FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 22 Nov 2010 21:34:59 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/22/t1290461892p6cywpt7vic7c4f.htm/, Retrieved Sun, 28 Apr 2024 01:25:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98749, Retrieved Sun, 28 Apr 2024 01:25:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [test] [2010-11-22 21:34:59] [23a9b79f355c69a75648521a893cf584] [Current]
-    D      [Multiple Regression] [Workshop 7, tutor...] [2010-11-22 21:53:32] [3635fb7041b1998c5a1332cf9de22bce]
-             [Multiple Regression] [Workshop 7 Multip...] [2010-11-23 11:23:19] [a9e130f95bad0a0597234e75c6380c5a]
-    D          [Multiple Regression] [WS 7 comp 1] [2010-11-23 19:49:41] [b659239b537e56f17142ee5c56ad6265]
- R  D          [Multiple Regression] [WS 7 - Deel 1] [2011-11-22 18:43:07] [74be16979710d4c4e7c6647856088456]
-    D        [Multiple Regression] [] [2010-11-23 14:09:42] [4cb9d9226ff0df1b8fdf89cde6bcc828]
-    D        [Multiple Regression] [] [2011-11-18 16:49:37] [b1eb71d4db1ceb5d347df987feb4a25e]
-    D        [Multiple Regression] [WS7 - Tutorial] [2011-11-19 08:53:42] [ea9976aa04c7322b215e949114660791]
- R PD        [Multiple Regression] [] [2011-11-20 10:15:29] [46896e8a404bb9354f2d070359621409]
-   PD        [Multiple Regression] [Workshop 7] [2011-11-22 09:16:23] [21b3d52ef28595defb5676e0f3570994]
-   PD        [Multiple Regression] [WS 7 - Multiple L...] [2011-11-22 15:01:08] [43239ed98a62e091c70785d80176537f]
- RM          [Multiple Regression] [] [2012-11-20 21:40:20] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [W7 RFC-tijd & logins] [2012-11-21 00:02:14] [3ae574fa1d645ef9b19cadb6c0dbd022]
-   PD        [Multiple Regression] [W7 RFC-tijd & log...] [2012-11-21 00:10:52] [3ae574fa1d645ef9b19cadb6c0dbd022]
-    D      [Multiple Regression] [WS 7 Multiple Lin...] [2010-11-22 21:47:12] [8081b8996d5947580de3eb171e82db4f]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5,6	5,5	6	4,8
4,4	3,5	4	4,4
2,4	8,5	4	5,6
4,8	5	4	4,8
3,2	6	4,5	8,4
4	6	3,5	4,8
4	5,5	2	8,8
4,4	5,5	5,5	4,4
6,4	6	3,5	4
4,4	6,5	3,5	5,2
5,2	7	6	4
4,8	8	5	3,2
3,2	5,5	5	6
4,8	5	4	5,6
4,4	5,5	4	4
1,6	7,5	2	5,6
3,6	4,5	4,5	5,6
3,2	5,5	4	4,4
3,2	8,5	3,5	4
5,6	8,5	5,5	5,2
6	5,5	4,5	2,8
6,4	9	5,5	5,6
3,6	7	6,5	4,8
5,6	5	4	5,6
4,4	5,5	4	4,4
3,2	7,5	4,5	3,6
3,6	7,5	3	4,4
3,6	6,5	4,5	6
3,6	8	4,5	5,6
3,6	6,5	3	5,2
4	4,5	3	3,6
6,4	9	8	6
4,4	9	2,5	4
3,2	6	3,5	4,4
3,6	8,5	4,5	5,2
6,4	4,5	3	3,2
4,4	4,5	3	8
6,4	6	2,5	4,8
4,8	9	6	4
4,8	6	3,5	4
5,6	9	5	3,6
3,6	7	4,5	5,6
4	7,5	4	3,2
3,6	8	2,5	5,6
4	5	4	4,4
4,8	5,5	4	5,2
5,6	7	5	3,6
5,6	4,5	3	4,4
4	6	4	6
5,6	8,5	3,5	4,4
6,4	2,5	2	4
3,6	6	4	5,6
4	6	4	7,2
2,4	3	2	5,6
3,2	12	10	4,4
5,2	6	4	4,8
4	6	4	5,2
3,2	7	3	3,6
2,8	3,5	2	4
6	6,5	4	6
3,6	6	4,5	8
4	6,5	3	4,8
4,8	7	3,5	4,8
5,2	4	4,5	5,6
4	5,5	2,5	5,2
4,4	4,5	2,5	4,4
3,2	5,5	4	6,8
3,6	6,5	4	4,8
5,2	5	3	5,2
4,4	5,5	4	5,6
3,2	6	3,5	5,2
3,6	4,5	3,5	6
3,6	7,5	4,5	5,2
6	9	5,5	4
3,6	7,5	3	4,4
4	6	4	7,6
5,6	6,5	3	5,2
4,8	7	4,5	6,8
4,8	5	4	5,2
4,4	6,5	3	3,6
5,6	6,5	5	4,4
2,4	5,5	4	4
4,8	6,5	4	3,6
3,2	8	5	4,8
5,6	4	2,5	4,8
4,4	8	3,5	5,2
4	5,5	2,5	5,2
5,6	4,5	4	4,8
4,8	8	7	6
4	6	3,5	8,8
5,6	7	4	5,2
2	4	3	6
4,4	4,5	2,5	5,2
4	7,5	3	6
3,6	5,5	5	4
4	10,5	6	4,4
6,4	7	4,5	6,4
5,2	9	6	4,4
3,6	6	3,5	4,4
4	6,5	4	4
4	7,5	5	4
2,8	6	3	6,4
3,6	9,5	5	4,8
3,2	7,5	5	4,4
5,6	5,5	5	6,4
5,6	5,5	2,5	7,6
3,2	5	3,5	4,4
3,6	6,5	5	6,4
5,6	7,5	5,5	6
5,6	6	3	9,6
3,2	6	3,5	5,6
3,2	8	6	6
3,2	4,5	5,5	4,4
2,8	9	5,5	6
2,4	4	5,5	4,8
3,2	6,5	2,5	4
2,4	8,5	4	5,6
4,4	4,5	3	5,2
5,6	7,5	4,5	3,6
4,4	4	2	6
4,4	3,5	2	6
4,4	6	3,5	5,6
5,6	7	5,5	4,4
3,2	3	3	3,2
8	4	3,5	4,4
4,4	8,5	4	4,4
3,2	5	2	3,2
4,4	5,5	4	4
4	7	4,5	4,4
5,6	5,5	4	5,2
4,4	6,5	5,5	4,4
3,6	6	4	8
3,6	5,5	2,5	4
3,2	4,5	2	6
4	6	4	4,8
5,2	10	5	5,6
5,2	6	3	9,2
4,8	6,5	4,5	5,6
3,2	6	4,5	6,4
5,2	6	6,5	4,4
5,6	4,5	4,5	4,8
4,8	7,5	5	4
5,6	12	10	5,6
6	3,5	2,5	4,8
5,2	8,5	5,5	4,8
6,4	5,5	3	4,4
3,6	8,5	4,5	4,8
3,6	5,5	3,5	5,2
3,6	6	4,5	4,4
3,2	7	5	7,6
2,8	5,5	4,5	4,8
6,4	8	4	6,8
4,4	10,5	3,5	3,6
3,6	7	3	4,8
4,4	10	6,5	7,6
3,6	6,5	3	7,2
5,6	5,5	4	6
5,2	7,5	5	5,6
6,4	9,5	8	4,4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98749&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98749&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98749&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 5.70984648766359 -0.0799607227294421Doubts[t] -0.021800622364926Expectat[t] -0.0155788482792036Criticism[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  5.70984648766359 -0.0799607227294421Doubts[t] -0.021800622364926Expectat[t] -0.0155788482792036Criticism[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98749&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  5.70984648766359 -0.0799607227294421Doubts[t] -0.021800622364926Expectat[t] -0.0155788482792036Criticism[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98749&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98749&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 5.70984648766359 -0.0799607227294421Doubts[t] -0.021800622364926Expectat[t] -0.0155788482792036Criticism[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.709846487663590.54612210.455300
Doubts-0.07996072272944210.091271-0.87610.3823430.191172
Expectat-0.0218006223649260.072838-0.29930.765110.382555
Criticism-0.01557884827920360.093796-0.16610.86830.43415

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 5.70984648766359 & 0.546122 & 10.4553 & 0 & 0 \tabularnewline
Doubts & -0.0799607227294421 & 0.091271 & -0.8761 & 0.382343 & 0.191172 \tabularnewline
Expectat & -0.021800622364926 & 0.072838 & -0.2993 & 0.76511 & 0.382555 \tabularnewline
Criticism & -0.0155788482792036 & 0.093796 & -0.1661 & 0.8683 & 0.43415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98749&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]5.70984648766359[/C][C]0.546122[/C][C]10.4553[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.0799607227294421[/C][C]0.091271[/C][C]-0.8761[/C][C]0.382343[/C][C]0.191172[/C][/ROW]
[ROW][C]Expectat[/C][C]-0.021800622364926[/C][C]0.072838[/C][C]-0.2993[/C][C]0.76511[/C][C]0.382555[/C][/ROW]
[ROW][C]Criticism[/C][C]-0.0155788482792036[/C][C]0.093796[/C][C]-0.1661[/C][C]0.8683[/C][C]0.43415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98749&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98749&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.709846487663590.54612210.455300
Doubts-0.07996072272944210.091271-0.87610.3823430.191172
Expectat-0.0218006223649260.072838-0.29930.765110.382555
Criticism-0.01557884827920360.093796-0.16610.86830.43415






Multiple Linear Regression - Regression Statistics
Multiple R0.0855457044326433
R-squared0.00731806754687717
Adjusted R-squared-0.0118951311457638
F-TEST (value)0.380887517167045
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value0.766914280365275
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.26562203627464
Sum Squared Residuals248.278866499113

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0855457044326433 \tabularnewline
R-squared & 0.00731806754687717 \tabularnewline
Adjusted R-squared & -0.0118951311457638 \tabularnewline
F-TEST (value) & 0.380887517167045 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.766914280365275 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.26562203627464 \tabularnewline
Sum Squared Residuals & 248.278866499113 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98749&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0855457044326433[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00731806754687717[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0118951311457638[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.380887517167045[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.766914280365275[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.26562203627464[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]248.278866499113[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98749&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98749&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.0855457044326433
R-squared0.00731806754687717
Adjusted R-squared-0.0118951311457638
F-TEST (value)0.380887517167045
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value0.766914280365275
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.26562203627464
Sum Squared Residuals248.278866499113






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.85.0486899276964-0.248689927696399
24.45.21940173625999-0.81940173625999
35.65.270320069894240.329679930105755
44.85.15471651362082-0.354716513620825
58.45.25306362348343.1469363765166
64.85.20467389357905-0.404673893579054
78.85.238942477180323.56105752281968
84.45.15243221911133-0.752432219111333
945.01276815902839-1.01276815902839
105.25.161789293304810.0382107066951858
1145.04797328324079-1.04797328324079
123.25.07373579824684-1.87373579824684
1365.256174510526270.743825489473734
145.65.154716513620820.445283486379175
1545.17580049153014-1.17580049153014
165.65.387246967001130.212753032998868
175.65.253780267939020.346219732060983
184.45.27175335880547-0.87175335880547
1945.21414091585029-1.21414091585029
205.24.991077484741220.208922515258775
212.85.04007391102343-2.24007391102343
225.64.916208595375210.683791404624792
234.85.16812101546829-0.368121015468295
245.65.090747935437270.509252064562729
254.45.17580049153014-0.775800491530138
263.65.22036268993602-1.62036268993602
274.45.21174667326304-0.811746673263044
2865.210179023209160.789820976790835
295.65.177478089661780.422521910338224
305.25.23354729562797-0.0335472956279698
313.65.24516425126605-1.64516425126604
3264.87726147467721.1227385253228
3345.1228665856717-1.1228665856717
344.45.26864247176261-0.868642471762608
355.25.166577778479310.0334222215206876
363.25.05325851671538-1.85325851671538
3785.213179962174272.78682003782573
384.85.0283470073076-0.228347007307597
3945.03635632760271-1.03635632760271
4045.1407053153955-1.1407053153955
413.64.98796659769836-1.38796659769836
425.65.19927871202670.400721287973298
433.25.16418353589206-1.96418353589206
445.65.208635786220180.391364213779817
454.45.21868509180438-0.818685091804378
465.25.143816202438360.0561837975616383
473.65.03156784242822-1.43156784242822
484.45.11722709489894-0.717227094898937
4965.196884469439450.803115530560548
504.45.02223518129963-0.622235181299632
5145.11243860972444-1.11243860972444
525.65.228868758531230.37113124146877
537.25.196884469439452.00311553056055
545.65.421381189459750.178618810540255
554.45.03657622375823-0.636576223758228
564.85.10093160216412-0.300931602164122
575.25.196884469439450.00311553056054765
583.65.25463127353728-1.65463127353728
5945.37849658918551-1.37849658918551
6065.02606271279810.973937287201895
6185.221079334391632.77892066560837
