Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 11:24:19 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/24/t12590871317jkygbwgjhk4zew.htm/, Retrieved Fri, 29 Mar 2024 02:15:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59206, Retrieved Fri, 29 Mar 2024 02:15:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2009-11-20 13:55:26] [5482608004c1d7bbf873930172393a2d]
-   PD        [Multiple Regression] [workshop 7/module1] [2009-11-24 18:24:19] [f94f05f163a3ee3ab544c4fef41db0eb] [Current]
Feedback Forum

Post a new message
Dataseries X:
114.08	136.49
112.95	142.62
135.31	141.71
134.31	149.51
133.03	147.39
140.11	131.96
124.69	136.38
131.68	127.34
150.95	133.85
137.26	125.14
130.51	141.25
143.15	149.32
118.01	120.92
122.56	134.85
147.97	131.93
135.74	134.22
151.62	143.07
154.82	145.37
145.59	134.32
147.12	126.31
175.86	162.21
140.66	124.09
152.69	153.91
154.38	154.34
132.45	138.70
136.44	150.98
153.24	146.39
154.11	178.30
155.93	168.23
142.53	162.52
148.73	158.86
147.73	152.17
166.79	171.01
144.30	171.49
156.07	189.62
161.70	177.46
152.10	179.98
140.45	156.96
155.56	167.89
174.53	194.78
167.16	192.78
159.48	165.06
173.22	196.60
176.13	151.64
180.31	187.02
185.84	210.99
169.43	219.08
195.25	235.68
174.99	241.44
156.42	187.46
182.08	229.57
182.00	208.44
153.28	215.09
136.72	217.00
130.19	171.08
132.04	178.41
143.89	196.34
133.38	172.11
127.98	154.93
150.45	182.26
133.55	181.74




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
InvoerEU[t] = + 82.0823843016541 + 0.405853219646718InvoerAM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
InvoerEU[t] =  +  82.0823843016541 +  0.405853219646718InvoerAM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]InvoerEU[t] =  +  82.0823843016541 +  0.405853219646718InvoerAM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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
InvoerEU[t] = + 82.0823843016541 + 0.405853219646718InvoerAM[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)82.082384301654110.0504378.16700
InvoerAM0.4058532196467180.0597946.787500

\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) & 82.0823843016541 & 10.050437 & 8.167 & 0 & 0 \tabularnewline
InvoerAM & 0.405853219646718 & 0.059794 & 6.7875 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]82.0823843016541[/C][C]10.050437[/C][C]8.167[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]InvoerAM[/C][C]0.405853219646718[/C][C]0.059794[/C][C]6.7875[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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)82.082384301654110.0504378.16700
InvoerAM0.4058532196467180.0597946.787500







Multiple Linear Regression - Regression Statistics
Multiple R0.662171795257425
R-squared0.438471486434441
Adjusted R-squared0.428954054001127
F-TEST (value)46.0703545317146
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value6.18692874709836e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14.0076440268092
Sum Squared Residuals11576.6313797264

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.662171795257425 \tabularnewline
R-squared & 0.438471486434441 \tabularnewline
Adjusted R-squared & 0.428954054001127 \tabularnewline
F-TEST (value) & 46.0703545317146 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 6.18692874709836e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.0076440268092 \tabularnewline
Sum Squared Residuals & 11576.6313797264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.662171795257425[/C][/ROW]
[ROW][C]R-squared[/C][C]0.438471486434441[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.428954054001127[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]46.0703545317146[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]6.18692874709836e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.0076440268092[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11576.6313797264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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 R0.662171795257425
R-squared0.438471486434441
Adjusted R-squared0.428954054001127
F-TEST (value)46.0703545317146
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value6.18692874709836e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14.0076440268092
Sum Squared Residuals11576.6313797264







