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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 28 Dec 2010 12:27:13 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t1293539424e739kc3ne541myy.htm/, Retrieved Sun, 05 May 2024 04:03:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116322, Retrieved Sun, 05 May 2024 04:03:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
- R  D  [Univariate Explorative Data Analysis] [WS7 Tutorial Popu...] [2010-11-22 10:43:52] [afe9379cca749d06b3d6872e02cc47ed]
- RMPD      [Multiple Regression] [] [2010-12-28 12:27:13] [f3ebcd8ed76b32fdadb8ee7c5dc81ded] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	14	11	12	24	26
9	11	7	8	25	23
9	6	17	8	30	25
9	12	10	8	19	23
9	8	12	9	22	19
9	10	12	7	22	29
10	10	11	4	25	25
10	11	11	11	23	21
10	16	12	7	17	22
10	11	13	7	21	25
10	13	14	12	19	24
10	12	16	10	19	18
10	8	11	10	15	22
10	12	10	8	16	15
10	11	11	8	23	22
10	4	15	4	27	28
10	9	9	9	22	20
10	8	11	8	14	12
10	8	17	7	22	24
10	14	17	11	23	20
10	15	11	9	23	21
10	16	18	11	21	20
10	9	14	13	19	21
10	14	10	8	18	23
10	11	11	8	20	28
10	8	15	9	23	24
10	9	15	6	25	24
10	9	13	9	19	24
10	9	16	9	24	23
10	9	13	6	22	23
10	10	9	6	25	29
10	16	18	16	26	24
10	11	18	5	29	18
10	8	12	7	32	25
10	9	17	9	25	21
10	16	9	6	29	26
10	11	9	6	28	22
10	16	12	5	17	22
10	12	18	12	28	22
10	12	12	7	29	23
10	14	18	10	26	30
10	9	14	9	25	23
10	10	15	8	14	17
10	9	16	5	25	23
10	10	10	8	26	23
10	12	11	8	20	25
10	14	14	10	18	24
10	14	9	6	32	24
10	10	12	8	25	23
10	14	17	7	25	21
10	16	5	4	23	24
10	9	12	8	21	24
10	10	12	8	20	28
10	6	6	4	15	16
10	8	24	20	30	20
10	13	12	8	24	29
10	10	12	8	26	27
10	8	14	6	24	22
10	7	7	4	22	28
10	15	13	8	14	16
10	9	12	9	24	25
10	10	13	6	24	24
10	12	14	7	24	28
10	13	8	9	24	24
10	10	11	5	19	23
10	11	9	5	31	30
10	8	11	8	22	24
10	9	13	8	27	21
10	13	10	6	19	25
10	11	11	8	25	25
10	8	12	7	20	22
10	9	9	7	21	23
10	9	15	9	27	26
10	15	18	11	23	23
10	9	15	6	25	25
10	10	12	8	20	21
10	14	13	6	21	25
10	12	14	9	22	24
10	12	10	8	23	29
10	11	13	6	25	22
10	14	13	10	25	27
10	6	11	8	17	26
10	12	13	8	19	22
10	8	16	10	25	24
10	14	8	5	19	27
10	11	16	7	20	24
10	10	11	5	26	24
10	14	9	8	23	29
10	12	16	14	27	22
10	10	12	7	17	21
10	14	14	8	17	24
10	5	8	6	19	24
10	11	9	5	17	23
10	10	15	6	22	20
10	9	11	10	21	27
10	10	21	12	32	26
10	16	14	9	21	25
10	13	18	12	21	21
10	9	12	7	18	21
10	10	13	8	18	19
10	10	15	10	23	21
10	7	12	6	19	21
10	9	19	10	20	16
10	8	15	10	21	22
10	14	11	10	20	29
10	14	11	5	17	15
10	8	10	7	18	17
10	9	13	10	19	15
10	14	15	11	22	21
10	14	12	6	15	21
10	8	12	7	14	19
10	8	16	12	18	24
10	8	9	11	24	20
10	7	18	11	35	17
10	6	8	11	29	23
10	8	13	5	21	24
10	6	17	8	25	14
10	11	9	6	20	19
10	14	15	9	22	24
10	11	8	4	13	13
10	11	7	4	26	22
10	11	12	7	17	16
10	14	14	11	25	19
10	8	6	6	20	25
10	20	8	7	19	25
10	11	17	8	21	23
10	8	10	4	22	24
10	11	11	8	24	26
10	10	14	9	21	26
10	14	11	8	26	25
10	11	13	11	24	18
10	9	12	8	16	21
10	9	11	5	23	26
10	8	9	4	18	23
10	10	12	8	16	23
10	13	20	10	26	22
10	13	12	6	19	20
10	12	13	9	21	13
10	8	12	9	21	24
10	13	12	13	22	15
10	14	9	9	23	14
10	12	15	10	29	22
10	14	24	20	21	10
10	15	7	5	21	24
10	13	17	11	23	22
10	16	11	6	27	24
10	9	17	9	25	19
10	9	11	7	21	20
10	9	12	9	10	13
10	8	14	10	20	20
10	7	11	9	26	22
10	16	16	8	24	24
10	11	21	7	29	29
10	9	14	6	19	12
10	11	20	13	24	20
10	9	13	6	19	21
10	14	11	8	24	24
10	13	15	10	22	22
10	16	19	16	17	20

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116322&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116322&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116322&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
PersonalStandards[t] = + 18.4826788440990 -0.99436172606256Maand[t] -0.112328105823988DoubtsAboutActions[t] + 0.339703753624901ParentalExpectations[t] + 0.093100809713022ParentalCriticism[t] + 0.437231123532580`Organization `[t] -0.00132568663676311t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PersonalStandards[t] =  +  18.4826788440990 -0.99436172606256Maand[t] -0.112328105823988DoubtsAboutActions[t] +  0.339703753624901ParentalExpectations[t] +  0.093100809713022ParentalCriticism[t] +  0.437231123532580`Organization
`[t] -0.00132568663676311t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116322&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PersonalStandards[t] =  +  18.4826788440990 -0.99436172606256Maand[t] -0.112328105823988DoubtsAboutActions[t] +  0.339703753624901ParentalExpectations[t] +  0.093100809713022ParentalCriticism[t] +  0.437231123532580`Organization
`[t] -0.00132568663676311t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116322&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116322&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
PersonalStandards[t] = + 18.4826788440990 -0.99436172606256Maand[t] -0.112328105823988DoubtsAboutActions[t] + 0.339703753624901ParentalExpectations[t] + 0.093100809713022ParentalCriticism[t] + 0.437231123532580`Organization `[t] -0.00132568663676311t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.482678844099016.7823811.10130.2725010.13625
Maand-0.994361726062561.68538-0.590.5560720.278036
DoubtsAboutActions-0.1123281058239880.110148-1.01980.3094450.154723
ParentalExpectations0.3397037536249010.1098033.09380.0023530.001176
ParentalCriticism0.0931008097130220.1430740.65070.516210.258105
`Organization `0.4372311235325800.0815165.363700
t-0.001325686636763110.00711-0.18650.8523380.426169

\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) & 18.4826788440990 & 16.782381 & 1.1013 & 0.272501 & 0.13625 \tabularnewline
Maand & -0.99436172606256 & 1.68538 & -0.59 & 0.556072 & 0.278036 \tabularnewline
DoubtsAboutActions & -0.112328105823988 & 0.110148 & -1.0198 & 0.309445 & 0.154723 \tabularnewline
ParentalExpectations & 0.339703753624901 & 0.109803 & 3.0938 & 0.002353 & 0.001176 \tabularnewline
ParentalCriticism & 0.093100809713022 & 0.143074 & 0.6507 & 0.51621 & 0.258105 \tabularnewline
`Organization
` & 0.437231123532580 & 0.081516 & 5.3637 & 0 & 0 \tabularnewline
t & -0.00132568663676311 & 0.00711 & -0.1865 & 0.852338 & 0.426169 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116322&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]18.4826788440990[/C][C]16.782381[/C][C]1.1013[/C][C]0.272501[/C][C]0.13625[/C][/ROW]
[ROW][C]Maand[/C][C]-0.99436172606256[/C][C]1.68538[/C][C]-0.59[/C][C]0.556072[/C][C]0.278036[/C][/ROW]
[ROW][C]DoubtsAboutActions[/C][C]-0.112328105823988[/C][C]0.110148[/C][C]-1.0198[/C][C]0.309445[/C][C]0.154723[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.339703753624901[/C][C]0.109803[/C][C]3.0938[/C][C]0.002353[/C][C]0.001176[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.093100809713022[/C][C]0.143074[/C][C]0.6507[/C][C]0.51621[/C][C]0.258105[/C][/ROW]
[ROW][C]`Organization
`[/C][C]0.437231123532580[/C][C]0.081516[/C][C]5.3637[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.00132568663676311[/C][C]0.00711[/C][C]-0.1865[/C][C]0.852338[/C][C]0.426169[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116322&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116322&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)18.482678844099016.7823811.10130.2725010.13625
Maand-0.994361726062561.68538-0.590.5560720.278036
DoubtsAboutActions-0.1123281058239880.110148-1.01980.3094450.154723
ParentalExpectations0.3397037536249010.1098033.09380.0023530.001176
ParentalCriticism0.0931008097130220.1430740.65070.516210.258105
`Organization `0.4372311235325800.0815165.363700
t-0.001325686636763110.00711-0.18650.8523380.426169






Multiple Linear Regression - Regression Statistics
Multiple R0.47458145992812
R-squared0.225227562107506
Adjusted R-squared0.194644439559118
F-TEST (value)7.36443970857313
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value6.29173045330056e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.78436635676899
Sum Squared Residuals2176.85716578124

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.47458145992812 \tabularnewline
R-squared & 0.225227562107506 \tabularnewline
Adjusted R-squared & 0.194644439559118 \tabularnewline
F-TEST (value) & 7.36443970857313 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 6.29173045330056e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.78436635676899 \tabularnewline
Sum Squared Residuals & 2176.85716578124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116322&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.47458145992812[/C][/ROW]
[ROW][C]R-squared[/C][C]0.225227562107506[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.194644439559118[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.36443970857313[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]6.29173045330056e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.78436635676899[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2176.85716578124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116322&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116322&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 R0.47458145992812
R-squared0.225227562107506
Adjusted R-squared0.194644439559118
F-TEST (value)7.36443970857313
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value6.29173045330056e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.78436635676899
Sum Squared Residuals2176.85716578124






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.1814643596406-0.181464359640563
22521.47421136652643.52578863347358
33026.30602599232383.69397400767622
41922.3783431483036-3.37834314830361
52221.84991370779530.150086292204692
62225.8100414254103-3.81004142541033
72522.44642333581672.55357666418329
82321.23555071721681.7644492827832
91721.0771161397655-4.07711613976549
102123.2888281064713-2.28882810647131
111923.430822886844-4.430822886844
121921.4116444526595-2.4116444526595
131521.9100369153245-6.91003691532451
141617.8728755676128-1.87287556761278
152321.38419960515301.61580039484703
162725.77896917612711.22103082387288
172220.14543549892551.85456450107452
181417.3448956273888-3.34489562738884
192224.5354651351794-2.53546513517943
202322.48364955832050.5163504416795
212320.58280274821692.41719725178312
222122.5960457270239-1.59604572702390
231922.6456345096141-3.64563450961408
241821.1328114778578-3.13281147785782
252023.9943294799808-3.99432947998082
262324.0329794408983-1.03297944089833
272523.64002321929851.35997678070149
281923.238592454551-4.23859245455101
292423.81914690525640.180853094743629
302222.5194075286058-0.519407528605839
312523.67032546284101.32967453715903
322624.79721740335171.2027825966483
332921.71003659779627.28996340220385
343223.25429219103618.74570780896394
352523.27643429199551.72356570800447
362921.67803502411557.32196497588452
372820.48942537246837.51057462753166
381720.8524696078733-3.85246960787331
392823.99038453427314.00961546572694
402921.92256340085447.07743659914564
412627.0747243181862-1.07472431818616
