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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 19 Nov 2010 10:09:00 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/19/t1290161479ifo5uutkxgnc431.htm/, Retrieved Fri, 03 May 2024 17:57:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97878, Retrieved Fri, 03 May 2024 17:57:34 +0000

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No (this computation is public)

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Estimated Impact

120

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

-       [Multiple Regression] [WS7: Mini-tutorial a] [2010-11-19 10:09:00] [380f6bceef280be3d93cc6fafd18141e] [Current]

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5	71,91	5,11	50	3
5	6,06	3,53	48	5
4	8,1	4,52	63	11
5	79,38	3,72	113	13
4	65,34	5,99	128	11
5	34,62	3,15	52	7
5	26,26	3,17	104	1
5	60,92	3,5	40	1
4	39,56	3,39	89	11
5	65,61	4,15	97	3
3	56,49	4,5	29	9
6	56,19	3,31	36	5
4	80,3	3,09	114	11
4	61,2	5,31	49	9
4	58,2	4,24	57	7
6	75,91	5,06	82	4
3	73,66	4,72	34	10
2	73,87	4,58	36	13
3	87,21	5,3	89	9
2	64,29	5,11	69	5
5	71,82	4,05	35	8
5	89,31	4,62	65	12
3	1,41	4,66	70	8
6	35,17	4,66	60	5
3	34,68	2,76	57	9
4	41,08	5,1	127	11
3	30,57	4,97	96	8
6	68,84	2,87	61	9
3	7,17	5,14	127	10
2	71,05	4,98	36	1
5	23,32	4,55	55	9
5	61,39	5,45	75	2
1	8,41	4,36	42	3
4	65,88	4,78	64	4
6	64,06	4,74	83	3
4	26,8	5,44	56	1
3	12,78	5,78	114	5
4	23,84	2,92	33	4
3	42,69	4,22	91	2
6	54,94	3,93	127	2
3	89,99	3,01	45	10
4	5,68	3,22	80	6
3	72,64	5,12	40	9
4	45,92	3,04	115	7
2	24,96	5,82	33	1
5	18,17	3,11	127	13
4	29,12	3,87	45	9
3	40,08	3,75	74	11
3	1,08	4,82	105	10
5	57,52	2,83	60	7

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97878&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97878&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97878&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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
verzekeraar[t] = + 5.36592818594656 + 0.0123624146327769kost[t] -0.498384874445766grootte[t] + 0.0107851551238493snelheid[t] -0.0819978444943991maand[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
verzekeraar[t] =  +  5.36592818594656 +  0.0123624146327769kost[t] -0.498384874445766grootte[t] +  0.0107851551238493snelheid[t] -0.0819978444943991maand[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97878&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]verzekeraar[t] =  +  5.36592818594656 +  0.0123624146327769kost[t] -0.498384874445766grootte[t] +  0.0107851551238493snelheid[t] -0.0819978444943991maand[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97878&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97878&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
verzekeraar[t] = + 5.36592818594656 + 0.0123624146327769kost[t] -0.498384874445766grootte[t] + 0.0107851551238493snelheid[t] -0.0819978444943991maand[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)	5.36592818594656	0.950501	5.6454	1e-06	1e-06
kost	0.0123624146327769	0.006352	1.9462	0.05789	0.028945
grootte	-0.498384874445766	0.180004	-2.7687	0.008144	0.004072
snelheid	0.0107851551238493	0.00546	1.9753	0.054382	0.027191
maand	-0.0819978444943991	0.045672	-1.7954	0.079313	0.039657

