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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 09:35:22 +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/t1485160804wku5smmx3btmpzz.htm/, Retrieved Wed, 15 May 2024 17:13:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=303990, Retrieved Wed, 15 May 2024 17:13:21 +0000

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-       [Multiple Regression] [] [2017-01-23 08:35:22] [1bf80170c5e6d32ce8f3ad7977dc404a] [Current]

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14 22 13 22 4 2 4 3 5 4
19 24 16 24 5 3 3 4 5 4
17 21 17 26 4 4 5 4 5 4
17 21 NA 21 3 4 3 3 4 4
15 24 NA 26 4 4 5 4 5 4
20 20 16 25 3 4 4 4 5 5
15 22 NA 21 3 4 4 3 3 4
19 20 NA 24 3 4 5 4 4 4
15 19 NA 27 4 5 4 4 5 5
15 23 17 28 4 5 5 4 5 5
19 21 17 23 4 4 2 4 5 4
NA 19 15 25 4 4 5 3 5 4
20 19 16 24 4 4 4 3 4 5
18 21 14 24 3 3 5 4 4 5
15 21 16 24 4 4 5 4 2 5
14 22 17 25 3 4 5 4 4 5
20 22 NA 25 3 4 5 4 4 5
NA 19 NA NA NA NA 5 NA 5
16 21 NA 25 5 5 4 3 4 4
16 21 NA 25 4 4 4 4 5 4
16 21 16 24 3 4 5 3 4 5
10 20 NA 26 4 4 4 4 5 5
19 22 16 26 4 4 5 4 4 5
19 22 NA 25 4 4 5 4 4 4
16 24 NA 26 4 4 5 4 4 5
15 21 NA 23 3 4 4 4 4 4
18 19 16 24 3 4 4 3 5 5
17 19 15 24 4 4 4 4 4 4
19 23 16 25 2 4 5 4 5 5
17 21 16 25 5 4 4 4 4 4
NA 21 13 24 4 3 5 4 4 4
19 19 15 28 4 5 5 4 5 5
20 21 17 27 5 4 5 4 4 5
5 19 NA NA 4 3 5 4 NA 5
19 21 13 23 2 3 5 4 5 4
16 21 17 23 4 5 2 4 4 4
15 23 NA 24 3 4 5 4 4 4
16 19 14 24 4 3 5 3 4 5
18 19 14 22 4 3 3 4 4 4
16 19 18 25 4 4 5 4 4 4
15 18 NA 25 5 4 4 4 4 4
17 22 17 28 4 5 5 4 5 5
NA 18 13 22 3 3 4 4 4 4
20 22 16 28 5 5 5 3 5 5
19 18 15 25 5 4 5 3 4 4
7 22 15 24 4 4 4 3 4 5
13 22 NA 24 4 4 4 4 4 4
16 19 15 23 3 5 5 3 3 4
16 22 13 25 4 4 4 4 5 4
NA 25 NA NA 2 3 4 2 NA 4
18 19 17 26 4 5 5 4 4 4
18 19 NA 25 5 5 2 4 5 4
16 19 NA 27 5 5 5 4 4 4
17 19 11 26 4 3 5 4 5 5
19 21 14 23 4 3 4 3 4 5
16 21 13 25 4 4 5 4 4 4
19 20 NA 21 3 4 4 3 3 4
13 19 17 22 3 4 4 4 4 3
16 19 16 24 4 4 4 3 5 4
13 22 NA 25 4 4 4 4 5 4
12 26 17 27 5 5 3 4 5 5
17 19 16 24 2 4 4 4 5 5
17 21 16 26 4 4 4 4 5 5
17 21 16 21 3 4 4 4 2 4
16 20 15 27 4 4 5 4 5 5
16 23 12 22 4 2 4 4 4 4
14 22 17 23 4 4 4 3 5 3
16 22 14 24 4 4 4 3 5 4
13 22 14 25 5 4 5 3 3 5
16 21 16 24 3 4 4 3 5 5
14 21 NA 23 3 4 4 3 4 5
20 22 NA 28 4 5 5 5 5 4
12 23 NA NA 4 4 3 4 NA 4
13 18 NA 24 4 4 4 4 4 4
18 24 NA 26 4 4 4 5 5 4
14 22 15 22 3 4 3 4 4 4
19 21 16 25 4 4 4 4 5 4
18 21 14 25 3 4 5 3 5 5
14 21 15 24 3 3 5 4 4 5
18 23 17 24 4 3 5 4 4 4
19 21 NA 26 4 4 5 4 4 5
15 23 10 21 3 3 3 4 4 4
14 21 NA 25 4 4 4 4 5 4
17 19 17 25 4 4 3 4 5 5
19 21 NA 26 4 4 4 4 5 5
13 21 20 25 5 4 4 4 4 4
19 21 17 26 5 4 3 5 4 5
18 23 18 27 4 4 5 4 5 5
20 23 NA 25 3 4 5 4 4 5
15 20 17 NA 3 NA 4 4 4 4
15 20 14 20 4 2 3 3 4 4
15 19 NA 24 4 4 5 4 4 3
20 23 17 26 4 4 5 4 4 5
15 22 NA 25 4 4 4 4 5 4
19 19 17 25 4 5 4 4 5 3
18 23 NA 24 3 4 4 3 5 5
18 22 16 26 4 4 5 4 4 5
15 22 18 25 5 4 3 4 4 5
20 21 18 28 5 4 5 5 4 5
17 21 16 27 4 5 4 4 5 5
12 21 NA 25 3 4 5 4 4 5
18 21 NA 26 5 3 4 4 5 5
19 22 15 26 4 4 5 4 4 5
20 25 13 26 5 4 4 4 4 5
NA 21 NA NA 3 4 4 3 NA 4
17 23 NA 28 5 4 4 5 5 5
15 19 NA NA 4 4 5 3 NA 5
16 22 NA 21 4 4 3 3 4 3
18 20 NA 25 4 4 5 4 4 4
18 21 16 25 4 4 5 4 4 4
14 25 NA 24 3 4 5 4 5 3
15 21 NA 24 4 4 4 4 4 4
12 19 NA 24 4 4 4 3 4 5
17 23 12 23 3 3 4 3 5 5
14 22 NA 23 4 4 4 3 4 4
18 21 16 24 3 4 5 4 4 4
17 24 16 24 4 4 5 4 3 4
17 21 NA 25 5 4 5 1 5 5
20 19 16 28 5 4 5 4 5 5
16 18 14 23 4 4 4 4 4 3
14 19 15 24 4 4 5 3 4 4
15 20 14 23 3 4 4 3 4 5
18 19 NA 24 4 4 4 4 4 4
20 22 15 25 4 4 4 4 5 4
17 21 NA 24 4 5 3 4 4 4
17 22 15 23 3 4 4 4 4 4
17 24 16 23 4 4 4 3 4 4
17 28 NA 25 4 4 4 4 4 5
15 19 NA 21 3 4 3 3 4 4
17 18 NA 22 4 4 4 3 4 3
18 23 11 19 3 2 4 2 4 4
17 19 NA 24 4 4 4 3 5 4
20 23 18 25 5 4 4 3 5 4
15 19 NA 21 2 4 4 3 3 5
16 22 11 22 3 3 4 4 4 4
15 21 NA 23 4 4 4 3 4 4
18 19 18 27 5 5 4 4 5 4
11 22 NA NA NA NA 2 NA NA NA
15 21 15 26 4 5 5 4 4 4
18 23 19 29 5 5 5 5 5 4
20 22 17 28 4 5 5 4 5 5
19 19 NA 24 4 4 4 3 4 5
14 19 14 25 3 4 5 4 5 4
16 21 NA 25 4 4 5 4 4 4
15 22 13 22 4 4 2 4 4 4
17 21 17 25 4 4 3 4 5 5
18 20 14 26 4 4 4 4 5 5
20 23 19 26 5 4 5 3 5 4
17 22 14 24 4 3 5 4 4 4
18 23 NA 25 4 4 5 4 4 4
15 22 NA 19 3 3 2 3 4 4
16 21 16 25 4 5 5 4 4 3
11 20 16 23 4 4 4 3 4 4
15 18 15 25 4 4 4 4 4 5
18 18 12 25 3 4 5 3 5 5
17 20 NA 26 4 4 5 4 4 5
16 19 17 27 5 4 5 4 5 4
12 21 NA 24 4 4 5 4 3 4
19 24 NA 22 2 3 5 4 4 4
18 19 18 25 4 4 4 4 4 5
15 20 15 24 4 3 4 3 5 5
17 19 18 23 4 4 4 4 4 3
19 23 15 27 4 5 5 5 4 4
18 22 NA 24 5 4 3 4 4 4
19 21 NA 24 5 4 4 3 4 4
16 24 NA 21 3 3 1 4 5 5
16 21 16 25 4 4 4 4 4 5
16 21 NA 25 4 4 4 4 5 4
