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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 17 Dec 2010 11:54:43 +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/2010/Dec/17/t1292586755k81cd0luv4lko98.htm/, Retrieved Tue, 23 Apr 2024 18:58:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111399, Retrieved Tue, 23 Apr 2024 18:58:00 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

190

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

-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
-  M D  [Classical Decomposition] [] [2010-11-26 09:51:05] [7789b9488494790f41ddb7f073cada1b]
- RMPD      [Multiple Regression] [] [2010-12-17 11:54:43] [c05c5ae4ce2db58f67fd725429d7f25c] [Current]
-   PD        [Multiple Regression] [] [2010-12-19 13:30:08] [7789b9488494790f41ddb7f073cada1b] 
-               [Multiple Regression] [] [2010-12-20 20:46:17] [504b6ff240ec7a3fcbc007044ae7a0bb] 
-   PD        [Multiple Regression] [] [2010-12-19 13:42:28] [7789b9488494790f41ddb7f073cada1b] 
-               [Multiple Regression] [] [2010-12-20 22:10:46] [504b6ff240ec7a3fcbc007044ae7a0bb] 

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Dataseries X:

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Histogram

Boxplots

101.82	107.34	93.63	99.85	101.76
101.68	107.34	93.63	99.91	102.37
101.68	107.34	93.63	99.87	102.38
102.45	107.34	96.13	99.86	102.86
102.45	107.34	96.13	100.10	102.87
102.45	107.34	96.13	100.10	102.92
102.45	107.34	96.13	100.12	102.95
102.45	107.34	96.13	99.95	103.02
102.45	112.60	96.13	99.94	104.08
102.52	112.60	96.13	100.18	104.16
102.52	112.60	96.13	100.31	104.24
102.85	112.60	96.13	100.65	104.33
102.85	112.61	96.13	100.65	104.73
102.85	112.61	96.13	100.69	104.86
103.25	112.61	96.13	101.26	105.03
103.25	112.61	98.73	101.26	105.62
103.25	112.61	98.73	101.38	105.63
103.25	112.61	98.73	101.38	105.63
104.45	112.61	98.73	101.38	105.94
104.45	112.61	98.73	101.44	106.61
104.45	118.65	98.73	101.40	107.69
104.80	118.65	98.73	101.40	107.78
104.80	118.65	98.73	100.58	107.93
105.29	118.65	98.73	100.58	108.48
105.29	114.29	98.73	100.58	108.14
105.29	114.29	98.73	100.59	108.48
105.29	114.29	98.73	100.81	108.48
106.04	114.29	101.67	100.75	108.89
105.94	114.29	101.67	100.75	108.93
105.94	114.29	101.67	100.96	109.21
105.94	114.29	101.67	101.31	109.47
106.28	114.29	101.67	101.64	109.80
106.48	123.33	101.67	101.46	111.73
107.19	123.33	101.67	101.73	111.85
108.14	123.33	101.67	101.73	112.12
108.22	123.33	101.67	101.64	112.15
108.22	123.33	101.67	101.77	112.17
108.61	123.33	101.67	101.74	112.67
108.61	123.33	101.67	101.89	112.80
108.61	123.33	107.94	101.89	113.44
108.61	123.33	107.94	101.93	113.53
109.06	123.33	107.94	101.93	114.53
109.06	123.33	107.94	102.32	114.51
112.93	123.33	107.94	102.41	115.05
115.84	129.03	107.94	103.58	116.67
118.57	128.76	107.94	104.12	117.07
118.57	128.76	107.94	104.10	116.92
118.86	128.76	107.94	104.15	117.00
118.98	128.76	107.94	104.15	117.02
119.27	128.76	107.94	104.16	117.35
119.39	128.76	107.94	102.94	117.36
119.49	128.76	110.30	103.07	117.82
119.59	128.76	110.30	103.04	117.88
120.12	128.76	110.30	103.06	118.24
120.14	128.76	110.30	103.05	118.50
120.14	128.76	110.30	102.95	118.80
120.14	132.63	110.30	102.95	119.76
120.14	132.63	110.30	103.05	120.09

