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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 computationThu, 03 Dec 2009 10:58:23 -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/03/t1259863152ov3xm0f62rlajum.htm/, Retrieved Thu, 28 Mar 2024 13:34:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62999, Retrieved Thu, 28 Mar 2024 13:34:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
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] [] [2009-12-03 17:58:23] [0545e25c765ce26b196961216dc11e13] [Current]
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Dataseries X:
1,4
1,2
1
1,7
2,4
2
2,1
2
1,8
2,7
2,3
1,9
2
2,3
2,8
2,4
2,3
2,7
2,7
2,9
3
2,2
2,3
2,8
2,8
2,8
2,2
2,6
2,8
2,5
2,4
2,3
1,9
1,7
2
2,1
1,7
1,8
1,8
1,8
1,3
1,3
1,3
1,2
1,4
2,2
2,9
3,1
3,5
3,6
4,4
4,1
5,1
5,8
5,9
5,4
5,5
4,8
3,2
2,7




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=62999&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=62999&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62999&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 )0.2593-0.11930.1924-0.0407-0.4992-0.2572-0.5549
(p-val)(0.6268 )(0.4903 )(0.1774 )(0.9399 )(0.1361 )(0.3175 )(0.2085 )
Estimates ( 2 )0.2204-0.11110.18880-0.4956-0.2568-0.5615
(p-val)(0.1003 )(0.4096 )(0.1647 )(NA )(0.1348 )(0.3176 )(0.199 )
Estimates ( 3 )0.199600.16860-0.4591-0.2331-0.6007
(p-val)(0.1271 )(NA )(0.2128 )(NA )(0.1787 )(0.3836 )(0.2066 )
Estimates ( 4 )0.199400.17480-0.26580-1.0001
(p-val)(0.1164 )(NA )(0.193 )(NA )(0.1008 )(NA )(0.1917 )
Estimates ( 5 )0.1928000-0.27810-1.0013
(p-val)(0.1352 )(NA )(NA )(NA )(0.0904 )(NA )(0.1394 )
Estimates ( 6 )0.1461000-0.622400
(p-val)(0.2637 )(NA )(NA )(NA )(0 )(NA )(NA )
Estimates ( 7 )0000-0.636600
(p-val)(NA )(NA )(NA )(NA )(0 )(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 ) & 0.2593 & -0.1193 & 0.1924 & -0.0407 & -0.4992 & -0.2572 & -0.5549 \tabularnewline
(p-val) & (0.6268 ) & (0.4903 ) & (0.1774 ) & (0.9399 ) & (0.1361 ) & (0.3175 ) & (0.2085 ) \tabularnewline
Estimates ( 2 ) & 0.2204 & -0.1111 & 0.1888 & 0 & -0.4956 & -0.2568 & -0.5615 \tabularnewline
(p-val) & (0.1003 ) & (0.4096 ) & (0.1647 ) & (NA ) & (0.1348 ) & (0.3176 ) & (0.199 ) \tabularnewline
Estimates ( 3 ) & 0.1996 & 0 & 0.1686 & 0 & -0.4591 & -0.2331 & -0.6007 \tabularnewline
(p-val) & (0.1271 ) & (NA ) & (0.2128 ) & (NA ) & (0.1787 ) & (0.3836 ) & (0.2066 ) \tabularnewline
Estimates ( 4 ) & 0.1994 & 0 & 0.1748 & 0 & -0.2658 & 0 & -1.0001 \tabularnewline
(p-val) & (0.1164 ) & (NA ) & (0.193 ) & (NA ) & (0.1008 ) & (NA ) & (0.1917 ) \tabularnewline
Estimates ( 5 ) & 0.1928 & 0 & 0 & 0 & -0.2781 & 0 & -1.0013 \tabularnewline
(p-val) & (0.1352 ) & (NA ) & (NA ) & (NA ) & (0.0904 ) & (NA ) & (0.1394 ) \tabularnewline
Estimates ( 6 ) & 0.1461 & 0 & 0 & 0 & -0.6224 & 0 & 0 \tabularnewline
(p-val) & (0.2637 ) & (NA ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & -0.6366 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) & (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=62999&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]0.2593[/C][C]-0.1193[/C][C]0.1924[/C][C]-0.0407[/C][C]-0.4992[/C][C]-0.2572[/C][C]-0.5549[/C][/ROW]
