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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 05:35:44 -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/t1353062441b7ljrvh97eguft5.htm/, Retrieved Sat, 27 Apr 2024 10:06:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189854, Retrieved Sat, 27 Apr 2024 10:06:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109

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] [7ac586d7aaad1f98cbd1d1bd98b37cf0] [Current]
-    D      [Multiple Regression] [WS7] [2012-11-16 11:28:24] [3e2c7966ca4198d187b4c59e4eb5d004]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.6	2.8	2.5	2.5
2.6	2.8	2.4	2.6
2.6	2.9	2.6	2.6
2.0	2.3	2.2	2.4
2.2	2.3	2.2	2.4
2.6	2.8	2.7	2.4
2.9	3.2	3.0	2.6
3.1	3.4	3.1	2.7
3.3	3.7	3.4	2.7
3.3	3.6	3.4	2.7
3.2	3.5	3.2	2.7
3.7	3.8	3.4	3.0
3.4	3.6	3.1	3.0
3.4	3.6	3.0	3.0
3.4	3.6	3.1	2.5
4.0	3.8	3.3	2.6
3.4	3.7	3.3	2.7
3.1	3.3	2.9	2.7
3.3	3.4	2.9	2.8
3.5	3.5	2.9	2.7
3.5	3.4	2.8	2.4
3.7	3.2	2.7	2.3
3.4	3.1	2.6	2.2
3.0	2.9	2.5	1.9
3.1	3.0	2.5	1.9
2.9	2.9	2.6	1.9
2.4	2.3	2.1	1.6
2.4	2.6	2.2	1.7
2.7	2.5	2.0	1.5
2.5	2.3	1.6	1.7
2.1	1.8	1.1	1.6
1.9	1.7	0.9	1.6
0.8	0.7	0.1	0.8
0.8	0.6	-0.1	0.9
0.3	0.3	-0.3	0.9
0.0	-0.1	-0.3	0.5
-0.9	-1.0	-0.6	-0.1
-1.0	-1.2	-0.6	-0.3
-0.7	-0.8	-0.2	-0.2
-1.7	-1.7	-0.7	-0.6
-1.0	-1.1	-0.1	-0.1
-0.2	-0.4	0.7	0.0
0.7	0.6	1.5	0.6
0.6	0.6	1.6	0.6
1.9	1.9	2.8	1.2
2.1	2.3	3.3	1.1
2.7	2.6	3.5	1.6
3.2	3.1	3.9	2.1
4.8	4.7	4.8	3.2
5.5	5.5	5.1	3.6
5.4	5.4	4.9	3.8
5.9	5.9	5.2	4.0
5.8	5.8	5.1	4.0
5.1	5.2	4.6	3.7
4.1	4.2	3.7	3.3
4.4	4.4	3.9	3.6
3.6	3.6	3.1	3.3
3.5	3.5	2.8	3.2
3.1	3.1	2.6	3.1
2.9	2.9	2.2	3.1
2.2	2.2	1.8	2.6
1.4	1.5	1.3	2.1
1.2	1.1	1.2	1.7
1.3	1.4	1.4	1.8
1.3	1.3	1.3	1.9
1.3	1.3	1.3	1.9
1.8	1.8	1.9	1.9
1.8	1.8	1.9	1.9
1.8	1.8	2.1	1.8
1.7	1.7	2.0	1.8
2.1	1.6	1.9	1.9
2.0	1.5	1.9	1.9
1.7	1.2	1.9	1.6
1.9	1.2	1.8	1.7
2.3	1.6	1.7	2.3
2.4	1.6	1.6	2.4
2.5	1.9	1.7	2.5
2.8	2.2	1.9	2.5
2.6	2.0	1.7	2.5
2.2	1.7	1.3	2.2
2.8	2.4	2.0	2.3
2.8	2.6	2.0	2.4
2.8	2.9	2.3	2.2
2.3	2.6	2.0	2.3
2.2	2.5	1.7	2.5
3.0	3.2	2.3	2.6
2.9	3.1	2.4	2.2
2.7	3.1	2.4	2.2
2.7	2.9	2.3	2.1
2.3	2.5	2.1	2.0
2.4	2.8	2.1	2.1
2.8	3.1	2.5	2.1
2.3	2.6	2.0	2.1
2.0	2.3	1.8	1.9
1.9	2.3	1.7	2.4
2.3	2.6	1.9	2.2
2.7	2.9	2.1	2.4
1.8	2.0	1.4	2.1
2.0	2.2	1.6	2.3
2.1	2.4	1.7	2.3
2.0	2.3	1.6	2.4
2.4	2.6	1.9	2.5
1.7	1.9	1.6	2.0
1.0	1.1	1.1	1.7
1.2	1.3	1.3	1.6
1.4	1.6	1.6	1.9
1.7	1.7	1.6	2.0
1.8	1.9	1.7	2.2
1.4	1.6	1.6	2.0
1.7	1.8	1.7	2.2
1.6	1.8	1.6	2.1
1.4	1.5	1.5	1.9
1.5	1.6	1.6	1.9
0.9	1.0	1.1	1.8
1.5	1.5	1.5	2.1
1.7	1.8	1.4	2.4
1.6	1.7	1.3	2.4
1.2	1.2	0.9	2.1
1.3	1.4	1.2	2.3
1.1	1.1	0.9	2.3
1.3	1.3	1.1	2.3
1.2	1.3	1.3	2.1
1.3	1.3	1.3	2.1
1.1	1.3	1.4	2.0
0.8	0.9	1.2	1.9
1.4	1.3	1.7	2.0
1.6	1.8	2.0	2.3
2.5	2.7	3.0	2.5
2.5	2.6	3.1	2.5
2.6	2.9	3.2	2.6
2.0	2.2	2.7	2.1
1.8	2.1	2.8	2.0
1.9	2.3	3.0	2.2
1.9	2.3	2.8	2.2
2.5	2.7	3.1	2.4
2.8	2.6	3.1	2.5
3.0	2.9	3.2	2.8
3.1	3.1	3.1	3.1
2.9	2.8	2.7	2.7
2.2	2.1	2.2	2.2
2.5	2.3	2.2	1.9
2.7	2.2	2.1	2.0
3.0	2.5	2.3	2.5
3.7	3.1	2.5	2.5
3.7	3.0	2.3	2.4
4.0	3.4	2.6	2.5
3.5	2.9	2.3	2.0
1.7	2.8	2.0	2.0
3.0	2.7	1.8	2.1
2.4	2.2	1.4	1.7
2.3	2.1	1.5	1.7
2.5	2.2	1.4	1.9
2.1	1.9	1.2	1.9
0.3	1.8	1.2	1.8

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=189854&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=189854&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189854&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 + 0.0374209394068693HICP_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 +  0.0374209394068693HICP_Belgie[t] +  0.577959286947339Consumptieprijsindex_Belgie[t] -0.0923002058402775Gezondheidsindex_Belgie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189854&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HICP_Eurogebied[t] =  +  0.921123714656244 +  0.0374209394068693HICP_Belgie[t] +  0.577959286947339Consumptieprijsindex_Belgie[t] -0.0923002058402775Gezondheidsindex_Belgie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189854&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189854&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 + 0.0374209394068693HICP_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_Belgie0.03742093940686930.0967810.38670.699560.34978
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 & 0.0374209394068693 & 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=189854&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]0.0374209394068693[/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=189854&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189854&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_Belgie0.03742093940686930.0967810.38670.699560.34978
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=189854&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=189854&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189854&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
12.52.405953645965960.0940463540340391
22.62.415183666549990.184816333450012
32.62.454519554076670.145480445923333
42.42.122211500600250.277788499399747
52.42.129695688481630.270304311518373
62.42.387493604797910.0125063952020945
72.62.60221353964682-0.00221353964681887
82.72.71605956433363-0.0160595643336326
92.72.86924147654713-0.169241476547125
102.72.81144554785239-0.111445547852391
112.72.76836756638503-0.0683675663850258
1232.942005781004610.0579942189953931
1332.842877703545160.157122296454838
1432.852107724129190.147892275870811
152.52.84287770354516-0.342877703545162
162.62.9624620834107-0.362462083410695
172.72.88221359107184-0.18221359107184
182.72.676723676806950.0232763231930459
192.82.742003793383060.0579962066169376
202.72.80728390995917-0.10728390995917
212.42.75871800184846-0.358718001848464
222.32.6598403529244-0.359840352924398
232.22.60004816299163-0.40004816299163
241.92.47871795042344-0.578717950423443
251.92.54025597305886-0.640255973058864
261.92.46574583589873-0.565745835898728
271.62.14640989694703-0.546409896947028
281.72.3105676624472-0.610567662447203
291.52.28245805674258-0.782458056742585
301.72.19630209380785-0.496302093807854
311.61.93850417749158-0.338504177491575
321.61.89168410208352-0.291684102083523
330.81.34640194646085-0.54640194646085
340.91.30706605893417-0.407066058934171
350.91.13342784431459-0.23342784431459
360.50.891017847713593-0.391017847713593
37-0.10.364865705746889-0.464865705746889
38-0.30.245531754416734-0.545531754416734
39-0.20.451021668681619-0.651021668681619
40-0.6-0.0604125260577164-0.539587473942284
41-0.10.257177580191329-0.357177580191329
4200.61784566790774-0.61784566790774
430.61.15564363564904-0.55564363564904
440.61.14267152112433-0.542671521124325
451.21.83190556837646-0.631905568376464
461.12.02442336811663-0.924423368116635
471.62.2018036766769-0.601803676676902
482.12.4725737075179-0.372573707517896
493.23.37411188442838-0.17411188442838
503.63.83498390981898-0.234983909818977
513.83.791905928351610.008094071648388
5244.07190597977663-0.071905979776633
5344.01959797772524-0.0195979777252395
543.73.692777850892170.00722214910783386
553.33.160467809794210.139532190205792
563.63.268825907837680.331174092162319
573.32.850361891426540.449638108573464
583.22.81651393054320.383486069456802
593.12.588821881169570.51117811883043
603.12.502665918234840.597334081765161
612.62.1088198421230.491180157876996
622.11.720461692654510.37953830734549
631.71.491023810578230.208976189421772
641.81.649693649435060.150306350564939
651.91.601127741324350.298872258675645
661.91.601127741324350.298872258675645
671.91.853437730997290.0465622690027072
681.91.853437730997290.0465622690027072
691.81.83497768982924-0.0349776898292372
701.81.782669687777840.017330312222156
711.91.749072155429890.150927844570114
721.91.687534132794460.212465867205535
731.61.50292006488820.097079935111798
741.71.51963427335360.180365726646396
752.31.775016384479310.524983615520685
762.41.787988499004030.61201150099597
772.51.955888358444890.544111641555109
782.52.12204238518310.377957614816902
792.52.017426381080310.482573618919688
802.21.865990301569470.334009698430527
812.32.228404221988540.0715957780114619
822.42.343996079378010.0560039206219941
832.22.48969380371012-0.289693803710124
842.32.32528560967457-0.0252856096745714
852.52.291437648791230.208562351208766
862.62.6705657776757-0.0705657776757001
872.22.59979773445625-0.399797734456251
882.22.59231354657488-0.392313546574877
892.12.48595170976944-0.385951709769437
9022.25825966039581-0.258259660395809
912.12.4353895404207-0.335389540420698
922.12.58682561993154-0.486825619931537
932.12.32528560967457-0.225285609674571
941.92.15913158293636-0.259131582936364
952.42.16461950957970.235380490420295
962.22.3345156302586-0.134515630258599
972.42.50441175093749-0.104411750937493
982.12.01517969130690.0848203086931008
992.32.119795695409690.180204304590314
1002.32.229899626155810.0701003738441872
1012.42.177591624104420.22240837589558
1022.52.338257724199290.161742275800714
10321.935181627503420.0648183724965771
1041.71.492769643280880.207230356719118
1051.61.597385647383670.00261435261633218
1061.91.750567559597160.14943244040284
10721.819589770113950.180410229886045
1082.21.929693700860080.270306299139918
10921.750567559597160.24943244040284
1102.21.868155678224660.331844321775339
1112.11.8736436048680.226356395131998
1121.91.702001651486450.197998348513546
1131.91.754309653537850.145690346462153
1141.81.431231620645460.368768379354539
1152.11.705743745427140.394256254572859
1162.41.895845739976740.504154260023255
1172.41.843537737925350.556462262074649
1182.11.576509801025050.523490198974955
1192.31.668153690603120.631846309396884
1202.31.514971778389620.785028221610376
1212.31.619587782492410.68041221750759
1222.11.597385647383670.502614352616332
1232.11.601127741324350.498872258675645
12421.584413532858950.415586467141047
1251.91.360463577426010.539536422573988
12621.567949752928930.432050247071069
1272.31.836723522531890.463276477468109
1282.52.29826552041040.201734479589598
1292.52.231239571131640.26876042886836
1302.62.39913943057250.200860569427499
1312.12.018265468985380.0817345310146197
13221.943755331825240.0562446681747552
1332.22.044629241987340.155370758012656
1342.22.06308928315540.136910716844601
1352.42.289035499826370.110964500173626
1362.52.24246585295370.257534147046299
1372.82.414107806335250.385892193664751
