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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 23 Jan 2017 09:53:35 +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/t1485161621cz0kp1m6netk4v4.htm/, Retrieved Wed, 15 May 2024 23:51:13 +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 23:51:13 +0200

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14 22 13 22 4 2 4
19 24 16 24 5 3 3
17 21 17 26 4 4 5
17 21 NA 21 3 4 3
15 24 NA 26 4 4 5
20 20 16 25 3 4 4
15 22 NA 21 3 4 4
19 20 NA 24 3 4 5
15 19 NA 27 4 5 4
15 23 17 28 4 5 5
19 21 17 23 4 4 2
NA 19 15 25 4 4 5
20 19 16 24 4 4 4
18 21 14 24 3 3 5
15 21 16 24 4 4 5
14 22 17 25 3 4 5
20 22 NA 25 3 4 5
NA 19 NA NA NA NA 5
16 21 NA 25 5 5 4
16 21 NA 25 4 4 4
16 21 16 24 3 4 5
10 20 NA 26 4 4 4
19 22 16 26 4 4 5
19 22 NA 25 4 4 5
16 24 NA 26 4 4 5
15 21 NA 23 3 4 4
18 19 16 24 3 4 4
17 19 15 24 4 4 4
19 23 16 25 2 4 5
17 21 16 25 5 4 4
NA 21 13 24 4 3 5
19 19 15 28 4 5 5
20 21 17 27 5 4 5
5 19 NA NA 4 3 5
19 21 13 23 2 3 5
16 21 17 23 4 5 2
15 23 NA 24 3 4 5
16 19 14 24 4 3 5
18 19 14 22 4 3 3
16 19 18 25 4 4 5
15 18 NA 25 5 4 4
17 22 17 28 4 5 5
NA 18 13 22 3 3 4
20 22 16 28 5 5 5
19 18 15 25 5 4 5
7 22 15 24 4 4 4
13 22 NA 24 4 4 4
16 19 15 23 3 5 5
16 22 13 25 4 4 4
NA 25 NA NA 2 3 4
18 19 17 26 4 5 5
18 19 NA 25 5 5 2
16 19 NA 27 5 5 5
17 19 11 26 4 3 5
19 21 14 23 4 3 4
16 21 13 25 4 4 5
19 20 NA 21 3 4 4
13 19 17 22 3 4 4
16 19 16 24 4 4 4
13 22 NA 25 4 4 4
12 26 17 27 5 5 3
17 19 16 24 2 4 4
17 21 16 26 4 4 4
17 21 16 21 3 4 4
16 20 15 27 4 4 5
16 23 12 22 4 2 4
14 22 17 23 4 4 4
16 22 14 24 4 4 4
13 22 14 25 5 4 5
16 21 16 24 3 4 4
14 21 NA 23 3 4 4
20 22 NA 28 4 5 5
12 23 NA NA 4 4 3
13 18 NA 24 4 4 4
18 24 NA 26 4 4 4
14 22 15 22 3 4 3
19 21 16 25 4 4 4
18 21 14 25 3 4 5
14 21 15 24 3 3 5
18 23 17 24 4 3 5
19 21 NA 26 4 4 5
15 23 10 21 3 3 3
14 21 NA 25 4 4 4
17 19 17 25 4 4 3
19 21 NA 26 4 4 4
13 21 20 25 5 4 4
19 21 17 26 5 4 3
18 23 18 27 4 4 5
20 23 NA 25 3 4 5
15 20 17 NA 3 NA 4
15 20 14 20 4 2 3
15 19 NA 24 4 4 5
20 23 17 26 4 4 5
15 22 NA 25 4 4 4
19 19 17 25 4 5 4
18 23 NA 24 3 4 4
18 22 16 26 4 4 5
15 22 18 25 5 4 3
20 21 18 28 5 4 5
17 21 16 27 4 5 4
12 21 NA 25 3 4 5
18 21 NA 26 5 3 4
19 22 15 26 4 4 5
20 25 13 26 5 4 4
NA 21 NA NA 3 4 4
17 23 NA 28 5 4 4
15 19 NA NA 4 4 5
16 22 NA 21 4 4 3
18 20 NA 25 4 4 5
18 21 16 25 4 4 5
14 25 NA 24 3 4 5
15 21 NA 24 4 4 4
12 19 NA 24 4 4 4
17 23 12 23 3 3 4
14 22 NA 23 4 4 4
18 21 16 24 3 4 5
17 24 16 24 4 4 5
17 21 NA 25 5 4 5
20 19 16 28 5 4 5
16 18 14 23 4 4 4
14 19 15 24 4 4 5
15 20 14 23 3 4 4
18 19 NA 24 4 4 4
20 22 15 25 4 4 4
17 21 NA 24 4 5 3
17 22 15 23 3 4 4
17 24 16 23 4 4 4
17 28 NA 25 4 4 4
15 19 NA 21 3 4 3
17 18 NA 22 4 4 4
18 23 11 19 3 2 4
17 19 NA 24 4 4 4
20 23 18 25 5 4 4
15 19 NA 21 2 4 4
16 22 11 22 3 3 4
15 21 NA 23 4 4 4
18 19 18 27 5 5 4
11 22 NA NA NA NA 2
15 21 15 26 4 5 5
18 23 19 29 5 5 5
20 22 17 28 4 5 5
19 19 NA 24 4 4 4
14 19 14 25 3 4 5
16 21 NA 25 4 4 5
15 22 13 22 4 4 2
17 21 17 25 4 4 3
18 20 14 26 4 4 4
20 23 19 26 5 4 5
17 22 14 24 4 3 5
18 23 NA 25 4 4 5
15 22 NA 19 3 3 2
16 21 16 25 4 5 5
11 20 16 23 4 4 4
15 18 15 25 4 4 4
18 18 12 25 3 4 5
17 20 NA 26 4 4 5
16 19 17 27 5 4 5
12 21 NA 24 4 4 5
19 24 NA 22 2 3 5
18 19 18 25 4 4 4
15 20 15 24 4 3 4
17 19 18 23 4 4 4
19 23 15 27 4 5 5
18 22 NA 24 5 4 3
19 21 NA 24 5 4 4
16 24 NA 21 3 3 1
16 21 16 25 4 4 4
16 21 NA 25 4 4 4
14 22 16 23 2 3 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 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]

8 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	8 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] = + 5.79651 -0.0266603Bevr_Leeftijd[t] + 0.0202327TVDC[t] + 0.613882SKEOUSUM[t] -0.159116SKEOU1[t] -0.720333SKEOU2[t] -0.0928654SKEOU3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  5.79651 -0.0266603Bevr_Leeftijd[t] +  0.0202327TVDC[t] +  0.613882SKEOUSUM[t] -0.159116SKEOU1[t] -0.720333SKEOU2[t] -0.0928654SKEOU3[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] =  +  5.79651 -0.0266603Bevr_Leeftijd[t] +  0.0202327TVDC[t] +  0.613882SKEOUSUM[t] -0.159116SKEOU1[t] -0.720333SKEOU2[t] -0.0928654SKEOU3[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] = + 5.79651 -0.0266603Bevr_Leeftijd[t] + 0.0202327TVDC[t] + 0.613882SKEOUSUM[t] -0.159116SKEOU1[t] -0.720333SKEOU2[t] -0.0928654SKEOU3[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)	+5.796	4.169	+1.3900e+00	0.1677	0.08387
Bevr_Leeftijd	-0.02666	0.1355	-1.9680e-01	0.8444	0.4222
TVDC	+0.02023	0.1468	+1.3780e-01	0.8907	0.4453
SKEOUSUM	+0.6139	0.2149	+2.8570e+00	0.005294	0.002647
SKEOU1	-0.1591	0.382	-4.1650e-01	0.678	0.339
SKEOU2	-0.7203	0.4668	-1.5430e+00	0.1262	0.06311
SKEOU3	-0.09287	0.365	-2.5440e-01	0.7997	0.3999

\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) & +5.796 &  4.169 & +1.3900e+00 &  0.1677 &  0.08387 \tabularnewline
