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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 computationFri, 20 Nov 2009 12:24:54 -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/20/t1258745160g74daxhesonvj5g.htm/, Retrieved Fri, 29 Mar 2024 13:10:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58439, Retrieved Fri, 29 Mar 2024 13:10:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165
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] [Ws 7 ] [2009-11-20 18:21:24] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
-   P       [Multiple Regression] [Ws 7 (2)] [2009-11-20 19:21:00] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
-   P           [Multiple Regression] [Ws 7 (3)] [2009-11-20 19:24:54] [ba02bcb7e07025bbb7f8a074d38ad767] [Current]
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Dataseries X:
18.0	16.4
19.6	17.8
23.3	22.3
23.7	22.8
20.3	18.3
22.8	22.4
24.3	23.9
21.5	21.3
23.5	23.0
22.2	21.4
20.9	21.2
22.2	20.9
19.5	17.9
21.1	20.7
22.0	22.2
19.2	19.8
17.8	17.7
19.2	19.6
19.9	20.8
19.6	19.8
18.1	18.6
20.4	21.
18.1	18.6
18.6	18.9
17.6	17.3
19.4	20.0
19.3	19.9
18.6	19.5
16.9	16.2
16.4	17.6
19.0	19.8
18.7	19.4
17.1	17.2
21.5	21.1
17.8	17.8
18.1	17.5
19.0	18.0
18.9	19.1
16.8	17.7
18.1	19.2
15.7	15.1
15.1	16.3
18.3	18.6
16.5	17.2
16.9	17.8
18.4	19.1
16.4	16.6
15.7	16.0
16.9	16.7
16.6	17.4
16.7	17.9
16.6	17.8
14.4	13.9
14.5	15.9
17.5	17.9
14.3	15.4
15.4	16.4
17.2	17.9
14.6	15.3
14.2	14.6
14.9	14.9
14.1	15.0
15.6	16.7
14.6	16.3
11.9	11.7
13.5	15.1
14.2	15.5
13.7	15.0
14.4	15.4
15.3	16.0
14.3	14.7
14.5	14.8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58439&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]3 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=58439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58439&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.54167091765125 + 0.98059228033681X[t] + 0.387904499998967M1[t] -0.390548095699390M2[t] -0.792460059946535M3[t] -1.03691565041127M4[t] + 0.366721482980735M5[t] -1.14491108900953M6[t] -0.737442655419468M7[t] -0.821530714152305M8[t] -0.660810912706852M9[t] -0.358194409032587M10[t] -0.471564152213164M11[t] -0.0264160821289604t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.54167091765125 +  0.98059228033681X[t] +  0.387904499998967M1[t] -0.390548095699390M2[t] -0.792460059946535M3[t] -1.03691565041127M4[t] +  0.366721482980735M5[t] -1.14491108900953M6[t] -0.737442655419468M7[t] -0.821530714152305M8[t] -0.660810912706852M9[t] -0.358194409032587M10[t] -0.471564152213164M11[t] -0.0264160821289604t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58439&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.54167091765125 +  0.98059228033681X[t] +  0.387904499998967M1[t] -0.390548095699390M2[t] -0.792460059946535M3[t] -1.03691565041127M4[t] +  0.366721482980735M5[t] -1.14491108900953M6[t] -0.737442655419468M7[t] -0.821530714152305M8[t] -0.660810912706852M9[t] -0.358194409032587M10[t] -0.471564152213164M11[t] -0.0264160821289604t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58439&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
Y[t] = + 1.54167091765125 + 0.98059228033681X[t] + 0.387904499998967M1[t] -0.390548095699390M2[t] -0.792460059946535M3[t] -1.03691565041127M4[t] + 0.366721482980735M5[t] -1.14491108900953M6[t] -0.737442655419468M7[t] -0.821530714152305M8[t] -0.660810912706852M9[t] -0.358194409032587M10[t] -0.471564152213164M11[t] -0.0264160821289604t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.541670917651251.2334141.24990.2163480.108174
X0.980592280336810.05747117.062400
M10.3879044999989670.2763791.40350.1657920.082896
M2-0.3905480956993900.266241-1.46690.1478060.073903
M3-0.7924600599465350.278754-2.84290.0061630.003082
M4-1.036915650411270.276503-3.75010.000410.000205
M50.3667214829807350.2963091.23760.220840.11042
M6-1.144911089009530.265079-4.31916.2e-053.1e-05
M7-0.7374426554194680.284798-2.58930.0121370.006069
M8-0.8215307141523050.266386-3.0840.0031260.001563
M9-0.6608109127068520.267358-2.47160.0164070.008203
M10-0.3581944090325870.291004-1.23090.2233320.111666
M11-0.4715641522131640.264668-1.78170.0800310.040015
t-0.02641608212896040.006086-4.34065.8e-052.9e-05

\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) & 1.54167091765125 & 1.233414 & 1.2499 & 0.216348 & 0.108174 \tabularnewline
X & 0.98059228033681 & 0.057471 & 17.0624 & 0 & 0 \tabularnewline
M1 & 0.387904499998967 & 0.276379 & 1.4035 & 0.165792 & 0.082896 \tabularnewline
M2 & -0.390548095699390 & 0.266241 & -1.4669 & 0.147806 & 0.073903 \tabularnewline
M3 & -0.792460059946535 & 0.278754 & -2.8429 & 0.006163 & 0.003082 \tabularnewline
M4 & -1.03691565041127 & 0.276503 & -3.7501 & 0.00041 & 0.000205 \tabularnewline
M5 & 0.366721482980735 & 0.296309 & 1.2376 & 0.22084 & 0.11042 \tabularnewline
