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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 10 Dec 2014 14:27:42 +0000

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/2014/Dec/10/t1418226012gzny0vaexgeyhh1.htm/, Retrieved Thu, 27 Aug 2026 14:25:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265407, Retrieved Thu, 27 Aug 2026 14:25:20 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

482

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

-     [Cronbach Alpha] [Intrinsic Motivat...] [2010-10-12 11:42:57] [b98453cac15ba1066b407e146608df68]
- RM D    [Multiple Regression] [MR] [2014-12-10 14:27:42] [cf0ec5d34597f312b7dfbfe84499cd1d] [Current]

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Dataseries X:

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5.0 62 72 11
3.0 56 61 6
7.5 57 68 7
7.0 51 61 10
6.0 56 64 9
6.0 30 65 7
1.0 61 69 4
6.0 47 63 4
5.0 56 75 4
1.0 50 63 8
6.5 67 73 4
0.0 41 75 7
3.5 45 63 4
7.5 48 63 4
3.5 44 62 9
6.0 37 64 4
3.5 56 60 10
7.5 66 56 4
6.5 38 59 5
3.5 34 68 4
4.0 49 66 4
7.5 55 73 4
4.5 49 72 4
0.0 59 71 6
3.5 40 59 10
5.5 58 64 7
5.0 60 66 4
4.5 63 78 4
2.5 56 68 7
7.5 54 73 4
7.0 52 62 8
0.0 34 65 11
4.5 69 68 6
3.0 32 65 14
1.5 48 60 5
3.5 67 71 4
2.5 58 65 8
5.5 57 68 9
8.0 42 64 4
1.0 64 74 4
5.0 58 69 5
4.5 66 76 4
3.0 26 68 5
3.0 61 72 4
8.0 52 67 4
2.5 51 63 7
7.0 55 59 10
0.0 50 73 4
1.0 60 66 5
3.5 56 62 4
5.5 63 69 4
5.5 61 66 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265407&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265407&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265407&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 12.6294 + 0.027654AMS.I[t] -0.130373AMS.E[t] -0.177139AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  12.6294 +  0.027654AMS.I[t] -0.130373AMS.E[t] -0.177139AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265407&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  12.6294 +  0.027654AMS.I[t] -0.130373AMS.E[t] -0.177139AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265407&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265407&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
Ex[t] = + 12.6294 + 0.027654AMS.I[t] -0.130373AMS.E[t] -0.177139AMS.A[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)	12.6294	4.85932	2.599	0.0123835	0.00619173
AMS.I	0.027654	0.0329802	0.8385	0.405905	0.202952
AMS.E	-0.130373	0.0688153	-1.895	0.064187	0.0320935
AMS.A	-0.177139	0.136753	-1.295	0.201406	0.100703

\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) & 12.6294 & 4.85932 & 2.599 & 0.0123835 & 0.00619173 \tabularnewline
AMS.I & 0.027654 & 0.0329802 & 0.8385 & 0.405905 & 0.202952 \tabularnewline
AMS.E & -0.130373 & 0.0688153 & -1.895 & 0.064187 & 0.0320935 \tabularnewline
AMS.A & -0.177139 & 0.136753 & -1.295 & 0.201406 & 0.100703 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265407&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]12.6294[/C][C]4.85932[/C][C]2.599[/C][C]0.0123835[/C][C]0.00619173[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.027654[/C][C]0.0329802[/C][C]0.8385[/C][C]0.405905[/C][C]0.202952[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.130373[/C][C]0.0688153[/C][C]-1.895[/C][C]0.064187[/C][C]0.0320935[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.177139[/C][C]0.136753[/C][C]-1.295[/C][C]0.201406[/C][C]0.100703[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265407&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265407&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)	12.6294	4.85932	2.599	0.0123835	0.00619173
AMS.I	0.027654	0.0329802	0.8385	0.405905	0.202952
AMS.E	-0.130373	0.0688153	-1.895	0.064187	0.0320935
AMS.A	-0.177139	0.136753	-1.295	0.201406	0.100703

