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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 computationTue, 13 Dec 2011 12:55:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t132379896481v8dzumyg3q6j8.htm/, Retrieved Fri, 03 May 2024 03:25:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154586, Retrieved Fri, 03 May 2024 03:25:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80

Tree of Dependent Computations

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- RMPD  [Multiple Regression] [Multiple Regression] [2011-12-12 20:14:09] [f2faabc3a2466a29562900bc59f67898]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	13	12	14	12
2	39	32	16	11	18	11
2	30	35	19	15	11	14
1	31	33	15	6	12	12
2	34	37	14	13	16	21
2	35	29	13	10	18	12
2	39	31	19	12	14	22
2	34	36	15	14	14	11
2	36	35	14	12	15	10
2	37	38	15	6	15	13
1	38	31	16	10	17	10
2	36	34	16	12	19	8
1	38	35	16	12	10	15
2	39	38	16	11	16	14
2	33	37	17	15	18	10
1	32	33	15	12	14	14
1	36	32	15	10	14	14
2	38	38	20	12	17	11
1	39	38	18	11	14	10
2	32	32	16	12	16	13
1	32	33	16	11	18	7
2	31	31	16	12	11	14
2	39	38	19	13	14	12
2	37	39	16	11	12	14
1	39	32	17	9	17	11
2	41	32	17	13	9	9
1	36	35	16	10	16	11
2	33	37	15	14	14	15
2	33	33	16	12	15	14
1	34	33	14	10	11	13
2	31	28	15	12	16	9
1	27	32	12	8	13	15
2	37	31	14	10	17	10
2	34	37	16	12	15	11
1	34	30	14	12	14	13
1	32	33	7	7	16	8
1	29	31	10	6	9	20
1	36	33	14	12	15	12
2	29	31	16	10	17	10
1	35	33	16	10	13	10
1	37	32	16	10	15	9
2	34	33	14	12	16	14
1	38	32	20	15	16	8
1	35	33	14	10	12	14
2	38	28	14	10	12	11
2	37	35	11	12	11	13
2	38	39	14	13	15	9
2	33	34	15	11	15	11
2	36	38	16	11	17	15
1	38	32	14	12	13	11
2	32	38	16	14	16	10
1	32	30	14	10	14	14
1	32	33	12	12	11	18
2	34	38	16	13	12	14
1	32	32	9	5	12	11
2	37	32	14	6	15	12
2	39	34	16	12	16	13
2	29	34	16	12	15	9
1	37	36	15	11	12	10
2	35	34	16	10	12	15
1	30	28	12	7	8	20
1	38	34	16	12	13	12
2	34	35	16	14	11	12
2	31	35	14	11	14	14
2	34	31	16	12	15	13
1	35	37	17	13	10	11
2	36	35	18	14	11	17
1	30	27	18	11	12	12
2	39	40	12	12	15	13
1	35	37	16	12	15	14
1	38	36	10	8	14	13
2	31	38	14	11	16	15
2	34	39	18	14	15	13
1	38	41	18	14	15	10
1	34	27	16	12	13	11
2	39	30	17	9	12	19
2	37	37	16	13	17	13
2	34	31	16	11	13	17
1	28	31	13	12	15	13
1	37	27	16	12	13	9
1	33	36	16	12	15	11
1	37	38	20	12	16	10
2	35	37	16	12	15	9
1	37	33	15	12	16	12
2	32	34	15	11	15	12
2	33	31	16	10	14	13
1	38	39	14	9	15	13
2	33	34	16	12	14	12
2	29	32	16	12	13	15
2	33	33	15	12	7	22
2	31	36	12	9	17	13
2	36	32	17	15	13	15
2	35	41	16	12	15	13
2	32	28	15	12	14	15
2	29	30	13	12	13	10
2	39	36	16	10	16	11
2	37	35	16	13	12	16
2	35	31	16	9	14	11
1	37	34	16	12	17	11
1	32	36	14	10	15	10
2	38	36	16	14	17	10
1	37	35	16	11	12	16
2	36	37	20	15	16	12
1	32	28	15	11	11	11
2	33	39	16	11	15	16
1	40	32	13	12	9	19
2	38	35	17	12	16	11
1	41	39	16	12	15	16
1	36	35	16	11	10	15
2	43	42	12	7	10	24
2	30	34	16	12	15	14
2	31	33	16	14	11	15
2	32	41	17	11	13	11
1	32	33	13	11	14	15
2	37	34	12	10	18	12
1	37	32	18	13	16	10
2	33	40	14	13	14	14
2	34	40	14	8	14	13
2	33	35	13	11	14	9
2	38	36	16	12	14	15
2	33	37	13	11	12	15
2	31	27	16	13	14	14
2	38	39	13	12	15	11
2	37	38	16	14	15	8
2	33	31	15	13	15	11
2	31	33	16	15	13	11
1	39	32	15	10	17	8
2	44	39	17	11	17	10
2	33	36	15	9	19	11
2	35	33	12	11	15	13
1	32	33	16	10	13	11
1	28	32	10	11	9	20
2	40	37	16	8	15	10
1	27	30	12	11	15	15
1	37	38	14	12	15	12
2	32	29	15	12	16	14
1	28	22	13	9	11	23
1	34	35	15	11	14	14
2	30	35	11	10	11	16
2	35	34	12	8	15	11
1	31	35	8	9	13	12
2	32	34	16	8	15	10
1	30	34	15	9	16	14
2	30	35	17	15	14	12
1	31	23	16	11	15	12
2	40	31	10	8	16	11
2	32	27	18	13	16	12
1	36	36	13	12	11	13
1	32	31	16	12	12	11
1	35	32	13	9	9	19
2	38	39	10	7	16	12
2	42	37	15	13	13	17
1	34	38	16	9	16	9
2	35	39	16	6	12	12
2	35	34	14	8	9	19
2	33	31	10	8	13	18
2	36	32	17	15	13	15
2	32	37	13	6	14	14
2	33	36	15	9	19	11
2	34	32	16	11	13	9
2	32	35	12	8	12	18
2	34	36	13	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154586&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154586&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154586&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Gender[t] = -0.875166605612026 -0.00630631412876438Connected[t] + 0.0302406863561791Separate[t] + 0.00833274094499445Learning[t] + 0.0320750552932809Software[t] + 0.0540009954117176Hapiness[t] + 0.0347363636631257Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Gender[t] =  -0.875166605612026 -0.00630631412876438Connected[t] +  0.0302406863561791Separate[t] +  0.00833274094499445Learning[t] +  0.0320750552932809Software[t] +  0.0540009954117176Hapiness[t] +  0.0347363636631257Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154586&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Gender[t] =  -0.875166605612026 -0.00630631412876438Connected[t] +  0.0302406863561791Separate[t] +  0.00833274094499445Learning[t] +  0.0320750552932809Software[t] +  0.0540009954117176Hapiness[t] +  0.0347363636631257Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154586&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154586&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
Gender[t] = -0.875166605612026 -0.00630631412876438Connected[t] + 0.0302406863561791Separate[t] + 0.00833274094499445Learning[t] + 0.0320750552932809Software[t] + 0.0540009954117176Hapiness[t] + 0.0347363636631257Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.8751666056120260.607151-1.44140.151480.07574
Connected-0.006306314128764380.011871-0.53120.5960090.298004
Separate0.03024068635617910.0110842.72830.0071010.00355
Learning0.008332740944994450.0200170.41630.6777750.338888
Software0.03207505529328090.0203241.57820.116570.058285
Hapiness0.05400099541171760.0187432.88110.0045250.002263
Depression0.03473636366312570.0138642.50560.0132590.006629

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.875166605612026 & 0.607151 & -1.4414 & 0.15148 & 0.07574 \tabularnewline
Connected & -0.00630631412876438 & 0.011871 & -0.5312 & 0.596009 & 0.298004 \tabularnewline
Separate & 0.0302406863561791 & 0.011084 & 2.7283 & 0.007101 & 0.00355 \tabularnewline
Learning & 0.00833274094499445 & 0.020017 & 0.4163 & 0.677775 & 0.338888 \tabularnewline
Software & 0.0320750552932809 & 0.020324 & 1.5782 & 0.11657 & 0.058285 \tabularnewline
Hapiness & 0.0540009954117176 & 0.018743 & 2.8811 & 0.004525 & 0.002263 \tabularnewline
Depression & 0.0347363636631257 & 0.013864 & 2.5056 & 0.013259 & 0.006629 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154586&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.875166605612026[/C][C]0.607151[/C][C]-1.4414[/C][C]0.15148[/C][C]0.07574[/C][/ROW]
[ROW][C]Connected[/C][C]-0.00630631412876438[/C][C]0.011871[/C][C]-0.5312[/C][C]0.596009[/C][C]0.298004[/C][/ROW]
[ROW][C]Separate[/C][C]0.0302406863561791[/C][C]0.011084[/C][C]2.7283[/C][C]0.007101[/C][C]0.00355[/C][/ROW]
[ROW][C]Learning[/C][C]0.00833274094499445[/C][C]0.020017[/C][C]0.4163[/C][C]0.677775[/C][C]0.338888[/C][/ROW]
[ROW][C]Software[/C][C]0.0320750552932809[/C][C]0.020324[/C][C]1.5782[/C][C]0.11657[/C][C]0.058285[/C][/ROW]
[ROW][C]Hapiness[/C][C]0.0540009954117176[/C][C]0.018743[/C][C]2.8811[/C][C]0.004525[/C][C]0.002263[/C][/ROW]
[ROW][C]Depression[/C][C]0.0347363636631257[/C][C]0.013864[/C][C]2.5056[/C][C]0.013259[/C][C]0.006629[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154586&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154586&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.8751666056120260.607151-1.44140.151480.07574
Connected-0.006306314128764380.011871-0.53120.5960090.298004
Separate0.03024068635617910.0110842.72830.0071010.00355
Learning0.008332740944994450.0200170.41630.6777750.338888
Software0.03207505529328090.0203241.57820.116570.058285
Hapiness0.05400099541171760.0187432.88110.0045250.002263
Depression0.03473636366312570.0138642.50560.0132590.006629






Multiple Linear Regression - Regression Statistics
Multiple R0.367748227018048
R-squared0.135238758474918
Adjusted R-squared0.101764129770721
F-TEST (value)4.04003759593495
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.000863533576349051
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.460628023881901
Sum Squared Residuals32.8876173397286

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.367748227018048 \tabularnewline
R-squared & 0.135238758474918 \tabularnewline
Adjusted R-squared & 0.101764129770721 \tabularnewline
F-TEST (value) & 4.04003759593495 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.000863533576349051 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.460628023881901 \tabularnewline
Sum Squared Residuals & 32.8876173397286 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154586&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.367748227018048[/C][/ROW]
[ROW][C]R-squared[/C][C]0.135238758474918[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.101764129770721[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.04003759593495[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.000863533576349051[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.460628023881901[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]32.8876173397286[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154586&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154586&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 R0.367748227018048
R-squared0.135238758474918
Adjusted R-squared0.101764129770721
F-TEST (value)4.04003759593495
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.000863533576349051
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.460628023881901
Sum Squared Residuals32.8876173397286






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.681497192169290.318502807830708
221.686856487815190.313143512184805
321.713835941158070.286164058841928
411.30957006098291-0.309570060982909
522.15643776474431-0.156437764744306
621.599022770796580.400977229203424
721.879785098234790.120214901765208
821.711239247171710.288760752828289
921.615167712775040.384832287224962
1021.61967495788950.380325042110503
1111.54210970121966-0.542109701219656
1221.748123762629470.251876237370533
1311.52289740766454-0.522897407664539
1421.864507706118210.135492293881789
1521.977794402823670.0222055971763313
1611.67318879676352-0.673188796763517
1711.55357274330572-0.553572743305719
1821.886011943742570.113988056257426
1911.63422574253226-0.634225742532261
2021.724546478512640.275453521487358
2111.62229591842022-0.622295918420221
2221.465343492889770.534656507110235
2321.776181321390070.223818678609931
2421.691357039085050.308642960914952
2511.57703812276191-0.577038122761909
2621.191245025057510.808754974942488
2711.65642044315331-0.656420443153309
2821.886731702309160.113268297690843
2921.729216218991470.270783781008535
3011.39135396707615-0.391353967076154
3121.462611851619190.537388148380807
3211.50191660529446-0.501916605294461
3321.531750533458430.468249466541569
3421.739663559298040.26033644070196
3511.52678500482933-0.526785004829331
3611.34573540158184-0.345735401581838
3711.33592553487296-0.33592553487296
3811.62415906738893-0.624159067388931
