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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 12:42:11 +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/t1485171783cbdszgdwfu87lo9.htm/, Retrieved Wed, 15 May 2024 06:14:36 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 15 May 2024 06:14:36 +0200

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22 13 4 2 4 3 5 4
24 16 5 3 3 4 5 4
21 17 4 4 5 4 5 4
21 NA 3 4 3 3 4 4
24 NA 4 4 5 4 5 4
20 16 3 4 4 4 5 5
22 NA 3 4 4 3 3 4
20 NA 3 4 5 4 4 4
19 NA 4 5 4 4 5 5
23 17 4 5 5 4 5 5
21 17 4 4 2 4 5 4
19 15 4 4 5 3 5 4
19 16 4 4 4 3 4 5
21 14 3 3 5 4 4 5
21 16 4 4 5 4 2 5
22 17 3 4 5 4 4 5
22 NA 3 4 5 4 4 5
19 NA NA NA 5 NA 5 5
21 NA 5 5 4 3 4 4
21 NA 4 4 4 4 5 4
21 16 3 4 5 3 4 5
20 NA 4 4 4 4 5 5
22 16 4 4 5 4 4 5
22 NA 4 4 5 4 4 4
24 NA 4 4 5 4 4 5
21 NA 3 4 4 4 4 4
19 16 3 4 4 3 5 5
19 15 4 4 4 4 4 4
23 16 2 4 5 4 5 5
21 16 5 4 4 4 4 4
21 13 4 3 5 4 4 4
19 15 4 5 5 4 5 5
21 17 5 4 5 4 4 5
19 NA 4 3 5 4 NA 5
21 13 2 3 5 4 5 4
21 17 4 5 2 4 4 4
23 NA 3 4 5 4 4 4
19 14 4 3 5 3 4 5
19 14 4 3 3 4 4 4
19 18 4 4 5 4 4 4
18 NA 5 4 4 4 4 4
22 17 4 5 5 4 5 5
18 13 3 3 4 4 4 4
22 16 5 5 5 3 5 5
18 15 5 4 5 3 4 4
22 15 4 4 4 3 4 5
22 NA 4 4 4 4 4 4
19 15 3 5 5 3 3 4
22 13 4 4 4 4 5 4
25 NA 2 3 4 2 NA 4
19 17 4 5 5 4 4 4
19 NA 5 5 2 4 5 4
19 NA 5 5 5 4 4 4
19 11 4 3 5 4 5 5
21 14 4 3 4 3 4 5
21 13 4 4 5 4 4 4
20 NA 3 4 4 3 3 4
19 17 3 4 4 4 4 3
19 16 4 4 4 3 5 4
22 NA 4 4 4 4 5 4
26 17 5 5 3 4 5 5
19 16 2 4 4 4 5 5
21 16 4 4 4 4 5 5
21 16 3 4 4 4 2 4
20 15 4 4 5 4 5 5
23 12 4 2 4 4 4 4
22 17 4 4 4 3 5 3
22 14 4 4 4 3 5 4
22 14 5 4 5 3 3 5
21 16 3 4 4 3 5 5
21 NA 3 4 4 3 4 5
22 NA 4 5 5 5 5 4
23 NA 4 4 3 4 NA 4
18 NA 4 4 4 4 4 4
24 NA 4 4 4 5 5 4
22 15 3 4 3 4 4 4
21 16 4 4 4 4 5 4
21 14 3 4 5 3 5 5
21 15 3 3 5 4 4 5
23 17 4 3 5 4 4 4
21 NA 4 4 5 4 4 5
23 10 3 3 3 4 4 4
21 NA 4 4 4 4 5 4
19 17 4 4 3 4 5 5
21 NA 4 4 4 4 5 5
21 20 5 4 4 4 4 4
21 17 5 4 3 5 4 5
23 18 4 4 5 4 5 5
23 NA 3 4 5 4 4 5
20 17 3 NA 4 4 4 4
20 14 4 2 3 3 4 4
19 NA 4 4 5 4 4 3
23 17 4 4 5 4 4 5
22 NA 4 4 4 4 5 4
19 17 4 5 4 4 5 3
23 NA 3 4 4 3 5 5
22 16 4 4 5 4 4 5
22 18 5 4 3 4 4 5
21 18 5 4 5 5 4 5
21 16 4 5 4 4 5 5
21 NA 3 4 5 4 4 5
21 NA 5 3 4 4 5 5
22 15 4 4 5 4 4 5
25 13 5 4 4 4 4 5
21 NA 3 4 4 3 NA 4
23 NA 5 4 4 5 5 5
19 NA 4 4 5 3 NA 5
22 NA 4 4 3 3 4 3
20 NA 4 4 5 4 4 4
21 16 4 4 5 4 4 4
25 NA 3 4 5 4 5 3
21 NA 4 4 4 4 4 4
19 NA 4 4 4 3 4 5
23 12 3 3 4 3 5 5
22 NA 4 4 4 3 4 4
21 16 3 4 5 4 4 4
24 16 4 4 5 4 3 4
21 NA 5 4 5 1 5 5
19 16 5 4 5 4 5 5
18 14 4 4 4 4 4 3
19 15 4 4 5 3 4 4
20 14 3 4 4 3 4 5
19 NA 4 4 4 4 4 4
22 15 4 4 4 4 5 4
21 NA 4 5 3 4 4 4
22 15 3 4 4 4 4 4
24 16 4 4 4 3 4 4
28 NA 4 4 4 4 4 5
19 NA 3 4 3 3 4 4
18 NA 4 4 4 3 4 3
23 11 3 2 4 2 4 4
19 NA 4 4 4 3 5 4
23 18 5 4 4 3 5 4
19 NA 2 4 4 3 3 5
22 11 3 3 4 4 4 4
21 NA 4 4 4 3 4 4
19 18 5 5 4 4 5 4
22 NA NA NA 2 NA NA NA
21 15 4 5 5 4 4 4
23 19 5 5 5 5 5 4
22 17 4 5 5 4 5 5
19 NA 4 4 4 3 4 5
19 14 3 4 5 4 5 4
21 NA 4 4 5 4 4 4
22 13 4 4 2 4 4 4
21 17 4 4 3 4 5 5
20 14 4 4 4 4 5 5
23 19 5 4 5 3 5 4
22 14 4 3 5 4 4 4
23 NA 4 4 5 4 4 4
22 NA 3 3 2 3 4 4
21 16 4 5 5 4 4 3
20 16 4 4 4 3 4 4
18 15 4 4 4 4 4 5
18 12 3 4 5 3 5 5
20 NA 4 4 5 4 4 5
19 17 5 4 5 4 5 4
21 NA 4 4 5 4 3 4
24 NA 2 3 5 4 4 4
19 18 4 4 4 4 4 5
20 15 4 3 4 3 5 5
19 18 4 4 4 4 4 3
23 15 4 5 5 5 4 4
22 NA 5 4 3 4 4 4
21 NA 5 4 4 3 4 4
24 NA 3 3 1 4 5 5
21 16 4 4 4 4 4 5
21 NA 4 4 4 4 5 4
22 16 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	7 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] [ROW]

R Framework error message[/C][C]

Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

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

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
Bevr_Leeftijd[t] = + 18.7922 -0.00548236TVDC[t] + 0.328489SKEOU1[t] -0.263176SKEOU2[t] -0.135571SKEOU3[t] + 0.363108SKEOU4[t] -0.0259349SKEOU5[t] + 0.306SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bevr_Leeftijd[t] =  +  18.7922 -0.00548236TVDC[t] +  0.328489SKEOU1[t] -0.263176SKEOU2[t] -0.135571SKEOU3[t] +  0.363108SKEOU4[t] -0.0259349SKEOU5[t] +  0.306SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bevr_Leeftijd[t] =  +  18.7922 -0.00548236TVDC[t] +  0.328489SKEOU1[t] -0.263176SKEOU2[t] -0.135571SKEOU3[t] +  0.363108SKEOU4[t] -0.0259349SKEOU5[t] +  0.306SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Bevr_Leeftijd[t] = + 18.7922 -0.00548236TVDC[t] + 0.328489SKEOU1[t] -0.263176SKEOU2[t] -0.135571SKEOU3[t] + 0.363108SKEOU4[t] -0.0259349SKEOU5[t] + 0.306SKEOU6[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)	+18.79	2.477	+7.5860e+00	2.342e-11	1.171e-11
TVDC	-0.005482	0.1141	-4.8050e-02	0.9618	0.4809
SKEOU1	+0.3285	0.2494	+1.3170e+00	0.191	0.09552
SKEOU2	-0.2632	0.2982	-8.8250e-01	0.3797	0.1899
SKEOU3	-0.1356	0.2263	-5.9890e-01	0.5507	0.2753
SKEOU4	+0.3631	0.3226	+1.1250e+00	0.2633	0.1316
SKEOU5	-0.02593	0.2681	-9.6730e-02	0.9232	0.4616
SKEOU6	+0.306	0.2858	+1.0710e+00	0.2871	0.1435

\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) & +18.79 &  2.477 & +7.5860e+00 &  2.342e-11 &  1.171e-11 \tabularnewline
TVDC & -0.005482 &  0.1141 & -4.8050e-02 &  0.9618 &  0.4809 \tabularnewline
SKEOU1 & +0.3285 &  0.2494 & +1.3170e+00 &  0.191 &  0.09552 \tabularnewline
SKEOU2 & -0.2632 &  0.2982 & -8.8250e-01 &  0.3797 &  0.1899 \tabularnewline
SKEOU3 & -0.1356 &  0.2263 & -5.9890e-01 &  0.5507 &  0.2753 \tabularnewline
SKEOU4 & +0.3631 &  0.3226 & +1.1250e+00 &  0.2633 &  0.1316 \tabularnewline
SKEOU5 & -0.02593 &  0.2681 & -9.6730e-02 &  0.9232 &  0.4616 \tabularnewline
SKEOU6 & +0.306 &  0.2858 & +1.0710e+00 &  0.2871 &  0.1435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+18.79[/C][C] 2.477[/C][C]+7.5860e+00[/C][C] 2.342e-11[/C][C] 1.171e-11[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.005482[/C][C] 0.1141[/C][C]-4.8050e-02[/C][C] 0.9618[/C][C] 0.4809[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.3285[/C][C] 0.2494[/C][C]+1.3170e+00[/C][C] 0.191[/C][C] 0.09552[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.2632[/C][C] 0.2982[/C][C]-8.8250e-01[/C][C] 0.3797[/C][C] 0.1899[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.1356[/C][C] 0.2263[/C][C]-5.9890e-01[/C][C] 0.5507[/C][C] 0.2753[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.3631[/C][C] 0.3226[/C][C]+1.1250e+00[/C][C] 0.2633[/C][C] 0.1316[/C][/ROW]
[ROW][C]SKEOU5[/C][C]-0.02593[/C][C] 0.2681[/C][C]-9.6730e-02[/C][C] 0.9232[/C][C] 0.4616[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.306[/C][C] 0.2858[/C][C]+1.0710e+00[/C][C] 0.2871[/C][C] 0.1435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+18.79	2.477	+7.5860e+00	2.342e-11	1.171e-11
TVDC	-0.005482	0.1141	-4.8050e-02	0.9618	0.4809
SKEOU1	+0.3285	0.2494	+1.3170e+00	0.191	0.09552
SKEOU2	-0.2632	0.2982	-8.8250e-01	0.3797	0.1899
SKEOU3	-0.1356	0.2263	-5.9890e-01	0.5507	0.2753
SKEOU4	+0.3631	0.3226	+1.1250e+00	0.2633	0.1316
SKEOU5	-0.02593	0.2681	-9.6730e-02	0.9232	0.4616
SKEOU6	+0.306	0.2858	+1.0710e+00	0.2871	0.1435

Multiple Linear Regression - Regression Statistics
Multiple R	0.2233
R-squared	0.04986
Adjusted R-squared	-0.02089
F-TEST (value)	0.7047
F-TEST (DF numerator)	7
F-TEST (DF denominator)	94
p-value	0.668
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.697
Sum Squared Residuals	270.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2233 \tabularnewline
R-squared &  0.04986 \tabularnewline
Adjusted R-squared & -0.02089 \tabularnewline
F-TEST (value) &  0.7047 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value &  0.668 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.697 \tabularnewline
Sum Squared Residuals &  270.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2233[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.04986[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.02089[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.7047[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C] 0.668[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.697[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 270.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

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

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.2233
R-squared	0.04986
Adjusted R-squared	-0.02089
F-TEST (value)	0.7047
F-TEST (DF numerator)	7
F-TEST (DF denominator)	94
p-value	0.668
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.697
Sum Squared Residuals	270.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	21.15	0.8501
2	24	21.7	2.303
3	21	20.83	0.1709
4	20	20.95	-0.9477
5	23	20.87	2.128
6	21	21.24	-0.2358
7	19	20.48	-1.477
8	19	20.94	-1.939
9	21	21.11	-0.1122
10	21	21.22	-0.2184
11	22	20.83	1.167
12	21	20.47	0.5251
13	22	21.17	0.8335
14	19	20.58	-1.585
15	19	21	-2.002
16	23	20.48	2.516
17	21	21.32	-0.3246
18	21	21.14	-0.1402
19	19	20.88	-1.883
20	21	21.49	-0.4895
21	21	20.46	0.5428
22	21	21	0.001413
23	19	21.08	-2.078
24	19	21.41	-2.406
25	19	20.85	-1.85
26	22	20.87	1.128
27	18	20.95	-2.947
28	22	20.84	1.157
29	18	20.83	-2.831
30	22	20.94	1.056
31	19	19.94	-0.9372
32	22	20.99	1.013
33	19	20.59	-1.592
34	19	21.43	-2.431
35	21	21.21	-0.2131
36	21	20.88	0.123
37	19	20.36	-1.356
38	19	20.61	-1.607
39	26	21.47	4.528
40	19	20.62	-1.619
41	21	21.28	-0.2762
42	21	20.72	0.2805
43	20	21.15	-1.146
44	23	21.54	1.456
45	22	20.3	1.704
46	22	20.62	1.382
47	22	21.17	0.8312
48	21	20.58	0.4154
49	22	20.81	1.191
50	21	20.97	0.02983
51	21	20.46	0.54
52	21	21.11	-0.1067
53	23	21.12	1.882
54	23	21.1	1.901
55	19	21.41	-2.406
56	21	21.3	-0.3027
57	21	22.12	-1.124
58	23	21.13	1.87
59	20	21.31	-1.306
60	23	21.16	1.839
61	19	20.4	-1.396
62	22	21.17	0.8335
63	22	21.76	0.2448
64	21	21.85	-0.8472
65	21	21.01	-0.01299
66	22	21.17	0.828
67	25	21.65	3.353
68	21	20.86	0.1395
69	23	20.87	2.13
70	21	20.53	0.468
71	24	20.89	3.114
72	19	21.47	-2.469
73	18	20.7	-2.701
74	19	20.5	-1.503
75	20	20.62	-0.6215
76	22	20.98	1.024
77	22	20.67	1.327
78	24	20.63	3.367
79	23	20.5	2.505
80	23	20.92	2.075
81	22	20.96	1.042
82	19	21.02	-2.025
83	21	20.6	0.3972
84	23	21.25	1.753
85	22	20.87	1.128
86	19	20.52	-1.517
87	22	21.28	0.7163
88	21	21.41	-0.4063
89	20	21.29	-1.287
90	23	20.78	2.216
91	22	21.13	0.8653
92	21	20.29	0.7086
93	20	20.63	-0.633
94	18	21.31	-3.308
95	18	20.47	-2.471
96	19	21.16	-2.158
97	19	21.29	-2.291
98	20	21.18	-1.182
99	19	20.68	-1.679
100	23	20.97	2.034
101	21	21.3	-0.3021
102	22	20.94	1.061

