Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationSat, 05 Dec 2009 03:58:06 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/05/t1260010728uqn72l8hcj88ood.htm/, Retrieved Thu, 31 Oct 2024 23:09:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64228, Retrieved Thu, 31 Oct 2024 23:09:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact200
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Backward Selection] [WS9 Berekening1 TVD] [2009-12-02 15:52:32] [42ad1186d39724f834063794eac7cea3]
-           [ARIMA Backward Selection] [TG 7] [2009-12-02 18:02:35] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-   PD        [ARIMA Backward Selection] [DSHW-WS9-ARIMABackwS] [2009-12-04 14:34:58] [f15cfb7053d35072d573abca87df96a0]
-   P             [ARIMA Backward Selection] [SHWWS9review1] [2009-12-05 10:58:06] [db49399df1e4a3dbe31268849cebfd7f] [Current]
Feedback Forum

Post a new message
Dataseries X:
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
8.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64228&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64228&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64228&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.4943-0.0974-0.4282-0.0481-0.22790.33450.9981
(p-val)(0.0288 )(0.5896 )(0.0032 )(0.835 )(0.3672 )(0.1755 )(0.1196 )
Estimates ( 2 )0.4574-0.0747-0.44120-0.23430.34410.9999
(p-val)(6e-04 )(0.584 )(5e-04 )(NA )(0.3473 )(0.1535 )(0.11 )
Estimates ( 3 )0.42020-0.47920-0.20230.32191.0003
(p-val)(2e-04 )(NA )(0 )(NA )(0.3967 )(0.1796 )(0.1107 )
Estimates ( 4 )0.42160-0.4799000.20330.6998
(p-val)(1e-04 )(NA )(0 )(NA )(NA )(0.2517 )(0.002 )
Estimates ( 5 )0.39150-0.46140000.7035
(p-val)(5e-04 )(NA )(0 )(NA )(NA )(NA )(0.0047 )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.4943 & -0.0974 & -0.4282 & -0.0481 & -0.2279 & 0.3345 & 0.9981 \tabularnewline
(p-val) & (0.0288 ) & (0.5896 ) & (0.0032 ) & (0.835 ) & (0.3672 ) & (0.1755 ) & (0.1196 ) \tabularnewline
Estimates ( 2 ) & 0.4574 & -0.0747 & -0.4412 & 0 & -0.2343 & 0.3441 & 0.9999 \tabularnewline
(p-val) & (6e-04 ) & (0.584 ) & (5e-04 ) & (NA ) & (0.3473 ) & (0.1535 ) & (0.11 ) \tabularnewline
Estimates ( 3 ) & 0.4202 & 0 & -0.4792 & 0 & -0.2023 & 0.3219 & 1.0003 \tabularnewline
(p-val) & (2e-04 ) & (NA ) & (0 ) & (NA ) & (0.3967 ) & (0.1796 ) & (0.1107 ) \tabularnewline
Estimates ( 4 ) & 0.4216 & 0 & -0.4799 & 0 & 0 & 0.2033 & 0.6998 \tabularnewline
(p-val) & (1e-04 ) & (NA ) & (0 ) & (NA ) & (NA ) & (0.2517 ) & (0.002 ) \tabularnewline
Estimates ( 5 ) & 0.3915 & 0 & -0.4614 & 0 & 0 & 0 & 0.7035 \tabularnewline
(p-val) & (5e-04 ) & (NA ) & (0 ) & (NA ) & (NA ) & (NA ) & (0.0047 ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64228&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.4943[/C][C]-0.0974[/C][C]-0.4282[/C][C]-0.0481[/C][C]-0.2279[/C][C]0.3345[/C][C]0.9981[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0288 )[/C][C](0.5896 )[/C][C](0.0032 )[/C][C](0.835 )[/C][C](0.3672 )[/C][C](0.1755 )[/C][C](0.1196 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4574[/C][C]-0.0747[/C][C]-0.4412[/C][C]0[/C][C]-0.2343[/C][C]0.3441[/C][C]0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](6e-04 )[/C][C](0.584 )[/C][C](5e-04 )[/C][C](NA )[/C][C](0.3473 )[/C][C](0.1535 )[/C][C](0.11 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4202[/C][C]0[/C][C]-0.4792[/C][C]0[/C][C]-0.2023[/C][C]0.3219[/C][C]1.0003[/C][/ROW]
[ROW][C](p-val)[/C][C](2e-04 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0.3967 )[/C][C](0.1796 )[/C][C](0.1107 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.4216[/C][C]0[/C][C]-0.4799[/C][C]0[/C][C]0[/C][C]0.2033[/C][C]0.6998[/C][/ROW]
[ROW][C](p-val)[/C][C](1e-04 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.2517 )[/C][C](0.002 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.3915[/C][C]0[/C][C]-0.4614[/C][C]0[/C][C]0[/C][C]0[/C][C]0.7035[/C][/ROW]
[ROW][C](p-val)[/C][C](5e-04 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0047 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64228&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64228&T=1

