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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 12 Dec 2010 18:20:28 +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/Dec/12/t1292177942142ssssezg8wrjn.htm/, Retrieved Tue, 07 May 2024 20:50:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108598, Retrieved Tue, 07 May 2024 20:50:53 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

133

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

-       [Multiple Regression] [Workshop 10 part ...] [2010-12-12 18:20:28] [c9b1b69acb8f4b2b921fdfd5091a94b7] [Current]

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Dataseries X:

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Histogram

Boxplots

2.0	1000	42.0	3	3
1.8	2547000	624.0	3	4
.7	10550	180.0	4	4
3.9	0,023	35.0	1	1
1.0	160000	392.0	4	4
3.6	3300	63.0	1	1
1.4	52160	230.0	1	1
1.5	0,425	112.0	5	4
.7	465000	281.0	5	5
2.1	0,075	42.0	1	1
4.1	0,785	42.0	2	2
1.2	0,2	120.0	2	2
.5	27660	148.0	5	5
3.4	0,12	16.0	3	2
1.5	85000	310.0	1	1
3.4	0,101	28.0	5	3
.8	1040	68.0	5	4
.8	521000	336.0	5	5
2.0	0,01	50.0	1	1
1.9	62000	267.0	1	1
1.3	0,023	19.0	4	3
5.6	1700	12.0	2	1
3.1	3500	120.0	2	1
1.8	0,48	140.0	2	2
.9	10000	170.0	4	4
1.8	1620	17.0	2	2
1.9	192000	115.0	4	4
.9	2500	31.0	5	5
2.6	0,28	21.0	3	3
2.4	4235	52.0	1	1
1.2	6800	164.0	2	2
.9	0,75	225.0	2	2
.5	3600	225.0	3	3
.6	55500	151.0	5	5
2.3	0,9	60.0	2	2
.5	2000	200.0	3	3
2.6	0,104	46.0	3	2
.6	4190	210.0	4	4
6.6	3500	14.0	2	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108598&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108598&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108598&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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.7791885546538 + 1.90547089316906e-06Wb[t] -0.00579207803438209Tg[t] + 0.798598377850773P[t] -1.32821473057373`D `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.7791885546538 +  1.90547089316906e-06Wb[t] -0.00579207803438209Tg[t] +  0.798598377850773P[t] -1.32821473057373`D
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108598&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.7791885546538 +  1.90547089316906e-06Wb[t] -0.00579207803438209Tg[t] +  0.798598377850773P[t] -1.32821473057373`D
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108598&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108598&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
PS[t] = + 3.7791885546538 + 1.90547089316906e-06Wb[t] -0.00579207803438209Tg[t] + 0.798598377850773P[t] -1.32821473057373`D `[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)	3.7791885546538	0.360856	10.4728	0	0
Wb	1.90547089316906e-06	0	3.8792	0.000457	0.000229
Tg	-0.00579207803438209	0.001741	-3.3265	0.002119	0.00106
P	0.798598377850773	0.313457	2.5477	0.015534	0.007767
`D `	-1.32821473057373	0.33622	-3.9504	0.000373	0.000187