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1114.08137.477290251235-23.3972902512352
2112.95139.965170487669-27.0151704876691
3135.31139.595844057791-4.2858440577906
4134.31142.761499171035-8.451499171035
5133.03141.901090345384-8.87109034538395
6140.11135.6387751662354.47122483376492
7124.69137.432646397074-12.7426463970736
8131.68133.763733291467-2.08373329146725
9150.95136.40583775136714.5441622486326
10137.26132.8708562082444.38914379175551
11130.51139.409151576753-8.89915157675312
12143.15142.6843870593020.465612940697883
13118.01131.158155621335-13.1481556213353
14122.56136.811690971014-14.2516909710141
15147.97135.62659956964612.3434004303543
16135.74136.556003442637-0.816003442636669
17151.62140.1478044365111.4721955634899
18154.82141.08126684169813.7387331583024
19145.59136.5965887646018.99341123539866
20147.12133.34570447523113.7742955247689
21175.86147.91583506054827.9441649394517
22140.66132.4447103276158.21528967238457
23152.69144.5472533374818.14274666251944
24154.38144.7217702219299.65822977807134
25132.45138.374225866654-5.92422586665398
26136.44143.358103403916-6.91810340391568
27153.24141.49523712573711.7447628742628
28154.11154.446013364664-0.336013364664014
29155.93150.3590714428225.57092855717844
30142.53148.041649558639-5.51164955863881
31148.73146.5562267747322.17377322526817
32147.73143.8410687352953.88893126470472
33166.79151.48734339343915.3026566065606
34144.3151.68215293887-7.38215293886986
35156.07159.040271811065-2.97027181106488
36161.7154.1050966601617.5949033398392
37152.1155.127846773671-3.02784677367051
38140.45145.785105657403-5.33510565740307
39155.56150.2210813481425.33891865185832
40174.53161.13447442444213.3955255755581
41167.16160.3227679851496.83723201485149
42159.48149.07251673654110.4074832634585
43173.22161.87312728419911.3468727158010
44176.13143.62596652888332.5040334711175
45180.31157.98505343998322.3249465600166
46185.84167.71335511491518.1266448850848
47169.43170.996707661857-1.56670766185720
48195.25177.73387110799317.5161288920073
49174.99180.071585653158-5.08158565315781
50156.42158.163628856628-1.74362885662798
51182.08175.2541079359516.82589206404874
52182166.67842940481615.3215705951839
53153.28169.377353315467-16.0973533154668
54136.72170.152532964992-33.432532964992
55130.19151.515753118815-21.3257531188147
56132.04154.490657218825-22.4506572188252
57143.89161.767605447091-17.8776054470908
58133.38151.933781935051-18.5537819350508
59127.98144.961223621520-16.9812236215202
60150.45156.053192114465-5.60319211446504
61133.55155.842148440249-22.2921484402487

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 114.08 & 137.477290251235 & -23.3972902512352 \tabularnewline
2 & 112.95 & 139.965170487669 & -27.0151704876691 \tabularnewline
3 & 135.31 & 139.595844057791 & -4.2858440577906 \tabularnewline
4 & 134.31 & 142.761499171035 & -8.451499171035 \tabularnewline
5 & 133.03 & 141.901090345384 & -8.87109034538395 \tabularnewline
6 & 140.11 & 135.638775166235 & 4.47122483376492 \tabularnewline
7 & 124.69 & 137.432646397074 & -12.7426463970736 \tabularnewline
8 & 131.68 & 133.763733291467 & -2.08373329146725 \tabularnewline
9 & 150.95 & 136.405837751367 & 14.5441622486326 \tabularnewline
10 & 137.26 & 132.870856208244 & 4.38914379175551 \tabularnewline
11 & 130.51 & 139.409151576753 & -8.89915157675312 \tabularnewline
12 & 143.15 & 142.684387059302 & 0.465612940697883 \tabularnewline
13 & 118.01 & 131.158155621335 & -13.1481556213353 \tabularnewline
14 & 122.56 & 136.811690971014 & -14.2516909710141 \tabularnewline
15 & 147.97 & 135.626599569646 & 12.3434004303543 \tabularnewline
16 & 135.74 & 136.556003442637 & -0.816003442636669 \tabularnewline
17 & 151.62 & 140.14780443651 & 11.4721955634899 \tabularnewline
18 & 154.82 & 141.081266841698 & 13.7387331583024 \tabularnewline
19 & 145.59 & 136.596588764601 & 8.99341123539866 \tabularnewline
20 & 147.12 & 133.345704475231 & 13.7742955247689 \tabularnewline
21 & 175.86 & 147.915835060548 & 27.9441649394517 \tabularnewline
22 & 140.66 & 132.444710327615 & 8.21528967238457 \tabularnewline
23 & 152.69 & 144.547253337481 & 8.14274666251944 \tabularnewline
24 & 154.38 & 144.721770221929 & 9.65822977807134 \tabularnewline
25 & 132.45 & 138.374225866654 & -5.92422586665398 \tabularnewline