3921.598866528378540.401133471621465
4011.40550603467144-0.405506034671436
4111.43591834721804-0.435918347218038
4221.760245418384430.239754581615571
4311.64258290508425-0.642582905084248
4411.47378501202223-0.473785012022233
4521.199453546865670.800546453134333
4621.47206828515380.527931714846203
4721.720856521572380.279143478427619
4821.614840018119990.385159981880008
4921.972164007579350.0278359924206528
5011.43856739828866-0.438567398288663
5121.86593161624690.134068383753099
5211.50998388616342-0.509983886163424
5311.62513304234588-0.625133042345884
5421.744185405701720.255814594298276
5511.15621519587159-0.156215195871592
5621.39516173514430.604838264855697
5721.740883652323650.25911634767635
5821.611000343547070.388999656452927
5911.45335678441901-0.453356784419014
6021.555427543931530.444572456068474
6111.23363670344721-0.233636703447211
6211.55045061655414-0.550450616554136
6321.56206467918850.437935320811501
6421.699568687366360.300431312633636
6521.627692168487220.372307831512783
6611.50376006434896-0.503760064348963
6721.739799351136590.260200648863413
6811.31980575627599-0.319805756275988
6921.834995811269030.165004188730971
7011.83756633615865-0.837566336158652
7111.52137268149825-0.521372681498246
7221.933029100921460.0669708990785375
7321.95043325181320.0495667481867999
7411.88148027702112-0.881480277021124
7511.32925470491281-0.329254704912814
7621.524442682295970.475557317704031
7721.930294390354710.0697056096452858
7821.6265605770230.373439422976997
7911.64053183042482-0.64053183042482
8011.24086303520027-0.24086303520027
8111.71572918707062-0.715729187070625
8211.8035808987965-0.803580898796495
8321.663884517843020.336115482156976
8411.68018648961688-0.680186489616879
8521.655882695911880.344117304088118
8621.51584737661770.484152623382298
8711.73150175505176-0.73150175505176
8821.635983182609680.364016817390325
8921.650935161990030.349064838009966
9021.566766424057740.433233575942265
9121.776260403818020.22373959618198
9221.711348869913520.288651130086479
9321.923792717920240.0762072820797569
9421.556721728645750.443278271354252
9521.391773748127060.608226251872936
9621.667742187123190.332257812876805
9721.704017131573140.295982868426855
9821.401686965740640.59831303425936
9911.73802454866664-0.738024548666645
10011.60648354505971-0.606483545059713
10121.821613354173670.178386645826325
10211.63986702098658-0.639867020986583
10321.945344419775180.054655580224825
10411.22369823246481-0.223698232464811
10521.948058009161510.0519419908384902
10611.47950895674427-0.479508956744275
10721.716290666427340.283709333572664
10811.92968255142468-0.929682551424676
10911.50343498062879-0.503434980628787
11021.821971674235720.178028325764281
11121.778375847733940.221624152266063
11221.624711339851810.375288660148189
11321.741494627808560.258505372191437
11411.65918462324337-0.659184623243373
11521.729280833374950.270719166625052
11611.63754635406271-0.637546354062713
11721.902309601476290.0976903985237072
11821.7008916472180.299108352782002
11921.504941499848210.495058500151787
12021.769142075667590.230857924332412
12121.665839063715890.33416093628411
12221.538458788993480.461541211006517
12321.749921452660360.250078547339643
12421.710926322865110.28907367713489
12521.588268069638020.411731930361984
12621.625842931316020.374157068683976
12711.48823860517582-0.488238605175825
12821.786605103534780.213394896465223
12921.827175261892660.172824738107342
13021.616461207997550.383538792002448
13111.45916134072086-0.459161340720855
13211.53284781182431-0.532847811824308
13321.53878908968920.461210910310796
13411.64566238928538-0.645662389285381
13511.76905618504106-0.769056185041062
13621.660228042162240.339771957837764
13711.40350014232375-0.403500142323752
13811.68898248592507-0.688982485925066
13921.556271464457960.443728535542037
14021.481004001147640.518995998852363
14111.46194840837187-0.461948408371867
14221.498517543650780.501482456349218
14311.72781893632082-0.727818936320818
14421.789700718176980.210299281823016
14511.33787420106767-0.337874201067671
14621.396085884957010.603914115042994
14721.587347220251890.412652779748109
14811.52528076752873-0.525280767528729
14911.40882908318334-0.408829083183341
15011.41481536150825-0.414815361508254
15121.653285312433810.346714687566188
15221.813371551771530.18662844822847
15311.65820734785984-0.658207347859842
15421.474121663549920.525878336450079
15521.451554419872330.548445580127675
15621.521381643265080.478618356734916
15721.711348869913520.288651130086479
15821.585035728538560.414964271461441
15921.827175261892660.172824738107342
16021.378910354074180.621089645925823
16121.611315189296840.388684810703164
16221.621804256425950.378195743574053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.68149719216929 & 0.318502807830708 \tabularnewline
2 & 2 & 1.68685648781519 & 0.313143512184805 \tabularnewline
3 & 2 & 1.71383594115807 & 0.286164058841928 \tabularnewline
4 & 1 & 1.30957006098291 & -0.309570060982909 \tabularnewline
5 & 2 & 2.15643776474431 & -0.156437764744306 \tabularnewline
6 & 2 & 1.59902277079658 & 0.400977229203424 \tabularnewline
7 & 2 & 1.87978509823479 & 0.120214901765208 \tabularnewline
8 & 2 & 1.71123924717171 & 0.288760752828289 \tabularnewline
9 & 2 & 1.61516771277504 & 0.384832287224962 \tabularnewline
10 & 2 & 1.6196749578895 & 0.380325042110503 \tabularnewline
11 & 1 & 1.54210970121966 & -0.542109701219656 \tabularnewline
12 & 2 & 1.74812376262947 & 0.251876237370533 \tabularnewline
13 & 1 & 1.52289740766454 & -0.522897407664539 \tabularnewline
14 & 2 & 1.86450770611821 & 0.135492293881789 \tabularnewline
15 & 2 & 1.97779440282367 & 0.0222055971763313 \tabularnewline
16 & 1 & 1.67318879676352 & -0.673188796763517 \tabularnewline
17 & 1 & 1.55357274330572 & -0.553572743305719 \tabularnewline
18 & 2 & 1.88601194374257 & 0.113988056257426 \tabularnewline
19 & 1 & 1.63422574253226 & -0.634225742532261 \tabularnewline
20 & 2 & 1.72454647851264 & 0.275453521487358 \tabularnewline
21 & 1 & 1.62229591842022 & -0.622295918420221 \tabularnewline
22 & 2 & 1.46534349288977 & 0.534656507110235 \tabularnewline
23 & 2 & 1.77618132139007 & 0.223818678609931 \tabularnewline
24 & 2 & 1.69135703908505 & 0.308642960914952 \tabularnewline
25 & 1 & 1.57703812276191 & -0.577038122761909 \tabularnewline
26 & 2 & 1.19124502505751 & 0.808754974942488 \tabularnewline
27 & 1 & 1.65642044315331 & -0.656420443153309 \tabularnewline
28 & 2 & 1.88673170230916 & 0.113268297690843 \tabularnewline
29 & 2 & 1.72921621899147 & 0.270783781008535 \tabularnewline
30 & 1 & 1.39135396707615 & -0.391353967076154 \tabularnewline
31 & 2 & 1.46261185161919 & 0.537388148380807 \tabularnewline
32 & 1 & 1.50191660529446 & -0.501916605294461 \tabularnewline
33 & 2 & 1.53175053345843 & 0.468249466541569 \tabularnewline
34 & 2 & 1.73966355929804 & 0.26033644070196 \tabularnewline
35 & 1 & 1.52678500482933 & -0.526785004829331 \tabularnewline
36 & 1 & 1.34573540158184 & -0.345735401581838 \tabularnewline
37 & 1 & 1.33592553487296 & -0.33592553487296 \tabularnewline
38 & 1 & 1.62415906738893 & -0.624159067388931 \tabularnewline
39 & 2 & 1.59886652837854 & 0.401133471621465 \tabularnewline
40 & 1 & 1.40550603467144 & -0.405506034671436 \tabularnewline
41 & 1 & 1.43591834721804 & -0.435918347218038 \tabularnewline
42 & 2 & 1.76024541838443 & 0.239754581615571 \tabularnewline
43 & 1 & 1.64258290508425 & -0.642582905084248 \tabularnewline
44 & 1 & 1.47378501202223 & -0.473785012022233 \tabularnewline
45 & 2 & 1.19945354686567 & 0.800546453134333 \tabularnewline
46 & 2 & 1.4720682851538 & 0.527931714846203 \tabularnewline
47 & 2 & 1.72085652157238 & 0.279143478427619 \tabularnewline
48 & 2 & 1.61484001811999 & 0.385159981880008 \tabularnewline
49 & 2 & 1.97216400757935 & 0.0278359924206528 \tabularnewline
50 & 1 & 1.43856739828866 & -0.438567398288663 \tabularnewline
51 & 2 & 1.8659316162469 & 0.134068383753099 \tabularnewline
52 & 1 & 1.50998388616342 & -0.509983886163424 \tabularnewline
53 & 1 & 1.62513304234588 & -0.625133042345884 \tabularnewline
54 & 2 & 1.74418540570172 & 0.255814594298276 \tabularnewline
55 & 1 & 1.15621519587159 & -0.156215195871592 \tabularnewline
56 & 2 & 1.3951617351443 & 0.604838264855697 \tabularnewline
57 & 2 & 1.74088365232365 & 0.25911634767635 \tabularnewline
58 & 2 & 1.61100034354707 & 0.388999656452927 \tabularnewline
59 & 1 & 1.45335678441901 & -0.453356784419014 \tabularnewline
60 & 2 & 1.55542754393153 & 0.444572456068474 \tabularnewline
61 & 1 & 1.23363670344721 & -0.233636703447211 \tabularnewline
62 & 1 & 1.55045061655414 & -0.550450616554136 \tabularnewline
63 & 2 & 1.5620646791885 & 0.437935320811501 \tabularnewline
64 & 2 & 1.69956868736636 & 0.300431312633636 \tabularnewline
65 & 2 & 1.62769216848722 & 0.372307831512783 \tabularnewline
66 & 1 & 1.50376006434896 & -0.503760064348963 \tabularnewline
67 & 2 & 1.73979935113659 & 0.260200648863413 \tabularnewline
68 & 1 & 1.31980575627599 & -0.319805756275988 \tabularnewline
69 & 2 & 1.83499581126903 & 0.165004188730971 \tabularnewline
70 & 1 & 1.83756633615865 & -0.837566336158652 \tabularnewline
71 & 1 & 1.52137268149825 & -0.521372681498246 \tabularnewline
72 & 2 & 1.93302910092146 & 0.0669708990785375 \tabularnewline
73 & 2 & 1.9504332518132 & 0.0495667481867999 \tabularnewline
74 & 1 & 1.88148027702112 & -0.881480277021124 \tabularnewline
75 & 1 & 1.32925470491281 & -0.329254704912814 \tabularnewline
76 & 2 & 1.52444268229597 & 0.475557317704031 \tabularnewline
77 & 2 & 1.93029439035471 & 0.0697056096452858 \tabularnewline
78 & 2 & 1.626560577023 & 0.373439422976997 \tabularnewline
79 & 1 & 1.64053183042482 & -0.64053183042482 \tabularnewline
80 & 1 & 1.24086303520027 & -0.24086303520027 \tabularnewline
81 & 1 & 1.71572918707062 & -0.715729187070625 \tabularnewline
82 & 1 & 1.8035808987965 & -0.803580898796495 \tabularnewline
83 & 2 & 1.66388451784302 & 0.336115482156976 \tabularnewline
84 & 1 & 1.68018648961688 & -0.680186489616879 \tabularnewline
85 & 2 & 1.65588269591188 & 0.344117304088118 \tabularnewline
86 & 2 & 1.5158473766177 & 0.484152623382298 \tabularnewline
87 & 1 & 1.73150175505176 & -0.73150175505176 \tabularnewline
88 & 2 & 1.63598318260968 & 0.364016817390325 \tabularnewline
89 & 2 & 1.65093516199003 & 0.349064838009966 \tabularnewline
90 & 2 & 1.56676642405774 & 0.433233575942265 \tabularnewline
91 & 2 & 1.77626040381802 & 0.22373959618198 \tabularnewline
92 & 2 & 1.71134886991352 & 0.288651130086479 \tabularnewline
93 & 2 & 1.92379271792024 & 0.0762072820797569 \tabularnewline
94 & 2 & 1.55672172864575 & 0.443278271354252 \tabularnewline
95 & 2 & 1.39177374812706 & 0.608226251872936 \tabularnewline
96 & 2 & 1.66774218712319 & 0.332257812876805 \tabularnewline
97 & 2 & 1.70401713157314 & 0.295982868426855 \tabularnewline
98 & 2 & 1.40168696574064 & 0.59831303425936 \tabularnewline
99 & 1 & 1.73802454866664 & -0.738024548666645 \tabularnewline
100 & 1 & 1.60648354505971 & -0.606483545059713 \tabularnewline
101 & 2 & 1.82161335417367 & 0.178386645826325 \tabularnewline