M6 & -1.14491108900953 & 0.265079 & -4.3191 & 6.2e-05 & 3.1e-05 \tabularnewline
M7 & -0.737442655419468 & 0.284798 & -2.5893 & 0.012137 & 0.006069 \tabularnewline
M8 & -0.821530714152305 & 0.266386 & -3.084 & 0.003126 & 0.001563 \tabularnewline
M9 & -0.660810912706852 & 0.267358 & -2.4716 & 0.016407 & 0.008203 \tabularnewline
M10 & -0.358194409032587 & 0.291004 & -1.2309 & 0.223332 & 0.111666 \tabularnewline
M11 & -0.471564152213164 & 0.264668 & -1.7817 & 0.080031 & 0.040015 \tabularnewline
t & -0.0264160821289604 & 0.006086 & -4.3406 & 5.8e-05 & 2.9e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58439&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]1.54167091765125[/C][C]1.233414[/C][C]1.2499[/C][C]0.216348[/C][C]0.108174[/C][/ROW]
[ROW][C]X[/C][C]0.98059228033681[/C][C]0.057471[/C][C]17.0624[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.387904499998967[/C][C]0.276379[/C][C]1.4035[/C][C]0.165792[/C][C]0.082896[/C][/ROW]
[ROW][C]M2[/C][C]-0.390548095699390[/C][C]0.266241[/C][C]-1.4669[/C][C]0.147806[/C][C]0.073903[/C][/ROW]
[ROW][C]M3[/C][C]-0.792460059946535[/C][C]0.278754[/C][C]-2.8429[/C][C]0.006163[/C][C]0.003082[/C][/ROW]
[ROW][C]M4[/C][C]-1.03691565041127[/C][C]0.276503[/C][C]-3.7501[/C][C]0.00041[/C][C]0.000205[/C][/ROW]
[ROW][C]M5[/C][C]0.366721482980735[/C][C]0.296309[/C][C]1.2376[/C][C]0.22084[/C][C]0.11042[/C][/ROW]
[ROW][C]M6[/C][C]-1.14491108900953[/C][C]0.265079[/C][C]-4.3191[/C][C]6.2e-05[/C][C]3.1e-05[/C][/ROW]
[ROW][C]M7[/C][C]-0.737442655419468[/C][C]0.284798[/C][C]-2.5893[/C][C]0.012137[/C][C]0.006069[/C][/ROW]
[ROW][C]M8[/C][C]-0.821530714152305[/C][C]0.266386[/C][C]-3.084[/C][C]0.003126[/C][C]0.001563[/C][/ROW]
[ROW][C]M9[/C][C]-0.660810912706852[/C][C]0.267358[/C][C]-2.4716[/C][C]0.016407[/C][C]0.008203[/C][/ROW]
[ROW][C]M10[/C][C]-0.358194409032587[/C][C]0.291004[/C][C]-1.2309[/C][C]0.223332[/C][C]0.111666[/C][/ROW]
[ROW][C]M11[/C][C]-0.471564152213164[/C][C]0.264668[/C][C]-1.7817[/C][C]0.080031[/C][C]0.040015[/C][/ROW]
[ROW][C]t[/C][C]-0.0264160821289604[/C][C]0.006086[/C][C]-4.3406[/C][C]5.8e-05[/C][C]2.9e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58439&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58439&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)1.541670917651251.2334141.24990.2163480.108174
X0.980592280336810.05747117.062400
M10.3879044999989670.2763791.40350.1657920.082896
M2-0.3905480956993900.266241-1.46690.1478060.073903
M3-0.7924600599465350.278754-2.84290.0061630.003082
M4-1.036915650411270.276503-3.75010.000410.000205
M50.3667214829807350.2963091.23760.220840.11042
M6-1.144911089009530.265079-4.31916.2e-053.1e-05
M7-0.7374426554194680.284798-2.58930.0121370.006069
M8-0.8215307141523050.266386-3.0840.0031260.001563
M9-0.6608109127068520.267358-2.47160.0164070.008203
M10-0.3581944090325870.291004-1.23090.2233320.111666
M11-0.4715641522131640.264668-1.78170.0800310.040015
t-0.02641608212896040.006086-4.34065.8e-052.9e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.98920334433598
R-squared0.978523256445487
Adjusted R-squared0.97370950357982
F-TEST (value)203.276587675413
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.458137829416737
Sum Squared Residuals12.1736357030754

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.98920334433598 \tabularnewline
R-squared & 0.978523256445487 \tabularnewline
Adjusted R-squared & 0.97370950357982 \tabularnewline
F-TEST (value) & 203.276587675413 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.458137829416737 \tabularnewline
Sum Squared Residuals & 12.1736357030754 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58439&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.98920334433598[/C][/ROW]
[ROW][C]R-squared[/C][C]0.978523256445487[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.97370950357982[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]203.276587675413[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.458137829416737[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12.1736357030754[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58439&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58439&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 R0.98920334433598
R-squared0.978523256445487
Adjusted R-squared0.97370950357982
F-TEST (value)203.276587675413
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.458137829416737
Sum Squared Residuals12.1736357030754







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11817.9848727330450.0151272669550136
219.618.55283324768921.04716675231082
323.322.53717046282870.762829537171278
423.722.75659493040340.943405069596563
520.319.72115072015080.578849279849173
622.822.20353041541250.596469584587477
724.324.05547118737880.244528812621157
821.521.39542711764130.104572882358660
923.523.19673771353040.303262286469589
1022.221.90399048653680.296009513463182