1383.12.542671778249430.557328221750569
1392.72.398719886619970.301280113380034
1402.22.014103831092160.185896168907841
1411.92.14092197030369-0.240921970303688
14222.09984025007436-0.0998402500743555
1432.52.265994276812560.234005723187437
1442.52.62050446539772-0.120504465397719
1452.42.58116857787104-0.181168577871041
1462.52.79588851271995-0.295888512719954
14722.51588846129493-0.515888461294933
14822.41842490341992-0.418424903419917
1492.12.42773623712217-0.327736237122169
1501.72.15322411234049-0.453224112340489
1511.72.08245606912104-0.38245606912104
1521.92.15696620628118-0.256966206281176
1531.91.98707008560228-0.0870700856022817
1541.81.86191646597518-0.0619164659751831

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.5 & 2.40595364596596 & 0.0940463540340391 \tabularnewline
2 & 2.6 & 2.41518366654999 & 0.184816333450012 \tabularnewline
3 & 2.6 & 2.45451955407667 & 0.145480445923333 \tabularnewline
4 & 2.4 & 2.12221150060025 & 0.277788499399747 \tabularnewline
5 & 2.4 & 2.12969568848163 & 0.270304311518373 \tabularnewline
6 & 2.4 & 2.38749360479791 & 0.0125063952020945 \tabularnewline
7 & 2.6 & 2.60221353964682 & -0.00221353964681887 \tabularnewline
8 & 2.7 & 2.71605956433363 & -0.0160595643336326 \tabularnewline
9 & 2.7 & 2.86924147654713 & -0.169241476547125 \tabularnewline
10 & 2.7 & 2.81144554785239 & -0.111445547852391 \tabularnewline
11 & 2.7 & 2.76836756638503 & -0.0683675663850258 \tabularnewline
12 & 3 & 2.94200578100461 & 0.0579942189953931 \tabularnewline
13 & 3 & 2.84287770354516 & 0.157122296454838 \tabularnewline
14 & 3 & 2.85210772412919 & 0.147892275870811 \tabularnewline
15 & 2.5 & 2.84287770354516 & -0.342877703545162 \tabularnewline
16 & 2.6 & 2.9624620834107 & -0.362462083410695 \tabularnewline
17 & 2.7 & 2.88221359107184 & -0.18221359107184 \tabularnewline
18 & 2.7 & 2.67672367680695 & 0.0232763231930459 \tabularnewline
19 & 2.8 & 2.74200379338306 & 0.0579962066169376 \tabularnewline
20 & 2.7 & 2.80728390995917 & -0.10728390995917 \tabularnewline
21 & 2.4 & 2.75871800184846 & -0.358718001848464 \tabularnewline
22 & 2.3 & 2.6598403529244 & -0.359840352924398 \tabularnewline
23 & 2.2 & 2.60004816299163 & -0.40004816299163 \tabularnewline
24 & 1.9 & 2.47871795042344 & -0.578717950423443 \tabularnewline
25 & 1.9 & 2.54025597305886 & -0.640255973058864 \tabularnewline
26 & 1.9 & 2.46574583589873 & -0.565745835898728 \tabularnewline
27 & 1.6 & 2.14640989694703 & -0.546409896947028 \tabularnewline
28 & 1.7 & 2.3105676624472 & -0.610567662447203 \tabularnewline
29 & 1.5 & 2.28245805674258 & -0.782458056742585 \tabularnewline
30 & 1.7 & 2.19630209380785 & -0.496302093807854 \tabularnewline
31 & 1.6 & 1.93850417749158 & -0.338504177491575 \tabularnewline
32 & 1.6 & 1.89168410208352 & -0.291684102083523 \tabularnewline
33 & 0.8 & 1.34640194646085 & -0.54640194646085 \tabularnewline
34 & 0.9 & 1.30706605893417 & -0.407066058934171 \tabularnewline
35 & 0.9 & 1.13342784431459 & -0.23342784431459 \tabularnewline
36 & 0.5 & 0.891017847713593 & -0.391017847713593 \tabularnewline
37 & -0.1 & 0.364865705746889 & -0.464865705746889 \tabularnewline
38 & -0.3 & 0.245531754416734 & -0.545531754416734 \tabularnewline
39 & -0.2 & 0.451021668681619 & -0.651021668681619 \tabularnewline
40 & -0.6 & -0.0604125260577164 & -0.539587473942284 \tabularnewline
41 & -0.1 & 0.257177580191329 & -0.357177580191329 \tabularnewline
42 & 0 & 0.61784566790774 & -0.61784566790774 \tabularnewline
43 & 0.6 & 1.15564363564904 & -0.55564363564904 \tabularnewline
44 & 0.6 & 1.14267152112433 & -0.542671521124325 \tabularnewline
45 & 1.2 & 1.83190556837646 & -0.631905568376464 \tabularnewline
46 & 1.1 & 2.02442336811663 & -0.924423368116635 \tabularnewline
47 & 1.6 & 2.2018036766769 & -0.601803676676902 \tabularnewline
48 & 2.1 & 2.4725737075179 & -0.372573707517896 \tabularnewline
49 & 3.2 & 3.37411188442838 & -0.17411188442838 \tabularnewline
50 & 3.6 & 3.83498390981898 & -0.234983909818977 \tabularnewline
51 & 3.8 & 3.79190592835161 & 0.008094071648388 \tabularnewline
52 & 4 & 4.07190597977663 & -0.071905979776633 \tabularnewline
53 & 4 & 4.01959797772524 & -0.0195979777252395 \tabularnewline
54 & 3.7 & 3.69277785089217 & 0.00722214910783386 \tabularnewline
55 & 3.3 & 3.16046780979421 & 0.139532190205792 \tabularnewline
56 & 3.6 & 3.26882590783768 & 0.331174092162319 \tabularnewline
57 & 3.3 & 2.85036189142654 & 0.449638108573464 \tabularnewline
58 & 3.2 & 2.8165139305432 & 0.383486069456802 \tabularnewline
59 & 3.1 & 2.58882188116957 & 0.51117811883043 \tabularnewline
60 & 3.1 & 2.50266591823484 & 0.597334081765161 \tabularnewline
61 & 2.6 & 2.108819842123 & 0.491180157876996 \tabularnewline
62 & 2.1 & 1.72046169265451 & 0.37953830734549 \tabularnewline
63 & 1.7 & 1.49102381057823 & 0.208976189421772 \tabularnewline
64 & 1.8 & 1.64969364943506 & 0.150306350564939 \tabularnewline
65 & 1.9 & 1.60112774132435 & 0.298872258675645 \tabularnewline
66 & 1.9 & 1.60112774132435 & 0.298872258675645 \tabularnewline
67 & 1.9 & 1.85343773099729 & 0.0465622690027072 \tabularnewline
68 & 1.9 & 1.85343773099729 & 0.0465622690027072 \tabularnewline
69 & 1.8 & 1.83497768982924 & -0.0349776898292372 \tabularnewline
70 & 1.8 & 1.78266968777784 & 0.017330312222156 \tabularnewline
71 & 1.9 & 1.74907215542989 & 0.150927844570114 \tabularnewline
72 & 1.9 & 1.68753413279446 & 0.212465867205535 \tabularnewline
73 & 1.6 & 1.5029200648882 & 0.097079935111798 \tabularnewline
74 & 1.7 & 1.5196342733536 & 0.180365726646396 \tabularnewline
75 & 2.3 & 1.77501638447931 & 0.524983615520685 \tabularnewline
76 & 2.4 & 1.78798849900403 & 0.61201150099597 \tabularnewline
77 & 2.5 & 1.95588835844489 & 0.544111641555109 \tabularnewline
78 & 2.5 & 2.1220423851831 & 0.377957614816902 \tabularnewline
79 & 2.5 & 2.01742638108031 & 0.482573618919688 \tabularnewline
80 & 2.2 & 1.86599030156947 & 0.334009698430527 \tabularnewline
81 & 2.3 & 2.22840422198854 & 0.0715957780114619 \tabularnewline
82 & 2.4 & 2.34399607937801 & 0.0560039206219941 \tabularnewline
83 & 2.2 & 2.48969380371012 & -0.289693803710124 \tabularnewline
84 & 2.3 & 2.32528560967457 & -0.0252856096745714 \tabularnewline
85 & 2.5 & 2.29143764879123 & 0.208562351208766 \tabularnewline
86 & 2.6 & 2.6705657776757 & -0.0705657776757001 \tabularnewline
87 & 2.2 & 2.59979773445625 & -0.399797734456251 \tabularnewline
88 & 2.2 & 2.59231354657488 & -0.392313546574877 \tabularnewline
89 & 2.1 & 2.48595170976944 & -0.385951709769437 \tabularnewline
90 & 2 & 2.25825966039581 & -0.258259660395809 \tabularnewline
91 & 2.1 & 2.4353895404207 & -0.335389540420698 \tabularnewline
92 & 2.1 & 2.58682561993154 & -0.486825619931537 \tabularnewline
93 & 2.1 & 2.32528560967457 & -0.225285609674571 \tabularnewline
94 & 1.9 & 2.15913158293636 & -0.259131582936364 \tabularnewline
95 & 2.4 & 2.1646195095797 & 0.235380490420295 \tabularnewline
96 & 2.2 & 2.3345156302586 & -0.134515630258599 \tabularnewline
97 & 2.4 & 2.50441175093749 & -0.104411750937493 \tabularnewline
98 & 2.1 & 2.0151796913069 & 0.0848203086931008 \tabularnewline
99 & 2.3 & 2.11979569540969 & 0.180204304590314 \tabularnewline
100 & 2.3 & 2.22989962615581 & 0.0701003738441872 \tabularnewline
101 & 2.4 & 2.17759162410442 & 0.22240837589558 \tabularnewline
102 & 2.5 & 2.33825772419929 & 0.161742275800714 \tabularnewline
103 & 2 & 1.93518162750342 & 0.0648183724965771 \tabularnewline
104 & 1.7 & 1.49276964328088 & 0.207230356719118 \tabularnewline
105 & 1.6 & 1.59738564738367 & 0.00261435261633218 \tabularnewline
106 & 1.9 & 1.75056755959716 & 0.14943244040284 \tabularnewline
107 & 2 & 1.81958977011395 & 0.180410229886045 \tabularnewline
108 & 2.2 & 1.92969370086008 & 0.270306299139918 \tabularnewline
109 & 2 & 1.75056755959716 & 0.24943244040284 \tabularnewline
110 & 2.2 & 1.86815567822466 & 0.331844321775339 \tabularnewline
111 & 2.1 & 1.873643604868 & 0.226356395131998 \tabularnewline
112 & 1.9 & 1.70200165148645 & 0.197998348513546 \tabularnewline
113 & 1.9 & 1.75430965353785 & 0.145690346462153 \tabularnewline
114 & 1.8 & 1.43123162064546 & 0.368768379354539 \tabularnewline
115 & 2.1 & 1.70574374542714 & 0.394256254572859 \tabularnewline
116 & 2.4 & 1.89584573997674 & 0.504154260023255 \tabularnewline
117 & 2.4 & 1.84353773792535 & 0.556462262074649 \tabularnewline
118 & 2.1 & 1.57650980102505 & 0.523490198974955 \tabularnewline
119 & 2.3 & 1.66815369060312 & 0.631846309396884 \tabularnewline
120 & 2.3 & 1.51497177838962 & 0.785028221610376 \tabularnewline
121 & 2.3 & 1.61958778249241 & 0.68041221750759 \tabularnewline
122 & 2.1 & 1.59738564738367 & 0.502614352616332 \tabularnewline
123 & 2.1 & 1.60112774132435 & 0.498872258675645 \tabularnewline
124 & 2 & 1.58441353285895 & 0.415586467141047 \tabularnewline
125 & 1.9 & 1.36046357742601 & 0.539536422573988 \tabularnewline
126 & 2 & 1.56794975292893 & 0.432050247071069 \tabularnewline
127 & 2.3 & 1.83672352253189 & 0.463276477468109 \tabularnewline
128 & 2.5 & 2.2982655204104 & 0.201734479589598 \tabularnewline
129 & 2.5 & 2.23123957113164 & 0.26876042886836 \tabularnewline
130 & 2.6 & 2.3991394305725 & 0.200860569427499 \tabularnewline
131 & 2.1 & 2.01826546898538 & 0.0817345310146197 \tabularnewline
132 & 2 & 1.94375533182524 & 0.0562446681747552 \tabularnewline
133 & 2.2 & 2.04462924198734 & 0.155370758012656 \tabularnewline
134 & 2.2 & 2.0630892831554 & 0.136910716844601 \tabularnewline
135 & 2.4 & 2.28903549982637 & 0.110964500173626 \tabularnewline
136 & 2.5 & 2.2424658529537 & 0.257534147046299 \tabularnewline
137 & 2.8 & 2.41410780633525 & 0.385892193664751 \tabularnewline
138 & 3.1 & 2.54267177824943 & 0.557328221750569 \tabularnewline
139 & 2.7 & 2.39871988661997 & 0.301280113380034 \tabularnewline
140 & 2.2 & 2.01410383109216 & 0.185896168907841 \tabularnewline
141 & 1.9 & 2.14092197030369 & -0.240921970303688 \tabularnewline
142 & 2 & 2.09984025007436 & -0.0998402500743555 \tabularnewline
143 & 2.5 & 2.26599427681256 & 0.234005723187437 \tabularnewline
144 & 2.5 & 2.62050446539772 & -0.120504465397719 \tabularnewline
145 & 2.4 & 2.58116857787104 & -0.181168577871041 \tabularnewline
146 & 2.5 & 2.79588851271995 & -0.295888512719954 \tabularnewline
147 & 2 & 2.51588846129493 & -0.515888461294933 \tabularnewline
