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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 21 Nov 2011 15:29:39 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t13219073876npr7oteji73y3u.htm/, Retrieved Sat, 20 Apr 2024 11:38:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145968, Retrieved Sat, 20 Apr 2024 11:38:59 +0000

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114

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-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [deterministische ...] [2010-11-19 12:14:25] [74deae64b71f9d77c839af86f7c687b5]
- R P       [Multiple Regression] [] [2011-11-21 20:29:39] [4be1b05f688f7fa8db5b9e9e4d3a7e33] [Current]

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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	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145968&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145968&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145968&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	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Cultuuruitgaven[t] = + 67.2314876034129 + 0.080894904216056JAAR[t] + 0.111902922150727Bioscoop[t] + 0.177271331756085Schouwburgabonnement[t] + 0.12737147086898Daguitstap[t] -0.0868387869852839HuurDVD[t] + 0.169151767769642t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cultuuruitgaven[t] =  +  67.2314876034129 +  0.080894904216056JAAR[t] +  0.111902922150727Bioscoop[t] +  0.177271331756085Schouwburgabonnement[t] +  0.12737147086898Daguitstap[t] -0.0868387869852839HuurDVD[t] +  0.169151767769642t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145968&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cultuuruitgaven[t] =  +  67.2314876034129 +  0.080894904216056JAAR[t] +  0.111902922150727Bioscoop[t] +  0.177271331756085Schouwburgabonnement[t] +  0.12737147086898Daguitstap[t] -0.0868387869852839HuurDVD[t] +  0.169151767769642t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145968&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145968&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
Cultuuruitgaven[t] = + 67.2314876034129 + 0.080894904216056JAAR[t] + 0.111902922150727Bioscoop[t] + 0.177271331756085Schouwburgabonnement[t] + 0.12737147086898Daguitstap[t] -0.0868387869852839HuurDVD[t] + 0.169151767769642t + 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)	67.2314876034129	8.061054	8.3403	0	0
JAAR	0.080894904216056	0.15474	0.5228	0.603391	0.301695
Bioscoop	0.111902922150727	0.020322	5.5064	1e-06	1e-06
Schouwburgabonnement	0.177271331756085	0.021622	8.1987	0	0
Daguitstap	0.12737147086898	0.032484	3.9211	0.000264	0.000132
HuurDVD	-0.0868387869852839	0.09024	-0.9623	0.340437	0.170218
t	0.169151767769642	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) & 67.2314876034129 & 8.061054 & 8.3403 & 0 & 0 \tabularnewline
JAAR & 0.080894904216056 & 0.15474 & 0.5228 & 0.603391 & 0.301695 \tabularnewline
Bioscoop & 0.111902922150727 & 0.020322 & 5.5064 & 1e-06 & 1e-06 \tabularnewline
Schouwburgabonnement & 0.177271331756085 & 0.021622 & 8.1987 & 0 & 0 \tabularnewline
Daguitstap & 0.12737147086898 & 0.032484 & 3.9211 & 0.000264 & 0.000132 \tabularnewline
HuurDVD & -0.0868387869852839 & 0.09024 & -0.9623 & 0.340437 & 0.170218 \tabularnewline
t & 0.169151767769642 & 0.020911 & 8.0889 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145968&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]67.2314876034129[/C][C]8.061054[/C][C]8.3403[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]JAAR[/C][C]0.080894904216056[/C][C]0.15474[/C][C]0.5228[/C][C]0.603391[/C][C]0.301695[/C][/ROW]
[ROW][C]Bioscoop[/C][C]0.111902922150727[/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]Daguitstap[/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]-0.0868387869852839[/C][C]0.09024[/C][C]-0.9623[/C][C]0.340437[/C][C]0.170218[/C][/ROW]
