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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 25 Jan 2017 09:41:58 +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/t1485333858im6vwk2f5f4rltz.htm/, Retrieved Tue, 14 May 2024 08:42:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305736, Retrieved Tue, 14 May 2024 08:42:22 +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 08:41:58] [80e93ef609913f8c49133402d38d300f] [Current]

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

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

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

R Framework error message[/C][C]

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

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305736&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 4.96664 -0.029231Bevr_Leeftijd[t] -0.036529ITHSUM[t] + 0.479318SKEOUSUM[t] -0.000805891t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  4.96664 -0.029231Bevr_Leeftijd[t] -0.036529ITHSUM[t] +  0.479318SKEOUSUM[t] -0.000805891t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  4.96664 -0.029231Bevr_Leeftijd[t] -0.036529ITHSUM[t] +  0.479318SKEOUSUM[t] -0.000805891t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&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] = + 4.96664 -0.029231Bevr_Leeftijd[t] -0.036529ITHSUM[t] + 0.479318SKEOUSUM[t] -0.000805891t + 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)	+4.967	3.117	+1.5930e+00	0.1145	0.05723
Bevr_Leeftijd	-0.02923	0.1024	-2.8530e-01	0.776	0.388
ITHSUM	-0.03653	0.07925	-4.6090e-01	0.6459	0.323
SKEOUSUM	+0.4793	0.09661	+4.9610e+00	3.106e-06	1.553e-06
t	-0.0008059	0.005947	-1.3550e-01	0.8925	0.4462

\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) & +4.967 &  3.117 & +1.5930e+00 &  0.1145 &  0.05723 \tabularnewline
Bevr_Leeftijd & -0.02923 &  0.1024 & -2.8530e-01 &  0.776 &  0.388 \tabularnewline
ITHSUM & -0.03653 &  0.07925 & -4.6090e-01 &  0.6459 &  0.323 \tabularnewline
SKEOUSUM & +0.4793 &  0.09661 & +4.9610e+00 &  3.106e-06 &  1.553e-06 \tabularnewline
t & -0.0008059 &  0.005947 & -1.3550e-01 &  0.8925 &  0.4462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&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]+4.967[/C][C] 3.117[/C][C]+1.5930e+00[/C][C] 0.1145[/C][C] 0.05723[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.02923[/C][C] 0.1024[/C][C]-2.8530e-01[/C][C] 0.776[/C][C] 0.388[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-0.03653[/C][C] 0.07925[/C][C]-4.6090e-01[/C][C] 0.6459[/C][C] 0.323[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.4793[/C][C] 0.09661[/C][C]+4.9610e+00[/C][C] 3.106e-06[/C][C] 1.553e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.0008059[/C][C] 0.005947[/C][C]-1.3550e-01[/C][C] 0.8925[/C][C] 0.4462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&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)	+4.967	3.117	+1.5930e+00	0.1145	0.05723
Bevr_Leeftijd	-0.02923	0.1024	-2.8530e-01	0.776	0.388
ITHSUM	-0.03653	0.07925	-4.6090e-01	0.6459	0.323
SKEOUSUM	+0.4793	0.09661	+4.9610e+00	3.106e-06	1.553e-06
t	-0.0008059	0.005947	-1.3550e-01	0.8925	0.4462

Multiple Linear Regression - Regression Statistics
Multiple R	0.4671
R-squared	0.2182
Adjusted R-squared	0.1849
F-TEST (value)	6.558
F-TEST (DF numerator)	4
F-TEST (DF denominator)	94
p-value	0.0001065
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.688
Sum Squared Residuals	268

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.4671 \tabularnewline
R-squared &  0.2182 \tabularnewline
Adjusted R-squared &  0.1849 \tabularnewline
F-TEST (value) &  6.558 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value &  0.0001065 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.688 \tabularnewline
Sum Squared Residuals &  268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.4671[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2182[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1849[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 6.558[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0001065[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.688[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&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	0.4671
R-squared	0.2182
Adjusted R-squared	0.1849
F-TEST (value)	6.558
F-TEST (DF numerator)	4
F-TEST (DF denominator)	94
p-value	0.0001065
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.688
Sum Squared Residuals	268

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	14.36	-1.356
2	16	15.07	0.9269
3	17	16.19	0.8084
4	16	15.63	0.3688
5	17	17.16	-0.1633
6	17	14.68	2.322
7	16	15.18	0.8213
8	14	15.19	-1.192
9	16	15.3	0.6988
10	17	15.79	1.213
11	16	15.26	0.7369
12	16	16.08	-0.08211
13	16	15.25	0.7531
14	15	15.28	-0.2826
15	16	15.57	0.4289
16	16	15.7	0.2981
17	15	17.12	-2.124
18	17	16.55	0.4507
19	13	14.67	-1.668
20	17	14.78	2.223
21	14	15.31	-1.313
22	14	14.28	-0.281
23	18	15.79	2.209
24	17	17.1	-0.1041
25	16	16.99	-0.9937
26	15	15.71	-0.7084
27	15	15.55	-0.5497
28	15	14.83	0.1715
29	13	15.7	-2.699
30	17	16.19	0.8082
31	11	16.23	-5.228
32	14	14.66	-0.6573
33	13	15.72	-2.725
34	17	14.45	2.546
35	16	15.3	0.6978
36	17	16.68	0.3191
37	16	15.26	0.7359
38	16	16.16	-0.1634
39	16	13.77	2.234
40	15	16.71	-1.707
41	12	14.22	-2.222
42	17	14.8	2.197
43	14	15.21	-1.208
44	14	15.8	-1.796
45	16	15.24	0.7643
46	15	14.32	0.6799
47	16	15.6	0.3962
48	14	15.64	-1.64
49	15	15.31	-0.3055
50	17	15.1	1.9
51	10	13.77	-3.771
52	17	15.73	1.269
53	20	15.82	4.182
54	17	16.08	0.9225
55	18	16.53	1.466
56	14	13.38	0.6247
57	17	15.98	1.02
58	17	15.65	1.347
59	16	16.08	-0.08076
60	18	15.71	2.29
61	18	16.99	1.006
62	16	16.62	-0.6234
63	15	16.04	-1.041
64	13	15.92	-2.916
65	16	15.63	0.3742
66	12	14.64	-2.644
67	16	15.14	0.8551
68	16	15.09	0.9071
69	16	17.05	-1.046
70	14	14.82	-0.8239
71	15	15.35	-0.3463
72	14	14.8	-0.8004
73	15	15.52	-0.5171
74	15	14.67	0.3328
75	16	14.61	1.392
76	11	12.68	-1.683
77	18	15.48	2.515
78	11	14.22	-3.221
79	18	16.63	1.368
80	15	16.2	-1.203
81	19	17.47	1.528
82	17	16.95	0.0522
83	14	15.82	-1.816
84	13	14.25	-1.253
85	17	15.65	1.354
86	14	16.12	-2.117
87	19	15.96	3.044
88	14	15.14	-1.135
89	16	15.68	0.3204
90	16	14.93	1.068
91	15	15.8	-0.8022
92	12	15.69	-3.692
93	17	16.69	0.3066
94	18	15.66	2.339
95	15	15.26	-0.2612
96	18	14.74	3.263
97	15	16.46	-1.464
98	16	15.67	0.3277
99	16	14.76	1.243