148 & 2 & 2.41842490341992 & -0.418424903419917 \tabularnewline
149 & 2.1 & 2.42773623712217 & -0.327736237122169 \tabularnewline
150 & 1.7 & 2.15322411234049 & -0.453224112340489 \tabularnewline
151 & 1.7 & 2.08245606912104 & -0.38245606912104 \tabularnewline
152 & 1.9 & 2.15696620628118 & -0.256966206281176 \tabularnewline
153 & 1.9 & 1.98707008560228 & -0.0870700856022817 \tabularnewline
154 & 1.8 & 1.86191646597518 & -0.0619164659751831 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189854&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]2.5[/C][C]2.40595364596596[/C][C]0.0940463540340391[/C][/ROW]
[ROW][C]2[/C][C]2.6[/C][C]2.41518366654999[/C][C]0.184816333450012[/C][/ROW]
[ROW][C]3[/C][C]2.6[/C][C]2.45451955407667[/C][C]0.145480445923333[/C][/ROW]
[ROW][C]4[/C][C]2.4[/C][C]2.12221150060025[/C][C]0.277788499399747[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]2.12969568848163[/C][C]0.270304311518373[/C][/ROW]
[ROW][C]6[/C][C]2.4[/C][C]2.38749360479791[/C][C]0.0125063952020945[/C][/ROW]
[ROW][C]7[/C][C]2.6[/C][C]2.60221353964682[/C][C]-0.00221353964681887[/C][/ROW]
[ROW][C]8[/C][C]2.7[/C][C]2.71605956433363[/C][C]-0.0160595643336326[/C][/ROW]
[ROW][C]9[/C][C]2.7[/C][C]2.86924147654713[/C][C]-0.169241476547125[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]2.81144554785239[/C][C]-0.111445547852391[/C][/ROW]
[ROW][C]11[/C][C]2.7[/C][C]2.76836756638503[/C][C]-0.0683675663850258[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]2.94200578100461[/C][C]0.0579942189953931[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]2.84287770354516[/C][C]0.157122296454838[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]2.85210772412919[/C][C]0.147892275870811[/C][/ROW]
[ROW][C]15[/C][C]2.5[/C][C]2.84287770354516[/C][C]-0.342877703545162[/C][/ROW]
[ROW][C]16[/C][C]2.6[/C][C]2.9624620834107[/C][C]-0.362462083410695[/C][/ROW]
[ROW][C]17[/C][C]2.7[/C][C]2.88221359107184[/C][C]-0.18221359107184[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]2.67672367680695[/C][C]0.0232763231930459[/C][/ROW]
[ROW][C]19[/C][C]2.8[/C][C]2.74200379338306[/C][C]0.0579962066169376[/C][/ROW]
[ROW][C]20[/C][C]2.7[/C][C]2.80728390995917[/C][C]-0.10728390995917[/C][/ROW]
[ROW][C]21[/C][C]2.4[/C][C]2.75871800184846[/C][C]-0.358718001848464[/C][/ROW]
[ROW][C]22[/C][C]2.3[/C][C]2.6598403529244[/C][C]-0.359840352924398[/C][/ROW]
[ROW][C]23[/C][C]2.2[/C][C]2.60004816299163[/C][C]-0.40004816299163[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]2.47871795042344[/C][C]-0.578717950423443[/C][/ROW]
[ROW][C]25[/C][C]1.9[/C][C]2.54025597305886[/C][C]-0.640255973058864[/C][/ROW]
[ROW][C]26[/C][C]1.9[/C][C]2.46574583589873[/C][C]-0.565745835898728[/C][/ROW]
[ROW][C]27[/C][C]1.6[/C][C]2.14640989694703[/C][C]-0.546409896947028[/C][/ROW]
[ROW][C]28[/C][C]1.7[/C][C]2.3105676624472[/C][C]-0.610567662447203[/C][/ROW]
[ROW][C]29[/C][C]1.5[/C][C]2.28245805674258[/C][C]-0.782458056742585[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]2.19630209380785[/C][C]-0.496302093807854[/C][/ROW]
[ROW][C]31[/C][C]1.6[/C][C]1.93850417749158[/C][C]-0.338504177491575[/C][/ROW]
[ROW][C]32[/C][C]1.6[/C][C]1.89168410208352[/C][C]-0.291684102083523[/C][/ROW]
[ROW][C]33[/C][C]0.8[/C][C]1.34640194646085[/C][C]-0.54640194646085[/C][/ROW]
[ROW][C]34[/C][C]0.9[/C][C]1.30706605893417[/C][C]-0.407066058934171[/C][/ROW]
[ROW][C]35[/C][C]0.9[/C][C]1.13342784431459[/C][C]-0.23342784431459[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.891017847713593[/C][C]-0.391017847713593[/C][/ROW]
[ROW][C]37[/C][C]-0.1[/C][C]0.364865705746889[/C][C]-0.464865705746889[/C][/ROW]
[ROW][C]38[/C][C]-0.3[/C][C]0.245531754416734[/C][C]-0.545531754416734[/C][/ROW]
[ROW][C]39[/C][C]-0.2[/C][C]0.451021668681619[/C][C]-0.651021668681619[/C][/ROW]
[ROW][C]40[/C][C]-0.6[/C][C]-0.0604125260577164[/C][C]-0.539587473942284[/C][/ROW]
[ROW][C]41[/C][C]-0.1[/C][C]0.257177580191329[/C][C]-0.357177580191329[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.61784566790774[/C][C]-0.61784566790774[/C][/ROW]
[ROW][C]43[/C][C]0.6[/C][C]1.15564363564904[/C][C]-0.55564363564904[/C][/ROW]
[ROW][C]44[/C][C]0.6[/C][C]1.14267152112433[/C][C]-0.542671521124325[/C][/ROW]
[ROW][C]45[/C][C]1.2[/C][C]1.83190556837646[/C][C]-0.631905568376464[/C][/ROW]
[ROW][C]46[/C][C]1.1[/C][C]2.02442336811663[/C][C]-0.924423368116635[/C][/ROW]
[ROW][C]47[/C][C]1.6[/C][C]2.2018036766769[/C][C]-0.601803676676902[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]2.4725737075179[/C][C]-0.372573707517896[/C][/ROW]
[ROW][C]49[/C][C]3.2[/C][C]3.37411188442838[/C][C]-0.17411188442838[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]3.83498390981898[/C][C]-0.234983909818977[/C][/ROW]
[ROW][C]51[/C][C]3.8[/C][C]3.79190592835161[/C][C]0.008094071648388[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]4.07190597977663[/C][C]-0.071905979776633[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.01959797772524[/C][C]-0.0195979777252395[/C][/ROW]
[ROW][C]54[/C][C]3.7[/C][C]3.69277785089217[/C][C]0.00722214910783386[/C][/ROW]
[ROW][C]55[/C][C]3.3[/C][C]3.16046780979421[/C][C]0.139532190205792[/C][/ROW]
[ROW][C]56[/C][C]3.6[/C][C]3.26882590783768[/C][C]0.331174092162319[/C][/ROW]
[ROW][C]57[/C][C]3.3[/C][C]2.85036189142654[/C][C]0.449638108573464[/C][/ROW]
[ROW][C]58[/C][C]3.2[/C][C]2.8165139305432[/C][C]0.383486069456802[/C][/ROW]
[ROW][C]59[/C][C]3.1[/C][C]2.58882188116957[/C][C]0.51117811883043[/C][/ROW]
[ROW][C]60[/C][C]3.1[/C][C]2.50266591823484[/C][C]0.597334081765161[/C][/ROW]
[ROW][C]61[/C][C]2.6[/C][C]2.108819842123[/C][C]0.491180157876996[/C][/ROW]
[ROW][C]62[/C][C]2.1[/C][C]1.72046169265451[/C][C]0.37953830734549[/C][/ROW]
[ROW][C]63[/C][C]1.7[/C][C]1.49102381057823[/C][C]0.208976189421772[/C][/ROW]
[ROW][C]64[/C][C]1.8[/C][C]1.64969364943506[/C][C]0.150306350564939[/C][/ROW]
[ROW][C]65[/C][C]1.9[/C][C]1.60112774132435[/C][C]0.298872258675645[/C][/ROW]
[ROW][C]66[/C][C]1.9[/C][C]1.60112774132435[/C][C]0.298872258675645[/C][/ROW]
[ROW][C]67[/C][C]1.9[/C][C]1.85343773099729[/C][C]0.0465622690027072[/C][/ROW]
[ROW][C]68[/C][C]1.9[/C][C]1.85343773099729[/C][C]0.0465622690027072[/C][/ROW]
[ROW][C]69[/C][C]1.8[/C][C]1.83497768982924[/C][C]-0.0349776898292372[/C][/ROW]
[ROW][C]70[/C][C]1.8[/C][C]1.78266968777784[/C][C]0.017330312222156[/C][/ROW]
[ROW][C]71[/C][C]1.9[/C][C]1.74907215542989[/C][C]0.150927844570114[/C][/ROW]
[ROW][C]72[/C][C]1.9[/C][C]1.68753413279446[/C][C]0.212465867205535[/C][/ROW]
[ROW][C]73[/C][C]1.6[/C][C]1.5029200648882[/C][C]0.097079935111798[/C][/ROW]
[ROW][C]74[/C][C]1.7[/C][C]1.5196342733536[/C][C]0.180365726646396[/C][/ROW]
[ROW][C]75[/C][C]2.3[/C][C]1.77501638447931[/C][C]0.524983615520685[/C][/ROW]
[ROW][C]76[/C][C]2.4[/C][C]1.78798849900403[/C][C]0.61201150099597[/C][/ROW]
[ROW][C]77[/C][C]2.5[/C][C]1.95588835844489[/C][C]0.544111641555109[/C][/ROW]
[ROW][C]78[/C][C]2.5[/C][C]2.1220423851831[/C][C]0.377957614816902[/C][/ROW]
[ROW][C]79[/C][C]2.5[/C][C]2.01742638108031[/C][C]0.482573618919688[/C][/ROW]
[ROW][C]80[/C][C]2.2[/C][C]1.86599030156947[/C][C]0.334009698430527[/C][/ROW]
[ROW][C]81[/C][C]2.3[/C][C]2.22840422198854[/C][C]0.0715957780114619[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.34399607937801[/C][C]0.0560039206219941[/C][/ROW]
[ROW][C]83[/C][C]2.2[/C][C]2.48969380371012[/C][C]-0.289693803710124[/C][/ROW]
[ROW][C]84[/C][C]2.3[/C][C]2.32528560967457[/C][C]-0.0252856096745714[/C][/ROW]
[ROW][C]85[/C][C]2.5[/C][C]2.29143764879123[/C][C]0.208562351208766[/C][/ROW]
[ROW][C]86[/C][C]2.6[/C][C]2.6705657776757[/C][C]-0.0705657776757001[/C][/ROW]
[ROW][C]87[/C][C]2.2[/C][C]2.59979773445625[/C][C]-0.399797734456251[/C][/ROW]
[ROW][C]88[/C][C]2.2[/C][C]2.59231354657488[/C][C]-0.392313546574877[/C][/ROW]
[ROW][C]89[/C][C]2.1[/C][C]2.48595170976944[/C][C]-0.385951709769437[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]2.25825966039581[/C][C]-0.258259660395809[/C][/ROW]
[ROW][C]91[/C][C]2.1[/C][C]2.4353895404207[/C][C]-0.335389540420698[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]2.58682561993154[/C][C]-0.486825619931537[/C][/ROW]
[ROW][C]93[/C][C]2.1[/C][C]2.32528560967457[/C][C]-0.225285609674571[/C][/ROW]
[ROW][C]94[/C][C]1.9[/C][C]2.15913158293636[/C][C]-0.259131582936364[/C][/ROW]
[ROW][C]95[/C][C]2.4[/C][C]2.1646195095797[/C][C]0.235380490420295[/C][/ROW]
[ROW][C]96[/C][C]2.2[/C][C]2.3345156302586[/C][C]-0.134515630258599[/C][/ROW]
[ROW][C]97[/C][C]2.4[/C][C]2.50441175093749[/C][C]-0.104411750937493[/C][/ROW]
[ROW][C]98[/C][C]2.1[/C][C]2.0151796913069[/C][C]0.0848203086931008[/C][/ROW]
[ROW][C]99[/C][C]2.3[/C][C]2.11979569540969[/C][C]0.180204304590314[/C][/ROW]
[ROW][C]100[/C][C]2.3[/C][C]2.22989962615581[/C][C]0.0701003738441872[/C][/ROW]
[ROW][C]101[/C][C]2.4[/C][C]2.17759162410442[/C][C]0.22240837589558[/C][/ROW]
[ROW][C]102[/C][C]2.5[/C][C]2.33825772419929[/C][C]0.161742275800714[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.93518162750342[/C][C]0.0648183724965771[/C][/ROW]
[ROW][C]104[/C][C]1.7[/C][C]1.49276964328088[/C][C]0.207230356719118[/C][/ROW]
[ROW][C]105[/C][C]1.6[/C][C]1.59738564738367[/C][C]0.00261435261633218[/C][/ROW]
[ROW][C]106[/C][C]1.9[/C][C]1.75056755959716[/C][C]0.14943244040284[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.81958977011395[/C][C]0.180410229886045[/C][/ROW]
[ROW][C]108[/C][C]2.2[/C][C]1.92969370086008[/C][C]0.270306299139918[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]1.75056755959716[/C][C]0.24943244040284[/C][/ROW]
[ROW][C]110[/C][C]2.2[/C][C]1.86815567822466[/C][C]0.331844321775339[/C][/ROW]
[ROW][C]111[/C][C]2.1[/C][C]1.873643604868[/C][C]0.226356395131998[/C][/ROW]
[ROW][C]112[/C][C]1.9[/C][C]1.70200165148645[/C][C]0.197998348513546[/C][/ROW]
[ROW][C]113[/C][C]1.9[/C][C]1.75430965353785[/C][C]0.145690346462153[/C][/ROW]
[ROW][C]114[/C][C]1.8[/C][C]1.43123162064546[/C][C]0.368768379354539[/C][/ROW]
[ROW][C]115[/C][C]2.1[/C][C]1.70574374542714[/C][C]0.394256254572859[/C][/ROW]
[ROW][C]116[/C][C]2.4[/C][C]1.89584573997674[/C][C]0.504154260023255[/C][/ROW]
[ROW][C]117[/C][C]2.4[/C][C]1.84353773792535[/C][C]0.556462262074649[/C][/ROW]
[ROW][C]118[/C][C]2.1[/C][C]1.57650980102505[/C][C]0.523490198974955[/C][/ROW]
[ROW][C]119[/C][C]2.3[/C][C]1.66815369060312[/C][C]0.631846309396884[/C][/ROW]
[ROW][C]120[/C][C]2.3[/C][C]1.51497177838962[/C][C]0.785028221610376[/C][/ROW]