As an alternative you can also use a QR Code:  

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

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.4943-0.0974-0.4282-0.0481-0.22790.33450.9981
(p-val)(0.0288 )(0.5896 )(0.0032 )(0.835 )(0.3672 )(0.1755 )(0.1196 )
Estimates ( 2 )0.4574-0.0747-0.44120-0.23430.34410.9999
(p-val)(6e-04 )(0.584 )(5e-04 )(NA )(0.3473 )(0.1535 )(0.11 )
Estimates ( 3 )0.42020-0.47920-0.20230.32191.0003
(p-val)(2e-04 )(NA )(0 )(NA )(0.3967 )(0.1796 )(0.1107 )
Estimates ( 4 )0.42160-0.4799000.20330.6998
(p-val)(1e-04 )(NA )(0 )(NA )(NA )(0.2517 )(0.002 )
Estimates ( 5 )0.39150-0.46140000.7035
(p-val)(5e-04 )(NA )(0 )(NA )(NA )(NA )(0.0047 )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.00779999034864656
-4.89709675237169e-06
2.81552229392772e-06
-0.214816398199664
0.092905332767352
-0.327669766264729
0.340008853760585
-0.120427842085302
-0.0621246057851888
0.214378827493945
-0.0474536119661323
0.189380021559409
0.0935008427108706
0.0476620342188599
0.0160501221112578
0.20526902237685
-0.311950574565648
-0.239670518987734
-0.377000341839179
-0.179940412924872
-0.0112188812931215
-0.173196798343509
-0.0936326225942414
-0.0314841874576638
0.073320055118207
-0.115326133668192
-0.129783622074488
0.132971557302108
-0.102246173213616
-0.164861202930899
0.678386869047689
-0.163200687965873
-0.319364806343827
0.191935481162590
-0.033881646959467
0.214339994971065
0.0437669687434575
-0.0967203444941957
-0.177104195644016
-0.0984576551837839
-0.0516856413330651
0.441625633842398
0.388835491663766
-0.33619841629815
-0.0644750372415551
-0.228224406115761
0.132969786515147
0.163515729514859
0.28491593563155
0.163453949053911
0.282671109937350
0.166902964311976
0.166649322196621
0.0984973352869936
-0.0138015680672850
0.0442642453801988