[ROW][C]t[/C][C]0.169151767769642[/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=145968&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145968&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)	67.2314876034129	8.061054	8.3403	0	0
JAAR	0.080894904216056	0.15474	0.5228	0.603391	0.301695
Bioscoop	0.111902922150727	0.020322	5.5064	1e-06	1e-06
Schouwburgabonnement	0.177271331756085	0.021622	8.1987	0	0
Daguitstap	0.12737147086898	0.032484	3.9211	0.000264	0.000132
HuurDVD	-0.0868387869852839	0.09024	-0.9623	0.340437	0.170218
t	0.169151767769642	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)	3213.08487701928
F-TEST (DF numerator)	6
F-TEST (DF denominator)	51
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.302735427596801
Sum Squared Residuals	4.67408569523312

\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) & 3213.08487701928 \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 & 0.302735427596801 \tabularnewline
Sum Squared Residuals & 4.67408569523312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145968&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]3213.08487701928[/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]0.302735427596801[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.67408569523312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145968&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145968&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)	3213.08487701928
F-TEST (DF numerator)	6
F-TEST (DF denominator)	51
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.302735427596801
Sum Squared Residuals	4.67408569523312

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	101.563207017546	0.196792982453787
2	102.37	101.711482048995	0.658517951004513
3	102.38	101.884107368245	0.495892631755448
4	102.86	102.458721451113	0.401278548887448
5	102.87	102.607031910006	0.262968089994279
6	102.92	102.776183677775	0.143816322224634
7	102.95	102.943598669805	0.00640133019470009
8	103.02	103.127513031362	-0.107513031362447
9	104.08	104.229980392039	-0.149980392038948
10	104.16	104.386124055483	-0.226124055482673
11	104.24	104.543986780944	-0.30398678094423
12	104.33	104.720541325449	-0.390541325448612
13	104.73	104.972360710752	-0.242360710751866
14	104.86	105.138038927042	-0.278038927042102
15	105.03	105.30245375509	-0.272453755090422
16	105.62	105.802771347119	-0.182771347119409
17	105.63	105.961502460451	-0.331502460450827
18	105.63	106.13065422822	-0.500654228220469
19	105.94	106.434089502571	-0.494089502570981
20	106.61	106.598030943122	0.0119690568784954
21	107.69	107.841375106177	-0.151375106177316
22	107.78	108.0496928967	-0.269692896699708
23	107.93	108.290052469797	-0.360052469797278
24	108.48	108.514036669421	-0.0340366694207803
25	108.14	107.99118033495	0.14881966505005
26	108.48	108.15946371485	0.320536285150265
27	108.48	108.309510949483	0.170489050517385
28	108.89	108.942272360439	-0.0522723604392227
29	108.93	109.100233835994	-0.170233835993785
30	109.21	109.251149458497	-0.0411494584965308
31	109.47	109.389907650821	0.0800923491786823
32	109.8	109.568449612417	0.231550387582934
33	111.73	111.378145785349	0.351854214650791
34	111.85	111.60330215536	0.246697844640152
35	112.12	111.878761699173	0.241238300827328
36	112.15	112.064681191543	0.0853188084569532
37	112.17	112.303438821221	-0.133438821220662
38	112.67	112.518837892239	0.151162107761353
39	112.8	112.67496384196	0.1250361580395
40	113.44	113.642734732079	-0.202734732078643
41	113.53	113.808412948369	-0.27841294836887
42	114.53	114.027921031106	0.502078968893661
43	114.51	114.163205671952	0.346794328048282
44	115.05	114.757606257616	0.292393742383994
45	116.67	116.161240739081	0.508759260918836
46	117.07	116.541131279776	0.5288687202239
47	116.92	116.712019823285	0.20798017671456
48	117	116.90928149913	0.09071850087047
49	117.02	117.172756521773	-0.15275652177332
50	117.35	117.373491749097	-0.0234917490968215
51	117.36	117.662015187647	-0.302015187646593
52	117.82	118.131664876574	-0.311664876574018
53	117.88	118.314612100168	-0.434612100168289