422523.12250547172871.87749452827135
431420.6320678819843-6.6320678819843
442523.42685836685281.57314163314716
452621.55428448178174.44571551821825
462022.5424685841871-2.54246858418707
471823.0845684426705-5.08456844267049
483221.012320749057110.9876792509429
492522.22838924248452.77161075751550
502522.50870684389812.4912931561019
512319.23867084357323.76132915642678
522122.7739714119308-1.77397141193077
532024.4092421136003-4.40924211360034
541517.1998296072671-2.19982960726708
553026.32705272376923.67294727623077
562424.5055118597507-0.505511859750671
572623.96670824352072.03329175647929
582422.49708903869281.50291096130722
592222.6673503042751-0.667350304275138
601418.931252049257-4.931252049257
612423.29237216544550.707627834554491
622422.8018885739381.19811142606199
632424.7576357331215-0.757635733121516
642421.04303654420712.95696345579292
651921.5881720735323-2.58817207353232
663123.85572863854987.14427136145017
672222.5267104645784-0.526710464578415
682721.78077080876975.21922919123028
691921.8737443126666-2.87374431266658
702522.62298021072872.37701978927126
712021.8935484148781-1.89354841487808
722121.1980144850752-0.198014485075207
732724.73280631021162.26719368978837
742323.9511314983339-0.951131498333947
752524.01362138426650.986378615733538
762021.3181334562267-1.31813345622673
772122.7699219746232-1.76992197462319
782223.1750275588658-1.17502755886579
792323.9079416656793-0.907941665679305
802521.79123586158713.20876413841287
812524.01148471399340.988515286006611
821723.6059436237401-6.6059436237401
831921.8611323152789-2.86113231527889
842524.3888941793040.611105820696009
851921.8421591507567-2.84215915075672
862023.7699560594194-3.76995605941943
872621.99623809105614.00376190894389
882323.3316505206756-0.33165052067556
892723.43089431461113.56910568538885
901721.2064730335990-4.20647303359902
911722.8400366112269-5.84003661122687
921921.6252397358306-2.62523973583056
931720.7593172346292-3.75931723462916
942221.68994961468110.310050385318928
952123.8751581229488-2.87515812294885
963226.90751236263065.09248763736943
972123.1377582130039-2.13775821300392
982123.3626097933475-2.36260979334747
991821.3068699596921-3.30686995969214
1001820.7515584835042-2.75155848350416
1012322.49030417060840.509695829391601
1021921.4344483017168-2.43444830171681
1032021.7726402999956-1.77264029999556
1042123.1482144458787-2.14821444587867
1052024.1747229745264-4.17472297452643
1061717.5866575098684-0.586657509868434
1071818.9802605710419-0.980260571041904
1081919.2905582215298-0.290558221529762
1092222.1234870639314-0.123487063931363
1101520.6375460678548-5.63754606785479
1111420.5288275788098-6.52882757880981
1121824.5379765729007-6.53797657290067
1132420.31669930704633.68330069295375
1143522.173342138259812.8266578617402
1152921.51069376239357.48930623760645
1162122.8618568974878-1.86185689748776
1172520.35099363081184.64900636918816
1182019.07035138429280.929648615707217
1192223.2357219487354-1.23572194873543
1201315.9184078967728-2.91840789677283
1212619.51245856830446.48754143169561
1221718.8655673377357-1.86556733773571
1232520.89076145032664.10923854967338
1242021.0036570622649-1.00365706226495
1251920.4269024227032-1.42690242270315
1262123.7125020337543-2.71250203375425
1272221.73506227389560.264937726104360
1282422.98332150932911.01667849067094
1292124.206535999104-3.20653599910401
1302622.2064546950513.79354530494901
1312420.4402053975473.559794602453
1321621.356223110392-5.35622311039199
1332322.92204685865420.077953141345844
1341820.9488475902808-2.94884759028082
1351622.1143801917229-6.11438019172287
1362624.24267071250681.75732928749319
1371920.2768495109536-1.27684951095359
1382117.94624024817673.05375975182328
1392122.8640655900694-1.86406559006939
1402218.73842250137163.26157749862845
1412316.79602308565146.20397691434857
1422922.64852593038576.35147406961428
1432121.1641124294643-0.164112429464339
1442120.00021840914110.999781590858933
1452323.3047290816143-0.304729081614261
1462721.33715475425625.66284524574382
1472522.25349514161292.74650485838710
1482120.46497643733330.535023562666729
1491017.9289382590194-7.9289382590194
1502021.8730668598975-1.87306685989750
1512621.74631945556224.25368054443784
1522423.21392102198610.786078978013853
1532927.56580944054371.43419055945629
1541917.88518378041371.11481621958627
1552423.84697906013020.153020939869807
1561921.4779087653085-2.47790876530853
1572421.73343003232582.26656996767420
1582222.5149868383735-0.514986838373517
1591723.2196344599774-6.21963445997736

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.1814643596406 & -0.181464359640563 \tabularnewline
2 & 25 & 21.4742113665264 & 3.52578863347358 \tabularnewline
3 & 30 & 26.3060259923238 & 3.69397400767622 \tabularnewline
4 & 19 & 22.3783431483036 & -3.37834314830361 \tabularnewline
5 & 22 & 21.8499137077953 & 0.150086292204692 \tabularnewline
6 & 22 & 25.8100414254103 & -3.81004142541033 \tabularnewline
7 & 25 & 22.4464233358167 & 2.55357666418329 \tabularnewline
8 & 23 & 21.2355507172168 & 1.7644492827832 \tabularnewline
9 & 17 & 21.0771161397655 & -4.07711613976549 \tabularnewline
10 & 21 & 23.2888281064713 & -2.28882810647131 \tabularnewline
11 & 19 & 23.430822886844 & -4.430822886844 \tabularnewline
12 & 19 & 21.4116444526595 & -2.4116444526595 \tabularnewline
13 & 15 & 21.9100369153245 & -6.91003691532451 \tabularnewline
14 & 16 & 17.8728755676128 & -1.87287556761278 \tabularnewline
15 & 23 & 21.3841996051530 & 1.61580039484703 \tabularnewline
16 & 27 & 25.7789691761271 & 1.22103082387288 \tabularnewline
17 & 22 & 20.1454354989255 & 1.85456450107452 \tabularnewline
18 & 14 & 17.3448956273888 & -3.34489562738884 \tabularnewline
19 & 22 & 24.5354651351794 & -2.53546513517943 \tabularnewline
20 & 23 & 22.4836495583205 & 0.5163504416795 \tabularnewline
21 & 23 & 20.5828027482169 & 2.41719725178312 \tabularnewline
22 & 21 & 22.5960457270239 & -1.59604572702390 \tabularnewline
23 & 19 & 22.6456345096141 & -3.64563450961408 \tabularnewline
24 & 18 & 21.1328114778578 & -3.13281147785782 \tabularnewline
25 & 20 & 23.9943294799808 & -3.99432947998082 \tabularnewline
26 & 23 & 24.0329794408983 & -1.03297944089833 \tabularnewline
27 & 25 & 23.6400232192985 & 1.35997678070149 \tabularnewline
28 & 19 & 23.238592454551 & -4.23859245455101 \tabularnewline
29 & 24 & 23.8191469052564 & 0.180853094743629 \tabularnewline
30 & 22 & 22.5194075286058 & -0.519407528605839 \tabularnewline
31 & 25 & 23.6703254628410 & 1.32967453715903 \tabularnewline
32 & 26 & 24.7972174033517 & 1.2027825966483 \tabularnewline
33 & 29 & 21.7100365977962 & 7.28996340220385 \tabularnewline
34 & 32 & 23.2542921910361 & 8.74570780896394 \tabularnewline
35 & 25 & 23.2764342919955 & 1.72356570800447 \tabularnewline
36 & 29 & 21.6780350241155 & 7.32196497588452 \tabularnewline
37 & 28 & 20.4894253724683 & 7.51057462753166 \tabularnewline
38 & 17 & 20.8524696078733 & -3.85246960787331 \tabularnewline
39 & 28 & 23.9903845342731 & 4.00961546572694 \tabularnewline
40 & 29 & 21.9225634008544 & 7.07743659914564 \tabularnewline
41 & 26 & 27.0747243181862 & -1.07472431818616 \tabularnewline
42 & 25 & 23.1225054717287 & 1.87749452827135 \tabularnewline
43 & 14 & 20.6320678819843 & -6.6320678819843 \tabularnewline
44 & 25 & 23.4268583668528 & 1.57314163314716 \tabularnewline
45 & 26 & 21.5542844817817 & 4.44571551821825 \tabularnewline
46 & 20 & 22.5424685841871 & -2.54246858418707 \tabularnewline
47 & 18 & 23.0845684426705 & -5.08456844267049 \tabularnewline
48 & 32 & 21.0123207490571 & 10.9876792509429 \tabularnewline
49 & 25 & 22.2283892424845 & 2.77161075751550 \tabularnewline
50 & 25 & 22.5087068438981 & 2.4912931561019 \tabularnewline
51 & 23 & 19.2386708435732 & 3.76132915642678 \tabularnewline
52 & 21 & 22.7739714119308 & -1.77397141193077 \tabularnewline
53 & 20 & 24.4092421136003 & -4.40924211360034 \tabularnewline
54 & 15 & 17.1998296072671 & -2.19982960726708 \tabularnewline
55 & 30 & 26.3270527237692 & 3.67294727623077 \tabularnewline
56 & 24 & 24.5055118597507 & -0.505511859750671 \tabularnewline
57 & 26 & 23.9667082435207 & 2.03329175647929 \tabularnewline
58 & 24 & 22.4970890386928 & 1.50291096130722 \tabularnewline
59 & 22 & 22.6673503042751 & -0.667350304275138 \tabularnewline
60 & 14 & 18.931252049257 & -4.931252049257 \tabularnewline
61 & 24 & 23.2923721654455 & 0.707627834554491 \tabularnewline
62 & 24 & 22.801888573938 & 1.19811142606199 \tabularnewline
63 & 24 & 24.7576357331215 & -0.757635733121516 \tabularnewline
64 & 24 & 21.0430365442071 & 2.95696345579292 \tabularnewline
65 & 19 & 21.5881720735323 & -2.58817207353232 \tabularnewline
66 & 31 & 23.8557286385498 & 7.14427136145017 \tabularnewline
67 & 22 & 22.5267104645784 & -0.526710464578415 \tabularnewline
68 & 27 & 21.7807708087697 & 5.21922919123028 \tabularnewline
69 & 19 & 21.8737443126666 & -2.87374431266658 \tabularnewline
70 & 25 & 22.6229802107287 & 2.37701978927126 \tabularnewline
71 & 20 & 21.8935484148781 & -1.89354841487808 \tabularnewline
72 & 21 & 21.1980144850752 & -0.198014485075207 \tabularnewline
73 & 27 & 24.7328063102116 & 2.26719368978837 \tabularnewline
74 & 23 & 23.9511314983339 & -0.951131498333947 \tabularnewline
75 & 25 & 24.0136213842665 & 0.986378615733538 \tabularnewline
76 & 20 & 21.3181334562267 & -1.31813345622673 \tabularnewline
77 & 21 & 22.7699219746232 & -1.76992197462319 \tabularnewline
78 & 22 & 23.1750275588658 & -1.17502755886579 \tabularnewline
79 & 23 & 23.9079416656793 & -0.907941665679305 \tabularnewline
80 & 25 & 21.7912358615871 & 3.20876413841287 \tabularnewline
81 & 25 & 24.0114847139934 & 0.988515286006611 \tabularnewline
82 & 17 & 23.6059436237401 & -6.6059436237401 \tabularnewline
83 & 19 & 21.8611323152789 & -2.86113231527889 \tabularnewline
84 & 25 & 24.388894179304 & 0.611105820696009 \tabularnewline
85 & 19 & 21.8421591507567 & -2.84215915075672 \tabularnewline
86 & 20 & 23.7699560594194 & -3.76995605941943 \tabularnewline
87 & 26 & 21.9962380910561 & 4.00376190894389 \tabularnewline
88 & 23 & 23.3316505206756 & -0.33165052067556 \tabularnewline
89 & 27 & 23.4308943146111 & 3.56910568538885 \tabularnewline
90 & 17 & 21.2064730335990 & -4.20647303359902 \tabularnewline
91 & 17 & 22.8400366112269 & -5.84003661122687 \tabularnewline
92 & 19 & 21.6252397358306 & -2.62523973583056 \tabularnewline
93 & 17 & 20.7593172346292 & -3.75931723462916 \tabularnewline
94 & 22 & 21.6899496146811 & 0.310050385318928 \tabularnewline
95 & 21 & 23.8751581229488 & -2.87515812294885 \tabularnewline
96 & 32 & 26.9075123626306 & 5.09248763736943 \tabularnewline
97 & 21 & 23.1377582130039 & -2.13775821300392 \tabularnewline
98 & 21 & 23.3626097933475 & -2.36260979334747 \tabularnewline
99 & 18 & 21.3068699596921 & -3.30686995969214 \tabularnewline
100 & 18 & 20.7515584835042 & -2.75155848350416 \tabularnewline
