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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 12:10:32 +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/t1485169842n0uppypwh531pnq.htm/, Retrieved Wed, 15 May 2024 16:19:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305001, Retrieved Wed, 15 May 2024 16:19:24 +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] [] [2017-01-23 11:10:32] [d06ec19b175650a2a09ee5879d174acf] [Current]

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14 22 13 22
19 24 16 24
17 21 17 26
17 21 NA 21
15 24 NA 26
20 20 16 25
15 22 NA 21
19 20 NA 24
15 19 NA 27
15 23 17 28
19 21 17 23
NA 19 15 25
20 19 16 24
18 21 14 24
15 21 16 24
14 22 17 25
20 22 NA 25
NA 19 NA NA
16 21 NA 25
16 21 NA 25
16 21 16 24
10 20 NA 26
19 22 16 26
19 22 NA 25
16 24 NA 26
15 21 NA 23
18 19 16 24
17 19 15 24
19 23 16 25
17 21 16 25
NA 21 13 24
19 19 15 28
20 21 17 27
5 19 NA NA
19 21 13 23
16 21 17 23
15 23 NA 24
16 19 14 24
18 19 14 22
16 19 18 25
15 18 NA 25
17 22 17 28
NA 18 13 22
20 22 16 28
19 18 15 25
7 22 15 24
13 22 NA 24
16 19 15 23
16 22 13 25
NA 25 NA NA
18 19 17 26
18 19 NA 25
16 19 NA 27
17 19 11 26
19 21 14 23
16 21 13 25
19 20 NA 21
13 19 17 22
16 19 16 24
13 22 NA 25
12 26 17 27
17 19 16 24
17 21 16 26
17 21 16 21
16 20 15 27
16 23 12 22
14 22 17 23
16 22 14 24
13 22 14 25
16 21 16 24
14 21 NA 23
20 22 NA 28
12 23 NA NA
13 18 NA 24
18 24 NA 26
14 22 15 22
19 21 16 25
18 21 14 25
14 21 15 24
18 23 17 24
19 21 NA 26
15 23 10 21
14 21 NA 25
17 19 17 25
19 21 NA 26
13 21 20 25
19 21 17 26
18 23 18 27
20 23 NA 25
15 20 17 NA
15 20 14 20
15 19 NA 24
20 23 17 26
15 22 NA 25
19 19 17 25
18 23 NA 24
18 22 16 26
15 22 18 25
20 21 18 28
17 21 16 27
12 21 NA 25
18 21 NA 26
19 22 15 26
20 25 13 26
NA 21 NA NA
17 23 NA 28
15 19 NA NA
16 22 NA 21
18 20 NA 25
18 21 16 25
14 25 NA 24
15 21 NA 24
12 19 NA 24
17 23 12 23
14 22 NA 23
18 21 16 24
17 24 16 24
17 21 NA 25
20 19 16 28
16 18 14 23
14 19 15 24
15 20 14 23
18 19 NA 24
20 22 15 25
17 21 NA 24
17 22 15 23
17 24 16 23
17 28 NA 25
15 19 NA 21
17 18 NA 22
18 23 11 19
17 19 NA 24
20 23 18 25
15 19 NA 21
16 22 11 22
15 21 NA 23
18 19 18 27
11 22 NA NA
15 21 15 26
18 23 19 29
20 22 17 28
19 19 NA 24
14 19 14 25
16 21 NA 25
15 22 13 22
17 21 17 25
18 20 14 26
20 23 19 26
17 22 14 24
18 23 NA 25
15 22 NA 19
16 21 16 25
11 20 16 23
15 18 15 25
18 18 12 25
17 20 NA 26
16 19 17 27
12 21 NA 24
19 24 NA 22
18 19 18 25
15 20 15 24
17 19 18 23
19 23 15 27
18 22 NA 24
19 21 NA 24
16 24 NA 21
16 21 16 25
16 21 NA 25
14 22 16 23

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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 time6 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=305001&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]

