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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 20 Dec 2008 11:01:29 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/20/t1229796279xm0emdsnj3bopcg.htm/, Retrieved Fri, 17 May 2024 11:58:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35428, Retrieved Fri, 17 May 2024 11:58:03 +0000

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Estimated Impact

209

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

-       [Multiple Regression] [Huur] [2008-12-20 18:01:29] [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	6 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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35428&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]6 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=35428&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35428&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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
Huur[t] = + 105.659534883721 + 4.31546511627907Dummy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Huur[t] =  +  105.659534883721 +  4.31546511627907Dummy[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35428&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Huur[t] =  +  105.659534883721 +  4.31546511627907Dummy[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35428&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35428&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
Huur[t] = + 105.659534883721 + 4.31546511627907Dummy[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)	105.659534883721	0.389009	271.6123	0	0
Dummy	4.31546511627907	1.333454	3.2363	0.002274	0.001137

\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) & 105.659534883721 & 0.389009 & 271.6123 & 0 & 0 \tabularnewline
Dummy & 4.31546511627907 & 1.333454 & 3.2363 & 0.002274 & 0.001137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35428&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]105.659534883721[/C][C]0.389009[/C][C]271.6123[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]4.31546511627907[/C][C]1.333454[/C][C]3.2363[/C][C]0.002274[/C][C]0.001137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35428&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35428&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)	105.659534883721	0.389009	271.6123	0	0
Dummy	4.31546511627907	1.333454	3.2363	0.002274	0.001137

Multiple Linear Regression - Regression Statistics
Multiple R	0.434516241095722
R-squared	0.188804363775956
Adjusted R-squared	0.170777794082088
F-TEST (value)	10.4736711965884
F-TEST (DF numerator)	1
F-TEST (DF denominator)	45
p-value	0.00227427025277960
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.55090001850185
Sum Squared Residuals	292.819090697674

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.434516241095722 \tabularnewline
R-squared & 0.188804363775956 \tabularnewline
Adjusted R-squared & 0.170777794082088 \tabularnewline
F-TEST (value) & 10.4736711965884 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 0.00227427025277960 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.55090001850185 \tabularnewline
Sum Squared Residuals & 292.819090697674 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35428&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.434516241095722[/C][/ROW]
[ROW][C]R-squared[/C][C]0.188804363775956[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.170777794082088[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.4736711965884[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/C][/ROW]
