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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 computationThu, 17 Nov 2011 09:10:13 -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/Nov/17/t1321539052v6p9vslsed55iuy.htm/, Retrieved Sat, 20 Apr 2024 09:32:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=144663, Retrieved Sat, 20 Apr 2024 09:32:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-17 14:10:13] [87b6e955a128bfb8d1e350b3ce0d281e] [Current]
-   PD    [Multiple Regression] [workshop 7 mini t...] [2011-11-22 10:22:08] [a9dc51245fb8ca00f931d89893d090c8]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	41	38	13	12	14	12	53
5	39	32	16	11	18	11	86
5	30	35	19	15	11	14	66
5	31	33	15	6	12	12	67
8	34	37	14	13	16	21	76
6	35	29	13	10	18	12	78
5	39	31	19	12	14	22	53
6	34	36	15	14	14	11	80
5	36	35	14	12	15	10	74
4	37	38	15	6	15	13	76
6	38	31	16	10	17	10	79
5	36	34	16	12	19	8	54
5	38	35	16	12	10	15	67
6	39	38	16	11	16	14	54
7	33	37	17	15	18	10	87
6	32	33	15	12	14	14	58
7	36	32	15	10	14	14	75
6	38	38	20	12	17	11	88
8	39	38	18	11	14	10	64
7	32	32	16	12	16	13	57
5	32	33	16	11	18	7	66
5	31	31	16	12	11	14	68
7	39	38	19	13	14	12	54
7	37	39	16	11	12	14	56
5	39	32	17	9	17	11	86
4	41	32	17	13	9	9	80
10	36	35	16	10	16	11	76
6	33	37	15	14	14	15	69
5	33	33	16	12	15	14	78
5	34	33	14	10	11	13	67
5	31	28	15	12	16	9	80
5	27	32	12	8	13	15	54
6	37	31	14	10	17	10	71
5	34	37	16	12	15	11	84
5	34	30	14	12	14	13	74
5	32	33	7	7	16	8	71
5	29	31	10	6	9	20	63
5	36	33	14	12	15	12	71
5	29	31	16	10	17	10	76
5	35	33	16	10	13	10	69
5	37	32	16	10	15	9	74
7	34	33	14	12	16	14	75
5	38	32	20	15	16	8	54
6	35	33	14	10	12	14	52
7	38	28	14	10	12	11	69
7	37	35	11	12	11	13	68
5	38	39	14	13	15	9	65
5	33	34	15	11	15	11	75
4	36	38	16	11	17	15	74
5	38	32	14	12	13	11	75
4	32	38	16	14	16	10	72
5	32	30	14	10	14	14	67
5	32	33	12	12	11	18	63
7	34	38	16	13	12	14	62
5	32	32	9	5	12	11	63
5	37	32	14	6	15	12	76
6	39	34	16	12	16	13	74
4	29	34	16	12	15	9	67
6	37	36	15	11	12	10	73
6	35	34	16	10	12	15	70
5	30	28	12	7	8	20	53
7	38	34	16	12	13	12	77
6	34	35	16	14	11	12	77
8	31	35	14	11	14	14	52
7	34	31	16	12	15	13	54
5	35	37	17	13	10	11	80
6	36	35	18	14	11	17	66
6	30	27	18	11	12	12	73
5	39	40	12	12	15	13	63
5	35	37	16	12	15	14	69
5	38	36	10	8	14	13	67
5	31	38	14	11	16	15	54
4	34	39	18	14	15	13	81
6	38	41	18	14	15	10	69
6	34	27	16	12	13	11	84
6	39	30	17	9	12	19	80
6	37	37	16	13	17	13	70
7	34	31	16	11	13	17	69
5	28	31	13	12	15	13	77
7	37	27	16	12	13	9	54
6	33	36	16	12	15	11	79
5	37	38	20	12	16	10	30
5	35	37	16	12	15	9	71
4	37	33	15	12	16	12	73
8	32	34	15	11	15	12	72
8	33	31	16	10	14	13	77
5	38	39	14	9	15	13	75
5	33	34	16	12	14	12	69
6	29	32	16	12	13	15	54
4	33	33	15	12	7	22	70
5	31	36	12	9	17	13	73
5	36	32	17	15	13	15	54
5	35	41	16	12	15	13	77
5	32	28	15	12	14	15	82
6	29	30	13	12	13	10	80
6	39	36	16	10	16	11	80
5	37	35	16	13	12	16	69
6	35	31	16	9	14	11	78
5	37	34	16	12	17	11	81
7	32	36	14	10	15	10	76
5	38	36	16	14	17	10	76
6	37	35	16	11	12	16	73
6	36	37	20	15	16	12	85
6	32	28	15	11	11	11	66
4	33	39	16	11	15	16	79
5	40	32	13	12	9	19	68
5	38	35	17	12	16	11	76
7	41	39	16	12	15	16	71
6	36	35	16	11	10	15	54
9	43	42	12	7	10	24	46
6	30	34	16	12	15	14	82
6	31	33	16	14	11	15	74
5	32	41	17	11	13	11	88
6	32	33	13	11	14	15	38
5	37	34	12	10	18	12	76
8	37	32	18	13	16	10	86
7	33	40	14	13	14	14	54
5	34	40	14	8	14	13	70
7	33	35	13	11	14	9	69
6	38	36	16	12	14	15	90
6	33	37	13	11	12	15	54
9	31	27	16	13	14	14	76
7	38	39	13	12	15	11	89
6	37	38	16	14	15	8	76
5	33	31	15	13	15	11	73
5	31	33	16	15	13	11	79
6	39	32	15	10	17	8	90
6	44	39	17	11	17	10	74
7	33	36	15	9	19	11	81
5	35	33	12	11	15	13	72
5	32	33	16	10	13	11	71
5	28	32	10	11	9	20	66
6	40	37	16	8	15	10	77
4	27	30	12	11	15	15	65
5	37	38	14	12	15	12	74
7	32	29	15	12	16	14	82
5	28	22	13	9	11	23	54
7	34	35	15	11	14	14	63
7	30	35	11	10	11	16	54
6	35	34	12	8	15	11	64
5	31	35	8	9	13	12	69
8	32	34	16	8	15	10	54
5	30	34	15	9	16	14	84
5	30	35	17	15	14	12	86
5	31	23	16	11	15	12	77
6	40	31	10	8	16	11	89
4	32	27	18	13	16	12	76
5	36	36	13	12	11	13	60
5	32	31	16	12	12	11	75
7	35	32	13	9	9	19	73
6	38	39	10	7	16	12	85
7	42	37	15	13	13	17	79
10	34	38	16	9	16	9	71
6	35	39	16	6	12	12	72
8	35	34	14	8	9	19	69
4	33	31	10	8	13	18	78
5	36	32	17	15	13	15	54
6	32	37	13	6	14	14	69
7	33	36	15	9	19	11	81
7	34	32	16	11	13	9	84
6	32	35	12	8	12	18	84

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144663&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144663&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144663&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
belonging[t] = + 68.679142391371 -0.639613029996487age[t] + 0.229187055817126connected[t] -0.261027129920707separated[t] + 0.0734088904386086learning[t] -0.0405829510525371software[t] + 0.92488985048133hapiness[t] -0.537478633913233depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
belonging[t] =  +  68.679142391371 -0.639613029996487age[t] +  0.229187055817126connected[t] -0.261027129920707separated[t] +  0.0734088904386086learning[t] -0.0405829510525371software[t] +  0.92488985048133hapiness[t] -0.537478633913233depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144663&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]belonging[t] =  +  68.679142391371 -0.639613029996487age[t] +  0.229187055817126connected[t] -0.261027129920707separated[t] +  0.0734088904386086learning[t] -0.0405829510525371software[t] +  0.92488985048133hapiness[t] -0.537478633913233depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144663&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144663&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
belonging[t] = + 68.679142391371 -0.639613029996487age[t] + 0.229187055817126connected[t] -0.261027129920707separated[t] + 0.0734088904386086learning[t] -0.0405829510525371software[t] + 0.92488985048133hapiness[t] -0.537478633913233depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)68.67914239137113.8367874.96352e-061e-06
age-0.6396130299964870.716796-0.89230.3736220.186811
connected0.2291870558171260.2683580.8540.3944210.197211
separated-0.2610271299207070.249997-1.04410.2980750.149038
learning0.07340889043860860.4512510.16270.8709860.435493
software-0.04058295105253710.46097-0.0880.9299620.464981
hapiness0.924889850481330.4212552.19560.029630.014815
depression-0.5374786339132330.312516-1.71980.0874830.043742

\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) & 68.679142391371 & 13.836787 & 4.9635 & 2e-06 & 1e-06 \tabularnewline
age & -0.639613029996487 & 0.716796 & -0.8923 & 0.373622 & 0.186811 \tabularnewline
connected & 0.229187055817126 & 0.268358 & 0.854 & 0.394421 & 0.197211 \tabularnewline
separated & -0.261027129920707 & 0.249997 & -1.0441 & 0.298075 & 0.149038 \tabularnewline
learning & 0.0734088904386086 & 0.451251 & 0.1627 & 0.870986 & 0.435493 \tabularnewline
software & -0.0405829510525371 & 0.46097 & -0.088 & 0.929962 & 0.464981 \tabularnewline
hapiness & 0.92488985048133 & 0.421255 & 2.1956 & 0.02963 & 0.014815 \tabularnewline
depression & -0.537478633913233 & 0.312516 & -1.7198 & 0.087483 & 0.043742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144663&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]68.679142391371[/C][C]13.836787[/C][C]4.9635[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]age[/C][C]-0.639613029996487[/C][C]0.716796[/C][C]-0.8923[/C][C]0.373622[/C][C]0.186811[/C][/ROW]
[ROW][C]connected[/C][C]0.229187055817126[/C][C]0.268358[/C][C]0.854[/C][C]0.394421[/C][C]0.197211[/C][/ROW]
[ROW][C]separated[/C][C]-0.261027129920707[/C][C]0.249997[/C][C]-1.0441[/C][C]0.298075[/C][C]0.149038[/C][/ROW]
[ROW][C]learning[/C][C]0.0734088904386086[/C][C]0.451251[/C][C]0.1627[/C][C]0.870986[/C][C]0.435493[/C][/ROW]
[ROW][C]software[/C][C]-0.0405829510525371[/C][C]0.46097[/C][C]-0.088[/C][C]0.929962[/C][C]0.464981[/C][/ROW]
[ROW][C]hapiness[/C][C]0.92488985048133[/C][C]0.421255[/C][C]2.1956[/C][C]0.02963[/C][C]0.014815[/C][/ROW]
[ROW][C]depression[/C][C]-0.537478633913233[/C][C]0.312516[/C][C]-1.7198[/C][C]0.087483[/C][C]0.043742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144663&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144663&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)68.67914239137113.8367874.96352e-061e-06
age-0.6396130299964870.716796-0.89230.3736220.186811
connected0.2291870558171260.2683580.8540.3944210.197211
separated-0.2610271299207070.249997-1.04410.2980750.149038
learning0.07340889043860860.4512510.16270.8709860.435493
software-0.04058295105253710.46097-0.0880.9299620.464981
hapiness0.924889850481330.4212552.19560.029630.014815
depression-0.5374786339132330.312516-1.71980.0874830.043742






Multiple Linear Regression - Regression Statistics
Multiple R0.33807999721712
R-squared0.114298084518328
Adjusted R-squared0.0737757746596895
F-TEST (value)2.82062115701339
F-TEST (DF numerator)7
F-TEST (DF denominator)153
p-value0.00860693625553599
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.3515704156854
Sum Squared Residuals16394.7165408468

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.33807999721712 \tabularnewline
R-squared & 0.114298084518328 \tabularnewline
Adjusted R-squared & 0.0737757746596895 \tabularnewline
F-TEST (value) & 2.82062115701339 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 0.00860693625553599 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.3515704156854 \tabularnewline
Sum Squared Residuals & 16394.7165408468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144663&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.33807999721712[/C][/ROW]
[ROW][C]R-squared[/C][C]0.114298084518328[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0737757746596895[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.82062115701339[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]0.00860693625553599[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.3515704156854[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16394.7165408468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144663&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144663&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 R0.33807999721712
R-squared0.114298084518328
Adjusted R-squared0.0737757746596895
F-TEST (value)2.82062115701339
F-TEST (DF numerator)7
F-TEST (DF denominator)153
p-value0.00860693625553599
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.3515704156854
Sum Squared Residuals16394.7165408468






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15370.6455239957622-17.6455239957622
28677.5303863818528.46961361814793
36666.6558515017325-0.655851501732471
46769.4785509334172-2.47855093341721
57665.707926640096110.2920733599039
67876.03998416417271.96001583582733
75368.3592328570652-15.3592328570652
