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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 15:08:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321906122qn0k4mh1pqpl5ne.htm/, Retrieved Thu, 28 Mar 2024 22:00:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145959, Retrieved Thu, 28 Mar 2024 22:00:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145959&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145959&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145959&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Cultuurenvrijetijdsbesteding[t] = + 43.1196518887865 + 1.06911178353785jaar[t] + 0.0955092964682526bioscoop[t] + 0.293827546929861schouwburgabonnement[t] + 0.271011310340612eendagsattractie[t] -0.140287440271962huurDVD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cultuurenvrijetijdsbesteding[t] =  +  43.1196518887865 +  1.06911178353785jaar[t] +  0.0955092964682526bioscoop[t] +  0.293827546929861schouwburgabonnement[t] +  0.271011310340612eendagsattractie[t] -0.140287440271962huurDVD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145959&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cultuurenvrijetijdsbesteding[t] =  +  43.1196518887865 +  1.06911178353785jaar[t] +  0.0955092964682526bioscoop[t] +  0.293827546929861schouwburgabonnement[t] +  0.271011310340612eendagsattractie[t] -0.140287440271962huurDVD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145959&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145959&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] = + 43.1196518887865 + 1.06911178353785jaar[t] + 0.0955092964682526bioscoop[t] + 0.293827546929861schouwburgabonnement[t] + 0.271011310340612eendagsattractie[t] -0.140287440271962huurDVD[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)43.119651888786511.207163.84750.0003280.000164
jaar1.069111783537850.1421077.523300
bioscoop0.09550929646825260.0302583.15650.0026560.001328
schouwburgabonnement0.2938275469298610.02412212.180800
eendagsattractie0.2710113103406120.0407016.658500
huurDVD-0.1402874402719620.134669-1.04170.3023590.15118

\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) & 43.1196518887865 & 11.20716 & 3.8475 & 0.000328 & 0.000164 \tabularnewline
jaar & 1.06911178353785 & 0.142107 & 7.5233 & 0 & 0 \tabularnewline
bioscoop & 0.0955092964682526 & 0.030258 & 3.1565 & 0.002656 & 0.001328 \tabularnewline
schouwburgabonnement & 0.293827546929861 & 0.024122 & 12.1808 & 0 & 0 \tabularnewline
eendagsattractie & 0.271011310340612 & 0.040701 & 6.6585 & 0 & 0 \tabularnewline
huurDVD & -0.140287440271962 & 0.134669 & -1.0417 & 0.302359 & 0.15118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145959&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]43.1196518887865[/C][C]11.20716[/C][C]3.8475[/C][C]0.000328[/C][C]0.000164[/C][/ROW]
[ROW][C]jaar[/C][C]1.06911178353785[/C][C]0.142107[/C][C]7.5233[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]bioscoop[/C][C]0.0955092964682526[/C][C]0.030258[/C][C]3.1565[/C][C]0.002656[/C][C]0.001328[/C][/ROW]
[ROW][C]schouwburgabonnement[/C][C]0.293827546929861[/C][C]0.024122[/C][C]12.1808[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]eendagsattractie[/C][C]0.271011310340612[/C][C]0.040701[/C][C]6.6585[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]huurDVD[/C][C]-0.140287440271962[/C][C]0.134669[/C][C]-1.0417[/C][C]0.302359[/C][C]0.15118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145959&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145959&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)43.119651888786511.207163.84750.0003280.000164
jaar1.069111783537850.1421077.523300
bioscoop0.09550929646825260.0302583.15650.0026560.001328
schouwburgabonnement0.2938275469298610.02412212.180800
eendagsattractie0.2710113103406120.0407016.658500
huurDVD-0.1402874402719620.134669-1.04170.3023590.15118







Multiple Linear Regression - Regression Statistics
Multiple R0.996983707496076
R-squared0.993976513012621
Adjusted R-squared0.993397331571527
F-TEST (value)1716.17466045679
F-TEST (DF numerator)5
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.452997679974217