\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) & 5.36592818594656 & 0.950501 & 5.6454 & 1e-06 & 1e-06 \tabularnewline
kost & 0.0123624146327769 & 0.006352 & 1.9462 & 0.05789 & 0.028945 \tabularnewline
grootte & -0.498384874445766 & 0.180004 & -2.7687 & 0.008144 & 0.004072 \tabularnewline
snelheid & 0.0107851551238493 & 0.00546 & 1.9753 & 0.054382 & 0.027191 \tabularnewline
maand & -0.0819978444943991 & 0.045672 & -1.7954 & 0.079313 & 0.039657 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97878&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]5.36592818594656[/C][C]0.950501[/C][C]5.6454[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]kost[/C][C]0.0123624146327769[/C][C]0.006352[/C][C]1.9462[/C][C]0.05789[/C][C]0.028945[/C][/ROW]
[ROW][C]grootte[/C][C]-0.498384874445766[/C][C]0.180004[/C][C]-2.7687[/C][C]0.008144[/C][C]0.004072[/C][/ROW]
[ROW][C]snelheid[/C][C]0.0107851551238493[/C][C]0.00546[/C][C]1.9753[/C][C]0.054382[/C][C]0.027191[/C][/ROW]
[ROW][C]maand[/C][C]-0.0819978444943991[/C][C]0.045672[/C][C]-1.7954[/C][C]0.079313[/C][C]0.039657[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97878&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97878&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)	5.36592818594656	0.950501	5.6454	1e-06	1e-06
kost	0.0123624146327769	0.006352	1.9462	0.05789	0.028945
grootte	-0.498384874445766	0.180004	-2.7687	0.008144	0.004072
snelheid	0.0107851551238493	0.00546	1.9753	0.054382	0.027191
maand	-0.0819978444943991	0.045672	-1.7954	0.079313	0.039657

Multiple Linear Regression - Regression Statistics
Multiple R	0.481622163648044
R-squared	0.231959908517023
Adjusted R-squared	0.163689678162981
F-TEST (value)	3.39767285556388
F-TEST (DF numerator)	4
F-TEST (DF denominator)	45
p-value	0.0164007963731352
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.13124951623703
Sum Squared Residuals	57.5876460593936

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.481622163648044 \tabularnewline
R-squared & 0.231959908517023 \tabularnewline
Adjusted R-squared & 0.163689678162981 \tabularnewline
F-TEST (value) & 3.39767285556388 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 0.0164007963731352 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.13124951623703 \tabularnewline
Sum Squared Residuals & 57.5876460593936 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97878&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.481622163648044[/C][/ROW]
[ROW][C]R-squared[/C][C]0.231959908517023[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.163689678162981[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.39767285556388[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/C][/ROW]
[ROW][C]p-value[/C][C]0.0164007963731352[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.13124951623703[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]57.5876460593936[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97878&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97878&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	0.481622163648044
R-squared	0.231959908517023
Adjusted R-squared	0.163689678162981
F-TEST (value)	3.39767285556388
F-TEST (DF numerator)	4
F-TEST (DF denominator)	45
p-value	0.0164007963731352
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.13124951623703
Sum Squared Residuals	57.5876460593936

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5	4.00142693648095	0.998573063519048
2	5	3.78924403530041	1.21075596469959
3	4	2.99085259534131	1.00914740465869
4	5	4.64601547712593	0.353984522874068
5	4	3.66688652653639	0.333113473463607
6	5	4.21084578100851	0.789154218991493
7	5	5.15034343059614	-0.150343430596137
8	5	4.72410778527472	0.275892214725275
9	4	4.22336310103227	-0.223363101032271
10	5	4.90889549458331	0.091104505416685
11	3	3.39633795168822	-0.396337951688222
12	6	4.38919469173339	1.61080530826661
13	4	5.14615221360157	-1.14615221360157
14	4	3.26657627878452	0.733423721215483
15	4	4.01303778052275	-0.0130377805227489
16	6	4.33892295820313	1.66107704179687
17	3	3.47088386967978	-0.470883869679781
18	2	3.31883063593957	-1.31883063593957
19	3	4.02451273708148	-1.02451273708148
20	2	3.94814759534353	-1.94814759534353
21	5	3.95683573674678	1.04316426325322
22	5	3.88453826597785	1.11546173402215
23	3	3.15986377837577	-0.159863778375768
24	6	3.71536087862302	2.28463912137698
25	3	4.29588771355077	-1.29588771355077
26	4	3.79975173067811	0.200248269321893
27	3	3.64626651120944	-0.646266511209439
28	6	4.70650608171279	1.29349391828721
29	3	3.44260469999721	-0.44260469999721
30	2	4.06858881082962	-2.06858881082962
31	5	3.24177144781681	1.75822855218319
32	5	4.05355019982321	0.946449800176786
33	1	3.50392102214315	-2.50392102214315
34	4	4.16034291205191	-0.160342912051905
35	6	4.44469450424562	1.55530549575438
36	4	3.50799802356118	0.492001976438821
37	3	3.46277373230375	-0.462773732303752
38	4	4.23328305851976	-0.233283058519758
39	3	4.60794892374017	-1.60794892374017
40	6	5.29218570103953	0.707814298960467
41	3	4.64363694229763	-1.64363694229763
42	4	4.20217274828692	-0.20217274828692
43	3	3.40562903221354	-0.405629032213537
44	4	5.08482817535043	-1.08482817535043
45	2	3.04780636049894	-1.04780636049894
46	5	4.34431902259946	0.655680977400536
47	4	3.54452361607154	0.455476383928461
48	3	3.8885956749831	-0.888595674983099
49	3	3.28952734198156	-0.289527341981558
50	5	4.73970947691254	0.260290523087461

