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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 19:51:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322182316oafrcekgxskgy41.htm/, Retrieved Fri, 29 Mar 2024 02:34:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147244, Retrieved Fri, 29 Mar 2024 02:34:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:55:52] [afe9379cca749d06b3d6872e02cc47ed]
-   PD    [Multiple Regression] [] [2010-12-02 19:12:54] [94f4aa1c01e87d8321fffb341ed4df07]
- R           [Multiple Regression] [] [2011-11-25 00:51:26] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R P           [Multiple Regression] [] [2011-11-27 17:12:25] [3931071255a6f7f4a767409781cc5f7d]
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Dataseries X:
9	24	14	11	12	24	26
9	25	11	7	8	25	23
9	17	6	17	8	30	25
9	18	12	10	8	19	23
9	18	8	12	9	22	19
9	16	10	12	7	22	29
10	20	10	11	4	25	25
10	16	11	11	11	23	21
10	18	16	12	7	17	22
10	17	11	13	7	21	25
10	23	13	14	12	19	24
10	30	12	16	10	19	18
10	23	8	11	10	15	22
10	18	12	10	8	16	15
10	15	11	11	8	23	22
10	12	4	15	4	27	28
10	21	9	9	9	22	20
10	15	8	11	8	14	12
10	20	8	17	7	22	24
10	31	14	17	11	23	20
10	27	15	11	9	23	21
10	34	16	18	11	21	20
10	21	9	14	13	19	21
10	31	14	10	8	18	23
10	19	11	11	8	20	28
10	16	8	15	9	23	24
10	20	9	15	6	25	24
10	21	9	13	9	19	24
10	22	9	16	9	24	23
10	17	9	13	6	22	23
10	24	10	9	6	25	29
10	25	16	18	16	26	24
10	26	11	18	5	29	18
10	25	8	12	7	32	25
10	17	9	17	9	25	21
10	32	16	9	6	29	26
10	33	11	9	6	28	22
10	13	16	12	5	17	22
10	32	12	18	12	28	22
10	25	12	12	7	29	23
10	29	14	18	10	26	30
10	22	9	14	9	25	23
10	18	10	15	8	14	17
10	17	9	16	5	25	23
10	20	10	10	8	26	23
10	15	12	11	8	20	25
10	20	14	14	10	18	24
10	33	14	9	6	32	24
10	29	10	12	8	25	23
10	23	14	17	7	25	21
10	26	16	5	4	23	24
10	18	9	12	8	21	24
10	20	10	12	8	20	28
10	11	6	6	4	15	16
10	28	8	24	20	30	20
10	26	13	12	8	24	29
10	22	10	12	8	26	27
10	17	8	14	6	24	22
10	12	7	7	4	22	28
10	14	15	13	8	14	16
10	17	9	12	9	24	25
10	21	10	13	6	24	24
10	19	12	14	7	24	28
10	18	13	8	9	24	24
10	10	10	11	5	19	23
10	29	11	9	5	31	30
10	31	8	11	8	22	24
10	19	9	13	8	27	21
10	9	13	10	6	19	25
10	20	11	11	8	25	25
10	28	8	12	7	20	22
10	19	9	9	7	21	23
10	30	9	15	9	27	26
10	29	15	18	11	23	23
10	26	9	15	6	25	25
10	23	10	12	8	20	21
10	13	14	13	6	21	25
10	21	12	14	9	22	24
10	19	12	10	8	23	29
10	28	11	13	6	25	22
10	23	14	13	10	25	27
10	18	6	11	8	17	26
10	21	12	13	8	19	22
10	20	8	16	10	25	24
10	23	14	8	5	19	27
10	21	11	16	7	20	24
10	21	10	11	5	26	24
10	15	14	9	8	23	29
10	28	12	16	14	27	22
10	19	10	12	7	17	21
10	26	14	14	8	17	24
10	10	5	8	6	19	24
10	16	11	9	5	17	23
10	22	10	15	6	22	20
10	19	9	11	10	21	27
10	31	10	21	12	32	26
10	31	16	14	9	21	25
10	29	13	18	12	21	21
10	19	9	12	7	18	21
10	22	10	13	8	18	19
10	23	10	15	10	23	21
10	15	7	12	6	19	21
10	20	9	19	10	20	16
10	18	8	15	10	21	22
10	23	14	11	10	20	29
10	25	14	11	5	17	15
10	21	8	10	7	18	17
10	24	9	13	10	19	15
10	25	14	15	11	22	21
10	17	14	12	6	15	21
10	13	8	12	7	14	19
10	28	8	16	12	18	24
10	21	8	9	11	24	20
10	25	7	18	11	35	17
10	9	6	8	11	29	23
10	16	8	13	5	21	24
10	19	6	17	8	25	14
10	17	11	9	6	20	19
10	25	14	15	9	22	24
10	20	11	8	4	13	13
10	29	11	7	4	26	22
10	14	11	12	7	17	16
10	22	14	14	11	25	19
10	15	8	6	6	20	25
10	19	20	8	7	19	25
10	20	11	17	8	21	23
10	15	8	10	4	22	24
10	20	11	11	8	24	26
10	18	10	14	9	21	26
10	33	14	11	8	26	25
10	22	11	13	11	24	18
10	16	9	12	8	16	21
10	17	9	11	5	23	26
10	16	8	9	4	18	23
10	21	10	12	8	16	23
10	26	13	20	10	26	22
10	18	13	12	6	19	20
10	18	12	13	9	21	13
10	17	8	12	9	21	24
10	22	13	12	13	22	15
10	30	14	9	9	23	14
10	30	12	15	10	29	22
10	24	14	24	20	21	10
10	21	15	7	5	21	24
10	21	13	17	11	23	22
10	29	16	11	6	27	24
10	31	9	17	9	25	19
10	20	9	11	7	21	20
10	16	9	12	9	10	13
10	22	8	14	10	20	20
10	20	7	11	9	26	22
10	28	16	16	8	24	24
10	38	11	21	7	29	29
10	22	9	14	6	19	12
10	20	11	20	13	24	20
10	17	9	13	6	19	21
10	28	14	11	8	24	24
10	22	13	15	10	22	22
10	31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \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=147244&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/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=147244&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147244&T=0

As an alternative you can also use a 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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
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] = + 22.4503451046679 -1.49932963098333month[t] + 0.330887218059848ConcernoverMistakes[t] -0.356681047835005Doubtsaboutactions[t] + 0.198030325271138ParentalExpectations[t] + 0.00613248856872827ParentalCriticism[t] + 0.393045515742641`Organization `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PersonalStandards[t] =  +  22.4503451046679 -1.49932963098333month[t] +  0.330887218059848ConcernoverMistakes[t] -0.356681047835005Doubtsaboutactions[t] +  0.198030325271138ParentalExpectations[t] +  0.00613248856872827ParentalCriticism[t] +  0.393045515742641`Organization
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147244&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PersonalStandards[t] =  +  22.4503451046679 -1.49932963098333month[t] +  0.330887218059848ConcernoverMistakes[t] -0.356681047835005Doubtsaboutactions[t] +  0.198030325271138ParentalExpectations[t] +  0.00613248856872827ParentalCriticism[t] +  0.393045515742641`Organization
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147244&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147244&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
PersonalStandards[t] = + 22.4503451046679 -1.49932963098333month[t] + 0.330887218059848ConcernoverMistakes[t] -0.356681047835005Doubtsaboutactions[t] + 0.198030325271138ParentalExpectations[t] + 0.00613248856872827ParentalCriticism[t] + 0.393045515742641`Organization `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22.450345104667914.5969821.5380.1261250.063062
month-1.499329630983331.442617-1.03930.300310.150155
ConcernoverMistakes0.3308872180598480.0555925.952100
Doubtsaboutactions-0.3566810478350050.107249-3.32570.0011060.000553
ParentalExpectations0.1980303252711380.1017141.94690.0533850.026693
ParentalCriticism0.006132488568728270.1296540.04730.9623370.481169
`Organization `0.3930455157426410.0721895.444700

\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) & 22.4503451046679 & 14.596982 & 1.538 & 0.126125 & 0.063062 \tabularnewline
month & -1.49932963098333 & 1.442617 & -1.0393 & 0.30031 & 0.150155 \tabularnewline
ConcernoverMistakes & 0.330887218059848 & 0.055592 & 5.9521 & 0 & 0 \tabularnewline
Doubtsaboutactions & -0.356681047835005 & 0.107249 & -3.3257 & 0.001106 & 0.000553 \tabularnewline
ParentalExpectations & 0.198030325271138 & 0.101714 & 1.9469 & 0.053385 & 0.026693 \tabularnewline
ParentalCriticism & 0.00613248856872827 & 0.129654 & 0.0473 & 0.962337 & 0.481169 \tabularnewline
`Organization
` & 0.393045515742641 & 0.072189 & 5.4447 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147244&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]22.4503451046679[/C][C]14.596982[/C][C]1.538[/C][C]0.126125[/C][C]0.063062[/C][/ROW]
[ROW][C]month[/C][C]-1.49932963098333[/C][C]1.442617[/C][C]-1.0393[/C][C]0.30031[/C][C]0.150155[/C][/ROW]
[ROW][C]ConcernoverMistakes[/C][C]0.330887218059848[/C][C]0.055592[/C][C]5.9521[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]-0.356681047835005[/C][C]0.107249[/C][C]-3.3257[/C][C]0.001106[/C][C]0.000553[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.198030325271138[/C][C]0.101714[/C][C]1.9469[/C][C]0.053385[/C][C]0.026693[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.00613248856872827[/C][C]0.129654[/C][C]0.0473[/C][C]0.962337[/C][C]0.481169[/C][/ROW]
[ROW][C]`Organization
`[/C][C]0.393045515742641[/C][C]0.072189[/C][C]5.4447[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147244&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147244&T=2

As an alternative you can also use a 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)22.450345104667914.5969821.5380.1261250.063062
month-1.499329630983331.442617-1.03930.300310.150155
ConcernoverMistakes0.3308872180598480.0555925.952100
Doubtsaboutactions-0.3566810478350050.107249-3.32570.0011060.000553
ParentalExpectations0.1980303252711380.1017141.94690.0533850.026693
ParentalCriticism0.006132488568728270.1296540.04730.9623370.481169
`Organization `0.3930455157426410.0721895.444700







Multiple Linear Regression - Regression Statistics
Multiple R0.60953344822544
R-squared0.371531024505596
Adjusted R-squared0.346723038630816
F-TEST (value)14.9762671738422
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.02393657389166e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.40838004054157
Sum Squared Residuals1765.79228411584

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.60953344822544 \tabularnewline
R-squared & 0.371531024505596 \tabularnewline
Adjusted R-squared & 0.346723038630816 \tabularnewline
F-TEST (value) & 14.9762671738422 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 2.02393657389166e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.40838004054157 \tabularnewline
Sum Squared Residuals & 1765.79228411584 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147244&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.60953344822544[/C][/ROW]
[ROW][C]R-squared[/C][C]0.371531024505596[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.346723038630816[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.9762671738422[/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]2.02393657389166e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.40838004054157[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1765.79228411584[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147244&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147244&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.60953344822544
R-squared0.371531024505596
Adjusted R-squared0.346723038630816
F-TEST (value)14.9762671738422
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.02393657389166e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.40838004054157
Sum Squared Residuals1765.79228411584







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.3752438396802-0.375243839680221
22523.78038639865771.21961360134235
33025.68308817755064.31691182244943
41921.7015858002171-2.70158580021713
52221.95832106769760.0416789323024097
62224.5013747161968-2.50137471619684
72522.5369841035052.46301589649499
82319.32749954044123.67250045955885
91718.7724146241247-1.77241462412469
102121.6020995177389-0.60209951773893
111922.7097079828001-3.70970798280015
121923.4081221360031-4.40812213600306
131523.100666237539-8.10066623753901
141617.0578920432927-1.05789204329267
152319.37126037241783.62873962758224
162724.00123049434872.99876950565126
172220.8939265829881.10607341701197
181416.5108483584964-2.51084835849636