Multiple Linear Regression - Regression Statistics
Multiple R	0.295172
R-squared	0.0871263
Adjusted R-squared	0.0300717
F-TEST (value)	1.52707
F-TEST (DF numerator)	3
F-TEST (DF denominator)	48
p-value	0.2195
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.29084
Sum Squared Residuals	251.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.295172 \tabularnewline
R-squared & 0.0871263 \tabularnewline
Adjusted R-squared & 0.0300717 \tabularnewline
F-TEST (value) & 1.52707 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.2195 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.29084 \tabularnewline
Sum Squared Residuals & 251.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265407&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.295172[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0871263[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0300717[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.52707[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.2195[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.29084[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]251.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265407&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265407&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	0.295172
R-squared	0.0871263
Adjusted R-squared	0.0300717
F-TEST (value)	1.52707
F-TEST (DF numerator)	3
F-TEST (DF denominator)	48
p-value	0.2195
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.29084
Sum Squared Residuals	251.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5	3.00854	1.99146
2	3	5.16242	-2.16242
3	7.5	4.10032	3.39968
4	7	4.31559	2.68441
5	6	4.23988	1.76012
6	6	3.74478	2.25522
7	1	4.61198	-3.61198
8	6	5.00706	0.992938
9	5	3.69147	1.30853
10	1	4.38147	-3.38147
11	6.5	4.25641	2.24359
12	0	2.74525	-2.74525
13	3.5	4.95175	-1.45175
14	7.5	5.03472	2.46528
15	3.5	4.16878	-0.66878
16	6	4.60015	1.39985
17	3.5	4.58423	-1.08423
18	7.5	6.4451	1.0549
19	6.5	5.10253	1.39747
20	3.5	3.9957	-0.495695
21	4	4.67125	-0.671251
22	7.5	3.92456	3.57544
23	4.5	3.88901	0.610987
24	0	3.94165	-3.94165
25	3.5	4.27214	-0.772145
26	5.5	4.64947	0.850533
27	5	4.97544	0.0245556
28	4.5	3.49393	1.00607
29	2.5	4.07267	-1.57267
30	7.5	3.89691	3.60309
31	7	4.56715	2.43285
32	0	3.14684	-3.14684
33	4.5	4.60931	-0.109307
34	3	2.56012	0.43988
35	1.5	5.2487	-3.7487
36	3.5	4.51716	-1.01716
37	2.5	4.34195	-1.84195
38	5.5	3.74604	1.75396
39	8	4.73842	3.26158
40	1	4.04308	-3.04308
41	5	4.35188	0.648121
42	4.5	3.83764	0.662362
43	3	3.59732	-0.597325
44	3	4.22086	-1.22086
45	8	4.62384	3.37616
46	2.5	4.58626	-2.08626
47	7	4.68695	2.31305
48	0	3.78629	-3.78629
49	1	4.79831	-3.79831
50	3.5	5.38632	-1.88632
51	5.5	4.66729	0.832713
52	5.5	5.0031	0.496902

