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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 10:50:40 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/25/t1485338337xmpn8swdp7yzgj5.htm/, Retrieved Mon, 13 May 2024 22:46:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306488, Retrieved Mon, 13 May 2024 22:46:06 +0000

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

-       [Multiple Regression] [Vraag 11] [2017-01-25 09:50:40] [31f526a885cd288e1bc58dc4a6a7fb1f] [Current]

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13 14 4 2 4 3 5 4
16 19 5 3 3 4 5 4
17 17 4 4 5 4 5 4
NA 17 3 4 3 3 4 4
NA 15 4 4 5 4 5 4
16 20 3 4 4 4 5 5
NA 15 3 4 4 3 3 4
NA 19 3 4 5 4 4 4
NA 15 4 5 4 4 5 5
17 15 4 5 5 4 5 5
17 19 4 4 2 4 5 4
15 NA 4 4 5 3 5 4
16 20 4 4 4 3 4 5
14 18 3 3 5 4 4 5
16 15 4 4 5 4 2 5
17 14 3 4 5 4 4 5
NA 20 3 4 5 4 4 5
NA NA NA NA 5 NA 5 5
NA 16 5 5 4 3 4 4
NA 16 4 4 4 4 5 4
16 16 3 4 5 3 4 5
NA 10 4 4 4 4 5 5
16 19 4 4 5 4 4 5
NA 19 4 4 5 4 4 4
NA 16 4 4 5 4 4 5
NA 15 3 4 4 4 4 4
16 18 3 4 4 3 5 5
15 17 4 4 4 4 4 4
16 19 2 4 5 4 5 5
16 17 5 4 4 4 4 4
13 NA 4 3 5 4 4 4
15 19 4 5 5 4 5 5
17 20 5 4 5 4 4 5
NA 5 4 3 5 4 NA 5
13 19 2 3 5 4 5 4
17 16 4 5 2 4 4 4
NA 15 3 4 5 4 4 4
14 16 4 3 5 3 4 5
14 18 4 3 3 4 4 4
18 16 4 4 5 4 4 4
NA 15 5 4 4 4 4 4
17 17 4 5 5 4 5 5
13 NA 3 3 4 4 4 4
16 20 5 5 5 3 5 5
15 19 5 4 5 3 4 4
15 7 4 4 4 3 4 5
NA 13 4 4 4 4 4 4
15 16 3 5 5 3 3 4
13 16 4 4 4 4 5 4
NA NA 2 3 4 2 NA 4
17 18 4 5 5 4 4 4
NA 18 5 5 2 4 5 4
NA 16 5 5 5 4 4 4
11 17 4 3 5 4 5 5
14 19 4 3 4 3 4 5
13 16 4 4 5 4 4 4
NA 19 3 4 4 3 3 4
17 13 3 4 4 4 4 3
16 16 4 4 4 3 5 4
NA 13 4 4 4 4 5 4
17 12 5 5 3 4 5 5
16 17 2 4 4 4 5 5
16 17 4 4 4 4 5 5
16 17 3 4 4 4 2 4
15 16 4 4 5 4 5 5
12 16 4 2 4 4 4 4
17 14 4 4 4 3 5 3
14 16 4 4 4 3 5 4
14 13 5 4 5 3 3 5
16 16 3 4 4 3 5 5
NA 14 3 4 4 3 4 5
NA 20 4 5 5 5 5 4
NA 12 4 4 3 4 NA 4
NA 13 4 4 4 4 4 4
NA 18 4 4 4 5 5 4
15 14 3 4 3 4 4 4
16 19 4 4 4 4 5 4
14 18 3 4 5 3 5 5
15 14 3 3 5 4 4 5
17 18 4 3 5 4 4 4
NA 19 4 4 5 4 4 5
10 15 3 3 3 4 4 4
NA 14 4 4 4 4 5 4
17 17 4 4 3 4 5 5
NA 19 4 4 4 4 5 5
20 13 5 4 4 4 4 4
17 19 5 4 3 5 4 5
18 18 4 4 5 4 5 5
NA 20 3 4 5 4 4 5
17 15 3 NA 4 4 4 4
14 15 4 2 3 3 4 4
NA 15 4 4 5 4 4 3
17 20 4 4 5 4 4 5
NA 15 4 4 4 4 5 4
17 19 4 5 4 4 5 3
NA 18 3 4 4 3 5 5
16 18 4 4 5 4 4 5
18 15 5 4 3 4 4 5
18 20 5 4 5 5 4 5
16 17 4 5 4 4 5 5
NA 12 3 4 5 4 4 5
NA 18 5 3 4 4 5 5
15 19 4 4 5 4 4 5
13 20 5 4 4 4 4 5
NA NA 3 4 4 3 NA 4
NA 17 5 4 4 5 5 5
NA 15 4 4 5 3 NA 5
NA 16 4 4 3 3 4 3
NA 18 4 4 5 4 4 4
16 18 4 4 5 4 4 4
NA 14 3 4 5 4 5 3
NA 15 4 4 4 4 4 4
NA 12 4 4 4 3 4 5
12 17 3 3 4 3 5 5
NA 14 4 4 4 3 4 4
16 18 3 4 5 4 4 4
16 17 4 4 5 4 3 4
NA 17 5 4 5 1 5 5
16 20 5 4 5 4 5 5
14 16 4 4 4 4 4 3
15 14 4 4 5 3 4 4
14 15 3 4 4 3 4 5
NA 18 4 4 4 4 4 4
15 20 4 4 4 4 5 4
NA 17 4 5 3 4 4 4
15 17 3 4 4 4 4 4
16 17 4 4 4 3 4 4
NA 17 4 4 4 4 4 5
NA 15 3 4 3 3 4 4
NA 17 4 4 4 3 4 3
11 18 3 2 4 2 4 4
NA 17 4 4 4 3 5 4
18 20 5 4 4 3 5 4
NA 15 2 4 4 3 3 5
11 16 3 3 4 4 4 4
NA 15 4 4 4 3 4 4
18 18 5 5 4 4 5 4
NA 11 NA NA 2 NA NA NA
15 15 4 5 5 4 4 4
19 18 5 5 5 5 5 4
17 20 4 5 5 4 5 5
NA 19 4 4 4 3 4 5
14 14 3 4 5 4 5 4
NA 16 4 4 5 4 4 4
13 15 4 4 2 4 4 4
17 17 4 4 3 4 5 5
14 18 4 4 4 4 5 5
19 20 5 4 5 3 5 4
14 17 4 3 5 4 4 4
NA 18 4 4 5 4 4 4
NA 15 3 3 2 3 4 4
16 16 4 5 5 4 4 3
16 11 4 4 4 3 4 4
15 15 4 4 4 4 4 5
12 18 3 4 5 3 5 5
NA 17 4 4 5 4 4 5
17 16 5 4 5 4 5 4
NA 12 4 4 5 4 3 4
NA 19 2 3 5 4 4 4
18 18 4 4 4 4 4 5
15 15 4 3 4 3 5 5
18 17 4 4 4 4 4 3
15 19 4 5 5 5 4 4
NA 18 5 4 3 4 4 4
NA 19 5 4 4 3 4 4
NA 16 3 3 1 4 5 5
16 16 4 4 4 4 4 5
NA 16 4 4 4 4 5 4
16 14 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