\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) & 3.7791885546538 & 0.360856 & 10.4728 & 0 & 0 \tabularnewline
Wb & 1.90547089316906e-06 & 0 & 3.8792 & 0.000457 & 0.000229 \tabularnewline
Tg & -0.00579207803438209 & 0.001741 & -3.3265 & 0.002119 & 0.00106 \tabularnewline
P & 0.798598377850773 & 0.313457 & 2.5477 & 0.015534 & 0.007767 \tabularnewline
`D
` & -1.32821473057373 & 0.33622 & -3.9504 & 0.000373 & 0.000187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108598&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]3.7791885546538[/C][C]0.360856[/C][C]10.4728[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Wb[/C][C]1.90547089316906e-06[/C][C]0[/C][C]3.8792[/C][C]0.000457[/C][C]0.000229[/C][/ROW]
[ROW][C]Tg[/C][C]-0.00579207803438209[/C][C]0.001741[/C][C]-3.3265[/C][C]0.002119[/C][C]0.00106[/C][/ROW]
[ROW][C]P[/C][C]0.798598377850773[/C][C]0.313457[/C][C]2.5477[/C][C]0.015534[/C][C]0.007767[/C][/ROW]
[ROW][C]`D
`[/C][C]-1.32821473057373[/C][C]0.33622[/C][C]-3.9504[/C][C]0.000373[/C][C]0.000187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108598&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108598&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)	3.7791885546538	0.360856	10.4728	0	0
Wb	1.90547089316906e-06	0	3.8792	0.000457	0.000229
Tg	-0.00579207803438209	0.001741	-3.3265	0.002119	0.00106
P	0.798598377850773	0.313457	2.5477	0.015534	0.007767
`D `	-1.32821473057373	0.33622	-3.9504	0.000373	0.000187

Multiple Linear Regression - Regression Statistics
Multiple R	0.808413774271965
R-squared	0.653532830432643
Adjusted R-squared	0.61277198695413
F-TEST (value)	16.0333490345252
F-TEST (DF numerator)	4
F-TEST (DF denominator)	34
p-value	1.80884655032187e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.874656246105222
Sum Squared Residuals	26.0108006609299

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.808413774271965 \tabularnewline
R-squared & 0.653532830432643 \tabularnewline
Adjusted R-squared & 0.61277198695413 \tabularnewline
F-TEST (value) & 16.0333490345252 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 1.80884655032187e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.874656246105222 \tabularnewline
Sum Squared Residuals & 26.0108006609299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108598&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.808413774271965[/C][/ROW]
[ROW][C]R-squared[/C][C]0.653532830432643[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.61277198695413[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.0333490345252[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]1.80884655032187e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.874656246105222[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]26.0108006609299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108598&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108598&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	0.808413774271965
R-squared	0.653532830432643
Adjusted R-squared	0.61277198695413
F-TEST (value)	16.0333490345252
F-TEST (DF numerator)	4
F-TEST (DF denominator)	34
p-value	1.80884655032187e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.874656246105222
Sum Squared Residuals	26.0108006609299

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2	1.94897768993404	0.0510223100659579
2	1.8	2.10110243735835	-0.301102437358345
3	0.7	0.638251815496113	0.0617481845038873
4	3.9	3.0468495145533	0.853150485446703
5	1	-0.304896102808774	1.30489610280877
6	3.6	2.89095933971223	0.709040660287774
7	1.4	2.01678361581066	-0.616783615810657
8	1.5	1.81060959158707	-0.310609591587066
9	0.7	0.38957682870124	0.31042317129876
10	2.1	3.00630506739711	-0.906305067397108
11	4.1	2.47669006755848	1.62330993244152
12	1.2	2.02490686617621	-0.824906866176207
13	0.5	0.326584566855503	0.173415433144497
14	3.4	3.42588120716505	-0.0258812071650459
15	1.5	1.61599303719176	-0.115993037191761
16	3.4	3.62535825967633	-0.225358259676327
17	0.8	2.06744190500364	-1.26744190500364
18	0.8	0.177718906827692	0.622281093172308
19	2	2.95996831926644	-0.959968319266444
20	1.9	1.8212265621273	0.0787734378726974
21	1.3	2.87888843550826	-1.57888843550826
22	5.6	3.98190494388742	1.61809505611258
23	3.1	3.35979036378185	-0.259790363781854
24	1.8	1.90906583902041	-0.109065839020415
25	0.9	0.695124586848692	0.204875413151308
26	1.8	2.62457738547032	-0.824577385470317
27	1.9	1.36048458129648	0.539515418703524
28	0.9	0.956316049206074	-0.0563160492060738
29	2.6	2.06870639129474	0.531293608705256
30	2.4	2.95645381337554	-0.556453813375542
31	1.2	1.78301225364277	-0.583012253642766
32	0.9	1.41673972057508	-0.516739720575078
33	0.5	0.893981633964356	-0.393981633964356
34	0.6	0.362256642418183	0.237743357581817
35	2.3	2.37243288206876	-0.0724328820687575
36	0.5	1.03573483139484	-0.535734831394838
37	2.6	3.25211883564605	-0.652118835646049
38	0.6	0.452370679584096	0.147629320415904
39	6.6	3.97375063542636	2.62624936457364