14 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	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=303990&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 19.5659 + 0.00539493Bevr_Leeftijd[t] + 0.30075TVDC[t] -0.0431738SKEOUSUM[t] -0.434135SKEOU1[t] -0.376296SKEOU2[t] -0.460387SKEOU3[t] -0.447221SKEOU4[t] + 0.0242015SKEOU5[t] + 0.0547998SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  19.5659 +  0.00539493Bevr_Leeftijd[t] +  0.30075TVDC[t] -0.0431738SKEOUSUM[t] -0.434135SKEOU1[t] -0.376296SKEOU2[t] -0.460387SKEOU3[t] -0.447221SKEOU4[t] +  0.0242015SKEOU5[t] +  0.0547998SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  19.5659 +  0.00539493Bevr_Leeftijd[t] +  0.30075TVDC[t] -0.0431738SKEOUSUM[t] -0.434135SKEOU1[t] -0.376296SKEOU2[t] -0.460387SKEOU3[t] -0.447221SKEOU4[t] +  0.0242015SKEOU5[t] +  0.0547998SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&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
ITHSUM[t] = + 19.5659 + 0.00539493Bevr_Leeftijd[t] + 0.30075TVDC[t] -0.0431738SKEOUSUM[t] -0.434135SKEOU1[t] -0.376296SKEOU2[t] -0.460387SKEOU3[t] -0.447221SKEOU4[t] + 0.0242015SKEOU5[t] + 0.0547998SKEOU6[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)	+19.57	2.729	+7.1700e+00	2.328e-10	1.164e-10
Bevr_Leeftijd	+0.005395	0.1244	+4.3380e-02	0.9655	0.4827
TVDC	+0.3008	0.1902	+1.5810e+00	0.1174	0.05872
SKEOUSUM	-0.04317	0.09473	-4.5580e-01	0.6497	0.3248
SKEOU1	-0.4341	0.398	-1.0910e+00	0.2783	0.1392
SKEOU2	-0.3763	0.3235	-1.1630e+00	0.2479	0.124
SKEOU3	-0.4604	0.3537	-1.3020e+00	0.1964	0.09821
SKEOU4	-0.4472	0.3727	-1.2000e+00	0.2334	0.1167
SKEOU5	+0.0242	0.3399	+7.1200e-02	0.9434	0.4717
SKEOU6	+0.0548	0.07223	+7.5870e-01	0.4501	0.225

\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) & +19.57 &  2.729 & +7.1700e+00 &  2.328e-10 &  1.164e-10 \tabularnewline
Bevr_Leeftijd & +0.005395 &  0.1244 & +4.3380e-02 &  0.9655 &  0.4827 \tabularnewline
TVDC & +0.3008 &  0.1902 & +1.5810e+00 &  0.1174 &  0.05872 \tabularnewline
SKEOUSUM & -0.04317 &  0.09473 & -4.5580e-01 &  0.6497 &  0.3248 \tabularnewline
SKEOU1 & -0.4341 &  0.398 & -1.0910e+00 &  0.2783 &  0.1392 \tabularnewline
SKEOU2 & -0.3763 &  0.3235 & -1.1630e+00 &  0.2479 &  0.124 \tabularnewline
SKEOU3 & -0.4604 &  0.3537 & -1.3020e+00 &  0.1964 &  0.09821 \tabularnewline
SKEOU4 & -0.4472 &  0.3727 & -1.2000e+00 &  0.2334 &  0.1167 \tabularnewline
SKEOU5 & +0.0242 &  0.3399 & +7.1200e-02 &  0.9434 &  0.4717 \tabularnewline
SKEOU6 & +0.0548 &  0.07223 & +7.5870e-01 &  0.4501 &  0.225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&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]+19.57[/C][C] 2.729[/C][C]+7.1700e+00[/C][C] 2.328e-10[/C][C] 1.164e-10[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.005395[/C][C] 0.1244[/C][C]+4.3380e-02[/C][C] 0.9655[/C][C] 0.4827[/C][/ROW]
[ROW][C]TVDC[/C][C]+0.3008[/C][C] 0.1902[/C][C]+1.5810e+00[/C][C] 0.1174[/C][C] 0.05872[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]-0.04317[/C][C] 0.09473[/C][C]-4.5580e-01[/C][C] 0.6497[/C][C] 0.3248[/C][/ROW]
[ROW][C]SKEOU1[/C][C]-0.4341[/C][C] 0.398[/C][C]-1.0910e+00[/C][C] 0.2783[/C][C] 0.1392[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.3763[/C][C] 0.3235[/C][C]-1.1630e+00[/C][C] 0.2479[/C][C] 0.124[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.4604[/C][C] 0.3537[/C][C]-1.3020e+00[/C][C] 0.1964[/C][C] 0.09821[/C][/ROW]
[ROW][C]SKEOU4[/C][C]-0.4472[/C][C] 0.3727[/C][C]-1.2000e+00[/C][C] 0.2334[/C][C] 0.1167[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.0242[/C][C] 0.3399[/C][C]+7.1200e-02[/C][C] 0.9434[/C][C] 0.4717[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.0548[/C][C] 0.07223[/C][C]+7.5870e-01[/C][C] 0.4501[/C][C] 0.225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&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)	+19.57	2.729	+7.1700e+00	2.328e-10	1.164e-10
Bevr_Leeftijd	+0.005395	0.1244	+4.3380e-02	0.9655	0.4827
TVDC	+0.3008	0.1902	+1.5810e+00	0.1174	0.05872
SKEOUSUM	-0.04317	0.09473	-4.5580e-01	0.6497	0.3248
SKEOU1	-0.4341	0.398	-1.0910e+00	0.2783	0.1392
SKEOU2	-0.3763	0.3235	-1.1630e+00	0.2479	0.124
SKEOU3	-0.4604	0.3537	-1.3020e+00	0.1964	0.09821
SKEOU4	-0.4472	0.3727	-1.2000e+00	0.2334	0.1167
SKEOU5	+0.0242	0.3399	+7.1200e-02	0.9434	0.4717
SKEOU6	+0.0548	0.07223	+7.5870e-01	0.4501	0.225

Multiple Linear Regression - Regression Statistics
Multiple R	0.5782
R-squared	0.3343
Adjusted R-squared	0.2654
F-TEST (value)	4.855
F-TEST (DF numerator)	9
F-TEST (DF denominator)	87
p-value	2.899e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.797
Sum Squared Residuals	281.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5782 \tabularnewline
R-squared &  0.3343 \tabularnewline
Adjusted R-squared &  0.2654 \tabularnewline
F-TEST (value) &  4.855 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 87 \tabularnewline
p-value &  2.899e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.797 \tabularnewline
