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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 03:20:13 -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/19/t125862651290sv808dkcw2y0u.htm/, Retrieved Thu, 28 Mar 2024 22:18:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57683, Retrieved Thu, 28 Mar 2024 22:18:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
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]
-    D      [Multiple Regression] [] [2009-11-19 10:20:13] [8551abdd6804649d94d88b1829ac2b1a] [Current]
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Dataseries X:
110.5	55
110.8	48.7
104.2	70.3
88.9	94.8
89.8	58.5
90	62.4
93.9	56.7
91.3	65.1
87.8	114.4
99.7	50.7
73.5	44.5
79.2	72
96.9	61.2
95.2	68.4
95.6	78.7
89.7	64.1
92.8	64.6
88	71.9
101.1	71
92.7	76.4
95.8	117.3
103.8	66.1
81.8	57.3
87.1	75
105.9	63.8
108.1	62.2
102.6	75.4
93.7	58
103.5	62.1
100.6	99.2
113.3	70.7
102.4	73.3
102.1	111.2
106.9	68.9
87.3	57.6
93.1	72.9
109.1	75.9
120.3	79.4
104.9	96.9
92.6	75.2
109.8	60.3
111.4	88.9
117.9	90.5
121.6	79.9
117.8	116.3
124.2	95.2
106.8	81.5
102.7	89.1
116.8	76
113.6	100.5
96.1	83.9
85	75.1
83.2	69.5
84.9	95.1
83	90.1
79.6	78.4
83.2	113.8
83.8	73.6
82.8	56.5
71.4	97.7




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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=57683&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]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=57683&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57683&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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
prod[t] = + 91.162508541583 + 0.0856714386324468`inv `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
prod[t] =  +  91.162508541583 +  0.0856714386324468`inv
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57683&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]prod[t] =  +  91.162508541583 +  0.0856714386324468`inv
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57683&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57683&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
prod[t] = + 91.162508541583 + 0.0856714386324468`inv `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)91.1625085415837.27637812.528600
`inv `0.08567143863244680.0929240.92190.3603740.180187

\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) & 91.162508541583 & 7.276378 & 12.5286 & 0 & 0 \tabularnewline
`inv
` & 0.0856714386324468 & 0.092924 & 0.9219 & 0.360374 & 0.180187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57683&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]91.162508541583[/C][C]7.276378[/C][C]12.5286[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`inv
`[/C][C]0.0856714386324468[/C][C]0.092924[/C][C]0.9219[/C][C]0.360374[/C][C]0.180187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57683&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57683&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)91.1625085415837.27637812.528600
`inv `0.08567143863244680.0929240.92190.3603740.180187







Multiple Linear Regression - Regression Statistics
Multiple R0.120180468187982
R-squared0.0144433449338825
Adjusted R-squared-0.00254901118794715
F-TEST (value)0.849990715256226
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.360374230373602
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.5822570001917
Sum Squared Residuals9182.16509069469

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.120180468187982 \tabularnewline
R-squared & 0.0144433449338825 \tabularnewline
Adjusted R-squared & -0.00254901118794715 \tabularnewline
F-TEST (value) & 0.849990715256226 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.360374230373602 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 12.5822570001917 \tabularnewline
Sum Squared Residuals & 9182.16509069469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57683&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.120180468187982[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0144433449338825[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00254901118794715[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.849990715256226[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.360374230373602[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]12.5822570001917[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9182.16509069469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57683&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57683&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 R0.120180468187982
