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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 09:32:58 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/23/t1485160389ztgm1f29bi4k8ak.htm/, Retrieved Wed, 15 May 2024 10:52:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=303905, Retrieved Wed, 15 May 2024 10:52:48 +0000

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

-       [Multiple Regression] [] [2017-01-23 08:32:58] [863feeaf19a0ddfce7bd9c25059c4d8a] [Current]

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22 13 22 4 2 4
24 16 24 5 3 3
21 17 26 4 4 5
21 NA 21 3 4 3
24 NA 26 4 4 5
20 16 25 3 4 4
22 NA 21 3 4 4
20 NA 24 3 4 5
19 NA 27 4 5 4
23 17 28 4 5 5
21 17 23 4 4 2
19 15 25 4 4 5
19 16 24 4 4 4
21 14 24 3 3 5
21 16 24 4 4 5
22 17 25 3 4 5
22 NA 25 3 4 5
19 NA NA NA NA 5
21 NA 25 5 5 4
21 NA 25 4 4 4
21 16 24 3 4 5
20 NA 26 4 4 4
22 16 26 4 4 5
22 NA 25 4 4 5
24 NA 26 4 4 5
21 NA 23 3 4 4
19 16 24 3 4 4
19 15 24 4 4 4
23 16 25 2 4 5
21 16 25 5 4 4
21 13 24 4 3 5
19 15 28 4 5 5
21 17 27 5 4 5
19 NA NA 4 3 5
21 13 23 2 3 5
21 17 23 4 5 2
23 NA 24 3 4 5
19 14 24 4 3 5
19 14 22 4 3 3
19 18 25 4 4 5
18 NA 25 5 4 4
22 17 28 4 5 5
18 13 22 3 3 4
22 16 28 5 5 5
18 15 25 5 4 5
22 15 24 4 4 4
22 NA 24 4 4 4
19 15 23 3 5 5
22 13 25 4 4 4
25 NA NA 2 3 4
19 17 26 4 5 5
19 NA 25 5 5 2
19 NA 27 5 5 5
19 11 26 4 3 5
21 14 23 4 3 4
21 13 25 4 4 5
20 NA 21 3 4 4
19 17 22 3 4 4
19 16 24 4 4 4
22 NA 25 4 4 4
26 17 27 5 5 3
19 16 24 2 4 4
21 16 26 4 4 4
21 16 21 3 4 4
20 15 27 4 4 5
23 12 22 4 2 4
22 17 23 4 4 4
22 14 24 4 4 4
22 14 25 5 4 5
21 16 24 3 4 4
21 NA 23 3 4 4
22 NA 28 4 5 5
23 NA NA 4 4 3
18 NA 24 4 4 4
24 NA 26 4 4 4
22 15 22 3 4 3
21 16 25 4 4 4
21 14 25 3 4 5
21 15 24 3 3 5
23 17 24 4 3 5
21 NA 26 4 4 5
23 10 21 3 3 3
21 NA 25 4 4 4
19 17 25 4 4 3
21 NA 26 4 4 4
21 20 25 5 4 4
21 17 26 5 4 3
23 18 27 4 4 5
23 NA 25 3 4 5
20 17 NA 3 NA 4
20 14 20 4 2 3
19 NA 24 4 4 5
23 17 26 4 4 5
22 NA 25 4 4 4
19 17 25 4 5 4
23 NA 24 3 4 4
22 16 26 4 4 5
22 18 25 5 4 3
21 18 28 5 4 5
21 16 27 4 5 4
21 NA 25 3 4 5
21 NA 26 5 3 4
22 15 26 4 4 5
25 13 26 5 4 4
21 NA NA 3 4 4
23 NA 28 5 4 4
19 NA NA 4 4 5
22 NA 21 4 4 3
20 NA 25 4 4 5
21 16 25 4 4 5
25 NA 24 3 4 5
21 NA 24 4 4 4
19 NA 24 4 4 4
23 12 23 3 3 4
22 NA 23 4 4 4
21 16 24 3 4 5
24 16 24 4 4 5
21 NA 25 5 4 5
19 16 28 5 4 5
18 14 23 4 4 4
19 15 24 4 4 5
20 14 23 3 4 4
19 NA 24 4 4 4
22 15 25 4 4 4
21 NA 24 4 5 3
22 15 23 3 4 4
24 16 23 4 4 4
28 NA 25 4 4 4
19 NA 21 3 4 3
18 NA 22 4 4 4
23 11 19 3 2 4
19 NA 24 4 4 4
23 18 25 5 4 4
19 NA 21 2 4 4
22 11 22 3 3 4
21 NA 23 4 4 4
19 18 27 5 5 4
22 NA NA NA NA 2
21 15 26 4 5 5
23 19 29 5 5 5
22 17 28 4 5 5
19 NA 24 4 4 4
19 14 25 3 4 5
21 NA 25 4 4 5
22 13 22 4 4 2
21 17 25 4 4 3
20 14 26 4 4 4
23 19 26 5 4 5
22 14 24 4 3 5
23 NA 25 4 4 5
22 NA 19 3 3 2
21 16 25 4 5 5
20 16 23 4 4 4
18 15 25 4 4 4
18 12 25 3 4 5
20 NA 26 4 4 5
19 17 27 5 4 5
21 NA 24 4 4 5
24 NA 22 2 3 5
19 18 25 4 4 4
20 15 24 4 3 4
19 18 23 4 4 4
23 15 27 4 5 5
22 NA 24 5 4 3
21 NA 24 5 4 4
24 NA 21 3 3 1
21 16 25 4 4 4
21 NA 25 4 4 4
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	7 seconds
R Server	Big Analytics Cloud Computing Center

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&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] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=303905&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&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

Multiple Linear Regression - Estimated Regression Equation
Bevr_Leeftijd[t] = + 18.871 -0.000499896TVDC[t] + 0.184702SKEOUSUM[t] + 0.146966SKEOU1[t] -0.44122SKEOU2[t] -0.298817SKEOU3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bevr_Leeftijd[t] =  +  18.871 -0.000499896TVDC[t] +  0.184702SKEOUSUM[t] +  0.146966SKEOU1[t] -0.44122SKEOU2[t] -0.298817SKEOU3[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bevr_Leeftijd[t] =  +  18.871 -0.000499896TVDC[t] +  0.184702SKEOUSUM[t] +  0.146966SKEOU1[t] -0.44122SKEOU2[t] -0.298817SKEOU3[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&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
Bevr_Leeftijd[t] = + 18.871 -0.000499896TVDC[t] + 0.184702SKEOUSUM[t] + 0.146966SKEOU1[t] -0.44122SKEOU2[t] -0.298817SKEOU3[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)	+18.87	2.464	+7.6590e+00	1.489e-11	7.446e-12
TVDC	-0.0004999	0.1124	-4.4470e-03	0.9965	0.4982
SKEOUSUM	+0.1847	0.1643	+1.1240e+00	0.2638	0.1319
SKEOU1	+0.147	0.2921	+5.0310e-01	0.6161	0.308
SKEOU2	-0.4412	0.3549	-1.2430e+00	0.2169	0.1084
SKEOU3	-0.2988	0.2767	-1.0800e+00	0.283	0.1415