54	118.24	118.541335640938	-0.301335640938112
55	118.5	118.713593855021	-0.213593855020616
56	118.8	118.891429501489	-0.0914295014887887
57	119.76	119.746621323154	0.0133786768455257
58	120.09	119.907089212226	0.18291078777441

\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 & 0.196792982453787 \tabularnewline
2 & 102.37 & 101.711482048995 & 0.658517951004513 \tabularnewline
3 & 102.38 & 101.884107368245 & 0.495892631755448 \tabularnewline
4 & 102.86 & 102.458721451113 & 0.401278548887448 \tabularnewline
5 & 102.87 & 102.607031910006 & 0.262968089994279 \tabularnewline
6 & 102.92 & 102.776183677775 & 0.143816322224634 \tabularnewline
7 & 102.95 & 102.943598669805 & 0.00640133019470009 \tabularnewline
8 & 103.02 & 103.127513031362 & -0.107513031362447 \tabularnewline
9 & 104.08 & 104.229980392039 & -0.149980392038948 \tabularnewline
10 & 104.16 & 104.386124055483 & -0.226124055482673 \tabularnewline
11 & 104.24 & 104.543986780944 & -0.30398678094423 \tabularnewline
12 & 104.33 & 104.720541325449 & -0.390541325448612 \tabularnewline
13 & 104.73 & 104.972360710752 & -0.242360710751866 \tabularnewline
14 & 104.86 & 105.138038927042 & -0.278038927042102 \tabularnewline
15 & 105.03 & 105.30245375509 & -0.272453755090422 \tabularnewline
16 & 105.62 & 105.802771347119 & -0.182771347119409 \tabularnewline
17 & 105.63 & 105.961502460451 & -0.331502460450827 \tabularnewline
18 & 105.63 & 106.13065422822 & -0.500654228220469 \tabularnewline
19 & 105.94 & 106.434089502571 & -0.494089502570981 \tabularnewline
20 & 106.61 & 106.598030943122 & 0.0119690568784954 \tabularnewline
21 & 107.69 & 107.841375106177 & -0.151375106177316 \tabularnewline
22 & 107.78 & 108.0496928967 & -0.269692896699708 \tabularnewline
23 & 107.93 & 108.290052469797 & -0.360052469797278 \tabularnewline
24 & 108.48 & 108.514036669421 & -0.0340366694207803 \tabularnewline
25 & 108.14 & 107.99118033495 & 0.14881966505005 \tabularnewline
26 & 108.48 & 108.15946371485 & 0.320536285150265 \tabularnewline
27 & 108.48 & 108.309510949483 & 0.170489050517385 \tabularnewline
28 & 108.89 & 108.942272360439 & -0.0522723604392227 \tabularnewline
29 & 108.93 & 109.100233835994 & -0.170233835993785 \tabularnewline
30 & 109.21 & 109.251149458497 & -0.0411494584965308 \tabularnewline
31 & 109.47 & 109.389907650821 & 0.0800923491786823 \tabularnewline
32 & 109.8 & 109.568449612417 & 0.231550387582934 \tabularnewline
33 & 111.73 & 111.378145785349 & 0.351854214650791 \tabularnewline
34 & 111.85 & 111.60330215536 & 0.246697844640152 \tabularnewline
35 & 112.12 & 111.878761699173 & 0.241238300827328 \tabularnewline
36 & 112.15 & 112.064681191543 & 0.0853188084569532 \tabularnewline
37 & 112.17 & 112.303438821221 & -0.133438821220662 \tabularnewline
38 & 112.67 & 112.518837892239 & 0.151162107761353 \tabularnewline
39 & 112.8 & 112.67496384196 & 0.1250361580395 \tabularnewline
40 & 113.44 & 113.642734732079 & -0.202734732078643 \tabularnewline
41 & 113.53 & 113.808412948369 & -0.27841294836887 \tabularnewline
42 & 114.53 & 114.027921031106 & 0.502078968893661 \tabularnewline
43 & 114.51 & 114.163205671952 & 0.346794328048282 \tabularnewline
44 & 115.05 & 114.757606257616 & 0.292393742383994 \tabularnewline
45 & 116.67 & 116.161240739081 & 0.508759260918836 \tabularnewline
46 & 117.07 & 116.541131279776 & 0.5288687202239 \tabularnewline
47 & 116.92 & 116.712019823285 & 0.20798017671456 \tabularnewline
48 & 117 & 116.90928149913 & 0.09071850087047 \tabularnewline
49 & 117.02 & 117.172756521773 & -0.15275652177332 \tabularnewline
50 & 117.35 & 117.373491749097 & -0.0234917490968215 \tabularnewline
51 & 117.36 & 117.662015187647 & -0.302015187646593 \tabularnewline
52 & 117.82 & 118.131664876574 & -0.311664876574018 \tabularnewline
53 & 117.88 & 118.314612100168 & -0.434612100168289 \tabularnewline
54 & 118.24 & 118.541335640938 & -0.301335640938112 \tabularnewline
55 & 118.5 & 118.713593855021 & -0.213593855020616 \tabularnewline