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  22 &  21.15 &  0.8501 \tabularnewline
2 &  24 &  21.7 &  2.303 \tabularnewline
3 &  21 &  20.83 &  0.1709 \tabularnewline
4 &  20 &  20.95 & -0.9477 \tabularnewline
5 &  23 &  20.87 &  2.128 \tabularnewline
6 &  21 &  21.24 & -0.2358 \tabularnewline
7 &  19 &  20.48 & -1.477 \tabularnewline
8 &  19 &  20.94 & -1.939 \tabularnewline
9 &  21 &  21.11 & -0.1122 \tabularnewline
10 &  21 &  21.22 & -0.2184 \tabularnewline
11 &  22 &  20.83 &  1.167 \tabularnewline
12 &  21 &  20.47 &  0.5251 \tabularnewline
13 &  22 &  21.17 &  0.8335 \tabularnewline
14 &  19 &  20.58 & -1.585 \tabularnewline
15 &  19 &  21 & -2.002 \tabularnewline
16 &  23 &  20.48 &  2.516 \tabularnewline
17 &  21 &  21.32 & -0.3246 \tabularnewline
18 &  21 &  21.14 & -0.1402 \tabularnewline
19 &  19 &  20.88 & -1.883 \tabularnewline
20 &  21 &  21.49 & -0.4895 \tabularnewline
21 &  21 &  20.46 &  0.5428 \tabularnewline
22 &  21 &  21 &  0.001413 \tabularnewline
23 &  19 &  21.08 & -2.078 \tabularnewline
24 &  19 &  21.41 & -2.406 \tabularnewline
25 &  19 &  20.85 & -1.85 \tabularnewline
26 &  22 &  20.87 &  1.128 \tabularnewline
27 &  18 &  20.95 & -2.947 \tabularnewline
28 &  22 &  20.84 &  1.157 \tabularnewline
29 &  18 &  20.83 & -2.831 \tabularnewline
30 &  22 &  20.94 &  1.056 \tabularnewline
31 &  19 &  19.94 & -0.9372 \tabularnewline
32 &  22 &  20.99 &  1.013 \tabularnewline
33 &  19 &  20.59 & -1.592 \tabularnewline
34 &  19 &  21.43 & -2.431 \tabularnewline
35 &  21 &  21.21 & -0.2131 \tabularnewline
36 &  21 &  20.88 &  0.123 \tabularnewline
37 &  19 &  20.36 & -1.356 \tabularnewline
38 &  19 &  20.61 & -1.607 \tabularnewline
39 &  26 &  21.47 &  4.528 \tabularnewline
40 &  19 &  20.62 & -1.619 \tabularnewline
41 &  21 &  21.28 & -0.2762 \tabularnewline
42 &  21 &  20.72 &  0.2805 \tabularnewline
43 &  20 &  21.15 & -1.146 \tabularnewline
44 &  23 &  21.54 &  1.456 \tabularnewline
45 &  22 &  20.3 &  1.704 \tabularnewline
46 &  22 &  20.62 &  1.382 \tabularnewline
47 &  22 &  21.17 &  0.8312 \tabularnewline
48 &  21 &  20.58 &  0.4154 \tabularnewline
49 &  22 &  20.81 &  1.191 \tabularnewline
50 &  21 &  20.97 &  0.02983 \tabularnewline
51 &  21 &  20.46 &  0.54 \tabularnewline
52 &  21 &  21.11 & -0.1067 \tabularnewline
53 &  23 &  21.12 &  1.882 \tabularnewline
54 &  23 &  21.1 &  1.901 \tabularnewline
55 &  19 &  21.41 & -2.406 \tabularnewline
56 &  21 &  21.3 & -0.3027 \tabularnewline
57 &  21 &  22.12 & -1.124 \tabularnewline
58 &  23 &  21.13 &  1.87 \tabularnewline
59 &  20 &  21.31 & -1.306 \tabularnewline
60 &  23 &  21.16 &  1.839 \tabularnewline
61 &  19 &  20.4 & -1.396 \tabularnewline
62 &  22 &  21.17 &  0.8335 \tabularnewline
63 &  22 &  21.76 &  0.2448 \tabularnewline
64 &  21 &  21.85 & -0.8472 \tabularnewline
65 &  21 &  21.01 & -0.01299 \tabularnewline
66 &  22 &  21.17 &  0.828 \tabularnewline
67 &  25 &  21.65 &  3.353 \tabularnewline
68 &  21 &  20.86 &  0.1395 \tabularnewline
69 &  23 &  20.87 &  2.13 \tabularnewline
70 &  21 &  20.53 &  0.468 \tabularnewline
71 &  24 &  20.89 &  3.114 \tabularnewline
72 &  19 &  21.47 & -2.469 \tabularnewline
73 &  18 &  20.7 & -2.701 \tabularnewline
74 &  19 &  20.5 & -1.503 \tabularnewline
75 &  20 &  20.62 & -0.6215 \tabularnewline
76 &  22 &  20.98 &  1.024 \tabularnewline
77 &  22 &  20.67 &  1.327 \tabularnewline
78 &  24 &  20.63 &  3.367 \tabularnewline
79 &  23 &  20.5 &  2.505 \tabularnewline
80 &  23 &  20.92 &  2.075 \tabularnewline
81 &  22 &  20.96 &  1.042 \tabularnewline
82 &  19 &  21.02 & -2.025 \tabularnewline
83 &  21 &  20.6 &  0.3972 \tabularnewline
84 &  23 &  21.25 &  1.753 \tabularnewline
85 &  22 &  20.87 &  1.128 \tabularnewline
86 &  19 &  20.52 & -1.517 \tabularnewline
87 &  22 &  21.28 &  0.7163 \tabularnewline
88 &  21 &  21.41 & -0.4063 \tabularnewline
89 &  20 &  21.29 & -1.287 \tabularnewline
90 &  23 &  20.78 &  2.216 \tabularnewline
91 &  22 &  21.13 &  0.8653 \tabularnewline
92 &  21 &  20.29 &  0.7086 \tabularnewline
93 &  20 &  20.63 & -0.633 \tabularnewline
94 &  18 &  21.31 & -3.308 \tabularnewline
95 &  18 &  20.47 & -2.471 \tabularnewline
96 &  19 &  21.16 & -2.158 \tabularnewline
97 &  19 &  21.29 & -2.291 \tabularnewline
98 &  20 &  21.18 & -1.182 \tabularnewline
99 &  19 &  20.68 & -1.679 \tabularnewline
100 &  23 &  20.97 &  2.034 \tabularnewline
101 &  21 &  21.3 & -0.3021 \tabularnewline
102 &  22 &  20.94 &  1.061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 22[/C][C] 21.15[/C][C] 0.8501[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 21.7[/C][C] 2.303[/C][/ROW]
[ROW][C]3[/C][C] 21[/C][C] 20.83[/C][C] 0.1709[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 20.95[/C][C]-0.9477[/C][/ROW]
[ROW][C]5[/C][C] 23[/C][C] 20.87[/C][C] 2.128[/C][/ROW]
[ROW][C]6[/C][C] 21[/C][C] 21.24[/C][C]-0.2358[/C][/ROW]
[ROW][C]7[/C][C] 19[/C][C] 20.48[/C][C]-1.477[/C][/ROW]
[ROW][C]8[/C][C] 19[/C][C] 20.94[/C][C]-1.939[/C][/ROW]
[ROW][C]9[/C][C] 21[/C][C] 21.11[/C][C]-0.1122[/C][/ROW]
[ROW][C]10[/C][C] 21[/C][C] 21.22[/C][C]-0.2184[/C][/ROW]
[ROW][C]11[/C][C] 22[/C][C] 20.83[/C][C] 1.167[/C][/ROW]
[ROW][C]12[/C][C] 21[/C][C] 20.47[/C][C] 0.5251[/C][/ROW]
[ROW][C]13[/C][C] 22[/C][C] 21.17[/C][C] 0.8335[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 20.58[/C][C]-1.585[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 21[/C][C]-2.002[/C][/ROW]
[ROW][C]16[/C][C] 23[/C][C] 20.48[/C][C] 2.516[/C][/ROW]
[ROW][C]17[/C][C] 21[/C][C] 21.32[/C][C]-0.3246[/C][/ROW]
[ROW][C]18[/C][C] 21[/C][C] 21.14[/C][C]-0.1402[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 20.88[/C][C]-1.883[/C][/ROW]
[ROW][C]20[/C][C] 21[/C][C] 21.49[/C][C]-0.4895[/C][/ROW]
[ROW][C]21[/C][C] 21[/C][C] 20.46[/C][C] 0.5428[/C][/ROW]
[ROW][C]22[/C][C] 21[/C][C] 21[/C][C] 0.001413[/C][/ROW]
[ROW][C]23[/C][C] 19[/C][C] 21.08[/C][C]-2.078[/C][/ROW]
[ROW][C]24[/C][C] 19[/C][C] 21.41[/C][C]-2.406[/C][/ROW]
[ROW][C]25[/C][C] 19[/C][C] 20.85[/C][C]-1.85[/C][/ROW]
[ROW][C]26[/C][C] 22[/C][C] 20.87[/C][C] 1.128[/C][/ROW]
[ROW][C]27[/C][C] 18[/C][C] 20.95[/C][C]-2.947[/C][/ROW]
[ROW][C]28[/C][C] 22[/C][C] 20.84[/C][C] 1.157[/C][/ROW]
[ROW][C]29[/C][C] 18[/C][C] 20.83[/C][C]-2.831[/C][/ROW]