\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) & +18.87 &  2.464 & +7.6590e+00 &  1.489e-11 &  7.446e-12 \tabularnewline
TVDC & -0.0004999 &  0.1124 & -4.4470e-03 &  0.9965 &  0.4982 \tabularnewline
SKEOUSUM & +0.1847 &  0.1643 & +1.1240e+00 &  0.2638 &  0.1319 \tabularnewline
SKEOU1 & +0.147 &  0.2921 & +5.0310e-01 &  0.6161 &  0.308 \tabularnewline
SKEOU2 & -0.4412 &  0.3549 & -1.2430e+00 &  0.2169 &  0.1084 \tabularnewline
SKEOU3 & -0.2988 &  0.2767 & -1.0800e+00 &  0.283 &  0.1415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&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]+18.87[/C][C] 2.464[/C][C]+7.6590e+00[/C][C] 1.489e-11[/C][C] 7.446e-12[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.0004999[/C][C] 0.1124[/C][C]-4.4470e-03[/C][C] 0.9965[/C][C] 0.4982[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.1847[/C][C] 0.1643[/C][C]+1.1240e+00[/C][C] 0.2638[/C][C] 0.1319[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.147[/C][C] 0.2921[/C][C]+5.0310e-01[/C][C] 0.6161[/C][C] 0.308[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.4412[/C][C] 0.3549[/C][C]-1.2430e+00[/C][C] 0.2169[/C][C] 0.1084[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.2988[/C][C] 0.2767[/C][C]-1.0800e+00[/C][C] 0.283[/C][C] 0.1415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&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)	+18.87	2.464	+7.6590e+00	1.489e-11	7.446e-12
TVDC	-0.0004999	0.1124	-4.4470e-03	0.9965	0.4982
SKEOUSUM	+0.1847	0.1643	+1.1240e+00	0.2638	0.1319
SKEOU1	+0.147	0.2921	+5.0310e-01	0.6161	0.308
SKEOU2	-0.4412	0.3549	-1.2430e+00	0.2169	0.1084
SKEOU3	-0.2988	0.2767	-1.0800e+00	0.283	0.1415

Multiple Linear Regression - Regression Statistics
Multiple R	0.1972
R-squared	0.03889
Adjusted R-squared	-0.01117
F-TEST (value)	0.7769
F-TEST (DF numerator)	5
F-TEST (DF denominator)	96
p-value	0.5688
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.689
Sum Squared Residuals	273.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1972 \tabularnewline
R-squared &  0.03889 \tabularnewline
Adjusted R-squared & -0.01117 \tabularnewline
F-TEST (value) &  0.7769 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value &  0.5688 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.689 \tabularnewline
Sum Squared Residuals &  273.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1972[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.03889[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01117[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.7769[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C] 0.5688[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.689[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 273.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&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	0.1972
R-squared	0.03889
Adjusted R-squared	-0.01117
F-TEST (value)	0.7769
F-TEST (DF numerator)	5
F-TEST (DF denominator)	96
p-value	0.5688
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.689
Sum Squared Residuals	273.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	21.44	0.5619
2	24	21.81	2.189
3	21	20.99	0.00631
4	20	20.96	-0.9613
5	23	20.92	2.078
6	21	21.34	-0.336
7	19	20.81	-1.81
8	19	20.92	-1.924
9	21	20.92	0.07996
10	21	20.62	0.3752
11	22	20.66	1.338
12	21	20.48	0.5222
13	22	20.99	1.006
14	19	20.78	-1.777
15	19	20.92	-1.924
16	23	20.52	2.484
17	21	21.26	-0.2553
18	21	21.07	-0.06751
19	19	20.92	-1.923
20	21	21.33	-0.3254
21	21	20.59	0.4111
22	21	20.89	0.1052
23	19	21.07	-2.067
24	19	21.3	-2.295
25	19	20.81	-1.808
26	22	20.92	1.078
27	18	20.85	-2.85
28	22	21.07	0.9307
29	18	20.96	-2.957
30	22	20.92	1.076
31	19	19.85	-0.8524
32	22	21.11	0.8902
33	19	20.55	-1.552
34	19	21.44	-2.438
35	21	21.18	-0.1811
36	21	20.81	0.189
37	19	20.41	-1.407
38	19	20.92	-1.924
39	26	21.48	4.518
40	19	20.63	-1.63
41	21	21.29	-0.293
42	21	20.22	0.7775
43	20	21.18	-1.179
44	23	21.44	1.561
45	22	20.74	1.262
46	22	20.92	1.075
47	22	20.96	1.043
48	21	20.78	0.2234
49	22	20.71	1.293
50	21	21.11	-0.1083
51	21	20.66	0.3365
52	21	20.92	0.08046
53	23	21.07	1.934
54	23	20.97	2.034
55	19	21.41	-2.407
56	21	21.25	-0.2533
57	21	21.74	-0.7383
58	23	21.18	1.822
59	20	21.37	-1.367
60	23	20.99	2.006
61	19	20.67	-1.667
62	22	20.99	1.006
63	22	21.55	0.4469
64	21	21.51	-0.5096
65	21	21.04	-0.03649
66	22	20.99	1.005
67	25	21.44	3.559
68	21	20.81	0.1905
69	23	21.04	1.965
70	21	20.48	0.5222
71	24	20.62	3.375
72	19	21.51	-2.511
73	18	20.74	-2.74
74	19	20.63	-1.625
75	20	20.59	-0.5929
76	22	21.11	0.8912
77	22	20.59	1.408
78	24	20.74	3.261
79	23	20.74	2.262
80	23	21.25	1.746
81	22	20.85	1.149
82	19	21.18	-2.182
83	21	20.55	0.4465
84	23	21.25	1.747
85	22	20.92	1.078
86	19	20.66	-1.664
87	22	21.15	0.8467
88	21	21.41	-0.4066
89	20	21.29	-1.294
90	23	21.14	1.86
91	22	21.07	0.933
92	21	20.37	0.6317
93	20	20.74	-0.7389
94	18	21.11	-3.109
95	18	20.66	-2.665
96	19	21.33	-2.325
97	19	21.11	-2.107
98	20	21.37	-1.365
99	19	20.74	-1.738
100	23	20.74	2.262
101	21	21.11	-0.1083
102	22	20.89	1.114