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

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306488&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.21559 + 0.0032006ITHSUM[t] + 0.647678SKEOU1[t] + 1.14898SKEOU2[t] + 0.0122695SKEOU3[t] + 0.497619SKEOU4[t] + 0.145986SKEOU5[t] -0.0798165SKEOU6[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.21559 +  0.0032006ITHSUM[t] +  0.647678SKEOU1[t] +  1.14898SKEOU2[t] +  0.0122695SKEOU3[t] +  0.497619SKEOU4[t] +  0.145986SKEOU5[t] -0.0798165SKEOU6[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.21559 +  0.0032006ITHSUM[t] +  0.647678SKEOU1[t] +  1.14898SKEOU2[t] +  0.0122695SKEOU3[t] +  0.497619SKEOU4[t] +  0.145986SKEOU5[t] -0.0798165SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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
TVDC[t] = + 6.21559 + 0.0032006ITHSUM[t] + 0.647678SKEOU1[t] + 1.14898SKEOU2[t] + 0.0122695SKEOU3[t] + 0.497619SKEOU4[t] + 0.145986SKEOU5[t] -0.0798165SKEOU6[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.216	2.218	+2.8020e+00	0.0062	0.0031
ITHSUM	+0.003201	0.0738	+4.3370e-02	0.9655	0.4828
SKEOU1	+0.6477	0.2212	+2.9280e+00	0.004311	0.002155
SKEOU2	+1.149	0.2463	+4.6640e+00	1.059e-05	5.297e-06
SKEOU3	+0.01227	0.2134	+5.7490e-02	0.9543	0.4771
SKEOU4	+0.4976	0.2995	+1.6620e+00	0.1	0.05002
SKEOU5	+0.146	0.252	+5.7930e-01	0.5638	0.2819
SKEOU6	-0.07982	0.2642	-3.0210e-01	0.7633	0.3816

\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) & +6.216 &  2.218 & +2.8020e+00 &  0.0062 &  0.0031 \tabularnewline
ITHSUM & +0.003201 &  0.0738 & +4.3370e-02 &  0.9655 &  0.4828 \tabularnewline
SKEOU1 & +0.6477 &  0.2212 & +2.9280e+00 &  0.004311 &  0.002155 \tabularnewline
SKEOU2 & +1.149 &  0.2463 & +4.6640e+00 &  1.059e-05 &  5.297e-06 \tabularnewline
SKEOU3 & +0.01227 &  0.2134 & +5.7490e-02 &  0.9543 &  0.4771 \tabularnewline
SKEOU4 & +0.4976 &  0.2995 & +1.6620e+00 &  0.1 &  0.05002 \tabularnewline
SKEOU5 & +0.146 &  0.252 & +5.7930e-01 &  0.5638 &  0.2819 \tabularnewline
SKEOU6 & -0.07982 &  0.2642 & -3.0210e-01 &  0.7633 &  0.3816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&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]+6.216[/C][C] 2.218[/C][C]+2.8020e+00[/C][C] 0.0062[/C][C] 0.0031[/C][/ROW]
[ROW][C]ITHSUM[/C][C]+0.003201[/C][C] 0.0738[/C][C]+4.3370e-02[/C][C] 0.9655[/C][C] 0.4828[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.6477[/C][C] 0.2212[/C][C]+2.9280e+00[/C][C] 0.004311[/C][C] 0.002155[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1.149[/C][C] 0.2463[/C][C]+4.6640e+00[/C][C] 1.059e-05[/C][C] 5.297e-06[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.01227[/C][C] 0.2134[/C][C]+5.7490e-02[/C][C] 0.9543[/C][C] 0.4771[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.4976[/C][C] 0.2995[/C][C]+1.6620e+00[/C][C] 0.1[/C][C] 0.05002[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.146[/C][C] 0.252[/C][C]+5.7930e-01[/C][C] 0.5638[/C][C] 0.2819[/C][/ROW]
[ROW][C]SKEOU6[/C][C]-0.07982[/C][C] 0.2642[/C][C]-3.0210e-01[/C][C] 0.7633[/C][C] 0.3816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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)	+6.216	2.218	+2.8020e+00	0.0062	0.0031
ITHSUM	+0.003201	0.0738	+4.3370e-02	0.9655	0.4828
SKEOU1	+0.6477	0.2212	+2.9280e+00	0.004311	0.002155
SKEOU2	+1.149	0.2463	+4.6640e+00	1.059e-05	5.297e-06
SKEOU3	+0.01227	0.2134	+5.7490e-02	0.9543	0.4771
SKEOU4	+0.4976	0.2995	+1.6620e+00	0.1	0.05002
SKEOU5	+0.146	0.252	+5.7930e-01	0.5638	0.2819
SKEOU6	-0.07982	0.2642	-3.0210e-01	0.7633	0.3816