192224.0638801007654-2.0638801007654
202324.015901103718-1.01590110371805
212321.5282697706221.471730229378
222124.4932309874987-3.49323098749872
231922.3016536793613-3.30165367936127
241823.7904279083418-5.79042790834182
252023.053082339113-3.053082339113
262322.35653555512120.643464444878811
272523.30500591381941.69499408618061
281923.2582299470431-4.25822994704315
292423.79016262517380.209837374826231
302221.52323809335490.476761906645071
312525.0489193653102-0.0489193653101529
322623.11809053077432.88190946922571
332922.80665251929736.19334748070269
343225.12121008045166.87878991954841
352521.54766582866043.45233417133961
362924.3767942755514.62320572444902
372824.91890466981533.08109533018471
381717.105713556688-0.105713556687993
392826.05040426277311.94959573722695
402922.90839485762636.09160514237371
412627.4764796617272-1.47647966172717
422523.39410197463151.60589802536851
431419.5474967968037-5.54749679680366
442522.11119658059962.88880341940038
452621.57739270102354.42260729897649
462020.1937158718107-0.19371587181068
471821.3481003036481-3.34810030364814
483224.63495255779567.36504744220444
492524.95143831410440.0485616858955817
502521.7373189207073.26268107929301
512320.80099365748462.1990063425154
522122.0614054790237-1.06140547902374
532023.938680930279-3.93868093027899
541516.4581620642669-1.45816206426694
553026.60473041056513.39526958943494
562425.2470066108757-1.24700661087571
572624.2074098506561.79259014934395
582421.68490395071842.31509604928157
592221.34694474867460.653055251325381
601415.6514365191043-1.65143651910432
612422.12969626527531.87030373472474
622422.8831514335021.11684856649804
632423.28435977852270.715640221477318
642419.84869247516794.15130752483211
651918.448153379990.551846620010017
663126.73358743494834.2664125650517
672226.5215900363656-4.52159003636563
682721.41118647512685.5888135248732
691917.6414162132081.35858378679201
702522.20483300994492.79516699005507
712024.9347351874032-4.93473518740321
722121.3990237169588-0.3990237169588
732727.4183665916093-0.418366591609338
742324.3746124922624-1.37461249226241
752525.6833747379211-0.68337473792112
762022.18002397426-2.18002397426005
772119.20237501342581.79762498657421
782222.3862171288093-0.38621712880927
792322.89141648174950.108583518250502
802524.05658988060060.943410119399394
812523.32186817978451.67813182021553
821723.7195093287429-6.7195093287429
831921.3959632834841-2.39596328348412
842523.88424724120041.11575275879955
851922.3010541105851-3.30105411058514
862023.1266938500491-3.1266938500491
872622.4809582943913.51904170560905
882320.6564751885692.34352481143104
892724.34305971712882.65694028287116
901720.8503426134519-3.85034261345193
911723.3211586348698-6.32115863486977
921920.036645647663-1.03664564766296
931719.6807349899718-2.68073498997179
942222.0379172391335-0.0379172391335173
952123.3856638961778-2.38566389617783
963228.59915217916723.4008478208328
972124.6614106338104-3.66141063381037
982124.3080160450159-3.30801604501586
991821.2070236612869-3.20702366128693
1001821.2610760499861-3.26107604998606
1012322.78637992721090.213620072789081
1021920.5907043961488-1.59070439614882
1032020.9772930432377-0.977293043237729
1042122.2383514483243-1.23835144832433
1052023.7118985607275-3.71189856072747
1061718.8403733336066-1.84037333360655
1071820.2572364317288-2.25723643172879
1081920.7196144481076-1.71961444810765
1092222.0275626605593-0.0275626605593229
1101518.7557114974235-3.75571149742348
1111418.7922903692776-4.79229036927757
1121826.5436099628167-8.54360996281669
1132421.26287260796052.73712739203951
1143523.546238908247211.4537610917528
1152918.986694308869110.0133056911309
1162121.935944950304-0.935944950303999
1172520.52203230951794.47796769048212
1182018.44557263362981.5544273663702
1192223.1944342306498-1.19443423064979
1201316.8696658909449-3.8696658909449
1212623.18703016989622.81296983010383
1221716.87399789660450.126002103395524
1232520.05077964962344.94922035037664
1242020.6180234596576-0.618023459657552
1251918.06359289698790.936407103012113
1262122.6069239300865-1.60692393008647
1272221.0048342678620.995165732137992
1282422.59787852568761.40212147431243
1292122.893008601785-1.89300860178502
1302625.43632370121790.563676298782067
1312420.52974695211463.47025304788541
1321620.2204944956761-4.22049449567612
1332322.30018150147180.699818498528152
1341820.7446456449081-2.74464564490808
1351622.3043405696256-6.30434056962563
1362624.09219557998381.90780442001622
1371919.0502342475757-0.050234247575695
1382116.87202447618954.12797552381046
1392122.0933317973676-1.09333179736762
1402218.4514829610833.54851703891702
1412319.73023321189583.26976678810421
1422924.78227387370254.21772612629751
1432119.21064009388921.78935990611078
1442119.90543175413141.09456824586863
1452321.84980100243991.15019899756015
1462722.99410224042844.00589775957157
1472525.393995850013-0.393995850012975
1482120.9468350383330.0531649616669982
1491017.0822628583037-7.08226285830372
1502022.5777789638073-2.5777789638073
1512622.45855314262573.54144685737425
1522423.66563162586170.334368374138266
1532931.7071557621354-2.70715576213541
1541919.0522038357563-0.052203835756255
1552422.05254080151561.94745919848439
1561920.7371470618696-1.73714706186965
1572423.38884209517610.611157904823948
1582221.77849508138870.221504918611303
1591723.729262101434-6.72926210143395

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.3752438396802 & -0.375243839680221 \tabularnewline
2 & 25 & 23.7803863986577 & 1.21961360134235 \tabularnewline
3 & 30 & 25.6830881775506 & 4.31691182244943 \tabularnewline
4 & 19 & 21.7015858002171 & -2.70158580021713 \tabularnewline
5 & 22 & 21.9583210676976 & 0.0416789323024097 \tabularnewline
6 & 22 & 24.5013747161968 & -2.50137471619684 \tabularnewline
7 & 25 & 22.536984103505 & 2.46301589649499 \tabularnewline
8 & 23 & 19.3274995404412 & 3.67250045955885 \tabularnewline
9 & 17 & 18.7724146241247 & -1.77241462412469 \tabularnewline
10 & 21 & 21.6020995177389 & -0.60209951773893 \tabularnewline
11 & 19 & 22.7097079828001 & -3.70970798280015 \tabularnewline
12 & 19 & 23.4081221360031 & -4.40812213600306 \tabularnewline
13 & 15 & 23.100666237539 & -8.10066623753901 \tabularnewline
14 & 16 & 17.0578920432927 & -1.05789204329267 \tabularnewline
15 & 23 & 19.3712603724178 & 3.62873962758224 \tabularnewline
16 & 27 & 24.0012304943487 & 2.99876950565126 \tabularnewline
17 & 22 & 20.893926582988 & 1.10607341701197 \tabularnewline
18 & 14 & 16.5108483584964 & -2.51084835849636 \tabularnewline
19 & 22 & 24.0638801007654 & -2.0638801007654 \tabularnewline
20 & 23 & 24.015901103718 & -1.01590110371805 \tabularnewline
21 & 23 & 21.528269770622 & 1.471730229378 \tabularnewline
22 & 21 & 24.4932309874987 & -3.49323098749872 \tabularnewline
23 & 19 & 22.3016536793613 & -3.30165367936127 \tabularnewline
24 & 18 & 23.7904279083418 & -5.79042790834182 \tabularnewline
25 & 20 & 23.053082339113 & -3.053082339113 \tabularnewline
26 & 23 & 22.3565355551212 & 0.643464444878811 \tabularnewline
27 & 25 & 23.3050059138194 & 1.69499408618061 \tabularnewline
28 & 19 & 23.2582299470431 & -4.25822994704315 \tabularnewline
29 & 24 & 23.7901626251738 & 0.209837374826231 \tabularnewline
30 & 22 & 21.5232380933549 & 0.476761906645071 \tabularnewline
31 & 25 & 25.0489193653102 & -0.0489193653101529 \tabularnewline
32 & 26 & 23.1180905307743 & 2.88190946922571 \tabularnewline
33 & 29 & 22.8066525192973 & 6.19334748070269 \tabularnewline
34 & 32 & 25.1212100804516 & 6.87878991954841 \tabularnewline
35 & 25 & 21.5476658286604 & 3.45233417133961 \tabularnewline
36 & 29 & 24.376794275551 & 4.62320572444902 \tabularnewline
37 & 28 & 24.9189046698153 & 3.08109533018471 \tabularnewline
38 & 17 & 17.105713556688 & -0.105713556687993 \tabularnewline
39 & 28 & 26.0504042627731 & 1.94959573722695 \tabularnewline
40 & 29 & 22.9083948576263 & 6.09160514237371 \tabularnewline
41 & 26 & 27.4764796617272 & -1.47647966172717 \tabularnewline
42 & 25 & 23.3941019746315 & 1.60589802536851 \tabularnewline
43 & 14 & 19.5474967968037 & -5.54749679680366 \tabularnewline
44 & 25 & 22.1111965805996 & 2.88880341940038 \tabularnewline
45 & 26 & 21.5773927010235 & 4.42260729897649 \tabularnewline
46 & 20 & 20.1937158718107 & -0.19371587181068 \tabularnewline
47 & 18 & 21.3481003036481 & -3.34810030364814 \tabularnewline
48 & 32 & 24.6349525577956 & 7.36504744220444 \tabularnewline
49 & 25 & 24.9514383141044 & 0.0485616858955817 \tabularnewline
50 & 25 & 21.737318920707 & 3.26268107929301 \tabularnewline
51 & 23 & 20.8009936574846 & 2.1990063425154 \tabularnewline
52 & 21 & 22.0614054790237 & -1.06140547902374 \tabularnewline
53 & 20 & 23.938680930279 & -3.93868093027899 \tabularnewline
54 & 15 & 16.4581620642669 & -1.45816206426694 \tabularnewline
55 & 30 & 26.6047304105651 & 3.39526958943494 \tabularnewline
56 & 24 & 25.2470066108757 & -1.24700661087571 \tabularnewline
57 & 26 & 24.207409850656 & 1.79259014934395 \tabularnewline
58 & 24 & 21.6849039507184 & 2.31509604928157 \tabularnewline
59 & 22 & 21.3469447486746 & 0.653055251325381 \tabularnewline
60 & 14 & 15.6514365191043 & -1.65143651910432 \tabularnewline
61 & 24 & 22.1296962652753 & 1.87030373472474 \tabularnewline
62 & 24 & 22.883151433502 & 1.11684856649804 \tabularnewline
63 & 24 & 23.2843597785227 & 0.715640221477318 \tabularnewline
64 & 24 & 19.8486924751679 & 4.15130752483211 \tabularnewline
65 & 19 & 18.44815337999 & 0.551846620010017 \tabularnewline
66 & 31 & 26.7335874349483 & 4.2664125650517 \tabularnewline
67 & 22 & 26.5215900363656 & -4.52159003636563 \tabularnewline
68 & 27 & 21.4111864751268 & 5.5888135248732 \tabularnewline
69 & 19 & 17.641416213208 & 1.35858378679201 \tabularnewline
70 & 25 & 22.2048330099449 & 2.79516699005507 \tabularnewline
71 & 20 & 24.9347351874032 & -4.93473518740321 \tabularnewline
72 & 21 & 21.3990237169588 & -0.3990237169588 \tabularnewline
73 & 27 & 27.4183665916093 & -0.418366591609338 \tabularnewline
74 & 23 & 24.3746124922624 & -1.37461249226241 \tabularnewline
75 & 25 & 25.6833747379211 & -0.68337473792112 \tabularnewline
76 & 20 & 22.18002397426 & -2.18002397426005 \tabularnewline
77 & 21 & 19.2023750134258 & 1.79762498657421 \tabularnewline
78 & 22 & 22.3862171288093 & -0.38621712880927 \tabularnewline
79 & 23 & 22.8914164817495 & 0.108583518250502 \tabularnewline
80 & 25 & 24.0565898806006 & 0.943410119399394 \tabularnewline
81 & 25 & 23.3218681797845 & 1.67813182021553 \tabularnewline
82 & 17 & 23.7195093287429 & -6.7195093287429 \tabularnewline
83 & 19 & 21.3959632834841 & -2.39596328348412 \tabularnewline
84 & 25 & 23.8842472412004 & 1.11575275879955 \tabularnewline
85 & 19 & 22.3010541105851 & -3.30105411058514 \tabularnewline
86 & 20 & 23.1266938500491 & -3.1266938500491 \tabularnewline
87 & 26 & 22.480958294391 & 3.51904170560905 \tabularnewline
88 & 23 & 20.656475188569 & 2.34352481143104 \tabularnewline
