2.92e-19	5.839e-19	1
77	0.4437	0.8874	0.5563
78	1	3.391e-10	1.696e-10
79	2.859e-10	5.719e-10	1
80	7.831e-12	1.566e-11	1
81	5.9e-08	1.18e-07	1
82	1	2.226e-05	1.113e-05
83	0.9989	0.002103	0.001051
84	0.6949	0.6102	0.3051
85	1	3.988e-08	1.994e-08
86	1	2.435e-07	1.217e-07
87	0.9993	0.001302	0.000651
88	1	2.04e-05	1.02e-05
89	0.3136	0.6273	0.6864
90	1	2.459e-06	1.23e-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	70	0.886076	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 & 70 & 0.886076 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&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]70[/C][C]0.886076[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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	70	0.886076	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.3884, df1 = 2, df2 = 91, p-value = 0.2547
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.9371, df1 = 16, df2 = 77, p-value = 0.02907
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.53034, df1 = 2, df2 = 91, p-value = 0.5902

\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 = 1.3884, df1 = 2, df2 = 91, p-value = 0.2547
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.9371, df1 = 16, df2 = 77, p-value = 0.02907
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.53034, df1 = 2, df2 = 91, p-value = 0.5902
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&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 = 1.3884, df1 = 2, df2 = 91, p-value = 0.2547
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.9371, df1 = 16, df2 = 77, p-value = 0.02907
[/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 = 0.53034, df1 = 2, df2 = 91, p-value = 0.5902
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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 = 1.3884, df1 = 2, df2 = 91, p-value = 0.2547
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.9371, df1 = 16, df2 = 77, p-value = 0.02907
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.53034, df1 = 2, df2 = 91, p-value = 0.5902

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOU6 SKEOU5 SKEOU4 1.052480 1.606652 1.048503 1.038075 1.111849 SKEOU3 SKEOU1 SKEOU2 1.063982 1.201001 1.500845

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC        SKEOU6        SKEOU5        SKEOU4 
     1.052480      1.606652      1.048503      1.038075      1.111849 
       SKEOU3        SKEOU1        SKEOU2 
     1.063982      1.201001      1.500845 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304900&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd          TVDC        SKEOU6        SKEOU5        SKEOU4 
     1.052480      1.606652      1.048503      1.038075      1.111849 
       SKEOU3        SKEOU1        SKEOU2 
     1.063982      1.201001      1.500845 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304900&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304900&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 SKEOU6 SKEOU5 SKEOU4 1.052480 1.606652 1.048503 1.038075 1.111849 SKEOU3 SKEOU1 SKEOU2 1.063982 1.201001 1.500845

Figure 1

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Figure 3

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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):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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 <- '1'
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