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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 16 Nov 2012 06:28:24 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/16/t1353065339aqqs60yru0h5qcv.htm/, Retrieved Sat, 27 Apr 2024 12:24:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189876, Retrieved Sat, 27 Apr 2024 12:24:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7] [2012-11-16 10:35:44] [3e2c7966ca4198d187b4c59e4eb5d004]
-    D      [Multiple Regression] [WS7] [2012-11-16 11:28:24] [7ac586d7aaad1f98cbd1d1bd98b37cf0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,3	1,8	1,2	1,8
2,1	1,9	1,2	1,9
2,5	2,2	1,4	1,9
2,3	2,1	1,5	1,7
2,4	2,2	1,4	1,7
3	2,7	1,8	2,1
1,7	2,8	2	2
3,5	2,9	2,3	2
4	3,4	2,6	2,5
3,7	3	2,3	2,4
3,7	3,1	2,5	2,5
3	2,5	2,3	2,5
2,7	2,2	2,1	2
2,5	2,3	2,2	1,9
2,2	2,1	2,2	2,2
2,9	2,8	2,7	2,7
3,1	3,1	3,1	3,1
3	2,9	3,2	2,8
2,8	2,6	3,1	2,5
2,5	2,7	3,1	2,4
1,9	2,3	2,8	2,2
1,9	2,3	3	2,2
1,8	2,1	2,8	2
2	2,2	2,7	2,1
2,6	2,9	3,2	2,6
2,5	2,6	3,1	2,5
2,5	2,7	3	2,5
1,6	1,8	2	2,3
1,4	1,3	1,7	2
0,8	0,9	1,2	1,9
1,1	1,3	1,4	2
1,3	1,3	1,3	2,1
1,2	1,3	1,3	2,1
1,3	1,3	1,1	2,3
1,1	1,1	0,9	2,3
1,3	1,4	1,2	2,3
1,2	1,2	0,9	2,1
1,6	1,7	1,3	2,4
1,7	1,8	1,4	2,4
1,5	1,5	1,5	2,1
0,9	1	1,1	1,8
1,5	1,6	1,6	1,9
1,4	1,5	1,5	1,9
1,6	1,8	1,6	2,1
1,7	1,8	1,7	2,2
1,4	1,6	1,6	2
1,8	1,9	1,7	2,2
1,7	1,7	1,6	2
1,4	1,6	1,6	1,9
1,2	1,3	1,3	1,6
1	1,1	1,1	1,7
1,7	1,9	1,6	2
2,4	2,6	1,9	2,5
2	2,3	1,6	2,4
2,1	2,4	1,7	2,3
2	2,2	1,6	2,3
1,8	2	1,4	2,1
2,7	2,9	2,1	2,4
2,3	2,6	1,9	2,2
1,9	2,3	1,7	2,4
2	2,3	1,8	1,9
2,3	2,6	2	2,1
2,8	3,1	2,5	2,1
2,4	2,8	2,1	2,1
2,3	2,5	2,1	2
2,7	2,9	2,3	2,1
2,7	3,1	2,4	2,2
2,9	3,1	2,4	2,2
3	3,2	2,3	2,6
2,2	2,5	1,7	2,5
2,3	2,6	2	2,3
2,8	2,9	2,3	2,2
2,8	2,6	2	2,4
2,8	2,4	2	2,3
2,2	1,7	1,3	2,2
2,6	2	1,7	2,5
2,8	2,2	1,9	2,5
2,5	1,9	1,7	2,5
2,4	1,6	1,6	2,4
2,3	1,6	1,7	2,3
1,9	1,2	1,8	1,7
1,7	1,2	1,9	1,6
2	1,5	1,9	1,9
2,1	1,6	1,9	1,9
1,7	1,7	2	1,8
1,8	1,8	2,1	1,8
1,8	1,8	1,9	1,9
1,8	1,8	1,9	1,9
1,3	1,3	1,3	1,9
1,3	1,3	1,3	1,9
1,3	1,4	1,4	1,8
1,2	1,1	1,2	1,7
1,4	1,5	1,3	2,1
2,2	2,2	1,8	2,6
2,9	2,9	2,2	3,1
3,1	3,1	2,6	3,1
3,5	3,5	2,8	3,2
3,6	3,6	3,1	3,3
4,4	4,4	3,9	3,6
4,1	4,2	3,7	3,3
5,1	5,2	4,6	3,7
5,8	5,8	5,1	4
5,9	5,9	5,2	4
5,4	5,4	4,9	3,8
5,5	5,5	5,1	3,6
4,8	4,7	4,8	3,2
3,2	3,1	3,9	2,1
2,7	2,6	3,5	1,6
2,1	2,3	3,3	1,1
1,9	1,9	2,8	1,2
0,6	0,6	1,6	0,6
0,7	0,6	1,5	0,6
-0,2	-0,4	0,7	0
-1	-1,1	-0,1	-0,1
-1,7	-1,7	-0,7	-0,6
-0,7	-0,8	-0,2	-0,2
-1	-1,2	-0,6	-0,3
-0,9	-1	-0,6	-0,1
0	-0,1	-0,3	0,5
0,3	0,3	-0,3	0,9
0,8	0,6	-0,1	0,9
0,8	0,7	0,1	0,8
1,9	1,7	0,9	1,6
2,1	1,8	1,1	1,6
2,5	2,3	1,6	1,7
2,7	2,5	2	1,5
2,4	2,6	2,2	1,7
2,4	2,3	2,1	1,6
2,9	2,9	2,6	1,9
3,1	3	2,5	1,9
3	2,9	2,5	1,9
3,4	3,1	2,6	2,2
3,7	3,2	2,7	2,3
3,5	3,4	2,8	2,4
3,5	3,5	2,9	2,7
3,3	3,4	2,9	2,8
3,1	3,3	2,9	2,7
3,4	3,7	3,3	2,7
4	3,8	3,3	2,6
3,4	3,6	3,1	2,5
3,4	3,6	3	3
3,4	3,6	3,1	3
3,7	3,8	3,4	3
3,2	3,5	3,2	2,7
3,3	3,6	3,4	2,7
3,3	3,7	3,4	2,7
3,1	3,4	3,1	2,7
2,9	3,2	3	2,6
2,6	2,8	2,7	2,4
2,2	2,3	2,2	2,4
2	2,3	2,2	2,4
2,6	2,9	2,6	2,6
2,6	2,8	2,4	2,6
2,6	2,8	2,5	2,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189876&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189876&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189876&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
HICP_Eurogebied[t] = + 0.921123714656244 + 0.0374209394068694HICP_Belgie[t] + 0.577959286947339Consumptieprijsindex_Belgie[t] -0.0923002058402775Gezondheidsindex_Belgie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HICP_Eurogebied[t] =  +  0.921123714656244 +  0.0374209394068694HICP_Belgie[t] +  0.577959286947339Consumptieprijsindex_Belgie[t] -0.0923002058402775Gezondheidsindex_Belgie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189876&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HICP_Eurogebied[t] =  +  0.921123714656244 +  0.0374209394068694HICP_Belgie[t] +  0.577959286947339Consumptieprijsindex_Belgie[t] -0.0923002058402775Gezondheidsindex_Belgie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189876&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189876&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
HICP_Eurogebied[t] = + 0.921123714656244 + 0.0374209394068694HICP_Belgie[t] + 0.577959286947339Consumptieprijsindex_Belgie[t] -0.0923002058402775Gezondheidsindex_Belgie[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.9211237146562440.0653914.086600
HICP_Belgie0.03742093940686940.0967810.38670.699560.34978
Consumptieprijsindex_Belgie0.5779592869473390.1100995.24941e-060
Gezondheidsindex_Belgie-0.09230020584027750.070701-1.30550.1937230.096861

\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) & 0.921123714656244 & 0.06539 & 14.0866 & 0 & 0 \tabularnewline
HICP_Belgie & 0.0374209394068694 & 0.096781 & 0.3867 & 0.69956 & 0.34978 \tabularnewline
Consumptieprijsindex_Belgie & 0.577959286947339 & 0.110099 & 5.2494 & 1e-06 & 0 \tabularnewline
Gezondheidsindex_Belgie & -0.0923002058402775 & 0.070701 & -1.3055 & 0.193723 & 0.096861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189876&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]0.921123714656244[/C][C]0.06539[/C][C]14.0866[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]HICP_Belgie[/C][C]0.0374209394068694[/C][C]0.096781[/C][C]0.3867[/C][C]0.69956[/C][C]0.34978[/C][/ROW]
[ROW][C]Consumptieprijsindex_Belgie[/C][C]0.577959286947339[/C][C]0.110099[/C][C]5.2494[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Gezondheidsindex_Belgie[/C][C]-0.0923002058402775[/C][C]0.070701[/C][C]-1.3055[/C][C]0.193723[/C][C]0.096861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189876&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189876&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.9211237146562440.0653914.086600
HICP_Belgie0.03742093940686940.0967810.38670.699560.34978
Consumptieprijsindex_Belgie0.5779592869473390.1100995.24941e-060
Gezondheidsindex_Belgie-0.09230020584027750.070701-1.30550.1937230.096861






Multiple Linear Regression - Regression Statistics
Multiple R0.878815424298261
R-squared0.772316549984532
Adjusted R-squared0.767762880984223
F-TEST (value)169.60313758687
F-TEST (DF numerator)3
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.363654451628317
Sum Squared Residuals19.8366840283638

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.878815424298261 \tabularnewline
R-squared & 0.772316549984532 \tabularnewline
Adjusted R-squared & 0.767762880984223 \tabularnewline
F-TEST (value) & 169.60313758687 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.363654451628317 \tabularnewline
Sum Squared Residuals & 19.8366840283638 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189876&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.878815424298261[/C][/ROW]
[ROW][C]R-squared[/C][C]0.772316549984532[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.767762880984223[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]169.60313758687[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/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]0.363654451628317[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19.8366840283638[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189876&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189876&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.878815424298261
R-squared0.772316549984532
Adjusted R-squared0.767762880984223
F-TEST (value)169.60313758687
F-TEST (DF numerator)3
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.363654451628317
Sum Squared Residuals19.8366840283638






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.81.86191646597518-0.0619164659751833
21.91.98707008560228-0.0870700856022813
31.92.15696620628118-0.256966206281176
41.72.08245606912104-0.38245606912104
51.72.15322411234049-0.453224112340489
62.12.42773623712217-0.327736237122169
722.41842490341992-0.418424903419917
822.51588846129493-0.515888461294933
92.52.79588851271995-0.295888512719954
102.42.58116857787104-0.181168577871041
112.52.62050446539772-0.120504465397719
122.52.265994276812560.234005723187437
1322.09984025007436-0.0998402500743555
141.92.14092197030369-0.240921970303688
152.22.014103831092160.185896168907841
162.72.398719886619970.301280113380034
173.12.542671778249430.557328221750569
182.82.414107806335250.385892193664752
192.52.24246585295370.257534147046299
202.42.289035499826370.110964500173626
212.22.06308928315540.136910716844601
222.22.044629241987340.155370758012656
2321.943755331825240.0562446681747552
242.12.018265468985380.0817345310146197
252.62.39913943057250.200860569427499
262.52.231239571131640.26876042886836
272.52.29826552041040.201734479589599
282.31.836723522531890.463276477468109
2921.567949752928930.432050247071069
301.91.360463577426010.539536422573988
3121.584413532858950.415586467141047
322.11.601127741324350.498872258675645
332.11.597385647383670.502614352616332
342.31.619587782492410.680412217507589
352.31.514971778389620.785028221610376
362.31.668153690603120.631846309396883
372.11.576509801025050.523490198974955
382.41.843537737925350.556462262074649
392.41.895845739976740.504154260023255
402.11.705743745427140.394256254572859
411.81.431231620645460.368768379354539
421.91.754309653537850.145690346462153
431.91.702001651486450.197998348513546
442.11.8736436048680.226356395131998
452.21.868155678224660.331844321775339
4621.750567559597160.24943244040284
472.21.929693700860080.270306299139918
4821.819589770113950.180410229886045
491.91.750567559597160.14943244040284
501.61.597385647383670.00261435261633222
511.71.492769643280880.207230356719118
5221.935181627503420.0648183724965771
532.52.338257724199290.161742275800714
542.42.177591624104420.22240837589558
552.32.229899626155810.0701003738441872
562.32.119795695409690.180204304590314
572.12.01517969130690.0848203086931008
582.42.50441175093749-0.104411750937493
592.22.3345156302586-0.134515630258599
602.42.16461950957970.235380490420295
611.92.15913158293636-0.259131582936364
622.12.32528560967457-0.225285609674571
632.12.58682561993154-0.486825619931537
642.12.4353895404207-0.335389540420698
6522.25825966039581-0.258259660395809
662.12.48595170976944-0.385951709769437
672.22.59231354657488-0.392313546574877
682.22.59979773445625-0.399797734456251
692.62.6705657776757-0.0705657776757001
702.52.291437648791230.208562351208767
712.32.32528560967457-0.0252856096745713
722.22.48969380371012-0.289693803710124
732.42.343996079378010.0560039206219941
742.32.228404221988540.0715957780114619
752.21.865990301569470.334009698430527
762.52.017426381080310.482573618919688
772.52.12204238518310.377957614816902
782.51.955888358444890.544111641555109
792.41.787988499004030.61201150099597
802.31.775016384479310.524983615520685
811.71.51963427335360.180365726646396