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 3.00854 & 1.99146 \tabularnewline
2 & 3 & 5.16242 & -2.16242 \tabularnewline
3 & 7.5 & 4.10032 & 3.39968 \tabularnewline
4 & 7 & 4.31559 & 2.68441 \tabularnewline
5 & 6 & 4.23988 & 1.76012 \tabularnewline
6 & 6 & 3.74478 & 2.25522 \tabularnewline
7 & 1 & 4.61198 & -3.61198 \tabularnewline
8 & 6 & 5.00706 & 0.992938 \tabularnewline
9 & 5 & 3.69147 & 1.30853 \tabularnewline
10 & 1 & 4.38147 & -3.38147 \tabularnewline
11 & 6.5 & 4.25641 & 2.24359 \tabularnewline
12 & 0 & 2.74525 & -2.74525 \tabularnewline
13 & 3.5 & 4.95175 & -1.45175 \tabularnewline
14 & 7.5 & 5.03472 & 2.46528 \tabularnewline
15 & 3.5 & 4.16878 & -0.66878 \tabularnewline
16 & 6 & 4.60015 & 1.39985 \tabularnewline
17 & 3.5 & 4.58423 & -1.08423 \tabularnewline
18 & 7.5 & 6.4451 & 1.0549 \tabularnewline
19 & 6.5 & 5.10253 & 1.39747 \tabularnewline
20 & 3.5 & 3.9957 & -0.495695 \tabularnewline
21 & 4 & 4.67125 & -0.671251 \tabularnewline
22 & 7.5 & 3.92456 & 3.57544 \tabularnewline
23 & 4.5 & 3.88901 & 0.610987 \tabularnewline
24 & 0 & 3.94165 & -3.94165 \tabularnewline
25 & 3.5 & 4.27214 & -0.772145 \tabularnewline
26 & 5.5 & 4.64947 & 0.850533 \tabularnewline
27 & 5 & 4.97544 & 0.0245556 \tabularnewline
28 & 4.5 & 3.49393 & 1.00607 \tabularnewline
29 & 2.5 & 4.07267 & -1.57267 \tabularnewline
30 & 7.5 & 3.89691 & 3.60309 \tabularnewline
31 & 7 & 4.56715 & 2.43285 \tabularnewline
32 & 0 & 3.14684 & -3.14684 \tabularnewline
33 & 4.5 & 4.60931 & -0.109307 \tabularnewline
34 & 3 & 2.56012 & 0.43988 \tabularnewline
35 & 1.5 & 5.2487 & -3.7487 \tabularnewline
36 & 3.5 & 4.51716 & -1.01716 \tabularnewline
37 & 2.5 & 4.34195 & -1.84195 \tabularnewline
38 & 5.5 & 3.74604 & 1.75396 \tabularnewline
39 & 8 & 4.73842 & 3.26158 \tabularnewline
40 & 1 & 4.04308 & -3.04308 \tabularnewline
41 & 5 & 4.35188 & 0.648121 \tabularnewline
42 & 4.5 & 3.83764 & 0.662362 \tabularnewline
43 & 3 & 3.59732 & -0.597325 \tabularnewline
44 & 3 & 4.22086 & -1.22086 \tabularnewline
45 & 8 & 4.62384 & 3.37616 \tabularnewline
46 & 2.5 & 4.58626 & -2.08626 \tabularnewline
47 & 7 & 4.68695 & 2.31305 \tabularnewline
48 & 0 & 3.78629 & -3.78629 \tabularnewline
49 & 1 & 4.79831 & -3.79831 \tabularnewline
50 & 3.5 & 5.38632 & -1.88632 \tabularnewline
51 & 5.5 & 4.66729 & 0.832713 \tabularnewline
52 & 5.5 & 5.0031 & 0.496902 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265407&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]5[/C][C]3.00854[/C][C]1.99146[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]5.16242[/C][C]-2.16242[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]4.10032[/C][C]3.39968[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]4.31559[/C][C]2.68441[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]4.23988[/C][C]1.76012[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]3.74478[/C][C]2.25522[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]4.61198[/C][C]-3.61198[/C][/ROW]
[ROW][C]8[/C][C]6[/C][C]5.00706[/C][C]0.992938[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]3.69147[/C][C]1.30853[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]4.38147[/C][C]-3.38147[/C][/ROW]
[ROW][C]11[/C][C]6.5[/C][C]4.25641[/C][C]2.24359[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]2.74525[/C][C]-2.74525[/C][/ROW]
[ROW][C]13[/C][C]3.5[/C][C]4.95175[/C][C]-1.45175[/C][/ROW]
[ROW][C]14[/C][C]7.5[/C][C]5.03472[/C][C]2.46528[/C][/ROW]
[ROW][C]15[/C][C]3.5[/C][C]4.16878[/C][C]-0.66878[/C][/ROW]
[ROW][C]16[/C][C]6[/C][C]4.60015[/C][C]1.39985[/C][/ROW]
[ROW][C]17[/C][C]3.5[/C][C]4.58423[/C][C]-1.08423[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]6.4451[/C][C]1.0549[/C][/ROW]
