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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sun, 21 Dec 2008 23:08:27 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/22/t122992622252vv7q0xzdfujsb.htm/, Retrieved Mon, 13 May 2024 20:24:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35935, Retrieved Mon, 13 May 2024 20:24:57 +0000

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Estimated Impact

174

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

-       [Multiple Regression] [Voedingsmiddelen 2] [2008-12-22 06:08:27] [8e1dd6a8d7300d49f515697199ea9e73] [Current]

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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	'Gwilym Jenkins' @ 72.249.127.135

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35935&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35935&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35935&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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
voedingsmiddelen[t] = + 98.372 + 2.01866666666667dummy[t] + 0.739722222222227M1[t] + 0.892444444444448M2[t] + 1.08266666666667M3[t] + 1.67038888888889M4[t] + 1.76561111111111M5[t] + 1.16833333333333M6[t] + 1.03605555555556M7[t] -0.0283888888888880M8[t] -0.210666666666671M9[t] -0.167944444444444M10[t] + 0.204777777777778M11[t] + 0.319777777777778t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
voedingsmiddelen[t] =  +  98.372 +  2.01866666666667dummy[t] +  0.739722222222227M1[t] +  0.892444444444448M2[t] +  1.08266666666667M3[t] +  1.67038888888889M4[t] +  1.76561111111111M5[t] +  1.16833333333333M6[t] +  1.03605555555556M7[t] -0.0283888888888880M8[t] -0.210666666666671M9[t] -0.167944444444444M10[t] +  0.204777777777778M11[t] +  0.319777777777778t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35935&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]voedingsmiddelen[t] =  +  98.372 +  2.01866666666667dummy[t] +  0.739722222222227M1[t] +  0.892444444444448M2[t] +  1.08266666666667M3[t] +  1.67038888888889M4[t] +  1.76561111111111M5[t] +  1.16833333333333M6[t] +  1.03605555555556M7[t] -0.0283888888888880M8[t] -0.210666666666671M9[t] -0.167944444444444M10[t] +  0.204777777777778M11[t] +  0.319777777777778t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35935&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35935&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
voedingsmiddelen[t] = + 98.372 + 2.01866666666667dummy[t] + 0.739722222222227M1[t] + 0.892444444444448M2[t] + 1.08266666666667M3[t] + 1.67038888888889M4[t] + 1.76561111111111M5[t] + 1.16833333333333M6[t] + 1.03605555555556M7[t] -0.0283888888888880M8[t] -0.210666666666671M9[t] -0.167944444444444M10[t] + 0.204777777777778M11[t] + 0.319777777777778t + 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)	98.372	0.910261	108.0701	0	0
dummy	2.01866666666667	0.910261	2.2177	0.033574	0.016787
M1	0.739722222222227	1.073458	0.6891	0.495576	0.247788
M2	0.892444444444448	1.072188	0.8324	0.41119	0.205595
M3	1.08266666666667	1.071199	1.0107	0.319511	0.159756
M4	1.67038888888889	1.070492	1.5604	0.128207	0.064103
M5	1.76561111111111	1.070067	1.65	0.108431	0.054215
M6	1.16833333333333	1.069926	1.092	0.282752	0.141376
M7	1.03605555555556	1.070067	0.9682	0.33998	0.16999
M8	-0.0283888888888880	1.091087	-0.026	0.979399	0.489699
M9	-0.210666666666671	1.090115	-0.1933	0.847946	0.423973
M10	-0.167944444444444	1.08942	-0.1542	0.878423	0.439211
M11	0.204777777777778	1.089003	0.188	0.851996	0.425998
t	0.319777777777778	0.017402	18.3756	0	0