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  14.36 & -1.356 \tabularnewline
2 &  16 &  15.07 &  0.9269 \tabularnewline
3 &  17 &  16.19 &  0.8084 \tabularnewline
4 &  16 &  15.63 &  0.3688 \tabularnewline
5 &  17 &  17.16 & -0.1633 \tabularnewline
6 &  17 &  14.68 &  2.322 \tabularnewline
7 &  16 &  15.18 &  0.8213 \tabularnewline
8 &  14 &  15.19 & -1.192 \tabularnewline
9 &  16 &  15.3 &  0.6988 \tabularnewline
10 &  17 &  15.79 &  1.213 \tabularnewline
11 &  16 &  15.26 &  0.7369 \tabularnewline
12 &  16 &  16.08 & -0.08211 \tabularnewline
13 &  16 &  15.25 &  0.7531 \tabularnewline
14 &  15 &  15.28 & -0.2826 \tabularnewline
15 &  16 &  15.57 &  0.4289 \tabularnewline
16 &  16 &  15.7 &  0.2981 \tabularnewline
17 &  15 &  17.12 & -2.124 \tabularnewline
18 &  17 &  16.55 &  0.4507 \tabularnewline
19 &  13 &  14.67 & -1.668 \tabularnewline
20 &  17 &  14.78 &  2.223 \tabularnewline
21 &  14 &  15.31 & -1.313 \tabularnewline
22 &  14 &  14.28 & -0.281 \tabularnewline
23 &  18 &  15.79 &  2.209 \tabularnewline
24 &  17 &  17.1 & -0.1041 \tabularnewline
25 &  16 &  16.99 & -0.9937 \tabularnewline
26 &  15 &  15.71 & -0.7084 \tabularnewline
27 &  15 &  15.55 & -0.5497 \tabularnewline
28 &  15 &  14.83 &  0.1715 \tabularnewline
29 &  13 &  15.7 & -2.699 \tabularnewline
30 &  17 &  16.19 &  0.8082 \tabularnewline
31 &  11 &  16.23 & -5.228 \tabularnewline
32 &  14 &  14.66 & -0.6573 \tabularnewline
33 &  13 &  15.72 & -2.725 \tabularnewline
34 &  17 &  14.45 &  2.546 \tabularnewline
35 &  16 &  15.3 &  0.6978 \tabularnewline
36 &  17 &  16.68 &  0.3191 \tabularnewline
37 &  16 &  15.26 &  0.7359 \tabularnewline
38 &  16 &  16.16 & -0.1634 \tabularnewline
39 &  16 &  13.77 &  2.234 \tabularnewline
40 &  15 &  16.71 & -1.707 \tabularnewline
41 &  12 &  14.22 & -2.222 \tabularnewline
42 &  17 &  14.8 &  2.197 \tabularnewline
43 &  14 &  15.21 & -1.208 \tabularnewline
44 &  14 &  15.8 & -1.796 \tabularnewline
45 &  16 &  15.24 &  0.7643 \tabularnewline
46 &  15 &  14.32 &  0.6799 \tabularnewline
47 &  16 &  15.6 &  0.3962 \tabularnewline
48 &  14 &  15.64 & -1.64 \tabularnewline
49 &  15 &  15.31 & -0.3055 \tabularnewline
50 &  17 &  15.1 &  1.9 \tabularnewline
51 &  10 &  13.77 & -3.771 \tabularnewline
52 &  17 &  15.73 &  1.269 \tabularnewline
53 &  20 &  15.82 &  4.182 \tabularnewline
54 &  17 &  16.08 &  0.9225 \tabularnewline
55 &  18 &  16.53 &  1.466 \tabularnewline
56 &  14 &  13.38 &  0.6247 \tabularnewline
57 &  17 &  15.98 &  1.02 \tabularnewline
58 &  17 &  15.65 &  1.347 \tabularnewline
59 &  16 &  16.08 & -0.08076 \tabularnewline
60 &  18 &  15.71 &  2.29 \tabularnewline
61 &  18 &  16.99 &  1.006 \tabularnewline
62 &  16 &  16.62 & -0.6234 \tabularnewline
63 &  15 &  16.04 & -1.041 \tabularnewline
64 &  13 &  15.92 & -2.916 \tabularnewline
65 &  16 &  15.63 &  0.3742 \tabularnewline
66 &  12 &  14.64 & -2.644 \tabularnewline
67 &  16 &  15.14 &  0.8551 \tabularnewline
68 &  16 &  15.09 &  0.9071 \tabularnewline
69 &  16 &  17.05 & -1.046 \tabularnewline
70 &  14 &  14.82 & -0.8239 \tabularnewline
71 &  15 &  15.35 & -0.3463 \tabularnewline
72 &  14 &  14.8 & -0.8004 \tabularnewline
73 &  15 &  15.52 & -0.5171 \tabularnewline
74 &  15 &  14.67 &  0.3328 \tabularnewline
75 &  16 &  14.61 &  1.392 \tabularnewline
76 &  11 &  12.68 & -1.683 \tabularnewline
77 &  18 &  15.48 &  2.515 \tabularnewline
78 &  11 &  14.22 & -3.221 \tabularnewline
79 &  18 &  16.63 &  1.368 \tabularnewline
80 &  15 &  16.2 & -1.203 \tabularnewline
81 &  19 &  17.47 &  1.528 \tabularnewline
82 &  17 &  16.95 &  0.0522 \tabularnewline
83 &  14 &  15.82 & -1.816 \tabularnewline
84 &  13 &  14.25 & -1.253 \tabularnewline
85 &  17 &  15.65 &  1.354 \tabularnewline
86 &  14 &  16.12 & -2.117 \tabularnewline
87 &  19 &  15.96 &  3.044 \tabularnewline
88 &  14 &  15.14 & -1.135 \tabularnewline
89 &  16 &  15.68 &  0.3204 \tabularnewline
90 &  16 &  14.93 &  1.068 \tabularnewline
91 &  15 &  15.8 & -0.8022 \tabularnewline
92 &  12 &  15.69 & -3.692 \tabularnewline
93 &  17 &  16.69 &  0.3066 \tabularnewline
94 &  18 &  15.66 &  2.339 \tabularnewline
95 &  15 &  15.26 & -0.2612 \tabularnewline
96 &  18 &  14.74 &  3.263 \tabularnewline
97 &  15 &  16.46 & -1.464 \tabularnewline
98 &  16 &  15.67 &  0.3277 \tabularnewline
99 &  16 &  14.76 &  1.243 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&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] 14.36[/C][C]-1.356[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.07[/C][C] 0.9269[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.19[/C][C] 0.8084[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.63[/C][C] 0.3688[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.16[/C][C]-0.1633[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 14.68[/C][C] 2.322[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.18[/C][C] 0.8213[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 15.19[/C][C]-1.192[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 15.3[/C][C] 0.6988[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.79[/C][C] 1.213[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 15.26[/C][C] 0.7369[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 16.08[/C][C]-0.08211[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.25[/C][C] 0.7531[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.28[/C][C]-0.2826[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.57[/C][C] 0.4289[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 15.7[/C][C] 0.2981[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 17.12[/C][C]-2.124[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.55[/C][C] 0.4507[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 14.67[/C][C]-1.668[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 14.78[/C][C] 2.223[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 15.31[/C][C]-1.313[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 14.28[/C][C]-0.281[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 15.79[/C][C] 2.209[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 17.1[/C][C]-0.1041[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 16.99[/C][C]-0.9937[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 15.71[/C][C]-0.7084[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.55[/C][C]-0.5497[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 14.83[/C][C] 0.1715[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 15.7[/C][C]-2.699[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 16.19[/C][C] 0.8082[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 16.23[/C][C]-5.228[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 14.66[/C][C]-0.6573[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 