Bevr_Leeftijd & -0.02666 &  0.1355 & -1.9680e-01 &  0.8444 &  0.4222 \tabularnewline
TVDC & +0.02023 &  0.1468 & +1.3780e-01 &  0.8907 &  0.4453 \tabularnewline
SKEOUSUM & +0.6139 &  0.2149 & +2.8570e+00 &  0.005294 &  0.002647 \tabularnewline
SKEOU1 & -0.1591 &  0.382 & -4.1650e-01 &  0.678 &  0.339 \tabularnewline
SKEOU2 & -0.7203 &  0.4668 & -1.5430e+00 &  0.1262 &  0.06311 \tabularnewline
SKEOU3 & -0.09287 &  0.365 & -2.5440e-01 &  0.7997 &  0.3999 \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]+5.796[/C][C] 4.169[/C][C]+1.3900e+00[/C][C] 0.1677[/C][C] 0.08387[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.02666[/C][C] 0.1355[/C][C]-1.9680e-01[/C][C] 0.8444[/C][C] 0.4222[/C][/ROW]
[ROW][C]TVDC[/C][C]+0.02023[/C][C] 0.1468[/C][C]+1.3780e-01[/C][C] 0.8907[/C][C] 0.4453[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.6139[/C][C] 0.2149[/C][C]+2.8570e+00[/C][C] 0.005294[/C][C] 0.002647[/C][/ROW]
[ROW][C]SKEOU1[/C][C]-0.1591[/C][C] 0.382[/C][C]-4.1650e-01[/C][C] 0.678[/C][C] 0.339[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.7203[/C][C] 0.4668[/C][C]-1.5430e+00[/C][C] 0.1262[/C][C] 0.06311[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.09287[/C][C] 0.365[/C][C]-2.5440e-01[/C][C] 0.7997[/C][C] 0.3999[/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)	+5.796	4.169	+1.3900e+00	0.1677	0.08387
Bevr_Leeftijd	-0.02666	0.1355	-1.9680e-01	0.8444	0.4222
TVDC	+0.02023	0.1468	+1.3780e-01	0.8907	0.4453
SKEOUSUM	+0.6139	0.2149	+2.8570e+00	0.005294	0.002647
SKEOU1	-0.1591	0.382	-4.1650e-01	0.678	0.339
SKEOU2	-0.7203	0.4668	-1.5430e+00	0.1262	0.06311
SKEOU3	-0.09287	0.365	-2.5440e-01	0.7997	0.3999

Multiple Linear Regression - Regression Statistics
Multiple R	0.3675
R-squared	0.1351
Adjusted R-squared	0.07868
F-TEST (value)	2.395
F-TEST (DF numerator)	6
F-TEST (DF denominator)	92
p-value	0.03397
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.193
Sum Squared Residuals	442.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3675 \tabularnewline
R-squared &  0.1351 \tabularnewline
Adjusted R-squared &  0.07868 \tabularnewline
F-TEST (value) &  2.395 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 92 \tabularnewline
p-value &  0.03397 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.193 \tabularnewline
Sum Squared Residuals &  442.3 \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] 0.3675[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1351[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.07868[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.395[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]92[/C][/ROW]
[ROW][C]p-value[/C][C] 0.03397[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.193[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 442.3[/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	0.3675
R-squared	0.1351
Adjusted R-squared	0.07868
F-TEST (value)	2.395
F-TEST (DF numerator)	6
F-TEST (DF denominator)	92
p-value	0.03397
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.193
Sum Squared Residuals	442.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.53	-2.53
2	19	16.98	2.022
3	17	17.56	-0.5594
4	20	17.2	2.796
5	15	18.01	-3.014
6	19	16	3.004
7	20	16.46	3.542
8	18	17.15	0.8496
9	15	16.31	-1.311
10	14	17.08	-3.078
11	16	16.47	-0.4705
12	19	17.51	1.487
13	18	16.62	1.383
14	17	16.44	0.5626
15	19	17.19	1.81
16	17	16.86	0.141
17	19	18.08	0.9203
18	20	18.01	1.986
19	19	16.68	2.325
20	16	15.28	0.724
21	16	17.04	-1.045
22	18	16	1.997
23	16	17.02	-1.019
24	17	18.04	-1.04
25	20	17.86	2.139
26	19	16.83	2.174
27	7	16.36	-9.357
28	16	15.17	0.8306
29	16	16.93	-0.9308
30	18	16.89	1.108
31	17	18.21	-1.212
32	19	16.47	2.53
33	16	16.86	-0.8646
34	13	15.41	-2.409
35	16	16.46	-0.4576
36	12	17.35	-5.346
37	17	16.78	0.2242
38	17	17.63	-0.632
39	17	14.72	2.278
40	16	18.16	-2.159
41	16	16.48	-0.4829
42	14	15.78	-1.784
43	16	16.34	-0.3371
44	13	16.7	-3.699
45	16	16.56	-0.5634
46	14	15.38	-1.382
47	19	17.02	1.982
48	18	17.04	0.9561
49	14	17.17	-3.171
50	18	17	1.001
51	15	15.36	-0.3602
52	17	17.18	-0.1846
53	13	16.94	-3.94
54	19	17.59	1.414
55	18	18.14	-0.1402
56	15	15.47	-0.4685
57	20	17.51	2.494
58	19	16.37	2.629
59	18	17.51	0.4875
60	15	16.97	-1.966
61	20	18.65	1.352
62	17	17.53	-0.5256
63	19	17.49	1.508
64	20	17.31	2.694
65	18	16.93	1.075
66	17	16.54	0.4644
67	18	16.47	1.529
68	17	16.23	0.7686
69	20	18.66	1.339
70	16	15.83	0.1701
71	14	16.34	-2.344
72	15	15.94	-0.9357
73	20	16.97	3.029
74	17	15.9	1.097
75	17	15.71	1.29
76	18	14.78	3.22
77	20	16.85	3.154
78	16	15.93	0.07187
79	18	17.46	0.5397
80	15	16.8	-1.799
81	18	18.51	-0.5087
82	20	18.04	1.96
83	14	17.1	-3.097
84	15	15.27	-0.2749
85	17	17.13	-0.1312
86	18	17.62	0.3818
87	20	17.39	2.613
88	17	16.96	0.03539
89	16	16.2	-0.205
90	11	15.82	-4.817
91	15	17.08	-2.078