89 & 27 & 24.3430597171288 & 2.65694028287116 \tabularnewline
90 & 17 & 20.8503426134519 & -3.85034261345193 \tabularnewline
91 & 17 & 23.3211586348698 & -6.32115863486977 \tabularnewline
92 & 19 & 20.036645647663 & -1.03664564766296 \tabularnewline
93 & 17 & 19.6807349899718 & -2.68073498997179 \tabularnewline
94 & 22 & 22.0379172391335 & -0.0379172391335173 \tabularnewline
95 & 21 & 23.3856638961778 & -2.38566389617783 \tabularnewline
96 & 32 & 28.5991521791672 & 3.4008478208328 \tabularnewline
97 & 21 & 24.6614106338104 & -3.66141063381037 \tabularnewline
98 & 21 & 24.3080160450159 & -3.30801604501586 \tabularnewline
99 & 18 & 21.2070236612869 & -3.20702366128693 \tabularnewline
100 & 18 & 21.2610760499861 & -3.26107604998606 \tabularnewline
101 & 23 & 22.7863799272109 & 0.213620072789081 \tabularnewline
102 & 19 & 20.5907043961488 & -1.59070439614882 \tabularnewline
103 & 20 & 20.9772930432377 & -0.977293043237729 \tabularnewline
104 & 21 & 22.2383514483243 & -1.23835144832433 \tabularnewline
105 & 20 & 23.7118985607275 & -3.71189856072747 \tabularnewline
106 & 17 & 18.8403733336066 & -1.84037333360655 \tabularnewline
107 & 18 & 20.2572364317288 & -2.25723643172879 \tabularnewline
108 & 19 & 20.7196144481076 & -1.71961444810765 \tabularnewline
109 & 22 & 22.0275626605593 & -0.0275626605593229 \tabularnewline
110 & 15 & 18.7557114974235 & -3.75571149742348 \tabularnewline
111 & 14 & 18.7922903692776 & -4.79229036927757 \tabularnewline
112 & 18 & 26.5436099628167 & -8.54360996281669 \tabularnewline
113 & 24 & 21.2628726079605 & 2.73712739203951 \tabularnewline
114 & 35 & 23.5462389082472 & 11.4537610917528 \tabularnewline
115 & 29 & 18.9866943088691 & 10.0133056911309 \tabularnewline
116 & 21 & 21.935944950304 & -0.935944950303999 \tabularnewline
117 & 25 & 20.5220323095179 & 4.47796769048212 \tabularnewline
118 & 20 & 18.4455726336298 & 1.5544273663702 \tabularnewline
119 & 22 & 23.1944342306498 & -1.19443423064979 \tabularnewline
120 & 13 & 16.8696658909449 & -3.8696658909449 \tabularnewline
121 & 26 & 23.1870301698962 & 2.81296983010383 \tabularnewline
122 & 17 & 16.8739978966045 & 0.126002103395524 \tabularnewline
123 & 25 & 20.0507796496234 & 4.94922035037664 \tabularnewline
124 & 20 & 20.6180234596576 & -0.618023459657552 \tabularnewline
125 & 19 & 18.0635928969879 & 0.936407103012113 \tabularnewline
126 & 21 & 22.6069239300865 & -1.60692393008647 \tabularnewline
127 & 22 & 21.004834267862 & 0.995165732137992 \tabularnewline
128 & 24 & 22.5978785256876 & 1.40212147431243 \tabularnewline
129 & 21 & 22.893008601785 & -1.89300860178502 \tabularnewline
130 & 26 & 25.4363237012179 & 0.563676298782067 \tabularnewline
131 & 24 & 20.5297469521146 & 3.47025304788541 \tabularnewline
132 & 16 & 20.2204944956761 & -4.22049449567612 \tabularnewline
133 & 23 & 22.3001815014718 & 0.699818498528152 \tabularnewline
134 & 18 & 20.7446456449081 & -2.74464564490808 \tabularnewline
135 & 16 & 22.3043405696256 & -6.30434056962563 \tabularnewline
136 & 26 & 24.0921955799838 & 1.90780442001622 \tabularnewline
137 & 19 & 19.0502342475757 & -0.050234247575695 \tabularnewline
138 & 21 & 16.8720244761895 & 4.12797552381046 \tabularnewline
139 & 21 & 22.0933317973676 & -1.09333179736762 \tabularnewline
140 & 22 & 18.451482961083 & 3.54851703891702 \tabularnewline
141 & 23 & 19.7302332118958 & 3.26976678810421 \tabularnewline
142 & 29 & 24.7822738737025 & 4.21772612629751 \tabularnewline
143 & 21 & 19.2106400938892 & 1.78935990611078 \tabularnewline
144 & 21 & 19.9054317541314 & 1.09456824586863 \tabularnewline
145 & 23 & 21.8498010024399 & 1.15019899756015 \tabularnewline
146 & 27 & 22.9941022404284 & 4.00589775957157 \tabularnewline
147 & 25 & 25.393995850013 & -0.393995850012975 \tabularnewline
148 & 21 & 20.946835038333 & 0.0531649616669982 \tabularnewline
149 & 10 & 17.0822628583037 & -7.08226285830372 \tabularnewline
150 & 20 & 22.5777789638073 & -2.5777789638073 \tabularnewline
151 & 26 & 22.4585531426257 & 3.54144685737425 \tabularnewline
152 & 24 & 23.6656316258617 & 0.334368374138266 \tabularnewline
153 & 29 & 31.7071557621354 & -2.70715576213541 \tabularnewline
154 & 19 & 19.0522038357563 & -0.052203835756255 \tabularnewline
155 & 24 & 22.0525408015156 & 1.94745919848439 \tabularnewline
156 & 19 & 20.7371470618696 & -1.73714706186965 \tabularnewline
157 & 24 & 23.3888420951761 & 0.611157904823948 \tabularnewline
158 & 22 & 21.7784950813887 & 0.221504918611303 \tabularnewline
159 & 17 & 23.729262101434 & -6.72926210143395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147244&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]24.3752438396802[/C][C]-0.375243839680221[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]23.7803863986577[/C][C]1.21961360134235[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]25.6830881775506[/C][C]4.31691182244943[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]21.7015858002171[/C][C]-2.70158580021713[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]21.9583210676976[/C][C]0.0416789323024097[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]24.5013747161968[/C][C]-2.50137471619684[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]22.536984103505[/C][C]2.46301589649499[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]19.3274995404412[/C][C]3.67250045955885[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]18.7724146241247[/C][C]-1.77241462412469[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]21.6020995177389[/C][C]-0.60209951773893[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]22.7097079828001[/C][C]-3.70970798280015[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]23.4081221360031[/C][C]-4.40812213600306[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]23.100666237539[/C][C]-8.10066623753901[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]17.0578920432927[/C][C]-1.05789204329267[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]19.3712603724178[/C][C]3.62873962758224[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]24.0012304943487[/C][C]2.99876950565126[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]20.893926582988[/C][C]1.10607341701197[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]16.5108483584964[/C][C]-2.51084835849636[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]24.0638801007654[/C][C]-2.0638801007654[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]24.015901103718[/C][C]-1.01590110371805[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]21.528269770622[/C][C]1.471730229378[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]24.4932309874987[/C][C]-3.49323098749872[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]22.3016536793613[/C][C]-3.30165367936127[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]23.7904279083418[/C][C]-5.79042790834182[/C][/ROW]
[ROW][C]25[/C][C]20[/C][C]23.053082339113[/C][C]-3.053082339113[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]22.3565355551212[/C][C]0.643464444878811[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.3050059138194[/C][C]1.69499408618061[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]23.2582299470431[/C][C]-4.25822994704315[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]23.7901626251738[/C][C]0.209837374826231[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]21.5232380933549[/C][C]0.476761906645071[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]25.0489193653102[/C][C]-0.0489193653101529[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]23.1180905307743[/C][C]2.88190946922571[/C][/ROW]
[ROW][C]33[/C][C]29[/C][C]22.8066525192973[/C][C]6.19334748070269[/C][/ROW]
[ROW][C]34[/C][C]32[/C][C]25.1212100804516[/C][C]6.87878991954841[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]21.5476658286604[/C][C]3.45233417133961[/C][/ROW]
[ROW][C]36[/C][C]29[/C][C]24.376794275551[/C][C]4.62320572444902[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]24.9189046698153[/C][C]3.08109533018471[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]17.105713556688[/C][C]-0.105713556687993[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]26.0504042627731[/C][C]1.94959573722695[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]22.9083948576263[/C][C]6.09160514237371[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]27.4764796617272[/C][C]-1.47647966172717[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]23.3941019746315[/C][C]1.60589802536851[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]19.5474967968037[/C][C]-5.54749679680366[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]22.1111965805996[/C][C]2.88880341940038[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]21.5773927010235[/C][C]4.42260729897649[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]20.1937158718107[/C][C]-0.19371587181068[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]21.3481003036481[/C][C]-3.34810030364814[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]24.6349525577956[/C][C]7.36504744220444[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]24.9514383141044[/C][C]0.0485616858955817[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]21.737318920707[/C][C]3.26268107929301[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]20.8009936574846[/C][C]2.1990063425154[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]22.0614054790237[/C][C]-1.06140547902374[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]23.938680930279[/C][C]-3.93868093027899[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]16.4581620642669[/C][C]-1.45816206426694[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]26.6047304105651[/C][C]3.39526958943494[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]25.2470066108757[/C][C]-1.24700661087571[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]24.207409850656[/C][C]1.79259014934395[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]21.6849039507184[/C][C]2.31509604928157[/C][/ROW]
[ROW][C]59[/C][C]22[/C][C]21.3469447486746[/C][C]0.653055251325381[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]15.6514365191043[/C][C]-1.65143651910432[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]22.1296962652753[/C][C]1.87030373472474[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]22.883151433502[/C][C]1.11684856649804[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]23.2843597785227[/C][C]0.715640221477318[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]19.8486924751679[/C][C]4.15130752483211[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]18.44815337999[/C][C]0.551846620010017[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]26.7335874349483[/C][C]4.2664125650517[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]26.5215900363656[/C][C]-4.52159003636563[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]21.4111864751268[/C][C]5.5888135248732[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]17.641416213208[/C][C]1.35858378679201[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]22.2048330099449[/C][C]2.79516699005507[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]24.9347351874032[/C][C]-4.93473518740321[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]21.3990237169588[/C][C]-0.3990237169588[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]27.4183665916093[/C][C]-0.418366591609338[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]24.3746124922624[/C][C]-1.37461249226241[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]25.6833747379211[/C][C]-0.68337473792112[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]22.18002397426[/C][C]-2.18002397426005[/C][/ROW]
