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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 19 Nov 2010 12:14:25 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/19/t1290168867e6iobr02r23me43.htm/, Retrieved Sat, 27 Apr 2024 23:30:27 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97939, Retrieved Sat, 27 Apr 2024 23:30:27 +0000

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197

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

-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [deterministische ...] [2010-11-19 12:14:25] [e665313c9926a9f4bdf6ad1ee5aefad6] [Current]
-   PD      [Multiple Regression] [] [2010-11-23 22:04:55] [8ef75e99f9f5061c72c54640f2f1c3e7] 
- R P       [Multiple Regression] [] [2011-11-21 20:29:39] [46d7ccc24e5d35a2decd922dfb3b3a39] 

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6	101.82	107.34	93.63	99.85	101.76
6	101.68	107.34	93.63	99.91	102.37
6	101.68	107.34	93.63	99.87	102.38
6	102.45	107.34	96.13	99.86	102.86
6	102.45	107.34	96.13	100.10	102.87
6	102.45	107.34	96.13	100.10	102.92
6	102.45	107.34	96.13	100.12	102.95
6	102.45	107.34	96.13	99.95	103.02
6	102.45	112.60	96.13	99.94	104.08
6	102.52	112.60	96.13	100.18	104.16
6	102.52	112.60	96.13	100.31	104.24
6	102.85	112.60	96.13	100.65	104.33
7	102.85	112.61	96.13	100.65	104.73
7	102.85	112.61	96.13	100.69	104.86
7	103.25	112.61	96.13	101.26	105.03
7	103.25	112.61	98.73	101.26	105.62
7	103.25	112.61	98.73	101.38	105.63
7	103.25	112.61	98.73	101.38	105.63
7	104.45	112.61	98.73	101.38	105.94
7	104.45	112.61	98.73	101.44	106.61
7	104.45	118.65	98.73	101.40	107.69
7	104.80	118.65	98.73	101.40	107.78
7	104.80	118.65	98.73	100.58	107.93
7	105.29	118.65	98.73	100.58	108.48
8	105.29	114.29	98.73	100.58	108.14
8	105.29	114.29	98.73	100.59	108.48
8	105.29	114.29	98.73	100.81	108.48
8	106.04	114.29	101.67	100.75	108.89
8	105.94	114.29	101.67	100.75	108.93
8	105.94	114.29	101.67	100.96	109.21
8	105.94	114.29	101.67	101.31	109.47
8	106.28	114.29	101.67	101.64	109.80
8	106.48	123.33	101.67	101.46	111.73
8	107.19	123.33	101.67	101.73	111.85
8	108.14	123.33	101.67	101.73	112.12
8	108.22	123.33	101.67	101.64	112.15
9	108.22	123.33	101.67	101.77	112.17
9	108.61	123.33	101.67	101.74	112.67
9	108.61	123.33	101.67	101.89	112.80
9	108.61	123.33	107.94	101.89	113.44
9	108.61	123.33	107.94	101.93	113.53
9	109.06	123.33	107.94	101.93	114.53
9	109.06	123.33	107.94	102.32	114.51
9	112.93	123.33	107.94	102.41	115.05
9	115.84	129.03	107.94	103.58	116.67
9	118.57	128.76	107.94	104.12	117.07
9	118.57	128.76	107.94	104.10	116.92
9	118.86	128.76	107.94	104.15	117.00
10	118.98	128.76	107.94	104.15	117.02
10	119.27	128.76	107.94	104.16	117.35
10	119.39	128.76	107.94	102.94	117.36
10	119.49	128.76	110.30	103.07	117.82
10	119.59	128.76	110.30	103.04	117.88
10	120.12	128.76	110.30	103.06	118.24
10	120.14	128.76	110.30	103.05	118.50
10	120.14	128.76	110.30	102.95	118.80
10	120.14	132.63	110.30	102.95	119.76
10	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=97939&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=97939&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97939&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
Cultuurenvrijetijdsbesteding[t] = + 67.2314876034128 + 0.0808949042160449jaar[t] + 0.111902922150725bioscoop[t] + 0.177271331756085schouwburgabonnement[t] + 0.12737147086898eendagsattractie[t] -0.0868387869852808huurDVD[t] + 0.169151767769644t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cultuurenvrijetijdsbesteding[t] =  +  67.2314876034128 +  0.0808949042160449jaar[t] +  0.111902922150725bioscoop[t] +  0.177271331756085schouwburgabonnement[t] +  0.12737147086898eendagsattractie[t] -0.0868387869852808huurDVD[t] +  0.169151767769644t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97939&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cultuurenvrijetijdsbesteding[t] =  +  67.2314876034128 +  0.0808949042160449jaar[t] +  0.111902922150725bioscoop[t] +  0.177271331756085schouwburgabonnement[t] +  0.12737147086898eendagsattractie[t] -0.0868387869852808huurDVD[t] +  0.169151767769644t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97939&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97939&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
Cultuurenvrijetijdsbesteding[t] = + 67.2314876034128 + 0.0808949042160449jaar[t] + 0.111902922150725bioscoop[t] + 0.177271331756085schouwburgabonnement[t] + 0.12737147086898eendagsattractie[t] -0.0868387869852808huurDVD[t] + 0.169151767769644t + 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)	67.2314876034128	8.061054	8.3403	0	0