6 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=305001&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&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	6 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
ITHSUM[t] = -0.601486 + 0.40812Bevr_Leeftijd[t] -0.104868TVDC[t] + 0.598312SKEOUSUM[t] -0.0712496`ITHSUM(t-1)`[t] -0.0170663`ITHSUM(t-1s)`[t] -0.0613353`ITHSUM(t-2s)`[t] -0.143811`ITHSUM(t-3s)`[t] + 0.0461709`ITHSUM(t-4s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  -0.601486 +  0.40812Bevr_Leeftijd[t] -0.104868TVDC[t] +  0.598312SKEOUSUM[t] -0.0712496`ITHSUM(t-1)`[t] -0.0170663`ITHSUM(t-1s)`[t] -0.0613353`ITHSUM(t-2s)`[t] -0.143811`ITHSUM(t-3s)`[t] +  0.0461709`ITHSUM(t-4s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  -0.601486 +  0.40812Bevr_Leeftijd[t] -0.104868TVDC[t] +  0.598312SKEOUSUM[t] -0.0712496`ITHSUM(t-1)`[t] -0.0170663`ITHSUM(t-1s)`[t] -0.0613353`ITHSUM(t-2s)`[t] -0.143811`ITHSUM(t-3s)`[t] +  0.0461709`ITHSUM(t-4s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = -0.601486 + 0.40812Bevr_Leeftijd[t] -0.104868TVDC[t] + 0.598312SKEOUSUM[t] -0.0712496`ITHSUM(t-1)`[t] -0.0170663`ITHSUM(t-1s)`[t] -0.0613353`ITHSUM(t-2s)`[t] -0.143811`ITHSUM(t-3s)`[t] + 0.0461709`ITHSUM(t-4s)`[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)	-0.6015	6.038	-9.9620e-02	0.9211	0.4606
Bevr_Leeftijd	+0.4081	0.152	+2.6840e+00	0.01044	0.005218
TVDC	-0.1049	0.1502	-6.9800e-01	0.4891	0.2446
SKEOUSUM	+0.5983	0.162	+3.6920e+00	0.0006492	0.0003246
`ITHSUM(t-1)`	-0.07125	0.1288	-5.5340e-01	0.583	0.2915
`ITHSUM(t-1s)`	-0.01707	0.1288	-1.3260e-01	0.8952	0.4476
`ITHSUM(t-2s)`	-0.06134	0.1115	-5.5000e-01	0.5853	0.2926
`ITHSUM(t-3s)`	-0.1438	0.1083	-1.3280e+00	0.1915	0.09577
`ITHSUM(t-4s)`	+0.04617	0.1086	+4.2520e-01	0.6729	0.3365