[ROW][C]77[/C][C]21[/C][C]19.2023750134258[/C][C]1.79762498657421[/C][/ROW]
[ROW][C]78[/C][C]22[/C][C]22.3862171288093[/C][C]-0.38621712880927[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]22.8914164817495[/C][C]0.108583518250502[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]24.0565898806006[/C][C]0.943410119399394[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]23.3218681797845[/C][C]1.67813182021553[/C][/ROW]
[ROW][C]82[/C][C]17[/C][C]23.7195093287429[/C][C]-6.7195093287429[/C][/ROW]
[ROW][C]83[/C][C]19[/C][C]21.3959632834841[/C][C]-2.39596328348412[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]23.8842472412004[/C][C]1.11575275879955[/C][/ROW]
[ROW][C]85[/C][C]19[/C][C]22.3010541105851[/C][C]-3.30105411058514[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]23.1266938500491[/C][C]-3.1266938500491[/C][/ROW]
[ROW][C]87[/C][C]26[/C][C]22.480958294391[/C][C]3.51904170560905[/C][/ROW]
[ROW][C]88[/C][C]23[/C][C]20.656475188569[/C][C]2.34352481143104[/C][/ROW]
[ROW][C]89[/C][C]27[/C][C]24.3430597171288[/C][C]2.65694028287116[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]20.8503426134519[/C][C]-3.85034261345193[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]23.3211586348698[/C][C]-6.32115863486977[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]20.036645647663[/C][C]-1.03664564766296[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]19.6807349899718[/C][C]-2.68073498997179[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]22.0379172391335[/C][C]-0.0379172391335173[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]23.3856638961778[/C][C]-2.38566389617783[/C][/ROW]
[ROW][C]96[/C][C]32[/C][C]28.5991521791672[/C][C]3.4008478208328[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]24.6614106338104[/C][C]-3.66141063381037[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]24.3080160450159[/C][C]-3.30801604501586[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]21.2070236612869[/C][C]-3.20702366128693[/C][/ROW]
[ROW][C]100[/C][C]18[/C][C]21.2610760499861[/C][C]-3.26107604998606[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.7863799272109[/C][C]0.213620072789081[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]20.5907043961488[/C][C]-1.59070439614882[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]20.9772930432377[/C][C]-0.977293043237729[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]22.2383514483243[/C][C]-1.23835144832433[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]23.7118985607275[/C][C]-3.71189856072747[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]18.8403733336066[/C][C]-1.84037333360655[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]20.2572364317288[/C][C]-2.25723643172879[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]20.7196144481076[/C][C]-1.71961444810765[/C][/ROW]
[ROW][C]109[/C][C]22[/C][C]22.0275626605593[/C][C]-0.0275626605593229[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]18.7557114974235[/C][C]-3.75571149742348[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]18.7922903692776[/C][C]-4.79229036927757[/C][/ROW]
[ROW][C]112[/C][C]18[/C][C]26.5436099628167[/C][C]-8.54360996281669[/C][/ROW]
[ROW][C]113[/C][C]24[/C][C]21.2628726079605[/C][C]2.73712739203951[/C][/ROW]
[ROW][C]114[/C][C]35[/C][C]23.5462389082472[/C][C]11.4537610917528[/C][/ROW]
[ROW][C]115[/C][C]29[/C][C]18.9866943088691[/C][C]10.0133056911309[/C][/ROW]
[ROW][C]116[/C][C]21[/C][C]21.935944950304[/C][C]-0.935944950303999[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]20.5220323095179[/C][C]4.47796769048212[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]18.4455726336298[/C][C]1.5544273663702[/C][/ROW]
[ROW][C]119[/C][C]22[/C][C]23.1944342306498[/C][C]-1.19443423064979[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]16.8696658909449[/C][C]-3.8696658909449[/C][/ROW]
[ROW][C]121[/C][C]26[/C][C]23.1870301698962[/C][C]2.81296983010383[/C][/ROW]
[ROW][C]122[/C][C]17[/C][C]16.8739978966045[/C][C]0.126002103395524[/C][/ROW]
[ROW][C]123[/C][C]25[/C][C]20.0507796496234[/C][C]4.94922035037664[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]20.6180234596576[/C][C]-0.618023459657552[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]18.0635928969879[/C][C]0.936407103012113[/C][/ROW]
[ROW][C]126[/C][C]21[/C][C]22.6069239300865[/C][C]-1.60692393008647[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]21.004834267862[/C][C]0.995165732137992[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]22.5978785256876[/C][C]1.40212147431243[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]22.893008601785[/C][C]-1.89300860178502[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]25.4363237012179[/C][C]0.563676298782067[/C][/ROW]
[ROW][C]131[/C][C]24[/C][C]20.5297469521146[/C][C]3.47025304788541[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]20.2204944956761[/C][C]-4.22049449567612[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]22.3001815014718[/C][C]0.699818498528152[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.7446456449081[/C][C]-2.74464564490808[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]22.3043405696256[/C][C]-6.30434056962563[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]24.0921955799838[/C][C]1.90780442001622[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]19.0502342475757[/C][C]-0.050234247575695[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]16.8720244761895[/C][C]4.12797552381046[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]22.0933317973676[/C][C]-1.09333179736762[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]18.451482961083[/C][C]3.54851703891702[/C][/ROW]
[ROW][C]141[/C][C]23[/C][C]19.7302332118958[/C][C]3.26976678810421[/C][/ROW]
[ROW][C]142[/C][C]29[/C][C]24.7822738737025[/C][C]4.21772612629751[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]19.2106400938892[/C][C]1.78935990611078[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]19.9054317541314[/C][C]1.09456824586863[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]21.8498010024399[/C][C]1.15019899756015[/C][/ROW]
[ROW][C]146[/C][C]27[/C][C]22.9941022404284[/C][C]4.00589775957157[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]25.393995850013[/C][C]-0.393995850012975[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]20.946835038333[/C][C]0.0531649616669982[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]17.0822628583037[/C][C]-7.08226285830372[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]22.5777789638073[/C][C]-2.5777789638073[/C][/ROW]
[ROW][C]151[/C][C]26[/C][C]22.4585531426257[/C][C]3.54144685737425[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.6656316258617[/C][C]0.334368374138266[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]31.7071557621354[/C][C]-2.70715576213541[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]19.0522038357563[/C][C]-0.052203835756255[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]22.0525408015156[/C][C]1.94745919848439[/C][/ROW]
[ROW][C]156[/C][C]19[/C][C]20.7371470618696[/C][C]-1.73714706186965[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.3888420951761[/C][C]0.611157904823948[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]21.7784950813887[/C][C]0.221504918611303[/C][/ROW]
[ROW][C]159[/C][C]17[/C][C]23.729262101434[/C][C]-6.72926210143395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147244&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147244&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.3752438396802-0.375243839680221
22523.78038639865771.21961360134235
33025.68308817755064.31691182244943
41921.7015858002171-2.70158580021713
52221.95832106769760.0416789323024097
62224.5013747161968-2.50137471619684
72522.5369841035052.46301589649499
82319.32749954044123.67250045955885
91718.7724146241247-1.77241462412469
102121.6020995177389-0.60209951773893
111922.7097079828001-3.70970798280015
121923.4081221360031-4.40812213600306
131523.100666237539-8.10066623753901
141617.0578920432927-1.05789204329267
152319.37126037241783.62873962758224
162724.00123049434872.99876950565126
172220.8939265829881.10607341701197
181416.5108483584964-2.51084835849636
192224.0638801007654-2.0638801007654
202324.015901103718-1.01590110371805
212321.5282697706221.471730229378
222124.4932309874987-3.49323098749872
231922.3016536793613-3.30165367936127
241823.7904279083418-5.79042790834182
252023.053082339113-3.053082339113
262322.35653555512120.643464444878811
272523.30500591381941.69499408618061
281923.2582299470431-4.25822994704315
292423.79016262517380.209837374826231
302221.52323809335490.476761906645071
312525.0489193653102-0.0489193653101529
322623.11809053077432.88190946922571
332922.80665251929736.19334748070269
343225.12121008045166.87878991954841
352521.54766582866043.45233417133961
362924.3767942755514.62320572444902
372824.91890466981533.08109533018471
381717.105713556688-0.105713556687993
392826.05040426277311.94959573722695
402922.90839485762636.09160514237371
412627.4764796617272-1.47647966172717
422523.39410197463151.60589802536851
431419.5474967968037-5.54749679680366
442522.11119658059962.88880341940038
452621.57739270102354.42260729897649
462020.1937158718107-0.19371587181068
471821.3481003036481-3.34810030364814
483224.63495255779567.36504744220444
492524.95143831410440.0485616858955817
502521.7373189207073.26268107929301
512320.80099365748462.1990063425154
522122.0614054790237-1.06140547902374
532023.938680930279-3.93868093027899
541516.4581620642669-1.45816206426694
553026.60473041056513.39526958943494
562425.2470066108757-1.24700661087571
572624.2074098506561.79259014934395
582421.68490395071842.31509604928157
592221.34694474867460.653055251325381
601415.6514365191043-1.65143651910432
612422.12969626527531.87030373472474
622422.8831514335021.11684856649804
632423.28435977852270.715640221477318
642419.84869247516794.15130752483211
651918.448153379990.551846620010017
663126.73358743494834.2664125650517
672226.5215900363656-4.52159003636563
682721.41118647512685.5888135248732
691917.6414162132081.35858378679201
702522.20483300994492.79516699005507
712024.9347351874032-4.93473518740321