15.72[/C][C]-2.725[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 14.45[/C][C] 2.546[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.3[/C][C] 0.6978[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 16.68[/C][C] 0.3191[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 15.26[/C][C] 0.7359[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 16.16[/C][C]-0.1634[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 13.77[/C][C] 2.234[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.71[/C][C]-1.707[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 14.22[/C][C]-2.222[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 14.8[/C][C] 2.197[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.21[/C][C]-1.208[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.8[/C][C]-1.796[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 15.24[/C][C] 0.7643[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 14.32[/C][C] 0.6799[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 15.6[/C][C] 0.3962[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 15.64[/C][C]-1.64[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.31[/C][C]-0.3055[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 15.1[/C][C] 1.9[/C][/ROW]
[ROW][C]51[/C][C] 10[/C][C] 13.77[/C][C]-3.771[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 15.73[/C][C] 1.269[/C][/ROW]
[ROW][C]53[/C][C] 20[/C][C] 15.82[/C][C] 4.182[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.08[/C][C] 0.9225[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 16.53[/C][C] 1.466[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 13.38[/C][C] 0.6247[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.98[/C][C] 1.02[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 15.65[/C][C] 1.347[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 16.08[/C][C]-0.08076[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 15.71[/C][C] 2.29[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 16.99[/C][C] 1.006[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.62[/C][C]-0.6234[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 16.04[/C][C]-1.041[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 15.92[/C][C]-2.916[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 15.63[/C][C] 0.3742[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 14.64[/C][C]-2.644[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.14[/C][C] 0.8551[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.09[/C][C] 0.9071[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 17.05[/C][C]-1.046[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 14.82[/C][C]-0.8239[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 15.35[/C][C]-0.3463[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.8[/C][C]-0.8004[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.52[/C][C]-0.5171[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 14.67[/C][C] 0.3328[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 14.61[/C][C] 1.392[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 12.68[/C][C]-1.683[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 15.48[/C][C] 2.515[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 14.22[/C][C]-3.221[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 16.63[/C][C] 1.368[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.2[/C][C]-1.203[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 17.47[/C][C] 1.528[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 16.95[/C][C] 0.0522[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 15.82[/C][C]-1.816[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.25[/C][C]-1.253[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 15.65[/C][C] 1.354[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 16.12[/C][C]-2.117[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 15.96[/C][C] 3.044[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 15.14[/C][C]-1.135[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 15.68[/C][C] 0.3204[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 14.93[/C][C] 1.068[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.8[/C][C]-0.8022[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 15.69[/C][C]-3.692[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.69[/C][C] 0.3066[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.66[/C][C] 2.339[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 15.26[/C][C]-0.2612[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 14.74[/C][C] 3.263[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 16.46[/C][C]-1.464[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.67[/C][C] 0.3277[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 14.76[/C][C] 1.243[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&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	14.36	-1.356
2	16	15.07	0.9269
3	17	16.19	0.8084
4	16	15.63	0.3688
5	17	17.16	-0.1633
6	17	14.68	2.322
7	16	15.18	0.8213
8	14	15.19	-1.192
9	16	15.3	0.6988
10	17	15.79	1.213
11	16	15.26	0.7369
12	16	16.08	-0.08211
13	16	15.25	0.7531
14	15	15.28	-0.2826
15	16	15.57	0.4289
16	16	15.7	0.2981
17	15	17.12	-2.124
18	17	16.55	0.4507
19	13	14.67	-1.668
20	17	14.78	2.223
21	14	15.31	-1.313
22	14	14.28	-0.281
23	18	15.79	2.209
24	17	17.1	-0.1041
25	16	16.99	-0.9937
26	15	15.71	-0.7084
27	15	15.55	-0.5497
28	15	14.83	0.1715
29	13	15.7	-2.699
30	17	16.19	0.8082
31	11	16.23	-5.228
32	14	14.66	-0.6573
33	13	15.72	-2.725
34	17	14.45	2.546
35	16	15.3	0.6978
36	17	16.68	0.3191
37	16	15.26	0.7359
38	16	16.16	-0.1634
39	16	13.77	2.234
40	15	16.71	-1.707
41	12	14.22	-2.222
42	17	14.8	2.197
43	14	15.21	-1.208
44	14	15.8	-1.796
45	16	15.24	0.7643
46	15	14.32	0.6799
47	16	15.6	0.3962
48	14	15.64	-1.64
49	15	15.31	-0.3055
50	17	15.1	1.9
51	10	13.77	-3.771
52	17	15.73	1.269
53	20	15.82	4.182
54	17	16.08	0.9225
55	18	16.53	1.466
56	14	13.38	0.6247
57	17	15.98	1.02
58	17	15.65	1.347
59	16	16.08	-0.08076
60	18	15.71	2.29
61	18	16.99	1.006
62	16	16.62	-0.6234
63	15	16.04	-1.041
64	13	15.92	-2.916
65	16	15.63	0.3742
66	12	14.64	-2.644
67	16	15.14	0.8551
68	16	15.09	0.9071
69	16	17.05	-1.046
70	14	14.82	-0.8239
71	15	15.35	-0.3463
72	14	14.8	-0.8004
73	15	15.52	-0.5171
74	15	14.67	0.3328
75	16	14.61	1.392
76	11	12.68	-1.683
77	18	15.48	2.515
78	11	14.22	-3.221
79	18	16.63	1.368
80	15	16.2	-1.203
81	19	17.47	1.528
82	17	16.95	0.0522
83	14	15.82	-1.816
84	13	14.25	-1.253
85	17	15.65	1.354
86	14	16.12	-2.117
87	19	15.96	3.044
88	14	15.14	-1.135
89	16	15.68	0.3204
90	16	14.93	1.068
91	15	15.8	-0.8022
92	12	15.69	-3.692
93	17	16.69	0.3066
94	18	15.66	2.339
95	15	15.26	-0.2612
96	18	14.74	3.263
97	15	16.46	-1.464
98	16	15.67	0.3277
99	16	14.76	1.243