[ROW][C]30[/C][C] 22[/C][C] 20.94[/C][C] 1.056[/C][/ROW]
[ROW][C]31[/C][C] 19[/C][C] 19.94[/C][C]-0.9372[/C][/ROW]
[ROW][C]32[/C][C] 22[/C][C] 20.99[/C][C] 1.013[/C][/ROW]
[ROW][C]33[/C][C] 19[/C][C] 20.59[/C][C]-1.592[/C][/ROW]
[ROW][C]34[/C][C] 19[/C][C] 21.43[/C][C]-2.431[/C][/ROW]
[ROW][C]35[/C][C] 21[/C][C] 21.21[/C][C]-0.2131[/C][/ROW]
[ROW][C]36[/C][C] 21[/C][C] 20.88[/C][C] 0.123[/C][/ROW]
[ROW][C]37[/C][C] 19[/C][C] 20.36[/C][C]-1.356[/C][/ROW]
[ROW][C]38[/C][C] 19[/C][C] 20.61[/C][C]-1.607[/C][/ROW]
[ROW][C]39[/C][C] 26[/C][C] 21.47[/C][C] 4.528[/C][/ROW]
[ROW][C]40[/C][C] 19[/C][C] 20.62[/C][C]-1.619[/C][/ROW]
[ROW][C]41[/C][C] 21[/C][C] 21.28[/C][C]-0.2762[/C][/ROW]
[ROW][C]42[/C][C] 21[/C][C] 20.72[/C][C] 0.2805[/C][/ROW]
[ROW][C]43[/C][C] 20[/C][C] 21.15[/C][C]-1.146[/C][/ROW]
[ROW][C]44[/C][C] 23[/C][C] 21.54[/C][C] 1.456[/C][/ROW]
[ROW][C]45[/C][C] 22[/C][C] 20.3[/C][C] 1.704[/C][/ROW]
[ROW][C]46[/C][C] 22[/C][C] 20.62[/C][C] 1.382[/C][/ROW]
[ROW][C]47[/C][C] 22[/C][C] 21.17[/C][C] 0.8312[/C][/ROW]
[ROW][C]48[/C][C] 21[/C][C] 20.58[/C][C] 0.4154[/C][/ROW]
[ROW][C]49[/C][C] 22[/C][C] 20.81[/C][C] 1.191[/C][/ROW]
[ROW][C]50[/C][C] 21[/C][C] 20.97[/C][C] 0.02983[/C][/ROW]
[ROW][C]51[/C][C] 21[/C][C] 20.46[/C][C] 0.54[/C][/ROW]
[ROW][C]52[/C][C] 21[/C][C] 21.11[/C][C]-0.1067[/C][/ROW]
[ROW][C]53[/C][C] 23[/C][C] 21.12[/C][C] 1.882[/C][/ROW]
[ROW][C]54[/C][C] 23[/C][C] 21.1[/C][C] 1.901[/C][/ROW]
[ROW][C]55[/C][C] 19[/C][C] 21.41[/C][C]-2.406[/C][/ROW]
[ROW][C]56[/C][C] 21[/C][C] 21.3[/C][C]-0.3027[/C][/ROW]
[ROW][C]57[/C][C] 21[/C][C] 22.12[/C][C]-1.124[/C][/ROW]
[ROW][C]58[/C][C] 23[/C][C] 21.13[/C][C] 1.87[/C][/ROW]
[ROW][C]59[/C][C] 20[/C][C] 21.31[/C][C]-1.306[/C][/ROW]
[ROW][C]60[/C][C] 23[/C][C] 21.16[/C][C] 1.839[/C][/ROW]
[ROW][C]61[/C][C] 19[/C][C] 20.4[/C][C]-1.396[/C][/ROW]
[ROW][C]62[/C][C] 22[/C][C] 21.17[/C][C] 0.8335[/C][/ROW]
[ROW][C]63[/C][C] 22[/C][C] 21.76[/C][C] 0.2448[/C][/ROW]
[ROW][C]64[/C][C] 21[/C][C] 21.85[/C][C]-0.8472[/C][/ROW]
[ROW][C]65[/C][C] 21[/C][C] 21.01[/C][C]-0.01299[/C][/ROW]
[ROW][C]66[/C][C] 22[/C][C] 21.17[/C][C] 0.828[/C][/ROW]
[ROW][C]67[/C][C] 25[/C][C] 21.65[/C][C] 3.353[/C][/ROW]
[ROW][C]68[/C][C] 21[/C][C] 20.86[/C][C] 0.1395[/C][/ROW]
[ROW][C]69[/C][C] 23[/C][C] 20.87[/C][C] 2.13[/C][/ROW]
[ROW][C]70[/C][C] 21[/C][C] 20.53[/C][C] 0.468[/C][/ROW]
[ROW][C]71[/C][C] 24[/C][C] 20.89[/C][C] 3.114[/C][/ROW]
[ROW][C]72[/C][C] 19[/C][C] 21.47[/C][C]-2.469[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 20.7[/C][C]-2.701[/C][/ROW]
[ROW][C]74[/C][C] 19[/C][C] 20.5[/C][C]-1.503[/C][/ROW]
[ROW][C]75[/C][C] 20[/C][C] 20.62[/C][C]-0.6215[/C][/ROW]
[ROW][C]76[/C][C] 22[/C][C] 20.98[/C][C] 1.024[/C][/ROW]
[ROW][C]77[/C][C] 22[/C][C] 20.67[/C][C] 1.327[/C][/ROW]
[ROW][C]78[/C][C] 24[/C][C] 20.63[/C][C] 3.367[/C][/ROW]
[ROW][C]79[/C][C] 23[/C][C] 20.5[/C][C] 2.505[/C][/ROW]
[ROW][C]80[/C][C] 23[/C][C] 20.92[/C][C] 2.075[/C][/ROW]
[ROW][C]81[/C][C] 22[/C][C] 20.96[/C][C] 1.042[/C][/ROW]
[ROW][C]82[/C][C] 19[/C][C] 21.02[/C][C]-2.025[/C][/ROW]
[ROW][C]83[/C][C] 21[/C][C] 20.6[/C][C] 0.3972[/C][/ROW]
[ROW][C]84[/C][C] 23[/C][C] 21.25[/C][C] 1.753[/C][/ROW]
[ROW][C]85[/C][C] 22[/C][C] 20.87[/C][C] 1.128[/C][/ROW]
[ROW][C]86[/C][C] 19[/C][C] 20.52[/C][C]-1.517[/C][/ROW]
[ROW][C]87[/C][C] 22[/C][C] 21.28[/C][C] 0.7163[/C][/ROW]
[ROW][C]88[/C][C] 21[/C][C] 21.41[/C][C]-0.4063[/C][/ROW]
[ROW][C]89[/C][C] 20[/C][C] 21.29[/C][C]-1.287[/C][/ROW]
[ROW][C]90[/C][C] 23[/C][C] 20.78[/C][C] 2.216[/C][/ROW]
[ROW][C]91[/C][C] 22[/C][C] 21.13[/C][C] 0.8653[/C][/ROW]
[ROW][C]92[/C][C] 21[/C][C] 20.29[/C][C] 0.7086[/C][/ROW]
[ROW][C]93[/C][C] 20[/C][C] 20.63[/C][C]-0.633[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 21.31[/C][C]-3.308[/C][/ROW]
[ROW][C]95[/C][C] 18[/C][C] 20.47[/C][C]-2.471[/C][/ROW]
[ROW][C]96[/C][C] 19[/C][C] 21.16[/C][C]-2.158[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 21.29[/C][C]-2.291[/C][/ROW]
[ROW][C]98[/C][C] 20[/C][C] 21.18[/C][C]-1.182[/C][/ROW]
[ROW][C]99[/C][C] 19[/C][C] 20.68[/C][C]-1.679[/C][/ROW]
[ROW][C]100[/C][C] 23[/C][C] 20.97[/C][C] 2.034[/C][/ROW]
[ROW][C]101[/C][C] 21[/C][C] 21.3[/C][C]-0.3021[/C][/ROW]
[ROW][C]102[/C][C] 22[/C][C] 20.94[/C][C] 1.061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	21.15	0.8501
2	24	21.7	2.303
3	21	20.83	0.1709
4	20	20.95	-0.9477
5	23	20.87	2.128
6	21	21.24	-0.2358
7	19	20.48	-1.477
8	19	20.94	-1.939
9	21	21.11	-0.1122
10	21	21.22	-0.2184
11	22	20.83	1.167
12	21	20.47	0.5251
13	22	21.17	0.8335
14	19	20.58	-1.585
15	19	21	-2.002
16	23	20.48	2.516
17	21	21.32	-0.3246
18	21	21.14	-0.1402
19	19	20.88	-1.883
20	21	21.49	-0.4895
21	21	20.46	0.5428
22	21	21	0.001413
23	19	21.08	-2.078
24	19	21.41	-2.406
25	19	20.85	-1.85
26	22	20.87	1.128
27	18	20.95	-2.947
28	22	20.84	1.157
29	18	20.83	-2.831
30	22	20.94	1.056
31	19	19.94	-0.9372
32	22	20.99	1.013
33	19	20.59	-1.592
34	19	21.43	-2.431
35	21	21.21	-0.2131
36	21	20.88	0.123
37	19	20.36	-1.356
38	19	20.61	-1.607
39	26	21.47	4.528
40	19	20.62	-1.619
41	21	21.28	-0.2762
42	21	20.72	0.2805
43	20	21.15	-1.146
44	23	21.54	1.456
45	22	20.3	1.704
46	22	20.62	1.382
47	22	21.17	0.8312
48	21	20.58	0.4154
49	22	20.81	1.191
50	21	20.97	0.02983
51	21	20.46	0.54
52	21	21.11	-0.1067
53	23	21.12	1.882
54	23	21.1	1.901
55	19	21.41	-2.406
56	21	21.3	-0.3027
57	21	22.12	-1.124
58	23	21.13	1.87
59	20	21.31	-1.306
60	23	21.16	1.839
61	19	20.4	-1.396
62	22	21.17	0.8335
63	22	21.76	0.2448
64	21	21.85	-0.8472
65	21	21.01	-0.01299
66	22	21.17	0.828
67	25	21.65	3.353
68	21	20.86	0.1395
69	23	20.87	2.13
70	21	20.53	0.468
71	24	20.89	3.114
72	19	21.47	-2.469
73	18	20.7	-2.701
74	19	20.5	-1.503
75	20	20.62	-0.6215
76	22	20.98	1.024
77	22	20.67	1.327
78	24	20.63	3.367
79	23	20.5	2.505
80	23	20.92	2.075
81	22	20.96	1.042
82	19	21.02	-2.025
83	21	20.6	0.3972
84	23	21.25	1.753
85	22	20.87	1.128
86	19	20.52	-1.517
87	22	21.28	0.7163
88	21	21.41	-0.4063
89	20	21.29	-1.287
90	23	20.78	2.216
91	22	21.13	0.8653
92	21	20.29	0.7086
93	20	20.63	-0.633
94	18	21.31	-3.308
95	18	20.47	-2.471
96	19	21.16	-2.158
97	19	21.29	-2.291
98	20	21.18	-1.182
99	19	20.68	-1.679
100	23	20.97	2.034
101	21	21.3	-0.3021
102	22	20.94	1.061