1120.921.5680862051599-0.668086205159919
1222.221.71905659114310.480943408856922
1319.519.13876816800270.361231831997348
1421.121.07955787511840.0204421248815975
152222.1221182492475-0.122118249247516
1619.219.4978251038455-0.297825103845481
1717.818.8158023664012-1.01580236640122
1819.219.14087904492190.0591209550780661
1919.920.6986421327872-0.79864213278721
2019.619.6075457115886-0.0075457115885984
2118.118.5651386945009-0.465138694500919
2220.421.1947605888546-0.794760588854571
2318.118.7015532907367-0.601553290736686
2418.619.4408790449219-0.84087904492193
2517.618.2334198142530-0.633419814253042
2619.420.0761502933351-0.676150293335115
2719.319.5497630189253-0.249763018925326
2818.618.8866544341969-0.286654434196911
2916.917.0279209603485-0.127920960348478
3016.416.8627014987008-0.462701498700789
311919.4010568669029-0.401056866902873
3218.718.8983158139064-0.19831581390635
3317.116.87531651648190.224683483518142
3421.520.97582683134070.524173168659272
3517.817.60008648091970.199913519080287
3618.117.75105686690290.348943133097128
371918.60284142494130.397158575058714
3818.918.87662425548450.0233757445155378
3916.817.0754670166368-0.275467016636818
4018.118.2754837645483-0.175483764548342
4115.715.63227646643050.0677235335695386
4215.115.2709385487154-0.170938548715409
4318.317.90735314495120.392646855048824
4416.516.42401981161780.075980188382158
4516.917.1466788991364-0.246678899136424
4618.418.6976492851196-0.297649285119583
4716.416.10638275896800.293617241031981
4815.715.9631754608501-0.263175460850134
4916.917.0110784749559-0.111078474955908
5016.616.8926243933644-0.292624393364354
5116.716.9545924871567-0.254592487156657
5216.616.58566158652930.0143384134707157
5314.414.13857274447880.261427255521237
5414.514.5617086510332-0.0617086510331594
5517.516.90394556316790.596054436832118
5614.314.3419607214641-0.0419607214640582
5715.415.4568567211174-0.0568567211173608
5817.217.2039455631679-0.00394556316788222
5914.614.51461980898260.0853801910173616
6014.214.2733532828311-0.0733532828310736
6114.914.9290193848021-0.0290193848021241
6214.114.2222099350085-0.122209935008487
6315.615.46088876520500.13911123479504
6414.614.7977801804765-0.197780180476546
6511.911.66427674219030.235723257809746
6613.513.46024184121620.0397581587838143
6714.214.233531104812-0.033531104812014
6813.713.63273082378180.0672691762181886
6914.414.15927145523300.240728544766973
7015.315.02382724498040.276172755019582
7114.313.60927145523300.690728544766975
7214.514.15247875335090.347521246649088

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18 & 17.984872733045 & 0.0151272669550136 \tabularnewline
2 & 19.6 & 18.5528332476892 & 1.04716675231082 \tabularnewline
3 & 23.3 & 22.5371704628287 & 0.762829537171278 \tabularnewline
4 & 23.7 & 22.7565949304034 & 0.943405069596563 \tabularnewline
5 & 20.3 & 19.7211507201508 & 0.578849279849173 \tabularnewline
6 & 22.8 & 22.2035304154125 & 0.596469584587477 \tabularnewline
7 & 24.3 & 24.0554711873788 & 0.244528812621157 \tabularnewline
8 & 21.5 & 21.3954271176413 & 0.104572882358660 \tabularnewline
9 & 23.5 & 23.1967377135304 & 0.303262286469589 \tabularnewline
10 & 22.2 & 21.9039904865368 & 0.296009513463182 \tabularnewline
11 & 20.9 & 21.5680862051599 & -0.668086205159919 \tabularnewline
12 & 22.2 & 21.7190565911431 & 0.480943408856922 \tabularnewline
13 & 19.5 & 19.1387681680027 & 0.361231831997348 \tabularnewline
14 & 21.1 & 21.0795578751184 & 0.0204421248815975 \tabularnewline
15 & 22 & 22.1221182492475 & -0.122118249247516 \tabularnewline
16 & 19.2 & 19.4978251038455 & -0.297825103845481 \tabularnewline
17 & 17.8 & 18.8158023664012 & -1.01580236640122 \tabularnewline
18 & 19.2 & 19.1408790449219 & 0.0591209550780661 \tabularnewline
19 & 19.9 & 20.6986421327872 & -0.79864213278721 \tabularnewline
20 & 19.6 & 19.6075457115886 & -0.0075457115885984 \tabularnewline
21 & 18.1 & 18.5651386945009 & -0.465138694500919 \tabularnewline
22 & 20.4 & 21.1947605888546 & -0.794760588854571 \tabularnewline
23 & 18.1 & 18.7015532907367 & -0.601553290736686 \tabularnewline
24 & 18.6 & 19.4408790449219 & -0.84087904492193 \tabularnewline
25 & 17.6 & 18.2334198142530 & -0.633419814253042 \tabularnewline
26 & 19.4 & 20.0761502933351 & -0.676150293335115 \tabularnewline
27 & 19.3 & 19.5497630189253 & -0.249763018925326 \tabularnewline
28 & 18.6 & 18.8866544341969 & -0.286654434196911 \tabularnewline
29 & 16.9 & 17.0279209603485 & -0.127920960348478 \tabularnewline
30 & 16.4 & 16.8627014987008 & -0.462701498700789 \tabularnewline
31 & 19 & 19.4010568669029 & -0.401056866902873 \tabularnewline
32 & 18.7 & 18.8983158139064 & -0.19831581390635 \tabularnewline
33 & 17.1 & 16.8753165164819 & 0.224683483518142 \tabularnewline
34 & 21.5 & 20.9758268313407 & 0.524173168659272 \tabularnewline
35 & 17.8 & 17.6000864809197 & 0.199913519080287 \tabularnewline
