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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 computationMon, 22 Nov 2010 21:19:35 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/22/t1290460784wzimxo3d1rsgr1v.htm/, Retrieved Sat, 04 May 2024 00:03:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98745, Retrieved Sat, 04 May 2024 00:03:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136

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]
-   PD    [Multiple Regression] [Workshop 7, tutor...] [2010-11-22 21:19:35] [23a9b79f355c69a75648521a893cf584] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5,6	5,5	6	12
4,4	3,5	4	11
2,4	8,5	4	14
4,8	5	4	12
3,2	6	4,5	21
4	6	3,5	12
4	5,5	2	22
4,4	5,5	5,5	11
6,4	6	3,5	10
4,4	6,5	3,5	13
5,2	7	6	10
4,8	8	5	8
3,2	5,5	5	15
4,8	5	4	14
4,4	5,5	4	10
1,6	7,5	2	14
3,6	4,5	4,5	14
3,2	5,5	4	11
3,2	8,5	3,5	10
5,6	8,5	5,5	13
6	5,5	4,5	7
6,4	9	5,5	14
3,6	7	6,5	12
5,6	5	4	14
4,4	5,5	4	11
3,2	7,5	4,5	9
3,6	7,5	3	11
3,6	6,5	4,5	15
3,6	8	4,5	14
3,6	6,5	3	13
4	4,5	3	9
6,4	9	8	15
4,4	9	2,5	10
3,2	6	3,5	11
3,6	8,5	4,5	13
6,4	4,5	3	8
4,4	4,5	3	20
6,4	6	2,5	12
4,8	9	6	10
4,8	6	3,5	10
5,6	9	5	9
3,6	7	4,5	14
4	7,5	4	8
3,6	8	2,5	14
4	5	4	11
4,8	5,5	4	13
5,6	7	5	9
5,6	4,5	3	11
4	6	4	15
5,6	8,5	3,5	11
6,4	2,5	2	10
3,6	6	4	14
4	6	4	18
2,4	3	2	14
3,2	12	10	11
5,2	6	4	12
4	6	4	13
3,2	7	3	9
2,8	3,5	2	10
6	6,5	4	15
3,6	6	4,5	20
4	6,5	3	12
4,8	7	3,5	12
5,2	4	4,5	14
4	5,5	2,5	13
4,4	4,5	2,5	11
3,2	5,5	4	17
3,6	6,5	4	12
5,2	5	3	13
4,4	5,5	4	14
3,2	6	3,5	13
3,6	4,5	3,5	15
3,6	7,5	4,5	13
6	9	5,5	10
3,6	7,5	3	11
4	6	4	19
5,6	6,5	3	13
4,8	7	4,5	17
4,8	5	4	13
4,4	6,5	3	9
5,6	6,5	5	11
2,4	5,5	4	10
4,8	6,5	4	9
3,2	8	5	12
5,6	4	2,5	12
4,4	8	3,5	13
4	5,5	2,5	13
5,6	4,5	4	12
4,8	8	7	15
4	6	3,5	22
5,6	7	4	13
2	4	3	15
4,4	4,5	2,5	13
4	7,5	3	15
3,6	5,5	5	10
4	10,5	6	11
6,4	7	4,5	16
5,2	9	6	11
3,6	6	3,5	11
4	6,5	4	10
4	7,5	5	10
2,8	6	3	16
3,6	9,5	5	12
3,2	7,5	5	11
5,6	5,5	5	16
5,6	5,5	2,5	19
3,2	5	3,5	11
3,6	6,5	5	16
5,6	7,5	5,5	15
5,6	6	3	24
3,2	6	3,5	14
3,2	8	6	15
3,2	4,5	5,5	11
2,8	9	5,5	15
2,4	4	5,5	12
3,2	6,5	2,5	10
2,4	8,5	4	14
4,4	4,5	3	13
5,6	7,5	4,5	9
4,4	4	2	15
4,4	3,5	2	15
4,4	6	3,5	14
5,6	7	5,5	11
3,2	3	3	8
8	4	3,5	11
4,4	8,5	4	11
3,2	5	2	8
4,4	5,5	4	10
4	7	4,5	11
5,6	5,5	4	13
4,4	6,5	5,5	11
3,6	6	4	20
3,6	5,5	2,5	10
3,2	4,5	2	15
4	6	4	12
5,2	10	5	14
5,2	6	3	23
4,8	6,5	4,5	14
3,2	6	4,5	16
5,2	6	6,5	11
5,6	4,5	4,5	12
4,8	7,5	5	10
5,6	12	10	14
6	3,5	2,5	12
5,2	8,5	5,5	12
6,4	5,5	3	11
3,6	8,5	4,5	12
3,6	5,5	3,5	13
3,6	6	4,5	11
3,2	7	5	19
2,8	5,5	4,5	12
6,4	8	4	17
4,4	10,5	3,5	9
3,6	7	3	12
4,4	10	6,5	19
3,6	6,5	3	18
5,6	5,5	4	15
5,2	7,5	5	14
6,4	9,5	8	11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98745&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98745&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98745&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 14.2746162191590 -0.199901806823605Doubts[t] -0.054501555912315Expect[t] -0.038947120698009Criticism[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  14.2746162191590 -0.199901806823605Doubts[t] -0.054501555912315Expect[t] -0.038947120698009Criticism[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98745&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  14.2746162191590 -0.199901806823605Doubts[t] -0.054501555912315Expect[t] -0.038947120698009Criticism[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98745&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98745&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
Depression[t] = + 14.2746162191590 -0.199901806823605Doubts[t] -0.054501555912315Expect[t] -0.038947120698009Criticism[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.27461621915901.36530510.455300
Doubts-0.1999018068236050.228178-0.87610.3823430.191172
Expect-0.0545015559123150.182095-0.29930.765110.382555
Criticism-0.0389471206980090.23449-0.16610.86830.43415

\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) & 14.2746162191590 & 1.365305 & 10.4553 & 0 & 0 \tabularnewline
Doubts & -0.199901806823605 & 0.228178 & -0.8761 & 0.382343 & 0.191172 \tabularnewline
Expect & -0.054501555912315 & 0.182095 & -0.2993 & 0.76511 & 0.382555 \tabularnewline
Criticism & -0.038947120698009 & 0.23449 & -0.1661 & 0.8683 & 0.43415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98745&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]14.2746162191590[/C][C]1.365305[/C][C]10.4553[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.199901806823605[/C][C]0.228178[/C][C]-0.8761[/C][C]0.382343[/C][C]0.191172[/C][/ROW]
[ROW][C]Expect[/C][C]-0.054501555912315[/C][C]0.182095[/C][C]-0.2993[/C][C]0.76511[/C][C]0.382555[/C][/ROW]
[ROW][C]Criticism[/C][C]-0.038947120698009[/C][C]0.23449[/C][C]-0.1661[/C][C]0.8683[/C][C]0.43415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98745&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98745&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)14.27461621915901.36530510.455300
Doubts-0.1999018068236050.228178-0.87610.3823430.191172
Expect-0.0545015559123150.182095-0.29930.765110.382555
Criticism-0.0389471206980090.23449-0.16610.86830.43415






Multiple Linear Regression - Regression Statistics
Multiple R0.0855457044326433
R-squared0.00731806754687717
Adjusted R-squared-0.0118951311457638
F-TEST (value)0.380887517167045
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value0.766914280365275
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16405509068659
Sum Squared Residuals1551.74291561946

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0855457044326433 \tabularnewline
R-squared & 0.00731806754687717 \tabularnewline
Adjusted R-squared & -0.0118951311457638 \tabularnewline
F-TEST (value) & 0.380887517167045 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.766914280365275 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.16405509068659 \tabularnewline
Sum Squared Residuals & 1551.74291561946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98745&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0855457044326433[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00731806754687717[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0118951311457638[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.380887517167045[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.766914280365275[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.16405509068659[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1551.74291561946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98745&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98745&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 R0.0855457044326433
R-squared0.00731806754687717
Adjusted R-squared-0.0118951311457638
F-TEST (value)0.380887517167045
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value0.766914280365275
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16405509068659
Sum Squared Residuals1551.74291561946






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.621724819241-0.621724819240985
21113.0485043406500-2.04850434064998
31413.17580017473560.824199825264388
41212.8867912840521-0.88679128405206
52113.13265905870857.86734094129149
61213.0116847339476-1.01168473394764
72213.09735619295088.9026438070492
81112.8810805477783-1.88108054777833
91012.5319203975710-2.53192039757098
101312.90447323326200.0955267667379638
111012.6199332081020-2.61993320810197
12812.6843394956171-4.68433949561711
131513.14043627631571.85956372368434
141412.88679128405211.11320871594794
151012.9395012288253-2.93950122882535
161413.46811741750280.53188258249717
171413.13445066984750.865549330152459
181113.1793833970137-2.17938339701367
191013.0353522896257-3.03535228962573
201312.47769371185310.522306288146938
21712.6001847775586-5.60018477755857
221412.29052148843801.70947851156198
231212.9203025386707-0.920302538670735
241412.72686983859321.27313016140682
251112.9395012288253-1.93950122882535
26913.0509067248400-4.05090672484004
271113.0293666831576-2.02936668315761
281513.02544755802291.97455244197709
291412.94369522415441.05630477584556
301313.0838682390699-0.083868239069925
31913.1129106281651-4.11291062816511
321512.1931536866932.80684631330700
331012.8071664641793-2.80716646417926
341113.1716061794065-2.17160617940652
351312.91644444619830.0835555538017188
36812.6331462917885-4.63314629178846
372013.03294990543576.96705009456433
381212.570867518269-0.570867518268993
391012.5908908190068-2.59089081900678
401012.8517632884888-2.85176328848875
41912.4699164942459-3.46991649424591
421412.99819678006681.00180321993325
43812.9104588397302-4.91045883973016
441413.02158946555050.978410534449543
451113.0467127295109-2.04671272951095
461312.85954050609590.140459493904095
47912.5789196060705-3.57891960607054
481112.7930677372473-1.79306773724734
491512.99221117359862.00778882640137
501112.5555879532491-1.55558795324908
511012.7810965243111-2.7810965243111
521413.07217189632810.927828103671927
531812.99221117359865.00778882640137
541413.55345297364940.446547026350637
551112.5914405593956-1.59144055939557
561212.7523290054103-0.752329005410305
571312.99221117359860.00778882640136876
58913.1365781838432-4.13657818384321
591013.4462414729638-3.44624147296376
601512.56515678199532.43484321800474
612013.05269833597916.94730166402093
621213.0039075163405-1.00390751634048
631212.7972617325764-0.797261732576437
641412.84185855688591.15814144311407
651313.0778826326018-0.0778826326018025
661113.0524234657847-2.05242346578468
671713.17938339701373.82061660298633
681213.0449211183719-1.04492111837192
691312.84577768202060.154222317979371
701412.93950122882531.06049877117465
711313.1716061794065-0.17160617940652
721513.17339779054561.82660220945445
731312.97094600211060.0290539978894038
741012.3704822111675-2.37048221116746
751113.0293666831576-2.02936668315761
761912.99221117359866.00778882640137
771312.68406462542270.315935374577285
781712.75831461187844.24168538812157
791312.88679128405210.113208715947938
80912.9239467936110-3.92394679361104
811112.6061703840267-1.60617038402670
821013.3393048424726-3.33930484247256
83912.8050389501836-3.80503895018359
841213.0041823865349-1.00418238653488
851212.8397920755525-0.839792075552507
861312.82272089939360.177279100606436
871313.0778826326018-0.0778826326018025
881212.7541206165493-0.754120616549335
891512.60644525422112.39355474577891
902213.01168473394768.98831526605236
911312.61786672676850.382133273231452
921513.53996501976851.46003498023152
931313.0524234657847-0.0524234657846753
941512.94940596042822.05059403957183
951013.0604755535862-3.06047555358622
961112.6690599305972-1.66905993059720
971612.43847172096073.56152827903934
981112.5109300962773-1.51093009627734
991113.0916454566771-2.09164545667708
1001012.9649603956425-2.96496039564247
1011012.8715117190321-2.87151171903215