[ROW][C]121[/C][C]2.3[/C][C]1.61958778249241[/C][C]0.68041221750759[/C][/ROW]
[ROW][C]122[/C][C]2.1[/C][C]1.59738564738367[/C][C]0.502614352616332[/C][/ROW]
[ROW][C]123[/C][C]2.1[/C][C]1.60112774132435[/C][C]0.498872258675645[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]1.58441353285895[/C][C]0.415586467141047[/C][/ROW]
[ROW][C]125[/C][C]1.9[/C][C]1.36046357742601[/C][C]0.539536422573988[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.56794975292893[/C][C]0.432050247071069[/C][/ROW]
[ROW][C]127[/C][C]2.3[/C][C]1.83672352253189[/C][C]0.463276477468109[/C][/ROW]
[ROW][C]128[/C][C]2.5[/C][C]2.2982655204104[/C][C]0.201734479589598[/C][/ROW]
[ROW][C]129[/C][C]2.5[/C][C]2.23123957113164[/C][C]0.26876042886836[/C][/ROW]
[ROW][C]130[/C][C]2.6[/C][C]2.3991394305725[/C][C]0.200860569427499[/C][/ROW]
[ROW][C]131[/C][C]2.1[/C][C]2.01826546898538[/C][C]0.0817345310146197[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.94375533182524[/C][C]0.0562446681747552[/C][/ROW]
[ROW][C]133[/C][C]2.2[/C][C]2.04462924198734[/C][C]0.155370758012656[/C][/ROW]
[ROW][C]134[/C][C]2.2[/C][C]2.0630892831554[/C][C]0.136910716844601[/C][/ROW]
[ROW][C]135[/C][C]2.4[/C][C]2.28903549982637[/C][C]0.110964500173626[/C][/ROW]
[ROW][C]136[/C][C]2.5[/C][C]2.2424658529537[/C][C]0.257534147046299[/C][/ROW]
[ROW][C]137[/C][C]2.8[/C][C]2.41410780633525[/C][C]0.385892193664751[/C][/ROW]
[ROW][C]138[/C][C]3.1[/C][C]2.54267177824943[/C][C]0.557328221750569[/C][/ROW]
[ROW][C]139[/C][C]2.7[/C][C]2.39871988661997[/C][C]0.301280113380034[/C][/ROW]
[ROW][C]140[/C][C]2.2[/C][C]2.01410383109216[/C][C]0.185896168907841[/C][/ROW]
[ROW][C]141[/C][C]1.9[/C][C]2.14092197030369[/C][C]-0.240921970303688[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]2.09984025007436[/C][C]-0.0998402500743555[/C][/ROW]
[ROW][C]143[/C][C]2.5[/C][C]2.26599427681256[/C][C]0.234005723187437[/C][/ROW]
[ROW][C]144[/C][C]2.5[/C][C]2.62050446539772[/C][C]-0.120504465397719[/C][/ROW]
[ROW][C]145[/C][C]2.4[/C][C]2.58116857787104[/C][C]-0.181168577871041[/C][/ROW]
[ROW][C]146[/C][C]2.5[/C][C]2.79588851271995[/C][C]-0.295888512719954[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]2.51588846129493[/C][C]-0.515888461294933[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]2.41842490341992[/C][C]-0.418424903419917[/C][/ROW]
[ROW][C]149[/C][C]2.1[/C][C]2.42773623712217[/C][C]-0.327736237122169[/C][/ROW]
[ROW][C]150[/C][C]1.7[/C][C]2.15322411234049[/C][C]-0.453224112340489[/C][/ROW]
[ROW][C]151[/C][C]1.7[/C][C]2.08245606912104[/C][C]-0.38245606912104[/C][/ROW]
[ROW][C]152[/C][C]1.9[/C][C]2.15696620628118[/C][C]-0.256966206281176[/C][/ROW]
[ROW][C]153[/C][C]1.9[/C][C]1.98707008560228[/C][C]-0.0870700856022817[/C][/ROW]
[ROW][C]154[/C][C]1.8[/C][C]1.86191646597518[/C][C]-0.0619164659751831[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189854&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189854&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
12.52.405953645965960.0940463540340391
22.62.415183666549990.184816333450012
32.62.454519554076670.145480445923333
42.42.122211500600250.277788499399747
52.42.129695688481630.270304311518373
62.42.387493604797910.0125063952020945
72.62.60221353964682-0.00221353964681887
82.72.71605956433363-0.0160595643336326
92.72.86924147654713-0.169241476547125
102.72.81144554785239-0.111445547852391
112.72.76836756638503-0.0683675663850258
1232.942005781004610.0579942189953931
1332.842877703545160.157122296454838
1432.852107724129190.147892275870811
152.52.84287770354516-0.342877703545162
162.62.9624620834107-0.362462083410695
172.72.88221359107184-0.18221359107184
182.72.676723676806950.0232763231930459
192.82.742003793383060.0579962066169376
202.72.80728390995917-0.10728390995917
212.42.75871800184846-0.358718001848464
222.32.6598403529244-0.359840352924398
232.22.60004816299163-0.40004816299163
241.92.47871795042344-0.578717950423443
251.92.54025597305886-0.640255973058864
261.92.46574583589873-0.565745835898728
271.62.14640989694703-0.546409896947028
281.72.3105676624472-0.610567662447203
291.52.28245805674258-0.782458056742585
301.72.19630209380785-0.496302093807854
311.61.93850417749158-0.338504177491575
321.61.89168410208352-0.291684102083523
330.81.34640194646085-0.54640194646085
340.91.30706605893417-0.407066058934171
350.91.13342784431459-0.23342784431459
360.50.891017847713593-0.391017847713593
37-0.10.364865705746889-0.464865705746889
38-0.30.245531754416734-0.545531754416734
39-0.20.451021668681619-0.651021668681619
40-0.6-0.0604125260577164-0.539587473942284
41-0.10.257177580191329-0.357177580191329
4200.61784566790774-0.61784566790774
430.61.15564363564904-0.55564363564904
440.61.14267152112433-0.542671521124325
451.21.83190556837646-0.631905568376464
461.12.02442336811663-0.924423368116635
471.62.2018036766769-0.601803676676902
482.12.4725737075179-0.372573707517896
493.23.37411188442838-0.17411188442838
503.63.83498390981898-0.234983909818977
513.83.791905928351610.008094071648388
5244.07190597977663-0.071905979776633
5344.01959797772524-0.0195979777252395
543.73.692777850892170.00722214910783386
553.33.160467809794210.139532190205792
563.63.268825907837680.331174092162319
573.32.850361891426540.449638108573464
583.22.81651393054320.383486069456802
593.12.588821881169570.51117811883043
603.12.502665918234840.597334081765161
612.62.1088198421230.491180157876996
622.11.720461692654510.37953830734549
631.71.491023810578230.208976189421772
641.81.649693649435060.150306350564939
651.91.601127741324350.298872258675645
661.91.601127741324350.298872258675645
671.91.853437730997290.0465622690027072
681.91.853437730997290.0465622690027072
691.81.83497768982924-0.0349776898292372
701.81.782669687777840.017330312222156
711.91.749072155429890.150927844570114
721.91.687534132794460.212465867205535
731.61.50292006488820.097079935111798
741.71.51963427335360.180365726646396
752.31.775016384479310.524983615520685
762.41.787988499004030.61201150099597
772.51.955888358444890.544111641555109
782.52.12204238518310.377957614816902
792.52.017426381080310.482573618919688
802.21.865990301569470.334009698430527
812.32.228404221988540.0715957780114619
822.42.343996079378010.0560039206219941
832.22.48969380371012-0.289693803710124
842.32.32528560967457-0.0252856096745714
852.52.291437648791230.208562351208766
862.62.6705657776757-0.0705657776757001
872.22.59979773445625-0.399797734456251
882.22.59231354657488-0.392313546574877
892.12.48595170976944-0.385951709769437
9022.25825966039581-0.258259660395809
912.12.4353895404207-0.335389540420698
922.12.58682561993154-0.486825619931537
932.12.32528560967457-0.225285609674571
941.92.15913158293636-0.259131582936364
952.42.16461950957970.235380490420295
962.22.3345156302586-0.134515630258599
972.42.50441175093749-0.104411750937493
982.12.01517969130690.0848203086931008
992.32.119795695409690.180204304590314
1002.32.229899626155810.0701003738441872
1012.42.177591624104420.22240837589558
1022.52.338257724199290.161742275800714
10321.935181627503420.0648183724965771
1041.71.492769643280880.207230356719118
1051.61.597385647383670.00261435261633218
1061.91.750567559597160.14943244040284
10721.819589770113950.180410229886045
1082.21.929693700860080.270306299139918
10921.750567559597160.24943244040284
1102.21.868155678224660.331844321775339
1112.11.8736436048680.226356395131998
1121.91.702001651486450.197998348513546
1131.91.754309653537850.145690346462153
1141.81.431231620645460.368768379354539
1152.11.705743745427140.394256254572859
1162.41.895845739976740.504154260023255
1172.41.843537737925350.556462262074649
1182.11.576509801025050.523490198974955
1192.31.668153690603120.631846309396884
1202.31.514971778389620.785028221610376
1212.31.619587782492410.68041221750759
1222.11.597385647383670.502614352616332
1232.11.601127741324350.498872258675645
12421.584413532858950.415586467141047
1251.91.360463577426010.539536422573988
12621.567949752928930.432050247071069
1272.31.836723522531890.463276477468109
1282.52.29826552041040.201734479589598
1292.52.231239571131640.26876042886836
1302.62.39913943057250.200860569427499
1312.12.018265468985380.0817345310146197
13221.943755331825240.0562446681747552
1332.22.044629241987340.155370758012656
1342.22.06308928315540.136910716844601
1352.42.289035499826370.110964500173626
1362.52.24246585295370.257534147046299
1372.82.414107806335250.385892193664751
1383.12.542671778249430.557328221750569
1392.72.398719886619970.301280113380034
1402.22.014103831092160.185896168907841
1411.92.14092197030369-0.240921970303688
14222.09984025007436-0.0998402500743555
1432.52.265994276812560.234005723187437
1442.52.62050446539772-0.120504465397719
1452.42.58116857787104-0.181168577871041
1462.52.79588851271995-0.295888512719954
14722.51588846129493-0.515888461294933
14822.41842490341992-0.418424903419917
1492.12.42773623712217-0.327736237122169
1501.72.15322411234049-0.453224112340489
1511.72.08245606912104-0.38245606912104
1521.92.15696620628118-0.256966206281176
1531.91.98707008560228-0.0870700856022817
1541.81.86191646597518-0.0619164659751831






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.003110525650424040.006221051300848080.996889474349576
80.0005270236035262540.001054047207052510.999472976396474
97.0001246624481e-050.0001400024932489620.999929998753376
101.42993739822207e-052.85987479644414e-050.999985700626018
111.60116465680517e-063.20232931361035e-060.999998398835343
124.8620810898998e-069.7241621797996e-060.99999513791891
132.24312468791e-064.48624937582e-060.999997756875312
143.60709975807873e-077.21419951615745e-070.999999639290024
150.0002248217803181260.0004496435606362520.999775178219682
160.000276792007056980.0005535840141139610.999723207992943
170.0001229839085855650.0002459678171711290.999877016091414
184.19699107065673e-058.39398214131346e-050.999958030089293
191.49287047950549e-052.98574095901098e-050.999985071295205
205.86644997482357e-061.17328999496471e-050.999994133550025
211.38065503116706e-052.76131006233412e-050.999986193449688
225.34286947604924e-061.06857389520985e-050.999994657130524
234.52155124034999e-069.04310248069997e-060.99999547844876
249.27434973071213e-050.0001854869946142430.999907256502693
250.0006481784572048980.00129635691440980.999351821542795
