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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 22 Jan 2016 10:01:37 +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/2016/Jan/22/t1453456903ur32elzadgen4ap.htm/, Retrieved Tue, 07 May 2024 10:55:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=291228, Retrieved Tue, 07 May 2024 10:55:00 +0000

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-       [Multiple Regression] [] [2016-01-22 10:01:37] [34fd9dc11cb4971d03f504d75aa2e25f] [Current]

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6 1 1 0 0 0 3.2 3.2 10.24
 10.24 3.2
 1 3.3 0 10.89
7 0 0 1 0 1 3 3 9
 10.89 3.3
 1 3.5 0 12.25
2 1 0 1 1 0 3.7 3.7 13.69
 9 3
 0 2.7 0 7.29
11 0 0 1 0 1 3.6 3.6 12.96
 12.25 3.5
 1 3.5 0 12.25
13 1 0 1 0 0 3.8 3.8 14.44
 13.69 3.7
 0 3.4 0 11.56
3 0 1 0 0 1 3.7 3.7 13.69
 7.29 2.7
 0 3.5 0 12.25
17 1 0 1 1 0 2.8 2.8 7.84
 12.96 3.6
 0 3.8 0 14.44
10 0 0 1 0 0 4.3 4.3 18.49
 12.25 3.5
 1 3.3 0 10.89
4 1 1 0 0 0 3.6 3.6 12.96
 14.44 3.8
 0 3.6 0 12.96
12 0 0 1 0 0 3.3 3.3 10.89
 11.56 3.4
 0 2.8 0 7.84

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	'George Udny Yule' @ yule.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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291228&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291228&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291228&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	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Numeracy[t] = + 8.82316 + 1.8686Geslacht[t] -0.458635Drugs[t] + 0.21214Fruit[t] -1.4107Sport[t] + 0.0786795Alcohol[t] -0.554974Gebgewicht[t] + 0.639093Inter[t] -0.148747Gebgew2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Numeracy[t] =  +  8.82316 +  1.8686Geslacht[t] -0.458635Drugs[t] +  0.21214Fruit[t] -1.4107Sport[t] +  0.0786795Alcohol[t] -0.554974Gebgewicht[t] +  0.639093Inter[t] -0.148747Gebgew2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291228&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Numeracy[t] =  +  8.82316 +  1.8686Geslacht[t] -0.458635Drugs[t] +  0.21214Fruit[t] -1.4107Sport[t] +  0.0786795Alcohol[t] -0.554974Gebgewicht[t] +  0.639093Inter[t] -0.148747Gebgew2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291228&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291228&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
Numeracy[t] = + 8.82316 + 1.8686Geslacht[t] -0.458635Drugs[t] + 0.21214Fruit[t] -1.4107Sport[t] + 0.0786795Alcohol[t] -0.554974Gebgewicht[t] + 0.639093Inter[t] -0.148747Gebgew2[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)	+8.823	3.368	+2.6200e+00	0.01602	0.008008
Geslacht	+1.869	0.8009	+2.3330e+00	0.02967	0.01483
Drugs	-0.4586	0.2705	-1.6960e+00	0.1047	0.05235
Fruit	+0.2121	0.298	+7.1200e-01	0.4843	0.2422
Sport	-1.411	0.7046	-2.0020e+00	0.05837	0.02918
Alcohol	+0.07868	0.2751	+2.8600e-01	0.7776	0.3888
Gebgewicht	-0.555	0.3229	-1.7190e+00	0.1004	0.05019
Inter	+0.6391	1.264	+5.0580e-01	0.6183	0.3091
Gebgew2	-0.1487	0.2335	-6.3710e-01	0.5309	0.2655

