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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 computationWed, 02 Dec 2009 10:37:24 -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/02/t125977552286zp2g1n38x8rue.htm/, Retrieved Sun, 28 Apr 2024 10:40:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62482, Retrieved Sun, 28 Apr 2024 10:40:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact226
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] [Workshop 9: ARIMA...] [2009-12-02 17:37:24] [63d6214c2814604a6f6cfa44dba5912e] [Current]
- RMP         [ARIMA Forecasting] [WS 10: Forecasting] [2009-12-10 14:58:18] [b00a5c3d5f6ccb867aa9e2de58adfa61]
-   P           [ARIMA Forecasting] [WS 10: Forecast t...] [2009-12-10 15:18:07] [b00a5c3d5f6ccb867aa9e2de58adfa61]
-    D          [ARIMA Forecasting] [W10] [2009-12-28 11:58:37] [0a7d38ad9c7f1a2c46637c75a8a0e083]
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Dataseries X:
8.1
7.7
7.5
7.6
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
7.0
7.1
7.2
7.1
6.9
7.0
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
8.0
8.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62482&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]6 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=62482&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62482&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.4806-0.0907-0.4427-0.012-0.20840.30750.871
(p-val)(0.0283 )(0.6074 )(0.0017 )(0.9581 )(0.8089 )(0.575 )(0.4396 )
Estimates ( 2 )0.4714-0.0849-0.4460-0.21450.31280.8781
(p-val)(4e-04 )(0.5328 )(4e-04 )(NA )(0.8037 )(0.5655 )(0.4484 )
Estimates ( 3 )0.4715-0.0837-0.4438000.18890.6449
(p-val)(4e-04 )(0.5376 )(4e-04 )(NA )(NA )(0.278 )(0.0012 )
Estimates ( 4 )0.42980-0.4861000.17950.6782
(p-val)(1e-04 )(NA )(0 )(NA )(NA )(0.3047 )(8e-04 )
Estimates ( 5 )0.40080-0.47430000.6721
(p-val)(3e-04 )(NA )(0 )(NA )(NA )(NA )(0.0015 )
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 ) & 0.4806 & -0.0907 & -0.4427 & -0.012 & -0.2084 & 0.3075 & 0.871 \tabularnewline
(p-val) & (0.0283 ) & (0.6074 ) & (0.0017 ) & (0.9581 ) & (0.8089 ) & (0.575 ) & (0.4396 ) \tabularnewline
Estimates ( 2 ) & 0.4714 & -0.0849 & -0.446 & 0 & -0.2145 & 0.3128 & 0.8781 \tabularnewline
(p-val) & (4e-04 ) & (0.5328 ) & (4e-04 ) & (NA ) & (0.8037 ) & (0.5655 ) & (0.4484 ) \tabularnewline
Estimates ( 3 ) & 0.4715 & -0.0837 & -0.4438 & 0 & 0 & 0.1889 & 0.6449 \tabularnewline
(p-val) & (4e-04 ) & (0.5376 ) & (4e-04 ) & (NA ) & (NA ) & (0.278 ) & (0.0012 ) \tabularnewline
Estimates ( 4 ) & 0.4298 & 0 & -0.4861 & 0 & 0 & 0.1795 & 0.6782 \tabularnewline
(p-val) & (1e-04 ) & (NA ) & (0 ) & (NA ) & (NA ) & (0.3047 ) & (8e-04 ) \tabularnewline
Estimates ( 5 ) & 0.4008 & 0 & -0.4743 & 0 & 0 & 0 & 0.6721 \tabularnewline
(p-val) & (3e-04 ) & (NA ) & (0 ) & (NA ) & (NA ) & (NA ) & (0.0015 ) \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=62482&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]0.4806[/C][C]-0.0907[/C][C]-0.4427[/C][C]-0.012[/C][C]-0.2084[/C][C]0.3075[/C][C]0.871[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0283 )[/C][C](0.6074 )[/C][C](0.0017 )[/C][C](0.9581 )[/C][C](0.8089 )[/C][C](0.575 )[/C][C](0.4396 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4714[/C][C]-0.0849[/C][C]-0.446[/C][C]0[/C][C]-0.2145[/C][C]0.3128[/C][C]0.8781[/C][/ROW]
[ROW][C](p-val)[/C][C](4e-04 )[/C][C](0.5328 )[/C][C](4e-04 )[/C][C](NA )[/C][C](0.8037 )[/C][C](0.5655 )[/C][C](0.4484 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4715[/C][C]-0.0837[/C][C]-0.4438[/C][C]0[/C][C]0[/C][C]0.1889[/C][C]0.6449[/C][/ROW]
[ROW][C](p-val)[/C][C](4e-04 )[/C][C](0.5376 )[/C][C](4e-04 )[/C][C](NA )[/C][C](NA )[/C][C](0.278 )[/C][C](0.0012 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.4298[/C][C]0[/C][C]-0.4861[/C][C]0[/C][C]0[/C][C]0.1795[/C][C]0.6782[/C][/ROW]