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111399&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111399&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111399&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	9 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
vrijetijdsbesteding[t] = + 35.2689359098687 + 0.0686290527084394bios[t] + 0.349632828334539schouwburg[t] + 0.533715451658278eedagsacttractie[t] -0.279710824870231huurDVD[t] + 0.316008799723908M1[t] + 0.695631656879148M2[t] + 0.734592742605774M3[t] -0.547693856402015M4[t] -0.484995255361616M5[t] -0.147579851748444M6[t] + 0.045631283121235M7[t] + 0.381593475385275M8[t] -0.370011739420981M9[t] -0.13577970566168M10[t] -0.188061168684507M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
vrijetijdsbesteding[t] =  +  35.2689359098687 +  0.0686290527084394bios[t] +  0.349632828334539schouwburg[t] +  0.533715451658278eedagsacttractie[t] -0.279710824870231huurDVD[t] +  0.316008799723908M1[t] +  0.695631656879148M2[t] +  0.734592742605774M3[t] -0.547693856402015M4[t] -0.484995255361616M5[t] -0.147579851748444M6[t] +  0.045631283121235M7[t] +  0.381593475385275M8[t] -0.370011739420981M9[t] -0.13577970566168M10[t] -0.188061168684507M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111399&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]vrijetijdsbesteding[t] =  +  35.2689359098687 +  0.0686290527084394bios[t] +  0.349632828334539schouwburg[t] +  0.533715451658278eedagsacttractie[t] -0.279710824870231huurDVD[t] +  0.316008799723908M1[t] +  0.695631656879148M2[t] +  0.734592742605774M3[t] -0.547693856402015M4[t] -0.484995255361616M5[t] -0.147579851748444M6[t] +  0.045631283121235M7[t] +  0.381593475385275M8[t] -0.370011739420981M9[t] -0.13577970566168M10[t] -0.188061168684507M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111399&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111399&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
vrijetijdsbesteding[t] = + 35.2689359098687 + 0.0686290527084394bios[t] + 0.349632828334539schouwburg[t] + 0.533715451658278eedagsacttractie[t] -0.279710824870231huurDVD[t] + 0.316008799723908M1[t] + 0.695631656879148M2[t] + 0.734592742605774M3[t] -0.547693856402015M4[t] -0.484995255361616M5[t] -0.147579851748444M6[t] + 0.045631283121235M7[t] + 0.381593475385275M8[t] -0.370011739420981M9[t] -0.13577970566168M10[t] -0.188061168684507M11[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)	35.2689359098687	14.406688	2.4481	0.018621	0.00931
bios	0.0686290527084394	0.040939	1.6764	0.101098	0.050549
schouwburg	0.349632828334539	0.056553	6.1824	0	0
eedagsacttractie	0.533715451658278	0.091555	5.8295	1e-06	0
huurDVD	-0.279710824870231	0.174489	-1.603	0.116423	0.058211
M1	0.316008799723908	0.397969	0.7941	0.431628	0.215814
M2	0.695631656879148	0.39816	1.7471	0.087926	0.043963
M3	0.734592742605774	0.398371	1.844	0.072247	0.036123
M4	-0.547693856402015	0.531577	-1.0303	0.308757	0.154379
M5	-0.484995255361616	0.531154	-0.9131	0.366404	0.183202
M6	-0.147579851748444	0.52955	-0.2787	0.781851	0.390925
M7	0.045631283121235	0.527995	0.0864	0.93154	0.46577
M8	0.381593475385275	0.522941	0.7297	0.46962	0.23481
M9	-0.370011739420981	0.393307	-0.9408	0.352202	0.176101
M10	-0.13577970566168	0.390147	-0.348	0.729562	0.364781
M11	-0.188061168684507	0.410028	-0.4587	0.648847	0.324423