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  22 &  21.44 &  0.5619 \tabularnewline
2 &  24 &  21.81 &  2.189 \tabularnewline
3 &  21 &  20.99 &  0.00631 \tabularnewline
4 &  20 &  20.96 & -0.9613 \tabularnewline
5 &  23 &  20.92 &  2.078 \tabularnewline
6 &  21 &  21.34 & -0.336 \tabularnewline
7 &  19 &  20.81 & -1.81 \tabularnewline
8 &  19 &  20.92 & -1.924 \tabularnewline
9 &  21 &  20.92 &  0.07996 \tabularnewline
10 &  21 &  20.62 &  0.3752 \tabularnewline
11 &  22 &  20.66 &  1.338 \tabularnewline
12 &  21 &  20.48 &  0.5222 \tabularnewline
13 &  22 &  20.99 &  1.006 \tabularnewline
14 &  19 &  20.78 & -1.777 \tabularnewline
15 &  19 &  20.92 & -1.924 \tabularnewline
16 &  23 &  20.52 &  2.484 \tabularnewline
17 &  21 &  21.26 & -0.2553 \tabularnewline
18 &  21 &  21.07 & -0.06751 \tabularnewline
19 &  19 &  20.92 & -1.923 \tabularnewline
20 &  21 &  21.33 & -0.3254 \tabularnewline
21 &  21 &  20.59 &  0.4111 \tabularnewline
22 &  21 &  20.89 &  0.1052 \tabularnewline
23 &  19 &  21.07 & -2.067 \tabularnewline
24 &  19 &  21.3 & -2.295 \tabularnewline
25 &  19 &  20.81 & -1.808 \tabularnewline
26 &  22 &  20.92 &  1.078 \tabularnewline
27 &  18 &  20.85 & -2.85 \tabularnewline
28 &  22 &  21.07 &  0.9307 \tabularnewline
29 &  18 &  20.96 & -2.957 \tabularnewline
30 &  22 &  20.92 &  1.076 \tabularnewline
31 &  19 &  19.85 & -0.8524 \tabularnewline
32 &  22 &  21.11 &  0.8902 \tabularnewline
33 &  19 &  20.55 & -1.552 \tabularnewline
34 &  19 &  21.44 & -2.438 \tabularnewline
35 &  21 &  21.18 & -0.1811 \tabularnewline
36 &  21 &  20.81 &  0.189 \tabularnewline
37 &  19 &  20.41 & -1.407 \tabularnewline
38 &  19 &  20.92 & -1.924 \tabularnewline
39 &  26 &  21.48 &  4.518 \tabularnewline
40 &  19 &  20.63 & -1.63 \tabularnewline
41 &  21 &  21.29 & -0.293 \tabularnewline
42 &  21 &  20.22 &  0.7775 \tabularnewline
43 &  20 &  21.18 & -1.179 \tabularnewline
44 &  23 &  21.44 &  1.561 \tabularnewline
45 &  22 &  20.74 &  1.262 \tabularnewline
46 &  22 &  20.92 &  1.075 \tabularnewline
47 &  22 &  20.96 &  1.043 \tabularnewline
48 &  21 &  20.78 &  0.2234 \tabularnewline
49 &  22 &  20.71 &  1.293 \tabularnewline
50 &  21 &  21.11 & -0.1083 \tabularnewline
51 &  21 &  20.66 &  0.3365 \tabularnewline
52 &  21 &  20.92 &  0.08046 \tabularnewline
53 &  23 &  21.07 &  1.934 \tabularnewline
54 &  23 &  20.97 &  2.034 \tabularnewline
55 &  19 &  21.41 & -2.407 \tabularnewline
56 &  21 &  21.25 & -0.2533 \tabularnewline
57 &  21 &  21.74 & -0.7383 \tabularnewline
58 &  23 &  21.18 &  1.822 \tabularnewline
59 &  20 &  21.37 & -1.367 \tabularnewline
60 &  23 &  20.99 &  2.006 \tabularnewline
61 &  19 &  20.67 & -1.667 \tabularnewline
62 &  22 &  20.99 &  1.006 \tabularnewline
63 &  22 &  21.55 &  0.4469 \tabularnewline
64 &  21 &  21.51 & -0.5096 \tabularnewline
65 &  21 &  21.04 & -0.03649 \tabularnewline
66 &  22 &  20.99 &  1.005 \tabularnewline
67 &  25 &  21.44 &  3.559 \tabularnewline
68 &  21 &  20.81 &  0.1905 \tabularnewline
69 &  23 &  21.04 &  1.965 \tabularnewline
70 &  21 &  20.48 &  0.5222 \tabularnewline
71 &  24 &  20.62 &  3.375 \tabularnewline
72 &  19 &  21.51 & -2.511 \tabularnewline
73 &  18 &  20.74 & -2.74 \tabularnewline
74 &  19 &  20.63 & -1.625 \tabularnewline
75 &  20 &  20.59 & -0.5929 \tabularnewline
76 &  22 &  21.11 &  0.8912 \tabularnewline
77 &  22 &  20.59 &  1.408 \tabularnewline
78 &  24 &  20.74 &  3.261 \tabularnewline
79 &  23 &  20.74 &  2.262 \tabularnewline
80 &  23 &  21.25 &  1.746 \tabularnewline
81 &  22 &  20.85 &  1.149 \tabularnewline
82 &  19 &  21.18 & -2.182 \tabularnewline
83 &  21 &  20.55 &  0.4465 \tabularnewline
84 &  23 &  21.25 &  1.747 \tabularnewline
85 &  22 &  20.92 &  1.078 \tabularnewline
86 &  19 &  20.66 & -1.664 \tabularnewline
87 &  22 &  21.15 &  0.8467 \tabularnewline
88 &  21 &  21.41 & -0.4066 \tabularnewline
89 &  20 &  21.29 & -1.294 \tabularnewline
90 &  23 &  21.14 &  1.86 \tabularnewline
91 &  22 &  21.07 &  0.933 \tabularnewline
92 &  21 &  20.37 &  0.6317 \tabularnewline
93 &  20 &  20.74 & -0.7389 \tabularnewline
94 &  18 &  21.11 & -3.109 \tabularnewline
95 &  18 &  20.66 & -2.665 \tabularnewline
96 &  19 &  21.33 & -2.325 \tabularnewline
97 &  19 &  21.11 & -2.107 \tabularnewline
98 &  20 &  21.37 & -1.365 \tabularnewline
99 &  19 &  20.74 & -1.738 \tabularnewline
100 &  23 &  20.74 &  2.262 \tabularnewline
101 &  21 &  21.11 & -0.1083 \tabularnewline
102 &  22 &  20.89 &  1.114 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&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] 22[/C][C] 21.44[/C][C] 0.5619[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 21.81[/C][C] 2.189[/C][/ROW]
[ROW][C]3[/C][C] 21[/C][C] 20.99[/C][C] 0.00631[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 20.96[/C][C]-0.9613[/C][/ROW]
[ROW][C]5[/C][C] 23[/C][C] 20.92[/C][C] 2.078[/C][/ROW]
[ROW][C]6[/C][C] 21[/C][C] 21.34[/C][C]-0.336[/C][/ROW]
[ROW][C]7[/C][C] 19[/C][C] 20.81[/C][C]-1.81[/C][/ROW]
[ROW][C]8[/C][C] 19[/C][C] 20.92[/C][C]-1.924[/C][/ROW]
[ROW][C]9[/C][C] 21[/C][C] 20.92[/C][C] 0.07996[/C][/ROW]
[ROW][C]10[/C][C] 21[/C][C] 20.62[/C][C] 0.3752[/C][/ROW]
[ROW][C]11[/C][C] 22[/C][C] 20.66[/C][C] 1.338[/C][/ROW]
[ROW][C]12[/C][C] 21[/C][C] 20.48[/C][C] 0.5222[/C][/ROW]
[ROW][C]13[/C][C] 22[/C][C] 20.99[/C][C] 1.006[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 20.78[/C][C]-1.777[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 20.92[/C][C]-1.924[/C][/ROW]
[ROW][C]16[/C][C] 23[/C][C] 20.52[/C][C] 2.484[/C][/ROW]
[ROW][C]17[/C][C] 21[/C][C] 21.26[/C][C]-0.2553[/C][/ROW]
[ROW][C]18[/C][C] 21[/C][C] 21.07[/C][C]-0.06751[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 20.92[/C][C]-1.923[/C][/ROW]
[ROW][C]20[/C][C] 21[/C][C] 21.33[/C][C]-0.3254[/C][/ROW]
[ROW][C]21[/C][C] 21[/C][C] 20.59[/C][C] 0.4111[/C][/ROW]
[ROW][C]22[/C][C] 21[/C][C] 20.89[/C][C] 0.1052[/C][/ROW]
[ROW][C]23[/C][C] 19[/C][C] 21.07[/C][C]-2.067[/C][/ROW]
[ROW][C]24[/C][C] 19[/C][C] 21.3[/C][C]-2.295[/C][/ROW]
[ROW][C]25[/C][C] 19[/C][C] 20.81[/C][C]-1.808[/C][/ROW]
[ROW][C]26[/C][C] 22[/C][C] 20.92[/C][C] 1.078[/C][/ROW]
[ROW][C]27[/C][C] 18[/C][C] 20.85[/C][C]-2.85[/C][/ROW]
[ROW][C]28[/C][C] 22[/C][C] 21.07[/C][C] 0.9307[/C][/ROW]
[ROW][C]29[/C][C] 18[/C][C] 20.96[/C][C]-2.957[/C][/ROW]