26 & 136.44 & 143.358103403916 & -6.91810340391568 \tabularnewline
27 & 153.24 & 141.495237125737 & 11.7447628742628 \tabularnewline
28 & 154.11 & 154.446013364664 & -0.336013364664014 \tabularnewline
29 & 155.93 & 150.359071442822 & 5.57092855717844 \tabularnewline
30 & 142.53 & 148.041649558639 & -5.51164955863881 \tabularnewline
31 & 148.73 & 146.556226774732 & 2.17377322526817 \tabularnewline
32 & 147.73 & 143.841068735295 & 3.88893126470472 \tabularnewline
33 & 166.79 & 151.487343393439 & 15.3026566065606 \tabularnewline
34 & 144.3 & 151.68215293887 & -7.38215293886986 \tabularnewline
35 & 156.07 & 159.040271811065 & -2.97027181106488 \tabularnewline
36 & 161.7 & 154.105096660161 & 7.5949033398392 \tabularnewline
37 & 152.1 & 155.127846773671 & -3.02784677367051 \tabularnewline
38 & 140.45 & 145.785105657403 & -5.33510565740307 \tabularnewline
39 & 155.56 & 150.221081348142 & 5.33891865185832 \tabularnewline
40 & 174.53 & 161.134474424442 & 13.3955255755581 \tabularnewline
41 & 167.16 & 160.322767985149 & 6.83723201485149 \tabularnewline
42 & 159.48 & 149.072516736541 & 10.4074832634585 \tabularnewline
43 & 173.22 & 161.873127284199 & 11.3468727158010 \tabularnewline
44 & 176.13 & 143.625966528883 & 32.5040334711175 \tabularnewline
45 & 180.31 & 157.985053439983 & 22.3249465600166 \tabularnewline
46 & 185.84 & 167.713355114915 & 18.1266448850848 \tabularnewline
47 & 169.43 & 170.996707661857 & -1.56670766185720 \tabularnewline
48 & 195.25 & 177.733871107993 & 17.5161288920073 \tabularnewline
49 & 174.99 & 180.071585653158 & -5.08158565315781 \tabularnewline
50 & 156.42 & 158.163628856628 & -1.74362885662798 \tabularnewline
51 & 182.08 & 175.254107935951 & 6.82589206404874 \tabularnewline
52 & 182 & 166.678429404816 & 15.3215705951839 \tabularnewline
53 & 153.28 & 169.377353315467 & -16.0973533154668 \tabularnewline
54 & 136.72 & 170.152532964992 & -33.432532964992 \tabularnewline
55 & 130.19 & 151.515753118815 & -21.3257531188147 \tabularnewline
56 & 132.04 & 154.490657218825 & -22.4506572188252 \tabularnewline
57 & 143.89 & 161.767605447091 & -17.8776054470908 \tabularnewline
58 & 133.38 & 151.933781935051 & -18.5537819350508 \tabularnewline
59 & 127.98 & 144.961223621520 & -16.9812236215202 \tabularnewline
60 & 150.45 & 156.053192114465 & -5.60319211446504 \tabularnewline
61 & 133.55 & 155.842148440249 & -22.2921484402487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]114.08[/C][C]137.477290251235[/C][C]-23.3972902512352[/C][/ROW]
[ROW][C]2[/C][C]112.95[/C][C]139.965170487669[/C][C]-27.0151704876691[/C][/ROW]
[ROW][C]3[/C][C]135.31[/C][C]139.595844057791[/C][C]-4.2858440577906[/C][/ROW]
[ROW][C]4[/C][C]134.31[/C][C]142.761499171035[/C][C]-8.451499171035[/C][/ROW]
[ROW][C]5[/C][C]133.03[/C][C]141.901090345384[/C][C]-8.87109034538395[/C][/ROW]
[ROW][C]6[/C][C]140.11[/C][C]135.638775166235[/C][C]4.47122483376492[/C][/ROW]
[ROW][C]7[/C][C]124.69[/C][C]137.432646397074[/C][C]-12.7426463970736[/C][/ROW]
[ROW][C]8[/C][C]131.68[/C][C]133.763733291467[/C][C]-2.08373329146725[/C][/ROW]
[ROW][C]9[/C][C]150.95[/C][C]136.405837751367[/C][C]14.5441622486326[/C][/ROW]
[ROW][C]10[/C][C]137.26[/C][C]132.870856208244[/C][C]4.38914379175551[/C][/ROW]
[ROW][C]11[/C][C]130.51[/C][C]139.409151576753[/C][C]-8.89915157675312[/C][/ROW]
[ROW][C]12[/C][C]143.15[/C][C]142.684387059302[/C][C]0.465612940697883[/C][/ROW]
[ROW][C]13[/C][C]118.01[/C][C]131.158155621335[/C][C]-13.1481556213353[/C][/ROW]
[ROW][C]14[/C][C]122.56[/C][C]136.811690971014[/C][C]-14.2516909710141[/C][/ROW]
[ROW][C]15[/C][C]147.97[/C][C]135.626599569646[/C][C]12.3434004303543[/C][/ROW]
[ROW][C]16[/C][C]135.74[/C][C]136.556003442637[/C][C]-0.816003442636669[/C][/ROW]
[ROW][C]17[/C][C]151.62[/C][C]140.14780443651[/C][C]11.4721955634899[/C][/ROW]
[ROW][C]18[/C][C]154.82[/C][C]141.081266841698[/C][C]13.7387331583024[/C][/ROW]
[ROW][C]19[/C][C]145.59[/C][C]136.596588764601[/C][C]8.99341123539866[/C][/ROW]
[ROW][C]20[/C][C]147.12[/C][C]133.345704475231[/C][C]13.7742955247689[/C][/ROW]
[ROW][C]21[/C][C]175.86[/C][C]147.915835060548[/C][C]27.9441649394517[/C][/ROW]
[ROW][C]22[/C][C]140.66[/C][C]132.444710327615[/C][C]8.21528967238457[/C][/ROW]
[ROW][C]23[/C][C]152.69[/C][C]144.547253337481[/C][C]8.14274666251944[/C][/ROW]