\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) & 35.2689359098687 & 14.406688 & 2.4481 & 0.018621 & 0.00931 \tabularnewline
bios & 0.0686290527084394 & 0.040939 & 1.6764 & 0.101098 & 0.050549 \tabularnewline
schouwburg & 0.349632828334539 & 0.056553 & 6.1824 & 0 & 0 \tabularnewline
eedagsacttractie & 0.533715451658278 & 0.091555 & 5.8295 & 1e-06 & 0 \tabularnewline
huurDVD & -0.279710824870231 & 0.174489 & -1.603 & 0.116423 & 0.058211 \tabularnewline
M1 & 0.316008799723908 & 0.397969 & 0.7941 & 0.431628 & 0.215814 \tabularnewline
M2 & 0.695631656879148 & 0.39816 & 1.7471 & 0.087926 & 0.043963 \tabularnewline
M3 & 0.734592742605774 & 0.398371 & 1.844 & 0.072247 & 0.036123 \tabularnewline
M4 & -0.547693856402015 & 0.531577 & -1.0303 & 0.308757 & 0.154379 \tabularnewline
M5 & -0.484995255361616 & 0.531154 & -0.9131 & 0.366404 & 0.183202 \tabularnewline
M6 & -0.147579851748444 & 0.52955 & -0.2787 & 0.781851 & 0.390925 \tabularnewline
M7 & 0.045631283121235 & 0.527995 & 0.0864 & 0.93154 & 0.46577 \tabularnewline
M8 & 0.381593475385275 & 0.522941 & 0.7297 & 0.46962 & 0.23481 \tabularnewline
M9 & -0.370011739420981 & 0.393307 & -0.9408 & 0.352202 & 0.176101 \tabularnewline
M10 & -0.13577970566168 & 0.390147 & -0.348 & 0.729562 & 0.364781 \tabularnewline
M11 & -0.188061168684507 & 0.410028 & -0.4587 & 0.648847 & 0.324423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111399&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]35.2689359098687[/C][C]14.406688[/C][C]2.4481[/C][C]0.018621[/C][C]0.00931[/C][/ROW]
[ROW][C]bios[/C][C]0.0686290527084394[/C][C]0.040939[/C][C]1.6764[/C][C]0.101098[/C][C]0.050549[/C][/ROW]
[ROW][C]schouwburg[/C][C]0.349632828334539[/C][C]0.056553[/C][C]6.1824[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]eedagsacttractie[/C][C]0.533715451658278[/C][C]0.091555[/C][C]5.8295[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]huurDVD[/C][C]-0.279710824870231[/C][C]0.174489[/C][C]-1.603[/C][C]0.116423[/C][C]0.058211[/C][/ROW]
[ROW][C]M1[/C][C]0.316008799723908[/C][C]0.397969[/C][C]0.7941[/C][C]0.431628[/C][C]0.215814[/C][/ROW]
[ROW][C]M2[/C][C]0.695631656879148[/C][C]0.39816[/C][C]1.7471[/C][C]0.087926[/C][C]0.043963[/C][/ROW]
[ROW][C]M3[/C][C]0.734592742605774[/C][C]0.398371[/C][C]1.844[/C][C]0.072247[/C][C]0.036123[/C][/ROW]
[ROW][C]M4[/C][C]-0.547693856402015[/C][C]0.531577[/C][C]-1.0303[/C][C]0.308757[/C][C]0.154379[/C][/ROW]
[ROW][C]M5[/C][C]-0.484995255361616[/C][C]0.531154[/C][C]-0.9131[/C][C]0.366404[/C][C]0.183202[/C][/ROW]
[ROW][C]M6[/C][C]-0.147579851748444[/C][C]0.52955[/C][C]-0.2787[/C][C]0.781851[/C][C]0.390925[/C][/ROW]
[ROW][C]M7[/C][C]0.045631283121235[/C][C]0.527995[/C][C]0.0864[/C][C]0.93154[/C][C]0.46577[/C][/ROW]
[ROW][C]M8[/C][C]0.381593475385275[/C][C]0.522941[/C][C]0.7297[/C][C]0.46962[/C][C]0.23481[/C][/ROW]
[ROW][C]M9[/C][C]-0.370011739420981[/C][C]0.393307[/C][C]-0.9408[/C][C]0.352202[/C][C]0.176101[/C][/ROW]
[ROW][C]M10[/C][C]-0.13577970566168[/C][C]0.390147[/C][C]-0.348[/C][C]0.729562[/C][C]0.364781[/C][/ROW]
[ROW][C]M11[/C][C]-0.188061168684507[/C][C]0.410028[/C][C]-0.4587[/C][C]0.648847[/C][C]0.324423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111399&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111399&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)	35.2689359098687	14.406688	2.4481	0.018621	0.00931
bios	0.0686290527084394	0.040939	1.6764	0.101098	0.050549
schouwburg	0.349632828334539	0.056553	6.1824	0	0
eedagsacttractie	0.533715451658278	0.091555	5.8295	1e-06	0
huurDVD	-0.279710824870231	0.174489	-1.603	0.116423	0.058211
M1	0.316008799723908	0.397969	0.7941	0.431628	0.215814
M2	0.695631656879148	0.39816	1.7471	0.087926	0.043963
M3	0.734592742605774	0.398371	1.844	0.072247	0.036123
M4	-0.547693856402015	0.531577	-1.0303	0.308757	0.154379
M5	-0.484995255361616	0.531154	-0.9131	0.366404	0.183202
M6	-0.147579851748444	0.52955	-0.2787	0.781851	0.390925
M7	0.045631283121235	0.527995	0.0864	0.93154	0.46577
M8	0.381593475385275	0.522941	0.7297	0.46962	0.23481
M9	-0.370011739420981	0.393307	-0.9408	0.352202	0.176101
M10	-0.13577970566168	0.390147	-0.348	0.729562	0.364781
M11	-0.188061168684507	0.410028	-0.4587	0.648847	0.324423

Multiple Linear Regression - Regression Statistics
Multiple R	0.99601068857855
R-squared	0.99203729176272
Adjusted R-squared	0.989193467392262
F-TEST (value)	348.839155493686
F-TEST (DF numerator)	15
F-TEST (DF denominator)	42
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.579534663866987
Sum Squared Residuals	14.1061379181837