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.4256	0.8512	0.5744
9	0.3519	0.7038	0.6481
10	0.2668	0.5335	0.7332
11	0.1647	0.3294	0.8353
12	0.1515	0.303	0.8485
13	0.09085	0.1817	0.9092
14	0.06285	0.1257	0.9372
15	0.03638	0.07276	0.9636
16	0.01951	0.03903	0.9805
17	0.03739	0.07478	0.9626
18	0.02275	0.0455	0.9773
19	0.03469	0.06938	0.9653
20	0.06216	0.1243	0.9378
21	0.05163	0.1033	0.9484
22	0.03317	0.06635	0.9668
23	0.06657	0.1331	0.9334
24	0.04448	0.08896	0.9555
25	0.03226	0.06451	0.9677
26	0.0213	0.04261	0.9787
27	0.01462	0.02925	0.9854
28	0.00909	0.01818	0.9909
29	0.01762	0.03524	0.9824
30	0.01575	0.0315	0.9843
31	0.1906	0.3812	0.8094
32	0.1493	0.2986	0.8507
33	0.169	0.338	0.831
34	0.2803	0.5606	0.7197
35	0.2556	0.5111	0.7444
36	0.2325	0.465	0.7675
37	0.2082	0.4164	0.7918
38	0.1711	0.3421	0.8289
39	0.1947	0.3893	0.8053
40	0.1799	0.3599	0.8201
41	0.2202	0.4404	0.7798
42	0.2618	0.5235	0.7382
43	0.231	0.4619	0.769
44	0.2354	0.4709	0.7646
45	0.2085	0.4171	0.7915
46	0.1725	0.3451	0.8275
47	0.1464	0.2928	0.8536
48	0.1362	0.2724	0.8638
49	0.1086	0.2172	0.8914
50	0.1237	0.2474	0.8763
51	0.317	0.6339	0.683
52	0.3081	0.6161	0.6919
53	0.583	0.834	0.417
54	0.5486	0.9029	0.4514
55	0.5376	0.9247	0.4624
56	0.4997	0.9994	0.5003
57	0.4636	0.9271	0.5364
58	0.4608	0.9216	0.5392
59	0.4009	0.8018	0.5991
60	0.4788	0.9575	0.5212
61	0.4518	0.9037	0.5482
62	0.3963	0.7925	0.6037
63	0.3502	0.7005	0.6498
64	0.4624	0.9249	0.5376
65	0.4112	0.8224	0.5888
66	0.476	0.9519	0.524
67	0.4426	0.8853	0.5574
68	0.3978	0.7956	0.6022
69	0.3482	0.6963	0.6518
70	0.2994	0.5988	0.7006
71	0.2535	0.5071	0.7465
72	0.2072	0.4145	0.7928
73	0.1635	0.3271	0.8365
74	0.1304	0.2609	0.8696
75	0.1234	0.2468	0.8766
76	0.1033	0.2066	0.8967
77	0.1474	0.2949	0.8526
78	0.2066	0.4131	0.7934
79	0.2223	0.4446	0.7777
80	0.1767	0.3534	0.8233
81	0.1789	0.3579	0.8211
82	0.1393	0.2786	0.8607
83	0.1065	0.213	0.8935
84	0.123	0.2461	0.877
85	0.09995	0.1999	0.9001
86	0.09041	0.1808	0.9096
87	0.2389	0.4779	0.7611
88	0.1693	0.3386	0.8307
89	0.1324	0.2647	0.8676
90	0.1465	0.293	0.8535
91	0.08977	0.1795	0.9102