56 & 118.8 & 118.891429501489 & -0.0914295014887887 \tabularnewline
57 & 119.76 & 119.746621323154 & 0.0133786768455257 \tabularnewline
58 & 120.09 & 119.907089212226 & 0.18291078777441 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145968&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]0.196792982453787[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]101.711482048995[/C][C]0.658517951004513[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]101.884107368245[/C][C]0.495892631755448[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.458721451113[/C][C]0.401278548887448[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.607031910006[/C][C]0.262968089994279[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.776183677775[/C][C]0.143816322224634[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.943598669805[/C][C]0.00640133019470009[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.127513031362[/C][C]-0.107513031362447[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.229980392039[/C][C]-0.149980392038948[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.386124055483[/C][C]-0.226124055482673[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.543986780944[/C][C]-0.30398678094423[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.720541325449[/C][C]-0.390541325448612[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]104.972360710752[/C][C]-0.242360710751866[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.138038927042[/C][C]-0.278038927042102[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.30245375509[/C][C]-0.272453755090422[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.802771347119[/C][C]-0.182771347119409[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]105.961502460451[/C][C]-0.331502460450827[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.13065422822[/C][C]-0.500654228220469[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.434089502571[/C][C]-0.494089502570981[/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]-0.151375106177316[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.0496928967[/C][C]-0.269692896699708[/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]-0.0340366694207803[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.99118033495[/C][C]0.14881966505005[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]108.15946371485[/C][C]0.320536285150265[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]108.309510949483[/C][C]0.170489050517385[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.942272360439[/C][C]-0.0522723604392227[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.100233835994[/C][C]-0.170233835993785[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]109.251149458497[/C][C]-0.0411494584965308[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]109.389907650821[/C][C]0.0800923491786823[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]109.568449612417[/C][C]0.231550387582934[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.378145785349[/C][C]0.351854214650791[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.60330215536[/C][C]0.246697844640152[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.878761699173[/C][C]0.241238300827328[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]112.064681191543[/C][C]0.0853188084569532[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.303438821221[/C][C]-0.133438821220662[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.518837892239[/C][C]0.151162107761353[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.67496384196[/C][C]0.1250361580395[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]113.642734732079[/C][C]-0.202734732078643[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]113.808412948369[/C][C]-0.27841294836887[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.027921031106[/C][C]0.502078968893661[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.163205671952[/C][C]0.346794328048282[