624.85.20156300653619-0.401563006536193
634.85.11890469303057-0.318904693030575
645.65.136743422754370.463256577245627
655.25.23115305304072-0.0311530530407207
664.45.22096938631387-0.82096938631387
676.85.271753358805471.52824664119453
684.85.21796844734877-0.417968447348766
695.25.138311072808250.0616889271917487
705.65.175800491530140.424199508469861
715.25.26864247176261-0.0686424717626078
7265.269359116218220.73064088378178
735.25.188378400844240.0116215991557616
7444.94819288446698-0.948192884466985
754.45.21174667326304-0.811746673263044
767.65.196884469439452.40311553056055
775.25.073625850169090.126374149830914
786.85.103325844751371.69667415524863
795.25.154716513620820.0452834863791753
803.65.16957871744442-1.56957871744442
814.45.04246815361068-0.642468153610678
8245.33572193698902-1.33572193698902
833.65.12201558007344-1.52201558007344
844.85.20167295461395-0.401672954613951
854.85.135916830221-0.335916830221003
865.25.129088359757430.0709116402425748
875.25.23115305304072-0.0311530530407207
884.85.10164824661973-0.301648246619734
8965.042578101688440.957421898311564
908.85.204673893579053.59532610642095
915.25.047146690707420.152853309292581
9265.415986007907390.584013992092608
935.25.22096938631387-0.0209693863138698
9465.179762384171270.820237615828733
9545.22419022143449-1.22419022143449
964.45.06762397223888-0.667623972238878
976.44.975388688384261.42461131161574
984.45.00437203851094-0.604372038510936
994.45.23665818267083-0.83665818267083
10045.18598415825699-1.18598415825699
10145.14860468761286-1.14860468761286
1026.45.308416184993991.09158381500601
1034.85.13698773197478-0.336987731974785
1044.45.21257326579641-0.812573265796413
1056.45.06426877597561.3357312240244
1067.65.103215896673612.49678410332639
1074.45.29044309412753-0.890443094127534
1086.45.202389599069561.19761040093044
10965.012878107106150.98712189289385
1109.65.084526161351554.51547383864845
1115.65.268642471762610.331357528237392
11265.186094106334750.813905893665253
1134.45.27018570875159-0.87018570875159
11465.20406719720120.7959328027988
1154.85.34505459811761-0.545054598117607
11645.27332100885935-1.27332100885935
1175.65.270320069894240.329679930105755
1185.25.21317996217427-0.013179962174268
1193.65.02845695538536-1.42845695538535
12065.239659121635930.760340878364065
12165.25055943281840.749440567181602
1225.65.172689604487280.427310395512722
1234.45.02377841828861-0.623778418288613
1243.25.34183376299699-2.14183376299699
1254.44.92843224739114-0.528432247391138
1264.45.11039862443536-0.71039862443536
1273.25.31381136654634-2.11381136654634
12845.17580049153014-1.17580049153014
1294.45.16729442293492-0.767294422934924
1305.25.079847624254810.120152375745192
1314.45.13063159674641-0.730631596746407
13285.228868758531232.77113124146877
13345.2631373421325-1.2631373421325
13465.32471167772880.675288322271198
1354.85.19688446943945-0.396884469439453
1365.64.998150264425210.601849735574785
1379.25.116510450443334.08348954955667
1385.65.114226155933830.485773844066166
1396.45.25306362348341.1469363765166
1404.45.06198448146611-0.661984481466113
1414.85.09385882248013-0.293858822480133
14245.08463610942931-1.08463610942931
1435.64.844670489207570.755329510792432
1444.85.11483285231169-0.314832852311689
1454.85.023061773833-0.223061773833002
1464.45.03145789435046-0.631457894350458
1474.85.16657777847931-0.366577778479313
1485.25.24755849385329-0.047558493853294
1494.45.22107933439163-0.821079334391627
1507.65.223473576978882.37652642302112
1514.85.29594822375764-0.495948223757644
1526.84.961377490158941.83862250984106
1533.65.07458680384511-1.47458680384511
1544.85.22264698444551-0.422646984445507
1557.65.038750570189962.56124942981004
1567.25.233547295627971.96645270437203
15765.079847624254810.920152375745192
1585.65.052651820337530.54734817966247
1594.44.86636116349474-0.466361163494736

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.8 & 5.0486899276964 & -0.248689927696399 \tabularnewline
2 & 4.4 & 5.21940173625999 & -0.81940173625999 \tabularnewline
3 & 5.6 & 5.27032006989424 & 0.329679930105755 \tabularnewline
4 & 4.8 & 5.15471651362082 & -0.354716513620825 \tabularnewline
5 & 8.4 & 5.2530636234834 & 3.1469363765166 \tabularnewline
6 & 4.8 & 5.20467389357905 & -0.404673893579054 \tabularnewline
7 & 8.8 & 5.23894247718032 & 3.56105752281968 \tabularnewline
8 & 4.4 & 5.15243221911133 & -0.752432219111333 \tabularnewline
9 & 4 & 5.01276815902839 & -1.01276815902839 \tabularnewline
10 & 5.2 & 5.16178929330481 & 0.0382107066951858 \tabularnewline
11 & 4 & 5.04797328324079 & -1.04797328324079 \tabularnewline
12 & 3.2 & 5.07373579824684 & -1.87373579824684 \tabularnewline
13 & 6 & 5.25617451052627 & 0.743825489473734 \tabularnewline
14 & 5.6 & 5.15471651362082 & 0.445283486379175 \tabularnewline
15 & 4 & 5.17580049153014 & -1.17580049153014 \tabularnewline
16 & 5.6 & 5.38724696700113 & 0.212753032998868 \tabularnewline
17 & 5.6 & 5.25378026793902 & 0.346219732060983 \tabularnewline
18 & 4.4 & 5.27175335880547 & -0.87175335880547 \tabularnewline
19 & 4 & 5.21414091585029 & -1.21414091585029 \tabularnewline
20 & 5.2 & 4.99107748474122 & 0.208922515258775 \tabularnewline
21 & 2.8 & 5.04007391102343 & -2.24007391102343 \tabularnewline
22 & 5.6 & 4.91620859537521 & 0.683791404624792 \tabularnewline
23 & 4.8 & 5.16812101546829 & -0.368121015468295 \tabularnewline
24 & 5.6 & 5.09074793543727 & 0.509252064562729 \tabularnewline
25 & 4.4 & 5.17580049153014 & -0.775800491530138 \tabularnewline
26 & 3.6 & 5.22036268993602 & -1.62036268993602 \tabularnewline
27 & 4.4 & 5.21174667326304 & -0.811746673263044 \tabularnewline
28 & 6 & 5.21017902320916 & 0.789820976790835 \tabularnewline
29 & 5.6 & 5.17747808966178 & 0.422521910338224 \tabularnewline
30 & 5.2 & 5.23354729562797 & -0.0335472956279698 \tabularnewline
31 & 3.6 & 5.24516425126605 & -1.64516425126604 \tabularnewline
32 & 6 & 4.8772614746772 & 1.1227385253228 \tabularnewline
33 & 4 & 5.1228665856717 & -1.1228665856717 \tabularnewline
34 & 4.4 & 5.26864247176261 & -0.868642471762608 \tabularnewline
35 & 5.2 & 5.16657777847931 & 0.0334222215206876 \tabularnewline
36 & 3.2 & 5.05325851671538 & -1.85325851671538 \tabularnewline
37 & 8 & 5.21317996217427 & 2.78682003782573 \tabularnewline
38 & 4.8 & 5.0283470073076 & -0.228347007307597 \tabularnewline
39 & 4 & 5.03635632760271 & -1.03635632760271 \tabularnewline
40 & 4 & 5.1407053153955 & -1.1407053153955 \tabularnewline
41 & 3.6 & 4.98796659769836 & -1.38796659769836 \tabularnewline
42 & 5.6 & 5.1992787120267 & 0.400721287973298 \tabularnewline
43 & 3.2 & 5.16418353589206 & -1.96418353589206 \tabularnewline
44 & 5.6 & 5.20863578622018 & 0.391364213779817 \tabularnewline
45 & 4.4 & 5.21868509180438 & -0.818685091804378 \tabularnewline
46 & 5.2 & 5.14381620243836 & 0.0561837975616383 \tabularnewline
47 & 3.6 & 5.03156784242822 & -1.43156784242822 \tabularnewline
48 & 4.4 & 5.11722709489894 & -0.717227094898937 \tabularnewline
49 & 6 & 5.19688446943945 & 0.803115530560548 \tabularnewline
50 & 4.4 & 5.02223518129963 & -0.622235181299632 \tabularnewline
51 & 4 & 5.11243860972444 & -1.11243860972444 \tabularnewline
52 & 5.6 & 5.22886875853123 & 0.37113124146877 \tabularnewline
53 & 7.2 & 5.19688446943945 & 2.00311553056055 \tabularnewline
54 & 5.6 & 5.42138118945975 & 0.178618810540255 \tabularnewline
55 & 4.4 & 5.03657622375823 & -0.636576223758228 \tabularnewline
56 & 4.8 & 5.10093160216412 & -0.300931602164122 \tabularnewline
57 & 5.2 & 5.19688446943945 & 0.00311553056054765 \tabularnewline
58 & 3.6 & 5.25463127353728 & -1.65463127353728 \tabularnewline
59 & 4 & 5.37849658918551 & -1.37849658918551 \tabularnewline
60 & 6 & 5.0260627127981 & 0.973937287201895 \tabularnewline
61 & 8 & 5.22107933439163 & 2.77892066560837 \tabularnewline
62 & 4.8 & 5.20156300653619 & -0.401563006536193 \tabularnewline
63 & 4.8 & 5.11890469303057 & -0.318904693030575 \tabularnewline
64 & 5.6 & 5.13674342275437 & 0.463256577245627 \tabularnewline
65 & 5.2 & 5.23115305304072 & -0.0311530530407207 \tabularnewline
66 & 4.4 & 5.22096938631387 & -0.82096938631387 \tabularnewline
67 & 6.8 & 5.27175335880547 & 1.52824664119453 \tabularnewline
68 & 4.8 & 5.21796844734877 & -0.417968447348766 \tabularnewline
69 & 5.2 & 5.13831107280825 & 0.0616889271917487 \tabularnewline
70 & 5.6 & 5.17580049153014 & 0.424199508469861 \tabularnewline
71 & 5.2 & 5.26864247176261 & -0.0686424717626078 \tabularnewline
72 & 6 & 5.26935911621822 & 0.73064088378178 \tabularnewline
73 & 5.2 & 5.18837840084424 & 0.0116215991557616 \tabularnewline
74 & 4 & 4.94819288446698 & -0.948192884466985 \tabularnewline
75 & 4.4 & 5.21174667326304 & -0.811746673263044 \tabularnewline
76 & 7.6 & 5.19688446943945 & 2.40311553056055 \tabularnewline
77 & 5.2 & 5.07362585016909 & 0.126374149830914 \tabularnewline
78 & 6.8 & 5.10332584475137 & 1.69667415524863 \tabularnewline
79 & 5.2 & 5.15471651362082 & 0.0452834863791753 \tabularnewline
80 & 3.6 & 5.16957871744442 & -1.56957871744442 \tabularnewline
81 & 4.4 & 5.04246815361068 & -0.642468153610678 \tabularnewline
82 & 4 & 5.33572193698902 & -1.33572193698902 \tabularnewline
83 & 3.6 & 5.12201558007344 & -1.52201558007344 \tabularnewline
84 & 4.8 & 5.20167295461395 & -0.401672954613951 \tabularnewline
85 & 4.8 & 5.135916830221 & -0.335916830221003 \tabularnewline
86 & 5.2 & 5.12908835975743 & 0.0709116402425748 \tabularnewline
87 & 5.2 & 5.23115305304072 & -0.0311530530407207 \tabularnewline
88 & 4.8 & 5.10164824661973 & -0.301648246619734 \tabularnewline
89 & 6 & 5.04257810168844 & 0.957421898311564 \tabularnewline
90 & 8.8 & 5.20467389357905 & 3.59532610642095 \tabularnewline
91 & 5.2 & 5.04714669070742 & 0.152853309292581 \tabularnewline
92 & 6 & 5.41598600790739 & 0.584013992092608 \tabularnewline
93 & 5.2 & 5.22096938631387 & -0.0209693863138698 \tabularnewline
94 & 6 & 5.17976238417127 & 0.820237615828733 \tabularnewline
95 & 4 & 5.22419022143449 & -1.22419022143449 \tabularnewline
96 & 4.4 & 5.06762397223888 & -0.667623972238878 \tabularnewline
97 & 6.4 & 4.97538868838426 & 1.42461131161574 \tabularnewline
98 & 4.4 & 5.00437203851094 & -0.604372038510936 \tabularnewline
99 & 4.4 & 5.23665818267083 & -0.83665818267083 \tabularnewline
100 & 4 & 5.18598415825699 & -1.18598415825699 \tabularnewline
101 & 4 & 5.14860468761286 & -1.14860468761286 \tabularnewline
102 & 6.4 & 5.30841618499399 & 1.09158381500601 \tabularnewline
103 & 4.8 & 5.13698773197478 & -0.336987731974785 \tabularnewline
104 & 4.4 & 5.21257326579641 & -0.812573265796413 \tabularnewline
105 & 6.4 & 5.0642687759756 & 1.3357312240244 \tabularnewline
106 & 7.6 & 5.10321589667361 & 2.49678410332639 \tabularnewline
107 & 4.4 & 5.29044309412753 & -0.890443094127534 \tabularnewline
108 & 6.4 & 5.20238959906956 & 1.19761040093044 \tabularnewline