821.61.50292006488820.097079935111798
831.91.687534132794460.212465867205535
841.91.749072155429890.150927844570114
851.81.782669687777840.017330312222156
861.81.83497768982924-0.0349776898292372
871.91.853437730997290.0465622690027072
881.91.853437730997290.0465622690027072
891.91.601127741324350.298872258675645
901.91.601127741324350.298872258675645
911.81.649693649435060.150306350564939
921.71.491023810578230.208976189421772
932.11.720461692654510.37953830734549
942.62.1088198421230.491180157876996
953.12.502665918234840.597334081765161
963.12.588821881169570.51117811883043
973.22.81651393054320.383486069456802
983.32.850361891426540.449638108573464
993.63.268825907837680.331174092162319
1003.33.160467809794210.139532190205792
1013.73.692777850892170.0072221491078338
10244.01959797772524-0.0195979777252396
10344.07190597977663-0.071905979776633
1043.83.791905928351610.00809407164838796
1053.63.83498390981898-0.234983909818977
1063.23.37411188442838-0.17411188442838
1072.12.4725737075179-0.372573707517896
1081.62.2018036766769-0.601803676676902
1091.12.02442336811663-0.924423368116635
1101.21.83190556837646-0.631905568376464
1110.61.14267152112433-0.542671521124325
1120.61.15564363564904-0.55564363564904
11300.617845667907741-0.617845667907741
114-0.10.257177580191329-0.357177580191329
115-0.6-0.0604125260577163-0.539587473942284
116-0.20.451021668681619-0.651021668681619
117-0.30.245531754416734-0.545531754416734
118-0.10.364865705746889-0.464865705746889
1190.50.891017847713594-0.391017847713594
1200.91.13342784431459-0.23342784431459
1210.91.30706605893417-0.407066058934171
1220.81.34640194646085-0.54640194646085
1231.61.89168410208352-0.291684102083523
1241.61.93850417749158-0.338504177491575
1251.72.19630209380785-0.496302093807854
1261.52.28245805674258-0.782458056742585
1271.72.3105676624472-0.610567662447203
1281.62.14640989694703-0.546409896947028
1291.92.46574583589873-0.565745835898728
1301.92.54025597305886-0.640255973058864
1311.92.47871795042344-0.578717950423443
1322.22.60004816299163-0.40004816299163
1332.32.6598403529244-0.359840352924398
1342.42.75871800184846-0.358718001848464
1352.72.80728390995917-0.10728390995917
1362.82.742003793383060.0579962066169376
1372.72.676723676806950.0232763231930459
1382.72.88221359107184-0.18221359107184
1392.62.9624620834107-0.362462083410695
1402.52.84287770354516-0.342877703545162
14132.852107724129190.147892275870811
14232.842877703545160.157122296454838
14332.942005781004610.0579942189953931
1442.72.76836756638503-0.0683675663850258
1452.72.81144554785239-0.111445547852391
1462.72.86924147654713-0.169241476547125
1472.72.71605956433363-0.0160595643336326
1482.62.60221353964682-0.00221353964681885
1492.42.387493604797910.0125063952020945
1502.42.129695688481630.270304311518373
1512.42.122211500600250.277788499399747
1522.62.454519554076670.145480445923333
1532.62.415183666549990.184816333450011
1542.52.405953645965960.0940463540340391

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.8 & 1.86191646597518 & -0.0619164659751833 \tabularnewline
2 & 1.9 & 1.98707008560228 & -0.0870700856022813 \tabularnewline
3 & 1.9 & 2.15696620628118 & -0.256966206281176 \tabularnewline
4 & 1.7 & 2.08245606912104 & -0.38245606912104 \tabularnewline
5 & 1.7 & 2.15322411234049 & -0.453224112340489 \tabularnewline
6 & 2.1 & 2.42773623712217 & -0.327736237122169 \tabularnewline
7 & 2 & 2.41842490341992 & -0.418424903419917 \tabularnewline
8 & 2 & 2.51588846129493 & -0.515888461294933 \tabularnewline
9 & 2.5 & 2.79588851271995 & -0.295888512719954 \tabularnewline
10 & 2.4 & 2.58116857787104 & -0.181168577871041 \tabularnewline
11 & 2.5 & 2.62050446539772 & -0.120504465397719 \tabularnewline
12 & 2.5 & 2.26599427681256 & 0.234005723187437 \tabularnewline
13 & 2 & 2.09984025007436 & -0.0998402500743555 \tabularnewline
14 & 1.9 & 2.14092197030369 & -0.240921970303688 \tabularnewline
15 & 2.2 & 2.01410383109216 & 0.185896168907841 \tabularnewline
16 & 2.7 & 2.39871988661997 & 0.301280113380034 \tabularnewline
17 & 3.1 & 2.54267177824943 & 0.557328221750569 \tabularnewline
18 & 2.8 & 2.41410780633525 & 0.385892193664752 \tabularnewline
19 & 2.5 & 2.2424658529537 & 0.257534147046299 \tabularnewline
20 & 2.4 & 2.28903549982637 & 0.110964500173626 \tabularnewline
21 & 2.2 & 2.0630892831554 & 0.136910716844601 \tabularnewline
22 & 2.2 & 2.04462924198734 & 0.155370758012656 \tabularnewline
23 & 2 & 1.94375533182524 & 0.0562446681747552 \tabularnewline
24 & 2.1 & 2.01826546898538 & 0.0817345310146197 \tabularnewline
25 & 2.6 & 2.3991394305725 & 0.200860569427499 \tabularnewline
26 & 2.5 & 2.23123957113164 & 0.26876042886836 \tabularnewline
27 & 2.5 & 2.2982655204104 & 0.201734479589599 \tabularnewline
28 & 2.3 & 1.83672352253189 & 0.463276477468109 \tabularnewline
29 & 2 & 1.56794975292893 & 0.432050247071069 \tabularnewline
30 & 1.9 & 1.36046357742601 & 0.539536422573988 \tabularnewline
31 & 2 & 1.58441353285895 & 0.415586467141047 \tabularnewline
32 & 2.1 & 1.60112774132435 & 0.498872258675645 \tabularnewline
33 & 2.1 & 1.59738564738367 & 0.502614352616332 \tabularnewline
34 & 2.3 & 1.61958778249241 & 0.680412217507589 \tabularnewline
35 & 2.3 & 1.51497177838962 & 0.785028221610376 \tabularnewline
36 & 2.3 & 1.66815369060312 & 0.631846309396883 \tabularnewline
37 & 2.1 & 1.57650980102505 & 0.523490198974955 \tabularnewline
38 & 2.4 & 1.84353773792535 & 0.556462262074649 \tabularnewline
39 & 2.4 & 1.89584573997674 & 0.504154260023255 \tabularnewline
40 & 2.1 & 1.70574374542714 & 0.394256254572859 \tabularnewline
41 & 1.8 & 1.43123162064546 & 0.368768379354539 \tabularnewline
42 & 1.9 & 1.75430965353785 & 0.145690346462153 \tabularnewline
43 & 1.9 & 1.70200165148645 & 0.197998348513546 \tabularnewline
44 & 2.1 & 1.873643604868 & 0.226356395131998 \tabularnewline
45 & 2.2 & 1.86815567822466 & 0.331844321775339 \tabularnewline
46 & 2 & 1.75056755959716 & 0.24943244040284 \tabularnewline
47 & 2.2 & 1.92969370086008 & 0.270306299139918 \tabularnewline
48 & 2 & 1.81958977011395 & 0.180410229886045 \tabularnewline
49 & 1.9 & 1.75056755959716 & 0.14943244040284 \tabularnewline
50 & 1.6 & 1.59738564738367 & 0.00261435261633222 \tabularnewline
51 & 1.7 & 1.49276964328088 & 0.207230356719118 \tabularnewline
52 & 2 & 1.93518162750342 & 0.0648183724965771 \tabularnewline
53 & 2.5 & 2.33825772419929 & 0.161742275800714 \tabularnewline
54 & 2.4 & 2.17759162410442 & 0.22240837589558 \tabularnewline
55 & 2.3 & 2.22989962615581 & 0.0701003738441872 \tabularnewline
56 & 2.3 & 2.11979569540969 & 0.180204304590314 \tabularnewline
57 & 2.1 & 2.0151796913069 & 0.0848203086931008 \tabularnewline
58 & 2.4 & 2.50441175093749 & -0.104411750937493 \tabularnewline
59 & 2.2 & 2.3345156302586 & -0.134515630258599 \tabularnewline
60 & 2.4 & 2.1646195095797 & 0.235380490420295 \tabularnewline
61 & 1.9 & 2.15913158293636 & -0.259131582936364 \tabularnewline
62 & 2.1 & 2.32528560967457 & -0.225285609674571 \tabularnewline
63 & 2.1 & 2.58682561993154 & -0.486825619931537 \tabularnewline
64 & 2.1 & 2.4353895404207 & -0.335389540420698 \tabularnewline
65 & 2 & 2.25825966039581 & -0.258259660395809 \tabularnewline
66 & 2.1 & 2.48595170976944 & -0.385951709769437 \tabularnewline
67 & 2.2 & 2.59231354657488 & -0.392313546574877 \tabularnewline
68 & 2.2 & 2.59979773445625 & -0.399797734456251 \tabularnewline
69 & 2.6 & 2.6705657776757 & -0.0705657776757001 \tabularnewline
70 & 2.5 & 2.29143764879123 & 0.208562351208767 \tabularnewline
71 & 2.3 & 2.32528560967457 & -0.0252856096745713 \tabularnewline
72 & 2.2 & 2.48969380371012 & -0.289693803710124 \tabularnewline
73 & 2.4 & 2.34399607937801 & 0.0560039206219941 \tabularnewline
74 & 2.3 & 2.22840422198854 & 0.0715957780114619 \tabularnewline
75 & 2.2 & 1.86599030156947 & 0.334009698430527 \tabularnewline
76 & 2.5 & 2.01742638108031 & 0.482573618919688 \tabularnewline
77 & 2.5 & 2.1220423851831 & 0.377957614816902 \tabularnewline
78 & 2.5 & 1.95588835844489 & 0.544111641555109 \tabularnewline
79 & 2.4 & 1.78798849900403 & 0.61201150099597 \tabularnewline
80 & 2.3 & 1.77501638447931 & 0.524983615520685 \tabularnewline
81 & 1.7 & 1.5196342733536 & 0.180365726646396 \tabularnewline
82 & 1.6 & 1.5029200648882 & 0.097079935111798 \tabularnewline
83 & 1.9 & 1.68753413279446 & 0.212465867205535 \tabularnewline
84 & 1.9 & 1.74907215542989 & 0.150927844570114 \tabularnewline
85 & 1.8 & 1.78266968777784 & 0.017330312222156 \tabularnewline
86 & 1.8 & 1.83497768982924 & -0.0349776898292372 \tabularnewline
87 & 1.9 & 1.85343773099729 & 0.0465622690027072 \tabularnewline
88 & 1.9 & 1.85343773099729 & 0.0465622690027072 \tabularnewline
89 & 1.9 & 1.60112774132435 & 0.298872258675645 \tabularnewline
90 & 1.9 & 1.60112774132435 & 0.298872258675645 \tabularnewline
91 & 1.8 & 1.64969364943506 & 0.150306350564939 \tabularnewline
92 & 1.7 & 1.49102381057823 & 0.208976189421772 \tabularnewline
93 & 2.1 & 1.72046169265451 & 0.37953830734549 \tabularnewline
94 & 2.6 & 2.108819842123 & 0.491180157876996 \tabularnewline
95 & 3.1 & 2.50266591823484 & 0.597334081765161 \tabularnewline
96 & 3.1 & 2.58882188116957 & 0.51117811883043 \tabularnewline
97 & 3.2 & 2.8165139305432 & 0.383486069456802 \tabularnewline
98 & 3.3 & 2.85036189142654 & 0.449638108573464 \tabularnewline
99 & 3.6 & 3.26882590783768 & 0.331174092162319 \tabularnewline
100 & 3.3 & 3.16046780979421 & 0.139532190205792 \tabularnewline
101 & 3.7 & 3.69277785089217 & 0.0072221491078338 \tabularnewline
102 & 4 & 4.01959797772524 & -0.0195979777252396 \tabularnewline
103 & 4 & 4.07190597977663 & -0.071905979776633 \tabularnewline
104 & 3.8 & 3.79190592835161 & 0.00809407164838796 \tabularnewline
105 & 3.6 & 3.83498390981898 & -0.234983909818977 \tabularnewline
106 & 3.2 & 3.37411188442838 & -0.17411188442838 \tabularnewline
107 & 2.1 & 2.4725737075179 & -0.372573707517896 \tabularnewline
108 & 1.6 & 2.2018036766769 & -0.601803676676902 \tabularnewline
109 & 1.1 & 2.02442336811663 & -0.924423368116635 \tabularnewline
110 & 1.2 & 1.83190556837646 & -0.631905568376464 \tabularnewline
111 & 0.6 & 1.14267152112433 & -0.542671521124325 \tabularnewline
112 & 0.6 & 1.15564363564904 & -0.55564363564904 \tabularnewline
113 & 0 & 0.617845667907741 & -0.617845667907741 \tabularnewline
114 & -0.1 & 0.257177580191329 & -0.357177580191329 \tabularnewline
115 & -0.6 & -0.0604125260577163 & -0.539587473942284 \tabularnewline
116 & -0.2 & 0.451021668681619 & -0.651021668681619 \tabularnewline
117 & -0.3 & 0.245531754416734 & -0.545531754416734 \tabularnewline
118 & -0.1 & 0.364865705746889 & -0.464865705746889 \tabularnewline
119 & 0.5 & 0.891017847713594 & -0.391017847713594 \tabularnewline