Sum Squared Residuals10.6707586992252

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.996983707496076 \tabularnewline
R-squared & 0.993976513012621 \tabularnewline
Adjusted R-squared & 0.993397331571527 \tabularnewline
F-TEST (value) & 1716.17466045679 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.452997679974217 \tabularnewline
Sum Squared Residuals & 10.6707586992252 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145959&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.996983707496076[/C][/ROW]
[ROW][C]R-squared[/C][C]0.993976513012621[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.993397331571527[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1716.17466045679[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/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]0.452997679974217[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.6707586992252[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145959&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145959&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 R0.996983707496076
R-squared0.993976513012621
Adjusted R-squared0.993397331571527
F-TEST (value)1716.17466045679
F-TEST (DF numerator)5
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.452997679974217
Sum Squared Residuals10.6707586992252







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.76102.165616119899-0.405616119898497
2102.37102.1438275719770.226172428023485
3102.38102.1494390695870.230560930412596
4102.86102.901912378122-0.0419123781222031
5102.87102.8682433924570.0017566075430726
6102.92102.8682433924570.0517566075430694
7102.95102.8654376436510.0845623563485112
8103.02102.8892865084980.130713491502271
9104.08104.436222279752-0.356222279751516
10104.16104.409238944839-0.249238944839022
11104.24104.391001577604-0.151001577603669
12104.33104.374821915746-0.0448219157457216
13104.73105.446871974753-0.716871974752862
14104.86105.441260477142-0.581260477141989
15105.03105.399500354774-0.369500354774269
16105.62106.10412976166-0.484129761659859
17105.63106.087295268827-0.457295268827234
18105.63106.087295268827-0.457295268827234
19105.94106.201906424589-0.261906424589135
20106.61106.1934891781730.416510821827185
21107.69107.97381905924-0.283819059240059
22107.78108.007247313004-0.227247313003943
23107.93108.122283014027-0.192283014026947
24108.48108.1690825692960.310917430703605
25108.14107.957106248220.182893751779952
26108.48107.9557033738170.524296626182676
27108.48107.9248401369570.555159863042507
28108.89108.8016626081260.0883373918735986
29108.93108.792111678480.137888321520431
30109.21108.7626513160220.447348683977529
31109.47108.7135507119270.756449288072722
32109.8108.6997290174371.10027098256326
33111.73111.4002836402250.329716359774719
34111.85111.4302176318440.419782368155682
35112.12111.5209514634890.599048536510851
36112.15111.5412180768310.608781923168915
37112.17112.592092493134-0.42209249313358
38112.67112.6335497419640.0364502580356428
39112.8112.6125066259240.187493374076433
40113.44114.311747541759-0.871747541759199
41113.53114.306136044148-0.776136044148316
42114.53114.3491152275590.18088477244097
43114.51114.2944031258530.215596874147037
44115.05114.6513982335610.398601766439368
45116.67116.4400109986650.229989001334743
46117.07116.5456627226060.524337277394332
47116.92116.5484684714110.3715315285889
48117116.5691517953730.430848204626704
49117.02117.649724694487-0.629724694487336
50117.35117.67601951606-0.326019516060412
51117.36117.858631308768-0.498631308768392
52117.82118.489531563584-0.669531563583711
53117.88118.503291116439-0.623291116438692
54118.24118.551105294761-0.311105294761428
55118.5118.554418355094-0.0544183550935074
56118.8118.5684470991210.231552900879294
57119.76119.7055597057390.0544402942607372
58120.09119.6915309617120.398469038287931

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 102.165616119899 & -0.405616119898497 \tabularnewline
2 & 102.37 & 102.143827571977 & 0.226172428023485 \tabularnewline
3 & 102.38 & 102.149439069587 & 0.230560930412596 \tabularnewline
4 & 102.86 & 102.901912378122 & -0.0419123781222031 \tabularnewline
5 & 102.87 & 102.868243392457 & 0.0017566075430726 \tabularnewline