102 & 1 & 1.63986702098658 & -0.639867020986583 \tabularnewline
103 & 2 & 1.94534441977518 & 0.054655580224825 \tabularnewline
104 & 1 & 1.22369823246481 & -0.223698232464811 \tabularnewline
105 & 2 & 1.94805800916151 & 0.0519419908384902 \tabularnewline
106 & 1 & 1.47950895674427 & -0.479508956744275 \tabularnewline
107 & 2 & 1.71629066642734 & 0.283709333572664 \tabularnewline
108 & 1 & 1.92968255142468 & -0.929682551424676 \tabularnewline
109 & 1 & 1.50343498062879 & -0.503434980628787 \tabularnewline
110 & 2 & 1.82197167423572 & 0.178028325764281 \tabularnewline
111 & 2 & 1.77837584773394 & 0.221624152266063 \tabularnewline
112 & 2 & 1.62471133985181 & 0.375288660148189 \tabularnewline
113 & 2 & 1.74149462780856 & 0.258505372191437 \tabularnewline
114 & 1 & 1.65918462324337 & -0.659184623243373 \tabularnewline
115 & 2 & 1.72928083337495 & 0.270719166625052 \tabularnewline
116 & 1 & 1.63754635406271 & -0.637546354062713 \tabularnewline
117 & 2 & 1.90230960147629 & 0.0976903985237072 \tabularnewline
118 & 2 & 1.700891647218 & 0.299108352782002 \tabularnewline
119 & 2 & 1.50494149984821 & 0.495058500151787 \tabularnewline
120 & 2 & 1.76914207566759 & 0.230857924332412 \tabularnewline
121 & 2 & 1.66583906371589 & 0.33416093628411 \tabularnewline
122 & 2 & 1.53845878899348 & 0.461541211006517 \tabularnewline
123 & 2 & 1.74992145266036 & 0.250078547339643 \tabularnewline
124 & 2 & 1.71092632286511 & 0.28907367713489 \tabularnewline
125 & 2 & 1.58826806963802 & 0.411731930361984 \tabularnewline
126 & 2 & 1.62584293131602 & 0.374157068683976 \tabularnewline
127 & 1 & 1.48823860517582 & -0.488238605175825 \tabularnewline
128 & 2 & 1.78660510353478 & 0.213394896465223 \tabularnewline
129 & 2 & 1.82717526189266 & 0.172824738107342 \tabularnewline
130 & 2 & 1.61646120799755 & 0.383538792002448 \tabularnewline
131 & 1 & 1.45916134072086 & -0.459161340720855 \tabularnewline
132 & 1 & 1.53284781182431 & -0.532847811824308 \tabularnewline
133 & 2 & 1.5387890896892 & 0.461210910310796 \tabularnewline
134 & 1 & 1.64566238928538 & -0.645662389285381 \tabularnewline
135 & 1 & 1.76905618504106 & -0.769056185041062 \tabularnewline
136 & 2 & 1.66022804216224 & 0.339771957837764 \tabularnewline
137 & 1 & 1.40350014232375 & -0.403500142323752 \tabularnewline
138 & 1 & 1.68898248592507 & -0.688982485925066 \tabularnewline
139 & 2 & 1.55627146445796 & 0.443728535542037 \tabularnewline
140 & 2 & 1.48100400114764 & 0.518995998852363 \tabularnewline
141 & 1 & 1.46194840837187 & -0.461948408371867 \tabularnewline
142 & 2 & 1.49851754365078 & 0.501482456349218 \tabularnewline
143 & 1 & 1.72781893632082 & -0.727818936320818 \tabularnewline
144 & 2 & 1.78970071817698 & 0.210299281823016 \tabularnewline
145 & 1 & 1.33787420106767 & -0.337874201067671 \tabularnewline
146 & 2 & 1.39608588495701 & 0.603914115042994 \tabularnewline
147 & 2 & 1.58734722025189 & 0.412652779748109 \tabularnewline
148 & 1 & 1.52528076752873 & -0.525280767528729 \tabularnewline
149 & 1 & 1.40882908318334 & -0.408829083183341 \tabularnewline
150 & 1 & 1.41481536150825 & -0.414815361508254 \tabularnewline
151 & 2 & 1.65328531243381 & 0.346714687566188 \tabularnewline
152 & 2 & 1.81337155177153 & 0.18662844822847 \tabularnewline
153 & 1 & 1.65820734785984 & -0.658207347859842 \tabularnewline
154 & 2 & 1.47412166354992 & 0.525878336450079 \tabularnewline
155 & 2 & 1.45155441987233 & 0.548445580127675 \tabularnewline
156 & 2 & 1.52138164326508 & 0.478618356734916 \tabularnewline
157 & 2 & 1.71134886991352 & 0.288651130086479 \tabularnewline
158 & 2 & 1.58503572853856 & 0.414964271461441 \tabularnewline
159 & 2 & 1.82717526189266 & 0.172824738107342 \tabularnewline
160 & 2 & 1.37891035407418 & 0.621089645925823 \tabularnewline
161 & 2 & 1.61131518929684 & 0.388684810703164 \tabularnewline
162 & 2 & 1.62180425642595 & 0.378195743574053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154586&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]2[/C][C]1.68149719216929[/C][C]0.318502807830708[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]1.68685648781519[/C][C]0.313143512184805[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]1.71383594115807[/C][C]0.286164058841928[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.30957006098291[/C][C]-0.309570060982909[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]2.15643776474431[/C][C]-0.156437764744306[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.59902277079658[/C][C]0.400977229203424[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.87978509823479[/C][C]0.120214901765208[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.71123924717171[/C][C]0.288760752828289[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]1.61516771277504[/C][C]0.384832287224962[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.6196749578895[/C][C]0.380325042110503[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.54210970121966[/C][C]-0.542109701219656[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.74812376262947[/C][C]0.251876237370533[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.52289740766454[/C][C]-0.522897407664539[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.86450770611821[/C][C]0.135492293881789[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.97779440282367[/C][C]0.0222055971763313[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.67318879676352[/C][C]-0.673188796763517[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.55357274330572[/C][C]-0.553572743305719[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.88601194374257[/C][C]0.113988056257426[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.63422574253226[/C][C]-0.634225742532261[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.72454647851264[/C][C]0.275453521487358[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.62229591842022[/C][C]-0.622295918420221[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.46534349288977[/C][C]0.534656507110235[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.77618132139007[/C][C]0.223818678609931[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.69135703908505[/C][C]0.308642960914952[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.57703812276191[/C][C]-0.577038122761909[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]1.19124502505751[/C][C]0.808754974942488[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.65642044315331[/C][C]-0.656420443153309[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.88673170230916[/C][C]0.113268297690843[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.72921621899147[/C][C]0.270783781008535[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.39135396707615[/C][C]-0.391353967076154[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.46261185161919[/C][C]0.537388148380807[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.50191660529446[/C][C]-0.501916605294461[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]1.53175053345843[/C][C]0.468249466541569[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.73966355929804[/C][C]0.26033644070196[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.52678500482933[/C][C]-0.526785004829331[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.34573540158184[/C][C]-0.345735401581838[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.33592553487296[/C][C]-0.33592553487296[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.62415906738893[/C][C]-0.624159067388931[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.59886652837854[/C][C]0.401133471621465[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.40550603467144[/C][C]-0.405506034671436[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.43591834721804[/C][C]-0.435918347218038[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.76024541838443[/C][C]0.239754581615571[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.64258290508425[/C][C]-0.642582905084248[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.47378501202223[/C][C]-0.473785012022233[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.19945354686567[/C][C]0.800546453134333[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.4720682851538[/C][C]0.527931714846203[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.72085652157238[/C][C]0.279143478427619[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.61484001811999[/C][C]0.385159981880008[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.97216400757935[/C][C]0.0278359924206528[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]1.43856739828866[/C][C]-0.438567398288663[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.8659316162469[/C][C]0.134068383753099[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.50998388616342[/C][C]-0.509983886163424[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.62513304234588[/C][C]-0.625133042345884[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.74418540570172[/C][C]0.255814594298276[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]1.15621519587159[/C][C]-0.156215195871592[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]1.3951617351443[/C][C]0.604838264855697[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]1.74088365232365[/C][C]0.25911634767635[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]1.61100034354707[/C][C]0.388999656452927[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.45335678441901[/C][C]-0.453356784419014[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.55542754393153[/C][C]0.444572456068474[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.23363670344721[/C][C]-0.233636703447211[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.55045061655414[/C][C]-0.550450616554136[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]1.5620646791885[/C][C]0.437935320811501[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.69956868736636[/C][C]0.300431312633636[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.62769216848722[/C][C]0.372307831512783[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.50376006434896[/C][C]-0.503760064348963[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.73979935113659[/C][C]0.260200648863413[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.31980575627599[/C][C]-0.319805756275988[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]1.83499581126903[/C][C]0.165004188730971[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.83756633615865[/C][C]-0.837566336158652[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.52137268149825[/C][C]-0.521372681498246[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.93302910092146[/C][C]0.0669708990785375[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.9504332518132[/C][C]0.0495667481867999[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.88148027702112[/C][C]-0.881480277021124[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.32925470491281[/C][C]-0.329254704912814[/C][/ROW]