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99601068857855 \tabularnewline
R-squared & 0.99203729176272 \tabularnewline
Adjusted R-squared & 0.989193467392262 \tabularnewline
F-TEST (value) & 348.839155493686 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.579534663866987 \tabularnewline
Sum Squared Residuals & 14.1061379181837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111399&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99601068857855[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99203729176272[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.989193467392262[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]348.839155493686[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.579534663866987[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14.1061379181837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111399&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111399&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	0.99601068857855
R-squared	0.99203729176272
Adjusted R-squared	0.989193467392262
F-TEST (value)	348.839155493686
F-TEST (DF numerator)	15
F-TEST (DF denominator)	42
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.579534663866987
Sum Squared Residuals	14.1061379181837

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	102.144994525268	-0.384994525267722
2	102.37	102.498226665551	-0.128226665551124
3	102.38	102.548376184273	-0.168376184272574
4	102.86	102.656019693245	0.203980306755325
5	102.87	102.651587696316	0.218412303683786
6	102.92	102.989003099929	-0.0690030999293898
7	102.95	103.176620018302	-0.22662001830166
8	103.02	103.560133050794	-0.540133050793647
9	104.08	104.650393621276	-0.570393621275765
10	104.16	104.822299090756	-0.662299090755801
11	104.24	104.733655220500	-0.493655220499847
12	104.33	104.849262296122	-0.519262296122256
13	104.73	105.168767424130	-0.438767424129506
14	104.86	105.53720184829	-0.677201848289943
15	105.03	105.444179384924	-0.414179384923909
16	105.62	105.549552960228	0.0704470397723576
17	105.63	105.578686262284	0.0513137377163745
18	105.63	105.916101665897	-0.286101665896798
19	105.94	106.191667664017	-0.251667664016602
20	106.61	106.510847206788	0.0991527932115737
21	107.69	107.882212708118	-0.192212708117599
22	107.78	108.140464910325	-0.36046491032485
23	107.93	108.317546323696	-0.387546323695609
24	108.48	108.539235728207	-0.0592357282072548
25	108.14	107.330845396393	0.809154603607425
26	108.48	107.707671145299	0.772328854700892
27	108.48	107.685095849554	0.794904150445718
28	108.89	108.040187117445	0.849812882554624
29	108.93	108.096022813215	0.833977186785076
30	109.21	108.374698943605	0.835301056394638
31	109.47	108.470011289770	0.999988710229548
32	109.8	108.737002787748	1.06299721225181
33	111.73	111.210152100104	0.51984789989551
34	111.85	111.417588838572	0.432411161428172
35	112.12	111.430504975622	0.689495024377992
36	112.15	111.649230442762	0.500769557238488
37	112.17	111.928876835252	0.241123164747705
38	112.67	112.34365634771	0.326343652290066
39	112.8	112.340660809706	0.459339190293973
40	113.44	114.404770092596	-0.964770092595634
41	113.53	114.456280260641	-0.92628026064122
42	114.53	114.824578737973	-0.29457873797319
43	114.51	114.908702651143	-0.398702651143479
44	115.05	115.485085303151	-0.435085303150866
45	116.67	116.598836088135	0.0711639118651307
46	117.07	116.774980726708	0.295019273292036
47	116.92	116.728293480183	0.191706519817464
48	117	116.922271532909	0.0777284670910233
49	117.02	117.246515818958	-0.226515818957902
50	117.35	117.64324399315	-0.293243993149891
51	117.36	118.031687771543	-0.671687771543208
52	117.82	117.979470136487	-0.159470136486672
53	117.88	118.057422967544	-0.177422967544017
54	118.24	118.425617552595	-0.18561755259526
55	118.5	118.622998376768	-0.122998376767807
56	118.8	118.986931651519	-0.186931651518871
57	119.76	119.588405482367	0.171594517632724
58	120.09	119.794666433640	0.295333566360443