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.4256 &  0.8512 &  0.5744 \tabularnewline
9 &  0.3519 &  0.7038 &  0.6481 \tabularnewline
10 &  0.2668 &  0.5335 &  0.7332 \tabularnewline
11 &  0.1647 &  0.3294 &  0.8353 \tabularnewline
12 &  0.1515 &  0.303 &  0.8485 \tabularnewline
13 &  0.09085 &  0.1817 &  0.9092 \tabularnewline
14 &  0.06285 &  0.1257 &  0.9372 \tabularnewline
15 &  0.03638 &  0.07276 &  0.9636 \tabularnewline
16 &  0.01951 &  0.03903 &  0.9805 \tabularnewline
17 &  0.03739 &  0.07478 &  0.9626 \tabularnewline
18 &  0.02275 &  0.0455 &  0.9773 \tabularnewline
19 &  0.03469 &  0.06938 &  0.9653 \tabularnewline
20 &  0.06216 &  0.1243 &  0.9378 \tabularnewline
21 &  0.05163 &  0.1033 &  0.9484 \tabularnewline
22 &  0.03317 &  0.06635 &  0.9668 \tabularnewline
23 &  0.06657 &  0.1331 &  0.9334 \tabularnewline
24 &  0.04448 &  0.08896 &  0.9555 \tabularnewline
25 &  0.03226 &  0.06451 &  0.9677 \tabularnewline
26 &  0.0213 &  0.04261 &  0.9787 \tabularnewline
27 &  0.01462 &  0.02925 &  0.9854 \tabularnewline
28 &  0.00909 &  0.01818 &  0.9909 \tabularnewline
29 &  0.01762 &  0.03524 &  0.9824 \tabularnewline
30 &  0.01575 &  0.0315 &  0.9843 \tabularnewline
31 &  0.1906 &  0.3812 &  0.8094 \tabularnewline
32 &  0.1493 &  0.2986 &  0.8507 \tabularnewline
33 &  0.169 &  0.338 &  0.831 \tabularnewline
34 &  0.2803 &  0.5606 &  0.7197 \tabularnewline
35 &  0.2556 &  0.5111 &  0.7444 \tabularnewline
36 &  0.2325 &  0.465 &  0.7675 \tabularnewline
37 &  0.2082 &  0.4164 &  0.7918 \tabularnewline
38 &  0.1711 &  0.3421 &  0.8289 \tabularnewline
39 &  0.1947 &  0.3893 &  0.8053 \tabularnewline
40 &  0.1799 &  0.3599 &  0.8201 \tabularnewline
41 &  0.2202 &  0.4404 &  0.7798 \tabularnewline
42 &  0.2618 &  0.5235 &  0.7382 \tabularnewline
43 &  0.231 &  0.4619 &  0.769 \tabularnewline
44 &  0.2354 &  0.4709 &  0.7646 \tabularnewline
45 &  0.2085 &  0.4171 &  0.7915 \tabularnewline
46 &  0.1725 &  0.3451 &  0.8275 \tabularnewline
47 &  0.1464 &  0.2928 &  0.8536 \tabularnewline
48 &  0.1362 &  0.2724 &  0.8638 \tabularnewline
49 &  0.1086 &  0.2172 &  0.8914 \tabularnewline
50 &  0.1237 &  0.2474 &  0.8763 \tabularnewline
51 &  0.317 &  0.6339 &  0.683 \tabularnewline
52 &  0.3081 &  0.6161 &  0.6919 \tabularnewline
53 &  0.583 &  0.834 &  0.417 \tabularnewline
54 &  0.5486 &  0.9029 &  0.4514 \tabularnewline
55 &  0.5376 &  0.9247 &  0.4624 \tabularnewline
56 &  0.4997 &  0.9994 &  0.5003 \tabularnewline
57 &  0.4636 &  0.9271 &  0.5364 \tabularnewline
58 &  0.4608 &  0.9216 &  0.5392 \tabularnewline
59 &  0.4009 &  0.8018 &  0.5991 \tabularnewline
60 &  0.4788 &  0.9575 &  0.5212 \tabularnewline
61 &  0.4518 &  0.9037 &  0.5482 \tabularnewline
62 &  0.3963 &  0.7925 &  0.6037 \tabularnewline
63 &  0.3502 &  0.7005 &  0.6498 \tabularnewline
64 &  0.4624 &  0.9249 &  0.5376 \tabularnewline
65 &  0.4112 &  0.8224 &  0.5888 \tabularnewline
66 &  0.476 &  0.9519 &  0.524 \tabularnewline
67 &  0.4426 &  0.8853 &  0.5574 \tabularnewline
68 &  0.3978 &  0.7956 &  0.6022 \tabularnewline
69 &  0.3482 &  0.6963 &  0.6518 \tabularnewline
70 &  0.2994 &  0.5988 &  0.7006 \tabularnewline
71 &  0.2535 &  0.5071 &  0.7465 \tabularnewline
72 &  0.2072 &  0.4145 &  0.7928 \tabularnewline
73 &  0.1635 &  0.3271 &  0.8365 \tabularnewline
74 &  0.1304 &  0.2609 &  0.8696 \tabularnewline
75 &  0.1234 &  0.2468 &  0.8766 \tabularnewline
76 &  0.1033 &  0.2066 &  0.8967 \tabularnewline
77 &  0.1474 &  0.2949 &  0.8526 \tabularnewline
78 &  0.2066 &  0.4131 &  0.7934 \tabularnewline
79 &  0.2223 &  0.4446 &  0.7777 \tabularnewline
80 &  0.1767 &  0.3534 &  0.8233 \tabularnewline
81 &  0.1789 &  0.3579 &  0.8211 \tabularnewline
82 &  0.1393 &  0.2786 &  0.8607 \tabularnewline
83 &  0.1065 &  0.213 &  0.8935 \tabularnewline
84 &  0.123 &  0.2461 &  0.877 \tabularnewline
85 &  0.09995 &  0.1999 &  0.9001 \tabularnewline
