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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 23 Jan 2017 09:54:02 +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/t1485161653z9tsoti1jumipo5.htm/, Retrieved Wed, 15 May 2024 05:19:31 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 15 May 2024 05:19:31 +0200

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Dataseries X:

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14 22 13 22 4
19 24 16 24 5
17 21 17 26 4
17 21 NA 21 3
15 24 NA 26 4
20 20 16 25 3
15 22 NA 21 3
19 20 NA 24 3
15 19 NA 27 4
15 23 17 28 4
19 21 17 23 4
NA 19 15 25 4
20 19 16 24 4
18 21 14 24 3
15 21 16 24 4
14 22 17 25 3
20 22 NA 25 3
NA 19 NA NA NA
16 21 NA 25 5
16 21 NA 25 4
16 21 16 24 3
10 20 NA 26 4
19 22 16 26 4
19 22 NA 25 4
16 24 NA 26 4
15 21 NA 23 3
18 19 16 24 3
17 19 15 24 4
19 23 16 25 2
17 21 16 25 5
NA 21 13 24 4
19 19 15 28 4
20 21 17 27 5
5 19 NA NA 4
19 21 13 23 2
16 21 17 23 4
15 23 NA 24 3
16 19 14 24 4
18 19 14 22 4
16 19 18 25 4
15 18 NA 25 5
17 22 17 28 4
NA 18 13 22 3
20 22 16 28 5
19 18 15 25 5
7 22 15 24 4
13 22 NA 24 4
16 19 15 23 3
16 22 13 25 4
NA 25 NA NA 2
18 19 17 26 4
18 19 NA 25 5
16 19 NA 27 5
17 19 11 26 4
19 21 14 23 4
16 21 13 25 4
19 20 NA 21 3
13 19 17 22 3
16 19 16 24 4
13 22 NA 25 4
12 26 17 27 5
17 19 16 24 2
17 21 16 26 4
17 21 16 21 3
16 20 15 27 4
16 23 12 22 4
14 22 17 23 4
16 22 14 24 4
13 22 14 25 5
16 21 16 24 3
14 21 NA 23 3
20 22 NA 28 4
12 23 NA NA 4
13 18 NA 24 4
18 24 NA 26 4
14 22 15 22 3
19 21 16 25 4
18 21 14 25 3
14 21 15 24 3
18 23 17 24 4
19 21 NA 26 4
15 23 10 21 3
14 21 NA 25 4
17 19 17 25 4
19 21 NA 26 4
13 21 20 25 5
19 21 17 26 5
18 23 18 27 4
20 23 NA 25 3
15 20 17 NA 3
15 20 14 20 4
15 19 NA 24 4
20 23 17 26 4
15 22 NA 25 4
19 19 17 25 4
18 23 NA 24 3
18 22 16 26 4
15 22 18 25 5
20 21 18 28 5
17 21 16 27 4
12 21 NA 25 3
18 21 NA 26 5
19 22 15 26 4
20 25 13 26 5
NA 21 NA NA 3
17 23 NA 28 5
15 19 NA NA 4
16 22 NA 21 4
18 20 NA 25 4
18 21 16 25 4
14 25 NA 24 3
15 21 NA 24 4
12 19 NA 24 4
17 23 12 23 3
14 22 NA 23 4
18 21 16 24 3
17 24 16 24 4
17 21 NA 25 5
20 19 16 28 5
16 18 14 23 4
14 19 15 24 4
15 20 14 23 3
18 19 NA 24 4
20 22 15 25 4
17 21 NA 24 4
17 22 15 23 3
17 24 16 23 4
17 28 NA 25 4
15 19 NA 21 3
17 18 NA 22 4
18 23 11 19 3
17 19 NA 24 4
20 23 18 25 5
15 19 NA 21 2
16 22 11 22 3
15 21 NA 23 4
18 19 18 27 5
11 22 NA NA NA
15 21 15 26 4
18 23 19 29 5
20 22 17 28 4
19 19 NA 24 4
14 19 14 25 3
16 21 NA 25 4
15 22 13 22 4
17 21 17 25 4
18 20 14 26 4
20 23 19 26 5
17 22 14 24 4
18 23 NA 25 4
15 22 NA 19 3
16 21 16 25 4
11 20 16 23 4
15 18 15 25 4
18 18 12 25 3
17 20 NA 26 4
16 19 17 27 5
12 21 NA 24 4
19 24 NA 22 2
18 19 18 25 4
15 20 15 24 4
17 19 18 23 4
19 23 15 27 4
18 22 NA 24 5
19 21 NA 24 5
16 24 NA 21 3
16 21 16 25 4
16 21 NA 25 4
14 22 16 23 2

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

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

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

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

R Framework error message[/C][C]

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

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

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

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 7.06356 -0.000298268Bevr_Leeftijd[t] -0.0583575TVDC[t] + 0.438942SKEOUSUM[t] -0.035048SKEOU1[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  7.06356 -0.000298268Bevr_Leeftijd[t] -0.0583575TVDC[t] +  0.438942SKEOUSUM[t] -0.035048SKEOU1[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  7.06356 -0.000298268Bevr_Leeftijd[t] -0.0583575TVDC[t] +  0.438942SKEOUSUM[t] -0.035048SKEOU1[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

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

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 7.06356 -0.000298268Bevr_Leeftijd[t] -0.0583575TVDC[t] + 0.438942SKEOUSUM[t] -0.035048SKEOU1[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)	+7.064	4.096	+1.7250e+00	0.08789	0.04394
Bevr_Leeftijd	-0.0002983	0.1343	-2.2220e-03	0.9982	0.4991
TVDC	-0.05836	0.1367	-4.2690e-01	0.6704	0.3352
SKEOUSUM	+0.4389	0.1446	+3.0350e+00	0.003113	0.001556
SKEOU1	-0.03505	0.3516	-9.9670e-02	0.9208	0.4604