\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) & 98.372 & 0.910261 & 108.0701 & 0 & 0 \tabularnewline
dummy & 2.01866666666667 & 0.910261 & 2.2177 & 0.033574 & 0.016787 \tabularnewline
M1 & 0.739722222222227 & 1.073458 & 0.6891 & 0.495576 & 0.247788 \tabularnewline
M2 & 0.892444444444448 & 1.072188 & 0.8324 & 0.41119 & 0.205595 \tabularnewline
M3 & 1.08266666666667 & 1.071199 & 1.0107 & 0.319511 & 0.159756 \tabularnewline
M4 & 1.67038888888889 & 1.070492 & 1.5604 & 0.128207 & 0.064103 \tabularnewline
M5 & 1.76561111111111 & 1.070067 & 1.65 & 0.108431 & 0.054215 \tabularnewline
M6 & 1.16833333333333 & 1.069926 & 1.092 & 0.282752 & 0.141376 \tabularnewline
M7 & 1.03605555555556 & 1.070067 & 0.9682 & 0.33998 & 0.16999 \tabularnewline
M8 & -0.0283888888888880 & 1.091087 & -0.026 & 0.979399 & 0.489699 \tabularnewline
M9 & -0.210666666666671 & 1.090115 & -0.1933 & 0.847946 & 0.423973 \tabularnewline
M10 & -0.167944444444444 & 1.08942 & -0.1542 & 0.878423 & 0.439211 \tabularnewline
M11 & 0.204777777777778 & 1.089003 & 0.188 & 0.851996 & 0.425998 \tabularnewline
t & 0.319777777777778 & 0.017402 & 18.3756 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35935&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]98.372[/C][C]0.910261[/C][C]108.0701[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]dummy[/C][C]2.01866666666667[/C][C]0.910261[/C][C]2.2177[/C][C]0.033574[/C][C]0.016787[/C][/ROW]
[ROW][C]M1[/C][C]0.739722222222227[/C][C]1.073458[/C][C]0.6891[/C][C]0.495576[/C][C]0.247788[/C][/ROW]
[ROW][C]M2[/C][C]0.892444444444448[/C][C]1.072188[/C][C]0.8324[/C][C]0.41119[/C][C]0.205595[/C][/ROW]
[ROW][C]M3[/C][C]1.08266666666667[/C][C]1.071199[/C][C]1.0107[/C][C]0.319511[/C][C]0.159756[/C][/ROW]
[ROW][C]M4[/C][C]1.67038888888889[/C][C]1.070492[/C][C]1.5604[/C][C]0.128207[/C][C]0.064103[/C][/ROW]
[ROW][C]M5[/C][C]1.76561111111111[/C][C]1.070067[/C][C]1.65[/C][C]0.108431[/C][C]0.054215[/C][/ROW]
[ROW][C]M6[/C][C]1.16833333333333[/C][C]1.069926[/C][C]1.092[/C][C]0.282752[/C][C]0.141376[/C][/ROW]
[ROW][C]M7[/C][C]1.03605555555556[/C][C]1.070067[/C][C]0.9682[/C][C]0.33998[/C][C]0.16999[/C][/ROW]
[ROW][C]M8[/C][C]-0.0283888888888880[/C][C]1.091087[/C][C]-0.026[/C][C]0.979399[/C][C]0.489699[/C][/ROW]
[ROW][C]M9[/C][C]-0.210666666666671[/C][C]1.090115[/C][C]-0.1933[/C][C]0.847946[/C][C]0.423973[/C][/ROW]
[ROW][C]M10[/C][C]-0.167944444444444[/C][C]1.08942[/C][C]-0.1542[/C][C]0.878423[/C][C]0.439211[/C][/ROW]
[ROW][C]M11[/C][C]0.204777777777778[/C][C]1.089003[/C][C]0.188[/C][C]0.851996[/C][C]0.425998[/C][/ROW]
[ROW][C]t[/C][C]0.319777777777778[/C][C]0.017402[/C][C]18.3756[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35935&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35935&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)	98.372	0.910261	108.0701	0	0
dummy	2.01866666666667	0.910261	2.2177	0.033574	0.016787
M1	0.739722222222227	1.073458	0.6891	0.495576	0.247788
M2	0.892444444444448	1.072188	0.8324	0.41119	0.205595
M3	1.08266666666667	1.071199	1.0107	0.319511	0.159756
M4	1.67038888888889	1.070492	1.5604	0.128207	0.064103
M5	1.76561111111111	1.070067	1.65	0.108431	0.054215
M6	1.16833333333333	1.069926	1.092	0.282752	0.141376
M7	1.03605555555556	1.070067	0.9682	0.33998	0.16999
M8	-0.0283888888888880	1.091087	-0.026	0.979399	0.489699
M9	-0.210666666666671	1.090115	-0.1933	0.847946	0.423973
M10	-0.167944444444444	1.08942	-0.1542	0.878423	0.439211
M11	0.204777777777778	1.089003	0.188	0.851996	0.425998
t	0.319777777777778	0.017402	18.3756	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.968552904813162
R-squared	0.938094729422014
Adjusted R-squared	0.913707804648868
F-TEST (value)	38.4671186772597
F-TEST (DF numerator)	13
F-TEST (DF denominator)	33
p-value	4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.40086189198081
Sum Squared Residuals	64.7596633333334