101 & 23 & 22.4903041706084 & 0.509695829391601 \tabularnewline
102 & 19 & 21.4344483017168 & -2.43444830171681 \tabularnewline
103 & 20 & 21.7726402999956 & -1.77264029999556 \tabularnewline
104 & 21 & 23.1482144458787 & -2.14821444587867 \tabularnewline
105 & 20 & 24.1747229745264 & -4.17472297452643 \tabularnewline
106 & 17 & 17.5866575098684 & -0.586657509868434 \tabularnewline
107 & 18 & 18.9802605710419 & -0.980260571041904 \tabularnewline
108 & 19 & 19.2905582215298 & -0.290558221529762 \tabularnewline
109 & 22 & 22.1234870639314 & -0.123487063931363 \tabularnewline
110 & 15 & 20.6375460678548 & -5.63754606785479 \tabularnewline
111 & 14 & 20.5288275788098 & -6.52882757880981 \tabularnewline
112 & 18 & 24.5379765729007 & -6.53797657290067 \tabularnewline
113 & 24 & 20.3166993070463 & 3.68330069295375 \tabularnewline
114 & 35 & 22.1733421382598 & 12.8266578617402 \tabularnewline
115 & 29 & 21.5106937623935 & 7.48930623760645 \tabularnewline
116 & 21 & 22.8618568974878 & -1.86185689748776 \tabularnewline
117 & 25 & 20.3509936308118 & 4.64900636918816 \tabularnewline
118 & 20 & 19.0703513842928 & 0.929648615707217 \tabularnewline
119 & 22 & 23.2357219487354 & -1.23572194873543 \tabularnewline
120 & 13 & 15.9184078967728 & -2.91840789677283 \tabularnewline
121 & 26 & 19.5124585683044 & 6.48754143169561 \tabularnewline
122 & 17 & 18.8655673377357 & -1.86556733773571 \tabularnewline
123 & 25 & 20.8907614503266 & 4.10923854967338 \tabularnewline
124 & 20 & 21.0036570622649 & -1.00365706226495 \tabularnewline
125 & 19 & 20.4269024227032 & -1.42690242270315 \tabularnewline
126 & 21 & 23.7125020337543 & -2.71250203375425 \tabularnewline
127 & 22 & 21.7350622738956 & 0.264937726104360 \tabularnewline
128 & 24 & 22.9833215093291 & 1.01667849067094 \tabularnewline
129 & 21 & 24.206535999104 & -3.20653599910401 \tabularnewline
130 & 26 & 22.206454695051 & 3.79354530494901 \tabularnewline
131 & 24 & 20.440205397547 & 3.559794602453 \tabularnewline
132 & 16 & 21.356223110392 & -5.35622311039199 \tabularnewline
133 & 23 & 22.9220468586542 & 0.077953141345844 \tabularnewline
134 & 18 & 20.9488475902808 & -2.94884759028082 \tabularnewline
135 & 16 & 22.1143801917229 & -6.11438019172287 \tabularnewline
136 & 26 & 24.2426707125068 & 1.75732928749319 \tabularnewline
137 & 19 & 20.2768495109536 & -1.27684951095359 \tabularnewline
138 & 21 & 17.9462402481767 & 3.05375975182328 \tabularnewline
139 & 21 & 22.8640655900694 & -1.86406559006939 \tabularnewline
140 & 22 & 18.7384225013716 & 3.26157749862845 \tabularnewline
141 & 23 & 16.7960230856514 & 6.20397691434857 \tabularnewline
142 & 29 & 22.6485259303857 & 6.35147406961428 \tabularnewline
143 & 21 & 21.1641124294643 & -0.164112429464339 \tabularnewline
144 & 21 & 20.0002184091411 & 0.999781590858933 \tabularnewline
145 & 23 & 23.3047290816143 & -0.304729081614261 \tabularnewline
146 & 27 & 21.3371547542562 & 5.66284524574382 \tabularnewline
147 & 25 & 22.2534951416129 & 2.74650485838710 \tabularnewline
148 & 21 & 20.4649764373333 & 0.535023562666729 \tabularnewline
149 & 10 & 17.9289382590194 & -7.9289382590194 \tabularnewline
150 & 20 & 21.8730668598975 & -1.87306685989750 \tabularnewline
151 & 26 & 21.7463194555622 & 4.25368054443784 \tabularnewline
152 & 24 & 23.2139210219861 & 0.786078978013853 \tabularnewline
153 & 29 & 27.5658094405437 & 1.43419055945629 \tabularnewline
154 & 19 & 17.8851837804137 & 1.11481621958627 \tabularnewline
155 & 24 & 23.8469790601302 & 0.153020939869807 \tabularnewline
156 & 19 & 21.4779087653085 & -2.47790876530853 \tabularnewline
157 & 24 & 21.7334300323258 & 2.26656996767420 \tabularnewline
158 & 22 & 22.5149868383735 & -0.514986838373517 \tabularnewline
159 & 17 & 23.2196344599774 & -6.21963445997736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116322&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]24[/C][C]24.1814643596406[/C][C]-0.181464359640563[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]21.4742113665264[/C][C]3.52578863347358[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]26.3060259923238[/C][C]3.69397400767622[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]22.3783431483036[/C][C]-3.37834314830361[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]21.8499137077953[/C][C]0.150086292204692[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]25.8100414254103[/C][C]-3.81004142541033[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]22.4464233358167[/C][C]2.55357666418329[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]21.2355507172168[/C][C]1.7644492827832[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]21.0771161397655[/C][C]-4.07711613976549[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]23.2888281064713[/C][C]-2.28882810647131[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]23.430822886844[/C][C]-4.430822886844[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]21.4116444526595[/C][C]-2.4116444526595[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]21.9100369153245[/C][C]-6.91003691532451[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]17.8728755676128[/C][C]-1.87287556761278[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]21.3841996051530[/C][C]1.61580039484703[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]25.7789691761271[/C][C]1.22103082387288[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]20.1454354989255[/C][C]1.85456450107452[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]17.3448956273888[/C][C]-3.34489562738884[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]24.5354651351794[/C][C]-2.53546513517943[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]22.4836495583205[/C][C]0.5163504416795[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]20.5828027482169[/C][C]2.41719725178312[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]22.5960457270239[/C][C]-1.59604572702390[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]22.6456345096141[/C][C]-3.64563450961408[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]21.1328114778578[/C][C]-3.13281147785782[/C][/ROW]
[ROW][C]25[/C][C]20[/C][C]23.9943294799808[/C][C]-3.99432947998082[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]24.0329794408983[/C][C]-1.03297944089833[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.6400232192985[/C][C]1.35997678070149[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]23.238592454551[/C][C]-4.23859245455101[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]23.8191469052564[/C][C]0.180853094743629[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]22.5194075286058[/C][C]-0.519407528605839[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]23.6703254628410[/C][C]1.32967453715903[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]24.7972174033517[/C][C]1.2027825966483[/C][/ROW]
[ROW][C]33[/C][C]29[/C][C]21.7100365977962[/C][C]7.28996340220385[/C][/ROW]
[ROW][C]34[/C][C]32[/C][C]23.2542921910361[/C][C]8.74570780896394[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]23.2764342919955[/C][C]1.72356570800447[/C][/ROW]
[ROW][C]36[/C][C]29[/C][C]21.6780350241155[/C][C]7.32196497588452[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]20.4894253724683[/C][C]7.51057462753166[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]20.8524696078733[/C][C]-3.85246960787331[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]23.9903845342731[/C][C]4.00961546572694[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]21.9225634008544[/C][C]7.07743659914564[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]27.0747243181862[/C][C]-1.07472431818616[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]23.1225054717287[/C][C]1.87749452827135[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]20.6320678819843[/C][C]-6.6320678819843[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]23.4268583668528[/C][C]1.57314163314716[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]21.5542844817817[/C][C]4.44571551821825[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]22.5424685841871[/C][C]-2.54246858418707[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]23.0845684426705[/C][C]-5.08456844267049[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]21.0123207490571[/C][C]10.9876792509429[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]22.2283892424845[/C][C]2.77161075751550[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]22.5087068438981[/C][C]2.4912931561019[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]19.2386708435732[/C][C]3.76132915642678[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]22.7739714119308[/C][C]-1.77397141193077[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]24.4092421136003[/C][C]-4.40924211360034[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]17.1998296072671[/C][C]-2.19982960726708[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]26.3270527237692[/C][C]3.67294727623077[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]24.5055118597507[/C][C]-0.505511859750671[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]23.9667082435207[/C][C]2.03329175647929[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]22.4970890386928[/C][C]1.50291096130722[/C][/ROW]
[ROW][C]59[/C][C]22[/C][C]22.6673503042751[/C][C]-0.667350304275138[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]18.931252049257[/C][C]-4.931252049257[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]23.2923721654455[/C][C]0.707627834554491[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]22.801888573938[/C][C]1.19811142606199[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]24.7576357331215[/C][C]-0.757635733121516[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]21.0430365442071[/C][C]2.95696345579292[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]21.5881720735323[/C][C]-2.58817207353232[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]23.8557286385498[/C][C]7.14427136145017[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]22.5267104645784[/C][C]-0.526710464578415[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]21.7807708087697[/C][C]5.21922919123028[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]21.8737443126666[/C][C]-2.87374431266658[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]22.6229802107287[/C][C]2.37701978927126[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]21.8935484148781[/C][C]-1.89354841487808[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]21.1980144850752[/C][C]-0.198014485075207[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]24.7328063102116[/C][C]2.26719368978837[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]23.9511314983339[/C][C]-0.951131498333947[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]24.0136213842665[/C][C]0.986378615733538[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]21.3181334562267[/C][C]-1.31813345622673[/C][/ROW]