\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) & +7.064 &  4.096 & +1.7250e+00 &  0.08789 &  0.04394 \tabularnewline
Bevr_Leeftijd & -0.0002983 &  0.1343 & -2.2220e-03 &  0.9982 &  0.4991 \tabularnewline
TVDC & -0.05836 &  0.1367 & -4.2690e-01 &  0.6704 &  0.3352 \tabularnewline
SKEOUSUM & +0.4389 &  0.1446 & +3.0350e+00 &  0.003113 &  0.001556 \tabularnewline
SKEOU1 & -0.03505 &  0.3516 & -9.9670e-02 &  0.9208 &  0.4604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+7.064[/C][C] 4.096[/C][C]+1.7250e+00[/C][C] 0.08789[/C][C] 0.04394[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.0002983[/C][C] 0.1343[/C][C]-2.2220e-03[/C][C] 0.9982[/C][C] 0.4991[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.05836[/C][C] 0.1367[/C][C]-4.2690e-01[/C][C] 0.6704[/C][C] 0.3352[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.4389[/C][C] 0.1446[/C][C]+3.0350e+00[/C][C] 0.003113[/C][C] 0.001556[/C][/ROW]
[ROW][C]SKEOU1[/C][C]-0.03505[/C][C] 0.3516[/C][C]-9.9670e-02[/C][C] 0.9208[/C][C] 0.4604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

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

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+7.064	4.096	+1.7250e+00	0.08789	0.04394
Bevr_Leeftijd	-0.0002983	0.1343	-2.2220e-03	0.9982	0.4991
TVDC	-0.05836	0.1367	-4.2690e-01	0.6704	0.3352
SKEOUSUM	+0.4389	0.1446	+3.0350e+00	0.003113	0.001556
SKEOU1	-0.03505	0.3516	-9.9670e-02	0.9208	0.4604

Multiple Linear Regression - Regression Statistics
Multiple R	0.3355
R-squared	0.1126
Adjusted R-squared	0.07481
F-TEST (value)	2.981
F-TEST (DF numerator)	4
F-TEST (DF denominator)	94
p-value	0.02296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.197
Sum Squared Residuals	453.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3355 \tabularnewline
R-squared &  0.1126 \tabularnewline
Adjusted R-squared &  0.07481 \tabularnewline
F-TEST (value) &  2.981 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value &  0.02296 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.197 \tabularnewline
Sum Squared Residuals &  453.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3355[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1126[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.07481[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.981[/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] 0.02296[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.197[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 453.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

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

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.3355
R-squared	0.1126
Adjusted R-squared	0.07481
F-TEST (value)	2.981
F-TEST (DF numerator)	4
F-TEST (DF denominator)	94
p-value	0.02296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.197
Sum Squared Residuals	453.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	15.81	-1.815
2	19	16.48	2.518
3	17	17.34	-0.3375
4	20	16.99	3.008
5	15	18.21	-3.215
6	19	16.02	2.979
7	20	16.52	3.481
8	18	16.67	1.33
9	15	16.52	-1.518
10	14	16.93	-2.933
11	16	16.55	-0.5531
12	19	17.4	1.604
13	18	16.55	1.446
14	17	16.58	0.423
15	19	17.03	1.974
16	17	16.92	0.0781
17	19	18.33	0.6673
18	20	17.74	2.259
19	19	16.32	2.676
20	16	16.02	-0.02071
21	16	16.64	-0.6353
22	18	15.76	2.243
23	16	16.84	-0.8408
24	17	18.22	-1.215
25	20	18.24	1.762
26	19	16.98	2.019
27	7	16.58	-9.576
28	16	16.17	-0.1731
29	16	17.13	-1.132
30	18	17.34	0.6619
31	17	17.69	-0.6883
32	19	16.2	2.804
33	16	17.13	-1.132
34	13	15.62	-2.617
35	16	16.52	-0.5186
36	12	17.74	-5.74
37	17	16.59	0.4113
38	17	17.4	-0.3959
39	17	15.24	1.764
40	16	17.89	-1.893
41	16	15.87	0.127
42	14	16.02	-2.02
43	16	16.63	-0.6344
44	13	17.04	-4.038
45	16	16.55	-0.5531
46	14	15.73	-1.733
47	19	16.96	2.043
48	18	17.11	0.8913
49	14	16.61	-2.611
50	18	16.46	1.541
51	15	15.59	-0.5858
52	17	16.9	0.1008
53	13	16.69	-3.688
54	19	17.3	1.698
55	18	17.72	0.2825
56	15	14.88	0.1207
57	20	17.34	2.663
58	19	16.9	2.101
59	18	17.4	0.6044
60	15	16.8	-1.805
61	20	18.12	1.878
62	17	17.83	-0.8348
63	19	17.45	1.546
64	20	17.53	2.465
65	18	16.96	1.043
66	17	16.35	0.6531
67	18	16.55	1.447
68	17	16.52	0.4829
69	20	18.24	1.761
70	16	16.2	-0.1967
71	14	16.58	-2.577
72	15	16.23	-1.231
73	20	17.02	2.985
74	17	16.17	0.8278
75	17	16.08	0.9218
76	18	14.65	3.35
77	20	16.8	3.195
78	16	15.97	0.03334
79	18	17.68	0.3163
80	15	17.45	-2.454
81	18	18.5	-0.502
82	20	18.22	1.785
83	14	17.11	-3.109
84	15	15.81	-0.8149
85	17	16.9	0.1014
86	18	17.51	0.4871
87	20	17.19	2.815
88	17	16.63	0.3656
89	16	16.96	-0.9569
90	11	16.08	-5.079
91	15	17.02	-2.016
92	18	17.23	0.7737
93	16	17.74	-1.742
94	18	16.84	1.159
95	15	16.58	-1.577
96	17	15.96	1.037
97	19	17.89	1.107
98	16	16.96	-0.9569
99	14	16.15	-2.149