88070.80601240756569.19398759243442
97473.6351521751780.364847824821947
107672.42534156624373.57465843375632
117976.57578516044362.4242148395564
125478.8175137557278-24.8175137557278
136766.92850164571670.0714983542832962
145471.862394969629-17.862394969629
158774.019458057494512.9805419425055
165869.5994496860588-11.5994496860588
177570.21877791135664.78122208864338
188874.423586276734813.5764137232652
196471.0301215252035-7.03012152520349
205771.6815310112976-14.6815310112975
216677.8149643968644-11.8149643968644
226867.83066925907420.169330740925788
235470.587020275707-16.587020275707
245666.8038212961522-10.8038212961522
258676.76007132391449.23992867608558
268071.37156512531088.62843487468917
277671.0524819247464.94751807525399
286968.16588368617480.834116313825191
297871.46654851279246.53345148720763
306768.4680029218253-1.46800292182527
318075.85218418037054.14781581962946
325467.8338912153901-13.8338912153901
337176.1997803237493-5.19978032374926
348472.264062950666411.7359370493336
357471.94458796092632.05541203907369
367174.9293578522512-3.92935785225118
376362.10068800668150.899311993318532
387173.082249167193-2.082249167193
397675.1527146880860.84728531191405
406972.306223361222-3.30622336122197
417475.4128829376528-1.41288293765283
427571.19458157822063.80541842177936
435477.1951592843563-23.1951592843563
445268.444988164214-16.444988164214
456971.4105078530121-2.41050785301213
466867.05289119602140.947108803978643
476573.5463134499902-8.54631344999017
487572.78513134522532.21486865477471
497472.84147107873871.15852892126133
507572.48934934169852.51065065830147
517273.5654773213974-1.56547732139739
526771.0299011174839-4.02990111748389
536365.0942519576426-2.09425195764256
546266.2961213565165-4.29612135651646
556370.1063733614892-7.10637336148916
567673.8159610592462.18403894075394
577473.40339917217250.5966008278275
586773.6157793591658-6.61577935916581
597370.30302136112512.69697863887486
607067.79330018125732.20669981874272
615362.2943014316041-9.29430143160407
627770.29740816882816.70259183117187
637767.82830024256779.17169975743233
645267.5361563710212-15.5361563710212
655471.4760424023712-17.4760424023712
668067.901626693462912.0983733065371
676665.7460989655130.253901034487028
687370.1932255431812.80677445681902
696371.258324010409-8.25832401040898
706970.8808141047438-1.8808141047438
716771.1638696471263-4.16386964712628
725469.984215138298-15.984215138298
738171.37231633176719.62768366823288
746972.100220136941-3.10022013694094
758472.384941518914311.6150584810857
768067.718234230046812.2817657699532
777073.046250570205-3.04625057020492
786967.51693111680811.48306888319189
797771.15991945614565.84008054385444
805473.5078469241956-19.5078469241957
817971.65628999477357.34371000522653
823074.446601034346-44.446601034346
837173.56820727431-2.56820727430996
847374.9493479939266-1.94934799392655
857270.09962656550551.90037343449453
867769.76351836818137.2364816318187
877571.55873071854743.44126928145261
886971.3555888002168-2.3555888002168
895467.7839560545722-13.7839560545722
907060.33380477719379.66619522280626
917372.4404646376750.559535362324932
925469.9795385125696-15.9795385125696
937770.37418421897426.6258157810258
948271.006719731745610.9932802682544
958070.7731768126649.22682318733605
968074.03746808226265.96253191773737
976967.97103270589651.02896729410353
987872.61665875868755.38334124131247
998175.58448520884255.41551479115747
1007671.25931666410094.74068333589906
1017675.74793073662640.252069263373597
1027367.41258557800515.58741442199494
1038572.64212195746912.3578780425309
1046669.7829346370106-3.7829346370106
1057969.50562444649079.49437555350931
1066864.8749260896633.12507391033701
1077674.70116417469621.29883582530377
1087169.37969885198571.6203011480143
1095465.8710974551385-11.8710974551385
1104658.7607663836606-12.7607663836606
1118269.878347185423812.1216528145762
1127466.0503574332187.949642566782
1138869.025792459877818.9742075401222
1143868.9557362223209-30.9557362223209
1157675.75942676576130.240573234238734
1168673.90652399195112.0934760080489
1175467.2478419609434-13.2478419609434
1187069.49664846592940.503351534070617
1196971.2481277917795-2.24812779177953
1209069.727420887724820.2725791122752
1215466.2910350574925-12.2910350574925
1227669.05041225916266.94958774083745
1238971.159304182784717.8406958172153
1247673.58225395783522.41774604216482
1257373.4870468328823-0.48704683288234
1267970.64908174877758.35091825122245
1279077.545493463727912.4545065362721
1287475.8955163953668-1.89551639536677
1298174.76457632942136.23542367057869
1307272.209348647638-0.20934864763796
1317171.0811835598574-0.0811835598573597
1326661.40755906568094.59244093431905
1337773.6993826936963.30061730630398
1346570.7235893530331-5.72358935303309
1357472.00630057340661.99369942659341
1368271.853724876707810.1462751232922
1375464.5565667375319-10.5565667375319
1386368.9367394589077-5.93673945890771
1395463.9173118056668-9.91731180566685
1406472.5054146087048-8.50541460870484
1416969.2457754378293-0.245775437829252
1425471.3697415769282-17.3697415769282
1438471.491190028620612.5088099713794
1448670.358660540125715.6413394598743
1457774.73398591924422.2660140807558
1468975.212503347156713.7874966528433
1477675.54921921462850.450780785371546
1486067.9887208511537-7.98872085115372
1497570.59718206711244.40281793288763
1507362.57151360374210.428486396258
1518572.169016513296112.8309834867039
1527967.629689991277211.3703100087228
1537170.92656664222860.0734333577713639
1547268.26293223760363.73706776239641
1556961.52383815539527.47616184460476
1567868.35040002759329.64959997240682
1575469.9795385125696-15.9795385125696
1586968.6520210918140.347978908186004
1598174.76457632942136.23542367057869
1608471.555733058193312.4442669418067
1618465.019806322496118.9801936775039

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 53 & 70.6455239957622 & -17.6455239957622 \tabularnewline
2 & 86 & 77.530386381852 & 8.46961361814793 \tabularnewline
3 & 66 & 66.6558515017325 & -0.655851501732471 \tabularnewline
4 & 67 & 69.4785509334172 & -2.47855093341721 \tabularnewline
5 & 76 & 65.7079266400961 & 10.2920733599039 \tabularnewline
6 & 78 & 76.0399841641727 & 1.96001583582733 \tabularnewline
7 & 53 & 68.3592328570652 & -15.3592328570652 \tabularnewline
8 & 80 & 70.8060124075656 & 9.19398759243442 \tabularnewline
9 & 74 & 73.635152175178 & 0.364847824821947 \tabularnewline
10 & 76 & 72.4253415662437 & 3.57465843375632 \tabularnewline
11 & 79 & 76.5757851604436 & 2.4242148395564 \tabularnewline
12 & 54 & 78.8175137557278 & -24.8175137557278 \tabularnewline
13 & 67 & 66.9285016457167 & 0.0714983542832962 \tabularnewline
14 & 54 & 71.862394969629 & -17.862394969629 \tabularnewline
15 & 87 & 74.0194580574945 & 12.9805419425055 \tabularnewline
16 & 58 & 69.5994496860588 & -11.5994496860588 \tabularnewline
17 & 75 & 70.2187779113566 & 4.78122208864338 \tabularnewline
18 & 88 & 74.4235862767348 & 13.5764137232652 \tabularnewline
19 & 64 & 71.0301215252035 & -7.03012152520349 \tabularnewline
20 & 57 & 71.6815310112976 & -14.6815310112975 \tabularnewline
21 & 66 & 77.8149643968644 & -11.8149643968644 \tabularnewline
22 & 68 & 67.8306692590742 & 0.169330740925788 \tabularnewline
23 & 54 & 70.587020275707 & -16.587020275707 \tabularnewline
24 & 56 & 66.8038212961522 & -10.8038212961522 \tabularnewline
25 & 86 & 76.7600713239144 & 9.23992867608558 \tabularnewline
26 & 80 & 71.3715651253108 & 8.62843487468917 \tabularnewline
27 & 76 & 71.052481924746 & 4.94751807525399 \tabularnewline
28 & 69 & 68.1658836861748 & 0.834116313825191 \tabularnewline
29 & 78 & 71.4665485127924 & 6.53345148720763 \tabularnewline
30 & 67 & 68.4680029218253 & -1.46800292182527 \tabularnewline
31 & 80 & 75.8521841803705 & 4.14781581962946 \tabularnewline
32 & 54 & 67.8338912153901 & -13.8338912153901 \tabularnewline
33 & 71 & 76.1997803237493 & -5.19978032374926 \tabularnewline
34 & 84 & 72.2640629506664 & 11.7359370493336 \tabularnewline
35 & 74 & 71.9445879609263 & 2.05541203907369 \tabularnewline
36 & 71 & 74.9293578522512 & -3.92935785225118 \tabularnewline
37 & 63 & 62.1006880066815 & 0.899311993318532 \tabularnewline
38 & 71 & 73.082249167193 & -2.082249167193 \tabularnewline
39 & 76 & 75.152714688086 & 0.84728531191405 \tabularnewline
40 & 69 & 72.306223361222 & -3.30622336122197 \tabularnewline
41 & 74 & 75.4128829376528 & -1.41288293765283 \tabularnewline
42 & 75 & 71.1945815782206 & 3.80541842177936 \tabularnewline
43 & 54 & 77.1951592843563 & -23.1951592843563 \tabularnewline
44 & 52 & 68.444988164214 & -16.444988164214 \tabularnewline
45 & 69 & 71.4105078530121 & -2.41050785301213 \tabularnewline
46 & 68 & 67.0528911960214 & 0.947108803978643 \tabularnewline
47 & 65 & 73.5463134499902 & -8.54631344999017 \tabularnewline
48 & 75 & 72.7851313452253 & 2.21486865477471 \tabularnewline
49 & 74 & 72.8414710787387 & 1.15852892126133 \tabularnewline
50 & 75 & 72.4893493416985 & 2.51065065830147 \tabularnewline
51 & 72 & 73.5654773213974 & -1.56547732139739 \tabularnewline
52 & 67 & 71.0299011174839 & -4.02990111748389 \tabularnewline
53 & 63 & 65.0942519576426 & -2.09425195764256 \tabularnewline
54 & 62 & 66.2961213565165 & -4.29612135651646 \tabularnewline
55 & 63 & 70.1063733614892 & -7.10637336148916 \tabularnewline
56 & 76 & 73.815961059246 & 2.18403894075394 \tabularnewline
57 & 74 & 73.4033991721725 & 0.5966008278275 \tabularnewline
58 & 67 & 73.6157793591658 & -6.61577935916581 \tabularnewline
59 & 73 & 70.3030213611251 & 2.69697863887486 \tabularnewline
60 & 70 & 67.7933001812573 & 2.20669981874272 \tabularnewline
61 & 53 & 62.2943014316041 & -9.29430143160407 \tabularnewline
62 & 77 & 70.2974081688281 & 6.70259183117187 \tabularnewline
63 & 77 & 67.8283002425677 & 9.17169975743233 \tabularnewline
64 & 52 & 67.5361563710212 & -15.5361563710212 \tabularnewline
65 & 54 & 71.4760424023712 & -17.4760424023712 \tabularnewline
66 & 80 & 67.9016266934629 & 12.0983733065371 \tabularnewline
67 & 66 & 65.746098965513 & 0.253901034487028 \tabularnewline
68 & 73 & 70.193225543181 & 2.80677445681902 \tabularnewline
69 & 63 & 71.258324010409 & -8.25832401040898 \tabularnewline
70 & 69 & 70.8808141047438 & -1.8808141047438 \tabularnewline
71 & 67 & 71.1638696471263 & -4.16386964712628 \tabularnewline
72 & 54 & 69.984215138298 & -15.984215138298 \tabularnewline
73 & 81 & 71.3723163317671 & 9.62768366823288 \tabularnewline
74 & 69 & 72.100220136941 & -3.10022013694094 \tabularnewline
75 & 84 & 72.3849415189143 & 11.6150584810857 \tabularnewline
76 & 80 & 67.7182342300468 & 12.2817657699532 \tabularnewline
77 & 70 & 73.046250570205 & -3.04625057020492 \tabularnewline
78 & 69 & 67.5169311168081 & 1.48306888319189 \tabularnewline