[ROW][C]77[/C][C]21[/C][C]22.7699219746232[/C][C]-1.76992197462319[/C][/ROW]
[ROW][C]78[/C][C]22[/C][C]23.1750275588658[/C][C]-1.17502755886579[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]23.9079416656793[/C][C]-0.907941665679305[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]21.7912358615871[/C][C]3.20876413841287[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]24.0114847139934[/C][C]0.988515286006611[/C][/ROW]
[ROW][C]82[/C][C]17[/C][C]23.6059436237401[/C][C]-6.6059436237401[/C][/ROW]
[ROW][C]83[/C][C]19[/C][C]21.8611323152789[/C][C]-2.86113231527889[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]24.388894179304[/C][C]0.611105820696009[/C][/ROW]
[ROW][C]85[/C][C]19[/C][C]21.8421591507567[/C][C]-2.84215915075672[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]23.7699560594194[/C][C]-3.76995605941943[/C][/ROW]
[ROW][C]87[/C][C]26[/C][C]21.9962380910561[/C][C]4.00376190894389[/C][/ROW]
[ROW][C]88[/C][C]23[/C][C]23.3316505206756[/C][C]-0.33165052067556[/C][/ROW]
[ROW][C]89[/C][C]27[/C][C]23.4308943146111[/C][C]3.56910568538885[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]21.2064730335990[/C][C]-4.20647303359902[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]22.8400366112269[/C][C]-5.84003661122687[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]21.6252397358306[/C][C]-2.62523973583056[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]20.7593172346292[/C][C]-3.75931723462916[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.6899496146811[/C][C]0.310050385318928[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]23.8751581229488[/C][C]-2.87515812294885[/C][/ROW]
[ROW][C]96[/C][C]32[/C][C]26.9075123626306[/C][C]5.09248763736943[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]23.1377582130039[/C][C]-2.13775821300392[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]23.3626097933475[/C][C]-2.36260979334747[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]21.3068699596921[/C][C]-3.30686995969214[/C][/ROW]
[ROW][C]100[/C][C]18[/C][C]20.7515584835042[/C][C]-2.75155848350416[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.4903041706084[/C][C]0.509695829391601[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]21.4344483017168[/C][C]-2.43444830171681[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.7726402999956[/C][C]-1.77264029999556[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]23.1482144458787[/C][C]-2.14821444587867[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]24.1747229745264[/C][C]-4.17472297452643[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]17.5866575098684[/C][C]-0.586657509868434[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]18.9802605710419[/C][C]-0.980260571041904[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]19.2905582215298[/C][C]-0.290558221529762[/C][/ROW]
[ROW][C]109[/C][C]22[/C][C]22.1234870639314[/C][C]-0.123487063931363[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]20.6375460678548[/C][C]-5.63754606785479[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]20.5288275788098[/C][C]-6.52882757880981[/C][/ROW]
[ROW][C]112[/C][C]18[/C][C]24.5379765729007[/C][C]-6.53797657290067[/C][/ROW]
[ROW][C]113[/C][C]24[/C][C]20.3166993070463[/C][C]3.68330069295375[/C][/ROW]
[ROW][C]114[/C][C]35[/C][C]22.1733421382598[/C][C]12.8266578617402[/C][/ROW]
[ROW][C]115[/C][C]29[/C][C]21.5106937623935[/C][C]7.48930623760645[/C][/ROW]
[ROW][C]116[/C][C]21[/C][C]22.8618568974878[/C][C]-1.86185689748776[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]20.3509936308118[/C][C]4.64900636918816[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]19.0703513842928[/C][C]0.929648615707217[/C][/ROW]
[ROW][C]119[/C][C]22[/C][C]23.2357219487354[/C][C]-1.23572194873543[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]15.9184078967728[/C][C]-2.91840789677283[/C][/ROW]
[ROW][C]121[/C][C]26[/C][C]19.5124585683044[/C][C]6.48754143169561[/C][/ROW]
[ROW][C]122[/C][C]17[/C][C]18.8655673377357[/C][C]-1.86556733773571[/C][/ROW]
[ROW][C]123[/C][C]25[/C][C]20.8907614503266[/C][C]4.10923854967338[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]21.0036570622649[/C][C]-1.00365706226495[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]20.4269024227032[/C][C]-1.42690242270315[/C][/ROW]
[ROW][C]126[/C][C]21[/C][C]23.7125020337543[/C][C]-2.71250203375425[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]21.7350622738956[/C][C]0.264937726104360[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]22.9833215093291[/C][C]1.01667849067094[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]24.206535999104[/C][C]-3.20653599910401[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]22.206454695051[/C][C]3.79354530494901[/C][/ROW]
[ROW][C]131[/C][C]24[/C][C]20.440205397547[/C][C]3.559794602453[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]21.356223110392[/C][C]-5.35622311039199[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]22.9220468586542[/C][C]0.077953141345844[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.9488475902808[/C][C]-2.94884759028082[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]22.1143801917229[/C][C]-6.11438019172287[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]24.2426707125068[/C][C]1.75732928749319[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]20.2768495109536[/C][C]-1.27684951095359[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]17.9462402481767[/C][C]3.05375975182328[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]22.8640655900694[/C][C]-1.86406559006939[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]18.7384225013716[/C][C]3.26157749862845[/C][/ROW]
[ROW][C]141[/C][C]23[/C][C]16.7960230856514[/C][C]6.20397691434857[/C][/ROW]
[ROW][C]142[/C][C]29[/C][C]22.6485259303857[/C][C]6.35147406961428[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]21.1641124294643[/C][C]-0.164112429464339[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]20.0002184091411[/C][C]0.999781590858933[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]23.3047290816143[/C][C]-0.304729081614261[/C][/ROW]
[ROW][C]146[/C][C]27[/C][C]21.3371547542562[/C][C]5.66284524574382[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]22.2534951416129[/C][C]2.74650485838710[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]20.4649764373333[/C][C]0.535023562666729[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]17.9289382590194[/C][C]-7.9289382590194[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]21.8730668598975[/C][C]-1.87306685989750[/C][/ROW]
[ROW][C]151[/C][C]26[/C][C]21.7463194555622[/C][C]4.25368054443784[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.2139210219861[/C][C]0.786078978013853[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]27.5658094405437[/C][C]1.43419055945629[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]17.8851837804137[/C][C]1.11481621958627[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]23.8469790601302[/C][C]0.153020939869807[/C][/ROW]
[ROW][C]156[/C][C]19[/C][C]21.4779087653085[/C][C]-2.47790876530853[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]21.7334300323258[/C][C]2.26656996767420[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]22.5149868383735[/C][C]-0.514986838373517[/C][/ROW]
[ROW][C]159[/C][C]17[/C][C]23.2196344599774[/C][C]-6.21963445997736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116322&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116322&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
12424.1814643596406-0.181464359640563
22521.47421136652643.52578863347358
33026.30602599232383.69397400767622
41922.3783431483036-3.37834314830361
52221.84991370779530.150086292204692
62225.8100414254103-3.81004142541033
72522.44642333581672.55357666418329
82321.23555071721681.7644492827832
91721.0771161397655-4.07711613976549
102123.2888281064713-2.28882810647131
111923.430822886844-4.430822886844
121921.4116444526595-2.4116444526595
131521.9100369153245-6.91003691532451
141617.8728755676128-1.87287556761278
152321.38419960515301.61580039484703
162725.77896917612711.22103082387288
172220.14543549892551.85456450107452
181417.3448956273888-3.34489562738884
192224.5354651351794-2.53546513517943
202322.48364955832050.5163504416795
212320.58280274821692.41719725178312
222122.5960457270239-1.59604572702390
231922.6456345096141-3.64563450961408
241821.1328114778578-3.13281147785782
252023.9943294799808-3.99432947998082
262324.0329794408983-1.03297944089833
272523.64002321929851.35997678070149
281923.238592454551-4.23859245455101
292423.81914690525640.180853094743629
302222.5194075286058-0.519407528605839
312523.67032546284101.32967453715903
322624.79721740335171.2027825966483
332921.71003659779627.28996340220385
343223.25429219103618.74570780896394
352523.27643429199551.72356570800447
362921.67803502411557.32196497588452
372820.48942537246837.51057462753166
381720.8524696078733-3.85246960787331
392823.99038453427314.00961546572694
402921.92256340085447.07743659914564
412627.0747243181862-1.07472431818616
422523.12250547172871.87749452827135
431420.6320678819843-6.6320678819843
442523.42685836685281.57314163314716
452621.55428448178174.44571551821825
462022.5424685841871-2.54246858418707
471823.0845684426705-5.08456844267049
483221.012320749057110.9876792509429
492522.22838924248452.77161075751550
502522.50870684389812.4912931561019
512319.23867084357323.76132915642678
522122.7739714119308-1.77397141193077
532024.4092421136003-4.40924211360034
541517.1998296072671-2.19982960726708
553026.32705272376923.67294727623077
562424.5055118597507-0.505511859750671
572623.96670824352072.03329175647929
582422.49708903869281.50291096130722
592222.6673503042751-0.667350304275138
601418.931252049257-4.931252049257
612423.29237216544550.707627834554491
622422.8018885739381.19811142606199
632424.7576357331215-0.757635733121516
642421.04303654420712.95696345579292
651921.5881720735323-2.58817207353232
663123.85572863854987.14427136145017
672222.5267104645784-0.526710464578415
682721.78077080876975.21922919123028
691921.8737443126666-2.87374431266658
702522.62298021072872.37701978927126
712021.8935484148781-1.89354841487808
722121.1980144850752-0.198014485075207
732724.73280631021162.26719368978837
742323.9511314983339-0.951131498333947
752524.01362138426650.986378615733538
762021.3181334562267-1.31813345622673
772122.7699219746232-1.76992197462319
782223.1750275588658-1.17502755886579
792323.9079416656793-0.907941665679305
802521.79123586158713.20876413841287
812524.01148471399340.988515286006611
821723.6059436237401-6.6059436237401
831921.8611323152789-2.86113231527889
842524.3888941793040.611105820696009
851921.8421591507567-2.84215915075672
862023.7699560594194-3.76995605941943