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  15.81 & -1.815 \tabularnewline
2 &  19 &  16.48 &  2.518 \tabularnewline
3 &  17 &  17.34 & -0.3375 \tabularnewline
4 &  20 &  16.99 &  3.008 \tabularnewline
5 &  15 &  18.21 & -3.215 \tabularnewline
6 &  19 &  16.02 &  2.979 \tabularnewline
7 &  20 &  16.52 &  3.481 \tabularnewline
8 &  18 &  16.67 &  1.33 \tabularnewline
9 &  15 &  16.52 & -1.518 \tabularnewline
10 &  14 &  16.93 & -2.933 \tabularnewline
11 &  16 &  16.55 & -0.5531 \tabularnewline
12 &  19 &  17.4 &  1.604 \tabularnewline
13 &  18 &  16.55 &  1.446 \tabularnewline
14 &  17 &  16.58 &  0.423 \tabularnewline
15 &  19 &  17.03 &  1.974 \tabularnewline
16 &  17 &  16.92 &  0.0781 \tabularnewline
17 &  19 &  18.33 &  0.6673 \tabularnewline
18 &  20 &  17.74 &  2.259 \tabularnewline
19 &  19 &  16.32 &  2.676 \tabularnewline
20 &  16 &  16.02 & -0.02071 \tabularnewline
21 &  16 &  16.64 & -0.6353 \tabularnewline
22 &  18 &  15.76 &  2.243 \tabularnewline
23 &  16 &  16.84 & -0.8408 \tabularnewline
24 &  17 &  18.22 & -1.215 \tabularnewline
25 &  20 &  18.24 &  1.762 \tabularnewline
26 &  19 &  16.98 &  2.019 \tabularnewline
27 &  7 &  16.58 & -9.576 \tabularnewline
28 &  16 &  16.17 & -0.1731 \tabularnewline
29 &  16 &  17.13 & -1.132 \tabularnewline
30 &  18 &  17.34 &  0.6619 \tabularnewline
31 &  17 &  17.69 & -0.6883 \tabularnewline
32 &  19 &  16.2 &  2.804 \tabularnewline
33 &  16 &  17.13 & -1.132 \tabularnewline
34 &  13 &  15.62 & -2.617 \tabularnewline
35 &  16 &  16.52 & -0.5186 \tabularnewline
36 &  12 &  17.74 & -5.74 \tabularnewline
37 &  17 &  16.59 &  0.4113 \tabularnewline
38 &  17 &  17.4 & -0.3959 \tabularnewline
39 &  17 &  15.24 &  1.764 \tabularnewline
40 &  16 &  17.89 & -1.893 \tabularnewline
41 &  16 &  15.87 &  0.127 \tabularnewline
42 &  14 &  16.02 & -2.02 \tabularnewline
43 &  16 &  16.63 & -0.6344 \tabularnewline
44 &  13 &  17.04 & -4.038 \tabularnewline
45 &  16 &  16.55 & -0.5531 \tabularnewline
46 &  14 &  15.73 & -1.733 \tabularnewline
47 &  19 &  16.96 &  2.043 \tabularnewline
48 &  18 &  17.11 &  0.8913 \tabularnewline
49 &  14 &  16.61 & -2.611 \tabularnewline
50 &  18 &  16.46 &  1.541 \tabularnewline
51 &  15 &  15.59 & -0.5858 \tabularnewline
52 &  17 &  16.9 &  0.1008 \tabularnewline
53 &  13 &  16.69 & -3.688 \tabularnewline
54 &  19 &  17.3 &  1.698 \tabularnewline
55 &  18 &  17.72 &  0.2825 \tabularnewline
56 &  15 &  14.88 &  0.1207 \tabularnewline
57 &  20 &  17.34 &  2.663 \tabularnewline
58 &  19 &  16.9 &  2.101 \tabularnewline
59 &  18 &  17.4 &  0.6044 \tabularnewline
60 &  15 &  16.8 & -1.805 \tabularnewline
61 &  20 &  18.12 &  1.878 \tabularnewline
62 &  17 &  17.83 & -0.8348 \tabularnewline
63 &  19 &  17.45 &  1.546 \tabularnewline
64 &  20 &  17.53 &  2.465 \tabularnewline
65 &  18 &  16.96 &  1.043 \tabularnewline
66 &  17 &  16.35 &  0.6531 \tabularnewline
67 &  18 &  16.55 &  1.447 \tabularnewline
68 &  17 &  16.52 &  0.4829 \tabularnewline
69 &  20 &  18.24 &  1.761 \tabularnewline
70 &  16 &  16.2 & -0.1967 \tabularnewline
71 &  14 &  16.58 & -2.577 \tabularnewline
72 &  15 &  16.23 & -1.231 \tabularnewline
73 &  20 &  17.02 &  2.985 \tabularnewline
74 &  17 &  16.17 &  0.8278 \tabularnewline
75 &  17 &  16.08 &  0.9218 \tabularnewline
76 &  18 &  14.65 &  3.35 \tabularnewline
77 &  20 &  16.8 &  3.195 \tabularnewline
78 &  16 &  15.97 &  0.03334 \tabularnewline
79 &  18 &  17.68 &  0.3163 \tabularnewline
80 &  15 &  17.45 & -2.454 \tabularnewline
81 &  18 &  18.5 & -0.502 \tabularnewline
82 &  20 &  18.22 &  1.785 \tabularnewline
83 &  14 &  17.11 & -3.109 \tabularnewline
84 &  15 &  15.81 & -0.8149 \tabularnewline
85 &  17 &  16.9 &  0.1014 \tabularnewline
86 &  18 &  17.51 &  0.4871 \tabularnewline
87 &  20 &  17.19 &  2.815 \tabularnewline
88 &  17 &  16.63 &  0.3656 \tabularnewline
89 &  16 &  16.96 & -0.9569 \tabularnewline
90 &  11 &  16.08 & -5.079 \tabularnewline
91 &  15 &  17.02 & -2.016 \tabularnewline
92 &  18 &  17.23 &  0.7737 \tabularnewline
93 &  16 &  17.74 & -1.742 \tabularnewline
94 &  18 &  16.84 &  1.159 \tabularnewline
95 &  15 &  16.58 & -1.577 \tabularnewline
96 &  17 &  15.96 &  1.037 \tabularnewline
97 &  19 &  17.89 &  1.107 \tabularnewline
98 &  16 &  16.96 & -0.9569 \tabularnewline
99 &  14 &  16.15 & -2.149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 14[/C][C] 15.81[/C][C]-1.815[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.48[/C][C] 2.518[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.34[/C][C]-0.3375[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 16.99[/C][C] 3.008[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 18.21[/C][C]-3.215[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 16.02[/C][C] 2.979[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 16.52[/C][C] 3.481[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 16.67[/C][C] 1.33[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 16.52[/C][C]-1.518[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 16.93[/C][C]-2.933[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.55[/C][C]-0.5531[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 17.4[/C][C] 1.604[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.55[/C][C] 1.446[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 16.58[/C][C] 0.423[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 17.03[/C][C] 1.974[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 16.92[/C][C] 0.0781[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 18.33[/C][C] 0.6673[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 17.74[/C][C] 2.259[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 16.32[/C][C] 2.676[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 16.02[/C][C]-0.02071[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 16.64[/C][C]-0.6353[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 15.76[/C][C] 2.243[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.84[/C][C]-0.8408[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 18.22[/C][C]-1.215[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 18.24[/C][C] 1.762[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 16.98[/C][C] 2.019[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 16.58[/C][C]-9.576[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 16.17[/C][C]-0.1731[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 17.13[/C][C]-1.132[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 17.34[/C][C] 0.6619[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 17.69[/C][C]-0.6883[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.2[/C][C] 2.804[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 17.13[/C][C]-1.132[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.62[/C][C]-2.617[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.52[/C][C]-0.5186[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 17.74[/C][C]-5.74[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 16.59[/C][C] 0.4113[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.4[/C][C]-0.3959[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 15.24[/C][C] 1.764[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 17.89[/C][C]-1.893[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.87[/C][C] 0.127[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 16.02[/C][C]-2.02[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.63[/C][C]-0.6344[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 17.04[/C][C]-4.038[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.55[/C][C]-0.5531[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.73[/C][C]-1.733[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 16.96[/C][C] 2.043[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 17.11[/C][C] 0.8913[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 16.61[/C][C]-2.611[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 16.46[/C][C] 1.541[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 15.59[/C][C]-0.5858[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 16.9[/C][C] 0.1008[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 16.69[/C][C]-3.688[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 17.3[/C][C] 1.698[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 17.72[/C][C] 0.2825[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 14.88[/C][C] 0.1207[/C][/ROW]
[ROW][C]57[/C][C] 20[/C][C] 17.34[/C][C] 2.663[/C][/ROW]
[ROW][C]58[/C][C] 19[/C][C] 16.9[/C][C] 2.101[/C][/ROW]
[ROW][C]59[/C][C] 18[/C][C] 17.4[/C][C] 0.6044[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 16.8[/C][C]-1.805[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 18.12[/C][C] 1.878[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 17.83[/C][C]-0.8348[/C][/ROW]
[ROW][C]63[/C][C] 19[/C][C] 17.45[/C][C] 1.546[/C][/ROW]
[ROW][C]64[/C][C] 20[/C][C] 17.53[/C][C] 2.465[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 16.96[/C][C] 1.043[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 16.35[/C][C] 0.6531[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 16.55[/C][C] 1.447[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 16.52[/C][C] 0.4829[/C][/ROW]
[ROW][C]69[/C][C] 20[/C][C] 18.24[/C][C] 1.761[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 16.2[/C][C]-0.1967[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 16.58[/C][C]-2.577[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 16.23[/C][C]-1.231[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 17.02[/C][C] 2.985[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 16.17[/C][C] 0.8278[/C][/ROW]
[ROW][C]75[/C][C] 17[/C][C] 16.08[/C][C] 0.9218[/C][/ROW]
[ROW][C]76[/C][C] 18[/C][C] 14.65[/C][C] 3.35[/C][/ROW]
[ROW][C]77[/C][C] 20[/C][C] 16.8[/C][C] 3.195[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.97[/C][C] 0.03334[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.68[/C][C] 0.3163[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 17.45[/C][C]-2.454[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 18.5[/C][C]-0.502[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 18.22[/C][C] 1.785[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 17.11[/C][C]-3.109[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.81[/C][C]-0.8149[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.9[/C][C] 0.1014[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 17.51[/C][C] 0.4871[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 17.19[/C][C] 2.815[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 16.63[/C][C] 0.3656[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.96[/C][C]-0.9569[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 16.08[/C][C]-5.079[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 17.02[/C][C]-2.016[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 17.23[/C][C] 0.7737[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 17.74[/C][C]-1.742[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.84[/C][C] 1.159[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.58[/C][C]-1.577[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 15.96[/C][C] 1.037[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 17.89[/C][C] 1.107[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.96[/C][C]-0.9569[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 16.15[/C][C]-2.149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