260.001778780287450880.003557560574901770.998221219712549
270.002717367037427960.005434734074855910.997282632962572
280.01288263147487580.02576526294975160.987117368525124
290.01899837196642270.03799674393284550.981001628033577
300.01443055019843880.02886110039687750.985569449801561
310.01296301145126290.02592602290252580.987036988548737
320.009793537360848850.01958707472169770.990206462639151
330.009060684947105950.01812136989421190.990939315052894
340.006937972517104370.01387594503420870.993062027482896
350.004851985786702270.009703971573404550.995148014213298
360.003593122560339930.007186245120679860.99640687743966
370.002812610188751590.005625220377503190.997187389811248
380.002376589875419110.004753179750838220.997623410124581
390.002804311149488030.005608622298976070.997195688850512
400.002732618743844520.005465237487689040.997267381256155
410.002709779310149710.005419558620299420.99729022068985
420.003289495972674030.006578991945348060.996710504027326
430.003757183539753740.007514367079507490.996242816460246
440.004831171530201590.009662343060403170.995168828469798
450.007537249860722280.01507449972144460.992462750139278
460.06519113808711910.1303822761742380.934808861912881
470.09323950340626860.1864790068125370.906760496593731
480.1183136534158160.2366273068316310.881686346584184
490.115082231209460.230164462418920.88491776879054
500.0930632212732240.1861264425464480.906936778726776
510.08433494112551680.1686698822510340.915665058874483
520.06866693043533410.1373338608706680.931333069564666
530.05889993319535870.1177998663907170.941100066804641
540.04976110649453380.09952221298906760.950238893505466
550.05160288101981790.1032057620396360.948397118980182
560.1134285131355510.2268570262711010.886571486864449
570.2820775939908250.5641551879816490.717922406009175
580.4411701474778340.8823402949556670.558829852522166
590.7043886287253710.5912227425492580.295611371274629
600.9112390389883540.1775219220232920.088760961011646
610.9639516382325620.07209672353487580.0360483617674379
620.9737914721878510.05241705562429770.0262085278121489
630.9833389497238810.03332210055223780.0166610502761189
640.9829615591132040.03407688177359130.0170384408867957
650.9869661307500270.02606773849994660.0130338692499733
660.989575389737250.02084922052550040.0104246102627502
670.9889654839577590.0220690320844820.011034516042241
680.9882846078307440.02343078433851120.0117153921692556
690.9890222636194940.02195547276101230.0109777363805062
700.9899054558021670.0201890883956660.010094544197833
710.9955159992716580.008968001456683450.00448400072834172
720.9978542831750060.004291433649988490.00214571682499425
730.9992907394191380.001418521161723320.000709260580861661
740.9997751395119920.0004497209760150660.000224860488007533
750.9998813715307290.0002372569385429980.000118628469271499
760.9999343676796580.0001312646406836036.56323203418014e-05
770.99995632082918.73583417989741e-054.3679170899487e-05
780.9999533006307029.33987385950752e-054.66993692975376e-05
790.9999622773798537.54452402944515e-053.77226201472258e-05
800.9999484200795880.000103159840823755.15799204118748e-05
810.9999166727355170.0001666545289669978.33272644834987e-05
820.9998773200742570.000245359851485040.00012267992574252
830.9998326022449710.0003347955100582860.000167397755029143
840.9997452072976570.0005095854046857010.00025479270234285
850.9997879830567120.0004240338865759970.000212016943287998
860.9997658978208180.0004682043583630380.000234102179181519
870.9997115934220660.0005768131558679590.00028840657793398
880.9996135468144230.0007729063711535350.000386453185576767
890.9995522595810850.0008954808378301210.000447740418915061
900.999462294040640.001075411918720270.000537705959360136
910.9992861958034970.001427608393005390.000713804196502694
920.9993408113799790.001318377240042160.000659188620021079
930.9990885995128280.001822800974344920.000911400487172461
940.9990289434997920.001942113000415870.000971056500207933
950.9990687732322130.001862453535573620.000931226767786808
960.9985898599016690.002820280196662030.00141014009833102
970.9979331654918760.004133669016248260.00206683450812413
980.9970839673199850.005832065360029320.00291603268001466
990.9964833330159330.007033333968134210.0035166669840671
1000.9953864476854240.009227104629152880.00461355231457644
1010.9955808789449440.008838242110111660.00441912105505583
1020.9958047984555560.008390403088888630.00419520154444431
1030.9942284859028890.0115430281942220.00577151409711102
1040.9939760857028190.01204782859436220.00602391429718108
1050.9953626894143520.009274621171296770.00463731058564838
1060.9945872933737160.01082541325256770.00541270662628383
1070.9929218012776380.01415639744472330.00707819872236167
1080.9911534239884860.0176931520230270.00884657601151351
1090.9892283719323280.02154325613534470.0107716280676724
1100.9871401969304480.02571960613910360.0128598030695518
1110.9832831382852140.0334337234295730.0167168617147865
1120.9800617618531830.03987647629363440.0199382381468172
1130.9766242707682680.04675145846346360.0233757292317318
1140.9753129078832650.04937418423346980.0246870921167349
1150.9701336846001630.05973263079967360.0298663153998368
1160.9762659365592960.04746812688140820.0237340634407041
1170.9839327550231830.03213448995363340.0160672449768167
1180.9830304110205510.03393917795889720.0169695889794486
1190.9883172481019650.02336550379607070.0116827518980353
1200.9956910286083750.008617942783250570.00430897139162529
1210.9985308343731790.002938331253641460.00146916562682073
1220.9986799283821730.002640143235654540.00132007161782727
1230.9989316088335130.002136782332973120.00106839116648656
1240.9988268606684420.002346278663116850.00117313933155842
1250.9991370072404170.001725985519166240.00086299275958312
1260.9991580446372460.001683910725507830.000841955362753917
1270.9996712108871340.0006575782257322190.00032878911286611
1280.9993820978832640.001235804233472430.000617902116736214
1290.9988868986680310.002226202663936990.00111310133196849
1300.9979745238668150.004050952266370280.00202547613318514
1310.9966737124405850.006652575118830620.00332628755941531
1320.9962394607812880.007521078437423910.00376053921871196
1330.995583167556580.008833664886839970.00441683244341998
1340.9944013376491070.0111973247017870.00559866235089348
1350.9945555851900240.01088882961995150.00544441480997577
1360.9934384270391490.01312314592170190.00656157296085097
1370.988345591767170.02330881646565980.0116544082328299
1380.9925056226359830.01498875472803330.00749437736401663
1390.9930027748410840.01399445031783220.00699722515891609
1400.9872998388142230.02540032237155450.0127001611857773
1410.986646971679480.02670605664103960.0133530283205198
1420.9846125613822510.03077487723549760.0153874386177488
1430.9781234694095920.04375306118081520.0218765305904076
1440.9702835581010590.05943288379788190.029716441898941
1450.9639000710620470.07219985787590690.0360999289379535
1460.9730176338362630.05396473232747460.0269823661637373
1470.9310439453384210.1379121093231570.0689560546615787

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.00311052565042404 & 0.00622105130084808 & 0.996889474349576 \tabularnewline
8 & 0.000527023603526254 & 0.00105404720705251 & 0.999472976396474 \tabularnewline
9 & 7.0001246624481e-05 & 0.000140002493248962 & 0.999929998753376 \tabularnewline
10 & 1.42993739822207e-05 & 2.85987479644414e-05 & 0.999985700626018 \tabularnewline
11 & 1.60116465680517e-06 & 3.20232931361035e-06 & 0.999998398835343 \tabularnewline
12 & 4.8620810898998e-06 & 9.7241621797996e-06 & 0.99999513791891 \tabularnewline
13 & 2.24312468791e-06 & 4.48624937582e-06 & 0.999997756875312 \tabularnewline
14 & 3.60709975807873e-07 & 7.21419951615745e-07 & 0.999999639290024 \tabularnewline
15 & 0.000224821780318126 & 0.000449643560636252 & 0.999775178219682 \tabularnewline
16 & 0.00027679200705698 & 0.000553584014113961 & 0.999723207992943 \tabularnewline
17 & 0.000122983908585565 & 0.000245967817171129 & 0.999877016091414 \tabularnewline
18 & 4.19699107065673e-05 & 8.39398214131346e-05 & 0.999958030089293 \tabularnewline
19 & 1.49287047950549e-05 & 2.98574095901098e-05 & 0.999985071295205 \tabularnewline
20 & 5.86644997482357e-06 & 1.17328999496471e-05 & 0.999994133550025 \tabularnewline
21 & 1.38065503116706e-05 & 2.76131006233412e-05 & 0.999986193449688 \tabularnewline
22 & 5.34286947604924e-06 & 1.06857389520985e-05 & 0.999994657130524 \tabularnewline
23 & 4.52155124034999e-06 & 9.04310248069997e-06 & 0.99999547844876 \tabularnewline
24 & 9.27434973071213e-05 & 0.000185486994614243 & 0.999907256502693 \tabularnewline
25 & 0.000648178457204898 & 0.0012963569144098 & 0.999351821542795 \tabularnewline
26 & 0.00177878028745088 & 0.00355756057490177 & 0.998221219712549 \tabularnewline
27 & 0.00271736703742796 & 0.00543473407485591 & 0.997282632962572 \tabularnewline
28 & 0.0128826314748758 & 0.0257652629497516 & 0.987117368525124 \tabularnewline
29 & 0.0189983719664227 & 0.0379967439328455 & 0.981001628033577 \tabularnewline
30 & 0.0144305501984388 & 0.0288611003968775 & 0.985569449801561 \tabularnewline
31 & 0.0129630114512629 & 0.0259260229025258 & 0.987036988548737 \tabularnewline
32 & 0.00979353736084885 & 0.0195870747216977 & 0.990206462639151 \tabularnewline
33 & 0.00906068494710595 & 0.0181213698942119 & 0.990939315052894 \tabularnewline
34 & 0.00693797251710437 & 0.0138759450342087 & 0.993062027482896 \tabularnewline
35 & 0.00485198578670227 & 0.00970397157340455 & 0.995148014213298 \tabularnewline
36 & 0.00359312256033993 & 0.00718624512067986 & 0.99640687743966 \tabularnewline
37 & 0.00281261018875159 & 0.00562522037750319 & 0.997187389811248 \tabularnewline
38 & 0.00237658987541911 & 0.00475317975083822 & 0.997623410124581 \tabularnewline
39 & 0.00280431114948803 & 0.00560862229897607 & 0.997195688850512 \tabularnewline
40 & 0.00273261874384452 & 0.00546523748768904 & 0.997267381256155 \tabularnewline
41 & 0.00270977931014971 & 0.00541955862029942 & 0.99729022068985 \tabularnewline
42 & 0.00328949597267403 & 0.00657899194534806 & 0.996710504027326 \tabularnewline
43 & 0.00375718353975374 & 0.00751436707950749 & 0.996242816460246 \tabularnewline
44 & 0.00483117153020159 & 0.00966234306040317 & 0.995168828469798 \tabularnewline