jaar	0.0808949042160449	0.15474	0.5228	0.603391	0.301695
bioscoop	0.111902922150725	0.020322	5.5064	1e-06	1e-06
schouwburgabonnement	0.177271331756085	0.021622	8.1987	0	0
eendagsattractie	0.12737147086898	0.032484	3.9211	0.000264	0.000132
huurDVD	-0.0868387869852808	0.09024	-0.9623	0.340437	0.170218
t	0.169151767769644	0.020911	8.0889	0	0

\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) & 67.2314876034128 & 8.061054 & 8.3403 & 0 & 0 \tabularnewline
jaar & 0.0808949042160449 & 0.15474 & 0.5228 & 0.603391 & 0.301695 \tabularnewline
bioscoop & 0.111902922150725 & 0.020322 & 5.5064 & 1e-06 & 1e-06 \tabularnewline
schouwburgabonnement & 0.177271331756085 & 0.021622 & 8.1987 & 0 & 0 \tabularnewline
eendagsattractie & 0.12737147086898 & 0.032484 & 3.9211 & 0.000264 & 0.000132 \tabularnewline
huurDVD & -0.0868387869852808 & 0.09024 & -0.9623 & 0.340437 & 0.170218 \tabularnewline
t & 0.169151767769644 & 0.020911 & 8.0889 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97939&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]67.2314876034128[/C][C]8.061054[/C][C]8.3403[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]jaar[/C][C]0.0808949042160449[/C][C]0.15474[/C][C]0.5228[/C][C]0.603391[/C][C]0.301695[/C][/ROW]
[ROW][C]bioscoop[/C][C]0.111902922150725[/C][C]0.020322[/C][C]5.5064[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]schouwburgabonnement[/C][C]0.177271331756085[/C][C]0.021622[/C][C]8.1987[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]eendagsattractie[/C][C]0.12737147086898[/C][C]0.032484[/C][C]3.9211[/C][C]0.000264[/C][C]0.000132[/C][/ROW]
[ROW][C]huurDVD[/C][C]-0.0868387869852808[/C][C]0.09024[/C][C]-0.9623[/C][C]0.340437[/C][C]0.170218[/C][/ROW]
[ROW][C]t[/C][C]0.169151767769644[/C][C]0.020911[/C][C]8.0889[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97939&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97939&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)	67.2314876034128	8.061054	8.3403	0	0
jaar	0.0808949042160449	0.15474	0.5228	0.603391	0.301695
bioscoop	0.111902922150725	0.020322	5.5064	1e-06	1e-06
schouwburgabonnement	0.177271331756085	0.021622	8.1987	0	0
eendagsattractie	0.12737147086898	0.032484	3.9211	0.000264	0.000132
huurDVD	-0.0868387869852808	0.09024	-0.9623	0.340437	0.170218
t	0.169151767769644	0.020911	8.0889	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.998679902214923
R-squared	0.997361547088008
Adjusted R-squared	0.997051140863068
F-TEST (value)	3213.08487701933
F-TEST (DF numerator)	6
F-TEST (DF denominator)	51
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.302735427596799
Sum Squared Residuals	4.67408569523305

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998679902214923 \tabularnewline
R-squared & 0.997361547088008 \tabularnewline
Adjusted R-squared & 0.997051140863068 \tabularnewline
F-TEST (value) & 3213.08487701933 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.302735427596799 \tabularnewline
Sum Squared Residuals & 4.67408569523305 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97939&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998679902214923[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997361547088008[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997051140863068[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3213.08487701933[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/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]0.302735427596799[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.67408569523305[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97939&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97939&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	0.998679902214923
R-squared	0.997361547088008
Adjusted R-squared	0.997051140863068
F-TEST (value)	3213.08487701933
F-TEST (DF numerator)	6
F-TEST (DF denominator)	51
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.302735427596799
Sum Squared Residuals	4.67408569523305

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	101.563207017546	0.196792982453661
2	102.37	101.711482048995	0.658517951004521
3	102.38	101.884107368245	0.495892631755464
4	102.86	102.458721451113	0.401278548887465
5	102.87	102.607031910006	0.262968089994293
6	102.92	102.776183677775	0.143816322224646
7	102.95	102.943598669805	0.00640133019470953
8	103.02	103.127513031362	-0.107513031362439