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

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	15.81	-1.815
2	19	16.48	2.518
3	17	17.34	-0.3375
4	20	16.99	3.008
5	15	18.21	-3.215
6	19	16.02	2.979
7	20	16.52	3.481
8	18	16.67	1.33
9	15	16.52	-1.518
10	14	16.93	-2.933
11	16	16.55	-0.5531
12	19	17.4	1.604
13	18	16.55	1.446
14	17	16.58	0.423
15	19	17.03	1.974
16	17	16.92	0.0781
17	19	18.33	0.6673
18	20	17.74	2.259
19	19	16.32	2.676
20	16	16.02	-0.02071
21	16	16.64	-0.6353
22	18	15.76	2.243
23	16	16.84	-0.8408
24	17	18.22	-1.215
25	20	18.24	1.762
26	19	16.98	2.019
27	7	16.58	-9.576
28	16	16.17	-0.1731
29	16	17.13	-1.132
30	18	17.34	0.6619
31	17	17.69	-0.6883
32	19	16.2	2.804
33	16	17.13	-1.132
34	13	15.62	-2.617
35	16	16.52	-0.5186
36	12	17.74	-5.74
37	17	16.59	0.4113
38	17	17.4	-0.3959
39	17	15.24	1.764
40	16	17.89	-1.893
41	16	15.87	0.127
42	14	16.02	-2.02
43	16	16.63	-0.6344
44	13	17.04	-4.038
45	16	16.55	-0.5531
46	14	15.73	-1.733
47	19	16.96	2.043
48	18	17.11	0.8913
49	14	16.61	-2.611
50	18	16.46	1.541
51	15	15.59	-0.5858
52	17	16.9	0.1008
53	13	16.69	-3.688
54	19	17.3	1.698
55	18	17.72	0.2825
56	15	14.88	0.1207
57	20	17.34	2.663
58	19	16.9	2.101
59	18	17.4	0.6044
60	15	16.8	-1.805
61	20	18.12	1.878
62	17	17.83	-0.8348
63	19	17.45	1.546
64	20	17.53	2.465
65	18	16.96	1.043
66	17	16.35	0.6531
67	18	16.55	1.447
68	17	16.52	0.4829
69	20	18.24	1.761
70	16	16.2	-0.1967
71	14	16.58	-2.577
72	15	16.23	-1.231
73	20	17.02	2.985
74	17	16.17	0.8278
75	17	16.08	0.9218
76	18	14.65	3.35
77	20	16.8	3.195
78	16	15.97	0.03334
79	18	17.68	0.3163
80	15	17.45	-2.454
81	18	18.5	-0.502
82	20	18.22	1.785
83	14	17.11	-3.109
84	15	15.81	-0.8149
85	17	16.9	0.1014
86	18	17.51	0.4871
87	20	17.19	2.815
88	17	16.63	0.3656
89	16	16.96	-0.9569
90	11	16.08	-5.079
91	15	17.02	-2.016
92	18	17.23	0.7737
93	16	17.74	-1.742
94	18	16.84	1.159
95	15	16.58	-1.577
96	17	15.96	1.037
97	19	17.89	1.107
98	16	16.96	-0.9569
99	14	16.15	-2.149