722121.3990237169588-0.3990237169588
732727.4183665916093-0.418366591609338
742324.3746124922624-1.37461249226241
752525.6833747379211-0.68337473792112
762022.18002397426-2.18002397426005
772119.20237501342581.79762498657421
782222.3862171288093-0.38621712880927
792322.89141648174950.108583518250502
802524.05658988060060.943410119399394
812523.32186817978451.67813182021553
821723.7195093287429-6.7195093287429
831921.3959632834841-2.39596328348412
842523.88424724120041.11575275879955
851922.3010541105851-3.30105411058514
862023.1266938500491-3.1266938500491
872622.4809582943913.51904170560905
882320.6564751885692.34352481143104
892724.34305971712882.65694028287116
901720.8503426134519-3.85034261345193
911723.3211586348698-6.32115863486977
921920.036645647663-1.03664564766296
931719.6807349899718-2.68073498997179
942222.0379172391335-0.0379172391335173
952123.3856638961778-2.38566389617783
963228.59915217916723.4008478208328
972124.6614106338104-3.66141063381037
982124.3080160450159-3.30801604501586
991821.2070236612869-3.20702366128693
1001821.2610760499861-3.26107604998606
1012322.78637992721090.213620072789081
1021920.5907043961488-1.59070439614882
1032020.9772930432377-0.977293043237729
1042122.2383514483243-1.23835144832433
1052023.7118985607275-3.71189856072747
1061718.8403733336066-1.84037333360655
1071820.2572364317288-2.25723643172879
1081920.7196144481076-1.71961444810765
1092222.0275626605593-0.0275626605593229
1101518.7557114974235-3.75571149742348
1111418.7922903692776-4.79229036927757
1121826.5436099628167-8.54360996281669
1132421.26287260796052.73712739203951
1143523.546238908247211.4537610917528
1152918.986694308869110.0133056911309
1162121.935944950304-0.935944950303999
1172520.52203230951794.47796769048212
1182018.44557263362981.5544273663702
1192223.1944342306498-1.19443423064979
1201316.8696658909449-3.8696658909449
1212623.18703016989622.81296983010383
1221716.87399789660450.126002103395524
1232520.05077964962344.94922035037664
1242020.6180234596576-0.618023459657552
1251918.06359289698790.936407103012113
1262122.6069239300865-1.60692393008647
1272221.0048342678620.995165732137992
1282422.59787852568761.40212147431243
1292122.893008601785-1.89300860178502
1302625.43632370121790.563676298782067
1312420.52974695211463.47025304788541
1321620.2204944956761-4.22049449567612
1332322.30018150147180.699818498528152
1341820.7446456449081-2.74464564490808
1351622.3043405696256-6.30434056962563
1362624.09219557998381.90780442001622
1371919.0502342475757-0.050234247575695
1382116.87202447618954.12797552381046
1392122.0933317973676-1.09333179736762
1402218.4514829610833.54851703891702
1412319.73023321189583.26976678810421
1422924.78227387370254.21772612629751
1432119.21064009388921.78935990611078
1442119.90543175413141.09456824586863
1452321.84980100243991.15019899756015
1462722.99410224042844.00589775957157
1472525.393995850013-0.393995850012975
1482120.9468350383330.0531649616669982
1491017.0822628583037-7.08226285830372
1502022.5777789638073-2.5777789638073
1512622.45855314262573.54144685737425
1522423.66563162586170.334368374138266
1532931.7071557621354-2.70715576213541
1541919.0522038357563-0.052203835756255
1552422.05254080151561.94745919848439
1561920.7371470618696-1.73714706186965
1572423.38884209517610.611157904823948
1582221.77849508138870.221504918611303
1591723.729262101434-6.72926210143395







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1677148899669330.3354297799338660.832285110033067
110.4762109914426040.9524219828852080.523789008557396
120.4057761244159390.8115522488318780.594223875584061
130.8306431499475920.3387137001048160.169356850052408
140.7534802294243580.4930395411512840.246519770575642
150.73548793031510.52902413936980.2645120696849
160.6537743989601620.6924512020796750.346225601039838
170.5933666357617280.8132667284765440.406633364238272
180.576949200610540.846101598778920.42305079938946
190.5002669581646210.9994660836707590.499733041835379
200.4965359656994520.9930719313989040.503464034300548
210.4927379940793450.9854759881586910.507262005920655
220.4229006680720160.8458013361440330.577099331927984
230.3622428019239970.7244856038479930.637757198076003
240.380403008269030.7608060165380590.61959699173097
250.3492601296197740.6985202592395480.650739870380226
260.2883000589943060.5766001179886120.711699941005694
270.2476443270884590.4952886541769180.752355672911541
280.2392792452215430.4785584904430860.760720754778457
290.1979242097405860.3958484194811720.802075790259414
300.1541724074902810.3083448149805620.845827592509719
310.1260052355241870.2520104710483740.873994764475813
320.1610260650248540.3220521300497090.838973934975146
330.2698306374760930.5396612749521860.730169362523907
340.5483485114667850.9033029770664310.451651488533216
350.5271291365163890.9457417269672220.472870863483611
360.6042615937631990.7914768124736010.395738406236801
370.5996999915009230.8006000169981550.400300008499077
380.5529432502447330.8941134995105350.447056749755267
390.520711430201340.958577139597320.47928856979866
400.6144375946405590.7711248107188820.385562405359441
410.5797437124125940.8405125751748110.420256287587406
420.5362814249594420.9274371500811160.463718575040558
430.6243800213324560.7512399573350870.375619978667544
440.595123184173710.8097536316525810.40487681582629
450.6300942683267320.7398114633465360.369905731673268
460.5788669528079330.8422660943841330.421133047192067
470.5653028631056980.8693942737886030.434697136894302
480.6809146704106830.6381706591786340.319085329589317
490.6374654900863870.7250690198272260.362534509913613
500.6236329429986330.7527341140027340.376367057001367
510.5839665175863410.8320669648273180.416033482413659
520.5392576501796610.9214846996406780.460742349820339
530.5648908512548920.8702182974902160.435109148745108
540.5270022788658780.9459954422682430.472997721134122
550.5902160307310930.8195679385378150.409783969268907
560.5529545597574830.8940908804850340.447045440242517
570.514684615603540.9706307687929210.48531538439646
580.4838469963797010.9676939927594030.516153003620299
590.4368000246573770.8736000493147550.563199975342623
600.395523010634590.7910460212691790.60447698936541
610.3667856277560460.7335712555120910.633214372243954
620.3273259504924190.6546519009848380.672674049507581
630.2872696974028320.5745393948056630.712730302597168
640.3279045371986010.6558090743972020.672095462801399
650.2871244978341060.5742489956682130.712875502165894
660.3013950225347250.602790045069450.698604977465275
670.3584244536740130.7168489073480270.641575546325987
680.4375987419192970.8751974838385950.562401258080703
690.4000535058499540.8001070116999090.599946494150046
700.3855561707047610.7711123414095230.614443829295239
710.4497925913152510.8995851826305010.550207408684749
720.4039105490321590.8078210980643170.596089450967841
730.3624374691425180.7248749382850350.637562530857482
740.3273843158817390.6547686317634770.672615684118261
750.2957956246778250.5915912493556490.704204375322175
760.2716364373465980.5432728746931960.728363562653402
770.2495479043070340.4990958086140670.750452095692966
780.2144650011250810.4289300022501620.785534998874919
790.1828275293129230.3656550586258470.817172470687077
800.1571916984521550.3143833969043110.842808301547845
810.1376995479746150.275399095949230.862300452025385
820.2213130549214940.4426261098429890.778686945078506
830.2032799569757070.4065599139514150.796720043024293
840.1762817580049780.3525635160099570.823718241995022
850.1768671986075890.3537343972151780.823132801392411
860.1726912644660910.3453825289321820.827308735533909
870.1797987832392730.3595975664785460.820201216760727
880.1686302939676030.3372605879352070.831369706032397
890.1586197982451480.3172395964902960.841380201754852
900.1622886262057440.3245772524114880.837711373794256
910.2348361730261840.4696723460523680.765163826973816
920.201399340955890.402798681911780.79860065904411
930.1847571991503680.3695143983007360.815242800849632
940.1559978934544120.3119957869088230.844002106545588
950.1409733207969480.2819466415938960.859026679203052
960.1443296515704160.2886593031408330.855670348429584
970.145774159414260.291548318828520.85422584058574
980.1418119271303820.2836238542607640.858188072869618
990.1345983108142240.2691966216284490.865401689185776
1000.1294164858327750.2588329716655490.870583514167225
1010.1057460530674360.2114921061348720.894253946932564
1020.08759526752296160.1751905350459230.912404732477038
1030.07016833790988550.1403366758197710.929831662090115
1040.05644097488913730.1128819497782750.943559025110863
1050.05965098516926370.1193019703385270.940349014830736
1060.04872863388118950.09745726776237910.95127136611881
1070.04258511029319480.08517022058638960.957414889706805
1080.03694512472935480.07389024945870960.963054875270645
1090.0280944805239470.05618896104789390.971905519476053
1100.02761203269233250.05522406538466510.972387967307668
1110.03476230996984070.06952461993968140.965237690030159
1120.1480319131636360.2960638263272730.851968086836364
1130.1361149519989620.2722299039979240.863885048001038
1140.5491035633682470.9017928732635060.450896436631753
1150.8509624561338380.2980750877323240.149037543866162
1160.8175467765604840.3649064468790330.182453223439516
1170.8725133502772160.2549732994455670.127486649722784
1180.8474548883968470.3050902232063060.152545111603153
1190.8187542316329260.3624915367341480.181245768367074
1200.8521828395693450.2956343208613110.147817160430655
1210.8288430235396780.3423139529206450.171156976460322
1220.7884172559405590.4231654881188820.211582744059441
1230.8197816141223260.3604367717553490.180218385877674
1240.7770040471536970.4459919056926060.222995952846303
1250.7374372043607650.5251255912784710.262562795639235
1260.6885490789281990.6229018421436010.311450921071801
1270.6515683568381680.6968632863236650.348431643161832
1280.6092736870518680.7814526258962640.390726312948132
1290.5502923126977580.8994153746044840.449707687302242
1300.4870146727942530.9740293455885070.512985327205747
1310.4847127237360070.9694254474720140.515287276263993
1320.482597109188470.965194218376940.51740289081153
1330.4320568646209030.8641137292418060.567943135379097
1340.3838081257144370.7676162514288740.616191874285563
1350.5314485663706680.9371028672586640.468551433629332
1360.4921688174696430.9843376349392860.507831182530357
1370.4186925637844270.8373851275688550.581307436215573
1380.4281675271882330.8563350543764670.571832472811767
1390.3565833426365370.7131666852730730.643416657363463
1400.3203594376791160.6407188753582330.679640562320884
1410.2859131825862710.5718263651725420.714086817413729
1420.3365044110543720.6730088221087450.663495588945628
1430.4780941135756880.9561882271513760.521905886424312
1440.400992750579690.8019855011593810.59900724942031
1450.3198841429387340.6397682858774680.680115857061266
1460.30794447329060.61588894658120.6920555267094
1470.254742419681060.509484839362120.74525758031894