79 & 77 & 71.1599194561456 & 5.84008054385444 \tabularnewline
80 & 54 & 73.5078469241956 & -19.5078469241957 \tabularnewline
81 & 79 & 71.6562899947735 & 7.34371000522653 \tabularnewline
82 & 30 & 74.446601034346 & -44.446601034346 \tabularnewline
83 & 71 & 73.56820727431 & -2.56820727430996 \tabularnewline
84 & 73 & 74.9493479939266 & -1.94934799392655 \tabularnewline
85 & 72 & 70.0996265655055 & 1.90037343449453 \tabularnewline
86 & 77 & 69.7635183681813 & 7.2364816318187 \tabularnewline
87 & 75 & 71.5587307185474 & 3.44126928145261 \tabularnewline
88 & 69 & 71.3555888002168 & -2.3555888002168 \tabularnewline
89 & 54 & 67.7839560545722 & -13.7839560545722 \tabularnewline
90 & 70 & 60.3338047771937 & 9.66619522280626 \tabularnewline
91 & 73 & 72.440464637675 & 0.559535362324932 \tabularnewline
92 & 54 & 69.9795385125696 & -15.9795385125696 \tabularnewline
93 & 77 & 70.3741842189742 & 6.6258157810258 \tabularnewline
94 & 82 & 71.0067197317456 & 10.9932802682544 \tabularnewline
95 & 80 & 70.773176812664 & 9.22682318733605 \tabularnewline
96 & 80 & 74.0374680822626 & 5.96253191773737 \tabularnewline
97 & 69 & 67.9710327058965 & 1.02896729410353 \tabularnewline
98 & 78 & 72.6166587586875 & 5.38334124131247 \tabularnewline
99 & 81 & 75.5844852088425 & 5.41551479115747 \tabularnewline
100 & 76 & 71.2593166641009 & 4.74068333589906 \tabularnewline
101 & 76 & 75.7479307366264 & 0.252069263373597 \tabularnewline
102 & 73 & 67.4125855780051 & 5.58741442199494 \tabularnewline
103 & 85 & 72.642121957469 & 12.3578780425309 \tabularnewline
104 & 66 & 69.7829346370106 & -3.7829346370106 \tabularnewline
105 & 79 & 69.5056244464907 & 9.49437555350931 \tabularnewline
106 & 68 & 64.874926089663 & 3.12507391033701 \tabularnewline
107 & 76 & 74.7011641746962 & 1.29883582530377 \tabularnewline
108 & 71 & 69.3796988519857 & 1.6203011480143 \tabularnewline
109 & 54 & 65.8710974551385 & -11.8710974551385 \tabularnewline
110 & 46 & 58.7607663836606 & -12.7607663836606 \tabularnewline
111 & 82 & 69.8783471854238 & 12.1216528145762 \tabularnewline
112 & 74 & 66.050357433218 & 7.949642566782 \tabularnewline
113 & 88 & 69.0257924598778 & 18.9742075401222 \tabularnewline
114 & 38 & 68.9557362223209 & -30.9557362223209 \tabularnewline
115 & 76 & 75.7594267657613 & 0.240573234238734 \tabularnewline
116 & 86 & 73.906523991951 & 12.0934760080489 \tabularnewline
117 & 54 & 67.2478419609434 & -13.2478419609434 \tabularnewline
118 & 70 & 69.4966484659294 & 0.503351534070617 \tabularnewline
119 & 69 & 71.2481277917795 & -2.24812779177953 \tabularnewline
120 & 90 & 69.7274208877248 & 20.2725791122752 \tabularnewline
121 & 54 & 66.2910350574925 & -12.2910350574925 \tabularnewline
122 & 76 & 69.0504122591626 & 6.94958774083745 \tabularnewline
123 & 89 & 71.1593041827847 & 17.8406958172153 \tabularnewline
124 & 76 & 73.5822539578352 & 2.41774604216482 \tabularnewline
125 & 73 & 73.4870468328823 & -0.48704683288234 \tabularnewline
126 & 79 & 70.6490817487775 & 8.35091825122245 \tabularnewline
127 & 90 & 77.5454934637279 & 12.4545065362721 \tabularnewline
128 & 74 & 75.8955163953668 & -1.89551639536677 \tabularnewline
129 & 81 & 74.7645763294213 & 6.23542367057869 \tabularnewline
130 & 72 & 72.209348647638 & -0.20934864763796 \tabularnewline
131 & 71 & 71.0811835598574 & -0.0811835598573597 \tabularnewline
132 & 66 & 61.4075590656809 & 4.59244093431905 \tabularnewline
133 & 77 & 73.699382693696 & 3.30061730630398 \tabularnewline
134 & 65 & 70.7235893530331 & -5.72358935303309 \tabularnewline
135 & 74 & 72.0063005734066 & 1.99369942659341 \tabularnewline
136 & 82 & 71.8537248767078 & 10.1462751232922 \tabularnewline
137 & 54 & 64.5565667375319 & -10.5565667375319 \tabularnewline
138 & 63 & 68.9367394589077 & -5.93673945890771 \tabularnewline
139 & 54 & 63.9173118056668 & -9.91731180566685 \tabularnewline
140 & 64 & 72.5054146087048 & -8.50541460870484 \tabularnewline
141 & 69 & 69.2457754378293 & -0.245775437829252 \tabularnewline
142 & 54 & 71.3697415769282 & -17.3697415769282 \tabularnewline
143 & 84 & 71.4911900286206 & 12.5088099713794 \tabularnewline
144 & 86 & 70.3586605401257 & 15.6413394598743 \tabularnewline
145 & 77 & 74.7339859192442 & 2.2660140807558 \tabularnewline
146 & 89 & 75.2125033471567 & 13.7874966528433 \tabularnewline
147 & 76 & 75.5492192146285 & 0.450780785371546 \tabularnewline
148 & 60 & 67.9887208511537 & -7.98872085115372 \tabularnewline
149 & 75 & 70.5971820671124 & 4.40281793288763 \tabularnewline
150 & 73 & 62.571513603742 & 10.428486396258 \tabularnewline
151 & 85 & 72.1690165132961 & 12.8309834867039 \tabularnewline
152 & 79 & 67.6296899912772 & 11.3703100087228 \tabularnewline
153 & 71 & 70.9265666422286 & 0.0734333577713639 \tabularnewline
154 & 72 & 68.2629322376036 & 3.73706776239641 \tabularnewline
155 & 69 & 61.5238381553952 & 7.47616184460476 \tabularnewline
156 & 78 & 68.3504000275932 & 9.64959997240682 \tabularnewline
157 & 54 & 69.9795385125696 & -15.9795385125696 \tabularnewline
158 & 69 & 68.652021091814 & 0.347978908186004 \tabularnewline
159 & 81 & 74.7645763294213 & 6.23542367057869 \tabularnewline
160 & 84 & 71.5557330581933 & 12.4442669418067 \tabularnewline
161 & 84 & 65.0198063224961 & 18.9801936775039 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144663&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]53[/C][C]70.6455239957622[/C][C]-17.6455239957622[/C][/ROW]
[ROW][C]2[/C][C]86[/C][C]77.530386381852[/C][C]8.46961361814793[/C][/ROW]
[ROW][C]3[/C][C]66[/C][C]66.6558515017325[/C][C]-0.655851501732471[/C][/ROW]
[ROW][C]4[/C][C]67[/C][C]69.4785509334172[/C][C]-2.47855093341721[/C][/ROW]
[ROW][C]5[/C][C]76[/C][C]65.7079266400961[/C][C]10.2920733599039[/C][/ROW]
[ROW][C]6[/C][C]78[/C][C]76.0399841641727[/C][C]1.96001583582733[/C][/ROW]
[ROW][C]7[/C][C]53[/C][C]68.3592328570652[/C][C]-15.3592328570652[/C][/ROW]
[ROW][C]8[/C][C]80[/C][C]70.8060124075656[/C][C]9.19398759243442[/C][/ROW]
[ROW][C]9[/C][C]74[/C][C]73.635152175178[/C][C]0.364847824821947[/C][/ROW]
[ROW][C]10[/C][C]76[/C][C]72.4253415662437[/C][C]3.57465843375632[/C][/ROW]
[ROW][C]11[/C][C]79[/C][C]76.5757851604436[/C][C]2.4242148395564[/C][/ROW]
[ROW][C]12[/C][C]54[/C][C]78.8175137557278[/C][C]-24.8175137557278[/C][/ROW]
[ROW][C]13[/C][C]67[/C][C]66.9285016457167[/C][C]0.0714983542832962[/C][/ROW]
[ROW][C]14[/C][C]54[/C][C]71.862394969629[/C][C]-17.862394969629[/C][/ROW]
[ROW][C]15[/C][C]87[/C][C]74.0194580574945[/C][C]12.9805419425055[/C][/ROW]
[ROW][C]16[/C][C]58[/C][C]69.5994496860588[/C][C]-11.5994496860588[/C][/ROW]
[ROW][C]17[/C][C]75[/C][C]70.2187779113566[/C][C]4.78122208864338[/C][/ROW]
[ROW][C]18[/C][C]88[/C][C]74.4235862767348[/C][C]13.5764137232652[/C][/ROW]
[ROW][C]19[/C][C]64[/C][C]71.0301215252035[/C][C]-7.03012152520349[/C][/ROW]
[ROW][C]20[/C][C]57[/C][C]71.6815310112976[/C][C]-14.6815310112975[/C][/ROW]
[ROW][C]21[/C][C]66[/C][C]77.8149643968644[/C][C]-11.8149643968644[/C][/ROW]
[ROW][C]22[/C][C]68[/C][C]67.8306692590742[/C][C]0.169330740925788[/C][/ROW]
[ROW][C]23[/C][C]54[/C][C]70.587020275707[/C][C]-16.587020275707[/C][/ROW]
[ROW][C]24[/C][C]56[/C][C]66.8038212961522[/C][C]-10.8038212961522[/C][/ROW]
[ROW][C]25[/C][C]86[/C][C]76.7600713239144[/C][C]9.23992867608558[/C][/ROW]
[ROW][C]26[/C][C]80[/C][C]71.3715651253108[/C][C]8.62843487468917[/C][/ROW]
[ROW][C]27[/C][C]76[/C][C]71.052481924746[/C][C]4.94751807525399[/C][/ROW]
[ROW][C]28[/C][C]69[/C][C]68.1658836861748[/C][C]0.834116313825191[/C][/ROW]
[ROW][C]29[/C][C]78[/C][C]71.4665485127924[/C][C]6.53345148720763[/C][/ROW]
[ROW][C]30[/C][C]67[/C][C]68.4680029218253[/C][C]-1.46800292182527[/C][/ROW]
[ROW][C]31[/C][C]80[/C][C]75.8521841803705[/C][C]4.14781581962946[/C][/ROW]
[ROW][C]32[/C][C]54[/C][C]67.8338912153901[/C][C]-13.8338912153901[/C][/ROW]
[ROW][C]33[/C][C]71[/C][C]76.1997803237493[/C][C]-5.19978032374926[/C][/ROW]
[ROW][C]34[/C][C]84[/C][C]72.2640629506664[/C][C]11.7359370493336[/C][/ROW]
[ROW][C]35[/C][C]74[/C][C]71.9445879609263[/C][C]2.05541203907369[/C][/ROW]
[ROW][C]36[/C][C]71[/C][C]74.9293578522512[/C][C]-3.92935785225118[/C][/ROW]
[ROW][C]37[/C][C]63[/C][C]62.1006880066815[/C][C]0.899311993318532[/C][/ROW]
[ROW][C]38[/C][C]71[/C][C]73.082249167193[/C][C]-2.082249167193[/C][/ROW]
[ROW][C]39[/C][C]76[/C][C]75.152714688086[/C][C]0.84728531191405[/C][/ROW]
[ROW][C]40[/C][C]69[/C][C]72.306223361222[/C][C]-3.30622336122197[/C][/ROW]
[ROW][C]41[/C][C]74[/C][C]75.4128829376528[/C][C]-1.41288293765283[/C][/ROW]
[ROW][C]42[/C][C]75[/C][C]71.1945815782206[/C][C]3.80541842177936[/C][/ROW]
[ROW][C]43[/C][C]54[/C][C]77.1951592843563[/C][C]-23.1951592843563[/C][/ROW]
[ROW][C]44[/C][C]52[/C][C]68.444988164214[/C][C]-16.444988164214[/C][/ROW]
[ROW][C]45[/C][C]69[/C][C]71.4105078530121[/C][C]-2.41050785301213[/C][/ROW]
[ROW][C]46[/C][C]68[/C][C]67.0528911960214[/C][C]0.947108803978643[/C][/ROW]
[ROW][C]47[/C][C]65[/C][C]73.5463134499902[/C][C]-8.54631344999017[/C][/ROW]
[ROW][C]48[/C][C]75[/C][C]72.7851313452253[/C][C]2.21486865477471[/C][/ROW]
[ROW][C]49[/C][C]74[/C][C]72.8414710787387[/C][C]1.15852892126133[/C][/ROW]
[ROW][C]50[/C][C]75[/C][C]72.4893493416985[/C][C]2.51065065830147[/C][/ROW]
[ROW][C]51[/C][C]72[/C][C]73.5654773213974[/C][C]-1.56547732139739[/C][/ROW]
[ROW][C]52[/C][C]67[/C][C]71.0299011174839[/C][C]-4.02990111748389[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]65.0942519576426[/C][C]-2.09425195764256[/C][/ROW]
[ROW][C]54[/C][C]62[/C][C]66.2961213565165[/C][C]-4.29612135651646[/C][/ROW]
[ROW][C]55[/C][C]63[/C][C]70.1063733614892[/C][C]-7.10637336148916[/C][/ROW]
[ROW][C]56[/C][C]76[/C][C]73.815961059246[/C][C]2.18403894075394[/C][/ROW]
[ROW][C]57[/C][C]74[/C][C]73.4033991721725[/C][C]0.5966008278275[/C][/ROW]
[ROW][C]58[/C][C]67[/C][C]73.6157793591658[/C][C]-6.61577935916581[/C][/ROW]
[ROW][C]59[/C][C]73[/C][C]70.3030213611251[/C][C]2.69697863887486[/C][/ROW]
[ROW][C]60[/C][C]70[/C][C]67.7933001812573[/C][C]2.20669981874272[/C][/ROW]
[ROW][C]61[/C][C]53[/C][C]62.2943014316041[/C][C]-9.29430143160407[/C][/ROW]
[ROW][C]62[/C][C]77[/C][C]70.2974081688281[/C][C]6.70259183117187[/C][/ROW]
[ROW][C]63[/C][C]77[/C][C]67.8283002425677[/C][C]9.17169975743233[/C][/ROW]
[ROW][C]64[/C][C]52[/C][C]67.5361563710212[/C][C]-15.5361563710212[/C][/ROW]
[ROW][C]65[/C][C]54[/C][C]71.4760424023712[/C][C]-17.4760424023712[/C][/ROW]
[ROW][C]66[/C][C]80[/C][C]67.9016266934629[/C][C]12.0983733065371[/C][/ROW]
[ROW][C]67[/C][C]66[/C][C]65.746098965513[/C][C]0.253901034487028[/C][/ROW]
[ROW][C]68[/C][C]73[/C][C]70.193225543181[/C][C]2.80677445681902[/C][/ROW]
[ROW][C]69[/C][C]63[/C][C]71.258324010409[/C][C]-8.25832401040898[/C][/ROW]
[ROW][C]70[/C][C]69[/C][C]70.8808141047438[/C][C]-1.8808141047438[/C][/ROW]