92	18	17.08	0.9165
93	16	18.07	-2.067
94	18	17.11	0.8881
95	15	17.13	-2.131
96	17	15.88	1.116
97	19	17.36	1.641
98	16	17.02	-1.018
99	14	16.8	-2.802

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.53 & -2.53 \tabularnewline
2 &  19 &  16.98 &  2.022 \tabularnewline
3 &  17 &  17.56 & -0.5594 \tabularnewline
4 &  20 &  17.2 &  2.796 \tabularnewline
5 &  15 &  18.01 & -3.014 \tabularnewline
6 &  19 &  16 &  3.004 \tabularnewline
7 &  20 &  16.46 &  3.542 \tabularnewline
8 &  18 &  17.15 &  0.8496 \tabularnewline
9 &  15 &  16.31 & -1.311 \tabularnewline
10 &  14 &  17.08 & -3.078 \tabularnewline
11 &  16 &  16.47 & -0.4705 \tabularnewline
12 &  19 &  17.51 &  1.487 \tabularnewline
13 &  18 &  16.62 &  1.383 \tabularnewline
14 &  17 &  16.44 &  0.5626 \tabularnewline
15 &  19 &  17.19 &  1.81 \tabularnewline
16 &  17 &  16.86 &  0.141 \tabularnewline
17 &  19 &  18.08 &  0.9203 \tabularnewline
18 &  20 &  18.01 &  1.986 \tabularnewline
19 &  19 &  16.68 &  2.325 \tabularnewline
20 &  16 &  15.28 &  0.724 \tabularnewline
21 &  16 &  17.04 & -1.045 \tabularnewline
22 &  18 &  16 &  1.997 \tabularnewline
23 &  16 &  17.02 & -1.019 \tabularnewline
24 &  17 &  18.04 & -1.04 \tabularnewline
25 &  20 &  17.86 &  2.139 \tabularnewline
26 &  19 &  16.83 &  2.174 \tabularnewline
27 &  7 &  16.36 & -9.357 \tabularnewline
28 &  16 &  15.17 &  0.8306 \tabularnewline
29 &  16 &  16.93 & -0.9308 \tabularnewline
30 &  18 &  16.89 &  1.108 \tabularnewline
31 &  17 &  18.21 & -1.212 \tabularnewline
32 &  19 &  16.47 &  2.53 \tabularnewline
33 &  16 &  16.86 & -0.8646 \tabularnewline
34 &  13 &  15.41 & -2.409 \tabularnewline
35 &  16 &  16.46 & -0.4576 \tabularnewline
36 &  12 &  17.35 & -5.346 \tabularnewline
37 &  17 &  16.78 &  0.2242 \tabularnewline
38 &  17 &  17.63 & -0.632 \tabularnewline
39 &  17 &  14.72 &  2.278 \tabularnewline
40 &  16 &  18.16 & -2.159 \tabularnewline
41 &  16 &  16.48 & -0.4829 \tabularnewline
42 &  14 &  15.78 & -1.784 \tabularnewline
43 &  16 &  16.34 & -0.3371 \tabularnewline
44 &  13 &  16.7 & -3.699 \tabularnewline
45 &  16 &  16.56 & -0.5634 \tabularnewline
46 &  14 &  15.38 & -1.382 \tabularnewline
47 &  19 &  17.02 &  1.982 \tabularnewline
48 &  18 &  17.04 &  0.9561 \tabularnewline
49 &  14 &  17.17 & -3.171 \tabularnewline
50 &  18 &  17 &  1.001 \tabularnewline
51 &  15 &  15.36 & -0.3602 \tabularnewline
52 &  17 &  17.18 & -0.1846 \tabularnewline
53 &  13 &  16.94 & -3.94 \tabularnewline
54 &  19 &  17.59 &  1.414 \tabularnewline
55 &  18 &  18.14 & -0.1402 \tabularnewline
56 &  15 &  15.47 & -0.4685 \tabularnewline
57 &  20 &  17.51 &  2.494 \tabularnewline
58 &  19 &  16.37 &  2.629 \tabularnewline
59 &  18 &  17.51 &  0.4875 \tabularnewline
60 &  15 &  16.97 & -1.966 \tabularnewline
61 &  20 &  18.65 &  1.352 \tabularnewline
62 &  17 &  17.53 & -0.5256 \tabularnewline
63 &  19 &  17.49 &  1.508 \tabularnewline
64 &  20 &  17.31 &  2.694 \tabularnewline
65 &  18 &  16.93 &  1.075 \tabularnewline
66 &  17 &  16.54 &  0.4644 \tabularnewline
67 &  18 &  16.47 &  1.529 \tabularnewline
68 &  17 &  16.23 &  0.7686 \tabularnewline
69 &  20 &  18.66 &  1.339 \tabularnewline
70 &  16 &  15.83 &  0.1701 \tabularnewline
71 &  14 &  16.34 & -2.344 \tabularnewline
72 &  15 &  15.94 & -0.9357 \tabularnewline
73 &  20 &  16.97 &  3.029 \tabularnewline
74 &  17 &  15.9 &  1.097 \tabularnewline
75 &  17 &  15.71 &  1.29 \tabularnewline
76 &  18 &  14.78 &  3.22 \tabularnewline
77 &  20 &  16.85 &  3.154 \tabularnewline
78 &  16 &  15.93 &  0.07187 \tabularnewline
79 &  18 &  17.46 &  0.5397 \tabularnewline
80 &  15 &  16.8 & -1.799 \tabularnewline
81 &  18 &  18.51 & -0.5087 \tabularnewline
82 &  20 &  18.04 &  1.96 \tabularnewline
83 &  14 &  17.1 & -3.097 \tabularnewline
84 &  15 &  15.27 & -0.2749 \tabularnewline
85 &  17 &  17.13 & -0.1312 \tabularnewline
86 &  18 &  17.62 &  0.3818 \tabularnewline
87 &  20 &  17.39 &  2.613 \tabularnewline
88 &  17 &  16.96 &  0.03539 \tabularnewline
89 &  16 &  16.2 & -0.205 \tabularnewline
90 &  11 &  15.82 & -4.817 \tabularnewline
91 &  15 &  17.08 & -2.078 \tabularnewline
92 &  18 &  17.08 &  0.9165 \tabularnewline
93 &  16 &  18.07 & -2.067 \tabularnewline
94 &  18 &  17.11 &  0.8881 \tabularnewline
95 &  15 &  17.13 & -2.131 \tabularnewline
96 &  17 &  15.88 &  1.116 \tabularnewline
97 &  19 &  17.36 &  1.641 \tabularnewline
98 &  16 &  17.02 & -1.018 \tabularnewline