[ROW][C](p-val)[/C][C](0.6268 )[/C][C](0.4903 )[/C][C](0.1774 )[/C][C](0.9399 )[/C][C](0.1361 )[/C][C](0.3175 )[/C][C](0.2085 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.2204[/C][C]-0.1111[/C][C]0.1888[/C][C]0[/C][C]-0.4956[/C][C]-0.2568[/C][C]-0.5615[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1003 )[/C][C](0.4096 )[/C][C](0.1647 )[/C][C](NA )[/C][C](0.1348 )[/C][C](0.3176 )[/C][C](0.199 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1996[/C][C]0[/C][C]0.1686[/C][C]0[/C][C]-0.4591[/C][C]-0.2331[/C][C]-0.6007[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1271 )[/C][C](NA )[/C][C](0.2128 )[/C][C](NA )[/C][C](0.1787 )[/C][C](0.3836 )[/C][C](0.2066 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1994[/C][C]0[/C][C]0.1748[/C][C]0[/C][C]-0.2658[/C][C]0[/C][C]-1.0001[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1164 )[/C][C](NA )[/C][C](0.193 )[/C][C](NA )[/C][C](0.1008 )[/C][C](NA )[/C][C](0.1917 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.1928[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.2781[/C][C]0[/C][C]-1.0013[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1352 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0904 )[/C][C](NA )[/C][C](0.1394 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.1461[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.6224[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2637 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.6366[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=62999&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62999&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 )0.2593-0.11930.1924-0.0407-0.4992-0.2572-0.5549
(p-val)(0.6268 )(0.4903 )(0.1774 )(0.9399 )(0.1361 )(0.3175 )(0.2085 )
Estimates ( 2 )0.2204-0.11110.18880-0.4956-0.2568-0.5615
(p-val)(0.1003 )(0.4096 )(0.1647 )(NA )(0.1348 )(0.3176 )(0.199 )
Estimates ( 3 )0.199600.16860-0.4591-0.2331-0.6007
(p-val)(0.1271 )(NA )(0.2128 )(NA )(0.1787 )(0.3836 )(0.2066 )
Estimates ( 4 )0.199400.17480-0.26580-1.0001
(p-val)(0.1164 )(NA )(0.193 )(NA )(0.1008 )(NA )(0.1917 )
Estimates ( 5 )0.1928000-0.27810-1.0013
(p-val)(0.1352 )(NA )(NA )(NA )(0.0904 )(NA )(0.1394 )
Estimates ( 6 )0.1461000-0.622400
(p-val)(0.2637 )(NA )(NA )(NA )(0 )(NA )(NA )
Estimates ( 7 )0000-0.636600
(p-val)(NA )(NA )(NA )(NA )(0 )(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
0.00139999883234530
-0.154853946205015
-0.133666919865533
0.570730561904452
0.467834216424491
-0.393096056392133
0.123997959584350
-0.0897002403020991
-0.145106188774748
0.727219948665105
-0.416259479337895
-0.269399550568173
0.110144224740825
0.16743256432121
0.349872931663255
-0.0191423574298892
0.330494622722537
0.101982442617903
0.0401835789714027
0.128663050753863
-0.0446118483193971
-0.236222598455854
-0.113948566228068
0.272784414446396
0.0255758687474428
0.177640886911225
-0.316055175423989
0.193205974151524
0.115694660892617
-0.0711450940834988
-0.0925468301559014
0.0390966283026413
-0.341332811150555
-0.648617321626082
0.464199801142564