45 & 0.00753724986072228 & 0.0150744997214446 & 0.992462750139278 \tabularnewline
46 & 0.0651911380871191 & 0.130382276174238 & 0.934808861912881 \tabularnewline
47 & 0.0932395034062686 & 0.186479006812537 & 0.906760496593731 \tabularnewline
48 & 0.118313653415816 & 0.236627306831631 & 0.881686346584184 \tabularnewline
49 & 0.11508223120946 & 0.23016446241892 & 0.88491776879054 \tabularnewline
50 & 0.093063221273224 & 0.186126442546448 & 0.906936778726776 \tabularnewline
51 & 0.0843349411255168 & 0.168669882251034 & 0.915665058874483 \tabularnewline
52 & 0.0686669304353341 & 0.137333860870668 & 0.931333069564666 \tabularnewline
53 & 0.0588999331953587 & 0.117799866390717 & 0.941100066804641 \tabularnewline
54 & 0.0497611064945338 & 0.0995222129890676 & 0.950238893505466 \tabularnewline
55 & 0.0516028810198179 & 0.103205762039636 & 0.948397118980182 \tabularnewline
56 & 0.113428513135551 & 0.226857026271101 & 0.886571486864449 \tabularnewline
57 & 0.282077593990825 & 0.564155187981649 & 0.717922406009175 \tabularnewline
58 & 0.441170147477834 & 0.882340294955667 & 0.558829852522166 \tabularnewline
59 & 0.704388628725371 & 0.591222742549258 & 0.295611371274629 \tabularnewline
60 & 0.911239038988354 & 0.177521922023292 & 0.088760961011646 \tabularnewline
61 & 0.963951638232562 & 0.0720967235348758 & 0.0360483617674379 \tabularnewline
62 & 0.973791472187851 & 0.0524170556242977 & 0.0262085278121489 \tabularnewline
63 & 0.983338949723881 & 0.0333221005522378 & 0.0166610502761189 \tabularnewline
64 & 0.982961559113204 & 0.0340768817735913 & 0.0170384408867957 \tabularnewline
65 & 0.986966130750027 & 0.0260677384999466 & 0.0130338692499733 \tabularnewline
66 & 0.98957538973725 & 0.0208492205255004 & 0.0104246102627502 \tabularnewline
67 & 0.988965483957759 & 0.022069032084482 & 0.011034516042241 \tabularnewline
68 & 0.988284607830744 & 0.0234307843385112 & 0.0117153921692556 \tabularnewline
69 & 0.989022263619494 & 0.0219554727610123 & 0.0109777363805062 \tabularnewline
70 & 0.989905455802167 & 0.020189088395666 & 0.010094544197833 \tabularnewline
71 & 0.995515999271658 & 0.00896800145668345 & 0.00448400072834172 \tabularnewline
72 & 0.997854283175006 & 0.00429143364998849 & 0.00214571682499425 \tabularnewline
73 & 0.999290739419138 & 0.00141852116172332 & 0.000709260580861661 \tabularnewline
74 & 0.999775139511992 & 0.000449720976015066 & 0.000224860488007533 \tabularnewline
75 & 0.999881371530729 & 0.000237256938542998 & 0.000118628469271499 \tabularnewline
76 & 0.999934367679658 & 0.000131264640683603 & 6.56323203418014e-05 \tabularnewline
77 & 0.9999563208291 & 8.73583417989741e-05 & 4.3679170899487e-05 \tabularnewline
78 & 0.999953300630702 & 9.33987385950752e-05 & 4.66993692975376e-05 \tabularnewline
79 & 0.999962277379853 & 7.54452402944515e-05 & 3.77226201472258e-05 \tabularnewline
80 & 0.999948420079588 & 0.00010315984082375 & 5.15799204118748e-05 \tabularnewline
81 & 0.999916672735517 & 0.000166654528966997 & 8.33272644834987e-05 \tabularnewline
82 & 0.999877320074257 & 0.00024535985148504 & 0.00012267992574252 \tabularnewline
83 & 0.999832602244971 & 0.000334795510058286 & 0.000167397755029143 \tabularnewline
84 & 0.999745207297657 & 0.000509585404685701 & 0.00025479270234285 \tabularnewline
85 & 0.999787983056712 & 0.000424033886575997 & 0.000212016943287998 \tabularnewline
86 & 0.999765897820818 & 0.000468204358363038 & 0.000234102179181519 \tabularnewline
87 & 0.999711593422066 & 0.000576813155867959 & 0.00028840657793398 \tabularnewline
88 & 0.999613546814423 & 0.000772906371153535 & 0.000386453185576767 \tabularnewline
89 & 0.999552259581085 & 0.000895480837830121 & 0.000447740418915061 \tabularnewline
90 & 0.99946229404064 & 0.00107541191872027 & 0.000537705959360136 \tabularnewline
91 & 0.999286195803497 & 0.00142760839300539 & 0.000713804196502694 \tabularnewline
92 & 0.999340811379979 & 0.00131837724004216 & 0.000659188620021079 \tabularnewline
93 & 0.999088599512828 & 0.00182280097434492 & 0.000911400487172461 \tabularnewline
94 & 0.999028943499792 & 0.00194211300041587 & 0.000971056500207933 \tabularnewline
95 & 0.999068773232213 & 0.00186245353557362 & 0.000931226767786808 \tabularnewline
96 & 0.998589859901669 & 0.00282028019666203 & 0.00141014009833102 \tabularnewline
97 & 0.997933165491876 & 0.00413366901624826 & 0.00206683450812413 \tabularnewline
98 & 0.997083967319985 & 0.00583206536002932 & 0.00291603268001466 \tabularnewline
99 & 0.996483333015933 & 0.00703333396813421 & 0.0035166669840671 \tabularnewline
100 & 0.995386447685424 & 0.00922710462915288 & 0.00461355231457644 \tabularnewline
101 & 0.995580878944944 & 0.00883824211011166 & 0.00441912105505583 \tabularnewline
102 & 0.995804798455556 & 0.00839040308888863 & 0.00419520154444431 \tabularnewline
103 & 0.994228485902889 & 0.011543028194222 & 0.00577151409711102 \tabularnewline
104 & 0.993976085702819 & 0.0120478285943622 & 0.00602391429718108 \tabularnewline
105 & 0.995362689414352 & 0.00927462117129677 & 0.00463731058564838 \tabularnewline
106 & 0.994587293373716 & 0.0108254132525677 & 0.00541270662628383 \tabularnewline
107 & 0.992921801277638 & 0.0141563974447233 & 0.00707819872236167 \tabularnewline
108 & 0.991153423988486 & 0.017693152023027 & 0.00884657601151351 \tabularnewline
109 & 0.989228371932328 & 0.0215432561353447 & 0.0107716280676724 \tabularnewline
110 & 0.987140196930448 & 0.0257196061391036 & 0.0128598030695518 \tabularnewline
111 & 0.983283138285214 & 0.033433723429573 & 0.0167168617147865 \tabularnewline
112 & 0.980061761853183 & 0.0398764762936344 & 0.0199382381468172 \tabularnewline
113 & 0.976624270768268 & 0.0467514584634636 & 0.0233757292317318 \tabularnewline
114 & 0.975312907883265 & 0.0493741842334698 & 0.0246870921167349 \tabularnewline
115 & 0.970133684600163 & 0.0597326307996736 & 0.0298663153998368 \tabularnewline
116 & 0.976265936559296 & 0.0474681268814082 & 0.0237340634407041 \tabularnewline
117 & 0.983932755023183 & 0.0321344899536334 & 0.0160672449768167 \tabularnewline
118 & 0.983030411020551 & 0.0339391779588972 & 0.0169695889794486 \tabularnewline
119 & 0.988317248101965 & 0.0233655037960707 & 0.0116827518980353 \tabularnewline
120 & 0.995691028608375 & 0.00861794278325057 & 0.00430897139162529 \tabularnewline
121 & 0.998530834373179 & 0.00293833125364146 & 0.00146916562682073 \tabularnewline
122 & 0.998679928382173 & 0.00264014323565454 & 0.00132007161782727 \tabularnewline
123 & 0.998931608833513 & 0.00213678233297312 & 0.00106839116648656 \tabularnewline
124 & 0.998826860668442 & 0.00234627866311685 & 0.00117313933155842 \tabularnewline
125 & 0.999137007240417 & 0.00172598551916624 & 0.00086299275958312 \tabularnewline
126 & 0.999158044637246 & 0.00168391072550783 & 0.000841955362753917 \tabularnewline
127 & 0.999671210887134 & 0.000657578225732219 & 0.00032878911286611 \tabularnewline
128 & 0.999382097883264 & 0.00123580423347243 & 0.000617902116736214 \tabularnewline
129 & 0.998886898668031 & 0.00222620266393699 & 0.00111310133196849 \tabularnewline
130 & 0.997974523866815 & 0.00405095226637028 & 0.00202547613318514 \tabularnewline
131 & 0.996673712440585 & 0.00665257511883062 & 0.00332628755941531 \tabularnewline
132 & 0.996239460781288 & 0.00752107843742391 & 0.00376053921871196 \tabularnewline
133 & 0.99558316755658 & 0.00883366488683997 & 0.00441683244341998 \tabularnewline
134 & 0.994401337649107 & 0.011197324701787 & 0.00559866235089348 \tabularnewline
135 & 0.994555585190024 & 0.0108888296199515 & 0.00544441480997577 \tabularnewline
136 & 0.993438427039149 & 0.0131231459217019 & 0.00656157296085097 \tabularnewline
137 & 0.98834559176717 & 0.0233088164656598 & 0.0116544082328299 \tabularnewline
138 & 0.992505622635983 & 0.0149887547280333 & 0.00749437736401663 \tabularnewline
139 & 0.993002774841084 & 0.0139944503178322 & 0.00699722515891609 \tabularnewline
140 & 0.987299838814223 & 0.0254003223715545 & 0.0127001611857773 \tabularnewline
141 & 0.98664697167948 & 0.0267060566410396 & 0.0133530283205198 \tabularnewline
142 & 0.984612561382251 & 0.0307748772354976 & 0.0153874386177488 \tabularnewline
143 & 0.978123469409592 & 0.0437530611808152 & 0.0218765305904076 \tabularnewline
144 & 0.970283558101059 & 0.0594328837978819 & 0.029716441898941 \tabularnewline
145 & 0.963900071062047 & 0.0721998578759069 & 0.0360999289379535 \tabularnewline
146 & 0.973017633836263 & 0.0539647323274746 & 0.0269823661637373 \tabularnewline
147 & 0.931043945338421 & 0.137912109323157 & 0.0689560546615787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189854&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]0.00311052565042404[/C][C]0.00622105130084808[/C][C]0.996889474349576[/C][/ROW]
[ROW][C]8[/C][C]0.000527023603526254[/C][C]0.00105404720705251[/C][C]0.999472976396474[/C][/ROW]
[ROW][C]9[/C][C]7.0001246624481e-05[/C][C]0.000140002493248962[/C][C]0.999929998753376[/C][/ROW]
[ROW][C]10[/C][C]1.42993739822207e-05[/C][C]2.85987479644414e-05[/C][C]0.999985700626018[/C][/ROW]
[ROW][C]11[/C][C]1.60116465680517e-06[/C][C]3.20232931361035e-06[/C][C]0.999998398835343[/C][/ROW]
[ROW][C]12[/C][C]4.8620810898998e-06[/C][C]9.7241621797996e-06[/C][C]0.99999513791891[/C][/ROW]
[ROW][C]13[/C][C]2.24312468791e-06[/C][C]4.48624937582e-06[/C][C]0.999997756875312[/C][/ROW]
[ROW][C]14[/C][C]3.60709975807873e-07[/C][C]7.21419951615745e-07[/C][C]0.999999639290024[/C][/ROW]
[ROW][C]15[/C][C]0.000224821780318126[/C][C]0.000449643560636252[/C][C]0.999775178219682[/C][/ROW]
[ROW][C]16[/C][C]0.00027679200705698[/C][C]0.000553584014113961[/C][C]0.999723207992943[/C][/ROW]
[ROW][C]17[/C][C]0.000122983908585565[/C][C]0.000245967817171129[/C][C]0.999877016091414[/C][/ROW]
[ROW][C]18[/C][C]4.19699107065673e-05[/C][C]8.39398214131346e-05[/C][C]0.999958030089293[/C][/ROW]
[ROW][C]19[/C][C]1.49287047950549e-05[/C][C]2.98574095901098e-05[/C][C]0.999985071295205[/C][/ROW]
[ROW][C]20[/C][C]5.86644997482357e-06[/C][C]1.17328999496471e-05[/C][C]0.999994133550025[/C][/ROW]
[ROW][C]21[/C][C]1.38065503116706e-05[/C][C]2.76131006233412e-05[/C][C]0.999986193449688[/C][/ROW]
[ROW][C]22[/C][C]5.34286947604924e-06[/C][C]1.06857389520985e-05[/C][C]0.999994657130524[/C][/ROW]
[ROW][C]23[/C][C]4.52155124034999e-06[/C][C]9.04310248069997e-06[/C][C]0.99999547844876[/C][/ROW]