99 &  14 &  16.8 & -2.802 \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] 16.53[/C][C]-2.53[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.98[/C][C] 2.022[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.56[/C][C]-0.5594[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 17.2[/C][C] 2.796[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 18.01[/C][C]-3.014[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 16[/C][C] 3.004[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 16.46[/C][C] 3.542[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 17.15[/C][C] 0.8496[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 16.31[/C][C]-1.311[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 17.08[/C][C]-3.078[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.47[/C][C]-0.4705[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 17.51[/C][C] 1.487[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.62[/C][C] 1.383[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 16.44[/C][C] 0.5626[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 17.19[/C][C] 1.81[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 16.86[/C][C] 0.141[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 18.08[/C][C] 0.9203[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 18.01[/C][C] 1.986[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 16.68[/C][C] 2.325[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 15.28[/C][C] 0.724[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 17.04[/C][C]-1.045[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 16[/C][C] 1.997[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 17.02[/C][C]-1.019[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 18.04[/C][C]-1.04[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 17.86[/C][C] 2.139[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 16.83[/C][C] 2.174[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 16.36[/C][C]-9.357[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 15.17[/C][C] 0.8306[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.93[/C][C]-0.9308[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 16.89[/C][C] 1.108[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 18.21[/C][C]-1.212[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.47[/C][C] 2.53[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 16.86[/C][C]-0.8646[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.41[/C][C]-2.409[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.46[/C][C]-0.4576[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 17.35[/C][C]-5.346[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 16.78[/C][C] 0.2242[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.63[/C][C]-0.632[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 14.72[/C][C] 2.278[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 18.16[/C][C]-2.159[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 16.48[/C][C]-0.4829[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 15.78[/C][C]-1.784[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.34[/C][C]-0.3371[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 16.7[/C][C]-3.699[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.56[/C][C]-0.5634[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.38[/C][C]-1.382[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 17.02[/C][C] 1.982[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 17.04[/C][C] 0.9561[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 17.17[/C][C]-3.171[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17[/C][C] 1.001[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 15.36[/C][C]-0.3602[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 17.18[/C][C]-0.1846[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 16.94[/C][C]-3.94[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 17.59[/C][C] 1.414[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 18.14[/C][C]-0.1402[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 15.47[/C][C]-0.4685[/C][/ROW]
[ROW][C]57[/C][C] 20[/C][C] 17.51[/C][C] 2.494[/C][/ROW]
[ROW][C]58[/C][C] 19[/C][C] 16.37[/C][C] 2.629[/C][/ROW]
[ROW][C]59[/C][C] 18[/C][C] 17.51[/C][C] 0.4875[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 16.97[/C][C]-1.966[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 18.65[/C][C] 1.352[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 17.53[/C][C]-0.5256[/C][/ROW]
[ROW][C]63[/C][C] 19[/C][C] 17.49[/C][C] 1.508[/C][/ROW]
[ROW][C]64[/C][C] 20[/C][C] 17.31[/C][C] 2.694[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 16.93[/C][C] 1.075[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 16.54[/C][C] 0.4644[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 16.47[/C][C] 1.529[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 16.23[/C][C] 0.7686[/C][/ROW]
[ROW][C]69[/C][C] 20[/C][C] 18.66[/C][C] 1.339[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 15.83[/C][C] 0.1701[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 16.34[/C][C]-2.344[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 15.94[/C][C]-0.9357[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 16.97[/C][C] 3.029[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 15.9[/C][C] 1.097[/C][/ROW]