[ROW][C]24[/C][C]154.38[/C][C]144.721770221929[/C][C]9.65822977807134[/C][/ROW]
[ROW][C]25[/C][C]132.45[/C][C]138.374225866654[/C][C]-5.92422586665398[/C][/ROW]
[ROW][C]26[/C][C]136.44[/C][C]143.358103403916[/C][C]-6.91810340391568[/C][/ROW]
[ROW][C]27[/C][C]153.24[/C][C]141.495237125737[/C][C]11.7447628742628[/C][/ROW]
[ROW][C]28[/C][C]154.11[/C][C]154.446013364664[/C][C]-0.336013364664014[/C][/ROW]
[ROW][C]29[/C][C]155.93[/C][C]150.359071442822[/C][C]5.57092855717844[/C][/ROW]
[ROW][C]30[/C][C]142.53[/C][C]148.041649558639[/C][C]-5.51164955863881[/C][/ROW]
[ROW][C]31[/C][C]148.73[/C][C]146.556226774732[/C][C]2.17377322526817[/C][/ROW]
[ROW][C]32[/C][C]147.73[/C][C]143.841068735295[/C][C]3.88893126470472[/C][/ROW]
[ROW][C]33[/C][C]166.79[/C][C]151.487343393439[/C][C]15.3026566065606[/C][/ROW]
[ROW][C]34[/C][C]144.3[/C][C]151.68215293887[/C][C]-7.38215293886986[/C][/ROW]
[ROW][C]35[/C][C]156.07[/C][C]159.040271811065[/C][C]-2.97027181106488[/C][/ROW]
[ROW][C]36[/C][C]161.7[/C][C]154.105096660161[/C][C]7.5949033398392[/C][/ROW]
[ROW][C]37[/C][C]152.1[/C][C]155.127846773671[/C][C]-3.02784677367051[/C][/ROW]
[ROW][C]38[/C][C]140.45[/C][C]145.785105657403[/C][C]-5.33510565740307[/C][/ROW]
[ROW][C]39[/C][C]155.56[/C][C]150.221081348142[/C][C]5.33891865185832[/C][/ROW]
[ROW][C]40[/C][C]174.53[/C][C]161.134474424442[/C][C]13.3955255755581[/C][/ROW]
[ROW][C]41[/C][C]167.16[/C][C]160.322767985149[/C][C]6.83723201485149[/C][/ROW]
[ROW][C]42[/C][C]159.48[/C][C]149.072516736541[/C][C]10.4074832634585[/C][/ROW]
[ROW][C]43[/C][C]173.22[/C][C]161.873127284199[/C][C]11.3468727158010[/C][/ROW]
[ROW][C]44[/C][C]176.13[/C][C]143.625966528883[/C][C]32.5040334711175[/C][/ROW]
[ROW][C]45[/C][C]180.31[/C][C]157.985053439983[/C][C]22.3249465600166[/C][/ROW]
[ROW][C]46[/C][C]185.84[/C][C]167.713355114915[/C][C]18.1266448850848[/C][/ROW]
[ROW][C]47[/C][C]169.43[/C][C]170.996707661857[/C][C]-1.56670766185720[/C][/ROW]
[ROW][C]48[/C][C]195.25[/C][C]177.733871107993[/C][C]17.5161288920073[/C][/ROW]
[ROW][C]49[/C][C]174.99[/C][C]180.071585653158[/C][C]-5.08158565315781[/C][/ROW]
[ROW][C]50[/C][C]156.42[/C][C]158.163628856628[/C][C]-1.74362885662798[/C][/ROW]
[ROW][C]51[/C][C]182.08[/C][C]175.254107935951[/C][C]6.82589206404874[/C][/ROW]
[ROW][C]52[/C][C]182[/C][C]166.678429404816[/C][C]15.3215705951839[/C][/ROW]
[ROW][C]53[/C][C]153.28[/C][C]169.377353315467[/C][C]-16.0973533154668[/C][/ROW]
[ROW][C]54[/C][C]136.72[/C][C]170.152532964992[/C][C]-33.432532964992[/C][/ROW]
[ROW][C]55[/C][C]130.19[/C][C]151.515753118815[/C][C]-21.3257531188147[/C][/ROW]
[ROW][C]56[/C][C]132.04[/C][C]154.490657218825[/C][C]-22.4506572188252[/C][/ROW]
[ROW][C]57[/C][C]143.89[/C][C]161.767605447091[/C][C]-17.8776054470908[/C][/ROW]
[ROW][C]58[/C][C]133.38[/C][C]151.933781935051[/C][C]-18.5537819350508[/C][/ROW]
[ROW][C]59[/C][C]127.98[/C][C]144.961223621520[/C][C]-16.9812236215202[/C][/ROW]
[ROW][C]60[/C][C]150.45[/C][C]156.053192114465[/C][C]-5.60319211446504[/C][/ROW]
[ROW][C]61[/C][C]133.55[/C][C]155.842148440249[/C][C]-22.2921484402487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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
1114.08137.477290251235-23.3972902512352
2112.95139.965170487669-27.0151704876691
3135.31139.595844057791-4.2858440577906
4134.31142.761499171035-8.451499171035
5133.03141.901090345384-8.87109034538395
6140.11135.6387751662354.47122483376492
7124.69137.432646397074-12.7426463970736
8131.68133.763733291467-2.08373329146725
9150.95136.40583775136714.5441622486326
10137.26132.8708562082444.38914379175551
11130.51139.409151576753-8.89915157675312
12143.15142.6843870593020.465612940697883
13118.01131.158155621335-13.1481556213353
14122.56136.811690971014-14.2516909710141
15147.97135.62659956964612.3434004303543
16135.74136.556003442637-0.816003442636669
17151.62140.1478044365111.4721955634899
18154.82141.08126684169813.7387331583024
19145.59136.5965887646018.99341123539866
20147.12133.34570447523113.7742955247689
21175.86147.91583506054827.9441649394517
22140.66132.4447103276158.21528967238457
23152.69144.5472533374818.14274666251944
24154.38144.7217702219299.65822977807134
25132.45138.374225866654-5.92422586665398
26136.44143.358103403916-6.91810340391568
27153.24141.49523712573711.7447628742628
28154.11154.446013364664-0.336013364664014
29155.93150.3590714428225.57092855717844