6 & 102.92 & 102.868243392457 & 0.0517566075430694 \tabularnewline
7 & 102.95 & 102.865437643651 & 0.0845623563485112 \tabularnewline
8 & 103.02 & 102.889286508498 & 0.130713491502271 \tabularnewline
9 & 104.08 & 104.436222279752 & -0.356222279751516 \tabularnewline
10 & 104.16 & 104.409238944839 & -0.249238944839022 \tabularnewline
11 & 104.24 & 104.391001577604 & -0.151001577603669 \tabularnewline
12 & 104.33 & 104.374821915746 & -0.0448219157457216 \tabularnewline
13 & 104.73 & 105.446871974753 & -0.716871974752862 \tabularnewline
14 & 104.86 & 105.441260477142 & -0.581260477141989 \tabularnewline
15 & 105.03 & 105.399500354774 & -0.369500354774269 \tabularnewline
16 & 105.62 & 106.10412976166 & -0.484129761659859 \tabularnewline
17 & 105.63 & 106.087295268827 & -0.457295268827234 \tabularnewline
18 & 105.63 & 106.087295268827 & -0.457295268827234 \tabularnewline
19 & 105.94 & 106.201906424589 & -0.261906424589135 \tabularnewline
20 & 106.61 & 106.193489178173 & 0.416510821827185 \tabularnewline
21 & 107.69 & 107.97381905924 & -0.283819059240059 \tabularnewline
22 & 107.78 & 108.007247313004 & -0.227247313003943 \tabularnewline
23 & 107.93 & 108.122283014027 & -0.192283014026947 \tabularnewline
24 & 108.48 & 108.169082569296 & 0.310917430703605 \tabularnewline
25 & 108.14 & 107.95710624822 & 0.182893751779952 \tabularnewline
26 & 108.48 & 107.955703373817 & 0.524296626182676 \tabularnewline
27 & 108.48 & 107.924840136957 & 0.555159863042507 \tabularnewline
28 & 108.89 & 108.801662608126 & 0.0883373918735986 \tabularnewline
29 & 108.93 & 108.79211167848 & 0.137888321520431 \tabularnewline
30 & 109.21 & 108.762651316022 & 0.447348683977529 \tabularnewline
31 & 109.47 & 108.713550711927 & 0.756449288072722 \tabularnewline
32 & 109.8 & 108.699729017437 & 1.10027098256326 \tabularnewline
33 & 111.73 & 111.400283640225 & 0.329716359774719 \tabularnewline
34 & 111.85 & 111.430217631844 & 0.419782368155682 \tabularnewline
35 & 112.12 & 111.520951463489 & 0.599048536510851 \tabularnewline
36 & 112.15 & 111.541218076831 & 0.608781923168915 \tabularnewline
37 & 112.17 & 112.592092493134 & -0.42209249313358 \tabularnewline
38 & 112.67 & 112.633549741964 & 0.0364502580356428 \tabularnewline
39 & 112.8 & 112.612506625924 & 0.187493374076433 \tabularnewline
40 & 113.44 & 114.311747541759 & -0.871747541759199 \tabularnewline
41 & 113.53 & 114.306136044148 & -0.776136044148316 \tabularnewline
42 & 114.53 & 114.349115227559 & 0.18088477244097 \tabularnewline
43 & 114.51 & 114.294403125853 & 0.215596874147037 \tabularnewline
44 & 115.05 & 114.651398233561 & 0.398601766439368 \tabularnewline
45 & 116.67 & 116.440010998665 & 0.229989001334743 \tabularnewline
46 & 117.07 & 116.545662722606 & 0.524337277394332 \tabularnewline
47 & 116.92 & 116.548468471411 & 0.3715315285889 \tabularnewline
48 & 117 & 116.569151795373 & 0.430848204626704 \tabularnewline
49 & 117.02 & 117.649724694487 & -0.629724694487336 \tabularnewline
50 & 117.35 & 117.67601951606 & -0.326019516060412 \tabularnewline
51 & 117.36 & 117.858631308768 & -0.498631308768392 \tabularnewline
52 & 117.82 & 118.489531563584 & -0.669531563583711 \tabularnewline
53 & 117.88 & 118.503291116439 & -0.623291116438692 \tabularnewline
54 & 118.24 & 118.551105294761 & -0.311105294761428 \tabularnewline
55 & 118.5 & 118.554418355094 & -0.0544183550935074 \tabularnewline
56 & 118.8 & 118.568447099121 & 0.231552900879294 \tabularnewline
57 & 119.76 & 119.705559705739 & 0.0544402942607372 \tabularnewline
58 & 120.09 & 119.691530961712 & 