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.968552904813162 \tabularnewline
R-squared & 0.938094729422014 \tabularnewline
Adjusted R-squared & 0.913707804648868 \tabularnewline
F-TEST (value) & 38.4671186772597 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 33 \tabularnewline
p-value & 4.44089209850063e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.40086189198081 \tabularnewline
Sum Squared Residuals & 64.7596633333334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35935&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.968552904813162[/C][/ROW]
[ROW][C]R-squared[/C][C]0.938094729422014[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.913707804648868[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]38.4671186772597[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]33[/C][/ROW]
[ROW][C]p-value[/C][C]4.44089209850063e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.40086189198081[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]64.7596633333334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35935&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35935&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	0.968552904813162
R-squared	0.938094729422014
Adjusted R-squared	0.913707804648868
F-TEST (value)	38.4671186772597
F-TEST (DF numerator)	13
F-TEST (DF denominator)	33
p-value	4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.40086189198081
Sum Squared Residuals	64.7596633333334

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.29	99.4315	0.858500000000033
2	101.12	99.904	1.21599999999999
3	102.65	100.414	2.23600000000001
4	102.71	101.3215	1.38849999999999
5	103.39	101.7365	1.65350000000000
6	102.8	101.459	1.34100000000000
7	102.07	101.6465	0.423499999999990
8	102.15	100.901833333333	1.24816666666667
9	101.21	101.039333333333	0.170666666666661
10	101.27	101.401833333333	-0.131833333333340
11	101.86	102.094333333333	-0.234333333333337
12	101.65	102.209333333333	-0.55933333333333
13	101.94	103.268833333333	-1.32883333333334
14	102.62	103.741333333333	-1.12133333333332
15	102.71	104.251333333333	-1.54133333333334
16	103.39	105.158833333333	-1.76883333333334
17	104.51	105.573833333333	-1.06383333333333
18	104.09	105.296333333333	-1.20633333333333
19	104.29	105.483833333333	-1.19383333333333
20	104.57	104.739166666667	-0.169166666666675
21	105.39	104.876666666667	0.513333333333336
22	105.15	105.239166666667	-0.0891666666666628
23	106.13	105.931666666667	0.198333333333328
24	105.46	106.046666666667	-0.586666666666675
25	106.47	107.106166666667	-0.636166666666675
26	106.62	107.578666666667	-0.958666666666668
27	106.52	108.088666666667	-1.56866666666667
28	108.04	108.996166666667	-0.956166666666662
29	107.15	109.411166666667	-2.26116666666666
30	107.32	109.133666666667	-1.81366666666667
31	107.76	109.321166666667	-1.56116666666666
32	107.26	108.5765	-1.31650000000000
33	107.89	108.714	-0.823999999999995
34	109.08	109.0765	0.00349999999999862
35	110.4	109.769	0.631000000000009
36	111.03	109.884	1.14600000000000
37	112.05	110.9435	1.10649999999999
38	112.28	111.416	0.863999999999999
39	112.8	111.926	0.874000000000003
40	114.17	112.8335	1.3365
41	114.92	113.2485	1.6715
42	114.65	112.971	1.67900000000001
43	115.49	113.1585	2.3315
44	114.67	114.4325	0.237499999999999
45	114.71	114.57	0.139999999999997
46	115.15	114.9325	0.217500000000004
47	115.03	115.625	-0.595