[ROW][C]71[/C][C]67[/C][C]71.1638696471263[/C][C]-4.16386964712628[/C][/ROW]
[ROW][C]72[/C][C]54[/C][C]69.984215138298[/C][C]-15.984215138298[/C][/ROW]
[ROW][C]73[/C][C]81[/C][C]71.3723163317671[/C][C]9.62768366823288[/C][/ROW]
[ROW][C]74[/C][C]69[/C][C]72.100220136941[/C][C]-3.10022013694094[/C][/ROW]
[ROW][C]75[/C][C]84[/C][C]72.3849415189143[/C][C]11.6150584810857[/C][/ROW]
[ROW][C]76[/C][C]80[/C][C]67.7182342300468[/C][C]12.2817657699532[/C][/ROW]
[ROW][C]77[/C][C]70[/C][C]73.046250570205[/C][C]-3.04625057020492[/C][/ROW]
[ROW][C]78[/C][C]69[/C][C]67.5169311168081[/C][C]1.48306888319189[/C][/ROW]
[ROW][C]79[/C][C]77[/C][C]71.1599194561456[/C][C]5.84008054385444[/C][/ROW]
[ROW][C]80[/C][C]54[/C][C]73.5078469241956[/C][C]-19.5078469241957[/C][/ROW]
[ROW][C]81[/C][C]79[/C][C]71.6562899947735[/C][C]7.34371000522653[/C][/ROW]
[ROW][C]82[/C][C]30[/C][C]74.446601034346[/C][C]-44.446601034346[/C][/ROW]
[ROW][C]83[/C][C]71[/C][C]73.56820727431[/C][C]-2.56820727430996[/C][/ROW]
[ROW][C]84[/C][C]73[/C][C]74.9493479939266[/C][C]-1.94934799392655[/C][/ROW]
[ROW][C]85[/C][C]72[/C][C]70.0996265655055[/C][C]1.90037343449453[/C][/ROW]
[ROW][C]86[/C][C]77[/C][C]69.7635183681813[/C][C]7.2364816318187[/C][/ROW]
[ROW][C]87[/C][C]75[/C][C]71.5587307185474[/C][C]3.44126928145261[/C][/ROW]
[ROW][C]88[/C][C]69[/C][C]71.3555888002168[/C][C]-2.3555888002168[/C][/ROW]
[ROW][C]89[/C][C]54[/C][C]67.7839560545722[/C][C]-13.7839560545722[/C][/ROW]
[ROW][C]90[/C][C]70[/C][C]60.3338047771937[/C][C]9.66619522280626[/C][/ROW]
[ROW][C]91[/C][C]73[/C][C]72.440464637675[/C][C]0.559535362324932[/C][/ROW]
[ROW][C]92[/C][C]54[/C][C]69.9795385125696[/C][C]-15.9795385125696[/C][/ROW]
[ROW][C]93[/C][C]77[/C][C]70.3741842189742[/C][C]6.6258157810258[/C][/ROW]
[ROW][C]94[/C][C]82[/C][C]71.0067197317456[/C][C]10.9932802682544[/C][/ROW]
[ROW][C]95[/C][C]80[/C][C]70.773176812664[/C][C]9.22682318733605[/C][/ROW]
[ROW][C]96[/C][C]80[/C][C]74.0374680822626[/C][C]5.96253191773737[/C][/ROW]
[ROW][C]97[/C][C]69[/C][C]67.9710327058965[/C][C]1.02896729410353[/C][/ROW]
[ROW][C]98[/C][C]78[/C][C]72.6166587586875[/C][C]5.38334124131247[/C][/ROW]
[ROW][C]99[/C][C]81[/C][C]75.5844852088425[/C][C]5.41551479115747[/C][/ROW]
[ROW][C]100[/C][C]76[/C][C]71.2593166641009[/C][C]4.74068333589906[/C][/ROW]
[ROW][C]101[/C][C]76[/C][C]75.7479307366264[/C][C]0.252069263373597[/C][/ROW]
[ROW][C]102[/C][C]73[/C][C]67.4125855780051[/C][C]5.58741442199494[/C][/ROW]
[ROW][C]103[/C][C]85[/C][C]72.642121957469[/C][C]12.3578780425309[/C][/ROW]
[ROW][C]104[/C][C]66[/C][C]69.7829346370106[/C][C]-3.7829346370106[/C][/ROW]
[ROW][C]105[/C][C]79[/C][C]69.5056244464907[/C][C]9.49437555350931[/C][/ROW]
[ROW][C]106[/C][C]68[/C][C]64.874926089663[/C][C]3.12507391033701[/C][/ROW]
[ROW][C]107[/C][C]76[/C][C]74.7011641746962[/C][C]1.29883582530377[/C][/ROW]
[ROW][C]108[/C][C]71[/C][C]69.3796988519857[/C][C]1.6203011480143[/C][/ROW]
[ROW][C]109[/C][C]54[/C][C]65.8710974551385[/C][C]-11.8710974551385[/C][/ROW]
[ROW][C]110[/C][C]46[/C][C]58.7607663836606[/C][C]-12.7607663836606[/C][/ROW]
[ROW][C]111[/C][C]82[/C][C]69.8783471854238[/C][C]12.1216528145762[/C][/ROW]
[ROW][C]112[/C][C]74[/C][C]66.050357433218[/C][C]7.949642566782[/C][/ROW]
[ROW][C]113[/C][C]88[/C][C]69.0257924598778[/C][C]18.9742075401222[/C][/ROW]
[ROW][C]114[/C][C]38[/C][C]68.9557362223209[/C][C]-30.9557362223209[/C][/ROW]
[ROW][C]115[/C][C]76[/C][C]75.7594267657613[/C][C]0.240573234238734[/C][/ROW]
[ROW][C]116[/C][C]86[/C][C]73.906523991951[/C][C]12.0934760080489[/C][/ROW]
[ROW][C]117[/C][C]54[/C][C]67.2478419609434[/C][C]-13.2478419609434[/C][/ROW]
[ROW][C]118[/C][C]70[/C][C]69.4966484659294[/C][C]0.503351534070617[/C][/ROW]
[ROW][C]119[/C][C]69[/C][C]71.2481277917795[/C][C]-2.24812779177953[/C][/ROW]
[ROW][C]120[/C][C]90[/C][C]69.7274208877248[/C][C]20.2725791122752[/C][/ROW]
[ROW][C]121[/C][C]54[/C][C]66.2910350574925[/C][C]-12.2910350574925[/C][/ROW]
[ROW][C]122[/C][C]76[/C][C]69.0504122591626[/C][C]6.94958774083745[/C][/ROW]
[ROW][C]123[/C][C]89[/C][C]71.1593041827847[/C][C]17.8406958172153[/C][/ROW]
[ROW][C]124[/C][C]76[/C][C]73.5822539578352[/C][C]2.41774604216482[/C][/ROW]
[ROW][C]125[/C][C]73[/C][C]73.4870468328823[/C][C]-0.48704683288234[/C][/ROW]
[ROW][C]126[/C][C]79[/C][C]70.6490817487775[/C][C]8.35091825122245[/C][/ROW]
[ROW][C]127[/C][C]90[/C][C]77.5454934637279[/C][C]12.4545065362721[/C][/ROW]
[ROW][C]128[/C][C]74[/C][C]75.8955163953668[/C][C]-1.89551639536677[/C][/ROW]
[ROW][C]129[/C][C]81[/C][C]74.7645763294213[/C][C]6.23542367057869[/C][/ROW]
[ROW][C]130[/C][C]72[/C][C]72.209348647638[/C][C]-0.20934864763796[/C][/ROW]
[ROW][C]131[/C][C]71[/C][C]71.0811835598574[/C][C]-0.0811835598573597[/C][/ROW]
[ROW][C]132[/C][C]66[/C][C]61.4075590656809[/C][C]4.59244093431905[/C][/ROW]
[ROW][C]133[/C][C]77[/C][C]73.699382693696[/C][C]3.30061730630398[/C][/ROW]
[ROW][C]134[/C][C]65[/C][C]70.7235893530331[/C][C]-5.72358935303309[/C][/ROW]
[ROW][C]135[/C][C]74[/C][C]72.0063005734066[/C][C]1.99369942659341[/C][/ROW]
[ROW][C]136[/C][C]82[/C][C]71.8537248767078[/C][C]10.1462751232922[/C][/ROW]
[ROW][C]137[/C][C]54[/C][C]64.5565667375319[/C][C]-10.5565667375319[/C][/ROW]
[ROW][C]138[/C][C]63[/C][C]68.9367394589077[/C][C]-5.93673945890771[/C][/ROW]
[ROW][C]139[/C][C]54[/C][C]63.9173118056668[/C][C]-9.91731180566685[/C][/ROW]
[ROW][C]140[/C][C]64[/C][C]72.5054146087048[/C][C]-8.50541460870484[/C][/ROW]
[ROW][C]141[/C][C]69[/C][C]69.2457754378293[/C][C]-0.245775437829252[/C][/ROW]
[ROW][C]142[/C][C]54[/C][C]71.3697415769282[/C][C]-17.3697415769282[/C][/ROW]
[ROW][C]143[/C][C]84[/C][C]71.4911900286206[/C][C]12.5088099713794[/C][/ROW]
[ROW][C]144[/C][C]86[/C][C]70.3586605401257[/C][C]15.6413394598743[/C][/ROW]
[ROW][C]145[/C][C]77[/C][C]74.7339859192442[/C][C]2.2660140807558[/C][/ROW]
[ROW][C]146[/C][C]89[/C][C]75.2125033471567[/C][C]13.7874966528433[/C][/ROW]
[ROW][C]147[/C][C]76[/C][C]75.5492192146285[/C][C]0.450780785371546[/C][/ROW]
[ROW][C]148[/C][C]60[/C][C]67.9887208511537[/C][C]-7.98872085115372[/C][/ROW]
[ROW][C]149[/C][C]75[/C][C]70.5971820671124[/C][C]4.40281793288763[/C][/ROW]
[ROW][C]150[/C][C]73[/C][C]62.571513603742[/C][C]10.428486396258[/C][/ROW]
[ROW][C]151[/C][C]85[/C][C]72.1690165132961[/C][C]12.8309834867039[/C][/ROW]
[ROW][C]152[/C][C]79[/C][C]67.6296899912772[/C][C]11.3703100087228[/C][/ROW]
[ROW][C]153[/C][C]71[/C][C]70.9265666422286[/C][C]0.0734333577713639[/C][/ROW]
[ROW][C]154[/C][C]72[/C][C]68.2629322376036[/C][C]3.73706776239641[/C][/ROW]
[ROW][C]155[/C][C]69[/C][C]61.5238381553952[/C][C]7.47616184460476[/C][/ROW]
[ROW][C]156[/C][C]78[/C][C]68.3504000275932[/C][C]9.64959997240682[/C][/ROW]
[ROW][C]157[/C][C]54[/C][C]69.9795385125696[/C][C]-15.9795385125696[/C][/ROW]
[ROW][C]158[/C][C]69[/C][C]68.652021091814[/C][C]0.347978908186004[/C][/ROW]
[ROW][C]159[/C][C]81[/C][C]74.7645763294213[/C][C]6.23542367057869[/C][/ROW]
[ROW][C]160[/C][C]84[/C][C]71.5557330581933[/C][C]12.4442669418067[/C][/ROW]
[ROW][C]161[/C][C]84[/C][C]65.0198063224961[/C][C]18.9801936775039[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144663&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144663&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
15370.6455239957622-17.6455239957622
28677.5303863818528.46961361814793
36666.6558515017325-0.655851501732471
46769.4785509334172-2.47855093341721
57665.707926640096110.2920733599039
67876.03998416417271.96001583582733
75368.3592328570652-15.3592328570652
88070.80601240756569.19398759243442
97473.6351521751780.364847824821947
107672.42534156624373.57465843375632
117976.57578516044362.4242148395564
125478.8175137557278-24.8175137557278
136766.92850164571670.0714983542832962
145471.862394969629-17.862394969629
158774.019458057494512.9805419425055
165869.5994496860588-11.5994496860588
177570.21877791135664.78122208864338
188874.423586276734813.5764137232652
196471.0301215252035-7.03012152520349
205771.6815310112976-14.6815310112975
216677.8149643968644-11.8149643968644
226867.83066925907420.169330740925788
235470.587020275707-16.587020275707
245666.8038212961522-10.8038212961522
258676.76007132391449.23992867608558
268071.37156512531088.62843487468917
277671.0524819247464.94751807525399
286968.16588368617480.834116313825191
297871.46654851279246.53345148720763
306768.4680029218253-1.46800292182527
318075.85218418037054.14781581962946
325467.8338912153901-13.8338912153901
337176.1997803237493-5.19978032374926
348472.264062950666411.7359370493336
357471.94458796092632.05541203907369
367174.9293578522512-3.92935785225118
376362.10068800668150.899311993318532
387173.082249167193-2.082249167193
397675.1527146880860.84728531191405
406972.306223361222-3.30622336122197
417475.4128829376528-1.41288293765283
427571.19458157822063.80541842177936
435477.1951592843563-23.1951592843563
445268.444988164214-16.444988164214
456971.4105078530121-2.41050785301213
466867.05289119602140.947108803978643
476573.5463134499902-8.54631344999017
487572.78513134522532.21486865477471
497472.84147107873871.15852892126133
507572.48934934169852.51065065830147
517273.5654773213974-1.56547732139739
526771.0299011174839-4.02990111748389
536365.0942519576426-2.09425195764256
546266.2961213565165-4.29612135651646
556370.1063733614892-7.10637336148916
567673.8159610592462.18403894075394
577473.40339917217250.5966008278275
586773.6157793591658-6.61577935916581
597370.30302136112512.69697863887486
607067.79330018125732.20669981874272
615362.2943014316041-9.29430143160407
627770.29740816882816.70259183117187
637767.82830024256779.17169975743233
645267.5361563710212-15.5361563710212
655471.4760424023712-17.4760424023712
668067.901626693462912.0983733065371
676665.7460989655130.253901034487028
687370.1932255431812.80677445681902
696371.258324010409-8.25832401040898
706970.8808141047438-1.8808141047438
716771.1638696471263-4.16386964712628
725469.984215138298-15.984215138298
738171.37231633176719.62768366823288
746972.100220136941-3.10022013694094
758472.384941518914311.6150584810857
768067.718234230046812.2817657699532
777073.046250570205-3.04625057020492
786967.51693111680811.48306888319189
797771.15991945614565.84008054385444
805473.5078469241956-19.5078469241957
817971.65628999477357.34371000522653
823074.446601034346-44.446601034346
837173.56820727431-2.56820727430996
847374.9493479939266-1.94934799392655
857270.09962656550551.90037343449453
867769.76351836818137.2364816318187
877571.55873071854743.44126928145261
886971.3555888002168-2.3555888002168
895467.7839560545722-13.7839560545722
907060.33380477719379.66619522280626
917372.4404646376750.559535362324932
925469.9795385125696-15.9795385125696
937770.37418421897426.6258157810258
948271.006719731745610.9932802682544
958070.7731768126649.22682318733605
968074.03746808226265.96253191773737