\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) & -0.6015 &  6.038 & -9.9620e-02 &  0.9211 &  0.4606 \tabularnewline
Bevr_Leeftijd & +0.4081 &  0.152 & +2.6840e+00 &  0.01044 &  0.005218 \tabularnewline
TVDC & -0.1049 &  0.1502 & -6.9800e-01 &  0.4891 &  0.2446 \tabularnewline
SKEOUSUM & +0.5983 &  0.162 & +3.6920e+00 &  0.0006492 &  0.0003246 \tabularnewline
`ITHSUM(t-1)` & -0.07125 &  0.1288 & -5.5340e-01 &  0.583 &  0.2915 \tabularnewline
`ITHSUM(t-1s)` & -0.01707 &  0.1288 & -1.3260e-01 &  0.8952 &  0.4476 \tabularnewline
`ITHSUM(t-2s)` & -0.06134 &  0.1115 & -5.5000e-01 &  0.5853 &  0.2926 \tabularnewline
`ITHSUM(t-3s)` & -0.1438 &  0.1083 & -1.3280e+00 &  0.1915 &  0.09577 \tabularnewline
`ITHSUM(t-4s)` & +0.04617 &  0.1086 & +4.2520e-01 &  0.6729 &  0.3365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&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]-0.6015[/C][C] 6.038[/C][C]-9.9620e-02[/C][C] 0.9211[/C][C] 0.4606[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.4081[/C][C] 0.152[/C][C]+2.6840e+00[/C][C] 0.01044[/C][C] 0.005218[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.1049[/C][C] 0.1502[/C][C]-6.9800e-01[/C][C] 0.4891[/C][C] 0.2446[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.5983[/C][C] 0.162[/C][C]+3.6920e+00[/C][C] 0.0006492[/C][C] 0.0003246[/C][/ROW]
[ROW][C]`ITHSUM(t-1)`[/C][C]-0.07125[/C][C] 0.1288[/C][C]-5.5340e-01[/C][C] 0.583[/C][C] 0.2915[/C][/ROW]
[ROW][C]`ITHSUM(t-1s)`[/C][C]-0.01707[/C][C] 0.1288[/C][C]-1.3260e-01[/C][C] 0.8952[/C][C] 0.4476[/C][/ROW]
[ROW][C]`ITHSUM(t-2s)`[/C][C]-0.06134[/C][C] 0.1115[/C][C]-5.5000e-01[/C][C] 0.5853[/C][C] 0.2926[/C][/ROW]
[ROW][C]`ITHSUM(t-3s)`[/C][C]-0.1438[/C][C] 0.1083[/C][C]-1.3280e+00[/C][C] 0.1915[/C][C] 0.09577[/C][/ROW]
[ROW][C]`ITHSUM(t-4s)`[/C][C]+0.04617[/C][C] 0.1086[/C][C]+4.2520e-01[/C][C] 0.6729[/C][C] 0.3365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&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)	-0.6015	6.038	-9.9620e-02	0.9211	0.4606
Bevr_Leeftijd	+0.4081	0.152	+2.6840e+00	0.01044	0.005218
TVDC	-0.1049	0.1502	-6.9800e-01	0.4891	0.2446
SKEOUSUM	+0.5983	0.162	+3.6920e+00	0.0006492	0.0003246
`ITHSUM(t-1)`	-0.07125	0.1288	-5.5340e-01	0.583	0.2915
`ITHSUM(t-1s)`	-0.01707	0.1288	-1.3260e-01	0.8952	0.4476
`ITHSUM(t-2s)`	-0.06134	0.1115	-5.5000e-01	0.5853	0.2926
`ITHSUM(t-3s)`	-0.1438	0.1083	-1.3280e+00	0.1915	0.09577
`ITHSUM(t-4s)`	+0.04617	0.1086	+4.2520e-01	0.6729	0.3365

Multiple Linear Regression - Regression Statistics
Multiple R	0.6188
R-squared	0.3829
Adjusted R-squared	0.2625
F-TEST (value)	3.18
F-TEST (DF numerator)	8
F-TEST (DF denominator)	41
p-value	0.006752
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.798
Sum Squared Residuals	132.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6188 \tabularnewline
R-squared &  0.3829 \tabularnewline
Adjusted R-squared &  0.2625 \tabularnewline
F-TEST (value) &  3.18 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value &  0.006752 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.798 \tabularnewline
Sum Squared Residuals &  132.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6188[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3829[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2625[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 3.18[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/C][/ROW]
[ROW][C]p-value[/C][C] 0.006752[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.798[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 132.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&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	0.6188
R-squared	0.3829
Adjusted R-squared	0.2625
F-TEST (value)	3.18
F-TEST (DF numerator)	8
F-TEST (DF denominator)	41
p-value	0.006752
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.798
Sum Squared Residuals	132.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	18	17.34	0.6585
2	15	16.35	-1.352
3	17	16.48	0.5167
4	13	16.32	-3.324
5	19	17.47	1.526
6	18	18.57	-0.5736
7	15	13.92	1.081
8	20	18.63	1.373
9	19	15.92	3.076
10	18	18.03	-0.03375
11	15	17.55	-2.554
12	20	18.44	1.561
13	17	17.72	-0.7232
14	19	19.72	-0.7209
15	20	19.65	0.347
16	18	17.2	0.803
17	17	17.16	-0.1574
18	18	16.58	1.417
19	17	17.55	-0.5455
20	20	18.13	1.869
21	16	15.39	0.6087
22	14	15.76	-1.764
23	15	16.56	-1.556
24	20	17.97	2.028
25	17	16.18	0.8214
26	17	16.7	0.3005
27	18	14.84	3.158
28	20	17.91	2.094
29	16	16.32	-0.3237
30	18	17.35	0.6477
31	15	18.47	-3.467
32	18	19.94	-1.945
33	20	19.21	0.7869
34	14	15.88	-1.881
35	15	15.97	-0.9687
36	17	17.28	-0.2788
37	18	17.3	0.6989
38	20	18.24	1.761
39	17	16.6	0.3965
40	16	17.46	-1.462
41	11	15.1	-4.102
42	15	16.08	-1.085
43	18	16.52	1.48
44	16	16.57	-0.5706
45	18	15.67	2.326
46	15	16.26	-1.256
47	17	15.46	1.545
48	19	18.41	0.5912
49	16	16.93	-0.9316
50	14	15.9	-1.897