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 102.144994525268 & -0.384994525267722 \tabularnewline
2 & 102.37 & 102.498226665551 & -0.128226665551124 \tabularnewline
3 & 102.38 & 102.548376184273 & -0.168376184272574 \tabularnewline
4 & 102.86 & 102.656019693245 & 0.203980306755325 \tabularnewline
5 & 102.87 & 102.651587696316 & 0.218412303683786 \tabularnewline
6 & 102.92 & 102.989003099929 & -0.0690030999293898 \tabularnewline
7 & 102.95 & 103.176620018302 & -0.22662001830166 \tabularnewline
8 & 103.02 & 103.560133050794 & -0.540133050793647 \tabularnewline
9 & 104.08 & 104.650393621276 & -0.570393621275765 \tabularnewline
10 & 104.16 & 104.822299090756 & -0.662299090755801 \tabularnewline
11 & 104.24 & 104.733655220500 & -0.493655220499847 \tabularnewline
12 & 104.33 & 104.849262296122 & -0.519262296122256 \tabularnewline
13 & 104.73 & 105.168767424130 & -0.438767424129506 \tabularnewline
14 & 104.86 & 105.53720184829 & -0.677201848289943 \tabularnewline
15 & 105.03 & 105.444179384924 & -0.414179384923909 \tabularnewline
16 & 105.62 & 105.549552960228 & 0.0704470397723576 \tabularnewline
17 & 105.63 & 105.578686262284 & 0.0513137377163745 \tabularnewline
18 & 105.63 & 105.916101665897 & -0.286101665896798 \tabularnewline
19 & 105.94 & 106.191667664017 & -0.251667664016602 \tabularnewline
20 & 106.61 & 106.510847206788 & 0.0991527932115737 \tabularnewline
21 & 107.69 & 107.882212708118 & -0.192212708117599 \tabularnewline
22 & 107.78 & 108.140464910325 & -0.36046491032485 \tabularnewline
23 & 107.93 & 108.317546323696 & -0.387546323695609 \tabularnewline
24 & 108.48 & 108.539235728207 & -0.0592357282072548 \tabularnewline
25 & 108.14 & 107.330845396393 & 0.809154603607425 \tabularnewline
26 & 108.48 & 107.707671145299 & 0.772328854700892 \tabularnewline
27 & 108.48 & 107.685095849554 & 0.794904150445718 \tabularnewline
28 & 108.89 & 108.040187117445 & 0.849812882554624 \tabularnewline
29 & 108.93 & 108.096022813215 & 0.833977186785076 \tabularnewline
30 & 109.21 & 108.374698943605 & 0.835301056394638 \tabularnewline
31 & 109.47 & 108.470011289770 & 0.999988710229548 \tabularnewline
32 & 109.8 & 108.737002787748 & 1.06299721225181 \tabularnewline
33 & 111.73 & 111.210152100104 & 0.51984789989551 \tabularnewline
34 & 111.85 & 111.417588838572 & 0.432411161428172 \tabularnewline
35 & 112.12 & 111.430504975622 & 0.689495024377992 \tabularnewline
36 & 112.15 & 111.649230442762 & 0.500769557238488 \tabularnewline
37 & 112.17 & 111.928876835252 & 0.241123164747705 \tabularnewline
38 & 112.67 & 112.34365634771 & 0.326343652290066 \tabularnewline
39 & 112.8 & 112.340660809706 & 0.459339190293973 \tabularnewline
40 & 113.44 & 114.404770092596 & -0.964770092595634 \tabularnewline
41 & 113.53 & 114.456280260641 & -0.92628026064122 \tabularnewline
42 & 114.53 & 114.824578737973 & -0.29457873797319 \tabularnewline
43 & 114.51 & 114.908702651143 & -0.398702651143479 \tabularnewline
44 & 115.05 & 115.485085303151 & -0.435085303150866 \tabularnewline
45 & 116.67 & 116.598836088135 & 0.0711639118651307 \tabularnewline
46 & 117.07 & 116.774980726708 & 0.295019273292036 \tabularnewline
47 & 116.92 & 116.728293480183 & 0.191706519817464 \tabularnewline
48 & 117 & 116.922271532909 & 0.0777284670910233 \tabularnewline
49 & 117.02 & 117.246515818958 & -0.226515818957902 \tabularnewline
50 & 117.35 & 117.64324399315 & -0.293243993149891 \tabularnewline
51 & 117.36 & 118.031687771543 & -0.671687771543208 \tabularnewline
52 & 117.82 & 117.979470136487 & -0.159470136486672 \tabularnewline
53 & 117.88 & 118.057422967544 & -0.177422967544017 \tabularnewline
54 & 118.24 & 118.425617552595 & -0.18561755259526 \tabularnewline
55 & 118.5 & 118.622998376768 & -0.122998376767807 \tabularnewline
56 & 118.8 & 118.986931651519 & -0.186931651518871 \tabularnewline
57 & 119.76 & 119.588405482367 & 0.171594517632724 \tabularnewline
58 & 120.09 & 119.794666433640 & 0.295333566360443 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111399&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]101.76[/C][C]102.144994525268[/C][C]-0.384994525267722[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]102.498226665551[/C][C]-0.128226665551124[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]102.548376184273[/C][C]-0.168376184272574[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.656019693245[/C][C]0.203980306755325[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.651587696316[/C][C]0.218412303683786[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.989003099929[/C][C]-0.0690030999293898[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]103.176620018302[/C][C]-0.22662001830166[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.560133050794[/C][C]-0.540133050793647[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.650393621276[/C][C]-0.570393621275765[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.822299090756[/C][C]-0.662299090755801[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.733655220500[/C][C]-0.493655220499847[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.849262296122[/C][C]-0.519262296122256[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]105.168767424130[/C][C]-0.438767424129506[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.53720184829[/C][C]-0.677201848289943[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.444179384924[/C][C]-0.414179384923909[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.549552960228[/C][C]0.0704470397723576[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]105.578686262284[/C][C]0.0513137377163745[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]105.916101665897[/C][C]-0.286101665896798[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.191667664017[/C][C]-0.251667664016602[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.510847206788[/C][C]0.0991527932115737[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.882212708118[/C][C]-0.192212708117599[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.140464910325[/C][C]-0.36046491032485[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