0.398469038287931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145959&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]101.76[/C][C]102.165616119899[/C][C]-0.405616119898497[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]102.143827571977[/C][C]0.226172428023485[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]102.149439069587[/C][C]0.230560930412596[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.901912378122[/C][C]-0.0419123781222031[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.868243392457[/C][C]0.0017566075430726[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.868243392457[/C][C]0.0517566075430694[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.865437643651[/C][C]0.0845623563485112[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]102.889286508498[/C][C]0.130713491502271[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.436222279752[/C][C]-0.356222279751516[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.409238944839[/C][C]-0.249238944839022[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.391001577604[/C][C]-0.151001577603669[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.374821915746[/C][C]-0.0448219157457216[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]105.446871974753[/C][C]-0.716871974752862[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.441260477142[/C][C]-0.581260477141989[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.399500354774[/C][C]-0.369500354774269[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]106.10412976166[/C][C]-0.484129761659859[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]106.087295268827[/C][C]-0.457295268827234[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.087295268827[/C][C]-0.457295268827234[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.201906424589[/C][C]-0.261906424589135[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.193489178173[/C][C]0.416510821827185[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.97381905924[/C][C]-0.283819059240059[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.007247313004[/C][C]-0.227247313003943[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.122283014027[/C][C]-0.192283014026947[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.169082569296[/C][C]0.310917430703605[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.95710624822[/C][C]0.182893751779952[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]107.955703373817[/C][C]0.524296626182676[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]107.924840136957[/C][C]0.555159863042507[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.801662608126[/C][C]0.0883373918735986[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]108.79211167848[/C][C]0.137888321520431[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]108.762651316022[/C][C]0.447348683977529[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]108.713550711927[/C][C]0.756449288072722[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]108.699729017437[/C][C]1.10027098256326[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.400283640225[/C][C]0.329716359774719[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.430217631844[/C][C]0.419782368155682[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.520951463489[/C][C]0.599048536510851[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]111.541218076831[/C][C]0.608781923168915[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.592092493134[/C][C]-0.42209249313358[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.633549741964[/C][C]0.0364502580356428[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.612506625924[/C][C]0.187493374076433[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]114.311747541759[/C][C]-0.871747541759199[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]114.306136044148[/C][C]-0.776136044148316[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.349115227559[/C][C]0.18088477244097[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.294403125853[/C][C]0.215596874147037[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.651398233561[/C][C]0.398601766439368[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.440010998665[/C][C]0.229989001334743[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.545662722606[/C][C]0.524337277394332[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.548468471411[/C][C]0.3715315285889[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.569151795373[/C][C]0.430848204626704[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.649724694487[/C][C]-0.629724694487336[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.67601951606[/C][C]-0.326019516060412[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