1021613.27104046248502.72895953751503
1031212.8424693299370-0.842469329936962
1041113.0314331644910-2.03143316449103
1051612.6606719399393.33932806006099
1061912.75803974168406.24196025831596
1071113.2261077353188-2.22610773531883
1081613.00597399767392.99402600232609
1091512.53219526776542.46780473223462
1102412.711315403378911.2886845966211
1111413.17160617940650.82839382059348
1121512.96523526583692.03476473416313
1131113.1754642718790-2.17546427187897
1141513.0101679930031.989832006997
1151213.3626364952940-1.36263649529402
1161013.1833025221484-3.18330252214837
1171413.17580017473560.824199825264388
1181313.0329499054357-0.0329499054356707
119912.5711423884634-3.57114238846339
1201513.09914780408981.90085219591016
1211513.1263985820461.87360141795401
1221412.93172401121821.06827598878181
1231112.5594460457215-1.55944604572153
124813.3545844074925-5.35458440749247
1251112.3210806184778-1.32108061847785
1261112.7759965610884-1.77599656108840
127813.2845284163659-5.28452841636585
1281012.9395012288253-2.93950122882535
1291112.9182360573373-1.91823605733731
1301312.69961906063700.300380939362979
1311112.8265789918660-1.82657899186602
1322013.07217189632816.92782810367193
1331013.1578433553312-3.15784335533124
1341513.3117791943221.68822080567799
1351212.9922111735986-0.992211173598631
1361412.49537566106301.50462433893696
1372312.791276126108310.2087238738917
1381412.78556538983461.21443461016541
1391613.13265905870852.86734094129149
1401112.6549612036653-1.65496120366528
1411212.7346470562003-0.734647056200331
1421012.7115902735733-2.71159027357327
1431412.11167622301891.88832377698108
1441212.7870821307792-0.787082130779222
1451212.5576544345825-0.557654434582504
1461112.5786447358761-1.57864473587615
1471212.9164444461983-0.916444446198281
1481313.1188962346332-0.118896234633235
1491113.0526983359791-2.05269833597907
1501913.05868394244725.94131605755281
1511213.2398705593941-1.23987055939411
1521712.40344372539734.59655627460265
153912.6864670096128-3.68646700961278
1541213.0566174611138-1.05661746111377
1551912.59687642547496.4031235745251
1561813.08386823906994.91613176093007
1571512.69961906063702.30038093936298
1581412.63162955084381.36837044915618
1591112.1659029087368-1.16590290873684

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.621724819241 & -0.621724819240985 \tabularnewline
2 & 11 & 13.0485043406500 & -2.04850434064998 \tabularnewline
3 & 14 & 13.1758001747356 & 0.824199825264388 \tabularnewline
4 & 12 & 12.8867912840521 & -0.88679128405206 \tabularnewline
5 & 21 & 13.1326590587085 & 7.86734094129149 \tabularnewline
6 & 12 & 13.0116847339476 & -1.01168473394764 \tabularnewline
7 & 22 & 13.0973561929508 & 8.9026438070492 \tabularnewline
8 & 11 & 12.8810805477783 & -1.88108054777833 \tabularnewline
9 & 10 & 12.5319203975710 & -2.53192039757098 \tabularnewline
10 & 13 & 12.9044732332620 & 0.0955267667379638 \tabularnewline
11 & 10 & 12.6199332081020 & -2.61993320810197 \tabularnewline
12 & 8 & 12.6843394956171 & -4.68433949561711 \tabularnewline
13 & 15 & 13.1404362763157 & 1.85956372368434 \tabularnewline
14 & 14 & 12.8867912840521 & 1.11320871594794 \tabularnewline
15 & 10 & 12.9395012288253 & -2.93950122882535 \tabularnewline
16 & 14 & 13.4681174175028 & 0.53188258249717 \tabularnewline
17 & 14 & 13.1344506698475 & 0.865549330152459 \tabularnewline
18 & 11 & 13.1793833970137 & -2.17938339701367 \tabularnewline
19 & 10 & 13.0353522896257 & -3.03535228962573 \tabularnewline
20 & 13 & 12.4776937118531 & 0.522306288146938 \tabularnewline
21 & 7 & 12.6001847775586 & -5.60018477755857 \tabularnewline
22 & 14 & 12.2905214884380 & 1.70947851156198 \tabularnewline
23 & 12 & 12.9203025386707 & -0.920302538670735 \tabularnewline
24 & 14 & 12.7268698385932 & 1.27313016140682 \tabularnewline
25 & 11 & 12.9395012288253 & -1.93950122882535 \tabularnewline
26 & 9 & 13.0509067248400 & -4.05090672484004 \tabularnewline
27 & 11 & 13.0293666831576 & -2.02936668315761 \tabularnewline
28 & 15 & 13.0254475580229 & 1.97455244197709 \tabularnewline
29 & 14 & 12.9436952241544 & 1.05630477584556 \tabularnewline
30 & 13 & 13.0838682390699 & -0.083868239069925 \tabularnewline
31 & 9 & 13.1129106281651 & -4.11291062816511 \tabularnewline
32 & 15 & 12.193153686693 & 2.80684631330700 \tabularnewline
33 & 10 & 12.8071664641793 & -2.80716646417926 \tabularnewline
34 & 11 & 13.1716061794065 & -2.17160617940652 \tabularnewline
35 & 13 & 12.9164444461983 & 0.0835555538017188 \tabularnewline
36 & 8 & 12.6331462917885 & -4.63314629178846 \tabularnewline
37 & 20 & 13.0329499054357 & 6.96705009456433 \tabularnewline
38 & 12 & 12.570867518269 & -0.570867518268993 \tabularnewline
39 & 10 & 12.5908908190068 & -2.59089081900678 \tabularnewline
40 & 10 & 12.8517632884888 & -2.85176328848875 \tabularnewline
41 & 9 & 12.4699164942459 & -3.46991649424591 \tabularnewline
42 & 14 & 12.9981967800668 & 1.00180321993325 \tabularnewline
43 & 8 & 12.9104588397302 & -4.91045883973016 \tabularnewline
44 & 14 & 13.0215894655505 & 0.978410534449543 \tabularnewline
45 & 11 & 13.0467127295109 & -2.04671272951095 \tabularnewline
46 & 13 & 12.8595405060959 & 0.140459493904095 \tabularnewline
47 & 9 & 12.5789196060705 & -3.57891960607054 \tabularnewline
48 & 11 & 12.7930677372473 & -1.79306773724734 \tabularnewline
49 & 15 & 12.9922111735986 & 2.00778882640137 \tabularnewline
50 & 11 & 12.5555879532491 & -1.55558795324908 \tabularnewline
51 & 10 & 12.7810965243111 & -2.7810965243111 \tabularnewline
52 & 14 & 13.0721718963281 & 0.927828103671927 \tabularnewline
53 & 18 & 12.9922111735986 & 5.00778882640137 \tabularnewline
54 & 14 & 13.5534529736494 & 0.446547026350637 \tabularnewline
55 & 11 & 12.5914405593956 & -1.59144055939557 \tabularnewline
56 & 12 & 12.7523290054103 & -0.752329005410305 \tabularnewline
57 & 13 & 12.9922111735986 & 0.00778882640136876 \tabularnewline
58 & 9 & 13.1365781838432 & -4.13657818384321 \tabularnewline
59 & 10 & 13.4462414729638 & -3.44624147296376 \tabularnewline
60 & 15 & 12.5651567819953 & 2.43484321800474 \tabularnewline
61 & 20 & 13.0526983359791 & 6.94730166402093 \tabularnewline
62 & 12 & 13.0039075163405 & -1.00390751634048 \tabularnewline
63 & 12 & 12.7972617325764 & -0.797261732576437 \tabularnewline
64 & 14 & 12.8418585568859 & 1.15814144311407 \tabularnewline
65 & 13 & 13.0778826326018 & -0.0778826326018025 \tabularnewline
66 & 11 & 13.0524234657847 & -2.05242346578468 \tabularnewline
67 & 17 & 13.1793833970137 & 3.82061660298633 \tabularnewline
68 & 12 & 13.0449211183719 & -1.04492111837192 \tabularnewline
69 & 13 & 12.8457776820206 & 0.154222317979371 \tabularnewline
70 & 14 & 12.9395012288253 & 1.06049877117465 \tabularnewline
71 & 13 & 13.1716061794065 & -0.17160617940652 \tabularnewline
72 & 15 & 13.1733977905456 & 1.82660220945445 \tabularnewline
73 & 13 & 12.9709460021106 & 0.0290539978894038 \tabularnewline
74 & 10 & 12.3704822111675 & -2.37048221116746 \tabularnewline
75 & 11 & 13.0293666831576 & -2.02936668315761 \tabularnewline
76 & 19 & 12.9922111735986 & 6.00778882640137 \tabularnewline
77 & 13 & 12.6840646254227 & 0.315935374577285 \tabularnewline
78 & 17 & 12.7583146118784 & 4.24168538812157 \tabularnewline
79 & 13 & 12.8867912840521 & 0.113208715947938 \tabularnewline
80 & 9 & 12.9239467936110 & -3.92394679361104 \tabularnewline
81 & 11 & 12.6061703840267 & -1.60617038402670 \tabularnewline
82 & 10 & 13.3393048424726 & -3.33930484247256 \tabularnewline
83 & 9 & 12.8050389501836 & -3.80503895018359 \tabularnewline
84 & 12 & 13.0041823865349 & -1.00418238653488 \tabularnewline
85 & 12 & 12.8397920755525 & -0.839792075552507 \tabularnewline
86 & 13 & 12.8227208993936 & 0.177279100606436 \tabularnewline
87 & 13 & 13.0778826326018 & -0.0778826326018025 \tabularnewline
88 & 12 & 12.7541206165493 & -0.754120616549335 \tabularnewline
89 & 15 & 12.6064452542211 & 2.39355474577891 \tabularnewline
90 & 22 & 13.0116847339476 & 8.98831526605236 \tabularnewline
91 & 13 & 12.6178667267685 & 0.382133273231452 \tabularnewline
92 & 15 & 13.5399650197685 & 1.46003498023152 \tabularnewline
93 & 13 & 13.0524234657847 & -0.0524234657846753 \tabularnewline
94 & 15 & 12.9494059604282 & 2.05059403957183 \tabularnewline
95 & 10 & 13.0604755535862 & -3.06047555358622 \tabularnewline
96 & 11 & 12.6690599305972 & -1.66905993059720 \tabularnewline
97 & 16 & 12.4384717209607 & 3.56152827903934 \tabularnewline
98 & 11 & 12.5109300962773 & -1.51093009627734 \tabularnewline
99 & 11 & 13.0916454566771 & -2.09164545667708 \tabularnewline
100 & 10 & 12.9649603956425 & -2.96496039564247 \tabularnewline
101 & 10 & 12.8715117190321 & -2.87151171903215 \tabularnewline
102 & 16 & 13.2710404624850 & 2.72895953751503 \tabularnewline
103 & 12 & 12.8424693299370 & -0.842469329936962 \tabularnewline
104 & 11 & 13.0314331644910 & -2.03143316449103 \tabularnewline
105 & 16 & 12.660671939939 & 3.33932806006099 \tabularnewline
106 & 19 & 12.7580397416840 & 6.24196025831596 \tabularnewline
107 & 11 & 13.2261077353188 & -2.22610773531883 \tabularnewline
108 & 16 & 13.0059739976739 & 2.99402600232609 \tabularnewline
109 & 15 & 12.5321952677654 & 2.46780473223462 \tabularnewline
110 & 24 & 12.7113154033789 & 11.2886845966211 \tabularnewline
111 & 14 & 13.1716061794065 & 0.82839382059348 \tabularnewline
112 & 15 & 12.9652352658369 & 2.03476473416313 \tabularnewline
113 & 11 & 13.1754642718790 & -2.17546427187897 \tabularnewline
114 & 15 & 13.010167993003 & 1.989832006997 \tabularnewline
115 & 12 & 13.3626364952940 & -1.36263649529402 \tabularnewline
116 & 10 & 13.1833025221484 & -3.18330252214837 \tabularnewline
117 & 14 & 13.1758001747356 & 0.824199825264388 \tabularnewline
118 & 13 & 13.0329499054357 & -0.0329499054356707 \tabularnewline
119 & 9 & 12.5711423884634 & -3.57114238846339 \tabularnewline
120 & 15 & 13.0991478040898 & 1.90085219591016 \tabularnewline
121 & 15 & 13.126398582046 & 1.87360141795401 \tabularnewline
122 & 14 & 12.9317240112182 & 1.06827598878181 \tabularnewline
123 & 11 & 12.5594460457215 & -1.55944604572153 \tabularnewline
124 & 8 & 13.3545844074925 & -5.35458440749247 \tabularnewline
125 & 11 & 12.3210806184778 & -1.32108061847785 \tabularnewline
126 & 11 & 12.7759965610884 & -1.77599656108840 \tabularnewline
127 & 8 & 13.2845284163659 & -5.28452841636585 \tabularnewline
128 & 10 & 12.9395012288253 & -2.93950122882535 \tabularnewline
129 & 11 & 12.9182360573373 & -1.91823605733731 \tabularnewline
130 & 13 & 12.6996190606370 & 0.300380939362979 \tabularnewline
131 & 11 & 12.8265789918660 & -1.82657899186602 \tabularnewline
132 & 20 & 13.0721718963281 & 6.92782810367193 \tabularnewline
133 & 10 & 13.1578433553312 & -3.15784335533124 \tabularnewline
134 & 15 & 13.311779194322 & 1.68822080567799 \tabularnewline
135 & 12 & 12.9922111735986 & -0.992211173598631 \tabularnewline