9	104.08	104.229980392039	-0.149980392038939
10	104.16	104.386124055483	-0.226124055482666
11	104.24	104.543986780944	-0.303986780944226
12	104.33	104.720541325449	-0.390541325448609
13	104.73	104.972360710752	-0.242360710751853
14	104.86	105.138038927042	-0.278038927042091
15	105.03	105.302453755090	-0.272453755090413
16	105.62	105.802771347119	-0.182771347119403
17	105.63	105.961502460451	-0.331502460450823
18	105.63	106.130654228220	-0.500654228220467
19	105.94	106.434089502571	-0.494089502570979
20	106.61	106.598030943122	0.0119690568784954
21	107.69	107.841375106177	-0.151375106177315
22	107.78	108.049692896700	-0.269692896699709
23	107.93	108.290052469797	-0.360052469797278
24	108.48	108.514036669421	-0.0340366694207805
25	108.14	107.99118033495	0.148819665050059
26	108.48	108.159463714850	0.320536285150271
27	108.48	108.309510949483	0.170489050517389
28	108.89	108.942272360439	-0.0522723604392201
29	108.93	109.100233835994	-0.170233835993784
30	109.21	109.251149458497	-0.0411494584965328
31	109.47	109.389907650821	0.0800923491786774
32	109.8	109.568449612417	0.231550387582927
33	111.73	111.378145785349	0.351854214650788
34	111.85	111.603302155360	0.246697844640147
35	112.12	111.878761699173	0.241238300827324
36	112.15	112.064681191543	0.0853188084569473
37	112.17	112.303438821221	-0.133438821220658
38	112.67	112.518837892239	0.151162107761357
39	112.8	112.674963841960	0.125036158039501
40	113.44	113.642734732079	-0.202734732078646
41	113.53	113.808412948369	-0.278412948368875
42	114.53	114.027921031106	0.502078968893654
43	114.51	114.163205671952	0.346794328048273
44	115.05	114.757606257616	0.292393742383989
45	116.67	116.161240739081	0.508759260918834
46	117.07	116.541131279776	0.528868720223899
47	116.92	116.712019823285	0.207980176714557
48	117	116.909281499130	0.0907185008704657
49	117.02	117.172756521773	-0.152756521773314
50	117.35	117.373491749097	-0.0234917490968169
51	117.36	117.662015187647	-0.302015187646585
52	117.82	118.131664876574	-0.311664876574014
53	117.88	118.314612100168	-0.434612100168287
54	118.24	118.541335640938	-0.301335640938110
55	118.5	118.713593855021	-0.213593855020616
56	118.8	118.891429501489	-0.0914295014887906
57	119.76	119.746621323154	0.0133786768455240
58	120.09	119.907089212226	0.182910787774406

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 101.563207017546 & 0.196792982453661 \tabularnewline
2 & 102.37 & 101.711482048995 & 0.658517951004521 \tabularnewline
3 & 102.38 & 101.884107368245 & 0.495892631755464 \tabularnewline
4 & 102.86 & 102.458721451113 & 0.401278548887465 \tabularnewline
5 & 102.87 & 102.607031910006 & 0.262968089994293 \tabularnewline
6 & 102.92 & 102.776183677775 & 0.143816322224646 \tabularnewline
7 & 102.95 & 102.943598669805 & 0.00640133019470953 \tabularnewline
8 & 103.02 & 103.127513031362 & -0.107513031362439 \tabularnewline
9 & 104.08 & 104.229980392039 & -0.149980392038939 \tabularnewline
10 & 104.16 & 104.386124055483 & -0.226124055482666 \tabularnewline
11 & 104.24 & 104.543986780944 & -0.303986780944226 \tabularnewline
12 & 104.33 & 104.720541325449 & -0.390541325448609 \tabularnewline
13 & 104.73 & 104.972360710752 & -0.242360710751853 \tabularnewline
14 & 104.86 & 105.138038927042 & -0.278038927042091 \tabularnewline
15 & 105.03 & 105.302453755090 & -0.272453755090413 \tabularnewline
16 & 105.62 & 105.802771347119 & -0.182771347119403 \tabularnewline
17 & 105.63 & 105.961502460451 & -0.331502460450823 \tabularnewline
18 & 105.63 & 106.130654228220 & -0.500654228220467 \tabularnewline
19 & 105.94 & 106.434089502571 & -0.494089502570979 \tabularnewline
20 & 106.61 & 106.598030943122 & 0.0119690568784954 \tabularnewline
21 & 107.69 & 107.841375106177 & -0.151375106177315 \tabularnewline
22 & 107.78 & 108.049692896700 & -0.269692896699709 \tabularnewline
23 & 107.93 & 108.290052469797 & -0.360052469797278 \tabularnewline
24 & 108.48 & 108.514036669421 & -0.0340366694207805 \tabularnewline
25 & 108.14 & 107.99118033495 & 0.148819665050059 \tabularnewline
26 & 108.48 & 108.159463714850 & 0.320536285150271 \tabularnewline
27 & 108.48 & 108.309510949483 & 0.170489050517389 \tabularnewline
28 & 108.89 & 108.942272360439 & -0.0522723604392201 \tabularnewline
29 & 108.93 & 109.100233835994 & -0.170233835993784 \tabularnewline
30 & 109.21 & 109.251149458497 & -0.0411494584965328 \tabularnewline
31 & 109.47 & 109.389907650821 & 0.0800923491786774 \tabularnewline
32 & 109.8 & 109.568449612417 & 0.231550387582927 \tabularnewline