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.7003	0.5995	0.2997
9	0.7807	0.4387	0.2193
10	0.8267	0.3467	0.1733
11	0.7401	0.5198	0.2599
12	0.7165	0.567	0.2835
13	0.6237	0.7526	0.3763
14	0.6037	0.7926	0.3963
15	0.6778	0.6443	0.3222
16	0.5905	0.8191	0.4095
17	0.5228	0.9544	0.4772
18	0.5018	0.9965	0.4982
19	0.5059	0.9883	0.4941
20	0.4445	0.8891	0.5555
21	0.4166	0.8331	0.5834
22	0.3641	0.7283	0.6359
23	0.3386	0.6772	0.6614
24	0.2819	0.5638	0.7181
25	0.2724	0.5448	0.7276
26	0.2336	0.4671	0.7664
27	0.988	0.02407	0.01203
28	0.9832	0.03364	0.01682
29	0.9764	0.04724	0.02362
30	0.967	0.066	0.033
31	0.955	0.09007	0.04503
32	0.9628	0.07442	0.03721
33	0.9508	0.09839	0.04919
34	0.9633	0.07334	0.03667
35	0.9512	0.09751	0.04876
36	0.9927	0.01467	0.007334
37	0.9897	0.02055	0.01028
38	0.9849	0.03027	0.01513
39	0.9843	0.03143	0.01572
40	0.983	0.0341	0.01705
41	0.976	0.048	0.024
42	0.9737	0.05254	0.02627
43	0.9644	0.0713	0.03565
44	0.989	0.02205	0.01103
45	0.9839	0.03218	0.01609
46	0.9806	0.0387	0.01935
47	0.981	0.03802	0.01901
48	0.9751	0.04987	0.02493
49	0.978	0.04405	0.02202
50	0.9754	0.04911	0.02456
51	0.969	0.0621	0.03105
52	0.9589	0.08214	0.04107
53	0.981	0.03808	0.01904
54	0.978	0.04408	0.02204
55	0.9712	0.05767	0.02884
56	0.9599	0.08022	0.04011
57	0.9656	0.06879	0.0344
58	0.9726	0.05475	0.02737
59	0.9621	0.07584	0.03792
60	0.9676	0.06483	0.03242
61	0.9627	0.0746	0.0373
62	0.9508	0.09845	0.04923
63	0.9401	0.1198	0.05991
64	0.9369	0.1261	0.06305
65	0.9196	0.1608	0.08042
66	0.893	0.214	0.107
67	0.8912	0.2175	0.1088
68	0.865	0.27	0.135
69	0.8614	0.2773	0.1386
70	0.8332	0.3337	0.1668
71	0.829	0.3421	0.171
72	0.786	0.4281	0.214
73	0.8169	0.3663	0.1831
74	0.775	0.45	0.225
75	0.724	0.552	0.276
76	0.8055	0.389	0.1945
77	0.8355	0.3291	0.1645
78	0.803	0.3941	0.197
79	0.742	0.516	0.258
80	0.7503	0.4995	0.2497
81	0.7743	0.4514	0.2257
82	0.7082	0.5836	0.2918
83	0.7122	0.5755	0.2878
84	0.6508	0.6984	0.3492
85	0.5525	0.895	0.4475
86	0.456	0.9121	0.544
87	0.4773	0.9545	0.5227
88	0.5071	0.9858	0.4929
89	0.3808	0.7617	0.6192
90	0.5135	0.9731	0.4865
91	0.4529	0.9058	0.5471