[ROW][C]75[/C][C] 17[/C][C] 15.71[/C][C] 1.29[/C][/ROW]
[ROW][C]76[/C][C] 18[/C][C] 14.78[/C][C] 3.22[/C][/ROW]
[ROW][C]77[/C][C] 20[/C][C] 16.85[/C][C] 3.154[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.93[/C][C] 0.07187[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.46[/C][C] 0.5397[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.8[/C][C]-1.799[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 18.51[/C][C]-0.5087[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 18.04[/C][C] 1.96[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 17.1[/C][C]-3.097[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.27[/C][C]-0.2749[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 17.13[/C][C]-0.1312[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 17.62[/C][C] 0.3818[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 17.39[/C][C] 2.613[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 16.96[/C][C] 0.03539[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.2[/C][C]-0.205[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 15.82[/C][C]-4.817[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 17.08[/C][C]-2.078[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 17.08[/C][C] 0.9165[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 18.07[/C][C]-2.067[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 17.11[/C][C] 0.8881[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 17.13[/C][C]-2.131[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 15.88[/C][C] 1.116[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 17.36[/C][C] 1.641[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 17.02[/C][C]-1.018[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 16.8[/C][C]-2.802[/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	16.53	-2.53
2	19	16.98	2.022
3	17	17.56	-0.5594
4	20	17.2	2.796
5	15	18.01	-3.014
6	19	16	3.004
7	20	16.46	3.542
8	18	17.15	0.8496
9	15	16.31	-1.311
10	14	17.08	-3.078
11	16	16.47	-0.4705
12	19	17.51	1.487
13	18	16.62	1.383
14	17	16.44	0.5626
15	19	17.19	1.81
16	17	16.86	0.141
17	19	18.08	0.9203
18	20	18.01	1.986
19	19	16.68	2.325
20	16	15.28	0.724
21	16	17.04	-1.045
22	18	16	1.997
23	16	17.02	-1.019
24	17	18.04	-1.04
25	20	17.86	2.139
26	19	16.83	2.174
27	7	16.36	-9.357
28	16	15.17	0.8306
29	16	16.93	-0.9308
30	18	16.89	1.108
31	17	18.21	-1.212
32	19	16.47	2.53
33	16	16.86	-0.8646
34	13	15.41	-2.409
35	16	16.46	-0.4576
36	12	17.35	-5.346
37	17	16.78	0.2242
38	17	17.63	-0.632
39	17	14.72	2.278
40	16	18.16	-2.159
41	16	16.48	-0.4829
42	14	15.78	-1.784
43	16	16.34	-0.3371
44	13	16.7	-3.699
45	16	16.56	-0.5634
46	14	15.38	-1.382
47	19	17.02	1.982
48	18	17.04	0.9561
49	14	17.17	-3.171
50	18	17	1.001
51	15	15.36	-0.3602
52	17	17.18	-0.1846
53	13	16.94	-3.94
54	19	17.59	1.414
55	18	18.14	-0.1402
56	15	15.47	-0.4685
57	20	17.51	2.494
58	19	16.37	2.629
59	18	17.51	0.4875
60	15	16.97	-1.966
61	20	18.65	1.352
62	17	17.53	-0.5256
63	19	17.49	1.508
64	20	17.31	2.694
65	18	16.93	1.075
66	17	16.54	0.4644
67	18	16.47	1.529
68	17	16.23	0.7686
69	20	18.66	1.339
70	16	15.83	0.1701
71	14	16.34	-2.344
72	15	15.94	-0.9357
73	20	16.97	3.029
74	17	15.9	1.097
75	17	15.71	1.29
76	18	14.78	3.22
77	20	16.85	3.154
78	16	15.93	0.07187
79	18	17.46	0.5397
80	15	16.8	-1.799
81	18	18.51	-0.5087
82	20	18.04	1.96
83	14	17.1	-3.097
84	15	15.27	-0.2749
85	17	17.13	-0.1312
86	18	17.62	0.3818
87	20	17.39	2.613
88	17	16.96	0.03539
89	16	16.2	-0.205
90	11	15.82	-4.817
91	15	17.08	-2.078
92	18	17.08	0.9165
93	16	18.07	-2.067
94	18	17.11	0.8881
95	15	17.13	-2.131
96	17	15.88	1.116
97	19	17.36	1.641
98	16	17.02	-1.018
99	14	16.8	-2.802

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.6199	0.7602	0.3801
11	0.5275	0.945	0.4725
12	0.5585	0.8831	0.4415
13	0.4704	0.9408	0.5296
14	0.4257	0.8515	0.5743
15	0.525	0.95	0.475
16	0.4192	0.8385	0.5808
17	0.3242	0.6483	0.6758
18	0.2962	0.5924	0.7038
19	0.3148	0.6295	0.6852
20	0.2442	0.4885	0.7558
21	0.2253	0.4505	0.7747
22	0.1781	0.3561	0.8219
23	0.1486	0.2972	0.8514
24	0.1156	0.2311	0.8844
25	0.1541	0.3081	0.8459
26	0.1539	0.3079	0.8461
27	0.9578	0.08431	0.04216
28	0.9615	0.07698	0.03849
29	0.9465	0.107	0.05352
30	0.9288	0.1424	0.07122
31	0.9244	0.1512	0.07558
32	0.9354	0.1292	0.06458
33	0.9152	0.1695	0.08476
34	0.929	0.142	0.07099
35	0.9112	0.1776	0.08881
36	0.9778	0.04437	0.02218
37	0.974	0.05208	0.02604
38	0.9656	0.06873	0.03436
39	0.9746	0.05083	0.02541
40	0.9756	0.0488	0.0244
41	0.9664	0.06722	0.03361
42	0.96	0.08005	0.04002
43	0.9465	0.1069	0.05346
44	0.9767	0.04651	0.02325
45	0.9675	0.06494	0.03247
46	0.9585	0.08292	0.04146
47	0.9574	0.08523	0.04261
48	0.9476	0.1049	0.05244
49	0.962	0.07605	0.03802
50	0.9543	0.09138	0.04569
51	0.9433	0.1133	0.05667
52	0.9334	0.1332	0.06659
53	0.9716	0.0569	0.02845