[ROW][C]19[/C][C]6.5[/C][C]5.10253[/C][C]1.39747[/C][/ROW]
[ROW][C]20[/C][C]3.5[/C][C]3.9957[/C][C]-0.495695[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.67125[/C][C]-0.671251[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]3.92456[/C][C]3.57544[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]3.88901[/C][C]0.610987[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]3.94165[/C][C]-3.94165[/C][/ROW]
[ROW][C]25[/C][C]3.5[/C][C]4.27214[/C][C]-0.772145[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]4.64947[/C][C]0.850533[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]4.97544[/C][C]0.0245556[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]3.49393[/C][C]1.00607[/C][/ROW]
[ROW][C]29[/C][C]2.5[/C][C]4.07267[/C][C]-1.57267[/C][/ROW]
[ROW][C]30[/C][C]7.5[/C][C]3.89691[/C][C]3.60309[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]4.56715[/C][C]2.43285[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]3.14684[/C][C]-3.14684[/C][/ROW]
[ROW][C]33[/C][C]4.5[/C][C]4.60931[/C][C]-0.109307[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]2.56012[/C][C]0.43988[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]5.2487[/C][C]-3.7487[/C][/ROW]
[ROW][C]36[/C][C]3.5[/C][C]4.51716[/C][C]-1.01716[/C][/ROW]
[ROW][C]37[/C][C]2.5[/C][C]4.34195[/C][C]-1.84195[/C][/ROW]
[ROW][C]38[/C][C]5.5[/C][C]3.74604[/C][C]1.75396[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]4.73842[/C][C]3.26158[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]4.04308[/C][C]-3.04308[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.35188[/C][C]0.648121[/C][/ROW]
[ROW][C]42[/C][C]4.5[/C][C]3.83764[/C][C]0.662362[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.59732[/C][C]-0.597325[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]4.22086[/C][C]-1.22086[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]4.62384[/C][C]3.37616[/C][/ROW]
[ROW][C]46[/C][C]2.5[/C][C]4.58626[/C][C]-2.08626[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]4.68695[/C][C]2.31305[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]3.78629[/C][C]-3.78629[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]4.79831[/C][C]-3.79831[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]5.38632[/C][C]-1.88632[/C][/ROW]
[ROW][C]51[/C][C]5.5[/C][C]4.66729[/C][C]0.832713[/C][/ROW]
[ROW][C]52[/C][C]5.5[/C][C]5.0031[/C][C]0.496902[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265407&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265407&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5	3.00854	1.99146
2	3	5.16242	-2.16242
3	7.5	4.10032	3.39968
4	7	4.31559	2.68441
5	6	4.23988	1.76012
6	6	3.74478	2.25522
7	1	4.61198	-3.61198
8	6	5.00706	0.992938
9	5	3.69147	1.30853
10	1	4.38147	-3.38147
11	6.5	4.25641	2.24359
12	0	2.74525	-2.74525
13	3.5	4.95175	-1.45175
14	7.5	5.03472	2.46528
15	3.5	4.16878	-0.66878
16	6	4.60015	1.39985
17	3.5	4.58423	-1.08423
18	7.5	6.4451	1.0549
19	6.5	5.10253	1.39747
20	3.5	3.9957	-0.495695
21	4	4.67125	-0.671251
22	7.5	3.92456	3.57544
23	4.5	3.88901	0.610987
24	0	3.94165	-3.94165
25	3.5	4.27214	-0.772145
26	5.5	4.64947	0.850533
27	5	4.97544	0.0245556
28	4.5	3.49393	1.00607
29	2.5	4.07267	-1.57267
30	7.5	3.89691	3.60309
31	7	4.56715	2.43285
32	0	3.14684	-3.14684
33	4.5	4.60931	-0.109307
34	3	2.56012	0.43988
35	1.5	5.2487	-3.7487
36	3.5	4.51716	-1.01716
37	2.5	4.34195	-1.84195
38	5.5	3.74604	1.75396
39	8	4.73842	3.26158
40	1	4.04308	-3.04308
41	5	4.35188	0.648121
42	4.5	3.83764	0.662362
43	3	3.59732	-0.597325
44	3	4.22086	-1.22086
45	8	4.62384	3.37616
46	2.5	4.58626	-2.08626
47	7	4.68695	2.31305
48	0	3.78629	-3.78629
49	1	4.79831	-3.79831
50	3.5	5.38632	-1.88632
51	5.5	4.66729	0.832713
52	5.5	5.0031	0.496902