.317546323696[/C][C]-0.387546323695609[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.539235728207[/C][C]-0.0592357282072548[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.330845396393[/C][C]0.809154603607425[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]107.707671145299[/C][C]0.772328854700892[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]107.685095849554[/C][C]0.794904150445718[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.040187117445[/C][C]0.849812882554624[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]108.096022813215[/C][C]0.833977186785076[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]108.374698943605[/C][C]0.835301056394638[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]108.470011289770[/C][C]0.999988710229548[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]108.737002787748[/C][C]1.06299721225181[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.210152100104[/C][C]0.51984789989551[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.417588838572[/C][C]0.432411161428172[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.430504975622[/C][C]0.689495024377992[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]111.649230442762[/C][C]0.500769557238488[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]111.928876835252[/C][C]0.241123164747705[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.34365634771[/C][C]0.326343652290066[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.340660809706[/C][C]0.459339190293973[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]114.404770092596[/C][C]-0.964770092595634[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]114.456280260641[/C][C]-0.92628026064122[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.824578737973[/C][C]-0.29457873797319[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.908702651143[/C][C]-0.398702651143479[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]115.485085303151[/C][C]-0.435085303150866[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.598836088135[/C][C]0.0711639118651307[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.774980726708[/C][C]0.295019273292036[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.728293480183[/C][C]0.191706519817464[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.922271532909[/C][C]0.0777284670910233[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.246515818958[/C][C]-0.226515818957902[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.64324399315[/C][C]-0.293243993149891[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]118.031687771543[/C][C]-0.671687771543208[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]117.979470136487[/C][C]-0.159470136486672[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.057422967544[/C][C]-0.177422967544017[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.425617552595[/C][C]-0.18561755259526[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.622998376768[/C][C]-0.122998376767807[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.986931651519[/C][C]-0.186931651518871[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.588405482367[/C][C]0.171594517632724[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.794666433640[/C][C]0.295333566360443[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111399&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111399&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	101.76	102.144994525268	-0.384994525267722
2	102.37	102.498226665551	-0.128226665551124
3	102.38	102.548376184273	-0.168376184272574
4	102.86	102.656019693245	0.203980306755325
5	102.87	102.651587696316	0.218412303683786
6	102.92	102.989003099929	-0.0690030999293898
7	102.95	103.176620018302	-0.22662001830166
8	103.02	103.560133050794	-0.540133050793647
9	104.08	104.650393621276	-0.570393621275765
10	104.16	104.822299090756	-0.662299090755801
11	104.24	104.733655220500	-0.493655220499847
12	104.33	104.849262296122	-0.519262296122256
13	104.73	105.168767424130	-0.438767424129506
14	104.86	105.53720184829	-0.677201848289943
15	105.03	105.444179384924	-0.414179384923909
16	105.62	105.549552960228	0.0704470397723576
17	105.63	105.578686262284	0.0513137377163745
18	105.63	105.916101665897	-0.286101665896798
19	105.94	106.191667664017	-0.251667664016602
20	106.61	106.510847206788	0.0991527932115737
21	107.69	107.882212708118	-0.192212708117599
22	107.78	108.140464910325	-0.36046491032485
23	107.93	108.317546323696	-0.387546323695609
24	108.48	108.539235728207	-0.0592357282072548
25	108.14	107.330845396393	0.809154603607425
26	108.48	107.707671145299	0.772328854700892
27	108.48	107.685095849554	0.794904150445718
28	108.89	108.040187117445	0.849812882554624
29	108.93	108.096022813215	0.833977186785076
30	109.21	108.374698943605	0.835301056394638
31	109.47	108.470011289770	0.999988710229548
32	109.8	108.737002787748	1.06299721225181
33	111.73	111.210152100104	0.51984789989551
34	111.85	111.417588838572	0.432411161428172
35	112.12	111.430504975622	0.689495024377992
36	112.15	111.649230442762	0.500769557238488
37	112.17	111.928876835252	0.241123164747705
38	112.67	112.34365634771	0.326343652290066
39	112.8	112.340660809706	0.459339190293973
40	113.44	114.404770092596	-0.964770092595634
41	113.53	114.456280260641	-0.92628026064122
42	114.53	114.824578737973	-0.29457873797319
43	114.51	114.908702651143	-0.398702651143479
44	115.05	115.485085303151	-0.435085303150866
45	116.67	116.598836088135	0.0711639118651307
46	117.07	116.774980726708	0.295019273292036
47	116.92	116.728293480183	0.191706519817464
48	117	116.922271532909	0.0777284670910233
49	117.02	117.246515818958	-0.226515818957902
50	117.35	117.64324399315	-0.293243993149891
51	117.36	118.031687771543	-0.671687771543208
52	117.82	117.979470136487	-0.159470136486672
53	117.88	118.057422967544	-0.177422967544017
54	118.24	118.425617552595	-0.18561755259526
55	118.5	118.622998376768	-0.122998376767807
56	118.8	118.986931651519	-0.186931651518871
57	119.76	119.588405482367	0.171594517632724
58	120.09	119.794666433640	0.295333566360443