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.29 & 99.4315 & 0.858500000000033 \tabularnewline
2 & 101.12 & 99.904 & 1.21599999999999 \tabularnewline
3 & 102.65 & 100.414 & 2.23600000000001 \tabularnewline
4 & 102.71 & 101.3215 & 1.38849999999999 \tabularnewline
5 & 103.39 & 101.7365 & 1.65350000000000 \tabularnewline
6 & 102.8 & 101.459 & 1.34100000000000 \tabularnewline
7 & 102.07 & 101.6465 & 0.423499999999990 \tabularnewline
8 & 102.15 & 100.901833333333 & 1.24816666666667 \tabularnewline
9 & 101.21 & 101.039333333333 & 0.170666666666661 \tabularnewline
10 & 101.27 & 101.401833333333 & -0.131833333333340 \tabularnewline
11 & 101.86 & 102.094333333333 & -0.234333333333337 \tabularnewline
12 & 101.65 & 102.209333333333 & -0.55933333333333 \tabularnewline
13 & 101.94 & 103.268833333333 & -1.32883333333334 \tabularnewline
14 & 102.62 & 103.741333333333 & -1.12133333333332 \tabularnewline
15 & 102.71 & 104.251333333333 & -1.54133333333334 \tabularnewline
16 & 103.39 & 105.158833333333 & -1.76883333333334 \tabularnewline
17 & 104.51 & 105.573833333333 & -1.06383333333333 \tabularnewline
18 & 104.09 & 105.296333333333 & -1.20633333333333 \tabularnewline
19 & 104.29 & 105.483833333333 & -1.19383333333333 \tabularnewline
20 & 104.57 & 104.739166666667 & -0.169166666666675 \tabularnewline
21 & 105.39 & 104.876666666667 & 0.513333333333336 \tabularnewline
22 & 105.15 & 105.239166666667 & -0.0891666666666628 \tabularnewline
23 & 106.13 & 105.931666666667 & 0.198333333333328 \tabularnewline
24 & 105.46 & 106.046666666667 & -0.586666666666675 \tabularnewline
25 & 106.47 & 107.106166666667 & -0.636166666666675 \tabularnewline
26 & 106.62 & 107.578666666667 & -0.958666666666668 \tabularnewline
27 & 106.52 & 108.088666666667 & -1.56866666666667 \tabularnewline
28 & 108.04 & 108.996166666667 & -0.956166666666662 \tabularnewline
29 & 107.15 & 109.411166666667 & -2.26116666666666 \tabularnewline
30 & 107.32 & 109.133666666667 & -1.81366666666667 \tabularnewline
31 & 107.76 & 109.321166666667 & -1.56116666666666 \tabularnewline
32 & 107.26 & 108.5765 & -1.31650000000000 \tabularnewline
33 & 107.89 & 108.714 & -0.823999999999995 \tabularnewline
34 & 109.08 & 109.0765 & 0.00349999999999862 \tabularnewline
35 & 110.4 & 109.769 & 0.631000000000009 \tabularnewline
36 & 111.03 & 109.884 & 1.14600000000000 \tabularnewline
37 & 112.05 & 110.9435 & 1.10649999999999 \tabularnewline
38 & 112.28 & 111.416 & 0.863999999999999 \tabularnewline
39 & 112.8 & 111.926 & 0.874000000000003 \tabularnewline
40 & 114.17 & 112.8335 & 1.3365 \tabularnewline
41 & 114.92 & 113.2485 & 1.6715 \tabularnewline
42 & 114.65 & 112.971 & 1.67900000000001 \tabularnewline
43 & 115.49 & 113.1585 & 2.3315 \tabularnewline
44 & 114.67 & 114.4325 & 0.237499999999999 \tabularnewline
45 & 114.71 & 114.57 & 0.139999999999997 \tabularnewline
46 & 115.15 & 114.9325 & 0.217500000000004 \tabularnewline