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  18 &  17.34 &  0.6585 \tabularnewline
2 &  15 &  16.35 & -1.352 \tabularnewline
3 &  17 &  16.48 &  0.5167 \tabularnewline
4 &  13 &  16.32 & -3.324 \tabularnewline
5 &  19 &  17.47 &  1.526 \tabularnewline
6 &  18 &  18.57 & -0.5736 \tabularnewline
7 &  15 &  13.92 &  1.081 \tabularnewline
8 &  20 &  18.63 &  1.373 \tabularnewline
9 &  19 &  15.92 &  3.076 \tabularnewline
10 &  18 &  18.03 & -0.03375 \tabularnewline
11 &  15 &  17.55 & -2.554 \tabularnewline
12 &  20 &  18.44 &  1.561 \tabularnewline
13 &  17 &  17.72 & -0.7232 \tabularnewline
14 &  19 &  19.72 & -0.7209 \tabularnewline
15 &  20 &  19.65 &  0.347 \tabularnewline
16 &  18 &  17.2 &  0.803 \tabularnewline
17 &  17 &  17.16 & -0.1574 \tabularnewline
18 &  18 &  16.58 &  1.417 \tabularnewline
19 &  17 &  17.55 & -0.5455 \tabularnewline
20 &  20 &  18.13 &  1.869 \tabularnewline
21 &  16 &  15.39 &  0.6087 \tabularnewline
22 &  14 &  15.76 & -1.764 \tabularnewline
23 &  15 &  16.56 & -1.556 \tabularnewline
24 &  20 &  17.97 &  2.028 \tabularnewline
25 &  17 &  16.18 &  0.8214 \tabularnewline
26 &  17 &  16.7 &  0.3005 \tabularnewline
27 &  18 &  14.84 &  3.158 \tabularnewline
28 &  20 &  17.91 &  2.094 \tabularnewline
29 &  16 &  16.32 & -0.3237 \tabularnewline
30 &  18 &  17.35 &  0.6477 \tabularnewline
31 &  15 &  18.47 & -3.467 \tabularnewline
32 &  18 &  19.94 & -1.945 \tabularnewline
33 &  20 &  19.21 &  0.7869 \tabularnewline
34 &  14 &  15.88 & -1.881 \tabularnewline
35 &  15 &  15.97 & -0.9687 \tabularnewline
36 &  17 &  17.28 & -0.2788 \tabularnewline
37 &  18 &  17.3 &  0.6989 \tabularnewline
38 &  20 &  18.24 &  1.761 \tabularnewline
39 &  17 &  16.6 &  0.3965 \tabularnewline
40 &  16 &  17.46 & -1.462 \tabularnewline
41 &  11 &  15.1 & -4.102 \tabularnewline
42 &  15 &  16.08 & -1.085 \tabularnewline
43 &  18 &  16.52 &  1.48 \tabularnewline
44 &  16 &  16.57 & -0.5706 \tabularnewline
45 &  18 &  15.67 &  2.326 \tabularnewline
46 &  15 &  16.26 & -1.256 \tabularnewline
47 &  17 &  15.46 &  1.545 \tabularnewline
48 &  19 &  18.41 &  0.5912 \tabularnewline
49 &  16 &  16.93 & -0.9316 \tabularnewline
50 &  14 &  15.9 & -1.897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&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] 18[/C][C] 17.34[/C][C] 0.6585[/C][/ROW]
[ROW][C]2[/C][C] 15[/C][C] 16.35[/C][C]-1.352[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.48[/C][C] 0.5167[/C][/ROW]
[ROW][C]4[/C][C] 13[/C][C] 16.32[/C][C]-3.324[/C][/ROW]
[ROW][C]5[/C][C] 19[/C][C] 17.47[/C][C] 1.526[/C][/ROW]
[ROW][C]6[/C][C] 18[/C][C] 18.57[/C][C]-0.5736[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 13.92[/C][C] 1.081[/C][/ROW]
[ROW][C]8[/C][C] 20[/C][C] 18.63[/C][C] 1.373[/C][/ROW]
[ROW][C]9[/C][C] 19[/C][C] 15.92[/C][C] 3.076[/C][/ROW]
[ROW][C]10[/C][C] 18[/C][C] 18.03[/C][C]-0.03375[/C][/ROW]
[ROW][C]11[/C][C] 15[/C][C] 17.55[/C][C]-2.554[/C][/ROW]
[ROW][C]12[/C][C] 20[/C][C] 18.44[/C][C] 1.561[/C][/ROW]
[ROW][C]13[/C][C] 17[/C][C] 17.72[/C][C]-0.7232[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 19.72[/C][C]-0.7209[/C][/ROW]
[ROW][C]15[/C][C] 20[/C][C] 19.65[/C][C] 0.347[/C][/ROW]
[ROW][C]16[/C][C] 18[/C][C] 17.2[/C][C] 0.803[/C][/ROW]