120 & 0.9 & 1.13342784431459 & -0.23342784431459 \tabularnewline
121 & 0.9 & 1.30706605893417 & -0.407066058934171 \tabularnewline
122 & 0.8 & 1.34640194646085 & -0.54640194646085 \tabularnewline
123 & 1.6 & 1.89168410208352 & -0.291684102083523 \tabularnewline
124 & 1.6 & 1.93850417749158 & -0.338504177491575 \tabularnewline
125 & 1.7 & 2.19630209380785 & -0.496302093807854 \tabularnewline
126 & 1.5 & 2.28245805674258 & -0.782458056742585 \tabularnewline
127 & 1.7 & 2.3105676624472 & -0.610567662447203 \tabularnewline
128 & 1.6 & 2.14640989694703 & -0.546409896947028 \tabularnewline
129 & 1.9 & 2.46574583589873 & -0.565745835898728 \tabularnewline
130 & 1.9 & 2.54025597305886 & -0.640255973058864 \tabularnewline
131 & 1.9 & 2.47871795042344 & -0.578717950423443 \tabularnewline
132 & 2.2 & 2.60004816299163 & -0.40004816299163 \tabularnewline
133 & 2.3 & 2.6598403529244 & -0.359840352924398 \tabularnewline
134 & 2.4 & 2.75871800184846 & -0.358718001848464 \tabularnewline
135 & 2.7 & 2.80728390995917 & -0.10728390995917 \tabularnewline
136 & 2.8 & 2.74200379338306 & 0.0579962066169376 \tabularnewline
137 & 2.7 & 2.67672367680695 & 0.0232763231930459 \tabularnewline
138 & 2.7 & 2.88221359107184 & -0.18221359107184 \tabularnewline
139 & 2.6 & 2.9624620834107 & -0.362462083410695 \tabularnewline
140 & 2.5 & 2.84287770354516 & -0.342877703545162 \tabularnewline
141 & 3 & 2.85210772412919 & 0.147892275870811 \tabularnewline
142 & 3 & 2.84287770354516 & 0.157122296454838 \tabularnewline
143 & 3 & 2.94200578100461 & 0.0579942189953931 \tabularnewline
144 & 2.7 & 2.76836756638503 & -0.0683675663850258 \tabularnewline
145 & 2.7 & 2.81144554785239 & -0.111445547852391 \tabularnewline
146 & 2.7 & 2.86924147654713 & -0.169241476547125 \tabularnewline
147 & 2.7 & 2.71605956433363 & -0.0160595643336326 \tabularnewline
148 & 2.6 & 2.60221353964682 & -0.00221353964681885 \tabularnewline
149 & 2.4 & 2.38749360479791 & 0.0125063952020945 \tabularnewline
150 & 2.4 & 2.12969568848163 & 0.270304311518373 \tabularnewline
151 & 2.4 & 2.12221150060025 & 0.277788499399747 \tabularnewline
152 & 2.6 & 2.45451955407667 & 0.145480445923333 \tabularnewline
153 & 2.6 & 2.41518366654999 & 0.184816333450011 \tabularnewline
154 & 2.5 & 2.40595364596596 & 0.0940463540340391 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189876&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]1.8[/C][C]1.86191646597518[/C][C]-0.0619164659751833[/C][/ROW]
[ROW][C]2[/C][C]1.9[/C][C]1.98707008560228[/C][C]-0.0870700856022813[/C][/ROW]
[ROW][C]3[/C][C]1.9[/C][C]2.15696620628118[/C][C]-0.256966206281176[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]2.08245606912104[/C][C]-0.38245606912104[/C][/ROW]
[ROW][C]5[/C][C]1.7[/C][C]2.15322411234049[/C][C]-0.453224112340489[/C][/ROW]
[ROW][C]6[/C][C]2.1[/C][C]2.42773623712217[/C][C]-0.327736237122169[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]2.41842490341992[/C][C]-0.418424903419917[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.51588846129493[/C][C]-0.515888461294933[/C][/ROW]
[ROW][C]9[/C][C]2.5[/C][C]2.79588851271995[/C][C]-0.295888512719954[/C][/ROW]
[ROW][C]10[/C][C]2.4[/C][C]2.58116857787104[/C][C]-0.181168577871041[/C][/ROW]
[ROW][C]11[/C][C]2.5[/C][C]2.62050446539772[/C][C]-0.120504465397719[/C][/ROW]
[ROW][C]12[/C][C]2.5[/C][C]2.26599427681256[/C][C]0.234005723187437[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]2.09984025007436[/C][C]-0.0998402500743555[/C][/ROW]
[ROW][C]14[/C][C]1.9[/C][C]2.14092197030369[/C][C]-0.240921970303688[/C][/ROW]
[ROW][C]15[/C][C]2.2[/C][C]2.01410383109216[/C][C]0.185896168907841[/C][/ROW]
[ROW][C]16[/C][C]2.7[/C][C]2.39871988661997[/C][C]0.301280113380034[/C][/ROW]
[ROW][C]17[/C][C]3.1[/C][C]2.54267177824943[/C][C]0.557328221750569[/C][/ROW]
[ROW][C]18[/C][C]2.8[/C][C]2.41410780633525[/C][C]0.385892193664752[/C][/ROW]
[ROW][C]19[/C][C]2.5[/C][C]2.2424658529537[/C][C]0.257534147046299[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.28903549982637[/C][C]0.110964500173626[/C][/ROW]
[ROW][C]21[/C][C]2.2[/C][C]2.0630892831554[/C][C]0.136910716844601[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]2.04462924198734[/C][C]0.155370758012656[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.94375533182524[/C][C]0.0562446681747552[/C][/ROW]
[ROW][C]24[/C][C]2.1[/C][C]2.01826546898538[/C][C]0.0817345310146197[/C][/ROW]
[ROW][C]25[/C][C]2.6[/C][C]2.3991394305725[/C][C]0.200860569427499[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]2.23123957113164[/C][C]0.26876042886836[/C][/ROW]
[ROW][C]27[/C][C]2.5[/C][C]2.2982655204104[/C][C]0.201734479589599[/C][/ROW]
[ROW][C]28[/C][C]2.3[/C][C]1.83672352253189[/C][C]0.463276477468109[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.56794975292893[/C][C]0.432050247071069[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.36046357742601[/C][C]0.539536422573988[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.58441353285895[/C][C]0.415586467141047[/C][/ROW]
[ROW][C]32[/C][C]2.1[/C][C]1.60112774132435[/C][C]0.498872258675645[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]1.59738564738367[/C][C]0.502614352616332[/C][/ROW]
[ROW][C]34[/C][C]2.3[/C][C]1.61958778249241[/C][C]0.680412217507589[/C][/ROW]
[ROW][C]35[/C][C]2.3[/C][C]1.51497177838962[/C][C]0.785028221610376[/C][/ROW]
[ROW][C]36[/C][C]2.3[/C][C]1.66815369060312[/C][C]0.631846309396883[/C][/ROW]
[ROW][C]37[/C][C]2.1[/C][C]1.57650980102505[/C][C]0.523490198974955[/C][/ROW]
[ROW][C]38[/C][C]2.4[/C][C]1.84353773792535[/C][C]0.556462262074649[/C][/ROW]
[ROW][C]39[/C][C]2.4[/C][C]1.89584573997674[/C][C]0.504154260023255[/C][/ROW]
[ROW][C]40[/C][C]2.1[/C][C]1.70574374542714[/C][C]0.394256254572859[/C][/ROW]
[ROW][C]41[/C][C]1.8[/C][C]1.43123162064546[/C][C]0.368768379354539[/C][/ROW]
[ROW][C]42[/C][C]1.9[/C][C]1.75430965353785[/C][C]0.145690346462153[/C][/ROW]
[ROW][C]43[/C][C]1.9[/C][C]1.70200165148645[/C][C]0.197998348513546[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]1.873643604868[/C][C]0.226356395131998[/C][/ROW]
[ROW][C]45[/C][C]2.2[/C][C]1.86815567822466[/C][C]0.331844321775339[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.75056755959716[/C][C]0.24943244040284[/C][/ROW]
[ROW][C]47[/C][C]2.2[/C][C]1.92969370086008[/C][C]0.270306299139918[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.81958977011395[/C][C]0.180410229886045[/C][/ROW]
[ROW][C]49[/C][C]1.9[/C][C]1.75056755959716[/C][C]0.14943244040284[/C][/ROW]
[ROW][C]50[/C][C]1.6[/C][C]1.59738564738367[/C][C]0.00261435261633222[/C][/ROW]
[ROW][C]51[/C][C]1.7[/C][C]1.49276964328088[/C][C]0.207230356719118[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]1.93518162750342[/C][C]0.0648183724965771[/C][/ROW]
[ROW][C]53[/C][C]2.5[/C][C]2.33825772419929[/C][C]0.161742275800714[/C][/ROW]
[ROW][C]54[/C][C]2.4[/C][C]2.17759162410442[/C][C]0.22240837589558[/C][/ROW]
[ROW][C]55[/C][C]2.3[/C][C]2.22989962615581[/C][C]0.0701003738441872[/C][/ROW]
[ROW][C]56[/C][C]2.3[/C][C]2.11979569540969[/C][C]0.180204304590314[/C][/ROW]
[ROW][C]57[/C][C]2.1[/C][C]2.0151796913069[/C][C]0.0848203086931008[/C][/ROW]
[ROW][C]58[/C][C]2.4[/C][C]2.50441175093749[/C][C]-0.104411750937493[/C][/ROW]
[ROW][C]59[/C][C]2.2[/C][C]2.3345156302586[/C][C]-0.134515630258599[/C][/ROW]
[ROW][C]60[/C][C]2.4[/C][C]2.1646195095797[/C][C]0.235380490420295[/C][/ROW]
[ROW][C]61[/C][C]1.9[/C][C]2.15913158293636[/C][C]-0.259131582936364[/C][/ROW]
[ROW][C]62[/C][C]2.1[/C][C]2.32528560967457[/C][C]-0.225285609674571[/C][/ROW]
[ROW][C]63[/C][C]2.1[/C][C]2.58682561993154[/C][C]-0.486825619931537[/C][/ROW]
[ROW][C]64[/C][C]2.1[/C][C]2.4353895404207[/C][C]-0.335389540420698[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]2.25825966039581[/C][C]-0.258259660395809[/C][/ROW]
[ROW][C]66[/C][C]2.1[/C][C]2.48595170976944[/C][C]-0.385951709769437[/C][/ROW]
[ROW][C]67[/C][C]2.2[/C][C]2.59231354657488[/C][C]-0.392313546574877[/C][/ROW]
[ROW][C]68[/C][C]2.2[/C][C]2.59979773445625[/C][C]-0.399797734456251[/C][/ROW]
[ROW][C]69[/C][C]2.6[/C][C]2.6705657776757[/C][C]-0.0705657776757001[/C][/ROW]
[ROW][C]70[/C][C]2.5[/C][C]2.29143764879123[/C][C]0.208562351208767[/C][/ROW]
[ROW][C]71[/C][C]2.3[/C][C]2.32528560967457[/C][C]-0.0252856096745713[/C][/ROW]
[ROW][C]72[/C][C]2.2[/C][C]2.48969380371012[/C][C]-0.289693803710124[/C][/ROW]
[ROW][C]73[/C][C]2.4[/C][C]2.34399607937801[/C][C]0.0560039206219941[/C][/ROW]
[ROW][C]74[/C][C]2.3[/C][C]2.22840422198854[/C][C]0.0715957780114619[/C][/ROW]
[ROW][C]75[/C][C]2.2[/C][C]1.86599030156947[/C][C]0.334009698430527[/C][/ROW]
[ROW][C]76[/C][C]2.5[/C][C]2.01742638108031[/C][C]0.482573618919688[/C][/ROW]
[ROW][C]77[/C][C]2.5[/C][C]2.1220423851831[/C][C]0.377957614816902[/C][/ROW]
[ROW][C]78[/C][C]2.5[/C][C]1.95588835844489[/C][C]0.544111641555109[/C][/ROW]
[ROW][C]79[/C][C]2.4[/C][C]1.78798849900403[/C][C]0.61201150099597[/C][/ROW]
[ROW][C]80[/C][C]2.3[/C][C]1.77501638447931[/C][C]0.524983615520685[/C][/ROW]
[ROW][C]81[/C][C]1.7[/C][C]1.5196342733536[/C][C]0.180365726646396[/C][/ROW]
[ROW][C]82[/C][C]1.6[/C][C]1.5029200648882[/C][C]0.097079935111798[/C][/ROW]
[ROW][C]83[/C][C]1.9[/C][C]1.68753413279446[/C][C]0.212465867205535[/C][/ROW]
[ROW][C]84[/C][C]1.9[/C][C]1.74907215542989[/C][C]0.150927844570114[/C][/ROW]
[ROW][C]85[/C][C]1.8[/C][C]1.78266968777784[/C][C]0.017330312222156[/C][/ROW]
[ROW][C]86[/C][C]1.8[/C][C]1.83497768982924[/C][C]-0.0349776898292372[/C][/ROW]
[ROW][C]87[/C][C]1.9[/C][C]1.85343773099729[/C][C]0.0465622690027072[/C][/ROW]
[ROW][C]88[/C][C]1.9[/C][C]1.85343773099729[/C][C]0.0465622690027072[/C][/ROW]
[ROW][C]89[/C][C]1.9[/C][C]1.60112774132435[/C][C]0.298872258675645[/C][/ROW]
[ROW][C]90[/C][C]1.9[/C][C]1.60112774132435[/C][C]0.298872258675645[/C][/ROW]
[ROW][C]91[/C][C]1.8[/C][C]1.64969364943506[/C][C]0.150306350564939[/C][/ROW]
[ROW][C]92[/C][C]1.7[/C][C]1.49102381057823[/C][C]0.208976189421772[/C][/ROW]
[ROW][C]93[/C][C]2.1[/C][C]1.72046169265451[/C][C]0.37953830734549[/C][/ROW]
[ROW][C]94[/C][C]2.6[/C][C]2.108819842123[/C][C]0.491180157876996[/C][/ROW]
[ROW][C]95[/C][C]3.1[/C][C]2.50266591823484[/C][C]0.597334081765161[/C][/ROW]
[ROW][C]96[/C][C]3.1[/C][C]2.58882188116957[/C][C]0.51117811883043[/C][/ROW]
[ROW][C]97[/C][C]3.2[/C][C]2.8165139305432[/C][C]0.383486069456802[/C][/ROW]
[ROW][C]98[/C][C]3.3[/C][C]2.85036189142654[/C][C]0.449638108573464[/C][/ROW]
[ROW][C]99[/C][C]3.6[/C][C]3.26882590783768[/C][C]0.331174092162319[/C][/ROW]
[ROW][C]100[/C][C]3.3[/C][C]3.16046780979421[/C][C]0.139532190205792[/C][/ROW]
[ROW][C]101[/C][C]3.7[/C][C]3.69277785089217[/C][C]0.0072221491078338[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]4.01959797772524[/C][C]-0.0195979777252396[/C][/ROW]
[ROW][C]103[/C][C]4[/C][C]4.07190597977663[/C][C]-0.071905979776633[/C][/ROW]