976967.97103270589651.02896729410353
987872.61665875868755.38334124131247
998175.58448520884255.41551479115747
1007671.25931666410094.74068333589906
1017675.74793073662640.252069263373597
1027367.41258557800515.58741442199494
1038572.64212195746912.3578780425309
1046669.7829346370106-3.7829346370106
1057969.50562444649079.49437555350931
1066864.8749260896633.12507391033701
1077674.70116417469621.29883582530377
1087169.37969885198571.6203011480143
1095465.8710974551385-11.8710974551385
1104658.7607663836606-12.7607663836606
1118269.878347185423812.1216528145762
1127466.0503574332187.949642566782
1138869.025792459877818.9742075401222
1143868.9557362223209-30.9557362223209
1157675.75942676576130.240573234238734
1168673.90652399195112.0934760080489
1175467.2478419609434-13.2478419609434
1187069.49664846592940.503351534070617
1196971.2481277917795-2.24812779177953
1209069.727420887724820.2725791122752
1215466.2910350574925-12.2910350574925
1227669.05041225916266.94958774083745
1238971.159304182784717.8406958172153
1247673.58225395783522.41774604216482
1257373.4870468328823-0.48704683288234
1267970.64908174877758.35091825122245
1279077.545493463727912.4545065362721
1287475.8955163953668-1.89551639536677
1298174.76457632942136.23542367057869
1307272.209348647638-0.20934864763796
1317171.0811835598574-0.0811835598573597
1326661.40755906568094.59244093431905
1337773.6993826936963.30061730630398
1346570.7235893530331-5.72358935303309
1357472.00630057340661.99369942659341
1368271.853724876707810.1462751232922
1375464.5565667375319-10.5565667375319
1386368.9367394589077-5.93673945890771
1395463.9173118056668-9.91731180566685
1406472.5054146087048-8.50541460870484
1416969.2457754378293-0.245775437829252
1425471.3697415769282-17.3697415769282
1438471.491190028620612.5088099713794
1448670.358660540125715.6413394598743
1457774.73398591924422.2660140807558
1468975.212503347156713.7874966528433
1477675.54921921462850.450780785371546
1486067.9887208511537-7.98872085115372
1497570.59718206711244.40281793288763
1507362.57151360374210.428486396258
1518572.169016513296112.8309834867039
1527967.629689991277211.3703100087228
1537170.92656664222860.0734333577713639
1547268.26293223760363.73706776239641
1556961.52383815539527.47616184460476
1567868.35040002759329.64959997240682
1575469.9795385125696-15.9795385125696
1586968.6520210918140.347978908186004
1598174.76457632942136.23542367057869
1608471.555733058193312.4442669418067
1618465.019806322496118.9801936775039






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1040936282276730.2081872564553460.895906371772327
120.9642529713382880.07149405732342390.0357470286617119
130.9380728035694290.1238543928611420.0619271964305709
140.9199245010004170.1601509979991650.0800754989995826
150.9363331150512580.1273337698974840.0636668849487419
160.9525540201085610.0948919597828770.0474459798914385
170.9298555987445680.1402888025108650.0701444012554324
180.93241127795730.1351774440854010.0675887220427007
190.9269057472934820.1461885054130350.0730942527065176
200.9520554624974820.09588907500503540.0479445375025177
210.9505319636092230.09893607278155420.0494680363907771
220.929644089778230.1407118204435390.0703559102217695
230.9390590523939220.1218818952121560.0609409476060779
240.9269601651755440.1460796696489120.0730398348244558
250.9336492914775720.1327014170448560.0663507085224279
260.9372003097691340.1255993804617320.062799690230866
270.9241360374557840.1517279250884320.0758639625442162
280.9004477395871120.1991045208257760.0995522604128881
290.8834254884742950.2331490230514090.116574511525705
300.8489189767328870.3021620465342270.151081023267113
310.811056975624680.3778860487506410.188943024375321
320.8182097653977720.3635804692044560.181790234602228
330.781547940221390.4369041195572210.218452059778611
340.8004166953079780.3991666093840430.199583304692022
350.75894023899430.48211952201140.2410597610057
360.7118505822830790.5762988354338420.288149417716921
370.6724804845924550.655039030815090.327519515407545
380.6191120143471160.7617759713057690.380887985652884
390.5630677952665460.8738644094669070.436932204733454
400.5097322200286430.9805355599427150.490267779971357
410.4535579361632970.9071158723265940.546442063836703
420.4104014266038780.8208028532077570.589598573396122
430.610018374620670.7799632507586590.38998162537933
440.6602889155665110.6794221688669780.339711084433489
450.6124538780002920.7750922439994160.387546121999708
460.5640199002396030.8719601995207950.435980099760397
470.5344883357392270.9310233285215470.465511664260773
480.4874639159739740.9749278319479480.512536084026026
490.4390935502588480.8781871005176970.560906449741151
500.3975104838485220.7950209676970450.602489516151478
510.3487531755966730.6975063511933470.651246824403327
520.3072575053381090.6145150106762180.692742494661891
530.264088244431870.5281764888637390.73591175556813
540.226998832311690.4539976646233810.77300116768831
550.2026365400022870.4052730800045740.797363459997713
560.1745049128018690.3490098256037370.825495087198131
570.1455257459918870.2910514919837730.854474254008113
580.1274424315960790.2548848631921580.872557568403921
590.1085915588666840.2171831177333680.891408441133316
600.08973890784791450.1794778156958290.910261092152086
610.08348748990639660.1669749798127930.916512510093603
620.07646739040774010.152934780815480.92353260959226
630.07617407107376540.1523481421475310.923825928926235
640.09384007303142560.1876801460628510.906159926968574
650.1291648086721440.2583296173442880.870835191327856
660.1407073465556240.2814146931112470.859292653444376
670.1155889472429450.231177894485890.884411052757055
680.09737927739570130.1947585547914030.902620722604299
690.08784189603580380.1756837920716080.912158103964196
700.07063496028804780.1412699205760960.929365039711952
710.05794315144954720.1158863028990940.942056848550453
720.0742557825914780.1485115651829560.925744217408522
730.0735465757611220.1470931515222440.926453424238878
740.05931625384107660.1186325076821530.940683746158923
750.06309032848201460.1261806569640290.936909671517985
760.07092785770961470.1418557154192290.929072142290385
770.05780598597361740.1156119719472350.942194014026383
780.04580537202820990.09161074405641980.95419462797179
790.03969444554332240.07938889108664470.960305554456678
800.07626815518731850.1525363103746370.923731844812681
810.06999691255757490.139993825115150.930003087442425
820.7344165346437210.5311669307125570.265583465356279
830.7042189380213360.5915621239573280.295781061978664
840.671252862029820.657494275940360.32874713797018
850.6327233401470250.7345533197059490.367276659852975
860.6119054734721120.7761890530557770.388094526527888
870.5780843174388920.8438313651222150.421915682561108
880.53959640426220.92080719147560.4604035957378
890.584088429775790.831823140448420.41591157022421
900.5740018979797480.8519962040405030.425998102020252
910.5326381402938650.934723719412270.467361859706135
920.6276428237846650.7447143524306710.372357176215335
930.5989125528592850.802174894281430.401087447140715
940.5948075020837470.8103849958325070.405192497916253
950.583741557525370.832516884949260.41625844247463
960.552601066437090.894797867125820.44739893356291
970.5059901040133840.9880197919732330.494009895986616
980.468218706235140.936437412470280.53178129376486
990.4310003876889440.8620007753778880.568999612311056
1000.3940184416199520.7880368832399040.605981558380048
1010.3584824449987280.7169648899974560.641517555001272
1020.3224386696353430.6448773392706870.677561330364657
1030.319109964854710.638219929709420.68089003514529
1040.2837837169911020.5675674339822040.716216283008898
1050.2659283396308750.5318566792617490.734071660369125
1060.2286490983071450.4572981966142890.771350901692855
1070.197360111533080.394720223066160.80263988846692
1080.1657775705178080.3315551410356170.834222429482192
1090.1845697827475930.3691395654951870.815430217252407
1100.2099254800247870.4198509600495750.790074519975212
1110.215676006708080.431352013416160.78432399329192
1120.1957495750101440.3914991500202870.804250424989856
1130.2806854097790970.5613708195581940.719314590220903
1140.7039763240851620.5920473518296770.296023675914838
1150.6725092884630910.6549814230738190.327490711536909
1160.6619557923814760.6760884152370480.338044207618524
1170.7209135621076630.5581728757846750.279086437892338
1180.6744469815452130.6511060369095740.325553018454787
1190.6284313010351070.7431373979297860.371568698964893
1200.7120217884919660.5759564230160680.287978211508034
1210.761586855568940.4768262888621180.238413144431059
1220.7291326708829910.5417346582340180.270867329117009
1230.7710262047806040.4579475904387930.228973795219396
1240.7237276034406720.5525447931186560.276272396559328
1250.6733235361770930.6533529276458140.326676463822907
1260.6571571233192060.6856857533615890.342842876680794
1270.654796988829830.6904060223403390.345203011170169
1280.636604090503850.72679181899230.36339590949615
1290.5818679569961390.8362640860077220.418132043003861
1300.5273611131974960.9452777736050070.472638886802504
1310.4617579789329940.9235159578659880.538242021067006
1320.4107334805128320.8214669610256630.589266519487168
1330.3571708047256240.7143416094512480.642829195274376
1340.313942018470920.6278840369418410.68605798152908
1350.2652914196456740.5305828392913490.734708580354326
1360.2559382265744680.5118764531489360.744061773425532
1370.280030801253060.560061602506120.71996919874694
1380.2560747349910120.5121494699820240.743925265008988
1390.2880695037179560.5761390074359120.711930496282044
1400.3081408305626190.6162816611252390.69185916943738
1410.3091563652427040.6183127304854080.690843634757296
1420.5631506697461150.8736986605077690.436849330253885
1430.5066396490020950.986720701995810.493360350997905
1440.7219005343567610.5561989312864780.278099465643239
1450.65185963680960.6962807263807990.348140363190399
1460.6263055038044820.7473889923910350.373694496195518
1470.5071562219337160.9856875561325670.492843778066284
1480.4732883131277290.9465766262554590.526711686872271
1490.3581307037093420.7162614074186840.641869296290658
1500.2306616990368950.461323398073790.769338300963105

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.104093628227673 & 0.208187256455346 & 0.895906371772327 \tabularnewline
12 & 0.964252971338288 & 0.0714940573234239 & 0.0357470286617119 \tabularnewline
13 & 0.938072803569429 & 0.123854392861142 & 0.0619271964305709 \tabularnewline
14 & 0.919924501000417 & 0.160150997999165 & 0.0800754989995826 \tabularnewline
15 & 0.936333115051258 & 0.127333769897484 & 0.0636668849487419 \tabularnewline
16 & 0.952554020108561 & 0.094891959782877 & 0.0474459798914385 \tabularnewline