[ROW][C]76[/C][C]2[/C][C]1.52444268229597[/C][C]0.475557317704031[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.93029439035471[/C][C]0.0697056096452858[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.626560577023[/C][C]0.373439422976997[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.64053183042482[/C][C]-0.64053183042482[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.24086303520027[/C][C]-0.24086303520027[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.71572918707062[/C][C]-0.715729187070625[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.8035808987965[/C][C]-0.803580898796495[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]1.66388451784302[/C][C]0.336115482156976[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.68018648961688[/C][C]-0.680186489616879[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.65588269591188[/C][C]0.344117304088118[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.5158473766177[/C][C]0.484152623382298[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.73150175505176[/C][C]-0.73150175505176[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.63598318260968[/C][C]0.364016817390325[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.65093516199003[/C][C]0.349064838009966[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]1.56676642405774[/C][C]0.433233575942265[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]1.77626040381802[/C][C]0.22373959618198[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.71134886991352[/C][C]0.288651130086479[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.92379271792024[/C][C]0.0762072820797569[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]1.55672172864575[/C][C]0.443278271354252[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]1.39177374812706[/C][C]0.608226251872936[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]1.66774218712319[/C][C]0.332257812876805[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.70401713157314[/C][C]0.295982868426855[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]1.40168696574064[/C][C]0.59831303425936[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.73802454866664[/C][C]-0.738024548666645[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.60648354505971[/C][C]-0.606483545059713[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.82161335417367[/C][C]0.178386645826325[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.63986702098658[/C][C]-0.639867020986583[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.94534441977518[/C][C]0.054655580224825[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.22369823246481[/C][C]-0.223698232464811[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.94805800916151[/C][C]0.0519419908384902[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.47950895674427[/C][C]-0.479508956744275[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.71629066642734[/C][C]0.283709333572664[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.92968255142468[/C][C]-0.929682551424676[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.50343498062879[/C][C]-0.503434980628787[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.82197167423572[/C][C]0.178028325764281[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]1.77837584773394[/C][C]0.221624152266063[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]1.62471133985181[/C][C]0.375288660148189[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]1.74149462780856[/C][C]0.258505372191437[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.65918462324337[/C][C]-0.659184623243373[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]1.72928083337495[/C][C]0.270719166625052[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]1.63754635406271[/C][C]-0.637546354062713[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]1.90230960147629[/C][C]0.0976903985237072[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.700891647218[/C][C]0.299108352782002[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.50494149984821[/C][C]0.495058500151787[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]1.76914207566759[/C][C]0.230857924332412[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]1.66583906371589[/C][C]0.33416093628411[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.53845878899348[/C][C]0.461541211006517[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.74992145266036[/C][C]0.250078547339643[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]1.71092632286511[/C][C]0.28907367713489[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.58826806963802[/C][C]0.411731930361984[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.62584293131602[/C][C]0.374157068683976[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.48823860517582[/C][C]-0.488238605175825[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.78660510353478[/C][C]0.213394896465223[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.82717526189266[/C][C]0.172824738107342[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.61646120799755[/C][C]0.383538792002448[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.45916134072086[/C][C]-0.459161340720855[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.53284781182431[/C][C]-0.532847811824308[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.5387890896892[/C][C]0.461210910310796[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.64566238928538[/C][C]-0.645662389285381[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.76905618504106[/C][C]-0.769056185041062[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.66022804216224[/C][C]0.339771957837764[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.40350014232375[/C][C]-0.403500142323752[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.68898248592507[/C][C]-0.688982485925066[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.55627146445796[/C][C]0.443728535542037[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]1.48100400114764[/C][C]0.518995998852363[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.46194840837187[/C][C]-0.461948408371867[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.49851754365078[/C][C]0.501482456349218[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.72781893632082[/C][C]-0.727818936320818[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]1.78970071817698[/C][C]0.210299281823016[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.33787420106767[/C][C]-0.337874201067671[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.39608588495701[/C][C]0.603914115042994[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.58734722025189[/C][C]0.412652779748109[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]1.52528076752873[/C][C]-0.525280767528729[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.40882908318334[/C][C]-0.408829083183341[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]1.41481536150825[/C][C]-0.414815361508254[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.65328531243381[/C][C]0.346714687566188[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.81337155177153[/C][C]0.18662844822847[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.65820734785984[/C][C]-0.658207347859842[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.47412166354992[/C][C]0.525878336450079[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.45155441987233[/C][C]0.548445580127675[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]1.52138164326508[/C][C]0.478618356734916[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.71134886991352[/C][C]0.288651130086479[/C][/ROW]
[ROW][C]158[/C][C]2[/C][C]1.58503572853856[/C][C]0.414964271461441[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]1.82717526189266[/C][C]0.172824738107342[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]1.37891035407418[/C][C]0.621089645925823[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]1.61131518929684[/C][C]0.388684810703164[/C][/ROW]
[ROW][C]162[/C][C]2[/C][C]1.62180425642595[/C][C]0.378195743574053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154586&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154586&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
121.681497192169290.318502807830708
221.686856487815190.313143512184805
321.713835941158070.286164058841928
411.30957006098291-0.309570060982909
522.15643776474431-0.156437764744306
621.599022770796580.400977229203424
721.879785098234790.120214901765208
821.711239247171710.288760752828289
921.615167712775040.384832287224962
1021.61967495788950.380325042110503
1111.54210970121966-0.542109701219656
1221.748123762629470.251876237370533
1311.52289740766454-0.522897407664539
1421.864507706118210.135492293881789
1521.977794402823670.0222055971763313
1611.67318879676352-0.673188796763517
1711.55357274330572-0.553572743305719
1821.886011943742570.113988056257426
1911.63422574253226-0.634225742532261
2021.724546478512640.275453521487358
2111.62229591842022-0.622295918420221
2221.465343492889770.534656507110235
2321.776181321390070.223818678609931
2421.691357039085050.308642960914952
2511.57703812276191-0.577038122761909
2621.191245025057510.808754974942488
2711.65642044315331-0.656420443153309
2821.886731702309160.113268297690843
2921.729216218991470.270783781008535
3011.39135396707615-0.391353967076154
3121.462611851619190.537388148380807
3211.50191660529446-0.501916605294461
3321.531750533458430.468249466541569
3421.739663559298040.26033644070196
3511.52678500482933-0.526785004829331
3611.34573540158184-0.345735401581838
3711.33592553487296-0.33592553487296
3811.62415906738893-0.624159067388931
3921.598866528378540.401133471621465
4011.40550603467144-0.405506034671436
4111.43591834721804-0.435918347218038
4221.760245418384430.239754581615571
4311.64258290508425-0.642582905084248
4411.47378501202223-0.473785012022233
4521.199453546865670.800546453134333
4621.47206828515380.527931714846203
4721.720856521572380.279143478427619
4821.614840018119990.385159981880008
4921.972164007579350.0278359924206528
5011.43856739828866-0.438567398288663
5121.86593161624690.134068383753099
5211.50998388616342-0.509983886163424
5311.62513304234588-0.625133042345884
5421.744185405701720.255814594298276
5511.15621519587159-0.156215195871592
5621.39516173514430.604838264855697
5721.740883652323650.25911634767635
5821.611000343547070.388999656452927
5911.45335678441901-0.453356784419014
6021.555427543931530.444572456068474
6111.23363670344721-0.233636703447211
6211.55045061655414-0.550450616554136
6321.56206467918850.437935320811501
6421.699568687366360.300431312633636
6521.627692168487220.372307831512783
6611.50376006434896-0.503760064348963
6721.739799351136590.260200648863413
6811.31980575627599-0.319805756275988
6921.834995811269030.165004188730971
7011.83756633615865-0.837566336158652
7111.52137268149825-0.521372681498246
7221.933029100921460.0669708990785375
7321.95043325181320.0495667481867999
7411.88148027702112-0.881480277021124
7511.32925470491281-0.329254704912814
7621.524442682295970.475557317704031