86 &  0.09041 &  0.1808 &  0.9096 \tabularnewline
87 &  0.2389 &  0.4779 &  0.7611 \tabularnewline
88 &  0.1693 &  0.3386 &  0.8307 \tabularnewline
89 &  0.1324 &  0.2647 &  0.8676 \tabularnewline
90 &  0.1465 &  0.293 &  0.8535 \tabularnewline
91 &  0.08977 &  0.1795 &  0.9102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&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]8[/C][C] 0.4256[/C][C] 0.8512[/C][C] 0.5744[/C][/ROW]
[ROW][C]9[/C][C] 0.3519[/C][C] 0.7038[/C][C] 0.6481[/C][/ROW]
[ROW][C]10[/C][C] 0.2668[/C][C] 0.5335[/C][C] 0.7332[/C][/ROW]
[ROW][C]11[/C][C] 0.1647[/C][C] 0.3294[/C][C] 0.8353[/C][/ROW]
[ROW][C]12[/C][C] 0.1515[/C][C] 0.303[/C][C] 0.8485[/C][/ROW]
[ROW][C]13[/C][C] 0.09085[/C][C] 0.1817[/C][C] 0.9092[/C][/ROW]
[ROW][C]14[/C][C] 0.06285[/C][C] 0.1257[/C][C] 0.9372[/C][/ROW]
[ROW][C]15[/C][C] 0.03638[/C][C] 0.07276[/C][C] 0.9636[/C][/ROW]
[ROW][C]16[/C][C] 0.01951[/C][C] 0.03903[/C][C] 0.9805[/C][/ROW]
[ROW][C]17[/C][C] 0.03739[/C][C] 0.07478[/C][C] 0.9626[/C][/ROW]
[ROW][C]18[/C][C] 0.02275[/C][C] 0.0455[/C][C] 0.9773[/C][/ROW]
[ROW][C]19[/C][C] 0.03469[/C][C] 0.06938[/C][C] 0.9653[/C][/ROW]
[ROW][C]20[/C][C] 0.06216[/C][C] 0.1243[/C][C] 0.9378[/C][/ROW]
[ROW][C]21[/C][C] 0.05163[/C][C] 0.1033[/C][C] 0.9484[/C][/ROW]
[ROW][C]22[/C][C] 0.03317[/C][C] 0.06635[/C][C] 0.9668[/C][/ROW]
[ROW][C]23[/C][C] 0.06657[/C][C] 0.1331[/C][C] 0.9334[/C][/ROW]
[ROW][C]24[/C][C] 0.04448[/C][C] 0.08896[/C][C] 0.9555[/C][/ROW]
[ROW][C]25[/C][C] 0.03226[/C][C] 0.06451[/C][C] 0.9677[/C][/ROW]
[ROW][C]26[/C][C] 0.0213[/C][C] 0.04261[/C][C] 0.9787[/C][/ROW]
[ROW][C]27[/C][C] 0.01462[/C][C] 0.02925[/C][C] 0.9854[/C][/ROW]
[ROW][C]28[/C][C] 0.00909[/C][C] 0.01818[/C][C] 0.9909[/C][/ROW]
[ROW][C]29[/C][C] 0.01762[/C][C] 0.03524[/C][C] 0.9824[/C][/ROW]
[ROW][C]30[/C][C] 0.01575[/C][C] 0.0315[/C][C] 0.9843[/C][/ROW]
[ROW][C]31[/C][C] 0.1906[/C][C] 0.3812[/C][C] 0.8094[/C][/ROW]
[ROW][C]32[/C][C] 0.1493[/C][C] 0.2986[/C][C] 0.8507[/C][/ROW]
[ROW][C]33[/C][C] 0.169[/C][C] 0.338[/C][C] 0.831[/C][/ROW]
[ROW][C]34[/C][C] 0.2803[/C][C] 0.5606[/C][C] 0.7197[/C][/ROW]
[ROW][C]35[/C][C] 0.2556[/C][C] 0.5111[/C][C] 0.7444[/C][/ROW]
[ROW][C]36[/C][C] 0.2325[/C][C] 0.465[/C][C] 0.7675[/C][/ROW]
[ROW][C]37[/C][C] 0.2082[/C][C] 0.4164[/C][C] 0.7918[/C][/ROW]
[ROW][C]38[/C][C] 0.1711[/C][C] 0.3421[/C][C] 0.8289[/C][/ROW]
[ROW][C]39[/C][C] 0.1947[/C][C] 0.3893[/C][C] 0.8053[/C][/ROW]
[ROW][C]40[/C][C] 0.1799[/C][C] 0.3599[/C][C] 0.8201[/C][/ROW]
[ROW][C]41[/C][C] 0.2202[/C][C] 0.4404[/C][C] 0.7798[/C][/ROW]
[ROW][C]42[/C][C] 0.2618[/C][C] 0.5235[/C][C] 0.7382[/C][/ROW]
[ROW][C]43[/C][C] 0.231[/C][C] 0.4619[/C][C] 0.769[/C][/ROW]
[ROW][C]44[/C][C] 0.2354[/C][C] 0.4709[/C][C] 0.7646[/C][/ROW]
[ROW][C]45[/C][C] 0.2085[/C][C] 0.4171[/C][C] 0.7915[/C][/ROW]
[ROW][C]46[/C][C] 0.1725[/C][C] 0.3451[/C][C] 0.8275[/C][/ROW]
[ROW][C]47[/C][C] 0.1464[/C][C] 0.2928[/C][C] 0.8536[/C][/ROW]
[ROW][C]48[/C][C] 0.1362[/C][C] 0.2724[/C][C] 0.8638[/C][/ROW]
[ROW][C]49[/C][C] 0.1086[/C][C] 0.2172[/C][C] 0.8914[/C][/ROW]
[ROW][C]50[/C][C] 0.1237[/C][C] 0.2474[/C][C] 0.8763[/C][/ROW]
[ROW][C]51[/C][C] 0.317[/C][C] 0.6339[/C][C] 0.683[/C][/ROW]
[ROW][C]52[/C][C] 0.3081[/C][C] 0.6161[/C][C] 0.6919[/C][/ROW]
[ROW][C]53[/C][C] 0.583[/C][C] 0.834[/C][C] 0.417[/C][/ROW]
[ROW][C]54[/C][C] 0.5486[/C][C] 0.9029[/C][C] 0.4514[/C][/ROW]
[ROW][C]55[/C][C] 0.5376[/C][C] 0.9247[/C][C] 0.4624[/C][/ROW]
[ROW][C]56[/C][C] 0.4997[/C][C] 0.9994[/C][C] 0.5003[/C][/ROW]
[ROW][C]57[/C][C] 0.4636[/C][C] 0.9271[/C][C] 0.5364[/C][/ROW]
[ROW][C]58[/C][C] 0.4608[/C][C] 0.9216[/C][C] 0.5392[/C][/ROW]
[ROW][C]59[/C][C] 0.4009[/C][C] 0.8018[/C][C] 0.5991[/C][/ROW]
[ROW][C]60[/C][C] 0.4788[/C][C] 0.9575[/C][C] 0.5212[/C][/ROW]
[ROW][C]61[/C][C] 0.4518[/C][C] 0.9037[/C][C] 0.5482[/C][/ROW]
[ROW][C]62[/C][C] 0.3963[/C][C] 0.7925[/C][C] 0.6037[/C][/ROW]
[ROW][C]63[/C][C] 0.3502[/C][C] 0.7005[/C][C] 0.6498[/C][/ROW]