[ROW][C]p-value[/C][C]0.00227427025277960[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.55090001850185[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]292.819090697674[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35428&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35428&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	0.434516241095722
R-squared	0.188804363775956
Adjusted R-squared	0.170777794082088
F-TEST (value)	10.4736711965884
F-TEST (DF numerator)	1
F-TEST (DF denominator)	45
p-value	0.00227427025277960
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.55090001850185
Sum Squared Residuals	292.819090697674

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.95	105.659534883721	-4.7095348837209
2	101.26	105.659534883721	-4.39953488372093
3	101.42	105.659534883721	-4.23953488372093
4	101.68	105.659534883721	-3.97953488372092
5	101.75	105.659534883721	-3.90953488372093
6	101.89	105.659534883721	-3.76953488372093
7	102.07	105.659534883721	-3.58953488372094
8	102.22	105.659534883721	-3.43953488372093
9	102.45	105.659534883721	-3.20953488372093
10	102.62	105.659534883721	-3.03953488372093
11	102.67	105.659534883721	-2.98953488372093
12	102.86	105.659534883721	-2.79953488372093
13	104.78	105.659534883721	-0.87953488372093
14	104.87	105.659534883721	-0.789534883720926
15	105.06	105.659534883721	-0.599534883720929
16	105.14	105.659534883721	-0.51953488372093
17	105.32	105.659534883721	-0.339534883720938
18	105.54	105.659534883721	-0.119534883720925
19	105.68	105.659534883721	0.020465116279076
20	105.77	105.659534883721	0.110465116279065
21	106.07	105.659534883721	0.410465116279062
22	106.03	105.659534883721	0.37046511627907
23	106.13	105.659534883721	0.470465116279064
24	106.28	105.659534883721	0.62046511627907
25	106.61	105.659534883721	0.950465116279068
26	106.74	105.659534883721	1.08046511627906
27	107.01	105.659534883721	1.35046511627907
28	107.1	105.659534883721	1.44046511627906
29	107.28	105.659534883721	1.62046511627907
30	107.4	105.659534883721	1.74046511627907
31	107.59	105.659534883721	1.93046511627907
32	107.69	105.659534883721	2.03046511627907
33	107.78	105.659534883721	2.12046511627907
34	108.02	105.659534883721	2.36046511627907
35	108	105.659534883721	2.34046511627907
36	108.07	105.659534883721	2.41046511627906
37	108.36	105.659534883721	2.70046511627907
38	108.74	105.659534883721	3.08046511627906
39	108.99	105.659534883721	3.33046511627906
40	109.21	105.659534883721	3.55046511627906
41	109.31	105.659534883721	3.65046511627907
42	109.41	105.659534883721	3.75046511627907
43	109.54	105.659534883721	3.88046511627907
44	109.81	109.975	-0.164999999999999
45	109.85	109.975	-0.125000000000007
46	110.01	109.975	0.0350000000000039
47	110.23	109.975	0.255000000000003

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.95 & 105.659534883721 & -4.7095348837209 \tabularnewline
2 & 101.26 & 105.659534883721 & -4.39953488372093 \tabularnewline
3 & 101.42 & 105.659534883721 & -4.23953488372093 \tabularnewline
4 & 101.68 & 105.659534883721 & -3.97953488372092 \tabularnewline
5 & 101.75 & 105.659534883721 & -3.90953488372093 \tabularnewline
6 & 101.89 & 105.659534883721 & -3.76953488372093 \tabularnewline
7 & 102.07 & 105.659534883721 & -3.58953488372094 \tabularnewline
8 & 102.22 & 105.659534883721 & -3.43953488372093 \tabularnewline
9 & 102.45 & 105.659534883721 & -3.20953488372093 \tabularnewline
10 & 102.62 & 105.659534883721 & -3.03953488372093 \tabularnewline
11 & 102.67 & 105.659534883721 & -2.98953488372093 \tabularnewline