47 & 115.03 & 115.625 & -0.595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35935&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]100.29[/C][C]99.4315[/C][C]0.858500000000033[/C][/ROW]
[ROW][C]2[/C][C]101.12[/C][C]99.904[/C][C]1.21599999999999[/C][/ROW]
[ROW][C]3[/C][C]102.65[/C][C]100.414[/C][C]2.23600000000001[/C][/ROW]
[ROW][C]4[/C][C]102.71[/C][C]101.3215[/C][C]1.38849999999999[/C][/ROW]
[ROW][C]5[/C][C]103.39[/C][C]101.7365[/C][C]1.65350000000000[/C][/ROW]
[ROW][C]6[/C][C]102.8[/C][C]101.459[/C][C]1.34100000000000[/C][/ROW]
[ROW][C]7[/C][C]102.07[/C][C]101.6465[/C][C]0.423499999999990[/C][/ROW]
[ROW][C]8[/C][C]102.15[/C][C]100.901833333333[/C][C]1.24816666666667[/C][/ROW]
[ROW][C]9[/C][C]101.21[/C][C]101.039333333333[/C][C]0.170666666666661[/C][/ROW]
[ROW][C]10[/C][C]101.27[/C][C]101.401833333333[/C][C]-0.131833333333340[/C][/ROW]
[ROW][C]11[/C][C]101.86[/C][C]102.094333333333[/C][C]-0.234333333333337[/C][/ROW]
[ROW][C]12[/C][C]101.65[/C][C]102.209333333333[/C][C]-0.55933333333333[/C][/ROW]
[ROW][C]13[/C][C]101.94[/C][C]103.268833333333[/C][C]-1.32883333333334[/C][/ROW]
[ROW][C]14[/C][C]102.62[/C][C]103.741333333333[/C][C]-1.12133333333332[/C][/ROW]
[ROW][C]15[/C][C]102.71[/C][C]104.251333333333[/C][C]-1.54133333333334[/C][/ROW]
[ROW][C]16[/C][C]103.39[/C][C]105.158833333333[/C][C]-1.76883333333334[/C][/ROW]
[ROW][C]17[/C][C]104.51[/C][C]105.573833333333[/C][C]-1.06383333333333[/C][/ROW]
[ROW][C]18[/C][C]104.09[/C][C]105.296333333333[/C][C]-1.20633333333333[/C][/ROW]
[ROW][C]19[/C][C]104.29[/C][C]105.483833333333[/C][C]-1.19383333333333[/C][/ROW]
[ROW][C]20[/C][C]104.57[/C][C]104.739166666667[/C][C]-0.169166666666675[/C][/ROW]
[ROW][C]21[/C][C]105.39[/C][C]104.876666666667[/C][C]0.513333333333336[/C][/ROW]
[ROW][C]22[/C][C]105.15[/C][C]105.239166666667[/C][C]-0.0891666666666628[/C][/ROW]
[ROW][C]23[/C][C]106.13[/C][C]105.931666666667[/C][C]0.198333333333328[/C][/ROW]
[ROW][C]24[/C][C]105.46[/C][C]106.046666666667[/C][C]-0.586666666666675[/C][/ROW]
[ROW][C]25[/C][C]106.47[/C][C]107.106166666667[/C][C]-0.636166666666675[/C][/ROW]
[ROW][C]26[/C][C]106.62[/C][C]107.578666666667[/C][C]-0.958666666666668[/C][/ROW]
[ROW][C]27[/C][C]106.52[/C][C]108.088666666667[/C][C]-1.56866666666667[/C][/ROW]
[ROW][C]28[/C][C]108.04[/C][C]108.996166666667[/C][C]-0.956166666666662[/C][/ROW]
[ROW][C]29[/C][C]107.15[/C][C]109.411166666667[/C][C]-2.26116666666666[/C][/ROW]
[ROW][C]30[/C][C]107.32[/C][C]109.133666666667[/C][C]-1.81366666666667[/C][/ROW]
[ROW][C]31[/C][C]107.76[/C][C]109.321166666667[/C][C]-1.56116666666666[/C][/ROW]
[ROW][C]32[/C][C]107.26[/C][C]108.5765[/C][C]-1.31650000000000[/C][/ROW]
[ROW][C]33[/C][C]107.89[/C][C]108.714[/C][C]-0.823999999999995[/C][/ROW]
[ROW][C]34[/C][C]109.08[/C][C]109.0765[/C][C]0.00349999999999862[/C][/ROW]
[ROW][C]35[/C][C]110.4[/C][C]109.769[/C][C]0.631000000000009[/C][/ROW]