[ROW][C]17[/C][C] 17[/C][C] 17.16[/C][C]-0.1574[/C][/ROW]
[ROW][C]18[/C][C] 18[/C][C] 16.58[/C][C] 1.417[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 17.55[/C][C]-0.5455[/C][/ROW]
[ROW][C]20[/C][C] 20[/C][C] 18.13[/C][C] 1.869[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 15.39[/C][C] 0.6087[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 15.76[/C][C]-1.764[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 16.56[/C][C]-1.556[/C][/ROW]
[ROW][C]24[/C][C] 20[/C][C] 17.97[/C][C] 2.028[/C][/ROW]
[ROW][C]25[/C][C] 17[/C][C] 16.18[/C][C] 0.8214[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.7[/C][C] 0.3005[/C][/ROW]
[ROW][C]27[/C][C] 18[/C][C] 14.84[/C][C] 3.158[/C][/ROW]
[ROW][C]28[/C][C] 20[/C][C] 17.91[/C][C] 2.094[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.32[/C][C]-0.3237[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 17.35[/C][C] 0.6477[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 18.47[/C][C]-3.467[/C][/ROW]
[ROW][C]32[/C][C] 18[/C][C] 19.94[/C][C]-1.945[/C][/ROW]
[ROW][C]33[/C][C] 20[/C][C] 19.21[/C][C] 0.7869[/C][/ROW]
[ROW][C]34[/C][C] 14[/C][C] 15.88[/C][C]-1.881[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 15.97[/C][C]-0.9687[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.28[/C][C]-0.2788[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 17.3[/C][C] 0.6989[/C][/ROW]
[ROW][C]38[/C][C] 20[/C][C] 18.24[/C][C] 1.761[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 16.6[/C][C] 0.3965[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 17.46[/C][C]-1.462[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 15.1[/C][C]-4.102[/C][/ROW]
[ROW][C]42[/C][C] 15[/C][C] 16.08[/C][C]-1.085[/C][/ROW]
[ROW][C]43[/C][C] 18[/C][C] 16.52[/C][C] 1.48[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 16.57[/C][C]-0.5706[/C][/ROW]
[ROW][C]45[/C][C] 18[/C][C] 15.67[/C][C] 2.326[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 16.26[/C][C]-1.256[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 15.46[/C][C] 1.545[/C][/ROW]
[ROW][C]48[/C][C] 19[/C][C] 18.41[/C][C] 0.5912[/C][/ROW]
[ROW][C]49[/C][C] 16[/C][C] 16.93[/C][C]-0.9316[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 15.9[/C][C]-1.897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&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	18	17.34	0.6585
2	15	16.35	-1.352
3	17	16.48	0.5167
4	13	16.32	-3.324
5	19	17.47	1.526
6	18	18.57	-0.5736
7	15	13.92	1.081
8	20	18.63	1.373
9	19	15.92	3.076
10	18	18.03	-0.03375
11	15	17.55	-2.554
12	20	18.44	1.561
13	17	17.72	-0.7232
14	19	19.72	-0.7209
15	20	19.65	0.347
16	18	17.2	0.803
17	17	17.16	-0.1574
18	18	16.58	1.417
19	17	17.55	-0.5455
20	20	18.13	1.869
21	16	15.39	0.6087
22	14	15.76	-1.764
23	15	16.56	-1.556
24	20	17.97	2.028
25	17	16.18	0.8214
26	17	16.7	0.3005
27	18	14.84	3.158
28	20	17.91	2.094
29	16	16.32	-0.3237
30	18	17.35	0.6477
31	15	18.47	-3.467
32	18	19.94	-1.945
33	20	19.21	0.7869
34	14	15.88	-1.881
35	15	15.97	-0.9687
36	17	17.28	-0.2788
37	18	17.3	0.6989
38	20	18.24	1.761
39	17	16.6	0.3965
40	16	17.46	-1.462
41	11	15.1	-4.102
42	15	16.08	-1.085
43	18	16.52	1.48
44	16	16.57	-0.5706
45	18	15.67	2.326
46	15	16.26	-1.256
47	17	15.46	1.545
48	19	18.41	0.5912
49	16	16.93	-0.9316
50	14	15.9	-1.897