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.597731	0.804538	0.402269
8	0.563103	0.873795	0.436897
9	0.475938	0.951876	0.524062
10	0.751332	0.497336	0.248668
11	0.760487	0.479026	0.239513
12	0.885858	0.228284	0.114142
13	0.838563	0.322874	0.161437
14	0.849412	0.301176	0.150588
15	0.797534	0.404932	0.202466
16	0.751353	0.497294	0.248647
17	0.696839	0.606323	0.303161
18	0.626925	0.746151	0.373075
19	0.56526	0.869479	0.43474
20	0.480518	0.961037	0.519482
21	0.401128	0.802255	0.598872
22	0.487751	0.975501	0.512249
23	0.407151	0.814303	0.592849
24	0.578304	0.843392	0.421696
25	0.500441	0.999118	0.499559
26	0.425019	0.850039	0.574981
27	0.343885	0.68777	0.656115
28	0.279592	0.559185	0.720408
29	0.240107	0.480215	0.759893
30	0.350777	0.701554	0.649223
31	0.356597	0.713194	0.643403
32	0.404593	0.809187	0.595407
33	0.324902	0.649804	0.675098
34	0.258723	0.517446	0.741277
35	0.381169	0.762338	0.618831
36	0.30589	0.611781	0.69411
37	0.28137	0.562741	0.71863
38	0.242161	0.484321	0.757839
39	0.311607	0.623214	0.688393
40	0.321725	0.643451	0.678275
41	0.23929	0.47858	0.76071
42	0.187935	0.37587	0.812065
43	0.123089	0.246177	0.876911
44	0.0693697	0.138739	0.93063
45	0.3986	0.7972	0.6014