Sum Squared Residuals &  281.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5782[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3343[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2654[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.855[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]87[/C][/ROW]
[ROW][C]p-value[/C][C] 2.899e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.797[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 281.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.5782
R-squared	0.3343
Adjusted R-squared	0.2654
F-TEST (value)	4.855
F-TEST (DF numerator)	9
F-TEST (DF denominator)	87
p-value	2.899e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.797
Sum Squared Residuals	281.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	17.31	-3.312
2	19	17.34	1.658
3	17	16.68	0.3229
4	20	17.36	2.637
5	15	16.28	-1.28
6	19	18.19	0.8122
7	20	17.39	2.61
8	18	16.7	1.298
9	15	16.44	-1.445
10	14	17.19	-3.19
11	21	20.62	0.3783
12	22	21.21	0.7872
13	19	20.93	-1.934
14	19	20.96	-1.958
15	23	20.44	2.558
16	21	21.01	-0.005063
17	19	20.98	-1.982
18	21	20.71	0.2914
19	21	20.18	0.821
20	21	20.77	0.2327
21	19	21.44	-2.44
22	19	21	-1.997
23	19	20.68	-1.679
24	18	20.88	-2.878
25	22	21.35	0.6498
26	18	20.64	-2.642
27	22	21.11	0.8862
28	19	20.63	-1.633
29	19	20.71	-1.705
30	19	21.17	-2.173
31	21	21.41	-0.4062
32	21	20.87	0.1283
33	19	20.22	-1.222
34	19	20.65	-1.648
35	26	21.24	4.763
36	19	20.52	-1.517
37	21	21.03	-0.03229
38	21	20.83	0.1656
39	20	20.9	-0.8965
40	23	20.93	2.065
41	22	20.49	1.507
42	22	20.64	1.363
43	22	21.6	0.3987
44	21	20.77	0.23
45	22	20.78	1.224
46	21	20.76	0.2379
47	21	20.68	0.3164
48	21	21.03	-0.02843
49	23	21.03	1.973
50	23	20.61	2.392
51	19	21.22	-2.223
52	21	21.24	-0.2426
53	21	21.41	-0.4124
54	23	21.13	1.868
55	20	21.24	-1.235
56	23	21	2.001
57	19	20.31	-1.309
58	22	20.99	1.006
59	22	21.69	0.3129
60	21	21.21	-0.2119
61	21	20.62	0.3751
62	22	21.26	0.7378
63	21	20.61	0.3861
64	23	20.88	2.118
65	21	20.52	0.4793
66	24	20.92	3.075
67	19	21.16	-2.16
68	18	20.35	-2.354
69	19	20.82	-1.823
70	20	21.12	-1.125
71	22	20.7	1.298
72	22	20.59	1.409
73	24	21.01	2.987
74	23	21.16	1.845
75	23	21.03	1.974
76	22	20.59	1.407
77	19	20.51	-1.514
78	21	20.69	0.3057
79	23	20.78	2.223
80	22	20.94	1.062
81	19	20.31	-1.309
82	22	20.99	1.011
83	21	21.17	-0.168
84	20	21.19	-1.186
85	23	21.07	1.935
86	22	20.96	1.044
87	21	19.99	1.009
88	20	20.9	-0.9038
89	18	21.23	-3.228
90	18	20.84	-2.837
91	19	20.62	-1.621
92	19	21.08	-2.08
93	20	21.32	-1.32
94	19	20.65	-1.649
95	23	20.53	2.465
96	21	21.12	-0.124
97	22	20	1.998

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  17.31 & -3.312 \tabularnewline
2 &  19 &  17.34 &  1.658 \tabularnewline
3 &  17 &  16.68 &  0.3229 \tabularnewline
4 &  20 &  17.36 &  2.637 \tabularnewline
5 &  15 &  16.28 & -1.28 \tabularnewline
6 &  19 &  18.19 &  0.8122 \tabularnewline
7 &  20 &  17.39 &  2.61 \tabularnewline
8 &  18 &  16.7 &  1.298 \tabularnewline
9 &  15 &  16.44 & -1.445 \tabularnewline
10 &  14 &  17.19 & -3.19 \tabularnewline
11 &  21 &  20.62 &  0.3783 \tabularnewline
12 &  22 &  21.21 &  0.7872 \tabularnewline
13 &  19 &  20.93 & -1.934 \tabularnewline
14 &  19 &  20.96 & -1.958 \tabularnewline
15 &  23 &  20.44 &  2.558 \tabularnewline
16 &  21 &  21.01 & -0.005063 \tabularnewline
17 &  19 &  20.98 & -1.982 \tabularnewline
18 &  21 &  20.71 &  0.2914 \tabularnewline
19 &  21 &  20.18 &  0.821 \tabularnewline
20 &  21 &  20.77 &  0.2327 \tabularnewline
21 &  19 &  21.44 & -2.44 \tabularnewline
22 &  19 &  21 & -1.997 \tabularnewline
23 &  19 &  20.68 & -1.679 \tabularnewline
24 &  18 &  20.88 & -2.878 \tabularnewline
25 &  22 &  21.35 &  0.6498 \tabularnewline
26 &  18 &  20.64 & -2.642 \tabularnewline
27 &  22 &  21.11 &  0.8862 \tabularnewline
28 &  19 &  20.63 & -1.633 \tabularnewline
29 &  19 &  20.71 & -1.705 \tabularnewline
30 &  19 &  21.17 & -2.173 \tabularnewline
31 &  21 &  21.41 & -0.4062 \tabularnewline
32 &  21 &  20.87 &  0.1283 \tabularnewline
33 &  19 &  20.22 & -1.222 \tabularnewline
34 &  19 &  20.65 & -1.648 \tabularnewline
35 &  26 &  21.24 &  4.763 \tabularnewline
36 &  19 &  20.52 & -1.517 \tabularnewline
37 &  21 &  21.03 & -0.03229 \tabularnewline
38 &  21 &  20.83 &  0.1656 \tabularnewline
39 &  20 &  20.9 & -0.8965 \tabularnewline
40 &  23 &  20.93 &  2.065 \tabularnewline
41 &  22 &  20.49 &  1.507 \tabularnewline
42 &  22 &  20.64 &  1.363 \tabularnewline
43 &  22 &  21.6 &  0.3987 \tabularnewline
44 &  21 &  20.77 &  0.23 \tabularnewline
45 &  22 &  20.78 &  1.224 \tabularnewline
46 &  21 &  20.76 &  0.2379 \tabularnewline
47 &  21 &  20.68 &  0.3164 \tabularnewline
48 &  21 &  21.03 & -0.02843 \tabularnewline
49 &  23 &  21.03 &  1.973 \tabularnewline
50 &  23 &  20.61 &  2.392 \tabularnewline
51 &  19 &  21.22 & -2.223 \tabularnewline
52 &  21 &  21.24 & -0.2426 \tabularnewline
53 &  21 &  21.41 & -0.4124 \tabularnewline
54 &  23 &  21.13 &  1.868 \tabularnewline