109 & 6 & 5.01287810710615 & 0.98712189289385 \tabularnewline
110 & 9.6 & 5.08452616135155 & 4.51547383864845 \tabularnewline
111 & 5.6 & 5.26864247176261 & 0.331357528237392 \tabularnewline
112 & 6 & 5.18609410633475 & 0.813905893665253 \tabularnewline
113 & 4.4 & 5.27018570875159 & -0.87018570875159 \tabularnewline
114 & 6 & 5.2040671972012 & 0.7959328027988 \tabularnewline
115 & 4.8 & 5.34505459811761 & -0.545054598117607 \tabularnewline
116 & 4 & 5.27332100885935 & -1.27332100885935 \tabularnewline
117 & 5.6 & 5.27032006989424 & 0.329679930105755 \tabularnewline
118 & 5.2 & 5.21317996217427 & -0.013179962174268 \tabularnewline
119 & 3.6 & 5.02845695538536 & -1.42845695538535 \tabularnewline
120 & 6 & 5.23965912163593 & 0.760340878364065 \tabularnewline
121 & 6 & 5.2505594328184 & 0.749440567181602 \tabularnewline
122 & 5.6 & 5.17268960448728 & 0.427310395512722 \tabularnewline
123 & 4.4 & 5.02377841828861 & -0.623778418288613 \tabularnewline
124 & 3.2 & 5.34183376299699 & -2.14183376299699 \tabularnewline
125 & 4.4 & 4.92843224739114 & -0.528432247391138 \tabularnewline
126 & 4.4 & 5.11039862443536 & -0.71039862443536 \tabularnewline
127 & 3.2 & 5.31381136654634 & -2.11381136654634 \tabularnewline
128 & 4 & 5.17580049153014 & -1.17580049153014 \tabularnewline
129 & 4.4 & 5.16729442293492 & -0.767294422934924 \tabularnewline
130 & 5.2 & 5.07984762425481 & 0.120152375745192 \tabularnewline
131 & 4.4 & 5.13063159674641 & -0.730631596746407 \tabularnewline
132 & 8 & 5.22886875853123 & 2.77113124146877 \tabularnewline
133 & 4 & 5.2631373421325 & -1.2631373421325 \tabularnewline
134 & 6 & 5.3247116777288 & 0.675288322271198 \tabularnewline
135 & 4.8 & 5.19688446943945 & -0.396884469439453 \tabularnewline
136 & 5.6 & 4.99815026442521 & 0.601849735574785 \tabularnewline
137 & 9.2 & 5.11651045044333 & 4.08348954955667 \tabularnewline
138 & 5.6 & 5.11422615593383 & 0.485773844066166 \tabularnewline
139 & 6.4 & 5.2530636234834 & 1.1469363765166 \tabularnewline
140 & 4.4 & 5.06198448146611 & -0.661984481466113 \tabularnewline
141 & 4.8 & 5.09385882248013 & -0.293858822480133 \tabularnewline
142 & 4 & 5.08463610942931 & -1.08463610942931 \tabularnewline
143 & 5.6 & 4.84467048920757 & 0.755329510792432 \tabularnewline
144 & 4.8 & 5.11483285231169 & -0.314832852311689 \tabularnewline
145 & 4.8 & 5.023061773833 & -0.223061773833002 \tabularnewline
146 & 4.4 & 5.03145789435046 & -0.631457894350458 \tabularnewline
147 & 4.8 & 5.16657777847931 & -0.366577778479313 \tabularnewline
148 & 5.2 & 5.24755849385329 & -0.047558493853294 \tabularnewline
149 & 4.4 & 5.22107933439163 & -0.821079334391627 \tabularnewline
150 & 7.6 & 5.22347357697888 & 2.37652642302112 \tabularnewline
151 & 4.8 & 5.29594822375764 & -0.495948223757644 \tabularnewline
152 & 6.8 & 4.96137749015894 & 1.83862250984106 \tabularnewline
153 & 3.6 & 5.07458680384511 & -1.47458680384511 \tabularnewline
154 & 4.8 & 5.22264698444551 & -0.422646984445507 \tabularnewline
155 & 7.6 & 5.03875057018996 & 2.56124942981004 \tabularnewline
156 & 7.2 & 5.23354729562797 & 1.96645270437203 \tabularnewline
157 & 6 & 5.07984762425481 & 0.920152375745192 \tabularnewline
158 & 5.6 & 5.05265182033753 & 0.54734817966247 \tabularnewline
159 & 4.4 & 4.86636116349474 & -0.466361163494736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98749&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]4.8[/C][C]5.0486899276964[/C][C]-0.248689927696399[/C][/ROW]
[ROW][C]2[/C][C]4.4[/C][C]5.21940173625999[/C][C]-0.81940173625999[/C][/ROW]
[ROW][C]3[/C][C]5.6[/C][C]5.27032006989424[/C][C]0.329679930105755[/C][/ROW]
[ROW][C]4[/C][C]4.8[/C][C]5.15471651362082[/C][C]-0.354716513620825[/C][/ROW]
[ROW][C]5[/C][C]8.4[/C][C]5.2530636234834[/C][C]3.1469363765166[/C][/ROW]
[ROW][C]6[/C][C]4.8[/C][C]5.20467389357905[/C][C]-0.404673893579054[/C][/ROW]
[ROW][C]7[/C][C]8.8[/C][C]5.23894247718032[/C][C]3.56105752281968[/C][/ROW]
[ROW][C]8[/C][C]4.4[/C][C]5.15243221911133[/C][C]-0.752432219111333[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.01276815902839[/C][C]-1.01276815902839[/C][/ROW]
[ROW][C]10[/C][C]5.2[/C][C]5.16178929330481[/C][C]0.0382107066951858[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]5.04797328324079[/C][C]-1.04797328324079[/C][/ROW]
[ROW][C]12[/C][C]3.2[/C][C]5.07373579824684[/C][C]-1.87373579824684[/C][/ROW]
[ROW][C]13[/C][C]6[/C][C]5.25617451052627[/C][C]0.743825489473734[/C][/ROW]
[ROW][C]14[/C][C]5.6[/C][C]5.15471651362082[/C][C]0.445283486379175[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]5.17580049153014[/C][C]-1.17580049153014[/C][/ROW]
[ROW][C]16[/C][C]5.6[/C][C]5.38724696700113[/C][C]0.212753032998868[/C][/ROW]
[ROW][C]17[/C][C]5.6[/C][C]5.25378026793902[/C][C]0.346219732060983[/C][/ROW]
[ROW][C]18[/C][C]4.4[/C][C]5.27175335880547[/C][C]-0.87175335880547[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]5.21414091585029[/C][C]-1.21414091585029[/C][/ROW]
[ROW][C]20[/C][C]5.2[/C][C]4.99107748474122[/C][C]0.208922515258775[/C][/ROW]
[ROW][C]21[/C][C]2.8[/C][C]5.04007391102343[/C][C]-2.24007391102343[/C][/ROW]
[ROW][C]22[/C][C]5.6[/C][C]4.91620859537521[/C][C]0.683791404624792[/C][/ROW]
[ROW][C]23[/C][C]4.8[/C][C]5.16812101546829[/C][C]-0.368121015468295[/C][/ROW]
[ROW][C]24[/C][C]5.6[/C][C]5.09074793543727[/C][C]0.509252064562729[/C][/ROW]
[ROW][C]25[/C][C]4.4[/C][C]5.17580049153014[/C][C]-0.775800491530138[/C][/ROW]
[ROW][C]26[/C][C]3.6[/C][C]5.22036268993602[/C][C]-1.62036268993602[/C][/ROW]
[ROW][C]27[/C][C]4.4[/C][C]5.21174667326304[/C][C]-0.811746673263044[/C][/ROW]
[ROW][C]28[/C][C]6[/C][C]5.21017902320916[/C][C]0.789820976790835[/C][/ROW]
[ROW][C]29[/C][C]5.6[/C][C]5.17747808966178[/C][C]0.422521910338224[/C][/ROW]
[ROW][C]30[/C][C]5.2[/C][C]5.23354729562797[/C][C]-0.0335472956279698[/C][/ROW]
[ROW][C]31[/C][C]3.6[/C][C]5.24516425126605[/C][C]-1.64516425126604[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]4.8772614746772[/C][C]1.1227385253228[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]5.1228665856717[/C][C]-1.1228665856717[/C][/ROW]
[ROW][C]34[/C][C]4.4[/C][C]5.26864247176261[/C][C]-0.868642471762608[/C][/ROW]
[ROW][C]35[/C][C]5.2[/C][C]5.16657777847931[/C][C]0.0334222215206876[/C][/ROW]
[ROW][C]36[/C][C]3.2[/C][C]5.05325851671538[/C][C]-1.85325851671538[/C][/ROW]
[ROW][C]37[/C][C]8[/C][C]5.21317996217427[/C][C]2.78682003782573[/C][/ROW]
[ROW][C]38[/C][C]4.8[/C][C]5.0283470073076[/C][C]-0.228347007307597[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]5.03635632760271[/C][C]-1.03635632760271[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]5.1407053153955[/C][C]-1.1407053153955[/C][/ROW]
[ROW][C]41[/C][C]3.6[/C][C]4.98796659769836[/C][C]-1.38796659769836[/C][/ROW]
[ROW][C]42[/C][C]5.6[/C][C]5.1992787120267[/C][C]0.400721287973298[/C][/ROW]
[ROW][C]43[/C][C]3.2[/C][C]5.16418353589206[/C][C]-1.96418353589206[/C][/ROW]
[ROW][C]44[/C][C]5.6[/C][C]5.20863578622018[/C][C]0.391364213779817[/C][/ROW]
[ROW][C]45[/C][C]4.4[/C][C]5.21868509180438[/C][C]-0.818685091804378[/C][/ROW]
[ROW][C]46[/C][C]5.2[/C][C]5.14381620243836[/C][C]0.0561837975616383[/C][/ROW]
[ROW][C]47[/C][C]3.6[/C][C]5.03156784242822[/C][C]-1.43156784242822[/C][/ROW]
[ROW][C]48[/C][C]4.4[/C][C]5.11722709489894[/C][C]-0.717227094898937[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]5.19688446943945[/C][C]0.803115530560548[/C][/ROW]
[ROW][C]50[/C][C]4.4[/C][C]5.02223518129963[/C][C]-0.622235181299632[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]5.11243860972444[/C][C]-1.11243860972444[/C][/ROW]
[ROW][C]52[/C][C]5.6[/C][C]5.22886875853123[/C][C]0.37113124146877[/C][/ROW]
[ROW][C]53[/C][C]7.2[/C][C]5.19688446943945[/C][C]2.00311553056055[/C][/ROW]
[ROW][C]54[/C][C]5.6[/C][C]5.42138118945975[/C][C]0.178618810540255[/C][/ROW]
[ROW][C]55[/C][C]4.4[/C][C]5.03657622375823[/C][C]-0.636576223758228[/C][/ROW]
[ROW][C]56[/C][C]4.8[/C][C]5.10093160216412[/C][C]-0.300931602164122[/C][/ROW]
[ROW][C]57[/C][C]5.2[/C][C]5.19688446943945[/C][C]0.00311553056054765[/C][/ROW]
[ROW][C]58[/C][C]3.6[/C][C]5.25463127353728[/C][C]-1.65463127353728[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]5.37849658918551[/C][C]-1.37849658918551[/C][/ROW]
[ROW][C]60[/C][C]6[/C][C]5.0260627127981[/C][C]0.973937287201895[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]5.22107933439163[/C][C]2.77892066560837[/C][/ROW]
[ROW][C]62[/C][C]4.8[/C][C]5.20156300653619[/C][C]-0.401563006536193[/C][/ROW]
[ROW][C]63[/C][C]4.8[/C][C]5.11890469303057[/C][C]-0.318904693030575[/C][/ROW]
[ROW][C]64[/C][C]5.6[/C][C]5.13674342275437[/C][C]0.463256577245627[/C][/ROW]
[ROW][C]65[/C][C]5.2[/C][C]5.23115305304072[/C][C]-0.0311530530407207[/C][/ROW]
[ROW][C]66[/C][C]4.4[/C][C]5.22096938631387[/C][C]-0.82096938631387[/C][/ROW]
[ROW][C]67[/C][C]6.8[/C][C]5.27175335880547[/C][C]1.52824664119453[/C][/ROW]
[ROW][C]68[/C][C]4.8[/C][C]5.21796844734877[/C][C]-0.417968447348766[/C][/ROW]
[ROW][C]69[/C][C]5.2[/C][C]5.13831107280825[/C][C]0.0616889271917487[/C][/ROW]
[ROW][C]70[/C][C]5.6[/C][C]5.17580049153014[/C][C]0.424199508469861[/C][/ROW]
[ROW][C]71[/C][C]5.2[/C][C]5.26864247176261[/C][C]-0.0686424717626078[/C][/ROW]
[ROW][C]72[/C][C]6[/C][C]5.26935911621822[/C][C]0.73064088378178[/C][/ROW]
[ROW][C]73[/C][C]5.2[/C][C]5.18837840084424[/C][C]0.0116215991557616[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]4.94819288446698[/C][C]-0.948192884466985[/C][/ROW]
[ROW][C]75[/C][C]4.4[/C][C]5.21174667326304[/C][C]-0.811746673263044[/C][/ROW]
[ROW][C]76[/C][C]7.6[/C][C]5.19688446943945[/C][C]2.40311553056055[/C][/ROW]
[ROW][C]77[/C][C]5.2[/C][C]5.07362585016909[/C][C]0.126374149830914[/C][/ROW]
[ROW][C]78[/C][C]6.8[/C][C]5.10332584475137[/C][C]1.69667415524863[/C][/ROW]
[ROW][C]79[/C][C]5.2[/C][C]5.15471651362082[/C][C]0.0452834863791753[/C][/ROW]
[ROW][C]80[/C][C]3.6[/C][C]5.16957871744442[/C][C]-1.56957871744442[/C][/ROW]
[ROW][C]81[/C][C]4.4[/C][C]5.04246815361068[/C][C]-0.642468153610678[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]5.33572193698902[/C][C]-1.33572193698902[/C][/ROW]
[ROW][C]83[/C][C]3.6[/C][C]5.12201558007344[/C][C]-1.52201558007344[/C][/ROW]
[ROW][C]84[/C][C]4.8[/C][C]5.20167295461395[/C][C]-0.401672954613951[/C][/ROW]
[ROW][C]85[/C][C]4.8[/C][C]5.135916830221[/C][C]-0.335916830221003[/C][/ROW]
[ROW][C]86[/C][C]5.2[/C][C]5.12908835975743[/C][C]0.0709116402425748[/C][/ROW]
[ROW][C]87[/C][C]5.2[/C][C]5.23115305304072[/C][C]-0.0311530530407207[/C][/ROW]
[ROW][C]88[/C][C]4.8[/C][C]5.10164824661973[/C][C]-0.301648246619734[/C][/ROW]
[ROW][C]89[/C][C]6[/C][C]5.04257810168844[/C][C]0.957421898311564[/C][/ROW]
[ROW][C]90[/C][C]8.8[/C][C]5.20467389357905[/C][C]3.59532610642095[/C][/ROW]
[ROW][C]91[/C][C]5.2[/C][C]5.04714669070742[/C][C]0.152853309292581[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]5.41598600790739[/C][C]0.584013992092608[/C][/ROW]