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.597731 & 0.804538 & 0.402269 \tabularnewline
8 & 0.563103 & 0.873795 & 0.436897 \tabularnewline
9 & 0.475938 & 0.951876 & 0.524062 \tabularnewline
10 & 0.751332 & 0.497336 & 0.248668 \tabularnewline
11 & 0.760487 & 0.479026 & 0.239513 \tabularnewline
12 & 0.885858 & 0.228284 & 0.114142 \tabularnewline
13 & 0.838563 & 0.322874 & 0.161437 \tabularnewline
14 & 0.849412 & 0.301176 & 0.150588 \tabularnewline
15 & 0.797534 & 0.404932 & 0.202466 \tabularnewline
16 & 0.751353 & 0.497294 & 0.248647 \tabularnewline
17 & 0.696839 & 0.606323 & 0.303161 \tabularnewline
18 & 0.626925 & 0.746151 & 0.373075 \tabularnewline
19 & 0.56526 & 0.869479 & 0.43474 \tabularnewline
20 & 0.480518 & 0.961037 & 0.519482 \tabularnewline
21 & 0.401128 & 0.802255 & 0.598872 \tabularnewline
22 & 0.487751 & 0.975501 & 0.512249 \tabularnewline
23 & 0.407151 & 0.814303 & 0.592849 \tabularnewline
24 & 0.578304 & 0.843392 & 0.421696 \tabularnewline
25 & 0.500441 & 0.999118 & 0.499559 \tabularnewline
26 & 0.425019 & 0.850039 & 0.574981 \tabularnewline
27 & 0.343885 & 0.68777 & 0.656115 \tabularnewline
28 & 0.279592 & 0.559185 & 0.720408 \tabularnewline
29 & 0.240107 & 0.480215 & 0.759893 \tabularnewline
30 & 0.350777 & 0.701554 & 0.649223 \tabularnewline
31 & 0.356597 & 0.713194 & 0.643403 \tabularnewline
32 & 0.404593 & 0.809187 & 0.595407 \tabularnewline
33 & 0.324902 & 0.649804 & 0.675098 \tabularnewline
34 & 0.258723 & 0.517446 & 0.741277 \tabularnewline
35 & 0.381169 & 0.762338 & 0.618831 \tabularnewline
36 & 0.30589 & 0.611781 & 0.69411 \tabularnewline
37 & 0.28137 & 0.562741 & 0.71863 \tabularnewline
38 & 0.242161 & 0.484321 & 0.757839 \tabularnewline
39 & 0.311607 & 0.623214 & 0.688393 \tabularnewline
40 & 0.321725 & 0.643451 & 0.678275 \tabularnewline
41 & 0.23929 & 0.47858 & 0.76071 \tabularnewline
42 & 0.187935 & 0.37587 & 0.812065 \tabularnewline
43 & 0.123089 & 0.246177 & 0.876911 \tabularnewline
44 & 0.0693697 & 0.138739 & 0.93063 \tabularnewline
45 & 0.3986 & 0.7972 & 0.6014 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265407&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]7[/C][C]0.597731[/C][C]0.804538[/C][C]0.402269[/C][/ROW]
[ROW][C]8[/C][C]0.563103[/C][C]0.873795[/C][C]0.436897[/C][/ROW]
[ROW][C]9[/C][C]0.475938[/C][C]0.951876[/C][C]0.524062[/C][/ROW]
[ROW][C]10[/C][C]0.751332[/C][C]0.497336[/C][C]0.248668[/C][/ROW]
[ROW][C]11[/C][C]0.760487[/C][C]0.479026[/C][C]0.239513[/C][/ROW]
[ROW][C]12[/C][C]0.885858[/C][C]0.228284[/C][C]0.114142[/C][/ROW]
[ROW][C]13[/C][C]0.838563[/C][C]0.322874[/C][C]0.161437[/C][/ROW]
[ROW][C]14[/C][C]0.849412[/C][C]0.301176[/C][C]0.150588[/C][/ROW]
[ROW][C]15[/C][C]0.797534[/C][C]0.404932[/C][C]0.202466[/C][/ROW]
[ROW][C]16[/C][C]0.751353[/C][C]0.497294[/C][C]0.248647[/C][/ROW]
[ROW][C]17[/C][C]0.696839[/C][C]0.606323[/C][C]0.303161[/C][/ROW]
[ROW][C]18[/C][C]0.626925[/C][C]0.746151[/C][C]0.373075[/C][/ROW]
[ROW][C]19[/C][C]0.56526[/C][C]0.869479[/C][C]0.43474[/C][/ROW]
[ROW][C]20[/C][C]0.480518[/C][C]0.961037[/C][C]0.519482[/C][/ROW]
[ROW][C]21[/C][C]0.401128[/C][C]0.802255[/C][C]0.598872[/C][/ROW]
[ROW][C]22[/C][C]0.487751[/C][C]0.975501[/C][C]0.512249[/C][/ROW]
