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Description of Statistical Computation

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 Sat, 07 Jun 2025 21:34:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59206, Retrieved Sat, 07 Jun 2025 21:34:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact281

Tree of Dependent Computations

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]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





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:  
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:  
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:  
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:  
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:  
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:  
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:  
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


Figures (Output of Computation)

Figure 1
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}