.858631308768[/C][C]-0.498631308768392[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.489531563584[/C][C]-0.669531563583711[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.503291116439[/C][C]-0.623291116438692[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.551105294761[/C][C]-0.311105294761428[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.554418355094[/C][C]-0.0544183550935074[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.568447099121[/C][C]0.231552900879294[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.705559705739[/C][C]0.0544402942607372[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.691530961712[/C][C]0.398469038287931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145959&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145959&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 IndexActualsInterpolationForecastResidualsPrediction Error
1101.76102.165616119899-0.405616119898497
2102.37102.1438275719770.226172428023485
3102.38102.1494390695870.230560930412596
4102.86102.901912378122-0.0419123781222031
5102.87102.8682433924570.0017566075430726
6102.92102.8682433924570.0517566075430694
7102.95102.8654376436510.0845623563485112
8103.02102.8892865084980.130713491502271
9104.08104.436222279752-0.356222279751516
10104.16104.409238944839-0.249238944839022
11104.24104.391001577604-0.151001577603669
12104.33104.374821915746-0.0448219157457216
13104.73105.446871974753-0.716871974752862
14104.86105.441260477142-0.581260477141989
15105.03105.399500354774-0.369500354774269
16105.62106.10412976166-0.484129761659859
17105.63106.087295268827-0.457295268827234
18105.63106.087295268827-0.457295268827234
19105.94106.201906424589-0.261906424589135
20106.61106.1934891781730.416510821827185
21107.69107.97381905924-0.283819059240059
22107.78108.007247313004-0.227247313003943
23107.93108.122283014027-0.192283014026947
24108.48108.1690825692960.310917430703605
25108.14107.957106248220.182893751779952
26108.48107.9557033738170.524296626182676
27108.48107.9248401369570.555159863042507
28108.89108.8016626081260.0883373918735986
29108.93108.792111678480.137888321520431
30109.21108.7626513160220.447348683977529
31109.47108.7135507119270.756449288072722
32109.8108.6997290174371.10027098256326
33111.73111.4002836402250.329716359774719
34111.85111.4302176318440.419782368155682
35112.12111.5209514634890.599048536510851
36112.15111.5412180768310.608781923168915
37112.17112.592092493134-0.42209249313358
38112.67112.6335497419640.0364502580356428
39112.8112.6125066259240.187493374076433
40113.44114.311747541759-0.871747541759199
41113.53114.306136044148-0.776136044148316
42114.53114.3491152275590.18088477244097
43114.51114.2944031258530.215596874147037
44115.05114.6513982335610.398601766439368
45116.67116.4400109986650.229989001334743
46117.07116.5456627226060.524337277394332
47116.92116.5484684714110.3715315285889
48117116.5691517953730.430848204626704
49117.02117.649724694487-0.629724694487336
50117.35117.67601951606-0.326019516060412
51117.36117.858631308768-0.498631308768392
52117.82118.489531563584-0.669531563583711
53117.88118.503291116439-0.623291116438692
54118.24118.551105294761-0.311105294761428
55118.5118.554418355094-0.0544183550935074
56118.8118.5684470991210.231552900879294
57119.76119.7055597057390.0544402942607372
58120.09119.6915309617120.398469038287931







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.005020724915884920.01004144983176980.994979275084115
100.006696880924325250.01339376184865050.993303119075675
110.00174015818935180.00348031637870360.998259841810648
120.007419596494613740.01483919298922750.992580403505386
130.002796029701444180.005592059402888350.997203970298556
140.001101943057151080.002203886114302160.998898056942849
150.0004766657191527540.0009533314383055080.999523334280847
160.0006499257240055840.001299851448011170.999350074275994
170.0003208095712814560.0006416191425629110.999679190428719
180.0001728282192871150.0003456564385742310.999827171780713
190.0001194580668090060.0002389161336180120.999880541933191
200.001861510633451410.003723021266902820.998138489366549
210.001715541174831450.003431082349662890.998284458825169