54	0.9649	0.0703	0.03515
55	0.9547	0.0906	0.0453
56	0.9391	0.1218	0.06089
57	0.9457	0.1086	0.0543
58	0.9674	0.06528	0.03264
59	0.9552	0.08966	0.04483
60	0.961	0.07792	0.03896
61	0.9486	0.1027	0.05137
62	0.9299	0.1402	0.07012
63	0.9149	0.1702	0.08508
64	0.916	0.1679	0.08396
65	0.8941	0.2118	0.1059
66	0.8609	0.2782	0.1391
67	0.8637	0.2726	0.1363
68	0.8339	0.3322	0.1661
69	0.8089	0.3822	0.1911
70	0.7779	0.4441	0.2221
71	0.7602	0.4796	0.2398
72	0.703	0.594	0.297
73	0.7392	0.5215	0.2607
74	0.698	0.6041	0.302
75	0.6396	0.7207	0.3604
76	0.7121	0.5758	0.2879
77	0.7418	0.5164	0.2582
78	0.6942	0.6117	0.3059
79	0.6164	0.7672	0.3836
80	0.5971	0.8058	0.4029
81	0.6221	0.7558	0.3779
82	0.538	0.924	0.462
83	0.5304	0.9391	0.4696
84	0.4741	0.9482	0.5259
85	0.3906	0.7812	0.6094
86	0.3427	0.6855	0.6573
87	0.2958	0.5916	0.7042
88	0.3062	0.6124	0.6938
89	0.193	0.3861	0.807

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.6199 &  0.7602 &  0.3801 \tabularnewline
11 &  0.5275 &  0.945 &  0.4725 \tabularnewline
12 &  0.5585 &  0.8831 &  0.4415 \tabularnewline
13 &  0.4704 &  0.9408 &  0.5296 \tabularnewline
14 &  0.4257 &  0.8515 &  0.5743 \tabularnewline
15 &  0.525 &  0.95 &  0.475 \tabularnewline
16 &  0.4192 &  0.8385 &  0.5808 \tabularnewline
17 &  0.3242 &  0.6483 &  0.6758 \tabularnewline
18 &  0.2962 &  0.5924 &  0.7038 \tabularnewline
19 &  0.3148 &  0.6295 &  0.6852 \tabularnewline
20 &  0.2442 &  0.4885 &  0.7558 \tabularnewline
21 &  0.2253 &  0.4505 &  0.7747 \tabularnewline
22 &  0.1781 &  0.3561 &  0.8219 \tabularnewline
23 &  0.1486 &  0.2972 &  0.8514 \tabularnewline
24 &  0.1156 &  0.2311 &  0.8844 \tabularnewline
25 &  0.1541 &  0.3081 &  0.8459 \tabularnewline
26 &  0.1539 &  0.3079 &  0.8461 \tabularnewline
27 &  0.9578 &  0.08431 &  0.04216 \tabularnewline
28 &  0.9615 &  0.07698 &  0.03849 \tabularnewline
29 &  0.9465 &  0.107 &  0.05352 \tabularnewline
30 &  0.9288 &  0.1424 &  0.07122 \tabularnewline
31 &  0.9244 &  0.1512 &  0.07558 \tabularnewline
32 &  0.9354 &  0.1292 &  0.06458 \tabularnewline
33 &  0.9152 &  0.1695 &  0.08476 \tabularnewline
34 &  0.929 &  0.142 &  0.07099 \tabularnewline
35 &  0.9112 &  0.1776 &  0.08881 \tabularnewline
36 &  0.9778 &  0.04437 &  0.02218 \tabularnewline
37 &  0.974 &  0.05208 &  0.02604 \tabularnewline
38 &  0.9656 &  0.06873 &  0.03436 \tabularnewline
39 &  0.9746 &  0.05083 &  0.02541 \tabularnewline
40 &  0.9756 &  0.0488 &  0.0244 \tabularnewline
41 &  0.9664 &  0.06722 &  0.03361 \tabularnewline
42 &  0.96 &  0.08005 &  0.04002 \tabularnewline
43 &  0.9465 &  0.1069 &  0.05346 \tabularnewline
44 &  0.9767 &  0.04651 &  0.02325 \tabularnewline
45 &  0.9675 &  0.06494 &  0.03247 \tabularnewline
46 &  0.9585 &  0.08292 &  0.04146 \tabularnewline
47 &  0.9574 &  0.08523 &  0.04261 \tabularnewline
48 &  0.9476 &  0.1049 &  0.05244 \tabularnewline
49 &  0.962 &  0.07605 &  0.03802 \tabularnewline
50 &  0.9543 &  0.09138 &  0.04569 \tabularnewline
51 &  0.9433 &  0.1133 &  0.05667 \tabularnewline
52 &  0.9334 &  0.1332 &  0.06659 \tabularnewline
53 &  0.9716 &  0.0569 &  0.02845 \tabularnewline
54 &  0.9649 &  0.0703 &  0.03515 \tabularnewline
55 &  0.9547 &  0.0906 &  0.0453 \tabularnewline
56 &  0.9391 &  0.1218 &  0.06089 \tabularnewline
57 &  0.9457 &  0.1086 &  0.0543 \tabularnewline
58 &  0.9674 &  0.06528 &  0.03264 \tabularnewline
59 &  0.9552 &  0.08966 &  0.04483 \tabularnewline
60 &  0.961 &  0.07792 &  0.03896 \tabularnewline
61 &  0.9486 &  0.1027 &  0.05137 \tabularnewline
62 &  0.9299 &  0.1402 &  0.07012 \tabularnewline
63 &  0.9149 &  0.1702 &  0.08508 \tabularnewline
64 &  0.916 &  0.1679 &  0.08396 \tabularnewline
65 &  0.8941 &  0.2118 &  0.1059 \tabularnewline
66 &  0.8609 &  0.2782 &  0.1391 \tabularnewline
67 &  0.8637 &  0.2726 &  0.1363 \tabularnewline
68 &  0.8339 &  0.3322 &  0.1661 \tabularnewline
69 &  0.8089 &  0.3822 &  0.1911 \tabularnewline
70 &  0.7779 &  0.4441 &  0.2221 \tabularnewline
71 &  0.7602 &  0.4796 &  0.2398 \tabularnewline
72 &  0.703 &  0.594 &  0.297 \tabularnewline
73 &  0.7392 &  0.5215 &  0.2607 \tabularnewline
74 &  0.698 &  0.6041 &  0.302 \tabularnewline
75 &  0.6396 &  0.7207 &  0.3604 \tabularnewline
76 &  0.7121 &  0.5758 &  0.2879 \tabularnewline
77 &  0.7418 &  0.5164 &  0.2582 \tabularnewline
78 &  0.6942 &  0.6117 &  0.3059 \tabularnewline
79 &  0.6164 &  0.7672 &  0.3836 \tabularnewline
80 &  0.5971 &  0.8058 &  0.4029 \tabularnewline
81 &  0.6221 &  0.7558 &  0.3779 \tabularnewline
82 &  0.538 &  0.924 &  0.462 \tabularnewline
83 &  0.5304 &  0.9391 &  0.4696 \tabularnewline
84 &  0.4741 &  0.9482 &  0.5259 \tabularnewline
85 &  0.3906 &  0.7812 &  0.6094 \tabularnewline
86 &  0.3427 &  0.6855 &  0.6573 \tabularnewline
87 &  0.2958 &  0.5916 &  0.7042 \tabularnewline
88 &  0.3062 &  0.6124 &  0.6938 \tabularnewline