7721.930294390354710.0697056096452858
7821.6265605770230.373439422976997
7911.64053183042482-0.64053183042482
8011.24086303520027-0.24086303520027
8111.71572918707062-0.715729187070625
8211.8035808987965-0.803580898796495
8321.663884517843020.336115482156976
8411.68018648961688-0.680186489616879
8521.655882695911880.344117304088118
8621.51584737661770.484152623382298
8711.73150175505176-0.73150175505176
8821.635983182609680.364016817390325
8921.650935161990030.349064838009966
9021.566766424057740.433233575942265
9121.776260403818020.22373959618198
9221.711348869913520.288651130086479
9321.923792717920240.0762072820797569
9421.556721728645750.443278271354252
9521.391773748127060.608226251872936
9621.667742187123190.332257812876805
9721.704017131573140.295982868426855
9821.401686965740640.59831303425936
9911.73802454866664-0.738024548666645
10011.60648354505971-0.606483545059713
10121.821613354173670.178386645826325
10211.63986702098658-0.639867020986583
10321.945344419775180.054655580224825
10411.22369823246481-0.223698232464811
10521.948058009161510.0519419908384902
10611.47950895674427-0.479508956744275
10721.716290666427340.283709333572664
10811.92968255142468-0.929682551424676
10911.50343498062879-0.503434980628787
11021.821971674235720.178028325764281
11121.778375847733940.221624152266063
11221.624711339851810.375288660148189
11321.741494627808560.258505372191437
11411.65918462324337-0.659184623243373
11521.729280833374950.270719166625052
11611.63754635406271-0.637546354062713
11721.902309601476290.0976903985237072
11821.7008916472180.299108352782002
11921.504941499848210.495058500151787
12021.769142075667590.230857924332412
12121.665839063715890.33416093628411
12221.538458788993480.461541211006517
12321.749921452660360.250078547339643
12421.710926322865110.28907367713489
12521.588268069638020.411731930361984
12621.625842931316020.374157068683976
12711.48823860517582-0.488238605175825
12821.786605103534780.213394896465223
12921.827175261892660.172824738107342
13021.616461207997550.383538792002448
13111.45916134072086-0.459161340720855
13211.53284781182431-0.532847811824308
13321.53878908968920.461210910310796
13411.64566238928538-0.645662389285381
13511.76905618504106-0.769056185041062
13621.660228042162240.339771957837764
13711.40350014232375-0.403500142323752
13811.68898248592507-0.688982485925066
13921.556271464457960.443728535542037
14021.481004001147640.518995998852363
14111.46194840837187-0.461948408371867
14221.498517543650780.501482456349218
14311.72781893632082-0.727818936320818
14421.789700718176980.210299281823016
14511.33787420106767-0.337874201067671
14621.396085884957010.603914115042994
14721.587347220251890.412652779748109
14811.52528076752873-0.525280767528729
14911.40882908318334-0.408829083183341
15011.41481536150825-0.414815361508254
15121.653285312433810.346714687566188
15221.813371551771530.18662844822847
15311.65820734785984-0.658207347859842
15421.474121663549920.525878336450079
15521.451554419872330.548445580127675
15621.521381643265080.478618356734916
15721.711348869913520.288651130086479
15821.585035728538560.414964271461441
15921.827175261892660.172824738107342
16021.378910354074180.621089645925823
16121.611315189296840.388684810703164
16221.621804256425950.378195743574053






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1733410320389520.3466820640779030.826658967961048
110.582893657082210.8342126858355790.41710634291779
120.4382913836207470.8765827672414940.561708616379253
130.4643195200258830.9286390400517650.535680479974117
140.3526629061705820.7053258123411650.647337093829418
150.3151110807672390.6302221615344790.684888919232761
160.413713444854910.8274268897098210.58628655514509
170.4035508088929330.8071016177858650.596449191107067
180.3287548053843330.6575096107686660.671245194615667
190.4229785108947430.8459570217894850.577021489105257
200.3676370169574260.7352740339148520.632362983042574
210.4471696821993420.8943393643986840.552830317800658
220.5450455521284540.9099088957430910.454954447871546
230.4887425642620120.9774851285240240.511257435737988
240.4429809817057020.8859619634114030.557019018294298
250.4363078001479450.8726156002958910.563692199852055
260.5101284500285580.9797430999428830.489871549971442
270.5440800339111480.9118399321777040.455919966088852
280.4774670154969340.9549340309938680.522532984503066
290.4371344229938460.8742688459876920.562865577006154
300.4241879710702570.8483759421405150.575812028929743
310.4353730451406070.8707460902812150.564626954859393
320.3990089769946680.7980179539893360.600991023005332
330.3933826507906590.7867653015813180.606617349209341
340.3542594937956760.7085189875913520.645740506204324
350.4084590555994480.8169181111988960.591540944400552
360.363618700477190.7272374009543790.63638129952281
370.3209769026931470.6419538053862930.679023097306853
380.3889527794763110.7779055589526220.611047220523689
390.3993340296253390.7986680592506780.600665970374661
400.3821555631859230.7643111263718470.617844436814077
410.3707738602861450.7415477205722910.629226139713855
420.3321946648560850.6643893297121690.667805335143915
430.4294279476755550.858855895351110.570572052324445
440.4171614558417590.8343229116835170.582838544158241
450.5325500502241640.9348998995516720.467449949775836
460.5255775646757510.9488448706484970.474422435324249
470.4844299887841690.9688599775683390.515570011215831
480.4742684039693050.948536807938610.525731596030695
490.4253143387379520.8506286774759040.574685661262048
500.4385818491659470.8771636983318930.561418150834053
510.3907170110398460.7814340220796920.609282988960154
520.3897925775518570.7795851551037150.610207422448143
530.4302762210042970.8605524420085950.569723778995702
540.3942615003724120.7885230007448240.605738499627588
550.3534685101683580.7069370203367150.646531489831642
560.4213454717794720.8426909435589440.578654528220528
570.38578343850160.7715668770031990.6142165614984
580.3744855806911710.7489711613823420.625514419308829
590.3766347548795350.7532695097590690.623365245120465
600.3812798795837770.7625597591675540.618720120416223
610.3462934174421680.6925868348843370.653706582557832
620.3678600007967060.7357200015934120.632139999203294
630.3590428206923570.7180856413847150.640957179307643
640.3349529648957160.6699059297914320.665047035104284
650.3197746986308610.6395493972617220.680225301369139
660.3309298296952210.6618596593904410.669070170304779
670.3017652644332780.6035305288665560.698234735566722
680.2793424791924920.5586849583849840.720657520807508
690.246231771669050.4924635433381010.75376822833095
700.3357668898709680.6715337797419350.664233110129032
710.3428195868693560.6856391737387110.657180413130644
720.3025752772873650.605150554574730.697424722712635
730.2642924031437820.5285848062875630.735707596856218
740.3685875158798160.7371750317596320.631412484120184
750.3521684680692230.7043369361384460.647831531930777
760.3556846742436780.7113693484873570.644315325756322
770.3150610642098220.6301221284196430.684938935790178
780.3000208169539360.6000416339078710.699979183046064
790.335482282170190.6709645643403810.66451771782981
800.3100290570242520.6200581140485040.689970942975748
810.3633984809131580.7267969618263170.636601519086841
820.4475039232360990.8950078464721980.552496076763901
830.428294294598410.856588589196820.57170570540159
840.4841792779505490.9683585559010980.515820722049451
850.4650584567082050.930116913416410.534941543291795
860.4669523817859180.9339047635718360.533047618214082
870.5326442313487510.9347115373024980.467355768651249
880.5130565366184110.9738869267631780.486943463381589
890.4914858144973920.9829716289947850.508514185502608
900.4872409743868150.974481948773630.512759025613185
910.4544832026373830.9089664052747650.545516797362617
920.4273273776482990.8546547552965980.572672622351701
930.3844439316036230.7688878632072460.615556068396377
940.3790232040490060.7580464080980120.620976795950994
950.4017287796397750.803457559279550.598271220360225
960.3801866987537530.7603733975075060.619813301246247
970.3568186957471650.713637391494330.643181304252835
980.3808584313079240.7617168626158470.619141568692076
990.4548110561341580.9096221122683160.545188943865842
1000.5014764575651660.9970470848696680.498523542434834
1010.4588840811218670.9177681622437350.541115918878133
1020.4978807386839120.9957614773678240.502119261316088
1030.4503527996853860.9007055993707730.549647200314613
1040.4182450968108680.8364901936217350.581754903189132
1050.3726498074971750.745299614994350.627350192502825
1060.3737166591704260.7474333183408510.626283340829574
1070.3421177388383740.6842354776767470.657882261161626
1080.4923272615408220.9846545230816450.507672738459178
1090.518925843308620.9621483133827610.48107415669138
1100.4851740274493830.9703480548987660.514825972550617
1110.4482378493511260.8964756987022510.551762150648874
1120.4332285967218880.8664571934437760.566771403278112
1130.3978084943166140.7956169886332280.602191505683386
1140.4481779354382950.896355870876590.551822064561705
1150.4087750919126370.8175501838252740.591224908087363
1160.4845164676338940.9690329352677890.515483532366106
1170.4344092276860740.8688184553721490.565590772313926
1180.3987516130325980.7975032260651960.601248386967402
1190.3996527707481440.7993055414962880.600347229251856
1200.3529808511347110.7059617022694220.647019148865289
1210.3285712050481420.6571424100962830.671428794951858
1220.3308500297720580.6617000595441160.669149970227942
1230.290276596033090.5805531920661790.70972340396691
1240.2584992944792760.5169985889585520.741500705520724
1250.2562441435609790.5124882871219570.743755856439021
1260.290401715047160.580803430094320.70959828495284
1270.3539655012101290.7079310024202580.646034498789871
1280.3199680528845780.6399361057691560.680031947115422
1290.2717219345847770.5434438691695540.728278065415223
1300.2536607534159030.5073215068318070.746339246584097
1310.2543932912081460.5087865824162920.745606708791854
1320.2255014213752470.4510028427504940.774498578624753
1330.1933361551924520.3866723103849050.806663844807548
1340.1824507581569240.3649015163138490.817549241843076
1350.2716497102676270.5432994205352530.728350289732373
1360.2491325748682130.4982651497364260.750867425131787
1370.2339144505428890.4678289010857780.766085549457111
1380.3207372315064780.6414744630129560.679262768493522
1390.353565984558090.7071319691161790.64643401544191
1400.3273068251765130.6546136503530250.672693174823487
1410.2773994591816260.5547989183632530.722600540818374
1420.2578624912900160.5157249825800310.742137508709984
1430.4219793720507870.8439587441015740.578020627949213
1440.431623339050730.8632466781014590.56837666094927
1450.5587898018463690.8824203963072610.441210198153631
1460.4822282163581780.9644564327163560.517771783641822
1470.3865458671131220.7730917342262450.613454132886878
1480.3022855963581140.6045711927162270.697714403641887
1490.2855951431704930.5711902863409850.714404856829507
1500.7129678036532840.5740643926934320.287032196346716
1510.6269819960655450.7460360078689090.373018003934455
1520.5626695103961840.8746609792076330.437330489603816