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.0333304328523004	0.0666608657046009	0.9666695671477
20	0.0565366688518876	0.113073337703775	0.943463331148112
21	0.0458266069010975	0.091653213802195	0.954173393098902
22	0.0520464973896608	0.104092994779322	0.94795350261034
23	0.103622472471212	0.207244944942424	0.896377527528788
24	0.302459205299881	0.604918410599762	0.697540794700119
25	0.239374227585357	0.478748455170714	0.760625772414643
26	0.170378287574510	0.340756575149019	0.82962171242549
27	0.105809059415743	0.211618118831487	0.894190940584257
28	0.207439272472346	0.414878544944693	0.792560727527654
29	0.189699787574926	0.379399575149851	0.810300212425074
30	0.127967688722201	0.255935377444402	0.8720323112778
31	0.145294584121482	0.290589168242964	0.854705415878518
32	0.609501014550346	0.780997970899309	0.390498985449654
33	0.594599409179392	0.810801181641216	0.405400590820608
34	0.689710410412257	0.620579179175485	0.310289589587743
35	0.578061433307055	0.84387713338589	0.421938566692945
36	0.487109584288505	0.97421916857701	0.512890415711495
37	0.463116476722374	0.926232953444748	0.536883523277626
38	0.426808221779824	0.853616443559648	0.573191778220176
39	0.408366854632799	0.816733709265597	0.591633145367201