[ROW][C]23[/C][C]0.407151[/C][C]0.814303[/C][C]0.592849[/C][/ROW]
[ROW][C]24[/C][C]0.578304[/C][C]0.843392[/C][C]0.421696[/C][/ROW]
[ROW][C]25[/C][C]0.500441[/C][C]0.999118[/C][C]0.499559[/C][/ROW]
[ROW][C]26[/C][C]0.425019[/C][C]0.850039[/C][C]0.574981[/C][/ROW]
[ROW][C]27[/C][C]0.343885[/C][C]0.68777[/C][C]0.656115[/C][/ROW]
[ROW][C]28[/C][C]0.279592[/C][C]0.559185[/C][C]0.720408[/C][/ROW]
[ROW][C]29[/C][C]0.240107[/C][C]0.480215[/C][C]0.759893[/C][/ROW]
[ROW][C]30[/C][C]0.350777[/C][C]0.701554[/C][C]0.649223[/C][/ROW]
[ROW][C]31[/C][C]0.356597[/C][C]0.713194[/C][C]0.643403[/C][/ROW]
[ROW][C]32[/C][C]0.404593[/C][C]0.809187[/C][C]0.595407[/C][/ROW]
[ROW][C]33[/C][C]0.324902[/C][C]0.649804[/C][C]0.675098[/C][/ROW]
[ROW][C]34[/C][C]0.258723[/C][C]0.517446[/C][C]0.741277[/C][/ROW]
[ROW][C]35[/C][C]0.381169[/C][C]0.762338[/C][C]0.618831[/C][/ROW]
[ROW][C]36[/C][C]0.30589[/C][C]0.611781[/C][C]0.69411[/C][/ROW]
[ROW][C]37[/C][C]0.28137[/C][C]0.562741[/C][C]0.71863[/C][/ROW]
[ROW][C]38[/C][C]0.242161[/C][C]0.484321[/C][C]0.757839[/C][/ROW]
[ROW][C]39[/C][C]0.311607[/C][C]0.623214[/C][C]0.688393[/C][/ROW]
[ROW][C]40[/C][C]0.321725[/C][C]0.643451[/C][C]0.678275[/C][/ROW]
[ROW][C]41[/C][C]0.23929[/C][C]0.47858[/C][C]0.76071[/C][/ROW]
[ROW][C]42[/C][C]0.187935[/C][C]0.37587[/C][C]0.812065[/C][/ROW]
[ROW][C]43[/C][C]0.123089[/C][C]0.246177[/C][C]0.876911[/C][/ROW]
[ROW][C]44[/C][C]0.0693697[/C][C]0.138739[/C][C]0.93063[/C][/ROW]
[ROW][C]45[/C][C]0.3986[/C][C]0.7972[/C][C]0.6014[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265407&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265407&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
7	0.597731	0.804538	0.402269
8	0.563103	0.873795	0.436897
9	0.475938	0.951876	0.524062
10	0.751332	0.497336	0.248668
11	0.760487	0.479026	0.239513
12	0.885858	0.228284	0.114142
13	0.838563	0.322874	0.161437
14	0.849412	0.301176	0.150588
15	0.797534	0.404932	0.202466
16	0.751353	0.497294	0.248647
17	0.696839	0.606323	0.303161
18	0.626925	0.746151	0.373075
19	0.56526	0.869479	0.43474
20	0.480518	0.961037	0.519482
21	0.401128	0.802255	0.598872
22	0.487751	0.975501	0.512249
23	0.407151	0.814303	0.592849
24	0.578304	0.843392	0.421696
25	0.500441	0.999118	0.499559
26	0.425019	0.850039	0.574981
27	0.343885	0.68777	0.656115
28	0.279592	0.559185	0.720408
29	0.240107	0.480215	0.759893
30	0.350777	0.701554	0.649223
31	0.356597	0.713194	0.643403
32	0.404593	0.809187	0.595407
33	0.324902	0.649804	0.675098
34	0.258723	0.517446	0.741277
35	0.381169	0.762338	0.618831
36	0.30589	0.611781	0.69411
37	0.28137	0.562741	0.71863
38	0.242161	0.484321	0.757839
39	0.311607	0.623214	0.688393
40	0.321725	0.643451	0.678275
41	0.23929	0.47858	0.76071
42	0.187935	0.37587	0.812065
43	0.123089	0.246177	0.876911
44	0.0693697	0.138739	0.93063
45	0.3986	0.7972	0.6014

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=265407&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=265407&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265407&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):

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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code