Multiple Linear Regression - Regression Statistics
Multiple R	0.6039
R-squared	0.3647
Adjusted R-squared	0.3158
F-TEST (value)	7.463
F-TEST (DF numerator)	7
F-TEST (DF denominator)	91
p-value	4.422e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.547
Sum Squared Residuals	217.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6039 \tabularnewline
R-squared &  0.3647 \tabularnewline
Adjusted R-squared &  0.3158 \tabularnewline
F-TEST (value) &  7.463 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 91 \tabularnewline
p-value &  4.422e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.547 \tabularnewline
Sum Squared Residuals &  217.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6039[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3647[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3158[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.463[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]91[/C][/ROW]
[ROW][C]p-value[/C][C] 4.422e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.547[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 217.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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	0.6039
R-squared	0.3647
Adjusted R-squared	0.3158
F-TEST (value)	7.463
F-TEST (DF numerator)	7
F-TEST (DF denominator)	91
p-value	4.422e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.547
Sum Squared Residuals	217.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.1	-0.1017
2	16	15.4	0.6003
3	17	15.92	1.081
4	16	15.19	0.811
5	17	16.98	0.01813
6	17	15.89	1.111
7	16	15.19	0.807
8	14	13.9	0.1001
9	16	15.39	0.6051
10	17	15.04	1.964
11	16	14.54	1.455
12	16	15.7	0.3003
13	16	14.68	1.315
14	15	15.76	-0.7609
15	16	14.55	1.45
16	16	16.41	-0.4085
17	15	16.99	-1.995
18	17	16.35	0.6494
19	13	13.48	-0.4812
20	17	16.88	0.1179
21	14	14.04	-0.04351
22	14	14.6	-0.6028
23	18	15.77	2.23
24	17	16.99	0.01173
25	16	17.15	-1.148
26	15	15.93	-0.9296
27	15	15.15	-0.1514
28	15	15.63	-0.6276
29	13	15.9	-2.904
30	17	16.93	0.07469
31	11	14.69	-3.69
32	14	14.04	-0.04085
33	13	15.77	-2.77
34	17	15.18	1.82
35	16	15.41	0.594
36	17	17.6	-0.5954
37	16	14.53	1.468
38	16	15.83	0.173
39	16	14.82	1.179
40	15	15.84	-0.8361
41	12	13.46	-1.46
42	17	15.48	1.521
43	14	15.41	-1.406
44	14	15.68	-1.685
45	16	14.68	1.321
46	15	15.09	-0.09131
47	16	15.91	0.08676
48	14	14.7	-0.6972
49	15	13.89	1.113
50	17	14.63	2.373
51	10	13.95	-3.946
52	17	15.81	1.185
53	20	16.4	3.604
54	17	16.82	0.1795
55	18	15.84	2.158
56	14	12.95	1.053
57	17	15.7	1.297
58	17	17.14	-0.142
59	16	15.7	0.3035
60	18	16.31	1.69
61	18	16.85	1.152
62	16	16.98	-0.976
63	15	15.7	-0.6997
64	13	16.34	-3.338
65	16	15.78	0.2237
66	12	13.53	-1.533
67	16	15.13	0.8713
68	16	15.63	0.3729
69	16	16.5	-0.4966
70	14	15.84	-1.837
71	15	15.27	-0.2659
72	14	14.53	-0.5293
73	15	15.92	-0.9164
74	15	15.11	-0.1132
75	16	15.26	0.7368
76	11	11.82	-0.8232
77	18	16.07	1.933
78	11	13.96	-2.961
79	18	17.71	0.2933
80	15	16.92	-1.916
81	19	18.22	0.7834
82	17	17	0.002124
83	14	15.26	-1.262
84	13	15.73	-2.73
85	17	15.81	1.185
86	14	15.83	-1.83
87	19	16.08	2.921
88	14	14.62	-0.6241
89	16	17	-0.9987
90	16	15.24	0.756
91	15	15.67	-0.6746
92	12	14.7	-2.697
93	17	16.56	0.4364
94	18	15.68	2.316
95	15	14.17	0.826
96	18	15.84	2.159
97	15	17.43	-2.426
98	16	15.68	0.3222
99	16	13.95	2.049