1480.1590915611281080.3181831222562150.840908438871892
1490.3041069572470940.6082139144941880.695893042752906

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.167714889966933 & 0.335429779933866 & 0.832285110033067 \tabularnewline
11 & 0.476210991442604 & 0.952421982885208 & 0.523789008557396 \tabularnewline
12 & 0.405776124415939 & 0.811552248831878 & 0.594223875584061 \tabularnewline
13 & 0.830643149947592 & 0.338713700104816 & 0.169356850052408 \tabularnewline
14 & 0.753480229424358 & 0.493039541151284 & 0.246519770575642 \tabularnewline
15 & 0.7354879303151 & 0.5290241393698 & 0.2645120696849 \tabularnewline
16 & 0.653774398960162 & 0.692451202079675 & 0.346225601039838 \tabularnewline
17 & 0.593366635761728 & 0.813266728476544 & 0.406633364238272 \tabularnewline
18 & 0.57694920061054 & 0.84610159877892 & 0.42305079938946 \tabularnewline
19 & 0.500266958164621 & 0.999466083670759 & 0.499733041835379 \tabularnewline
20 & 0.496535965699452 & 0.993071931398904 & 0.503464034300548 \tabularnewline
21 & 0.492737994079345 & 0.985475988158691 & 0.507262005920655 \tabularnewline
22 & 0.422900668072016 & 0.845801336144033 & 0.577099331927984 \tabularnewline
23 & 0.362242801923997 & 0.724485603847993 & 0.637757198076003 \tabularnewline
24 & 0.38040300826903 & 0.760806016538059 & 0.61959699173097 \tabularnewline
25 & 0.349260129619774 & 0.698520259239548 & 0.650739870380226 \tabularnewline
26 & 0.288300058994306 & 0.576600117988612 & 0.711699941005694 \tabularnewline
27 & 0.247644327088459 & 0.495288654176918 & 0.752355672911541 \tabularnewline
28 & 0.239279245221543 & 0.478558490443086 & 0.760720754778457 \tabularnewline
29 & 0.197924209740586 & 0.395848419481172 & 0.802075790259414 \tabularnewline
30 & 0.154172407490281 & 0.308344814980562 & 0.845827592509719 \tabularnewline
31 & 0.126005235524187 & 0.252010471048374 & 0.873994764475813 \tabularnewline
32 & 0.161026065024854 & 0.322052130049709 & 0.838973934975146 \tabularnewline
33 & 0.269830637476093 & 0.539661274952186 & 0.730169362523907 \tabularnewline
34 & 0.548348511466785 & 0.903302977066431 & 0.451651488533216 \tabularnewline
35 & 0.527129136516389 & 0.945741726967222 & 0.472870863483611 \tabularnewline
36 & 0.604261593763199 & 0.791476812473601 & 0.395738406236801 \tabularnewline
37 & 0.599699991500923 & 0.800600016998155 & 0.400300008499077 \tabularnewline
38 & 0.552943250244733 & 0.894113499510535 & 0.447056749755267 \tabularnewline
39 & 0.52071143020134 & 0.95857713959732 & 0.47928856979866 \tabularnewline
40 & 0.614437594640559 & 0.771124810718882 & 0.385562405359441 \tabularnewline
41 & 0.579743712412594 & 0.840512575174811 & 0.420256287587406 \tabularnewline
42 & 0.536281424959442 & 0.927437150081116 & 0.463718575040558 \tabularnewline
43 & 0.624380021332456 & 0.751239957335087 & 0.375619978667544 \tabularnewline
44 & 0.59512318417371 & 0.809753631652581 & 0.40487681582629 \tabularnewline
45 & 0.630094268326732 & 0.739811463346536 & 0.369905731673268 \tabularnewline
46 & 0.578866952807933 & 0.842266094384133 & 0.421133047192067 \tabularnewline
47 & 0.565302863105698 & 0.869394273788603 & 0.434697136894302 \tabularnewline
48 & 0.680914670410683 & 0.638170659178634 & 0.319085329589317 \tabularnewline
49 & 0.637465490086387 & 0.725069019827226 & 0.362534509913613 \tabularnewline
50 & 0.623632942998633 & 0.752734114002734 & 0.376367057001367 \tabularnewline
51 & 0.583966517586341 & 0.832066964827318 & 0.416033482413659 \tabularnewline
52 & 0.539257650179661 & 0.921484699640678 & 0.460742349820339 \tabularnewline
53 & 0.564890851254892 & 0.870218297490216 & 0.435109148745108 \tabularnewline
54 & 0.527002278865878 & 0.945995442268243 & 0.472997721134122 \tabularnewline
55 & 0.590216030731093 & 0.819567938537815 & 0.409783969268907 \tabularnewline
56 & 0.552954559757483 & 0.894090880485034 & 0.447045440242517 \tabularnewline
57 & 0.51468461560354 & 0.970630768792921 & 0.48531538439646 \tabularnewline
58 & 0.483846996379701 & 0.967693992759403 & 0.516153003620299 \tabularnewline
59 & 0.436800024657377 & 0.873600049314755 & 0.563199975342623 \tabularnewline
60 & 0.39552301063459 & 0.791046021269179 & 0.60447698936541 \tabularnewline
61 & 0.366785627756046 & 0.733571255512091 & 0.633214372243954 \tabularnewline
62 & 0.327325950492419 & 0.654651900984838 & 0.672674049507581 \tabularnewline
63 & 0.287269697402832 & 0.574539394805663 & 0.712730302597168 \tabularnewline
64 & 0.327904537198601 & 0.655809074397202 & 0.672095462801399 \tabularnewline
65 & 0.287124497834106 & 0.574248995668213 & 0.712875502165894 \tabularnewline
66 & 0.301395022534725 & 0.60279004506945 & 0.698604977465275 \tabularnewline
67 & 0.358424453674013 & 0.716848907348027 & 0.641575546325987 \tabularnewline
68 & 0.437598741919297 & 0.875197483838595 & 0.562401258080703 \tabularnewline
69 & 0.400053505849954 & 0.800107011699909 & 0.599946494150046 \tabularnewline
70 & 0.385556170704761 & 0.771112341409523 & 0.614443829295239 \tabularnewline
71 & 0.449792591315251 & 0.899585182630501 & 0.550207408684749 \tabularnewline
72 & 0.403910549032159 & 0.807821098064317 & 0.596089450967841 \tabularnewline
73 & 0.362437469142518 & 0.724874938285035 & 0.637562530857482 \tabularnewline
74 & 0.327384315881739 & 0.654768631763477 & 0.672615684118261 \tabularnewline
75 & 0.295795624677825 & 0.591591249355649 & 0.704204375322175 \tabularnewline
76 & 0.271636437346598 & 0.543272874693196 & 0.728363562653402 \tabularnewline
77 & 0.249547904307034 & 0.499095808614067 & 0.750452095692966 \tabularnewline
78 & 0.214465001125081 & 0.428930002250162 & 0.785534998874919 \tabularnewline
79 & 0.182827529312923 & 0.365655058625847 & 0.817172470687077 \tabularnewline
80 & 0.157191698452155 & 0.314383396904311 & 0.842808301547845 \tabularnewline
81 & 0.137699547974615 & 0.27539909594923 & 0.862300452025385 \tabularnewline
82 & 0.221313054921494 & 0.442626109842989 & 0.778686945078506 \tabularnewline
83 & 0.203279956975707 & 0.406559913951415 & 0.796720043024293 \tabularnewline
84 & 0.176281758004978 & 0.352563516009957 & 0.823718241995022 \tabularnewline
85 & 0.176867198607589 & 0.353734397215178 & 0.823132801392411 \tabularnewline
86 & 0.172691264466091 & 0.345382528932182 & 0.827308735533909 \tabularnewline
87 & 0.179798783239273 & 0.359597566478546 & 0.820201216760727 \tabularnewline
88 & 0.168630293967603 & 0.337260587935207 & 0.831369706032397 \tabularnewline
89 & 0.158619798245148 & 0.317239596490296 & 0.841380201754852 \tabularnewline
90 & 0.162288626205744 & 0.324577252411488 & 0.837711373794256 \tabularnewline
91 & 0.234836173026184 & 0.469672346052368 & 0.765163826973816 \tabularnewline
92 & 0.20139934095589 & 0.40279868191178 & 0.79860065904411 \tabularnewline
93 & 0.184757199150368 & 0.369514398300736 & 0.815242800849632 \tabularnewline
94 & 0.155997893454412 & 0.311995786908823 & 0.844002106545588 \tabularnewline
95 & 0.140973320796948 & 0.281946641593896 & 0.859026679203052 \tabularnewline
96 & 0.144329651570416 & 0.288659303140833 & 0.855670348429584 \tabularnewline
97 & 0.14577415941426 & 0.29154831882852 & 0.85422584058574 \tabularnewline
98 & 0.141811927130382 & 0.283623854260764 & 0.858188072869618 \tabularnewline
99 & 0.134598310814224 & 0.269196621628449 & 0.865401689185776 \tabularnewline
100 & 0.129416485832775 & 0.258832971665549 & 0.870583514167225 \tabularnewline
101 & 0.105746053067436 & 0.211492106134872 & 0.894253946932564 \tabularnewline
102 & 0.0875952675229616 & 0.175190535045923 & 0.912404732477038 \tabularnewline
103 & 0.0701683379098855 & 0.140336675819771 & 0.929831662090115 \tabularnewline
104 & 0.0564409748891373 & 0.112881949778275 & 0.943559025110863 \tabularnewline
105 & 0.0596509851692637 & 0.119301970338527 & 0.940349014830736 \tabularnewline
106 & 0.0487286338811895 & 0.0974572677623791 & 0.95127136611881 \tabularnewline
107 & 0.0425851102931948 & 0.0851702205863896 & 0.957414889706805 \tabularnewline
108 & 0.0369451247293548 & 0.0738902494587096 & 0.963054875270645 \tabularnewline
109 & 0.028094480523947 & 0.0561889610478939 & 0.971905519476053 \tabularnewline
110 & 0.0276120326923325 & 0.0552240653846651 & 0.972387967307668 \tabularnewline
111 & 0.0347623099698407 & 0.0695246199396814 & 0.965237690030159 \tabularnewline
112 & 0.148031913163636 & 0.296063826327273 & 0.851968086836364 \tabularnewline
113 & 0.136114951998962 & 0.272229903997924 & 0.863885048001038 \tabularnewline
114 & 0.549103563368247 & 0.901792873263506 & 0.450896436631753 \tabularnewline
115 & 0.850962456133838 & 0.298075087732324 & 0.149037543866162 \tabularnewline
116 & 0.817546776560484 & 0.364906446879033 & 0.182453223439516 \tabularnewline
117 & 0.872513350277216 & 0.254973299445567 & 0.127486649722784 \tabularnewline
118 & 0.847454888396847 & 0.305090223206306 & 0.152545111603153 \tabularnewline
119 & 0.818754231632926 & 0.362491536734148 & 0.181245768367074 \tabularnewline
120 & 0.852182839569345 & 0.295634320861311 & 0.147817160430655 \tabularnewline
121 & 0.828843023539678 & 0.342313952920645 & 0.171156976460322 \tabularnewline
122 & 0.788417255940559 & 0.423165488118882 & 0.211582744059441 \tabularnewline
123 & 0.819781614122326 & 0.360436771755349 & 0.180218385877674 \tabularnewline
124 & 0.777004047153697 & 0.445991905692606 & 0.222995952846303 \tabularnewline
125 & 0.737437204360765 & 0.525125591278471 & 0.262562795639235 \tabularnewline
126 & 0.688549078928199 & 0.622901842143601 & 0.311450921071801 \tabularnewline
127 & 0.651568356838168 & 0.696863286323665 & 0.348431643161832 \tabularnewline
128 & 0.609273687051868 & 0.781452625896264 & 0.390726312948132 \tabularnewline
129 & 0.550292312697758 & 0.899415374604484 & 0.449707687302242 \tabularnewline
130 & 0.487014672794253 & 0.974029345588507 & 0.512985327205747 \tabularnewline
131 & 0.484712723736007 & 0.969425447472014 & 0.515287276263993 \tabularnewline
132 & 0.48259710918847 & 0.96519421837694 & 0.51740289081153 \tabularnewline
133 & 0.432056864620903 & 0.864113729241806 & 0.567943135379097 \tabularnewline
134 & 0.383808125714437 & 0.767616251428874 & 0.616191874285563 \tabularnewline
135 & 0.531448566370668 & 0.937102867258664 & 0.468551433629332 \tabularnewline
136 & 0.492168817469643 & 0.984337634939286 & 0.507831182530357 \tabularnewline
137 & 0.418692563784427 & 0.837385127568855 & 0.581307436215573 \tabularnewline
138 & 0.428167527188233 & 0.856335054376467 & 0.571832472811767 \tabularnewline
139 & 0.356583342636537 & 0.713166685273073 & 0.643416657363463 \tabularnewline
140 & 0.320359437679116 & 0.640718875358233 & 0.679640562320884 \tabularnewline
141 & 0.285913182586271 & 0.571826365172542 & 0.714086817413729 \tabularnewline
142 & 0.336504411054372 & 0.673008822108745 & 0.663495588945628 \tabularnewline
143 & 0.478094113575688 & 0.956188227151376 & 0.521905886424312 \tabularnewline
144 & 0.40099275057969 & 0.801985501159381 & 0.59900724942031 \tabularnewline