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.757606257616[/C][C]0.292393742383994[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.161240739081[/C][C]0.508759260918836[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.541131279776[/C][C]0.5288687202239[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.712019823285[/C][C]0.20798017671456[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.90928149913[/C][C]0.09071850087047[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.172756521773[/C][C]-0.15275652177332[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.373491749097[/C][C]-0.0234917490968215[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.662015187647[/C][C]-0.302015187646593[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.131664876574[/C][C]-0.311664876574018[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.314612100168[/C][C]-0.434612100168289[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.541335640938[/C][C]-0.301335640938112[/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]-0.0914295014887887[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.746621323154[/C][C]0.0133786768455257[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.907089212226[/C][C]0.18291078777441[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145968&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145968&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	0.196792982453787
2	102.37	101.711482048995	0.658517951004513
3	102.38	101.884107368245	0.495892631755448
4	102.86	102.458721451113	0.401278548887448
5	102.87	102.607031910006	0.262968089994279
6	102.92	102.776183677775	0.143816322224634
7	102.95	102.943598669805	0.00640133019470009
8	103.02	103.127513031362	-0.107513031362447
9	104.08	104.229980392039	-0.149980392038948
10	104.16	104.386124055483	-0.226124055482673
11	104.24	104.543986780944	-0.30398678094423
12	104.33	104.720541325449	-0.390541325448612
13	104.73	104.972360710752	-0.242360710751866
14	104.86	105.138038927042	-0.278038927042102
15	105.03	105.30245375509	-0.272453755090422
16	105.62	105.802771347119	-0.182771347119409
17	105.63	105.961502460451	-0.331502460450827
18	105.63	106.13065422822	-0.500654228220469
19	105.94	106.434089502571	-0.494089502570981
20	106.61	106.598030943122	0.0119690568784954
21	107.69	107.841375106177	-0.151375106177316
22	107.78	108.0496928967	-0.269692896699708
23	107.93	108.290052469797	-0.360052469797278
24	108.48	108.514036669421	-0.0340366694207803
25	108.14	107.99118033495	0.14881966505005
26	108.48	108.15946371485	0.320536285150265
27	108.48	108.309510949483	0.170489050517385
28	108.89	108.942272360439	-0.0522723604392227
29	108.93	109.100233835994	-0.170233835993785
30	109.21	109.251149458497	-0.0411494584965308
31	109.47	109.389907650821	0.0800923491786823
32	109.8	109.568449612417	0.231550387582934
33	111.73	111.378145785349	0.351854214650791
34	111.85	111.60330215536	0.246697844640152
35	112.12	111.878761699173	0.241238300827328
36	112.15	112.064681191543	0.0853188084569532
37	112.17	112.303438821221	-0.133438821220662
38	112.67	112.518837892239	0.151162107761353
39	112.8	112.67496384196	0.1250361580395
40	113.44	113.642734732079	-0.202734732078643
41	113.53	113.808412948369	-0.27841294836887
42	114.53	114.027921031106	0.502078968893661
43	114.51	114.163205671952	0.346794328048282
44	115.05	114.757606257616	0.292393742383994
45	116.67	116.161240739081	0.508759260918836
46	117.07	116.541131279776	0.5288687202239
47	116.92	116.712019823285	0.20798017671456
48	117	116.90928149913	0.09071850087047
49	117.02	117.172756521773	-0.15275652177332
50	117.35	117.373491749097	-0.0234917490968215
51	117.36	117.662015187647	-0.302015187646593
52	117.82	118.131664876574	-0.311664876574018
53	117.88	118.314612100168	-0.434612100168289
54	118.24	118.541335640938	-0.301335640938112
55	118.5	118.713593855021	-0.213593855020616
56	118.8	118.891429501489	-0.0914295014887887
57	119.76	119.746621323154	0.0133786768455257