33 & 111.73 & 111.378145785349 & 0.351854214650788 \tabularnewline
34 & 111.85 & 111.603302155360 & 0.246697844640147 \tabularnewline
35 & 112.12 & 111.878761699173 & 0.241238300827324 \tabularnewline
36 & 112.15 & 112.064681191543 & 0.0853188084569473 \tabularnewline
37 & 112.17 & 112.303438821221 & -0.133438821220658 \tabularnewline
38 & 112.67 & 112.518837892239 & 0.151162107761357 \tabularnewline
39 & 112.8 & 112.674963841960 & 0.125036158039501 \tabularnewline
40 & 113.44 & 113.642734732079 & -0.202734732078646 \tabularnewline
41 & 113.53 & 113.808412948369 & -0.278412948368875 \tabularnewline
42 & 114.53 & 114.027921031106 & 0.502078968893654 \tabularnewline
43 & 114.51 & 114.163205671952 & 0.346794328048273 \tabularnewline
44 & 115.05 & 114.757606257616 & 0.292393742383989 \tabularnewline
45 & 116.67 & 116.161240739081 & 0.508759260918834 \tabularnewline
46 & 117.07 & 116.541131279776 & 0.528868720223899 \tabularnewline
47 & 116.92 & 116.712019823285 & 0.207980176714557 \tabularnewline
48 & 117 & 116.909281499130 & 0.0907185008704657 \tabularnewline
49 & 117.02 & 117.172756521773 & -0.152756521773314 \tabularnewline
50 & 117.35 & 117.373491749097 & -0.0234917490968169 \tabularnewline
51 & 117.36 & 117.662015187647 & -0.302015187646585 \tabularnewline
52 & 117.82 & 118.131664876574 & -0.311664876574014 \tabularnewline
53 & 117.88 & 118.314612100168 & -0.434612100168287 \tabularnewline
54 & 118.24 & 118.541335640938 & -0.301335640938110 \tabularnewline
55 & 118.5 & 118.713593855021 & -0.213593855020616 \tabularnewline
56 & 118.8 & 118.891429501489 & -0.0914295014887906 \tabularnewline
57 & 119.76 & 119.746621323154 & 0.0133786768455240 \tabularnewline
58 & 120.09 & 119.907089212226 & 0.182910787774406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97939&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]101.563207017546[/C][C]0.196792982453661[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]101.711482048995[/C][C]0.658517951004521[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]101.884107368245[/C][C]0.495892631755464[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.458721451113[/C][C]0.401278548887465[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.607031910006[/C][C]0.262968089994293[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.776183677775[/C][C]0.143816322224646[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.943598669805[/C][C]0.00640133019470953[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.127513031362[/C][C]-0.107513031362439[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.229980392039[/C][C]-0.149980392038939[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.386124055483[/C][C]-0.226124055482666[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.543986780944[/C][C]-0.303986780944226[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.720541325449[/C][C]-0.390541325448609[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]104.972360710752[/C][C]-0.242360710751853[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.138038927042[/C][C]-0.278038927042091[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.302453755090[/C][C]-0.272453755090413[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.802771347119[/C][C]-0.182771347119403[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]105.961502460451[/C][C]-0.331502460450823[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.130654228220[/C][C]-0.500654228220467[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.434089502571[/C][C]-0.494089502570979[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.598030943122[/C][C]0.0119690568784954[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.841375106177[/C][C]-0.151375106177315[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.049692896700[/C][C]-0.269692896699709[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.290052469797[/C][C]-0.360052469797278[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.514036669421[/C][C]-0.0340366694207805[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.99118033495[/C][C]0.148819665050059[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]108.159463714850[/C][C]0.320536285150271[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]108.309510949483[/C][C]0.170489050517389[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.942272360439[/C][C]-0.0522723604392201[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.100233835994[/C][C]-0.170233835993784[