[ROW][C]104[/C][C]3.8[/C][C]3.79190592835161[/C][C]0.00809407164838796[/C][/ROW]
[ROW][C]105[/C][C]3.6[/C][C]3.83498390981898[/C][C]-0.234983909818977[/C][/ROW]
[ROW][C]106[/C][C]3.2[/C][C]3.37411188442838[/C][C]-0.17411188442838[/C][/ROW]
[ROW][C]107[/C][C]2.1[/C][C]2.4725737075179[/C][C]-0.372573707517896[/C][/ROW]
[ROW][C]108[/C][C]1.6[/C][C]2.2018036766769[/C][C]-0.601803676676902[/C][/ROW]
[ROW][C]109[/C][C]1.1[/C][C]2.02442336811663[/C][C]-0.924423368116635[/C][/ROW]
[ROW][C]110[/C][C]1.2[/C][C]1.83190556837646[/C][C]-0.631905568376464[/C][/ROW]
[ROW][C]111[/C][C]0.6[/C][C]1.14267152112433[/C][C]-0.542671521124325[/C][/ROW]
[ROW][C]112[/C][C]0.6[/C][C]1.15564363564904[/C][C]-0.55564363564904[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.617845667907741[/C][C]-0.617845667907741[/C][/ROW]
[ROW][C]114[/C][C]-0.1[/C][C]0.257177580191329[/C][C]-0.357177580191329[/C][/ROW]
[ROW][C]115[/C][C]-0.6[/C][C]-0.0604125260577163[/C][C]-0.539587473942284[/C][/ROW]
[ROW][C]116[/C][C]-0.2[/C][C]0.451021668681619[/C][C]-0.651021668681619[/C][/ROW]
[ROW][C]117[/C][C]-0.3[/C][C]0.245531754416734[/C][C]-0.545531754416734[/C][/ROW]
[ROW][C]118[/C][C]-0.1[/C][C]0.364865705746889[/C][C]-0.464865705746889[/C][/ROW]
[ROW][C]119[/C][C]0.5[/C][C]0.891017847713594[/C][C]-0.391017847713594[/C][/ROW]
[ROW][C]120[/C][C]0.9[/C][C]1.13342784431459[/C][C]-0.23342784431459[/C][/ROW]
[ROW][C]121[/C][C]0.9[/C][C]1.30706605893417[/C][C]-0.407066058934171[/C][/ROW]
[ROW][C]122[/C][C]0.8[/C][C]1.34640194646085[/C][C]-0.54640194646085[/C][/ROW]
[ROW][C]123[/C][C]1.6[/C][C]1.89168410208352[/C][C]-0.291684102083523[/C][/ROW]
[ROW][C]124[/C][C]1.6[/C][C]1.93850417749158[/C][C]-0.338504177491575[/C][/ROW]
[ROW][C]125[/C][C]1.7[/C][C]2.19630209380785[/C][C]-0.496302093807854[/C][/ROW]
[ROW][C]126[/C][C]1.5[/C][C]2.28245805674258[/C][C]-0.782458056742585[/C][/ROW]
[ROW][C]127[/C][C]1.7[/C][C]2.3105676624472[/C][C]-0.610567662447203[/C][/ROW]
[ROW][C]128[/C][C]1.6[/C][C]2.14640989694703[/C][C]-0.546409896947028[/C][/ROW]
[ROW][C]129[/C][C]1.9[/C][C]2.46574583589873[/C][C]-0.565745835898728[/C][/ROW]
[ROW][C]130[/C][C]1.9[/C][C]2.54025597305886[/C][C]-0.640255973058864[/C][/ROW]
[ROW][C]131[/C][C]1.9[/C][C]2.47871795042344[/C][C]-0.578717950423443[/C][/ROW]
[ROW][C]132[/C][C]2.2[/C][C]2.60004816299163[/C][C]-0.40004816299163[/C][/ROW]
[ROW][C]133[/C][C]2.3[/C][C]2.6598403529244[/C][C]-0.359840352924398[/C][/ROW]
[ROW][C]134[/C][C]2.4[/C][C]2.75871800184846[/C][C]-0.358718001848464[/C][/ROW]
[ROW][C]135[/C][C]2.7[/C][C]2.80728390995917[/C][C]-0.10728390995917[/C][/ROW]
[ROW][C]136[/C][C]2.8[/C][C]2.74200379338306[/C][C]0.0579962066169376[/C][/ROW]
[ROW][C]137[/C][C]2.7[/C][C]2.67672367680695[/C][C]0.0232763231930459[/C][/ROW]
[ROW][C]138[/C][C]2.7[/C][C]2.88221359107184[/C][C]-0.18221359107184[/C][/ROW]
[ROW][C]139[/C][C]2.6[/C][C]2.9624620834107[/C][C]-0.362462083410695[/C][/ROW]
[ROW][C]140[/C][C]2.5[/C][C]2.84287770354516[/C][C]-0.342877703545162[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]2.85210772412919[/C][C]0.147892275870811[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]2.84287770354516[/C][C]0.157122296454838[/C][/ROW]
[ROW][C]143[/C][C]3[/C][C]2.94200578100461[/C][C]0.0579942189953931[/C][/ROW]
[ROW][C]144[/C][C]2.7[/C][C]2.76836756638503[/C][C]-0.0683675663850258[/C][/ROW]
[ROW][C]145[/C][C]2.7[/C][C]2.81144554785239[/C][C]-0.111445547852391[/C][/ROW]
[ROW][C]146[/C][C]2.7[/C][C]2.86924147654713[/C][C]-0.169241476547125[/C][/ROW]
[ROW][C]147[/C][C]2.7[/C][C]2.71605956433363[/C][C]-0.0160595643336326[/C][/ROW]
[ROW][C]148[/C][C]2.6[/C][C]2.60221353964682[/C][C]-0.00221353964681885[/C][/ROW]
[ROW][C]149[/C][C]2.4[/C][C]2.38749360479791[/C][C]0.0125063952020945[/C][/ROW]
[ROW][C]150[/C][C]2.4[/C][C]2.12969568848163[/C][C]0.270304311518373[/C][/ROW]
[ROW][C]151[/C][C]2.4[/C][C]2.12221150060025[/C][C]0.277788499399747[/C][/ROW]
[ROW][C]152[/C][C]2.6[/C][C]2.45451955407667[/C][C]0.145480445923333[/C][/ROW]
[ROW][C]153[/C][C]2.6[/C][C]2.41518366654999[/C][C]0.184816333450011[/C][/ROW]
[ROW][C]154[/C][C]2.5[/C][C]2.40595364596596[/C][C]0.0940463540340391[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189876&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189876&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.81.86191646597518-0.0619164659751833
21.91.98707008560228-0.0870700856022813
31.92.15696620628118-0.256966206281176
41.72.08245606912104-0.38245606912104
51.72.15322411234049-0.453224112340489
62.12.42773623712217-0.327736237122169
722.41842490341992-0.418424903419917
822.51588846129493-0.515888461294933
92.52.79588851271995-0.295888512719954
102.42.58116857787104-0.181168577871041
112.52.62050446539772-0.120504465397719
122.52.265994276812560.234005723187437
1322.09984025007436-0.0998402500743555
141.92.14092197030369-0.240921970303688
152.22.014103831092160.185896168907841
162.72.398719886619970.301280113380034
173.12.542671778249430.557328221750569
182.82.414107806335250.385892193664752
192.52.24246585295370.257534147046299
202.42.289035499826370.110964500173626
212.22.06308928315540.136910716844601
222.22.044629241987340.155370758012656
2321.943755331825240.0562446681747552
242.12.018265468985380.0817345310146197
252.62.39913943057250.200860569427499
262.52.231239571131640.26876042886836
272.52.29826552041040.201734479589599
282.31.836723522531890.463276477468109
2921.567949752928930.432050247071069
301.91.360463577426010.539536422573988
3121.584413532858950.415586467141047
322.11.601127741324350.498872258675645
332.11.597385647383670.502614352616332
342.31.619587782492410.680412217507589
352.31.514971778389620.785028221610376
362.31.668153690603120.631846309396883
372.11.576509801025050.523490198974955
382.41.843537737925350.556462262074649
392.41.895845739976740.504154260023255
402.11.705743745427140.394256254572859
411.81.431231620645460.368768379354539
421.91.754309653537850.145690346462153
431.91.702001651486450.197998348513546
442.11.8736436048680.226356395131998
452.21.868155678224660.331844321775339
4621.750567559597160.24943244040284
472.21.929693700860080.270306299139918
4821.819589770113950.180410229886045
491.91.750567559597160.14943244040284
501.61.597385647383670.00261435261633222
511.71.492769643280880.207230356719118
5221.935181627503420.0648183724965771
532.52.338257724199290.161742275800714
542.42.177591624104420.22240837589558
552.32.229899626155810.0701003738441872
562.32.119795695409690.180204304590314
572.12.01517969130690.0848203086931008
582.42.50441175093749-0.104411750937493
592.22.3345156302586-0.134515630258599
602.42.16461950957970.235380490420295
611.92.15913158293636-0.259131582936364
622.12.32528560967457-0.225285609674571
632.12.58682561993154-0.486825619931537
642.12.4353895404207-0.335389540420698
6522.25825966039581-0.258259660395809
662.12.48595170976944-0.385951709769437
672.22.59231354657488-0.392313546574877
682.22.59979773445625-0.399797734456251
692.62.6705657776757-0.0705657776757001
702.52.291437648791230.208562351208767
712.32.32528560967457-0.0252856096745713
722.22.48969380371012-0.289693803710124
732.42.343996079378010.0560039206219941
742.32.228404221988540.0715957780114619
752.21.865990301569470.334009698430527
762.52.017426381080310.482573618919688
772.52.12204238518310.377957614816902
782.51.955888358444890.544111641555109
792.41.787988499004030.61201150099597
802.31.775016384479310.524983615520685
811.71.51963427335360.180365726646396
821.61.50292006488820.097079935111798
831.91.687534132794460.212465867205535
841.91.749072155429890.150927844570114
851.81.782669687777840.017330312222156
861.81.83497768982924-0.0349776898292372
871.91.853437730997290.0465622690027072
881.91.853437730997290.0465622690027072
891.91.601127741324350.298872258675645
901.91.601127741324350.298872258675645
911.81.649693649435060.150306350564939
921.71.491023810578230.208976189421772
932.11.720461692654510.37953830734549
942.62.1088198421230.491180157876996
953.12.502665918234840.597334081765161
963.12.588821881169570.51117811883043
973.22.81651393054320.383486069456802
983.32.850361891426540.449638108573464
993.63.268825907837680.331174092162319
1003.33.160467809794210.139532190205792
1013.73.692777850892170.0072221491078338
10244.01959797772524-0.0195979777252396
10344.07190597977663-0.071905979776633
1043.83.791905928351610.00809407164838796
1053.63.83498390981898-0.234983909818977
1063.23.37411188442838-0.17411188442838
1072.12.4725737075179-0.372573707517896
1081.62.2018036766769-0.601803676676902
1091.12.02442336811663-0.924423368116635
1101.21.83190556837646-0.631905568376464
1110.61.14267152112433-0.542671521124325
1120.61.15564363564904-0.55564363564904
11300.617845667907741-0.617845667907741
114-0.10.257177580191329-0.357177580191329
115-0.6-0.0604125260577163-0.539587473942284
116-0.20.451021668681619-0.651021668681619
117-0.30.245531754416734-0.545531754416734
118-0.10.364865705746889-0.464865705746889
1190.50.891017847713594-0.391017847713594
1200.91.13342784431459-0.23342784431459
1210.91.30706605893417-0.407066058934171
1220.81.34640194646085-0.54640194646085
1231.61.89168410208352-0.291684102083523
1241.61.93850417749158-0.338504177491575
1251.72.19630209380785-0.496302093807854
1261.52.28245805674258-0.782458056742585
1271.72.3105676624472-0.610567662447203
1281.62.14640989694703-0.546409896947028
1291.92.46574583589873-0.565745835898728
1301.92.54025597305886-0.640255973058864
1311.92.47871795042344-0.578717950423443
1322.22.60004816299163-0.40004816299163
1332.32.6598403529244-0.359840352924398
1342.42.75871800184846-0.358718001848464
1352.72.80728390995917-0.10728390995917
1362.82.742003793383060.0579962066169376
1372.72.676723676806950.0232763231930459
1382.72.88221359107184-0.18221359107184
1392.62.9624620834107-0.362462083410695
1402.52.84287770354516-0.342877703545162
14132.852107724129190.147892275870811
14232.842877703545160.157122296454838
14332.942005781004610.0579942189953931
1442.72.76836756638503-0.0683675663850258
1452.72.81144554785239-0.111445547852391
1462.72.86924147654713-0.169241476547125
1472.72.71605956433363-0.0160595643336326
1482.62.60221353964682-0.00221353964681885
1492.42.387493604797910.0125063952020945
1502.42.129695688481630.270304311518373
1512.42.122211500600250.277788499399747
1522.62.454519554076670.145480445923333
1532.62.415183666549990.184816333450011
1542.52.405953645965960.0940463540340391






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.06895605466157870.1379121093231570.931043945338421
80.02698236616373730.05396473232747470.973017633836263
90.03609992893795360.07219985787590720.963900071062046
100.02971644189894090.05943288379788180.970283558101059