12 & 102.86 & 105.659534883721 & -2.79953488372093 \tabularnewline
13 & 104.78 & 105.659534883721 & -0.87953488372093 \tabularnewline
14 & 104.87 & 105.659534883721 & -0.789534883720926 \tabularnewline
15 & 105.06 & 105.659534883721 & -0.599534883720929 \tabularnewline
16 & 105.14 & 105.659534883721 & -0.51953488372093 \tabularnewline
17 & 105.32 & 105.659534883721 & -0.339534883720938 \tabularnewline
18 & 105.54 & 105.659534883721 & -0.119534883720925 \tabularnewline
19 & 105.68 & 105.659534883721 & 0.020465116279076 \tabularnewline
20 & 105.77 & 105.659534883721 & 0.110465116279065 \tabularnewline
21 & 106.07 & 105.659534883721 & 0.410465116279062 \tabularnewline
22 & 106.03 & 105.659534883721 & 0.37046511627907 \tabularnewline
23 & 106.13 & 105.659534883721 & 0.470465116279064 \tabularnewline
24 & 106.28 & 105.659534883721 & 0.62046511627907 \tabularnewline
25 & 106.61 & 105.659534883721 & 0.950465116279068 \tabularnewline
26 & 106.74 & 105.659534883721 & 1.08046511627906 \tabularnewline
27 & 107.01 & 105.659534883721 & 1.35046511627907 \tabularnewline
28 & 107.1 & 105.659534883721 & 1.44046511627906 \tabularnewline
29 & 107.28 & 105.659534883721 & 1.62046511627907 \tabularnewline
30 & 107.4 & 105.659534883721 & 1.74046511627907 \tabularnewline
31 & 107.59 & 105.659534883721 & 1.93046511627907 \tabularnewline
32 & 107.69 & 105.659534883721 & 2.03046511627907 \tabularnewline
33 & 107.78 & 105.659534883721 & 2.12046511627907 \tabularnewline
34 & 108.02 & 105.659534883721 & 2.36046511627907 \tabularnewline
35 & 108 & 105.659534883721 & 2.34046511627907 \tabularnewline
36 & 108.07 & 105.659534883721 & 2.41046511627906 \tabularnewline
37 & 108.36 & 105.659534883721 & 2.70046511627907 \tabularnewline
38 & 108.74 & 105.659534883721 & 3.08046511627906 \tabularnewline
39 & 108.99 & 105.659534883721 & 3.33046511627906 \tabularnewline
40 & 109.21 & 105.659534883721 & 3.55046511627906 \tabularnewline
41 & 109.31 & 105.659534883721 & 3.65046511627907 \tabularnewline
42 & 109.41 & 105.659534883721 & 3.75046511627907 \tabularnewline
43 & 109.54 & 105.659534883721 & 3.88046511627907 \tabularnewline
44 & 109.81 & 109.975 & -0.164999999999999 \tabularnewline
45 & 109.85 & 109.975 & -0.125000000000007 \tabularnewline
46 & 110.01 & 109.975 & 0.0350000000000039 \tabularnewline
47 & 110.23 & 109.975 & 0.255000000000003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35428&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]100.95[/C][C]105.659534883721[/C][C]-4.7095348837209[/C][/ROW]
[ROW][C]2[/C][C]101.26[/C][C]105.659534883721[/C][C]-4.39953488372093[/C][/ROW]
[ROW][C]3[/C][C]101.42[/C][C]105.659534883721[/C][C]-4.23953488372093[/C][/ROW]
[ROW][C]4[/C][C]101.68[/C][C]105.659534883721[/C][C]-3.97953488372092[/C][/ROW]
[ROW][C]5[/C][C]101.75[/C][C]105.659534883721[/C][C]-3.90953488372093[/C][/ROW]
[ROW][C]6[/C][C]101.89[/C][C]105.659534883721[/C][C]-3.76953488372093[/C][/ROW]
[ROW][C]7[/C][C]102.07[/C][C]105.659534883721[/C][C]-3.58953488372094[/C][/ROW]
[ROW][C]8[/C][C]102.22[/C][C]105.659534883721[/C][C]-3.43953488372093[/C][/ROW]
[ROW][C]9[/C][C]102.45[/C][C]105.659534883721[/C][C]-3.20953488372093[/C][/ROW]
[ROW][C]10[/C][C]102.62[/C][C]105.659534883721[/C][C]-3.03953488372093[/C][/ROW]
[ROW][C]11[/C][C]102.67[/C][C]105.659534883721[/C][C]-2.98953488372093[/C][/ROW]
[ROW][C]12[/C][C]102.86[/C][C]105.659534883721[/C][C]-2.79953488372093[/C][/ROW]
[ROW][C]13[/C][C]104.78[/C][C]105.659534883721[/C][C]-0.87953488372093[/C][/ROW]
[ROW][C]14[/C][C]104.87[/C][C]105.659534883721[/C][C]-0.789534883720926[/C][/ROW]