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.7003 &  0.5995 &  0.2997 \tabularnewline
9 &  0.7807 &  0.4387 &  0.2193 \tabularnewline
10 &  0.8267 &  0.3467 &  0.1733 \tabularnewline
11 &  0.7401 &  0.5198 &  0.2599 \tabularnewline
12 &  0.7165 &  0.567 &  0.2835 \tabularnewline
13 &  0.6237 &  0.7526 &  0.3763 \tabularnewline
14 &  0.6037 &  0.7926 &  0.3963 \tabularnewline
15 &  0.6778 &  0.6443 &  0.3222 \tabularnewline
16 &  0.5905 &  0.8191 &  0.4095 \tabularnewline
17 &  0.5228 &  0.9544 &  0.4772 \tabularnewline
18 &  0.5018 &  0.9965 &  0.4982 \tabularnewline
19 &  0.5059 &  0.9883 &  0.4941 \tabularnewline
20 &  0.4445 &  0.8891 &  0.5555 \tabularnewline
21 &  0.4166 &  0.8331 &  0.5834 \tabularnewline
22 &  0.3641 &  0.7283 &  0.6359 \tabularnewline
23 &  0.3386 &  0.6772 &  0.6614 \tabularnewline
24 &  0.2819 &  0.5638 &  0.7181 \tabularnewline
25 &  0.2724 &  0.5448 &  0.7276 \tabularnewline
26 &  0.2336 &  0.4671 &  0.7664 \tabularnewline
27 &  0.988 &  0.02407 &  0.01203 \tabularnewline
28 &  0.9832 &  0.03364 &  0.01682 \tabularnewline
29 &  0.9764 &  0.04724 &  0.02362 \tabularnewline
30 &  0.967 &  0.066 &  0.033 \tabularnewline
31 &  0.955 &  0.09007 &  0.04503 \tabularnewline
32 &  0.9628 &  0.07442 &  0.03721 \tabularnewline
33 &  0.9508 &  0.09839 &  0.04919 \tabularnewline
34 &  0.9633 &  0.07334 &  0.03667 \tabularnewline
35 &  0.9512 &  0.09751 &  0.04876 \tabularnewline
36 &  0.9927 &  0.01467 &  0.007334 \tabularnewline
37 &  0.9897 &  0.02055 &  0.01028 \tabularnewline
38 &  0.9849 &  0.03027 &  0.01513 \tabularnewline
39 &  0.9843 &  0.03143 &  0.01572 \tabularnewline
40 &  0.983 &  0.0341 &  0.01705 \tabularnewline
41 &  0.976 &  0.048 &  0.024 \tabularnewline
42 &  0.9737 &  0.05254 &  0.02627 \tabularnewline
43 &  0.9644 &  0.0713 &  0.03565 \tabularnewline
44 &  0.989 &  0.02205 &  0.01103 \tabularnewline
45 &  0.9839 &  0.03218 &  0.01609 \tabularnewline
46 &  0.9806 &  0.0387 &  0.01935 \tabularnewline
47 &  0.981 &  0.03802 &  0.01901 \tabularnewline
48 &  0.9751 &  0.04987 &  0.02493 \tabularnewline
49 &  0.978 &  0.04405 &  0.02202 \tabularnewline
50 &  0.9754 &  0.04911 &  0.02456 \tabularnewline
51 &  0.969 &  0.0621 &  0.03105 \tabularnewline
52 &  0.9589 &  0.08214 &  0.04107 \tabularnewline
53 &  0.981 &  0.03808 &  0.01904 \tabularnewline
54 &  0.978 &  0.04408 &  0.02204 \tabularnewline
55 &  0.9712 &  0.05767 &  0.02884 \tabularnewline
56 &  0.9599 &  0.08022 &  0.04011 \tabularnewline
57 &  0.9656 &  0.06879 &  0.0344 \tabularnewline
58 &  0.9726 &  0.05475 &  0.02737 \tabularnewline
59 &  0.9621 &  0.07584 &  0.03792 \tabularnewline
60 &  0.9676 &  0.06483 &  0.03242 \tabularnewline
61 &  0.9627 &  0.0746 &  0.0373 \tabularnewline
62 &  0.9508 &  0.09845 &  0.04923 \tabularnewline
63 &  0.9401 &  0.1198 &  0.05991 \tabularnewline
64 &  0.9369 &  0.1261 &  0.06305 \tabularnewline
65 &  0.9196 &  0.1608 &  0.08042 \tabularnewline
66 &  0.893 &  0.214 &  0.107 \tabularnewline
67 &  0.8912 &  0.2175 &  0.1088 \tabularnewline
68 &  0.865 &  0.27 &  0.135 \tabularnewline
69 &  0.8614 &  0.2773 &  0.1386 \tabularnewline
70 &  0.8332 &  0.3337 &  0.1668 \tabularnewline
71 &  0.829 &  0.3421 &  0.171 \tabularnewline
72 &  0.786 &  0.4281 &  0.214 \tabularnewline
73 &  0.8169 &  0.3663 &  0.1831 \tabularnewline
74 &  0.775 &  0.45 &  0.225 \tabularnewline
75 &  0.724 &  0.552 &  0.276 \tabularnewline
76 &  0.8055 &  0.389 &  0.1945 \tabularnewline
77 &  0.8355 &  0.3291 &  0.1645 \tabularnewline
78 &  0.803 &  0.3941 &  0.197 \tabularnewline
79 &  0.742 &  0.516 &  0.258 \tabularnewline
80 &  0.7503 &  0.4995 &  0.2497 \tabularnewline
81 &  0.7743 &  0.4514 &  0.2257 \tabularnewline
82 &  0.7082 &  0.5836 &  0.2918 \tabularnewline
83 &  0.7122 &  0.5755 &  0.2878 \tabularnewline
84 &  0.6508 &  0.6984 &  0.3492 \tabularnewline
85 &  0.5525 &  0.895 &  0.4475 \tabularnewline
86 &  0.456 &  0.9121 &  0.544 \tabularnewline
87 &  0.4773 &  0.9545 &  0.5227 \tabularnewline
88 &  0.5071 &  0.9858 &  0.4929 \tabularnewline
89 &  0.3808 &  0.7617 &  0.6192 \tabularnewline
90 &  0.5135 &  0.9731 &  0.4865 \tabularnewline
91 &  0.4529 &  0.9058 &  0.5471 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C] 0.7003[/C][C] 0.5995[/C][C] 0.2997[/C][/ROW]
[ROW][C]9[/C][C] 0.7807[/C][C] 0.4387[/C][C] 0.2193[/C][/ROW]
[ROW][C]10[/C][C] 0.8267[/C][C] 0.3467[/C][C] 0.1733[/C][/ROW]
[ROW][C]11[/C][C] 0.7401[/C][C] 0.5198[/C][C] 0.2599[/C][/ROW]
[ROW][C]12[/C][C] 0.7165[/C][C] 0.567[/C][C] 0.2835[/C][/ROW]
[ROW][C]13[/C][C] 0.6237[/C][C] 0.7526[/C][C] 0.3763[/C][/ROW]
[ROW][C]14[/C][C] 0.6037[/C][C] 0.7926[/C][C] 0.3963[/C][/ROW]
[ROW][C]15[/C][C] 0.6778[/C][C] 0.6443[/C][C] 0.3222[/C][/ROW]
[ROW][C]16[/C][C] 0.5905[/C][C] 0.8191[/C][C] 0.4095[/C][/ROW]