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 4.00142693648095 & 0.998573063519048 \tabularnewline
2 & 5 & 3.78924403530041 & 1.21075596469959 \tabularnewline
3 & 4 & 2.99085259534131 & 1.00914740465869 \tabularnewline
4 & 5 & 4.64601547712593 & 0.353984522874068 \tabularnewline
5 & 4 & 3.66688652653639 & 0.333113473463607 \tabularnewline
6 & 5 & 4.21084578100851 & 0.789154218991493 \tabularnewline
7 & 5 & 5.15034343059614 & -0.150343430596137 \tabularnewline
8 & 5 & 4.72410778527472 & 0.275892214725275 \tabularnewline
9 & 4 & 4.22336310103227 & -0.223363101032271 \tabularnewline
10 & 5 & 4.90889549458331 & 0.091104505416685 \tabularnewline
11 & 3 & 3.39633795168822 & -0.396337951688222 \tabularnewline
12 & 6 & 4.38919469173339 & 1.61080530826661 \tabularnewline
13 & 4 & 5.14615221360157 & -1.14615221360157 \tabularnewline
14 & 4 & 3.26657627878452 & 0.733423721215483 \tabularnewline
15 & 4 & 4.01303778052275 & -0.0130377805227489 \tabularnewline
16 & 6 & 4.33892295820313 & 1.66107704179687 \tabularnewline
17 & 3 & 3.47088386967978 & -0.470883869679781 \tabularnewline
18 & 2 & 3.31883063593957 & -1.31883063593957 \tabularnewline
19 & 3 & 4.02451273708148 & -1.02451273708148 \tabularnewline
20 & 2 & 3.94814759534353 & -1.94814759534353 \tabularnewline
21 & 5 & 3.95683573674678 & 1.04316426325322 \tabularnewline
22 & 5 & 3.88453826597785 & 1.11546173402215 \tabularnewline
23 & 3 & 3.15986377837577 & -0.159863778375768 \tabularnewline
24 & 6 & 3.71536087862302 & 2.28463912137698 \tabularnewline
25 & 3 & 4.29588771355077 & -1.29588771355077 \tabularnewline
26 & 4 & 3.79975173067811 & 0.200248269321893 \tabularnewline
27 & 3 & 3.64626651120944 & -0.646266511209439 \tabularnewline
28 & 6 & 4.70650608171279 & 1.29349391828721 \tabularnewline
29 & 3 & 3.44260469999721 & -0.44260469999721 \tabularnewline
30 & 2 & 4.06858881082962 & -2.06858881082962 \tabularnewline
31 & 5 & 3.24177144781681 & 1.75822855218319 \tabularnewline
32 & 5 & 4.05355019982321 & 0.946449800176786 \tabularnewline
33 & 1 & 3.50392102214315 & -2.50392102214315 \tabularnewline
34 & 4 & 4.16034291205191 & -0.160342912051905 \tabularnewline
35 & 6 & 4.44469450424562 & 1.55530549575438 \tabularnewline
36 & 4 & 3.50799802356118 & 0.492001976438821 \tabularnewline
37 & 3 & 3.46277373230375 & -0.462773732303752 \tabularnewline
38 & 4 & 4.23328305851976 & -0.233283058519758 \tabularnewline
39 & 3 & 4.60794892374017 & -1.60794892374017 \tabularnewline
40 & 6 & 5.29218570103953 & 0.707814298960467 \tabularnewline
41 & 3 & 4.64363694229763 & -1.64363694229763 \tabularnewline
42 & 4 & 4.20217274828692 & -0.20217274828692 \tabularnewline
43 & 3 & 3.40562903221354 & -0.405629032213537 \tabularnewline
44 & 4 & 5.08482817535043 & -1.08482817535043 \tabularnewline
45 & 2 & 3.04780636049894 & -1.04780636049894 \tabularnewline
46 & 5 & 4.34431902259946 & 0.655680977400536 \tabularnewline
47 & 4 & 3.54452361607154 & 0.455476383928461 \tabularnewline
48 & 3 & 3.8885956749831 & -0.888595674983099 \tabularnewline
49 & 3 & 3.28952734198156 & -0.289527341981558 \tabularnewline
50 & 5 & 4.73970947691254 & 0.260290523087461 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97878&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]5[/C][C]4.00142693648095[/C][C]0.998573063519048[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]3.78924403530041[/C][C]1.21075596469959[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]2.99085259534131[/C][C]1.00914740465869[/C][/ROW]
[ROW][C]4[/C][C]5[/C][C]4.64601547712593[/C][C]0.353984522874068[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]3.66688652653639[/C][C]0.333113473463607[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]4.21084578100851[/C][C]0.789154218991493[/C][/ROW]
[ROW][C]7[/C][C]5[/C][C]5.15034343059614[/C][C]-0.150343430596137[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]4.72410778527472[/C][C]0.275892214725275[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.22336310103227[/C][C]-0.223363101032271[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]4.90889549458331[/C][C]0.091104505416685[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]3.39633795168822[/C][C]-0.396337951688222[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]4.38919469173339[/C][C]1.61080530826661[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]5.14615221360157[/C][C]-1.14615221360157[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.26657627878452[/C][C]0.733423721215483[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]4.01303778052275[/C][C]-0.0130377805227489[/C][/ROW]
[ROW][C]16[/C][C]6[/C][C]4.33892295820313[/C][C]1.66107704179687[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.47088386967978[/C][C]-0.470883869679781[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]3.31883063593957[/C][C]-1.31883063593957[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]4.02451273708148[/C][C]-1.02451273708148[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]3.94814759534353[/C][C]-1.94814759534353[/C][/ROW]