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.1 & -0.1017 \tabularnewline
2 &  16 &  15.4 &  0.6003 \tabularnewline
3 &  17 &  15.92 &  1.081 \tabularnewline
4 &  16 &  15.19 &  0.811 \tabularnewline
5 &  17 &  16.98 &  0.01813 \tabularnewline
6 &  17 &  15.89 &  1.111 \tabularnewline
7 &  16 &  15.19 &  0.807 \tabularnewline
8 &  14 &  13.9 &  0.1001 \tabularnewline
9 &  16 &  15.39 &  0.6051 \tabularnewline
10 &  17 &  15.04 &  1.964 \tabularnewline
11 &  16 &  14.54 &  1.455 \tabularnewline
12 &  16 &  15.7 &  0.3003 \tabularnewline
13 &  16 &  14.68 &  1.315 \tabularnewline
14 &  15 &  15.76 & -0.7609 \tabularnewline
15 &  16 &  14.55 &  1.45 \tabularnewline
16 &  16 &  16.41 & -0.4085 \tabularnewline
17 &  15 &  16.99 & -1.995 \tabularnewline
18 &  17 &  16.35 &  0.6494 \tabularnewline
19 &  13 &  13.48 & -0.4812 \tabularnewline
20 &  17 &  16.88 &  0.1179 \tabularnewline
21 &  14 &  14.04 & -0.04351 \tabularnewline
22 &  14 &  14.6 & -0.6028 \tabularnewline
23 &  18 &  15.77 &  2.23 \tabularnewline
24 &  17 &  16.99 &  0.01173 \tabularnewline
25 &  16 &  17.15 & -1.148 \tabularnewline
26 &  15 &  15.93 & -0.9296 \tabularnewline
27 &  15 &  15.15 & -0.1514 \tabularnewline
28 &  15 &  15.63 & -0.6276 \tabularnewline
29 &  13 &  15.9 & -2.904 \tabularnewline
30 &  17 &  16.93 &  0.07469 \tabularnewline
31 &  11 &  14.69 & -3.69 \tabularnewline
32 &  14 &  14.04 & -0.04085 \tabularnewline
33 &  13 &  15.77 & -2.77 \tabularnewline
34 &  17 &  15.18 &  1.82 \tabularnewline
35 &  16 &  15.41 &  0.594 \tabularnewline
36 &  17 &  17.6 & -0.5954 \tabularnewline
37 &  16 &  14.53 &  1.468 \tabularnewline
38 &  16 &  15.83 &  0.173 \tabularnewline
39 &  16 &  14.82 &  1.179 \tabularnewline
40 &  15 &  15.84 & -0.8361 \tabularnewline
41 &  12 &  13.46 & -1.46 \tabularnewline
42 &  17 &  15.48 &  1.521 \tabularnewline
43 &  14 &  15.41 & -1.406 \tabularnewline
44 &  14 &  15.68 & -1.685 \tabularnewline
45 &  16 &  14.68 &  1.321 \tabularnewline
46 &  15 &  15.09 & -0.09131 \tabularnewline
47 &  16 &  15.91 &  0.08676 \tabularnewline
48 &  14 &  14.7 & -0.6972 \tabularnewline
49 &  15 &  13.89 &  1.113 \tabularnewline
50 &  17 &  14.63 &  2.373 \tabularnewline
51 &  10 &  13.95 & -3.946 \tabularnewline
52 &  17 &  15.81 &  1.185 \tabularnewline
53 &  20 &  16.4 &  3.604 \tabularnewline
54 &  17 &  16.82 &  0.1795 \tabularnewline
55 &  18 &  15.84 &  2.158 \tabularnewline
56 &  14 &  12.95 &  1.053 \tabularnewline
57 &  17 &  15.7 &  1.297 \tabularnewline
58 &  17 &  17.14 & -0.142 \tabularnewline
59 &  16 &  15.7 &  0.3035 \tabularnewline
60 &  18 &  16.31 &  1.69 \tabularnewline
61 &  18 &  16.85 &  1.152 \tabularnewline
62 &  16 &  16.98 & -0.976 \tabularnewline
63 &  15 &  15.7 & -0.6997 \tabularnewline
64 &  13 &  16.34 & -3.338 \tabularnewline
65 &  16 &  15.78 &  0.2237 \tabularnewline
66 &  12 &  13.53 & -1.533 \tabularnewline
67 &  16 &  15.13 &  0.8713 \tabularnewline
68 &  16 &  15.63 &  0.3729 \tabularnewline
69 &  16 &  16.5 & -0.4966 \tabularnewline
70 &  14 &  15.84 & -1.837 \tabularnewline
71 &  15 &  15.27 & -0.2659 \tabularnewline
72 &  14 &  14.53 & -0.5293 \tabularnewline
73 &  15 &  15.92 & -0.9164 \tabularnewline
74 &  15 &  15.11 & -0.1132 \tabularnewline
75 &  16 &  15.26 &  0.7368 \tabularnewline
76 &  11 &  11.82 & -0.8232 \tabularnewline
77 &  18 &  16.07 &  1.933 \tabularnewline
78 &  11 &  13.96 & -2.961 \tabularnewline
79 &  18 &  17.71 &  0.2933 \tabularnewline
80 &  15 &  16.92 & -1.916 \tabularnewline
81 &  19 &  18.22 &  0.7834 \tabularnewline
82 &  17 &  17 &  0.002124 \tabularnewline
83 &  14 &  15.26 & -1.262 \tabularnewline
84 &  13 &  15.73 & -2.73 \tabularnewline
85 &  17 &  15.81 &  1.185 \tabularnewline
86 &  14 &  15.83 & -1.83 \tabularnewline
87 &  19 &  16.08 &  2.921 \tabularnewline
88 &  14 &  14.62 & -0.6241 \tabularnewline
89 &  16 &  17 & -0.9987 \tabularnewline
90 &  16 &  15.24 &  0.756 \tabularnewline
91 &  15 &  15.67 & -0.6746 \tabularnewline
92 &  12 &  14.7 & -2.697 \tabularnewline
93 &  17 &  16.56 &  0.4364 \tabularnewline
94 &  18 &  15.68 &  2.316 \tabularnewline
95 &  15 &  14.17 &  0.826 \tabularnewline
96 &  18 &  15.84 &  2.159 \tabularnewline
97 &  15 &  17.43 & -2.426 \tabularnewline
98 &  16 &  15.68 &  0.3222 \tabularnewline
99 &  16 &  13.95 &  2.049 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&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] 13[/C][C] 13.1[/C][C]-0.1017[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.4[/C][C] 0.6003[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.92[/C][C] 1.081[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.19[/C][C] 0.811[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 16.98[/C][C] 0.01813[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.89[/C][C] 1.111[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.19[/C][C] 0.807[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 13.9[/C][C] 0.1001[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 15.39[/C][C] 0.6051[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.04[/C][C] 1.964[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 14.54[/C][C] 1.455[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.7[/C][C] 0.3003[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.68[/C][C] 1.315[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.76[/C][C]-0.7609[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 14.55[/C][C] 1.45[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 16.41[/C][C]-0.4085[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 16.99[/C][C]-1.995[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.35[/C][C] 0.6494[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 13.48[/C][C]-0.4812[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.88[/C][C] 0.1179[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 14.04[/C][C]-0.04351[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 14.6[/C][C]-0.6028[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 15.77[/C][C] 2.23[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 16.99[/C][C] 0.01173[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 17.15[/C][C]-1.148[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 15.93[/C][C]-0.9296[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.15[/C][C]-0.1514[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 15.63[/C][C]-0.6276[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 15.9[/C][C]-2.904[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 16.93[/C][C] 0.07469[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 14.69[/C][C]-3.69[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 14.04[/C][C]-0.04085[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 15.77[/C][C]-2.77[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 15.18[/C][C] 1.82[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.41[/C][C] 0.594[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.6[/C][C]-0.5954[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 14.53[/C][C] 1.468[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.83[/C][C] 0.173[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 14.82[/C][C] 1.179[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.84[/C][C]-0.8361[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 13.46[/C][C]-1.46[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 15.48[/C][C] 1.521[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.41[/C][C]-1.406[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.68[/C][C]-1.685[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 14.68[/C][C] 1.321[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.09[/C][C]-0.09131[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 15.91[/C][C] 0.08676[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 14.7[/C][C]-0.6972[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 13.89[/C][C] 1.113[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.63[/C][C] 2.373[/C][/ROW]
[ROW][C]51[/C][C] 10[/C][C] 13.95[/C][C]-3.946[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 15.81[/C][C] 1.185[/C][/ROW]
[ROW][C]53[/C][C] 20[/C][C] 16.4[/C][C] 3.604[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.82[/C][C] 0.1795[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 15.84[/C][C] 2.158[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 12.95[/C][C] 1.053[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.7[/C][C] 1.297[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 17.14[/C][C]-0.142[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 15.7[/C][C] 0.3035[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 16.31[/C][C] 1.69[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 16.85[/C][C] 1.152[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.98[/C][C]-0.976[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.7[/C][C]-0.6997[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 16.34[/C][C]-3.338[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 15.78[/C][C] 0.2237[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 13.53[/C][C]-1.533[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.13[/C][C] 0.8713[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.63[/C][C] 0.3729[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 16.5[/C][C]-0.4966[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 15.84[/C][C]-1.837[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 15.27[/C][C]-0.2659[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.53[/C][C]-0.5293[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.92[/C][C]-0.9164[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.11[/C][C]-0.1132[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.26[/C][C] 0.7368[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.82[/C][C]-0.8232[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 16.07[/C][C] 1.933[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 13.96[/C][C]-2.961[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.71[/C][C] 0.2933[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.92[/C][C]-1.916[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 18.22[/C][C] 0.7834[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 17[/C][C] 0.002124[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 15.26[/C][C]-1.262[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 15.73[/C][C]-2.73[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 15.81[/C][C] 1.185[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.83[/C][C]-1.83[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 16.08[/C][C] 2.921[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 14.62[/C][C]-0.6241[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 17[/C][C]-0.9987[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.24[/C][C] 0.756[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.67[/C][C]-0.6746[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 14.7[/C][C]-2.697[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.56[/C][C] 0.4364[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.68[/C][C] 2.316[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 14.17[/C][C] 0.826[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 15.84[/C][C] 2.159[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 17.43[/C][C]-2.426[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.68[/C][C] 0.3222[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 13.95[/C][C] 2.049[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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	13	13.1	-0.1017
2	16	15.4	0.6003
3	17	15.92	1.081
4	16	15.19	0.811
5	17	16.98	0.01813
6	17	15.89	1.111
7	16	15.19	0.807
8	14	13.9	0.1001
9	16	15.39	0.6051
10	17	15.04	1.964
11	16	14.54	1.455
12	16	15.7	0.3003
13	16	14.68	1.315
14	15	15.76	-0.7609
15	16	14.55	1.45
16	16	16.41	-0.4085
17	15	16.99	-1.995
18	17	16.35	0.6494
19	13	13.48	-0.4812
20	17	16.88	0.1179
21	14	14.04	-0.04351
22	14	14.6	-0.6028
23	18	15.77	2.23
24	17	16.99	0.01173
25	16	17.15	-1.148
26	15	15.93	-0.9296
27	15	15.15	-0.1514
28	15	15.63	-0.6276
29	13	15.9	-2.904
30	17	16.93	0.07469
31	11	14.69	-3.69
32	14	14.04	-0.04085
33	13	15.77	-2.77
34	17	15.18	1.82
35	16	15.41	0.594
36	17	17.6	-0.5954
37	16	14.53	1.468
38	16	15.83	0.173
39	16	14.82	1.179
40	15	15.84	-0.8361
41	12	13.46	-1.46
42	17	15.48	1.521
43	14	15.41	-1.406
44	14	15.68	-1.685
45	16	14.68	1.321
46	15	15.09	-0.09131
47	16	15.91	0.08676
48	14	14.7	-0.6972
49	15	13.89	1.113
50	17	14.63	2.373
51	10	13.95	-3.946
52	17	15.81	1.185
53	20	16.4	3.604
54	17	16.82	0.1795
55	18	15.84	2.158
56	14	12.95	1.053
57	17	15.7	1.297
58	17	17.14	-0.142
59	16	15.7	0.3035
60	18	16.31	1.69
61	18	16.85	1.152
62	16	16.98	-0.976
63	15	15.7	-0.6997
64	13	16.34	-3.338
65	16	15.78	0.2237
66	12	13.53	-1.533
67	16	15.13	0.8713
68	16	15.63	0.3729
69	16	16.5	-0.4966
70	14	15.84	-1.837
71	15	15.27	-0.2659
72	14	14.53	-0.5293
73	15	15.92	-0.9164
74	15	15.11	-0.1132
75	16	15.26	0.7368
76	11	11.82	-0.8232
77	18	16.07	1.933
78	11	13.96	-2.961
79	18	17.71	0.2933
80	15	16.92	-1.916
81	19	18.22	0.7834
82	17	17	0.002124
83	14	15.26	-1.262
84	13	15.73	-2.73
85	17	15.81	1.185
86	14	15.83	-1.83
87	19	16.08	2.921
88	14	14.62	-0.6241
89	16	17	-0.9987
90	16	15.24	0.756
91	15	15.67	-0.6746
92	12	14.7	-2.697
93	17	16.56	0.4364
94	18	15.68	2.316
95	15	14.17	0.826
96	18	15.84	2.159
97	15	17.43	-2.426
98	16	15.68	0.3222
99	16	13.95	2.049