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.4073	0.8147	0.5927
12	0.4429	0.8858	0.5571
13	0.3025	0.6049	0.6975
14	0.2151	0.4302	0.7849
15	0.139	0.2779	0.861
16	0.2299	0.4598	0.7701
17	0.1533	0.3065	0.8467
18	0.1005	0.2009	0.8995
19	0.07073	0.1415	0.9293
20	0.09212	0.1842	0.9079
21	0.05915	0.1183	0.9409
22	0.06591	0.1318	0.9341
23	0.05348	0.107	0.9465
24	0.06699	0.134	0.933
25	0.1579	0.3159	0.8421
26	0.1284	0.2568	0.8716
27	0.1659	0.3318	0.8341
28	0.1782	0.3564	0.8218
29	0.1802	0.3603	0.8198
30	0.2078	0.4156	0.7922
31	0.2117	0.4234	0.7883
32	0.2082	0.4165	0.7917
33	0.1877	0.3753	0.8123
34	0.2291	0.4583	0.7709
35	0.1879	0.3758	0.8121
36	0.1748	0.3495	0.8252
37	0.1467	0.2933	0.8533
38	0.1338	0.2677	0.8662
39	0.3979	0.7957	0.6021
40	0.4264	0.8528	0.5736
41	0.3819	0.7638	0.6181
42	0.3726	0.7453	0.6274
43	0.3502	0.7004	0.6498
44	0.3722	0.7444	0.6278
45	0.4131	0.8263	0.5869
46	0.4171	0.8342	0.5829
47	0.3874	0.7749	0.6126
48	0.334	0.668	0.666
49	0.312	0.624	0.688
50	0.2608	0.5217	0.7392
51	0.221	0.442	0.779
52	0.1842	0.3685	0.8158
53	0.1833	0.3665	0.8167
54	0.2076	0.4152	0.7924
55	0.2564	0.5127	0.7436
56	0.2116	0.4231	0.7884
57	0.1879	0.3758	0.8121
58	0.1928	0.3857	0.8072
59	0.1833	0.3667	0.8167
60	0.1799	0.3598	0.8201
61	0.1586	0.3172	0.8414
62	0.1292	0.2584	0.8708
63	0.09911	0.1982	0.9009
64	0.08226	0.1645	0.9177
65	0.06301	0.126	0.937
66	0.04811	0.09622	0.9519
67	0.1129	0.2258	0.8871
68	0.08536	0.1707	0.9146
69	0.1082	0.2164	0.8918
70	0.08311	0.1662	0.9169
71	0.1335	0.267	0.8665
72	0.1404	0.2809	0.8596
73	0.2488	0.4975	0.7512
74	0.2483	0.4967	0.7517
75	0.1974	0.3949	0.8026
76	0.1622	0.3245	0.8378
77	0.136	0.272	0.864
78	0.2484	0.4969	0.7516
79	0.316	0.632	0.684
80	0.3867	0.7734	0.6133
81	0.3502	0.7003	0.6499
82	0.4145	0.829	0.5855
83	0.3355	0.671	0.6645
84	0.2712	0.5424	0.7288
85	0.2654	0.5309	0.7346
86	0.2282	0.4565	0.7718
87	0.2115	0.423	0.7885
88	0.1947	0.3894	0.8053
89	0.16	0.3199	0.84
90	0.2857	0.5713	0.7143
91	0.1696	0.3393	0.8304