[ROW][C]15[/C][C]105.06[/C][C]105.659534883721[/C][C]-0.599534883720929[/C][/ROW]
[ROW][C]16[/C][C]105.14[/C][C]105.659534883721[/C][C]-0.51953488372093[/C][/ROW]
[ROW][C]17[/C][C]105.32[/C][C]105.659534883721[/C][C]-0.339534883720938[/C][/ROW]
[ROW][C]18[/C][C]105.54[/C][C]105.659534883721[/C][C]-0.119534883720925[/C][/ROW]
[ROW][C]19[/C][C]105.68[/C][C]105.659534883721[/C][C]0.020465116279076[/C][/ROW]
[ROW][C]20[/C][C]105.77[/C][C]105.659534883721[/C][C]0.110465116279065[/C][/ROW]
[ROW][C]21[/C][C]106.07[/C][C]105.659534883721[/C][C]0.410465116279062[/C][/ROW]
[ROW][C]22[/C][C]106.03[/C][C]105.659534883721[/C][C]0.37046511627907[/C][/ROW]
[ROW][C]23[/C][C]106.13[/C][C]105.659534883721[/C][C]0.470465116279064[/C][/ROW]
[ROW][C]24[/C][C]106.28[/C][C]105.659534883721[/C][C]0.62046511627907[/C][/ROW]
[ROW][C]25[/C][C]106.61[/C][C]105.659534883721[/C][C]0.950465116279068[/C][/ROW]
[ROW][C]26[/C][C]106.74[/C][C]105.659534883721[/C][C]1.08046511627906[/C][/ROW]
[ROW][C]27[/C][C]107.01[/C][C]105.659534883721[/C][C]1.35046511627907[/C][/ROW]
[ROW][C]28[/C][C]107.1[/C][C]105.659534883721[/C][C]1.44046511627906[/C][/ROW]
[ROW][C]29[/C][C]107.28[/C][C]105.659534883721[/C][C]1.62046511627907[/C][/ROW]
[ROW][C]30[/C][C]107.4[/C][C]105.659534883721[/C][C]1.74046511627907[/C][/ROW]
[ROW][C]31[/C][C]107.59[/C][C]105.659534883721[/C][C]1.93046511627907[/C][/ROW]
[ROW][C]32[/C][C]107.69[/C][C]105.659534883721[/C][C]2.03046511627907[/C][/ROW]
[ROW][C]33[/C][C]107.78[/C][C]105.659534883721[/C][C]2.12046511627907[/C][/ROW]
[ROW][C]34[/C][C]108.02[/C][C]105.659534883721[/C][C]2.36046511627907[/C][/ROW]
[ROW][C]35[/C][C]108[/C][C]105.659534883721[/C][C]2.34046511627907[/C][/ROW]
[ROW][C]36[/C][C]108.07[/C][C]105.659534883721[/C][C]2.41046511627906[/C][/ROW]
[ROW][C]37[/C][C]108.36[/C][C]105.659534883721[/C][C]2.70046511627907[/C][/ROW]
[ROW][C]38[/C][C]108.74[/C][C]105.659534883721[/C][C]3.08046511627906[/C][/ROW]
[ROW][C]39[/C][C]108.99[/C][C]105.659534883721[/C][C]3.33046511627906[/C][/ROW]
[ROW][C]40[/C][C]109.21[/C][C]105.659534883721[/C][C]3.55046511627906[/C][/ROW]
[ROW][C]41[/C][C]109.31[/C][C]105.659534883721[/C][C]3.65046511627907[/C][/ROW]
[ROW][C]42[/C][C]109.41[/C][C]105.659534883721[/C][C]3.75046511627907[/C][/ROW]
[ROW][C]43[/C][C]109.54[/C][C]105.659534883721[/C][C]3.88046511627907[/C][/ROW]
[ROW][C]44[/C][C]109.81[/C][C]109.975[/C][C]-0.164999999999999[/C][/ROW]
[ROW][C]45[/C][C]109.85[/C][C]109.975[/C][C]-0.125000000000007[/C][/ROW]
[ROW][C]46[/C][C]110.01[/C][C]109.975[/C][C]0.0350000000000039[/C][/ROW]
[ROW][C]47[/C][C]110.23[/C][C]109.975[/C][C]0.255000000000003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35428&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35428&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	100.95	105.659534883721	-4.7095348837209
2	101.26	105.659534883721	-4.39953488372093
3	101.42	105.659534883721	-4.23953488372093
4	101.68	105.659534883721	-3.97953488372092
5	101.75	105.659534883721	-3.90953488372093
6	101.89	105.659534883721	-3.76953488372093
7	102.07	105.659534883721	-3.58953488372094
8	102.22	105.659534883721	-3.43953488372093
9	102.45	105.659534883721	-3.20953488372093
10	102.62	105.659534883721	-3.03953488372093
11	102.67	105.659534883721	-2.98953488372093
12	102.86	105.659534883721	-2.79953488372093
13	104.78	105.659534883721	-0.87953488372093
14	104.87	105.659534883721	-0.789534883720926
15	105.06	105.659534883721	-0.599534883720929
16	105.14	105.659534883721	-0.51953488372093
17	105.32	105.659534883721	-0.339534883720938