[ROW][C]30[/C][C] 22[/C][C] 20.92[/C][C] 1.076[/C][/ROW]
[ROW][C]31[/C][C] 19[/C][C] 19.85[/C][C]-0.8524[/C][/ROW]
[ROW][C]32[/C][C] 22[/C][C] 21.11[/C][C] 0.8902[/C][/ROW]
[ROW][C]33[/C][C] 19[/C][C] 20.55[/C][C]-1.552[/C][/ROW]
[ROW][C]34[/C][C] 19[/C][C] 21.44[/C][C]-2.438[/C][/ROW]
[ROW][C]35[/C][C] 21[/C][C] 21.18[/C][C]-0.1811[/C][/ROW]
[ROW][C]36[/C][C] 21[/C][C] 20.81[/C][C] 0.189[/C][/ROW]
[ROW][C]37[/C][C] 19[/C][C] 20.41[/C][C]-1.407[/C][/ROW]
[ROW][C]38[/C][C] 19[/C][C] 20.92[/C][C]-1.924[/C][/ROW]
[ROW][C]39[/C][C] 26[/C][C] 21.48[/C][C] 4.518[/C][/ROW]
[ROW][C]40[/C][C] 19[/C][C] 20.63[/C][C]-1.63[/C][/ROW]
[ROW][C]41[/C][C] 21[/C][C] 21.29[/C][C]-0.293[/C][/ROW]
[ROW][C]42[/C][C] 21[/C][C] 20.22[/C][C] 0.7775[/C][/ROW]
[ROW][C]43[/C][C] 20[/C][C] 21.18[/C][C]-1.179[/C][/ROW]
[ROW][C]44[/C][C] 23[/C][C] 21.44[/C][C] 1.561[/C][/ROW]
[ROW][C]45[/C][C] 22[/C][C] 20.74[/C][C] 1.262[/C][/ROW]
[ROW][C]46[/C][C] 22[/C][C] 20.92[/C][C] 1.075[/C][/ROW]
[ROW][C]47[/C][C] 22[/C][C] 20.96[/C][C] 1.043[/C][/ROW]
[ROW][C]48[/C][C] 21[/C][C] 20.78[/C][C] 0.2234[/C][/ROW]
[ROW][C]49[/C][C] 22[/C][C] 20.71[/C][C] 1.293[/C][/ROW]
[ROW][C]50[/C][C] 21[/C][C] 21.11[/C][C]-0.1083[/C][/ROW]
[ROW][C]51[/C][C] 21[/C][C] 20.66[/C][C] 0.3365[/C][/ROW]
[ROW][C]52[/C][C] 21[/C][C] 20.92[/C][C] 0.08046[/C][/ROW]
[ROW][C]53[/C][C] 23[/C][C] 21.07[/C][C] 1.934[/C][/ROW]
[ROW][C]54[/C][C] 23[/C][C] 20.97[/C][C] 2.034[/C][/ROW]
[ROW][C]55[/C][C] 19[/C][C] 21.41[/C][C]-2.407[/C][/ROW]
[ROW][C]56[/C][C] 21[/C][C] 21.25[/C][C]-0.2533[/C][/ROW]
[ROW][C]57[/C][C] 21[/C][C] 21.74[/C][C]-0.7383[/C][/ROW]
[ROW][C]58[/C][C] 23[/C][C] 21.18[/C][C] 1.822[/C][/ROW]
[ROW][C]59[/C][C] 20[/C][C] 21.37[/C][C]-1.367[/C][/ROW]
[ROW][C]60[/C][C] 23[/C][C] 20.99[/C][C] 2.006[/C][/ROW]
[ROW][C]61[/C][C] 19[/C][C] 20.67[/C][C]-1.667[/C][/ROW]
[ROW][C]62[/C][C] 22[/C][C] 20.99[/C][C] 1.006[/C][/ROW]
[ROW][C]63[/C][C] 22[/C][C] 21.55[/C][C] 0.4469[/C][/ROW]
[ROW][C]64[/C][C] 21[/C][C] 21.51[/C][C]-0.5096[/C][/ROW]
[ROW][C]65[/C][C] 21[/C][C] 21.04[/C][C]-0.03649[/C][/ROW]
[ROW][C]66[/C][C] 22[/C][C] 20.99[/C][C] 1.005[/C][/ROW]
[ROW][C]67[/C][C] 25[/C][C] 21.44[/C][C] 3.559[/C][/ROW]
[ROW][C]68[/C][C] 21[/C][C] 20.81[/C][C] 0.1905[/C][/ROW]
[ROW][C]69[/C][C] 23[/C][C] 21.04[/C][C] 1.965[/C][/ROW]
[ROW][C]70[/C][C] 21[/C][C] 20.48[/C][C] 0.5222[/C][/ROW]
[ROW][C]71[/C][C] 24[/C][C] 20.62[/C][C] 3.375[/C][/ROW]
[ROW][C]72[/C][C] 19[/C][C] 21.51[/C][C]-2.511[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 20.74[/C][C]-2.74[/C][/ROW]
[ROW][C]74[/C][C] 19[/C][C] 20.63[/C][C]-1.625[/C][/ROW]
[ROW][C]75[/C][C] 20[/C][C] 20.59[/C][C]-0.5929[/C][/ROW]
[ROW][C]76[/C][C] 22[/C][C] 21.11[/C][C] 0.8912[/C][/ROW]
[ROW][C]77[/C][C] 22[/C][C] 20.59[/C][C] 1.408[/C][/ROW]
[ROW][C]78[/C][C] 24[/C][C] 20.74[/C][C] 3.261[/C][/ROW]
[ROW][C]79[/C][C] 23[/C][C] 20.74[/C][C] 2.262[/C][/ROW]
[ROW][C]80[/C][C] 23[/C][C] 21.25[/C][C] 1.746[/C][/ROW]
[ROW][C]81[/C][C] 22[/C][C] 20.85[/C][C] 1.149[/C][/ROW]
[ROW][C]82[/C][C] 19[/C][C] 21.18[/C][C]-2.182[/C][/ROW]
[ROW][C]83[/C][C] 21[/C][C] 20.55[/C][C] 0.4465[/C][/ROW]
[ROW][C]84[/C][C] 23[/C][C] 21.25[/C][C] 1.747[/C][/ROW]
[ROW][C]85[/C][C] 22[/C][C] 20.92[/C][C] 1.078[/C][/ROW]
[ROW][C]86[/C][C] 19[/C][C] 20.66[/C][C]-1.664[/C][/ROW]
[ROW][C]87[/C][C] 22[/C][C] 21.15[/C][C] 0.8467[/C][/ROW]
[ROW][C]88[/C][C] 21[/C][C] 21.41[/C][C]-0.4066[/C][/ROW]
[ROW][C]89[/C][C] 20[/C][C] 21.29[/C][C]-1.294[/C][/ROW]
[ROW][C]90[/C][C] 23[/C][C] 21.14[/C][C] 1.86[/C][/ROW]
[ROW][C]91[/C][C] 22[/C][C] 21.07[/C][C] 0.933[/C][/ROW]
[ROW][C]92[/C][C] 21[/C][C] 20.37[/C][C] 0.6317[/C][/ROW]
[ROW][C]93[/C][C] 20[/C][C] 20.74[/C][C]-0.7389[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 21.11[/C][C]-3.109[/C][/ROW]
[ROW][C]95[/C][C] 18[/C][C] 20.66[/C][C]-2.665[/C][/ROW]
[ROW][C]96[/C][C] 19[/C][C] 21.33[/C][C]-2.325[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 21.11[/C][C]-2.107[/C][/ROW]
[ROW][C]98[/C][C] 20[/C][C] 21.37[/C][C]-1.365[/C][/ROW]
[ROW][C]99[/C][C] 19[/C][C] 20.74[/C][C]-1.738[/C][/ROW]
[ROW][C]100[/C][C] 23[/C][C] 20.74[/C][C] 2.262[/C][/ROW]
[ROW][C]101[/C][C] 21[/C][C] 21.11[/C][C]-0.1083[/C][/ROW]
[ROW][C]102[/C][C] 22[/C][C] 20.89[/C][C] 1.114[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&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	22	21.44	0.5619
2	24	21.81	2.189
3	21	20.99	0.00631
4	20	20.96	-0.9613
5	23	20.92	2.078
6	21	21.34	-0.336
7	19	20.81	-1.81
8	19	20.92	-1.924
9	21	20.92	0.07996
10	21	20.62	0.3752
11	22	20.66	1.338
12	21	20.48	0.5222
13	22	20.99	1.006
14	19	20.78	-1.777
15	19	20.92	-1.924
16	23	20.52	2.484
17	21	21.26	-0.2553
18	21	21.07	-0.06751
19	19	20.92	-1.923
20	21	21.33	-0.3254
21	21	20.59	0.4111
22	21	20.89	0.1052
23	19	21.07	-2.067
24	19	21.3	-2.295
25	19	20.81	-1.808
26	22	20.92	1.078
27	18	20.85	-2.85
28	22	21.07	0.9307
29	18	20.96	-2.957
30	22	20.92	1.076
31	19	19.85	-0.8524
32	22	21.11	0.8902
33	19	20.55	-1.552
34	19	21.44	-2.438
35	21	21.18	-0.1811
36	21	20.81	0.189
37	19	20.41	-1.407
38	19	20.92	-1.924
39	26	21.48	4.518
40	19	20.63	-1.63
41	21	21.29	-0.293
42	21	20.22	0.7775
43	20	21.18	-1.179
44	23	21.44	1.561
45	22	20.74	1.262
46	22	20.92	1.075
47	22	20.96	1.043
48	21	20.78	0.2234
49	22	20.71	1.293
50	21	21.11	-0.1083
51	21	20.66	0.3365
52	21	20.92	0.08046
53	23	21.07	1.934
54	23	20.97	2.034
55	19	21.41	-2.407
56	21	21.25	-0.2533
57	21	21.74	-0.7383
58	23	21.18	1.822
59	20	21.37	-1.367
60	23	20.99	2.006
61	19	20.67	-1.667
62	22	20.99	1.006
63	22	21.55	0.4469
64	21	21.51	-0.5096
65	21	21.04	-0.03649
66	22	20.99	1.005
67	25	21.44	3.559
68	21	20.81	0.1905
69	23	21.04	1.965
70	21	20.48	0.5222
71	24	20.62	3.375
72	19	21.51	-2.511
73	18	20.74	-2.74
74	19	20.63	-1.625
75	20	20.59	-0.5929
76	22	21.11	0.8912
77	22	20.59	1.408
78	24	20.74	3.261
79	23	20.74	2.262
80	23	21.25	1.746
81	22	20.85	1.149
82	19	21.18	-2.182
83	21	20.55	0.4465
84	23	21.25	1.747
85	22	20.92	1.078
86	19	20.66	-1.664
87	22	21.15	0.8467
88	21	21.41	-0.4066
89	20	21.29	-1.294
90	23	21.14	1.86
91	22	21.07	0.933
92	21	20.37	0.6317
93	20	20.74	-0.7389
94	18	21.11	-3.109
95	18	20.66	-2.665
96	19	21.33	-2.325
97	19	21.11	-2.107
98	20	21.37	-1.365
99	19	20.74	-1.738
100	23	20.74	2.262
101	21	21.11	-0.1083
102	22	20.89	1.114