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]109.251149458497[/C][C]-0.0411494584965328[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]109.389907650821[/C][C]0.0800923491786774[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]109.568449612417[/C][C]0.231550387582927[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.378145785349[/C][C]0.351854214650788[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.603302155360[/C][C]0.246697844640147[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.878761699173[/C][C]0.241238300827324[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]112.064681191543[/C][C]0.0853188084569473[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.303438821221[/C][C]-0.133438821220658[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.518837892239[/C][C]0.151162107761357[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.674963841960[/C][C]0.125036158039501[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]113.642734732079[/C][C]-0.202734732078646[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]113.808412948369[/C][C]-0.278412948368875[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.027921031106[/C][C]0.502078968893654[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.163205671952[/C][C]0.346794328048273[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.757606257616[/C][C]0.292393742383989[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.161240739081[/C][C]0.508759260918834[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.541131279776[/C][C]0.528868720223899[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.712019823285[/C][C]0.207980176714557[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.909281499130[/C][C]0.0907185008704657[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.172756521773[/C][C]-0.152756521773314[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.373491749097[/C][C]-0.0234917490968169[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.662015187647[/C][C]-0.302015187646585[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.131664876574[/C][C]-0.311664876574014[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.314612100168[/C][C]-0.434612100168287[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.541335640938[/C][C]-0.301335640938110[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.713593855021[/C][C]-0.213593855020616[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.891429501489[/C][C]-0.0914295014887906[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.746621323154[/C][C]0.0133786768455240[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.907089212226[/C][C]0.182910787774406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97939&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97939&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	101.563207017546	0.196792982453661
2	102.37	101.711482048995	0.658517951004521
3	102.38	101.884107368245	0.495892631755464
4	102.86	102.458721451113	0.401278548887465
5	102.87	102.607031910006	0.262968089994293
6	102.92	102.776183677775	0.143816322224646
7	102.95	102.943598669805	0.00640133019470953
8	103.02	103.127513031362	-0.107513031362439
9	104.08	104.229980392039	-0.149980392038939
10	104.16	104.386124055483	-0.226124055482666
11	104.24	104.543986780944	-0.303986780944226
12	104.33	104.720541325449	-0.390541325448609
13	104.73	104.972360710752	-0.242360710751853
14	104.86	105.138038927042	-0.278038927042091
15	105.03	105.302453755090	-0.272453755090413
16	105.62	105.802771347119	-0.182771347119403
17	105.63	105.961502460451	-0.331502460450823
18	105.63	106.130654228220	-0.500654228220467
19	105.94	106.434089502571	-0.494089502570979
20	106.61	106.598030943122	0.0119690568784954
21	107.69	107.841375106177	-0.151375106177315
22	107.78	108.049692896700	-0.269692896699709
23	107.93	108.290052469797	-0.360052469797278
24	108.48	108.514036669421	-0.0340366694207805
25	108.14	107.99118033495	0.148819665050059
26	108.48	108.159463714850	0.320536285150271
27	108.48	108.309510949483	0.170489050517389
28	108.89	108.942272360439	-0.0522723604392201
29	108.93	109.100233835994	-0.170233835993784
30	109.21	109.251149458497	-0.0411494584965328
31	109.47	109.389907650821	0.0800923491786774
32	109.8	109.568449612417	0.231550387582927
33	111.73	111.378145785349	0.351854214650788
34	111.85	111.603302155360	0.246697844640147
35	112.12	111.878761699173	0.241238300827324
36	112.15	112.064681191543	0.0853188084569473