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.173341032038952 & 0.346682064077903 & 0.826658967961048 \tabularnewline
11 & 0.58289365708221 & 0.834212685835579 & 0.41710634291779 \tabularnewline
12 & 0.438291383620747 & 0.876582767241494 & 0.561708616379253 \tabularnewline
13 & 0.464319520025883 & 0.928639040051765 & 0.535680479974117 \tabularnewline
14 & 0.352662906170582 & 0.705325812341165 & 0.647337093829418 \tabularnewline
15 & 0.315111080767239 & 0.630222161534479 & 0.684888919232761 \tabularnewline
16 & 0.41371344485491 & 0.827426889709821 & 0.58628655514509 \tabularnewline
17 & 0.403550808892933 & 0.807101617785865 & 0.596449191107067 \tabularnewline
18 & 0.328754805384333 & 0.657509610768666 & 0.671245194615667 \tabularnewline
19 & 0.422978510894743 & 0.845957021789485 & 0.577021489105257 \tabularnewline
20 & 0.367637016957426 & 0.735274033914852 & 0.632362983042574 \tabularnewline
21 & 0.447169682199342 & 0.894339364398684 & 0.552830317800658 \tabularnewline
22 & 0.545045552128454 & 0.909908895743091 & 0.454954447871546 \tabularnewline
23 & 0.488742564262012 & 0.977485128524024 & 0.511257435737988 \tabularnewline
24 & 0.442980981705702 & 0.885961963411403 & 0.557019018294298 \tabularnewline
25 & 0.436307800147945 & 0.872615600295891 & 0.563692199852055 \tabularnewline
26 & 0.510128450028558 & 0.979743099942883 & 0.489871549971442 \tabularnewline
27 & 0.544080033911148 & 0.911839932177704 & 0.455919966088852 \tabularnewline
28 & 0.477467015496934 & 0.954934030993868 & 0.522532984503066 \tabularnewline
29 & 0.437134422993846 & 0.874268845987692 & 0.562865577006154 \tabularnewline
30 & 0.424187971070257 & 0.848375942140515 & 0.575812028929743 \tabularnewline
31 & 0.435373045140607 & 0.870746090281215 & 0.564626954859393 \tabularnewline
32 & 0.399008976994668 & 0.798017953989336 & 0.600991023005332 \tabularnewline
33 & 0.393382650790659 & 0.786765301581318 & 0.606617349209341 \tabularnewline
34 & 0.354259493795676 & 0.708518987591352 & 0.645740506204324 \tabularnewline
35 & 0.408459055599448 & 0.816918111198896 & 0.591540944400552 \tabularnewline
36 & 0.36361870047719 & 0.727237400954379 & 0.63638129952281 \tabularnewline
37 & 0.320976902693147 & 0.641953805386293 & 0.679023097306853 \tabularnewline
38 & 0.388952779476311 & 0.777905558952622 & 0.611047220523689 \tabularnewline
39 & 0.399334029625339 & 0.798668059250678 & 0.600665970374661 \tabularnewline
40 & 0.382155563185923 & 0.764311126371847 & 0.617844436814077 \tabularnewline
41 & 0.370773860286145 & 0.741547720572291 & 0.629226139713855 \tabularnewline
42 & 0.332194664856085 & 0.664389329712169 & 0.667805335143915 \tabularnewline
43 & 0.429427947675555 & 0.85885589535111 & 0.570572052324445 \tabularnewline
44 & 0.417161455841759 & 0.834322911683517 & 0.582838544158241 \tabularnewline
45 & 0.532550050224164 & 0.934899899551672 & 0.467449949775836 \tabularnewline
46 & 0.525577564675751 & 0.948844870648497 & 0.474422435324249 \tabularnewline
47 & 0.484429988784169 & 0.968859977568339 & 0.515570011215831 \tabularnewline
48 & 0.474268403969305 & 0.94853680793861 & 0.525731596030695 \tabularnewline
49 & 0.425314338737952 & 0.850628677475904 & 0.574685661262048 \tabularnewline
50 & 0.438581849165947 & 0.877163698331893 & 0.561418150834053 \tabularnewline
51 & 0.390717011039846 & 0.781434022079692 & 0.609282988960154 \tabularnewline
52 & 0.389792577551857 & 0.779585155103715 & 0.610207422448143 \tabularnewline
53 & 0.430276221004297 & 0.860552442008595 & 0.569723778995702 \tabularnewline
54 & 0.394261500372412 & 0.788523000744824 & 0.605738499627588 \tabularnewline
55 & 0.353468510168358 & 0.706937020336715 & 0.646531489831642 \tabularnewline
56 & 0.421345471779472 & 0.842690943558944 & 0.578654528220528 \tabularnewline
57 & 0.3857834385016 & 0.771566877003199 & 0.6142165614984 \tabularnewline
58 & 0.374485580691171 & 0.748971161382342 & 0.625514419308829 \tabularnewline
59 & 0.376634754879535 & 0.753269509759069 & 0.623365245120465 \tabularnewline
60 & 0.381279879583777 & 0.762559759167554 & 0.618720120416223 \tabularnewline
61 & 0.346293417442168 & 0.692586834884337 & 0.653706582557832 \tabularnewline
62 & 0.367860000796706 & 0.735720001593412 & 0.632139999203294 \tabularnewline
63 & 0.359042820692357 & 0.718085641384715 & 0.640957179307643 \tabularnewline
64 & 0.334952964895716 & 0.669905929791432 & 0.665047035104284 \tabularnewline
65 & 0.319774698630861 & 0.639549397261722 & 0.680225301369139 \tabularnewline
66 & 0.330929829695221 & 0.661859659390441 & 0.669070170304779 \tabularnewline
67 & 0.301765264433278 & 0.603530528866556 & 0.698234735566722 \tabularnewline
68 & 0.279342479192492 & 0.558684958384984 & 0.720657520807508 \tabularnewline
69 & 0.24623177166905 & 0.492463543338101 & 0.75376822833095 \tabularnewline
70 & 0.335766889870968 & 0.671533779741935 & 0.664233110129032 \tabularnewline
71 & 0.342819586869356 & 0.685639173738711 & 0.657180413130644 \tabularnewline
72 & 0.302575277287365 & 0.60515055457473 & 0.697424722712635 \tabularnewline
73 & 0.264292403143782 & 0.528584806287563 & 0.735707596856218 \tabularnewline
74 & 0.368587515879816 & 0.737175031759632 & 0.631412484120184 \tabularnewline
75 & 0.352168468069223 & 0.704336936138446 & 0.647831531930777 \tabularnewline
76 & 0.355684674243678 & 0.711369348487357 & 0.644315325756322 \tabularnewline
77 & 0.315061064209822 & 0.630122128419643 & 0.684938935790178 \tabularnewline
78 & 0.300020816953936 & 0.600041633907871 & 0.699979183046064 \tabularnewline
79 & 0.33548228217019 & 0.670964564340381 & 0.66451771782981 \tabularnewline
80 & 0.310029057024252 & 0.620058114048504 & 0.689970942975748 \tabularnewline
81 & 0.363398480913158 & 0.726796961826317 & 0.636601519086841 \tabularnewline
82 & 0.447503923236099 & 0.895007846472198 & 0.552496076763901 \tabularnewline
83 & 0.42829429459841 & 0.85658858919682 & 0.57170570540159 \tabularnewline
84 & 0.484179277950549 & 0.968358555901098 & 0.515820722049451 \tabularnewline
85 & 0.465058456708205 & 0.93011691341641 & 0.534941543291795 \tabularnewline
86 & 0.466952381785918 & 0.933904763571836 & 0.533047618214082 \tabularnewline
87 & 0.532644231348751 & 0.934711537302498 & 0.467355768651249 \tabularnewline
88 & 0.513056536618411 & 0.973886926763178 & 0.486943463381589 \tabularnewline
89 & 0.491485814497392 & 0.982971628994785 & 0.508514185502608 \tabularnewline
90 & 0.487240974386815 & 0.97448194877363 & 0.512759025613185 \tabularnewline
91 & 0.454483202637383 & 0.908966405274765 & 0.545516797362617 \tabularnewline
92 & 0.427327377648299 & 0.854654755296598 & 0.572672622351701 \tabularnewline
93 & 0.384443931603623 & 0.768887863207246 & 0.615556068396377 \tabularnewline
94 & 0.379023204049006 & 0.758046408098012 & 0.620976795950994 \tabularnewline
95 & 0.401728779639775 & 0.80345755927955 & 0.598271220360225 \tabularnewline
96 & 0.380186698753753 & 0.760373397507506 & 0.619813301246247 \tabularnewline
97 & 0.356818695747165 & 0.71363739149433 & 0.643181304252835 \tabularnewline
98 & 0.380858431307924 & 0.761716862615847 & 0.619141568692076 \tabularnewline
99 & 0.454811056134158 & 0.909622112268316 & 0.545188943865842 \tabularnewline
100 & 0.501476457565166 & 0.997047084869668 & 0.498523542434834 \tabularnewline
101 & 0.458884081121867 & 0.917768162243735 & 0.541115918878133 \tabularnewline
102 & 0.497880738683912 & 0.995761477367824 & 0.502119261316088 \tabularnewline
103 & 0.450352799685386 & 0.900705599370773 & 0.549647200314613 \tabularnewline
104 & 0.418245096810868 & 0.836490193621735 & 0.581754903189132 \tabularnewline
105 & 0.372649807497175 & 0.74529961499435 & 0.627350192502825 \tabularnewline
106 & 0.373716659170426 & 0.747433318340851 & 0.626283340829574 \tabularnewline
107 & 0.342117738838374 & 0.684235477676747 & 0.657882261161626 \tabularnewline
108 & 0.492327261540822 & 0.984654523081645 & 0.507672738459178 \tabularnewline
109 & 0.51892584330862 & 0.962148313382761 & 0.48107415669138 \tabularnewline
110 & 0.485174027449383 & 0.970348054898766 & 0.514825972550617 \tabularnewline
111 & 0.448237849351126 & 0.896475698702251 & 0.551762150648874 \tabularnewline
112 & 0.433228596721888 & 0.866457193443776 & 0.566771403278112 \tabularnewline
113 & 0.397808494316614 & 0.795616988633228 & 0.602191505683386 \tabularnewline
114 & 0.448177935438295 & 0.89635587087659 & 0.551822064561705 \tabularnewline
115 & 0.408775091912637 & 0.817550183825274 & 0.591224908087363 \tabularnewline
116 & 0.484516467633894 & 0.969032935267789 & 0.515483532366106 \tabularnewline
117 & 0.434409227686074 & 0.868818455372149 & 0.565590772313926 \tabularnewline
118 & 0.398751613032598 & 0.797503226065196 & 0.601248386967402 \tabularnewline
119 & 0.399652770748144 & 0.799305541496288 & 0.600347229251856 \tabularnewline
120 & 0.352980851134711 & 0.705961702269422 & 0.647019148865289 \tabularnewline
121 & 0.328571205048142 & 0.657142410096283 & 0.671428794951858 \tabularnewline
122 & 0.330850029772058 & 0.661700059544116 & 0.669149970227942 \tabularnewline
123 & 0.29027659603309 & 0.580553192066179 & 0.70972340396691 \tabularnewline
124 & 0.258499294479276 & 0.516998588958552 & 0.741500705520724 \tabularnewline
125 & 0.256244143560979 & 0.512488287121957 & 0.743755856439021 \tabularnewline
126 & 0.29040171504716 & 0.58080343009432 & 0.70959828495284 \tabularnewline
127 & 0.353965501210129 & 0.707931002420258 & 0.646034498789871 \tabularnewline
128 & 0.319968052884578 & 0.639936105769156 & 0.680031947115422 \tabularnewline
129 & 0.271721934584777 & 0.543443869169554 & 0.728278065415223 \tabularnewline
130 & 0.253660753415903 & 0.507321506831807 & 0.746339246584097 \tabularnewline
131 & 0.254393291208146 & 0.508786582416292 & 0.745606708791854 \tabularnewline
132 & 0.225501421375247 & 0.451002842750494 & 0.774498578624753 \tabularnewline
133 & 0.193336155192452 & 0.386672310384905 & 0.806663844807548 \tabularnewline
134 & 0.182450758156924 & 0.364901516313849 & 0.817549241843076 \tabularnewline
135 & 0.271649710267627 & 0.543299420535253 & 0.728350289732373 \tabularnewline
136 & 0.249132574868213 & 0.498265149736426 & 0.750867425131787 \tabularnewline
137 & 0.233914450542889 & 0.467828901085778 & 0.766085549457111 \tabularnewline
138 & 0.320737231506478 & 0.641474463012956 & 0.679262768493522 \tabularnewline
139 & 0.35356598455809 & 0.707131969116179 & 0.64643401544191 \tabularnewline
140 & 0.327306825176513 & 0.654613650353025 & 0.672693174823487 \tabularnewline
141 & 0.277399459181626 & 0.554798918363253 & 0.722600540818374 \tabularnewline
142 & 0.257862491290016 & 0.515724982580031 & 0.742137508709984 \tabularnewline
143 & 0.421979372050787 & 0.843958744101574 & 0.578020627949213 \tabularnewline
144 & 0.43162333905073 & 0.863246678101459 & 0.56837666094927 \tabularnewline
145 & 0.558789801846369 & 0.882420396307261 & 0.441210198153631 \tabularnewline
146 & 0.482228216358178 & 0.964456432716356 & 0.517771783641822 \tabularnewline
147 & 0.386545867113122 & 0.773091734226245 & 0.613454132886878 \tabularnewline
148 & 0.302285596358114 & 0.604571192716227 & 0.697714403641887 \tabularnewline