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.149	0.298	0.851
12	0.06014	0.1203	0.9399
13	0.02332	0.04664	0.9767
14	0.06596	0.1319	0.934
15	0.03324	0.06647	0.9668
16	0.01608	0.03216	0.9839
17	0.05662	0.1132	0.9434
18	0.05675	0.1135	0.9433
19	0.04134	0.08268	0.9587
20	0.02678	0.05355	0.9732
21	0.01846	0.03692	0.9815
22	0.01417	0.02835	0.9858
23	0.03952	0.07904	0.9605
24	0.02467	0.04934	0.9753
25	0.01857	0.03714	0.9814
26	0.01162	0.02323	0.9884
27	0.009174	0.01835	0.9908
28	0.006228	0.01246	0.9938
29	0.03155	0.06309	0.9685
30	0.02128	0.04256	0.9787
31	0.1489	0.2979	0.8511
32	0.1129	0.2258	0.8871
33	0.1806	0.3613	0.8194
34	0.1983	0.3965	0.8017
35	0.1631	0.3263	0.8369
36	0.1277	0.2554	0.8723
37	0.1159	0.2318	0.8841
38	0.08736	0.1747	0.9126
39	0.08577	0.1715	0.9142
40	0.06908	0.1382	0.9309
41	0.07154	0.1431	0.9285
42	0.07032	0.1406	0.9297
43	0.07391	0.1478	0.9261
44	0.07916	0.1583	0.9208
45	0.07362	0.1472	0.9264
46	0.06546	0.1309	0.9345
47	0.04794	0.09589	0.9521
48	0.03931	0.07861	0.9607
49	0.03309	0.06619	0.9669
50	0.06037	0.1207	0.9396
51	0.2782	0.5565	0.7218
52	0.2653	0.5307	0.7347
53	0.4874	0.9748	0.5126
54	0.4264	0.8529	0.5736
55	0.4781	0.9561	0.5219
56	0.4346	0.8692	0.5654
57	0.4318	0.8637	0.5682
58	0.3723	0.7445	0.6277
59	0.3211	0.6422	0.6789
60	0.3257	0.6514	0.6743
61	0.3039	0.6078	0.6961
62	0.2636	0.5272	0.7364
63	0.2195	0.4391	0.7805
64	0.3965	0.793	0.6035
65	0.3359	0.6718	0.6641
66	0.3203	0.6405	0.6797
67	0.3179	0.6358	0.6821
68	0.2895	0.5789	0.7105
69	0.2589	0.5179	0.7411
70	0.2719	0.5438	0.7281
71	0.2161	0.4323	0.7839
72	0.1816	0.3632	0.8184
73	0.1627	0.3255	0.8373
74	0.1379	0.2759	0.8621
75	0.1207	0.2414	0.8793
76	0.09047	0.1809	0.9095
77	0.07884	0.1577	0.9212
78	0.1587	0.3174	0.8413
79	0.1131	0.2263	0.8869
80	0.08934	0.1787	0.9107
81	0.06163	0.1233	0.9384
82	0.04894	0.09788	0.9511
83	0.03326	0.06652	0.9667
84	0.3097	0.6193	0.6903
85	0.2458	0.4915	0.7542
86	0.5688	0.8625	0.4312
87	0.6703	0.6594	0.3297
88	0.4994	0.9987	0.5006