R-squared0.0144433449338825
Adjusted R-squared-0.00254901118794715
F-TEST (value)0.849990715256226
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.360374230373602
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.5822570001917
Sum Squared Residuals9182.16509069469







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1110.595.874437666368114.6255623336319
2110.895.334707602983215.4652923970168
3104.297.1852106774447.01478932255595
488.999.284160923939-10.384160923939
589.896.1742877015812-6.37428770158119
69096.5084063122477-6.50840631224773
793.996.0200791120428-2.12007911204278
891.396.7397191965553-5.43971919655534
987.8100.963321121135-13.1633211211350
1099.795.5060504802484.1939495197519
1173.594.974887560727-21.4748875607269
1279.297.3308521231192-18.1308521231192
1396.996.40560058588880.494399414111213
1495.297.0224349440424-1.82243494404241
1595.697.9048507619566-2.30485076195662
1689.796.6540477579229-6.95404775792288
1792.896.696883477239-3.89688347723911
188897.322284979256-9.32228497925597
19101.197.24518068448683.85481931551322
2092.797.707806453102-5.00780645310198
2195.8101.211768293169-5.41176829316906
22103.896.82539063518786.97460936481222
2381.896.0714819752223-14.2714819752223
2487.197.5878664390166-10.4878664390166
25105.996.62834632633329.27165367366685
26108.196.491272024521211.6087279754788
27102.697.62213501446954.97786498553046
2893.796.131451982265-2.43145198226496
29103.596.4827048806587.017295119342
30100.699.66111525392180.938884746078225
31113.397.21947925289716.0805207471030
32102.497.44222499334144.95777500665861
33102.1100.6891725175111.41082748248886
34106.997.06527066335869.83472933664137
3587.396.097183406812-8.79718340681199
3693.197.4079564178884-4.30795641788843
37109.197.664970733785811.4350292662142
38120.397.964820768999322.3351792310007
39104.999.46407094506715.43592905493286
4092.697.605000726743-5.00500072674305
41109.896.328496291119613.4715037088804
42111.498.778699436007612.6213005639924
43117.998.915773737819518.9842262621805
44121.698.007656488315523.5923435116844
45117.8101.12609685453716.6739031454634
46124.299.31842949939224.8815705006080
47106.898.14473079012758.65526920987254
48102.798.7958337237343.90416627626595
49116.897.67353787764919.126462122351
50113.699.77248812414413.8275118758560
5196.198.3503422428453-2.25034224284534
528597.5964335828798-12.5964335828798
5383.297.116673526538-13.9166735265381
5484.999.3098623555287-14.4098623555287
558398.8815051623665-15.8815051623665
5679.697.8791493303669-18.2791493303669
5783.2100.911918257955-17.7119182579555
5883.897.4679264249311-13.6679264249311
5982.896.0029448243163-13.2029448243163
6071.499.5326080959731-28.1326080959731

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 110.5 & 95.8744376663681 & 14.6255623336319 \tabularnewline
2 & 110.8 & 95.3347076029832 & 15.4652923970168 \tabularnewline
3 & 104.2 & 97.185210677444 & 7.01478932255595 \tabularnewline
4 & 88.9 & 99.284160923939 & -10.384160923939 \tabularnewline
5 & 89.8 & 96.1742877015812 & -6.37428770158119 \tabularnewline
6 & 90 & 96.5084063122477 & -6.50840631224773 \tabularnewline
7 & 93.9 & 96.0200791120428 & -2.12007911204278 \tabularnewline
8 & 91.3 & 96.7397191965553 & -5.43971919655534 \tabularnewline
9 & 87.8 & 100.963321121135 & -13.1633211211350 \tabularnewline
10 & 99.7 & 95.506050480248 & 4.1939495197519 \tabularnewline
11 & 73.5 & 94.974887560727 & -21.4748875607269 \tabularnewline
12 & 79.2 & 97.3308521231192 & -18.1308521231192 \tabularnewline
13 & 96.9 & 96.4056005858888 & 0.494399414111213 \tabularnewline
14 & 95.2 & 97.0224349440424 & -1.82243494404241 \tabularnewline
15 & 95.6 & 97.9048507619566 & -2.30485076195662 \tabularnewline
16 & 89.7 & 96.6540477579229 & -6.95404775792288 \tabularnewline
17 & 92.8 & 96.696883477239 & -3.89688347723911 \tabularnewline
18 & 88 & 97.322284979256 & -9.32228497925597 \tabularnewline
19 & 101.1 & 97.2451806844868 & 3.85481931551322 \tabularnewline
20 & 92.7 & 97.707806453102 & -5.00780645310198 \tabularnewline