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.94897768993404 & 0.0510223100659579 \tabularnewline
2 & 1.8 & 2.10110243735835 & -0.301102437358345 \tabularnewline
3 & 0.7 & 0.638251815496113 & 0.0617481845038873 \tabularnewline
4 & 3.9 & 3.0468495145533 & 0.853150485446703 \tabularnewline
5 & 1 & -0.304896102808774 & 1.30489610280877 \tabularnewline
6 & 3.6 & 2.89095933971223 & 0.709040660287774 \tabularnewline
7 & 1.4 & 2.01678361581066 & -0.616783615810657 \tabularnewline
8 & 1.5 & 1.81060959158707 & -0.310609591587066 \tabularnewline
9 & 0.7 & 0.38957682870124 & 0.31042317129876 \tabularnewline
10 & 2.1 & 3.00630506739711 & -0.906305067397108 \tabularnewline
11 & 4.1 & 2.47669006755848 & 1.62330993244152 \tabularnewline
12 & 1.2 & 2.02490686617621 & -0.824906866176207 \tabularnewline
13 & 0.5 & 0.326584566855503 & 0.173415433144497 \tabularnewline
14 & 3.4 & 3.42588120716505 & -0.0258812071650459 \tabularnewline
15 & 1.5 & 1.61599303719176 & -0.115993037191761 \tabularnewline
16 & 3.4 & 3.62535825967633 & -0.225358259676327 \tabularnewline
17 & 0.8 & 2.06744190500364 & -1.26744190500364 \tabularnewline
18 & 0.8 & 0.177718906827692 & 0.622281093172308 \tabularnewline
19 & 2 & 2.95996831926644 & -0.959968319266444 \tabularnewline
20 & 1.9 & 1.8212265621273 & 0.0787734378726974 \tabularnewline
21 & 1.3 & 2.87888843550826 & -1.57888843550826 \tabularnewline
22 & 5.6 & 3.98190494388742 & 1.61809505611258 \tabularnewline
23 & 3.1 & 3.35979036378185 & -0.259790363781854 \tabularnewline
24 & 1.8 & 1.90906583902041 & -0.109065839020415 \tabularnewline
25 & 0.9 & 0.695124586848692 & 0.204875413151308 \tabularnewline
26 & 1.8 & 2.62457738547032 & -0.824577385470317 \tabularnewline
27 & 1.9 & 1.36048458129648 & 0.539515418703524 \tabularnewline
28 & 0.9 & 0.956316049206074 & -0.0563160492060738 \tabularnewline
29 & 2.6 & 2.06870639129474 & 0.531293608705256 \tabularnewline
30 & 2.4 & 2.95645381337554 & -0.556453813375542 \tabularnewline
31 & 1.2 & 1.78301225364277 & -0.583012253642766 \tabularnewline
32 & 0.9 & 1.41673972057508 & -0.516739720575078 \tabularnewline
33 & 0.5 & 0.893981633964356 & -0.393981633964356 \tabularnewline
34 & 0.6 & 0.362256642418183 & 0.237743357581817 \tabularnewline
35 & 2.3 & 2.37243288206876 & -0.0724328820687575 \tabularnewline
36 & 0.5 & 1.03573483139484 & -0.535734831394838 \tabularnewline
37 & 2.6 & 3.25211883564605 & -0.652118835646049 \tabularnewline
38 & 0.6 & 0.452370679584096 & 0.147629320415904 \tabularnewline
39 & 6.6 & 3.97375063542636 & 2.62624936457364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108598&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]2[/C][C]1.94897768993404[/C][C]0.0510223100659579[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]2.10110243735835[/C][C]-0.301102437358345[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.638251815496113[/C][C]0.0617481845038873[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]3.0468495145533[/C][C]0.853150485446703[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]-0.304896102808774[/C][C]1.30489610280877[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]2.89095933971223[/C][C]0.709040660287774[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]2.01678361581066[/C][C]-0.616783615810657[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]1.81060959158707[/C][C]-0.310609591587066[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.38957682870124[/C][C]0.31042317129876[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]3.00630506739711[/C][C]-0.906305067397108[/C][/ROW]
[ROW][C]11[/C][C]4.1[/C][C]2.47669006755848[/C][C]1.62330993244152[/C][/ROW]
[ROW][C]12[/C][C]1.2[/C][C]2.02490686617621[/C][C]-0.824906866176207[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]0.326584566855503[/C][C]0.173415433144497[/C][/ROW]
[ROW][C]14[/C][C]3.4[/C][C]3.42588120716505[/C][C]-0.0258812071650459[/C][/ROW]