89 &  0.193 &  0.3861 &  0.807 \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]10[/C][C] 0.6199[/C][C] 0.7602[/C][C] 0.3801[/C][/ROW]
[ROW][C]11[/C][C] 0.5275[/C][C] 0.945[/C][C] 0.4725[/C][/ROW]
[ROW][C]12[/C][C] 0.5585[/C][C] 0.8831[/C][C] 0.4415[/C][/ROW]
[ROW][C]13[/C][C] 0.4704[/C][C] 0.9408[/C][C] 0.5296[/C][/ROW]
[ROW][C]14[/C][C] 0.4257[/C][C] 0.8515[/C][C] 0.5743[/C][/ROW]
[ROW][C]15[/C][C] 0.525[/C][C] 0.95[/C][C] 0.475[/C][/ROW]
[ROW][C]16[/C][C] 0.4192[/C][C] 0.8385[/C][C] 0.5808[/C][/ROW]
[ROW][C]17[/C][C] 0.3242[/C][C] 0.6483[/C][C] 0.6758[/C][/ROW]
[ROW][C]18[/C][C] 0.2962[/C][C] 0.5924[/C][C] 0.7038[/C][/ROW]
[ROW][C]19[/C][C] 0.3148[/C][C] 0.6295[/C][C] 0.6852[/C][/ROW]
[ROW][C]20[/C][C] 0.2442[/C][C] 0.4885[/C][C] 0.7558[/C][/ROW]
[ROW][C]21[/C][C] 0.2253[/C][C] 0.4505[/C][C] 0.7747[/C][/ROW]
[ROW][C]22[/C][C] 0.1781[/C][C] 0.3561[/C][C] 0.8219[/C][/ROW]
[ROW][C]23[/C][C] 0.1486[/C][C] 0.2972[/C][C] 0.8514[/C][/ROW]
[ROW][C]24[/C][C] 0.1156[/C][C] 0.2311[/C][C] 0.8844[/C][/ROW]
[ROW][C]25[/C][C] 0.1541[/C][C] 0.3081[/C][C] 0.8459[/C][/ROW]
[ROW][C]26[/C][C] 0.1539[/C][C] 0.3079[/C][C] 0.8461[/C][/ROW]
[ROW][C]27[/C][C] 0.9578[/C][C] 0.08431[/C][C] 0.04216[/C][/ROW]
[ROW][C]28[/C][C] 0.9615[/C][C] 0.07698[/C][C] 0.03849[/C][/ROW]
[ROW][C]29[/C][C] 0.9465[/C][C] 0.107[/C][C] 0.05352[/C][/ROW]
[ROW][C]30[/C][C] 0.9288[/C][C] 0.1424[/C][C] 0.07122[/C][/ROW]
[ROW][C]31[/C][C] 0.9244[/C][C] 0.1512[/C][C] 0.07558[/C][/ROW]
[ROW][C]32[/C][C] 0.9354[/C][C] 0.1292[/C][C] 0.06458[/C][/ROW]
[ROW][C]33[/C][C] 0.9152[/C][C] 0.1695[/C][C] 0.08476[/C][/ROW]
[ROW][C]34[/C][C] 0.929[/C][C] 0.142[/C][C] 0.07099[/C][/ROW]
[ROW][C]35[/C][C] 0.9112[/C][C] 0.1776[/C][C] 0.08881[/C][/ROW]
[ROW][C]36[/C][C] 0.9778[/C][C] 0.04437[/C][C] 0.02218[/C][/ROW]
[ROW][C]37[/C][C] 0.974[/C][C] 0.05208[/C][C] 0.02604[/C][/ROW]
[ROW][C]38[/C][C] 0.9656[/C][C] 0.06873[/C][C] 0.03436[/C][/ROW]
[ROW][C]39[/C][C] 0.9746[/C][C] 0.05083[/C][C] 0.02541[/C][/ROW]
[ROW][C]40[/C][C] 0.9756[/C][C] 0.0488[/C][C] 0.0244[/C][/ROW]
[ROW][C]41[/C][C] 0.9664[/C][C] 0.06722[/C][C] 0.03361[/C][/ROW]
[ROW][C]42[/C][C] 0.96[/C][C] 0.08005[/C][C] 0.04002[/C][/ROW]
[ROW][C]43[/C][C] 0.9465[/C][C] 0.1069[/C][C] 0.05346[/C][/ROW]
[ROW][C]44[/C][C] 0.9767[/C][C] 0.04651[/C][C] 0.02325[/C][/ROW]
[ROW][C]45[/C][C] 0.9675[/C][C] 0.06494[/C][C] 0.03247[/C][/ROW]
[ROW][C]46[/C][C] 0.9585[/C][C] 0.08292[/C][C] 0.04146[/C][/ROW]
[ROW][C]47[/C][C] 0.9574[/C][C] 0.08523[/C][C] 0.04261[/C][/ROW]
[ROW][C]48[/C][C] 0.9476[/C][C] 0.1049[/C][C] 0.05244[/C][/ROW]
[ROW][C]49[/C][C] 0.962[/C][C] 0.07605[/C][C] 0.03802[/C][/ROW]
[ROW][C]50[/C][C] 0.9543[/C][C] 0.09138[/C][C] 0.04569[/C][/ROW]
[ROW][C]51[/C][C] 0.9433[/C][C] 0.1133[/C][C] 0.05667[/C][/ROW]
[ROW][C]52[/C][C] 0.9334[/C][C] 0.1332[/C][C] 0.06659[/C][/ROW]
[ROW][C]53[/C][C] 0.9716[/C][C] 0.0569[/C][C] 0.02845[/C][/ROW]
[ROW][C]54[/C][C] 0.9649[/C][C] 0.0703[/C][C] 0.03515[/C][/ROW]
[ROW][C]55[/C][C] 0.9547[/C][C] 0.0906[/C][C] 0.0453[/C][/ROW]
[ROW][C]56[/C][C] 0.9391[/C][C] 0.1218[/C][C] 0.06089[/C][/ROW]
[ROW][C]57[/C][C] 0.9457[/C][C] 0.1086[/C][C] 0.0543[/C][/ROW]
[ROW][C]58[/C][C] 0.9674[/C][C] 0.06528[/C][C] 0.03264[/C][/ROW]
[ROW][C]59[/C][C] 0.9552[/C][C] 0.08966[/C][C] 0.04483[/C][/ROW]
[ROW][C]60[/C][C] 0.961[/C][C] 0.07792[/C][C] 0.03896[/C][/ROW]
[ROW][C]61[/C][C] 0.9486[/C][C] 0.1027[/C][C] 0.05137[/C][/ROW]
[ROW][C]62[/C][C] 0.9299[/C][C] 0.1402[/C][C] 0.07012[/C][/ROW]
[ROW][C]63[/C][C] 0.9149[/C][C] 0.1702[/C][C] 0.08508[/C][/ROW]
[ROW][C]64[/C][C] 0.916[/C][C] 0.1679[/C][C] 0.08396[/C][/ROW]
[ROW][C]65[/C][C] 0.8941[/C][C] 0.2118[/C][C] 0.1059[/C][/ROW]
[ROW][C]66[/C][C] 0.8609[/C][C] 0.2782[/C][C] 0.1391[/C][/ROW]
[ROW][C]67[/C][C] 0.8637[/C][C] 0.2726[/C][C] 0.1363[/C][/ROW]
[ROW][C]68[/C][C] 0.8339[/C][C] 0.3322[/C][C] 0.1661[/C][/ROW]
[ROW][C]69[/C][C] 0.8089[/C][C] 0.3822[/C][C] 0.1911[/C][/ROW]
[ROW][C]70[/C][C] 0.7779[/C][C] 0.4441[/C][C] 0.2221[/C][/ROW]
[ROW][C]71[/C][C] 0.7602[/C][C] 0.4796[/C][C] 0.2398[/C][/ROW]
[ROW][C]72[/C][C] 0.703[/C][C] 0.594[/C][C] 0.297[/C][/ROW]
[ROW][C]73[/C][C] 0.7392[/C][C] 0.5215[/C][C] 0.2607[/C][/ROW]
[ROW][C]74[/C][C] 0.698[/C][C] 0.6041[/C][C] 0.302[/C][/ROW]
[ROW][C]75[/C][C] 0.6396[/C][C] 0.7207[/C][C] 0.3604[/C][/ROW]
[ROW][C]76[/C][C] 0.7121[/C][C] 0.5758[/C][C] 0.2879[/C][/ROW]
[ROW][C]77[/C][C] 0.7418[/C][C] 0.5164[/C][C] 0.2582[/C][/ROW]
[ROW][C]78[/C][C] 0.6942[/C][C] 0.6117[/C][C] 0.3059[/C][/ROW]
[ROW][C]79[/C][C] 0.6164[/C][C] 0.7672[/C][C] 0.3836[/C][/ROW]
[ROW][C]80[/C][C] 0.5971[/C][C] 0.8058[/C][C] 0.4029[/C][/ROW]
[ROW][C]81[/C][C] 0.6221[/C][C] 0.7558[/C][C] 0.3779[/C][/ROW]
[ROW][C]82[/C][C] 0.538[/C][C] 0.924[/C][C] 0.462[/C][/ROW]
[ROW][C]83[/C][C] 0.5304[/C][C] 0.9391[/C][C] 0.4696[/C][/ROW]
[ROW][C]84[/C][C] 0.4741[/C][C] 0.9482[/C][C] 0.5259[/C][/ROW]