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.149 &  0.298 &  0.851 \tabularnewline
12 &  0.06014 &  0.1203 &  0.9399 \tabularnewline
13 &  0.02332 &  0.04664 &  0.9767 \tabularnewline
14 &  0.06596 &  0.1319 &  0.934 \tabularnewline
15 &  0.03324 &  0.06647 &  0.9668 \tabularnewline
16 &  0.01608 &  0.03216 &  0.9839 \tabularnewline
17 &  0.05662 &  0.1132 &  0.9434 \tabularnewline
18 &  0.05675 &  0.1135 &  0.9433 \tabularnewline
19 &  0.04134 &  0.08268 &  0.9587 \tabularnewline
20 &  0.02678 &  0.05355 &  0.9732 \tabularnewline
21 &  0.01846 &  0.03692 &  0.9815 \tabularnewline
22 &  0.01417 &  0.02835 &  0.9858 \tabularnewline
23 &  0.03952 &  0.07904 &  0.9605 \tabularnewline
24 &  0.02467 &  0.04934 &  0.9753 \tabularnewline
25 &  0.01857 &  0.03714 &  0.9814 \tabularnewline
26 &  0.01162 &  0.02323 &  0.9884 \tabularnewline
27 &  0.009174 &  0.01835 &  0.9908 \tabularnewline
28 &  0.006228 &  0.01246 &  0.9938 \tabularnewline
29 &  0.03155 &  0.06309 &  0.9685 \tabularnewline
30 &  0.02128 &  0.04256 &  0.9787 \tabularnewline
31 &  0.1489 &  0.2979 &  0.8511 \tabularnewline
32 &  0.1129 &  0.2258 &  0.8871 \tabularnewline
33 &  0.1806 &  0.3613 &  0.8194 \tabularnewline
34 &  0.1983 &  0.3965 &  0.8017 \tabularnewline
35 &  0.1631 &  0.3263 &  0.8369 \tabularnewline
36 &  0.1277 &  0.2554 &  0.8723 \tabularnewline
37 &  0.1159 &  0.2318 &  0.8841 \tabularnewline
38 &  0.08736 &  0.1747 &  0.9126 \tabularnewline
39 &  0.08577 &  0.1715 &  0.9142 \tabularnewline
40 &  0.06908 &  0.1382 &  0.9309 \tabularnewline
41 &  0.07154 &  0.1431 &  0.9285 \tabularnewline
42 &  0.07032 &  0.1406 &  0.9297 \tabularnewline
43 &  0.07391 &  0.1478 &  0.9261 \tabularnewline
44 &  0.07916 &  0.1583 &  0.9208 \tabularnewline
45 &  0.07362 &  0.1472 &  0.9264 \tabularnewline
46 &  0.06546 &  0.1309 &  0.9345 \tabularnewline
47 &  0.04794 &  0.09589 &  0.9521 \tabularnewline
48 &  0.03931 &  0.07861 &  0.9607 \tabularnewline
49 &  0.03309 &  0.06619 &  0.9669 \tabularnewline
50 &  0.06037 &  0.1207 &  0.9396 \tabularnewline
51 &  0.2782 &  0.5565 &  0.7218 \tabularnewline
52 &  0.2653 &  0.5307 &  0.7347 \tabularnewline
53 &  0.4874 &  0.9748 &  0.5126 \tabularnewline
54 &  0.4264 &  0.8529 &  0.5736 \tabularnewline
55 &  0.4781 &  0.9561 &  0.5219 \tabularnewline
56 &  0.4346 &  0.8692 &  0.5654 \tabularnewline
57 &  0.4318 &  0.8637 &  0.5682 \tabularnewline
58 &  0.3723 &  0.7445 &  0.6277 \tabularnewline
59 &  0.3211 &  0.6422 &  0.6789 \tabularnewline
60 &  0.3257 &  0.6514 &  0.6743 \tabularnewline
61 &  0.3039 &  0.6078 &  0.6961 \tabularnewline
62 &  0.2636 &  0.5272 &  0.7364 \tabularnewline
63 &  0.2195 &  0.4391 &  0.7805 \tabularnewline
64 &  0.3965 &  0.793 &  0.6035 \tabularnewline
65 &  0.3359 &  0.6718 &  0.6641 \tabularnewline
66 &  0.3203 &  0.6405 &  0.6797 \tabularnewline
67 &  0.3179 &  0.6358 &  0.6821 \tabularnewline
68 &  0.2895 &  0.5789 &  0.7105 \tabularnewline
69 &  0.2589 &  0.5179 &  0.7411 \tabularnewline
70 &  0.2719 &  0.5438 &  0.7281 \tabularnewline
71 &  0.2161 &  0.4323 &  0.7839 \tabularnewline
72 &  0.1816 &  0.3632 &  0.8184 \tabularnewline
73 &  0.1627 &  0.3255 &  0.8373 \tabularnewline
74 &  0.1379 &  0.2759 &  0.8621 \tabularnewline
75 &  0.1207 &  0.2414 &  0.8793 \tabularnewline
76 &  0.09047 &  0.1809 &  0.9095 \tabularnewline
77 &  0.07884 &  0.1577 &  0.9212 \tabularnewline
78 &  0.1587 &  0.3174 &  0.8413 \tabularnewline
79 &  0.1131 &  0.2263 &  0.8869 \tabularnewline
80 &  0.08934 &  0.1787 &  0.9107 \tabularnewline
81 &  0.06163 &  0.1233 &  0.9384 \tabularnewline
82 &  0.04894 &  0.09788 &  0.9511 \tabularnewline
83 &  0.03326 &  0.06652 &  0.9667 \tabularnewline
84 &  0.3097 &  0.6193 &  0.6903 \tabularnewline
85 &  0.2458 &  0.4915 &  0.7542 \tabularnewline
86 &  0.5688 &  0.8625 &  0.4312 \tabularnewline
87 &  0.6703 &  0.6594 &  0.3297 \tabularnewline
88 &  0.4994 &  0.9987 &  0.5006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&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] 0.149[/C][C] 0.298[/C][C] 0.851[/C][/ROW]
[ROW][C]12[/C][C] 0.06014[/C][C] 0.1203[/C][C] 0.9399[/C][/ROW]
[ROW][C]13[/C][C] 0.02332[/C][C] 0.04664[/C][C] 0.9767[/C][/ROW]
[ROW][C]14[/C][C] 0.06596[/C][C] 0.1319[/C][C] 0.934[/C][/ROW]
[ROW][C]15[/C][C] 0.03324[/C][C] 0.06647[/C][C] 0.9668[/C][/ROW]
[ROW][C]16[/C][C] 0.01608[/C][C] 0.03216[/C][C] 0.9839[/C][/ROW]
[ROW][C]17[/C][C] 0.05662[/C][C] 0.1132[/C][C] 0.9434[/C][/ROW]
[ROW][C]18[/C][C] 0.05675[/C][C] 0.1135[/C][C] 0.9433[/C][/ROW]
[ROW][C]19[/C][C] 0.04134[/C][C] 0.08268[/C][C] 0.9587[/C][/ROW]
[ROW][C]20[/C][C] 0.02678[/C][C] 0.05355[/C][C] 0.9732[/C][/ROW]
[ROW][C]21[/C][C] 0.01846[/C][C] 0.03692[/C][C] 0.9815[/C][/ROW]
[ROW][C]22[/C][C] 0.01417[/C][C] 0.02835[/C][C] 0.9858[/C][/ROW]
[ROW][C]23[/C][C] 0.03952[/C][C] 0.07904[/C][C] 0.9605[/C][/ROW]
[ROW][C]24[/C][C] 0.02467[/C][C] 0.04934[/C][C] 0.9753[/C][/ROW]
[ROW][C]25[/C][C] 0.01857[/C][C] 0.03714[/C][C] 0.9814[/C][/ROW]
[ROW][C]26[/C][C] 0.01162[/C][C] 0.02323[/C][C] 0.9884[/C][/ROW]
[ROW][C]27[/C][C] 0.009174[/C][C] 0.01835[/C][C] 0.9908[/C][/ROW]
[ROW][C]28[/C][C] 0.006228[/C][C] 0.01246[/C][C] 0.9938[/C][/ROW]
[ROW][C]29[/C][C] 0.03155[/C][C] 0.06309[/C][C] 0.9685[/C][/ROW]
[ROW][C]30[/C][C] 0.02128[/C][C] 0.04256[/C][C] 0.9787[/C][/ROW]
[ROW][C]31[/C][C] 0.1489[/C][C] 0.2979[/C][C] 0.8511[/C][/ROW]
[ROW][C]32[/C][C] 0.1129[/C][C] 0.2258[/C][C] 0.8871[/C][/ROW]
[ROW][C]33[/C][C] 0.1806[/C][C] 0.3613[/C][C] 0.8194[/C][/ROW]