18	105.54	105.659534883721	-0.119534883720925
19	105.68	105.659534883721	0.020465116279076
20	105.77	105.659534883721	0.110465116279065
21	106.07	105.659534883721	0.410465116279062
22	106.03	105.659534883721	0.37046511627907
23	106.13	105.659534883721	0.470465116279064
24	106.28	105.659534883721	0.62046511627907
25	106.61	105.659534883721	0.950465116279068
26	106.74	105.659534883721	1.08046511627906
27	107.01	105.659534883721	1.35046511627907
28	107.1	105.659534883721	1.44046511627906
29	107.28	105.659534883721	1.62046511627907
30	107.4	105.659534883721	1.74046511627907
31	107.59	105.659534883721	1.93046511627907
32	107.69	105.659534883721	2.03046511627907
33	107.78	105.659534883721	2.12046511627907
34	108.02	105.659534883721	2.36046511627907
35	108	105.659534883721	2.34046511627907
36	108.07	105.659534883721	2.41046511627906
37	108.36	105.659534883721	2.70046511627907
38	108.74	105.659534883721	3.08046511627906
39	108.99	105.659534883721	3.33046511627906
40	109.21	105.659534883721	3.55046511627906
41	109.31	105.659534883721	3.65046511627907
42	109.41	105.659534883721	3.75046511627907
43	109.54	105.659534883721	3.88046511627907
44	109.81	109.975	-0.164999999999999
45	109.85	109.975	-0.125000000000007
46	110.01	109.975	0.0350000000000039
47	110.23	109.975	0.255000000000003

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.00697129767113578	0.0139425953422716	0.993028702328864
6	0.00243896024340733	0.00487792048681465	0.997561039756593
7	0.00127532036408377	0.00255064072816753	0.998724679635916
8	0.000892065394180972	0.00178413078836194	0.99910793460582
9	0.000999824169824392	0.00199964833964878	0.999000175830176
10	0.00145354040303869	0.00290708080607739	0.998546459596961
11	0.00230921058233913	0.00461842116467826	0.99769078941766
12	0.00564404108057715	0.0112880821611543	0.994355958919423
13	0.149096538880494	0.298193077760988	0.850903461119506
14	0.408395239508713	0.816790479017426	0.591604760491287
15	0.649441135551283	0.701117728897434	0.350558864448717
16	0.809516565718246	0.380966868563508	0.190483434281754
17	0.905313659246696	0.189372681506609	0.0946863407533044
18	0.955873103877648	0.0882537922447045	0.0441268961223522
19	0.980068759877558	0.0398624802448848	0.0199312401224424
20	0.991327653734543	0.0173446925309142	0.00867234626545708
21	0.996115669681208	0.00776866063758388	0.00388433031879194
22	0.998379930743627	0.00324013851274644	0.00162006925637322
23	0.999376789048358	0.00124642190328351	0.000623210951641755
24	0.999776284600451	0.000447430799097476	0.000223715399548738
25	0.999905457608469	0.000189084783062839	9.45423915314194e-05
26	0.99995964855057	8.07028988611744e-05	4.03514494305872e-05
27	0.999978477819116	4.30443617687342e-05	2.15221808843671e-05
28	0.999988346179528	2.33076409446776e-05	1.16538204723388e-05
29	0.99999267017427	1.4659651461187e-05	7.3298257305935e-06
30	0.999995100392363	9.79921527389211e-06	4.89960763694606e-06
31	0.999995982365791	8.03526841802743e-06	4.01763420901371e-06
32	0.999996587993467	6.82401306588417e-06	3.41200653294208e-06
33	0.999997211948562	5.57610287633372e-06	2.78805143816686e-06
34	0.999996763336849	6.47332630281564e-06	3.23666315140782e-06
35	0.999997594245702	4.81150859589008e-06	2.40575429794504e-06
36	0.99999915790079	1.68419841893783e-06	8.42099209468916e-07
37	0.999999757220106	4.85559788529107e-07	2.42779894264554e-07
38	0.999999793547308	4.12905383777792e-07	2.06452691888896e-07
39	0.999999531076732	9.37846536516586e-07	4.68923268258293e-07
40	0.999996348811864	7.30237627236877e-06	3.65118813618439e-06