[ROW][C]24[/C][C]9.27434973071213e-05[/C][C]0.000185486994614243[/C][C]0.999907256502693[/C][/ROW]
[ROW][C]25[/C][C]0.000648178457204898[/C][C]0.0012963569144098[/C][C]0.999351821542795[/C][/ROW]
[ROW][C]26[/C][C]0.00177878028745088[/C][C]0.00355756057490177[/C][C]0.998221219712549[/C][/ROW]
[ROW][C]27[/C][C]0.00271736703742796[/C][C]0.00543473407485591[/C][C]0.997282632962572[/C][/ROW]
[ROW][C]28[/C][C]0.0128826314748758[/C][C]0.0257652629497516[/C][C]0.987117368525124[/C][/ROW]
[ROW][C]29[/C][C]0.0189983719664227[/C][C]0.0379967439328455[/C][C]0.981001628033577[/C][/ROW]
[ROW][C]30[/C][C]0.0144305501984388[/C][C]0.0288611003968775[/C][C]0.985569449801561[/C][/ROW]
[ROW][C]31[/C][C]0.0129630114512629[/C][C]0.0259260229025258[/C][C]0.987036988548737[/C][/ROW]
[ROW][C]32[/C][C]0.00979353736084885[/C][C]0.0195870747216977[/C][C]0.990206462639151[/C][/ROW]
[ROW][C]33[/C][C]0.00906068494710595[/C][C]0.0181213698942119[/C][C]0.990939315052894[/C][/ROW]
[ROW][C]34[/C][C]0.00693797251710437[/C][C]0.0138759450342087[/C][C]0.993062027482896[/C][/ROW]
[ROW][C]35[/C][C]0.00485198578670227[/C][C]0.00970397157340455[/C][C]0.995148014213298[/C][/ROW]
[ROW][C]36[/C][C]0.00359312256033993[/C][C]0.00718624512067986[/C][C]0.99640687743966[/C][/ROW]
[ROW][C]37[/C][C]0.00281261018875159[/C][C]0.00562522037750319[/C][C]0.997187389811248[/C][/ROW]
[ROW][C]38[/C][C]0.00237658987541911[/C][C]0.00475317975083822[/C][C]0.997623410124581[/C][/ROW]
[ROW][C]39[/C][C]0.00280431114948803[/C][C]0.00560862229897607[/C][C]0.997195688850512[/C][/ROW]
[ROW][C]40[/C][C]0.00273261874384452[/C][C]0.00546523748768904[/C][C]0.997267381256155[/C][/ROW]
[ROW][C]41[/C][C]0.00270977931014971[/C][C]0.00541955862029942[/C][C]0.99729022068985[/C][/ROW]
[ROW][C]42[/C][C]0.00328949597267403[/C][C]0.00657899194534806[/C][C]0.996710504027326[/C][/ROW]
[ROW][C]43[/C][C]0.00375718353975374[/C][C]0.00751436707950749[/C][C]0.996242816460246[/C][/ROW]
[ROW][C]44[/C][C]0.00483117153020159[/C][C]0.00966234306040317[/C][C]0.995168828469798[/C][/ROW]
[ROW][C]45[/C][C]0.00753724986072228[/C][C]0.0150744997214446[/C][C]0.992462750139278[/C][/ROW]
[ROW][C]46[/C][C]0.0651911380871191[/C][C]0.130382276174238[/C][C]0.934808861912881[/C][/ROW]
[ROW][C]47[/C][C]0.0932395034062686[/C][C]0.186479006812537[/C][C]0.906760496593731[/C][/ROW]
[ROW][C]48[/C][C]0.118313653415816[/C][C]0.236627306831631[/C][C]0.881686346584184[/C][/ROW]
[ROW][C]49[/C][C]0.11508223120946[/C][C]0.23016446241892[/C][C]0.88491776879054[/C][/ROW]
[ROW][C]50[/C][C]0.093063221273224[/C][C]0.186126442546448[/C][C]0.906936778726776[/C][/ROW]
[ROW][C]51[/C][C]0.0843349411255168[/C][C]0.168669882251034[/C][C]0.915665058874483[/C][/ROW]
[ROW][C]52[/C][C]0.0686669304353341[/C][C]0.137333860870668[/C][C]0.931333069564666[/C][/ROW]
[ROW][C]53[/C][C]0.0588999331953587[/C][C]0.117799866390717[/C][C]0.941100066804641[/C][/ROW]
[ROW][C]54[/C][C]0.0497611064945338[/C][C]0.0995222129890676[/C][C]0.950238893505466[/C][/ROW]
[ROW][C]55[/C][C]0.0516028810198179[/C][C]0.103205762039636[/C][C]0.948397118980182[/C][/ROW]
[ROW][C]56[/C][C]0.113428513135551[/C][C]0.226857026271101[/C][C]0.886571486864449[/C][/ROW]
[ROW][C]57[/C][C]0.282077593990825[/C][C]0.564155187981649[/C][C]0.717922406009175[/C][/ROW]
[ROW][C]58[/C][C]0.441170147477834[/C][C]0.882340294955667[/C][C]0.558829852522166[/C][/ROW]
[ROW][C]59[/C][C]0.704388628725371[/C][C]0.591222742549258[/C][C]0.295611371274629[/C][/ROW]
[ROW][C]60[/C][C]0.911239038988354[/C][C]0.177521922023292[/C][C]0.088760961011646[/C][/ROW]
[ROW][C]61[/C][C]0.963951638232562[/C][C]0.0720967235348758[/C][C]0.0360483617674379[/C][/ROW]
[ROW][C]62[/C][C]0.973791472187851[/C][C]0.0524170556242977[/C][C]0.0262085278121489[/C][/ROW]
[ROW][C]63[/C][C]0.983338949723881[/C][C]0.0333221005522378[/C][C]0.0166610502761189[/C][/ROW]
[ROW][C]64[/C][C]0.982961559113204[/C][C]0.0340768817735913[/C][C]0.0170384408867957[/C][/ROW]
[ROW][C]65[/C][C]0.986966130750027[/C][C]0.0260677384999466[/C][C]0.0130338692499733[/C][/ROW]
[ROW][C]66[/C][C]0.98957538973725[/C][C]0.0208492205255004[/C][C]0.0104246102627502[/C][/ROW]
[ROW][C]67[/C][C]0.988965483957759[/C][C]0.022069032084482[/C][C]0.011034516042241[/C][/ROW]
[ROW][C]68[/C][C]0.988284607830744[/C][C]0.0234307843385112[/C][C]0.0117153921692556[/C][/ROW]
[ROW][C]69[/C][C]0.989022263619494[/C][C]0.0219554727610123[/C][C]0.0109777363805062[/C][/ROW]
[ROW][C]70[/C][C]0.989905455802167[/C][C]0.020189088395666[/C][C]0.010094544197833[/C][/ROW]
[ROW][C]71[/C][C]0.995515999271658[/C][C]0.00896800145668345[/C][C]0.00448400072834172[/C][/ROW]
[ROW][C]72[/C][C]0.997854283175006[/C][C]0.00429143364998849[/C][C]0.00214571682499425[/C][/ROW]
[ROW][C]73[/C][C]0.999290739419138[/C][C]0.00141852116172332[/C][C]0.000709260580861661[/C][/ROW]
[ROW][C]74[/C][C]0.999775139511992[/C][C]0.000449720976015066[/C][C]0.000224860488007533[/C][/ROW]
[ROW][C]75[/C][C]0.999881371530729[/C][C]0.000237256938542998[/C][C]0.000118628469271499[/C][/ROW]
[ROW][C]76[/C][C]0.999934367679658[/C][C]0.000131264640683603[/C][C]6.56323203418014e-05[/C][/ROW]
[ROW][C]77[/C][C]0.9999563208291[/C][C]8.73583417989741e-05[/C][C]4.3679170899487e-05[/C][/ROW]
[ROW][C]78[/C][C]0.999953300630702[/C][C]9.33987385950752e-05[/C][C]4.66993692975376e-05[/C][/ROW]
[ROW][C]79[/C][C]0.999962277379853[/C][C]7.54452402944515e-05[/C][C]3.77226201472258e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999948420079588[/C][C]0.00010315984082375[/C][C]5.15799204118748e-05[/C][/ROW]
[ROW][C]81[/C][C]0.999916672735517[/C][C]0.000166654528966997[/C][C]8.33272644834987e-05[/C][/ROW]
[ROW][C]82[/C][C]0.999877320074257[/C][C]0.00024535985148504[/C][C]0.00012267992574252[/C][/ROW]
[ROW][C]83[/C][C]0.999832602244971[/C][C]0.000334795510058286[/C][C]0.000167397755029143[/C][/ROW]
[ROW][C]84[/C][C]0.999745207297657[/C][C]0.000509585404685701[/C][C]0.00025479270234285[/C][/ROW]
[ROW][C]85[/C][C]0.999787983056712[/C][C]0.000424033886575997[/C][C]0.000212016943287998[/C][/ROW]
[ROW][C]86[/C][C]0.999765897820818[/C][C]0.000468204358363038[/C][C]0.000234102179181519[/C][/ROW]
[ROW][C]87[/C][C]0.999711593422066[/C][C]0.000576813155867959[/C][C]0.00028840657793398[/C][/ROW]
[ROW][C]88[/C][C]0.999613546814423[/C][C]0.000772906371153535[/C][C]0.000386453185576767[/C][/ROW]
[ROW][C]89[/C][C]0.999552259581085[/C][C]0.000895480837830121[/C][C]0.000447740418915061[/C][/ROW]
[ROW][C]90[/C][C]0.99946229404064[/C][C]0.00107541191872027[/C][C]0.000537705959360136[/C][/ROW]
[ROW][C]91[/C][C]0.999286195803497[/C][C]0.00142760839300539[/C][C]0.000713804196502694[/C][/ROW]
[ROW][C]92[/C][C]0.999340811379979[/C][C]0.00131837724004216[/C][C]0.000659188620021079[/C][/ROW]
[ROW][C]93[/C][C]0.999088599512828[/C][C]0.00182280097434492[/C][C]0.000911400487172461[/C][/ROW]
[ROW][C]94[/C][C]0.999028943499792[/C][C]0.00194211300041587[/C][C]0.000971056500207933[/C][/ROW]
[ROW][C]95[/C][C]0.999068773232213[/C][C]0.00186245353557362[/C][C]0.000931226767786808[/C][/ROW]
[ROW][C]96[/C][C]0.998589859901669[/C][C]0.00282028019666203[/C][C]0.00141014009833102[/C][/ROW]
[ROW][C]97[/C][C]0.997933165491876[/C][C]0.00413366901624826[/C][C]0.00206683450812413[/C][/ROW]
[ROW][C]98[/C][C]0.997083967319985[/C][C]0.00583206536002932[/C][C]0.00291603268001466[/C][/ROW]
[ROW][C]99[/C][C]0.996483333015933[/C][C]0.00703333396813421[/C][C]0.0035166669840671[/C][/ROW]
[ROW][C]100[/C][C]0.995386447685424[/C][C]0.00922710462915288[/C][C]0.00461355231457644[/C][/ROW]
[ROW][C]101[/C][C]0.995580878944944[/C][C]0.00883824211011166[/C][C]0.00441912105505583[/C][/ROW]
[ROW][C]102[/C][C]0.995804798455556[/C][C]0.00839040308888863[/C][C]0.00419520154444431[/C][/ROW]
[ROW][C]103[/C][C]0.994228485902889[/C][C]0.011543028194222[/C][C]0.00577151409711102[/C][/ROW]
[ROW][C]104[/C][C]0.993976085702819[/C][C]0.0120478285943622[/C][C]0.00602391429718108[/C][/ROW]
[ROW][C]105[/C][C]0.995362689414352[/C][C]0.00927462117129677[/C][C]0.00463731058564838[/C][/ROW]
[ROW][C]106[/C][C]0.994587293373716[/C][C]0.0108254132525677[/C][C]0.00541270662628383[/C][/ROW]
[ROW][C]107[/C][C]0.992921801277638[/C][C]0.0141563974447233[/C][C]0.00707819872236167[/C][/ROW]
[ROW][C]108[/C][C]0.991153423988486[/C][C]0.017693152023027[/C][C]0.00884657601151351[/C][/ROW]
[ROW][C]109[/C][C]0.989228371932328[/C][C]0.0215432561353447[/C][C]0.0107716280676724[/C][/ROW]
[ROW][C]110[/C][C]0.987140196930448[/C][C]0.0257196061391036[/C][C]0.0128598030695518[/C][/ROW]
[ROW][C]111[/C][C]0.983283138285214[/C][C]0.033433723429573[/C][C]0.0167168617147865[/C][/ROW]
[ROW][C]112[/C][C]0.980061761853183[/C][C]0.0398764762936344[/C][C]0.0199382381468172[/C][/ROW]
[ROW][C]113[/C][C]0.976624270768268[/C][C]0.0467514584634636[/C][C]0.0233757292317318[/C][/ROW]
[ROW][C]114[/C][C]0.975312907883265[/C][C]0.0493741842334698[/C][C]0.0246870921167349[/C][/ROW]
[ROW][C]115[/C][C]0.970133684600163[/C][C]0.0597326307996736[/C][C]0.0298663153998368[/C][/ROW]
[ROW][C]116[/C][C]0.976265936559296[/C][C]0.0474681268814082[/C][C]0.0237340634407041[/C][/ROW]
[ROW][C]117[/C][C]0.983932755023183[/C][C]0.0321344899536334[/C][C]0.0160672449768167[/C][/ROW]
[ROW][C]118[/C][C]0.983030411020551[/C][C]0.0339391779588972[/C][C]0.0169695889794486[/C][/ROW]
[ROW][C]119[/C][C]0.988317248101965[/C][C]0.0233655037960707[/C][C]0.0116827518980353[/C][/ROW]
[ROW][C]120[/C][C]0.995691028608375[/C][C]0.00861794278325057[/C][C]0.00430897139162529[/C][/ROW]
[ROW][C]121[/C][C]0.998530834373179[/C][C]0.00293833125364146[/C][C]0.00146916562682073[/C][/ROW]
[ROW][C]122[/C][C]0.998679928382173[/C][C]0.00264014323565454[/C][C]0.00132007161782727[/C][/ROW]
[ROW][C]123[/C][C]0.998931608833513[/C][C]0.00213678233297312[/C][C]0.00106839116648656[/C][/ROW]
[ROW][C]124[/C][C]0.998826860668442[/C][C]0.00234627866311685[/C][C]0.00117313933155842[/C][/ROW]
[ROW][C]125[/C][C]0.999137007240417[/C][C]0.00172598551916624[/C][C]0.00086299275958312[/C][/ROW]
[ROW][C]126[/C][C]0.999158044637246[/C][C]0.00168391072550783[/C][C]0.000841955362753917[/C][/ROW]
[ROW][C]127[/C][C]0.999671210887134[/C][C]0.000657578225732219[/C][C]0.00032878911286611[/C][/ROW]
[ROW][C]128[/C][C]0.999382097883264[/C][C]0.00123580423347243[/C][C]0.000617902116736214[/C][/ROW]
[ROW][C]129[/C][C]0.998886898668031[/C][C]0.00222620266393699[/C][C]0.00111310133196849[/C][/ROW]