[ROW][C]93[/C][C]5.2[/C][C]5.22096938631387[/C][C]-0.0209693863138698[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]5.17976238417127[/C][C]0.820237615828733[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]5.22419022143449[/C][C]-1.22419022143449[/C][/ROW]
[ROW][C]96[/C][C]4.4[/C][C]5.06762397223888[/C][C]-0.667623972238878[/C][/ROW]
[ROW][C]97[/C][C]6.4[/C][C]4.97538868838426[/C][C]1.42461131161574[/C][/ROW]
[ROW][C]98[/C][C]4.4[/C][C]5.00437203851094[/C][C]-0.604372038510936[/C][/ROW]
[ROW][C]99[/C][C]4.4[/C][C]5.23665818267083[/C][C]-0.83665818267083[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]5.18598415825699[/C][C]-1.18598415825699[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]5.14860468761286[/C][C]-1.14860468761286[/C][/ROW]
[ROW][C]102[/C][C]6.4[/C][C]5.30841618499399[/C][C]1.09158381500601[/C][/ROW]
[ROW][C]103[/C][C]4.8[/C][C]5.13698773197478[/C][C]-0.336987731974785[/C][/ROW]
[ROW][C]104[/C][C]4.4[/C][C]5.21257326579641[/C][C]-0.812573265796413[/C][/ROW]
[ROW][C]105[/C][C]6.4[/C][C]5.0642687759756[/C][C]1.3357312240244[/C][/ROW]
[ROW][C]106[/C][C]7.6[/C][C]5.10321589667361[/C][C]2.49678410332639[/C][/ROW]
[ROW][C]107[/C][C]4.4[/C][C]5.29044309412753[/C][C]-0.890443094127534[/C][/ROW]
[ROW][C]108[/C][C]6.4[/C][C]5.20238959906956[/C][C]1.19761040093044[/C][/ROW]
[ROW][C]109[/C][C]6[/C][C]5.01287810710615[/C][C]0.98712189289385[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]5.08452616135155[/C][C]4.51547383864845[/C][/ROW]
[ROW][C]111[/C][C]5.6[/C][C]5.26864247176261[/C][C]0.331357528237392[/C][/ROW]
[ROW][C]112[/C][C]6[/C][C]5.18609410633475[/C][C]0.813905893665253[/C][/ROW]
[ROW][C]113[/C][C]4.4[/C][C]5.27018570875159[/C][C]-0.87018570875159[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]5.2040671972012[/C][C]0.7959328027988[/C][/ROW]
[ROW][C]115[/C][C]4.8[/C][C]5.34505459811761[/C][C]-0.545054598117607[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]5.27332100885935[/C][C]-1.27332100885935[/C][/ROW]
[ROW][C]117[/C][C]5.6[/C][C]5.27032006989424[/C][C]0.329679930105755[/C][/ROW]
[ROW][C]118[/C][C]5.2[/C][C]5.21317996217427[/C][C]-0.013179962174268[/C][/ROW]
[ROW][C]119[/C][C]3.6[/C][C]5.02845695538536[/C][C]-1.42845695538535[/C][/ROW]
[ROW][C]120[/C][C]6[/C][C]5.23965912163593[/C][C]0.760340878364065[/C][/ROW]
[ROW][C]121[/C][C]6[/C][C]5.2505594328184[/C][C]0.749440567181602[/C][/ROW]
[ROW][C]122[/C][C]5.6[/C][C]5.17268960448728[/C][C]0.427310395512722[/C][/ROW]
[ROW][C]123[/C][C]4.4[/C][C]5.02377841828861[/C][C]-0.623778418288613[/C][/ROW]
[ROW][C]124[/C][C]3.2[/C][C]5.34183376299699[/C][C]-2.14183376299699[/C][/ROW]
[ROW][C]125[/C][C]4.4[/C][C]4.92843224739114[/C][C]-0.528432247391138[/C][/ROW]
[ROW][C]126[/C][C]4.4[/C][C]5.11039862443536[/C][C]-0.71039862443536[/C][/ROW]
[ROW][C]127[/C][C]3.2[/C][C]5.31381136654634[/C][C]-2.11381136654634[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]5.17580049153014[/C][C]-1.17580049153014[/C][/ROW]
[ROW][C]129[/C][C]4.4[/C][C]5.16729442293492[/C][C]-0.767294422934924[/C][/ROW]
[ROW][C]130[/C][C]5.2[/C][C]5.07984762425481[/C][C]0.120152375745192[/C][/ROW]
[ROW][C]131[/C][C]4.4[/C][C]5.13063159674641[/C][C]-0.730631596746407[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]5.22886875853123[/C][C]2.77113124146877[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]5.2631373421325[/C][C]-1.2631373421325[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.3247116777288[/C][C]0.675288322271198[/C][/ROW]
[ROW][C]135[/C][C]4.8[/C][C]5.19688446943945[/C][C]-0.396884469439453[/C][/ROW]
[ROW][C]136[/C][C]5.6[/C][C]4.99815026442521[/C][C]0.601849735574785[/C][/ROW]
[ROW][C]137[/C][C]9.2[/C][C]5.11651045044333[/C][C]4.08348954955667[/C][/ROW]
[ROW][C]138[/C][C]5.6[/C][C]5.11422615593383[/C][C]0.485773844066166[/C][/ROW]
[ROW][C]139[/C][C]6.4[/C][C]5.2530636234834[/C][C]1.1469363765166[/C][/ROW]
[ROW][C]140[/C][C]4.4[/C][C]5.06198448146611[/C][C]-0.661984481466113[/C][/ROW]
[ROW][C]141[/C][C]4.8[/C][C]5.09385882248013[/C][C]-0.293858822480133[/C][/ROW]
[ROW][C]142[/C][C]4[/C][C]5.08463610942931[/C][C]-1.08463610942931[/C][/ROW]
[ROW][C]143[/C][C]5.6[/C][C]4.84467048920757[/C][C]0.755329510792432[/C][/ROW]
[ROW][C]144[/C][C]4.8[/C][C]5.11483285231169[/C][C]-0.314832852311689[/C][/ROW]
[ROW][C]145[/C][C]4.8[/C][C]5.023061773833[/C][C]-0.223061773833002[/C][/ROW]
[ROW][C]146[/C][C]4.4[/C][C]5.03145789435046[/C][C]-0.631457894350458[/C][/ROW]
[ROW][C]147[/C][C]4.8[/C][C]5.16657777847931[/C][C]-0.366577778479313[/C][/ROW]
[ROW][C]148[/C][C]5.2[/C][C]5.24755849385329[/C][C]-0.047558493853294[/C][/ROW]
[ROW][C]149[/C][C]4.4[/C][C]5.22107933439163[/C][C]-0.821079334391627[/C][/ROW]
[ROW][C]150[/C][C]7.6[/C][C]5.22347357697888[/C][C]2.37652642302112[/C][/ROW]
[ROW][C]151[/C][C]4.8[/C][C]5.29594822375764[/C][C]-0.495948223757644[/C][/ROW]
[ROW][C]152[/C][C]6.8[/C][C]4.96137749015894[/C][C]1.83862250984106[/C][/ROW]
[ROW][C]153[/C][C]3.6[/C][C]5.07458680384511[/C][C]-1.47458680384511[/C][/ROW]
[ROW][C]154[/C][C]4.8[/C][C]5.22264698444551[/C][C]-0.422646984445507[/C][/ROW]
[ROW][C]155[/C][C]7.6[/C][C]5.03875057018996[/C][C]2.56124942981004[/C][/ROW]
[ROW][C]156[/C][C]7.2[/C][C]5.23354729562797[/C][C]1.96645270437203[/C][/ROW]
[ROW][C]157[/C][C]6[/C][C]5.07984762425481[/C][C]0.920152375745192[/C][/ROW]
[ROW][C]158[/C][C]5.6[/C][C]5.05265182033753[/C][C]0.54734817966247[/C][/ROW]
[ROW][C]159[/C][C]4.4[/C][C]4.86636116349474[/C][C]-0.466361163494736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98749&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98749&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.85.0486899276964-0.248689927696399
24.45.21940173625999-0.81940173625999
35.65.270320069894240.329679930105755
44.85.15471651362082-0.354716513620825
58.45.25306362348343.1469363765166
64.85.20467389357905-0.404673893579054
78.85.238942477180323.56105752281968
84.45.15243221911133-0.752432219111333
945.01276815902839-1.01276815902839
105.25.161789293304810.0382107066951858
1145.04797328324079-1.04797328324079
123.25.07373579824684-1.87373579824684
1365.256174510526270.743825489473734
145.65.154716513620820.445283486379175
1545.17580049153014-1.17580049153014
165.65.387246967001130.212753032998868
175.65.253780267939020.346219732060983
184.45.27175335880547-0.87175335880547
1945.21414091585029-1.21414091585029
205.24.991077484741220.208922515258775
212.85.04007391102343-2.24007391102343
225.64.916208595375210.683791404624792
234.85.16812101546829-0.368121015468295
245.65.090747935437270.509252064562729
254.45.17580049153014-0.775800491530138
263.65.22036268993602-1.62036268993602
274.45.21174667326304-0.811746673263044
2865.210179023209160.789820976790835
295.65.177478089661780.422521910338224
305.25.23354729562797-0.0335472956279698
313.65.24516425126605-1.64516425126604
3264.87726147467721.1227385253228
3345.1228665856717-1.1228665856717
344.45.26864247176261-0.868642471762608
355.25.166577778479310.0334222215206876
363.25.05325851671538-1.85325851671538
3785.213179962174272.78682003782573
384.85.0283470073076-0.228347007307597
3945.03635632760271-1.03635632760271
4045.1407053153955-1.1407053153955
413.64.98796659769836-1.38796659769836
425.65.19927871202670.400721287973298
433.25.16418353589206-1.96418353589206
445.65.208635786220180.391364213779817
454.45.21868509180438-0.818685091804378
465.25.143816202438360.0561837975616383
473.65.03156784242822-1.43156784242822
484.45.11722709489894-0.717227094898937
4965.196884469439450.803115530560548
504.45.02223518129963-0.622235181299632
5145.11243860972444-1.11243860972444
525.65.228868758531230.37113124146877
537.25.196884469439452.00311553056055
545.65.421381189459750.178618810540255
554.45.03657622375823-0.636576223758228
564.85.10093160216412-0.300931602164122
575.25.196884469439450.00311553056054765
583.65.25463127353728-1.65463127353728
5945.37849658918551-1.37849658918551
6065.02606271279810.973937287201895
6185.221079334391632.77892066560837
624.85.20156300653619-0.401563006536193
634.85.11890469303057-0.318904693030575
645.65.136743422754370.463256577245627
655.25.23115305304072-0.0311530530407207
664.45.22096938631387-0.82096938631387
676.85.271753358805471.52824664119453
684.85.21796844734877-0.417968447348766
695.25.138311072808250.0616889271917487
705.65.175800491530140.424199508469861
715.25.26864247176261-0.0686424717626078
7265.269359116218220.73064088378178
735.25.188378400844240.0116215991557616
7444.94819288446698-0.948192884466985
754.45.21174667326304-0.811746673263044
767.65.196884469439452.40311553056055
775.25.073625850169090.126374149830914
786.85.103325844751371.69667415524863
795.25.154716513620820.0452834863791753
803.65.16957871744442-1.56957871744442
814.45.04246815361068-0.642468153610678
8245.33572193698902-1.33572193698902
833.65.12201558007344-1.52201558007344
844.85.20167295461395-0.401672954613951
854.85.135916830221-0.335916830221003
865.25.129088359757430.0709116402425748
875.25.23115305304072-0.0311530530407207
884.85.10164824661973-0.301648246619734
8965.042578101688440.957421898311564
908.85.204673893579053.59532610642095
915.25.047146690707420.152853309292581
9265.415986007907390.584013992092608
935.25.22096938631387-0.0209693863138698
9465.179762384171270.820237615828733
9545.22419022143449-1.22419022143449
964.45.06762397223888-0.667623972238878
976.44.975388688384261.42461131161574
984.45.00437203851094-0.604372038510936
994.45.23665818267083-0.83665818267083
10045.18598415825699-1.18598415825699
10145.14860468761286-1.14860468761286
1026.45.308416184993991.09158381500601
1034.85.13698773197478-0.336987731974785
1044.45.21257326579641-0.812573265796413
1056.45.06426877597561.3357312240244
1067.65.103215896673612.49678410332639
1074.45.29044309412753-0.890443094127534
1086.45.202389599069561.19761040093044
10965.012878107106150.98712189289385
1109.65.084526161351554.51547383864845
1115.65.268642471762610.331357528237392
11265.186094106334750.813905893665253
1134.45.27018570875159-0.87018570875159
11465.20406719720120.7959328027988
1154.85.34505459811761-0.545054598117607
11645.27332100885935-1.27332100885935
1175.65.270320069894240.329679930105755
1185.25.21317996217427-0.013179962174268
1193.65.02845695538536-1.42845695538535
12065.239659121635930.760340878364065
12165.25055943281840.749440567181602
1225.65.172689604487280.427310395512722
1234.45.02377841828861-0.623778418288613
1243.25.34183376299699-2.14183376299699
1254.44.92843224739114-0.528432247391138
1264.45.11039862443536-0.71039862443536
1273.25.31381136654634-2.11381136654634
12845.17580049153014-1.17580049153014
1294.45.16729442293492-0.767294422934924
1305.25.079847624254810.120152375745192
1314.45.13063159674641-0.730631596746407
13285.228868758531232.77113124146877