41	0.999960073393361	7.98532132771774e-05	3.99266066385887e-05
42	0.999467838530096	0.00106432293980854	0.00053216146990427

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00697129767113578 & 0.0139425953422716 & 0.993028702328864 \tabularnewline
6 & 0.00243896024340733 & 0.00487792048681465 & 0.997561039756593 \tabularnewline
7 & 0.00127532036408377 & 0.00255064072816753 & 0.998724679635916 \tabularnewline
8 & 0.000892065394180972 & 0.00178413078836194 & 0.99910793460582 \tabularnewline
9 & 0.000999824169824392 & 0.00199964833964878 & 0.999000175830176 \tabularnewline
10 & 0.00145354040303869 & 0.00290708080607739 & 0.998546459596961 \tabularnewline
11 & 0.00230921058233913 & 0.00461842116467826 & 0.99769078941766 \tabularnewline
12 & 0.00564404108057715 & 0.0112880821611543 & 0.994355958919423 \tabularnewline
13 & 0.149096538880494 & 0.298193077760988 & 0.850903461119506 \tabularnewline
14 & 0.408395239508713 & 0.816790479017426 & 0.591604760491287 \tabularnewline
15 & 0.649441135551283 & 0.701117728897434 & 0.350558864448717 \tabularnewline
16 & 0.809516565718246 & 0.380966868563508 & 0.190483434281754 \tabularnewline
17 & 0.905313659246696 & 0.189372681506609 & 0.0946863407533044 \tabularnewline
18 & 0.955873103877648 & 0.0882537922447045 & 0.0441268961223522 \tabularnewline
19 & 0.980068759877558 & 0.0398624802448848 & 0.0199312401224424 \tabularnewline
20 & 0.991327653734543 & 0.0173446925309142 & 0.00867234626545708 \tabularnewline
21 & 0.996115669681208 & 0.00776866063758388 & 0.00388433031879194 \tabularnewline
22 & 0.998379930743627 & 0.00324013851274644 & 0.00162006925637322 \tabularnewline
23 & 0.999376789048358 & 0.00124642190328351 & 0.000623210951641755 \tabularnewline
24 & 0.999776284600451 & 0.000447430799097476 & 0.000223715399548738 \tabularnewline
25 & 0.999905457608469 & 0.000189084783062839 & 9.45423915314194e-05 \tabularnewline
26 & 0.99995964855057 & 8.07028988611744e-05 & 4.03514494305872e-05 \tabularnewline
27 & 0.999978477819116 & 4.30443617687342e-05 & 2.15221808843671e-05 \tabularnewline
28 & 0.999988346179528 & 2.33076409446776e-05 & 1.16538204723388e-05 \tabularnewline
29 & 0.99999267017427 & 1.4659651461187e-05 & 7.3298257305935e-06 \tabularnewline
30 & 0.999995100392363 & 9.79921527389211e-06 & 4.89960763694606e-06 \tabularnewline
31 & 0.999995982365791 & 8.03526841802743e-06 & 4.01763420901371e-06 \tabularnewline
32 & 0.999996587993467 & 6.82401306588417e-06 & 3.41200653294208e-06 \tabularnewline
33 & 0.999997211948562 & 5.57610287633372e-06 & 2.78805143816686e-06 \tabularnewline
34 & 0.999996763336849 & 6.47332630281564e-06 & 3.23666315140782e-06 \tabularnewline
35 & 0.999997594245702 & 4.81150859589008e-06 & 2.40575429794504e-06 \tabularnewline
36 & 0.99999915790079 & 1.68419841893783e-06 & 8.42099209468916e-07 \tabularnewline
37 & 0.999999757220106 & 4.85559788529107e-07 & 2.42779894264554e-07 \tabularnewline
38 & 0.999999793547308 & 4.12905383777792e-07 & 2.06452691888896e-07 \tabularnewline
39 & 0.999999531076732 & 9.37846536516586e-07 & 4.68923268258293e-07 \tabularnewline
40 & 0.999996348811864 & 7.30237627236877e-06 & 3.65118813618439e-06 \tabularnewline
41 & 0.999960073393361 & 7.98532132771774e-05 & 3.99266066385887e-05 \tabularnewline
42 & 0.999467838530096 & 0.00106432293980854 & 0.00053216146990427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35428&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]5[/C][C]0.00697129767113578[/C][C]0.0139425953422716[/C][C]0.993028702328864[/C][/ROW]