[ROW][C]21[/C][C]5[/C][C]3.95683573674678[/C][C]1.04316426325322[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]3.88453826597785[/C][C]1.11546173402215[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]3.15986377837577[/C][C]-0.159863778375768[/C][/ROW]
[ROW][C]24[/C][C]6[/C][C]3.71536087862302[/C][C]2.28463912137698[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]4.29588771355077[/C][C]-1.29588771355077[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.79975173067811[/C][C]0.200248269321893[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]3.64626651120944[/C][C]-0.646266511209439[/C][/ROW]
[ROW][C]28[/C][C]6[/C][C]4.70650608171279[/C][C]1.29349391828721[/C][/ROW]
[ROW][C]29[/C][C]3[/C][C]3.44260469999721[/C][C]-0.44260469999721[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]4.06858881082962[/C][C]-2.06858881082962[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]3.24177144781681[/C][C]1.75822855218319[/C][/ROW]
[ROW][C]32[/C][C]5[/C][C]4.05355019982321[/C][C]0.946449800176786[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]3.50392102214315[/C][C]-2.50392102214315[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.16034291205191[/C][C]-0.160342912051905[/C][/ROW]
[ROW][C]35[/C][C]6[/C][C]4.44469450424562[/C][C]1.55530549575438[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]3.50799802356118[/C][C]0.492001976438821[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.46277373230375[/C][C]-0.462773732303752[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.23328305851976[/C][C]-0.233283058519758[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]4.60794892374017[/C][C]-1.60794892374017[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]5.29218570103953[/C][C]0.707814298960467[/C][/ROW]
[ROW][C]41[/C][C]3[/C][C]4.64363694229763[/C][C]-1.64363694229763[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.20217274828692[/C][C]-0.20217274828692[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.40562903221354[/C][C]-0.405629032213537[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]5.08482817535043[/C][C]-1.08482817535043[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]3.04780636049894[/C][C]-1.04780636049894[/C][/ROW]
[ROW][C]46[/C][C]5[/C][C]4.34431902259946[/C][C]0.655680977400536[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.54452361607154[/C][C]0.455476383928461[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]3.8885956749831[/C][C]-0.888595674983099[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]3.28952734198156[/C][C]-0.289527341981558[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]4.73970947691254[/C][C]0.260290523087461[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97878&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97878&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	5	4.00142693648095	0.998573063519048
2	5	3.78924403530041	1.21075596469959
3	4	2.99085259534131	1.00914740465869
4	5	4.64601547712593	0.353984522874068
5	4	3.66688652653639	0.333113473463607
6	5	4.21084578100851	0.789154218991493
7	5	5.15034343059614	-0.150343430596137
8	5	4.72410778527472	0.275892214725275
9	4	4.22336310103227	-0.223363101032271
10	5	4.90889549458331	0.091104505416685
11	3	3.39633795168822	-0.396337951688222
12	6	4.38919469173339	1.61080530826661
13	4	5.14615221360157	-1.14615221360157
14	4	3.26657627878452	0.733423721215483
15	4	4.01303778052275	-0.0130377805227489
16	6	4.33892295820313	1.66107704179687
17	3	3.47088386967978	-0.470883869679781
18	2	3.31883063593957	-1.31883063593957
19	3	4.02451273708148	-1.02451273708148
20	2	3.94814759534353	-1.94814759534353
21	5	3.95683573674678	1.04316426325322
22	5	3.88453826597785	1.11546173402215
23	3	3.15986377837577	-0.159863778375768
24	6	3.71536087862302	2.28463912137698
25	3	4.29588771355077	-1.29588771355077
26	4	3.79975173067811	0.200248269321893
27	3	3.64626651120944	-0.646266511209439
28	6	4.70650608171279	1.29349391828721
29	3	3.44260469999721	-0.44260469999721
30	2	4.06858881082962	-2.06858881082962
31	5	3.24177144781681	1.75822855218319
32	5	4.05355019982321	0.946449800176786
33	1	3.50392102214315	-2.50392102214315
34	4	4.16034291205191	-0.160342912051905
35	6	4.44469450424562	1.55530549575438
36	4	3.50799802356118	0.492001976438821
37	3	3.46277373230375	-0.462773732303752
38	4	4.23328305851976	-0.233283058519758
39	3	4.60794892374017	-1.60794892374017
40	6	5.29218570103953	0.707814298960467
41	3	4.64363694229763	-1.64363694229763
42	4	4.20217274828692	-0.20217274828692
43	3	3.40562903221354	-0.405629032213537
44	4	5.08482817535043	-1.08482817535043
45	2	3.04780636049894	-1.04780636049894
46	5	4.34431902259946	0.655680977400536
47	4	3.54452361607154	0.455476383928461
48	3	3.8885956749831	-0.888595674983099
49	3	3.28952734198156	-0.289527341981558
50	5	4.73970947691254	0.260290523087461