110.02187653059040760.04375306118081520.978123469409592
120.01538743861774870.03077487723549750.984612561382251
130.01335302832051980.02670605664103960.98664697167948
140.01270016118577730.02540032237155460.987299838814223
150.006997225158916090.01399445031783220.993002774841084
160.007494377364016630.01498875472803330.992505622635983
170.01165440823282990.02330881646565980.98834559176717
180.006561572960850970.01312314592170190.993438427039149
190.005444414809975770.01088882961995150.994555585190024
200.005598662350893450.01119732470178690.994401337649107
210.004416832443419980.008833664886839950.99558316755658
220.003760539218711980.007521078437423960.996239460781288
230.003326287559415290.006652575118830580.996673712440585
240.002025476133185150.004050952266370290.997974523866815
250.00111310133196850.0022262026639370.998886898668031
260.0006179021167362140.001235804233472430.999382097883264
270.000328789112866110.0006575782257322190.999671210887134
280.0008419553627539190.001683910725507840.999158044637246
290.0008629927595831170.001725985519166230.999137007240417
300.001173139331558430.002346278663116860.998826860668442
310.001068391166486560.002136782332973120.998931608833513
320.001320071617827270.002640143235654540.998679928382173
330.001469165626820720.002938331253641450.998530834373179
340.004308971391625290.008617942783250570.995691028608375
350.01168275189803540.02336550379607070.988317248101965
360.01696958897944870.03393917795889740.983030411020551
370.01606724497681670.03213448995363330.983932755023183
380.0237340634407040.0474681268814080.976265936559296
390.02986631539983690.05973263079967380.970133684600163
400.02468709211673490.04937418423346980.975312907883265
410.02337572923173180.04675145846346360.976624270768268
420.01993823814681730.03987647629363460.980061761853183
430.01671686171478650.03343372342957290.983283138285214
440.01285980306955180.02571960613910350.987140196930448
450.01077162806767240.02154325613534470.989228371932328
460.008846576011513460.01769315202302690.991153423988486
470.007078198722361640.01415639744472330.992921801277638
480.005412706626283830.01082541325256770.994587293373716
490.004637310585648380.009274621171296770.995362689414352
500.00602391429718110.01204782859436220.993976085702819
510.005771514097110980.0115430281942220.994228485902889
520.00419520154444430.008390403088888590.995804798455556
530.00441912105505580.00883824211011160.995580878944944
540.004613552314576420.009227104629152850.995386447685424
550.003516666984067110.007033333968134220.996483333015933
560.002916032680014650.005832065360029290.997083967319985
570.002066834508124130.004133669016248260.997933165491876
580.001410140098331030.002820280196662050.998589859901669
590.0009312267677868140.001862453535573630.999068773232213
600.0009710565002079230.001942113000415850.999028943499792
610.0009114004871724620.001822800974344920.999088599512828
620.000659188620021080.001318377240042160.999340811379979
630.0007138041965026980.00142760839300540.999286195803497
640.0005377059593601330.001075411918720270.99946229404064
650.0004477404189150620.0008954808378301240.999552259581085
660.0003864531855767650.0007729063711535310.999613546814423
670.0002884065779339790.0005768131558679580.999711593422066
680.0002341021791815180.0004682043583630360.999765897820818
690.0002120169432879940.0004240338865759880.999787983056712
700.0002547927023428510.0005095854046857030.999745207297657
710.0001673977550291430.0003347955100582860.999832602244971
720.0001226799257425190.0002453598514850390.999877320074257
738.33272644834981e-050.0001666545289669960.999916672735517
745.15799204118747e-050.0001031598408237490.999948420079588
753.77226201472258e-057.54452402944515e-050.999962277379853
764.66993692975376e-059.33987385950752e-050.999953300630702
774.36791708994873e-058.73583417989747e-050.9999563208291
786.56323203418009e-050.0001312646406836020.999934367679658
790.0001186284692714990.0002372569385429980.999881371530729
800.0002248604880075330.0004497209760150660.999775139511992
810.0007092605808616610.001418521161723320.999290739419138
820.002145716824994270.004291433649988530.997854283175006
830.004484000728341760.008968001456683530.995515999271658
840.01009454419783310.02018908839566620.989905455802167
850.01097773638050620.02195547276101230.989022263619494
860.01171539216925560.02343078433851130.988284607830744
870.0110345160422410.0220690320844820.988965483957759
880.01042461026275010.02084922052550030.98957538973725
890.01303386924997320.02606773849994640.986966130750027
900.01703844088679580.03407688177359160.982961559113204
910.01666105027611910.03332210055223810.983338949723881
920.02620852781214860.05241705562429720.973791472187851
930.0360483617674380.0720967235348760.963951638232562
940.08876096101164520.177521922023290.911239038988355
950.2956113712746330.5912227425492650.704388628725367
960.5588298525221650.8823402949556690.441170147477835
970.7179224060091740.5641551879816530.282077593990826
980.8865714868644490.2268570262711020.113428513135551
990.9483971189801820.1032057620396360.051602881019818
1000.9502388935054660.09952221298906780.0497611064945339
1010.9411000668046420.1177998663907170.0588999331953585
1020.9313330695646660.1373338608706690.0686669304353344
1030.9156650588744830.1686698822510330.0843349411255166
1040.9069367787267770.1861264425464470.0930632212732233
1050.884917768790540.230164462418920.11508223120946
1060.8816863465841840.2366273068316320.118313653415816
1070.9067604965937310.1864790068125380.0932395034062689
1080.9348088619128810.1303822761742390.0651911380871193
1090.9924627501392780.01507449972144460.0075372498607223
1100.9951688284697980.009662343060403270.00483117153020163
1110.9962428164602460.00751436707950750.00375718353975375
1120.9967105040273260.006578991945348060.00328949597267403
1130.997290220689850.005419558620299490.00270977931014974
1140.9972673812561560.005465237487688990.00273261874384449
1150.9971956888505120.00560862229897610.00280431114948805
1160.9976234101245810.004753179750838210.00237658987541911
1170.9971873898112480.005625220377503190.00281261018875159
1180.996406877439660.007186245120679910.00359312256033996
1190.9951480142132980.009703971573404540.00485198578670227
1200.9930620274828960.01387594503420880.00693797251710441
1210.9909393150528940.01812136989421190.00906068494710593
1220.9902064626391510.01958707472169780.00979353736084888
1230.9870369885487370.02592602290252570.0129630114512629
1240.9855694498015610.02886110039687750.0144305501984387
1250.9810016280335770.03799674393284550.0189983719664227
1260.9871173685251240.02576526294975150.0128826314748757
1270.9972826329625720.005434734074855850.00271736703742793
1280.9982212197125490.003557560574901730.00177878028745087
1290.9993518215427950.001296356914409790.000648178457204894
1300.9999072565026930.0001854869946142449.27434973071218e-05
1310.999995478448769.04310248069987e-064.52155124034994e-06
1320.9999946571305241.06857389520985e-055.34286947604924e-06
1330.9999861934496882.76131006233408e-051.38065503116704e-05
1340.9999941335500251.17328999496472e-055.86644997482358e-06
1350.9999850712952052.98574095901096e-051.49287047950548e-05
1360.9999580300892938.39398214131343e-054.19699107065671e-05
1370.9998770160914140.0002459678171711280.000122983908585564
1380.9997232079929430.0005535840141139550.000276792007056977
1390.9997751782196820.0004496435606362430.000224821780318122
1400.9999996392900247.21419951615766e-073.60709975807883e-07
1410.9999977568753124.48624937582006e-062.24312468791003e-06
1420.999995137918919.72416217979956e-064.86208108989978e-06
1430.9999983988353433.20232931361038e-061.60116465680519e-06
1440.9999857006260182.85987479644417e-051.42993739822208e-05
1450.9999299987533760.0001400024932489587.00012466244788e-05
1460.9994729763964740.001054047207052530.000527023603526264
1470.9968894743495760.006221051300848140.00311052565042407

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0689560546615787 & 0.137912109323157 & 0.931043945338421 \tabularnewline
8 & 0.0269823661637373 & 0.0539647323274747 & 0.973017633836263 \tabularnewline
9 & 0.0360999289379536 & 0.0721998578759072 & 0.963900071062046 \tabularnewline
10 & 0.0297164418989409 & 0.0594328837978818 & 0.970283558101059 \tabularnewline
11 & 0.0218765305904076 & 0.0437530611808152 & 0.978123469409592 \tabularnewline
12 & 0.0153874386177487 & 0.0307748772354975 & 0.984612561382251 \tabularnewline
13 & 0.0133530283205198 & 0.0267060566410396 & 0.98664697167948 \tabularnewline
14 & 0.0127001611857773 & 0.0254003223715546 & 0.987299838814223 \tabularnewline
15 & 0.00699722515891609 & 0.0139944503178322 & 0.993002774841084 \tabularnewline
16 & 0.00749437736401663 & 0.0149887547280333 & 0.992505622635983 \tabularnewline
17 & 0.0116544082328299 & 0.0233088164656598 & 0.98834559176717 \tabularnewline
18 & 0.00656157296085097 & 0.0131231459217019 & 0.993438427039149 \tabularnewline
19 & 0.00544441480997577 & 0.0108888296199515 & 0.994555585190024 \tabularnewline
20 & 0.00559866235089345 & 0.0111973247017869 & 0.994401337649107 \tabularnewline
21 & 0.00441683244341998 & 0.00883366488683995 & 0.99558316755658 \tabularnewline
22 & 0.00376053921871198 & 0.00752107843742396 & 0.996239460781288 \tabularnewline
23 & 0.00332628755941529 & 0.00665257511883058 & 0.996673712440585 \tabularnewline
24 & 0.00202547613318515 & 0.00405095226637029 & 0.997974523866815 \tabularnewline
25 & 0.0011131013319685 & 0.002226202663937 & 0.998886898668031 \tabularnewline
26 & 0.000617902116736214 & 0.00123580423347243 & 0.999382097883264 \tabularnewline
27 & 0.00032878911286611 & 0.000657578225732219 & 0.999671210887134 \tabularnewline
28 & 0.000841955362753919 & 0.00168391072550784 & 0.999158044637246 \tabularnewline
29 & 0.000862992759583117 & 0.00172598551916623 & 0.999137007240417 \tabularnewline
30 & 0.00117313933155843 & 0.00234627866311686 & 0.998826860668442 \tabularnewline
31 & 0.00106839116648656 & 0.00213678233297312 & 0.998931608833513 \tabularnewline
32 & 0.00132007161782727 & 0.00264014323565454 & 0.998679928382173 \tabularnewline
33 & 0.00146916562682072 & 0.00293833125364145 & 0.998530834373179 \tabularnewline
34 & 0.00430897139162529 & 0.00861794278325057 & 0.995691028608375 \tabularnewline
35 & 0.0116827518980354 & 0.0233655037960707 & 0.988317248101965 \tabularnewline
36 & 0.0169695889794487 & 0.0339391779588974 & 0.983030411020551 \tabularnewline
37 & 0.0160672449768167 & 0.0321344899536333 & 0.983932755023183 \tabularnewline
38 & 0.023734063440704 & 0.047468126881408 & 0.976265936559296 \tabularnewline
39 & 0.0298663153998369 & 0.0597326307996738 & 0.970133684600163 \tabularnewline