[ROW][C]64[/C][C] 0.4624[/C][C] 0.9249[/C][C] 0.5376[/C][/ROW]
[ROW][C]65[/C][C] 0.4112[/C][C] 0.8224[/C][C] 0.5888[/C][/ROW]
[ROW][C]66[/C][C] 0.476[/C][C] 0.9519[/C][C] 0.524[/C][/ROW]
[ROW][C]67[/C][C] 0.4426[/C][C] 0.8853[/C][C] 0.5574[/C][/ROW]
[ROW][C]68[/C][C] 0.3978[/C][C] 0.7956[/C][C] 0.6022[/C][/ROW]
[ROW][C]69[/C][C] 0.3482[/C][C] 0.6963[/C][C] 0.6518[/C][/ROW]
[ROW][C]70[/C][C] 0.2994[/C][C] 0.5988[/C][C] 0.7006[/C][/ROW]
[ROW][C]71[/C][C] 0.2535[/C][C] 0.5071[/C][C] 0.7465[/C][/ROW]
[ROW][C]72[/C][C] 0.2072[/C][C] 0.4145[/C][C] 0.7928[/C][/ROW]
[ROW][C]73[/C][C] 0.1635[/C][C] 0.3271[/C][C] 0.8365[/C][/ROW]
[ROW][C]74[/C][C] 0.1304[/C][C] 0.2609[/C][C] 0.8696[/C][/ROW]
[ROW][C]75[/C][C] 0.1234[/C][C] 0.2468[/C][C] 0.8766[/C][/ROW]
[ROW][C]76[/C][C] 0.1033[/C][C] 0.2066[/C][C] 0.8967[/C][/ROW]
[ROW][C]77[/C][C] 0.1474[/C][C] 0.2949[/C][C] 0.8526[/C][/ROW]
[ROW][C]78[/C][C] 0.2066[/C][C] 0.4131[/C][C] 0.7934[/C][/ROW]
[ROW][C]79[/C][C] 0.2223[/C][C] 0.4446[/C][C] 0.7777[/C][/ROW]
[ROW][C]80[/C][C] 0.1767[/C][C] 0.3534[/C][C] 0.8233[/C][/ROW]
[ROW][C]81[/C][C] 0.1789[/C][C] 0.3579[/C][C] 0.8211[/C][/ROW]
[ROW][C]82[/C][C] 0.1393[/C][C] 0.2786[/C][C] 0.8607[/C][/ROW]
[ROW][C]83[/C][C] 0.1065[/C][C] 0.213[/C][C] 0.8935[/C][/ROW]
[ROW][C]84[/C][C] 0.123[/C][C] 0.2461[/C][C] 0.877[/C][/ROW]
[ROW][C]85[/C][C] 0.09995[/C][C] 0.1999[/C][C] 0.9001[/C][/ROW]
[ROW][C]86[/C][C] 0.09041[/C][C] 0.1808[/C][C] 0.9096[/C][/ROW]
[ROW][C]87[/C][C] 0.2389[/C][C] 0.4779[/C][C] 0.7611[/C][/ROW]
[ROW][C]88[/C][C] 0.1693[/C][C] 0.3386[/C][C] 0.8307[/C][/ROW]
[ROW][C]89[/C][C] 0.1324[/C][C] 0.2647[/C][C] 0.8676[/C][/ROW]
[ROW][C]90[/C][C] 0.1465[/C][C] 0.293[/C][C] 0.8535[/C][/ROW]
[ROW][C]91[/C][C] 0.08977[/C][C] 0.1795[/C][C] 0.9102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&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
8	0.4256	0.8512	0.5744
9	0.3519	0.7038	0.6481
10	0.2668	0.5335	0.7332
11	0.1647	0.3294	0.8353
12	0.1515	0.303	0.8485
13	0.09085	0.1817	0.9092
14	0.06285	0.1257	0.9372
15	0.03638	0.07276	0.9636
16	0.01951	0.03903	0.9805
17	0.03739	0.07478	0.9626
18	0.02275	0.0455	0.9773
19	0.03469	0.06938	0.9653
20	0.06216	0.1243	0.9378
21	0.05163	0.1033	0.9484
22	0.03317	0.06635	0.9668
23	0.06657	0.1331	0.9334
24	0.04448	0.08896	0.9555
25	0.03226	0.06451	0.9677
26	0.0213	0.04261	0.9787
27	0.01462	0.02925	0.9854
28	0.00909	0.01818	0.9909
29	0.01762	0.03524	0.9824
30	0.01575	0.0315	0.9843
31	0.1906	0.3812	0.8094
32	0.1493	0.2986	0.8507
33	0.169	0.338	0.831
34	0.2803	0.5606	0.7197
35	0.2556	0.5111	0.7444
36	0.2325	0.465	0.7675
37	0.2082	0.4164	0.7918
38	0.1711	0.3421	0.8289
39	0.1947	0.3893	0.8053
40	0.1799	0.3599	0.8201
41	0.2202	0.4404	0.7798
42	0.2618	0.5235	0.7382
43	0.231	0.4619	0.769
44	0.2354	0.4709	0.7646
45	0.2085	0.4171	0.7915
46	0.1725	0.3451	0.8275
47	0.1464	0.2928	0.8536
48	0.1362	0.2724	0.8638
49	0.1086	0.2172	0.8914
50	0.1237	0.2474	0.8763
51	0.317	0.6339	0.683
52	0.3081	0.6161	0.6919
53	0.583	0.834	0.417
54	0.5486	0.9029	0.4514
55	0.5376	0.9247	0.4624
56	0.4997	0.9994	0.5003
57	0.4636	0.9271	0.5364
58	0.4608	0.9216	0.5392
59	0.4009	0.8018	0.5991
60	0.4788	0.9575	0.5212
61	0.4518	0.9037	0.5482
62	0.3963	0.7925	0.6037
63	0.3502	0.7005	0.6498
64	0.4624	0.9249	0.5376
65	0.4112	0.8224	0.5888
66	0.476	0.9519	0.524
67	0.4426	0.8853	0.5574
68	0.3978	0.7956	0.6022
69	0.3482	0.6963	0.6518
70	0.2994	0.5988	0.7006
71	0.2535	0.5071	0.7465
72	0.2072	0.4145	0.7928
73	0.1635	0.3271	0.8365
74	0.1304	0.2609	0.8696
75	0.1234	0.2468	0.8766
76	0.1033	0.2066	0.8967
77	0.1474	0.2949	0.8526
78	0.2066	0.4131	0.7934
79	0.2223	0.4446	0.7777
80	0.1767	0.3534	0.8233
81	0.1789	0.3579	0.8211
82	0.1393	0.2786	0.8607
83	0.1065	0.213	0.8935
84	0.123	0.2461	0.877
85	0.09995	0.1999	0.9001
86	0.09041	0.1808	0.9096
87	0.2389	0.4779	0.7611
88	0.1693	0.3386	0.8307
89	0.1324	0.2647	0.8676
90	0.1465	0.293	0.8535
91	0.08977	0.1795	0.9102