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0218798757621692	0.0437597515243385	0.97812012423783
9	0.0258534321253834	0.0517068642507667	0.974146567874617
10	0.00741110595143635	0.0148222119028727	0.992588894048564
11	0.0409218199910557	0.0818436399821114	0.959078180008944
12	0.0707901949015031	0.141580389803006	0.929209805098497
13	0.0632795478757941	0.126559095751588	0.936720452124206
14	0.0343682500198694	0.0687365000397388	0.96563174998013
15	0.0207359557875945	0.041471911575189	0.979264044212406
16	0.0353435526250505	0.070687105250101	0.96465644737495
17	0.0322248287052334	0.0644496574104668	0.967775171294767
18	0.0436545046640218	0.0873090093280437	0.956345495335978
19	0.0473160535126379	0.0946321070252758	0.952683946487362
20	0.218117314024552	0.436234628049103	0.781882685975448
21	0.210861899129325	0.421723798258651	0.789138100870675
22	0.229000475271917	0.458000950543833	0.770999524728083
23	0.180063436079921	0.360126872159841	0.81993656392008
24	0.371717831231681	0.743435662463362	0.628282168768319
25	0.390729110427177	0.781458220854354	0.609270889572823
26	0.308484684071439	0.616969368142879	0.69151531592856
27	0.27159983289198	0.54319966578396	0.72840016710802
28	0.314657263414631	0.629314526829261	0.68534273658537
29	0.264323654762475	0.52864730952495	0.735676345237525
30	0.447234521652516	0.894469043305032	0.552765478347484
31	0.614570035122386	0.770859929755228	0.385429964877614
32	0.581403588686708	0.837192822626583	0.418596411313292
33	0.825501755645941	0.348996488708117	0.174498244354059
34	0.75115636924724	0.49768726150552	0.24884363075276
35	0.888136907561896	0.223726184876209	0.111863092438104
36	0.883984431110642	0.232031137778717	0.116015568889358
37	0.81681384042679	0.366372319146419	0.183186159573210
38	0.727595286353613	0.544809427292774	0.272404713646387
39	0.767517469996835	0.46496506000633	0.232482530003165
40	0.846079803073215	0.307840393853569	0.153920196926785
41	0.898291698496123	0.203416603007754	0.101708301503877
42	0.789622418226498	0.420755163547003	0.210377581773502