58	120.09	119.907089212226	0.18291078777441

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.112420926588103	0.224841853176206	0.887579073411897
11	0.0476101742046356	0.0952203484092712	0.952389825795364
12	0.226824863996475	0.453649727992949	0.773175136003525
13	0.137016396197795	0.27403279239559	0.862983603802205
14	0.0768929576814086	0.153785915362817	0.923107042318591
15	0.0569124814266819	0.113824962853364	0.943087518573318
16	0.041245412076641	0.0824908241532821	0.958754587923359
17	0.0255529792967069	0.0511059585934137	0.974447020703293
18	0.0230581027798352	0.0461162055596705	0.976941897220165
19	0.0286498153864832	0.0572996307729664	0.971350184613517
20	0.22815237999668	0.45630475999336	0.77184762000332
21	0.274059147912992	0.548118295825985	0.725940852087008
22	0.271369270516541	0.542738541033082	0.728630729483459
23	0.312026723424918	0.624053446849837	0.687973276575082
24	0.355381698770129	0.710763397540258	0.644618301229871
25	0.320637473621241	0.641274947242482	0.679362526378759
26	0.479181607158862	0.958363214317724	0.520818392841138
27	0.497548358899305	0.99509671779861	0.502451641100695
28	0.490891716538726	0.981783433077453	0.509108283461273
29	0.425269774691414	0.850539549382828	0.574730225308586
30	0.366770523694436	0.733541047388872	0.633229476305564
31	0.387496712763042	0.774993425526083	0.612503287236958
32	0.42561946720428	0.851238934408561	0.57438053279572
33	0.661252217570701	0.677495564858599	0.338747782429299
34	0.592120657924967	0.815758684150066	0.407879342075033
35	0.524422772577523	0.951154454844954	0.475577227422477
36	0.580118632948819	0.839762734102361	0.419881367051181
37	0.655190680535213	0.689618638929573	0.344809319464787
38	0.582566476572724	0.834867046854552	0.417433523427276
39	0.521416848573992	0.957166302852016	0.478583151426008
40	0.502458639916676	0.995082720166647	0.497541360083324
41	0.901180212205303	0.197639575589395	0.0988197877946974
42	0.905651051585639	0.188697896828723	0.0943489484143614
43	0.917404012542562	0.165191974914876	0.0825959874574382
44	0.885062565211635	0.22987486957673	0.114937434788365
45	0.824122136902134	0.351755726195731	0.175877863097866
46	0.983833829500158	0.032332340999684	0.016166170499842
47	0.983414399178296	0.0331712016434076	0.0165856008217038
48	0.944905285659128	0.110189428681745	0.0550947143408724

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.112420926588103 & 0.224841853176206 & 0.887579073411897 \tabularnewline
11 & 0.0476101742046356 & 0.0952203484092712 & 0.952389825795364 \tabularnewline
12 & 0.226824863996475 & 0.453649727992949 & 0.773175136003525 \tabularnewline
13 & 0.137016396197795 & 0.27403279239559 & 0.862983603802205 \tabularnewline
14 & 0.0768929576814086 & 0.153785915362817 & 0.923107042318591 \tabularnewline
15 & 0.0569124814266819 & 0.113824962853364 & 0.943087518573318 \tabularnewline
16 & 0.041245412076641 & 0.0824908241532821 & 0.958754587923359 \tabularnewline
17 & 0.0255529792967069 & 0.0511059585934137 & 0.974447020703293 \tabularnewline
18 & 0.0230581027798352 & 0.0461162055596705 & 0.976941897220165 \tabularnewline
19 & 0.0286498153864832 & 0.0572996307729664 & 0.971350184613517 \tabularnewline
20 & 0.22815237999668 & 0.45630475999336 & 0.77184762000332 \tabularnewline
21 & 0.274059147912992 & 0.548118295825985 & 0.725940852087008 \tabularnewline
22 & 0.271369270516541 & 0.542738541033082 & 0.728630729483459 \tabularnewline
23 & 0.312026723424918 & 0.624053446849837 & 0.687973276575082 \tabularnewline
24 & 0.355381698770129 & 0.710763397540258 & 0.644618301229871 \tabularnewline
25 & 0.320637473621241 & 0.641274947242482 & 0.679362526378759 \tabularnewline
26 & 0.479181607158862 & 0.958363214317724 & 0.520818392841138 \tabularnewline
27 & 0.497548358899305 & 0.99509671779861 & 0.502451641100695 \tabularnewline
28 & 0.490891716538726 & 0.981783433077453 & 0.509108283461273 \tabularnewline
29 & 0.425269774691414 & 0.850539549382828 & 0.574730225308586 \tabularnewline