21 & 95.8 & 101.211768293169 & -5.41176829316906 \tabularnewline
22 & 103.8 & 96.8253906351878 & 6.97460936481222 \tabularnewline
23 & 81.8 & 96.0714819752223 & -14.2714819752223 \tabularnewline
24 & 87.1 & 97.5878664390166 & -10.4878664390166 \tabularnewline
25 & 105.9 & 96.6283463263332 & 9.27165367366685 \tabularnewline
26 & 108.1 & 96.4912720245212 & 11.6087279754788 \tabularnewline
27 & 102.6 & 97.6221350144695 & 4.97786498553046 \tabularnewline
28 & 93.7 & 96.131451982265 & -2.43145198226496 \tabularnewline
29 & 103.5 & 96.482704880658 & 7.017295119342 \tabularnewline
30 & 100.6 & 99.6611152539218 & 0.938884746078225 \tabularnewline
31 & 113.3 & 97.219479252897 & 16.0805207471030 \tabularnewline
32 & 102.4 & 97.4422249933414 & 4.95777500665861 \tabularnewline
33 & 102.1 & 100.689172517511 & 1.41082748248886 \tabularnewline
34 & 106.9 & 97.0652706633586 & 9.83472933664137 \tabularnewline
35 & 87.3 & 96.097183406812 & -8.79718340681199 \tabularnewline
36 & 93.1 & 97.4079564178884 & -4.30795641788843 \tabularnewline
37 & 109.1 & 97.6649707337858 & 11.4350292662142 \tabularnewline
38 & 120.3 & 97.9648207689993 & 22.3351792310007 \tabularnewline
39 & 104.9 & 99.4640709450671 & 5.43592905493286 \tabularnewline
40 & 92.6 & 97.605000726743 & -5.00500072674305 \tabularnewline
41 & 109.8 & 96.3284962911196 & 13.4715037088804 \tabularnewline
42 & 111.4 & 98.7786994360076 & 12.6213005639924 \tabularnewline
43 & 117.9 & 98.9157737378195 & 18.9842262621805 \tabularnewline
44 & 121.6 & 98.0076564883155 & 23.5923435116844 \tabularnewline
45 & 117.8 & 101.126096854537 & 16.6739031454634 \tabularnewline
46 & 124.2 & 99.318429499392 & 24.8815705006080 \tabularnewline
47 & 106.8 & 98.1447307901275 & 8.65526920987254 \tabularnewline
48 & 102.7 & 98.795833723734 & 3.90416627626595 \tabularnewline
49 & 116.8 & 97.673537877649 & 19.126462122351 \tabularnewline
50 & 113.6 & 99.772488124144 & 13.8275118758560 \tabularnewline
51 & 96.1 & 98.3503422428453 & -2.25034224284534 \tabularnewline
52 & 85 & 97.5964335828798 & -12.5964335828798 \tabularnewline
53 & 83.2 & 97.116673526538 & -13.9166735265381 \tabularnewline
54 & 84.9 & 99.3098623555287 & -14.4098623555287 \tabularnewline
55 & 83 & 98.8815051623665 & -15.8815051623665 \tabularnewline
56 & 79.6 & 97.8791493303669 & -18.2791493303669 \tabularnewline
57 & 83.2 & 100.911918257955 & -17.7119182579555 \tabularnewline
58 & 83.8 & 97.4679264249311 & -13.6679264249311 \tabularnewline
59 & 82.8 & 96.0029448243163 & -13.2029448243163 \tabularnewline
60 & 71.4 & 99.5326080959731 & -28.1326080959731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57683&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]110.5[/C][C]95.8744376663681[/C][C]14.6255623336319[/C][/ROW]
[ROW][C]2[/C][C]110.8[/C][C]95.3347076029832[/C][C]15.4652923970168[/C][/ROW]
[ROW][C]3[/C][C]104.2[/C][C]97.185210677444[/C][C]7.01478932255595[/C][/ROW]
[ROW][C]4[/C][C]88.9[/C][C]99.284160923939[/C][C]-10.384160923939[/C][/ROW]
[ROW][C]5[/C][C]89.8[/C][C]96.1742877015812[/C][C]-6.37428770158119[/C][/ROW]
[ROW][C]6[/C][C]90[/C][C]96.5084063122477[/C][C]-6.50840631224773[/C][/ROW]
[ROW][C]7[/C][C]93.9[/C][C]96.0200791120428[/C][C]-2.12007911204278[/C][/ROW]
[ROW][C]8[/C][C]91.3[/C][C]96.7397191965553[/C][C]-5.43971919655534[/C][/ROW]
[ROW][C]9[/C][C]87.8[/C][C]100.963321121135[/C][C]-13.1633211211350[/C][/ROW]
[ROW][C]10[/C][C]99.7[/C][C]95.506050480248[/C][C]4.1939495197519[/C][/ROW]
[ROW][C]11[/C][C]73.5[/C][C]94.974887560727[/C][C]-21.4748875607269[/C][/ROW]
[ROW][C]12[/C][C]79.2[/C][C]97.3308521231192[/C][C]-18.1308521231192[/C][/ROW]
[ROW][C]13[/C][C]96.9[/C][C]96.4056005858888[/C][C]0.494399414111213[/C][/ROW]
[ROW][C]14[/C][C]95.2[/C][C]97.0224349440424[/C][C]-1.82243494404241[/C][/ROW]
[ROW][C]15[/C][C]95.6[/C][C]97.9048507619566[/C][C]-2.30485076195662[/C][/ROW]
[ROW][C]16[/C][C]89.7[/C][C]96.6540477579229[/C][C]-6.95404775792288[/C][/ROW]
[ROW][C]17[/C][C]92.8[/C][C]96.696883477239[/C][C]-3.89688347723911[/C][/ROW]
[ROW][C]18[/C][C]88[/C][C]97.322284979256[/C][C]-9.32228497925597[/C][/ROW]
[ROW][C]19[/C][C]101.1[/C][C]97.2451806844868[/C][C]3.85481931551322[/C][/ROW]
[ROW][C]20[/C][C]92.7[/C][C]97.707806453102[/C][C]-5.00780645310198[/C][/ROW]
[ROW][C]21[/C][C]95.8[/C][C]101.211768293169[/C][C]-5.41176829316906[/C][/ROW]