37	112.17	112.303438821221	-0.133438821220658
38	112.67	112.518837892239	0.151162107761357
39	112.8	112.674963841960	0.125036158039501
40	113.44	113.642734732079	-0.202734732078646
41	113.53	113.808412948369	-0.278412948368875
42	114.53	114.027921031106	0.502078968893654
43	114.51	114.163205671952	0.346794328048273
44	115.05	114.757606257616	0.292393742383989
45	116.67	116.161240739081	0.508759260918834
46	117.07	116.541131279776	0.528868720223899
47	116.92	116.712019823285	0.207980176714557
48	117	116.909281499130	0.0907185008704657
49	117.02	117.172756521773	-0.152756521773314
50	117.35	117.373491749097	-0.0234917490968169
51	117.36	117.662015187647	-0.302015187646585
52	117.82	118.131664876574	-0.311664876574014
53	117.88	118.314612100168	-0.434612100168287
54	118.24	118.541335640938	-0.301335640938110
55	118.5	118.713593855021	-0.213593855020616
56	118.8	118.891429501489	-0.0914295014887906
57	119.76	119.746621323154	0.0133786768455240
58	120.09	119.907089212226	0.182910787774406

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.112420926588230	0.224841853176459	0.88757907341177
11	0.0476101742046196	0.0952203484092391	0.95238982579538
12	0.226824863996256	0.453649727992511	0.773175136003744
13	0.137016396197778	0.274032792395556	0.862983603802222
14	0.0768929576815509	0.153785915363102	0.923107042318449
15	0.0569124814266924	0.113824962853385	0.943087518573308
16	0.0412454120766625	0.082490824153325	0.958754587923337
17	0.0255529792966954	0.0511059585933908	0.974447020703305
18	0.0230581027798625	0.0461162055597251	0.976941897220137
19	0.0286498153864716	0.0572996307729431	0.971350184613528
20	0.228152379996633	0.456304759993266	0.771847620003367
21	0.274059147913091	0.548118295826183	0.725940852086909
22	0.271369270516584	0.542738541033167	0.728630729483416
23	0.312026723424966	0.624053446849932	0.687973276575034
24	0.355381698770068	0.710763397540136	0.644618301229932
25	0.320637473621236	0.641274947242472	0.679362526378764
26	0.479181607158958	0.958363214317916	0.520818392841042
27	0.497548358899356	0.995096717798711	0.502451641100644
28	0.49089171653865	0.9817834330773	0.50910828346135
29	0.425269774691213	0.850539549382427	0.574730225308787
30	0.366770523694434	0.733541047388867	0.633229476305566
31	0.387496712763241	0.774993425526483	0.612503287236759
32	0.425619467204316	0.851238934408632	0.574380532795684
33	0.661252217570711	0.677495564858577	0.338747782429289
34	0.592120657925002	0.815758684149997	0.407879342074998
35	0.524422772577486	0.951154454845027	0.475577227422514
36	0.580118632948774	0.839762734102451	0.419881367051226
37	0.655190680535218	0.689618638929563	0.344809319464782
38	0.582566476572728	0.834867046854545	0.417433523427272
39	0.521416848573965	0.95716630285207	0.478583151426035
40	0.502458639916762	0.995082720166476	0.497541360083238
41	0.901180212205295	0.197639575589409	0.0988197877947046
42	0.905651051585636	0.188697896828728	0.094348948414364
43	0.91740401254256	0.165191974914879	0.0825959874574393
44	0.885062565211636	0.229874869576727	0.114937434788364
45	0.824122136902139	0.351755726195722	0.175877863097861
46	0.983833829500153	0.0323323409996938	0.0161661704998469
47	0.983414399178296	0.0331712016434077	0.0165856008217039
48	0.944905285659149	0.110189428681702	0.0550947143408512

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.112420926588230 & 0.224841853176459 & 0.88757907341177 \tabularnewline
11 & 0.0476101742046196 & 0.0952203484092391 & 0.95238982579538 \tabularnewline
12 & 0.226824863996256 & 0.453649727992511 & 0.773175136003744 \tabularnewline
13 & 0.137016396197778 & 0.274032792395556 & 0.862983603802222 \tabularnewline
14 & 0.0768929576815509 & 0.153785915363102 & 0.923107042318449 \tabularnewline
15 & 0.0569124814266924 & 0.113824962853385 & 0.943087518573308 \tabularnewline
16 & 0.0412454120766625 & 0.082490824153325 & 0.958754587923337 \tabularnewline
17 & 0.0255529792966954 & 0.0511059585933908 & 0.974447020703305 \tabularnewline
18 & 0.0230581027798625 & 0.0461162055597251 & 0.976941897220137 \tabularnewline
19 & 0.0286498153864716 & 0.0572996307729431 & 0.971350184613528 \tabularnewline
20 & 0.228152379996633 & 0.456304759993266 & 0.771847620003367 \tabularnewline
21 & 0.274059147913091 & 0.548118295826183 & 0.725940852086909 \tabularnewline
22 & 0.271369270516584 & 0.542738541033167 & 0.728630729483416 \tabularnewline
23 & 0.312026723424966 & 0.624053446849932 & 0.687973276575034 \tabularnewline
24 & 0.355381698770068 & 0.710763397540136 & 0.644618301229932 \tabularnewline