\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) & +8.823 &  3.368 & +2.6200e+00 &  0.01602 &  0.008008 \tabularnewline
Geslacht & +1.869 &  0.8009 & +2.3330e+00 &  0.02967 &  0.01483 \tabularnewline
Drugs & -0.4586 &  0.2705 & -1.6960e+00 &  0.1047 &  0.05235 \tabularnewline
Fruit & +0.2121 &  0.298 & +7.1200e-01 &  0.4843 &  0.2422 \tabularnewline
Sport & -1.411 &  0.7046 & -2.0020e+00 &  0.05837 &  0.02918 \tabularnewline
Alcohol & +0.07868 &  0.2751 & +2.8600e-01 &  0.7776 &  0.3888 \tabularnewline
Gebgewicht & -0.555 &  0.3229 & -1.7190e+00 &  0.1004 &  0.05019 \tabularnewline
Inter & +0.6391 &  1.264 & +5.0580e-01 &  0.6183 &  0.3091 \tabularnewline
Gebgew2 & -0.1487 &  0.2335 & -6.3710e-01 &  0.5309 &  0.2655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291228&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]+8.823[/C][C] 3.368[/C][C]+2.6200e+00[/C][C] 0.01602[/C][C] 0.008008[/C][/ROW]
[ROW][C]Geslacht[/C][C]+1.869[/C][C] 0.8009[/C][C]+2.3330e+00[/C][C] 0.02967[/C][C] 0.01483[/C][/ROW]
[ROW][C]Drugs[/C][C]-0.4586[/C][C] 0.2705[/C][C]-1.6960e+00[/C][C] 0.1047[/C][C] 0.05235[/C][/ROW]
[ROW][C]Fruit[/C][C]+0.2121[/C][C] 0.298[/C][C]+7.1200e-01[/C][C] 0.4843[/C][C] 0.2422[/C][/ROW]
[ROW][C]Sport[/C][C]-1.411[/C][C] 0.7046[/C][C]-2.0020e+00[/C][C] 0.05837[/C][C] 0.02918[/C][/ROW]
[ROW][C]Alcohol[/C][C]+0.07868[/C][C] 0.2751[/C][C]+2.8600e-01[/C][C] 0.7776[/C][C] 0.3888[/C][/ROW]
[ROW][C]Gebgewicht[/C][C]-0.555[/C][C] 0.3229[/C][C]-1.7190e+00[/C][C] 0.1004[/C][C] 0.05019[/C][/ROW]
[ROW][C]Inter[/C][C]+0.6391[/C][C] 1.264[/C][C]+5.0580e-01[/C][C] 0.6183[/C][C] 0.3091[/C][/ROW]
[ROW][C]Gebgew2[/C][C]-0.1487[/C][C] 0.2335[/C][C]-6.3710e-01[/C][C] 0.5309[/C][C] 0.2655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291228&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291228&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)	+8.823	3.368	+2.6200e+00	0.01602	0.008008
Geslacht	+1.869	0.8009	+2.3330e+00	0.02967	0.01483
Drugs	-0.4586	0.2705	-1.6960e+00	0.1047	0.05235
Fruit	+0.2121	0.298	+7.1200e-01	0.4843	0.2422
Sport	-1.411	0.7046	-2.0020e+00	0.05837	0.02918
Alcohol	+0.07868	0.2751	+2.8600e-01	0.7776	0.3888
Gebgewicht	-0.555	0.3229	-1.7190e+00	0.1004	0.05019
Inter	+0.6391	1.264	+5.0580e-01	0.6183	0.3091
Gebgew2	-0.1487	0.2335	-6.3710e-01	0.5309	0.2655

Multiple Linear Regression - Regression Statistics
Multiple R	0.8181
R-squared	0.6692
Adjusted R-squared	0.5432
F-TEST (value)	5.311
F-TEST (DF numerator)	8
F-TEST (DF denominator)	21
p-value	0.0009963
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.013
Sum Squared Residuals	190.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8181 \tabularnewline
R-squared &  0.6692 \tabularnewline
Adjusted R-squared &  0.5432 \tabularnewline
F-TEST (value) &  5.311 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 21 \tabularnewline
p-value &  0.0009963 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.013 \tabularnewline
Sum Squared Residuals &  190.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291228&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8181[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6692[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5432[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.311[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]21[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0009963[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 3.013[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 190.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291228&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291228&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	0.8181
R-squared	0.6692
Adjusted R-squared	0.5432
F-TEST (value)	5.311
F-TEST (DF numerator)	8
F-TEST (DF denominator)	21
p-value	0.0009963
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.013
Sum Squared Residuals	190.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6	8.979	-2.979
2	10.24	12.02	-1.776
3	1	1.394	-0.3935
4	3.5	2.303	1.197
5	3.7	4.553	-0.8526
6	11	7.489	3.511
7	12.25	10.04	2.215
8	1	0.1718	0.8282
9	3.4	4.088	-0.6877
10	3.7	3.431	0.2687
11	17	8.563	8.437
12	12.96	11.94	1.017
13	1	0.4138	0.5862
14	3.3	3.345	-0.04516
15	3.6	3.383	0.2168
16	12	7.693	4.307
17	11.56	13.55	-1.988
18	0	2.007	-2.007
19	3.3	4.61	-1.31
20	3	4.27	-1.27
21	2	7.768	-5.768
22	9	9.471	-0.4706
23	1	0.359	0.641
24	3.5	3.997	-0.497
25	3.8	3.379	0.4207
26	3	6.718	-3.718
27	7.29	6.779	0.5108
28	1	3.061	-2.061
29	3.8	3.767	0.0331
30	4.3	2.667	1.633