[ROW][C]22[/C][C]103.8[/C][C]96.8253906351878[/C][C]6.97460936481222[/C][/ROW]
[ROW][C]23[/C][C]81.8[/C][C]96.0714819752223[/C][C]-14.2714819752223[/C][/ROW]
[ROW][C]24[/C][C]87.1[/C][C]97.5878664390166[/C][C]-10.4878664390166[/C][/ROW]
[ROW][C]25[/C][C]105.9[/C][C]96.6283463263332[/C][C]9.27165367366685[/C][/ROW]
[ROW][C]26[/C][C]108.1[/C][C]96.4912720245212[/C][C]11.6087279754788[/C][/ROW]
[ROW][C]27[/C][C]102.6[/C][C]97.6221350144695[/C][C]4.97786498553046[/C][/ROW]
[ROW][C]28[/C][C]93.7[/C][C]96.131451982265[/C][C]-2.43145198226496[/C][/ROW]
[ROW][C]29[/C][C]103.5[/C][C]96.482704880658[/C][C]7.017295119342[/C][/ROW]
[ROW][C]30[/C][C]100.6[/C][C]99.6611152539218[/C][C]0.938884746078225[/C][/ROW]
[ROW][C]31[/C][C]113.3[/C][C]97.219479252897[/C][C]16.0805207471030[/C][/ROW]
[ROW][C]32[/C][C]102.4[/C][C]97.4422249933414[/C][C]4.95777500665861[/C][/ROW]
[ROW][C]33[/C][C]102.1[/C][C]100.689172517511[/C][C]1.41082748248886[/C][/ROW]
[ROW][C]34[/C][C]106.9[/C][C]97.0652706633586[/C][C]9.83472933664137[/C][/ROW]
[ROW][C]35[/C][C]87.3[/C][C]96.097183406812[/C][C]-8.79718340681199[/C][/ROW]
[ROW][C]36[/C][C]93.1[/C][C]97.4079564178884[/C][C]-4.30795641788843[/C][/ROW]
[ROW][C]37[/C][C]109.1[/C][C]97.6649707337858[/C][C]11.4350292662142[/C][/ROW]
[ROW][C]38[/C][C]120.3[/C][C]97.9648207689993[/C][C]22.3351792310007[/C][/ROW]
[ROW][C]39[/C][C]104.9[/C][C]99.4640709450671[/C][C]5.43592905493286[/C][/ROW]
[ROW][C]40[/C][C]92.6[/C][C]97.605000726743[/C][C]-5.00500072674305[/C][/ROW]
[ROW][C]41[/C][C]109.8[/C][C]96.3284962911196[/C][C]13.4715037088804[/C][/ROW]
[ROW][C]42[/C][C]111.4[/C][C]98.7786994360076[/C][C]12.6213005639924[/C][/ROW]
[ROW][C]43[/C][C]117.9[/C][C]98.9157737378195[/C][C]18.9842262621805[/C][/ROW]
[ROW][C]44[/C][C]121.6[/C][C]98.0076564883155[/C][C]23.5923435116844[/C][/ROW]
[ROW][C]45[/C][C]117.8[/C][C]101.126096854537[/C][C]16.6739031454634[/C][/ROW]
[ROW][C]46[/C][C]124.2[/C][C]99.318429499392[/C][C]24.8815705006080[/C][/ROW]
[ROW][C]47[/C][C]106.8[/C][C]98.1447307901275[/C][C]8.65526920987254[/C][/ROW]
[ROW][C]48[/C][C]102.7[/C][C]98.795833723734[/C][C]3.90416627626595[/C][/ROW]
[ROW][C]49[/C][C]116.8[/C][C]97.673537877649[/C][C]19.126462122351[/C][/ROW]
[ROW][C]50[/C][C]113.6[/C][C]99.772488124144[/C][C]13.8275118758560[/C][/ROW]
[ROW][C]51[/C][C]96.1[/C][C]98.3503422428453[/C][C]-2.25034224284534[/C][/ROW]
[ROW][C]52[/C][C]85[/C][C]97.5964335828798[/C][C]-12.5964335828798[/C][/ROW]
[ROW][C]53[/C][C]83.2[/C][C]97.116673526538[/C][C]-13.9166735265381[/C][/ROW]
[ROW][C]54[/C][C]84.9[/C][C]99.3098623555287[/C][C]-14.4098623555287[/C][/ROW]
[ROW][C]55[/C][C]83[/C][C]98.8815051623665[/C][C]-15.8815051623665[/C][/ROW]
[ROW][C]56[/C][C]79.6[/C][C]97.8791493303669[/C][C]-18.2791493303669[/C][/ROW]
[ROW][C]57[/C][C]83.2[/C][C]100.911918257955[/C][C]-17.7119182579555[/C][/ROW]
[ROW][C]58[/C][C]83.8[/C][C]97.4679264249311[/C][C]-13.6679264249311[/C][/ROW]
[ROW][C]59[/C][C]82.8[/C][C]96.0029448243163[/C][C]-13.2029448243163[/C][/ROW]
[ROW][C]60[/C][C]71.4[/C][C]99.5326080959731[/C][C]-28.1326080959731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57683&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57683&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
1110.595.874437666368114.6255623336319
2110.895.334707602983215.4652923970168
3104.297.1852106774447.01478932255595
488.999.284160923939-10.384160923939
589.896.1742877015812-6.37428770158119
69096.5084063122477-6.50840631224773
793.996.0200791120428-2.12007911204278
891.396.7397191965553-5.43971919655534
987.8100.963321121135-13.1633211211350
1099.795.5060504802484.1939495197519
1173.594.974887560727-21.4748875607269
1279.297.3308521231192-18.1308521231192
1396.996.40560058588880.494399414111213
1495.297.0224349440424-1.82243494404241
1595.697.9048507619566-2.30485076195662
1689.796.6540477579229-6.95404775792288
1792.896.696883477239-3.89688347723911
188897.322284979256-9.32228497925597
19101.197.24518068448683.85481931551322
2092.797.707806453102-5.00780645310198
2195.8101.211768293169-5.41176829316906
22103.896.82539063518786.97460936481222
2381.896.0714819752223-14.2714819752223
2487.197.5878664390166-10.4878664390166
25105.996.62834632633329.27165367366685
26108.196.491272024521211.6087279754788
27102.697.62213501446954.97786498553046