25 & 0.320637473621236 & 0.641274947242472 & 0.679362526378764 \tabularnewline
26 & 0.479181607158958 & 0.958363214317916 & 0.520818392841042 \tabularnewline
27 & 0.497548358899356 & 0.995096717798711 & 0.502451641100644 \tabularnewline
28 & 0.49089171653865 & 0.9817834330773 & 0.50910828346135 \tabularnewline
29 & 0.425269774691213 & 0.850539549382427 & 0.574730225308787 \tabularnewline
30 & 0.366770523694434 & 0.733541047388867 & 0.633229476305566 \tabularnewline
31 & 0.387496712763241 & 0.774993425526483 & 0.612503287236759 \tabularnewline
32 & 0.425619467204316 & 0.851238934408632 & 0.574380532795684 \tabularnewline
33 & 0.661252217570711 & 0.677495564858577 & 0.338747782429289 \tabularnewline
34 & 0.592120657925002 & 0.815758684149997 & 0.407879342074998 \tabularnewline
35 & 0.524422772577486 & 0.951154454845027 & 0.475577227422514 \tabularnewline
36 & 0.580118632948774 & 0.839762734102451 & 0.419881367051226 \tabularnewline
37 & 0.655190680535218 & 0.689618638929563 & 0.344809319464782 \tabularnewline
38 & 0.582566476572728 & 0.834867046854545 & 0.417433523427272 \tabularnewline
39 & 0.521416848573965 & 0.95716630285207 & 0.478583151426035 \tabularnewline
40 & 0.502458639916762 & 0.995082720166476 & 0.497541360083238 \tabularnewline
41 & 0.901180212205295 & 0.197639575589409 & 0.0988197877947046 \tabularnewline
42 & 0.905651051585636 & 0.188697896828728 & 0.094348948414364 \tabularnewline
43 & 0.91740401254256 & 0.165191974914879 & 0.0825959874574393 \tabularnewline
44 & 0.885062565211636 & 0.229874869576727 & 0.114937434788364 \tabularnewline
45 & 0.824122136902139 & 0.351755726195722 & 0.175877863097861 \tabularnewline
46 & 0.983833829500153 & 0.0323323409996938 & 0.0161661704998469 \tabularnewline
47 & 0.983414399178296 & 0.0331712016434077 & 0.0165856008217039 \tabularnewline
48 & 0.944905285659149 & 0.110189428681702 & 0.0550947143408512 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97939&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]10[/C][C]0.112420926588230[/C][C]0.224841853176459[/C][C]0.88757907341177[/C][/ROW]
[ROW][C]11[/C][C]0.0476101742046196[/C][C]0.0952203484092391[/C][C]0.95238982579538[/C][/ROW]
[ROW][C]12[/C][C]0.226824863996256[/C][C]0.453649727992511[/C][C]0.773175136003744[/C][/ROW]
[ROW][C]13[/C][C]0.137016396197778[/C][C]0.274032792395556[/C][C]0.862983603802222[/C][/ROW]
[ROW][C]14[/C][C]0.0768929576815509[/C][C]0.153785915363102[/C][C]0.923107042318449[/C][/ROW]
[ROW][C]15[/C][C]0.0569124814266924[/C][C]0.113824962853385[/C][C]0.943087518573308[/C][/ROW]
[ROW][C]16[/C][C]0.0412454120766625[/C][C]0.082490824153325[/C][C]0.958754587923337[/C][/ROW]
[ROW][C]17[/C][C]0.0255529792966954[/C][C]0.0511059585933908[/C][C]0.974447020703305[/C][/ROW]
[ROW][C]18[/C][C]0.0230581027798625[/C][C]0.0461162055597251[/C][C]0.976941897220137[/C][/ROW]
[ROW][C]19[/C][C]0.0286498153864716[/C][C]0.0572996307729431[/C][C]0.971350184613528[/C][/ROW]
[ROW][C]20[/C][C]0.228152379996633[/C][C]0.456304759993266[/C][C]0.771847620003367[/C][/ROW]
[ROW][C]21[/C][C]0.274059147913091[/C][C]0.548118295826183[/C][C]0.725940852086909[/C][/ROW]
[ROW][C]22[/C][C]0.271369270516584[/C][C]0.542738541033167[/C][C]0.728630729483416[/C][/ROW]
[ROW][C]23[/C][C]0.312026723424966[/C][C]0.624053446849932[/C][C]0.687973276575034[/C][/ROW]
[ROW][C]24[/C][C]0.355381698770068[/C][C]0.710763397540136[/C][C]0.644618301229932[/C][/ROW]
[ROW][C]25[/C][C]0.320637473621236[/C][C]0.641274947242472[/C][C]0.679362526378764[/C][/ROW]
[ROW][C]26[/C][C]0.479181607158958[/C][C]0.958363214317916[/C][C]0.520818392841042[/C][/ROW]
[ROW][C]27[/C][C]0.497548358899356[/C][C]0.995096717798711[/C][C]0.502451641100644[/C][/ROW]
[ROW][C]28[/C][C]0.49089171653865[/C][C]0.9817834330773[/C][C]0.50910828346135[/C][/ROW]
[ROW][C]29[/C][C]0.425269774691213[/C][C]0.850539549382427[/C][C]0.574730225308787[/C][/ROW]
[ROW][C]30[/C][C]0.366770523694434[/C][C]0.733541047388867[/C][C]0.633229476305566[/C][/ROW]
[ROW][C]31[/C][C]0.387496712763241[/C][C]0.774993425526483[/C][C]0.612503287236759[/C][/ROW]
[ROW][C]32[/C][C]0.425619467204316[/C][C]0.851238934408632[/C][C]0.574380532795684[/C][/ROW]
[ROW][C]33[/C][C]0.661252217570711[/C][C]0.677495564858577[/C][C]0.338747782429289[/C][/ROW]
[ROW][C]34[/C][C]0.592120657925002[/C][C]0.815758684149997[/C][C]0.407879342074998[/C][/ROW]
[ROW][C]35[/C][C]0.524422772577486[/C][C]0.951154454845027[/C][C]0.475577227422514[/C][/ROW]