[ROW][C]6[/C][C]0.00243896024340733[/C][C]0.00487792048681465[/C][C]0.997561039756593[/C][/ROW]
[ROW][C]7[/C][C]0.00127532036408377[/C][C]0.00255064072816753[/C][C]0.998724679635916[/C][/ROW]
[ROW][C]8[/C][C]0.000892065394180972[/C][C]0.00178413078836194[/C][C]0.99910793460582[/C][/ROW]
[ROW][C]9[/C][C]0.000999824169824392[/C][C]0.00199964833964878[/C][C]0.999000175830176[/C][/ROW]
[ROW][C]10[/C][C]0.00145354040303869[/C][C]0.00290708080607739[/C][C]0.998546459596961[/C][/ROW]
[ROW][C]11[/C][C]0.00230921058233913[/C][C]0.00461842116467826[/C][C]0.99769078941766[/C][/ROW]
[ROW][C]12[/C][C]0.00564404108057715[/C][C]0.0112880821611543[/C][C]0.994355958919423[/C][/ROW]
[ROW][C]13[/C][C]0.149096538880494[/C][C]0.298193077760988[/C][C]0.850903461119506[/C][/ROW]
[ROW][C]14[/C][C]0.408395239508713[/C][C]0.816790479017426[/C][C]0.591604760491287[/C][/ROW]
[ROW][C]15[/C][C]0.649441135551283[/C][C]0.701117728897434[/C][C]0.350558864448717[/C][/ROW]
[ROW][C]16[/C][C]0.809516565718246[/C][C]0.380966868563508[/C][C]0.190483434281754[/C][/ROW]
[ROW][C]17[/C][C]0.905313659246696[/C][C]0.189372681506609[/C][C]0.0946863407533044[/C][/ROW]
[ROW][C]18[/C][C]0.955873103877648[/C][C]0.0882537922447045[/C][C]0.0441268961223522[/C][/ROW]
[ROW][C]19[/C][C]0.980068759877558[/C][C]0.0398624802448848[/C][C]0.0199312401224424[/C][/ROW]
[ROW][C]20[/C][C]0.991327653734543[/C][C]0.0173446925309142[/C][C]0.00867234626545708[/C][/ROW]
[ROW][C]21[/C][C]0.996115669681208[/C][C]0.00776866063758388[/C][C]0.00388433031879194[/C][/ROW]
[ROW][C]22[/C][C]0.998379930743627[/C][C]0.00324013851274644[/C][C]0.00162006925637322[/C][/ROW]
[ROW][C]23[/C][C]0.999376789048358[/C][C]0.00124642190328351[/C][C]0.000623210951641755[/C][/ROW]
[ROW][C]24[/C][C]0.999776284600451[/C][C]0.000447430799097476[/C][C]0.000223715399548738[/C][/ROW]
[ROW][C]25[/C][C]0.999905457608469[/C][C]0.000189084783062839[/C][C]9.45423915314194e-05[/C][/ROW]
[ROW][C]26[/C][C]0.99995964855057[/C][C]8.07028988611744e-05[/C][C]4.03514494305872e-05[/C][/ROW]
[ROW][C]27[/C][C]0.999978477819116[/C][C]4.30443617687342e-05[/C][C]2.15221808843671e-05[/C][/ROW]
[ROW][C]28[/C][C]0.999988346179528[/C][C]2.33076409446776e-05[/C][C]1.16538204723388e-05[/C][/ROW]
[ROW][C]29[/C][C]0.99999267017427[/C][C]1.4659651461187e-05[/C][C]7.3298257305935e-06[/C][/ROW]
[ROW][C]30[/C][C]0.999995100392363[/C][C]9.79921527389211e-06[/C][C]4.89960763694606e-06[/C][/ROW]
[ROW][C]31[/C][C]0.999995982365791[/C][C]8.03526841802743e-06[/C][C]4.01763420901371e-06[/C][/ROW]
[ROW][C]32[/C][C]0.999996587993467[/C][C]6.82401306588417e-06[/C][C]3.41200653294208e-06[/C][/ROW]
[ROW][C]33[/C][C]0.999997211948562[/C][C]5.57610287633372e-06[/C][C]2.78805143816686e-06[/C][/ROW]
[ROW][C]34[/C][C]0.999996763336849[/C][C]6.47332630281564e-06[/C][C]3.23666315140782e-06[/C][/ROW]
[ROW][C]35[/C][C]0.999997594245702[/C][C]4.81150859589008e-06[/C][C]2.40575429794504e-06[/C][/ROW]
[ROW][C]36[/C][C]0.99999915790079[/C][C]1.68419841893783e-06[/C][C]8.42099209468916e-07[/C][/ROW]
[ROW][C]37[/C][C]0.999999757220106[/C][C]4.85559788529107e-07[/C][C]2.42779894264554e-07[/C][/ROW]
[ROW][C]38[/C][C]0.999999793547308[/C][C]4.12905383777792e-07[/C][C]2.06452691888896e-07[/C][/ROW]
[ROW][C]39[/C][C]0.999999531076732[/C][C]9.37846536516586e-07[/C][C]4.68923268258293e-07[/C][/ROW]
[ROW][C]40[/C][C]0.999996348811864[/C][C]7.30237627236877e-06[/C][C]3.65118813618439e-06[/C][/ROW]
[ROW][C]41[/C][C]0.999960073393361[/C][C]7.98532132771774e-05[/C][C]3.99266066385887e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999467838530096[/C][C]0.00106432293980854[/C][C]0.00053216146990427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35428&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35428&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