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.4895	0.9791	0.5105
13	0.3316	0.6632	0.6684
14	0.6014	0.7973	0.3986
15	0.4625	0.925	0.5375
16	0.3496	0.6992	0.6504
17	0.2647	0.5294	0.7353
18	0.2877	0.5754	0.7123
19	0.2032	0.4065	0.7968
20	0.1425	0.2849	0.8575
21	0.0898	0.1796	0.9102
22	0.1941	0.3882	0.8059
23	0.1995	0.399	0.8005
24	0.2358	0.4716	0.7642
25	0.1848	0.3696	0.8152
26	0.162	0.324	0.838
27	0.292	0.584	0.708
28	0.3764	0.7528	0.6236
29	0.313	0.6259	0.687
30	0.2279	0.4558	0.7721
31	0.3689	0.7377	0.6311
32	0.4587	0.9174	0.5413
33	0.4417	0.8834	0.5583
34	0.4052	0.8103	0.5948
35	0.3065	0.6131	0.6935
36	0.2011	0.4022	0.7989
37	0.1224	0.2447	0.8776
38	0.06838	0.1368	0.9316

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.4895 &  0.9791 &  0.5105 \tabularnewline
13 &  0.3316 &  0.6632 &  0.6684 \tabularnewline
14 &  0.6014 &  0.7973 &  0.3986 \tabularnewline
15 &  0.4625 &  0.925 &  0.5375 \tabularnewline
16 &  0.3496 &  0.6992 &  0.6504 \tabularnewline
17 &  0.2647 &  0.5294 &  0.7353 \tabularnewline
18 &  0.2877 &  0.5754 &  0.7123 \tabularnewline
19 &  0.2032 &  0.4065 &  0.7968 \tabularnewline
20 &  0.1425 &  0.2849 &  0.8575 \tabularnewline
21 &  0.0898 &  0.1796 &  0.9102 \tabularnewline
22 &  0.1941 &  0.3882 &  0.8059 \tabularnewline
23 &  0.1995 &  0.399 &  0.8005 \tabularnewline
24 &  0.2358 &  0.4716 &  0.7642 \tabularnewline
25 &  0.1848 &  0.3696 &  0.8152 \tabularnewline
26 &  0.162 &  0.324 &  0.838 \tabularnewline
27 &  0.292 &  0.584 &  0.708 \tabularnewline
28 &  0.3764 &  0.7528 &  0.6236 \tabularnewline
29 &  0.313 &  0.6259 &  0.687 \tabularnewline
30 &  0.2279 &  0.4558 &  0.7721 \tabularnewline
31 &  0.3689 &  0.7377 &  0.6311 \tabularnewline
32 &  0.4587 &  0.9174 &  0.5413 \tabularnewline
33 &  0.4417 &  0.8834 &  0.5583 \tabularnewline
34 &  0.4052 &  0.8103 &  0.5948 \tabularnewline
35 &  0.3065 &  0.6131 &  0.6935 \tabularnewline
36 &  0.2011 &  0.4022 &  0.7989 \tabularnewline
37 &  0.1224 &  0.2447 &  0.8776 \tabularnewline
38 &  0.06838 &  0.1368 &  0.9316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C] 0.4895[/C][C] 0.9791[/C][C] 0.5105[/C][/ROW]
[ROW][C]13[/C][C] 0.3316[/C][C] 0.6632[/C][C] 0.6684[/C][/ROW]