13345.2631373421325-1.2631373421325
13465.32471167772880.675288322271198
1354.85.19688446943945-0.396884469439453
1365.64.998150264425210.601849735574785
1379.25.116510450443334.08348954955667
1385.65.114226155933830.485773844066166
1396.45.25306362348341.1469363765166
1404.45.06198448146611-0.661984481466113
1414.85.09385882248013-0.293858822480133
14245.08463610942931-1.08463610942931
1435.64.844670489207570.755329510792432
1444.85.11483285231169-0.314832852311689
1454.85.023061773833-0.223061773833002
1464.45.03145789435046-0.631457894350458
1474.85.16657777847931-0.366577778479313
1485.25.24755849385329-0.047558493853294
1494.45.22107933439163-0.821079334391627
1507.65.223473576978882.37652642302112
1514.85.29594822375764-0.495948223757644
1526.84.961377490158941.83862250984106
1533.65.07458680384511-1.47458680384511
1544.85.22264698444551-0.422646984445507
1557.65.038750570189962.56124942981004
1567.25.233547295627971.96645270437203
15765.079847624254810.920152375745192
1585.65.052651820337530.54734817966247
1594.44.86636116349474-0.466361163494736






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9763945343223430.04721093135531380.0236054656776569
80.9519033864738520.09619322705229550.0480966135261478
90.919862850969680.1602742980606410.0801371490303205
100.8734981349108290.2530037301783420.126501865089171
110.81206607002410.3758678599518010.1879339299759
120.7875880455577190.4248239088845620.212411954442281
130.7107855469434470.5784289061131060.289214453056553
140.6275139946371140.7449720107257730.372486005362886
150.6550043036572310.6899913926855380.344995696342769
160.7231855862794660.5536288274410670.276814413720534
170.6548202727026850.690359454594630.345179727297315
180.671761928492560.6564761430148810.328238071507441
190.6481642799530960.7036714400938080.351835720046904
200.6801043632379490.6397912735241010.319895636762051
210.7239831092510410.5520337814979180.276016890748959
220.7757135223738430.4485729552523150.224286477626157
230.7213784112292490.5572431775415030.278621588770751
240.6809238141058710.6381523717882580.319076185894129
250.6409547019220620.7180905961558750.359045298077938
260.6709884044313020.6580231911373960.329011595568698
270.6397386130684430.7205227738631140.360261386931557
280.6074907111638090.7850185776723820.392509288836191
290.5590874947117190.8818250105765630.440912505288281
300.49882720516290.99765441032580.5011727948371
310.5521156235527370.8957687528945260.447884376447263
320.5875479371278360.8249041257443290.412452062872164
330.559270668049350.88145866390130.44072933195065
340.5248368770783180.9503262458433650.475163122921682
350.4674083881988330.9348167763976660.532591611801167
360.4713865671429050.942773134285810.528613432857095
370.7110003344301010.5779993311397970.288999665569899
380.6671047364263150.665790527147370.332895263573685
390.6380647697654220.7238704604691560.361935230234578
400.6156478983205740.7687042033588520.384352101679426
410.598069122896710.803861754206580.40193087710329
420.5520874783404270.8958250433191460.447912521659573
430.6041859239779240.7916281520441520.395814076022076
440.5649215840851170.8701568318297670.435078415914883
450.5331389735170790.9337220529658420.466861026482921
460.4839402159407610.9678804318815230.516059784059239
470.4756295819723370.9512591639446750.524370418027663
480.4342053895758120.8684107791516230.565794610424188
490.4092801291161460.8185602582322910.590719870883854
500.3687095324497430.7374190648994870.631290467550257
510.3461149953245360.6922299906490730.653885004675464
520.3051913028609870.6103826057219740.694808697139013
530.3845158578042750.769031715608550.615484142195725
540.3408710699859130.6817421399718250.659128930014087
550.3064383857865790.6128767715731580.693561614213421
560.2671610987884230.5343221975768460.732838901211577
570.2285880992830120.4571761985660230.771411900716988
580.2568831810490850.5137663620981690.743116818950916
590.2745880595697550.549176119139510.725411940430245
600.279156326473110.558312652946220.72084367352689
610.4554114919013560.9108229838027130.544588508098644
620.4122932075406010.8245864150812010.587706792459399
630.3701274628886060.7402549257772120.629872537111394
640.3334285615732030.6668571231464060.666571438426797
650.2921198280752720.5842396561505430.707880171924728
660.2681159144557260.5362318289114520.731884085544274
670.2834864028708270.5669728057416550.716513597129173
680.2490556492467850.4981112984935710.750944350753215
690.2157105354925420.4314210709850830.784289464507458
700.1872142120409470.3744284240818940.812785787959053
710.1575481542610030.3150963085220070.842451845738997
720.1384071295983720.2768142591967450.861592870401628
730.1143033090028130.2286066180056260.885696690997187
740.1039474776663580.2078949553327170.896052522333642
750.09184987705740020.18369975411480.9081501229426
760.1567894774278830.3135789548557660.843210522572117
770.1356587137988720.2713174275977440.864341286201128
780.1624841312496690.3249682624993390.83751586875033
790.1356913843047010.2713827686094020.864308615695299
800.1504863610716010.3009727221432020.849513638928399
810.1315156059765910.2630312119531810.86848439402341
820.1398427199632090.2796854399264170.860157280036791
830.1527122610588740.3054245221177490.847287738941126
840.1295234287280740.2590468574561480.870476571271926
850.1098277463201570.2196554926403130.890172253679843
860.09196942469493250.1839388493898650.908030575305067
870.07484782119113250.1496956423822650.925152178808867
880.06110176336013210.1222035267202640.938898236639868
890.05618713967745890.1123742793549180.94381286032254
900.2206192822434050.4412385644868110.779380717756595
910.1917346453349030.3834692906698060.808265354665097
920.1715339776665610.3430679553331220.828466022333439
930.1438210971347150.2876421942694290.856178902865285
940.128877093529040.257754187058080.87112290647096
950.1268348373574090.2536696747148190.87316516264259
960.1113944000625050.222788800125010.888605599937495
970.1166238470471290.2332476940942580.883376152952871
980.1022680855837870.2045361711675740.897731914416213
990.09135894350606630.1827178870121330.908641056493934
1000.0902812303778820.1805624607557640.909718769622118
1010.08822234698612240.1764446939722450.911777653013878
1020.0824345309079990.1648690618159980.917565469092001
1030.06839921857331340.1367984371466270.931600781426687
1040.05994715340278720.1198943068055740.940052846597213
1050.06023543065923270.1204708613184650.939764569340767
1060.1032293560501180.2064587121002360.896770643949882
1070.0920132622354370.1840265244708740.907986737764563
1080.08872883547709980.17745767095420.9112711645229
1090.07911388361759820.1582277672351960.920886116382402
1100.4694955174981110.9389910349962220.530504482501889
1110.4226012129416360.8452024258832730.577398787058363
1120.3901185754757490.7802371509514980.609881424524251
1130.3582048904587480.7164097809174970.641795109541252
1140.3244309034064680.6488618068129360.675569096593532
1150.2841011877408250.568202375481650.715898812259175
1160.2865558433895450.573111686779090.713444156610455
1170.2448946266746760.4897892533493510.755105373325324
1180.2053927789849160.4107855579698310.794607221015084
1190.2220611367925610.4441222735851230.777938863207439
1200.1981849617377760.3963699234755520.801815038262224
1210.1791478713483690.3582957426967390.82085212865163
1220.1488822848268990.2977645696537980.8511177151731
1230.1274058136315780.2548116272631550.872594186368422
1240.1658552504021990.3317105008043980.8341447495978
1250.1379903326775870.2759806653551740.862009667322413
1260.1227200304925090.2454400609850190.87727996950749
1270.1854987574403240.3709975148806470.814501242559676
1280.1870855288297060.3741710576594110.812914471170294
1290.1732312817887210.3464625635774420.826768718211279
1300.1378118073419510.2756236146839030.862188192658049
1310.1234524859534590.2469049719069180.876547514046541
1320.2216726277150680.4433452554301370.778327372284932
1330.2416486224226450.4832972448452910.758351377577354
1340.197335687546180.394671375092360.80266431245382
1350.1664140782409560.3328281564819120.833585921759044
1360.1301232111664740.2602464223329480.869876788833526
1370.594958682119830.810082635760340.40504131788017
1380.5275513942361820.9448972115276360.472448605763818
1390.4885350858341140.9770701716682270.511464914165886
1400.4479763225580.8959526451159990.552023677442
1410.3792579954715690.7585159909431380.620742004528431
1420.3872579397152110.7745158794304220.612742060284789
1430.3165923067498940.6331846134997870.683407693250106
1440.2486312826476230.4972625652952460.751368717352377
1450.1982100256484440.3964200512968880.801789974351556
1460.168858314784440.3377166295688810.83114168521556
1470.1258886329907130.2517772659814250.874111367009287
1480.08647370444620570.1729474088924110.913526295553794
1490.09387707512965530.1877541502593110.906122924870345
1500.1159225677690260.2318451355380520.884077432230974
1510.1127012689504090.2254025379008170.887298731049591
1520.3545981200104820.7091962400209640.645401879989518

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.976394534322343 & 0.0472109313553138 & 0.0236054656776569 \tabularnewline
8 & 0.951903386473852 & 0.0961932270522955 & 0.0480966135261478 \tabularnewline
9 & 0.91986285096968 & 0.160274298060641 & 0.0801371490303205 \tabularnewline
10 & 0.873498134910829 & 0.253003730178342 & 0.126501865089171 \tabularnewline
11 & 0.8120660700241 & 0.375867859951801 & 0.1879339299759 \tabularnewline
12 & 0.787588045557719 & 0.424823908884562 & 0.212411954442281 \tabularnewline
13 & 0.710785546943447 & 0.578428906113106 & 0.289214453056553 \tabularnewline
14 & 0.627513994637114 & 0.744972010725773 & 0.372486005362886 \tabularnewline
15 & 0.655004303657231 & 0.689991392685538 & 0.344995696342769 \tabularnewline
16 & 0.723185586279466 & 0.553628827441067 & 0.276814413720534 \tabularnewline
17 & 0.654820272702685 & 0.69035945459463 & 0.345179727297315 \tabularnewline
18 & 0.67176192849256 & 0.656476143014881 & 0.328238071507441 \tabularnewline
19 & 0.648164279953096 & 0.703671440093808 & 0.351835720046904 \tabularnewline
20 & 0.680104363237949 & 0.639791273524101 & 0.319895636762051 \tabularnewline
21 & 0.723983109251041 & 0.552033781497918 & 0.276016890748959 \tabularnewline
22 & 0.775713522373843 & 0.448572955252315 & 0.224286477626157 \tabularnewline
23 & 0.721378411229249 & 0.557243177541503 & 0.278621588770751 \tabularnewline
24 & 0.680923814105871 & 0.638152371788258 & 0.319076185894129 \tabularnewline
25 & 0.640954701922062 & 0.718090596155875 & 0.359045298077938 \tabularnewline
26 & 0.670988404431302 & 0.658023191137396 & 0.329011595568698 \tabularnewline
27 & 0.639738613068443 & 0.720522773863114 & 0.360261386931557 \tabularnewline
28 & 0.607490711163809 & 0.785018577672382 & 0.392509288836191 \tabularnewline