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00779999034864656 \tabularnewline
-4.89709675237169e-06 \tabularnewline
2.81552229392772e-06 \tabularnewline
-0.214816398199664 \tabularnewline
0.092905332767352 \tabularnewline
-0.327669766264729 \tabularnewline
0.340008853760585 \tabularnewline
-0.120427842085302 \tabularnewline
-0.0621246057851888 \tabularnewline
0.214378827493945 \tabularnewline
-0.0474536119661323 \tabularnewline
0.189380021559409 \tabularnewline
0.0935008427108706 \tabularnewline
0.0476620342188599 \tabularnewline
0.0160501221112578 \tabularnewline
0.20526902237685 \tabularnewline
-0.311950574565648 \tabularnewline
-0.239670518987734 \tabularnewline
-0.377000341839179 \tabularnewline
-0.179940412924872 \tabularnewline
-0.0112188812931215 \tabularnewline
-0.173196798343509 \tabularnewline
-0.0936326225942414 \tabularnewline
-0.0314841874576638 \tabularnewline
0.073320055118207 \tabularnewline
-0.115326133668192 \tabularnewline
-0.129783622074488 \tabularnewline
0.132971557302108 \tabularnewline
-0.102246173213616 \tabularnewline
-0.164861202930899 \tabularnewline
0.678386869047689 \tabularnewline
-0.163200687965873 \tabularnewline
-0.319364806343827 \tabularnewline
0.191935481162590 \tabularnewline
-0.033881646959467 \tabularnewline
0.214339994971065 \tabularnewline
0.0437669687434575 \tabularnewline
-0.0967203444941957 \tabularnewline
-0.177104195644016 \tabularnewline
-0.0984576551837839 \tabularnewline
-0.0516856413330651 \tabularnewline
0.441625633842398 \tabularnewline
0.388835491663766 \tabularnewline
-0.33619841629815 \tabularnewline
-0.0644750372415551 \tabularnewline
-0.228224406115761 \tabularnewline
0.132969786515147 \tabularnewline
0.163515729514859 \tabularnewline
0.28491593563155 \tabularnewline
0.163453949053911 \tabularnewline
0.282671109937350 \tabularnewline
0.166902964311976 \tabularnewline
0.166649322196621 \tabularnewline
0.0984973352869936 \tabularnewline
-0.0138015680672850 \tabularnewline
0.0442642453801988 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64228&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00779999034864656[/C][/ROW]
[ROW][C]-4.89709675237169e-06[/C][/ROW]
[ROW][C]2.81552229392772e-06[/C][/ROW]
[ROW][C]-0.214816398199664[/C][/ROW]
[ROW][C]0.092905332767352[/C][/ROW]
[ROW][C]-0.327669766264729[/C][/ROW]
[ROW][C]0.340008853760585[/C][/ROW]
[ROW][C]-0.120427842085302[/C][/ROW]
[ROW][C]-0.0621246057851888[/C][/ROW]
[ROW][C]0.214378827493945[/C][/ROW]
[ROW][C]-0.0474536119661323[/C][/ROW]
[ROW][C]0.189380021559409[/C][/ROW]
[ROW][C]0.0935008427108706[/C][/ROW]
[ROW][C]0.0476620342188599[/C][/ROW]
[ROW][C]0.0160501221112578[/C][/ROW]
[ROW][C]0.20526902237685[/C][/ROW]
[ROW][C]-0.311950574565648[/C][/ROW]
[ROW][C]-0.239670518987734[/C][/ROW]
[ROW][C]-0.377000341839179[/C][/ROW]
[ROW][C]-0.179940412924872[/C][/ROW]
[ROW][C]-0.0112188812931215[/C][/ROW]
[ROW][C]-0.173196798343509[/C][/ROW]
[ROW][C]-0.0936326225942414[/C][/ROW]
[ROW][C]-0.0314841874576638[/C][/ROW]
[ROW][C]0.073320055118207[/C][/ROW]
[ROW][C]-0.115326133668192[/C][/ROW]
[ROW][C]-0.129783622074488[/C][/ROW]
[ROW][C]0.132971557302108[/C][/ROW]
[ROW][C]-0.102246173213616[/C][/ROW]
[ROW][C]-0.164861202930899[/C][/ROW]
[ROW][C]0.678386869047689[/C][/ROW]
[ROW][C]-0.163200687965873[/C][/ROW]
[ROW][C]-0.319364806343827[/C][/ROW]
[ROW][C]0.191935481162590[/C][/ROW]
[ROW][C]-0.033881646959467[/C][/ROW]
[ROW][C]0.214339994971065[/C][/ROW]
[ROW][C]0.0437669687434575[/C][/ROW]
[ROW][C]-0.0967203444941957[/C][/ROW]
[ROW][C]-0.177104195644016[/C][/ROW]
[ROW][C]-0.0984576551837839[/C][/ROW]
[ROW][C]-0.0516856413330651[/C][/ROW]
[ROW][C]0.441625633842398[/C][/ROW]
[ROW][C]0.388835491663766[/C][/ROW]
[ROW][C]-0.33619841629815[/C][/ROW]
[ROW][C]-0.0644750372415551[/C][/ROW]
[ROW][C]-0.228224406115761[/C][/ROW]
[ROW][C]0.132969786515147[/C][/ROW]
[ROW][C]0.163515729514859[/C][/ROW]
[ROW][C]0.28491593563155[/C][/ROW]
[ROW][C]0.163453949053911[/C][/ROW]
[ROW][C]0.282671109937350[/C][/ROW]
[ROW][C]0.166902964311976[/C][/ROW]
[ROW][C]0.166649322196621[/C][/ROW]
[ROW][C]0.0984973352869936[/C][/ROW]
[ROW][C]-0.0138015680672850[/C][/ROW]
[ROW][C]0.0442642453801988[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64228&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64228&T=2

As an alternative you can also use a QR Code:  

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

Estimated ARIMA Residuals
Value
0.00779999034864656
-4.89709675237169e-06
2.81552229392772e-06
-0.214816398199664
0.092905332767352
-0.327669766264729
0.340008853760585
-0.120427842085302
-0.0621246057851888
0.214378827493945
-0.0474536119661323
0.189380021559409
0.0935008427108706
0.0476620342188599
0.0160501221112578
0.20526902237685
-0.311950574565648
-0.239670518987734
-0.377000341839179
-0.179940412924872
-0.0112188812931215
-0.173196798343509
-0.0936326225942414
-0.0314841874576638
0.073320055118207
-0.115326133668192
-0.129783622074488
0.132971557302108
-0.102246173213616
-0.164861202930899
0.678386869047689
-0.163200687965873
-0.319364806343827
0.191935481162590
-0.033881646959467
0.214339994971065
0.0437669687434575
-0.0967203444941957
-0.177104195644016
-0.0984576551837839
-0.0516856413330651
0.441625633842398
0.388835491663766
-0.33619841629815
-0.0644750372415551
-0.228224406115761
0.132969786515147
0.163515729514859
0.28491593563155
0.163453949053911
0.282671109937350
0.166902964311976
0.166649322196621
0.0984973352869936
-0.0138015680672850
0.0442642453801988



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')