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6 &  8.979 & -2.979 \tabularnewline
2 &  10.24 &  12.02 & -1.776 \tabularnewline
3 &  1 &  1.394 & -0.3935 \tabularnewline
4 &  3.5 &  2.303 &  1.197 \tabularnewline
5 &  3.7 &  4.553 & -0.8526 \tabularnewline
6 &  11 &  7.489 &  3.511 \tabularnewline
7 &  12.25 &  10.04 &  2.215 \tabularnewline
8 &  1 &  0.1718 &  0.8282 \tabularnewline
9 &  3.4 &  4.088 & -0.6877 \tabularnewline
10 &  3.7 &  3.431 &  0.2687 \tabularnewline
11 &  17 &  8.563 &  8.437 \tabularnewline
12 &  12.96 &  11.94 &  1.017 \tabularnewline
13 &  1 &  0.4138 &  0.5862 \tabularnewline
14 &  3.3 &  3.345 & -0.04516 \tabularnewline
15 &  3.6 &  3.383 &  0.2168 \tabularnewline
16 &  12 &  7.693 &  4.307 \tabularnewline
17 &  11.56 &  13.55 & -1.988 \tabularnewline
18 &  0 &  2.007 & -2.007 \tabularnewline
19 &  3.3 &  4.61 & -1.31 \tabularnewline
20 &  3 &  4.27 & -1.27 \tabularnewline
21 &  2 &  7.768 & -5.768 \tabularnewline
22 &  9 &  9.471 & -0.4706 \tabularnewline
23 &  1 &  0.359 &  0.641 \tabularnewline
24 &  3.5 &  3.997 & -0.497 \tabularnewline
25 &  3.8 &  3.379 &  0.4207 \tabularnewline
26 &  3 &  6.718 & -3.718 \tabularnewline
27 &  7.29 &  6.779 &  0.5108 \tabularnewline
28 &  1 &  3.061 & -2.061 \tabularnewline
29 &  3.8 &  3.767 &  0.0331 \tabularnewline
30 &  4.3 &  2.667 &  1.633 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291228&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] 6[/C][C] 8.979[/C][C]-2.979[/C][/ROW]
[ROW][C]2[/C][C] 10.24[/C][C] 12.02[/C][C]-1.776[/C][/ROW]
[ROW][C]3[/C][C] 1[/C][C] 1.394[/C][C]-0.3935[/C][/ROW]
[ROW][C]4[/C][C] 3.5[/C][C] 2.303[/C][C] 1.197[/C][/ROW]
[ROW][C]5[/C][C] 3.7[/C][C] 4.553[/C][C]-0.8526[/C][/ROW]
[ROW][C]6[/C][C] 11[/C][C] 7.489[/C][C] 3.511[/C][/ROW]
[ROW][C]7[/C][C] 12.25[/C][C] 10.04[/C][C] 2.215[/C][/ROW]
[ROW][C]8[/C][C] 1[/C][C] 0.1718[/C][C] 0.8282[/C][/ROW]
[ROW][C]9[/C][C] 3.4[/C][C] 4.088[/C][C]-0.6877[/C][/ROW]
[ROW][C]10[/C][C] 3.7[/C][C] 3.431[/C][C] 0.2687[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 8.563[/C][C] 8.437[/C][/ROW]
[ROW][C]12[/C][C] 12.96[/C][C] 11.94[/C][C] 1.017[/C][/ROW]
[ROW][C]13[/C][C] 1[/C][C] 0.4138[/C][C] 0.5862[/C][/ROW]
[ROW][C]14[/C][C] 3.3[/C][C] 3.345[/C][C]-0.04516[/C][/ROW]
[ROW][C]15[/C][C] 3.6[/C][C] 3.383[/C][C] 0.2168[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 7.693[/C][C] 4.307[/C][/ROW]
[ROW][C]17[/C][C] 11.56[/C][C] 13.55[/C][C]-1.988[/C][/ROW]
[ROW][C]18[/C][C] 0[/C][C] 2.007[/C][C]-2.007[/C][/ROW]
[ROW][C]19[/C][C] 3.3[/C][C] 4.61[/C][C]-1.31[/C][/ROW]
[ROW][C]20[/C][C] 3[/C][C] 4.27[/C][C]-1.27[/C][/ROW]
[ROW][C]21[/C][C] 2[/C][C] 7.768[/C][C]-5.768[/C][/ROW]
[ROW][C]22[/C][C] 9[/C][C] 9.471[/C][C]-0.4706[/C][/ROW]
[ROW][C]23[/C][C] 1[/C][C] 0.359[/C][C] 0.641[/C][/ROW]
[ROW][C]24[/C][C] 3.5[/C][C] 3.997[/C][C]-0.497[/C][/ROW]
[ROW][C]25[/C][C] 3.8[/C][C] 3.379[/C][C] 0.4207[/C][/ROW]
[ROW][C]26[/C][C] 3[/C][C] 6.718[/C][C]-3.718[/C][/ROW]
[ROW][C]27[/C][C] 7.29[/C][C] 6.779[/C][C] 0.5108[/C][/ROW]
[ROW][C]28[/C][C] 1[/C][C] 3.061[/C][C]-2.061[/C][/ROW]
[ROW][C]29[/C][C] 3.8[/C][C] 3.767[/C][C] 0.0331[/C][/ROW]
[ROW][C]30[/C][C] 4.3[/C][C] 2.667[/C][C] 1.633[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291228&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291228&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	6	8.979	-2.979
2	10.24	12.02	-1.776
3	1	1.394	-0.3935
4	3.5	2.303	1.197
5	3.7	4.553	-0.8526
6	11	7.489	3.511
7	12.25	10.04	2.215
8	1	0.1718	0.8282
9	3.4	4.088	-0.6877
10	3.7	3.431	0.2687
11	17	8.563	8.437
12	12.96	11.94	1.017
13	1	0.4138	0.5862
14	3.3	3.345	-0.04516
15	3.6	3.383	0.2168
16	12	7.693	4.307
17	11.56	13.55	-1.988
18	0	2.007	-2.007
19	3.3	4.61	-1.31
20	3	4.27	-1.27
21	2	7.768	-5.768
22	9	9.471	-0.4706
23	1	0.359	0.641
24	3.5	3.997	-0.497
25	3.8	3.379	0.4207
26	3	6.718	-3.718
27	7.29	6.779	0.5108
28	1	3.061	-2.061
29	3.8	3.767	0.0331
30	4.3	2.667	1.633