30142.53148.041649558639-5.51164955863881
31148.73146.5562267747322.17377322526817
32147.73143.8410687352953.88893126470472
33166.79151.48734339343915.3026566065606
34144.3151.68215293887-7.38215293886986
35156.07159.040271811065-2.97027181106488
36161.7154.1050966601617.5949033398392
37152.1155.127846773671-3.02784677367051
38140.45145.785105657403-5.33510565740307
39155.56150.2210813481425.33891865185832
40174.53161.13447442444213.3955255755581
41167.16160.3227679851496.83723201485149
42159.48149.07251673654110.4074832634585
43173.22161.87312728419911.3468727158010
44176.13143.62596652888332.5040334711175
45180.31157.98505343998322.3249465600166
46185.84167.71335511491518.1266448850848
47169.43170.996707661857-1.56670766185720
48195.25177.73387110799317.5161288920073
49174.99180.071585653158-5.08158565315781
50156.42158.163628856628-1.74362885662798
51182.08175.2541079359516.82589206404874
52182166.67842940481615.3215705951839
53153.28169.377353315467-16.0973533154668
54136.72170.152532964992-33.432532964992
55130.19151.515753118815-21.3257531188147
56132.04154.490657218825-22.4506572188252
57143.89161.767605447091-17.8776054470908
58133.38151.933781935051-18.5537819350508
59127.98144.961223621520-16.9812236215202
60150.45156.053192114465-5.60319211446504
61133.55155.842148440249-22.2921484402487







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.3261894041131740.6523788082263490.673810595886826
60.533955551110650.93208889777870.46604444888935
70.4016833451838350.803366690367670.598316654816165
80.2874838741547230.5749677483094450.712516125845277
90.4449469768994850.8898939537989690.555053023100515
100.3344277567840600.6688555135681190.66557224321594
110.2456672233573780.4913344467147560.754332776642622
120.2454554562849120.4909109125698230.754544543715088
130.2558821709263440.5117643418526880.744117829073656
140.2286070122015520.4572140244031050.771392987798448
150.2706528010469520.5413056020939040.729347198953048
160.2071735493071490.4143470986142980.792826450692851
170.2472993059053330.4945986118106670.752700694094667
180.2939799288096910.5879598576193830.706020071190309
190.2649753526014410.5299507052028820.735024647398559
200.2604983268258260.5209966536516520.739501673174174
210.4890716729358620.9781433458717230.510928327064138
220.4467929376824330.8935858753648670.553207062317567
230.3841358203505930.7682716407011870.615864179649407
240.330852284687710.661704569375420.66914771531229
250.2755466281888830.5510932563777660.724453371811117
260.2375204773043890.4750409546087780.762479522695611
270.2124677418857580.4249354837715170.787532258114242
280.1687500333361410.3375000666722810.83124996666386
290.1278373778763340.2556747557526690.872162622123666
300.1011384161004870.2022768322009740.898861583899513
310.07165341285565180.1433068257113040.928346587144348
320.05023573305082960.1004714661016590.94976426694917
330.04854252423797130.09708504847594250.951457475762029
340.03965264688544780.07930529377089570.960347353114552
350.02801881351823690.05603762703647370.971981186481763
360.01977078502202630.03954157004405270.980229214977974
370.01296258663400420.02592517326800840.987037413365996
380.008385779492598330.01677155898519670.991614220507402
390.005282974079980740.01056594815996150.99471702592002
400.004417986548855280.008835973097710560.995582013451145
410.002730569662325260.005461139324650510.997269430337675
420.002193127961576510.004386255923153020.997806872038423
430.001636189910974580.003272379821949160.998363810089025
440.05500377237204410.1100075447440880.944996227627956
450.1815388921186530.3630777842373050.818461107881347
460.2731318582087460.5462637164174930.726868141791254
470.2263600099610550.452720019922110.773639990038945
480.2820695785350980.5641391570701950.717930421464902
490.2404646256848580.4809292513697150.759535374315143
500.2242937870683090.4485875741366190.77570621293169
510.2364747936613680.4729495873227360.763525206338632
520.89519518532790.2096096293441980.104804814672099
530.9012832993283060.1974334013433870.0987167006716937
540.9433033006195550.1133933987608900.0566966993804448
550.8967304964191420.2065390071617150.103269503580857
560.8358650267201330.3282699465597340.164134973279867