220.00179872864038890.00359745728077780.998201271359611
230.003841673201352260.007683346402704520.996158326798648
240.009652781799922270.01930556359984450.990347218200078
250.007936588290587680.01587317658117540.992063411709412
260.01010814159900470.02021628319800950.989891858400995
270.009230094307806070.01846018861561210.990769905692194
280.01220626731982320.02441253463964650.987793732680177
290.01249054850512360.02498109701024730.987509451494876
300.01142835576115810.02285671152231630.988571644238842
310.01909475543004680.03818951086009360.980905244569953
320.05354325953515980.107086519070320.94645674046484
330.05929021428084010.118580428561680.94070978571916
340.04166179709094530.08332359418189060.958338202909055
350.04097801455153980.08195602910307960.95902198544846
360.03719944801097480.07439889602194960.962800551989025
370.1469171703917280.2938343407834550.853082829608272
380.1330948610819010.2661897221638020.866905138918099
390.1075052512528770.2150105025057540.892494748747123
400.3926227862313740.7852455724627490.607377213768626
410.7698056739080080.4603886521839830.230194326091992
420.6929113645550420.6141772708899160.307088635444958
430.60160505552490.7967898889501990.3983949444751
440.8458962985930030.3082074028139930.154103701406997
450.9699615593402050.06007688131959020.0300384406597951
460.9594854533744530.08102909325109440.0405145466255472
470.9334781827019150.133043634596170.0665218172980849
480.8575499837527110.2849000324945780.142450016247289
490.7774457775112020.4451084449775970.222554222488798

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00502072491588492 & 0.0100414498317698 & 0.994979275084115 \tabularnewline
10 & 0.00669688092432525 & 0.0133937618486505 & 0.993303119075675 \tabularnewline
11 & 0.0017401581893518 & 0.0034803163787036 & 0.998259841810648 \tabularnewline
12 & 0.00741959649461374 & 0.0148391929892275 & 0.992580403505386 \tabularnewline
13 & 0.00279602970144418 & 0.00559205940288835 & 0.997203970298556 \tabularnewline
14 & 0.00110194305715108 & 0.00220388611430216 & 0.998898056942849 \tabularnewline
15 & 0.000476665719152754 & 0.000953331438305508 & 0.999523334280847 \tabularnewline
16 & 0.000649925724005584 & 0.00129985144801117 & 0.999350074275994 \tabularnewline
17 & 0.000320809571281456 & 0.000641619142562911 & 0.999679190428719 \tabularnewline
18 & 0.000172828219287115 & 0.000345656438574231 & 0.999827171780713 \tabularnewline
19 & 0.000119458066809006 & 0.000238916133618012 & 0.999880541933191 \tabularnewline
20 & 0.00186151063345141 & 0.00372302126690282 & 0.998138489366549 \tabularnewline
21 & 0.00171554117483145 & 0.00343108234966289 & 0.998284458825169 \tabularnewline
22 & 0.0017987286403889 & 0.0035974572807778 & 0.998201271359611 \tabularnewline
23 & 0.00384167320135226 & 0.00768334640270452 & 0.996158326798648 \tabularnewline
24 & 0.00965278179992227 & 0.0193055635998445 & 0.990347218200078 \tabularnewline
25 & 0.00793658829058768 & 0.0158731765811754 & 0.992063411709412 \tabularnewline
26 & 0.0101081415990047 & 0.0202162831980095 & 0.989891858400995 \tabularnewline
27 & 0.00923009430780607 & 0.0184601886156121 & 0.990769905692194 \tabularnewline
28 & 0.0122062673198232 & 0.0244125346396465 & 0.987793732680177 \tabularnewline
29 & 0.0124905485051236 & 0.0249810970102473 & 0.987509451494876 \tabularnewline
30 & 0.0114283557611581 & 0.0228567115223163 & 0.988571644238842 \tabularnewline
31 & 0.0190947554300468 & 0.0381895108600936 & 0.980905244569953 \tabularnewline
32 & 0.0535432595351598 & 0.10708651907032 & 0.94645674046484 \tabularnewline
33 & 0.0592902142808401 & 0.11858042856168 & 0.94070978571916 \tabularnewline
34 & 0.0416617970909453 & 0.0833235941818906 & 0.958338202909055 \tabularnewline
35 & 0.0409780145515398 & 0.0819560291030796 & 0.95902198544846 \tabularnewline
36 & 0.0371994480109748 & 0.0743988960219496 & 0.962800551989025 \tabularnewline
37 & 0.146917170391728 & 0.293834340783455 & 0.853082829608272 \tabularnewline
38 & 0.133094861081901 & 0.266189722163802 & 0.866905138918099 \tabularnewline
39 & 0.107505251252877 & 0.215010502505754 & 0.892494748747123 \tabularnewline