30 & 0.366770523694436 & 0.733541047388872 & 0.633229476305564 \tabularnewline
31 & 0.387496712763042 & 0.774993425526083 & 0.612503287236958 \tabularnewline
32 & 0.42561946720428 & 0.851238934408561 & 0.57438053279572 \tabularnewline
33 & 0.661252217570701 & 0.677495564858599 & 0.338747782429299 \tabularnewline
34 & 0.592120657924967 & 0.815758684150066 & 0.407879342075033 \tabularnewline
35 & 0.524422772577523 & 0.951154454844954 & 0.475577227422477 \tabularnewline
36 & 0.580118632948819 & 0.839762734102361 & 0.419881367051181 \tabularnewline
37 & 0.655190680535213 & 0.689618638929573 & 0.344809319464787 \tabularnewline
38 & 0.582566476572724 & 0.834867046854552 & 0.417433523427276 \tabularnewline
39 & 0.521416848573992 & 0.957166302852016 & 0.478583151426008 \tabularnewline
40 & 0.502458639916676 & 0.995082720166647 & 0.497541360083324 \tabularnewline
41 & 0.901180212205303 & 0.197639575589395 & 0.0988197877946974 \tabularnewline
42 & 0.905651051585639 & 0.188697896828723 & 0.0943489484143614 \tabularnewline
43 & 0.917404012542562 & 0.165191974914876 & 0.0825959874574382 \tabularnewline
44 & 0.885062565211635 & 0.22987486957673 & 0.114937434788365 \tabularnewline
45 & 0.824122136902134 & 0.351755726195731 & 0.175877863097866 \tabularnewline
46 & 0.983833829500158 & 0.032332340999684 & 0.016166170499842 \tabularnewline
47 & 0.983414399178296 & 0.0331712016434076 & 0.0165856008217038 \tabularnewline
48 & 0.944905285659128 & 0.110189428681745 & 0.0550947143408724 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145968&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]0.112420926588103[/C][C]0.224841853176206[/C][C]0.887579073411897[/C][/ROW]
[ROW][C]11[/C][C]0.0476101742046356[/C][C]0.0952203484092712[/C][C]0.952389825795364[/C][/ROW]
[ROW][C]12[/C][C]0.226824863996475[/C][C]0.453649727992949[/C][C]0.773175136003525[/C][/ROW]
[ROW][C]13[/C][C]0.137016396197795[/C][C]0.27403279239559[/C][C]0.862983603802205[/C][/ROW]
[ROW][C]14[/C][C]0.0768929576814086[/C][C]0.153785915362817[/C][C]0.923107042318591[/C][/ROW]
[ROW][C]15[/C][C]0.0569124814266819[/C][C]0.113824962853364[/C][C]0.943087518573318[/C][/ROW]
[ROW][C]16[/C][C]0.041245412076641[/C][C]0.0824908241532821[/C][C]0.958754587923359[/C][/ROW]
[ROW][C]17[/C][C]0.0255529792967069[/C][C]0.0511059585934137[/C][C]0.974447020703293[/C][/ROW]
[ROW][C]18[/C][C]0.0230581027798352[/C][C]0.0461162055596705[/C][C]0.976941897220165[/C][/ROW]
[ROW][C]19[/C][C]0.0286498153864832[/C][C]0.0572996307729664[/C][C]0.971350184613517[/C][/ROW]
[ROW][C]20[/C][C]0.22815237999668[/C][C]0.45630475999336[/C][C]0.77184762000332[/C][/ROW]
[ROW][C]21[/C][C]0.274059147912992[/C][C]0.548118295825985[/C][C]0.725940852087008[/C][/ROW]
[ROW][C]22[/C][C]0.271369270516541[/C][C]0.542738541033082[/C][C]0.728630729483459[/C][/ROW]
[ROW][C]23[/C][C]0.312026723424918[/C][C]0.624053446849837[/C][C]0.687973276575082[/C][/ROW]
[ROW][C]24[/C][C]0.355381698770129[/C][C]0.710763397540258[/C][C]0.644618301229871[/C][/ROW]
[ROW][C]25[/C][C]0.320637473621241[/C][C]0.641274947242482[/C][C]0.679362526378759[/C][/ROW]
[ROW][C]26[/C][C]0.479181607158862[/C][C]0.958363214317724[/C][C]0.520818392841138[/C][/ROW]
[ROW][C]27[/C][C]0.497548358899305[/C][C]0.99509671779861[/C][C]0.502451641100695[/C][/ROW]
[ROW][C]28[/C][C]0.490891716538726[/C][C]0.981783433077453[/C][C]0.509108283461273[/C][/ROW]
[ROW][C]29[/C][C]0.425269774691414[/C][C]0.850539549382828[/C][C]0.574730225308586[/C][/ROW]
[ROW][C]30[/C][C]0.366770523694436[/C][C]0.733541047388872[/C][C]0.633229476305564[/C][/ROW]
[ROW][C]31[/C][C]0.387496712763042[/C][C]0.774993425526083[/C][C]0.612503287236958[/C][/ROW]
[ROW][C]32[/C][C]0.42561946720428[/C][C]0.851238934408561[/C][C]0.57438053279572[/C][/ROW]
[ROW][C]33[/C][C]0.661252217570701[/C][C]0.677495564858599[/C][C]0.338747782429299[/C][/ROW]
[ROW][C]34[/C][C]0.592120657924967[/C][C]0.815758684150066[/C][C]0.407879342075033[/C][/ROW]
[ROW][C]35[/C][C]0.524422772577523[/C][C]0.951154454844954[/C][C]0.475577227422477[/C][/ROW]