[ROW][C]15[/C][C]1.5[/C][C]1.61599303719176[/C][C]-0.115993037191761[/C][/ROW]
[ROW][C]16[/C][C]3.4[/C][C]3.62535825967633[/C][C]-0.225358259676327[/C][/ROW]
[ROW][C]17[/C][C]0.8[/C][C]2.06744190500364[/C][C]-1.26744190500364[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.177718906827692[/C][C]0.622281093172308[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]2.95996831926644[/C][C]-0.959968319266444[/C][/ROW]
[ROW][C]20[/C][C]1.9[/C][C]1.8212265621273[/C][C]0.0787734378726974[/C][/ROW]
[ROW][C]21[/C][C]1.3[/C][C]2.87888843550826[/C][C]-1.57888843550826[/C][/ROW]
[ROW][C]22[/C][C]5.6[/C][C]3.98190494388742[/C][C]1.61809505611258[/C][/ROW]
[ROW][C]23[/C][C]3.1[/C][C]3.35979036378185[/C][C]-0.259790363781854[/C][/ROW]
[ROW][C]24[/C][C]1.8[/C][C]1.90906583902041[/C][C]-0.109065839020415[/C][/ROW]
[ROW][C]25[/C][C]0.9[/C][C]0.695124586848692[/C][C]0.204875413151308[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]2.62457738547032[/C][C]-0.824577385470317[/C][/ROW]
[ROW][C]27[/C][C]1.9[/C][C]1.36048458129648[/C][C]0.539515418703524[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]0.956316049206074[/C][C]-0.0563160492060738[/C][/ROW]
[ROW][C]29[/C][C]2.6[/C][C]2.06870639129474[/C][C]0.531293608705256[/C][/ROW]
[ROW][C]30[/C][C]2.4[/C][C]2.95645381337554[/C][C]-0.556453813375542[/C][/ROW]
[ROW][C]31[/C][C]1.2[/C][C]1.78301225364277[/C][C]-0.583012253642766[/C][/ROW]
[ROW][C]32[/C][C]0.9[/C][C]1.41673972057508[/C][C]-0.516739720575078[/C][/ROW]
[ROW][C]33[/C][C]0.5[/C][C]0.893981633964356[/C][C]-0.393981633964356[/C][/ROW]
[ROW][C]34[/C][C]0.6[/C][C]0.362256642418183[/C][C]0.237743357581817[/C][/ROW]
[ROW][C]35[/C][C]2.3[/C][C]2.37243288206876[/C][C]-0.0724328820687575[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]1.03573483139484[/C][C]-0.535734831394838[/C][/ROW]
[ROW][C]37[/C][C]2.6[/C][C]3.25211883564605[/C][C]-0.652118835646049[/C][/ROW]
[ROW][C]38[/C][C]0.6[/C][C]0.452370679584096[/C][C]0.147629320415904[/C][/ROW]
[ROW][C]39[/C][C]6.6[/C][C]3.97375063542636[/C][C]2.62624936457364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108598&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108598&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	2	1.94897768993404	0.0510223100659579
2	1.8	2.10110243735835	-0.301102437358345
3	0.7	0.638251815496113	0.0617481845038873
4	3.9	3.0468495145533	0.853150485446703
5	1	-0.304896102808774	1.30489610280877
6	3.6	2.89095933971223	0.709040660287774
7	1.4	2.01678361581066	-0.616783615810657
8	1.5	1.81060959158707	-0.310609591587066
9	0.7	0.38957682870124	0.31042317129876
10	2.1	3.00630506739711	-0.906305067397108
11	4.1	2.47669006755848	1.62330993244152
12	1.2	2.02490686617621	-0.824906866176207
13	0.5	0.326584566855503	0.173415433144497
14	3.4	3.42588120716505	-0.0258812071650459
15	1.5	1.61599303719176	-0.115993037191761
16	3.4	3.62535825967633	-0.225358259676327
17	0.8	2.06744190500364	-1.26744190500364
18	0.8	0.177718906827692	0.622281093172308
19	2	2.95996831926644	-0.959968319266444
20	1.9	1.8212265621273	0.0787734378726974
21	1.3	2.87888843550826	-1.57888843550826
22	5.6	3.98190494388742	1.61809505611258
23	3.1	3.35979036378185	-0.259790363781854
24	1.8	1.90906583902041	-0.109065839020415
25	0.9	0.695124586848692	0.204875413151308
26	1.8	2.62457738547032	-0.824577385470317
27	1.9	1.36048458129648	0.539515418703524
28	0.9	0.956316049206074	-0.0563160492060738
29	2.6	2.06870639129474	0.531293608705256
30	2.4	2.95645381337554	-0.556453813375542
31	1.2	1.78301225364277	-0.583012253642766
32	0.9	1.41673972057508	-0.516739720575078
33	0.5	0.893981633964356	-0.393981633964356
34	0.6	0.362256642418183	0.237743357581817
35	2.3	2.37243288206876	-0.0724328820687575
36	0.5	1.03573483139484	-0.535734831394838
37	2.6	3.25211883564605	-0.652118835646049
38	0.6	0.452370679584096	0.147629320415904
39	6.6	3.97375063542636	2.62624936457364