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.326189404113174 & 0.652378808226349 & 0.673810595886826 \tabularnewline
6 & 0.53395555111065 & 0.9320888977787 & 0.46604444888935 \tabularnewline
7 & 0.401683345183835 & 0.80336669036767 & 0.598316654816165 \tabularnewline
8 & 0.287483874154723 & 0.574967748309445 & 0.712516125845277 \tabularnewline
9 & 0.444946976899485 & 0.889893953798969 & 0.555053023100515 \tabularnewline
10 & 0.334427756784060 & 0.668855513568119 & 0.66557224321594 \tabularnewline
11 & 0.245667223357378 & 0.491334446714756 & 0.754332776642622 \tabularnewline
12 & 0.245455456284912 & 0.490910912569823 & 0.754544543715088 \tabularnewline
13 & 0.255882170926344 & 0.511764341852688 & 0.744117829073656 \tabularnewline
14 & 0.228607012201552 & 0.457214024403105 & 0.771392987798448 \tabularnewline
15 & 0.270652801046952 & 0.541305602093904 & 0.729347198953048 \tabularnewline
16 & 0.207173549307149 & 0.414347098614298 & 0.792826450692851 \tabularnewline
17 & 0.247299305905333 & 0.494598611810667 & 0.752700694094667 \tabularnewline
18 & 0.293979928809691 & 0.587959857619383 & 0.706020071190309 \tabularnewline
19 & 0.264975352601441 & 0.529950705202882 & 0.735024647398559 \tabularnewline
20 & 0.260498326825826 & 0.520996653651652 & 0.739501673174174 \tabularnewline
21 & 0.489071672935862 & 0.978143345871723 & 0.510928327064138 \tabularnewline
22 & 0.446792937682433 & 0.893585875364867 & 0.553207062317567 \tabularnewline
23 & 0.384135820350593 & 0.768271640701187 & 0.615864179649407 \tabularnewline
24 & 0.33085228468771 & 0.66170456937542 & 0.66914771531229 \tabularnewline
25 & 0.275546628188883 & 0.551093256377766 & 0.724453371811117 \tabularnewline
26 & 0.237520477304389 & 0.475040954608778 & 0.762479522695611 \tabularnewline
27 & 0.212467741885758 & 0.424935483771517 & 0.787532258114242 \tabularnewline
28 & 0.168750033336141 & 0.337500066672281 & 0.83124996666386 \tabularnewline
29 & 0.127837377876334 & 0.255674755752669 & 0.872162622123666 \tabularnewline
30 & 0.101138416100487 & 0.202276832200974 & 0.898861583899513 \tabularnewline
31 & 0.0716534128556518 & 0.143306825711304 & 0.928346587144348 \tabularnewline
32 & 0.0502357330508296 & 0.100471466101659 & 0.94976426694917 \tabularnewline
33 & 0.0485425242379713 & 0.0970850484759425 & 0.951457475762029 \tabularnewline
34 & 0.0396526468854478 & 0.0793052937708957 & 0.960347353114552 \tabularnewline
35 & 0.0280188135182369 & 0.0560376270364737 & 0.971981186481763 \tabularnewline
36 & 0.0197707850220263 & 0.0395415700440527 & 0.980229214977974 \tabularnewline
37 & 0.0129625866340042 & 0.0259251732680084 & 0.987037413365996 \tabularnewline
38 & 0.00838577949259833 & 0.0167715589851967 & 0.991614220507402 \tabularnewline
39 & 0.00528297407998074 & 0.0105659481599615 & 0.99471702592002 \tabularnewline
40 & 0.00441798654885528 & 0.00883597309771056 & 0.995582013451145 \tabularnewline
41 & 0.00273056966232526 & 0.00546113932465051 & 0.997269430337675 \tabularnewline
42 & 0.00219312796157651 & 0.00438625592315302 & 0.997806872038423 \tabularnewline
43 & 0.00163618991097458 & 0.00327237982194916 & 0.998363810089025 \tabularnewline
44 & 0.0550037723720441 & 0.110007544744088 & 0.944996227627956 \tabularnewline
45 & 0.181538892118653 & 0.363077784237305 & 0.818461107881347 \tabularnewline
46 & 0.273131858208746 & 0.546263716417493 & 0.726868141791254 \tabularnewline
47 & 0.226360009961055 & 0.45272001992211 & 0.773639990038945 \tabularnewline
48 & 0.282069578535098 & 0.564139157070195 & 0.717930421464902 \tabularnewline
49 & 0.240464625684858 & 0.480929251369715 & 0.759535374315143 \tabularnewline
50 & 0.224293787068309 & 0.448587574136619 & 0.77570621293169 \tabularnewline
51 & 0.236474793661368 & 0.472949587322736 & 0.763525206338632 \tabularnewline
52 & 0.8951951853279 & 0.209609629344198 & 0.104804814672099 \tabularnewline
53 & 0.901283299328306 & 0.197433401343387 & 0.0987167006716937 \tabularnewline
54 & 0.943303300619555 & 0.113393398760890 & 0.0566966993804448 \tabularnewline
55 & 0.896730496419142 & 0.206539007161715 & 0.103269503580857 \tabularnewline
56 & 0.835865026720133 & 0.328269946559734 & 0.164134973279867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]5[/C][C]0.326189404113174[/C][C]0.652378808226349[/C][C]0.673810595886826[/C][/ROW]
[ROW][C]6[/C][C]0.53395555111065[/C][C]0.9320888977787[/C][C]0.46604444888935[/C][/ROW]