17 & 0.929855598744568 & 0.140288802510865 & 0.0701444012554324 \tabularnewline
18 & 0.9324112779573 & 0.135177444085401 & 0.0675887220427007 \tabularnewline
19 & 0.926905747293482 & 0.146188505413035 & 0.0730942527065176 \tabularnewline
20 & 0.952055462497482 & 0.0958890750050354 & 0.0479445375025177 \tabularnewline
21 & 0.950531963609223 & 0.0989360727815542 & 0.0494680363907771 \tabularnewline
22 & 0.92964408977823 & 0.140711820443539 & 0.0703559102217695 \tabularnewline
23 & 0.939059052393922 & 0.121881895212156 & 0.0609409476060779 \tabularnewline
24 & 0.926960165175544 & 0.146079669648912 & 0.0730398348244558 \tabularnewline
25 & 0.933649291477572 & 0.132701417044856 & 0.0663507085224279 \tabularnewline
26 & 0.937200309769134 & 0.125599380461732 & 0.062799690230866 \tabularnewline
27 & 0.924136037455784 & 0.151727925088432 & 0.0758639625442162 \tabularnewline
28 & 0.900447739587112 & 0.199104520825776 & 0.0995522604128881 \tabularnewline
29 & 0.883425488474295 & 0.233149023051409 & 0.116574511525705 \tabularnewline
30 & 0.848918976732887 & 0.302162046534227 & 0.151081023267113 \tabularnewline
31 & 0.81105697562468 & 0.377886048750641 & 0.188943024375321 \tabularnewline
32 & 0.818209765397772 & 0.363580469204456 & 0.181790234602228 \tabularnewline
33 & 0.78154794022139 & 0.436904119557221 & 0.218452059778611 \tabularnewline
34 & 0.800416695307978 & 0.399166609384043 & 0.199583304692022 \tabularnewline
35 & 0.7589402389943 & 0.4821195220114 & 0.2410597610057 \tabularnewline
36 & 0.711850582283079 & 0.576298835433842 & 0.288149417716921 \tabularnewline
37 & 0.672480484592455 & 0.65503903081509 & 0.327519515407545 \tabularnewline
38 & 0.619112014347116 & 0.761775971305769 & 0.380887985652884 \tabularnewline
39 & 0.563067795266546 & 0.873864409466907 & 0.436932204733454 \tabularnewline
40 & 0.509732220028643 & 0.980535559942715 & 0.490267779971357 \tabularnewline
41 & 0.453557936163297 & 0.907115872326594 & 0.546442063836703 \tabularnewline
42 & 0.410401426603878 & 0.820802853207757 & 0.589598573396122 \tabularnewline
43 & 0.61001837462067 & 0.779963250758659 & 0.38998162537933 \tabularnewline
44 & 0.660288915566511 & 0.679422168866978 & 0.339711084433489 \tabularnewline
45 & 0.612453878000292 & 0.775092243999416 & 0.387546121999708 \tabularnewline
46 & 0.564019900239603 & 0.871960199520795 & 0.435980099760397 \tabularnewline
47 & 0.534488335739227 & 0.931023328521547 & 0.465511664260773 \tabularnewline
48 & 0.487463915973974 & 0.974927831947948 & 0.512536084026026 \tabularnewline
49 & 0.439093550258848 & 0.878187100517697 & 0.560906449741151 \tabularnewline
50 & 0.397510483848522 & 0.795020967697045 & 0.602489516151478 \tabularnewline
51 & 0.348753175596673 & 0.697506351193347 & 0.651246824403327 \tabularnewline
52 & 0.307257505338109 & 0.614515010676218 & 0.692742494661891 \tabularnewline
53 & 0.26408824443187 & 0.528176488863739 & 0.73591175556813 \tabularnewline
54 & 0.22699883231169 & 0.453997664623381 & 0.77300116768831 \tabularnewline
55 & 0.202636540002287 & 0.405273080004574 & 0.797363459997713 \tabularnewline
56 & 0.174504912801869 & 0.349009825603737 & 0.825495087198131 \tabularnewline
57 & 0.145525745991887 & 0.291051491983773 & 0.854474254008113 \tabularnewline
58 & 0.127442431596079 & 0.254884863192158 & 0.872557568403921 \tabularnewline
59 & 0.108591558866684 & 0.217183117733368 & 0.891408441133316 \tabularnewline
60 & 0.0897389078479145 & 0.179477815695829 & 0.910261092152086 \tabularnewline
61 & 0.0834874899063966 & 0.166974979812793 & 0.916512510093603 \tabularnewline
62 & 0.0764673904077401 & 0.15293478081548 & 0.92353260959226 \tabularnewline
63 & 0.0761740710737654 & 0.152348142147531 & 0.923825928926235 \tabularnewline
64 & 0.0938400730314256 & 0.187680146062851 & 0.906159926968574 \tabularnewline
65 & 0.129164808672144 & 0.258329617344288 & 0.870835191327856 \tabularnewline
66 & 0.140707346555624 & 0.281414693111247 & 0.859292653444376 \tabularnewline
67 & 0.115588947242945 & 0.23117789448589 & 0.884411052757055 \tabularnewline
68 & 0.0973792773957013 & 0.194758554791403 & 0.902620722604299 \tabularnewline
69 & 0.0878418960358038 & 0.175683792071608 & 0.912158103964196 \tabularnewline
70 & 0.0706349602880478 & 0.141269920576096 & 0.929365039711952 \tabularnewline
71 & 0.0579431514495472 & 0.115886302899094 & 0.942056848550453 \tabularnewline
72 & 0.074255782591478 & 0.148511565182956 & 0.925744217408522 \tabularnewline
73 & 0.073546575761122 & 0.147093151522244 & 0.926453424238878 \tabularnewline
74 & 0.0593162538410766 & 0.118632507682153 & 0.940683746158923 \tabularnewline
75 & 0.0630903284820146 & 0.126180656964029 & 0.936909671517985 \tabularnewline
76 & 0.0709278577096147 & 0.141855715419229 & 0.929072142290385 \tabularnewline
77 & 0.0578059859736174 & 0.115611971947235 & 0.942194014026383 \tabularnewline
78 & 0.0458053720282099 & 0.0916107440564198 & 0.95419462797179 \tabularnewline
79 & 0.0396944455433224 & 0.0793888910866447 & 0.960305554456678 \tabularnewline
80 & 0.0762681551873185 & 0.152536310374637 & 0.923731844812681 \tabularnewline
81 & 0.0699969125575749 & 0.13999382511515 & 0.930003087442425 \tabularnewline
82 & 0.734416534643721 & 0.531166930712557 & 0.265583465356279 \tabularnewline
83 & 0.704218938021336 & 0.591562123957328 & 0.295781061978664 \tabularnewline
84 & 0.67125286202982 & 0.65749427594036 & 0.32874713797018 \tabularnewline
85 & 0.632723340147025 & 0.734553319705949 & 0.367276659852975 \tabularnewline
86 & 0.611905473472112 & 0.776189053055777 & 0.388094526527888 \tabularnewline
87 & 0.578084317438892 & 0.843831365122215 & 0.421915682561108 \tabularnewline
88 & 0.5395964042622 & 0.9208071914756 & 0.4604035957378 \tabularnewline
89 & 0.58408842977579 & 0.83182314044842 & 0.41591157022421 \tabularnewline
90 & 0.574001897979748 & 0.851996204040503 & 0.425998102020252 \tabularnewline
91 & 0.532638140293865 & 0.93472371941227 & 0.467361859706135 \tabularnewline
92 & 0.627642823784665 & 0.744714352430671 & 0.372357176215335 \tabularnewline
93 & 0.598912552859285 & 0.80217489428143 & 0.401087447140715 \tabularnewline
94 & 0.594807502083747 & 0.810384995832507 & 0.405192497916253 \tabularnewline
95 & 0.58374155752537 & 0.83251688494926 & 0.41625844247463 \tabularnewline
96 & 0.55260106643709 & 0.89479786712582 & 0.44739893356291 \tabularnewline
97 & 0.505990104013384 & 0.988019791973233 & 0.494009895986616 \tabularnewline
98 & 0.46821870623514 & 0.93643741247028 & 0.53178129376486 \tabularnewline
99 & 0.431000387688944 & 0.862000775377888 & 0.568999612311056 \tabularnewline
100 & 0.394018441619952 & 0.788036883239904 & 0.605981558380048 \tabularnewline
101 & 0.358482444998728 & 0.716964889997456 & 0.641517555001272 \tabularnewline
102 & 0.322438669635343 & 0.644877339270687 & 0.677561330364657 \tabularnewline
103 & 0.31910996485471 & 0.63821992970942 & 0.68089003514529 \tabularnewline
104 & 0.283783716991102 & 0.567567433982204 & 0.716216283008898 \tabularnewline
105 & 0.265928339630875 & 0.531856679261749 & 0.734071660369125 \tabularnewline
106 & 0.228649098307145 & 0.457298196614289 & 0.771350901692855 \tabularnewline
107 & 0.19736011153308 & 0.39472022306616 & 0.80263988846692 \tabularnewline
108 & 0.165777570517808 & 0.331555141035617 & 0.834222429482192 \tabularnewline
109 & 0.184569782747593 & 0.369139565495187 & 0.815430217252407 \tabularnewline
110 & 0.209925480024787 & 0.419850960049575 & 0.790074519975212 \tabularnewline
111 & 0.21567600670808 & 0.43135201341616 & 0.78432399329192 \tabularnewline
112 & 0.195749575010144 & 0.391499150020287 & 0.804250424989856 \tabularnewline
113 & 0.280685409779097 & 0.561370819558194 & 0.719314590220903 \tabularnewline
114 & 0.703976324085162 & 0.592047351829677 & 0.296023675914838 \tabularnewline
115 & 0.672509288463091 & 0.654981423073819 & 0.327490711536909 \tabularnewline
116 & 0.661955792381476 & 0.676088415237048 & 0.338044207618524 \tabularnewline
117 & 0.720913562107663 & 0.558172875784675 & 0.279086437892338 \tabularnewline
118 & 0.674446981545213 & 0.651106036909574 & 0.325553018454787 \tabularnewline
119 & 0.628431301035107 & 0.743137397929786 & 0.371568698964893 \tabularnewline
120 & 0.712021788491966 & 0.575956423016068 & 0.287978211508034 \tabularnewline
121 & 0.76158685556894 & 0.476826288862118 & 0.238413144431059 \tabularnewline
122 & 0.729132670882991 & 0.541734658234018 & 0.270867329117009 \tabularnewline
123 & 0.771026204780604 & 0.457947590438793 & 0.228973795219396 \tabularnewline
124 & 0.723727603440672 & 0.552544793118656 & 0.276272396559328 \tabularnewline
125 & 0.673323536177093 & 0.653352927645814 & 0.326676463822907 \tabularnewline
126 & 0.657157123319206 & 0.685685753361589 & 0.342842876680794 \tabularnewline
127 & 0.65479698882983 & 0.690406022340339 & 0.345203011170169 \tabularnewline
128 & 0.63660409050385 & 0.7267918189923 & 0.36339590949615 \tabularnewline
129 & 0.581867956996139 & 0.836264086007722 & 0.418132043003861 \tabularnewline
130 & 0.527361113197496 & 0.945277773605007 & 0.472638886802504 \tabularnewline
131 & 0.461757978932994 & 0.923515957865988 & 0.538242021067006 \tabularnewline
132 & 0.410733480512832 & 0.821466961025663 & 0.589266519487168 \tabularnewline
133 & 0.357170804725624 & 0.714341609451248 & 0.642829195274376 \tabularnewline
134 & 0.31394201847092 & 0.627884036941841 & 0.68605798152908 \tabularnewline
135 & 0.265291419645674 & 0.530582839291349 & 0.734708580354326 \tabularnewline
136 & 0.255938226574468 & 0.511876453148936 & 0.744061773425532 \tabularnewline
137 & 0.28003080125306 & 0.56006160250612 & 0.71996919874694 \tabularnewline
138 & 0.256074734991012 & 0.512149469982024 & 0.743925265008988 \tabularnewline
139 & 0.288069503717956 & 0.576139007435912 & 0.711930496282044 \tabularnewline
140 & 0.308140830562619 & 0.616281661125239 & 0.69185916943738 \tabularnewline
141 & 0.309156365242704 & 0.618312730485408 & 0.690843634757296 \tabularnewline
142 & 0.563150669746115 & 0.873698660507769 & 0.436849330253885 \tabularnewline
143 & 0.506639649002095 & 0.98672070199581 & 0.493360350997905 \tabularnewline
144 & 0.721900534356761 & 0.556198931286478 & 0.278099465643239 \tabularnewline
145 & 0.6518596368096 & 0.696280726380799 & 0.348140363190399 \tabularnewline
146 & 0.626305503804482 & 0.747388992391035 & 0.373694496195518 \tabularnewline
147 & 0.507156221933716 & 0.985687556132567 & 0.492843778066284 \tabularnewline
148 & 0.473288313127729 & 0.946576626255459 & 0.526711686872271 \tabularnewline
149 & 0.358130703709342 & 0.716261407418684 & 0.641869296290658 \tabularnewline
150 & 0.230661699036895 & 0.46132339807379 & 0.769338300963105 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144663&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]11[/C][C]0.104093628227673[/C][C]0.208187256455346[/C][C]0.895906371772327[/C][/ROW]