36 & 18.1 & 17.7510568669029 & 0.348943133097128 \tabularnewline
37 & 19 & 18.6028414249413 & 0.397158575058714 \tabularnewline
38 & 18.9 & 18.8766242554845 & 0.0233757445155378 \tabularnewline
39 & 16.8 & 17.0754670166368 & -0.275467016636818 \tabularnewline
40 & 18.1 & 18.2754837645483 & -0.175483764548342 \tabularnewline
41 & 15.7 & 15.6322764664305 & 0.0677235335695386 \tabularnewline
42 & 15.1 & 15.2709385487154 & -0.170938548715409 \tabularnewline
43 & 18.3 & 17.9073531449512 & 0.392646855048824 \tabularnewline
44 & 16.5 & 16.4240198116178 & 0.075980188382158 \tabularnewline
45 & 16.9 & 17.1466788991364 & -0.246678899136424 \tabularnewline
46 & 18.4 & 18.6976492851196 & -0.297649285119583 \tabularnewline
47 & 16.4 & 16.1063827589680 & 0.293617241031981 \tabularnewline
48 & 15.7 & 15.9631754608501 & -0.263175460850134 \tabularnewline
49 & 16.9 & 17.0110784749559 & -0.111078474955908 \tabularnewline
50 & 16.6 & 16.8926243933644 & -0.292624393364354 \tabularnewline
51 & 16.7 & 16.9545924871567 & -0.254592487156657 \tabularnewline
52 & 16.6 & 16.5856615865293 & 0.0143384134707157 \tabularnewline
53 & 14.4 & 14.1385727444788 & 0.261427255521237 \tabularnewline
54 & 14.5 & 14.5617086510332 & -0.0617086510331594 \tabularnewline
55 & 17.5 & 16.9039455631679 & 0.596054436832118 \tabularnewline
56 & 14.3 & 14.3419607214641 & -0.0419607214640582 \tabularnewline
57 & 15.4 & 15.4568567211174 & -0.0568567211173608 \tabularnewline
58 & 17.2 & 17.2039455631679 & -0.00394556316788222 \tabularnewline
59 & 14.6 & 14.5146198089826 & 0.0853801910173616 \tabularnewline
60 & 14.2 & 14.2733532828311 & -0.0733532828310736 \tabularnewline
61 & 14.9 & 14.9290193848021 & -0.0290193848021241 \tabularnewline
62 & 14.1 & 14.2222099350085 & -0.122209935008487 \tabularnewline
63 & 15.6 & 15.4608887652050 & 0.13911123479504 \tabularnewline
64 & 14.6 & 14.7977801804765 & -0.197780180476546 \tabularnewline
65 & 11.9 & 11.6642767421903 & 0.235723257809746 \tabularnewline
66 & 13.5 & 13.4602418412162 & 0.0397581587838143 \tabularnewline
67 & 14.2 & 14.233531104812 & -0.033531104812014 \tabularnewline
68 & 13.7 & 13.6327308237818 & 0.0672691762181886 \tabularnewline
69 & 14.4 & 14.1592714552330 & 0.240728544766973 \tabularnewline
70 & 15.3 & 15.0238272449804 & 0.276172755019582 \tabularnewline
71 & 14.3 & 13.6092714552330 & 0.690728544766975 \tabularnewline
72 & 14.5 & 14.1524787533509 & 0.347521246649088 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58439&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]18[/C][C]17.984872733045[/C][C]0.0151272669550136[/C][/ROW]
[ROW][C]2[/C][C]19.6[/C][C]18.5528332476892[/C][C]1.04716675231082[/C][/ROW]
[ROW][C]3[/C][C]23.3[/C][C]22.5371704628287[/C][C]0.762829537171278[/C][/ROW]
[ROW][C]4[/C][C]23.7[/C][C]22.7565949304034[/C][C]0.943405069596563[/C][/ROW]
[ROW][C]5[/C][C]20.3[/C][C]19.7211507201508[/C][C]0.578849279849173[/C][/ROW]
[ROW][C]6[/C][C]22.8[/C][C]22.2035304154125[/C][C]0.596469584587477[/C][/ROW]
[ROW][C]7[/C][C]24.3[/C][C]24.0554711873788[/C][C]0.244528812621157[/C][/ROW]
[ROW][C]8[/C][C]21.5[/C][C]21.3954271176413[/C][C]0.104572882358660[/C][/ROW]
[ROW][C]9[/C][C]23.5[/C][C]23.1967377135304[/C][C]0.303262286469589[/C][/ROW]
[ROW][C]10[/C][C]22.2[/C][C]21.9039904865368[/C][C]0.296009513463182[/C][/ROW]
[ROW][C]11[/C][C]20.9[/C][C]21.5680862051599[/C][C]-0.668086205159919[/C][/ROW]
[ROW][C]12[/C][C]22.2[/C][C]21.7190565911431[/C][C]0.480943408856922[/C][/ROW]
[ROW][C]13[/C][C]19.5[/C][C]19.1387681680027[/C][C]0.361231831997348[/C][/ROW]
[ROW][C]14[/C][C]21.1[/C][C]21.0795578751184[/C][C]0.0204421248815975[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]22.1221182492475[/C][C]-0.122118249247516[/C][/ROW]
[ROW][C]16[/C][C]19.2[/C][C]19.4978251038455[/C][C]-0.297825103845481[/C][/ROW]
[ROW][C]17[/C][C]17.8[/C][C]18.8158023664012[/C][C]-1.01580236640122[/C][/ROW]
[ROW][C]18[/C][C]19.2[/C][C]19.1408790449219[/C][C]0.0591209550780661[/C][/ROW]
[ROW][C]19[/C][C]19.9[/C][C]20.6986421327872[/C][C]-0.79864213278721[/C][/ROW]
[ROW][C]20[/C][C]19.6[/C][C]19.6075457115886[/C][C]-0.0075457115885984[/C][/ROW]
[ROW][C]21[/C][C]18.1[/C][C]18.5651386945009[/C][C]-0.465138694500919[/C][/ROW]
[ROW][C]22[/C][C]20.4[/C][C]21.1947605888546[/C][C]-0.794760588854571[/C][/ROW]
[ROW][C]23[/C][C]18.1[/C][C]18.7015532907367[/C][C]-0.601553290736686[/C][/ROW]
[ROW][C]24[/C][C]18.6[/C][C]19.4408790449219[/C][C]-0.84087904492193[/C][/ROW]
[ROW][C]25[/C][C]17.6[/C][C]18.2334198142530[/C][C]-0.633419814253042[/C][/ROW]
[ROW][C]26[/C][C]19.4[/C][C]20.0761502933351[/C][C]-0.676150293335115[/C][/ROW]
[ROW][C]27[/C][C]19.3[/C][C]19.5497630189253[/C][C]-0.249763018925326[/C][/ROW]
[ROW][C]28[/C][C]18.6[/C][C]18.8866544341969[/C][C]-0.286654434196911[/C][/ROW]
[ROW][C]29[/C][C]16.9[/C][C]17.0279209603485[/C][C]-0.127920960348478[/C][/ROW]