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.1419	0.2837	0.8581
10	0.4028	0.8056	0.5972
11	0.335	0.67	0.665
12	0.2853	0.5705	0.7147
13	0.1895	0.379	0.8105
14	0.1479	0.2959	0.8521
15	0.0963	0.1926	0.9037
16	0.1572	0.3143	0.8428
17	0.1041	0.2082	0.8959
18	0.07347	0.1469	0.9265
19	0.05187	0.1037	0.9481
20	0.07358	0.1472	0.9264
21	0.04938	0.09876	0.9506
22	0.07389	0.1478	0.9261
23	0.08523	0.1705	0.9148
24	0.08508	0.1702	0.9149
25	0.1386	0.2772	0.8614
26	0.1112	0.2224	0.8888
27	0.1283	0.2565	0.8717
28	0.1211	0.2422	0.8789
29	0.133	0.266	0.867
30	0.1631	0.3263	0.8369
31	0.1574	0.3147	0.8426
32	0.1532	0.3065	0.8468
33	0.1387	0.2774	0.8613
34	0.1701	0.3402	0.8299
35	0.1388	0.2775	0.8612
36	0.1285	0.2571	0.8715
37	0.1097	0.2194	0.8903
38	0.1117	0.2233	0.8883
39	0.3709	0.7418	0.6291
40	0.3957	0.7914	0.6043
41	0.3513	0.7027	0.6487
42	0.3703	0.7405	0.6297
43	0.3488	0.6975	0.6512
44	0.3855	0.7711	0.6145
45	0.3688	0.7377	0.6312
46	0.3494	0.6988	0.6506
47	0.3302	0.6605	0.6698
48	0.2786	0.5573	0.7214
49	0.2663	0.5326	0.7337
50	0.2199	0.4397	0.7801
51	0.1825	0.365	0.8175
52	0.1482	0.2963	0.8518
53	0.1497	0.2995	0.8503
54	0.1798	0.3597	0.8202
55	0.2253	0.4506	0.7747
56	0.1837	0.3674	0.8163
57	0.1536	0.3072	0.8464
58	0.1548	0.3097	0.8451
59	0.1479	0.2958	0.8521
60	0.1576	0.3151	0.8424
61	0.1526	0.3052	0.8474
62	0.1293	0.2586	0.8707
63	0.1015	0.203	0.8985
64	0.07946	0.1589	0.9205
65	0.0598	0.1196	0.9402
66	0.04838	0.09676	0.9516
67	0.1396	0.2791	0.8604
68	0.1077	0.2155	0.8923
69	0.1242	0.2483	0.8758
70	0.09664	0.1933	0.9034
71	0.1699	0.3399	0.8301
72	0.1841	0.3683	0.8159
73	0.2634	0.5268	0.7366
74	0.2828	0.5656	0.7172
75	0.2398	0.4796	0.7602
76	0.2074	0.4148	0.7926
77	0.1783	0.3565	0.8217
78	0.2655	0.531	0.7345
79	0.2891	0.5782	0.7109
80	0.3066	0.6132	0.6934
81	0.3199	0.6398	0.6801
82	0.3773	0.7545	0.6227
83	0.3032	0.6064	0.6968
84	0.2737	0.5473	0.7263
85	0.2343	0.4686	0.7657
86	0.199	0.398	0.801
87	0.2931	0.5861	0.7069
88	0.2827	0.5655	0.7173
89	0.2366	0.4733	0.7634
90	0.1984	0.3968	0.8016
91	0.2502	0.5004	0.7498
92	0.1621	0.3243	0.8379
93	0.118	0.2361	0.882