2893.796.131451982265-2.43145198226496
29103.596.4827048806587.017295119342
30100.699.66111525392180.938884746078225
31113.397.21947925289716.0805207471030
32102.497.44222499334144.95777500665861
33102.1100.6891725175111.41082748248886
34106.997.06527066335869.83472933664137
3587.396.097183406812-8.79718340681199
3693.197.4079564178884-4.30795641788843
37109.197.664970733785811.4350292662142
38120.397.964820768999322.3351792310007
39104.999.46407094506715.43592905493286
4092.697.605000726743-5.00500072674305
41109.896.328496291119613.4715037088804
42111.498.778699436007612.6213005639924
43117.998.915773737819518.9842262621805
44121.698.007656488315523.5923435116844
45117.8101.12609685453716.6739031454634
46124.299.31842949939224.8815705006080
47106.898.14473079012758.65526920987254
48102.798.7958337237343.90416627626595
49116.897.67353787764919.126462122351
50113.699.77248812414413.8275118758560
5196.198.3503422428453-2.25034224284534
528597.5964335828798-12.5964335828798
5383.297.116673526538-13.9166735265381
5484.999.3098623555287-14.4098623555287
558398.8815051623665-15.8815051623665
5679.697.8791493303669-18.2791493303669
5783.2100.911918257955-17.7119182579555
5883.897.4679264249311-13.6679264249311
5982.896.0029448243163-13.2029448243163
6071.499.5326080959731-28.1326080959731







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.3439500642712860.6879001285425730.656049935728714
60.3251434021088280.6502868042176560.674856597891172
70.2445570971359220.4891141942718440.755442902864078
80.1713379813868790.3426759627737570.828662018613121
90.1151451023717000.2302902047433990.8848548976283
100.06531084795277210.1306216959055440.934689152047228
110.366411723225320.732823446450640.63358827677468
120.4090314148459790.8180628296919590.590968585154021
130.3179805644124230.6359611288248450.682019435587577
140.2373074468090650.474614893618130.762692553190935
150.1740708782049420.3481417564098830.825929121795058
160.1287835089105560.2575670178211110.871216491089444
170.08750464296186860.1750092859237370.912495357038131
180.06448864094033050.1289772818806610.93551135905967
190.05017100665224090.1003420133044820.949828993347759
200.03215068256672250.0643013651334450.967849317433278
210.02322359974033180.04644719948066360.976776400259668
220.01950886261845940.03901772523691880.98049113738154
230.02396596618274710.04793193236549430.976034033817253
240.01874085391114850.03748170782229710.981259146088851
250.01833813036394630.03667626072789260.981661869636054
260.02050727959170850.0410145591834170.979492720408291
270.01540824087035580.03081648174071150.984591759129644
280.009405848267400830.01881169653480170.9905941517326
290.006938975069594610.01387795013918920.993061024930405
300.004651254093392840.009302508186785690.995348745906607
310.008491102130670760.01698220426134150.99150889786933
320.005678869138736720.01135773827747340.994321130861263
330.00368984566812140.00737969133624280.996310154331879
340.003170734635089740.006341469270179490.99682926536491
350.002352878709638420.004705757419276840.997647121290362
360.001345371329913660.002690742659827320.998654628670086
370.001315647427591800.002631294855183590.998684352572408
380.005235149897349970.01047029979469990.99476485010265
390.003315802277728130.006631604555456260.996684197722272
400.001945389476331110.003890778952662230.998054610523669
410.002355979958694360.004711959917388710.997644020041306
420.002346212186165140.004692424372330280.997653787813835
430.004679101137199570.009358202274399140.9953208988628
440.02041034475509280.04082068951018570.979589655244907
450.02460162081341890.04920324162683780.975398379186581
460.1181978106939980.2363956213879960.881802189306002
470.1295737229160210.2591474458320420.87042627708398
480.1196761314076540.2393522628153080.880323868592346
490.4581432780285400.9162865560570810.54185672197146
500.9543213335553030.09135733288939480.0456786664446974
510.992128539029370.01574292194125890.00787146097062947
520.9837476282803920.03250474343921650.0162523717196082
530.9619589154570320.07608216908593540.0380410845429677
540.9361557279477570.1276885441044860.0638442720522432
550.8684782108697020.2630435782605960.131521789130298

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.343950064271286 & 0.687900128542573 & 0.656049935728714 \tabularnewline