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.9167	0.1667	0.08333
13	0.8536	0.2928	0.1464
14	0.7377	0.5246	0.2623
15	0.5783	0.8435	0.4217
16	0.9982	0.003618	0.001809
17	0.9919	0.01624	0.008119
18	0.9859	0.02826	0.01413

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.9167 &  0.1667 &  0.08333 \tabularnewline
13 &  0.8536 &  0.2928 &  0.1464 \tabularnewline
14 &  0.7377 &  0.5246 &  0.2623 \tabularnewline
15 &  0.5783 &  0.8435 &  0.4217 \tabularnewline
16 &  0.9982 &  0.003618 &  0.001809 \tabularnewline
17 &  0.9919 &  0.01624 &  0.008119 \tabularnewline
18 &  0.9859 &  0.02826 &  0.01413 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291228&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]12[/C][C] 0.9167[/C][C] 0.1667[/C][C] 0.08333[/C][/ROW]
[ROW][C]13[/C][C] 0.8536[/C][C] 0.2928[/C][C] 0.1464[/C][/ROW]
[ROW][C]14[/C][C] 0.7377[/C][C] 0.5246[/C][C] 0.2623[/C][/ROW]
[ROW][C]15[/C][C] 0.5783[/C][C] 0.8435[/C][C] 0.4217[/C][/ROW]
[ROW][C]16[/C][C] 0.9982[/C][C] 0.003618[/C][C] 0.001809[/C][/ROW]
[ROW][C]17[/C][C] 0.9919[/C][C] 0.01624[/C][C] 0.008119[/C][/ROW]
[ROW][C]18[/C][C] 0.9859[/C][C] 0.02826[/C][C] 0.01413[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291228&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291228&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
12	0.9167	0.1667	0.08333
13	0.8536	0.2928	0.1464
14	0.7377	0.5246	0.2623
15	0.5783	0.8435	0.4217
16	0.9982	0.003618	0.001809
17	0.9919	0.01624	0.008119
18	0.9859	0.02826	0.01413

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.1429	NOK
5% type I error level	3	0.428571	NOK
10% type I error level	3	0.428571	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 & 1 &  0.1429 & NOK \tabularnewline
5% type I error level & 3 & 0.428571 & NOK \tabularnewline
10% type I error level & 3 & 0.428571 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291228&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]1[/C][C] 0.1429[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.428571[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.428571[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291228&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291228&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	1	0.1429	NOK
5% type I error level	3	0.428571	NOK
10% type I error level	3	0.428571	NOK

Figure 1

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Postscript link

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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

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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