[ROW][C]14[/C][C] 0.6014[/C][C] 0.7973[/C][C] 0.3986[/C][/ROW]
[ROW][C]15[/C][C] 0.4625[/C][C] 0.925[/C][C] 0.5375[/C][/ROW]
[ROW][C]16[/C][C] 0.3496[/C][C] 0.6992[/C][C] 0.6504[/C][/ROW]
[ROW][C]17[/C][C] 0.2647[/C][C] 0.5294[/C][C] 0.7353[/C][/ROW]
[ROW][C]18[/C][C] 0.2877[/C][C] 0.5754[/C][C] 0.7123[/C][/ROW]
[ROW][C]19[/C][C] 0.2032[/C][C] 0.4065[/C][C] 0.7968[/C][/ROW]
[ROW][C]20[/C][C] 0.1425[/C][C] 0.2849[/C][C] 0.8575[/C][/ROW]
[ROW][C]21[/C][C] 0.0898[/C][C] 0.1796[/C][C] 0.9102[/C][/ROW]
[ROW][C]22[/C][C] 0.1941[/C][C] 0.3882[/C][C] 0.8059[/C][/ROW]
[ROW][C]23[/C][C] 0.1995[/C][C] 0.399[/C][C] 0.8005[/C][/ROW]
[ROW][C]24[/C][C] 0.2358[/C][C] 0.4716[/C][C] 0.7642[/C][/ROW]
[ROW][C]25[/C][C] 0.1848[/C][C] 0.3696[/C][C] 0.8152[/C][/ROW]
[ROW][C]26[/C][C] 0.162[/C][C] 0.324[/C][C] 0.838[/C][/ROW]
[ROW][C]27[/C][C] 0.292[/C][C] 0.584[/C][C] 0.708[/C][/ROW]
[ROW][C]28[/C][C] 0.3764[/C][C] 0.7528[/C][C] 0.6236[/C][/ROW]
[ROW][C]29[/C][C] 0.313[/C][C] 0.6259[/C][C] 0.687[/C][/ROW]
[ROW][C]30[/C][C] 0.2279[/C][C] 0.4558[/C][C] 0.7721[/C][/ROW]
[ROW][C]31[/C][C] 0.3689[/C][C] 0.7377[/C][C] 0.6311[/C][/ROW]
[ROW][C]32[/C][C] 0.4587[/C][C] 0.9174[/C][C] 0.5413[/C][/ROW]
[ROW][C]33[/C][C] 0.4417[/C][C] 0.8834[/C][C] 0.5583[/C][/ROW]
[ROW][C]34[/C][C] 0.4052[/C][C] 0.8103[/C][C] 0.5948[/C][/ROW]
[ROW][C]35[/C][C] 0.3065[/C][C] 0.6131[/C][C] 0.6935[/C][/ROW]
[ROW][C]36[/C][C] 0.2011[/C][C] 0.4022[/C][C] 0.7989[/C][/ROW]
[ROW][C]37[/C][C] 0.1224[/C][C] 0.2447[/C][C] 0.8776[/C][/ROW]
[ROW][C]38[/C][C] 0.06838[/C][C] 0.1368[/C][C] 0.9316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.4895	0.9791	0.5105
13	0.3316	0.6632	0.6684
14	0.6014	0.7973	0.3986
15	0.4625	0.925	0.5375
16	0.3496	0.6992	0.6504
17	0.2647	0.5294	0.7353
18	0.2877	0.5754	0.7123
19	0.2032	0.4065	0.7968
20	0.1425	0.2849	0.8575
21	0.0898	0.1796	0.9102
22	0.1941	0.3882	0.8059
23	0.1995	0.399	0.8005
24	0.2358	0.4716	0.7642
25	0.1848	0.3696	0.8152
26	0.162	0.324	0.838
27	0.292	0.584	0.708
28	0.3764	0.7528	0.6236
29	0.313	0.6259	0.687
30	0.2279	0.4558	0.7721
31	0.3689	0.7377	0.6311
32	0.4587	0.9174	0.5413
33	0.4417	0.8834	0.5583
34	0.4052	0.8103	0.5948
35	0.3065	0.6131	0.6935
36	0.2011	0.4022	0.7989
37	0.1224	0.2447	0.8776
38	0.06838	0.1368	0.9316