[ROW][C]34[/C][C] 0.1983[/C][C] 0.3965[/C][C] 0.8017[/C][/ROW]
[ROW][C]35[/C][C] 0.1631[/C][C] 0.3263[/C][C] 0.8369[/C][/ROW]
[ROW][C]36[/C][C] 0.1277[/C][C] 0.2554[/C][C] 0.8723[/C][/ROW]
[ROW][C]37[/C][C] 0.1159[/C][C] 0.2318[/C][C] 0.8841[/C][/ROW]
[ROW][C]38[/C][C] 0.08736[/C][C] 0.1747[/C][C] 0.9126[/C][/ROW]
[ROW][C]39[/C][C] 0.08577[/C][C] 0.1715[/C][C] 0.9142[/C][/ROW]
[ROW][C]40[/C][C] 0.06908[/C][C] 0.1382[/C][C] 0.9309[/C][/ROW]
[ROW][C]41[/C][C] 0.07154[/C][C] 0.1431[/C][C] 0.9285[/C][/ROW]
[ROW][C]42[/C][C] 0.07032[/C][C] 0.1406[/C][C] 0.9297[/C][/ROW]
[ROW][C]43[/C][C] 0.07391[/C][C] 0.1478[/C][C] 0.9261[/C][/ROW]
[ROW][C]44[/C][C] 0.07916[/C][C] 0.1583[/C][C] 0.9208[/C][/ROW]
[ROW][C]45[/C][C] 0.07362[/C][C] 0.1472[/C][C] 0.9264[/C][/ROW]
[ROW][C]46[/C][C] 0.06546[/C][C] 0.1309[/C][C] 0.9345[/C][/ROW]
[ROW][C]47[/C][C] 0.04794[/C][C] 0.09589[/C][C] 0.9521[/C][/ROW]
[ROW][C]48[/C][C] 0.03931[/C][C] 0.07861[/C][C] 0.9607[/C][/ROW]
[ROW][C]49[/C][C] 0.03309[/C][C] 0.06619[/C][C] 0.9669[/C][/ROW]
[ROW][C]50[/C][C] 0.06037[/C][C] 0.1207[/C][C] 0.9396[/C][/ROW]
[ROW][C]51[/C][C] 0.2782[/C][C] 0.5565[/C][C] 0.7218[/C][/ROW]
[ROW][C]52[/C][C] 0.2653[/C][C] 0.5307[/C][C] 0.7347[/C][/ROW]
[ROW][C]53[/C][C] 0.4874[/C][C] 0.9748[/C][C] 0.5126[/C][/ROW]
[ROW][C]54[/C][C] 0.4264[/C][C] 0.8529[/C][C] 0.5736[/C][/ROW]
[ROW][C]55[/C][C] 0.4781[/C][C] 0.9561[/C][C] 0.5219[/C][/ROW]
[ROW][C]56[/C][C] 0.4346[/C][C] 0.8692[/C][C] 0.5654[/C][/ROW]
[ROW][C]57[/C][C] 0.4318[/C][C] 0.8637[/C][C] 0.5682[/C][/ROW]
[ROW][C]58[/C][C] 0.3723[/C][C] 0.7445[/C][C] 0.6277[/C][/ROW]
[ROW][C]59[/C][C] 0.3211[/C][C] 0.6422[/C][C] 0.6789[/C][/ROW]
[ROW][C]60[/C][C] 0.3257[/C][C] 0.6514[/C][C] 0.6743[/C][/ROW]
[ROW][C]61[/C][C] 0.3039[/C][C] 0.6078[/C][C] 0.6961[/C][/ROW]
[ROW][C]62[/C][C] 0.2636[/C][C] 0.5272[/C][C] 0.7364[/C][/ROW]
[ROW][C]63[/C][C] 0.2195[/C][C] 0.4391[/C][C] 0.7805[/C][/ROW]
[ROW][C]64[/C][C] 0.3965[/C][C] 0.793[/C][C] 0.6035[/C][/ROW]
[ROW][C]65[/C][C] 0.3359[/C][C] 0.6718[/C][C] 0.6641[/C][/ROW]
[ROW][C]66[/C][C] 0.3203[/C][C] 0.6405[/C][C] 0.6797[/C][/ROW]
[ROW][C]67[/C][C] 0.3179[/C][C] 0.6358[/C][C] 0.6821[/C][/ROW]
[ROW][C]68[/C][C] 0.2895[/C][C] 0.5789[/C][C] 0.7105[/C][/ROW]
[ROW][C]69[/C][C] 0.2589[/C][C] 0.5179[/C][C] 0.7411[/C][/ROW]
[ROW][C]70[/C][C] 0.2719[/C][C] 0.5438[/C][C] 0.7281[/C][/ROW]
[ROW][C]71[/C][C] 0.2161[/C][C] 0.4323[/C][C] 0.7839[/C][/ROW]
[ROW][C]72[/C][C] 0.1816[/C][C] 0.3632[/C][C] 0.8184[/C][/ROW]
[ROW][C]73[/C][C] 0.1627[/C][C] 0.3255[/C][C] 0.8373[/C][/ROW]
[ROW][C]74[/C][C] 0.1379[/C][C] 0.2759[/C][C] 0.8621[/C][/ROW]
[ROW][C]75[/C][C] 0.1207[/C][C] 0.2414[/C][C] 0.8793[/C][/ROW]
[ROW][C]76[/C][C] 0.09047[/C][C] 0.1809[/C][C] 0.9095[/C][/ROW]
[ROW][C]77[/C][C] 0.07884[/C][C] 0.1577[/C][C] 0.9212[/C][/ROW]
[ROW][C]78[/C][C] 0.1587[/C][C] 0.3174[/C][C] 0.8413[/C][/ROW]
[ROW][C]79[/C][C] 0.1131[/C][C] 0.2263[/C][C] 0.8869[/C][/ROW]
[ROW][C]80[/C][C] 0.08934[/C][C] 0.1787[/C][C] 0.9107[/C][/ROW]
[ROW][C]81[/C][C] 0.06163[/C][C] 0.1233[/C][C] 0.9384[/C][/ROW]
[ROW][C]82[/C][C] 0.04894[/C][C] 0.09788[/C][C] 0.9511[/C][/ROW]
[ROW][C]83[/C][C] 0.03326[/C][C] 0.06652[/C][C] 0.9667[/C][/ROW]
[ROW][C]84[/C][C] 0.3097[/C][C] 0.6193[/C][C] 0.6903[/C][/ROW]
[ROW][C]85[/C][C] 0.2458[/C][C] 0.4915[/C][C] 0.7542[/C][/ROW]
[ROW][C]86[/C][C] 0.5688[/C][C] 0.8625[/C][C] 0.4312[/C][/ROW]
[ROW][C]87[/C][C] 0.6703[/C][C] 0.6594[/C][C] 0.3297[/C][/ROW]
[ROW][C]88[/C][C] 0.4994[/C][C] 0.9987[/C][C] 0.5006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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	0.149	0.298	0.851
12	0.06014	0.1203	0.9399
13	0.02332	0.04664	0.9767
14	0.06596	0.1319	0.934
15	0.03324	0.06647	0.9668
16	0.01608	0.03216	0.9839
17	0.05662	0.1132	0.9434
18	0.05675	0.1135	0.9433
19	0.04134	0.08268	0.9587
20	0.02678	0.05355	0.9732
21	0.01846	0.03692	0.9815
22	0.01417	0.02835	0.9858
23	0.03952	0.07904	0.9605
24	0.02467	0.04934	0.9753
25	0.01857	0.03714	0.9814
26	0.01162	0.02323	0.9884
27	0.009174	0.01835	0.9908
28	0.006228	0.01246	0.9938
29	0.03155	0.06309	0.9685
30	0.02128	0.04256	0.9787
31	0.1489	0.2979	0.8511
32	0.1129	0.2258	0.8871
33	0.1806	0.3613	0.8194
34	0.1983	0.3965	0.8017
35	0.1631	0.3263	0.8369
36	0.1277	0.2554	0.8723
37	0.1159	0.2318	0.8841
38	0.08736	0.1747	0.9126
39	0.08577	0.1715	0.9142
40	0.06908	0.1382	0.9309
41	0.07154	0.1431	0.9285
42	0.07032	0.1406	0.9297
43	0.07391	0.1478	0.9261
44	0.07916	0.1583	0.9208
45	0.07362	0.1472	0.9264
46	0.06546	0.1309	0.9345
47	0.04794	0.09589	0.9521
48	0.03931	0.07861	0.9607
49	0.03309	0.06619	0.9669
50	0.06037	0.1207	0.9396
51	0.2782	0.5565	0.7218
52	0.2653	0.5307	0.7347
53	0.4874	0.9748	0.5126
54	0.4264	0.8529	0.5736
55	0.4781	0.9561	0.5219
56	0.4346	0.8692	0.5654
57	0.4318	0.8637	0.5682
58	0.3723	0.7445	0.6277
59	0.3211	0.6422	0.6789
60	0.3257	0.6514	0.6743
61	0.3039	0.6078	0.6961
62	0.2636	0.5272	0.7364
63	0.2195	0.4391	0.7805
64	0.3965	0.793	0.6035
65	0.3359	0.6718	0.6641
66	0.3203	0.6405	0.6797
67	0.3179	0.6358	0.6821
68	0.2895	0.5789	0.7105
69	0.2589	0.5179	0.7411
70	0.2719	0.5438	0.7281
71	0.2161	0.4323	0.7839
72	0.1816	0.3632	0.8184
73	0.1627	0.3255	0.8373
74	0.1379	0.2759	0.8621
75	0.1207	0.2414	0.8793
76	0.09047	0.1809	0.9095
77	0.07884	0.1577	0.9212
78	0.1587	0.3174	0.8413
79	0.1131	0.2263	0.8869
80	0.08934	0.1787	0.9107
81	0.06163	0.1233	0.9384
82	0.04894	0.09788	0.9511
83	0.03326	0.06652	0.9667
84	0.3097	0.6193	0.6903
85	0.2458	0.4915	0.7542
86	0.5688	0.8625	0.4312
87	0.6703	0.6594	0.3297
88	0.4994	0.9987	0.5006