[ROW][C]12[/C][C]0.964252971338288[/C][C]0.0714940573234239[/C][C]0.0357470286617119[/C][/ROW]
[ROW][C]13[/C][C]0.938072803569429[/C][C]0.123854392861142[/C][C]0.0619271964305709[/C][/ROW]
[ROW][C]14[/C][C]0.919924501000417[/C][C]0.160150997999165[/C][C]0.0800754989995826[/C][/ROW]
[ROW][C]15[/C][C]0.936333115051258[/C][C]0.127333769897484[/C][C]0.0636668849487419[/C][/ROW]
[ROW][C]16[/C][C]0.952554020108561[/C][C]0.094891959782877[/C][C]0.0474459798914385[/C][/ROW]
[ROW][C]17[/C][C]0.929855598744568[/C][C]0.140288802510865[/C][C]0.0701444012554324[/C][/ROW]
[ROW][C]18[/C][C]0.9324112779573[/C][C]0.135177444085401[/C][C]0.0675887220427007[/C][/ROW]
[ROW][C]19[/C][C]0.926905747293482[/C][C]0.146188505413035[/C][C]0.0730942527065176[/C][/ROW]
[ROW][C]20[/C][C]0.952055462497482[/C][C]0.0958890750050354[/C][C]0.0479445375025177[/C][/ROW]
[ROW][C]21[/C][C]0.950531963609223[/C][C]0.0989360727815542[/C][C]0.0494680363907771[/C][/ROW]
[ROW][C]22[/C][C]0.92964408977823[/C][C]0.140711820443539[/C][C]0.0703559102217695[/C][/ROW]
[ROW][C]23[/C][C]0.939059052393922[/C][C]0.121881895212156[/C][C]0.0609409476060779[/C][/ROW]
[ROW][C]24[/C][C]0.926960165175544[/C][C]0.146079669648912[/C][C]0.0730398348244558[/C][/ROW]
[ROW][C]25[/C][C]0.933649291477572[/C][C]0.132701417044856[/C][C]0.0663507085224279[/C][/ROW]
[ROW][C]26[/C][C]0.937200309769134[/C][C]0.125599380461732[/C][C]0.062799690230866[/C][/ROW]
[ROW][C]27[/C][C]0.924136037455784[/C][C]0.151727925088432[/C][C]0.0758639625442162[/C][/ROW]
[ROW][C]28[/C][C]0.900447739587112[/C][C]0.199104520825776[/C][C]0.0995522604128881[/C][/ROW]
[ROW][C]29[/C][C]0.883425488474295[/C][C]0.233149023051409[/C][C]0.116574511525705[/C][/ROW]
[ROW][C]30[/C][C]0.848918976732887[/C][C]0.302162046534227[/C][C]0.151081023267113[/C][/ROW]
[ROW][C]31[/C][C]0.81105697562468[/C][C]0.377886048750641[/C][C]0.188943024375321[/C][/ROW]
[ROW][C]32[/C][C]0.818209765397772[/C][C]0.363580469204456[/C][C]0.181790234602228[/C][/ROW]
[ROW][C]33[/C][C]0.78154794022139[/C][C]0.436904119557221[/C][C]0.218452059778611[/C][/ROW]
[ROW][C]34[/C][C]0.800416695307978[/C][C]0.399166609384043[/C][C]0.199583304692022[/C][/ROW]
[ROW][C]35[/C][C]0.7589402389943[/C][C]0.4821195220114[/C][C]0.2410597610057[/C][/ROW]
[ROW][C]36[/C][C]0.711850582283079[/C][C]0.576298835433842[/C][C]0.288149417716921[/C][/ROW]
[ROW][C]37[/C][C]0.672480484592455[/C][C]0.65503903081509[/C][C]0.327519515407545[/C][/ROW]
[ROW][C]38[/C][C]0.619112014347116[/C][C]0.761775971305769[/C][C]0.380887985652884[/C][/ROW]
[ROW][C]39[/C][C]0.563067795266546[/C][C]0.873864409466907[/C][C]0.436932204733454[/C][/ROW]
[ROW][C]40[/C][C]0.509732220028643[/C][C]0.980535559942715[/C][C]0.490267779971357[/C][/ROW]
[ROW][C]41[/C][C]0.453557936163297[/C][C]0.907115872326594[/C][C]0.546442063836703[/C][/ROW]
[ROW][C]42[/C][C]0.410401426603878[/C][C]0.820802853207757[/C][C]0.589598573396122[/C][/ROW]
[ROW][C]43[/C][C]0.61001837462067[/C][C]0.779963250758659[/C][C]0.38998162537933[/C][/ROW]
[ROW][C]44[/C][C]0.660288915566511[/C][C]0.679422168866978[/C][C]0.339711084433489[/C][/ROW]
[ROW][C]45[/C][C]0.612453878000292[/C][C]0.775092243999416[/C][C]0.387546121999708[/C][/ROW]
[ROW][C]46[/C][C]0.564019900239603[/C][C]0.871960199520795[/C][C]0.435980099760397[/C][/ROW]
[ROW][C]47[/C][C]0.534488335739227[/C][C]0.931023328521547[/C][C]0.465511664260773[/C][/ROW]
[ROW][C]48[/C][C]0.487463915973974[/C][C]0.974927831947948[/C][C]0.512536084026026[/C][/ROW]
[ROW][C]49[/C][C]0.439093550258848[/C][C]0.878187100517697[/C][C]0.560906449741151[/C][/ROW]
[ROW][C]50[/C][C]0.397510483848522[/C][C]0.795020967697045[/C][C]0.602489516151478[/C][/ROW]
[ROW][C]51[/C][C]0.348753175596673[/C][C]0.697506351193347[/C][C]0.651246824403327[/C][/ROW]
[ROW][C]52[/C][C]0.307257505338109[/C][C]0.614515010676218[/C][C]0.692742494661891[/C][/ROW]
[ROW][C]53[/C][C]0.26408824443187[/C][C]0.528176488863739[/C][C]0.73591175556813[/C][/ROW]
[ROW][C]54[/C][C]0.22699883231169[/C][C]0.453997664623381[/C][C]0.77300116768831[/C][/ROW]
[ROW][C]55[/C][C]0.202636540002287[/C][C]0.405273080004574[/C][C]0.797363459997713[/C][/ROW]
[ROW][C]56[/C][C]0.174504912801869[/C][C]0.349009825603737[/C][C]0.825495087198131[/C][/ROW]
[ROW][C]57[/C][C]0.145525745991887[/C][C]0.291051491983773[/C][C]0.854474254008113[/C][/ROW]
[ROW][C]58[/C][C]0.127442431596079[/C][C]0.254884863192158[/C][C]0.872557568403921[/C][/ROW]
[ROW][C]59[/C][C]0.108591558866684[/C][C]0.217183117733368[/C][C]0.891408441133316[/C][/ROW]
[ROW][C]60[/C][C]0.0897389078479145[/C][C]0.179477815695829[/C][C]0.910261092152086[/C][/ROW]
[ROW][C]61[/C][C]0.0834874899063966[/C][C]0.166974979812793[/C][C]0.916512510093603[/C][/ROW]
[ROW][C]62[/C][C]0.0764673904077401[/C][C]0.15293478081548[/C][C]0.92353260959226[/C][/ROW]
[ROW][C]63[/C][C]0.0761740710737654[/C][C]0.152348142147531[/C][C]0.923825928926235[/C][/ROW]
[ROW][C]64[/C][C]0.0938400730314256[/C][C]0.187680146062851[/C][C]0.906159926968574[/C][/ROW]
[ROW][C]65[/C][C]0.129164808672144[/C][C]0.258329617344288[/C][C]0.870835191327856[/C][/ROW]
[ROW][C]66[/C][C]0.140707346555624[/C][C]0.281414693111247[/C][C]0.859292653444376[/C][/ROW]
[ROW][C]67[/C][C]0.115588947242945[/C][C]0.23117789448589[/C][C]0.884411052757055[/C][/ROW]
[ROW][C]68[/C][C]0.0973792773957013[/C][C]0.194758554791403[/C][C]0.902620722604299[/C][/ROW]
[ROW][C]69[/C][C]0.0878418960358038[/C][C]0.175683792071608[/C][C]0.912158103964196[/C][/ROW]
[ROW][C]70[/C][C]0.0706349602880478[/C][C]0.141269920576096[/C][C]0.929365039711952[/C][/ROW]
[ROW][C]71[/C][C]0.0579431514495472[/C][C]0.115886302899094[/C][C]0.942056848550453[/C][/ROW]
[ROW][C]72[/C][C]0.074255782591478[/C][C]0.148511565182956[/C][C]0.925744217408522[/C][/ROW]
[ROW][C]73[/C][C]0.073546575761122[/C][C]0.147093151522244[/C][C]0.926453424238878[/C][/ROW]
[ROW][C]74[/C][C]0.0593162538410766[/C][C]0.118632507682153[/C][C]0.940683746158923[/C][/ROW]
[ROW][C]75[/C][C]0.0630903284820146[/C][C]0.126180656964029[/C][C]0.936909671517985[/C][/ROW]
[ROW][C]76[/C][C]0.0709278577096147[/C][C]0.141855715419229[/C][C]0.929072142290385[/C][/ROW]
[ROW][C]77[/C][C]0.0578059859736174[/C][C]0.115611971947235[/C][C]0.942194014026383[/C][/ROW]
[ROW][C]78[/C][C]0.0458053720282099[/C][C]0.0916107440564198[/C][C]0.95419462797179[/C][/ROW]
[ROW][C]79[/C][C]0.0396944455433224[/C][C]0.0793888910866447[/C][C]0.960305554456678[/C][/ROW]
[ROW][C]80[/C][C]0.0762681551873185[/C][C]0.152536310374637[/C][C]0.923731844812681[/C][/ROW]
[ROW][C]81[/C][C]0.0699969125575749[/C][C]0.13999382511515[/C][C]0.930003087442425[/C][/ROW]
[ROW][C]82[/C][C]0.734416534643721[/C][C]0.531166930712557[/C][C]0.265583465356279[/C][/ROW]
[ROW][C]83[/C][C]0.704218938021336[/C][C]0.591562123957328[/C][C]0.295781061978664[/C][/ROW]
[ROW][C]84[/C][C]0.67125286202982[/C][C]0.65749427594036[/C][C]0.32874713797018[/C][/ROW]
[ROW][C]85[/C][C]0.632723340147025[/C][C]0.734553319705949[/C][C]0.367276659852975[/C][/ROW]
[ROW][C]86[/C][C]0.611905473472112[/C][C]0.776189053055777[/C][C]0.388094526527888[/C][/ROW]
[ROW][C]87[/C][C]0.578084317438892[/C][C]0.843831365122215[/C][C]0.421915682561108[/C][/ROW]
[ROW][C]88[/C][C]0.5395964042622[/C][C]0.9208071914756[/C][C]0.4604035957378[/C][/ROW]
[ROW][C]89[/C][C]0.58408842977579[/C][C]0.83182314044842[/C][C]0.41591157022421[/C][/ROW]
[ROW][C]90[/C][C]0.574001897979748[/C][C]0.851996204040503[/C][C]0.425998102020252[/C][/ROW]
[ROW][C]91[/C][C]0.532638140293865[/C][C]0.93472371941227[/C][C]0.467361859706135[/C][/ROW]
[ROW][C]92[/C][C]0.627642823784665[/C][C]0.744714352430671[/C][C]0.372357176215335[/C][/ROW]
[ROW][C]93[/C][C]0.598912552859285[/C][C]0.80217489428143[/C][C]0.401087447140715[/C][/ROW]
[ROW][C]94[/C][C]0.594807502083747[/C][C]0.810384995832507[/C][C]0.405192497916253[/C][/ROW]
[ROW][C]95[/C][C]0.58374155752537[/C][C]0.83251688494926[/C][C]0.41625844247463[/C][/ROW]
[ROW][C]96[/C][C]0.55260106643709[/C][C]0.89479786712582[/C][C]0.44739893356291[/C][/ROW]
[ROW][C]97[/C][C]0.505990104013384[/C][C]0.988019791973233[/C][C]0.494009895986616[/C][/ROW]
[ROW][C]98[/C][C]0.46821870623514[/C][C]0.93643741247028[/C][C]0.53178129376486[/C][/ROW]
[ROW][C]99[/C][C]0.431000387688944[/C][C]0.862000775377888[/C][C]0.568999612311056[/C][/ROW]
[ROW][C]100[/C][C]0.394018441619952[/C][C]0.788036883239904[/C][C]0.605981558380048[/C][/ROW]
[ROW][C]101[/C][C]0.358482444998728[/C][C]0.716964889997456[/C][C]0.641517555001272[/C][/ROW]
[ROW][C]102[/C][C]0.322438669635343[/C][C]0.644877339270687[/C][C]0.677561330364657[/C][/ROW]
[ROW][C]103[/C][C]0.31910996485471[/C][C]0.63821992970942[/C][C]0.68089003514529[/C][/ROW]
[ROW][C]104[/C][C]0.283783716991102[/C][C]0.567567433982204[/C][C]0.716216283008898[/C][/ROW]
[ROW][C]105[/C][C]0.265928339630875[/C][C]0.531856679261749[/C][C]0.734071660369125[/C][/ROW]
[ROW][C]106[/C][C]0.228649098307145[/C][C]0.457298196614289[/C][C]0.771350901692855[/C][/ROW]
[ROW][C]107[/C][C]0.19736011153308[/C][C]0.39472022306616[/C][C]0.80263988846692[/C][/ROW]
[ROW][C]108[/C][C]0.165777570517808[/C][C]0.331555141035617[/C][C]0.834222429482192[/C][/ROW]
[ROW][C]109[/C][C]0.184569782747593[/C][C]0.369139565495187[/C][C]0.815430217252407[/C][/ROW]
[ROW][C]110[/C][C]0.209925480024787[/C][C]0.419850960049575[/C][C]0.790074519975212[/C][/ROW]
[ROW][C]111[/C][C]0.21567600670808[/C][C]0.43135201341616[/C][C]0.78432399329192[/C][/ROW]
[ROW][C]112[/C][C]0.195749575010144[/C][C]0.391499150020287[/C][C]0.804250424989856[/C][/ROW]
[ROW][C]113[/C][C]0.280685409779097[/C][C]0.561370819558194[/C][C]0.719314590220903[/C][/ROW]
[ROW][C]114[/C][C]0.703976324085162[/C][C]0.592047351829677[/C][C]0.296023675914838[/C][/ROW]
[ROW][C]115[/C][C]0.672509288463091[/C][C]0.654981423073819[/C][C]0.327490711536909[/C][/ROW]
[ROW][C]116[/C][C]0.661955792381476[/C][C]0.676088415237048[/C][C]0.338044207618524[/C][/ROW]
[ROW][C]117[/C][C]0.720913562107663[/C][C]0.558172875784675[/C][C]0.279086437892338[/C][/ROW]
[ROW][C]118[/C][C]0.674446981545213[/C][C]0.651106036909574[/C][C]0.325553018454787[/C][/ROW]
[ROW][C]119[/C][C]0.628431301035107[/C][C]0.743137397929786[/C][C]0.371568698964893[/C][/ROW]
[ROW][C]120[/C][C]0.712021788491966[/C][C]0.575956423016068[/C][C]0.287978211508034[/C][/ROW]
[ROW][C]121[/C][C]0.76158685556894[/C][C]0.476826288862118[/C][C]0.238413144431059[/C][/ROW]
[ROW][C]122[/C][C]0.729132670882991[/C][C]0.541734658234018[/C][C]0.270867329117009[/C][/ROW]
[ROW][C]123[/C][C]0.771026204780604[/C][C]0.457947590438793[/C][C]0.228973795219396[/C][/ROW]
[ROW][C]124[/C][C]0.723727603440672[/C][C]0.552544793118656[/C][C]0.276272396559328[/C][/ROW]
[ROW][C]125[/C][C]0.673323536177093[/C][C]0.653352927645814[/C][C]0.326676463822907[/C][/ROW]