29 & 0.559087494711719 & 0.881825010576563 & 0.440912505288281 \tabularnewline
30 & 0.4988272051629 & 0.9976544103258 & 0.5011727948371 \tabularnewline
31 & 0.552115623552737 & 0.895768752894526 & 0.447884376447263 \tabularnewline
32 & 0.587547937127836 & 0.824904125744329 & 0.412452062872164 \tabularnewline
33 & 0.55927066804935 & 0.8814586639013 & 0.44072933195065 \tabularnewline
34 & 0.524836877078318 & 0.950326245843365 & 0.475163122921682 \tabularnewline
35 & 0.467408388198833 & 0.934816776397666 & 0.532591611801167 \tabularnewline
36 & 0.471386567142905 & 0.94277313428581 & 0.528613432857095 \tabularnewline
37 & 0.711000334430101 & 0.577999331139797 & 0.288999665569899 \tabularnewline
38 & 0.667104736426315 & 0.66579052714737 & 0.332895263573685 \tabularnewline
39 & 0.638064769765422 & 0.723870460469156 & 0.361935230234578 \tabularnewline
40 & 0.615647898320574 & 0.768704203358852 & 0.384352101679426 \tabularnewline
41 & 0.59806912289671 & 0.80386175420658 & 0.40193087710329 \tabularnewline
42 & 0.552087478340427 & 0.895825043319146 & 0.447912521659573 \tabularnewline
43 & 0.604185923977924 & 0.791628152044152 & 0.395814076022076 \tabularnewline
44 & 0.564921584085117 & 0.870156831829767 & 0.435078415914883 \tabularnewline
45 & 0.533138973517079 & 0.933722052965842 & 0.466861026482921 \tabularnewline
46 & 0.483940215940761 & 0.967880431881523 & 0.516059784059239 \tabularnewline
47 & 0.475629581972337 & 0.951259163944675 & 0.524370418027663 \tabularnewline
48 & 0.434205389575812 & 0.868410779151623 & 0.565794610424188 \tabularnewline
49 & 0.409280129116146 & 0.818560258232291 & 0.590719870883854 \tabularnewline
50 & 0.368709532449743 & 0.737419064899487 & 0.631290467550257 \tabularnewline
51 & 0.346114995324536 & 0.692229990649073 & 0.653885004675464 \tabularnewline
52 & 0.305191302860987 & 0.610382605721974 & 0.694808697139013 \tabularnewline
53 & 0.384515857804275 & 0.76903171560855 & 0.615484142195725 \tabularnewline
54 & 0.340871069985913 & 0.681742139971825 & 0.659128930014087 \tabularnewline
55 & 0.306438385786579 & 0.612876771573158 & 0.693561614213421 \tabularnewline
56 & 0.267161098788423 & 0.534322197576846 & 0.732838901211577 \tabularnewline
57 & 0.228588099283012 & 0.457176198566023 & 0.771411900716988 \tabularnewline
58 & 0.256883181049085 & 0.513766362098169 & 0.743116818950916 \tabularnewline
59 & 0.274588059569755 & 0.54917611913951 & 0.725411940430245 \tabularnewline
60 & 0.27915632647311 & 0.55831265294622 & 0.72084367352689 \tabularnewline
61 & 0.455411491901356 & 0.910822983802713 & 0.544588508098644 \tabularnewline
62 & 0.412293207540601 & 0.824586415081201 & 0.587706792459399 \tabularnewline
63 & 0.370127462888606 & 0.740254925777212 & 0.629872537111394 \tabularnewline
64 & 0.333428561573203 & 0.666857123146406 & 0.666571438426797 \tabularnewline
65 & 0.292119828075272 & 0.584239656150543 & 0.707880171924728 \tabularnewline
66 & 0.268115914455726 & 0.536231828911452 & 0.731884085544274 \tabularnewline
67 & 0.283486402870827 & 0.566972805741655 & 0.716513597129173 \tabularnewline
68 & 0.249055649246785 & 0.498111298493571 & 0.750944350753215 \tabularnewline
69 & 0.215710535492542 & 0.431421070985083 & 0.784289464507458 \tabularnewline
70 & 0.187214212040947 & 0.374428424081894 & 0.812785787959053 \tabularnewline
71 & 0.157548154261003 & 0.315096308522007 & 0.842451845738997 \tabularnewline
72 & 0.138407129598372 & 0.276814259196745 & 0.861592870401628 \tabularnewline
73 & 0.114303309002813 & 0.228606618005626 & 0.885696690997187 \tabularnewline
74 & 0.103947477666358 & 0.207894955332717 & 0.896052522333642 \tabularnewline
75 & 0.0918498770574002 & 0.1836997541148 & 0.9081501229426 \tabularnewline
76 & 0.156789477427883 & 0.313578954855766 & 0.843210522572117 \tabularnewline
77 & 0.135658713798872 & 0.271317427597744 & 0.864341286201128 \tabularnewline
78 & 0.162484131249669 & 0.324968262499339 & 0.83751586875033 \tabularnewline
79 & 0.135691384304701 & 0.271382768609402 & 0.864308615695299 \tabularnewline
80 & 0.150486361071601 & 0.300972722143202 & 0.849513638928399 \tabularnewline
81 & 0.131515605976591 & 0.263031211953181 & 0.86848439402341 \tabularnewline
82 & 0.139842719963209 & 0.279685439926417 & 0.860157280036791 \tabularnewline
83 & 0.152712261058874 & 0.305424522117749 & 0.847287738941126 \tabularnewline
84 & 0.129523428728074 & 0.259046857456148 & 0.870476571271926 \tabularnewline
85 & 0.109827746320157 & 0.219655492640313 & 0.890172253679843 \tabularnewline
86 & 0.0919694246949325 & 0.183938849389865 & 0.908030575305067 \tabularnewline
87 & 0.0748478211911325 & 0.149695642382265 & 0.925152178808867 \tabularnewline
88 & 0.0611017633601321 & 0.122203526720264 & 0.938898236639868 \tabularnewline
89 & 0.0561871396774589 & 0.112374279354918 & 0.94381286032254 \tabularnewline
90 & 0.220619282243405 & 0.441238564486811 & 0.779380717756595 \tabularnewline
91 & 0.191734645334903 & 0.383469290669806 & 0.808265354665097 \tabularnewline
92 & 0.171533977666561 & 0.343067955333122 & 0.828466022333439 \tabularnewline
93 & 0.143821097134715 & 0.287642194269429 & 0.856178902865285 \tabularnewline
94 & 0.12887709352904 & 0.25775418705808 & 0.87112290647096 \tabularnewline
95 & 0.126834837357409 & 0.253669674714819 & 0.87316516264259 \tabularnewline
96 & 0.111394400062505 & 0.22278880012501 & 0.888605599937495 \tabularnewline
97 & 0.116623847047129 & 0.233247694094258 & 0.883376152952871 \tabularnewline
98 & 0.102268085583787 & 0.204536171167574 & 0.897731914416213 \tabularnewline
99 & 0.0913589435060663 & 0.182717887012133 & 0.908641056493934 \tabularnewline
100 & 0.090281230377882 & 0.180562460755764 & 0.909718769622118 \tabularnewline
101 & 0.0882223469861224 & 0.176444693972245 & 0.911777653013878 \tabularnewline
102 & 0.082434530907999 & 0.164869061815998 & 0.917565469092001 \tabularnewline
103 & 0.0683992185733134 & 0.136798437146627 & 0.931600781426687 \tabularnewline
104 & 0.0599471534027872 & 0.119894306805574 & 0.940052846597213 \tabularnewline
105 & 0.0602354306592327 & 0.120470861318465 & 0.939764569340767 \tabularnewline
106 & 0.103229356050118 & 0.206458712100236 & 0.896770643949882 \tabularnewline
107 & 0.092013262235437 & 0.184026524470874 & 0.907986737764563 \tabularnewline
108 & 0.0887288354770998 & 0.1774576709542 & 0.9112711645229 \tabularnewline
109 & 0.0791138836175982 & 0.158227767235196 & 0.920886116382402 \tabularnewline
110 & 0.469495517498111 & 0.938991034996222 & 0.530504482501889 \tabularnewline
111 & 0.422601212941636 & 0.845202425883273 & 0.577398787058363 \tabularnewline
112 & 0.390118575475749 & 0.780237150951498 & 0.609881424524251 \tabularnewline
113 & 0.358204890458748 & 0.716409780917497 & 0.641795109541252 \tabularnewline
114 & 0.324430903406468 & 0.648861806812936 & 0.675569096593532 \tabularnewline
115 & 0.284101187740825 & 0.56820237548165 & 0.715898812259175 \tabularnewline
116 & 0.286555843389545 & 0.57311168677909 & 0.713444156610455 \tabularnewline
117 & 0.244894626674676 & 0.489789253349351 & 0.755105373325324 \tabularnewline
118 & 0.205392778984916 & 0.410785557969831 & 0.794607221015084 \tabularnewline
119 & 0.222061136792561 & 0.444122273585123 & 0.777938863207439 \tabularnewline
120 & 0.198184961737776 & 0.396369923475552 & 0.801815038262224 \tabularnewline
121 & 0.179147871348369 & 0.358295742696739 & 0.82085212865163 \tabularnewline
122 & 0.148882284826899 & 0.297764569653798 & 0.8511177151731 \tabularnewline
123 & 0.127405813631578 & 0.254811627263155 & 0.872594186368422 \tabularnewline
124 & 0.165855250402199 & 0.331710500804398 & 0.8341447495978 \tabularnewline
125 & 0.137990332677587 & 0.275980665355174 & 0.862009667322413 \tabularnewline
126 & 0.122720030492509 & 0.245440060985019 & 0.87727996950749 \tabularnewline
127 & 0.185498757440324 & 0.370997514880647 & 0.814501242559676 \tabularnewline
128 & 0.187085528829706 & 0.374171057659411 & 0.812914471170294 \tabularnewline
129 & 0.173231281788721 & 0.346462563577442 & 0.826768718211279 \tabularnewline
130 & 0.137811807341951 & 0.275623614683903 & 0.862188192658049 \tabularnewline
131 & 0.123452485953459 & 0.246904971906918 & 0.876547514046541 \tabularnewline
132 & 0.221672627715068 & 0.443345255430137 & 0.778327372284932 \tabularnewline
133 & 0.241648622422645 & 0.483297244845291 & 0.758351377577354 \tabularnewline
134 & 0.19733568754618 & 0.39467137509236 & 0.80266431245382 \tabularnewline
135 & 0.166414078240956 & 0.332828156481912 & 0.833585921759044 \tabularnewline
136 & 0.130123211166474 & 0.260246422332948 & 0.869876788833526 \tabularnewline
137 & 0.59495868211983 & 0.81008263576034 & 0.40504131788017 \tabularnewline
138 & 0.527551394236182 & 0.944897211527636 & 0.472448605763818 \tabularnewline
139 & 0.488535085834114 & 0.977070171668227 & 0.511464914165886 \tabularnewline
140 & 0.447976322558 & 0.895952645115999 & 0.552023677442 \tabularnewline
141 & 0.379257995471569 & 0.758515990943138 & 0.620742004528431 \tabularnewline
142 & 0.387257939715211 & 0.774515879430422 & 0.612742060284789 \tabularnewline
143 & 0.316592306749894 & 0.633184613499787 & 0.683407693250106 \tabularnewline
144 & 0.248631282647623 & 0.497262565295246 & 0.751368717352377 \tabularnewline
145 & 0.198210025648444 & 0.396420051296888 & 0.801789974351556 \tabularnewline
146 & 0.16885831478444 & 0.337716629568881 & 0.83114168521556 \tabularnewline
147 & 0.125888632990713 & 0.251777265981425 & 0.874111367009287 \tabularnewline
148 & 0.0864737044462057 & 0.172947408892411 & 0.913526295553794 \tabularnewline
149 & 0.0938770751296553 & 0.187754150259311 & 0.906122924870345 \tabularnewline
150 & 0.115922567769026 & 0.231845135538052 & 0.884077432230974 \tabularnewline
151 & 0.112701268950409 & 0.225402537900817 & 0.887298731049591 \tabularnewline
152 & 0.354598120010482 & 0.709196240020964 & 0.645401879989518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98749&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.976394534322343[/C][C]0.0472109313553138[/C][C]0.0236054656776569[/C][/ROW]
[ROW][C]8[/C][C]0.951903386473852[/C][C]0.0961932270522955[/C][C]0.0480966135261478[/C][/ROW]
[ROW][C]9[/C][C]0.91986285096968[/C][C]0.160274298060641[/C][C]0.0801371490303205[/C][/ROW]
[ROW][C]10[/C][C]0.873498134910829[/C][C]0.253003730178342[/C][C]0.126501865089171[/C][/ROW]
[ROW][C]11[/C][C]0.8120660700241[/C][C]0.375867859951801[/C][C]0.1879339299759[/C][/ROW]
[ROW][C]12[/C][C]0.787588045557719[/C][C]0.424823908884562[/C][C]0.212411954442281[/C][/ROW]
[ROW][C]13[/C][C]0.710785546943447[/C][C]0.578428906113106[/C][C]0.289214453056553[/C][/ROW]
[ROW][C]14[/C][C]0.627513994637114[/C][C]0.744972010725773[/C][C]0.372486005362886[/C][/ROW]
[ROW][C]15[/C][C]0.655004303657231[/C][C]0.689991392685538[/C][C]0.344995696342769[/C][/ROW]