[ROW][C]36[/C][C]0.580118632948819[/C][C]0.839762734102361[/C][C]0.419881367051181[/C][/ROW]
[ROW][C]37[/C][C]0.655190680535213[/C][C]0.689618638929573[/C][C]0.344809319464787[/C][/ROW]
[ROW][C]38[/C][C]0.582566476572724[/C][C]0.834867046854552[/C][C]0.417433523427276[/C][/ROW]
[ROW][C]39[/C][C]0.521416848573992[/C][C]0.957166302852016[/C][C]0.478583151426008[/C][/ROW]
[ROW][C]40[/C][C]0.502458639916676[/C][C]0.995082720166647[/C][C]0.497541360083324[/C][/ROW]
[ROW][C]41[/C][C]0.901180212205303[/C][C]0.197639575589395[/C][C]0.0988197877946974[/C][/ROW]
[ROW][C]42[/C][C]0.905651051585639[/C][C]0.188697896828723[/C][C]0.0943489484143614[/C][/ROW]
[ROW][C]43[/C][C]0.917404012542562[/C][C]0.165191974914876[/C][C]0.0825959874574382[/C][/ROW]
[ROW][C]44[/C][C]0.885062565211635[/C][C]0.22987486957673[/C][C]0.114937434788365[/C][/ROW]
[ROW][C]45[/C][C]0.824122136902134[/C][C]0.351755726195731[/C][C]0.175877863097866[/C][/ROW]
[ROW][C]46[/C][C]0.983833829500158[/C][C]0.032332340999684[/C][C]0.016166170499842[/C][/ROW]
[ROW][C]47[/C][C]0.983414399178296[/C][C]0.0331712016434076[/C][C]0.0165856008217038[/C][/ROW]
[ROW][C]48[/C][C]0.944905285659128[/C][C]0.110189428681745[/C][C]0.0550947143408724[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145968&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145968&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	0.112420926588103	0.224841853176206	0.887579073411897
11	0.0476101742046356	0.0952203484092712	0.952389825795364
12	0.226824863996475	0.453649727992949	0.773175136003525
13	0.137016396197795	0.27403279239559	0.862983603802205
14	0.0768929576814086	0.153785915362817	0.923107042318591
15	0.0569124814266819	0.113824962853364	0.943087518573318
16	0.041245412076641	0.0824908241532821	0.958754587923359
17	0.0255529792967069	0.0511059585934137	0.974447020703293
18	0.0230581027798352	0.0461162055596705	0.976941897220165
19	0.0286498153864832	0.0572996307729664	0.971350184613517
20	0.22815237999668	0.45630475999336	0.77184762000332
21	0.274059147912992	0.548118295825985	0.725940852087008
22	0.271369270516541	0.542738541033082	0.728630729483459
23	0.312026723424918	0.624053446849837	0.687973276575082
24	0.355381698770129	0.710763397540258	0.644618301229871
25	0.320637473621241	0.641274947242482	0.679362526378759
26	0.479181607158862	0.958363214317724	0.520818392841138
27	0.497548358899305	0.99509671779861	0.502451641100695
28	0.490891716538726	0.981783433077453	0.509108283461273
29	0.425269774691414	0.850539549382828	0.574730225308586
30	0.366770523694436	0.733541047388872	0.633229476305564
31	0.387496712763042	0.774993425526083	0.612503287236958
32	0.42561946720428	0.851238934408561	0.57438053279572
33	0.661252217570701	0.677495564858599	0.338747782429299
34	0.592120657924967	0.815758684150066	0.407879342075033
35	0.524422772577523	0.951154454844954	0.475577227422477
36	0.580118632948819	0.839762734102361	0.419881367051181
37	0.655190680535213	0.689618638929573	0.344809319464787
38	0.582566476572724	0.834867046854552	0.417433523427276
39	0.521416848573992	0.957166302852016	0.478583151426008
40	0.502458639916676	0.995082720166647	0.497541360083324
41	0.901180212205303	0.197639575589395	0.0988197877946974
42	0.905651051585639	0.188697896828723	0.0943489484143614
43	0.917404012542562	0.165191974914876	0.0825959874574382
44	0.885062565211635	0.22987486957673	0.114937434788365
45	0.824122136902134	0.351755726195731	0.175877863097866
46	0.983833829500158	0.032332340999684	0.016166170499842
47	0.983414399178296	0.0331712016434076	0.0165856008217038
48	0.944905285659128	0.110189428681745	0.0550947143408724

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=145968&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=145968&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145968&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 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No 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