136 & 14 & 12.4953756610630 & 1.50462433893696 \tabularnewline
137 & 23 & 12.7912761261083 & 10.2087238738917 \tabularnewline
138 & 14 & 12.7855653898346 & 1.21443461016541 \tabularnewline
139 & 16 & 13.1326590587085 & 2.86734094129149 \tabularnewline
140 & 11 & 12.6549612036653 & -1.65496120366528 \tabularnewline
141 & 12 & 12.7346470562003 & -0.734647056200331 \tabularnewline
142 & 10 & 12.7115902735733 & -2.71159027357327 \tabularnewline
143 & 14 & 12.1116762230189 & 1.88832377698108 \tabularnewline
144 & 12 & 12.7870821307792 & -0.787082130779222 \tabularnewline
145 & 12 & 12.5576544345825 & -0.557654434582504 \tabularnewline
146 & 11 & 12.5786447358761 & -1.57864473587615 \tabularnewline
147 & 12 & 12.9164444461983 & -0.916444446198281 \tabularnewline
148 & 13 & 13.1188962346332 & -0.118896234633235 \tabularnewline
149 & 11 & 13.0526983359791 & -2.05269833597907 \tabularnewline
150 & 19 & 13.0586839424472 & 5.94131605755281 \tabularnewline
151 & 12 & 13.2398705593941 & -1.23987055939411 \tabularnewline
152 & 17 & 12.4034437253973 & 4.59655627460265 \tabularnewline
153 & 9 & 12.6864670096128 & -3.68646700961278 \tabularnewline
154 & 12 & 13.0566174611138 & -1.05661746111377 \tabularnewline
155 & 19 & 12.5968764254749 & 6.4031235745251 \tabularnewline
156 & 18 & 13.0838682390699 & 4.91613176093007 \tabularnewline
157 & 15 & 12.6996190606370 & 2.30038093936298 \tabularnewline
158 & 14 & 12.6316295508438 & 1.36837044915618 \tabularnewline
159 & 11 & 12.1659029087368 & -1.16590290873684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98745&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]12[/C][C]12.621724819241[/C][C]-0.621724819240985[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]13.0485043406500[/C][C]-2.04850434064998[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]13.1758001747356[/C][C]0.824199825264388[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.8867912840521[/C][C]-0.88679128405206[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]13.1326590587085[/C][C]7.86734094129149[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]13.0116847339476[/C][C]-1.01168473394764[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.0973561929508[/C][C]8.9026438070492[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.8810805477783[/C][C]-1.88108054777833[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.5319203975710[/C][C]-2.53192039757098[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.9044732332620[/C][C]0.0955267667379638[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.6199332081020[/C][C]-2.61993320810197[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]12.6843394956171[/C][C]-4.68433949561711[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.1404362763157[/C][C]1.85956372368434[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.8867912840521[/C][C]1.11320871594794[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]12.9395012288253[/C][C]-2.93950122882535[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.4681174175028[/C][C]0.53188258249717[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.1344506698475[/C][C]0.865549330152459[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]13.1793833970137[/C][C]-2.17938339701367[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]13.0353522896257[/C][C]-3.03535228962573[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.4776937118531[/C][C]0.522306288146938[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]12.6001847775586[/C][C]-5.60018477755857[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]12.2905214884380[/C][C]1.70947851156198[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.9203025386707[/C][C]-0.920302538670735[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]12.7268698385932[/C][C]1.27313016140682[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.9395012288253[/C][C]-1.93950122882535[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]13.0509067248400[/C][C]-4.05090672484004[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]13.0293666831576[/C][C]-2.02936668315761[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.0254475580229[/C][C]1.97455244197709[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9436952241544[/C][C]1.05630477584556[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]13.0838682390699[/C][C]-0.083868239069925[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]13.1129106281651[/C][C]-4.11291062816511[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]12.193153686693[/C][C]2.80684631330700[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.8071664641793[/C][C]-2.80716646417926[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]13.1716061794065[/C][C]-2.17160617940652[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.9164444461983[/C][C]0.0835555538017188[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.6331462917885[/C][C]-4.63314629178846[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]13.0329499054357[/C][C]6.96705009456433[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.570867518269[/C][C]-0.570867518268993[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.5908908190068[/C][C]-2.59089081900678[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.8517632884888[/C][C]-2.85176328848875[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.4699164942459[/C][C]-3.46991649424591[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.9981967800668[/C][C]1.00180321993325[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.9104588397302[/C][C]-4.91045883973016[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.0215894655505[/C][C]0.978410534449543[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]13.0467127295109[/C][C]-2.04671272951095[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.8595405060959[/C][C]0.140459493904095[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.5789196060705[/C][C]-3.57891960607054[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.7930677372473[/C][C]-1.79306773724734[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.9922111735986[/C][C]2.00778882640137[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.5555879532491[/C][C]-1.55558795324908[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.7810965243111[/C][C]-2.7810965243111[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.0721718963281[/C][C]0.927828103671927[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]12.9922111735986[/C][C]5.00778882640137[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]13.5534529736494[/C][C]0.446547026350637[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.5914405593956[/C][C]-1.59144055939557[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.7523290054103[/C][C]-0.752329005410305[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.9922111735986[/C][C]0.00778882640136876[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.1365781838432[/C][C]-4.13657818384321[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.4462414729638[/C][C]-3.44624147296376[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]12.5651567819953[/C][C]2.43484321800474[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]13.0526983359791[/C][C]6.94730166402093[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.0039075163405[/C][C]-1.00390751634048[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.7972617325764[/C][C]-0.797261732576437[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.8418585568859[/C][C]1.15814144311407[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.0778826326018[/C][C]-0.0778826326018025[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]13.0524234657847[/C][C]-2.05242346578468[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]13.1793833970137[/C][C]3.82061660298633[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.0449211183719[/C][C]-1.04492111837192[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.8457776820206[/C][C]0.154222317979371[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.9395012288253[/C][C]1.06049877117465[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.1716061794065[/C][C]-0.17160617940652[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.1733977905456[/C][C]1.82660220945445[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.9709460021106[/C][C]0.0290539978894038[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.3704822111675[/C][C]-2.37048221116746[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]13.0293666831576[/C][C]-2.02936668315761[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.9922111735986[/C][C]6.00778882640137[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.6840646254227[/C][C]0.315935374577285[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]12.7583146118784[/C][C]4.24168538812157[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.8867912840521[/C][C]0.113208715947938[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.9239467936110[/C][C]-3.92394679361104[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.6061703840267[/C][C]-1.60617038402670[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]13.3393048424726[/C][C]-3.33930484247256[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.8050389501836[/C][C]-3.80503895018359[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]13.0041823865349[/C][C]-1.00418238653488[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.8397920755525[/C][C]-0.839792075552507[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.8227208993936[/C][C]0.177279100606436[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]13.0778826326018[/C][C]-0.0778826326018025[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.7541206165493[/C][C]-0.754120616549335[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.6064452542211[/C][C]2.39355474577891[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]13.0116847339476[/C][C]8.98831526605236[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.6178667267685[/C][C]0.382133273231452[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.5399650197685[/C][C]1.46003498023152[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.0524234657847[/C][C]-0.0524234657846753[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.9494059604282[/C][C]2.05059403957183[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.0604755535862[/C][C]-3.06047555358622[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.6690599305972[/C][C]-1.66905993059720[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.4384717209607[/C][C]3.56152827903934[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.5109300962773[/C][C]-1.51093009627734[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.0916454566771[/C][C]-2.09164545667708[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