145 & 0.319884142938734 & 0.639768285877468 & 0.680115857061266 \tabularnewline
146 & 0.3079444732906 & 0.6158889465812 & 0.6920555267094 \tabularnewline
147 & 0.25474241968106 & 0.50948483936212 & 0.74525758031894 \tabularnewline
148 & 0.159091561128108 & 0.318183122256215 & 0.840908438871892 \tabularnewline
149 & 0.304106957247094 & 0.608213914494188 & 0.695893042752906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147244&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]0.167714889966933[/C][C]0.335429779933866[/C][C]0.832285110033067[/C][/ROW]
[ROW][C]11[/C][C]0.476210991442604[/C][C]0.952421982885208[/C][C]0.523789008557396[/C][/ROW]
[ROW][C]12[/C][C]0.405776124415939[/C][C]0.811552248831878[/C][C]0.594223875584061[/C][/ROW]
[ROW][C]13[/C][C]0.830643149947592[/C][C]0.338713700104816[/C][C]0.169356850052408[/C][/ROW]
[ROW][C]14[/C][C]0.753480229424358[/C][C]0.493039541151284[/C][C]0.246519770575642[/C][/ROW]
[ROW][C]15[/C][C]0.7354879303151[/C][C]0.5290241393698[/C][C]0.2645120696849[/C][/ROW]
[ROW][C]16[/C][C]0.653774398960162[/C][C]0.692451202079675[/C][C]0.346225601039838[/C][/ROW]
[ROW][C]17[/C][C]0.593366635761728[/C][C]0.813266728476544[/C][C]0.406633364238272[/C][/ROW]
[ROW][C]18[/C][C]0.57694920061054[/C][C]0.84610159877892[/C][C]0.42305079938946[/C][/ROW]
[ROW][C]19[/C][C]0.500266958164621[/C][C]0.999466083670759[/C][C]0.499733041835379[/C][/ROW]
[ROW][C]20[/C][C]0.496535965699452[/C][C]0.993071931398904[/C][C]0.503464034300548[/C][/ROW]
[ROW][C]21[/C][C]0.492737994079345[/C][C]0.985475988158691[/C][C]0.507262005920655[/C][/ROW]
[ROW][C]22[/C][C]0.422900668072016[/C][C]0.845801336144033[/C][C]0.577099331927984[/C][/ROW]
[ROW][C]23[/C][C]0.362242801923997[/C][C]0.724485603847993[/C][C]0.637757198076003[/C][/ROW]
[ROW][C]24[/C][C]0.38040300826903[/C][C]0.760806016538059[/C][C]0.61959699173097[/C][/ROW]
[ROW][C]25[/C][C]0.349260129619774[/C][C]0.698520259239548[/C][C]0.650739870380226[/C][/ROW]
[ROW][C]26[/C][C]0.288300058994306[/C][C]0.576600117988612[/C][C]0.711699941005694[/C][/ROW]
[ROW][C]27[/C][C]0.247644327088459[/C][C]0.495288654176918[/C][C]0.752355672911541[/C][/ROW]
[ROW][C]28[/C][C]0.239279245221543[/C][C]0.478558490443086[/C][C]0.760720754778457[/C][/ROW]
[ROW][C]29[/C][C]0.197924209740586[/C][C]0.395848419481172[/C][C]0.802075790259414[/C][/ROW]
[ROW][C]30[/C][C]0.154172407490281[/C][C]0.308344814980562[/C][C]0.845827592509719[/C][/ROW]
[ROW][C]31[/C][C]0.126005235524187[/C][C]0.252010471048374[/C][C]0.873994764475813[/C][/ROW]
[ROW][C]32[/C][C]0.161026065024854[/C][C]0.322052130049709[/C][C]0.838973934975146[/C][/ROW]
[ROW][C]33[/C][C]0.269830637476093[/C][C]0.539661274952186[/C][C]0.730169362523907[/C][/ROW]
[ROW][C]34[/C][C]0.548348511466785[/C][C]0.903302977066431[/C][C]0.451651488533216[/C][/ROW]
[ROW][C]35[/C][C]0.527129136516389[/C][C]0.945741726967222[/C][C]0.472870863483611[/C][/ROW]
[ROW][C]36[/C][C]0.604261593763199[/C][C]0.791476812473601[/C][C]0.395738406236801[/C][/ROW]
[ROW][C]37[/C][C]0.599699991500923[/C][C]0.800600016998155[/C][C]0.400300008499077[/C][/ROW]
[ROW][C]38[/C][C]0.552943250244733[/C][C]0.894113499510535[/C][C]0.447056749755267[/C][/ROW]
[ROW][C]39[/C][C]0.52071143020134[/C][C]0.95857713959732[/C][C]0.47928856979866[/C][/ROW]
[ROW][C]40[/C][C]0.614437594640559[/C][C]0.771124810718882[/C][C]0.385562405359441[/C][/ROW]
[ROW][C]41[/C][C]0.579743712412594[/C][C]0.840512575174811[/C][C]0.420256287587406[/C][/ROW]
[ROW][C]42[/C][C]0.536281424959442[/C][C]0.927437150081116[/C][C]0.463718575040558[/C][/ROW]
[ROW][C]43[/C][C]0.624380021332456[/C][C]0.751239957335087[/C][C]0.375619978667544[/C][/ROW]
[ROW][C]44[/C][C]0.59512318417371[/C][C]0.809753631652581[/C][C]0.40487681582629[/C][/ROW]
[ROW][C]45[/C][C]0.630094268326732[/C][C]0.739811463346536[/C][C]0.369905731673268[/C][/ROW]
[ROW][C]46[/C][C]0.578866952807933[/C][C]0.842266094384133[/C][C]0.421133047192067[/C][/ROW]
[ROW][C]47[/C][C]0.565302863105698[/C][C]0.869394273788603[/C][C]0.434697136894302[/C][/ROW]
[ROW][C]48[/C][C]0.680914670410683[/C][C]0.638170659178634[/C][C]0.319085329589317[/C][/ROW]
[ROW][C]49[/C][C]0.637465490086387[/C][C]0.725069019827226[/C][C]0.362534509913613[/C][/ROW]
[ROW][C]50[/C][C]0.623632942998633[/C][C]0.752734114002734[/C][C]0.376367057001367[/C][/ROW]
[ROW][C]51[/C][C]0.583966517586341[/C][C]0.832066964827318[/C][C]0.416033482413659[/C][/ROW]
[ROW][C]52[/C][C]0.539257650179661[/C][C]0.921484699640678[/C][C]0.460742349820339[/C][/ROW]
[ROW][C]53[/C][C]0.564890851254892[/C][C]0.870218297490216[/C][C]0.435109148745108[/C][/ROW]
[ROW][C]54[/C][C]0.527002278865878[/C][C]0.945995442268243[/C][C]0.472997721134122[/C][/ROW]
[ROW][C]55[/C][C]0.590216030731093[/C][C]0.819567938537815[/C][C]0.409783969268907[/C][/ROW]
[ROW][C]56[/C][C]0.552954559757483[/C][C]0.894090880485034[/C][C]0.447045440242517[/C][/ROW]
[ROW][C]57[/C][C]0.51468461560354[/C][C]0.970630768792921[/C][C]0.48531538439646[/C][/ROW]
[ROW][C]58[/C][C]0.483846996379701[/C][C]0.967693992759403[/C][C]0.516153003620299[/C][/ROW]
[ROW][C]59[/C][C]0.436800024657377[/C][C]0.873600049314755[/C][C]0.563199975342623[/C][/ROW]
[ROW][C]60[/C][C]0.39552301063459[/C][C]0.791046021269179[/C][C]0.60447698936541[/C][/ROW]
[ROW][C]61[/C][C]0.366785627756046[/C][C]0.733571255512091[/C][C]0.633214372243954[/C][/ROW]
[ROW][C]62[/C][C]0.327325950492419[/C][C]0.654651900984838[/C][C]0.672674049507581[/C][/ROW]
[ROW][C]63[/C][C]0.287269697402832[/C][C]0.574539394805663[/C][C]0.712730302597168[/C][/ROW]
[ROW][C]64[/C][C]0.327904537198601[/C][C]0.655809074397202[/C][C]0.672095462801399[/C][/ROW]
[ROW][C]65[/C][C]0.287124497834106[/C][C]0.574248995668213[/C][C]0.712875502165894[/C][/ROW]
[ROW][C]66[/C][C]0.301395022534725[/C][C]0.60279004506945[/C][C]0.698604977465275[/C][/ROW]
[ROW][C]67[/C][C]0.358424453674013[/C][C]0.716848907348027[/C][C]0.641575546325987[/C][/ROW]
[ROW][C]68[/C][C]0.437598741919297[/C][C]0.875197483838595[/C][C]0.562401258080703[/C][/ROW]
[ROW][C]69[/C][C]0.400053505849954[/C][C]0.800107011699909[/C][C]0.599946494150046[/C][/ROW]
[ROW][C]70[/C][C]0.385556170704761[/C][C]0.771112341409523[/C][C]0.614443829295239[/C][/ROW]
[ROW][C]71[/C][C]0.449792591315251[/C][C]0.899585182630501[/C][C]0.550207408684749[/C][/ROW]
[ROW][C]72[/C][C]0.403910549032159[/C][C]0.807821098064317[/C][C]0.596089450967841[/C][/ROW]
[ROW][C]73[/C][C]0.362437469142518[/C][C]0.724874938285035[/C][C]0.637562530857482[/C][/ROW]
[ROW][C]74[/C][C]0.327384315881739[/C][C]0.654768631763477[/C][C]0.672615684118261[/C][/ROW]
[ROW][C]75[/C][C]0.295795624677825[/C][C]0.591591249355649[/C][C]0.704204375322175[/C][/ROW]
[ROW][C]76[/C][C]0.271636437346598[/C][C]0.543272874693196[/C][C]0.728363562653402[/C][/ROW]
[ROW][C]77[/C][C]0.249547904307034[/C][C]0.499095808614067[/C][C]0.750452095692966[/C][/ROW]
[ROW][C]78[/C][C]0.214465001125081[/C][C]0.428930002250162[/C][C]0.785534998874919[/C][/ROW]
[ROW][C]79[/C][C]0.182827529312923[/C][C]0.365655058625847[/C][C]0.817172470687077[/C][/ROW]
[ROW][C]80[/C][C]0.157191698452155[/C][C]0.314383396904311[/C][C]0.842808301547845[/C][/ROW]
[ROW][C]81[/C][C]0.137699547974615[/C][C]0.27539909594923[/C][C]0.862300452025385[/C][/ROW]
[ROW][C]82[/C][C]0.221313054921494[/C][C]0.442626109842989[/C][C]0.778686945078506[/C][/ROW]
[ROW][C]83[/C][C]0.203279956975707[/C][C]0.406559913951415[/C][C]0.796720043024293[/C][/ROW]
[ROW][C]84[/C][C]0.176281758004978[/C][C]0.352563516009957[/C][C]0.823718241995022[/C][/ROW]
[ROW][C]85[/C][C]0.176867198607589[/C][C]0.353734397215178[/C][C]0.823132801392411[/C][/ROW]
[ROW][C]86[/C][C]0.172691264466091[/C][C]0.345382528932182[/C][C]0.827308735533909[/C][/ROW]
[ROW][C]87[/C][C]0.179798783239273[/C][C]0.359597566478546[/C][C]0.820201216760727[/C][/ROW]
[ROW][C]88[/C][C]0.168630293967603[/C][C]0.337260587935207[/C][C]0.831369706032397[/C][/ROW]
[ROW][C]89[/C][C]0.158619798245148[/C][C]0.317239596490296[/C][C]0.841380201754852[/C][/ROW]
[ROW][C]90[/C][C]0.162288626205744[/C][C]0.324577252411488[/C][C]0.837711373794256[/C][/ROW]
[ROW][C]91[/C][C]0.234836173026184[/C][C]0.469672346052368[/C][C]0.765163826973816[/C][/ROW]
[ROW][C]92[/C][C]0.20139934095589[/C][C]0.40279868191178[/C][C]0.79860065904411[/C][/ROW]
[ROW][C]93[/C][C]0.184757199150368[/C][C]0.369514398300736[/C][C]0.815242800849632[/C][/ROW]
[ROW][C]94[/C][C]0.155997893454412[/C][C]0.311995786908823[/C][C]0.844002106545588[/C][/ROW]
[ROW][C]95[/C][C]0.140973320796948[/C][C]0.281946641593896[/C][C]0.859026679203052[/C][/ROW]
[ROW][C]96[/C][C]0.144329651570416[/C][C]0.288659303140833[/C][C]0.855670348429584[/C][/ROW]
[ROW][C]97[/C][C]0.14577415941426[/C][C]0.29154831882852[/C][C]0.85422584058574[/C][/ROW]
[ROW][C]98[/C][C]0.141811927130382[/C][C]0.283623854260764[/C][C]0.858188072869618[/C][/ROW]
[ROW][C]99[/C][C]0.134598310814224[/C][C]0.269196621628449[/C][C]0.865401689185776[/C][/ROW]
[ROW][C]100[/C][C]0.129416485832775[/C][C]0.258832971665549[/C][C]0.870583514167225[/C][/ROW]
[ROW][C]101[/C][C]0.105746053067436[/C][C]0.211492106134872[/C][C]0.894253946932564[/C][/ROW]
[ROW][C]102[/C][C]0.0875952675229616[/C][C]0.175190535045923[/C][C]0.912404732477038[/C][/ROW]
[ROW][C]103[/C][C]0.0701683379098855[/C][C]0.140336675819771[/C][C]0.929831662090115[/C][/ROW]
[ROW][C]104[/C][C]0.0564409748891373[/C][C]0.112881949778275[/C][C]0.943559025110863[/C][/ROW]
[ROW][C]105[/C][C]0.0596509851692637[/C][C]0.119301970338527[/C][C]0.940349014830736[/C][/ROW]
[ROW][C]106[/C][C]0.0487286338811895[/C][C]0.0974572677623791[/C][C]0.95127136611881[/C][/ROW]
[ROW][C]107[/C][C]0.0425851102931948[/C][C]0.0851702205863896[/C][C]0.957414889706805[/C][/ROW]
[ROW][C]108[/C][C]0.0369451247293548[/C][C]0.0738902494587096[/C][C]0.963054875270645[/C][/ROW]
[ROW][C]109[/C][C]0.028094480523947[/C][C]0.0561889610478939[/C][C]0.971905519476053[/C][/ROW]
[ROW][C]110[/C][C]0.0276120326923325[/C][C]0.0552240653846651[/C][C]0.972387967307668[/C][/ROW]
[ROW][C]111[/C][C]0.0347623099698407[/C][C]0.0695246199396814[/C][C]0.965237690030159[/C][/ROW]
[ROW][C]112[/C][C]0.148031913163636[/C][C]0.296063826327273[/C][C]0.851968086836364[/C][/ROW]
[ROW][C]113[/C][C]0.136114951998962[/C][C]0.272229903997924[/C][C]0.863885048001038[/C][/ROW]
[ROW][C]114[/C][C]0.549103563368247[/C][C]0.901792873263506[/C][C]0.450896436631753[/C][/ROW]
[ROW][C]115[/C][C]0.850962456133838[/C][C]0.298075087732324[/C][C]0.149037543866162[/C][/ROW]
[ROW][C]116[/C][C]0.817546776560484[/C][C]0.364906446879033[/C][C]0.182453223439516[/C][/ROW]
[ROW][C]117[/C][C]0.872513350277216[/C][C]0.254973299445567[/C][C]0.127486649722784[/C][/ROW]
[ROW][C]118[/C][C]0.847454888396847[/C][C]0.305090223206306[/C][C]0.152545111603153[/C][/ROW]
[ROW][C]119[/C][C]0.818754231632926[/C][C]0.362491536734148[/C][C]0.181245768367074[/C][/ROW]
[ROW][C]120[/C][C]0.852182839569345[/C][C]0.295634320861311[/C][C]0.147817160430655[/C][/ROW]
[ROW][C]121[/C][C]0.828843023539678[/C][C]0.342313952920645[/C][C]0.171156976460322[/C][/ROW]