[ROW][C]30[/C][C]16.4[/C][C]16.8627014987008[/C][C]-0.462701498700789[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]19.4010568669029[/C][C]-0.401056866902873[/C][/ROW]
[ROW][C]32[/C][C]18.7[/C][C]18.8983158139064[/C][C]-0.19831581390635[/C][/ROW]
[ROW][C]33[/C][C]17.1[/C][C]16.8753165164819[/C][C]0.224683483518142[/C][/ROW]
[ROW][C]34[/C][C]21.5[/C][C]20.9758268313407[/C][C]0.524173168659272[/C][/ROW]
[ROW][C]35[/C][C]17.8[/C][C]17.6000864809197[/C][C]0.199913519080287[/C][/ROW]
[ROW][C]36[/C][C]18.1[/C][C]17.7510568669029[/C][C]0.348943133097128[/C][/ROW]
[ROW][C]37[/C][C]19[/C][C]18.6028414249413[/C][C]0.397158575058714[/C][/ROW]
[ROW][C]38[/C][C]18.9[/C][C]18.8766242554845[/C][C]0.0233757445155378[/C][/ROW]
[ROW][C]39[/C][C]16.8[/C][C]17.0754670166368[/C][C]-0.275467016636818[/C][/ROW]
[ROW][C]40[/C][C]18.1[/C][C]18.2754837645483[/C][C]-0.175483764548342[/C][/ROW]
[ROW][C]41[/C][C]15.7[/C][C]15.6322764664305[/C][C]0.0677235335695386[/C][/ROW]
[ROW][C]42[/C][C]15.1[/C][C]15.2709385487154[/C][C]-0.170938548715409[/C][/ROW]
[ROW][C]43[/C][C]18.3[/C][C]17.9073531449512[/C][C]0.392646855048824[/C][/ROW]
[ROW][C]44[/C][C]16.5[/C][C]16.4240198116178[/C][C]0.075980188382158[/C][/ROW]
[ROW][C]45[/C][C]16.9[/C][C]17.1466788991364[/C][C]-0.246678899136424[/C][/ROW]
[ROW][C]46[/C][C]18.4[/C][C]18.6976492851196[/C][C]-0.297649285119583[/C][/ROW]
[ROW][C]47[/C][C]16.4[/C][C]16.1063827589680[/C][C]0.293617241031981[/C][/ROW]
[ROW][C]48[/C][C]15.7[/C][C]15.9631754608501[/C][C]-0.263175460850134[/C][/ROW]
[ROW][C]49[/C][C]16.9[/C][C]17.0110784749559[/C][C]-0.111078474955908[/C][/ROW]
[ROW][C]50[/C][C]16.6[/C][C]16.8926243933644[/C][C]-0.292624393364354[/C][/ROW]
[ROW][C]51[/C][C]16.7[/C][C]16.9545924871567[/C][C]-0.254592487156657[/C][/ROW]
[ROW][C]52[/C][C]16.6[/C][C]16.5856615865293[/C][C]0.0143384134707157[/C][/ROW]
[ROW][C]53[/C][C]14.4[/C][C]14.1385727444788[/C][C]0.261427255521237[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]14.5617086510332[/C][C]-0.0617086510331594[/C][/ROW]
[ROW][C]55[/C][C]17.5[/C][C]16.9039455631679[/C][C]0.596054436832118[/C][/ROW]
[ROW][C]56[/C][C]14.3[/C][C]14.3419607214641[/C][C]-0.0419607214640582[/C][/ROW]
[ROW][C]57[/C][C]15.4[/C][C]15.4568567211174[/C][C]-0.0568567211173608[/C][/ROW]
[ROW][C]58[/C][C]17.2[/C][C]17.2039455631679[/C][C]-0.00394556316788222[/C][/ROW]
[ROW][C]59[/C][C]14.6[/C][C]14.5146198089826[/C][C]0.0853801910173616[/C][/ROW]
[ROW][C]60[/C][C]14.2[/C][C]14.2733532828311[/C][C]-0.0733532828310736[/C][/ROW]
[ROW][C]61[/C][C]14.9[/C][C]14.9290193848021[/C][C]-0.0290193848021241[/C][/ROW]
[ROW][C]62[/C][C]14.1[/C][C]14.2222099350085[/C][C]-0.122209935008487[/C][/ROW]
[ROW][C]63[/C][C]15.6[/C][C]15.4608887652050[/C][C]0.13911123479504[/C][/ROW]
[ROW][C]64[/C][C]14.6[/C][C]14.7977801804765[/C][C]-0.197780180476546[/C][/ROW]
[ROW][C]65[/C][C]11.9[/C][C]11.6642767421903[/C][C]0.235723257809746[/C][/ROW]
[ROW][C]66[/C][C]13.5[/C][C]13.4602418412162[/C][C]0.0397581587838143[/C][/ROW]
[ROW][C]67[/C][C]14.2[/C][C]14.233531104812[/C][C]-0.033531104812014[/C][/ROW]
[ROW][C]68[/C][C]13.7[/C][C]13.6327308237818[/C][C]0.0672691762181886[/C][/ROW]
[ROW][C]69[/C][C]14.4[/C][C]14.1592714552330[/C][C]0.240728544766973[/C][/ROW]
[ROW][C]70[/C][C]15.3[/C][C]15.0238272449804[/C][C]0.276172755019582[/C][/ROW]
[ROW][C]71[/C][C]14.3[/C][C]13.6092714552330[/C][C]0.690728544766975[/C][/ROW]
[ROW][C]72[/C][C]14.5[/C][C]14.1524787533509[/C][C]0.347521246649088[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58439&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58439&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
11817.9848727330450.0151272669550136
219.618.55283324768921.04716675231082
323.322.53717046282870.762829537171278
423.722.75659493040340.943405069596563
520.319.72115072015080.578849279849173
622.822.20353041541250.596469584587477
724.324.05547118737880.244528812621157
821.521.39542711764130.104572882358660
923.523.19673771353040.303262286469589
1022.221.90399048653680.296009513463182
1120.921.5680862051599-0.668086205159919
1222.221.71905659114310.480943408856922
1319.519.13876816800270.361231831997348
1421.121.07955787511840.0204421248815975
152222.1221182492475-0.122118249247516
1619.219.4978251038455-0.297825103845481
1717.818.8158023664012-1.01580236640122
1819.219.14087904492190.0591209550780661
1919.920.6986421327872-0.79864213278721
2019.619.6075457115886-0.0075457115885984
2118.118.5651386945009-0.465138694500919
2220.421.1947605888546-0.794760588854571
2318.118.7015532907367-0.601553290736686
2418.619.4408790449219-0.84087904492193
2517.618.2334198142530-0.633419814253042
2619.420.0761502933351-0.676150293335115
2719.319.5497630189253-0.249763018925326
2818.618.8866544341969-0.286654434196911
2916.917.0279209603485-0.127920960348478
3016.416.8627014987008-0.462701498700789
311919.4010568669029-0.401056866902873