[ROW][C]36[/C][C]111.03[/C][C]109.884[/C][C]1.14600000000000[/C][/ROW]
[ROW][C]37[/C][C]112.05[/C][C]110.9435[/C][C]1.10649999999999[/C][/ROW]
[ROW][C]38[/C][C]112.28[/C][C]111.416[/C][C]0.863999999999999[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]111.926[/C][C]0.874000000000003[/C][/ROW]
[ROW][C]40[/C][C]114.17[/C][C]112.8335[/C][C]1.3365[/C][/ROW]
[ROW][C]41[/C][C]114.92[/C][C]113.2485[/C][C]1.6715[/C][/ROW]
[ROW][C]42[/C][C]114.65[/C][C]112.971[/C][C]1.67900000000001[/C][/ROW]
[ROW][C]43[/C][C]115.49[/C][C]113.1585[/C][C]2.3315[/C][/ROW]
[ROW][C]44[/C][C]114.67[/C][C]114.4325[/C][C]0.237499999999999[/C][/ROW]
[ROW][C]45[/C][C]114.71[/C][C]114.57[/C][C]0.139999999999997[/C][/ROW]
[ROW][C]46[/C][C]115.15[/C][C]114.9325[/C][C]0.217500000000004[/C][/ROW]
[ROW][C]47[/C][C]115.03[/C][C]115.625[/C][C]-0.595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35935&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35935&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	100.29	99.4315	0.858500000000033
2	101.12	99.904	1.21599999999999
3	102.65	100.414	2.23600000000001
4	102.71	101.3215	1.38849999999999
5	103.39	101.7365	1.65350000000000
6	102.8	101.459	1.34100000000000
7	102.07	101.6465	0.423499999999990
8	102.15	100.901833333333	1.24816666666667
9	101.21	101.039333333333	0.170666666666661
10	101.27	101.401833333333	-0.131833333333340
11	101.86	102.094333333333	-0.234333333333337
12	101.65	102.209333333333	-0.55933333333333
13	101.94	103.268833333333	-1.32883333333334
14	102.62	103.741333333333	-1.12133333333332
15	102.71	104.251333333333	-1.54133333333334
16	103.39	105.158833333333	-1.76883333333334
17	104.51	105.573833333333	-1.06383333333333
18	104.09	105.296333333333	-1.20633333333333
19	104.29	105.483833333333	-1.19383333333333
20	104.57	104.739166666667	-0.169166666666675
21	105.39	104.876666666667	0.513333333333336
22	105.15	105.239166666667	-0.0891666666666628
23	106.13	105.931666666667	0.198333333333328
24	105.46	106.046666666667	-0.586666666666675
25	106.47	107.106166666667	-0.636166666666675
26	106.62	107.578666666667	-0.958666666666668
27	106.52	108.088666666667	-1.56866666666667
28	108.04	108.996166666667	-0.956166666666662
29	107.15	109.411166666667	-2.26116666666666
30	107.32	109.133666666667	-1.81366666666667
31	107.76	109.321166666667	-1.56116666666666
32	107.26	108.5765	-1.31650000000000
33	107.89	108.714	-0.823999999999995
34	109.08	109.0765	0.00349999999999862
35	110.4	109.769	0.631000000000009
36	111.03	109.884	1.14600000000000
37	112.05	110.9435	1.10649999999999
38	112.28	111.416	0.863999999999999
39	112.8	111.926	0.874000000000003
40	114.17	112.8335	1.3365
41	114.92	113.2485	1.6715
42	114.65	112.971	1.67900000000001
43	115.49	113.1585	2.3315
44	114.67	114.4325	0.237499999999999
45	114.71	114.57	0.139999999999997
46	115.15	114.9325	0.217500000000004
47	115.03	115.625	-0.595