55 &  20 &  21.24 & -1.235 \tabularnewline
56 &  23 &  21 &  2.001 \tabularnewline
57 &  19 &  20.31 & -1.309 \tabularnewline
58 &  22 &  20.99 &  1.006 \tabularnewline
59 &  22 &  21.69 &  0.3129 \tabularnewline
60 &  21 &  21.21 & -0.2119 \tabularnewline
61 &  21 &  20.62 &  0.3751 \tabularnewline
62 &  22 &  21.26 &  0.7378 \tabularnewline
63 &  21 &  20.61 &  0.3861 \tabularnewline
64 &  23 &  20.88 &  2.118 \tabularnewline
65 &  21 &  20.52 &  0.4793 \tabularnewline
66 &  24 &  20.92 &  3.075 \tabularnewline
67 &  19 &  21.16 & -2.16 \tabularnewline
68 &  18 &  20.35 & -2.354 \tabularnewline
69 &  19 &  20.82 & -1.823 \tabularnewline
70 &  20 &  21.12 & -1.125 \tabularnewline
71 &  22 &  20.7 &  1.298 \tabularnewline
72 &  22 &  20.59 &  1.409 \tabularnewline
73 &  24 &  21.01 &  2.987 \tabularnewline
74 &  23 &  21.16 &  1.845 \tabularnewline
75 &  23 &  21.03 &  1.974 \tabularnewline
76 &  22 &  20.59 &  1.407 \tabularnewline
77 &  19 &  20.51 & -1.514 \tabularnewline
78 &  21 &  20.69 &  0.3057 \tabularnewline
79 &  23 &  20.78 &  2.223 \tabularnewline
80 &  22 &  20.94 &  1.062 \tabularnewline
81 &  19 &  20.31 & -1.309 \tabularnewline
82 &  22 &  20.99 &  1.011 \tabularnewline
83 &  21 &  21.17 & -0.168 \tabularnewline
84 &  20 &  21.19 & -1.186 \tabularnewline
85 &  23 &  21.07 &  1.935 \tabularnewline
86 &  22 &  20.96 &  1.044 \tabularnewline
87 &  21 &  19.99 &  1.009 \tabularnewline
88 &  20 &  20.9 & -0.9038 \tabularnewline
89 &  18 &  21.23 & -3.228 \tabularnewline
90 &  18 &  20.84 & -2.837 \tabularnewline
91 &  19 &  20.62 & -1.621 \tabularnewline
92 &  19 &  21.08 & -2.08 \tabularnewline
93 &  20 &  21.32 & -1.32 \tabularnewline
94 &  19 &  20.65 & -1.649 \tabularnewline
95 &  23 &  20.53 &  2.465 \tabularnewline
96 &  21 &  21.12 & -0.124 \tabularnewline
97 &  22 &  20 &  1.998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&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] 14[/C][C] 17.31[/C][C]-3.312[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 17.34[/C][C] 1.658[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.68[/C][C] 0.3229[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 17.36[/C][C] 2.637[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 16.28[/C][C]-1.28[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 18.19[/C][C] 0.8122[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 17.39[/C][C] 2.61[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 16.7[/C][C] 1.298[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 16.44[/C][C]-1.445[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 17.19[/C][C]-3.19[/C][/ROW]
[ROW][C]11[/C][C] 21[/C][C] 20.62[/C][C] 0.3783[/C][/ROW]
[ROW][C]12[/C][C] 22[/C][C] 21.21[/C][C] 0.7872[/C][/ROW]
[ROW][C]13[/C][C] 19[/C][C] 20.93[/C][C]-1.934[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 20.96[/C][C]-1.958[/C][/ROW]
[ROW][C]15[/C][C] 23[/C][C] 20.44[/C][C] 2.558[/C][/ROW]
[ROW][C]16[/C][C] 21[/C][C] 21.01[/C][C]-0.005063[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 20.98[/C][C]-1.982[/C][/ROW]
[ROW][C]18[/C][C] 21[/C][C] 20.71[/C][C] 0.2914[/C][/ROW]
[ROW][C]19[/C][C] 21[/C][C] 20.18[/C][C] 0.821[/C][/ROW]
[ROW][C]20[/C][C] 21[/C][C] 20.77[/C][C] 0.2327[/C][/ROW]
[ROW][C]21[/C][C] 19[/C][C] 21.44[/C][C]-2.44[/C][/ROW]
[ROW][C]22[/C][C] 19[/C][C] 21[/C][C]-1.997[/C][/ROW]
[ROW][C]23[/C][C] 19[/C][C] 20.68[/C][C]-1.679[/C][/ROW]
[ROW][C]24[/C][C] 18[/C][C] 20.88[/C][C]-2.878[/C][/ROW]
[ROW][C]25[/C][C] 22[/C][C] 21.35[/C][C] 0.6498[/C][/ROW]
[ROW][C]26[/C][C] 18[/C][C] 20.64[/C][C]-2.642[/C][/ROW]
[ROW][C]27[/C][C] 22[/C][C] 21.11[/C][C] 0.8862[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 20.63[/C][C]-1.633[/C][/ROW]
[ROW][C]29[/C][C] 19[/C][C] 20.71[/C][C]-1.705[/C][/ROW]
[ROW][C]30[/C][C] 19[/C][C] 21.17[/C][C]-2.173[/C][/ROW]
[ROW][C]31[/C][C] 21[/C][C] 21.41[/C][C]-0.4062[/C][/ROW]
[ROW][C]32[/C][C] 21[/C][C] 20.87[/C][C] 0.1283[/C][/ROW]
[ROW][C]33[/C][C] 19[/C][C] 20.22[/C][C]-1.222[/C][/ROW]
[ROW][C]34[/C][C] 19[/C][C] 20.65[/C][C]-1.648[/C][/ROW]
[ROW][C]35[/C][C] 26[/C][C] 21.24[/C][C] 4.763[/C][/ROW]
[ROW][C]36[/C][C] 19[/C][C] 20.52[/C][C]-1.517[/C][/ROW]
[ROW][C]37[/C][C] 21[/C][C] 21.03[/C][C]-0.03229[/C][/ROW]
[ROW][C]38[/C][C] 21[/C][C] 20.83[/C][C] 0.1656[/C][/ROW]
[ROW][C]39[/C][C] 20[/C][C] 20.9[/C][C]-0.8965[/C][/ROW]
[ROW][C]40[/C][C] 23[/C][C] 20.93[/C][C] 2.065[/C][/ROW]
[ROW][C]41[/C][C] 22[/C][C] 20.49[/C][C] 1.507[/C][/ROW]
[ROW][C]42[/C][C] 22[/C][C] 20.64[/C][C] 1.363[/C][/ROW]
[ROW][C]43[/C][C] 22[/C][C] 21.6[/C][C] 0.3987[/C][/ROW]
[ROW][C]44[/C][C] 21[/C][C] 20.77[/C][C] 0.23[/C][/ROW]
[ROW][C]45[/C][C] 22[/C][C] 20.78[/C][C] 1.224[/C][/ROW]
[ROW][C]46[/C][C] 21[/C][C] 20.76[/C][C] 0.2379[/C][/ROW]
[ROW][C]47[/C][C] 21[/C][C] 20.68[/C][C] 0.3164[/C][/ROW]