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	0	0	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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&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	0	0	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.4797, df1 = 2, df2 = 39, p-value = 0.2402
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3871, df1 = 16, df2 = 25, p-value = 0.2254
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.22505, df1 = 2, df2 = 39, p-value = 0.7995

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.4797, df1 = 2, df2 = 39, p-value = 0.2402
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3871, df1 = 16, df2 = 25, p-value = 0.2254
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.22505, df1 = 2, df2 = 39, p-value = 0.7995
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.4797, df1 = 2, df2 = 39, p-value = 0.2402
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3871, df1 = 16, df2 = 25, p-value = 0.2254
[/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 = 0.22505, df1 = 2, df2 = 39, p-value = 0.7995
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.4797, df1 = 2, df2 = 39, p-value = 0.2402
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3871, df1 = 16, df2 = 25, p-value = 0.2254
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.22505, df1 = 2, df2 = 39, p-value = 0.7995

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM `ITHSUM(t-1)` `ITHSUM(t-1s)` 1.077479 1.616282 1.633460 1.101334 1.050907 `ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 1.154976 1.070766 1.054595

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
 Bevr_Leeftijd           TVDC       SKEOUSUM  `ITHSUM(t-1)` `ITHSUM(t-1s)` 
      1.077479       1.616282       1.633460       1.101334       1.050907 
`ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 
      1.154976       1.070766       1.054595 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305001&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
 Bevr_Leeftijd           TVDC       SKEOUSUM  `ITHSUM(t-1)` `ITHSUM(t-1s)` 
      1.077479       1.616282       1.633460       1.101334       1.050907 
`ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 
      1.154976       1.070766       1.054595 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305001&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305001&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM `ITHSUM(t-1)` `ITHSUM(t-1s)` 1.077479 1.616282 1.633460 1.101334 1.050907 `ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 1.154976 1.070766 1.054595

Figure 1

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Figure 10

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Parameters (Session):

par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 4 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 4 ;

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