.9649603956425[/C][C]-2.96496039564247[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]12.8715117190321[/C][C]-2.87151171903215[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.2710404624850[/C][C]2.72895953751503[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]12.8424693299370[/C][C]-0.842469329936962[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.0314331644910[/C][C]-2.03143316449103[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.660671939939[/C][C]3.33932806006099[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.7580397416840[/C][C]6.24196025831596[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]13.2261077353188[/C][C]-2.22610773531883[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]13.0059739976739[/C][C]2.99402600232609[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]12.5321952677654[/C][C]2.46780473223462[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]12.7113154033789[/C][C]11.2886845966211[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.1716061794065[/C][C]0.82839382059348[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.9652352658369[/C][C]2.03476473416313[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.1754642718790[/C][C]-2.17546427187897[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.010167993003[/C][C]1.989832006997[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.3626364952940[/C][C]-1.36263649529402[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]13.1833025221484[/C][C]-3.18330252214837[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.1758001747356[/C][C]0.824199825264388[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.0329499054357[/C][C]-0.0329499054356707[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]12.5711423884634[/C][C]-3.57114238846339[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.0991478040898[/C][C]1.90085219591016[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]13.126398582046[/C][C]1.87360141795401[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.9317240112182[/C][C]1.06827598878181[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.5594460457215[/C][C]-1.55944604572153[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]13.3545844074925[/C][C]-5.35458440749247[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.3210806184778[/C][C]-1.32108061847785[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]12.7759965610884[/C][C]-1.77599656108840[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.2845284163659[/C][C]-5.28452841636585[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]12.9395012288253[/C][C]-2.93950122882535[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]12.9182360573373[/C][C]-1.91823605733731[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.6996190606370[/C][C]0.300380939362979[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.8265789918660[/C][C]-1.82657899186602[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.0721718963281[/C][C]6.92782810367193[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]13.1578433553312[/C][C]-3.15784335533124[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.311779194322[/C][C]1.68822080567799[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.9922111735986[/C][C]-0.992211173598631[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.4953756610630[/C][C]1.50462433893696[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]12.7912761261083[/C][C]10.2087238738917[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.7855653898346[/C][C]1.21443461016541[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]13.1326590587085[/C][C]2.86734094129149[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.6549612036653[/C][C]-1.65496120366528[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.7346470562003[/C][C]-0.734647056200331[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.7115902735733[/C][C]-2.71159027357327[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.1116762230189[/C][C]1.88832377698108[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.7870821307792[/C][C]-0.787082130779222[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.5576544345825[/C][C]-0.557654434582504[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.5786447358761[/C][C]-1.57864473587615[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]12.9164444461983[/C][C]-0.916444446198281[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]13.1188962346332[/C][C]-0.118896234633235[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.0526983359791[/C][C]-2.05269833597907[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]13.0586839424472[/C][C]5.94131605755281[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.2398705593941[/C][C]-1.23987055939411[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]12.4034437253973[/C][C]4.59655627460265[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.6864670096128[/C][C]-3.68646700961278[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.0566174611138[/C][C]-1.05661746111377[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]12.5968764254749[/C][C]6.4031235745251[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.0838682390699[/C][C]4.91613176093007[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.6996190606370[/C][C]2.30038093936298[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]12.6316295508438[/C][C]1.36837044915618[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.1659029087368[/C][C]-1.16590290873684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98745&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98745&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
11212.621724819241-0.621724819240985
21113.0485043406500-2.04850434064998
31413.17580017473560.824199825264388
41212.8867912840521-0.88679128405206
52113.13265905870857.86734094129149
61213.0116847339476-1.01168473394764
72213.09735619295088.9026438070492
81112.8810805477783-1.88108054777833
91012.5319203975710-2.53192039757098
101312.90447323326200.0955267667379638
111012.6199332081020-2.61993320810197
12812.6843394956171-4.68433949561711
131513.14043627631571.85956372368434
141412.88679128405211.11320871594794
151012.9395012288253-2.93950122882535
161413.46811741750280.53188258249717
171413.13445066984750.865549330152459
181113.1793833970137-2.17938339701367
191013.0353522896257-3.03535228962573
201312.47769371185310.522306288146938
21712.6001847775586-5.60018477755857
221412.29052148843801.70947851156198
231212.9203025386707-0.920302538670735
241412.72686983859321.27313016140682
251112.9395012288253-1.93950122882535
26913.0509067248400-4.05090672484004
271113.0293666831576-2.02936668315761
281513.02544755802291.97455244197709
291412.94369522415441.05630477584556
301313.0838682390699-0.083868239069925
31913.1129106281651-4.11291062816511
321512.1931536866932.80684631330700
331012.8071664641793-2.80716646417926
341113.1716061794065-2.17160617940652
351312.91644444619830.0835555538017188
36812.6331462917885-4.63314629178846
372013.03294990543576.96705009456433
381212.570867518269-0.570867518268993
391012.5908908190068-2.59089081900678
401012.8517632884888-2.85176328848875
41912.4699164942459-3.46991649424591
421412.99819678006681.00180321993325
43812.9104588397302-4.91045883973016
441413.02158946555050.978410534449543
451113.0467127295109-2.04671272951095
461312.85954050609590.140459493904095
47912.5789196060705-3.57891960607054
481112.7930677372473-1.79306773724734
491512.99221117359862.00778882640137
501112.5555879532491-1.55558795324908
511012.7810965243111-2.7810965243111
521413.07217189632810.927828103671927
531812.99221117359865.00778882640137
541413.55345297364940.446547026350637
551112.5914405593956-1.59144055939557
561212.7523290054103-0.752329005410305
571312.99221117359860.00778882640136876
58913.1365781838432-4.13657818384321
591013.4462414729638-3.44624147296376
601512.56515678199532.43484321800474
612013.05269833597916.94730166402093
621213.0039075163405-1.00390751634048
631212.7972617325764-0.797261732576437
641412.84185855688591.15814144311407
651313.0778826326018-0.0778826326018025
661113.0524234657847-2.05242346578468
671713.17938339701373.82061660298633
681213.0449211183719-1.04492111837192
691312.84577768202060.154222317979371
701412.93950122882531.06049877117465
711313.1716061794065-0.17160617940652
721513.17339779054561.82660220945445
731312.97094600211060.0290539978894038
741012.3704822111675-2.37048221116746
751113.0293666831576-2.02936668315761
761912.99221117359866.00778882640137
771312.68406462542270.315935374577285
781712.75831461187844.24168538812157
791312.88679128405210.113208715947938
80912.9239467936110-3.92394679361104
811112.6061703840267-1.60617038402670
821013.3393048424726-3.33930484247256
83912.8050389501836-3.80503895018359
841213.0041823865349-1.00418238653488
851212.8397920755525-0.839792075552507
861312.82272089939360.177279100606436
871313.0778826326018-0.0778826326018025
881212.7541206165493-0.754120616549335
891512.60644525422112.39355474577891
902213.01168473394768.98831526605236
911312.61786672676850.382133273231452
921513.53996501976851.46003498023152
931313.0524234657847-0.0524234657846753
941512.94940596042822.05059403957183
951013.0604755535862-3.06047555358622
961112.6690599305972-1.66905993059720
971612.43847172096073.56152827903934
981112.5109300962773-1.51093009627734
991113.0916454566771-2.09164545667708
1001012.9649603956425-2.96496039564247
1011012.8715117190321-2.87151171903215
1021613.27104046248502.72895953751503
1031212.8424693299370-0.842469329936962
1041113.0314331644910-2.03143316449103
1051612.6606719399393.33932806006099
1061912.75803974168406.24196025831596
1071113.2261077353188-2.22610773531883
1081613.00597399767392.99402600232609
1091512.53219526776542.46780473223462
1102412.711315403378911.2886845966211
1111413.17160617940650.82839382059348
1121512.96523526583692.03476473416313
1131113.1754642718790-2.17546427187897
1141513.0101679930031.989832006997
1151213.3626364952940-1.36263649529402
1161013.1833025221484-3.18330252214837
1171413.17580017473560.824199825264388
1181313.0329499054357-0.0329499054356707
119912.5711423884634-3.57114238846339
1201513.09914780408981.90085219591016
1211513.1263985820461.87360141795401
1221412.93172401121821.06827598878181
1231112.5594460457215-1.55944604572153
124813.3545844074925-5.35458440749247
1251112.3210806184778-1.32108061847785
1261112.7759965610884-1.77599656108840
127813.2845284163659-5.28452841636585
1281012.9395012288253-2.93950122882535
1291112.9182360573373-1.91823605733731
1301312.69961906063700.300380939362979
1311112.8265789918660-1.82657899186602
1322013.07217189632816.92782810367193
1331013.1578433553312-3.15784335533124
1341513.3117791943221.68822080567799