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.4073 &  0.8147 &  0.5927 \tabularnewline
12 &  0.4429 &  0.8858 &  0.5571 \tabularnewline
13 &  0.3025 &  0.6049 &  0.6975 \tabularnewline
14 &  0.2151 &  0.4302 &  0.7849 \tabularnewline
15 &  0.139 &  0.2779 &  0.861 \tabularnewline
16 &  0.2299 &  0.4598 &  0.7701 \tabularnewline
17 &  0.1533 &  0.3065 &  0.8467 \tabularnewline
18 &  0.1005 &  0.2009 &  0.8995 \tabularnewline
19 &  0.07073 &  0.1415 &  0.9293 \tabularnewline
20 &  0.09212 &  0.1842 &  0.9079 \tabularnewline
21 &  0.05915 &  0.1183 &  0.9409 \tabularnewline
22 &  0.06591 &  0.1318 &  0.9341 \tabularnewline
23 &  0.05348 &  0.107 &  0.9465 \tabularnewline
24 &  0.06699 &  0.134 &  0.933 \tabularnewline
25 &  0.1579 &  0.3159 &  0.8421 \tabularnewline
26 &  0.1284 &  0.2568 &  0.8716 \tabularnewline
27 &  0.1659 &  0.3318 &  0.8341 \tabularnewline
28 &  0.1782 &  0.3564 &  0.8218 \tabularnewline
29 &  0.1802 &  0.3603 &  0.8198 \tabularnewline
30 &  0.2078 &  0.4156 &  0.7922 \tabularnewline
31 &  0.2117 &  0.4234 &  0.7883 \tabularnewline
32 &  0.2082 &  0.4165 &  0.7917 \tabularnewline
33 &  0.1877 &  0.3753 &  0.8123 \tabularnewline
34 &  0.2291 &  0.4583 &  0.7709 \tabularnewline
35 &  0.1879 &  0.3758 &  0.8121 \tabularnewline
36 &  0.1748 &  0.3495 &  0.8252 \tabularnewline
37 &  0.1467 &  0.2933 &  0.8533 \tabularnewline
38 &  0.1338 &  0.2677 &  0.8662 \tabularnewline
39 &  0.3979 &  0.7957 &  0.6021 \tabularnewline
40 &  0.4264 &  0.8528 &  0.5736 \tabularnewline
41 &  0.3819 &  0.7638 &  0.6181 \tabularnewline
42 &  0.3726 &  0.7453 &  0.6274 \tabularnewline
43 &  0.3502 &  0.7004 &  0.6498 \tabularnewline
44 &  0.3722 &  0.7444 &  0.6278 \tabularnewline
45 &  0.4131 &  0.8263 &  0.5869 \tabularnewline
46 &  0.4171 &  0.8342 &  0.5829 \tabularnewline
47 &  0.3874 &  0.7749 &  0.6126 \tabularnewline
48 &  0.334 &  0.668 &  0.666 \tabularnewline
49 &  0.312 &  0.624 &  0.688 \tabularnewline
50 &  0.2608 &  0.5217 &  0.7392 \tabularnewline
51 &  0.221 &  0.442 &  0.779 \tabularnewline
52 &  0.1842 &  0.3685 &  0.8158 \tabularnewline
53 &  0.1833 &  0.3665 &  0.8167 \tabularnewline
54 &  0.2076 &  0.4152 &  0.7924 \tabularnewline
55 &  0.2564 &  0.5127 &  0.7436 \tabularnewline
56 &  0.2116 &  0.4231 &  0.7884 \tabularnewline
57 &  0.1879 &  0.3758 &  0.8121 \tabularnewline
58 &  0.1928 &  0.3857 &  0.8072 \tabularnewline
59 &  0.1833 &  0.3667 &  0.8167 \tabularnewline
60 &  0.1799 &  0.3598 &  0.8201 \tabularnewline
61 &  0.1586 &  0.3172 &  0.8414 \tabularnewline
62 &  0.1292 &  0.2584 &  0.8708 \tabularnewline
63 &  0.09911 &  0.1982 &  0.9009 \tabularnewline
64 &  0.08226 &  0.1645 &  0.9177 \tabularnewline
65 &  0.06301 &  0.126 &  0.937 \tabularnewline
66 &  0.04811 &  0.09622 &  0.9519 \tabularnewline
67 &  0.1129 &  0.2258 &  0.8871 \tabularnewline
68 &  0.08536 &  0.1707 &  0.9146 \tabularnewline
69 &  0.1082 &  0.2164 &  0.8918 \tabularnewline
70 &  0.08311 &  0.1662 &  0.9169 \tabularnewline
71 &  0.1335 &  0.267 &  0.8665 \tabularnewline
72 &  0.1404 &  0.2809 &  0.8596 \tabularnewline
73 &  0.2488 &  0.4975 &  0.7512 \tabularnewline
74 &  0.2483 &  0.4967 &  0.7517 \tabularnewline
75 &  0.1974 &  0.3949 &  0.8026 \tabularnewline
76 &  0.1622 &  0.3245 &  0.8378 \tabularnewline
77 &  0.136 &  0.272 &  0.864 \tabularnewline
78 &  0.2484 &  0.4969 &  0.7516 \tabularnewline
79 &  0.316 &  0.632 &  0.684 \tabularnewline
80 &  0.3867 &  0.7734 &  0.6133 \tabularnewline
81 &  0.3502 &  0.7003 &  0.6499 \tabularnewline
82 &  0.4145 &  0.829 &  0.5855 \tabularnewline
83 &  0.3355 &  0.671 &  0.6645 \tabularnewline
84 &  0.2712 &  0.5424 &  0.7288 \tabularnewline
85 &  0.2654 &  0.5309 &  0.7346 \tabularnewline
86 &  0.2282 &  0.4565 &  0.7718 \tabularnewline
87 &  0.2115 &  0.423 &  0.7885 \tabularnewline
88 &  0.1947 &  0.3894 &  0.8053 \tabularnewline
89 &  0.16 &  0.3199 &  0.84 \tabularnewline
90 &  0.2857 &  0.5713 &  0.7143 \tabularnewline
91 &  0.1696 &  0.3393 &  0.8304 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C] 0.4073[/C][C] 0.8147[/C][C] 0.5927[/C][/ROW]
[ROW][C]12[/C][C] 0.4429[/C][C] 0.8858[/C][C] 0.5571[/C][/ROW]
[ROW][C]13[/C][C] 0.3025[/C][C] 0.6049[/C][C] 0.6975[/C][/ROW]
[ROW][C]14[/C][C] 0.2151[/C][C] 0.4302[/C][C] 0.7849[/C][/ROW]
[ROW][C]15[/C][C] 0.139[/C][C] 0.2779[/C][C] 0.861[/C][/ROW]
[ROW][C]16[/C][C] 0.2299[/C][C] 0.4598[/C][C] 0.7701[/C][/ROW]
[ROW][C]17[/C][C] 0.1533[/C][C] 0.3065[/C][C] 0.8467[/C][/ROW]
[ROW][C]18[/C][C] 0.1005[/C][C] 0.2009[/C][C] 0.8995[/C][/ROW]
[ROW][C]19[/C][C] 0.07073[/C][C] 0.1415[/C][C] 0.9293[/C][/ROW]