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.1419 &  0.2837 &  0.8581 \tabularnewline
10 &  0.4028 &  0.8056 &  0.5972 \tabularnewline
11 &  0.335 &  0.67 &  0.665 \tabularnewline
12 &  0.2853 &  0.5705 &  0.7147 \tabularnewline
13 &  0.1895 &  0.379 &  0.8105 \tabularnewline
14 &  0.1479 &  0.2959 &  0.8521 \tabularnewline
15 &  0.0963 &  0.1926 &  0.9037 \tabularnewline
16 &  0.1572 &  0.3143 &  0.8428 \tabularnewline
17 &  0.1041 &  0.2082 &  0.8959 \tabularnewline
18 &  0.07347 &  0.1469 &  0.9265 \tabularnewline
19 &  0.05187 &  0.1037 &  0.9481 \tabularnewline
20 &  0.07358 &  0.1472 &  0.9264 \tabularnewline
21 &  0.04938 &  0.09876 &  0.9506 \tabularnewline
22 &  0.07389 &  0.1478 &  0.9261 \tabularnewline
23 &  0.08523 &  0.1705 &  0.9148 \tabularnewline
24 &  0.08508 &  0.1702 &  0.9149 \tabularnewline
25 &  0.1386 &  0.2772 &  0.8614 \tabularnewline
26 &  0.1112 &  0.2224 &  0.8888 \tabularnewline
27 &  0.1283 &  0.2565 &  0.8717 \tabularnewline
28 &  0.1211 &  0.2422 &  0.8789 \tabularnewline
29 &  0.133 &  0.266 &  0.867 \tabularnewline
30 &  0.1631 &  0.3263 &  0.8369 \tabularnewline
31 &  0.1574 &  0.3147 &  0.8426 \tabularnewline
32 &  0.1532 &  0.3065 &  0.8468 \tabularnewline
33 &  0.1387 &  0.2774 &  0.8613 \tabularnewline
34 &  0.1701 &  0.3402 &  0.8299 \tabularnewline
35 &  0.1388 &  0.2775 &  0.8612 \tabularnewline
36 &  0.1285 &  0.2571 &  0.8715 \tabularnewline
37 &  0.1097 &  0.2194 &  0.8903 \tabularnewline
38 &  0.1117 &  0.2233 &  0.8883 \tabularnewline
39 &  0.3709 &  0.7418 &  0.6291 \tabularnewline
40 &  0.3957 &  0.7914 &  0.6043 \tabularnewline
41 &  0.3513 &  0.7027 &  0.6487 \tabularnewline
42 &  0.3703 &  0.7405 &  0.6297 \tabularnewline
43 &  0.3488 &  0.6975 &  0.6512 \tabularnewline
44 &  0.3855 &  0.7711 &  0.6145 \tabularnewline
45 &  0.3688 &  0.7377 &  0.6312 \tabularnewline
46 &  0.3494 &  0.6988 &  0.6506 \tabularnewline
47 &  0.3302 &  0.6605 &  0.6698 \tabularnewline
48 &  0.2786 &  0.5573 &  0.7214 \tabularnewline
49 &  0.2663 &  0.5326 &  0.7337 \tabularnewline
50 &  0.2199 &  0.4397 &  0.7801 \tabularnewline
51 &  0.1825 &  0.365 &  0.8175 \tabularnewline
52 &  0.1482 &  0.2963 &  0.8518 \tabularnewline
53 &  0.1497 &  0.2995 &  0.8503 \tabularnewline
54 &  0.1798 &  0.3597 &  0.8202 \tabularnewline
55 &  0.2253 &  0.4506 &  0.7747 \tabularnewline
56 &  0.1837 &  0.3674 &  0.8163 \tabularnewline
57 &  0.1536 &  0.3072 &  0.8464 \tabularnewline
58 &  0.1548 &  0.3097 &  0.8451 \tabularnewline
59 &  0.1479 &  0.2958 &  0.8521 \tabularnewline
60 &  0.1576 &  0.3151 &  0.8424 \tabularnewline
61 &  0.1526 &  0.3052 &  0.8474 \tabularnewline
62 &  0.1293 &  0.2586 &  0.8707 \tabularnewline
63 &  0.1015 &  0.203 &  0.8985 \tabularnewline
64 &  0.07946 &  0.1589 &  0.9205 \tabularnewline
65 &  0.0598 &  0.1196 &  0.9402 \tabularnewline
66 &  0.04838 &  0.09676 &  0.9516 \tabularnewline
67 &  0.1396 &  0.2791 &  0.8604 \tabularnewline
68 &  0.1077 &  0.2155 &  0.8923 \tabularnewline
69 &  0.1242 &  0.2483 &  0.8758 \tabularnewline
70 &  0.09664 &  0.1933 &  0.9034 \tabularnewline
71 &  0.1699 &  0.3399 &  0.8301 \tabularnewline
72 &  0.1841 &  0.3683 &  0.8159 \tabularnewline
73 &  0.2634 &  0.5268 &  0.7366 \tabularnewline
74 &  0.2828 &  0.5656 &  0.7172 \tabularnewline
75 &  0.2398 &  0.4796 &  0.7602 \tabularnewline
76 &  0.2074 &  0.4148 &  0.7926 \tabularnewline
77 &  0.1783 &  0.3565 &  0.8217 \tabularnewline
78 &  0.2655 &  0.531 &  0.7345 \tabularnewline
79 &  0.2891 &  0.5782 &  0.7109 \tabularnewline
80 &  0.3066 &  0.6132 &  0.6934 \tabularnewline
81 &  0.3199 &  0.6398 &  0.6801 \tabularnewline
82 &  0.3773 &  0.7545 &  0.6227 \tabularnewline
83 &  0.3032 &  0.6064 &  0.6968 \tabularnewline
84 &  0.2737 &  0.5473 &  0.7263 \tabularnewline
85 &  0.2343 &  0.4686 &  0.7657 \tabularnewline
86 &  0.199 &  0.398 &  0.801 \tabularnewline
87 &  0.2931 &  0.5861 &  0.7069 \tabularnewline
88 &  0.2827 &  0.5655 &  0.7173 \tabularnewline
89 &  0.2366 &  0.4733 &  0.7634 \tabularnewline
90 &  0.1984 &  0.3968 &  0.8016 \tabularnewline
91 &  0.2502 &  0.5004 &  0.7498 \tabularnewline
92 &  0.1621 &  0.3243 &  0.8379 \tabularnewline
93 &  0.118 &  0.2361 &  0.882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&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]9[/C][C] 0.1419[/C][C] 0.2837[/C][C] 0.8581[/C][/ROW]
[ROW][C]10[/C][C] 0.4028[/C][C] 0.8056[/C][C] 0.5972[/C][/ROW]
[ROW][C]11[/C][C] 0.335[/C][C] 0.67[/C][C] 0.665[/C][/ROW]
[ROW][C]12[/C][C] 0.2853[/C][C] 0.5705[/C][C] 0.7147[/C][/ROW]
[ROW][C]13[/C][C] 0.1895[/C][C] 0.379[/C][C] 0.8105[/C][/ROW]
[ROW][C]14[/C][C] 0.1479[/C][C] 0.2959[/C][C] 0.8521[/C][/ROW]
[ROW][C]15[/C][C] 0.0963[/C][C] 0.1926[/C][C] 0.9037[/C][/ROW]
[ROW][C]16[/C][C] 0.1572[/C][C] 0.3143[/C][C] 0.8428[/C][/ROW]
[ROW][C]17[/C][C] 0.1041[/C][C] 0.2082[/C][C] 0.8959[/C][/ROW]
[ROW][C]18[/C][C] 0.07347[/C][C] 0.1469[/C][C] 0.9265[/C][/ROW]
[ROW][C]19[/C][C] 0.05187[/C][C] 0.1037[/C][C] 0.9481[/C][/ROW]
[ROW][C]20[/C][C] 0.07358[/C][C] 0.1472[/C][C] 0.9264[/C][/ROW]
[ROW][C]21[/C][C] 0.04938[/C][C] 0.09876[/C][C] 0.9506[/C][/ROW]
[ROW][C]22[/C][C] 0.07389[/C][C] 0.1478[/C][C] 0.9261[/C][/ROW]
[ROW][C]23[/C][C] 0.08523[/C][C] 0.1705[/C][C] 0.9148[/C][/ROW]
[ROW][C]24[/C][C] 0.08508[/C][C] 0.1702[/C][C] 0.9149[/C][/ROW]
[ROW][C]25[/C][C] 0.1386[/C][C] 0.2772[/C][C] 0.8614[/C][/ROW]
[ROW][C]26[/C][C] 0.1112[/C][C] 0.2224[/C][C] 0.8888[/C][/ROW]
[ROW][C]27[/C][C] 0.1283[/C][C] 0.2565[/C][C] 0.8717[/C][/ROW]
[ROW][C]28[/C][C] 0.1211[/C][C] 0.2422[/C][C] 0.8789[/C][/ROW]
[ROW][C]29[/C][C] 0.133[/C][C] 0.266[/C][C] 0.867[/C][/ROW]
[ROW][C]30[/C][C] 0.1631[/C][C] 0.3263[/C][C] 0.8369[/C][/ROW]
[ROW][C]31[/C][C] 0.1574[/C][C] 0.3147[/C][C] 0.8426[/C][/ROW]
[ROW][C]32[/C][C] 0.1532[/C][C] 0.3065[/C][C] 0.8468[/C][/ROW]
[ROW][C]33[/C][C] 0.1387[/C][C] 0.2774[/C][C] 0.8613[/C][/ROW]