1351212.9922111735986-0.992211173598631
1361412.49537566106301.50462433893696
1372312.791276126108310.2087238738917
1381412.78556538983461.21443461016541
1391613.13265905870852.86734094129149
1401112.6549612036653-1.65496120366528
1411212.7346470562003-0.734647056200331
1421012.7115902735733-2.71159027357327
1431412.11167622301891.88832377698108
1441212.7870821307792-0.787082130779222
1451212.5576544345825-0.557654434582504
1461112.5786447358761-1.57864473587615
1471212.9164444461983-0.916444446198281
1481313.1188962346332-0.118896234633235
1491113.0526983359791-2.05269833597907
1501913.05868394244725.94131605755281
1511213.2398705593941-1.23987055939411
1521712.40344372539734.59655627460265
153912.6864670096128-3.68646700961278
1541213.0566174611138-1.05661746111377
1551912.59687642547496.4031235745251
1561813.08386823906994.91613176093007
1571512.69961906063702.30038093936298
1581412.63162955084381.36837044915618
1591112.1659029087368-1.16590290873684






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9763945343223430.0472109313553140.023605465677657
80.9519033864738520.09619322705229530.0480966135261477
90.919862850969680.1602742980606410.0801371490303206
100.8734981349108290.2530037301783420.126501865089171
110.8120660700240990.3758678599518020.187933929975901
120.7875880455577190.4248239088845630.212411954442281
130.7107855469434470.5784289061131060.289214453056553
140.6275139946371130.7449720107257740.372486005362887
150.655004303657230.6899913926855390.344995696342770
160.7231855862794660.5536288274410680.276814413720534
170.6548202727026850.690359454594630.345179727297315
180.6717619284925590.6564761430148820.328238071507441
190.6481642799530950.703671440093810.351835720046905
200.6801043632379490.6397912735241020.319895636762051
210.7239831092510410.5520337814979180.276016890748959
220.7757135223738430.4485729552523150.224286477626157
230.7213784112292490.5572431775415020.278621588770751
240.6809238141058710.6381523717882580.319076185894129
250.6409547019220630.7180905961558740.359045298077937
260.6709884044313020.6580231911373960.329011595568698
270.6397386130684420.7205227738631150.360261386931558
280.6074907111638080.7850185776723840.392509288836192
290.5590874947117170.8818250105765650.440912505288282
300.4988272051628970.9976544103257950.501172794837103
310.5521156235527370.8957687528945260.447884376447263
320.5875479371278360.8249041257443280.412452062872164
330.5592706680493490.8814586639013030.440729331950651
340.5248368770783180.9503262458433640.475163122921682
350.4674083881988340.9348167763976680.532591611801166
360.471386567142910.942773134285820.52861343285709
370.7110003344301020.5779993311397960.288999665569898
380.6671047364263160.6657905271473690.332895263573684
390.6380647697654220.7238704604691550.361935230234578
400.6156478983205740.7687042033588520.384352101679426
410.5980691228967110.8038617542065770.401930877103289
420.5520874783404260.8958250433191470.447912521659574
430.6041859239779230.7916281520441530.395814076022076
440.5649215840851150.870156831829770.435078415914885
450.5331389735170780.9337220529658450.466861026482922
460.4839402159407610.9678804318815230.516059784059239
470.475629581972340.951259163944680.52437041802766
480.4342053895758110.8684107791516220.565794610424189
490.4092801291161460.8185602582322910.590719870883854
500.3687095324497430.7374190648994850.631290467550257
510.3461149953245370.6922299906490730.653885004675463
520.3051913028609880.6103826057219770.694808697139012
530.3845158578042760.7690317156085520.615484142195724
540.3408710699859120.6817421399718240.659128930014088
550.3064383857865780.6128767715731560.693561614213422
560.2671610987884230.5343221975768460.732838901211577
570.2285880992830110.4571761985660230.771411900716989
580.2568831810490840.5137663620981670.743116818950916
590.2745880595697560.5491761191395110.725411940430244
600.2791563264731100.5583126529462190.72084367352689
610.4554114919013560.9108229838027130.544588508098644
620.4122932075406010.8245864150812020.587706792459399
630.3701274628886050.7402549257772110.629872537111395
640.3334285615732030.6668571231464070.666571438426797
650.2921198280752720.5842396561505440.707880171924728
660.2681159144557270.5362318289114540.731884085544273
670.2834864028708270.5669728057416540.716513597129173
680.2490556492467850.498111298493570.750944350753215
690.2157105354925420.4314210709850830.784289464507459
700.1872142120409470.3744284240818930.812785787959053
710.1575481542610030.3150963085220060.842451845738997
720.1384071295983720.2768142591967430.861592870401628
730.1143033090028130.2286066180056260.885696690997187
740.1039474776663580.2078949553327160.896052522333642
750.09184987705739970.1836997541147990.9081501229426
760.1567894774278830.3135789548557660.843210522572117
770.1356587137988730.2713174275977460.864341286201127
780.1624841312496690.3249682624993390.83751586875033
790.1356913843047010.2713827686094010.8643086156953
800.1504863610716010.3009727221432010.8495136389284
810.1315156059765900.2630312119531810.86848439402341
820.1398427199632080.2796854399264160.860157280036792
830.1527122610588750.305424522117750.847287738941125
840.1295234287280730.2590468574561470.870476571271927
850.1098277463201570.2196554926403130.890172253679843
860.0919694246949320.1839388493898640.908030575305068
870.07484782119113260.1496956423822650.925152178808867
880.0611017633601320.1222035267202640.938898236639868
890.05618713967745850.1123742793549170.943812860322542
900.2206192822434070.4412385644868140.779380717756593
910.1917346453349020.3834692906698040.808265354665098
920.1715339776665610.3430679553331220.828466022333439
930.1438210971347130.2876421942694270.856178902865287
940.1288770935290390.2577541870580780.871122906470961
950.1268348373574080.2536696747148170.873165162642592
960.1113944000625050.222788800125010.888605599937495
970.1166238470471300.2332476940942600.88337615295287
980.1022680855837870.2045361711675730.897731914416213
990.09135894350606570.1827178870121310.908641056493934
1000.09028123037788170.1805624607557630.909718769622118
1010.08822234698612260.1764446939722450.911777653013877
1020.08243453090799940.1648690618159990.917565469092
1030.06839921857331290.1367984371466260.931600781426687
1040.05994715340278720.1198943068055740.940052846597213
1050.06023543065923190.1204708613184640.939764569340768
1060.1032293560501170.2064587121002350.896770643949883
1070.09201326223543640.1840265244708730.907986737764564
1080.08872883547709940.1774576709541990.9112711645229
1090.07911388361759850.1582277672351970.920886116382401
1100.4694955174981090.9389910349962190.530504482501891
1110.4226012129416330.8452024258832660.577398787058367
1120.3901185754757490.7802371509514970.609881424524251
1130.3582048904587480.7164097809174960.641795109541252
1140.3244309034064660.6488618068129330.675569096593534
1150.2841011877408250.568202375481650.715898812259175
1160.2865558433895440.5731116867790870.713444156610456
1170.2448946266746740.4897892533493480.755105373325326
1180.2053927789849160.4107855579698310.794607221015085
1190.2220611367925620.4441222735851240.777938863207438
1200.1981849617377750.3963699234755510.801815038262225
1210.1791478713483680.3582957426967360.820852128651632
1220.1488822848268990.2977645696537970.851117715173101
1230.1274058136315770.2548116272631550.872594186368423
1240.1658552504021980.3317105008043960.834144749597802
1250.1379903326775870.2759806653551740.862009667322413
1260.1227200304925090.2454400609850190.87727996950749
1270.1854987574403240.3709975148806480.814501242559676
1280.1870855288297060.3741710576594120.812914471170294
1290.1732312817887210.3464625635774410.82676871821128
1300.1378118073419510.2756236146839020.862188192658049
1310.1234524859534590.2469049719069170.876547514046541
1320.2216726277150680.4433452554301360.778327372284932
1330.2416486224226460.4832972448452920.758351377577354
1340.1973356875461800.3946713750923590.80266431245382
1350.1664140782409560.3328281564819110.833585921759044
1360.1301232111664730.2602464223329470.869876788833527
1370.594958682119830.810082635760340.40504131788017
1380.5275513942361810.9448972115276370.472448605763819
1390.4885350858341130.9770701716682260.511464914165887
1400.4479763225579990.8959526451159980.552023677442001
1410.3792579954715690.7585159909431380.620742004528431
1420.3872579397152110.7745158794304230.612742060284789
1430.3165923067498930.6331846134997860.683407693250107
1440.2486312826476220.4972625652952450.751368717352378
1450.1982100256484440.3964200512968870.801789974351556
1460.168858314784440.337716629568880.83114168521556
1470.1258886329907120.2517772659814250.874111367009288
1480.08647370444620570.1729474088924110.913526295553794
1490.09387707512965520.1877541502593100.906122924870345
1500.1159225677690260.2318451355380520.884077432230974
1510.1127012689504090.2254025379008170.887298731049591
1520.3545981200104810.7091962400209630.645401879989519

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.976394534322343 & 0.047210931355314 & 0.023605465677657 \tabularnewline
8 & 0.951903386473852 & 0.0961932270522953 & 0.0480966135261477 \tabularnewline
9 & 0.91986285096968 & 0.160274298060641 & 0.0801371490303206 \tabularnewline
10 & 0.873498134910829 & 0.253003730178342 & 0.126501865089171 \tabularnewline
11 & 0.812066070024099 & 0.375867859951802 & 0.187933929975901 \tabularnewline
12 & 0.787588045557719 & 0.424823908884563 & 0.212411954442281 \tabularnewline
13 & 0.710785546943447 & 0.578428906113106 & 0.289214453056553 \tabularnewline
14 & 0.627513994637113 & 0.744972010725774 & 0.372486005362887 \tabularnewline
15 & 0.65500430365723 & 0.689991392685539 & 0.344995696342770 \tabularnewline
16 & 0.723185586279466 & 0.553628827441068 & 0.276814413720534 \tabularnewline
17 & 0.654820272702685 & 0.69035945459463 & 0.345179727297315 \tabularnewline
18 & 0.671761928492559 & 0.656476143014882 & 0.328238071507441 \tabularnewline
19 & 0.648164279953095 & 0.70367144009381 & 0.351835720046905 \tabularnewline
20 & 0.680104363237949 & 0.639791273524102 & 0.319895636762051 \tabularnewline
21 & 0.723983109251041 & 0.552033781497918 & 0.276016890748959 \tabularnewline
22 & 0.775713522373843 & 0.448572955252315 & 0.224286477626157 \tabularnewline
23 & 0.721378411229249 & 0.557243177541502 & 0.278621588770751 \tabularnewline
24 & 0.680923814105871 & 0.638152371788258 & 0.319076185894129 \tabularnewline
25 & 0.640954701922063 & 0.718090596155874 & 0.359045298077937 \tabularnewline
26 & 0.670988404431302 & 0.658023191137396 & 0.329011595568698 \tabularnewline
27 & 0.639738613068442 & 0.720522773863115 & 0.360261386931558 \tabularnewline
28 & 0.607490711163808 & 0.785018577672384 & 0.392509288836192 \tabularnewline
29 & 0.559087494711717 & 0.881825010576565 & 0.440912505288282 \tabularnewline