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.586840715909917	0.826318568180165	0.413159284090083
9	0.412293834211476	0.824587668422952	0.587706165788524
10	0.45425694402563	0.90851388805126	0.54574305597437
11	0.657867005129615	0.68426598974077	0.342132994870385
12	0.680291483240805	0.639417033518389	0.319708516759195
13	0.595874286601288	0.808251426797424	0.404125713398712
14	0.488132956515164	0.976265913030327	0.511867043484836
15	0.376855442148438	0.753710884296875	0.623144557851562
16	0.280404167918775	0.56080833583755	0.719595832081225
17	0.374908648507673	0.749817297015346	0.625091351492327
18	0.304474533131021	0.608949066262041	0.69552546686898
19	0.310921518013934	0.621843036027868	0.689078481986066
20	0.222577130315356	0.445154260630711	0.777422869684644
21	0.541226277646141	0.917547444707718	0.458773722353859
22	0.722816936817387	0.554366126365225	0.277183063182613
23	0.687299426998904	0.625401146002192	0.312700573001096
24	0.588167412435265	0.82366517512947	0.411832587564735
25	0.488458143955143	0.976916287910285	0.511541856044857
26	0.457229551144933	0.914459102289866	0.542770448855067
27	0.3595745255284	0.7191490510568	0.6404254744716
28	0.253062670633113	0.506125341266225	0.746937329366887
29	0.185543134904849	0.371086269809698	0.81445686509515
30	0.131119586633957	0.262239173267914	0.868880413366043
31	0.0870261008037946	0.174052201607589	0.912973899196205