40 & 0.0246870921167349 & 0.0493741842334698 & 0.975312907883265 \tabularnewline
41 & 0.0233757292317318 & 0.0467514584634636 & 0.976624270768268 \tabularnewline
42 & 0.0199382381468173 & 0.0398764762936346 & 0.980061761853183 \tabularnewline
43 & 0.0167168617147865 & 0.0334337234295729 & 0.983283138285214 \tabularnewline
44 & 0.0128598030695518 & 0.0257196061391035 & 0.987140196930448 \tabularnewline
45 & 0.0107716280676724 & 0.0215432561353447 & 0.989228371932328 \tabularnewline
46 & 0.00884657601151346 & 0.0176931520230269 & 0.991153423988486 \tabularnewline
47 & 0.00707819872236164 & 0.0141563974447233 & 0.992921801277638 \tabularnewline
48 & 0.00541270662628383 & 0.0108254132525677 & 0.994587293373716 \tabularnewline
49 & 0.00463731058564838 & 0.00927462117129677 & 0.995362689414352 \tabularnewline
50 & 0.0060239142971811 & 0.0120478285943622 & 0.993976085702819 \tabularnewline
51 & 0.00577151409711098 & 0.011543028194222 & 0.994228485902889 \tabularnewline
52 & 0.0041952015444443 & 0.00839040308888859 & 0.995804798455556 \tabularnewline
53 & 0.0044191210550558 & 0.0088382421101116 & 0.995580878944944 \tabularnewline
54 & 0.00461355231457642 & 0.00922710462915285 & 0.995386447685424 \tabularnewline
55 & 0.00351666698406711 & 0.00703333396813422 & 0.996483333015933 \tabularnewline
56 & 0.00291603268001465 & 0.00583206536002929 & 0.997083967319985 \tabularnewline
57 & 0.00206683450812413 & 0.00413366901624826 & 0.997933165491876 \tabularnewline
58 & 0.00141014009833103 & 0.00282028019666205 & 0.998589859901669 \tabularnewline
59 & 0.000931226767786814 & 0.00186245353557363 & 0.999068773232213 \tabularnewline
60 & 0.000971056500207923 & 0.00194211300041585 & 0.999028943499792 \tabularnewline
61 & 0.000911400487172462 & 0.00182280097434492 & 0.999088599512828 \tabularnewline
62 & 0.00065918862002108 & 0.00131837724004216 & 0.999340811379979 \tabularnewline
63 & 0.000713804196502698 & 0.0014276083930054 & 0.999286195803497 \tabularnewline
64 & 0.000537705959360133 & 0.00107541191872027 & 0.99946229404064 \tabularnewline
65 & 0.000447740418915062 & 0.000895480837830124 & 0.999552259581085 \tabularnewline
66 & 0.000386453185576765 & 0.000772906371153531 & 0.999613546814423 \tabularnewline
67 & 0.000288406577933979 & 0.000576813155867958 & 0.999711593422066 \tabularnewline
68 & 0.000234102179181518 & 0.000468204358363036 & 0.999765897820818 \tabularnewline
69 & 0.000212016943287994 & 0.000424033886575988 & 0.999787983056712 \tabularnewline
70 & 0.000254792702342851 & 0.000509585404685703 & 0.999745207297657 \tabularnewline
71 & 0.000167397755029143 & 0.000334795510058286 & 0.999832602244971 \tabularnewline
72 & 0.000122679925742519 & 0.000245359851485039 & 0.999877320074257 \tabularnewline
73 & 8.33272644834981e-05 & 0.000166654528966996 & 0.999916672735517 \tabularnewline
74 & 5.15799204118747e-05 & 0.000103159840823749 & 0.999948420079588 \tabularnewline
75 & 3.77226201472258e-05 & 7.54452402944515e-05 & 0.999962277379853 \tabularnewline
76 & 4.66993692975376e-05 & 9.33987385950752e-05 & 0.999953300630702 \tabularnewline
77 & 4.36791708994873e-05 & 8.73583417989747e-05 & 0.9999563208291 \tabularnewline
78 & 6.56323203418009e-05 & 0.000131264640683602 & 0.999934367679658 \tabularnewline
79 & 0.000118628469271499 & 0.000237256938542998 & 0.999881371530729 \tabularnewline
80 & 0.000224860488007533 & 0.000449720976015066 & 0.999775139511992 \tabularnewline
81 & 0.000709260580861661 & 0.00141852116172332 & 0.999290739419138 \tabularnewline
82 & 0.00214571682499427 & 0.00429143364998853 & 0.997854283175006 \tabularnewline
83 & 0.00448400072834176 & 0.00896800145668353 & 0.995515999271658 \tabularnewline
84 & 0.0100945441978331 & 0.0201890883956662 & 0.989905455802167 \tabularnewline
85 & 0.0109777363805062 & 0.0219554727610123 & 0.989022263619494 \tabularnewline
86 & 0.0117153921692556 & 0.0234307843385113 & 0.988284607830744 \tabularnewline
87 & 0.011034516042241 & 0.022069032084482 & 0.988965483957759 \tabularnewline
88 & 0.0104246102627501 & 0.0208492205255003 & 0.98957538973725 \tabularnewline
89 & 0.0130338692499732 & 0.0260677384999464 & 0.986966130750027 \tabularnewline
90 & 0.0170384408867958 & 0.0340768817735916 & 0.982961559113204 \tabularnewline
91 & 0.0166610502761191 & 0.0333221005522381 & 0.983338949723881 \tabularnewline
92 & 0.0262085278121486 & 0.0524170556242972 & 0.973791472187851 \tabularnewline
93 & 0.036048361767438 & 0.072096723534876 & 0.963951638232562 \tabularnewline
94 & 0.0887609610116452 & 0.17752192202329 & 0.911239038988355 \tabularnewline
95 & 0.295611371274633 & 0.591222742549265 & 0.704388628725367 \tabularnewline
96 & 0.558829852522165 & 0.882340294955669 & 0.441170147477835 \tabularnewline
97 & 0.717922406009174 & 0.564155187981653 & 0.282077593990826 \tabularnewline
98 & 0.886571486864449 & 0.226857026271102 & 0.113428513135551 \tabularnewline
99 & 0.948397118980182 & 0.103205762039636 & 0.051602881019818 \tabularnewline
100 & 0.950238893505466 & 0.0995222129890678 & 0.0497611064945339 \tabularnewline
101 & 0.941100066804642 & 0.117799866390717 & 0.0588999331953585 \tabularnewline
102 & 0.931333069564666 & 0.137333860870669 & 0.0686669304353344 \tabularnewline
103 & 0.915665058874483 & 0.168669882251033 & 0.0843349411255166 \tabularnewline
104 & 0.906936778726777 & 0.186126442546447 & 0.0930632212732233 \tabularnewline
105 & 0.88491776879054 & 0.23016446241892 & 0.11508223120946 \tabularnewline
106 & 0.881686346584184 & 0.236627306831632 & 0.118313653415816 \tabularnewline
107 & 0.906760496593731 & 0.186479006812538 & 0.0932395034062689 \tabularnewline
108 & 0.934808861912881 & 0.130382276174239 & 0.0651911380871193 \tabularnewline
109 & 0.992462750139278 & 0.0150744997214446 & 0.0075372498607223 \tabularnewline
110 & 0.995168828469798 & 0.00966234306040327 & 0.00483117153020163 \tabularnewline
111 & 0.996242816460246 & 0.0075143670795075 & 0.00375718353975375 \tabularnewline
112 & 0.996710504027326 & 0.00657899194534806 & 0.00328949597267403 \tabularnewline
113 & 0.99729022068985 & 0.00541955862029949 & 0.00270977931014974 \tabularnewline
114 & 0.997267381256156 & 0.00546523748768899 & 0.00273261874384449 \tabularnewline
115 & 0.997195688850512 & 0.0056086222989761 & 0.00280431114948805 \tabularnewline
116 & 0.997623410124581 & 0.00475317975083821 & 0.00237658987541911 \tabularnewline
117 & 0.997187389811248 & 0.00562522037750319 & 0.00281261018875159 \tabularnewline
118 & 0.99640687743966 & 0.00718624512067991 & 0.00359312256033996 \tabularnewline
119 & 0.995148014213298 & 0.00970397157340454 & 0.00485198578670227 \tabularnewline
120 & 0.993062027482896 & 0.0138759450342088 & 0.00693797251710441 \tabularnewline
121 & 0.990939315052894 & 0.0181213698942119 & 0.00906068494710593 \tabularnewline
122 & 0.990206462639151 & 0.0195870747216978 & 0.00979353736084888 \tabularnewline
123 & 0.987036988548737 & 0.0259260229025257 & 0.0129630114512629 \tabularnewline
124 & 0.985569449801561 & 0.0288611003968775 & 0.0144305501984387 \tabularnewline
125 & 0.981001628033577 & 0.0379967439328455 & 0.0189983719664227 \tabularnewline
126 & 0.987117368525124 & 0.0257652629497515 & 0.0128826314748757 \tabularnewline
127 & 0.997282632962572 & 0.00543473407485585 & 0.00271736703742793 \tabularnewline
128 & 0.998221219712549 & 0.00355756057490173 & 0.00177878028745087 \tabularnewline
129 & 0.999351821542795 & 0.00129635691440979 & 0.000648178457204894 \tabularnewline
130 & 0.999907256502693 & 0.000185486994614244 & 9.27434973071218e-05 \tabularnewline
131 & 0.99999547844876 & 9.04310248069987e-06 & 4.52155124034994e-06 \tabularnewline
132 & 0.999994657130524 & 1.06857389520985e-05 & 5.34286947604924e-06 \tabularnewline
133 & 0.999986193449688 & 2.76131006233408e-05 & 1.38065503116704e-05 \tabularnewline
134 & 0.999994133550025 & 1.17328999496472e-05 & 5.86644997482358e-06 \tabularnewline
135 & 0.999985071295205 & 2.98574095901096e-05 & 1.49287047950548e-05 \tabularnewline
136 & 0.999958030089293 & 8.39398214131343e-05 & 4.19699107065671e-05 \tabularnewline
137 & 0.999877016091414 & 0.000245967817171128 & 0.000122983908585564 \tabularnewline
138 & 0.999723207992943 & 0.000553584014113955 & 0.000276792007056977 \tabularnewline
139 & 0.999775178219682 & 0.000449643560636243 & 0.000224821780318122 \tabularnewline
140 & 0.999999639290024 & 7.21419951615766e-07 & 3.60709975807883e-07 \tabularnewline
141 & 0.999997756875312 & 4.48624937582006e-06 & 2.24312468791003e-06 \tabularnewline
142 & 0.99999513791891 & 9.72416217979956e-06 & 4.86208108989978e-06 \tabularnewline
143 & 0.999998398835343 & 3.20232931361038e-06 & 1.60116465680519e-06 \tabularnewline
144 & 0.999985700626018 & 2.85987479644417e-05 & 1.42993739822208e-05 \tabularnewline
145 & 0.999929998753376 & 0.000140002493248958 & 7.00012466244788e-05 \tabularnewline
146 & 0.999472976396474 & 0.00105404720705253 & 0.000527023603526264 \tabularnewline
147 & 0.996889474349576 & 0.00622105130084814 & 0.00311052565042407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189876&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]7[/C][C]0.0689560546615787[/C][C]0.137912109323157[/C][C]0.931043945338421[/C][/ROW]
[ROW][C]8[/C][C]0.0269823661637373[/C][C]0.0539647323274747[/C][C]0.973017633836263[/C][/ROW]
[ROW][C]9[/C][C]0.0360999289379536[/C][C]0.0721998578759072[/C][C]0.963900071062046[/C][/ROW]
[ROW][C]10[/C][C]0.0297164418989409[/C][C]0.0594328837978818[/C][C]0.970283558101059[/C][/ROW]
[ROW][C]11[/C][C]0.0218765305904076[/C][C]0.0437530611808152[/C][C]0.978123469409592[/C][/ROW]
[ROW][C]12[/C][C]0.0153874386177487[/C][C]0.0307748772354975[/C][C]0.984612561382251[/C][/ROW]
[ROW][C]13[/C][C]0.0133530283205198[/C][C]0.0267060566410396[/C][C]0.98664697167948[/C][/ROW]
[ROW][C]14[/C][C]0.0127001611857773[/C][C]0.0254003223715546[/C][C]0.987299838814223[/C][/ROW]
[ROW][C]15[/C][C]0.00699722515891609[/C][C]0.0139944503178322[/C][C]0.993002774841084[/C][/ROW]
[ROW][C]16[/C][C]0.00749437736401663[/C][C]0.0149887547280333[/C][C]0.992505622635983[/C][/ROW]
[ROW][C]17[/C][C]0.0116544082328299[/C][C]0.0233088164656598[/C][C]0.98834559176717[/C][/ROW]
[ROW][C]18[/C][C]0.00656157296085097[/C][C]0.0131231459217019[/C][C]0.993438427039149[/C][/ROW]
[ROW][C]19[/C][C]0.00544441480997577[/C][C]0.0108888296199515[/C][C]0.994555585190024[/C][/ROW]
[ROW][C]20[/C][C]0.00559866235089345[/C][C]0.0111973247017869[/C][C]0.994401337649107[/C][/ROW]
[ROW][C]21[/C][C]0.00441683244341998[/C][C]0.00883366488683995[/C][C]0.99558316755658[/C][/ROW]
[ROW][C]22[/C][C]0.00376053921871198[/C][C]0.00752107843742396[/C][C]0.996239460781288[/C][/ROW]
[ROW][C]23[/C][C]0.00332628755941529[/C][C]0.00665257511883058[/C][C]0.996673712440585[/C][/ROW]