30 & 0.498827205162897 & 0.997654410325795 & 0.501172794837103 \tabularnewline
31 & 0.552115623552737 & 0.895768752894526 & 0.447884376447263 \tabularnewline
32 & 0.587547937127836 & 0.824904125744328 & 0.412452062872164 \tabularnewline
33 & 0.559270668049349 & 0.881458663901303 & 0.440729331950651 \tabularnewline
34 & 0.524836877078318 & 0.950326245843364 & 0.475163122921682 \tabularnewline
35 & 0.467408388198834 & 0.934816776397668 & 0.532591611801166 \tabularnewline
36 & 0.47138656714291 & 0.94277313428582 & 0.52861343285709 \tabularnewline
37 & 0.711000334430102 & 0.577999331139796 & 0.288999665569898 \tabularnewline
38 & 0.667104736426316 & 0.665790527147369 & 0.332895263573684 \tabularnewline
39 & 0.638064769765422 & 0.723870460469155 & 0.361935230234578 \tabularnewline
40 & 0.615647898320574 & 0.768704203358852 & 0.384352101679426 \tabularnewline
41 & 0.598069122896711 & 0.803861754206577 & 0.401930877103289 \tabularnewline
42 & 0.552087478340426 & 0.895825043319147 & 0.447912521659574 \tabularnewline
43 & 0.604185923977923 & 0.791628152044153 & 0.395814076022076 \tabularnewline
44 & 0.564921584085115 & 0.87015683182977 & 0.435078415914885 \tabularnewline
45 & 0.533138973517078 & 0.933722052965845 & 0.466861026482922 \tabularnewline
46 & 0.483940215940761 & 0.967880431881523 & 0.516059784059239 \tabularnewline
47 & 0.47562958197234 & 0.95125916394468 & 0.52437041802766 \tabularnewline
48 & 0.434205389575811 & 0.868410779151622 & 0.565794610424189 \tabularnewline
49 & 0.409280129116146 & 0.818560258232291 & 0.590719870883854 \tabularnewline
50 & 0.368709532449743 & 0.737419064899485 & 0.631290467550257 \tabularnewline
51 & 0.346114995324537 & 0.692229990649073 & 0.653885004675463 \tabularnewline
52 & 0.305191302860988 & 0.610382605721977 & 0.694808697139012 \tabularnewline
53 & 0.384515857804276 & 0.769031715608552 & 0.615484142195724 \tabularnewline
54 & 0.340871069985912 & 0.681742139971824 & 0.659128930014088 \tabularnewline
55 & 0.306438385786578 & 0.612876771573156 & 0.693561614213422 \tabularnewline
56 & 0.267161098788423 & 0.534322197576846 & 0.732838901211577 \tabularnewline
57 & 0.228588099283011 & 0.457176198566023 & 0.771411900716989 \tabularnewline
58 & 0.256883181049084 & 0.513766362098167 & 0.743116818950916 \tabularnewline
59 & 0.274588059569756 & 0.549176119139511 & 0.725411940430244 \tabularnewline
60 & 0.279156326473110 & 0.558312652946219 & 0.72084367352689 \tabularnewline
61 & 0.455411491901356 & 0.910822983802713 & 0.544588508098644 \tabularnewline
62 & 0.412293207540601 & 0.824586415081202 & 0.587706792459399 \tabularnewline
63 & 0.370127462888605 & 0.740254925777211 & 0.629872537111395 \tabularnewline
64 & 0.333428561573203 & 0.666857123146407 & 0.666571438426797 \tabularnewline
65 & 0.292119828075272 & 0.584239656150544 & 0.707880171924728 \tabularnewline
66 & 0.268115914455727 & 0.536231828911454 & 0.731884085544273 \tabularnewline
67 & 0.283486402870827 & 0.566972805741654 & 0.716513597129173 \tabularnewline
68 & 0.249055649246785 & 0.49811129849357 & 0.750944350753215 \tabularnewline
69 & 0.215710535492542 & 0.431421070985083 & 0.784289464507459 \tabularnewline
70 & 0.187214212040947 & 0.374428424081893 & 0.812785787959053 \tabularnewline
71 & 0.157548154261003 & 0.315096308522006 & 0.842451845738997 \tabularnewline
72 & 0.138407129598372 & 0.276814259196743 & 0.861592870401628 \tabularnewline
73 & 0.114303309002813 & 0.228606618005626 & 0.885696690997187 \tabularnewline
74 & 0.103947477666358 & 0.207894955332716 & 0.896052522333642 \tabularnewline
75 & 0.0918498770573997 & 0.183699754114799 & 0.9081501229426 \tabularnewline
76 & 0.156789477427883 & 0.313578954855766 & 0.843210522572117 \tabularnewline
77 & 0.135658713798873 & 0.271317427597746 & 0.864341286201127 \tabularnewline
78 & 0.162484131249669 & 0.324968262499339 & 0.83751586875033 \tabularnewline
79 & 0.135691384304701 & 0.271382768609401 & 0.8643086156953 \tabularnewline
80 & 0.150486361071601 & 0.300972722143201 & 0.8495136389284 \tabularnewline
81 & 0.131515605976590 & 0.263031211953181 & 0.86848439402341 \tabularnewline
82 & 0.139842719963208 & 0.279685439926416 & 0.860157280036792 \tabularnewline
83 & 0.152712261058875 & 0.30542452211775 & 0.847287738941125 \tabularnewline
84 & 0.129523428728073 & 0.259046857456147 & 0.870476571271927 \tabularnewline
85 & 0.109827746320157 & 0.219655492640313 & 0.890172253679843 \tabularnewline
86 & 0.091969424694932 & 0.183938849389864 & 0.908030575305068 \tabularnewline
87 & 0.0748478211911326 & 0.149695642382265 & 0.925152178808867 \tabularnewline
88 & 0.061101763360132 & 0.122203526720264 & 0.938898236639868 \tabularnewline
89 & 0.0561871396774585 & 0.112374279354917 & 0.943812860322542 \tabularnewline
90 & 0.220619282243407 & 0.441238564486814 & 0.779380717756593 \tabularnewline
91 & 0.191734645334902 & 0.383469290669804 & 0.808265354665098 \tabularnewline
92 & 0.171533977666561 & 0.343067955333122 & 0.828466022333439 \tabularnewline
93 & 0.143821097134713 & 0.287642194269427 & 0.856178902865287 \tabularnewline
94 & 0.128877093529039 & 0.257754187058078 & 0.871122906470961 \tabularnewline
95 & 0.126834837357408 & 0.253669674714817 & 0.873165162642592 \tabularnewline
96 & 0.111394400062505 & 0.22278880012501 & 0.888605599937495 \tabularnewline
97 & 0.116623847047130 & 0.233247694094260 & 0.88337615295287 \tabularnewline
98 & 0.102268085583787 & 0.204536171167573 & 0.897731914416213 \tabularnewline
99 & 0.0913589435060657 & 0.182717887012131 & 0.908641056493934 \tabularnewline
100 & 0.0902812303778817 & 0.180562460755763 & 0.909718769622118 \tabularnewline
101 & 0.0882223469861226 & 0.176444693972245 & 0.911777653013877 \tabularnewline
102 & 0.0824345309079994 & 0.164869061815999 & 0.917565469092 \tabularnewline
103 & 0.0683992185733129 & 0.136798437146626 & 0.931600781426687 \tabularnewline
104 & 0.0599471534027872 & 0.119894306805574 & 0.940052846597213 \tabularnewline
105 & 0.0602354306592319 & 0.120470861318464 & 0.939764569340768 \tabularnewline
106 & 0.103229356050117 & 0.206458712100235 & 0.896770643949883 \tabularnewline
107 & 0.0920132622354364 & 0.184026524470873 & 0.907986737764564 \tabularnewline
108 & 0.0887288354770994 & 0.177457670954199 & 0.9112711645229 \tabularnewline
109 & 0.0791138836175985 & 0.158227767235197 & 0.920886116382401 \tabularnewline
110 & 0.469495517498109 & 0.938991034996219 & 0.530504482501891 \tabularnewline
111 & 0.422601212941633 & 0.845202425883266 & 0.577398787058367 \tabularnewline
112 & 0.390118575475749 & 0.780237150951497 & 0.609881424524251 \tabularnewline
113 & 0.358204890458748 & 0.716409780917496 & 0.641795109541252 \tabularnewline
114 & 0.324430903406466 & 0.648861806812933 & 0.675569096593534 \tabularnewline
115 & 0.284101187740825 & 0.56820237548165 & 0.715898812259175 \tabularnewline
116 & 0.286555843389544 & 0.573111686779087 & 0.713444156610456 \tabularnewline
117 & 0.244894626674674 & 0.489789253349348 & 0.755105373325326 \tabularnewline
118 & 0.205392778984916 & 0.410785557969831 & 0.794607221015085 \tabularnewline
119 & 0.222061136792562 & 0.444122273585124 & 0.777938863207438 \tabularnewline
120 & 0.198184961737775 & 0.396369923475551 & 0.801815038262225 \tabularnewline
121 & 0.179147871348368 & 0.358295742696736 & 0.820852128651632 \tabularnewline
122 & 0.148882284826899 & 0.297764569653797 & 0.851117715173101 \tabularnewline
123 & 0.127405813631577 & 0.254811627263155 & 0.872594186368423 \tabularnewline
124 & 0.165855250402198 & 0.331710500804396 & 0.834144749597802 \tabularnewline
125 & 0.137990332677587 & 0.275980665355174 & 0.862009667322413 \tabularnewline
126 & 0.122720030492509 & 0.245440060985019 & 0.87727996950749 \tabularnewline
127 & 0.185498757440324 & 0.370997514880648 & 0.814501242559676 \tabularnewline
128 & 0.187085528829706 & 0.374171057659412 & 0.812914471170294 \tabularnewline
129 & 0.173231281788721 & 0.346462563577441 & 0.82676871821128 \tabularnewline
130 & 0.137811807341951 & 0.275623614683902 & 0.862188192658049 \tabularnewline
131 & 0.123452485953459 & 0.246904971906917 & 0.876547514046541 \tabularnewline
132 & 0.221672627715068 & 0.443345255430136 & 0.778327372284932 \tabularnewline
133 & 0.241648622422646 & 0.483297244845292 & 0.758351377577354 \tabularnewline
134 & 0.197335687546180 & 0.394671375092359 & 0.80266431245382 \tabularnewline
135 & 0.166414078240956 & 0.332828156481911 & 0.833585921759044 \tabularnewline
136 & 0.130123211166473 & 0.260246422332947 & 0.869876788833527 \tabularnewline
137 & 0.59495868211983 & 0.81008263576034 & 0.40504131788017 \tabularnewline
138 & 0.527551394236181 & 0.944897211527637 & 0.472448605763819 \tabularnewline
139 & 0.488535085834113 & 0.977070171668226 & 0.511464914165887 \tabularnewline
140 & 0.447976322557999 & 0.895952645115998 & 0.552023677442001 \tabularnewline
141 & 0.379257995471569 & 0.758515990943138 & 0.620742004528431 \tabularnewline
142 & 0.387257939715211 & 0.774515879430423 & 0.612742060284789 \tabularnewline
143 & 0.316592306749893 & 0.633184613499786 & 0.683407693250107 \tabularnewline
144 & 0.248631282647622 & 0.497262565295245 & 0.751368717352378 \tabularnewline
145 & 0.198210025648444 & 0.396420051296887 & 0.801789974351556 \tabularnewline
146 & 0.16885831478444 & 0.33771662956888 & 0.83114168521556 \tabularnewline
147 & 0.125888632990712 & 0.251777265981425 & 0.874111367009288 \tabularnewline
148 & 0.0864737044462057 & 0.172947408892411 & 0.913526295553794 \tabularnewline
149 & 0.0938770751296552 & 0.187754150259310 & 0.906122924870345 \tabularnewline
150 & 0.115922567769026 & 0.231845135538052 & 0.884077432230974 \tabularnewline
151 & 0.112701268950409 & 0.225402537900817 & 0.887298731049591 \tabularnewline
152 & 0.354598120010481 & 0.709196240020963 & 0.645401879989519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98745&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]0.976394534322343[/C][C]0.047210931355314[/C][C]0.023605465677657[/C][/ROW]
[ROW][C]8[/C][C]0.951903386473852[/C][C]0.0961932270522953[/C][C]0.0480966135261477[/C][/ROW]
[ROW][C]9[/C][C]0.91986285096968[/C][C]0.160274298060641[/C][C]0.0801371490303206[/C][/ROW]
[ROW][C]10[/C][C]0.873498134910829[/C][C]0.253003730178342[/C][C]0.126501865089171[/C][/ROW]
[ROW][C]11[/C][C]0.812066070024099[/C][C]0.375867859951802[/C][C]0.187933929975901[/C][/ROW]
[ROW][C]12[/C][C]0.787588045557719[/C][C]0.424823908884563[/C][C]0.212411954442281[/C][/ROW]
[ROW][C]13[/C][C]0.710785546943447[/C][C]0.578428906113106[/C][C]0.289214453056553[/C][/ROW]
[ROW][C]14[/C][C]0.627513994637113[/C][C]0.744972010725774[/C][C]0.372486005362887[/C][/ROW]
[ROW][C]15[/C][C]0.65500430365723[/C][C]0.689991392685539[/C][C]0.344995696342770[/C][/ROW]
[ROW][C]16[/C][C]0.723185586279466[/C][C]0.553628827441068[/C][C]0.276814413720534[/C][/ROW]