40 & 0.392622786231374 & 0.785245572462749 & 0.607377213768626 \tabularnewline
41 & 0.769805673908008 & 0.460388652183983 & 0.230194326091992 \tabularnewline
42 & 0.692911364555042 & 0.614177270889916 & 0.307088635444958 \tabularnewline
43 & 0.6016050555249 & 0.796789888950199 & 0.3983949444751 \tabularnewline
44 & 0.845896298593003 & 0.308207402813993 & 0.154103701406997 \tabularnewline
45 & 0.969961559340205 & 0.0600768813195902 & 0.0300384406597951 \tabularnewline
46 & 0.959485453374453 & 0.0810290932510944 & 0.0405145466255472 \tabularnewline
47 & 0.933478182701915 & 0.13304363459617 & 0.0665218172980849 \tabularnewline
48 & 0.857549983752711 & 0.284900032494578 & 0.142450016247289 \tabularnewline
49 & 0.777445777511202 & 0.445108444977597 & 0.222554222488798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145959&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]9[/C][C]0.00502072491588492[/C][C]0.0100414498317698[/C][C]0.994979275084115[/C][/ROW]
[ROW][C]10[/C][C]0.00669688092432525[/C][C]0.0133937618486505[/C][C]0.993303119075675[/C][/ROW]
[ROW][C]11[/C][C]0.0017401581893518[/C][C]0.0034803163787036[/C][C]0.998259841810648[/C][/ROW]
[ROW][C]12[/C][C]0.00741959649461374[/C][C]0.0148391929892275[/C][C]0.992580403505386[/C][/ROW]
[ROW][C]13[/C][C]0.00279602970144418[/C][C]0.00559205940288835[/C][C]0.997203970298556[/C][/ROW]
[ROW][C]14[/C][C]0.00110194305715108[/C][C]0.00220388611430216[/C][C]0.998898056942849[/C][/ROW]
[ROW][C]15[/C][C]0.000476665719152754[/C][C]0.000953331438305508[/C][C]0.999523334280847[/C][/ROW]
[ROW][C]16[/C][C]0.000649925724005584[/C][C]0.00129985144801117[/C][C]0.999350074275994[/C][/ROW]
[ROW][C]17[/C][C]0.000320809571281456[/C][C]0.000641619142562911[/C][C]0.999679190428719[/C][/ROW]
[ROW][C]18[/C][C]0.000172828219287115[/C][C]0.000345656438574231[/C][C]0.999827171780713[/C][/ROW]
[ROW][C]19[/C][C]0.000119458066809006[/C][C]0.000238916133618012[/C][C]0.999880541933191[/C][/ROW]
[ROW][C]20[/C][C]0.00186151063345141[/C][C]0.00372302126690282[/C][C]0.998138489366549[/C][/ROW]
[ROW][C]21[/C][C]0.00171554117483145[/C][C]0.00343108234966289[/C][C]0.998284458825169[/C][/ROW]
[ROW][C]22[/C][C]0.0017987286403889[/C][C]0.0035974572807778[/C][C]0.998201271359611[/C][/ROW]
[ROW][C]23[/C][C]0.00384167320135226[/C][C]0.00768334640270452[/C][C]0.996158326798648[/C][/ROW]
[ROW][C]24[/C][C]0.00965278179992227[/C][C]0.0193055635998445[/C][C]0.990347218200078[/C][/ROW]
[ROW][C]25[/C][C]0.00793658829058768[/C][C]0.0158731765811754[/C][C]0.992063411709412[/C][/ROW]
[ROW][C]26[/C][C]0.0101081415990047[/C][C]0.0202162831980095[/C][C]0.989891858400995[/C][/ROW]
[ROW][C]27[/C][C]0.00923009430780607[/C][C]0.0184601886156121[/C][C]0.990769905692194[/C][/ROW]
[ROW][C]28[/C][C]0.0122062673198232[/C][C]0.0244125346396465[/C][C]0.987793732680177[/C][/ROW]
[ROW][C]29[/C][C]0.0124905485051236[/C][C]0.0249810970102473[/C][C]0.987509451494876[/C][/ROW]
[ROW][C]30[/C][C]0.0114283557611581[/C][C]0.0228567115223163[/C][C]0.988571644238842[/C][/ROW]
[ROW][C]31[/C][C]0.0190947554300468[/C][C]0.0381895108600936[/C][C]0.980905244569953[/C][/ROW]
[ROW][C]32[/C][C]0.0535432595351598[/C][C]0.10708651907032[/C][C]0.94645674046484[/C][/ROW]
[ROW][C]33[/C][C]0.0592902142808401[/C][C]0.11858042856168[/C][C]0.94070978571916[/C][/ROW]
[ROW][C]34[/C][C]0.0416617970909453[/C][C]0.0833235941818906[/C][C]0.958338202909055[/C][/ROW]
[ROW][C]35[/C][C]0.0409780145515398[/C][C]0.0819560291030796[/C][C]0.95902198544846[/C][/ROW]
[ROW][C]36[/C][C]0.0371994480109748[/C][C]0.0743988960219496[/C][C]0.962800551989025[/C][/ROW]
[ROW][C]37[/C][C]0.146917170391728[/C][C]0.293834340783455[/C][C]0.853082829608272[/C][/ROW]
[ROW][C]38[/C][C]0.133094861081901[/C][C]0.266189722163802[/C][C]0.866905138918099[/C][/ROW]
[ROW][C]39[/C][C]0.107505251252877[/C][C]0.215010502505754[/C][C]0.892494748747123[/C][/ROW]
[ROW][C]40[/C][C]0.392622786231374[/C][C]0.785245572462749[/C][C]0.607377213768626[/C][/ROW]
[ROW][C]41[/C][C]0.769805673908008[/C][C]0.460388652183983[/C][C]0.230194326091992[/C][/ROW]
[ROW][C]42[/C][C]0.692911364555042[/C][C]0.614177270889916[/C][C]0.307088635444958[/C][/ROW]
[ROW][C]43[/C][C]0.6016050555249[/C][C]0.796789888950199[/C][C]0.3983949444751[/C][/ROW]