6 & 0.325143402108828 & 0.650286804217656 & 0.674856597891172 \tabularnewline
7 & 0.244557097135922 & 0.489114194271844 & 0.755442902864078 \tabularnewline
8 & 0.171337981386879 & 0.342675962773757 & 0.828662018613121 \tabularnewline
9 & 0.115145102371700 & 0.230290204743399 & 0.8848548976283 \tabularnewline
10 & 0.0653108479527721 & 0.130621695905544 & 0.934689152047228 \tabularnewline
11 & 0.36641172322532 & 0.73282344645064 & 0.63358827677468 \tabularnewline
12 & 0.409031414845979 & 0.818062829691959 & 0.590968585154021 \tabularnewline
13 & 0.317980564412423 & 0.635961128824845 & 0.682019435587577 \tabularnewline
14 & 0.237307446809065 & 0.47461489361813 & 0.762692553190935 \tabularnewline
15 & 0.174070878204942 & 0.348141756409883 & 0.825929121795058 \tabularnewline
16 & 0.128783508910556 & 0.257567017821111 & 0.871216491089444 \tabularnewline
17 & 0.0875046429618686 & 0.175009285923737 & 0.912495357038131 \tabularnewline
18 & 0.0644886409403305 & 0.128977281880661 & 0.93551135905967 \tabularnewline
19 & 0.0501710066522409 & 0.100342013304482 & 0.949828993347759 \tabularnewline
20 & 0.0321506825667225 & 0.064301365133445 & 0.967849317433278 \tabularnewline
21 & 0.0232235997403318 & 0.0464471994806636 & 0.976776400259668 \tabularnewline
22 & 0.0195088626184594 & 0.0390177252369188 & 0.98049113738154 \tabularnewline
23 & 0.0239659661827471 & 0.0479319323654943 & 0.976034033817253 \tabularnewline
24 & 0.0187408539111485 & 0.0374817078222971 & 0.981259146088851 \tabularnewline
25 & 0.0183381303639463 & 0.0366762607278926 & 0.981661869636054 \tabularnewline
26 & 0.0205072795917085 & 0.041014559183417 & 0.979492720408291 \tabularnewline
27 & 0.0154082408703558 & 0.0308164817407115 & 0.984591759129644 \tabularnewline
28 & 0.00940584826740083 & 0.0188116965348017 & 0.9905941517326 \tabularnewline
29 & 0.00693897506959461 & 0.0138779501391892 & 0.993061024930405 \tabularnewline
30 & 0.00465125409339284 & 0.00930250818678569 & 0.995348745906607 \tabularnewline
31 & 0.00849110213067076 & 0.0169822042613415 & 0.99150889786933 \tabularnewline
32 & 0.00567886913873672 & 0.0113577382774734 & 0.994321130861263 \tabularnewline
33 & 0.0036898456681214 & 0.0073796913362428 & 0.996310154331879 \tabularnewline
34 & 0.00317073463508974 & 0.00634146927017949 & 0.99682926536491 \tabularnewline
35 & 0.00235287870963842 & 0.00470575741927684 & 0.997647121290362 \tabularnewline
36 & 0.00134537132991366 & 0.00269074265982732 & 0.998654628670086 \tabularnewline
37 & 0.00131564742759180 & 0.00263129485518359 & 0.998684352572408 \tabularnewline
38 & 0.00523514989734997 & 0.0104702997946999 & 0.99476485010265 \tabularnewline
39 & 0.00331580227772813 & 0.00663160455545626 & 0.996684197722272 \tabularnewline
40 & 0.00194538947633111 & 0.00389077895266223 & 0.998054610523669 \tabularnewline
41 & 0.00235597995869436 & 0.00471195991738871 & 0.997644020041306 \tabularnewline
42 & 0.00234621218616514 & 0.00469242437233028 & 0.997653787813835 \tabularnewline
43 & 0.00467910113719957 & 0.00935820227439914 & 0.9953208988628 \tabularnewline
44 & 0.0204103447550928 & 0.0408206895101857 & 0.979589655244907 \tabularnewline
45 & 0.0246016208134189 & 0.0492032416268378 & 0.975398379186581 \tabularnewline
46 & 0.118197810693998 & 0.236395621387996 & 0.881802189306002 \tabularnewline
47 & 0.129573722916021 & 0.259147445832042 & 0.87042627708398 \tabularnewline
48 & 0.119676131407654 & 0.239352262815308 & 0.880323868592346 \tabularnewline
49 & 0.458143278028540 & 0.916286556057081 & 0.54185672197146 \tabularnewline
50 & 0.954321333555303 & 0.0913573328893948 & 0.0456786664446974 \tabularnewline
51 & 0.99212853902937 & 0.0157429219412589 & 0.00787146097062947 \tabularnewline
52 & 0.983747628280392 & 0.0325047434392165 & 0.0162523717196082 \tabularnewline
53 & 0.961958915457032 & 0.0760821690859354 & 0.0380410845429677 \tabularnewline
54 & 0.936155727947757 & 0.127688544104486 & 0.0638442720522432 \tabularnewline
55 & 0.868478210869702 & 0.263043578260596 & 0.131521789130298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57683&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]0.343950064271286[/C][C]0.687900128542573[/C][C]0.656049935728714[/C][/ROW]
[ROW][C]6[/C][C]0.325143402108828[/C][C]0.650286804217656[/C][C]0.674856597891172[/C][/ROW]