[ROW][C]126[/C][C]0.657157123319206[/C][C]0.685685753361589[/C][C]0.342842876680794[/C][/ROW]
[ROW][C]127[/C][C]0.65479698882983[/C][C]0.690406022340339[/C][C]0.345203011170169[/C][/ROW]
[ROW][C]128[/C][C]0.63660409050385[/C][C]0.7267918189923[/C][C]0.36339590949615[/C][/ROW]
[ROW][C]129[/C][C]0.581867956996139[/C][C]0.836264086007722[/C][C]0.418132043003861[/C][/ROW]
[ROW][C]130[/C][C]0.527361113197496[/C][C]0.945277773605007[/C][C]0.472638886802504[/C][/ROW]
[ROW][C]131[/C][C]0.461757978932994[/C][C]0.923515957865988[/C][C]0.538242021067006[/C][/ROW]
[ROW][C]132[/C][C]0.410733480512832[/C][C]0.821466961025663[/C][C]0.589266519487168[/C][/ROW]
[ROW][C]133[/C][C]0.357170804725624[/C][C]0.714341609451248[/C][C]0.642829195274376[/C][/ROW]
[ROW][C]134[/C][C]0.31394201847092[/C][C]0.627884036941841[/C][C]0.68605798152908[/C][/ROW]
[ROW][C]135[/C][C]0.265291419645674[/C][C]0.530582839291349[/C][C]0.734708580354326[/C][/ROW]
[ROW][C]136[/C][C]0.255938226574468[/C][C]0.511876453148936[/C][C]0.744061773425532[/C][/ROW]
[ROW][C]137[/C][C]0.28003080125306[/C][C]0.56006160250612[/C][C]0.71996919874694[/C][/ROW]
[ROW][C]138[/C][C]0.256074734991012[/C][C]0.512149469982024[/C][C]0.743925265008988[/C][/ROW]
[ROW][C]139[/C][C]0.288069503717956[/C][C]0.576139007435912[/C][C]0.711930496282044[/C][/ROW]
[ROW][C]140[/C][C]0.308140830562619[/C][C]0.616281661125239[/C][C]0.69185916943738[/C][/ROW]
[ROW][C]141[/C][C]0.309156365242704[/C][C]0.618312730485408[/C][C]0.690843634757296[/C][/ROW]
[ROW][C]142[/C][C]0.563150669746115[/C][C]0.873698660507769[/C][C]0.436849330253885[/C][/ROW]
[ROW][C]143[/C][C]0.506639649002095[/C][C]0.98672070199581[/C][C]0.493360350997905[/C][/ROW]
[ROW][C]144[/C][C]0.721900534356761[/C][C]0.556198931286478[/C][C]0.278099465643239[/C][/ROW]
[ROW][C]145[/C][C]0.6518596368096[/C][C]0.696280726380799[/C][C]0.348140363190399[/C][/ROW]
[ROW][C]146[/C][C]0.626305503804482[/C][C]0.747388992391035[/C][C]0.373694496195518[/C][/ROW]
[ROW][C]147[/C][C]0.507156221933716[/C][C]0.985687556132567[/C][C]0.492843778066284[/C][/ROW]
[ROW][C]148[/C][C]0.473288313127729[/C][C]0.946576626255459[/C][C]0.526711686872271[/C][/ROW]
[ROW][C]149[/C][C]0.358130703709342[/C][C]0.716261407418684[/C][C]0.641869296290658[/C][/ROW]
[ROW][C]150[/C][C]0.230661699036895[/C][C]0.46132339807379[/C][C]0.769338300963105[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144663&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144663&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
110.1040936282276730.2081872564553460.895906371772327
120.9642529713382880.07149405732342390.0357470286617119
130.9380728035694290.1238543928611420.0619271964305709
140.9199245010004170.1601509979991650.0800754989995826
150.9363331150512580.1273337698974840.0636668849487419
160.9525540201085610.0948919597828770.0474459798914385
170.9298555987445680.1402888025108650.0701444012554324
180.93241127795730.1351774440854010.0675887220427007
190.9269057472934820.1461885054130350.0730942527065176
200.9520554624974820.09588907500503540.0479445375025177
210.9505319636092230.09893607278155420.0494680363907771
220.929644089778230.1407118204435390.0703559102217695
230.9390590523939220.1218818952121560.0609409476060779
240.9269601651755440.1460796696489120.0730398348244558
250.9336492914775720.1327014170448560.0663507085224279
260.9372003097691340.1255993804617320.062799690230866
270.9241360374557840.1517279250884320.0758639625442162
280.9004477395871120.1991045208257760.0995522604128881
290.8834254884742950.2331490230514090.116574511525705
300.8489189767328870.3021620465342270.151081023267113
310.811056975624680.3778860487506410.188943024375321
320.8182097653977720.3635804692044560.181790234602228
330.781547940221390.4369041195572210.218452059778611
340.8004166953079780.3991666093840430.199583304692022
350.75894023899430.48211952201140.2410597610057
360.7118505822830790.5762988354338420.288149417716921
370.6724804845924550.655039030815090.327519515407545
380.6191120143471160.7617759713057690.380887985652884
390.5630677952665460.8738644094669070.436932204733454
400.5097322200286430.9805355599427150.490267779971357
410.4535579361632970.9071158723265940.546442063836703
420.4104014266038780.8208028532077570.589598573396122
430.610018374620670.7799632507586590.38998162537933
440.6602889155665110.6794221688669780.339711084433489
450.6124538780002920.7750922439994160.387546121999708
460.5640199002396030.8719601995207950.435980099760397
470.5344883357392270.9310233285215470.465511664260773
480.4874639159739740.9749278319479480.512536084026026
490.4390935502588480.8781871005176970.560906449741151
500.3975104838485220.7950209676970450.602489516151478
510.3487531755966730.6975063511933470.651246824403327
520.3072575053381090.6145150106762180.692742494661891
530.264088244431870.5281764888637390.73591175556813
540.226998832311690.4539976646233810.77300116768831
550.2026365400022870.4052730800045740.797363459997713
560.1745049128018690.3490098256037370.825495087198131
570.1455257459918870.2910514919837730.854474254008113
580.1274424315960790.2548848631921580.872557568403921
590.1085915588666840.2171831177333680.891408441133316
600.08973890784791450.1794778156958290.910261092152086
610.08348748990639660.1669749798127930.916512510093603
620.07646739040774010.152934780815480.92353260959226
630.07617407107376540.1523481421475310.923825928926235
640.09384007303142560.1876801460628510.906159926968574
650.1291648086721440.2583296173442880.870835191327856
660.1407073465556240.2814146931112470.859292653444376
670.1155889472429450.231177894485890.884411052757055
680.09737927739570130.1947585547914030.902620722604299
690.08784189603580380.1756837920716080.912158103964196
700.07063496028804780.1412699205760960.929365039711952
710.05794315144954720.1158863028990940.942056848550453
720.0742557825914780.1485115651829560.925744217408522
730.0735465757611220.1470931515222440.926453424238878
740.05931625384107660.1186325076821530.940683746158923
750.06309032848201460.1261806569640290.936909671517985
760.07092785770961470.1418557154192290.929072142290385
770.05780598597361740.1156119719472350.942194014026383
780.04580537202820990.09161074405641980.95419462797179
790.03969444554332240.07938889108664470.960305554456678
800.07626815518731850.1525363103746370.923731844812681
810.06999691255757490.139993825115150.930003087442425
820.7344165346437210.5311669307125570.265583465356279
830.7042189380213360.5915621239573280.295781061978664
840.671252862029820.657494275940360.32874713797018
850.6327233401470250.7345533197059490.367276659852975
860.6119054734721120.7761890530557770.388094526527888
870.5780843174388920.8438313651222150.421915682561108
880.53959640426220.92080719147560.4604035957378
890.584088429775790.831823140448420.41591157022421
900.5740018979797480.8519962040405030.425998102020252
910.5326381402938650.934723719412270.467361859706135
920.6276428237846650.7447143524306710.372357176215335
930.5989125528592850.802174894281430.401087447140715
940.5948075020837470.8103849958325070.405192497916253
950.583741557525370.832516884949260.41625844247463
960.552601066437090.894797867125820.44739893356291
970.5059901040133840.9880197919732330.494009895986616
980.468218706235140.936437412470280.53178129376486
990.4310003876889440.8620007753778880.568999612311056
1000.3940184416199520.7880368832399040.605981558380048
1010.3584824449987280.7169648899974560.641517555001272
1020.3224386696353430.6448773392706870.677561330364657
1030.319109964854710.638219929709420.68089003514529
1040.2837837169911020.5675674339822040.716216283008898
1050.2659283396308750.5318566792617490.734071660369125
1060.2286490983071450.4572981966142890.771350901692855
1070.197360111533080.394720223066160.80263988846692
1080.1657775705178080.3315551410356170.834222429482192
1090.1845697827475930.3691395654951870.815430217252407
1100.2099254800247870.4198509600495750.790074519975212
1110.215676006708080.431352013416160.78432399329192
1120.1957495750101440.3914991500202870.804250424989856
1130.2806854097790970.5613708195581940.719314590220903
1140.7039763240851620.5920473518296770.296023675914838
1150.6725092884630910.6549814230738190.327490711536909
1160.6619557923814760.6760884152370480.338044207618524
1170.7209135621076630.5581728757846750.279086437892338
1180.6744469815452130.6511060369095740.325553018454787
1190.6284313010351070.7431373979297860.371568698964893
1200.7120217884919660.5759564230160680.287978211508034
1210.761586855568940.4768262888621180.238413144431059
1220.7291326708829910.5417346582340180.270867329117009
1230.7710262047806040.4579475904387930.228973795219396
1240.7237276034406720.5525447931186560.276272396559328
1250.6733235361770930.6533529276458140.326676463822907
1260.6571571233192060.6856857533615890.342842876680794
1270.654796988829830.6904060223403390.345203011170169
1280.636604090503850.72679181899230.36339590949615
1290.5818679569961390.8362640860077220.418132043003861
1300.5273611131974960.9452777736050070.472638886802504
1310.4617579789329940.9235159578659880.538242021067006
1320.4107334805128320.8214669610256630.589266519487168
1330.3571708047256240.7143416094512480.642829195274376
1340.313942018470920.6278840369418410.68605798152908
1350.2652914196456740.5305828392913490.734708580354326
1360.2559382265744680.5118764531489360.744061773425532
1370.280030801253060.560061602506120.71996919874694
1380.2560747349910120.5121494699820240.743925265008988
1390.2880695037179560.5761390074359120.711930496282044
1400.3081408305626190.6162816611252390.69185916943738
1410.3091563652427040.6183127304854080.690843634757296
1420.5631506697461150.8736986605077690.436849330253885
1430.5066396490020950.986720701995810.493360350997905
1440.7219005343567610.5561989312864780.278099465643239
1450.65185963680960.6962807263807990.348140363190399
1460.6263055038044820.7473889923910350.373694496195518
1470.5071562219337160.9856875561325670.492843778066284
1480.4732883131277290.9465766262554590.526711686872271
1490.3581307037093420.7162614074186840.641869296290658
1500.2306616990368950.461323398073790.769338300963105






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 level60.0428571428571429OK

\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 & 6 & 0.0428571428571429 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144663&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]6[/C][C]0.0428571428571429[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144663&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144663&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 level60.0428571428571429OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 8 ; 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')
}