[ROW][C]16[/C][C]0.723185586279466[/C][C]0.553628827441067[/C][C]0.276814413720534[/C][/ROW]
[ROW][C]17[/C][C]0.654820272702685[/C][C]0.69035945459463[/C][C]0.345179727297315[/C][/ROW]
[ROW][C]18[/C][C]0.67176192849256[/C][C]0.656476143014881[/C][C]0.328238071507441[/C][/ROW]
[ROW][C]19[/C][C]0.648164279953096[/C][C]0.703671440093808[/C][C]0.351835720046904[/C][/ROW]
[ROW][C]20[/C][C]0.680104363237949[/C][C]0.639791273524101[/C][C]0.319895636762051[/C][/ROW]
[ROW][C]21[/C][C]0.723983109251041[/C][C]0.552033781497918[/C][C]0.276016890748959[/C][/ROW]
[ROW][C]22[/C][C]0.775713522373843[/C][C]0.448572955252315[/C][C]0.224286477626157[/C][/ROW]
[ROW][C]23[/C][C]0.721378411229249[/C][C]0.557243177541503[/C][C]0.278621588770751[/C][/ROW]
[ROW][C]24[/C][C]0.680923814105871[/C][C]0.638152371788258[/C][C]0.319076185894129[/C][/ROW]
[ROW][C]25[/C][C]0.640954701922062[/C][C]0.718090596155875[/C][C]0.359045298077938[/C][/ROW]
[ROW][C]26[/C][C]0.670988404431302[/C][C]0.658023191137396[/C][C]0.329011595568698[/C][/ROW]
[ROW][C]27[/C][C]0.639738613068443[/C][C]0.720522773863114[/C][C]0.360261386931557[/C][/ROW]
[ROW][C]28[/C][C]0.607490711163809[/C][C]0.785018577672382[/C][C]0.392509288836191[/C][/ROW]
[ROW][C]29[/C][C]0.559087494711719[/C][C]0.881825010576563[/C][C]0.440912505288281[/C][/ROW]
[ROW][C]30[/C][C]0.4988272051629[/C][C]0.9976544103258[/C][C]0.5011727948371[/C][/ROW]
[ROW][C]31[/C][C]0.552115623552737[/C][C]0.895768752894526[/C][C]0.447884376447263[/C][/ROW]
[ROW][C]32[/C][C]0.587547937127836[/C][C]0.824904125744329[/C][C]0.412452062872164[/C][/ROW]
[ROW][C]33[/C][C]0.55927066804935[/C][C]0.8814586639013[/C][C]0.44072933195065[/C][/ROW]
[ROW][C]34[/C][C]0.524836877078318[/C][C]0.950326245843365[/C][C]0.475163122921682[/C][/ROW]
[ROW][C]35[/C][C]0.467408388198833[/C][C]0.934816776397666[/C][C]0.532591611801167[/C][/ROW]
[ROW][C]36[/C][C]0.471386567142905[/C][C]0.94277313428581[/C][C]0.528613432857095[/C][/ROW]
[ROW][C]37[/C][C]0.711000334430101[/C][C]0.577999331139797[/C][C]0.288999665569899[/C][/ROW]
[ROW][C]38[/C][C]0.667104736426315[/C][C]0.66579052714737[/C][C]0.332895263573685[/C][/ROW]
[ROW][C]39[/C][C]0.638064769765422[/C][C]0.723870460469156[/C][C]0.361935230234578[/C][/ROW]
[ROW][C]40[/C][C]0.615647898320574[/C][C]0.768704203358852[/C][C]0.384352101679426[/C][/ROW]
[ROW][C]41[/C][C]0.59806912289671[/C][C]0.80386175420658[/C][C]0.40193087710329[/C][/ROW]
[ROW][C]42[/C][C]0.552087478340427[/C][C]0.895825043319146[/C][C]0.447912521659573[/C][/ROW]
[ROW][C]43[/C][C]0.604185923977924[/C][C]0.791628152044152[/C][C]0.395814076022076[/C][/ROW]
[ROW][C]44[/C][C]0.564921584085117[/C][C]0.870156831829767[/C][C]0.435078415914883[/C][/ROW]
[ROW][C]45[/C][C]0.533138973517079[/C][C]0.933722052965842[/C][C]0.466861026482921[/C][/ROW]
[ROW][C]46[/C][C]0.483940215940761[/C][C]0.967880431881523[/C][C]0.516059784059239[/C][/ROW]
[ROW][C]47[/C][C]0.475629581972337[/C][C]0.951259163944675[/C][C]0.524370418027663[/C][/ROW]
[ROW][C]48[/C][C]0.434205389575812[/C][C]0.868410779151623[/C][C]0.565794610424188[/C][/ROW]
[ROW][C]49[/C][C]0.409280129116146[/C][C]0.818560258232291[/C][C]0.590719870883854[/C][/ROW]
[ROW][C]50[/C][C]0.368709532449743[/C][C]0.737419064899487[/C][C]0.631290467550257[/C][/ROW]
[ROW][C]51[/C][C]0.346114995324536[/C][C]0.692229990649073[/C][C]0.653885004675464[/C][/ROW]
[ROW][C]52[/C][C]0.305191302860987[/C][C]0.610382605721974[/C][C]0.694808697139013[/C][/ROW]
[ROW][C]53[/C][C]0.384515857804275[/C][C]0.76903171560855[/C][C]0.615484142195725[/C][/ROW]
[ROW][C]54[/C][C]0.340871069985913[/C][C]0.681742139971825[/C][C]0.659128930014087[/C][/ROW]
[ROW][C]55[/C][C]0.306438385786579[/C][C]0.612876771573158[/C][C]0.693561614213421[/C][/ROW]
[ROW][C]56[/C][C]0.267161098788423[/C][C]0.534322197576846[/C][C]0.732838901211577[/C][/ROW]
[ROW][C]57[/C][C]0.228588099283012[/C][C]0.457176198566023[/C][C]0.771411900716988[/C][/ROW]
[ROW][C]58[/C][C]0.256883181049085[/C][C]0.513766362098169[/C][C]0.743116818950916[/C][/ROW]
[ROW][C]59[/C][C]0.274588059569755[/C][C]0.54917611913951[/C][C]0.725411940430245[/C][/ROW]
[ROW][C]60[/C][C]0.27915632647311[/C][C]0.55831265294622[/C][C]0.72084367352689[/C][/ROW]
[ROW][C]61[/C][C]0.455411491901356[/C][C]0.910822983802713[/C][C]0.544588508098644[/C][/ROW]
[ROW][C]62[/C][C]0.412293207540601[/C][C]0.824586415081201[/C][C]0.587706792459399[/C][/ROW]
[ROW][C]63[/C][C]0.370127462888606[/C][C]0.740254925777212[/C][C]0.629872537111394[/C][/ROW]
[ROW][C]64[/C][C]0.333428561573203[/C][C]0.666857123146406[/C][C]0.666571438426797[/C][/ROW]
[ROW][C]65[/C][C]0.292119828075272[/C][C]0.584239656150543[/C][C]0.707880171924728[/C][/ROW]
[ROW][C]66[/C][C]0.268115914455726[/C][C]0.536231828911452[/C][C]0.731884085544274[/C][/ROW]
[ROW][C]67[/C][C]0.283486402870827[/C][C]0.566972805741655[/C][C]0.716513597129173[/C][/ROW]
[ROW][C]68[/C][C]0.249055649246785[/C][C]0.498111298493571[/C][C]0.750944350753215[/C][/ROW]
[ROW][C]69[/C][C]0.215710535492542[/C][C]0.431421070985083[/C][C]0.784289464507458[/C][/ROW]
[ROW][C]70[/C][C]0.187214212040947[/C][C]0.374428424081894[/C][C]0.812785787959053[/C][/ROW]
[ROW][C]71[/C][C]0.157548154261003[/C][C]0.315096308522007[/C][C]0.842451845738997[/C][/ROW]
[ROW][C]72[/C][C]0.138407129598372[/C][C]0.276814259196745[/C][C]0.861592870401628[/C][/ROW]
[ROW][C]73[/C][C]0.114303309002813[/C][C]0.228606618005626[/C][C]0.885696690997187[/C][/ROW]
[ROW][C]74[/C][C]0.103947477666358[/C][C]0.207894955332717[/C][C]0.896052522333642[/C][/ROW]
[ROW][C]75[/C][C]0.0918498770574002[/C][C]0.1836997541148[/C][C]0.9081501229426[/C][/ROW]
[ROW][C]76[/C][C]0.156789477427883[/C][C]0.313578954855766[/C][C]0.843210522572117[/C][/ROW]
[ROW][C]77[/C][C]0.135658713798872[/C][C]0.271317427597744[/C][C]0.864341286201128[/C][/ROW]
[ROW][C]78[/C][C]0.162484131249669[/C][C]0.324968262499339[/C][C]0.83751586875033[/C][/ROW]
[ROW][C]79[/C][C]0.135691384304701[/C][C]0.271382768609402[/C][C]0.864308615695299[/C][/ROW]
[ROW][C]80[/C][C]0.150486361071601[/C][C]0.300972722143202[/C][C]0.849513638928399[/C][/ROW]
[ROW][C]81[/C][C]0.131515605976591[/C][C]0.263031211953181[/C][C]0.86848439402341[/C][/ROW]
[ROW][C]82[/C][C]0.139842719963209[/C][C]0.279685439926417[/C][C]0.860157280036791[/C][/ROW]
[ROW][C]83[/C][C]0.152712261058874[/C][C]0.305424522117749[/C][C]0.847287738941126[/C][/ROW]
[ROW][C]84[/C][C]0.129523428728074[/C][C]0.259046857456148[/C][C]0.870476571271926[/C][/ROW]
[ROW][C]85[/C][C]0.109827746320157[/C][C]0.219655492640313[/C][C]0.890172253679843[/C][/ROW]
[ROW][C]86[/C][C]0.0919694246949325[/C][C]0.183938849389865[/C][C]0.908030575305067[/C][/ROW]
[ROW][C]87[/C][C]0.0748478211911325[/C][C]0.149695642382265[/C][C]0.925152178808867[/C][/ROW]
[ROW][C]88[/C][C]0.0611017633601321[/C][C]0.122203526720264[/C][C]0.938898236639868[/C][/ROW]
[ROW][C]89[/C][C]0.0561871396774589[/C][C]0.112374279354918[/C][C]0.94381286032254[/C][/ROW]
[ROW][C]90[/C][C]0.220619282243405[/C][C]0.441238564486811[/C][C]0.779380717756595[/C][/ROW]
[ROW][C]91[/C][C]0.191734645334903[/C][C]0.383469290669806[/C][C]0.808265354665097[/C][/ROW]
[ROW][C]92[/C][C]0.171533977666561[/C][C]0.343067955333122[/C][C]0.828466022333439[/C][/ROW]
[ROW][C]93[/C][C]0.143821097134715[/C][C]0.287642194269429[/C][C]0.856178902865285[/C][/ROW]
[ROW][C]94[/C][C]0.12887709352904[/C][C]0.25775418705808[/C][C]0.87112290647096[/C][/ROW]
[ROW][C]95[/C][C]0.126834837357409[/C][C]0.253669674714819[/C][C]0.87316516264259[/C][/ROW]
[ROW][C]96[/C][C]0.111394400062505[/C][C]0.22278880012501[/C][C]0.888605599937495[/C][/ROW]
[ROW][C]97[/C][C]0.116623847047129[/C][C]0.233247694094258[/C][C]0.883376152952871[/C][/ROW]
[ROW][C]98[/C][C]0.102268085583787[/C][C]0.204536171167574[/C][C]0.897731914416213[/C][/ROW]
[ROW][C]99[/C][C]0.0913589435060663[/C][C]0.182717887012133[/C][C]0.908641056493934[/C][/ROW]
[ROW][C]100[/C][C]0.090281230377882[/C][C]0.180562460755764[/C][C]0.909718769622118[/C][/ROW]
[ROW][C]101[/C][C]0.0882223469861224[/C][C]0.176444693972245[/C][C]0.911777653013878[/C][/ROW]
[ROW][C]102[/C][C]0.082434530907999[/C][C]0.164869061815998[/C][C]0.917565469092001[/C][/ROW]
[ROW][C]103[/C][C]0.0683992185733134[/C][C]0.136798437146627[/C][C]0.931600781426687[/C][/ROW]
[ROW][C]104[/C][C]0.0599471534027872[/C][C]0.119894306805574[/C][C]0.940052846597213[/C][/ROW]
[ROW][C]105[/C][C]0.0602354306592327[/C][C]0.120470861318465[/C][C]0.939764569340767[/C][/ROW]
[ROW][C]106[/C][C]0.103229356050118[/C][C]0.206458712100236[/C][C]0.896770643949882[/C][/ROW]
[ROW][C]107[/C][C]0.092013262235437[/C][C]0.184026524470874[/C][C]0.907986737764563[/C][/ROW]
[ROW][C]108[/C][C]0.0887288354770998[/C][C]0.1774576709542[/C][C]0.9112711645229[/C][/ROW]
[ROW][C]109[/C][C]0.0791138836175982[/C][C]0.158227767235196[/C][C]0.920886116382402[/C][/ROW]
[ROW][C]110[/C][C]0.469495517498111[/C][C]0.938991034996222[/C][C]0.530504482501889[/C][/ROW]
[ROW][C]111[/C][C]0.422601212941636[/C][C]0.845202425883273[/C][C]0.577398787058363[/C][/ROW]
[ROW][C]112[/C][C]0.390118575475749[/C][C]0.780237150951498[/C][C]0.609881424524251[/C][/ROW]
[ROW][C]113[/C][C]0.358204890458748[/C][C]0.716409780917497[/C][C]0.641795109541252[/C][/ROW]
[ROW][C]114[/C][C]0.324430903406468[/C][C]0.648861806812936[/C][C]0.675569096593532[/C][/ROW]
[ROW][C]115[/C][C]0.284101187740825[/C][C]0.56820237548165[/C][C]0.715898812259175[/C][/ROW]
[ROW][C]116[/C][C]0.286555843389545[/C][C]0.57311168677909[/C][C]0.713444156610455[/C][/ROW]
[ROW][C]117[/C][C]0.244894626674676[/C][C]0.489789253349351[/C][C]0.755105373325324[/C][/ROW]
[ROW][C]118[/C][C]0.205392778984916[/C][C]0.410785557969831[/C][C]0.794607221015084[/C][/ROW]
[ROW][C]119[/C][C]0.222061136792561[/C][C]0.444122273585123[/C][C]0.777938863207439[/C][/ROW]
[ROW][C]120[/C][C]0.198184961737776[/C][C]0.396369923475552[/C][C]0.801815038262224[/C][/ROW]
[ROW][C]121[/C][C]0.179147871348369[/C][C]0.358295742696739[/C][C]0.82085212865163[/C][/ROW]
[ROW][C]122[/C][C]0.148882284826899[/C][C]0.297764569653798[/C][C]0.8511177151731[/C][/ROW]
[ROW][C]123[/C][C]0.127405813631578[/C][C]0.254811627263155[/C][C]0.872594186368422[/C][/ROW]
[ROW][C]124[/C][C]0.165855250402199[/C][C]0.331710500804398[/C][C]0.8341447495978[/C][/ROW]
[ROW][C]125[/C][C]0.137990332677587[/C][C]0.275980665355174[/C][C]0.862009667322413[/C][/ROW]
[ROW][C]126[/C][C]0.122720030492509[/C][C]0.245440060985019[/C][C]0.87727996950749[/C][/ROW]
[ROW][C]127[/C][C]0.185498757440324[/C][C]0.370997514880647[/C][C]0.814501242559676[/C][/ROW]
[ROW][C]128[/C][C]0.187085528829706[/C][C]0.374171057659411[/C][C]0.812914471170294[/C][/ROW]
[ROW][C]129[/C][C]0.173231281788721[/C][C]0.346462563577442[/C][C]0.826768718211279[/C][/ROW]
[ROW][C]130[/C][C]0.137811807341951[/C][C]0.275623614683903[/C][C]0.862188192658049[/C][/ROW]
[ROW][C]131[/C][C]0.123452485953459[/C][C]0.246904971906918[/C][C]0.876547514046541[/C][/ROW]