[ROW][C]7[/C][C]0.401683345183835[/C][C]0.80336669036767[/C][C]0.598316654816165[/C][/ROW]
[ROW][C]8[/C][C]0.287483874154723[/C][C]0.574967748309445[/C][C]0.712516125845277[/C][/ROW]
[ROW][C]9[/C][C]0.444946976899485[/C][C]0.889893953798969[/C][C]0.555053023100515[/C][/ROW]
[ROW][C]10[/C][C]0.334427756784060[/C][C]0.668855513568119[/C][C]0.66557224321594[/C][/ROW]
[ROW][C]11[/C][C]0.245667223357378[/C][C]0.491334446714756[/C][C]0.754332776642622[/C][/ROW]
[ROW][C]12[/C][C]0.245455456284912[/C][C]0.490910912569823[/C][C]0.754544543715088[/C][/ROW]
[ROW][C]13[/C][C]0.255882170926344[/C][C]0.511764341852688[/C][C]0.744117829073656[/C][/ROW]
[ROW][C]14[/C][C]0.228607012201552[/C][C]0.457214024403105[/C][C]0.771392987798448[/C][/ROW]
[ROW][C]15[/C][C]0.270652801046952[/C][C]0.541305602093904[/C][C]0.729347198953048[/C][/ROW]
[ROW][C]16[/C][C]0.207173549307149[/C][C]0.414347098614298[/C][C]0.792826450692851[/C][/ROW]
[ROW][C]17[/C][C]0.247299305905333[/C][C]0.494598611810667[/C][C]0.752700694094667[/C][/ROW]
[ROW][C]18[/C][C]0.293979928809691[/C][C]0.587959857619383[/C][C]0.706020071190309[/C][/ROW]
[ROW][C]19[/C][C]0.264975352601441[/C][C]0.529950705202882[/C][C]0.735024647398559[/C][/ROW]
[ROW][C]20[/C][C]0.260498326825826[/C][C]0.520996653651652[/C][C]0.739501673174174[/C][/ROW]
[ROW][C]21[/C][C]0.489071672935862[/C][C]0.978143345871723[/C][C]0.510928327064138[/C][/ROW]
[ROW][C]22[/C][C]0.446792937682433[/C][C]0.893585875364867[/C][C]0.553207062317567[/C][/ROW]
[ROW][C]23[/C][C]0.384135820350593[/C][C]0.768271640701187[/C][C]0.615864179649407[/C][/ROW]
[ROW][C]24[/C][C]0.33085228468771[/C][C]0.66170456937542[/C][C]0.66914771531229[/C][/ROW]
[ROW][C]25[/C][C]0.275546628188883[/C][C]0.551093256377766[/C][C]0.724453371811117[/C][/ROW]
[ROW][C]26[/C][C]0.237520477304389[/C][C]0.475040954608778[/C][C]0.762479522695611[/C][/ROW]
[ROW][C]27[/C][C]0.212467741885758[/C][C]0.424935483771517[/C][C]0.787532258114242[/C][/ROW]
[ROW][C]28[/C][C]0.168750033336141[/C][C]0.337500066672281[/C][C]0.83124996666386[/C][/ROW]
[ROW][C]29[/C][C]0.127837377876334[/C][C]0.255674755752669[/C][C]0.872162622123666[/C][/ROW]
[ROW][C]30[/C][C]0.101138416100487[/C][C]0.202276832200974[/C][C]0.898861583899513[/C][/ROW]
[ROW][C]31[/C][C]0.0716534128556518[/C][C]0.143306825711304[/C][C]0.928346587144348[/C][/ROW]
[ROW][C]32[/C][C]0.0502357330508296[/C][C]0.100471466101659[/C][C]0.94976426694917[/C][/ROW]
[ROW][C]33[/C][C]0.0485425242379713[/C][C]0.0970850484759425[/C][C]0.951457475762029[/C][/ROW]
[ROW][C]34[/C][C]0.0396526468854478[/C][C]0.0793052937708957[/C][C]0.960347353114552[/C][/ROW]
[ROW][C]35[/C][C]0.0280188135182369[/C][C]0.0560376270364737[/C][C]0.971981186481763[/C][/ROW]
[ROW][C]36[/C][C]0.0197707850220263[/C][C]0.0395415700440527[/C][C]0.980229214977974[/C][/ROW]
[ROW][C]37[/C][C]0.0129625866340042[/C][C]0.0259251732680084[/C][C]0.987037413365996[/C][/ROW]
[ROW][C]38[/C][C]0.00838577949259833[/C][C]0.0167715589851967[/C][C]0.991614220507402[/C][/ROW]
[ROW][C]39[/C][C]0.00528297407998074[/C][C]0.0105659481599615[/C][C]0.99471702592002[/C][/ROW]
[ROW][C]40[/C][C]0.00441798654885528[/C][C]0.00883597309771056[/C][C]0.995582013451145[/C][/ROW]
[ROW][C]41[/C][C]0.00273056966232526[/C][C]0.00546113932465051[/C][C]0.997269430337675[/C][/ROW]
[ROW][C]42[/C][C]0.00219312796157651[/C][C]0.00438625592315302[/C][C]0.997806872038423[/C][/ROW]
[ROW][C]43[/C][C]0.00163618991097458[/C][C]0.00327237982194916[/C][C]0.998363810089025[/C][/ROW]
[ROW][C]44[/C][C]0.0550037723720441[/C][C]0.110007544744088[/C][C]0.944996227627956[/C][/ROW]
[ROW][C]45[/C][C]0.181538892118653[/C][C]0.363077784237305[/C][C]0.818461107881347[/C][/ROW]
[ROW][C]46[/C][C]0.273131858208746[/C][C]0.546263716417493[/C][C]0.726868141791254[/C][/ROW]
[ROW][C]47[/C][C]0.226360009961055[/C][C]0.45272001992211[/C][C]0.773639990038945[/C][/ROW]
[ROW][C]48[/C][C]0.282069578535098[/C][C]0.564139157070195[/C][C]0.717930421464902[/C][/ROW]
[ROW][C]49[/C][C]0.240464625684858[/C][C]0.480929251369715[/C][C]0.759535374315143[/C][/ROW]
[ROW][C]50[/C][C]0.224293787068309[/C][C]0.448587574136619[/C][C]0.77570621293169[/C][/ROW]
[ROW][C]51[/C][C]0.236474793661368[/C][C]0.472949587322736[/C][C]0.763525206338632[/C][/ROW]