[ROW][C]48[/C][C] 21[/C][C] 21.03[/C][C]-0.02843[/C][/ROW]
[ROW][C]49[/C][C] 23[/C][C] 21.03[/C][C] 1.973[/C][/ROW]
[ROW][C]50[/C][C] 23[/C][C] 20.61[/C][C] 2.392[/C][/ROW]
[ROW][C]51[/C][C] 19[/C][C] 21.22[/C][C]-2.223[/C][/ROW]
[ROW][C]52[/C][C] 21[/C][C] 21.24[/C][C]-0.2426[/C][/ROW]
[ROW][C]53[/C][C] 21[/C][C] 21.41[/C][C]-0.4124[/C][/ROW]
[ROW][C]54[/C][C] 23[/C][C] 21.13[/C][C] 1.868[/C][/ROW]
[ROW][C]55[/C][C] 20[/C][C] 21.24[/C][C]-1.235[/C][/ROW]
[ROW][C]56[/C][C] 23[/C][C] 21[/C][C] 2.001[/C][/ROW]
[ROW][C]57[/C][C] 19[/C][C] 20.31[/C][C]-1.309[/C][/ROW]
[ROW][C]58[/C][C] 22[/C][C] 20.99[/C][C] 1.006[/C][/ROW]
[ROW][C]59[/C][C] 22[/C][C] 21.69[/C][C] 0.3129[/C][/ROW]
[ROW][C]60[/C][C] 21[/C][C] 21.21[/C][C]-0.2119[/C][/ROW]
[ROW][C]61[/C][C] 21[/C][C] 20.62[/C][C] 0.3751[/C][/ROW]
[ROW][C]62[/C][C] 22[/C][C] 21.26[/C][C] 0.7378[/C][/ROW]
[ROW][C]63[/C][C] 21[/C][C] 20.61[/C][C] 0.3861[/C][/ROW]
[ROW][C]64[/C][C] 23[/C][C] 20.88[/C][C] 2.118[/C][/ROW]
[ROW][C]65[/C][C] 21[/C][C] 20.52[/C][C] 0.4793[/C][/ROW]
[ROW][C]66[/C][C] 24[/C][C] 20.92[/C][C] 3.075[/C][/ROW]
[ROW][C]67[/C][C] 19[/C][C] 21.16[/C][C]-2.16[/C][/ROW]
[ROW][C]68[/C][C] 18[/C][C] 20.35[/C][C]-2.354[/C][/ROW]
[ROW][C]69[/C][C] 19[/C][C] 20.82[/C][C]-1.823[/C][/ROW]
[ROW][C]70[/C][C] 20[/C][C] 21.12[/C][C]-1.125[/C][/ROW]
[ROW][C]71[/C][C] 22[/C][C] 20.7[/C][C] 1.298[/C][/ROW]
[ROW][C]72[/C][C] 22[/C][C] 20.59[/C][C] 1.409[/C][/ROW]
[ROW][C]73[/C][C] 24[/C][C] 21.01[/C][C] 2.987[/C][/ROW]
[ROW][C]74[/C][C] 23[/C][C] 21.16[/C][C] 1.845[/C][/ROW]
[ROW][C]75[/C][C] 23[/C][C] 21.03[/C][C] 1.974[/C][/ROW]
[ROW][C]76[/C][C] 22[/C][C] 20.59[/C][C] 1.407[/C][/ROW]
[ROW][C]77[/C][C] 19[/C][C] 20.51[/C][C]-1.514[/C][/ROW]
[ROW][C]78[/C][C] 21[/C][C] 20.69[/C][C] 0.3057[/C][/ROW]
[ROW][C]79[/C][C] 23[/C][C] 20.78[/C][C] 2.223[/C][/ROW]
[ROW][C]80[/C][C] 22[/C][C] 20.94[/C][C] 1.062[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 20.31[/C][C]-1.309[/C][/ROW]
[ROW][C]82[/C][C] 22[/C][C] 20.99[/C][C] 1.011[/C][/ROW]
[ROW][C]83[/C][C] 21[/C][C] 21.17[/C][C]-0.168[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 21.19[/C][C]-1.186[/C][/ROW]
[ROW][C]85[/C][C] 23[/C][C] 21.07[/C][C] 1.935[/C][/ROW]
[ROW][C]86[/C][C] 22[/C][C] 20.96[/C][C] 1.044[/C][/ROW]
[ROW][C]87[/C][C] 21[/C][C] 19.99[/C][C] 1.009[/C][/ROW]
[ROW][C]88[/C][C] 20[/C][C] 20.9[/C][C]-0.9038[/C][/ROW]
[ROW][C]89[/C][C] 18[/C][C] 21.23[/C][C]-3.228[/C][/ROW]
[ROW][C]90[/C][C] 18[/C][C] 20.84[/C][C]-2.837[/C][/ROW]
[ROW][C]91[/C][C] 19[/C][C] 20.62[/C][C]-1.621[/C][/ROW]
[ROW][C]92[/C][C] 19[/C][C] 21.08[/C][C]-2.08[/C][/ROW]
[ROW][C]93[/C][C] 20[/C][C] 21.32[/C][C]-1.32[/C][/ROW]
[ROW][C]94[/C][C] 19[/C][C] 20.65[/C][C]-1.649[/C][/ROW]
[ROW][C]95[/C][C] 23[/C][C] 20.53[/C][C] 2.465[/C][/ROW]
[ROW][C]96[/C][C] 21[/C][C] 21.12[/C][C]-0.124[/C][/ROW]
[ROW][C]97[/C][C] 22[/C][C] 20[/C][C] 1.998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&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	14	17.31	-3.312
2	19	17.34	1.658
3	17	16.68	0.3229
4	20	17.36	2.637
5	15	16.28	-1.28
6	19	18.19	0.8122
7	20	17.39	2.61
8	18	16.7	1.298
9	15	16.44	-1.445
10	14	17.19	-3.19
11	21	20.62	0.3783
12	22	21.21	0.7872
13	19	20.93	-1.934
14	19	20.96	-1.958
15	23	20.44	2.558
16	21	21.01	-0.005063
17	19	20.98	-1.982
18	21	20.71	0.2914
19	21	20.18	0.821
20	21	20.77	0.2327
21	19	21.44	-2.44
22	19	21	-1.997
23	19	20.68	-1.679
24	18	20.88	-2.878
25	22	21.35	0.6498
26	18	20.64	-2.642
27	22	21.11	0.8862
28	19	20.63	-1.633
29	19	20.71	-1.705
30	19	21.17	-2.173
31	21	21.41	-0.4062
32	21	20.87	0.1283
33	19	20.22	-1.222
34	19	20.65	-1.648
35	26	21.24	4.763
36	19	20.52	-1.517
37	21	21.03	-0.03229
38	21	20.83	0.1656
39	20	20.9	-0.8965
40	23	20.93	2.065
41	22	20.49	1.507
42	22	20.64	1.363
43	22	21.6	0.3987
44	21	20.77	0.23
45	22	20.78	1.224
46	21	20.76	0.2379
47	21	20.68	0.3164
48	21	21.03	-0.02843
49	23	21.03	1.973
50	23	20.61	2.392
51	19	21.22	-2.223
52	21	21.24	-0.2426
53	21	21.41	-0.4124
54	23	21.13	1.868
55	20	21.24	-1.235
56	23	21	2.001
57	19	20.31	-1.309
58	22	20.99	1.006
59	22	21.69	0.3129
60	21	21.21	-0.2119
61	21	20.62	0.3751
62	22	21.26	0.7378
63	21	20.61	0.3861
64	23	20.88	2.118
65	21	20.52	0.4793
66	24	20.92	3.075
67	19	21.16	-2.16
68	18	20.35	-2.354
69	19	20.82	-1.823
70	20	21.12	-1.125
71	22	20.7	1.298
72	22	20.59	1.409
73	24	21.01	2.987
74	23	21.16	1.845
75	23	21.03	1.974
76	22	20.59	1.407
77	19	20.51	-1.514
78	21	20.69	0.3057
79	23	20.78	2.223
80	22	20.94	1.062
81	19	20.31	-1.309
82	22	20.99	1.011
83	21	21.17	-0.168
84	20	21.19	-1.186
85	23	21.07	1.935
86	22	20.96	1.044
87	21	19.99	1.009
88	20	20.9	-0.9038
89	18	21.23	-3.228
90	18	20.84	-2.837
91	19	20.62	-1.621
92	19	21.08	-2.08
93	20	21.32	-1.32
94	19	20.65	-1.649
95	23	20.53	2.465
96	21	21.12	-0.124
97	22	20	1.998

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.964	0.07202	0.03601