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.120015601660755	0.240031203321509	0.879984398339245
18	0.048637052519212	0.097274105038424	0.951362947480788
19	0.0492622795460677	0.0985245590921354	0.950737720453932
20	0.0570705209720304	0.114141041944061	0.94292947902797
21	0.370702473713990	0.741404947427981	0.62929752628601
22	0.537124632087676	0.925750735824648	0.462875367912324
23	0.873944811581093	0.252110376837814	0.126055188418907
24	0.868173743185215	0.263652513629571	0.131826256814785
25	0.884504905459659	0.230990189080683	0.115495094540341
26	0.877911517854512	0.244176964290976	0.122088482145488
27	0.823794841957972	0.352410316084055	0.176205158042028
28	0.829059840705331	0.341880318589337	0.170940159294669
29	0.710021473996684	0.579957052006633	0.289978526003316
30	0.537460413802705	0.92507917239459	0.462539586197295

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.120015601660755 & 0.240031203321509 & 0.879984398339245 \tabularnewline
18 & 0.048637052519212 & 0.097274105038424 & 0.951362947480788 \tabularnewline
19 & 0.0492622795460677 & 0.0985245590921354 & 0.950737720453932 \tabularnewline
20 & 0.0570705209720304 & 0.114141041944061 & 0.94292947902797 \tabularnewline
21 & 0.370702473713990 & 0.741404947427981 & 0.62929752628601 \tabularnewline
22 & 0.537124632087676 & 0.925750735824648 & 0.462875367912324 \tabularnewline
23 & 0.873944811581093 & 0.252110376837814 & 0.126055188418907 \tabularnewline
24 & 0.868173743185215 & 0.263652513629571 & 0.131826256814785 \tabularnewline
25 & 0.884504905459659 & 0.230990189080683 & 0.115495094540341 \tabularnewline
26 & 0.877911517854512 & 0.244176964290976 & 0.122088482145488 \tabularnewline
27 & 0.823794841957972 & 0.352410316084055 & 0.176205158042028 \tabularnewline
28 & 0.829059840705331 & 0.341880318589337 & 0.170940159294669 \tabularnewline
29 & 0.710021473996684 & 0.579957052006633 & 0.289978526003316 \tabularnewline
30 & 0.537460413802705 & 0.92507917239459 & 0.462539586197295 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35935&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]17[/C][C]0.120015601660755[/C][C]0.240031203321509[/C][C]0.879984398339245[/C][/ROW]
[ROW][C]18[/C][C]0.048637052519212[/C][C]0.097274105038424[/C][C]0.951362947480788[/C][/ROW]
[ROW][C]19[/C][C]0.0492622795460677[/C][C]0.0985245590921354[/C][C]0.950737720453932[/C][/ROW]
[ROW][C]20[/C][C]0.0570705209720304[/C][C]0.114141041944061[/C][C]0.94292947902797[/C][/ROW]
[ROW][C]21[/C][C]0.370702473713990[/C][C]0.741404947427981[/C][C]0.62929752628601[/C][/ROW]
[ROW][C]22[/C][C]0.537124632087676[/C][C]0.925750735824648[/C][C]0.462875367912324[/C][/ROW]
[ROW][C]23[/C][C]0.873944811581093[/C][C]0.252110376837814[/C][C]0.126055188418907[/C][/ROW]
[ROW][C]24[/C][C]0.868173743185215[/C][C]0.263652513629571[/C][C]0.131826256814785[/C][/ROW]
[ROW][C]25[/C][C]0.884504905459659[/C][C]0.230990189080683[/C][C]0.115495094540341[/C][/ROW]
[ROW][C]26[/C][C]0.877911517854512[/C][C]0.244176964290976[/C][C]0.122088482145488[/C][/ROW]
[ROW][C]27[/C][C]0.823794841957972[/C][C]0.352410316084055[/C][C]0.176205158042028[/C][/ROW]
[ROW][C]28[/C][C]0.829059840705331[/C][C]0.341880318589337[/C][C]0.170940159294669[/C][/ROW]
[ROW][C]29[/C][C]0.710021473996684[/C][C]0.579957052006633[/C][C]0.289978526003316[/C][/ROW]
[ROW][C]30[/C][C]0.537460413802705[/C][C]0.92507917239459[/C][C]0.462539586197295[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35935&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35935&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
17	0.120015601660755	0.240031203321509	0.879984398339245
18	0.048637052519212	0.097274105038424	0.951362947480788
19	0.0492622795460677	0.0985245590921354	0.950737720453932
20	0.0570705209720304	0.114141041944061	0.94292947902797
21	0.370702473713990	0.741404947427981	0.62929752628601
22	0.537124632087676	0.925750735824648	0.462875367912324
23	0.873944811581093	0.252110376837814	0.126055188418907
24	0.868173743185215	0.263652513629571	0.131826256814785
25	0.884504905459659	0.230990189080683	0.115495094540341
26	0.877911517854512	0.244176964290976	0.122088482145488
27	0.823794841957972	0.352410316084055	0.176205158042028
28	0.829059840705331	0.341880318589337	0.170940159294669
29	0.710021473996684	0.579957052006633	0.289978526003316
30	0.537460413802705	0.92507917239459	0.462539586197295

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	0.142857142857143	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 & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.142857142857143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35935&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]0.142857142857143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35935&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35935&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	0.142857142857143	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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