[ROW][C]20[/C][C] 0.09212[/C][C] 0.1842[/C][C] 0.9079[/C][/ROW]
[ROW][C]21[/C][C] 0.05915[/C][C] 0.1183[/C][C] 0.9409[/C][/ROW]
[ROW][C]22[/C][C] 0.06591[/C][C] 0.1318[/C][C] 0.9341[/C][/ROW]
[ROW][C]23[/C][C] 0.05348[/C][C] 0.107[/C][C] 0.9465[/C][/ROW]
[ROW][C]24[/C][C] 0.06699[/C][C] 0.134[/C][C] 0.933[/C][/ROW]
[ROW][C]25[/C][C] 0.1579[/C][C] 0.3159[/C][C] 0.8421[/C][/ROW]
[ROW][C]26[/C][C] 0.1284[/C][C] 0.2568[/C][C] 0.8716[/C][/ROW]
[ROW][C]27[/C][C] 0.1659[/C][C] 0.3318[/C][C] 0.8341[/C][/ROW]
[ROW][C]28[/C][C] 0.1782[/C][C] 0.3564[/C][C] 0.8218[/C][/ROW]
[ROW][C]29[/C][C] 0.1802[/C][C] 0.3603[/C][C] 0.8198[/C][/ROW]
[ROW][C]30[/C][C] 0.2078[/C][C] 0.4156[/C][C] 0.7922[/C][/ROW]
[ROW][C]31[/C][C] 0.2117[/C][C] 0.4234[/C][C] 0.7883[/C][/ROW]
[ROW][C]32[/C][C] 0.2082[/C][C] 0.4165[/C][C] 0.7917[/C][/ROW]
[ROW][C]33[/C][C] 0.1877[/C][C] 0.3753[/C][C] 0.8123[/C][/ROW]
[ROW][C]34[/C][C] 0.2291[/C][C] 0.4583[/C][C] 0.7709[/C][/ROW]
[ROW][C]35[/C][C] 0.1879[/C][C] 0.3758[/C][C] 0.8121[/C][/ROW]
[ROW][C]36[/C][C] 0.1748[/C][C] 0.3495[/C][C] 0.8252[/C][/ROW]
[ROW][C]37[/C][C] 0.1467[/C][C] 0.2933[/C][C] 0.8533[/C][/ROW]
[ROW][C]38[/C][C] 0.1338[/C][C] 0.2677[/C][C] 0.8662[/C][/ROW]
[ROW][C]39[/C][C] 0.3979[/C][C] 0.7957[/C][C] 0.6021[/C][/ROW]
[ROW][C]40[/C][C] 0.4264[/C][C] 0.8528[/C][C] 0.5736[/C][/ROW]
[ROW][C]41[/C][C] 0.3819[/C][C] 0.7638[/C][C] 0.6181[/C][/ROW]
[ROW][C]42[/C][C] 0.3726[/C][C] 0.7453[/C][C] 0.6274[/C][/ROW]
[ROW][C]43[/C][C] 0.3502[/C][C] 0.7004[/C][C] 0.6498[/C][/ROW]
[ROW][C]44[/C][C] 0.3722[/C][C] 0.7444[/C][C] 0.6278[/C][/ROW]
[ROW][C]45[/C][C] 0.4131[/C][C] 0.8263[/C][C] 0.5869[/C][/ROW]
[ROW][C]46[/C][C] 0.4171[/C][C] 0.8342[/C][C] 0.5829[/C][/ROW]
[ROW][C]47[/C][C] 0.3874[/C][C] 0.7749[/C][C] 0.6126[/C][/ROW]
[ROW][C]48[/C][C] 0.334[/C][C] 0.668[/C][C] 0.666[/C][/ROW]
[ROW][C]49[/C][C] 0.312[/C][C] 0.624[/C][C] 0.688[/C][/ROW]
[ROW][C]50[/C][C] 0.2608[/C][C] 0.5217[/C][C] 0.7392[/C][/ROW]
[ROW][C]51[/C][C] 0.221[/C][C] 0.442[/C][C] 0.779[/C][/ROW]
[ROW][C]52[/C][C] 0.1842[/C][C] 0.3685[/C][C] 0.8158[/C][/ROW]
[ROW][C]53[/C][C] 0.1833[/C][C] 0.3665[/C][C] 0.8167[/C][/ROW]
[ROW][C]54[/C][C] 0.2076[/C][C] 0.4152[/C][C] 0.7924[/C][/ROW]
[ROW][C]55[/C][C] 0.2564[/C][C] 0.5127[/C][C] 0.7436[/C][/ROW]
[ROW][C]56[/C][C] 0.2116[/C][C] 0.4231[/C][C] 0.7884[/C][/ROW]
[ROW][C]57[/C][C] 0.1879[/C][C] 0.3758[/C][C] 0.8121[/C][/ROW]
[ROW][C]58[/C][C] 0.1928[/C][C] 0.3857[/C][C] 0.8072[/C][/ROW]
[ROW][C]59[/C][C] 0.1833[/C][C] 0.3667[/C][C] 0.8167[/C][/ROW]
[ROW][C]60[/C][C] 0.1799[/C][C] 0.3598[/C][C] 0.8201[/C][/ROW]
[ROW][C]61[/C][C] 0.1586[/C][C] 0.3172[/C][C] 0.8414[/C][/ROW]
[ROW][C]62[/C][C] 0.1292[/C][C] 0.2584[/C][C] 0.8708[/C][/ROW]
[ROW][C]63[/C][C] 0.09911[/C][C] 0.1982[/C][C] 0.9009[/C][/ROW]
[ROW][C]64[/C][C] 0.08226[/C][C] 0.1645[/C][C] 0.9177[/C][/ROW]
[ROW][C]65[/C][C] 0.06301[/C][C] 0.126[/C][C] 0.937[/C][/ROW]
[ROW][C]66[/C][C] 0.04811[/C][C] 0.09622[/C][C] 0.9519[/C][/ROW]
[ROW][C]67[/C][C] 0.1129[/C][C] 0.2258[/C][C] 0.8871[/C][/ROW]
[ROW][C]68[/C][C] 0.08536[/C][C] 0.1707[/C][C] 0.9146[/C][/ROW]
[ROW][C]69[/C][C] 0.1082[/C][C] 0.2164[/C][C] 0.8918[/C][/ROW]
[ROW][C]70[/C][C] 0.08311[/C][C] 0.1662[/C][C] 0.9169[/C][/ROW]
[ROW][C]71[/C][C] 0.1335[/C][C] 0.267[/C][C] 0.8665[/C][/ROW]
[ROW][C]72[/C][C] 0.1404[/C][C] 0.2809[/C][C] 0.8596[/C][/ROW]
[ROW][C]73[/C][C] 0.2488[/C][C] 0.4975[/C][C] 0.7512[/C][/ROW]
[ROW][C]74[/C][C] 0.2483[/C][C] 0.4967[/C][C] 0.7517[/C][/ROW]
[ROW][C]75[/C][C] 0.1974[/C][C] 0.3949[/C][C] 0.8026[/C][/ROW]
[ROW][C]76[/C][C] 0.1622[/C][C] 0.3245[/C][C] 0.8378[/C][/ROW]
[ROW][C]77[/C][C] 0.136[/C][C] 0.272[/C][C] 0.864[/C][/ROW]
[ROW][C]78[/C][C] 0.2484[/C][C] 0.4969[/C][C] 0.7516[/C][/ROW]
[ROW][C]79[/C][C] 0.316[/C][C] 0.632[/C][C] 0.684[/C][/ROW]
[ROW][C]80[/C][C] 0.3867[/C][C] 0.7734[/C][C] 0.6133[/C][/ROW]
[ROW][C]81[/C][C] 0.3502[/C][C] 0.7003[/C][C] 0.6499[/C][/ROW]
[ROW][C]82[/C][C] 0.4145[/C][C] 0.829[/C][C] 0.5855[/C][/ROW]
[ROW][C]83[/C][C] 0.3355[/C][C] 0.671[/C][C] 0.6645[/C][/ROW]
[ROW][C]84[/C][C] 0.2712[/C][C] 0.5424[/C][C] 0.7288[/C][/ROW]
[ROW][C]85[/C][C] 0.2654[/C][C] 0.5309[/C][C] 0.7346[/C][/ROW]
[ROW][C]86[/C][C] 0.2282[/C][C] 0.4565[/C][C] 0.7718[/C][/ROW]
[ROW][C]87[/C][C] 0.2115[/C][C] 0.423[/C][C] 0.7885[/C][/ROW]
[ROW][C]88[/C][C] 0.1947[/C][C] 0.3894[/C][C] 0.8053[/C][/ROW]
[ROW][C]89[/C][C] 0.16[/C][C] 0.3199[/C][C] 0.84[/C][/ROW]
[ROW][C]90[/C][C] 0.2857[/C][C] 0.5713[/C][C] 0.7143[/C][/ROW]
[ROW][C]91[/C][C] 0.1696[/C][C] 0.3393[/C][C] 0.8304[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