14	0.9434	0.1132	0.05658
15	0.9491	0.1017	0.05086
16	0.9167	0.1667	0.08333
17	0.9064	0.1872	0.09358
18	0.8839	0.2322	0.1161
19	0.841	0.3181	0.159
20	0.8581	0.2837	0.1419
21	0.8696	0.2608	0.1304
22	0.8416	0.3168	0.1584
23	0.7966	0.4068	0.2034
24	0.8154	0.3692	0.1846
25	0.7654	0.4692	0.2346
26	0.8478	0.3043	0.1522
27	0.815	0.37	0.185
28	0.7814	0.4373	0.2186
29	0.7421	0.5158	0.2579
30	0.7802	0.4396	0.2198
31	0.729	0.5421	0.271
32	0.6879	0.6242	0.3121
33	0.6444	0.7113	0.3556
34	0.6156	0.7687	0.3844
35	0.9112	0.1776	0.0888
36	0.9036	0.1928	0.09641
37	0.8718	0.2564	0.1282
38	0.8673	0.2654	0.1327
39	0.8376	0.3249	0.1624
40	0.8691	0.2617	0.1309
41	0.8766	0.2467	0.1234
42	0.8627	0.2746	0.1373
43	0.8289	0.3422	0.1711
44	0.7861	0.4278	0.2139
45	0.7583	0.4835	0.2417
46	0.7066	0.5868	0.2934
47	0.6509	0.6982	0.3491
48	0.6114	0.7772	0.3886
49	0.6306	0.7387	0.3694
50	0.664	0.6719	0.336
51	0.6865	0.6271	0.3135
52	0.6391	0.7218	0.3609
53	0.5776	0.8448	0.4224
54	0.5726	0.8547	0.4274
55	0.5463	0.9074	0.4537
56	0.5523	0.8953	0.4477
57	0.534	0.932	0.466
58	0.4959	0.9919	0.5041
59	0.4302	0.8605	0.5698
60	0.3652	0.7303	0.6348
61	0.3408	0.6816	0.6592
62	0.2885	0.5769	0.7115
63	0.2359	0.4718	0.7641
64	0.3214	0.6428	0.6786
65	0.2689	0.5379	0.7311
66	0.3535	0.7071	0.6465
67	0.3301	0.6602	0.6699
68	0.4484	0.8967	0.5516
69	0.4334	0.8669	0.5666
70	0.3682	0.7364	0.6318
71	0.3123	0.6247	0.6877
72	0.2648	0.5296	0.7352
73	0.3621	0.7241	0.6379
74	0.3848	0.7697	0.6152
75	0.4499	0.8999	0.5501
76	0.4289	0.8578	0.5711
77	0.3932	0.7863	0.6068
78	0.3043	0.6087	0.6957
79	0.2401	0.4801	0.7599
80	0.2068	0.4135	0.7932
81	0.1646	0.3292	0.8354
82	0.1562	0.3123	0.8438
83	0.1288	0.2575	0.8712
84	0.07906	0.1581	0.9209

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 &  0.964 &  0.07202 &  0.03601 \tabularnewline
14 &  0.9434 &  0.1132 &  0.05658 \tabularnewline
15 &  0.9491 &  0.1017 &  0.05086 \tabularnewline
16 &  0.9167 &  0.1667 &  0.08333 \tabularnewline
17 &  0.9064 &  0.1872 &  0.09358 \tabularnewline
18 &  0.8839 &  0.2322 &  0.1161 \tabularnewline
19 &  0.841 &  0.3181 &  0.159 \tabularnewline
20 &  0.8581 &  0.2837 &  0.1419 \tabularnewline
21 &  0.8696 &  0.2608 &  0.1304 \tabularnewline
22 &  0.8416 &  0.3168 &  0.1584 \tabularnewline
23 &  0.7966 &  0.4068 &  0.2034 \tabularnewline
24 &  0.8154 &  0.3692 &  0.1846 \tabularnewline
25 &  0.7654 &  0.4692 &  0.2346 \tabularnewline
26 &  0.8478 &  0.3043 &  0.1522 \tabularnewline
27 &  0.815 &  0.37 &  0.185 \tabularnewline
28 &  0.7814 &  0.4373 &  0.2186 \tabularnewline
29 &  0.7421 &  0.5158 &  0.2579 \tabularnewline
30 &  0.7802 &  0.4396 &  0.2198 \tabularnewline
31 &  0.729 &  0.5421 &  0.271 \tabularnewline
32 &  0.6879 &  0.6242 &  0.3121 \tabularnewline
33 &  0.6444 &  0.7113 &  0.3556 \tabularnewline
34 &  0.6156 &  0.7687 &  0.3844 \tabularnewline
35 &  0.9112 &  0.1776 &  0.0888 \tabularnewline
36 &  0.9036 &  0.1928 &  0.09641 \tabularnewline
37 &  0.8718 &  0.2564 &  0.1282 \tabularnewline
38 &  0.8673 &  0.2654 &  0.1327 \tabularnewline
39 &  0.8376 &  0.3249 &  0.1624 \tabularnewline
40 &  0.8691 &  0.2617 &  0.1309 \tabularnewline
41 &  0.8766 &  0.2467 &  0.1234 \tabularnewline
42 &  0.8627 &  0.2746 &  0.1373 \tabularnewline
43 &  0.8289 &  0.3422 &  0.1711 \tabularnewline
44 &  0.7861 &  0.4278 &  0.2139 \tabularnewline
45 &  0.7583 &  0.4835 &  0.2417 \tabularnewline
46 &  0.7066 &  0.5868 &  0.2934 \tabularnewline
47 &  0.6509 &  0.6982 &  0.3491 \tabularnewline
48 &  0.6114 &  0.7772 &  0.3886 \tabularnewline
49 &  0.6306 &  0.7387 &  0.3694 \tabularnewline
50 &  0.664 &  0.6719 &  0.336 \tabularnewline
51 &  0.6865 &  0.6271 &  0.3135 \tabularnewline
52 &  0.6391 &  0.7218 &  0.3609 \tabularnewline
53 &  0.5776 &  0.8448 &  0.4224 \tabularnewline
54 &  0.5726 &  0.8547 &  0.4274 \tabularnewline
55 &  0.5463 &  0.9074 &  0.4537 \tabularnewline
56 &  0.5523 &  0.8953 &  0.4477 \tabularnewline
57 &  0.534 &  0.932 &  0.466 \tabularnewline
58 &  0.4959 &  0.9919 &  0.5041 \tabularnewline
59 &  0.4302 &  0.8605 &  0.5698 \tabularnewline
60 &  0.3652 &  0.7303 &  0.6348 \tabularnewline
61 &  0.3408 &  0.6816 &  0.6592 \tabularnewline
62 &  0.2885 &  0.5769 &  0.7115 \tabularnewline
63 &  0.2359 &  0.4718 &  0.7641 \tabularnewline
64 &  0.3214 &  0.6428 &  0.6786 \tabularnewline
65 &  0.2689 &  0.5379 &  0.7311 \tabularnewline
66 &  0.3535 &  0.7071 &  0.6465 \tabularnewline
67 &  0.3301 &  0.6602 &  0.6699 \tabularnewline
68 &  0.4484 &  0.8967 &  0.5516 \tabularnewline
69 &  0.4334 &  0.8669 &  0.5666 \tabularnewline
70 &  0.3682 &  0.7364 &  0.6318 \tabularnewline
71 &  0.3123 &  0.6247 &  0.6877 \tabularnewline
72 &  0.2648 &  0.5296 &  0.7352 \tabularnewline
73 &  0.3621 &  0.7241 &  0.6379 \tabularnewline
74 &  0.3848 &  0.7697 &  0.6152 \tabularnewline
75 &  0.4499 &  0.8999 &  0.5501 \tabularnewline