[ROW][C]17[/C][C]0.654820272702685[/C][C]0.69035945459463[/C][C]0.345179727297315[/C][/ROW]
[ROW][C]18[/C][C]0.671761928492559[/C][C]0.656476143014882[/C][C]0.328238071507441[/C][/ROW]
[ROW][C]19[/C][C]0.648164279953095[/C][C]0.70367144009381[/C][C]0.351835720046905[/C][/ROW]
[ROW][C]20[/C][C]0.680104363237949[/C][C]0.639791273524102[/C][C]0.319895636762051[/C][/ROW]
[ROW][C]21[/C][C]0.723983109251041[/C][C]0.552033781497918[/C][C]0.276016890748959[/C][/ROW]
[ROW][C]22[/C][C]0.775713522373843[/C][C]0.448572955252315[/C][C]0.224286477626157[/C][/ROW]
[ROW][C]23[/C][C]0.721378411229249[/C][C]0.557243177541502[/C][C]0.278621588770751[/C][/ROW]
[ROW][C]24[/C][C]0.680923814105871[/C][C]0.638152371788258[/C][C]0.319076185894129[/C][/ROW]
[ROW][C]25[/C][C]0.640954701922063[/C][C]0.718090596155874[/C][C]0.359045298077937[/C][/ROW]
[ROW][C]26[/C][C]0.670988404431302[/C][C]0.658023191137396[/C][C]0.329011595568698[/C][/ROW]
[ROW][C]27[/C][C]0.639738613068442[/C][C]0.720522773863115[/C][C]0.360261386931558[/C][/ROW]
[ROW][C]28[/C][C]0.607490711163808[/C][C]0.785018577672384[/C][C]0.392509288836192[/C][/ROW]
[ROW][C]29[/C][C]0.559087494711717[/C][C]0.881825010576565[/C][C]0.440912505288282[/C][/ROW]
[ROW][C]30[/C][C]0.498827205162897[/C][C]0.997654410325795[/C][C]0.501172794837103[/C][/ROW]
[ROW][C]31[/C][C]0.552115623552737[/C][C]0.895768752894526[/C][C]0.447884376447263[/C][/ROW]
[ROW][C]32[/C][C]0.587547937127836[/C][C]0.824904125744328[/C][C]0.412452062872164[/C][/ROW]
[ROW][C]33[/C][C]0.559270668049349[/C][C]0.881458663901303[/C][C]0.440729331950651[/C][/ROW]
[ROW][C]34[/C][C]0.524836877078318[/C][C]0.950326245843364[/C][C]0.475163122921682[/C][/ROW]
[ROW][C]35[/C][C]0.467408388198834[/C][C]0.934816776397668[/C][C]0.532591611801166[/C][/ROW]
[ROW][C]36[/C][C]0.47138656714291[/C][C]0.94277313428582[/C][C]0.52861343285709[/C][/ROW]
[ROW][C]37[/C][C]0.711000334430102[/C][C]0.577999331139796[/C][C]0.288999665569898[/C][/ROW]
[ROW][C]38[/C][C]0.667104736426316[/C][C]0.665790527147369[/C][C]0.332895263573684[/C][/ROW]
[ROW][C]39[/C][C]0.638064769765422[/C][C]0.723870460469155[/C][C]0.361935230234578[/C][/ROW]
[ROW][C]40[/C][C]0.615647898320574[/C][C]0.768704203358852[/C][C]0.384352101679426[/C][/ROW]
[ROW][C]41[/C][C]0.598069122896711[/C][C]0.803861754206577[/C][C]0.401930877103289[/C][/ROW]
[ROW][C]42[/C][C]0.552087478340426[/C][C]0.895825043319147[/C][C]0.447912521659574[/C][/ROW]
[ROW][C]43[/C][C]0.604185923977923[/C][C]0.791628152044153[/C][C]0.395814076022076[/C][/ROW]
[ROW][C]44[/C][C]0.564921584085115[/C][C]0.87015683182977[/C][C]0.435078415914885[/C][/ROW]
[ROW][C]45[/C][C]0.533138973517078[/C][C]0.933722052965845[/C][C]0.466861026482922[/C][/ROW]
[ROW][C]46[/C][C]0.483940215940761[/C][C]0.967880431881523[/C][C]0.516059784059239[/C][/ROW]
[ROW][C]47[/C][C]0.47562958197234[/C][C]0.95125916394468[/C][C]0.52437041802766[/C][/ROW]
[ROW][C]48[/C][C]0.434205389575811[/C][C]0.868410779151622[/C][C]0.565794610424189[/C][/ROW]
[ROW][C]49[/C][C]0.409280129116146[/C][C]0.818560258232291[/C][C]0.590719870883854[/C][/ROW]
[ROW][C]50[/C][C]0.368709532449743[/C][C]0.737419064899485[/C][C]0.631290467550257[/C][/ROW]
[ROW][C]51[/C][C]0.346114995324537[/C][C]0.692229990649073[/C][C]0.653885004675463[/C][/ROW]
[ROW][C]52[/C][C]0.305191302860988[/C][C]0.610382605721977[/C][C]0.694808697139012[/C][/ROW]
[ROW][C]53[/C][C]0.384515857804276[/C][C]0.769031715608552[/C][C]0.615484142195724[/C][/ROW]
[ROW][C]54[/C][C]0.340871069985912[/C][C]0.681742139971824[/C][C]0.659128930014088[/C][/ROW]
[ROW][C]55[/C][C]0.306438385786578[/C][C]0.612876771573156[/C][C]0.693561614213422[/C][/ROW]
[ROW][C]56[/C][C]0.267161098788423[/C][C]0.534322197576846[/C][C]0.732838901211577[/C][/ROW]
[ROW][C]57[/C][C]0.228588099283011[/C][C]0.457176198566023[/C][C]0.771411900716989[/C][/ROW]
[ROW][C]58[/C][C]0.256883181049084[/C][C]0.513766362098167[/C][C]0.743116818950916[/C][/ROW]
[ROW][C]59[/C][C]0.274588059569756[/C][C]0.549176119139511[/C][C]0.725411940430244[/C][/ROW]
[ROW][C]60[/C][C]0.279156326473110[/C][C]0.558312652946219[/C][C]0.72084367352689[/C][/ROW]
[ROW][C]61[/C][C]0.455411491901356[/C][C]0.910822983802713[/C][C]0.544588508098644[/C][/ROW]
[ROW][C]62[/C][C]0.412293207540601[/C][C]0.824586415081202[/C][C]0.587706792459399[/C][/ROW]
[ROW][C]63[/C][C]0.370127462888605[/C][C]0.740254925777211[/C][C]0.629872537111395[/C][/ROW]
[ROW][C]64[/C][C]0.333428561573203[/C][C]0.666857123146407[/C][C]0.666571438426797[/C][/ROW]
[ROW][C]65[/C][C]0.292119828075272[/C][C]0.584239656150544[/C][C]0.707880171924728[/C][/ROW]
[ROW][C]66[/C][C]0.268115914455727[/C][C]0.536231828911454[/C][C]0.731884085544273[/C][/ROW]
[ROW][C]67[/C][C]0.283486402870827[/C][C]0.566972805741654[/C][C]0.716513597129173[/C][/ROW]
[ROW][C]68[/C][C]0.249055649246785[/C][C]0.49811129849357[/C][C]0.750944350753215[/C][/ROW]
[ROW][C]69[/C][C]0.215710535492542[/C][C]0.431421070985083[/C][C]0.784289464507459[/C][/ROW]
[ROW][C]70[/C][C]0.187214212040947[/C][C]0.374428424081893[/C][C]0.812785787959053[/C][/ROW]
[ROW][C]71[/C][C]0.157548154261003[/C][C]0.315096308522006[/C][C]0.842451845738997[/C][/ROW]
[ROW][C]72[/C][C]0.138407129598372[/C][C]0.276814259196743[/C][C]0.861592870401628[/C][/ROW]
[ROW][C]73[/C][C]0.114303309002813[/C][C]0.228606618005626[/C][C]0.885696690997187[/C][/ROW]
[ROW][C]74[/C][C]0.103947477666358[/C][C]0.207894955332716[/C][C]0.896052522333642[/C][/ROW]
[ROW][C]75[/C][C]0.0918498770573997[/C][C]0.183699754114799[/C][C]0.9081501229426[/C][/ROW]
[ROW][C]76[/C][C]0.156789477427883[/C][C]0.313578954855766[/C][C]0.843210522572117[/C][/ROW]
[ROW][C]77[/C][C]0.135658713798873[/C][C]0.271317427597746[/C][C]0.864341286201127[/C][/ROW]
[ROW][C]78[/C][C]0.162484131249669[/C][C]0.324968262499339[/C][C]0.83751586875033[/C][/ROW]
[ROW][C]79[/C][C]0.135691384304701[/C][C]0.271382768609401[/C][C]0.8643086156953[/C][/ROW]
[ROW][C]80[/C][C]0.150486361071601[/C][C]0.300972722143201[/C][C]0.8495136389284[/C][/ROW]
[ROW][C]81[/C][C]0.131515605976590[/C][C]0.263031211953181[/C][C]0.86848439402341[/C][/ROW]
[ROW][C]82[/C][C]0.139842719963208[/C][C]0.279685439926416[/C][C]0.860157280036792[/C][/ROW]
[ROW][C]83[/C][C]0.152712261058875[/C][C]0.30542452211775[/C][C]0.847287738941125[/C][/ROW]
[ROW][C]84[/C][C]0.129523428728073[/C][C]0.259046857456147[/C][C]0.870476571271927[/C][/ROW]
[ROW][C]85[/C][C]0.109827746320157[/C][C]0.219655492640313[/C][C]0.890172253679843[/C][/ROW]
[ROW][C]86[/C][C]0.091969424694932[/C][C]0.183938849389864[/C][C]0.908030575305068[/C][/ROW]
[ROW][C]87[/C][C]0.0748478211911326[/C][C]0.149695642382265[/C][C]0.925152178808867[/C][/ROW]
[ROW][C]88[/C][C]0.061101763360132[/C][C]0.122203526720264[/C][C]0.938898236639868[/C][/ROW]
[ROW][C]89[/C][C]0.0561871396774585[/C][C]0.112374279354917[/C][C]0.943812860322542[/C][/ROW]
[ROW][C]90[/C][C]0.220619282243407[/C][C]0.441238564486814[/C][C]0.779380717756593[/C][/ROW]
[ROW][C]91[/C][C]0.191734645334902[/C][C]0.383469290669804[/C][C]0.808265354665098[/C][/ROW]
[ROW][C]92[/C][C]0.171533977666561[/C][C]0.343067955333122[/C][C]0.828466022333439[/C][/ROW]
[ROW][C]93[/C][C]0.143821097134713[/C][C]0.287642194269427[/C][C]0.856178902865287[/C][/ROW]
[ROW][C]94[/C][C]0.128877093529039[/C][C]0.257754187058078[/C][C]0.871122906470961[/C][/ROW]
[ROW][C]95[/C][C]0.126834837357408[/C][C]0.253669674714817[/C][C]0.873165162642592[/C][/ROW]
[ROW][C]96[/C][C]0.111394400062505[/C][C]0.22278880012501[/C][C]0.888605599937495[/C][/ROW]
[ROW][C]97[/C][C]0.116623847047130[/C][C]0.233247694094260[/C][C]0.88337615295287[/C][/ROW]
[ROW][C]98[/C][C]0.102268085583787[/C][C]0.204536171167573[/C][C]0.897731914416213[/C][/ROW]
[ROW][C]99[/C][C]0.0913589435060657[/C][C]0.182717887012131[/C][C]0.908641056493934[/C][/ROW]
[ROW][C]100[/C][C]0.0902812303778817[/C][C]0.180562460755763[/C][C]0.909718769622118[/C][/ROW]
[ROW][C]101[/C][C]0.0882223469861226[/C][C]0.176444693972245[/C][C]0.911777653013877[/C][/ROW]
[ROW][C]102[/C][C]0.0824345309079994[/C][C]0.164869061815999[/C][C]0.917565469092[/C][/ROW]
[ROW][C]103[/C][C]0.0683992185733129[/C][C]0.136798437146626[/C][C]0.931600781426687[/C][/ROW]
[ROW][C]104[/C][C]0.0599471534027872[/C][C]0.119894306805574[/C][C]0.940052846597213[/C][/ROW]
[ROW][C]105[/C][C]0.0602354306592319[/C][C]0.120470861318464[/C][C]0.939764569340768[/C][/ROW]
[ROW][C]106[/C][C]0.103229356050117[/C][C]0.206458712100235[/C][C]0.896770643949883[/C][/ROW]
[ROW][C]107[/C][C]0.0920132622354364[/C][C]0.184026524470873[/C][C]0.907986737764564[/C][/ROW]
[ROW][C]108[/C][C]0.0887288354770994[/C][C]0.177457670954199[/C][C]0.9112711645229[/C][/ROW]
[ROW][C]109[/C][C]0.0791138836175985[/C][C]0.158227767235197[/C][C]0.920886116382401[/C][/ROW]
[ROW][C]110[/C][C]0.469495517498109[/C][C]0.938991034996219[/C][C]0.530504482501891[/C][/ROW]
[ROW][C]111[/C][C]0.422601212941633[/C][C]0.845202425883266[/C][C]0.577398787058367[/C][/ROW]
[ROW][C]112[/C][C]0.390118575475749[/C][C]0.780237150951497[/C][C]0.609881424524251[/C][/ROW]
[ROW][C]113[/C][C]0.358204890458748[/C][C]0.716409780917496[/C][C]0.641795109541252[/C][/ROW]
[ROW][C]114[/C][C]0.324430903406466[/C][C]0.648861806812933[/C][C]0.675569096593534[/C][/ROW]
[ROW][C]115[/C][C]0.284101187740825[/C][C]0.56820237548165[/C][C]0.715898812259175[/C][/ROW]
[ROW][C]116[/C][C]0.286555843389544[/C][C]0.573111686779087[/C][C]0.713444156610456[/C][/ROW]
[ROW][C]117[/C][C]0.244894626674674[/C][C]0.489789253349348[/C][C]0.755105373325326[/C][/ROW]
[ROW][C]118[/C][C]0.205392778984916[/C][C]0.410785557969831[/C][C]0.794607221015085[/C][/ROW]
[ROW][C]119[/C][C]0.222061136792562[/C][C]0.444122273585124[/C][C]0.777938863207438[/C][/ROW]
[ROW][C]120[/C][C]0.198184961737775[/C][C]0.396369923475551[/C][C]0.801815038262225[/C][/ROW]
[ROW][C]121[/C][C]0.179147871348368[/C][C]0.358295742696736[/C][C]0.820852128651632[/C][/ROW]
[ROW][C]122[/C][C]0.148882284826899[/C][C]0.297764569653797[/C][C]0.851117715173101[/C][/ROW]
[ROW][C]123[/C][C]0.127405813631577[/C][C]0.254811627263155[/C][C]0.872594186368423[/C][/ROW]
[ROW][C]124[/C][C]0.165855250402198[/C][C]0.331710500804396[/C][C]0.834144749597802[/C][/ROW]
[ROW][C]125[/C][C]0.137990332677587[/C][C]0.275980665355174[/C][C]0.862009667322413[/C][/ROW]
[ROW][C]126[/C][C]0.122720030492509[/C][C]0.245440060985019[/C][C]0.87727996950749[/C][/ROW]
[ROW][C]127[/C][C]0.185498757440324[/C][C]0.370997514880648[/C][C]0.814501242559676[/C][/ROW]
[ROW][C]128[/C][C]0.187085528829706[/C][C]0.374171057659412[/C][C]0.812914471170294[/C][/ROW]
[ROW][C]129[/C][C]0.173231281788721[/C][C]0.346462563577441[/C][C]0.82676871821128[/C][/ROW]
[ROW][C]130[/C][C]0.137811807341951[/C][C]0.275623614683902[/C][C]0.862188192658049[/C][/ROW]
[ROW][C]131[/C][C]0.123452485953459[/C][C]0.246904971906917[/C][C]0.876547514046541[/C][/ROW]
[ROW][C]132[/C][C]0.221672627715068[/C][C]0.443345255430136[/C][C]0.778327372284932[/C][/ROW]