[ROW][C]7[/C][C]0.244557097135922[/C][C]0.489114194271844[/C][C]0.755442902864078[/C][/ROW]
[ROW][C]8[/C][C]0.171337981386879[/C][C]0.342675962773757[/C][C]0.828662018613121[/C][/ROW]
[ROW][C]9[/C][C]0.115145102371700[/C][C]0.230290204743399[/C][C]0.8848548976283[/C][/ROW]
[ROW][C]10[/C][C]0.0653108479527721[/C][C]0.130621695905544[/C][C]0.934689152047228[/C][/ROW]
[ROW][C]11[/C][C]0.36641172322532[/C][C]0.73282344645064[/C][C]0.63358827677468[/C][/ROW]
[ROW][C]12[/C][C]0.409031414845979[/C][C]0.818062829691959[/C][C]0.590968585154021[/C][/ROW]
[ROW][C]13[/C][C]0.317980564412423[/C][C]0.635961128824845[/C][C]0.682019435587577[/C][/ROW]
[ROW][C]14[/C][C]0.237307446809065[/C][C]0.47461489361813[/C][C]0.762692553190935[/C][/ROW]
[ROW][C]15[/C][C]0.174070878204942[/C][C]0.348141756409883[/C][C]0.825929121795058[/C][/ROW]
[ROW][C]16[/C][C]0.128783508910556[/C][C]0.257567017821111[/C][C]0.871216491089444[/C][/ROW]
[ROW][C]17[/C][C]0.0875046429618686[/C][C]0.175009285923737[/C][C]0.912495357038131[/C][/ROW]
[ROW][C]18[/C][C]0.0644886409403305[/C][C]0.128977281880661[/C][C]0.93551135905967[/C][/ROW]
[ROW][C]19[/C][C]0.0501710066522409[/C][C]0.100342013304482[/C][C]0.949828993347759[/C][/ROW]
[ROW][C]20[/C][C]0.0321506825667225[/C][C]0.064301365133445[/C][C]0.967849317433278[/C][/ROW]
[ROW][C]21[/C][C]0.0232235997403318[/C][C]0.0464471994806636[/C][C]0.976776400259668[/C][/ROW]
[ROW][C]22[/C][C]0.0195088626184594[/C][C]0.0390177252369188[/C][C]0.98049113738154[/C][/ROW]
[ROW][C]23[/C][C]0.0239659661827471[/C][C]0.0479319323654943[/C][C]0.976034033817253[/C][/ROW]
[ROW][C]24[/C][C]0.0187408539111485[/C][C]0.0374817078222971[/C][C]0.981259146088851[/C][/ROW]
[ROW][C]25[/C][C]0.0183381303639463[/C][C]0.0366762607278926[/C][C]0.981661869636054[/C][/ROW]
[ROW][C]26[/C][C]0.0205072795917085[/C][C]0.041014559183417[/C][C]0.979492720408291[/C][/ROW]
[ROW][C]27[/C][C]0.0154082408703558[/C][C]0.0308164817407115[/C][C]0.984591759129644[/C][/ROW]
[ROW][C]28[/C][C]0.00940584826740083[/C][C]0.0188116965348017[/C][C]0.9905941517326[/C][/ROW]
[ROW][C]29[/C][C]0.00693897506959461[/C][C]0.0138779501391892[/C][C]0.993061024930405[/C][/ROW]
[ROW][C]30[/C][C]0.00465125409339284[/C][C]0.00930250818678569[/C][C]0.995348745906607[/C][/ROW]
[ROW][C]31[/C][C]0.00849110213067076[/C][C]0.0169822042613415[/C][C]0.99150889786933[/C][/ROW]
[ROW][C]32[/C][C]0.00567886913873672[/C][C]0.0113577382774734[/C][C]0.994321130861263[/C][/ROW]
[ROW][C]33[/C][C]0.0036898456681214[/C][C]0.0073796913362428[/C][C]0.996310154331879[/C][/ROW]
[ROW][C]34[/C][C]0.00317073463508974[/C][C]0.00634146927017949[/C][C]0.99682926536491[/C][/ROW]
[ROW][C]35[/C][C]0.00235287870963842[/C][C]0.00470575741927684[/C][C]0.997647121290362[/C][/ROW]
[ROW][C]36[/C][C]0.00134537132991366[/C][C]0.00269074265982732[/C][C]0.998654628670086[/C][/ROW]
[ROW][C]37[/C][C]0.00131564742759180[/C][C]0.00263129485518359[/C][C]0.998684352572408[/C][/ROW]
[ROW][C]38[/C][C]0.00523514989734997[/C][C]0.0104702997946999[/C][C]0.99476485010265[/C][/ROW]
[ROW][C]39[/C][C]0.00331580227772813[/C][C]0.00663160455545626[/C][C]0.996684197722272[/C][/ROW]
[ROW][C]40[/C][C]0.00194538947633111[/C][C]0.00389077895266223[/C][C]0.998054610523669[/C][/ROW]
[ROW][C]41[/C][C]0.00235597995869436[/C][C]0.00471195991738871[/C][C]0.997644020041306[/C][/ROW]
[ROW][C]42[/C][C]0.00234621218616514[/C][C]0.00469242437233028[/C][C]0.997653787813835[/C][/ROW]
[ROW][C]43[/C][C]0.00467910113719957[/C][C]0.00935820227439914[/C][C]0.9953208988628[/C][/ROW]
[ROW][C]44[/C][C]0.0204103447550928[/C][C]0.0408206895101857[/C][C]0.979589655244907[/C][/ROW]
[ROW][C]45[/C][C]0.0246016208134189[/C][C]0.0492032416268378[/C][C]0.975398379186581[/C][/ROW]
[ROW][C]46[/C][C]0.118197810693998[/C][C]0.236395621387996[/C][C]0.881802189306002[/C][/ROW]
[ROW][C]47[/C][C]0.129573722916021[/C][C]0.259147445832042[/C][C]0.87042627708398[/C][/ROW]
[ROW][C]48[/C][C]0.119676131407654[/C][C]0.239352262815308[/C][C]0.880323868592346[/C][/ROW]
[ROW][C]49[/C][C]0.458143278028540[/C][C]0.916286556057081[/C][C]0.54185672197146[/C][/ROW]
[ROW][C]50[/C][C]0.954321333555303[/C][C]0.0913573328893948[/C][C]0.0456786664446974[/C][/ROW]
[ROW][C]51[/C][C]0.99212853902937[/C][C]0.0157429219412589[/C][C]0.00787146097062947[/C][/ROW]
[ROW][C]52[/C][C]0.983747628280392[/C][C]0.0325047434392165[/C][C]0.0162523717196082[/C][/ROW]