0.358306674318094
-0.460070161258523
0.158430840895702
-0.388074464460074
0.303532777398949
-0.411881042748962
-0.131879806412285
-0.0349669884185999
-0.153151968832439
-0.0252776357265871
0.682665622301111
0.788056675740509
0.132713016851122
0.112714253187797
0.140183578971334
0.776299799569155
-0.416861681791404
0.732600835474987
0.599385348795343
-0.00225397156747764
-0.576852169263284
0.306619959405262
-0.23483701832290
-1.13477473681237
-0.205435149786912

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00139999883234530 \tabularnewline
-0.154853946205015 \tabularnewline
-0.133666919865533 \tabularnewline
0.570730561904452 \tabularnewline
0.467834216424491 \tabularnewline
-0.393096056392133 \tabularnewline
0.123997959584350 \tabularnewline
-0.0897002403020991 \tabularnewline
-0.145106188774748 \tabularnewline
0.727219948665105 \tabularnewline
-0.416259479337895 \tabularnewline
-0.269399550568173 \tabularnewline
0.110144224740825 \tabularnewline
0.16743256432121 \tabularnewline
0.349872931663255 \tabularnewline
-0.0191423574298892 \tabularnewline
0.330494622722537 \tabularnewline
0.101982442617903 \tabularnewline
0.0401835789714027 \tabularnewline
0.128663050753863 \tabularnewline
-0.0446118483193971 \tabularnewline
-0.236222598455854 \tabularnewline
-0.113948566228068 \tabularnewline
0.272784414446396 \tabularnewline
0.0255758687474428 \tabularnewline
0.177640886911225 \tabularnewline
-0.316055175423989 \tabularnewline
0.193205974151524 \tabularnewline
0.115694660892617 \tabularnewline
-0.0711450940834988 \tabularnewline
-0.0925468301559014 \tabularnewline
0.0390966283026413 \tabularnewline
-0.341332811150555 \tabularnewline
-0.648617321626082 \tabularnewline
0.464199801142564 \tabularnewline
0.358306674318094 \tabularnewline
-0.460070161258523 \tabularnewline
0.158430840895702 \tabularnewline
-0.388074464460074 \tabularnewline
0.303532777398949 \tabularnewline
-0.411881042748962 \tabularnewline
-0.131879806412285 \tabularnewline
-0.0349669884185999 \tabularnewline
-0.153151968832439 \tabularnewline
-0.0252776357265871 \tabularnewline
0.682665622301111 \tabularnewline
0.788056675740509 \tabularnewline
0.132713016851122 \tabularnewline
0.112714253187797 \tabularnewline
0.140183578971334 \tabularnewline
0.776299799569155 \tabularnewline
-0.416861681791404 \tabularnewline
0.732600835474987 \tabularnewline
0.599385348795343 \tabularnewline
-0.00225397156747764 \tabularnewline
-0.576852169263284 \tabularnewline
0.306619959405262 \tabularnewline
-0.23483701832290 \tabularnewline
-1.13477473681237 \tabularnewline
-0.205435149786912 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62999&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00139999883234530[/C][/ROW]
[ROW][C]-0.154853946205015[/C][/ROW]
[ROW][C]-0.133666919865533[/C][/ROW]
[ROW][C]0.570730561904452[/C][/ROW]
[ROW][C]0.467834216424491[/C][/ROW]
[ROW][C]-0.393096056392133[/C][/ROW]
[ROW][C]0.123997959584350[/C][/ROW]
[ROW][C]-0.0897002403020991[/C][/ROW]
[ROW][C]-0.145106188774748[/C][/ROW]
[ROW][C]0.727219948665105[/C][/ROW]
[ROW][C]-0.416259479337895[/C][/ROW]
[ROW][C]-0.269399550568173[/C][/ROW]