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.586840715909917 & 0.826318568180165 & 0.413159284090083 \tabularnewline
9 & 0.412293834211476 & 0.824587668422952 & 0.587706165788524 \tabularnewline
10 & 0.45425694402563 & 0.90851388805126 & 0.54574305597437 \tabularnewline
11 & 0.657867005129615 & 0.68426598974077 & 0.342132994870385 \tabularnewline
12 & 0.680291483240805 & 0.639417033518389 & 0.319708516759195 \tabularnewline
13 & 0.595874286601288 & 0.808251426797424 & 0.404125713398712 \tabularnewline
14 & 0.488132956515164 & 0.976265913030327 & 0.511867043484836 \tabularnewline
15 & 0.376855442148438 & 0.753710884296875 & 0.623144557851562 \tabularnewline
16 & 0.280404167918775 & 0.56080833583755 & 0.719595832081225 \tabularnewline
17 & 0.374908648507673 & 0.749817297015346 & 0.625091351492327 \tabularnewline
18 & 0.304474533131021 & 0.608949066262041 & 0.69552546686898 \tabularnewline
19 & 0.310921518013934 & 0.621843036027868 & 0.689078481986066 \tabularnewline
20 & 0.222577130315356 & 0.445154260630711 & 0.777422869684644 \tabularnewline
21 & 0.541226277646141 & 0.917547444707718 & 0.458773722353859 \tabularnewline
22 & 0.722816936817387 & 0.554366126365225 & 0.277183063182613 \tabularnewline
23 & 0.687299426998904 & 0.625401146002192 & 0.312700573001096 \tabularnewline
24 & 0.588167412435265 & 0.82366517512947 & 0.411832587564735 \tabularnewline
25 & 0.488458143955143 & 0.976916287910285 & 0.511541856044857 \tabularnewline
26 & 0.457229551144933 & 0.914459102289866 & 0.542770448855067 \tabularnewline
27 & 0.3595745255284 & 0.7191490510568 & 0.6404254744716 \tabularnewline
28 & 0.253062670633113 & 0.506125341266225 & 0.746937329366887 \tabularnewline
29 & 0.185543134904849 & 0.371086269809698 & 0.81445686509515 \tabularnewline
30 & 0.131119586633957 & 0.262239173267914 & 0.868880413366043 \tabularnewline
31 & 0.0870261008037946 & 0.174052201607589 & 0.912973899196205 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108598&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]0.586840715909917[/C][C]0.826318568180165[/C][C]0.413159284090083[/C][/ROW]
[ROW][C]9[/C][C]0.412293834211476[/C][C]0.824587668422952[/C][C]0.587706165788524[/C][/ROW]
[ROW][C]10[/C][C]0.45425694402563[/C][C]0.90851388805126[/C][C]0.54574305597437[/C][/ROW]
[ROW][C]11[/C][C]0.657867005129615[/C][C]0.68426598974077[/C][C]0.342132994870385[/C][/ROW]
[ROW][C]12[/C][C]0.680291483240805[/C][C]0.639417033518389[/C][C]0.319708516759195[/C][/ROW]
[ROW][C]13[/C][C]0.595874286601288[/C][C]0.808251426797424[/C][C]0.404125713398712[/C][/ROW]
[ROW][C]14[/C][C]0.488132956515164[/C][C]0.976265913030327[/C][C]0.511867043484836[/C][/ROW]
[ROW][C]15[/C][C]0.376855442148438[/C][C]0.753710884296875[/C][C]0.623144557851562[/C][/ROW]
[ROW][C]16[/C][C]0.280404167918775[/C][C]0.56080833583755[/C][C]0.719595832081225[/C][/ROW]
[ROW][C]17[/C][C]0.374908648507673[/C][C]0.749817297015346[/C][C]0.625091351492327[/C][/ROW]
[ROW][C]18[/C][C]0.304474533131021[/C][C]0.608949066262041[/C][C]0.69552546686898[/C][/ROW]
[ROW][C]19[/C][C]0.310921518013934[/C][C]0.621843036027868[/C][C]0.689078481986066[/C][/ROW]
[ROW][C]20[/C][C]0.222577130315356[/C][C]0.445154260630711[/C][C]0.777422869684644[/C][/ROW]