[ROW][C]24[/C][C]0.00202547613318515[/C][C]0.00405095226637029[/C][C]0.997974523866815[/C][/ROW]
[ROW][C]25[/C][C]0.0011131013319685[/C][C]0.002226202663937[/C][C]0.998886898668031[/C][/ROW]
[ROW][C]26[/C][C]0.000617902116736214[/C][C]0.00123580423347243[/C][C]0.999382097883264[/C][/ROW]
[ROW][C]27[/C][C]0.00032878911286611[/C][C]0.000657578225732219[/C][C]0.999671210887134[/C][/ROW]
[ROW][C]28[/C][C]0.000841955362753919[/C][C]0.00168391072550784[/C][C]0.999158044637246[/C][/ROW]
[ROW][C]29[/C][C]0.000862992759583117[/C][C]0.00172598551916623[/C][C]0.999137007240417[/C][/ROW]
[ROW][C]30[/C][C]0.00117313933155843[/C][C]0.00234627866311686[/C][C]0.998826860668442[/C][/ROW]
[ROW][C]31[/C][C]0.00106839116648656[/C][C]0.00213678233297312[/C][C]0.998931608833513[/C][/ROW]
[ROW][C]32[/C][C]0.00132007161782727[/C][C]0.00264014323565454[/C][C]0.998679928382173[/C][/ROW]
[ROW][C]33[/C][C]0.00146916562682072[/C][C]0.00293833125364145[/C][C]0.998530834373179[/C][/ROW]
[ROW][C]34[/C][C]0.00430897139162529[/C][C]0.00861794278325057[/C][C]0.995691028608375[/C][/ROW]
[ROW][C]35[/C][C]0.0116827518980354[/C][C]0.0233655037960707[/C][C]0.988317248101965[/C][/ROW]
[ROW][C]36[/C][C]0.0169695889794487[/C][C]0.0339391779588974[/C][C]0.983030411020551[/C][/ROW]
[ROW][C]37[/C][C]0.0160672449768167[/C][C]0.0321344899536333[/C][C]0.983932755023183[/C][/ROW]
[ROW][C]38[/C][C]0.023734063440704[/C][C]0.047468126881408[/C][C]0.976265936559296[/C][/ROW]
[ROW][C]39[/C][C]0.0298663153998369[/C][C]0.0597326307996738[/C][C]0.970133684600163[/C][/ROW]
[ROW][C]40[/C][C]0.0246870921167349[/C][C]0.0493741842334698[/C][C]0.975312907883265[/C][/ROW]
[ROW][C]41[/C][C]0.0233757292317318[/C][C]0.0467514584634636[/C][C]0.976624270768268[/C][/ROW]
[ROW][C]42[/C][C]0.0199382381468173[/C][C]0.0398764762936346[/C][C]0.980061761853183[/C][/ROW]
[ROW][C]43[/C][C]0.0167168617147865[/C][C]0.0334337234295729[/C][C]0.983283138285214[/C][/ROW]
[ROW][C]44[/C][C]0.0128598030695518[/C][C]0.0257196061391035[/C][C]0.987140196930448[/C][/ROW]
[ROW][C]45[/C][C]0.0107716280676724[/C][C]0.0215432561353447[/C][C]0.989228371932328[/C][/ROW]
[ROW][C]46[/C][C]0.00884657601151346[/C][C]0.0176931520230269[/C][C]0.991153423988486[/C][/ROW]
[ROW][C]47[/C][C]0.00707819872236164[/C][C]0.0141563974447233[/C][C]0.992921801277638[/C][/ROW]
[ROW][C]48[/C][C]0.00541270662628383[/C][C]0.0108254132525677[/C][C]0.994587293373716[/C][/ROW]
[ROW][C]49[/C][C]0.00463731058564838[/C][C]0.00927462117129677[/C][C]0.995362689414352[/C][/ROW]
[ROW][C]50[/C][C]0.0060239142971811[/C][C]0.0120478285943622[/C][C]0.993976085702819[/C][/ROW]
[ROW][C]51[/C][C]0.00577151409711098[/C][C]0.011543028194222[/C][C]0.994228485902889[/C][/ROW]
[ROW][C]52[/C][C]0.0041952015444443[/C][C]0.00839040308888859[/C][C]0.995804798455556[/C][/ROW]
[ROW][C]53[/C][C]0.0044191210550558[/C][C]0.0088382421101116[/C][C]0.995580878944944[/C][/ROW]
[ROW][C]54[/C][C]0.00461355231457642[/C][C]0.00922710462915285[/C][C]0.995386447685424[/C][/ROW]
[ROW][C]55[/C][C]0.00351666698406711[/C][C]0.00703333396813422[/C][C]0.996483333015933[/C][/ROW]
[ROW][C]56[/C][C]0.00291603268001465[/C][C]0.00583206536002929[/C][C]0.997083967319985[/C][/ROW]
[ROW][C]57[/C][C]0.00206683450812413[/C][C]0.00413366901624826[/C][C]0.997933165491876[/C][/ROW]
[ROW][C]58[/C][C]0.00141014009833103[/C][C]0.00282028019666205[/C][C]0.998589859901669[/C][/ROW]
[ROW][C]59[/C][C]0.000931226767786814[/C][C]0.00186245353557363[/C][C]0.999068773232213[/C][/ROW]
[ROW][C]60[/C][C]0.000971056500207923[/C][C]0.00194211300041585[/C][C]0.999028943499792[/C][/ROW]
[ROW][C]61[/C][C]0.000911400487172462[/C][C]0.00182280097434492[/C][C]0.999088599512828[/C][/ROW]
[ROW][C]62[/C][C]0.00065918862002108[/C][C]0.00131837724004216[/C][C]0.999340811379979[/C][/ROW]
[ROW][C]63[/C][C]0.000713804196502698[/C][C]0.0014276083930054[/C][C]0.999286195803497[/C][/ROW]
[ROW][C]64[/C][C]0.000537705959360133[/C][C]0.00107541191872027[/C][C]0.99946229404064[/C][/ROW]
[ROW][C]65[/C][C]0.000447740418915062[/C][C]0.000895480837830124[/C][C]0.999552259581085[/C][/ROW]
[ROW][C]66[/C][C]0.000386453185576765[/C][C]0.000772906371153531[/C][C]0.999613546814423[/C][/ROW]
[ROW][C]67[/C][C]0.000288406577933979[/C][C]0.000576813155867958[/C][C]0.999711593422066[/C][/ROW]
[ROW][C]68[/C][C]0.000234102179181518[/C][C]0.000468204358363036[/C][C]0.999765897820818[/C][/ROW]
[ROW][C]69[/C][C]0.000212016943287994[/C][C]0.000424033886575988[/C][C]0.999787983056712[/C][/ROW]
[ROW][C]70[/C][C]0.000254792702342851[/C][C]0.000509585404685703[/C][C]0.999745207297657[/C][/ROW]
[ROW][C]71[/C][C]0.000167397755029143[/C][C]0.000334795510058286[/C][C]0.999832602244971[/C][/ROW]
[ROW][C]72[/C][C]0.000122679925742519[/C][C]0.000245359851485039[/C][C]0.999877320074257[/C][/ROW]
[ROW][C]73[/C][C]8.33272644834981e-05[/C][C]0.000166654528966996[/C][C]0.999916672735517[/C][/ROW]
[ROW][C]74[/C][C]5.15799204118747e-05[/C][C]0.000103159840823749[/C][C]0.999948420079588[/C][/ROW]
[ROW][C]75[/C][C]3.77226201472258e-05[/C][C]7.54452402944515e-05[/C][C]0.999962277379853[/C][/ROW]
[ROW][C]76[/C][C]4.66993692975376e-05[/C][C]9.33987385950752e-05[/C][C]0.999953300630702[/C][/ROW]
[ROW][C]77[/C][C]4.36791708994873e-05[/C][C]8.73583417989747e-05[/C][C]0.9999563208291[/C][/ROW]
[ROW][C]78[/C][C]6.56323203418009e-05[/C][C]0.000131264640683602[/C][C]0.999934367679658[/C][/ROW]
[ROW][C]79[/C][C]0.000118628469271499[/C][C]0.000237256938542998[/C][C]0.999881371530729[/C][/ROW]
[ROW][C]80[/C][C]0.000224860488007533[/C][C]0.000449720976015066[/C][C]0.999775139511992[/C][/ROW]
[ROW][C]81[/C][C]0.000709260580861661[/C][C]0.00141852116172332[/C][C]0.999290739419138[/C][/ROW]
[ROW][C]82[/C][C]0.00214571682499427[/C][C]0.00429143364998853[/C][C]0.997854283175006[/C][/ROW]
[ROW][C]83[/C][C]0.00448400072834176[/C][C]0.00896800145668353[/C][C]0.995515999271658[/C][/ROW]
[ROW][C]84[/C][C]0.0100945441978331[/C][C]0.0201890883956662[/C][C]0.989905455802167[/C][/ROW]
[ROW][C]85[/C][C]0.0109777363805062[/C][C]0.0219554727610123[/C][C]0.989022263619494[/C][/ROW]
[ROW][C]86[/C][C]0.0117153921692556[/C][C]0.0234307843385113[/C][C]0.988284607830744[/C][/ROW]
[ROW][C]87[/C][C]0.011034516042241[/C][C]0.022069032084482[/C][C]0.988965483957759[/C][/ROW]
[ROW][C]88[/C][C]0.0104246102627501[/C][C]0.0208492205255003[/C][C]0.98957538973725[/C][/ROW]
[ROW][C]89[/C][C]0.0130338692499732[/C][C]0.0260677384999464[/C][C]0.986966130750027[/C][/ROW]
[ROW][C]90[/C][C]0.0170384408867958[/C][C]0.0340768817735916[/C][C]0.982961559113204[/C][/ROW]
[ROW][C]91[/C][C]0.0166610502761191[/C][C]0.0333221005522381[/C][C]0.983338949723881[/C][/ROW]
[ROW][C]92[/C][C]0.0262085278121486[/C][C]0.0524170556242972[/C][C]0.973791472187851[/C][/ROW]
[ROW][C]93[/C][C]0.036048361767438[/C][C]0.072096723534876[/C][C]0.963951638232562[/C][/ROW]
[ROW][C]94[/C][C]0.0887609610116452[/C][C]0.17752192202329[/C][C]0.911239038988355[/C][/ROW]
[ROW][C]95[/C][C]0.295611371274633[/C][C]0.591222742549265[/C][C]0.704388628725367[/C][/ROW]
[ROW][C]96[/C][C]0.558829852522165[/C][C]0.882340294955669[/C][C]0.441170147477835[/C][/ROW]
[ROW][C]97[/C][C]0.717922406009174[/C][C]0.564155187981653[/C][C]0.282077593990826[/C][/ROW]
[ROW][C]98[/C][C]0.886571486864449[/C][C]0.226857026271102[/C][C]0.113428513135551[/C][/ROW]
[ROW][C]99[/C][C]0.948397118980182[/C][C]0.103205762039636[/C][C]0.051602881019818[/C][/ROW]
[ROW][C]100[/C][C]0.950238893505466[/C][C]0.0995222129890678[/C][C]0.0497611064945339[/C][/ROW]
[ROW][C]101[/C][C]0.941100066804642[/C][C]0.117799866390717[/C][C]0.0588999331953585[/C][/ROW]
[ROW][C]102[/C][C]0.931333069564666[/C][C]0.137333860870669[/C][C]0.0686669304353344[/C][/ROW]
[ROW][C]103[/C][C]0.915665058874483[/C][C]0.168669882251033[/C][C]0.0843349411255166[/C][/ROW]
[ROW][C]104[/C][C]0.906936778726777[/C][C]0.186126442546447[/C][C]0.0930632212732233[/C][/ROW]
[ROW][C]105[/C][C]0.88491776879054[/C][C]0.23016446241892[/C][C]0.11508223120946[/C][/ROW]
[ROW][C]106[/C][C]0.881686346584184[/C][C]0.236627306831632[/C][C]0.118313653415816[/C][/ROW]
[ROW][C]107[/C][C]0.906760496593731[/C][C]0.186479006812538[/C][C]0.0932395034062689[/C][/ROW]
[ROW][C]108[/C][C]0.934808861912881[/C][C]0.130382276174239[/C][C]0.0651911380871193[/C][/ROW]
[ROW][C]109[/C][C]0.992462750139278[/C][C]0.0150744997214446[/C][C]0.0075372498607223[/C][/ROW]
[ROW][C]110[/C][C]0.995168828469798[/C][C]0.00966234306040327[/C][C]0.00483117153020163[/C][/ROW]
[ROW][C]111[/C][C]0.996242816460246[/C][C]0.0075143670795075[/C][C]0.00375718353975375[/C][/ROW]
[ROW][C]112[/C][C]0.996710504027326[/C][C]0.00657899194534806[/C][C]0.00328949597267403[/C][/ROW]
[ROW][C]113[/C][C]0.99729022068985[/C][C]0.00541955862029949[/C][C]0.00270977931014974[/C][/ROW]
[ROW][C]114[/C][C]0.997267381256156[/C][C]0.00546523748768899[/C][C]0.00273261874384449[/C][/ROW]
[ROW][C]115[/C][C]0.997195688850512[/C][C]0.0056086222989761[/C][C]0.00280431114948805[/C][/ROW]
[ROW][C]116[/C][C]0.997623410124581[/C][C]0.00475317975083821[/C][C]0.00237658987541911[/C][/ROW]
[ROW][C]117[/C][C]0.997187389811248[/C][C]0.00562522037750319[/C][C]0.00281261018875159[/C][/ROW]
[ROW][C]118[/C][C]0.99640687743966[/C][C]0.00718624512067991[/C][C]0.00359312256033996[/C][/ROW]
[ROW][C]119[/C][C]0.995148014213298[/C][C]0.00970397157340454[/C][C]0.00485198578670227[/C][/ROW]
[ROW][C]120[/C][C]0.993062027482896[/C][C]0.0138759450342088[/C][C]0.00693797251710441[/C][/ROW]
[ROW][C]121[/C][C]0.990939315052894[/C][C]0.0181213698942119[/C][C]0.00906068494710593[/C][/ROW]
[ROW][C]122[/C][C]0.990206462639151[/C][C]0.0195870747216978[/C][C]0.00979353736084888[/C][/ROW]
[ROW][C]123[/C][C]0.987036988548737[/C][C]0.0259260229025257[/C][C]0.0129630114512629[/C][/ROW]
[ROW][C]124[/C][C]0.985569449801561[/C][C]0.0288611003968775[/C][C]0.0144305501984387[/C][/ROW]
[ROW][C]125[/C][C]0.981001628033577[/C][C]0.0379967439328455[/C][C]0.0189983719664227[/C][/ROW]
[ROW][C]126[/C][C]0.987117368525124[/C][C]0.0257652629497515[/C][C]0.0128826314748757[/C][/ROW]
[ROW][C]127[/C][C]0.997282632962572[/C][C]0.00543473407485585[/C][C]0.00271736703742793[/C][/ROW]
[ROW][C]128[/C][C]0.998221219712549[/C][C]0.00355756057490173[/C][C]0.00177878028745087[/C][/ROW]
[ROW][C]129[/C][C]0.999351821542795[/C][C]0.00129635691440979[/C][C]0.000648178457204894[/C][/ROW]
[ROW][C]130[/C][C]0.999907256502693[/C][C]0.000185486994614244[/C][C]9.27434973071218e-05[/C][/ROW]
[ROW][C]131[/C][C]0.99999547844876[/C][C]9.04310248069987e-06[/C][C]4.52155124034994e-06[/C][/ROW]
[ROW][C]132[/C][C]0.999994657130524[/C][C]1.06857389520985e-05[/C][C]5.34286947604924e-06[/C][/ROW]
[ROW][C]133[/C][C]0.999986193449688[/C][C]2.76131006233408e-05[/C][C]1.38065503116704e-05[/C][/ROW]
[ROW][C]134[/C][C]0.999994133550025[/C][C]1.17328999496472e-05[/C][C]5.86644997482358e-06[/C][/ROW]