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0218798757621692 & 0.0437597515243385 & 0.97812012423783 \tabularnewline
9 & 0.0258534321253834 & 0.0517068642507667 & 0.974146567874617 \tabularnewline
10 & 0.00741110595143635 & 0.0148222119028727 & 0.992588894048564 \tabularnewline
11 & 0.0409218199910557 & 0.0818436399821114 & 0.959078180008944 \tabularnewline
12 & 0.0707901949015031 & 0.141580389803006 & 0.929209805098497 \tabularnewline
13 & 0.0632795478757941 & 0.126559095751588 & 0.936720452124206 \tabularnewline
14 & 0.0343682500198694 & 0.0687365000397388 & 0.96563174998013 \tabularnewline
15 & 0.0207359557875945 & 0.041471911575189 & 0.979264044212406 \tabularnewline
16 & 0.0353435526250505 & 0.070687105250101 & 0.96465644737495 \tabularnewline
17 & 0.0322248287052334 & 0.0644496574104668 & 0.967775171294767 \tabularnewline
18 & 0.0436545046640218 & 0.0873090093280437 & 0.956345495335978 \tabularnewline
19 & 0.0473160535126379 & 0.0946321070252758 & 0.952683946487362 \tabularnewline
20 & 0.218117314024552 & 0.436234628049103 & 0.781882685975448 \tabularnewline
21 & 0.210861899129325 & 0.421723798258651 & 0.789138100870675 \tabularnewline
22 & 0.229000475271917 & 0.458000950543833 & 0.770999524728083 \tabularnewline
23 & 0.180063436079921 & 0.360126872159841 & 0.81993656392008 \tabularnewline
24 & 0.371717831231681 & 0.743435662463362 & 0.628282168768319 \tabularnewline
25 & 0.390729110427177 & 0.781458220854354 & 0.609270889572823 \tabularnewline
26 & 0.308484684071439 & 0.616969368142879 & 0.69151531592856 \tabularnewline
27 & 0.27159983289198 & 0.54319966578396 & 0.72840016710802 \tabularnewline
28 & 0.314657263414631 & 0.629314526829261 & 0.68534273658537 \tabularnewline
29 & 0.264323654762475 & 0.52864730952495 & 0.735676345237525 \tabularnewline
30 & 0.447234521652516 & 0.894469043305032 & 0.552765478347484 \tabularnewline
31 & 0.614570035122386 & 0.770859929755228 & 0.385429964877614 \tabularnewline
32 & 0.581403588686708 & 0.837192822626583 & 0.418596411313292 \tabularnewline
33 & 0.825501755645941 & 0.348996488708117 & 0.174498244354059 \tabularnewline
34 & 0.75115636924724 & 0.49768726150552 & 0.24884363075276 \tabularnewline
35 & 0.888136907561896 & 0.223726184876209 & 0.111863092438104 \tabularnewline
36 & 0.883984431110642 & 0.232031137778717 & 0.116015568889358 \tabularnewline
37 & 0.81681384042679 & 0.366372319146419 & 0.183186159573210 \tabularnewline
38 & 0.727595286353613 & 0.544809427292774 & 0.272404713646387 \tabularnewline
39 & 0.767517469996835 & 0.46496506000633 & 0.232482530003165 \tabularnewline
40 & 0.846079803073215 & 0.307840393853569 & 0.153920196926785 \tabularnewline
41 & 0.898291698496123 & 0.203416603007754 & 0.101708301503877 \tabularnewline
42 & 0.789622418226498 & 0.420755163547003 & 0.210377581773502 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97878&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.0218798757621692[/C][C]0.0437597515243385[/C][C]0.97812012423783[/C][/ROW]
[ROW][C]9[/C][C]0.0258534321253834[/C][C]0.0517068642507667[/C][C]0.974146567874617[/C][/ROW]