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

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.4073	0.8147	0.5927
12	0.4429	0.8858	0.5571
13	0.3025	0.6049	0.6975
14	0.2151	0.4302	0.7849
15	0.139	0.2779	0.861
16	0.2299	0.4598	0.7701
17	0.1533	0.3065	0.8467
18	0.1005	0.2009	0.8995
19	0.07073	0.1415	0.9293
20	0.09212	0.1842	0.9079
21	0.05915	0.1183	0.9409
22	0.06591	0.1318	0.9341
23	0.05348	0.107	0.9465
24	0.06699	0.134	0.933
25	0.1579	0.3159	0.8421
26	0.1284	0.2568	0.8716
27	0.1659	0.3318	0.8341
28	0.1782	0.3564	0.8218
29	0.1802	0.3603	0.8198
30	0.2078	0.4156	0.7922
31	0.2117	0.4234	0.7883
32	0.2082	0.4165	0.7917
33	0.1877	0.3753	0.8123
34	0.2291	0.4583	0.7709
35	0.1879	0.3758	0.8121
36	0.1748	0.3495	0.8252
37	0.1467	0.2933	0.8533
38	0.1338	0.2677	0.8662
39	0.3979	0.7957	0.6021
40	0.4264	0.8528	0.5736
41	0.3819	0.7638	0.6181
42	0.3726	0.7453	0.6274
43	0.3502	0.7004	0.6498
44	0.3722	0.7444	0.6278
45	0.4131	0.8263	0.5869
46	0.4171	0.8342	0.5829
47	0.3874	0.7749	0.6126
48	0.334	0.668	0.666
49	0.312	0.624	0.688
50	0.2608	0.5217	0.7392
51	0.221	0.442	0.779
52	0.1842	0.3685	0.8158
53	0.1833	0.3665	0.8167
54	0.2076	0.4152	0.7924
55	0.2564	0.5127	0.7436
56	0.2116	0.4231	0.7884
57	0.1879	0.3758	0.8121
58	0.1928	0.3857	0.8072
59	0.1833	0.3667	0.8167
60	0.1799	0.3598	0.8201
61	0.1586	0.3172	0.8414
62	0.1292	0.2584	0.8708
63	0.09911	0.1982	0.9009
64	0.08226	0.1645	0.9177
65	0.06301	0.126	0.937
66	0.04811	0.09622	0.9519
67	0.1129	0.2258	0.8871
68	0.08536	0.1707	0.9146
69	0.1082	0.2164	0.8918
70	0.08311	0.1662	0.9169
71	0.1335	0.267	0.8665
72	0.1404	0.2809	0.8596
73	0.2488	0.4975	0.7512
74	0.2483	0.4967	0.7517
75	0.1974	0.3949	0.8026
76	0.1622	0.3245	0.8378
77	0.136	0.272	0.864
78	0.2484	0.4969	0.7516
79	0.316	0.632	0.684
80	0.3867	0.7734	0.6133
81	0.3502	0.7003	0.6499
82	0.4145	0.829	0.5855
83	0.3355	0.671	0.6645
84	0.2712	0.5424	0.7288
85	0.2654	0.5309	0.7346
86	0.2282	0.4565	0.7718
87	0.2115	0.423	0.7885
88	0.1947	0.3894	0.8053
89	0.16	0.3199	0.84
90	0.2857	0.5713	0.7143
91	0.1696	0.3393	0.8304

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	0.0123457	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 & 0.0123457 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]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]0.0123457[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0123457	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.1782, df1 = 2, df2 = 92, p-value = 0.8371
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.70209, df1 = 14, df2 = 80, p-value = 0.7659
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.8305, df1 = 2, df2 = 92, p-value = 0.1661

\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 = 0.1782, df1 = 2, df2 = 92, p-value = 0.8371
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.70209, df1 = 14, df2 = 80, p-value = 0.7659
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8305, df1 = 2, df2 = 92, p-value = 0.1661
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.1782, df1 = 2, df2 = 92, p-value = 0.8371
[/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 = 0.70209, df1 = 14, df2 = 80, p-value = 0.7659
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8305, df1 = 2, df2 = 92, p-value = 0.1661
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.1782, df1 = 2, df2 = 92, p-value = 0.8371
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.70209, df1 = 14, df2 = 80, p-value = 0.7659
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.8305, df1 = 2, df2 = 92, p-value = 0.1661

Variance Inflation Factors (Multicollinearity)
> vif TVDC SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.606612 1.179242 1.488512 1.059937 1.097065 1.037972 1.035871

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    TVDC   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6 
1.606612 1.179242 1.488512 1.059937 1.097065 1.037972 1.035871 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    TVDC   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6 
1.606612 1.179242 1.488512 1.059937 1.097065 1.037972 1.035871 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=8

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

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif TVDC SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.606612 1.179242 1.488512 1.059937 1.097065 1.037972 1.035871

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 = 812181112121211211 ; par2 = 0Single2022Do not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal Dummies ; par3 = additive30.990.99No Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear Trend ; par4 = 1212TRUEtwo.sidedtwo.sided ; par5 = unpairedunpaired ; par6 = 0 ;

Parameters (R input):

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

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code