[ROW][C]133[/C][C]0.241648622422646[/C][C]0.483297244845292[/C][C]0.758351377577354[/C][/ROW]
[ROW][C]134[/C][C]0.197335687546180[/C][C]0.394671375092359[/C][C]0.80266431245382[/C][/ROW]
[ROW][C]135[/C][C]0.166414078240956[/C][C]0.332828156481911[/C][C]0.833585921759044[/C][/ROW]
[ROW][C]136[/C][C]0.130123211166473[/C][C]0.260246422332947[/C][C]0.869876788833527[/C][/ROW]
[ROW][C]137[/C][C]0.59495868211983[/C][C]0.81008263576034[/C][C]0.40504131788017[/C][/ROW]
[ROW][C]138[/C][C]0.527551394236181[/C][C]0.944897211527637[/C][C]0.472448605763819[/C][/ROW]
[ROW][C]139[/C][C]0.488535085834113[/C][C]0.977070171668226[/C][C]0.511464914165887[/C][/ROW]
[ROW][C]140[/C][C]0.447976322557999[/C][C]0.895952645115998[/C][C]0.552023677442001[/C][/ROW]
[ROW][C]141[/C][C]0.379257995471569[/C][C]0.758515990943138[/C][C]0.620742004528431[/C][/ROW]
[ROW][C]142[/C][C]0.387257939715211[/C][C]0.774515879430423[/C][C]0.612742060284789[/C][/ROW]
[ROW][C]143[/C][C]0.316592306749893[/C][C]0.633184613499786[/C][C]0.683407693250107[/C][/ROW]
[ROW][C]144[/C][C]0.248631282647622[/C][C]0.497262565295245[/C][C]0.751368717352378[/C][/ROW]
[ROW][C]145[/C][C]0.198210025648444[/C][C]0.396420051296887[/C][C]0.801789974351556[/C][/ROW]
[ROW][C]146[/C][C]0.16885831478444[/C][C]0.33771662956888[/C][C]0.83114168521556[/C][/ROW]
[ROW][C]147[/C][C]0.125888632990712[/C][C]0.251777265981425[/C][C]0.874111367009288[/C][/ROW]
[ROW][C]148[/C][C]0.0864737044462057[/C][C]0.172947408892411[/C][C]0.913526295553794[/C][/ROW]
[ROW][C]149[/C][C]0.0938770751296552[/C][C]0.187754150259310[/C][C]0.906122924870345[/C][/ROW]
[ROW][C]150[/C][C]0.115922567769026[/C][C]0.231845135538052[/C][C]0.884077432230974[/C][/ROW]
[ROW][C]151[/C][C]0.112701268950409[/C][C]0.225402537900817[/C][C]0.887298731049591[/C][/ROW]
[ROW][C]152[/C][C]0.354598120010481[/C][C]0.709196240020963[/C][C]0.645401879989519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98745&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98745&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
70.9763945343223430.0472109313553140.023605465677657
80.9519033864738520.09619322705229530.0480966135261477
90.919862850969680.1602742980606410.0801371490303206
100.8734981349108290.2530037301783420.126501865089171
110.8120660700240990.3758678599518020.187933929975901
120.7875880455577190.4248239088845630.212411954442281
130.7107855469434470.5784289061131060.289214453056553
140.6275139946371130.7449720107257740.372486005362887
150.655004303657230.6899913926855390.344995696342770
160.7231855862794660.5536288274410680.276814413720534
170.6548202727026850.690359454594630.345179727297315
180.6717619284925590.6564761430148820.328238071507441
190.6481642799530950.703671440093810.351835720046905
200.6801043632379490.6397912735241020.319895636762051
210.7239831092510410.5520337814979180.276016890748959
220.7757135223738430.4485729552523150.224286477626157
230.7213784112292490.5572431775415020.278621588770751
240.6809238141058710.6381523717882580.319076185894129
250.6409547019220630.7180905961558740.359045298077937
260.6709884044313020.6580231911373960.329011595568698
270.6397386130684420.7205227738631150.360261386931558
280.6074907111638080.7850185776723840.392509288836192
290.5590874947117170.8818250105765650.440912505288282
300.4988272051628970.9976544103257950.501172794837103
310.5521156235527370.8957687528945260.447884376447263
320.5875479371278360.8249041257443280.412452062872164
330.5592706680493490.8814586639013030.440729331950651
340.5248368770783180.9503262458433640.475163122921682
350.4674083881988340.9348167763976680.532591611801166
360.471386567142910.942773134285820.52861343285709
370.7110003344301020.5779993311397960.288999665569898
380.6671047364263160.6657905271473690.332895263573684
390.6380647697654220.7238704604691550.361935230234578
400.6156478983205740.7687042033588520.384352101679426
410.5980691228967110.8038617542065770.401930877103289
420.5520874783404260.8958250433191470.447912521659574
430.6041859239779230.7916281520441530.395814076022076
440.5649215840851150.870156831829770.435078415914885
450.5331389735170780.9337220529658450.466861026482922
460.4839402159407610.9678804318815230.516059784059239
470.475629581972340.951259163944680.52437041802766
480.4342053895758110.8684107791516220.565794610424189
490.4092801291161460.8185602582322910.590719870883854
500.3687095324497430.7374190648994850.631290467550257
510.3461149953245370.6922299906490730.653885004675463
520.3051913028609880.6103826057219770.694808697139012
530.3845158578042760.7690317156085520.615484142195724
540.3408710699859120.6817421399718240.659128930014088
550.3064383857865780.6128767715731560.693561614213422
560.2671610987884230.5343221975768460.732838901211577
570.2285880992830110.4571761985660230.771411900716989
580.2568831810490840.5137663620981670.743116818950916
590.2745880595697560.5491761191395110.725411940430244
600.2791563264731100.5583126529462190.72084367352689
610.4554114919013560.9108229838027130.544588508098644
620.4122932075406010.8245864150812020.587706792459399
630.3701274628886050.7402549257772110.629872537111395
640.3334285615732030.6668571231464070.666571438426797
650.2921198280752720.5842396561505440.707880171924728
660.2681159144557270.5362318289114540.731884085544273
670.2834864028708270.5669728057416540.716513597129173
680.2490556492467850.498111298493570.750944350753215
690.2157105354925420.4314210709850830.784289464507459
700.1872142120409470.3744284240818930.812785787959053
710.1575481542610030.3150963085220060.842451845738997
720.1384071295983720.2768142591967430.861592870401628
730.1143033090028130.2286066180056260.885696690997187
740.1039474776663580.2078949553327160.896052522333642
750.09184987705739970.1836997541147990.9081501229426
760.1567894774278830.3135789548557660.843210522572117
770.1356587137988730.2713174275977460.864341286201127
780.1624841312496690.3249682624993390.83751586875033
790.1356913843047010.2713827686094010.8643086156953
800.1504863610716010.3009727221432010.8495136389284
810.1315156059765900.2630312119531810.86848439402341
820.1398427199632080.2796854399264160.860157280036792
830.1527122610588750.305424522117750.847287738941125
840.1295234287280730.2590468574561470.870476571271927
850.1098277463201570.2196554926403130.890172253679843
860.0919694246949320.1839388493898640.908030575305068
870.07484782119113260.1496956423822650.925152178808867
880.0611017633601320.1222035267202640.938898236639868
890.05618713967745850.1123742793549170.943812860322542
900.2206192822434070.4412385644868140.779380717756593
910.1917346453349020.3834692906698040.808265354665098
920.1715339776665610.3430679553331220.828466022333439
930.1438210971347130.2876421942694270.856178902865287
940.1288770935290390.2577541870580780.871122906470961
950.1268348373574080.2536696747148170.873165162642592
960.1113944000625050.222788800125010.888605599937495
970.1166238470471300.2332476940942600.88337615295287
980.1022680855837870.2045361711675730.897731914416213
990.09135894350606570.1827178870121310.908641056493934
1000.09028123037788170.1805624607557630.909718769622118
1010.08822234698612260.1764446939722450.911777653013877
1020.08243453090799940.1648690618159990.917565469092
1030.06839921857331290.1367984371466260.931600781426687
1040.05994715340278720.1198943068055740.940052846597213
1050.06023543065923190.1204708613184640.939764569340768
1060.1032293560501170.2064587121002350.896770643949883
1070.09201326223543640.1840265244708730.907986737764564
1080.08872883547709940.1774576709541990.9112711645229
1090.07911388361759850.1582277672351970.920886116382401
1100.4694955174981090.9389910349962190.530504482501891
1110.4226012129416330.8452024258832660.577398787058367
1120.3901185754757490.7802371509514970.609881424524251
1130.3582048904587480.7164097809174960.641795109541252
1140.3244309034064660.6488618068129330.675569096593534
1150.2841011877408250.568202375481650.715898812259175
1160.2865558433895440.5731116867790870.713444156610456
1170.2448946266746740.4897892533493480.755105373325326
1180.2053927789849160.4107855579698310.794607221015085
1190.2220611367925620.4441222735851240.777938863207438
1200.1981849617377750.3963699234755510.801815038262225
1210.1791478713483680.3582957426967360.820852128651632
1220.1488822848268990.2977645696537970.851117715173101
1230.1274058136315770.2548116272631550.872594186368423
1240.1658552504021980.3317105008043960.834144749597802
1250.1379903326775870.2759806653551740.862009667322413
1260.1227200304925090.2454400609850190.87727996950749
1270.1854987574403240.3709975148806480.814501242559676
1280.1870855288297060.3741710576594120.812914471170294
1290.1732312817887210.3464625635774410.82676871821128
1300.1378118073419510.2756236146839020.862188192658049
1310.1234524859534590.2469049719069170.876547514046541
1320.2216726277150680.4433452554301360.778327372284932
1330.2416486224226460.4832972448452920.758351377577354
1340.1973356875461800.3946713750923590.80266431245382
1350.1664140782409560.3328281564819110.833585921759044
1360.1301232111664730.2602464223329470.869876788833527
1370.594958682119830.810082635760340.40504131788017
1380.5275513942361810.9448972115276370.472448605763819
1390.4885350858341130.9770701716682260.511464914165887
1400.4479763225579990.8959526451159980.552023677442001
1410.3792579954715690.7585159909431380.620742004528431
1420.3872579397152110.7745158794304230.612742060284789
1430.3165923067498930.6331846134997860.683407693250107
1440.2486312826476220.4972625652952450.751368717352378
1450.1982100256484440.3964200512968870.801789974351556
1460.168858314784440.337716629568880.83114168521556
1470.1258886329907120.2517772659814250.874111367009288
1480.08647370444620570.1729474088924110.913526295553794
1490.09387707512965520.1877541502593100.906122924870345
1500.1159225677690260.2318451355380520.884077432230974
1510.1127012689504090.2254025379008170.887298731049591
1520.3545981200104810.7091962400209630.645401879989519






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00684931506849315OK
10% type I error level20.0136986301369863OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00684931506849315 & OK \tabularnewline
10% type I error level & 2 & 0.0136986301369863 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98745&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00684931506849315[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0136986301369863[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98745&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98745&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 level00OK
5% type I error level10.00684931506849315OK
10% type I error level20.0136986301369863OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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')
}