76 &  0.4289 &  0.8578 &  0.5711 \tabularnewline
77 &  0.3932 &  0.7863 &  0.6068 \tabularnewline
78 &  0.3043 &  0.6087 &  0.6957 \tabularnewline
79 &  0.2401 &  0.4801 &  0.7599 \tabularnewline
80 &  0.2068 &  0.4135 &  0.7932 \tabularnewline
81 &  0.1646 &  0.3292 &  0.8354 \tabularnewline
82 &  0.1562 &  0.3123 &  0.8438 \tabularnewline
83 &  0.1288 &  0.2575 &  0.8712 \tabularnewline
84 &  0.07906 &  0.1581 &  0.9209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]13[/C][C] 0.964[/C][C] 0.07202[/C][C] 0.03601[/C][/ROW]
[ROW][C]14[/C][C] 0.9434[/C][C] 0.1132[/C][C] 0.05658[/C][/ROW]
[ROW][C]15[/C][C] 0.9491[/C][C] 0.1017[/C][C] 0.05086[/C][/ROW]
[ROW][C]16[/C][C] 0.9167[/C][C] 0.1667[/C][C] 0.08333[/C][/ROW]
[ROW][C]17[/C][C] 0.9064[/C][C] 0.1872[/C][C] 0.09358[/C][/ROW]
[ROW][C]18[/C][C] 0.8839[/C][C] 0.2322[/C][C] 0.1161[/C][/ROW]
[ROW][C]19[/C][C] 0.841[/C][C] 0.3181[/C][C] 0.159[/C][/ROW]
[ROW][C]20[/C][C] 0.8581[/C][C] 0.2837[/C][C] 0.1419[/C][/ROW]
[ROW][C]21[/C][C] 0.8696[/C][C] 0.2608[/C][C] 0.1304[/C][/ROW]
[ROW][C]22[/C][C] 0.8416[/C][C] 0.3168[/C][C] 0.1584[/C][/ROW]
[ROW][C]23[/C][C] 0.7966[/C][C] 0.4068[/C][C] 0.2034[/C][/ROW]
[ROW][C]24[/C][C] 0.8154[/C][C] 0.3692[/C][C] 0.1846[/C][/ROW]
[ROW][C]25[/C][C] 0.7654[/C][C] 0.4692[/C][C] 0.2346[/C][/ROW]
[ROW][C]26[/C][C] 0.8478[/C][C] 0.3043[/C][C] 0.1522[/C][/ROW]
[ROW][C]27[/C][C] 0.815[/C][C] 0.37[/C][C] 0.185[/C][/ROW]
[ROW][C]28[/C][C] 0.7814[/C][C] 0.4373[/C][C] 0.2186[/C][/ROW]
[ROW][C]29[/C][C] 0.7421[/C][C] 0.5158[/C][C] 0.2579[/C][/ROW]
[ROW][C]30[/C][C] 0.7802[/C][C] 0.4396[/C][C] 0.2198[/C][/ROW]
[ROW][C]31[/C][C] 0.729[/C][C] 0.5421[/C][C] 0.271[/C][/ROW]
[ROW][C]32[/C][C] 0.6879[/C][C] 0.6242[/C][C] 0.3121[/C][/ROW]
[ROW][C]33[/C][C] 0.6444[/C][C] 0.7113[/C][C] 0.3556[/C][/ROW]
[ROW][C]34[/C][C] 0.6156[/C][C] 0.7687[/C][C] 0.3844[/C][/ROW]
[ROW][C]35[/C][C] 0.9112[/C][C] 0.1776[/C][C] 0.0888[/C][/ROW]
[ROW][C]36[/C][C] 0.9036[/C][C] 0.1928[/C][C] 0.09641[/C][/ROW]
[ROW][C]37[/C][C] 0.8718[/C][C] 0.2564[/C][C] 0.1282[/C][/ROW]
[ROW][C]38[/C][C] 0.8673[/C][C] 0.2654[/C][C] 0.1327[/C][/ROW]
[ROW][C]39[/C][C] 0.8376[/C][C] 0.3249[/C][C] 0.1624[/C][/ROW]
[ROW][C]40[/C][C] 0.8691[/C][C] 0.2617[/C][C] 0.1309[/C][/ROW]
[ROW][C]41[/C][C] 0.8766[/C][C] 0.2467[/C][C] 0.1234[/C][/ROW]
[ROW][C]42[/C][C] 0.8627[/C][C] 0.2746[/C][C] 0.1373[/C][/ROW]
[ROW][C]43[/C][C] 0.8289[/C][C] 0.3422[/C][C] 0.1711[/C][/ROW]
[ROW][C]44[/C][C] 0.7861[/C][C] 0.4278[/C][C] 0.2139[/C][/ROW]
[ROW][C]45[/C][C] 0.7583[/C][C] 0.4835[/C][C] 0.2417[/C][/ROW]
[ROW][C]46[/C][C] 0.7066[/C][C] 0.5868[/C][C] 0.2934[/C][/ROW]
[ROW][C]47[/C][C] 0.6509[/C][C] 0.6982[/C][C] 0.3491[/C][/ROW]
[ROW][C]48[/C][C] 0.6114[/C][C] 0.7772[/C][C] 0.3886[/C][/ROW]
[ROW][C]49[/C][C] 0.6306[/C][C] 0.7387[/C][C] 0.3694[/C][/ROW]
[ROW][C]50[/C][C] 0.664[/C][C] 0.6719[/C][C] 0.336[/C][/ROW]
[ROW][C]51[/C][C] 0.6865[/C][C] 0.6271[/C][C] 0.3135[/C][/ROW]
[ROW][C]52[/C][C] 0.6391[/C][C] 0.7218[/C][C] 0.3609[/C][/ROW]
[ROW][C]53[/C][C] 0.5776[/C][C] 0.8448[/C][C] 0.4224[/C][/ROW]
[ROW][C]54[/C][C] 0.5726[/C][C] 0.8547[/C][C] 0.4274[/C][/ROW]
[ROW][C]55[/C][C] 0.5463[/C][C] 0.9074[/C][C] 0.4537[/C][/ROW]
[ROW][C]56[/C][C] 0.5523[/C][C] 0.8953[/C][C] 0.4477[/C][/ROW]
[ROW][C]57[/C][C] 0.534[/C][C] 0.932[/C][C] 0.466[/C][/ROW]
[ROW][C]58[/C][C] 0.4959[/C][C] 0.9919[/C][C] 0.5041[/C][/ROW]
[ROW][C]59[/C][C] 0.4302[/C][C] 0.8605[/C][C] 0.5698[/C][/ROW]
[ROW][C]60[/C][C] 0.3652[/C][C] 0.7303[/C][C] 0.6348[/C][/ROW]
[ROW][C]61[/C][C] 0.3408[/C][C] 0.6816[/C][C] 0.6592[/C][/ROW]
[ROW][C]62[/C][C] 0.2885[/C][C] 0.5769[/C][C] 0.7115[/C][/ROW]
[ROW][C]63[/C][C] 0.2359[/C][C] 0.4718[/C][C] 0.7641[/C][/ROW]
[ROW][C]64[/C][C] 0.3214[/C][C] 0.6428[/C][C] 0.6786[/C][/ROW]
[ROW][C]65[/C][C] 0.2689[/C][C] 0.5379[/C][C] 0.7311[/C][/ROW]
[ROW][C]66[/C][C] 0.3535[/C][C] 0.7071[/C][C] 0.6465[/C][/ROW]
[ROW][C]67[/C][C] 0.3301[/C][C] 0.6602[/C][C] 0.6699[/C][/ROW]
[ROW][C]68[/C][C] 0.4484[/C][C] 0.8967[/C][C] 0.5516[/C][/ROW]
[ROW][C]69[/C][C] 0.4334[/C][C] 0.8669[/C][C] 0.5666[/C][/ROW]
[ROW][C]70[/C][C] 0.3682[/C][C] 0.7364[/C][C] 0.6318[/C][/ROW]
[ROW][C]71[/C][C] 0.3123[/C][C] 0.6247[/C][C] 0.6877[/C][/ROW]
[ROW][C]72[/C][C] 0.2648[/C][C] 0.5296[/C][C] 0.7352[/C][/ROW]
[ROW][C]73[/C][C] 0.3621[/C][C] 0.7241[/C][C] 0.6379[/C][/ROW]
[ROW][C]74[/C][C] 0.3848[/C][C] 0.7697[/C][C] 0.6152[/C][/ROW]
[ROW][C]75[/C][C] 0.4499[/C][C] 0.8999[/C][C] 0.5501[/C][/ROW]
[ROW][C]76[/C][C] 0.4289[/C][C] 0.8578[/C][C] 0.5711[/C][/ROW]
[ROW][C]77[/C][C] 0.3932[/C][C] 0.7863[/C][C] 0.6068[/C][/ROW]
[ROW][C]78[/C][C] 0.3043[/C][C] 0.6087[/C][C] 0.6957[/C][/ROW]
[ROW][C]79[/C][C] 0.2401[/C][C] 0.4801[/C][C] 0.7599[/C][/ROW]
[ROW][C]80[/C][C] 0.2068[/C][C] 0.4135[/C][C] 0.7932[/C][/ROW]
[ROW][C]81[/C][C] 0.1646[/C][C] 0.3292[/C][C] 0.8354[/C][/ROW]