[ROW][C]122[/C][C]0.788417255940559[/C][C]0.423165488118882[/C][C]0.211582744059441[/C][/ROW]
[ROW][C]123[/C][C]0.819781614122326[/C][C]0.360436771755349[/C][C]0.180218385877674[/C][/ROW]
[ROW][C]124[/C][C]0.777004047153697[/C][C]0.445991905692606[/C][C]0.222995952846303[/C][/ROW]
[ROW][C]125[/C][C]0.737437204360765[/C][C]0.525125591278471[/C][C]0.262562795639235[/C][/ROW]
[ROW][C]126[/C][C]0.688549078928199[/C][C]0.622901842143601[/C][C]0.311450921071801[/C][/ROW]
[ROW][C]127[/C][C]0.651568356838168[/C][C]0.696863286323665[/C][C]0.348431643161832[/C][/ROW]
[ROW][C]128[/C][C]0.609273687051868[/C][C]0.781452625896264[/C][C]0.390726312948132[/C][/ROW]
[ROW][C]129[/C][C]0.550292312697758[/C][C]0.899415374604484[/C][C]0.449707687302242[/C][/ROW]
[ROW][C]130[/C][C]0.487014672794253[/C][C]0.974029345588507[/C][C]0.512985327205747[/C][/ROW]
[ROW][C]131[/C][C]0.484712723736007[/C][C]0.969425447472014[/C][C]0.515287276263993[/C][/ROW]
[ROW][C]132[/C][C]0.48259710918847[/C][C]0.96519421837694[/C][C]0.51740289081153[/C][/ROW]
[ROW][C]133[/C][C]0.432056864620903[/C][C]0.864113729241806[/C][C]0.567943135379097[/C][/ROW]
[ROW][C]134[/C][C]0.383808125714437[/C][C]0.767616251428874[/C][C]0.616191874285563[/C][/ROW]
[ROW][C]135[/C][C]0.531448566370668[/C][C]0.937102867258664[/C][C]0.468551433629332[/C][/ROW]
[ROW][C]136[/C][C]0.492168817469643[/C][C]0.984337634939286[/C][C]0.507831182530357[/C][/ROW]
[ROW][C]137[/C][C]0.418692563784427[/C][C]0.837385127568855[/C][C]0.581307436215573[/C][/ROW]
[ROW][C]138[/C][C]0.428167527188233[/C][C]0.856335054376467[/C][C]0.571832472811767[/C][/ROW]
[ROW][C]139[/C][C]0.356583342636537[/C][C]0.713166685273073[/C][C]0.643416657363463[/C][/ROW]
[ROW][C]140[/C][C]0.320359437679116[/C][C]0.640718875358233[/C][C]0.679640562320884[/C][/ROW]
[ROW][C]141[/C][C]0.285913182586271[/C][C]0.571826365172542[/C][C]0.714086817413729[/C][/ROW]
[ROW][C]142[/C][C]0.336504411054372[/C][C]0.673008822108745[/C][C]0.663495588945628[/C][/ROW]
[ROW][C]143[/C][C]0.478094113575688[/C][C]0.956188227151376[/C][C]0.521905886424312[/C][/ROW]
[ROW][C]144[/C][C]0.40099275057969[/C][C]0.801985501159381[/C][C]0.59900724942031[/C][/ROW]
[ROW][C]145[/C][C]0.319884142938734[/C][C]0.639768285877468[/C][C]0.680115857061266[/C][/ROW]
[ROW][C]146[/C][C]0.3079444732906[/C][C]0.6158889465812[/C][C]0.6920555267094[/C][/ROW]
[ROW][C]147[/C][C]0.25474241968106[/C][C]0.50948483936212[/C][C]0.74525758031894[/C][/ROW]
[ROW][C]148[/C][C]0.159091561128108[/C][C]0.318183122256215[/C][C]0.840908438871892[/C][/ROW]
[ROW][C]149[/C][C]0.304106957247094[/C][C]0.608213914494188[/C][C]0.695893042752906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147244&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147244&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1677148899669330.3354297799338660.832285110033067
110.4762109914426040.9524219828852080.523789008557396
120.4057761244159390.8115522488318780.594223875584061
130.8306431499475920.3387137001048160.169356850052408
140.7534802294243580.4930395411512840.246519770575642
150.73548793031510.52902413936980.2645120696849
160.6537743989601620.6924512020796750.346225601039838
170.5933666357617280.8132667284765440.406633364238272
180.576949200610540.846101598778920.42305079938946
190.5002669581646210.9994660836707590.499733041835379
200.4965359656994520.9930719313989040.503464034300548
210.4927379940793450.9854759881586910.507262005920655
220.4229006680720160.8458013361440330.577099331927984
230.3622428019239970.7244856038479930.637757198076003
240.380403008269030.7608060165380590.61959699173097
250.3492601296197740.6985202592395480.650739870380226
260.2883000589943060.5766001179886120.711699941005694
270.2476443270884590.4952886541769180.752355672911541
280.2392792452215430.4785584904430860.760720754778457
290.1979242097405860.3958484194811720.802075790259414
300.1541724074902810.3083448149805620.845827592509719
310.1260052355241870.2520104710483740.873994764475813
320.1610260650248540.3220521300497090.838973934975146
330.2698306374760930.5396612749521860.730169362523907
340.5483485114667850.9033029770664310.451651488533216
350.5271291365163890.9457417269672220.472870863483611
360.6042615937631990.7914768124736010.395738406236801
370.5996999915009230.8006000169981550.400300008499077
380.5529432502447330.8941134995105350.447056749755267
390.520711430201340.958577139597320.47928856979866
400.6144375946405590.7711248107188820.385562405359441
410.5797437124125940.8405125751748110.420256287587406
420.5362814249594420.9274371500811160.463718575040558
430.6243800213324560.7512399573350870.375619978667544
440.595123184173710.8097536316525810.40487681582629
450.6300942683267320.7398114633465360.369905731673268
460.5788669528079330.8422660943841330.421133047192067
470.5653028631056980.8693942737886030.434697136894302
480.6809146704106830.6381706591786340.319085329589317
490.6374654900863870.7250690198272260.362534509913613
500.6236329429986330.7527341140027340.376367057001367
510.5839665175863410.8320669648273180.416033482413659
520.5392576501796610.9214846996406780.460742349820339
530.5648908512548920.8702182974902160.435109148745108
540.5270022788658780.9459954422682430.472997721134122
550.5902160307310930.8195679385378150.409783969268907
560.5529545597574830.8940908804850340.447045440242517
570.514684615603540.9706307687929210.48531538439646
580.4838469963797010.9676939927594030.516153003620299
590.4368000246573770.8736000493147550.563199975342623
600.395523010634590.7910460212691790.60447698936541
610.3667856277560460.7335712555120910.633214372243954
620.3273259504924190.6546519009848380.672674049507581
630.2872696974028320.5745393948056630.712730302597168
640.3279045371986010.6558090743972020.672095462801399
650.2871244978341060.5742489956682130.712875502165894
660.3013950225347250.602790045069450.698604977465275
670.3584244536740130.7168489073480270.641575546325987
680.4375987419192970.8751974838385950.562401258080703
690.4000535058499540.8001070116999090.599946494150046
700.3855561707047610.7711123414095230.614443829295239
710.4497925913152510.8995851826305010.550207408684749
720.4039105490321590.8078210980643170.596089450967841
730.3624374691425180.7248749382850350.637562530857482
740.3273843158817390.6547686317634770.672615684118261
750.2957956246778250.5915912493556490.704204375322175
760.2716364373465980.5432728746931960.728363562653402
770.2495479043070340.4990958086140670.750452095692966
780.2144650011250810.4289300022501620.785534998874919
790.1828275293129230.3656550586258470.817172470687077
800.1571916984521550.3143833969043110.842808301547845
810.1376995479746150.275399095949230.862300452025385
820.2213130549214940.4426261098429890.778686945078506
830.2032799569757070.4065599139514150.796720043024293
840.1762817580049780.3525635160099570.823718241995022
850.1768671986075890.3537343972151780.823132801392411
860.1726912644660910.3453825289321820.827308735533909
870.1797987832392730.3595975664785460.820201216760727
880.1686302939676030.3372605879352070.831369706032397
890.1586197982451480.3172395964902960.841380201754852
900.1622886262057440.3245772524114880.837711373794256
910.2348361730261840.4696723460523680.765163826973816
920.201399340955890.402798681911780.79860065904411
930.1847571991503680.3695143983007360.815242800849632
940.1559978934544120.3119957869088230.844002106545588
950.1409733207969480.2819466415938960.859026679203052
960.1443296515704160.2886593031408330.855670348429584
970.145774159414260.291548318828520.85422584058574
980.1418119271303820.2836238542607640.858188072869618
990.1345983108142240.2691966216284490.865401689185776
1000.1294164858327750.2588329716655490.870583514167225
1010.1057460530674360.2114921061348720.894253946932564
1020.08759526752296160.1751905350459230.912404732477038
1030.07016833790988550.1403366758197710.929831662090115
1040.05644097488913730.1128819497782750.943559025110863
1050.05965098516926370.1193019703385270.940349014830736
1060.04872863388118950.09745726776237910.95127136611881
1070.04258511029319480.08517022058638960.957414889706805
1080.03694512472935480.07389024945870960.963054875270645
1090.0280944805239470.05618896104789390.971905519476053
1100.02761203269233250.05522406538466510.972387967307668
1110.03476230996984070.06952461993968140.965237690030159
1120.1480319131636360.2960638263272730.851968086836364
1130.1361149519989620.2722299039979240.863885048001038
1140.5491035633682470.9017928732635060.450896436631753
1150.8509624561338380.2980750877323240.149037543866162
1160.8175467765604840.3649064468790330.182453223439516
1170.8725133502772160.2549732994455670.127486649722784
1180.8474548883968470.3050902232063060.152545111603153
1190.8187542316329260.3624915367341480.181245768367074
1200.8521828395693450.2956343208613110.147817160430655
1210.8288430235396780.3423139529206450.171156976460322
1220.7884172559405590.4231654881188820.211582744059441
1230.8197816141223260.3604367717553490.180218385877674
1240.7770040471536970.4459919056926060.222995952846303
1250.7374372043607650.5251255912784710.262562795639235
1260.6885490789281990.6229018421436010.311450921071801
1270.6515683568381680.6968632863236650.348431643161832
1280.6092736870518680.7814526258962640.390726312948132
1290.5502923126977580.8994153746044840.449707687302242
1300.4870146727942530.9740293455885070.512985327205747
1310.4847127237360070.9694254474720140.515287276263993
1320.482597109188470.965194218376940.51740289081153
1330.4320568646209030.8641137292418060.567943135379097
1340.3838081257144370.7676162514288740.616191874285563
1350.5314485663706680.9371028672586640.468551433629332
1360.4921688174696430.9843376349392860.507831182530357
1370.4186925637844270.8373851275688550.581307436215573
1380.4281675271882330.8563350543764670.571832472811767
1390.3565833426365370.7131666852730730.643416657363463
1400.3203594376791160.6407188753582330.679640562320884
1410.2859131825862710.5718263651725420.714086817413729
1420.3365044110543720.6730088221087450.663495588945628
1430.4780941135756880.9561882271513760.521905886424312
1440.400992750579690.8019855011593810.59900724942031
1450.3198841429387340.6397682858774680.680115857061266
1460.30794447329060.61588894658120.6920555267094
1470.254742419681060.509484839362120.74525758031894
1480.1590915611281080.3181831222562150.840908438871892
1490.3041069572470940.6082139144941880.695893042752906







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.0428571428571429OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.0428571428571429 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147244&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0428571428571429[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147244&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147244&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.0428571428571429OK



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