3218.718.8983158139064-0.19831581390635
3317.116.87531651648190.224683483518142
3421.520.97582683134070.524173168659272
3517.817.60008648091970.199913519080287
3618.117.75105686690290.348943133097128
371918.60284142494130.397158575058714
3818.918.87662425548450.0233757445155378
3916.817.0754670166368-0.275467016636818
4018.118.2754837645483-0.175483764548342
4115.715.63227646643050.0677235335695386
4215.115.2709385487154-0.170938548715409
4318.317.90735314495120.392646855048824
4416.516.42401981161780.075980188382158
4516.917.1466788991364-0.246678899136424
4618.418.6976492851196-0.297649285119583
4716.416.10638275896800.293617241031981
4815.715.9631754608501-0.263175460850134
4916.917.0110784749559-0.111078474955908
5016.616.8926243933644-0.292624393364354
5116.716.9545924871567-0.254592487156657
5216.616.58566158652930.0143384134707157
5314.414.13857274447880.261427255521237
5414.514.5617086510332-0.0617086510331594
5517.516.90394556316790.596054436832118
5614.314.3419607214641-0.0419607214640582
5715.415.4568567211174-0.0568567211173608
5817.217.2039455631679-0.00394556316788222
5914.614.51461980898260.0853801910173616
6014.214.2733532828311-0.0733532828310736
6114.914.9290193848021-0.0290193848021241
6214.114.2222099350085-0.122209935008487
6315.615.46088876520500.13911123479504
6414.614.7977801804765-0.197780180476546
6511.911.66427674219030.235723257809746
6613.513.46024184121620.0397581587838143
6714.214.233531104812-0.033531104812014
6813.713.63273082378180.0672691762181886
6914.414.15927145523300.240728544766973
7015.315.02382724498040.276172755019582
7114.313.60927145523300.690728544766975
7214.514.15247875335090.347521246649088







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.9694197962846420.0611604074307160.030580203715358
180.9730641476632130.05387170467357420.0269358523367871
190.9596795757175580.08064084856488370.0403204242824419
200.9741411723513660.05171765529726730.0258588276486336
210.954011021961950.09197795607609790.0459889780380489
220.9506729279411680.09865414411766360.0493270720588318
230.9817316971908420.03653660561831580.0182683028091579
240.9921834509509550.01563309809808930.00781654904904467
250.9900595514964190.01988089700716300.00994044850358148
260.9892215679564660.0215568640870670.0107784320435335
270.9882518962564240.02349620748715140.0117481037435757
280.984188974857960.03162205028408070.0158110251420403
290.9949509070635930.01009818587281320.00504909293640658
300.9918822732419950.01623545351600910.00811772675800454
310.9978598307456270.004280338508746240.00214016925437312
320.998069912151240.003860175697518570.00193008784875928
330.9997442013651750.0005115972696497260.000255798634824863
340.9999779508837564.40982324873151e-052.20491162436576e-05
350.9999897732031582.04535936849318e-051.02267968424659e-05
360.999997369216995.2615660195967e-062.63078300979835e-06
370.9999995787420398.42515922650204e-074.21257961325102e-07
380.9999991133948341.77321033240766e-068.8660516620383e-07
390.9999978229901964.35401960696193e-062.17700980348097e-06
400.9999931153791341.37692417315975e-056.88462086579875e-06
410.9999844523755073.10952489858162e-051.55476244929081e-05
420.9999690714146846.18571706325659e-053.09285853162829e-05
430.9999762268224884.75463550246575e-052.37731775123287e-05
440.9999632579380997.34841238029508e-053.67420619014754e-05
450.9998919742186340.0002160515627314300.000108025781365715
460.999817839012380.0003643219752423930.000182160987621196
470.9996877926927820.0006244146144369920.000312207307218496
480.9990630049267870.001873990146425390.000936995073212693
490.997361370597350.005277258805298840.00263862940264942
500.9958504060085420.008299187982916190.00414959399145809
510.9922943941481830.01541121170363490.00770560585181744
520.984352217703680.03129556459263840.0156477822963192
530.9645828580532980.07083428389340420.0354171419467021
540.917349684389050.1653006312219010.0826503156109505
550.9690297322794130.06194053544117460.0309702677205873

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.969419796284642 & 0.061160407430716 & 0.030580203715358 \tabularnewline
18 & 0.973064147663213 & 0.0538717046735742 & 0.0269358523367871 \tabularnewline
19 & 0.959679575717558 & 0.0806408485648837 & 0.0403204242824419 \tabularnewline
20 & 0.974141172351366 & 0.0517176552972673 & 0.0258588276486336 \tabularnewline
21 & 0.95401102196195 & 0.0919779560760979 & 0.0459889780380489 \tabularnewline
22 & 0.950672927941168 & 0.0986541441176636 & 0.0493270720588318 \tabularnewline
23 & 0.981731697190842 & 0.0365366056183158 & 0.0182683028091579 \tabularnewline
24 & 0.992183450950955 & 0.0156330980980893 & 0.00781654904904467 \tabularnewline
25 & 0.990059551496419 & 0.0198808970071630 & 0.00994044850358148 \tabularnewline
26 & 0.989221567956466 & 0.021556864087067 & 0.0107784320435335 \tabularnewline