[ROW][C]44[/C][C]0.845896298593003[/C][C]0.308207402813993[/C][C]0.154103701406997[/C][/ROW]
[ROW][C]45[/C][C]0.969961559340205[/C][C]0.0600768813195902[/C][C]0.0300384406597951[/C][/ROW]
[ROW][C]46[/C][C]0.959485453374453[/C][C]0.0810290932510944[/C][C]0.0405145466255472[/C][/ROW]
[ROW][C]47[/C][C]0.933478182701915[/C][C]0.13304363459617[/C][C]0.0665218172980849[/C][/ROW]
[ROW][C]48[/C][C]0.857549983752711[/C][C]0.284900032494578[/C][C]0.142450016247289[/C][/ROW]
[ROW][C]49[/C][C]0.777445777511202[/C][C]0.445108444977597[/C][C]0.222554222488798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145959&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145959&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.005020724915884920.01004144983176980.994979275084115
100.006696880924325250.01339376184865050.993303119075675
110.00174015818935180.00348031637870360.998259841810648
120.007419596494613740.01483919298922750.992580403505386
130.002796029701444180.005592059402888350.997203970298556
140.001101943057151080.002203886114302160.998898056942849
150.0004766657191527540.0009533314383055080.999523334280847
160.0006499257240055840.001299851448011170.999350074275994
170.0003208095712814560.0006416191425629110.999679190428719
180.0001728282192871150.0003456564385742310.999827171780713
190.0001194580668090060.0002389161336180120.999880541933191
200.001861510633451410.003723021266902820.998138489366549
210.001715541174831450.003431082349662890.998284458825169
220.00179872864038890.00359745728077780.998201271359611
230.003841673201352260.007683346402704520.996158326798648
240.009652781799922270.01930556359984450.990347218200078
250.007936588290587680.01587317658117540.992063411709412
260.01010814159900470.02021628319800950.989891858400995
270.009230094307806070.01846018861561210.990769905692194
280.01220626731982320.02441253463964650.987793732680177
290.01249054850512360.02498109701024730.987509451494876
300.01142835576115810.02285671152231630.988571644238842
310.01909475543004680.03818951086009360.980905244569953
320.05354325953515980.107086519070320.94645674046484
330.05929021428084010.118580428561680.94070978571916
340.04166179709094530.08332359418189060.958338202909055
350.04097801455153980.08195602910307960.95902198544846
360.03719944801097480.07439889602194960.962800551989025
370.1469171703917280.2938343407834550.853082829608272
380.1330948610819010.2661897221638020.866905138918099
390.1075052512528770.2150105025057540.892494748747123
400.3926227862313740.7852455724627490.607377213768626
410.7698056739080080.4603886521839830.230194326091992
420.6929113645550420.6141772708899160.307088635444958
430.60160505552490.7967898889501990.3983949444751
440.8458962985930030.3082074028139930.154103701406997
450.9699615593402050.06007688131959020.0300384406597951
460.9594854533744530.08102909325109440.0405145466255472
470.9334781827019150.133043634596170.0665218172980849
480.8575499837527110.2849000324945780.142450016247289
490.7774457775112020.4451084449775970.222554222488798







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.292682926829268NOK
5% type I error level230.560975609756098NOK
10% type I error level280.682926829268293NOK

\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 & 12 & 0.292682926829268 & NOK \tabularnewline
5% type I error level & 23 & 0.560975609756098 & NOK \tabularnewline
10% type I error level & 28 & 0.682926829268293 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145959&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]12[/C][C]0.292682926829268[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.560975609756098[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.682926829268293[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145959&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145959&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 testsOK/NOK
1% type I error level120.292682926829268NOK
5% type I error level230.560975609756098NOK
10% type I error level280.682926829268293NOK



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 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}