[ROW][C](p-val)[/C][C](1e-04 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.3047 )[/C][C](8e-04 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.4008[/C][C]0[/C][C]-0.4743[/C][C]0[/C][C]0[/C][C]0[/C][C]0.6721[/C][/ROW]
[ROW][C](p-val)[/C][C](3e-04 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0015 )[/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=62482&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62482&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 )0.4806-0.0907-0.4427-0.012-0.20840.30750.871
(p-val)(0.0283 )(0.6074 )(0.0017 )(0.9581 )(0.8089 )(0.575 )(0.4396 )
Estimates ( 2 )0.4714-0.0849-0.4460-0.21450.31280.8781
(p-val)(4e-04 )(0.5328 )(4e-04 )(NA )(0.8037 )(0.5655 )(0.4484 )
Estimates ( 3 )0.4715-0.0837-0.4438000.18890.6449
(p-val)(4e-04 )(0.5376 )(4e-04 )(NA )(NA )(0.278 )(0.0012 )
Estimates ( 4 )0.42980-0.4861000.17950.6782
(p-val)(1e-04 )(NA )(0 )(NA )(NA )(0.3047 )(8e-04 )
Estimates ( 5 )0.40080-0.47430000.6721
(p-val)(3e-04 )(NA )(0 )(NA )(NA )(NA )(0.0015 )
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
0.00809999011424678
-0.256030106273241
-0.0261995982030688
0.0719851902337171
-0.0224258429805746
-0.124301476471403
0.064140545701854
-0.159502946771157
0.0759231305523105
-0.375004746715671
0.307941434557564
-0.117246775711441
-0.0129582186710077
0.325131270254925
-0.0204708244308402
0.188125836709736
0.176849438764125
0.101909987488730
0.0162069702051449
0.197310304512539
-0.303769207080201
-0.229203946742343
-0.347399624038535
-0.180554444762140
-0.0258467800321024
-0.213369819474686
-0.107150249948112
-0.0357645786100917
0.0436447597557989
-0.127445907697642
-0.126663795361469
0.138953687852875
-0.112828366648820
-0.186239291483964
0.664918433419096
-0.173809666264785
-0.313842006404744
0.224264649084628
-0.0268040736855746
0.224288989151266
0.0638262077516528
-0.0938925644723778
-0.177897591007898
-0.0924846033451493
-0.0503322220330691
0.441057957228204
0.403771058311445
-0.35078141035461
-0.0753978387485018
-0.238864071365307
0.133754558457539
0.163839803390978
0.267968889629529
0.147687166628877
0.275677926776287
0.171383689436019
0.162926992817567
0.101618386695402
-0.00624828425395776
0.0348199773931945

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00809999011424678 \tabularnewline
-0.256030106273241 \tabularnewline
-0.0261995982030688 \tabularnewline
0.0719851902337171 \tabularnewline
-0.0224258429805746 \tabularnewline
-0.124301476471403 \tabularnewline
0.064140545701854 \tabularnewline
-0.159502946771157 \tabularnewline
0.0759231305523105 \tabularnewline
-0.375004746715671 \tabularnewline
0.307941434557564 \tabularnewline
-0.117246775711441 \tabularnewline
-0.0129582186710077 \tabularnewline
0.325131270254925 \tabularnewline
-0.0204708244308402 \tabularnewline
0.188125836709736 \tabularnewline
0.176849438764125 \tabularnewline
0.101909987488730 \tabularnewline
0.0162069702051449 \tabularnewline
0.197310304512539 \tabularnewline
-0.303769207080201 \tabularnewline
-0.229203946742343 \tabularnewline
-0.347399624038535 \tabularnewline
-0.180554444762140 \tabularnewline
-0.0258467800321024 \tabularnewline
-0.213369819474686 \tabularnewline
-0.107150249948112 \tabularnewline
-0.0357645786100917 \tabularnewline
0.0436447597557989 \tabularnewline
-0.127445907697642 \tabularnewline
-0.126663795361469 \tabularnewline
0.138953687852875 \tabularnewline
-0.112828366648820 \tabularnewline
-0.186239291483964 \tabularnewline
0.664918433419096 \tabularnewline
-0.173809666264785 \tabularnewline
-0.313842006404744 \tabularnewline
0.224264649084628 \tabularnewline
-0.0268040736855746 \tabularnewline
0.224288989151266 \tabularnewline
0.0638262077516528 \tabularnewline
-0.0938925644723778 \tabularnewline
-0.177897591007898 \tabularnewline
-0.0924846033451493 \tabularnewline
-0.0503322220330691 \tabularnewline
0.441057957228204 \tabularnewline
0.403771058311445 \tabularnewline
-0.35078141035461 \tabularnewline
-0.0753978387485018 \tabularnewline
-0.238864071365307 \tabularnewline
0.133754558457539 \tabularnewline