[ROW][C]10[/C][C]0.00741110595143635[/C][C]0.0148222119028727[/C][C]0.992588894048564[/C][/ROW]
[ROW][C]11[/C][C]0.0409218199910557[/C][C]0.0818436399821114[/C][C]0.959078180008944[/C][/ROW]
[ROW][C]12[/C][C]0.0707901949015031[/C][C]0.141580389803006[/C][C]0.929209805098497[/C][/ROW]
[ROW][C]13[/C][C]0.0632795478757941[/C][C]0.126559095751588[/C][C]0.936720452124206[/C][/ROW]
[ROW][C]14[/C][C]0.0343682500198694[/C][C]0.0687365000397388[/C][C]0.96563174998013[/C][/ROW]
[ROW][C]15[/C][C]0.0207359557875945[/C][C]0.041471911575189[/C][C]0.979264044212406[/C][/ROW]
[ROW][C]16[/C][C]0.0353435526250505[/C][C]0.070687105250101[/C][C]0.96465644737495[/C][/ROW]
[ROW][C]17[/C][C]0.0322248287052334[/C][C]0.0644496574104668[/C][C]0.967775171294767[/C][/ROW]
[ROW][C]18[/C][C]0.0436545046640218[/C][C]0.0873090093280437[/C][C]0.956345495335978[/C][/ROW]
[ROW][C]19[/C][C]0.0473160535126379[/C][C]0.0946321070252758[/C][C]0.952683946487362[/C][/ROW]
[ROW][C]20[/C][C]0.218117314024552[/C][C]0.436234628049103[/C][C]0.781882685975448[/C][/ROW]
[ROW][C]21[/C][C]0.210861899129325[/C][C]0.421723798258651[/C][C]0.789138100870675[/C][/ROW]
[ROW][C]22[/C][C]0.229000475271917[/C][C]0.458000950543833[/C][C]0.770999524728083[/C][/ROW]
[ROW][C]23[/C][C]0.180063436079921[/C][C]0.360126872159841[/C][C]0.81993656392008[/C][/ROW]
[ROW][C]24[/C][C]0.371717831231681[/C][C]0.743435662463362[/C][C]0.628282168768319[/C][/ROW]
[ROW][C]25[/C][C]0.390729110427177[/C][C]0.781458220854354[/C][C]0.609270889572823[/C][/ROW]
[ROW][C]26[/C][C]0.308484684071439[/C][C]0.616969368142879[/C][C]0.69151531592856[/C][/ROW]
[ROW][C]27[/C][C]0.27159983289198[/C][C]0.54319966578396[/C][C]0.72840016710802[/C][/ROW]
[ROW][C]28[/C][C]0.314657263414631[/C][C]0.629314526829261[/C][C]0.68534273658537[/C][/ROW]
[ROW][C]29[/C][C]0.264323654762475[/C][C]0.52864730952495[/C][C]0.735676345237525[/C][/ROW]
[ROW][C]30[/C][C]0.447234521652516[/C][C]0.894469043305032[/C][C]0.552765478347484[/C][/ROW]
[ROW][C]31[/C][C]0.614570035122386[/C][C]0.770859929755228[/C][C]0.385429964877614[/C][/ROW]
[ROW][C]32[/C][C]0.581403588686708[/C][C]0.837192822626583[/C][C]0.418596411313292[/C][/ROW]
[ROW][C]33[/C][C]0.825501755645941[/C][C]0.348996488708117[/C][C]0.174498244354059[/C][/ROW]
[ROW][C]34[/C][C]0.75115636924724[/C][C]0.49768726150552[/C][C]0.24884363075276[/C][/ROW]
[ROW][C]35[/C][C]0.888136907561896[/C][C]0.223726184876209[/C][C]0.111863092438104[/C][/ROW]
[ROW][C]36[/C][C]0.883984431110642[/C][C]0.232031137778717[/C][C]0.116015568889358[/C][/ROW]
[ROW][C]37[/C][C]0.81681384042679[/C][C]0.366372319146419[/C][C]0.183186159573210[/C][/ROW]
[ROW][C]38[/C][C]0.727595286353613[/C][C]0.544809427292774[/C][C]0.272404713646387[/C][/ROW]
[ROW][C]39[/C][C]0.767517469996835[/C][C]0.46496506000633[/C][C]0.232482530003165[/C][/ROW]
[ROW][C]40[/C][C]0.846079803073215[/C][C]0.307840393853569[/C][C]0.153920196926785[/C][/ROW]
[ROW][C]41[/C][C]0.898291698496123[/C][C]0.203416603007754[/C][C]0.101708301503877[/C][/ROW]