27 & 0.988251896256424 & 0.0234962074871514 & 0.0117481037435757 \tabularnewline
28 & 0.98418897485796 & 0.0316220502840807 & 0.0158110251420403 \tabularnewline
29 & 0.994950907063593 & 0.0100981858728132 & 0.00504909293640658 \tabularnewline
30 & 0.991882273241995 & 0.0162354535160091 & 0.00811772675800454 \tabularnewline
31 & 0.997859830745627 & 0.00428033850874624 & 0.00214016925437312 \tabularnewline
32 & 0.99806991215124 & 0.00386017569751857 & 0.00193008784875928 \tabularnewline
33 & 0.999744201365175 & 0.000511597269649726 & 0.000255798634824863 \tabularnewline
34 & 0.999977950883756 & 4.40982324873151e-05 & 2.20491162436576e-05 \tabularnewline
35 & 0.999989773203158 & 2.04535936849318e-05 & 1.02267968424659e-05 \tabularnewline
36 & 0.99999736921699 & 5.2615660195967e-06 & 2.63078300979835e-06 \tabularnewline
37 & 0.999999578742039 & 8.42515922650204e-07 & 4.21257961325102e-07 \tabularnewline
38 & 0.999999113394834 & 1.77321033240766e-06 & 8.8660516620383e-07 \tabularnewline
39 & 0.999997822990196 & 4.35401960696193e-06 & 2.17700980348097e-06 \tabularnewline
40 & 0.999993115379134 & 1.37692417315975e-05 & 6.88462086579875e-06 \tabularnewline
41 & 0.999984452375507 & 3.10952489858162e-05 & 1.55476244929081e-05 \tabularnewline
42 & 0.999969071414684 & 6.18571706325659e-05 & 3.09285853162829e-05 \tabularnewline
43 & 0.999976226822488 & 4.75463550246575e-05 & 2.37731775123287e-05 \tabularnewline
44 & 0.999963257938099 & 7.34841238029508e-05 & 3.67420619014754e-05 \tabularnewline
45 & 0.999891974218634 & 0.000216051562731430 & 0.000108025781365715 \tabularnewline
46 & 0.99981783901238 & 0.000364321975242393 & 0.000182160987621196 \tabularnewline
47 & 0.999687792692782 & 0.000624414614436992 & 0.000312207307218496 \tabularnewline
48 & 0.999063004926787 & 0.00187399014642539 & 0.000936995073212693 \tabularnewline
49 & 0.99736137059735 & 0.00527725880529884 & 0.00263862940264942 \tabularnewline
50 & 0.995850406008542 & 0.00829918798291619 & 0.00414959399145809 \tabularnewline
51 & 0.992294394148183 & 0.0154112117036349 & 0.00770560585181744 \tabularnewline
52 & 0.98435221770368 & 0.0312955645926384 & 0.0156477822963192 \tabularnewline
53 & 0.964582858053298 & 0.0708342838934042 & 0.0354171419467021 \tabularnewline
54 & 0.91734968438905 & 0.165300631221901 & 0.0826503156109505 \tabularnewline
55 & 0.969029732279413 & 0.0619405354411746 & 0.0309702677205873 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58439&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]17[/C][C]0.969419796284642[/C][C]0.061160407430716[/C][C]0.030580203715358[/C][/ROW]
[ROW][C]18[/C][C]0.973064147663213[/C][C]0.0538717046735742[/C][C]0.0269358523367871[/C][/ROW]
[ROW][C]19[/C][C]0.959679575717558[/C][C]0.0806408485648837[/C][C]0.0403204242824419[/C][/ROW]
[ROW][C]20[/C][C]0.974141172351366[/C][C]0.0517176552972673[/C][C]0.0258588276486336[/C][/ROW]
[ROW][C]21[/C][C]0.95401102196195[/C][C]0.0919779560760979[/C][C]0.0459889780380489[/C][/ROW]
[ROW][C]22[/C][C]0.950672927941168[/C][C]0.0986541441176636[/C][C]0.0493270720588318[/C][/ROW]
[ROW][C]23[/C][C]0.981731697190842[/C][C]0.0365366056183158[/C][C]0.0182683028091579[/C][/ROW]
[ROW][C]24[/C][C]0.992183450950955[/C][C]0.0156330980980893[/C][C]0.00781654904904467[/C][/ROW]
[ROW][C]25[/C][C]0.990059551496419[/C][C]0.0198808970071630[/C][C]0.00994044850358148[/C][/ROW]
[ROW][C]26[/C][C]0.989221567956466[/C][C]0.021556864087067[/C][C]0.0107784320435335[/C][/ROW]
[ROW][C]27[/C][C]0.988251896256424[/C][C]0.0234962074871514[/C][C]0.0117481037435757[/C][/ROW]
[ROW][C]28[/C][C]0.98418897485796[/C][C]0.0316220502840807[/C][C]0.0158110251420403[/C][/ROW]
[ROW][C]29[/C][C]0.994950907063593[/C][C]0.0100981858728132[/C][C]0.00504909293640658[/C][/ROW]
[ROW][C]30[/C][C]0.991882273241995[/C][C]0.0162354535160091[/C][C]0.00811772675800454[/C][/ROW]
[ROW][C]31[/C][C]0.997859830745627[/C][C]0.00428033850874624[/C][C]0.00214016925437312[/C][/ROW]
[ROW][C]32[/C][C]0.99806991215124[/C][C]0.00386017569751857[/C][C]0.00193008784875928[/C][/ROW]
[ROW][C]33[/C][C]0.999744201365175[/C][C]0.000511597269649726[/C][C]0.000255798634824863[/C][/ROW]
[ROW][C]34[/C][C]0.999977950883756[/C][C]4.40982324873151e-05[/C][C]2.20491162436576e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999989773203158[/C][C]2.04535936849318e-05[/C][C]1.02267968424659e-05[/C][/ROW]
[ROW][C]36[/C][C]0.99999736921699[/C][C]5.2615660195967e-06[/C][C]2.63078300979835e-06[/C][/ROW]
[ROW][C]37[/C][C]0.999999578742039[/C][C]8.42515922650204e-07[/C][C]4.21257961325102e-07[/C][/ROW]
[ROW][C]38[/C][C]0.999999113394834[/C][C]1.77321033240766e-06[/C][C]8.8660516620383e-07[/C][/ROW]
[ROW][C]39[/C][C]0.999997822990196[/C][C]4.35401960696193e-06[/C][C]2.17700980348097e-06[/C][/ROW]
[ROW][C]40[/C][C]0.999993115379134[/C][C]1.37692417315975e-05[/C][C]6.88462086579875e-06[/C][/ROW]