[ROW][C]0.110144224740825[/C][/ROW]
[ROW][C]0.16743256432121[/C][/ROW]
[ROW][C]0.349872931663255[/C][/ROW]
[ROW][C]-0.0191423574298892[/C][/ROW]
[ROW][C]0.330494622722537[/C][/ROW]
[ROW][C]0.101982442617903[/C][/ROW]
[ROW][C]0.0401835789714027[/C][/ROW]
[ROW][C]0.128663050753863[/C][/ROW]
[ROW][C]-0.0446118483193971[/C][/ROW]
[ROW][C]-0.236222598455854[/C][/ROW]
[ROW][C]-0.113948566228068[/C][/ROW]
[ROW][C]0.272784414446396[/C][/ROW]
[ROW][C]0.0255758687474428[/C][/ROW]
[ROW][C]0.177640886911225[/C][/ROW]
[ROW][C]-0.316055175423989[/C][/ROW]
[ROW][C]0.193205974151524[/C][/ROW]
[ROW][C]0.115694660892617[/C][/ROW]
[ROW][C]-0.0711450940834988[/C][/ROW]
[ROW][C]-0.0925468301559014[/C][/ROW]
[ROW][C]0.0390966283026413[/C][/ROW]
[ROW][C]-0.341332811150555[/C][/ROW]
[ROW][C]-0.648617321626082[/C][/ROW]
[ROW][C]0.464199801142564[/C][/ROW]
[ROW][C]0.358306674318094[/C][/ROW]
[ROW][C]-0.460070161258523[/C][/ROW]
[ROW][C]0.158430840895702[/C][/ROW]
[ROW][C]-0.388074464460074[/C][/ROW]
[ROW][C]0.303532777398949[/C][/ROW]
[ROW][C]-0.411881042748962[/C][/ROW]
[ROW][C]-0.131879806412285[/C][/ROW]
[ROW][C]-0.0349669884185999[/C][/ROW]
[ROW][C]-0.153151968832439[/C][/ROW]
[ROW][C]-0.0252776357265871[/C][/ROW]
[ROW][C]0.682665622301111[/C][/ROW]
[ROW][C]0.788056675740509[/C][/ROW]
[ROW][C]0.132713016851122[/C][/ROW]
[ROW][C]0.112714253187797[/C][/ROW]
[ROW][C]0.140183578971334[/C][/ROW]
[ROW][C]0.776299799569155[/C][/ROW]
[ROW][C]-0.416861681791404[/C][/ROW]
[ROW][C]0.732600835474987[/C][/ROW]
[ROW][C]0.599385348795343[/C][/ROW]
[ROW][C]-0.00225397156747764[/C][/ROW]
[ROW][C]-0.576852169263284[/C][/ROW]
[ROW][C]0.306619959405262[/C][/ROW]
[ROW][C]-0.23483701832290[/C][/ROW]
[ROW][C]-1.13477473681237[/C][/ROW]
[ROW][C]-0.205435149786912[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62999&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62999&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
0.00139999883234530
-0.154853946205015
-0.133666919865533
0.570730561904452
0.467834216424491
-0.393096056392133
0.123997959584350
-0.0897002403020991
-0.145106188774748
0.727219948665105
-0.416259479337895
-0.269399550568173
0.110144224740825
0.16743256432121
0.349872931663255
-0.0191423574298892
0.330494622722537
0.101982442617903
0.0401835789714027
0.128663050753863
-0.0446118483193971
-0.236222598455854
-0.113948566228068
0.272784414446396
0.0255758687474428
0.177640886911225
-0.316055175423989
0.193205974151524
0.115694660892617
-0.0711450940834988
-0.0925468301559014
0.0390966283026413
-0.341332811150555
-0.648617321626082
0.464199801142564
0.358306674318094
-0.460070161258523
0.158430840895702
-0.388074464460074
0.303532777398949
-0.411881042748962
-0.131879806412285
-0.0349669884185999
-0.153151968832439
-0.0252776357265871
0.682665622301111
0.788056675740509
0.132713016851122
0.112714253187797
0.140183578971334
0.776299799569155
-0.416861681791404
0.732600835474987
0.599385348795343
-0.00225397156747764
-0.576852169263284
0.306619959405262
-0.23483701832290
-1.13477473681237
-0.205435149786912



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')