5	0.00697129767113578	0.0139425953422716	0.993028702328864
6	0.00243896024340733	0.00487792048681465	0.997561039756593
7	0.00127532036408377	0.00255064072816753	0.998724679635916
8	0.000892065394180972	0.00178413078836194	0.99910793460582
9	0.000999824169824392	0.00199964833964878	0.999000175830176
10	0.00145354040303869	0.00290708080607739	0.998546459596961
11	0.00230921058233913	0.00461842116467826	0.99769078941766
12	0.00564404108057715	0.0112880821611543	0.994355958919423
13	0.149096538880494	0.298193077760988	0.850903461119506
14	0.408395239508713	0.816790479017426	0.591604760491287
15	0.649441135551283	0.701117728897434	0.350558864448717
16	0.809516565718246	0.380966868563508	0.190483434281754
17	0.905313659246696	0.189372681506609	0.0946863407533044
18	0.955873103877648	0.0882537922447045	0.0441268961223522
19	0.980068759877558	0.0398624802448848	0.0199312401224424
20	0.991327653734543	0.0173446925309142	0.00867234626545708
21	0.996115669681208	0.00776866063758388	0.00388433031879194
22	0.998379930743627	0.00324013851274644	0.00162006925637322
23	0.999376789048358	0.00124642190328351	0.000623210951641755
24	0.999776284600451	0.000447430799097476	0.000223715399548738
25	0.999905457608469	0.000189084783062839	9.45423915314194e-05
26	0.99995964855057	8.07028988611744e-05	4.03514494305872e-05
27	0.999978477819116	4.30443617687342e-05	2.15221808843671e-05
28	0.999988346179528	2.33076409446776e-05	1.16538204723388e-05
29	0.99999267017427	1.4659651461187e-05	7.3298257305935e-06
30	0.999995100392363	9.79921527389211e-06	4.89960763694606e-06
31	0.999995982365791	8.03526841802743e-06	4.01763420901371e-06
32	0.999996587993467	6.82401306588417e-06	3.41200653294208e-06
33	0.999997211948562	5.57610287633372e-06	2.78805143816686e-06
34	0.999996763336849	6.47332630281564e-06	3.23666315140782e-06
35	0.999997594245702	4.81150859589008e-06	2.40575429794504e-06
36	0.99999915790079	1.68419841893783e-06	8.42099209468916e-07
37	0.999999757220106	4.85559788529107e-07	2.42779894264554e-07
38	0.999999793547308	4.12905383777792e-07	2.06452691888896e-07
39	0.999999531076732	9.37846536516586e-07	4.68923268258293e-07
40	0.999996348811864	7.30237627236877e-06	3.65118813618439e-06
41	0.999960073393361	7.98532132771774e-05	3.99266066385887e-05
42	0.999467838530096	0.00106432293980854	0.00053216146990427

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	28	0.736842105263158	NOK
5% type I error level	32	0.842105263157895	NOK
10% type I error level	33	0.868421052631579	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 & 28 & 0.736842105263158 & NOK \tabularnewline
5% type I error level & 32 & 0.842105263157895 & NOK \tabularnewline
10% type I error level & 33 & 0.868421052631579 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35428&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]28[/C][C]0.736842105263158[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.842105263157895[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.868421052631579[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35428&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35428&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	28	0.736842105263158	NOK
5% type I error level	32	0.842105263157895	NOK
10% type I error level	33	0.868421052631579	NOK

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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