149 & 0.285595143170493 & 0.571190286340985 & 0.714404856829507 \tabularnewline
150 & 0.712967803653284 & 0.574064392693432 & 0.287032196346716 \tabularnewline
151 & 0.626981996065545 & 0.746036007868909 & 0.373018003934455 \tabularnewline
152 & 0.562669510396184 & 0.874660979207633 & 0.437330489603816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154586&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]10[/C][C]0.173341032038952[/C][C]0.346682064077903[/C][C]0.826658967961048[/C][/ROW]
[ROW][C]11[/C][C]0.58289365708221[/C][C]0.834212685835579[/C][C]0.41710634291779[/C][/ROW]
[ROW][C]12[/C][C]0.438291383620747[/C][C]0.876582767241494[/C][C]0.561708616379253[/C][/ROW]
[ROW][C]13[/C][C]0.464319520025883[/C][C]0.928639040051765[/C][C]0.535680479974117[/C][/ROW]
[ROW][C]14[/C][C]0.352662906170582[/C][C]0.705325812341165[/C][C]0.647337093829418[/C][/ROW]
[ROW][C]15[/C][C]0.315111080767239[/C][C]0.630222161534479[/C][C]0.684888919232761[/C][/ROW]
[ROW][C]16[/C][C]0.41371344485491[/C][C]0.827426889709821[/C][C]0.58628655514509[/C][/ROW]
[ROW][C]17[/C][C]0.403550808892933[/C][C]0.807101617785865[/C][C]0.596449191107067[/C][/ROW]
[ROW][C]18[/C][C]0.328754805384333[/C][C]0.657509610768666[/C][C]0.671245194615667[/C][/ROW]
[ROW][C]19[/C][C]0.422978510894743[/C][C]0.845957021789485[/C][C]0.577021489105257[/C][/ROW]
[ROW][C]20[/C][C]0.367637016957426[/C][C]0.735274033914852[/C][C]0.632362983042574[/C][/ROW]
[ROW][C]21[/C][C]0.447169682199342[/C][C]0.894339364398684[/C][C]0.552830317800658[/C][/ROW]
[ROW][C]22[/C][C]0.545045552128454[/C][C]0.909908895743091[/C][C]0.454954447871546[/C][/ROW]
[ROW][C]23[/C][C]0.488742564262012[/C][C]0.977485128524024[/C][C]0.511257435737988[/C][/ROW]
[ROW][C]24[/C][C]0.442980981705702[/C][C]0.885961963411403[/C][C]0.557019018294298[/C][/ROW]
[ROW][C]25[/C][C]0.436307800147945[/C][C]0.872615600295891[/C][C]0.563692199852055[/C][/ROW]
[ROW][C]26[/C][C]0.510128450028558[/C][C]0.979743099942883[/C][C]0.489871549971442[/C][/ROW]
[ROW][C]27[/C][C]0.544080033911148[/C][C]0.911839932177704[/C][C]0.455919966088852[/C][/ROW]
[ROW][C]28[/C][C]0.477467015496934[/C][C]0.954934030993868[/C][C]0.522532984503066[/C][/ROW]
[ROW][C]29[/C][C]0.437134422993846[/C][C]0.874268845987692[/C][C]0.562865577006154[/C][/ROW]
[ROW][C]30[/C][C]0.424187971070257[/C][C]0.848375942140515[/C][C]0.575812028929743[/C][/ROW]
[ROW][C]31[/C][C]0.435373045140607[/C][C]0.870746090281215[/C][C]0.564626954859393[/C][/ROW]
[ROW][C]32[/C][C]0.399008976994668[/C][C]0.798017953989336[/C][C]0.600991023005332[/C][/ROW]
[ROW][C]33[/C][C]0.393382650790659[/C][C]0.786765301581318[/C][C]0.606617349209341[/C][/ROW]
[ROW][C]34[/C][C]0.354259493795676[/C][C]0.708518987591352[/C][C]0.645740506204324[/C][/ROW]
[ROW][C]35[/C][C]0.408459055599448[/C][C]0.816918111198896[/C][C]0.591540944400552[/C][/ROW]
[ROW][C]36[/C][C]0.36361870047719[/C][C]0.727237400954379[/C][C]0.63638129952281[/C][/ROW]
[ROW][C]37[/C][C]0.320976902693147[/C][C]0.641953805386293[/C][C]0.679023097306853[/C][/ROW]
[ROW][C]38[/C][C]0.388952779476311[/C][C]0.777905558952622[/C][C]0.611047220523689[/C][/ROW]
[ROW][C]39[/C][C]0.399334029625339[/C][C]0.798668059250678[/C][C]0.600665970374661[/C][/ROW]
[ROW][C]40[/C][C]0.382155563185923[/C][C]0.764311126371847[/C][C]0.617844436814077[/C][/ROW]
[ROW][C]41[/C][C]0.370773860286145[/C][C]0.741547720572291[/C][C]0.629226139713855[/C][/ROW]
[ROW][C]42[/C][C]0.332194664856085[/C][C]0.664389329712169[/C][C]0.667805335143915[/C][/ROW]
[ROW][C]43[/C][C]0.429427947675555[/C][C]0.85885589535111[/C][C]0.570572052324445[/C][/ROW]
[ROW][C]44[/C][C]0.417161455841759[/C][C]0.834322911683517[/C][C]0.582838544158241[/C][/ROW]
[ROW][C]45[/C][C]0.532550050224164[/C][C]0.934899899551672[/C][C]0.467449949775836[/C][/ROW]
[ROW][C]46[/C][C]0.525577564675751[/C][C]0.948844870648497[/C][C]0.474422435324249[/C][/ROW]
[ROW][C]47[/C][C]0.484429988784169[/C][C]0.968859977568339[/C][C]0.515570011215831[/C][/ROW]
[ROW][C]48[/C][C]0.474268403969305[/C][C]0.94853680793861[/C][C]0.525731596030695[/C][/ROW]
[ROW][C]49[/C][C]0.425314338737952[/C][C]0.850628677475904[/C][C]0.574685661262048[/C][/ROW]
[ROW][C]50[/C][C]0.438581849165947[/C][C]0.877163698331893[/C][C]0.561418150834053[/C][/ROW]
[ROW][C]51[/C][C]0.390717011039846[/C][C]0.781434022079692[/C][C]0.609282988960154[/C][/ROW]
[ROW][C]52[/C][C]0.389792577551857[/C][C]0.779585155103715[/C][C]0.610207422448143[/C][/ROW]
[ROW][C]53[/C][C]0.430276221004297[/C][C]0.860552442008595[/C][C]0.569723778995702[/C][/ROW]
[ROW][C]54[/C][C]0.394261500372412[/C][C]0.788523000744824[/C][C]0.605738499627588[/C][/ROW]
[ROW][C]55[/C][C]0.353468510168358[/C][C]0.706937020336715[/C][C]0.646531489831642[/C][/ROW]
[ROW][C]56[/C][C]0.421345471779472[/C][C]0.842690943558944[/C][C]0.578654528220528[/C][/ROW]
[ROW][C]57[/C][C]0.3857834385016[/C][C]0.771566877003199[/C][C]0.6142165614984[/C][/ROW]
[ROW][C]58[/C][C]0.374485580691171[/C][C]0.748971161382342[/C][C]0.625514419308829[/C][/ROW]
[ROW][C]59[/C][C]0.376634754879535[/C][C]0.753269509759069[/C][C]0.623365245120465[/C][/ROW]
[ROW][C]60[/C][C]0.381279879583777[/C][C]0.762559759167554[/C][C]0.618720120416223[/C][/ROW]
[ROW][C]61[/C][C]0.346293417442168[/C][C]0.692586834884337[/C][C]0.653706582557832[/C][/ROW]
[ROW][C]62[/C][C]0.367860000796706[/C][C]0.735720001593412[/C][C]0.632139999203294[/C][/ROW]
[ROW][C]63[/C][C]0.359042820692357[/C][C]0.718085641384715[/C][C]0.640957179307643[/C][/ROW]
[ROW][C]64[/C][C]0.334952964895716[/C][C]0.669905929791432[/C][C]0.665047035104284[/C][/ROW]
[ROW][C]65[/C][C]0.319774698630861[/C][C]0.639549397261722[/C][C]0.680225301369139[/C][/ROW]
[ROW][C]66[/C][C]0.330929829695221[/C][C]0.661859659390441[/C][C]0.669070170304779[/C][/ROW]
[ROW][C]67[/C][C]0.301765264433278[/C][C]0.603530528866556[/C][C]0.698234735566722[/C][/ROW]
[ROW][C]68[/C][C]0.279342479192492[/C][C]0.558684958384984[/C][C]0.720657520807508[/C][/ROW]
[ROW][C]69[/C][C]0.24623177166905[/C][C]0.492463543338101[/C][C]0.75376822833095[/C][/ROW]
[ROW][C]70[/C][C]0.335766889870968[/C][C]0.671533779741935[/C][C]0.664233110129032[/C][/ROW]
[ROW][C]71[/C][C]0.342819586869356[/C][C]0.685639173738711[/C][C]0.657180413130644[/C][/ROW]
[ROW][C]72[/C][C]0.302575277287365[/C][C]0.60515055457473[/C][C]0.697424722712635[/C][/ROW]
[ROW][C]73[/C][C]0.264292403143782[/C][C]0.528584806287563[/C][C]0.735707596856218[/C][/ROW]
[ROW][C]74[/C][C]0.368587515879816[/C][C]0.737175031759632[/C][C]0.631412484120184[/C][/ROW]
[ROW][C]75[/C][C]0.352168468069223[/C][C]0.704336936138446[/C][C]0.647831531930777[/C][/ROW]
[ROW][C]76[/C][C]0.355684674243678[/C][C]0.711369348487357[/C][C]0.644315325756322[/C][/ROW]
[ROW][C]77[/C][C]0.315061064209822[/C][C]0.630122128419643[/C][C]0.684938935790178[/C][/ROW]
[ROW][C]78[/C][C]0.300020816953936[/C][C]0.600041633907871[/C][C]0.699979183046064[/C][/ROW]
[ROW][C]79[/C][C]0.33548228217019[/C][C]0.670964564340381[/C][C]0.66451771782981[/C][/ROW]
[ROW][C]80[/C][C]0.310029057024252[/C][C]0.620058114048504[/C][C]0.689970942975748[/C][/ROW]
[ROW][C]81[/C][C]0.363398480913158[/C][C]0.726796961826317[/C][C]0.636601519086841[/C][/ROW]
[ROW][C]82[/C][C]0.447503923236099[/C][C]0.895007846472198[/C][C]0.552496076763901[/C][/ROW]
[ROW][C]83[/C][C]0.42829429459841[/C][C]0.85658858919682[/C][C]0.57170570540159[/C][/ROW]
[ROW][C]84[/C][C]0.484179277950549[/C][C]0.968358555901098[/C][C]0.515820722049451[/C][/ROW]
[ROW][C]85[/C][C]0.465058456708205[/C][C]0.93011691341641[/C][C]0.534941543291795[/C][/ROW]
[ROW][C]86[/C][C]0.466952381785918[/C][C]0.933904763571836[/C][C]0.533047618214082[/C][/ROW]
[ROW][C]87[/C][C]0.532644231348751[/C][C]0.934711537302498[/C][C]0.467355768651249[/C][/ROW]
[ROW][C]88[/C][C]0.513056536618411[/C][C]0.973886926763178[/C][C]0.486943463381589[/C][/ROW]
[ROW][C]89[/C][C]0.491485814497392[/C][C]0.982971628994785[/C][C]0.508514185502608[/C][/ROW]
[ROW][C]90[/C][C]0.487240974386815[/C][C]0.97448194877363[/C][C]0.512759025613185[/C][/ROW]
[ROW][C]91[/C][C]0.454483202637383[/C][C]0.908966405274765[/C][C]0.545516797362617[/C][/ROW]
[ROW][C]92[/C][C]0.427327377648299[/C][C]0.854654755296598[/C][C]0.572672622351701[/C][/ROW]
[ROW][C]93[/C][C]0.384443931603623[/C][C]0.768887863207246[/C][C]0.615556068396377[/C][/ROW]
[ROW][C]94[/C][C]0.379023204049006[/C][C]0.758046408098012[/C][C]0.620976795950994[/C][/ROW]
[ROW][C]95[/C][C]0.401728779639775[/C][C]0.80345755927955[/C][C]0.598271220360225[/C][/ROW]
[ROW][C]96[/C][C]0.380186698753753[/C][C]0.760373397507506[/C][C]0.619813301246247[/C][/ROW]
[ROW][C]97[/C][C]0.356818695747165[/C][C]0.71363739149433[/C][C]0.643181304252835[/C][/ROW]
[ROW][C]98[/C][C]0.380858431307924[/C][C]0.761716862615847[/C][C]0.619141568692076[/C][/ROW]
[ROW][C]99[/C][C]0.454811056134158[/C][C]0.909622112268316[/C][C]0.545188943865842[/C][/ROW]
[ROW][C]100[/C][C]0.501476457565166[/C][C]0.997047084869668[/C][C]0.498523542434834[/C][/ROW]
[ROW][C]101[/C][C]0.458884081121867[/C][C]0.917768162243735[/C][C]0.541115918878133[/C][/ROW]
[ROW][C]102[/C][C]0.497880738683912[/C][C]0.995761477367824[/C][C]0.502119261316088[/C][/ROW]
[ROW][C]103[/C][C]0.450352799685386[/C][C]0.900705599370773[/C][C]0.549647200314613[/C][/ROW]
[ROW][C]104[/C][C]0.418245096810868[/C][C]0.836490193621735[/C][C]0.581754903189132[/C][/ROW]
[ROW][C]105[/C][C]0.372649807497175[/C][C]0.74529961499435[/C][C]0.627350192502825[/C][/ROW]
[ROW][C]106[/C][C]0.373716659170426[/C][C]0.747433318340851[/C][C]0.626283340829574[/C][/ROW]
[ROW][C]107[/C][C]0.342117738838374[/C][C]0.684235477676747[/C][C]0.657882261161626[/C][/ROW]
[ROW][C]108[/C][C]0.492327261540822[/C][C]0.984654523081645[/C][C]0.507672738459178[/C][/ROW]
[ROW][C]109[/C][C]0.51892584330862[/C][C]0.962148313382761[/C][C]0.48107415669138[/C][/ROW]
[ROW][C]110[/C][C]0.485174027449383[/C][C]0.970348054898766[/C][C]0.514825972550617[/C][/ROW]
[ROW][C]111[/C][C]0.448237849351126[/C][C]0.896475698702251[/C][C]0.551762150648874[/C][/ROW]
[ROW][C]112[/C][C]0.433228596721888[/C][C]0.866457193443776[/C][C]0.566771403278112[/C][/ROW]
[ROW][C]113[/C][C]0.397808494316614[/C][C]0.795616988633228[/C][C]0.602191505683386[/C][/ROW]
[ROW][C]114[/C][C]0.448177935438295[/C][C]0.89635587087659[/C][C]0.551822064561705[/C][/ROW]
[ROW][C]115[/C][C]0.408775091912637[/C][C]0.817550183825274[/C][C]0.591224908087363[/C][/ROW]
[ROW][C]116[/C][C]0.484516467633894[/C][C]0.969032935267789[/C][C]0.515483532366106[/C][/ROW]
[ROW][C]117[/C][C]0.434409227686074[/C][C]0.868818455372149[/C][C]0.565590772313926[/C][/ROW]
[ROW][C]118[/C][C]0.398751613032598[/C][C]0.797503226065196[/C][C]0.601248386967402[/C][/ROW]
[ROW][C]119[/C][C]0.399652770748144[/C][C]0.799305541496288[/C][C]0.600347229251856[/C][/ROW]
[ROW][C]120[/C][C]0.352980851134711[/C][C]0.705961702269422[/C][C]0.647019148865289[/C][/ROW]
[ROW][C]121[/C][C]0.328571205048142[/C][C]0.657142410096283[/C][C]0.671428794951858[/C][/ROW]
[ROW][C]122[/C][C]0.330850029772058[/C][C]0.661700059544116[/C][C]0.669149970227942[/C][/ROW]
[ROW][C]123[/C][C]0.29027659603309[/C][C]0.580553192066179[/C][C]0.70972340396691[/C][/ROW]