[ROW][C]41[/C][C]0.999984452375507[/C][C]3.10952489858162e-05[/C][C]1.55476244929081e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999969071414684[/C][C]6.18571706325659e-05[/C][C]3.09285853162829e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999976226822488[/C][C]4.75463550246575e-05[/C][C]2.37731775123287e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999963257938099[/C][C]7.34841238029508e-05[/C][C]3.67420619014754e-05[/C][/ROW]
[ROW][C]45[/C][C]0.999891974218634[/C][C]0.000216051562731430[/C][C]0.000108025781365715[/C][/ROW]
[ROW][C]46[/C][C]0.99981783901238[/C][C]0.000364321975242393[/C][C]0.000182160987621196[/C][/ROW]
[ROW][C]47[/C][C]0.999687792692782[/C][C]0.000624414614436992[/C][C]0.000312207307218496[/C][/ROW]
[ROW][C]48[/C][C]0.999063004926787[/C][C]0.00187399014642539[/C][C]0.000936995073212693[/C][/ROW]
[ROW][C]49[/C][C]0.99736137059735[/C][C]0.00527725880529884[/C][C]0.00263862940264942[/C][/ROW]
[ROW][C]50[/C][C]0.995850406008542[/C][C]0.00829918798291619[/C][C]0.00414959399145809[/C][/ROW]
[ROW][C]51[/C][C]0.992294394148183[/C][C]0.0154112117036349[/C][C]0.00770560585181744[/C][/ROW]
[ROW][C]52[/C][C]0.98435221770368[/C][C]0.0312955645926384[/C][C]0.0156477822963192[/C][/ROW]
[ROW][C]53[/C][C]0.964582858053298[/C][C]0.0708342838934042[/C][C]0.0354171419467021[/C][/ROW]
[ROW][C]54[/C][C]0.91734968438905[/C][C]0.165300631221901[/C][C]0.0826503156109505[/C][/ROW]
[ROW][C]55[/C][C]0.969029732279413[/C][C]0.0619405354411746[/C][C]0.0309702677205873[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58439&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58439&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
170.9694197962846420.0611604074307160.030580203715358
180.9730641476632130.05387170467357420.0269358523367871
190.9596795757175580.08064084856488370.0403204242824419
200.9741411723513660.05171765529726730.0258588276486336
210.954011021961950.09197795607609790.0459889780380489
220.9506729279411680.09865414411766360.0493270720588318
230.9817316971908420.03653660561831580.0182683028091579
240.9921834509509550.01563309809808930.00781654904904467
250.9900595514964190.01988089700716300.00994044850358148
260.9892215679564660.0215568640870670.0107784320435335
270.9882518962564240.02349620748715140.0117481037435757
280.984188974857960.03162205028408070.0158110251420403
290.9949509070635930.01009818587281320.00504909293640658
300.9918822732419950.01623545351600910.00811772675800454
310.9978598307456270.004280338508746240.00214016925437312
320.998069912151240.003860175697518570.00193008784875928
330.9997442013651750.0005115972696497260.000255798634824863
340.9999779508837564.40982324873151e-052.20491162436576e-05
350.9999897732031582.04535936849318e-051.02267968424659e-05
360.999997369216995.2615660195967e-062.63078300979835e-06
370.9999995787420398.42515922650204e-074.21257961325102e-07
380.9999991133948341.77321033240766e-068.8660516620383e-07
390.9999978229901964.35401960696193e-062.17700980348097e-06
400.9999931153791341.37692417315975e-056.88462086579875e-06
410.9999844523755073.10952489858162e-051.55476244929081e-05
420.9999690714146846.18571706325659e-053.09285853162829e-05
430.9999762268224884.75463550246575e-052.37731775123287e-05
440.9999632579380997.34841238029508e-053.67420619014754e-05
450.9998919742186340.0002160515627314300.000108025781365715
460.999817839012380.0003643219752423930.000182160987621196
470.9996877926927820.0006244146144369920.000312207307218496
480.9990630049267870.001873990146425390.000936995073212693
490.997361370597350.005277258805298840.00263862940264942
500.9958504060085420.008299187982916190.00414959399145809
510.9922943941481830.01541121170363490.00770560585181744
520.984352217703680.03129556459263840.0156477822963192
530.9645828580532980.07083428389340420.0354171419467021
540.917349684389050.1653006312219010.0826503156109505
550.9690297322794130.06194053544117460.0309702677205873







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level200.512820512820513NOK
5% type I error level300.769230769230769NOK
10% type I error level380.974358974358974NOK

\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 & 20 & 0.512820512820513 & NOK \tabularnewline
5% type I error level & 30 & 0.769230769230769 & NOK \tabularnewline
10% type I error level & 38 & 0.974358974358974 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58439&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]20[/C][C]0.512820512820513[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]38[/C][C]0.974358974358974[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58439&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58439&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 level200.512820512820513NOK
5% type I error level300.769230769230769NOK
10% type I error level380.974358974358974NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}