872621.99623809105614.00376190894389
882323.3316505206756-0.33165052067556
892723.43089431461113.56910568538885
901721.2064730335990-4.20647303359902
911722.8400366112269-5.84003661122687
921921.6252397358306-2.62523973583056
931720.7593172346292-3.75931723462916
942221.68994961468110.310050385318928
952123.8751581229488-2.87515812294885
963226.90751236263065.09248763736943
972123.1377582130039-2.13775821300392
982123.3626097933475-2.36260979334747
991821.3068699596921-3.30686995969214
1001820.7515584835042-2.75155848350416
1012322.49030417060840.509695829391601
1021921.4344483017168-2.43444830171681
1032021.7726402999956-1.77264029999556
1042123.1482144458787-2.14821444587867
1052024.1747229745264-4.17472297452643
1061717.5866575098684-0.586657509868434
1071818.9802605710419-0.980260571041904
1081919.2905582215298-0.290558221529762
1092222.1234870639314-0.123487063931363
1101520.6375460678548-5.63754606785479
1111420.5288275788098-6.52882757880981
1121824.5379765729007-6.53797657290067
1132420.31669930704633.68330069295375
1143522.173342138259812.8266578617402
1152921.51069376239357.48930623760645
1162122.8618568974878-1.86185689748776
1172520.35099363081184.64900636918816
1182019.07035138429280.929648615707217
1192223.2357219487354-1.23572194873543
1201315.9184078967728-2.91840789677283
1212619.51245856830446.48754143169561
1221718.8655673377357-1.86556733773571
1232520.89076145032664.10923854967338
1242021.0036570622649-1.00365706226495
1251920.4269024227032-1.42690242270315
1262123.7125020337543-2.71250203375425
1272221.73506227389560.264937726104360
1282422.98332150932911.01667849067094
1292124.206535999104-3.20653599910401
1302622.2064546950513.79354530494901
1312420.4402053975473.559794602453
1321621.356223110392-5.35622311039199
1332322.92204685865420.077953141345844
1341820.9488475902808-2.94884759028082
1351622.1143801917229-6.11438019172287
1362624.24267071250681.75732928749319
1371920.2768495109536-1.27684951095359
1382117.94624024817673.05375975182328
1392122.8640655900694-1.86406559006939
1402218.73842250137163.26157749862845
1412316.79602308565146.20397691434857
1422922.64852593038576.35147406961428
1432121.1641124294643-0.164112429464339
1442120.00021840914110.999781590858933
1452323.3047290816143-0.304729081614261
1462721.33715475425625.66284524574382
1472522.25349514161292.74650485838710
1482120.46497643733330.535023562666729
1491017.9289382590194-7.9289382590194
1502021.8730668598975-1.87306685989750
1512621.74631945556224.25368054443784
1522423.21392102198610.786078978013853
1532927.56580944054371.43419055945629
1541917.88518378041371.11481621958627
1552423.84697906013020.153020939869807
1561921.4779087653085-2.47790876530853
1572421.73343003232582.26656996767420
1582222.5149868383735-0.514986838373517
1591723.2196344599774-6.21963445997736






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.04309352232343630.08618704464687250.956906477676564
110.01247722401795910.02495444803591830.98752277598204
120.01098170550282460.02196341100564910.989018294497175
130.02115175395994320.04230350791988630.978848246040057
140.03771368702247180.07542737404494360.962286312977528
150.3352547933313060.6705095866626130.664745206668694
160.2807244523841900.5614489047683790.71927554761581
170.3000847199959610.6001694399919210.699915280004039
180.2597971324654250.5195942649308510.740202867534575
190.1999521050741750.3999042101483510.800047894925825
200.299167685876430.598335371752860.70083231412357
210.3225050591817820.6450101183635640.677494940818218
220.2550831691612600.5101663383225190.74491683083874
230.2146573624317870.4293147248635750.785342637568213
240.1825603584733960.3651207169467920.817439641526604
250.1549692756969460.3099385513938920.845030724303054
260.1184135332913860.2368270665827720.881586466708614
270.09810109910630980.1962021982126200.90189890089369
280.08618771536456260.1723754307291250.913812284635437
290.06820237837101710.1364047567420340.931797621628983
300.04839910621432930.09679821242865870.95160089378567
310.04042707177520610.08085414355041210.959572928224794
320.04426363534359300.08852727068718610.955736364656407
330.07372729615859560.1474545923171910.926272703841404
340.2055863689825470.4111727379650930.794413631017453
350.1649137890786870.3298275781573730.835086210921313
360.1887017688286820.3774035376573650.811298231171318
370.2041349013266100.4082698026532210.79586509867339
380.3880233000397620.7760466000795230.611976699960239
390.3611611035427870.7223222070855740.638838896457213
400.3839322091954620.7678644183909230.616067790804538
410.3622225382015370.7244450764030730.637777461798463
420.3201975054982880.6403950109965750.679802494501712
430.5883002847364110.8233994305271780.411699715263589
440.5468469434300570.9063061131398860.453153056569943
450.5161928720087770.9676142559824450.483807127991223
460.548348695055890.903302609888220.45165130494411
470.6329809035418150.734038192916370.367019096458185
480.8179449296213880.3641101407572250.182055070378612
490.7893579884459150.4212840231081710.210642011554085
500.7609738502755670.4780522994488660.239026149724433
510.7472780979327670.5054438041344660.252721902067233
520.7472103235382180.5055793529235650.252789676461782
530.798174305879940.403651388240120.20182569412006
540.8059864990240390.3880270019519220.194013500975961
550.8212428006953620.3575143986092750.178757199304638
560.7981424327015290.4037151345969430.201857567298471
570.7674619812876490.4650760374247020.232538018712351
580.7358653871479980.5282692257040040.264134612852002
590.7083548298469950.583290340306010.291645170153005
600.763669361961660.472661276076680.23633063803834
610.7258329761982120.5483340476035760.274167023801788
620.689868348932660.620263302134680.31013165106734
630.6591318782888760.6817362434222470.340868121711124
640.6327547029722510.7344905940554980.367245297027749
650.6247083056934090.7505833886131830.375291694306591
660.7130667558192570.5738664883614860.286933244180743
670.6768967676231040.6462064647537920.323103232376896
680.7068995591604120.5862008816791750.293100440839588
690.7059993900403260.5880012199193470.294000609959674
700.6816813851873750.6366372296252510.318318614812625
710.6569968804700030.6860062390599930.343003119529997
720.617220968708310.765558062583380.38277903129169
730.5915410267601960.8169179464796070.408458973239804
740.5526048614809860.8947902770380280.447395138519014
750.5195773329006520.9608453341986970.480422667099348
760.4812623834919890.9625247669839790.518737616508011
770.4535605929885310.9071211859770620.546439407011469
780.4140660499587020.8281320999174040.585933950041298
790.3771751074479540.7543502148959070.622824892552046
800.3773898457510060.7547796915020120.622610154248994
810.3431917380553410.6863834761106830.656808261944659
820.4250910535893540.8501821071787080.574908946410646
830.3996090441214030.7992180882428060.600390955878597
840.3597636056604050.719527211320810.640236394339595
850.3394764836456630.6789529672913270.660523516354337
860.3288216327695370.6576432655390740.671178367230463
870.3544422454878370.7088844909756740.645557754512163
880.3149991994469820.6299983988939640.685000800553018
890.3251808084917880.6503616169835770.674819191508212
900.3191866199009980.6383732398019960.680813380099002
910.3502407982423560.7004815964847130.649759201757644
920.3191349057637950.638269811527590.680865094236205
930.3027951418982610.6055902837965230.697204858101739
940.2666313520427910.5332627040855820.733368647957209
950.2420265305759690.4840530611519370.757973469424031
960.3009299430369070.6018598860738140.699070056963093
970.2649513388891370.5299026777782740.735048661110863
980.2312938956600830.4625877913201660.768706104339917
990.2096876620088490.4193753240176980.790312337991151
1000.1843666275316690.3687332550633380.815633372468331
1010.1570248700748770.3140497401497530.842975129925123
1020.1341115286882350.2682230573764710.865888471311765
1030.1106325882245010.2212651764490030.889367411775499
1040.09230450571330620.1846090114266120.907695494286694
1050.09163062750623640.1832612550124730.908369372493764
1060.07343730751519260.1468746150303850.926562692484807
1070.05925957638129280.1185191527625860.940740423618707
1080.04761078422068250.0952215684413650.952389215779317
1090.03691390612709050.07382781225418110.96308609387291
1100.04856024489571440.09712048979142890.951439755104286
1110.08208734698788850.1641746939757770.917912653012111
1120.1543367418370090.3086734836740170.845663258162991
1130.1489954769225210.2979909538450420.85100452307748
1140.5542977114722140.8914045770555720.445702288527786
1150.6928402159686470.6143195680627060.307159784031353
1160.6530544443785640.6938911112428710.346945555621436
1170.7059463806612410.5881072386775180.294053619338759
1180.6590015103857710.6819969792284580.340998489614229
1190.6146923517200570.7706152965598850.385307648279943
1200.6150338842697330.7699322314605350.384966115730267
1210.695117039499310.609765921001380.30488296050069
1220.6616179076596150.6767641846807710.338382092340385
1230.6600213830973850.679957233805230.339978616902615
1240.6038083762187620.7923832475624760.396191623781238
1250.6282929355613150.743414128877370.371707064438685
1260.601738297028990.796523405942020.39826170297101
1270.540800599324830.918398801350340.45919940067517
1280.4810034287519530.9620068575039050.518996571248047
1290.4529713324313050.905942664862610.547028667568695
1300.4149368438112460.8298736876224910.585063156188754
1310.4072547705653820.8145095411307640.592745229434618
1320.4491794046370950.898358809274190.550820595362905
1330.3818016028497750.763603205699550.618198397150225
1340.3706897701890770.7413795403781540.629310229810923
1350.5955336403028840.8089327193942330.404466359697116
1360.5297215702524180.9405568594951650.470278429747582
1370.6124419048154060.7751161903691890.387558095184594
1380.5401975494009270.9196049011981460.459802450599073
1390.6319327111697070.7361345776605860.368067288830293
1400.5558195653490450.888360869301910.444180434650955
1410.5971379350523240.8057241298953520.402862064947676
1420.5941422588938770.8117154822122460.405857741106123
1430.60996527743080.78006944513840.3900347225692
1440.5618586863934210.8762826272131590.438141313606579
1450.4587651777827580.9175303555655150.541234822217242
1460.4071074100447240.8142148200894470.592892589955276
1470.389774869234750.77954973846950.61022513076525
1480.2667489489642480.5334978979284970.733251051035752
1490.4445026580675760.8890053161351520.555497341932424

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0430935223234363 & 0.0861870446468725 & 0.956906477676564 \tabularnewline
11 & 0.0124772240179591 & 0.0249544480359183 & 0.98752277598204 \tabularnewline
12 & 0.0109817055028246 & 0.0219634110056491 & 0.989018294497175 \tabularnewline
13 & 0.0211517539599432 & 0.0423035079198863 & 0.978848246040057 \tabularnewline