[ROW][C]34[/C][C] 0.1701[/C][C] 0.3402[/C][C] 0.8299[/C][/ROW]
[ROW][C]35[/C][C] 0.1388[/C][C] 0.2775[/C][C] 0.8612[/C][/ROW]
[ROW][C]36[/C][C] 0.1285[/C][C] 0.2571[/C][C] 0.8715[/C][/ROW]
[ROW][C]37[/C][C] 0.1097[/C][C] 0.2194[/C][C] 0.8903[/C][/ROW]
[ROW][C]38[/C][C] 0.1117[/C][C] 0.2233[/C][C] 0.8883[/C][/ROW]
[ROW][C]39[/C][C] 0.3709[/C][C] 0.7418[/C][C] 0.6291[/C][/ROW]
[ROW][C]40[/C][C] 0.3957[/C][C] 0.7914[/C][C] 0.6043[/C][/ROW]
[ROW][C]41[/C][C] 0.3513[/C][C] 0.7027[/C][C] 0.6487[/C][/ROW]
[ROW][C]42[/C][C] 0.3703[/C][C] 0.7405[/C][C] 0.6297[/C][/ROW]
[ROW][C]43[/C][C] 0.3488[/C][C] 0.6975[/C][C] 0.6512[/C][/ROW]
[ROW][C]44[/C][C] 0.3855[/C][C] 0.7711[/C][C] 0.6145[/C][/ROW]
[ROW][C]45[/C][C] 0.3688[/C][C] 0.7377[/C][C] 0.6312[/C][/ROW]
[ROW][C]46[/C][C] 0.3494[/C][C] 0.6988[/C][C] 0.6506[/C][/ROW]
[ROW][C]47[/C][C] 0.3302[/C][C] 0.6605[/C][C] 0.6698[/C][/ROW]
[ROW][C]48[/C][C] 0.2786[/C][C] 0.5573[/C][C] 0.7214[/C][/ROW]
[ROW][C]49[/C][C] 0.2663[/C][C] 0.5326[/C][C] 0.7337[/C][/ROW]
[ROW][C]50[/C][C] 0.2199[/C][C] 0.4397[/C][C] 0.7801[/C][/ROW]
[ROW][C]51[/C][C] 0.1825[/C][C] 0.365[/C][C] 0.8175[/C][/ROW]
[ROW][C]52[/C][C] 0.1482[/C][C] 0.2963[/C][C] 0.8518[/C][/ROW]
[ROW][C]53[/C][C] 0.1497[/C][C] 0.2995[/C][C] 0.8503[/C][/ROW]
[ROW][C]54[/C][C] 0.1798[/C][C] 0.3597[/C][C] 0.8202[/C][/ROW]
[ROW][C]55[/C][C] 0.2253[/C][C] 0.4506[/C][C] 0.7747[/C][/ROW]
[ROW][C]56[/C][C] 0.1837[/C][C] 0.3674[/C][C] 0.8163[/C][/ROW]
[ROW][C]57[/C][C] 0.1536[/C][C] 0.3072[/C][C] 0.8464[/C][/ROW]
[ROW][C]58[/C][C] 0.1548[/C][C] 0.3097[/C][C] 0.8451[/C][/ROW]
[ROW][C]59[/C][C] 0.1479[/C][C] 0.2958[/C][C] 0.8521[/C][/ROW]
[ROW][C]60[/C][C] 0.1576[/C][C] 0.3151[/C][C] 0.8424[/C][/ROW]
[ROW][C]61[/C][C] 0.1526[/C][C] 0.3052[/C][C] 0.8474[/C][/ROW]
[ROW][C]62[/C][C] 0.1293[/C][C] 0.2586[/C][C] 0.8707[/C][/ROW]
[ROW][C]63[/C][C] 0.1015[/C][C] 0.203[/C][C] 0.8985[/C][/ROW]
[ROW][C]64[/C][C] 0.07946[/C][C] 0.1589[/C][C] 0.9205[/C][/ROW]
[ROW][C]65[/C][C] 0.0598[/C][C] 0.1196[/C][C] 0.9402[/C][/ROW]
[ROW][C]66[/C][C] 0.04838[/C][C] 0.09676[/C][C] 0.9516[/C][/ROW]
[ROW][C]67[/C][C] 0.1396[/C][C] 0.2791[/C][C] 0.8604[/C][/ROW]
[ROW][C]68[/C][C] 0.1077[/C][C] 0.2155[/C][C] 0.8923[/C][/ROW]
[ROW][C]69[/C][C] 0.1242[/C][C] 0.2483[/C][C] 0.8758[/C][/ROW]
[ROW][C]70[/C][C] 0.09664[/C][C] 0.1933[/C][C] 0.9034[/C][/ROW]
[ROW][C]71[/C][C] 0.1699[/C][C] 0.3399[/C][C] 0.8301[/C][/ROW]
[ROW][C]72[/C][C] 0.1841[/C][C] 0.3683[/C][C] 0.8159[/C][/ROW]
[ROW][C]73[/C][C] 0.2634[/C][C] 0.5268[/C][C] 0.7366[/C][/ROW]
[ROW][C]74[/C][C] 0.2828[/C][C] 0.5656[/C][C] 0.7172[/C][/ROW]
[ROW][C]75[/C][C] 0.2398[/C][C] 0.4796[/C][C] 0.7602[/C][/ROW]
[ROW][C]76[/C][C] 0.2074[/C][C] 0.4148[/C][C] 0.7926[/C][/ROW]
[ROW][C]77[/C][C] 0.1783[/C][C] 0.3565[/C][C] 0.8217[/C][/ROW]
[ROW][C]78[/C][C] 0.2655[/C][C] 0.531[/C][C] 0.7345[/C][/ROW]
[ROW][C]79[/C][C] 0.2891[/C][C] 0.5782[/C][C] 0.7109[/C][/ROW]
[ROW][C]80[/C][C] 0.3066[/C][C] 0.6132[/C][C] 0.6934[/C][/ROW]
[ROW][C]81[/C][C] 0.3199[/C][C] 0.6398[/C][C] 0.6801[/C][/ROW]
[ROW][C]82[/C][C] 0.3773[/C][C] 0.7545[/C][C] 0.6227[/C][/ROW]
[ROW][C]83[/C][C] 0.3032[/C][C] 0.6064[/C][C] 0.6968[/C][/ROW]
[ROW][C]84[/C][C] 0.2737[/C][C] 0.5473[/C][C] 0.7263[/C][/ROW]
[ROW][C]85[/C][C] 0.2343[/C][C] 0.4686[/C][C] 0.7657[/C][/ROW]
[ROW][C]86[/C][C] 0.199[/C][C] 0.398[/C][C] 0.801[/C][/ROW]
[ROW][C]87[/C][C] 0.2931[/C][C] 0.5861[/C][C] 0.7069[/C][/ROW]
[ROW][C]88[/C][C] 0.2827[/C][C] 0.5655[/C][C] 0.7173[/C][/ROW]
[ROW][C]89[/C][C] 0.2366[/C][C] 0.4733[/C][C] 0.7634[/C][/ROW]
[ROW][C]90[/C][C] 0.1984[/C][C] 0.3968[/C][C] 0.8016[/C][/ROW]
[ROW][C]91[/C][C] 0.2502[/C][C] 0.5004[/C][C] 0.7498[/C][/ROW]
[ROW][C]92[/C][C] 0.1621[/C][C] 0.3243[/C][C] 0.8379[/C][/ROW]
[ROW][C]93[/C][C] 0.118[/C][C] 0.2361[/C][C] 0.882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&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
9	0.1419	0.2837	0.8581
10	0.4028	0.8056	0.5972
11	0.335	0.67	0.665
12	0.2853	0.5705	0.7147
13	0.1895	0.379	0.8105
14	0.1479	0.2959	0.8521
15	0.0963	0.1926	0.9037
16	0.1572	0.3143	0.8428
17	0.1041	0.2082	0.8959
18	0.07347	0.1469	0.9265
19	0.05187	0.1037	0.9481
20	0.07358	0.1472	0.9264
21	0.04938	0.09876	0.9506
22	0.07389	0.1478	0.9261
23	0.08523	0.1705	0.9148
24	0.08508	0.1702	0.9149
25	0.1386	0.2772	0.8614
26	0.1112	0.2224	0.8888
27	0.1283	0.2565	0.8717
28	0.1211	0.2422	0.8789
29	0.133	0.266	0.867
30	0.1631	0.3263	0.8369
31	0.1574	0.3147	0.8426
32	0.1532	0.3065	0.8468
33	0.1387	0.2774	0.8613
34	0.1701	0.3402	0.8299
35	0.1388	0.2775	0.8612
36	0.1285	0.2571	0.8715
37	0.1097	0.2194	0.8903
38	0.1117	0.2233	0.8883
39	0.3709	0.7418	0.6291
40	0.3957	0.7914	0.6043
41	0.3513	0.7027	0.6487
42	0.3703	0.7405	0.6297
43	0.3488	0.6975	0.6512
44	0.3855	0.7711	0.6145
45	0.3688	0.7377	0.6312
46	0.3494	0.6988	0.6506
47	0.3302	0.6605	0.6698
48	0.2786	0.5573	0.7214
49	0.2663	0.5326	0.7337
50	0.2199	0.4397	0.7801
51	0.1825	0.365	0.8175
52	0.1482	0.2963	0.8518
53	0.1497	0.2995	0.8503
54	0.1798	0.3597	0.8202
55	0.2253	0.4506	0.7747
56	0.1837	0.3674	0.8163
57	0.1536	0.3072	0.8464
58	0.1548	0.3097	0.8451
59	0.1479	0.2958	0.8521
60	0.1576	0.3151	0.8424
61	0.1526	0.3052	0.8474
62	0.1293	0.2586	0.8707
63	0.1015	0.203	0.8985
64	0.07946	0.1589	0.9205
65	0.0598	0.1196	0.9402
66	0.04838	0.09676	0.9516
67	0.1396	0.2791	0.8604
68	0.1077	0.2155	0.8923
69	0.1242	0.2483	0.8758
70	0.09664	0.1933	0.9034
71	0.1699	0.3399	0.8301
72	0.1841	0.3683	0.8159
73	0.2634	0.5268	0.7366
74	0.2828	0.5656	0.7172
75	0.2398	0.4796	0.7602
76	0.2074	0.4148	0.7926
77	0.1783	0.3565	0.8217
78	0.2655	0.531	0.7345
79	0.2891	0.5782	0.7109
80	0.3066	0.6132	0.6934
81	0.3199	0.6398	0.6801
82	0.3773	0.7545	0.6227
83	0.3032	0.6064	0.6968
84	0.2737	0.5473	0.7263
85	0.2343	0.4686	0.7657
86	0.199	0.398	0.801
87	0.2931	0.5861	0.7069
88	0.2827	0.5655	0.7173
89	0.2366	0.4733	0.7634
90	0.1984	0.3968	0.8016
91	0.2502	0.5004	0.7498
92	0.1621	0.3243	0.8379
93	0.118	0.2361	0.882