[ROW][C]42[/C][C]0.789622418226498[/C][C]0.420755163547003[/C][C]0.210377581773502[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97878&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97878&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0218798757621692	0.0437597515243385	0.97812012423783
9	0.0258534321253834	0.0517068642507667	0.974146567874617
10	0.00741110595143635	0.0148222119028727	0.992588894048564
11	0.0409218199910557	0.0818436399821114	0.959078180008944
12	0.0707901949015031	0.141580389803006	0.929209805098497
13	0.0632795478757941	0.126559095751588	0.936720452124206
14	0.0343682500198694	0.0687365000397388	0.96563174998013
15	0.0207359557875945	0.041471911575189	0.979264044212406
16	0.0353435526250505	0.070687105250101	0.96465644737495
17	0.0322248287052334	0.0644496574104668	0.967775171294767
18	0.0436545046640218	0.0873090093280437	0.956345495335978
19	0.0473160535126379	0.0946321070252758	0.952683946487362
20	0.218117314024552	0.436234628049103	0.781882685975448
21	0.210861899129325	0.421723798258651	0.789138100870675
22	0.229000475271917	0.458000950543833	0.770999524728083
23	0.180063436079921	0.360126872159841	0.81993656392008
24	0.371717831231681	0.743435662463362	0.628282168768319
25	0.390729110427177	0.781458220854354	0.609270889572823
26	0.308484684071439	0.616969368142879	0.69151531592856
27	0.27159983289198	0.54319966578396	0.72840016710802
28	0.314657263414631	0.629314526829261	0.68534273658537
29	0.264323654762475	0.52864730952495	0.735676345237525
30	0.447234521652516	0.894469043305032	0.552765478347484
31	0.614570035122386	0.770859929755228	0.385429964877614
32	0.581403588686708	0.837192822626583	0.418596411313292
33	0.825501755645941	0.348996488708117	0.174498244354059
34	0.75115636924724	0.49768726150552	0.24884363075276
35	0.888136907561896	0.223726184876209	0.111863092438104
36	0.883984431110642	0.232031137778717	0.116015568889358
37	0.81681384042679	0.366372319146419	0.183186159573210
38	0.727595286353613	0.544809427292774	0.272404713646387
39	0.767517469996835	0.46496506000633	0.232482530003165
40	0.846079803073215	0.307840393853569	0.153920196926785
41	0.898291698496123	0.203416603007754	0.101708301503877
42	0.789622418226498	0.420755163547003	0.210377581773502

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	0.0857142857142857	NOK
10% type I error level	10	0.285714285714286	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 & 0.0857142857142857 & NOK \tabularnewline
10% type I error level & 10 & 0.285714285714286 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97878&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]0.0857142857142857[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97878&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97878&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	0.0857142857142857	NOK
10% type I error level	10	0.285714285714286	NOK

Figure 1

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

Parameters (R input):

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

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code