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.0333304328523004 & 0.0666608657046009 & 0.9666695671477 \tabularnewline
20 & 0.0565366688518876 & 0.113073337703775 & 0.943463331148112 \tabularnewline
21 & 0.0458266069010975 & 0.091653213802195 & 0.954173393098902 \tabularnewline
22 & 0.0520464973896608 & 0.104092994779322 & 0.94795350261034 \tabularnewline
23 & 0.103622472471212 & 0.207244944942424 & 0.896377527528788 \tabularnewline
24 & 0.302459205299881 & 0.604918410599762 & 0.697540794700119 \tabularnewline
25 & 0.239374227585357 & 0.478748455170714 & 0.760625772414643 \tabularnewline
26 & 0.170378287574510 & 0.340756575149019 & 0.82962171242549 \tabularnewline
27 & 0.105809059415743 & 0.211618118831487 & 0.894190940584257 \tabularnewline
28 & 0.207439272472346 & 0.414878544944693 & 0.792560727527654 \tabularnewline
29 & 0.189699787574926 & 0.379399575149851 & 0.810300212425074 \tabularnewline
30 & 0.127967688722201 & 0.255935377444402 & 0.8720323112778 \tabularnewline
31 & 0.145294584121482 & 0.290589168242964 & 0.854705415878518 \tabularnewline
32 & 0.609501014550346 & 0.780997970899309 & 0.390498985449654 \tabularnewline
33 & 0.594599409179392 & 0.810801181641216 & 0.405400590820608 \tabularnewline
34 & 0.689710410412257 & 0.620579179175485 & 0.310289589587743 \tabularnewline
35 & 0.578061433307055 & 0.84387713338589 & 0.421938566692945 \tabularnewline
36 & 0.487109584288505 & 0.97421916857701 & 0.512890415711495 \tabularnewline
37 & 0.463116476722374 & 0.926232953444748 & 0.536883523277626 \tabularnewline
38 & 0.426808221779824 & 0.853616443559648 & 0.573191778220176 \tabularnewline
39 & 0.408366854632799 & 0.816733709265597 & 0.591633145367201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111399&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]19[/C][C]0.0333304328523004[/C][C]0.0666608657046009[/C][C]0.9666695671477[/C][/ROW]
[ROW][C]20[/C][C]0.0565366688518876[/C][C]0.113073337703775[/C][C]0.943463331148112[/C][/ROW]
[ROW][C]21[/C][C]0.0458266069010975[/C][C]0.091653213802195[/C][C]0.954173393098902[/C][/ROW]
[ROW][C]22[/C][C]0.0520464973896608[/C][C]0.104092994779322[/C][C]0.94795350261034[/C][/ROW]
[ROW][C]23[/C][C]0.103622472471212[/C][C]0.207244944942424[/C][C]0.896377527528788[/C][/ROW]
[ROW][C]24[/C][C]0.302459205299881[/C][C]0.604918410599762[/C][C]0.697540794700119[/C][/ROW]
[ROW][C]25[/C][C]0.239374227585357[/C][C]0.478748455170714[/C][C]0.760625772414643[/C][/ROW]
[ROW][C]26[/C][C]0.170378287574510[/C][C]0.340756575149019[/C][C]0.82962171242549[/C][/ROW]
[ROW][C]27[/C][C]0.105809059415743[/C][C]0.211618118831487[/C][C]0.894190940584257[/C][/ROW]
[ROW][C]28[/C][C]0.207439272472346[/C][C]0.414878544944693[/C][C]0.792560727527654[/C][/ROW]
[ROW][C]29[/C][C]0.189699787574926[/C][C]0.379399575149851[/C][C]0.810300212425074[/C][/ROW]
[ROW][C]30[/C][C]0.127967688722201[/C][C]0.255935377444402[/C][C]0.8720323112778[/C][/ROW]
[ROW][C]31[/C][C]0.145294584121482[/C][C]0.290589168242964[/C][C]0.854705415878518[/C][/ROW]
[ROW][C]32[/C][C]0.609501014550346[/C][C]0.780997970899309[/C][C]0.390498985449654[/C][/ROW]
[ROW][C]33[/C][C]0.594599409179392[/C][C]0.810801181641216[/C][C]0.405400590820608[/C][/ROW]
[ROW][C]34[/C][C]0.689710410412257[/C][C]0.620579179175485[/C][C]0.310289589587743[/C][/ROW]
[ROW][C]35[/C][C]0.578061433307055[/C][C]0.84387713338589[/C][C]0.421938566692945[/C][/ROW]
[ROW][C]36[/C][C]0.487109584288505[/C][C]0.97421916857701[/C][C]0.512890415711495[/C][/ROW]
[ROW][C]37[/C][C]0.463116476722374[/C][C]0.926232953444748[/C][C]0.536883523277626[/C][/ROW]
[ROW][C]38[/C][C]0.426808221779824[/C][C]0.853616443559648[/C][C]0.573191778220176[/C][/ROW]
[ROW][C]39[/C][C]0.408366854632799[/C][C]0.816733709265597[/C][C]0.591633145367201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111399&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111399&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
19	0.0333304328523004	0.0666608657046009	0.9666695671477
20	0.0565366688518876	0.113073337703775	0.943463331148112
21	0.0458266069010975	0.091653213802195	0.954173393098902
22	0.0520464973896608	0.104092994779322	0.94795350261034
23	0.103622472471212	0.207244944942424	0.896377527528788
24	0.302459205299881	0.604918410599762	0.697540794700119
25	0.239374227585357	0.478748455170714	0.760625772414643
26	0.170378287574510	0.340756575149019	0.82962171242549
27	0.105809059415743	0.211618118831487	0.894190940584257
28	0.207439272472346	0.414878544944693	0.792560727527654
29	0.189699787574926	0.379399575149851	0.810300212425074
30	0.127967688722201	0.255935377444402	0.8720323112778
31	0.145294584121482	0.290589168242964	0.854705415878518
32	0.609501014550346	0.780997970899309	0.390498985449654
33	0.594599409179392	0.810801181641216	0.405400590820608
34	0.689710410412257	0.620579179175485	0.310289589587743
35	0.578061433307055	0.84387713338589	0.421938566692945
36	0.487109584288505	0.97421916857701	0.512890415711495
37	0.463116476722374	0.926232953444748	0.536883523277626
38	0.426808221779824	0.853616443559648	0.573191778220176
39	0.408366854632799	0.816733709265597	0.591633145367201

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0952380952380952	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 & 2 & 0.0952380952380952 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111399&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0952380952380952[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111399&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111399&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0952380952380952	OK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = Include Monthly 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