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0235294	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0235294 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0235294[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0235294	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.49046, df1 = 2, df2 = 94, p-value = 0.6139
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.70849, df1 = 10, df2 = 86, p-value = 0.7141
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.7535, df1 = 2, df2 = 94, p-value = 0.06885

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.49046, df1 = 2, df2 = 94, p-value = 0.6139
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.70849, df1 = 10, df2 = 86, p-value = 0.7141
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.7535, df1 = 2, df2 = 94, p-value = 0.06885
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.49046, df1 = 2, df2 = 94, p-value = 0.6139
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.70849, df1 = 10, df2 = 86, p-value = 0.7141
[/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 = 2.7535, df1 = 2, df2 = 94, p-value = 0.06885
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.49046, df1 = 2, df2 = 94, p-value = 0.6139
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.70849, df1 = 10, df2 = 86, p-value = 0.7141
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.7535, df1 = 2, df2 = 94, p-value = 0.06885

Variance Inflation Factors (Multicollinearity)
> vif TVDC SKEOUSUM SKEOU1 SKEOU2 SKEOU3 1.574278 3.330100 1.633175 2.129250 1.599800

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    TVDC SKEOUSUM   SKEOU1   SKEOU2   SKEOU3 
1.574278 3.330100 1.633175 2.129250 1.599800 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303905&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    TVDC SKEOUSUM   SKEOU1   SKEOU2   SKEOU3 
1.574278 3.330100 1.633175 2.129250 1.599800 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303905&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303905&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 TVDC SKEOUSUM SKEOU1 SKEOU2 SKEOU3 1.574278 3.330100 1.633175 2.129250 1.599800

Figure 1

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Parameters (Session):

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