[ROW][C]130[/C][C]0.997974523866815[/C][C]0.00405095226637028[/C][C]0.00202547613318514[/C][/ROW]
[ROW][C]131[/C][C]0.996673712440585[/C][C]0.00665257511883062[/C][C]0.00332628755941531[/C][/ROW]
[ROW][C]132[/C][C]0.996239460781288[/C][C]0.00752107843742391[/C][C]0.00376053921871196[/C][/ROW]
[ROW][C]133[/C][C]0.99558316755658[/C][C]0.00883366488683997[/C][C]0.00441683244341998[/C][/ROW]
[ROW][C]134[/C][C]0.994401337649107[/C][C]0.011197324701787[/C][C]0.00559866235089348[/C][/ROW]
[ROW][C]135[/C][C]0.994555585190024[/C][C]0.0108888296199515[/C][C]0.00544441480997577[/C][/ROW]
[ROW][C]136[/C][C]0.993438427039149[/C][C]0.0131231459217019[/C][C]0.00656157296085097[/C][/ROW]
[ROW][C]137[/C][C]0.98834559176717[/C][C]0.0233088164656598[/C][C]0.0116544082328299[/C][/ROW]
[ROW][C]138[/C][C]0.992505622635983[/C][C]0.0149887547280333[/C][C]0.00749437736401663[/C][/ROW]
[ROW][C]139[/C][C]0.993002774841084[/C][C]0.0139944503178322[/C][C]0.00699722515891609[/C][/ROW]
[ROW][C]140[/C][C]0.987299838814223[/C][C]0.0254003223715545[/C][C]0.0127001611857773[/C][/ROW]
[ROW][C]141[/C][C]0.98664697167948[/C][C]0.0267060566410396[/C][C]0.0133530283205198[/C][/ROW]
[ROW][C]142[/C][C]0.984612561382251[/C][C]0.0307748772354976[/C][C]0.0153874386177488[/C][/ROW]
[ROW][C]143[/C][C]0.978123469409592[/C][C]0.0437530611808152[/C][C]0.0218765305904076[/C][/ROW]
[ROW][C]144[/C][C]0.970283558101059[/C][C]0.0594328837978819[/C][C]0.029716441898941[/C][/ROW]
[ROW][C]145[/C][C]0.963900071062047[/C][C]0.0721998578759069[/C][C]0.0360999289379535[/C][/ROW]
[ROW][C]146[/C][C]0.973017633836263[/C][C]0.0539647323274746[/C][C]0.0269823661637373[/C][/ROW]
[ROW][C]147[/C][C]0.931043945338421[/C][C]0.137912109323157[/C][C]0.0689560546615787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189854&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189854&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
70.003110525650424040.006221051300848080.996889474349576
80.0005270236035262540.001054047207052510.999472976396474
97.0001246624481e-050.0001400024932489620.999929998753376
101.42993739822207e-052.85987479644414e-050.999985700626018
111.60116465680517e-063.20232931361035e-060.999998398835343
124.8620810898998e-069.7241621797996e-060.99999513791891
132.24312468791e-064.48624937582e-060.999997756875312
143.60709975807873e-077.21419951615745e-070.999999639290024
150.0002248217803181260.0004496435606362520.999775178219682
160.000276792007056980.0005535840141139610.999723207992943
170.0001229839085855650.0002459678171711290.999877016091414
184.19699107065673e-058.39398214131346e-050.999958030089293
191.49287047950549e-052.98574095901098e-050.999985071295205
205.86644997482357e-061.17328999496471e-050.999994133550025
211.38065503116706e-052.76131006233412e-050.999986193449688
225.34286947604924e-061.06857389520985e-050.999994657130524
234.52155124034999e-069.04310248069997e-060.99999547844876
249.27434973071213e-050.0001854869946142430.999907256502693
250.0006481784572048980.00129635691440980.999351821542795
260.001778780287450880.003557560574901770.998221219712549
270.002717367037427960.005434734074855910.997282632962572
280.01288263147487580.02576526294975160.987117368525124
290.01899837196642270.03799674393284550.981001628033577
300.01443055019843880.02886110039687750.985569449801561
310.01296301145126290.02592602290252580.987036988548737
320.009793537360848850.01958707472169770.990206462639151
330.009060684947105950.01812136989421190.990939315052894
340.006937972517104370.01387594503420870.993062027482896
350.004851985786702270.009703971573404550.995148014213298
360.003593122560339930.007186245120679860.99640687743966
370.002812610188751590.005625220377503190.997187389811248
380.002376589875419110.004753179750838220.997623410124581
390.002804311149488030.005608622298976070.997195688850512
400.002732618743844520.005465237487689040.997267381256155
410.002709779310149710.005419558620299420.99729022068985
420.003289495972674030.006578991945348060.996710504027326
430.003757183539753740.007514367079507490.996242816460246
440.004831171530201590.009662343060403170.995168828469798
450.007537249860722280.01507449972144460.992462750139278
460.06519113808711910.1303822761742380.934808861912881
470.09323950340626860.1864790068125370.906760496593731
480.1183136534158160.2366273068316310.881686346584184
490.115082231209460.230164462418920.88491776879054
500.0930632212732240.1861264425464480.906936778726776
510.08433494112551680.1686698822510340.915665058874483
520.06866693043533410.1373338608706680.931333069564666
530.05889993319535870.1177998663907170.941100066804641
540.04976110649453380.09952221298906760.950238893505466
550.05160288101981790.1032057620396360.948397118980182
560.1134285131355510.2268570262711010.886571486864449
570.2820775939908250.5641551879816490.717922406009175
580.4411701474778340.8823402949556670.558829852522166
590.7043886287253710.5912227425492580.295611371274629
600.9112390389883540.1775219220232920.088760961011646
610.9639516382325620.07209672353487580.0360483617674379
620.9737914721878510.05241705562429770.0262085278121489
630.9833389497238810.03332210055223780.0166610502761189
640.9829615591132040.03407688177359130.0170384408867957
650.9869661307500270.02606773849994660.0130338692499733
660.989575389737250.02084922052550040.0104246102627502
670.9889654839577590.0220690320844820.011034516042241
680.9882846078307440.02343078433851120.0117153921692556
690.9890222636194940.02195547276101230.0109777363805062
700.9899054558021670.0201890883956660.010094544197833
710.9955159992716580.008968001456683450.00448400072834172
720.9978542831750060.004291433649988490.00214571682499425
730.9992907394191380.001418521161723320.000709260580861661
740.9997751395119920.0004497209760150660.000224860488007533
750.9998813715307290.0002372569385429980.000118628469271499
760.9999343676796580.0001312646406836036.56323203418014e-05
770.99995632082918.73583417989741e-054.3679170899487e-05
780.9999533006307029.33987385950752e-054.66993692975376e-05
790.9999622773798537.54452402944515e-053.77226201472258e-05
800.9999484200795880.000103159840823755.15799204118748e-05
810.9999166727355170.0001666545289669978.33272644834987e-05
820.9998773200742570.000245359851485040.00012267992574252
830.9998326022449710.0003347955100582860.000167397755029143
840.9997452072976570.0005095854046857010.00025479270234285
850.9997879830567120.0004240338865759970.000212016943287998
860.9997658978208180.0004682043583630380.000234102179181519
870.9997115934220660.0005768131558679590.00028840657793398
880.9996135468144230.0007729063711535350.000386453185576767
890.9995522595810850.0008954808378301210.000447740418915061
900.999462294040640.001075411918720270.000537705959360136
910.9992861958034970.001427608393005390.000713804196502694
920.9993408113799790.001318377240042160.000659188620021079
930.9990885995128280.001822800974344920.000911400487172461
940.9990289434997920.001942113000415870.000971056500207933
950.9990687732322130.001862453535573620.000931226767786808
960.9985898599016690.002820280196662030.00141014009833102
970.9979331654918760.004133669016248260.00206683450812413
980.9970839673199850.005832065360029320.00291603268001466
990.9964833330159330.007033333968134210.0035166669840671
1000.9953864476854240.009227104629152880.00461355231457644
1010.9955808789449440.008838242110111660.00441912105505583
1020.9958047984555560.008390403088888630.00419520154444431
1030.9942284859028890.0115430281942220.00577151409711102
1040.9939760857028190.01204782859436220.00602391429718108
1050.9953626894143520.009274621171296770.00463731058564838
1060.9945872933737160.01082541325256770.00541270662628383
1070.9929218012776380.01415639744472330.00707819872236167
1080.9911534239884860.0176931520230270.00884657601151351
1090.9892283719323280.02154325613534470.0107716280676724
1100.9871401969304480.02571960613910360.0128598030695518
1110.9832831382852140.0334337234295730.0167168617147865
1120.9800617618531830.03987647629363440.0199382381468172
1130.9766242707682680.04675145846346360.0233757292317318
1140.9753129078832650.04937418423346980.0246870921167349
1150.9701336846001630.05973263079967360.0298663153998368
1160.9762659365592960.04746812688140820.0237340634407041
1170.9839327550231830.03213448995363340.0160672449768167
1180.9830304110205510.03393917795889720.0169695889794486
1190.9883172481019650.02336550379607070.0116827518980353
1200.9956910286083750.008617942783250570.00430897139162529
1210.9985308343731790.002938331253641460.00146916562682073
1220.9986799283821730.002640143235654540.00132007161782727
1230.9989316088335130.002136782332973120.00106839116648656
1240.9988268606684420.002346278663116850.00117313933155842
1250.9991370072404170.001725985519166240.00086299275958312
1260.9991580446372460.001683910725507830.000841955362753917
1270.9996712108871340.0006575782257322190.00032878911286611
1280.9993820978832640.001235804233472430.000617902116736214
1290.9988868986680310.002226202663936990.00111310133196849
1300.9979745238668150.004050952266370280.00202547613318514
1310.9966737124405850.006652575118830620.00332628755941531
1320.9962394607812880.007521078437423910.00376053921871196
1330.995583167556580.008833664886839970.00441683244341998
1340.9944013376491070.0111973247017870.00559866235089348
1350.9945555851900240.01088882961995150.00544441480997577
1360.9934384270391490.01312314592170190.00656157296085097
1370.988345591767170.02330881646565980.0116544082328299
1380.9925056226359830.01498875472803330.00749437736401663
1390.9930027748410840.01399445031783220.00699722515891609
1400.9872998388142230.02540032237155450.0127001611857773
1410.986646971679480.02670605664103960.0133530283205198
1420.9846125613822510.03077487723549760.0153874386177488
1430.9781234694095920.04375306118081520.0218765305904076
1440.9702835581010590.05943288379788190.029716441898941
1450.9639000710620470.07219985787590690.0360999289379535
1460.9730176338362630.05396473232747460.0269823661637373
1470.9310439453384210.1379121093231570.0689560546615787






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=189854&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=189854&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189854&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')
}