[ROW][C]21[/C][C]0.541226277646141[/C][C]0.917547444707718[/C][C]0.458773722353859[/C][/ROW]
[ROW][C]22[/C][C]0.722816936817387[/C][C]0.554366126365225[/C][C]0.277183063182613[/C][/ROW]
[ROW][C]23[/C][C]0.687299426998904[/C][C]0.625401146002192[/C][C]0.312700573001096[/C][/ROW]
[ROW][C]24[/C][C]0.588167412435265[/C][C]0.82366517512947[/C][C]0.411832587564735[/C][/ROW]
[ROW][C]25[/C][C]0.488458143955143[/C][C]0.976916287910285[/C][C]0.511541856044857[/C][/ROW]
[ROW][C]26[/C][C]0.457229551144933[/C][C]0.914459102289866[/C][C]0.542770448855067[/C][/ROW]
[ROW][C]27[/C][C]0.3595745255284[/C][C]0.7191490510568[/C][C]0.6404254744716[/C][/ROW]
[ROW][C]28[/C][C]0.253062670633113[/C][C]0.506125341266225[/C][C]0.746937329366887[/C][/ROW]
[ROW][C]29[/C][C]0.185543134904849[/C][C]0.371086269809698[/C][C]0.81445686509515[/C][/ROW]
[ROW][C]30[/C][C]0.131119586633957[/C][C]0.262239173267914[/C][C]0.868880413366043[/C][/ROW]
[ROW][C]31[/C][C]0.0870261008037946[/C][C]0.174052201607589[/C][C]0.912973899196205[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108598&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108598&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	0.586840715909917	0.826318568180165	0.413159284090083
9	0.412293834211476	0.824587668422952	0.587706165788524
10	0.45425694402563	0.90851388805126	0.54574305597437
11	0.657867005129615	0.68426598974077	0.342132994870385
12	0.680291483240805	0.639417033518389	0.319708516759195
13	0.595874286601288	0.808251426797424	0.404125713398712
14	0.488132956515164	0.976265913030327	0.511867043484836
15	0.376855442148438	0.753710884296875	0.623144557851562
16	0.280404167918775	0.56080833583755	0.719595832081225
17	0.374908648507673	0.749817297015346	0.625091351492327
18	0.304474533131021	0.608949066262041	0.69552546686898
19	0.310921518013934	0.621843036027868	0.689078481986066
20	0.222577130315356	0.445154260630711	0.777422869684644
21	0.541226277646141	0.917547444707718	0.458773722353859
22	0.722816936817387	0.554366126365225	0.277183063182613
23	0.687299426998904	0.625401146002192	0.312700573001096
24	0.588167412435265	0.82366517512947	0.411832587564735
25	0.488458143955143	0.976916287910285	0.511541856044857
26	0.457229551144933	0.914459102289866	0.542770448855067
27	0.3595745255284	0.7191490510568	0.6404254744716
28	0.253062670633113	0.506125341266225	0.746937329366887
29	0.185543134904849	0.371086269809698	0.81445686509515
30	0.131119586633957	0.262239173267914	0.868880413366043
31	0.0870261008037946	0.174052201607589	0.912973899196205

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	0	0	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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108598&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108598&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108598&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	0	0	OK

Figure 1

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Figure 2

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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 = 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