[ROW][C]53[/C][C]0.961958915457032[/C][C]0.0760821690859354[/C][C]0.0380410845429677[/C][/ROW]
[ROW][C]54[/C][C]0.936155727947757[/C][C]0.127688544104486[/C][C]0.0638442720522432[/C][/ROW]
[ROW][C]55[/C][C]0.868478210869702[/C][C]0.263043578260596[/C][C]0.131521789130298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57683&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57683&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
50.3439500642712860.6879001285425730.656049935728714
60.3251434021088280.6502868042176560.674856597891172
70.2445570971359220.4891141942718440.755442902864078
80.1713379813868790.3426759627737570.828662018613121
90.1151451023717000.2302902047433990.8848548976283
100.06531084795277210.1306216959055440.934689152047228
110.366411723225320.732823446450640.63358827677468
120.4090314148459790.8180628296919590.590968585154021
130.3179805644124230.6359611288248450.682019435587577
140.2373074468090650.474614893618130.762692553190935
150.1740708782049420.3481417564098830.825929121795058
160.1287835089105560.2575670178211110.871216491089444
170.08750464296186860.1750092859237370.912495357038131
180.06448864094033050.1289772818806610.93551135905967
190.05017100665224090.1003420133044820.949828993347759
200.03215068256672250.0643013651334450.967849317433278
210.02322359974033180.04644719948066360.976776400259668
220.01950886261845940.03901772523691880.98049113738154
230.02396596618274710.04793193236549430.976034033817253
240.01874085391114850.03748170782229710.981259146088851
250.01833813036394630.03667626072789260.981661869636054
260.02050727959170850.0410145591834170.979492720408291
270.01540824087035580.03081648174071150.984591759129644
280.009405848267400830.01881169653480170.9905941517326
290.006938975069594610.01387795013918920.993061024930405
300.004651254093392840.009302508186785690.995348745906607
310.008491102130670760.01698220426134150.99150889786933
320.005678869138736720.01135773827747340.994321130861263
330.00368984566812140.00737969133624280.996310154331879
340.003170734635089740.006341469270179490.99682926536491
350.002352878709638420.004705757419276840.997647121290362
360.001345371329913660.002690742659827320.998654628670086
370.001315647427591800.002631294855183590.998684352572408
380.005235149897349970.01047029979469990.99476485010265
390.003315802277728130.006631604555456260.996684197722272
400.001945389476331110.003890778952662230.998054610523669
410.002355979958694360.004711959917388710.997644020041306
420.002346212186165140.004692424372330280.997653787813835
430.004679101137199570.009358202274399140.9953208988628
440.02041034475509280.04082068951018570.979589655244907
450.02460162081341890.04920324162683780.975398379186581
460.1181978106939980.2363956213879960.881802189306002
470.1295737229160210.2591474458320420.87042627708398
480.1196761314076540.2393522628153080.880323868592346
490.4581432780285400.9162865560570810.54185672197146
500.9543213335553030.09135733288939480.0456786664446974
510.992128539029370.01574292194125890.00787146097062947
520.9837476282803920.03250474343921650.0162523717196082
530.9619589154570320.07608216908593540.0380410845429677
540.9361557279477570.1276885441044860.0638442720522432
550.8684782108697020.2630435782605960.131521789130298







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.215686274509804NOK
5% type I error level270.529411764705882NOK
10% type I error level300.588235294117647NOK

\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 & 11 & 0.215686274509804 & NOK \tabularnewline
5% type I error level & 27 & 0.529411764705882 & NOK \tabularnewline
10% type I error level & 30 & 0.588235294117647 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57683&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]11[/C][C]0.215686274509804[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.529411764705882[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.588235294117647[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57683&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57683&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 level110.215686274509804NOK
5% type I error level270.529411764705882NOK
10% type I error level300.588235294117647NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')
}