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	10	0.128205	NOK
10% type I error level	20	0.25641	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 10 & 0.128205 & NOK \tabularnewline
10% type I error level & 20 & 0.25641 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&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]10[/C][C]0.128205[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.25641[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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	10	0.128205	NOK
10% type I error level	20	0.25641	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.87317, df1 = 2, df2 = 89, p-value = 0.4212
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.90208, df1 = 14, df2 = 77, p-value = 0.5599
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.73694, df1 = 2, df2 = 89, p-value = 0.4815

\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 = 0.87317, df1 = 2, df2 = 89, p-value = 0.4212
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.90208, df1 = 14, df2 = 77, p-value = 0.5599
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.73694, df1 = 2, df2 = 89, p-value = 0.4815
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&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 = 0.87317, df1 = 2, df2 = 89, p-value = 0.4212
[/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 = 0.90208, df1 = 14, df2 = 77, p-value = 0.5599
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.73694, df1 = 2, df2 = 89, p-value = 0.4815
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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 = 0.87317, df1 = 2, df2 = 89, p-value = 0.4212
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.90208, df1 = 14, df2 = 77, p-value = 0.5599
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.73694, df1 = 2, df2 = 89, p-value = 0.4815

Variance Inflation Factors (Multicollinearity)
> vif ITHSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.164189 1.099822 1.179477 1.113710 1.111735 1.085730 1.050983

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
  ITHSUM   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6 
1.164189 1.099822 1.179477 1.113710 1.111735 1.085730 1.050983 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306488&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
  ITHSUM   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6 
1.164189 1.099822 1.179477 1.113710 1.111735 1.085730 1.050983 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306488&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306488&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 ITHSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.164189 1.099822 1.179477 1.113710 1.111735 1.085730 1.050983

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

PNG link

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Parameters (Session):

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code