14 & 0.0377136870224718 & 0.0754273740449436 & 0.962286312977528 \tabularnewline
15 & 0.335254793331306 & 0.670509586662613 & 0.664745206668694 \tabularnewline
16 & 0.280724452384190 & 0.561448904768379 & 0.71927554761581 \tabularnewline
17 & 0.300084719995961 & 0.600169439991921 & 0.699915280004039 \tabularnewline
18 & 0.259797132465425 & 0.519594264930851 & 0.740202867534575 \tabularnewline
19 & 0.199952105074175 & 0.399904210148351 & 0.800047894925825 \tabularnewline
20 & 0.29916768587643 & 0.59833537175286 & 0.70083231412357 \tabularnewline
21 & 0.322505059181782 & 0.645010118363564 & 0.677494940818218 \tabularnewline
22 & 0.255083169161260 & 0.510166338322519 & 0.74491683083874 \tabularnewline
23 & 0.214657362431787 & 0.429314724863575 & 0.785342637568213 \tabularnewline
24 & 0.182560358473396 & 0.365120716946792 & 0.817439641526604 \tabularnewline
25 & 0.154969275696946 & 0.309938551393892 & 0.845030724303054 \tabularnewline
26 & 0.118413533291386 & 0.236827066582772 & 0.881586466708614 \tabularnewline
27 & 0.0981010991063098 & 0.196202198212620 & 0.90189890089369 \tabularnewline
28 & 0.0861877153645626 & 0.172375430729125 & 0.913812284635437 \tabularnewline
29 & 0.0682023783710171 & 0.136404756742034 & 0.931797621628983 \tabularnewline
30 & 0.0483991062143293 & 0.0967982124286587 & 0.95160089378567 \tabularnewline
31 & 0.0404270717752061 & 0.0808541435504121 & 0.959572928224794 \tabularnewline
32 & 0.0442636353435930 & 0.0885272706871861 & 0.955736364656407 \tabularnewline
33 & 0.0737272961585956 & 0.147454592317191 & 0.926272703841404 \tabularnewline
34 & 0.205586368982547 & 0.411172737965093 & 0.794413631017453 \tabularnewline
35 & 0.164913789078687 & 0.329827578157373 & 0.835086210921313 \tabularnewline
36 & 0.188701768828682 & 0.377403537657365 & 0.811298231171318 \tabularnewline
37 & 0.204134901326610 & 0.408269802653221 & 0.79586509867339 \tabularnewline
38 & 0.388023300039762 & 0.776046600079523 & 0.611976699960239 \tabularnewline
39 & 0.361161103542787 & 0.722322207085574 & 0.638838896457213 \tabularnewline
40 & 0.383932209195462 & 0.767864418390923 & 0.616067790804538 \tabularnewline
41 & 0.362222538201537 & 0.724445076403073 & 0.637777461798463 \tabularnewline
42 & 0.320197505498288 & 0.640395010996575 & 0.679802494501712 \tabularnewline
43 & 0.588300284736411 & 0.823399430527178 & 0.411699715263589 \tabularnewline
44 & 0.546846943430057 & 0.906306113139886 & 0.453153056569943 \tabularnewline
45 & 0.516192872008777 & 0.967614255982445 & 0.483807127991223 \tabularnewline
46 & 0.54834869505589 & 0.90330260988822 & 0.45165130494411 \tabularnewline
47 & 0.632980903541815 & 0.73403819291637 & 0.367019096458185 \tabularnewline
48 & 0.817944929621388 & 0.364110140757225 & 0.182055070378612 \tabularnewline
49 & 0.789357988445915 & 0.421284023108171 & 0.210642011554085 \tabularnewline
50 & 0.760973850275567 & 0.478052299448866 & 0.239026149724433 \tabularnewline
51 & 0.747278097932767 & 0.505443804134466 & 0.252721902067233 \tabularnewline
52 & 0.747210323538218 & 0.505579352923565 & 0.252789676461782 \tabularnewline
53 & 0.79817430587994 & 0.40365138824012 & 0.20182569412006 \tabularnewline
54 & 0.805986499024039 & 0.388027001951922 & 0.194013500975961 \tabularnewline
55 & 0.821242800695362 & 0.357514398609275 & 0.178757199304638 \tabularnewline
56 & 0.798142432701529 & 0.403715134596943 & 0.201857567298471 \tabularnewline
57 & 0.767461981287649 & 0.465076037424702 & 0.232538018712351 \tabularnewline
58 & 0.735865387147998 & 0.528269225704004 & 0.264134612852002 \tabularnewline
59 & 0.708354829846995 & 0.58329034030601 & 0.291645170153005 \tabularnewline
60 & 0.76366936196166 & 0.47266127607668 & 0.23633063803834 \tabularnewline
61 & 0.725832976198212 & 0.548334047603576 & 0.274167023801788 \tabularnewline
62 & 0.68986834893266 & 0.62026330213468 & 0.31013165106734 \tabularnewline
63 & 0.659131878288876 & 0.681736243422247 & 0.340868121711124 \tabularnewline
64 & 0.632754702972251 & 0.734490594055498 & 0.367245297027749 \tabularnewline
65 & 0.624708305693409 & 0.750583388613183 & 0.375291694306591 \tabularnewline
66 & 0.713066755819257 & 0.573866488361486 & 0.286933244180743 \tabularnewline
67 & 0.676896767623104 & 0.646206464753792 & 0.323103232376896 \tabularnewline
68 & 0.706899559160412 & 0.586200881679175 & 0.293100440839588 \tabularnewline
69 & 0.705999390040326 & 0.588001219919347 & 0.294000609959674 \tabularnewline
70 & 0.681681385187375 & 0.636637229625251 & 0.318318614812625 \tabularnewline
71 & 0.656996880470003 & 0.686006239059993 & 0.343003119529997 \tabularnewline
72 & 0.61722096870831 & 0.76555806258338 & 0.38277903129169 \tabularnewline
73 & 0.591541026760196 & 0.816917946479607 & 0.408458973239804 \tabularnewline
74 & 0.552604861480986 & 0.894790277038028 & 0.447395138519014 \tabularnewline
75 & 0.519577332900652 & 0.960845334198697 & 0.480422667099348 \tabularnewline
76 & 0.481262383491989 & 0.962524766983979 & 0.518737616508011 \tabularnewline
77 & 0.453560592988531 & 0.907121185977062 & 0.546439407011469 \tabularnewline
78 & 0.414066049958702 & 0.828132099917404 & 0.585933950041298 \tabularnewline
79 & 0.377175107447954 & 0.754350214895907 & 0.622824892552046 \tabularnewline
80 & 0.377389845751006 & 0.754779691502012 & 0.622610154248994 \tabularnewline
81 & 0.343191738055341 & 0.686383476110683 & 0.656808261944659 \tabularnewline
82 & 0.425091053589354 & 0.850182107178708 & 0.574908946410646 \tabularnewline
83 & 0.399609044121403 & 0.799218088242806 & 0.600390955878597 \tabularnewline
84 & 0.359763605660405 & 0.71952721132081 & 0.640236394339595 \tabularnewline
85 & 0.339476483645663 & 0.678952967291327 & 0.660523516354337 \tabularnewline
86 & 0.328821632769537 & 0.657643265539074 & 0.671178367230463 \tabularnewline
87 & 0.354442245487837 & 0.708884490975674 & 0.645557754512163 \tabularnewline
88 & 0.314999199446982 & 0.629998398893964 & 0.685000800553018 \tabularnewline
89 & 0.325180808491788 & 0.650361616983577 & 0.674819191508212 \tabularnewline
90 & 0.319186619900998 & 0.638373239801996 & 0.680813380099002 \tabularnewline
91 & 0.350240798242356 & 0.700481596484713 & 0.649759201757644 \tabularnewline
92 & 0.319134905763795 & 0.63826981152759 & 0.680865094236205 \tabularnewline
93 & 0.302795141898261 & 0.605590283796523 & 0.697204858101739 \tabularnewline
94 & 0.266631352042791 & 0.533262704085582 & 0.733368647957209 \tabularnewline
95 & 0.242026530575969 & 0.484053061151937 & 0.757973469424031 \tabularnewline
96 & 0.300929943036907 & 0.601859886073814 & 0.699070056963093 \tabularnewline
97 & 0.264951338889137 & 0.529902677778274 & 0.735048661110863 \tabularnewline
98 & 0.231293895660083 & 0.462587791320166 & 0.768706104339917 \tabularnewline
99 & 0.209687662008849 & 0.419375324017698 & 0.790312337991151 \tabularnewline
100 & 0.184366627531669 & 0.368733255063338 & 0.815633372468331 \tabularnewline
101 & 0.157024870074877 & 0.314049740149753 & 0.842975129925123 \tabularnewline
102 & 0.134111528688235 & 0.268223057376471 & 0.865888471311765 \tabularnewline
103 & 0.110632588224501 & 0.221265176449003 & 0.889367411775499 \tabularnewline
104 & 0.0923045057133062 & 0.184609011426612 & 0.907695494286694 \tabularnewline
105 & 0.0916306275062364 & 0.183261255012473 & 0.908369372493764 \tabularnewline
106 & 0.0734373075151926 & 0.146874615030385 & 0.926562692484807 \tabularnewline
107 & 0.0592595763812928 & 0.118519152762586 & 0.940740423618707 \tabularnewline
108 & 0.0476107842206825 & 0.095221568441365 & 0.952389215779317 \tabularnewline
109 & 0.0369139061270905 & 0.0738278122541811 & 0.96308609387291 \tabularnewline
110 & 0.0485602448957144 & 0.0971204897914289 & 0.951439755104286 \tabularnewline
111 & 0.0820873469878885 & 0.164174693975777 & 0.917912653012111 \tabularnewline
112 & 0.154336741837009 & 0.308673483674017 & 0.845663258162991 \tabularnewline
113 & 0.148995476922521 & 0.297990953845042 & 0.85100452307748 \tabularnewline
114 & 0.554297711472214 & 0.891404577055572 & 0.445702288527786 \tabularnewline
115 & 0.692840215968647 & 0.614319568062706 & 0.307159784031353 \tabularnewline
116 & 0.653054444378564 & 0.693891111242871 & 0.346945555621436 \tabularnewline
117 & 0.705946380661241 & 0.588107238677518 & 0.294053619338759 \tabularnewline
118 & 0.659001510385771 & 0.681996979228458 & 0.340998489614229 \tabularnewline
119 & 0.614692351720057 & 0.770615296559885 & 0.385307648279943 \tabularnewline
120 & 0.615033884269733 & 0.769932231460535 & 0.384966115730267 \tabularnewline
121 & 0.69511703949931 & 0.60976592100138 & 0.30488296050069 \tabularnewline
122 & 0.661617907659615 & 0.676764184680771 & 0.338382092340385 \tabularnewline
123 & 0.660021383097385 & 0.67995723380523 & 0.339978616902615 \tabularnewline
124 & 0.603808376218762 & 0.792383247562476 & 0.396191623781238 \tabularnewline
125 & 0.628292935561315 & 0.74341412887737 & 0.371707064438685 \tabularnewline
126 & 0.60173829702899 & 0.79652340594202 & 0.39826170297101 \tabularnewline
127 & 0.54080059932483 & 0.91839880135034 & 0.45919940067517 \tabularnewline
128 & 0.481003428751953 & 0.962006857503905 & 0.518996571248047 \tabularnewline
129 & 0.452971332431305 & 0.90594266486261 & 0.547028667568695 \tabularnewline
130 & 0.414936843811246 & 0.829873687622491 & 0.585063156188754 \tabularnewline
131 & 0.407254770565382 & 0.814509541130764 & 0.592745229434618 \tabularnewline
132 & 0.449179404637095 & 0.89835880927419 & 0.550820595362905 \tabularnewline
133 & 0.381801602849775 & 0.76360320569955 & 0.618198397150225 \tabularnewline
134 & 0.370689770189077 & 0.741379540378154 & 0.629310229810923 \tabularnewline
135 & 0.595533640302884 & 0.808932719394233 & 0.404466359697116 \tabularnewline
136 & 0.529721570252418 & 0.940556859495165 & 0.470278429747582 \tabularnewline
137 & 0.612441904815406 & 0.775116190369189 & 0.387558095184594 \tabularnewline
138 & 0.540197549400927 & 0.919604901198146 & 0.459802450599073 \tabularnewline
139 & 0.631932711169707 & 0.736134577660586 & 0.368067288830293 \tabularnewline
140 & 0.555819565349045 & 0.88836086930191 & 0.444180434650955 \tabularnewline
141 & 0.597137935052324 & 0.805724129895352 & 0.402862064947676 \tabularnewline
142 & 0.594142258893877 & 0.811715482212246 & 0.405857741106123 \tabularnewline
143 & 0.6099652774308 & 0.7800694451384 & 0.3900347225692 \tabularnewline
144 & 0.561858686393421 & 0.876282627213159 & 0.438141313606579 \tabularnewline
145 & 0.458765177782758 & 0.917530355565515 & 0.541234822217242 \tabularnewline
146 & 0.407107410044724 & 0.814214820089447 & 0.592892589955276 \tabularnewline
147 & 0.38977486923475 & 0.7795497384695 & 0.61022513076525 \tabularnewline
148 & 0.266748948964248 & 0.533497897928497 & 0.733251051035752 \tabularnewline