[ROW][C]82[/C][C] 0.1562[/C][C] 0.3123[/C][C] 0.8438[/C][/ROW]
[ROW][C]83[/C][C] 0.1288[/C][C] 0.2575[/C][C] 0.8712[/C][/ROW]
[ROW][C]84[/C][C] 0.07906[/C][C] 0.1581[/C][C] 0.9209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.964	0.07202	0.03601
14	0.9434	0.1132	0.05658
15	0.9491	0.1017	0.05086
16	0.9167	0.1667	0.08333
17	0.9064	0.1872	0.09358
18	0.8839	0.2322	0.1161
19	0.841	0.3181	0.159
20	0.8581	0.2837	0.1419
21	0.8696	0.2608	0.1304
22	0.8416	0.3168	0.1584
23	0.7966	0.4068	0.2034
24	0.8154	0.3692	0.1846
25	0.7654	0.4692	0.2346
26	0.8478	0.3043	0.1522
27	0.815	0.37	0.185
28	0.7814	0.4373	0.2186
29	0.7421	0.5158	0.2579
30	0.7802	0.4396	0.2198
31	0.729	0.5421	0.271
32	0.6879	0.6242	0.3121
33	0.6444	0.7113	0.3556
34	0.6156	0.7687	0.3844
35	0.9112	0.1776	0.0888
36	0.9036	0.1928	0.09641
37	0.8718	0.2564	0.1282
38	0.8673	0.2654	0.1327
39	0.8376	0.3249	0.1624
40	0.8691	0.2617	0.1309
41	0.8766	0.2467	0.1234
42	0.8627	0.2746	0.1373
43	0.8289	0.3422	0.1711
44	0.7861	0.4278	0.2139
45	0.7583	0.4835	0.2417
46	0.7066	0.5868	0.2934
47	0.6509	0.6982	0.3491
48	0.6114	0.7772	0.3886
49	0.6306	0.7387	0.3694
50	0.664	0.6719	0.336
51	0.6865	0.6271	0.3135
52	0.6391	0.7218	0.3609
53	0.5776	0.8448	0.4224
54	0.5726	0.8547	0.4274
55	0.5463	0.9074	0.4537
56	0.5523	0.8953	0.4477
57	0.534	0.932	0.466
58	0.4959	0.9919	0.5041
59	0.4302	0.8605	0.5698
60	0.3652	0.7303	0.6348
61	0.3408	0.6816	0.6592
62	0.2885	0.5769	0.7115
63	0.2359	0.4718	0.7641
64	0.3214	0.6428	0.6786
65	0.2689	0.5379	0.7311
66	0.3535	0.7071	0.6465
67	0.3301	0.6602	0.6699
68	0.4484	0.8967	0.5516
69	0.4334	0.8669	0.5666
70	0.3682	0.7364	0.6318
71	0.3123	0.6247	0.6877
72	0.2648	0.5296	0.7352
73	0.3621	0.7241	0.6379
74	0.3848	0.7697	0.6152
75	0.4499	0.8999	0.5501
76	0.4289	0.8578	0.5711
77	0.3932	0.7863	0.6068
78	0.3043	0.6087	0.6957
79	0.2401	0.4801	0.7599
80	0.2068	0.4135	0.7932
81	0.1646	0.3292	0.8354
82	0.1562	0.3123	0.8438
83	0.1288	0.2575	0.8712
84	0.07906	0.1581	0.9209

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0138889	OK

\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 & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0138889 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0138889[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0138889	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.90148, df1 = 2, df2 = 85, p-value = 0.4098
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77689, df1 = 18, df2 = 69, p-value = 0.7189
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.7124, df1 = 2, df2 = 85, p-value = 0.07213

\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 = 0.90148, df1 = 2, df2 = 85, p-value = 0.4098
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77689, df1 = 18, df2 = 69, p-value = 0.7189
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.7124, df1 = 2, df2 = 85, p-value = 0.07213
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&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 = 0.90148, df1 = 2, df2 = 85, p-value = 0.4098
[/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 = 0.77689, df1 = 18, df2 = 69, p-value = 0.7189
[/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 = 2.7124, df1 = 2, df2 = 85, p-value = 0.07213
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&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 = 0.90148, df1 = 2, df2 = 85, p-value = 0.4098
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77689, df1 = 18, df2 = 69, p-value = 0.7189
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.7124, df1 = 2, df2 = 85, p-value = 0.07213

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM SKEOU1 SKEOU2 3.147578 11.253434 10.813190 2.165192 1.880614 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.520561 1.544051 1.462116 3.057153

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     3.147578     11.253434     10.813190      2.165192      1.880614 
       SKEOU3        SKEOU4        SKEOU5        SKEOU6 
     1.520561      1.544051      1.462116      3.057153 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303990&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     3.147578     11.253434     10.813190      2.165192      1.880614 
       SKEOU3        SKEOU4        SKEOU5        SKEOU6 
     1.520561      1.544051      1.462116      3.057153 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303990&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303990&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 3.147578 11.253434 10.813190 2.165192 1.880614 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.520561 1.544051 1.462116 3.057153

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 12 ; par2 = Triple ; par3 = additive ; par4 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Include Monthly 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