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	7	0.0833333	NOK
10% type I error level	13	0.154762	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 & 7 & 0.0833333 & NOK \tabularnewline
10% type I error level & 13 & 0.154762 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&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]7[/C][C]0.0833333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.154762[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&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	7	0.0833333	NOK
10% type I error level	13	0.154762	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.2645, df1 = 2, df2 = 92, p-value = 0.2873
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.82143, df1 = 8, df2 = 86, p-value = 0.5859
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.28494, df1 = 2, df2 = 92, p-value = 0.7527

\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 = 1.2645, df1 = 2, df2 = 92, p-value = 0.2873
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.82143, df1 = 8, df2 = 86, p-value = 0.5859
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.28494, df1 = 2, df2 = 92, p-value = 0.7527
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&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 = 1.2645, df1 = 2, df2 = 92, p-value = 0.2873
[/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 = 0.82143, df1 = 8, df2 = 86, p-value = 0.5859
[/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 = 0.28494, df1 = 2, df2 = 92, p-value = 0.7527
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305736&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 = 1.2645, df1 = 2, df2 = 92, p-value = 0.2873
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.82143, df1 = 8, df2 = 86, p-value = 0.5859
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.28494, df1 = 2, df2 = 92, p-value = 0.7527

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd ITHSUM SKEOUSUM t 1.001342 1.126756 1.127390 1.003106

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd        ITHSUM      SKEOUSUM             t 
     1.001342      1.126756      1.127390      1.003106 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305736&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd        ITHSUM      SKEOUSUM             t 
     1.001342      1.126756      1.127390      1.003106 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305736&T=8

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Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd ITHSUM SKEOUSUM t 1.001342 1.126756 1.127390 1.003106

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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