[ROW][C]17[/C][C] 0.5228[/C][C] 0.9544[/C][C] 0.4772[/C][/ROW]
[ROW][C]18[/C][C] 0.5018[/C][C] 0.9965[/C][C] 0.4982[/C][/ROW]
[ROW][C]19[/C][C] 0.5059[/C][C] 0.9883[/C][C] 0.4941[/C][/ROW]
[ROW][C]20[/C][C] 0.4445[/C][C] 0.8891[/C][C] 0.5555[/C][/ROW]
[ROW][C]21[/C][C] 0.4166[/C][C] 0.8331[/C][C] 0.5834[/C][/ROW]
[ROW][C]22[/C][C] 0.3641[/C][C] 0.7283[/C][C] 0.6359[/C][/ROW]
[ROW][C]23[/C][C] 0.3386[/C][C] 0.6772[/C][C] 0.6614[/C][/ROW]
[ROW][C]24[/C][C] 0.2819[/C][C] 0.5638[/C][C] 0.7181[/C][/ROW]
[ROW][C]25[/C][C] 0.2724[/C][C] 0.5448[/C][C] 0.7276[/C][/ROW]
[ROW][C]26[/C][C] 0.2336[/C][C] 0.4671[/C][C] 0.7664[/C][/ROW]
[ROW][C]27[/C][C] 0.988[/C][C] 0.02407[/C][C] 0.01203[/C][/ROW]
[ROW][C]28[/C][C] 0.9832[/C][C] 0.03364[/C][C] 0.01682[/C][/ROW]
[ROW][C]29[/C][C] 0.9764[/C][C] 0.04724[/C][C] 0.02362[/C][/ROW]
[ROW][C]30[/C][C] 0.967[/C][C] 0.066[/C][C] 0.033[/C][/ROW]
[ROW][C]31[/C][C] 0.955[/C][C] 0.09007[/C][C] 0.04503[/C][/ROW]
[ROW][C]32[/C][C] 0.9628[/C][C] 0.07442[/C][C] 0.03721[/C][/ROW]
[ROW][C]33[/C][C] 0.9508[/C][C] 0.09839[/C][C] 0.04919[/C][/ROW]
[ROW][C]34[/C][C] 0.9633[/C][C] 0.07334[/C][C] 0.03667[/C][/ROW]
[ROW][C]35[/C][C] 0.9512[/C][C] 0.09751[/C][C] 0.04876[/C][/ROW]
[ROW][C]36[/C][C] 0.9927[/C][C] 0.01467[/C][C] 0.007334[/C][/ROW]
[ROW][C]37[/C][C] 0.9897[/C][C] 0.02055[/C][C] 0.01028[/C][/ROW]
[ROW][C]38[/C][C] 0.9849[/C][C] 0.03027[/C][C] 0.01513[/C][/ROW]
[ROW][C]39[/C][C] 0.9843[/C][C] 0.03143[/C][C] 0.01572[/C][/ROW]
[ROW][C]40[/C][C] 0.983[/C][C] 0.0341[/C][C] 0.01705[/C][/ROW]
[ROW][C]41[/C][C] 0.976[/C][C] 0.048[/C][C] 0.024[/C][/ROW]
[ROW][C]42[/C][C] 0.9737[/C][C] 0.05254[/C][C] 0.02627[/C][/ROW]
[ROW][C]43[/C][C] 0.9644[/C][C] 0.0713[/C][C] 0.03565[/C][/ROW]
[ROW][C]44[/C][C] 0.989[/C][C] 0.02205[/C][C] 0.01103[/C][/ROW]
[ROW][C]45[/C][C] 0.9839[/C][C] 0.03218[/C][C] 0.01609[/C][/ROW]
[ROW][C]46[/C][C] 0.9806[/C][C] 0.0387[/C][C] 0.01935[/C][/ROW]
[ROW][C]47[/C][C] 0.981[/C][C] 0.03802[/C][C] 0.01901[/C][/ROW]
[ROW][C]48[/C][C] 0.9751[/C][C] 0.04987[/C][C] 0.02493[/C][/ROW]
[ROW][C]49[/C][C] 0.978[/C][C] 0.04405[/C][C] 0.02202[/C][/ROW]
[ROW][C]50[/C][C] 0.9754[/C][C] 0.04911[/C][C] 0.02456[/C][/ROW]
[ROW][C]51[/C][C] 0.969[/C][C] 0.0621[/C][C] 0.03105[/C][/ROW]
[ROW][C]52[/C][C] 0.9589[/C][C] 0.08214[/C][C] 0.04107[/C][/ROW]
[ROW][C]53[/C][C] 0.981[/C][C] 0.03808[/C][C] 0.01904[/C][/ROW]
[ROW][C]54[/C][C] 0.978[/C][C] 0.04408[/C][C] 0.02204[/C][/ROW]
[ROW][C]55[/C][C] 0.9712[/C][C] 0.05767[/C][C] 0.02884[/C][/ROW]
[ROW][C]56[/C][C] 0.9599[/C][C] 0.08022[/C][C] 0.04011[/C][/ROW]
[ROW][C]57[/C][C] 0.9656[/C][C] 0.06879[/C][C] 0.0344[/C][/ROW]
[ROW][C]58[/C][C] 0.9726[/C][C] 0.05475[/C][C] 0.02737[/C][/ROW]
[ROW][C]59[/C][C] 0.9621[/C][C] 0.07584[/C][C] 0.03792[/C][/ROW]
[ROW][C]60[/C][C] 0.9676[/C][C] 0.06483[/C][C] 0.03242[/C][/ROW]
[ROW][C]61[/C][C] 0.9627[/C][C] 0.0746[/C][C] 0.0373[/C][/ROW]
[ROW][C]62[/C][C] 0.9508[/C][C] 0.09845[/C][C] 0.04923[/C][/ROW]
[ROW][C]63[/C][C] 0.9401[/C][C] 0.1198[/C][C] 0.05991[/C][/ROW]
[ROW][C]64[/C][C] 0.9369[/C][C] 0.1261[/C][C] 0.06305[/C][/ROW]
[ROW][C]65[/C][C] 0.9196[/C][C] 0.1608[/C][C] 0.08042[/C][/ROW]
[ROW][C]66[/C][C] 0.893[/C][C] 0.214[/C][C] 0.107[/C][/ROW]
[ROW][C]67[/C][C] 0.8912[/C][C] 0.2175[/C][C] 0.1088[/C][/ROW]
[ROW][C]68[/C][C] 0.865[/C][C] 0.27[/C][C] 0.135[/C][/ROW]
[ROW][C]69[/C][C] 0.8614[/C][C] 0.2773[/C][C] 0.1386[/C][/ROW]
[ROW][C]70[/C][C] 0.8332[/C][C] 0.3337[/C][C] 0.1668[/C][/ROW]
[ROW][C]71[/C][C] 0.829[/C][C] 0.3421[/C][C] 0.171[/C][/ROW]
[ROW][C]72[/C][C] 0.786[/C][C] 0.4281[/C][C] 0.214[/C][/ROW]
[ROW][C]73[/C][C] 0.8169[/C][C] 0.3663[/C][C] 0.1831[/C][/ROW]
[ROW][C]74[/C][C] 0.775[/C][C] 0.45[/C][C] 0.225[/C][/ROW]
[ROW][C]75[/C][C] 0.724[/C][C] 0.552[/C][C] 0.276[/C][/ROW]
[ROW][C]76[/C][C] 0.8055[/C][C] 0.389[/C][C] 0.1945[/C][/ROW]
[ROW][C]77[/C][C] 0.8355[/C][C] 0.3291[/C][C] 0.1645[/C][/ROW]
[ROW][C]78[/C][C] 0.803[/C][C] 0.3941[/C][C] 0.197[/C][/ROW]
[ROW][C]79[/C][C] 0.742[/C][C] 0.516[/C][C] 0.258[/C][/ROW]
[ROW][C]80[/C][C] 0.7503[/C][C] 0.4995[/C][C] 0.2497[/C][/ROW]
[ROW][C]81[/C][C] 0.7743[/C][C] 0.4514[/C][C] 0.2257[/C][/ROW]
[ROW][C]82[/C][C] 0.7082[/C][C] 0.5836[/C][C] 0.2918[/C][/ROW]
[ROW][C]83[/C][C] 0.7122[/C][C] 0.5755[/C][C] 0.2878[/C][/ROW]
[ROW][C]84[/C][C] 0.6508[/C][C] 0.6984[/C][C] 0.3492[/C][/ROW]
[ROW][C]85[/C][C] 0.5525[/C][C] 0.895[/C][C] 0.4475[/C][/ROW]
[ROW][C]86[/C][C] 0.456[/C][C] 0.9121[/C][C] 0.544[/C][/ROW]
[ROW][C]87[/C][C] 0.4773[/C][C] 0.9545[/C][C] 0.5227[/C][/ROW]
[ROW][C]88[/C][C] 0.5071[/C][C] 0.9858[/C][C] 0.4929[/C][/ROW]
[ROW][C]89[/C][C] 0.3808[/C][C] 0.7617[/C][C] 0.6192[/C][/ROW]
[ROW][C]90[/C][C] 0.5135[/C][C] 0.9731[/C][C] 0.4865[/C][/ROW]
[ROW][C]91[/C][C] 0.4529[/C][C] 0.9058[/C][C] 0.5471[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