[ROW][C]52[/C][C]0.8951951853279[/C][C]0.209609629344198[/C][C]0.104804814672099[/C][/ROW]
[ROW][C]53[/C][C]0.901283299328306[/C][C]0.197433401343387[/C][C]0.0987167006716937[/C][/ROW]
[ROW][C]54[/C][C]0.943303300619555[/C][C]0.113393398760890[/C][C]0.0566966993804448[/C][/ROW]
[ROW][C]55[/C][C]0.896730496419142[/C][C]0.206539007161715[/C][C]0.103269503580857[/C][/ROW]
[ROW][C]56[/C][C]0.835865026720133[/C][C]0.328269946559734[/C][C]0.164134973279867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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
50.3261894041131740.6523788082263490.673810595886826
60.533955551110650.93208889777870.46604444888935
70.4016833451838350.803366690367670.598316654816165
80.2874838741547230.5749677483094450.712516125845277
90.4449469768994850.8898939537989690.555053023100515
100.3344277567840600.6688555135681190.66557224321594
110.2456672233573780.4913344467147560.754332776642622
120.2454554562849120.4909109125698230.754544543715088
130.2558821709263440.5117643418526880.744117829073656
140.2286070122015520.4572140244031050.771392987798448
150.2706528010469520.5413056020939040.729347198953048
160.2071735493071490.4143470986142980.792826450692851
170.2472993059053330.4945986118106670.752700694094667
180.2939799288096910.5879598576193830.706020071190309
190.2649753526014410.5299507052028820.735024647398559
200.2604983268258260.5209966536516520.739501673174174
210.4890716729358620.9781433458717230.510928327064138
220.4467929376824330.8935858753648670.553207062317567
230.3841358203505930.7682716407011870.615864179649407
240.330852284687710.661704569375420.66914771531229
250.2755466281888830.5510932563777660.724453371811117
260.2375204773043890.4750409546087780.762479522695611
270.2124677418857580.4249354837715170.787532258114242
280.1687500333361410.3375000666722810.83124996666386
290.1278373778763340.2556747557526690.872162622123666
300.1011384161004870.2022768322009740.898861583899513
310.07165341285565180.1433068257113040.928346587144348
320.05023573305082960.1004714661016590.94976426694917
330.04854252423797130.09708504847594250.951457475762029
340.03965264688544780.07930529377089570.960347353114552
350.02801881351823690.05603762703647370.971981186481763
360.01977078502202630.03954157004405270.980229214977974
370.01296258663400420.02592517326800840.987037413365996
380.008385779492598330.01677155898519670.991614220507402
390.005282974079980740.01056594815996150.99471702592002
400.004417986548855280.008835973097710560.995582013451145
410.002730569662325260.005461139324650510.997269430337675
420.002193127961576510.004386255923153020.997806872038423
430.001636189910974580.003272379821949160.998363810089025
440.05500377237204410.1100075447440880.944996227627956
450.1815388921186530.3630777842373050.818461107881347
460.2731318582087460.5462637164174930.726868141791254
470.2263600099610550.452720019922110.773639990038945
480.2820695785350980.5641391570701950.717930421464902
490.2404646256848580.4809292513697150.759535374315143
500.2242937870683090.4485875741366190.77570621293169
510.2364747936613680.4729495873227360.763525206338632
520.89519518532790.2096096293441980.104804814672099
530.9012832993283060.1974334013433870.0987167006716937
540.9433033006195550.1133933987608900.0566966993804448
550.8967304964191420.2065390071617150.103269503580857
560.8358650267201330.3282699465597340.164134973279867







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0769230769230769NOK
5% type I error level80.153846153846154NOK
10% type I error level110.211538461538462NOK

\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 & 4 & 0.0769230769230769 & NOK \tabularnewline
5% type I error level & 8 & 0.153846153846154 & NOK \tabularnewline
10% type I error level & 11 & 0.211538461538462 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]4[/C][C]0.0769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.153846153846154[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.211538461538462[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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 level40.0769230769230769NOK
5% type I error level80.153846153846154NOK
10% type I error level110.211538461538462NOK



Parameters (Session):
Parameters (R input):
par1 = 1 ; 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')
}