149 & 0.444502658067576 & 0.889005316135152 & 0.555497341932424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116322&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.0430935223234363[/C][C]0.0861870446468725[/C][C]0.956906477676564[/C][/ROW]
[ROW][C]11[/C][C]0.0124772240179591[/C][C]0.0249544480359183[/C][C]0.98752277598204[/C][/ROW]
[ROW][C]12[/C][C]0.0109817055028246[/C][C]0.0219634110056491[/C][C]0.989018294497175[/C][/ROW]
[ROW][C]13[/C][C]0.0211517539599432[/C][C]0.0423035079198863[/C][C]0.978848246040057[/C][/ROW]
[ROW][C]14[/C][C]0.0377136870224718[/C][C]0.0754273740449436[/C][C]0.962286312977528[/C][/ROW]
[ROW][C]15[/C][C]0.335254793331306[/C][C]0.670509586662613[/C][C]0.664745206668694[/C][/ROW]
[ROW][C]16[/C][C]0.280724452384190[/C][C]0.561448904768379[/C][C]0.71927554761581[/C][/ROW]
[ROW][C]17[/C][C]0.300084719995961[/C][C]0.600169439991921[/C][C]0.699915280004039[/C][/ROW]
[ROW][C]18[/C][C]0.259797132465425[/C][C]0.519594264930851[/C][C]0.740202867534575[/C][/ROW]
[ROW][C]19[/C][C]0.199952105074175[/C][C]0.399904210148351[/C][C]0.800047894925825[/C][/ROW]
[ROW][C]20[/C][C]0.29916768587643[/C][C]0.59833537175286[/C][C]0.70083231412357[/C][/ROW]
[ROW][C]21[/C][C]0.322505059181782[/C][C]0.645010118363564[/C][C]0.677494940818218[/C][/ROW]
[ROW][C]22[/C][C]0.255083169161260[/C][C]0.510166338322519[/C][C]0.74491683083874[/C][/ROW]
[ROW][C]23[/C][C]0.214657362431787[/C][C]0.429314724863575[/C][C]0.785342637568213[/C][/ROW]
[ROW][C]24[/C][C]0.182560358473396[/C][C]0.365120716946792[/C][C]0.817439641526604[/C][/ROW]
[ROW][C]25[/C][C]0.154969275696946[/C][C]0.309938551393892[/C][C]0.845030724303054[/C][/ROW]
[ROW][C]26[/C][C]0.118413533291386[/C][C]0.236827066582772[/C][C]0.881586466708614[/C][/ROW]
[ROW][C]27[/C][C]0.0981010991063098[/C][C]0.196202198212620[/C][C]0.90189890089369[/C][/ROW]
[ROW][C]28[/C][C]0.0861877153645626[/C][C]0.172375430729125[/C][C]0.913812284635437[/C][/ROW]
[ROW][C]29[/C][C]0.0682023783710171[/C][C]0.136404756742034[/C][C]0.931797621628983[/C][/ROW]
[ROW][C]30[/C][C]0.0483991062143293[/C][C]0.0967982124286587[/C][C]0.95160089378567[/C][/ROW]
[ROW][C]31[/C][C]0.0404270717752061[/C][C]0.0808541435504121[/C][C]0.959572928224794[/C][/ROW]
[ROW][C]32[/C][C]0.0442636353435930[/C][C]0.0885272706871861[/C][C]0.955736364656407[/C][/ROW]
[ROW][C]33[/C][C]0.0737272961585956[/C][C]0.147454592317191[/C][C]0.926272703841404[/C][/ROW]
[ROW][C]34[/C][C]0.205586368982547[/C][C]0.411172737965093[/C][C]0.794413631017453[/C][/ROW]
[ROW][C]35[/C][C]0.164913789078687[/C][C]0.329827578157373[/C][C]0.835086210921313[/C][/ROW]
[ROW][C]36[/C][C]0.188701768828682[/C][C]0.377403537657365[/C][C]0.811298231171318[/C][/ROW]
[ROW][C]37[/C][C]0.204134901326610[/C][C]0.408269802653221[/C][C]0.79586509867339[/C][/ROW]
[ROW][C]38[/C][C]0.388023300039762[/C][C]0.776046600079523[/C][C]0.611976699960239[/C][/ROW]
[ROW][C]39[/C][C]0.361161103542787[/C][C]0.722322207085574[/C][C]0.638838896457213[/C][/ROW]
[ROW][C]40[/C][C]0.383932209195462[/C][C]0.767864418390923[/C][C]0.616067790804538[/C][/ROW]
[ROW][C]41[/C][C]0.362222538201537[/C][C]0.724445076403073[/C][C]0.637777461798463[/C][/ROW]
[ROW][C]42[/C][C]0.320197505498288[/C][C]0.640395010996575[/C][C]0.679802494501712[/C][/ROW]
[ROW][C]43[/C][C]0.588300284736411[/C][C]0.823399430527178[/C][C]0.411699715263589[/C][/ROW]
[ROW][C]44[/C][C]0.546846943430057[/C][C]0.906306113139886[/C][C]0.453153056569943[/C][/ROW]
[ROW][C]45[/C][C]0.516192872008777[/C][C]0.967614255982445[/C][C]0.483807127991223[/C][/ROW]
[ROW][C]46[/C][C]0.54834869505589[/C][C]0.90330260988822[/C][C]0.45165130494411[/C][/ROW]
[ROW][C]47[/C][C]0.632980903541815[/C][C]0.73403819291637[/C][C]0.367019096458185[/C][/ROW]
[ROW][C]48[/C][C]0.817944929621388[/C][C]0.364110140757225[/C][C]0.182055070378612[/C][/ROW]
[ROW][C]49[/C][C]0.789357988445915[/C][C]0.421284023108171[/C][C]0.210642011554085[/C][/ROW]
[ROW][C]50[/C][C]0.760973850275567[/C][C]0.478052299448866[/C][C]0.239026149724433[/C][/ROW]
[ROW][C]51[/C][C]0.747278097932767[/C][C]0.505443804134466[/C][C]0.252721902067233[/C][/ROW]
[ROW][C]52[/C][C]0.747210323538218[/C][C]0.505579352923565[/C][C]0.252789676461782[/C][/ROW]
[ROW][C]53[/C][C]0.79817430587994[/C][C]0.40365138824012[/C][C]0.20182569412006[/C][/ROW]
[ROW][C]54[/C][C]0.805986499024039[/C][C]0.388027001951922[/C][C]0.194013500975961[/C][/ROW]
[ROW][C]55[/C][C]0.821242800695362[/C][C]0.357514398609275[/C][C]0.178757199304638[/C][/ROW]
[ROW][C]56[/C][C]0.798142432701529[/C][C]0.403715134596943[/C][C]0.201857567298471[/C][/ROW]
[ROW][C]57[/C][C]0.767461981287649[/C][C]0.465076037424702[/C][C]0.232538018712351[/C][/ROW]
[ROW][C]58[/C][C]0.735865387147998[/C][C]0.528269225704004[/C][C]0.264134612852002[/C][/ROW]
[ROW][C]59[/C][C]0.708354829846995[/C][C]0.58329034030601[/C][C]0.291645170153005[/C][/ROW]
[ROW][C]60[/C][C]0.76366936196166[/C][C]0.47266127607668[/C][C]0.23633063803834[/C][/ROW]
[ROW][C]61[/C][C]0.725832976198212[/C][C]0.548334047603576[/C][C]0.274167023801788[/C][/ROW]
[ROW][C]62[/C][C]0.68986834893266[/C][C]0.62026330213468[/C][C]0.31013165106734[/C][/ROW]
[ROW][C]63[/C][C]0.659131878288876[/C][C]0.681736243422247[/C][C]0.340868121711124[/C][/ROW]
[ROW][C]64[/C][C]0.632754702972251[/C][C]0.734490594055498[/C][C]0.367245297027749[/C][/ROW]
[ROW][C]65[/C][C]0.624708305693409[/C][C]0.750583388613183[/C][C]0.375291694306591[/C][/ROW]
[ROW][C]66[/C][C]0.713066755819257[/C][C]0.573866488361486[/C][C]0.286933244180743[/C][/ROW]
[ROW][C]67[/C][C]0.676896767623104[/C][C]0.646206464753792[/C][C]0.323103232376896[/C][/ROW]
[ROW][C]68[/C][C]0.706899559160412[/C][C]0.586200881679175[/C][C]0.293100440839588[/C][/ROW]
[ROW][C]69[/C][C]0.705999390040326[/C][C]0.588001219919347[/C][C]0.294000609959674[/C][/ROW]
[ROW][C]70[/C][C]0.681681385187375[/C][C]0.636637229625251[/C][C]0.318318614812625[/C][/ROW]
[ROW][C]71[/C][C]0.656996880470003[/C][C]0.686006239059993[/C][C]0.343003119529997[/C][/ROW]
[ROW][C]72[/C][C]0.61722096870831[/C][C]0.76555806258338[/C][C]0.38277903129169[/C][/ROW]
[ROW][C]73[/C][C]0.591541026760196[/C][C]0.816917946479607[/C][C]0.408458973239804[/C][/ROW]
[ROW][C]74[/C][C]0.552604861480986[/C][C]0.894790277038028[/C][C]0.447395138519014[/C][/ROW]
[ROW][C]75[/C][C]0.519577332900652[/C][C]0.960845334198697[/C][C]0.480422667099348[/C][/ROW]
[ROW][C]76[/C][C]0.481262383491989[/C][C]0.962524766983979[/C][C]0.518737616508011[/C][/ROW]
[ROW][C]77[/C][C]0.453560592988531[/C][C]0.907121185977062[/C][C]0.546439407011469[/C][/ROW]
[ROW][C]78[/C][C]0.414066049958702[/C][C]0.828132099917404[/C][C]0.585933950041298[/C][/ROW]
[ROW][C]79[/C][C]0.377175107447954[/C][C]0.754350214895907[/C][C]0.622824892552046[/C][/ROW]
[ROW][C]80[/C][C]0.377389845751006[/C][C]0.754779691502012[/C][C]0.622610154248994[/C][/ROW]
[ROW][C]81[/C][C]0.343191738055341[/C][C]0.686383476110683[/C][C]0.656808261944659[/C][/ROW]
[ROW][C]82[/C][C]0.425091053589354[/C][C]0.850182107178708[/C][C]0.574908946410646[/C][/ROW]
[ROW][C]83[/C][C]0.399609044121403[/C][C]0.799218088242806[/C][C]0.600390955878597[/C][/ROW]
[ROW][C]84[/C][C]0.359763605660405[/C][C]0.71952721132081[/C][C]0.640236394339595[/C][/ROW]
[ROW][C]85[/C][C]0.339476483645663[/C][C]0.678952967291327[/C][C]0.660523516354337[/C][/ROW]
[ROW][C]86[/C][C]0.328821632769537[/C][C]0.657643265539074[/C][C]0.671178367230463[/C][/ROW]
[ROW][C]87[/C][C]0.354442245487837[/C][C]0.708884490975674[/C][C]0.645557754512163[/C][/ROW]
[ROW][C]88[/C][C]0.314999199446982[/C][C]0.629998398893964[/C][C]0.685000800553018[/C][/ROW]
[ROW][C]89[/C][C]0.325180808491788[/C][C]0.650361616983577[/C][C]0.674819191508212[/C][/ROW]
[ROW][C]90[/C][C]0.319186619900998[/C][C]0.638373239801996[/C][C]0.680813380099002[/C][/ROW]
[ROW][C]91[/C][C]0.350240798242356[/C][C]0.700481596484713[/C][C]0.649759201757644[/C][/ROW]
[ROW][C]92[/C][C]0.319134905763795[/C][C]0.63826981152759[/C][C]0.680865094236205[/C][/ROW]
[ROW][C]93[/C][C]0.302795141898261[/C][C]0.605590283796523[/C][C]0.697204858101739[/C][/ROW]
[ROW][C]94[/C][C]0.266631352042791[/C][C]0.533262704085582[/C][C]0.733368647957209[/C][/ROW]
[ROW][C]95[/C][C]0.242026530575969[/C][C]0.484053061151937[/C][C]0.757973469424031[/C][/ROW]
[ROW][C]96[/C][C]0.300929943036907[/C][C]0.601859886073814[/C][C]0.699070056963093[/C][/ROW]
[ROW][C]97[/C][C]0.264951338889137[/C][C]0.529902677778274[/C][C]0.735048661110863[/C][/ROW]
[ROW][C]98[/C][C]0.231293895660083[/C][C]0.462587791320166[/C][C]0.768706104339917[/C][/ROW]
[ROW][C]99[/C][C]0.209687662008849[/C][C]0.419375324017698[/C][C]0.790312337991151[/C][/ROW]
[ROW][C]100[/C][C]0.184366627531669[/C][C]0.368733255063338[/C][C]0.815633372468331[/C][/ROW]
[ROW][C]101[/C][C]0.157024870074877[/C][C]0.314049740149753[/C][C]0.842975129925123[/C][/ROW]
[ROW][C]102[/C][C]0.134111528688235[/C][C]0.268223057376471[/C][C]0.865888471311765[/C][/ROW]
[ROW][C]103[/C][C]0.110632588224501[/C][C]0.221265176449003[/C][C]0.889367411775499[/C][/ROW]
[ROW][C]104[/C][C]0.0923045057133062[/C][C]0.184609011426612[/C][C]0.907695494286694[/C][/ROW]
[ROW][C]105[/C][C]0.0916306275062364[/C][C]0.183261255012473[/C][C]0.908369372493764[/C][/ROW]
[ROW][C]106[/C][C]0.0734373075151926[/C][C]0.146874615030385[/C][C]0.926562692484807[/C][/ROW]
[ROW][C]107[/C][C]0.0592595763812928[/C][C]0.118519152762586[/C][C]0.940740423618707[/C][/ROW]
[ROW][C]108[/C][C]0.0476107842206825[/C][C]0.095221568441365[/C][C]0.952389215779317[/C][/ROW]
[ROW][C]109[/C][C]0.0369139061270905[/C][C]0.0738278122541811[/C][C]0.96308609387291[/C][/ROW]
[ROW][C]110[/C][C]0.0485602448957144[/C][C]0.0971204897914289[/C][C]0.951439755104286[/C][/ROW]
[ROW][C]111[/C][C]0.0820873469878885[/C][C]0.164174693975777[/C][C]0.917912653012111[/C][/ROW]
[ROW][C]112[/C][C]0.154336741837009[/C][C]0.308673483674017[/C][C]0.845663258162991[/C][/ROW]
[ROW][C]113[/C][C]0.148995476922521[/C][C]0.297990953845042[/C][C]0.85100452307748[/C][/ROW]
[ROW][C]114[/C][C]0.554297711472214[/C][C]0.891404577055572[/C][C]0.445702288527786[/C][/ROW]
[ROW][C]115[/C][C]0.692840215968647[/C][C]0.614319568062706[/C][C]0.307159784031353[/C][/ROW]
[ROW][C]116[/C][C]0.653054444378564[/C][C]0.693891111242871[/C][C]0.346945555621436[/C][/ROW]
[ROW][C]117[/C][C]0.705946380661241[/C][C]0.588107238677518[/C][C]0.294053619338759[/C][/ROW]
[ROW][C]118[/C][C]0.659001510385771[/C][C]0.681996979228458[/C][C]0.340998489614229[/C][/ROW]
[ROW][C]119[/C][C]0.614692351720057[/C][C]0.770615296559885[/C][C]0.385307648279943[/C][/ROW]
[ROW][C]120[/C][C]0.615033884269733[/C][C]0.769932231460535[/C][C]0.384966115730267[/C][/ROW]
[ROW][C]121[/C][C]0.69511703949931[/C][C]0.60976592100138[/C][C]0.30488296050069[/C][/ROW]
[ROW][C]122[/C][C]0.661617907659615[/C][C]0.676764184680771[/C][C]0.338382092340385[/C][/ROW]
[ROW][C]123[/C][C]0.660021383097385[/C][C]0.67995723380523[/C][C]0.339978616902615[/C][/ROW]
[ROW][C]124[/C][C]0.603808376218762[/C][C]0.792383247562476[/C][C]0.396191623781238[/C][/ROW]