[ROW][C]132[/C][C]0.221672627715068[/C][C]0.443345255430137[/C][C]0.778327372284932[/C][/ROW]
[ROW][C]133[/C][C]0.241648622422645[/C][C]0.483297244845291[/C][C]0.758351377577354[/C][/ROW]
[ROW][C]134[/C][C]0.19733568754618[/C][C]0.39467137509236[/C][C]0.80266431245382[/C][/ROW]
[ROW][C]135[/C][C]0.166414078240956[/C][C]0.332828156481912[/C][C]0.833585921759044[/C][/ROW]
[ROW][C]136[/C][C]0.130123211166474[/C][C]0.260246422332948[/C][C]0.869876788833526[/C][/ROW]
[ROW][C]137[/C][C]0.59495868211983[/C][C]0.81008263576034[/C][C]0.40504131788017[/C][/ROW]
[ROW][C]138[/C][C]0.527551394236182[/C][C]0.944897211527636[/C][C]0.472448605763818[/C][/ROW]
[ROW][C]139[/C][C]0.488535085834114[/C][C]0.977070171668227[/C][C]0.511464914165886[/C][/ROW]
[ROW][C]140[/C][C]0.447976322558[/C][C]0.895952645115999[/C][C]0.552023677442[/C][/ROW]
[ROW][C]141[/C][C]0.379257995471569[/C][C]0.758515990943138[/C][C]0.620742004528431[/C][/ROW]
[ROW][C]142[/C][C]0.387257939715211[/C][C]0.774515879430422[/C][C]0.612742060284789[/C][/ROW]
[ROW][C]143[/C][C]0.316592306749894[/C][C]0.633184613499787[/C][C]0.683407693250106[/C][/ROW]
[ROW][C]144[/C][C]0.248631282647623[/C][C]0.497262565295246[/C][C]0.751368717352377[/C][/ROW]
[ROW][C]145[/C][C]0.198210025648444[/C][C]0.396420051296888[/C][C]0.801789974351556[/C][/ROW]
[ROW][C]146[/C][C]0.16885831478444[/C][C]0.337716629568881[/C][C]0.83114168521556[/C][/ROW]
[ROW][C]147[/C][C]0.125888632990713[/C][C]0.251777265981425[/C][C]0.874111367009287[/C][/ROW]
[ROW][C]148[/C][C]0.0864737044462057[/C][C]0.172947408892411[/C][C]0.913526295553794[/C][/ROW]
[ROW][C]149[/C][C]0.0938770751296553[/C][C]0.187754150259311[/C][C]0.906122924870345[/C][/ROW]
[ROW][C]150[/C][C]0.115922567769026[/C][C]0.231845135538052[/C][C]0.884077432230974[/C][/ROW]
[ROW][C]151[/C][C]0.112701268950409[/C][C]0.225402537900817[/C][C]0.887298731049591[/C][/ROW]
[ROW][C]152[/C][C]0.354598120010482[/C][C]0.709196240020964[/C][C]0.645401879989518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98749&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98749&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9763945343223430.04721093135531380.0236054656776569
80.9519033864738520.09619322705229550.0480966135261478
90.919862850969680.1602742980606410.0801371490303205
100.8734981349108290.2530037301783420.126501865089171
110.81206607002410.3758678599518010.1879339299759
120.7875880455577190.4248239088845620.212411954442281
130.7107855469434470.5784289061131060.289214453056553
140.6275139946371140.7449720107257730.372486005362886
150.6550043036572310.6899913926855380.344995696342769
160.7231855862794660.5536288274410670.276814413720534
170.6548202727026850.690359454594630.345179727297315
180.671761928492560.6564761430148810.328238071507441
190.6481642799530960.7036714400938080.351835720046904
200.6801043632379490.6397912735241010.319895636762051
210.7239831092510410.5520337814979180.276016890748959
220.7757135223738430.4485729552523150.224286477626157
230.7213784112292490.5572431775415030.278621588770751
240.6809238141058710.6381523717882580.319076185894129
250.6409547019220620.7180905961558750.359045298077938
260.6709884044313020.6580231911373960.329011595568698
270.6397386130684430.7205227738631140.360261386931557
280.6074907111638090.7850185776723820.392509288836191
290.5590874947117190.8818250105765630.440912505288281
300.49882720516290.99765441032580.5011727948371
310.5521156235527370.8957687528945260.447884376447263
320.5875479371278360.8249041257443290.412452062872164
330.559270668049350.88145866390130.44072933195065
340.5248368770783180.9503262458433650.475163122921682
350.4674083881988330.9348167763976660.532591611801167
360.4713865671429050.942773134285810.528613432857095
370.7110003344301010.5779993311397970.288999665569899
380.6671047364263150.665790527147370.332895263573685
390.6380647697654220.7238704604691560.361935230234578
400.6156478983205740.7687042033588520.384352101679426
410.598069122896710.803861754206580.40193087710329
420.5520874783404270.8958250433191460.447912521659573
430.6041859239779240.7916281520441520.395814076022076
440.5649215840851170.8701568318297670.435078415914883
450.5331389735170790.9337220529658420.466861026482921
460.4839402159407610.9678804318815230.516059784059239
470.4756295819723370.9512591639446750.524370418027663
480.4342053895758120.8684107791516230.565794610424188
490.4092801291161460.8185602582322910.590719870883854
500.3687095324497430.7374190648994870.631290467550257
510.3461149953245360.6922299906490730.653885004675464
520.3051913028609870.6103826057219740.694808697139013
530.3845158578042750.769031715608550.615484142195725
540.3408710699859130.6817421399718250.659128930014087
550.3064383857865790.6128767715731580.693561614213421
560.2671610987884230.5343221975768460.732838901211577
570.2285880992830120.4571761985660230.771411900716988
580.2568831810490850.5137663620981690.743116818950916
590.2745880595697550.549176119139510.725411940430245
600.279156326473110.558312652946220.72084367352689
610.4554114919013560.9108229838027130.544588508098644
620.4122932075406010.8245864150812010.587706792459399
630.3701274628886060.7402549257772120.629872537111394
640.3334285615732030.6668571231464060.666571438426797
650.2921198280752720.5842396561505430.707880171924728
660.2681159144557260.5362318289114520.731884085544274
670.2834864028708270.5669728057416550.716513597129173
680.2490556492467850.4981112984935710.750944350753215
690.2157105354925420.4314210709850830.784289464507458
700.1872142120409470.3744284240818940.812785787959053
710.1575481542610030.3150963085220070.842451845738997
720.1384071295983720.2768142591967450.861592870401628
730.1143033090028130.2286066180056260.885696690997187
740.1039474776663580.2078949553327170.896052522333642
750.09184987705740020.18369975411480.9081501229426
760.1567894774278830.3135789548557660.843210522572117
770.1356587137988720.2713174275977440.864341286201128
780.1624841312496690.3249682624993390.83751586875033
790.1356913843047010.2713827686094020.864308615695299
800.1504863610716010.3009727221432020.849513638928399
810.1315156059765910.2630312119531810.86848439402341
820.1398427199632090.2796854399264170.860157280036791
830.1527122610588740.3054245221177490.847287738941126
840.1295234287280740.2590468574561480.870476571271926
850.1098277463201570.2196554926403130.890172253679843
860.09196942469493250.1839388493898650.908030575305067
870.07484782119113250.1496956423822650.925152178808867
880.06110176336013210.1222035267202640.938898236639868
890.05618713967745890.1123742793549180.94381286032254
900.2206192822434050.4412385644868110.779380717756595
910.1917346453349030.3834692906698060.808265354665097
920.1715339776665610.3430679553331220.828466022333439
930.1438210971347150.2876421942694290.856178902865285
940.128877093529040.257754187058080.87112290647096
950.1268348373574090.2536696747148190.87316516264259
960.1113944000625050.222788800125010.888605599937495
970.1166238470471290.2332476940942580.883376152952871
980.1022680855837870.2045361711675740.897731914416213
990.09135894350606630.1827178870121330.908641056493934
1000.0902812303778820.1805624607557640.909718769622118
1010.08822234698612240.1764446939722450.911777653013878
1020.0824345309079990.1648690618159980.917565469092001
1030.06839921857331340.1367984371466270.931600781426687
1040.05994715340278720.1198943068055740.940052846597213
1050.06023543065923270.1204708613184650.939764569340767
1060.1032293560501180.2064587121002360.896770643949882
1070.0920132622354370.1840265244708740.907986737764563
1080.08872883547709980.17745767095420.9112711645229
1090.07911388361759820.1582277672351960.920886116382402
1100.4694955174981110.9389910349962220.530504482501889
1110.4226012129416360.8452024258832730.577398787058363
1120.3901185754757490.7802371509514980.609881424524251
1130.3582048904587480.7164097809174970.641795109541252
1140.3244309034064680.6488618068129360.675569096593532
1150.2841011877408250.568202375481650.715898812259175
1160.2865558433895450.573111686779090.713444156610455
1170.2448946266746760.4897892533493510.755105373325324
1180.2053927789849160.4107855579698310.794607221015084
1190.2220611367925610.4441222735851230.777938863207439
1200.1981849617377760.3963699234755520.801815038262224
1210.1791478713483690.3582957426967390.82085212865163
1220.1488822848268990.2977645696537980.8511177151731
1230.1274058136315780.2548116272631550.872594186368422
1240.1658552504021990.3317105008043980.8341447495978
1250.1379903326775870.2759806653551740.862009667322413
1260.1227200304925090.2454400609850190.87727996950749
1270.1854987574403240.3709975148806470.814501242559676
1280.1870855288297060.3741710576594110.812914471170294
1290.1732312817887210.3464625635774420.826768718211279
1300.1378118073419510.2756236146839030.862188192658049
1310.1234524859534590.2469049719069180.876547514046541
1320.2216726277150680.4433452554301370.778327372284932
1330.2416486224226450.4832972448452910.758351377577354
1340.197335687546180.394671375092360.80266431245382
1350.1664140782409560.3328281564819120.833585921759044
1360.1301232111664740.2602464223329480.869876788833526
1370.594958682119830.810082635760340.40504131788017
1380.5275513942361820.9448972115276360.472448605763818
1390.4885350858341140.9770701716682270.511464914165886
1400.4479763225580.8959526451159990.552023677442
1410.3792579954715690.7585159909431380.620742004528431
1420.3872579397152110.7745158794304220.612742060284789
1430.3165923067498940.6331846134997870.683407693250106
1440.2486312826476230.4972625652952460.751368717352377
1450.1982100256484440.3964200512968880.801789974351556
1460.168858314784440.3377166295688810.83114168521556
1470.1258886329907130.2517772659814250.874111367009287
1480.08647370444620570.1729474088924110.913526295553794
1490.09387707512965530.1877541502593110.906122924870345
1500.1159225677690260.2318451355380520.884077432230974
1510.1127012689504090.2254025379008170.887298731049591
1520.3545981200104820.7091962400209640.645401879989518






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00684931506849315 & OK \tabularnewline
10% type I error level & 2 & 0.0136986301369863 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98749&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00684931506849315[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0136986301369863[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98749&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98749&T=6

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


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

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


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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