[ROW][C]135[/C][C]0.999985071295205[/C][C]2.98574095901096e-05[/C][C]1.49287047950548e-05[/C][/ROW]
[ROW][C]136[/C][C]0.999958030089293[/C][C]8.39398214131343e-05[/C][C]4.19699107065671e-05[/C][/ROW]
[ROW][C]137[/C][C]0.999877016091414[/C][C]0.000245967817171128[/C][C]0.000122983908585564[/C][/ROW]
[ROW][C]138[/C][C]0.999723207992943[/C][C]0.000553584014113955[/C][C]0.000276792007056977[/C][/ROW]
[ROW][C]139[/C][C]0.999775178219682[/C][C]0.000449643560636243[/C][C]0.000224821780318122[/C][/ROW]
[ROW][C]140[/C][C]0.999999639290024[/C][C]7.21419951615766e-07[/C][C]3.60709975807883e-07[/C][/ROW]
[ROW][C]141[/C][C]0.999997756875312[/C][C]4.48624937582006e-06[/C][C]2.24312468791003e-06[/C][/ROW]
[ROW][C]142[/C][C]0.99999513791891[/C][C]9.72416217979956e-06[/C][C]4.86208108989978e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999998398835343[/C][C]3.20232931361038e-06[/C][C]1.60116465680519e-06[/C][/ROW]
[ROW][C]144[/C][C]0.999985700626018[/C][C]2.85987479644417e-05[/C][C]1.42993739822208e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999929998753376[/C][C]0.000140002493248958[/C][C]7.00012466244788e-05[/C][/ROW]
[ROW][C]146[/C][C]0.999472976396474[/C][C]0.00105404720705253[/C][C]0.000527023603526264[/C][/ROW]
[ROW][C]147[/C][C]0.996889474349576[/C][C]0.00622105130084814[/C][C]0.00311052565042407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189876&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189876&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.06895605466157870.1379121093231570.931043945338421
80.02698236616373730.05396473232747470.973017633836263
90.03609992893795360.07219985787590720.963900071062046
100.02971644189894090.05943288379788180.970283558101059
110.02187653059040760.04375306118081520.978123469409592
120.01538743861774870.03077487723549750.984612561382251
130.01335302832051980.02670605664103960.98664697167948
140.01270016118577730.02540032237155460.987299838814223
150.006997225158916090.01399445031783220.993002774841084
160.007494377364016630.01498875472803330.992505622635983
170.01165440823282990.02330881646565980.98834559176717
180.006561572960850970.01312314592170190.993438427039149
190.005444414809975770.01088882961995150.994555585190024
200.005598662350893450.01119732470178690.994401337649107
210.004416832443419980.008833664886839950.99558316755658
220.003760539218711980.007521078437423960.996239460781288
230.003326287559415290.006652575118830580.996673712440585
240.002025476133185150.004050952266370290.997974523866815
250.00111310133196850.0022262026639370.998886898668031
260.0006179021167362140.001235804233472430.999382097883264
270.000328789112866110.0006575782257322190.999671210887134
280.0008419553627539190.001683910725507840.999158044637246
290.0008629927595831170.001725985519166230.999137007240417
300.001173139331558430.002346278663116860.998826860668442
310.001068391166486560.002136782332973120.998931608833513
320.001320071617827270.002640143235654540.998679928382173
330.001469165626820720.002938331253641450.998530834373179
340.004308971391625290.008617942783250570.995691028608375
350.01168275189803540.02336550379607070.988317248101965
360.01696958897944870.03393917795889740.983030411020551
370.01606724497681670.03213448995363330.983932755023183
380.0237340634407040.0474681268814080.976265936559296
390.02986631539983690.05973263079967380.970133684600163
400.02468709211673490.04937418423346980.975312907883265
410.02337572923173180.04675145846346360.976624270768268
420.01993823814681730.03987647629363460.980061761853183
430.01671686171478650.03343372342957290.983283138285214
440.01285980306955180.02571960613910350.987140196930448
450.01077162806767240.02154325613534470.989228371932328
460.008846576011513460.01769315202302690.991153423988486
470.007078198722361640.01415639744472330.992921801277638
480.005412706626283830.01082541325256770.994587293373716
490.004637310585648380.009274621171296770.995362689414352
500.00602391429718110.01204782859436220.993976085702819
510.005771514097110980.0115430281942220.994228485902889
520.00419520154444430.008390403088888590.995804798455556
530.00441912105505580.00883824211011160.995580878944944
540.004613552314576420.009227104629152850.995386447685424
550.003516666984067110.007033333968134220.996483333015933
560.002916032680014650.005832065360029290.997083967319985
570.002066834508124130.004133669016248260.997933165491876
580.001410140098331030.002820280196662050.998589859901669
590.0009312267677868140.001862453535573630.999068773232213
600.0009710565002079230.001942113000415850.999028943499792
610.0009114004871724620.001822800974344920.999088599512828
620.000659188620021080.001318377240042160.999340811379979
630.0007138041965026980.00142760839300540.999286195803497
640.0005377059593601330.001075411918720270.99946229404064
650.0004477404189150620.0008954808378301240.999552259581085
660.0003864531855767650.0007729063711535310.999613546814423
670.0002884065779339790.0005768131558679580.999711593422066
680.0002341021791815180.0004682043583630360.999765897820818
690.0002120169432879940.0004240338865759880.999787983056712
700.0002547927023428510.0005095854046857030.999745207297657
710.0001673977550291430.0003347955100582860.999832602244971
720.0001226799257425190.0002453598514850390.999877320074257
738.33272644834981e-050.0001666545289669960.999916672735517
745.15799204118747e-050.0001031598408237490.999948420079588
753.77226201472258e-057.54452402944515e-050.999962277379853
764.66993692975376e-059.33987385950752e-050.999953300630702
774.36791708994873e-058.73583417989747e-050.9999563208291
786.56323203418009e-050.0001312646406836020.999934367679658
790.0001186284692714990.0002372569385429980.999881371530729
800.0002248604880075330.0004497209760150660.999775139511992
810.0007092605808616610.001418521161723320.999290739419138
820.002145716824994270.004291433649988530.997854283175006
830.004484000728341760.008968001456683530.995515999271658
840.01009454419783310.02018908839566620.989905455802167
850.01097773638050620.02195547276101230.989022263619494
860.01171539216925560.02343078433851130.988284607830744
870.0110345160422410.0220690320844820.988965483957759
880.01042461026275010.02084922052550030.98957538973725
890.01303386924997320.02606773849994640.986966130750027
900.01703844088679580.03407688177359160.982961559113204
910.01666105027611910.03332210055223810.983338949723881
920.02620852781214860.05241705562429720.973791472187851
930.0360483617674380.0720967235348760.963951638232562
940.08876096101164520.177521922023290.911239038988355
950.2956113712746330.5912227425492650.704388628725367
960.5588298525221650.8823402949556690.441170147477835
970.7179224060091740.5641551879816530.282077593990826
980.8865714868644490.2268570262711020.113428513135551
990.9483971189801820.1032057620396360.051602881019818
1000.9502388935054660.09952221298906780.0497611064945339
1010.9411000668046420.1177998663907170.0588999331953585
1020.9313330695646660.1373338608706690.0686669304353344
1030.9156650588744830.1686698822510330.0843349411255166
1040.9069367787267770.1861264425464470.0930632212732233
1050.884917768790540.230164462418920.11508223120946
1060.8816863465841840.2366273068316320.118313653415816
1070.9067604965937310.1864790068125380.0932395034062689
1080.9348088619128810.1303822761742390.0651911380871193
1090.9924627501392780.01507449972144460.0075372498607223
1100.9951688284697980.009662343060403270.00483117153020163
1110.9962428164602460.00751436707950750.00375718353975375
1120.9967105040273260.006578991945348060.00328949597267403
1130.997290220689850.005419558620299490.00270977931014974
1140.9972673812561560.005465237487688990.00273261874384449
1150.9971956888505120.00560862229897610.00280431114948805
1160.9976234101245810.004753179750838210.00237658987541911
1170.9971873898112480.005625220377503190.00281261018875159
1180.996406877439660.007186245120679910.00359312256033996
1190.9951480142132980.009703971573404540.00485198578670227
1200.9930620274828960.01387594503420880.00693797251710441
1210.9909393150528940.01812136989421190.00906068494710593
1220.9902064626391510.01958707472169780.00979353736084888
1230.9870369885487370.02592602290252570.0129630114512629
1240.9855694498015610.02886110039687750.0144305501984387
1250.9810016280335770.03799674393284550.0189983719664227
1260.9871173685251240.02576526294975150.0128826314748757
1270.9972826329625720.005434734074855850.00271736703742793
1280.9982212197125490.003557560574901730.00177878028745087
1290.9993518215427950.001296356914409790.000648178457204894
1300.9999072565026930.0001854869946142449.27434973071218e-05
1310.999995478448769.04310248069987e-064.52155124034994e-06
1320.9999946571305241.06857389520985e-055.34286947604924e-06
1330.9999861934496882.76131006233408e-051.38065503116704e-05
1340.9999941335500251.17328999496472e-055.86644997482358e-06
1350.9999850712952052.98574095901096e-051.49287047950548e-05
1360.9999580300892938.39398214131343e-054.19699107065671e-05
1370.9998770160914140.0002459678171711280.000122983908585564
1380.9997232079929430.0005535840141139550.000276792007056977
1390.9997751782196820.0004496435606362430.000224821780318122
1400.9999996392900247.21419951615766e-073.60709975807883e-07
1410.9999977568753124.48624937582006e-062.24312468791003e-06
1420.999995137918919.72416217979956e-064.86208108989978e-06
1430.9999983988353433.20232931361038e-061.60116465680519e-06
1440.9999857006260182.85987479644417e-051.42993739822208e-05
1450.9999299987533760.0001400024932489587.00012466244788e-05
1460.9994729763964740.001054047207052530.000527023603526264
1470.9968894743495760.006221051300848140.00311052565042407






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level780.553191489361702NOK
5% type I error level1190.843971631205674NOK
10% type I error level1260.893617021276596NOK

\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 & 78 & 0.553191489361702 & NOK \tabularnewline
5% type I error level & 119 & 0.843971631205674 & NOK \tabularnewline
10% type I error level & 126 & 0.893617021276596 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189876&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]78[/C][C]0.553191489361702[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]119[/C][C]0.843971631205674[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]126[/C][C]0.893617021276596[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189876&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189876&T=6

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level780.553191489361702NOK
5% type I error level1190.843971631205674NOK
10% type I error level1260.893617021276596NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}