[ROW][C]36[/C][C]0.580118632948774[/C][C]0.839762734102451[/C][C]0.419881367051226[/C][/ROW]
[ROW][C]37[/C][C]0.655190680535218[/C][C]0.689618638929563[/C][C]0.344809319464782[/C][/ROW]
[ROW][C]38[/C][C]0.582566476572728[/C][C]0.834867046854545[/C][C]0.417433523427272[/C][/ROW]
[ROW][C]39[/C][C]0.521416848573965[/C][C]0.95716630285207[/C][C]0.478583151426035[/C][/ROW]
[ROW][C]40[/C][C]0.502458639916762[/C][C]0.995082720166476[/C][C]0.497541360083238[/C][/ROW]
[ROW][C]41[/C][C]0.901180212205295[/C][C]0.197639575589409[/C][C]0.0988197877947046[/C][/ROW]
[ROW][C]42[/C][C]0.905651051585636[/C][C]0.188697896828728[/C][C]0.094348948414364[/C][/ROW]
[ROW][C]43[/C][C]0.91740401254256[/C][C]0.165191974914879[/C][C]0.0825959874574393[/C][/ROW]
[ROW][C]44[/C][C]0.885062565211636[/C][C]0.229874869576727[/C][C]0.114937434788364[/C][/ROW]
[ROW][C]45[/C][C]0.824122136902139[/C][C]0.351755726195722[/C][C]0.175877863097861[/C][/ROW]
[ROW][C]46[/C][C]0.983833829500153[/C][C]0.0323323409996938[/C][C]0.0161661704998469[/C][/ROW]
[ROW][C]47[/C][C]0.983414399178296[/C][C]0.0331712016434077[/C][C]0.0165856008217039[/C][/ROW]
[ROW][C]48[/C][C]0.944905285659149[/C][C]0.110189428681702[/C][C]0.0550947143408512[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97939&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97939&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
10	0.112420926588230	0.224841853176459	0.88757907341177
11	0.0476101742046196	0.0952203484092391	0.95238982579538
12	0.226824863996256	0.453649727992511	0.773175136003744
13	0.137016396197778	0.274032792395556	0.862983603802222
14	0.0768929576815509	0.153785915363102	0.923107042318449
15	0.0569124814266924	0.113824962853385	0.943087518573308
16	0.0412454120766625	0.082490824153325	0.958754587923337
17	0.0255529792966954	0.0511059585933908	0.974447020703305
18	0.0230581027798625	0.0461162055597251	0.976941897220137
19	0.0286498153864716	0.0572996307729431	0.971350184613528
20	0.228152379996633	0.456304759993266	0.771847620003367
21	0.274059147913091	0.548118295826183	0.725940852086909
22	0.271369270516584	0.542738541033167	0.728630729483416
23	0.312026723424966	0.624053446849932	0.687973276575034
24	0.355381698770068	0.710763397540136	0.644618301229932
25	0.320637473621236	0.641274947242472	0.679362526378764
26	0.479181607158958	0.958363214317916	0.520818392841042
27	0.497548358899356	0.995096717798711	0.502451641100644
28	0.49089171653865	0.9817834330773	0.50910828346135
29	0.425269774691213	0.850539549382427	0.574730225308787
30	0.366770523694434	0.733541047388867	0.633229476305566
31	0.387496712763241	0.774993425526483	0.612503287236759
32	0.425619467204316	0.851238934408632	0.574380532795684
33	0.661252217570711	0.677495564858577	0.338747782429289
34	0.592120657925002	0.815758684149997	0.407879342074998
35	0.524422772577486	0.951154454845027	0.475577227422514
36	0.580118632948774	0.839762734102451	0.419881367051226
37	0.655190680535218	0.689618638929563	0.344809319464782
38	0.582566476572728	0.834867046854545	0.417433523427272
39	0.521416848573965	0.95716630285207	0.478583151426035
40	0.502458639916762	0.995082720166476	0.497541360083238
41	0.901180212205295	0.197639575589409	0.0988197877947046
42	0.905651051585636	0.188697896828728	0.094348948414364
43	0.91740401254256	0.165191974914879	0.0825959874574393
44	0.885062565211636	0.229874869576727	0.114937434788364
45	0.824122136902139	0.351755726195722	0.175877863097861
46	0.983833829500153	0.0323323409996938	0.0161661704998469
47	0.983414399178296	0.0331712016434077	0.0165856008217039
48	0.944905285659149	0.110189428681702	0.0550947143408512

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	3	0.0769230769230769	NOK
10% type I error level	7	0.179487179487179	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 & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0769230769230769 & NOK \tabularnewline
10% type I error level & 7 & 0.179487179487179 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97939&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]3[/C][C]0.0769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.179487179487179[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97939&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97939&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	3	0.0769230769230769	NOK
10% type I error level	7	0.179487179487179	NOK

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 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = 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