0.163839803390978 \tabularnewline
0.267968889629529 \tabularnewline
0.147687166628877 \tabularnewline
0.275677926776287 \tabularnewline
0.171383689436019 \tabularnewline
0.162926992817567 \tabularnewline
0.101618386695402 \tabularnewline
-0.00624828425395776 \tabularnewline
0.0348199773931945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62482&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00809999011424678[/C][/ROW]
[ROW][C]-0.256030106273241[/C][/ROW]
[ROW][C]-0.0261995982030688[/C][/ROW]
[ROW][C]0.0719851902337171[/C][/ROW]
[ROW][C]-0.0224258429805746[/C][/ROW]
[ROW][C]-0.124301476471403[/C][/ROW]
[ROW][C]0.064140545701854[/C][/ROW]
[ROW][C]-0.159502946771157[/C][/ROW]
[ROW][C]0.0759231305523105[/C][/ROW]
[ROW][C]-0.375004746715671[/C][/ROW]
[ROW][C]0.307941434557564[/C][/ROW]
[ROW][C]-0.117246775711441[/C][/ROW]
[ROW][C]-0.0129582186710077[/C][/ROW]
[ROW][C]0.325131270254925[/C][/ROW]
[ROW][C]-0.0204708244308402[/C][/ROW]
[ROW][C]0.188125836709736[/C][/ROW]
[ROW][C]0.176849438764125[/C][/ROW]
[ROW][C]0.101909987488730[/C][/ROW]
[ROW][C]0.0162069702051449[/C][/ROW]
[ROW][C]0.197310304512539[/C][/ROW]
[ROW][C]-0.303769207080201[/C][/ROW]
[ROW][C]-0.229203946742343[/C][/ROW]
[ROW][C]-0.347399624038535[/C][/ROW]
[ROW][C]-0.180554444762140[/C][/ROW]
[ROW][C]-0.0258467800321024[/C][/ROW]
[ROW][C]-0.213369819474686[/C][/ROW]
[ROW][C]-0.107150249948112[/C][/ROW]
[ROW][C]-0.0357645786100917[/C][/ROW]
[ROW][C]0.0436447597557989[/C][/ROW]
[ROW][C]-0.127445907697642[/C][/ROW]
[ROW][C]-0.126663795361469[/C][/ROW]
[ROW][C]0.138953687852875[/C][/ROW]
[ROW][C]-0.112828366648820[/C][/ROW]
[ROW][C]-0.186239291483964[/C][/ROW]
[ROW][C]0.664918433419096[/C][/ROW]
[ROW][C]-0.173809666264785[/C][/ROW]
[ROW][C]-0.313842006404744[/C][/ROW]
[ROW][C]0.224264649084628[/C][/ROW]
[ROW][C]-0.0268040736855746[/C][/ROW]
[ROW][C]0.224288989151266[/C][/ROW]
[ROW][C]0.0638262077516528[/C][/ROW]
[ROW][C]-0.0938925644723778[/C][/ROW]
[ROW][C]-0.177897591007898[/C][/ROW]
[ROW][C]-0.0924846033451493[/C][/ROW]
[ROW][C]-0.0503322220330691[/C][/ROW]
[ROW][C]0.441057957228204[/C][/ROW]
[ROW][C]0.403771058311445[/C][/ROW]
[ROW][C]-0.35078141035461[/C][/ROW]
[ROW][C]-0.0753978387485018[/C][/ROW]
[ROW][C]-0.238864071365307[/C][/ROW]
[ROW][C]0.133754558457539[/C][/ROW]
[ROW][C]0.163839803390978[/C][/ROW]
[ROW][C]0.267968889629529[/C][/ROW]
[ROW][C]0.147687166628877[/C][/ROW]
[ROW][C]0.275677926776287[/C][/ROW]
[ROW][C]0.171383689436019[/C][/ROW]
[ROW][C]0.162926992817567[/C][/ROW]
[ROW][C]0.101618386695402[/C][/ROW]
[ROW][C]-0.00624828425395776[/C][/ROW]
[ROW][C]0.0348199773931945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62482&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62482&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
0.00809999011424678
-0.256030106273241
-0.0261995982030688
0.0719851902337171
-0.0224258429805746
-0.124301476471403
0.064140545701854
-0.159502946771157
0.0759231305523105
-0.375004746715671
0.307941434557564
-0.117246775711441
-0.0129582186710077
0.325131270254925
-0.0204708244308402
0.188125836709736
0.176849438764125
0.101909987488730
0.0162069702051449
0.197310304512539
-0.303769207080201
-0.229203946742343
-0.347399624038535
-0.180554444762140
-0.0258467800321024
-0.213369819474686
-0.107150249948112
-0.0357645786100917
0.0436447597557989
-0.127445907697642
-0.126663795361469
0.138953687852875
-0.112828366648820
-0.186239291483964
0.664918433419096
-0.173809666264785
-0.313842006404744
0.224264649084628
-0.0268040736855746
0.224288989151266
0.0638262077516528
-0.0938925644723778
-0.177897591007898
-0.0924846033451493
-0.0503322220330691
0.441057957228204
0.403771058311445
-0.35078141035461
-0.0753978387485018
-0.238864071365307
0.133754558457539
0.163839803390978
0.267968889629529
0.147687166628877
0.275677926776287
0.171383689436019
0.162926992817567
0.101618386695402
-0.00624828425395776
0.0348199773931945



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')