[ROW][C]85[/C][C] 0.3906[/C][C] 0.7812[/C][C] 0.6094[/C][/ROW]
[ROW][C]86[/C][C] 0.3427[/C][C] 0.6855[/C][C] 0.6573[/C][/ROW]
[ROW][C]87[/C][C] 0.2958[/C][C] 0.5916[/C][C] 0.7042[/C][/ROW]
[ROW][C]88[/C][C] 0.3062[/C][C] 0.6124[/C][C] 0.6938[/C][/ROW]
[ROW][C]89[/C][C] 0.193[/C][C] 0.3861[/C][C] 0.807[/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
10	0.6199	0.7602	0.3801
11	0.5275	0.945	0.4725
12	0.5585	0.8831	0.4415
13	0.4704	0.9408	0.5296
14	0.4257	0.8515	0.5743
15	0.525	0.95	0.475
16	0.4192	0.8385	0.5808
17	0.3242	0.6483	0.6758
18	0.2962	0.5924	0.7038
19	0.3148	0.6295	0.6852
20	0.2442	0.4885	0.7558
21	0.2253	0.4505	0.7747
22	0.1781	0.3561	0.8219
23	0.1486	0.2972	0.8514
24	0.1156	0.2311	0.8844
25	0.1541	0.3081	0.8459
26	0.1539	0.3079	0.8461
27	0.9578	0.08431	0.04216
28	0.9615	0.07698	0.03849
29	0.9465	0.107	0.05352
30	0.9288	0.1424	0.07122
31	0.9244	0.1512	0.07558
32	0.9354	0.1292	0.06458
33	0.9152	0.1695	0.08476
34	0.929	0.142	0.07099
35	0.9112	0.1776	0.08881
36	0.9778	0.04437	0.02218
37	0.974	0.05208	0.02604
38	0.9656	0.06873	0.03436
39	0.9746	0.05083	0.02541
40	0.9756	0.0488	0.0244
41	0.9664	0.06722	0.03361
42	0.96	0.08005	0.04002
43	0.9465	0.1069	0.05346
44	0.9767	0.04651	0.02325
45	0.9675	0.06494	0.03247
46	0.9585	0.08292	0.04146
47	0.9574	0.08523	0.04261
48	0.9476	0.1049	0.05244
49	0.962	0.07605	0.03802
50	0.9543	0.09138	0.04569
51	0.9433	0.1133	0.05667
52	0.9334	0.1332	0.06659
53	0.9716	0.0569	0.02845
54	0.9649	0.0703	0.03515
55	0.9547	0.0906	0.0453
56	0.9391	0.1218	0.06089
57	0.9457	0.1086	0.0543
58	0.9674	0.06528	0.03264
59	0.9552	0.08966	0.04483
60	0.961	0.07792	0.03896
61	0.9486	0.1027	0.05137
62	0.9299	0.1402	0.07012
63	0.9149	0.1702	0.08508
64	0.916	0.1679	0.08396
65	0.8941	0.2118	0.1059
66	0.8609	0.2782	0.1391
67	0.8637	0.2726	0.1363
68	0.8339	0.3322	0.1661
69	0.8089	0.3822	0.1911
70	0.7779	0.4441	0.2221
71	0.7602	0.4796	0.2398
72	0.703	0.594	0.297
73	0.7392	0.5215	0.2607
74	0.698	0.6041	0.302
75	0.6396	0.7207	0.3604
76	0.7121	0.5758	0.2879
77	0.7418	0.5164	0.2582
78	0.6942	0.6117	0.3059
79	0.6164	0.7672	0.3836
80	0.5971	0.8058	0.4029
81	0.6221	0.7558	0.3779
82	0.538	0.924	0.462
83	0.5304	0.9391	0.4696
84	0.4741	0.9482	0.5259
85	0.3906	0.7812	0.6094
86	0.3427	0.6855	0.6573
87	0.2958	0.5916	0.7042
88	0.3062	0.6124	0.6938
89	0.193	0.3861	0.807

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	3	0.0375	OK
10% type I error level	21	0.2625	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 & 3 & 0.0375 & OK \tabularnewline
10% type I error level & 21 & 0.2625 & 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]3[/C][C]0.0375[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.2625[/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	3	0.0375	OK
10% type I error level	21	0.2625	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.2495, df1 = 2, df2 = 90, p-value = 0.2916
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0811, df1 = 12, df2 = 80, p-value = 0.3872
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1524, df1 = 2, df2 = 90, p-value = 0.3205

\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 = 1.2495, df1 = 2, df2 = 90, p-value = 0.2916
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0811, df1 = 12, df2 = 80, p-value = 0.3872
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1524, df1 = 2, df2 = 90, p-value = 0.3205
 \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 = 1.2495, df1 = 2, df2 = 90, p-value = 0.2916
[/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 = 1.0811, df1 = 12, df2 = 80, p-value = 0.3872
[/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 = 1.1524, df1 = 2, df2 = 90, p-value = 0.3205
[/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 = 1.2495, df1 = 2, df2 = 90, p-value = 0.2916
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0811, df1 = 12, df2 = 80, p-value = 0.3872
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1524, df1 = 2, df2 = 90, p-value = 0.3205

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM SKEOU1 SKEOU2 1.038339 1.535778 3.307318 1.632322 2.107571 SKEOU3 1.621058

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM        SKEOU1        SKEOU2 
     1.038339      1.535778      3.307318      1.632322      2.107571 
       SKEOU3 
     1.621058 
 \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        SKEOU2 
     1.038339      1.535778      3.307318      1.632322      2.107571 
       SKEOU3 
     1.621058 
[/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 SKEOU2 1.038339 1.535778 3.307318 1.632322 2.107571 SKEOU3 1.621058

Figure 1

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Figure 3

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 128811211 ; par2 = Double00Do 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 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