[ROW][C]124[/C][C]0.258499294479276[/C][C]0.516998588958552[/C][C]0.741500705520724[/C][/ROW]
[ROW][C]125[/C][C]0.256244143560979[/C][C]0.512488287121957[/C][C]0.743755856439021[/C][/ROW]
[ROW][C]126[/C][C]0.29040171504716[/C][C]0.58080343009432[/C][C]0.70959828495284[/C][/ROW]
[ROW][C]127[/C][C]0.353965501210129[/C][C]0.707931002420258[/C][C]0.646034498789871[/C][/ROW]
[ROW][C]128[/C][C]0.319968052884578[/C][C]0.639936105769156[/C][C]0.680031947115422[/C][/ROW]
[ROW][C]129[/C][C]0.271721934584777[/C][C]0.543443869169554[/C][C]0.728278065415223[/C][/ROW]
[ROW][C]130[/C][C]0.253660753415903[/C][C]0.507321506831807[/C][C]0.746339246584097[/C][/ROW]
[ROW][C]131[/C][C]0.254393291208146[/C][C]0.508786582416292[/C][C]0.745606708791854[/C][/ROW]
[ROW][C]132[/C][C]0.225501421375247[/C][C]0.451002842750494[/C][C]0.774498578624753[/C][/ROW]
[ROW][C]133[/C][C]0.193336155192452[/C][C]0.386672310384905[/C][C]0.806663844807548[/C][/ROW]
[ROW][C]134[/C][C]0.182450758156924[/C][C]0.364901516313849[/C][C]0.817549241843076[/C][/ROW]
[ROW][C]135[/C][C]0.271649710267627[/C][C]0.543299420535253[/C][C]0.728350289732373[/C][/ROW]
[ROW][C]136[/C][C]0.249132574868213[/C][C]0.498265149736426[/C][C]0.750867425131787[/C][/ROW]
[ROW][C]137[/C][C]0.233914450542889[/C][C]0.467828901085778[/C][C]0.766085549457111[/C][/ROW]
[ROW][C]138[/C][C]0.320737231506478[/C][C]0.641474463012956[/C][C]0.679262768493522[/C][/ROW]
[ROW][C]139[/C][C]0.35356598455809[/C][C]0.707131969116179[/C][C]0.64643401544191[/C][/ROW]
[ROW][C]140[/C][C]0.327306825176513[/C][C]0.654613650353025[/C][C]0.672693174823487[/C][/ROW]
[ROW][C]141[/C][C]0.277399459181626[/C][C]0.554798918363253[/C][C]0.722600540818374[/C][/ROW]
[ROW][C]142[/C][C]0.257862491290016[/C][C]0.515724982580031[/C][C]0.742137508709984[/C][/ROW]
[ROW][C]143[/C][C]0.421979372050787[/C][C]0.843958744101574[/C][C]0.578020627949213[/C][/ROW]
[ROW][C]144[/C][C]0.43162333905073[/C][C]0.863246678101459[/C][C]0.56837666094927[/C][/ROW]
[ROW][C]145[/C][C]0.558789801846369[/C][C]0.882420396307261[/C][C]0.441210198153631[/C][/ROW]
[ROW][C]146[/C][C]0.482228216358178[/C][C]0.964456432716356[/C][C]0.517771783641822[/C][/ROW]
[ROW][C]147[/C][C]0.386545867113122[/C][C]0.773091734226245[/C][C]0.613454132886878[/C][/ROW]
[ROW][C]148[/C][C]0.302285596358114[/C][C]0.604571192716227[/C][C]0.697714403641887[/C][/ROW]
[ROW][C]149[/C][C]0.285595143170493[/C][C]0.571190286340985[/C][C]0.714404856829507[/C][/ROW]
[ROW][C]150[/C][C]0.712967803653284[/C][C]0.574064392693432[/C][C]0.287032196346716[/C][/ROW]
[ROW][C]151[/C][C]0.626981996065545[/C][C]0.746036007868909[/C][C]0.373018003934455[/C][/ROW]
[ROW][C]152[/C][C]0.562669510396184[/C][C]0.874660979207633[/C][C]0.437330489603816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154586&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154586&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
100.1733410320389520.3466820640779030.826658967961048
110.582893657082210.8342126858355790.41710634291779
120.4382913836207470.8765827672414940.561708616379253
130.4643195200258830.9286390400517650.535680479974117
140.3526629061705820.7053258123411650.647337093829418
150.3151110807672390.6302221615344790.684888919232761
160.413713444854910.8274268897098210.58628655514509
170.4035508088929330.8071016177858650.596449191107067
180.3287548053843330.6575096107686660.671245194615667
190.4229785108947430.8459570217894850.577021489105257
200.3676370169574260.7352740339148520.632362983042574
210.4471696821993420.8943393643986840.552830317800658
220.5450455521284540.9099088957430910.454954447871546
230.4887425642620120.9774851285240240.511257435737988
240.4429809817057020.8859619634114030.557019018294298
250.4363078001479450.8726156002958910.563692199852055
260.5101284500285580.9797430999428830.489871549971442
270.5440800339111480.9118399321777040.455919966088852
280.4774670154969340.9549340309938680.522532984503066
290.4371344229938460.8742688459876920.562865577006154
300.4241879710702570.8483759421405150.575812028929743
310.4353730451406070.8707460902812150.564626954859393
320.3990089769946680.7980179539893360.600991023005332
330.3933826507906590.7867653015813180.606617349209341
340.3542594937956760.7085189875913520.645740506204324
350.4084590555994480.8169181111988960.591540944400552
360.363618700477190.7272374009543790.63638129952281
370.3209769026931470.6419538053862930.679023097306853
380.3889527794763110.7779055589526220.611047220523689
390.3993340296253390.7986680592506780.600665970374661
400.3821555631859230.7643111263718470.617844436814077
410.3707738602861450.7415477205722910.629226139713855
420.3321946648560850.6643893297121690.667805335143915
430.4294279476755550.858855895351110.570572052324445
440.4171614558417590.8343229116835170.582838544158241
450.5325500502241640.9348998995516720.467449949775836
460.5255775646757510.9488448706484970.474422435324249
470.4844299887841690.9688599775683390.515570011215831
480.4742684039693050.948536807938610.525731596030695
490.4253143387379520.8506286774759040.574685661262048
500.4385818491659470.8771636983318930.561418150834053
510.3907170110398460.7814340220796920.609282988960154
520.3897925775518570.7795851551037150.610207422448143
530.4302762210042970.8605524420085950.569723778995702
540.3942615003724120.7885230007448240.605738499627588
550.3534685101683580.7069370203367150.646531489831642
560.4213454717794720.8426909435589440.578654528220528
570.38578343850160.7715668770031990.6142165614984
580.3744855806911710.7489711613823420.625514419308829
590.3766347548795350.7532695097590690.623365245120465
600.3812798795837770.7625597591675540.618720120416223
610.3462934174421680.6925868348843370.653706582557832
620.3678600007967060.7357200015934120.632139999203294
630.3590428206923570.7180856413847150.640957179307643
640.3349529648957160.6699059297914320.665047035104284
650.3197746986308610.6395493972617220.680225301369139
660.3309298296952210.6618596593904410.669070170304779
670.3017652644332780.6035305288665560.698234735566722
680.2793424791924920.5586849583849840.720657520807508
690.246231771669050.4924635433381010.75376822833095
700.3357668898709680.6715337797419350.664233110129032
710.3428195868693560.6856391737387110.657180413130644
720.3025752772873650.605150554574730.697424722712635
730.2642924031437820.5285848062875630.735707596856218
740.3685875158798160.7371750317596320.631412484120184
750.3521684680692230.7043369361384460.647831531930777
760.3556846742436780.7113693484873570.644315325756322
770.3150610642098220.6301221284196430.684938935790178
780.3000208169539360.6000416339078710.699979183046064
790.335482282170190.6709645643403810.66451771782981
800.3100290570242520.6200581140485040.689970942975748
810.3633984809131580.7267969618263170.636601519086841
820.4475039232360990.8950078464721980.552496076763901
830.428294294598410.856588589196820.57170570540159
840.4841792779505490.9683585559010980.515820722049451
850.4650584567082050.930116913416410.534941543291795
860.4669523817859180.9339047635718360.533047618214082
870.5326442313487510.9347115373024980.467355768651249
880.5130565366184110.9738869267631780.486943463381589
890.4914858144973920.9829716289947850.508514185502608
900.4872409743868150.974481948773630.512759025613185
910.4544832026373830.9089664052747650.545516797362617
920.4273273776482990.8546547552965980.572672622351701
930.3844439316036230.7688878632072460.615556068396377
940.3790232040490060.7580464080980120.620976795950994
950.4017287796397750.803457559279550.598271220360225
960.3801866987537530.7603733975075060.619813301246247
970.3568186957471650.713637391494330.643181304252835
980.3808584313079240.7617168626158470.619141568692076
990.4548110561341580.9096221122683160.545188943865842
1000.5014764575651660.9970470848696680.498523542434834
1010.4588840811218670.9177681622437350.541115918878133
1020.4978807386839120.9957614773678240.502119261316088
1030.4503527996853860.9007055993707730.549647200314613
1040.4182450968108680.8364901936217350.581754903189132
1050.3726498074971750.745299614994350.627350192502825
1060.3737166591704260.7474333183408510.626283340829574
1070.3421177388383740.6842354776767470.657882261161626
1080.4923272615408220.9846545230816450.507672738459178
1090.518925843308620.9621483133827610.48107415669138
1100.4851740274493830.9703480548987660.514825972550617
1110.4482378493511260.8964756987022510.551762150648874
1120.4332285967218880.8664571934437760.566771403278112
1130.3978084943166140.7956169886332280.602191505683386
1140.4481779354382950.896355870876590.551822064561705
1150.4087750919126370.8175501838252740.591224908087363
1160.4845164676338940.9690329352677890.515483532366106
1170.4344092276860740.8688184553721490.565590772313926
1180.3987516130325980.7975032260651960.601248386967402
1190.3996527707481440.7993055414962880.600347229251856
1200.3529808511347110.7059617022694220.647019148865289
1210.3285712050481420.6571424100962830.671428794951858
1220.3308500297720580.6617000595441160.669149970227942
1230.290276596033090.5805531920661790.70972340396691
1240.2584992944792760.5169985889585520.741500705520724
1250.2562441435609790.5124882871219570.743755856439021
1260.290401715047160.580803430094320.70959828495284
1270.3539655012101290.7079310024202580.646034498789871
1280.3199680528845780.6399361057691560.680031947115422
1290.2717219345847770.5434438691695540.728278065415223
1300.2536607534159030.5073215068318070.746339246584097
1310.2543932912081460.5087865824162920.745606708791854
1320.2255014213752470.4510028427504940.774498578624753
1330.1933361551924520.3866723103849050.806663844807548
1340.1824507581569240.3649015163138490.817549241843076
1350.2716497102676270.5432994205352530.728350289732373
1360.2491325748682130.4982651497364260.750867425131787
1370.2339144505428890.4678289010857780.766085549457111
1380.3207372315064780.6414744630129560.679262768493522
1390.353565984558090.7071319691161790.64643401544191
1400.3273068251765130.6546136503530250.672693174823487
1410.2773994591816260.5547989183632530.722600540818374
1420.2578624912900160.5157249825800310.742137508709984
1430.4219793720507870.8439587441015740.578020627949213
1440.431623339050730.8632466781014590.56837666094927
1450.5587898018463690.8824203963072610.441210198153631
1460.4822282163581780.9644564327163560.517771783641822
1470.3865458671131220.7730917342262450.613454132886878
1480.3022855963581140.6045711927162270.697714403641887
1490.2855951431704930.5711902863409850.714404856829507
1500.7129678036532840.5740643926934320.287032196346716
1510.6269819960655450.7460360078689090.373018003934455
1520.5626695103961840.8746609792076330.437330489603816






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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154586&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154586&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154586&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 level00OK
10% type I error level00OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}