[ROW][C]125[/C][C]0.628292935561315[/C][C]0.74341412887737[/C][C]0.371707064438685[/C][/ROW]
[ROW][C]126[/C][C]0.60173829702899[/C][C]0.79652340594202[/C][C]0.39826170297101[/C][/ROW]
[ROW][C]127[/C][C]0.54080059932483[/C][C]0.91839880135034[/C][C]0.45919940067517[/C][/ROW]
[ROW][C]128[/C][C]0.481003428751953[/C][C]0.962006857503905[/C][C]0.518996571248047[/C][/ROW]
[ROW][C]129[/C][C]0.452971332431305[/C][C]0.90594266486261[/C][C]0.547028667568695[/C][/ROW]
[ROW][C]130[/C][C]0.414936843811246[/C][C]0.829873687622491[/C][C]0.585063156188754[/C][/ROW]
[ROW][C]131[/C][C]0.407254770565382[/C][C]0.814509541130764[/C][C]0.592745229434618[/C][/ROW]
[ROW][C]132[/C][C]0.449179404637095[/C][C]0.89835880927419[/C][C]0.550820595362905[/C][/ROW]
[ROW][C]133[/C][C]0.381801602849775[/C][C]0.76360320569955[/C][C]0.618198397150225[/C][/ROW]
[ROW][C]134[/C][C]0.370689770189077[/C][C]0.741379540378154[/C][C]0.629310229810923[/C][/ROW]
[ROW][C]135[/C][C]0.595533640302884[/C][C]0.808932719394233[/C][C]0.404466359697116[/C][/ROW]
[ROW][C]136[/C][C]0.529721570252418[/C][C]0.940556859495165[/C][C]0.470278429747582[/C][/ROW]
[ROW][C]137[/C][C]0.612441904815406[/C][C]0.775116190369189[/C][C]0.387558095184594[/C][/ROW]
[ROW][C]138[/C][C]0.540197549400927[/C][C]0.919604901198146[/C][C]0.459802450599073[/C][/ROW]
[ROW][C]139[/C][C]0.631932711169707[/C][C]0.736134577660586[/C][C]0.368067288830293[/C][/ROW]
[ROW][C]140[/C][C]0.555819565349045[/C][C]0.88836086930191[/C][C]0.444180434650955[/C][/ROW]
[ROW][C]141[/C][C]0.597137935052324[/C][C]0.805724129895352[/C][C]0.402862064947676[/C][/ROW]
[ROW][C]142[/C][C]0.594142258893877[/C][C]0.811715482212246[/C][C]0.405857741106123[/C][/ROW]
[ROW][C]143[/C][C]0.6099652774308[/C][C]0.7800694451384[/C][C]0.3900347225692[/C][/ROW]
[ROW][C]144[/C][C]0.561858686393421[/C][C]0.876282627213159[/C][C]0.438141313606579[/C][/ROW]
[ROW][C]145[/C][C]0.458765177782758[/C][C]0.917530355565515[/C][C]0.541234822217242[/C][/ROW]
[ROW][C]146[/C][C]0.407107410044724[/C][C]0.814214820089447[/C][C]0.592892589955276[/C][/ROW]
[ROW][C]147[/C][C]0.38977486923475[/C][C]0.7795497384695[/C][C]0.61022513076525[/C][/ROW]
[ROW][C]148[/C][C]0.266748948964248[/C][C]0.533497897928497[/C][C]0.733251051035752[/C][/ROW]
[ROW][C]149[/C][C]0.444502658067576[/C][C]0.889005316135152[/C][C]0.555497341932424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116322&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116322&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.04309352232343630.08618704464687250.956906477676564
110.01247722401795910.02495444803591830.98752277598204
120.01098170550282460.02196341100564910.989018294497175
130.02115175395994320.04230350791988630.978848246040057
140.03771368702247180.07542737404494360.962286312977528
150.3352547933313060.6705095866626130.664745206668694
160.2807244523841900.5614489047683790.71927554761581
170.3000847199959610.6001694399919210.699915280004039
180.2597971324654250.5195942649308510.740202867534575
190.1999521050741750.3999042101483510.800047894925825
200.299167685876430.598335371752860.70083231412357
210.3225050591817820.6450101183635640.677494940818218
220.2550831691612600.5101663383225190.74491683083874
230.2146573624317870.4293147248635750.785342637568213
240.1825603584733960.3651207169467920.817439641526604
250.1549692756969460.3099385513938920.845030724303054
260.1184135332913860.2368270665827720.881586466708614
270.09810109910630980.1962021982126200.90189890089369
280.08618771536456260.1723754307291250.913812284635437
290.06820237837101710.1364047567420340.931797621628983
300.04839910621432930.09679821242865870.95160089378567
310.04042707177520610.08085414355041210.959572928224794
320.04426363534359300.08852727068718610.955736364656407
330.07372729615859560.1474545923171910.926272703841404
340.2055863689825470.4111727379650930.794413631017453
350.1649137890786870.3298275781573730.835086210921313
360.1887017688286820.3774035376573650.811298231171318
370.2041349013266100.4082698026532210.79586509867339
380.3880233000397620.7760466000795230.611976699960239
390.3611611035427870.7223222070855740.638838896457213
400.3839322091954620.7678644183909230.616067790804538
410.3622225382015370.7244450764030730.637777461798463
420.3201975054982880.6403950109965750.679802494501712
430.5883002847364110.8233994305271780.411699715263589
440.5468469434300570.9063061131398860.453153056569943
450.5161928720087770.9676142559824450.483807127991223
460.548348695055890.903302609888220.45165130494411
470.6329809035418150.734038192916370.367019096458185
480.8179449296213880.3641101407572250.182055070378612
490.7893579884459150.4212840231081710.210642011554085
500.7609738502755670.4780522994488660.239026149724433
510.7472780979327670.5054438041344660.252721902067233
520.7472103235382180.5055793529235650.252789676461782
530.798174305879940.403651388240120.20182569412006
540.8059864990240390.3880270019519220.194013500975961
550.8212428006953620.3575143986092750.178757199304638
560.7981424327015290.4037151345969430.201857567298471
570.7674619812876490.4650760374247020.232538018712351
580.7358653871479980.5282692257040040.264134612852002
590.7083548298469950.583290340306010.291645170153005
600.763669361961660.472661276076680.23633063803834
610.7258329761982120.5483340476035760.274167023801788
620.689868348932660.620263302134680.31013165106734
630.6591318782888760.6817362434222470.340868121711124
640.6327547029722510.7344905940554980.367245297027749
650.6247083056934090.7505833886131830.375291694306591
660.7130667558192570.5738664883614860.286933244180743
670.6768967676231040.6462064647537920.323103232376896
680.7068995591604120.5862008816791750.293100440839588
690.7059993900403260.5880012199193470.294000609959674
700.6816813851873750.6366372296252510.318318614812625
710.6569968804700030.6860062390599930.343003119529997
720.617220968708310.765558062583380.38277903129169
730.5915410267601960.8169179464796070.408458973239804
740.5526048614809860.8947902770380280.447395138519014
750.5195773329006520.9608453341986970.480422667099348
760.4812623834919890.9625247669839790.518737616508011
770.4535605929885310.9071211859770620.546439407011469
780.4140660499587020.8281320999174040.585933950041298
790.3771751074479540.7543502148959070.622824892552046
800.3773898457510060.7547796915020120.622610154248994
810.3431917380553410.6863834761106830.656808261944659
820.4250910535893540.8501821071787080.574908946410646
830.3996090441214030.7992180882428060.600390955878597
840.3597636056604050.719527211320810.640236394339595
850.3394764836456630.6789529672913270.660523516354337
860.3288216327695370.6576432655390740.671178367230463
870.3544422454878370.7088844909756740.645557754512163
880.3149991994469820.6299983988939640.685000800553018
890.3251808084917880.6503616169835770.674819191508212
900.3191866199009980.6383732398019960.680813380099002
910.3502407982423560.7004815964847130.649759201757644
920.3191349057637950.638269811527590.680865094236205
930.3027951418982610.6055902837965230.697204858101739
940.2666313520427910.5332627040855820.733368647957209
950.2420265305759690.4840530611519370.757973469424031
960.3009299430369070.6018598860738140.699070056963093
970.2649513388891370.5299026777782740.735048661110863
980.2312938956600830.4625877913201660.768706104339917
990.2096876620088490.4193753240176980.790312337991151
1000.1843666275316690.3687332550633380.815633372468331
1010.1570248700748770.3140497401497530.842975129925123
1020.1341115286882350.2682230573764710.865888471311765
1030.1106325882245010.2212651764490030.889367411775499
1040.09230450571330620.1846090114266120.907695494286694
1050.09163062750623640.1832612550124730.908369372493764
1060.07343730751519260.1468746150303850.926562692484807
1070.05925957638129280.1185191527625860.940740423618707
1080.04761078422068250.0952215684413650.952389215779317
1090.03691390612709050.07382781225418110.96308609387291
1100.04856024489571440.09712048979142890.951439755104286
1110.08208734698788850.1641746939757770.917912653012111
1120.1543367418370090.3086734836740170.845663258162991
1130.1489954769225210.2979909538450420.85100452307748
1140.5542977114722140.8914045770555720.445702288527786
1150.6928402159686470.6143195680627060.307159784031353
1160.6530544443785640.6938911112428710.346945555621436
1170.7059463806612410.5881072386775180.294053619338759
1180.6590015103857710.6819969792284580.340998489614229
1190.6146923517200570.7706152965598850.385307648279943
1200.6150338842697330.7699322314605350.384966115730267
1210.695117039499310.609765921001380.30488296050069
1220.6616179076596150.6767641846807710.338382092340385
1230.6600213830973850.679957233805230.339978616902615
1240.6038083762187620.7923832475624760.396191623781238
1250.6282929355613150.743414128877370.371707064438685
1260.601738297028990.796523405942020.39826170297101
1270.540800599324830.918398801350340.45919940067517
1280.4810034287519530.9620068575039050.518996571248047
1290.4529713324313050.905942664862610.547028667568695
1300.4149368438112460.8298736876224910.585063156188754
1310.4072547705653820.8145095411307640.592745229434618
1320.4491794046370950.898358809274190.550820595362905
1330.3818016028497750.763603205699550.618198397150225
1340.3706897701890770.7413795403781540.629310229810923
1350.5955336403028840.8089327193942330.404466359697116
1360.5297215702524180.9405568594951650.470278429747582
1370.6124419048154060.7751161903691890.387558095184594
1380.5401975494009270.9196049011981460.459802450599073
1390.6319327111697070.7361345776605860.368067288830293
1400.5558195653490450.888360869301910.444180434650955
1410.5971379350523240.8057241298953520.402862064947676
1420.5941422588938770.8117154822122460.405857741106123
1430.60996527743080.78006944513840.3900347225692
1440.5618586863934210.8762826272131590.438141313606579
1450.4587651777827580.9175303555655150.541234822217242
1460.4071074100447240.8142148200894470.592892589955276
1470.389774869234750.77954973846950.61022513076525
1480.2667489489642480.5334978979284970.733251051035752
1490.4445026580675760.8890053161351520.555497341932424






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0214285714285714OK
10% type I error level110.0785714285714286OK

\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 & 3 & 0.0214285714285714 & OK \tabularnewline
10% type I error level & 11 & 0.0785714285714286 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116322&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]3[/C][C]0.0214285714285714[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.0785714285714286[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116322&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116322&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 level30.0214285714285714OK
10% type I error level110.0785714285714286OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}