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

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.7003	0.5995	0.2997
9	0.7807	0.4387	0.2193
10	0.8267	0.3467	0.1733
11	0.7401	0.5198	0.2599
12	0.7165	0.567	0.2835
13	0.6237	0.7526	0.3763
14	0.6037	0.7926	0.3963
15	0.6778	0.6443	0.3222
16	0.5905	0.8191	0.4095
17	0.5228	0.9544	0.4772
18	0.5018	0.9965	0.4982
19	0.5059	0.9883	0.4941
20	0.4445	0.8891	0.5555
21	0.4166	0.8331	0.5834
22	0.3641	0.7283	0.6359
23	0.3386	0.6772	0.6614
24	0.2819	0.5638	0.7181
25	0.2724	0.5448	0.7276
26	0.2336	0.4671	0.7664
27	0.988	0.02407	0.01203
28	0.9832	0.03364	0.01682
29	0.9764	0.04724	0.02362
30	0.967	0.066	0.033
31	0.955	0.09007	0.04503
32	0.9628	0.07442	0.03721
33	0.9508	0.09839	0.04919
34	0.9633	0.07334	0.03667
35	0.9512	0.09751	0.04876
36	0.9927	0.01467	0.007334
37	0.9897	0.02055	0.01028
38	0.9849	0.03027	0.01513
39	0.9843	0.03143	0.01572
40	0.983	0.0341	0.01705
41	0.976	0.048	0.024
42	0.9737	0.05254	0.02627
43	0.9644	0.0713	0.03565
44	0.989	0.02205	0.01103
45	0.9839	0.03218	0.01609
46	0.9806	0.0387	0.01935
47	0.981	0.03802	0.01901
48	0.9751	0.04987	0.02493
49	0.978	0.04405	0.02202
50	0.9754	0.04911	0.02456
51	0.969	0.0621	0.03105
52	0.9589	0.08214	0.04107
53	0.981	0.03808	0.01904
54	0.978	0.04408	0.02204
55	0.9712	0.05767	0.02884
56	0.9599	0.08022	0.04011
57	0.9656	0.06879	0.0344
58	0.9726	0.05475	0.02737
59	0.9621	0.07584	0.03792
60	0.9676	0.06483	0.03242
61	0.9627	0.0746	0.0373
62	0.9508	0.09845	0.04923
63	0.9401	0.1198	0.05991
64	0.9369	0.1261	0.06305
65	0.9196	0.1608	0.08042
66	0.893	0.214	0.107
67	0.8912	0.2175	0.1088
68	0.865	0.27	0.135
69	0.8614	0.2773	0.1386
70	0.8332	0.3337	0.1668
71	0.829	0.3421	0.171
72	0.786	0.4281	0.214
73	0.8169	0.3663	0.1831
74	0.775	0.45	0.225
75	0.724	0.552	0.276
76	0.8055	0.389	0.1945
77	0.8355	0.3291	0.1645
78	0.803	0.3941	0.197
79	0.742	0.516	0.258
80	0.7503	0.4995	0.2497
81	0.7743	0.4514	0.2257
82	0.7082	0.5836	0.2918
83	0.7122	0.5755	0.2878
84	0.6508	0.6984	0.3492
85	0.5525	0.895	0.4475
86	0.456	0.9121	0.544
87	0.4773	0.9545	0.5227
88	0.5071	0.9858	0.4929
89	0.3808	0.7617	0.6192
90	0.5135	0.9731	0.4865
91	0.4529	0.9058	0.5471

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	18	0.214286	NOK
10% type I error level	36	0.428571	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 & 18 & 0.214286 & NOK \tabularnewline
10% type I error level & 36 & 0.428571 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.214286[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]36[/C][C]0.428571[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

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

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	18	0.214286	NOK
10% type I error level	36	0.428571	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1665, df1 = 2, df2 = 92, p-value = 0.316
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2423, df1 = 8, df2 = 86, p-value = 0.2847
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3244, df1 = 2, df2 = 92, p-value = 0.271

\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 = 1.1665, df1 = 2, df2 = 92, p-value = 0.316
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2423, df1 = 8, df2 = 86, p-value = 0.2847
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3244, df1 = 2, df2 = 92, p-value = 0.271
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.1665, df1 = 2, df2 = 92, p-value = 0.316
[/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 = 1.2423, df1 = 8, df2 = 86, p-value = 0.2847
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3244, df1 = 2, df2 = 92, p-value = 0.271
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

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

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1665, df1 = 2, df2 = 92, p-value = 0.316
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2423, df1 = 8, df2 = 86, p-value = 0.2847
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3244, df1 = 2, df2 = 92, p-value = 0.271

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM SKEOU1 1.015174 1.326223 1.491878 1.377300

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1 
     1.015174      1.326223      1.491878      1.377300 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1 
     1.015174      1.326223      1.491878      1.377300 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=8

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

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM SKEOU1 1.015174 1.326223 1.491878 1.377300

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Parameters (Session):

par1 = 128811211121 ; par2 = Double00Do not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal Dummies ; par3 = additiveNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear Trend ; par4 = 12 ;

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code