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Description of Statistical Computation

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 computationTue, 01 Dec 2009 13:26:03 -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/01/t1259699280moco00s820444bt.htm/, Retrieved Wed, 24 Apr 2024 17:23:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62245, Retrieved Wed, 24 Apr 2024 17:23:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact200

Tree of Dependent Computations

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]
-    D      [ARIMA Backward Selection] [BBWS9-Arimabackward1] [2009-12-01 20:26:03] [b32ceebc68d054278e6bda97f3d57f91] [Current]
- RM D        [Harrell-Davis Quantiles] [BBWS9-Harolddavis] [2009-12-01 20:39:11] [408e92805dcb18620260f240a7fb9d53]
- RM            [Mean Plot] [BBWS9-Meanplot] [2009-12-01 20:43:16] [408e92805dcb18620260f240a7fb9d53]
-   PD            [Mean Plot] [W9: Mean plot] [2009-12-02 10:46:07] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D              [Mean Plot] [WS9 Mean Plot Yt ...] [2009-12-04 16:04:10] [1b4c3bbe3f2ba180dd536c5a6a81a8e6]
-    D              [Mean Plot] [W9.9] [2009-12-04 17:33:15] [d31db4f83c6a129f6d3e47077769e868]
-   PD            [Mean Plot] [workshop 9] [2009-12-04 11:57:21] [28d531aeb5ea2ff1b676cbab66947a19]
-    D              [Mean Plot] [workshop 9] [2009-12-08 21:17:21] [28d531aeb5ea2ff1b676cbab66947a19]
- R PD            [Mean Plot] [shw-ws9] [2009-12-04 13:33:50] [2663058f2a5dda519058ac6b2228468f]
-   PD              [Mean Plot] [ws 9 mean plot] [2009-12-04 19:27:12] [134dc66689e3d457a82860db6471d419]
-    D                [Mean Plot] [Paper MP IGP] [2009-12-15 20:54:47] [134dc66689e3d457a82860db6471d419]
-    D                [Mean Plot] [Paper MP ICP] [2009-12-15 21:02:57] [134dc66689e3d457a82860db6471d419]
-   PD          [Harrell-Davis Quantiles] [W9: Harrell Davis] [2009-12-02 10:38:10] [03d5b865e91ca35b5a5d21b8d6da5aba]
- RMPD            [Univariate Data Series] [W9.1] [2009-12-04 14:13:58] [d31db4f83c6a129f6d3e47077769e868]
- RMPD            [(Partial) Autocorrelation Function] [W9.2] [2009-12-04 14:24:14] [d31db4f83c6a129f6d3e47077769e868]
-   PD            [Harrell-Davis Quantiles] [WS9 Harell-Davis ...] [2009-12-04 15:49:07] [1b4c3bbe3f2ba180dd536c5a6a81a8e6]
-   PD            [Harrell-Davis Quantiles] [W9.6] [2009-12-04 17:24:17] [d31db4f83c6a129f6d3e47077769e868]
- R PD          [Harrell-Davis Quantiles] [shw-ws9] [2009-12-04 13:29:38] [2663058f2a5dda519058ac6b2228468f]
-    D            [Harrell-Davis Quantiles] [ws 9 h d c] [2009-12-04 19:21:58] [134dc66689e3d457a82860db6471d419]
-    D              [Harrell-Davis Quantiles] [Paper HDQ IGP] [2009-12-15 20:51:01] [134dc66689e3d457a82860db6471d419]
-    D              [Harrell-Davis Quantiles] [Paper HDQ ICP] [2009-12-15 21:00:13] [134dc66689e3d457a82860db6471d419]
-   PD        [ARIMA Backward Selection] [W9: Arima Backwards] [2009-12-02 10:25:46] [03d5b865e91ca35b5a5d21b8d6da5aba]
-   PD          [ARIMA Backward Selection] [] [2009-12-04 14:15:29] [4d62210f0915d3a20cbf115865da7cd4]
-                 [ARIMA Backward Selection] [] [2009-12-04 19:48:04] [8d2349dc1d6314bc274adc9ad027c980]
-                   [ARIMA Backward Selection] [] [2009-12-13 12:30:44] [0a7d38ad9c7f1a2c46637c75a8a0e083]
-   PD          [ARIMA Backward Selection] [CVM Paper: Arima ...] [2009-12-17 17:39:13] [1094881e1ea5909e0ae8508a91bf9041]
-    D        [ARIMA Backward Selection] [shw-ws9] [2009-12-04 13:12:35] [2663058f2a5dda519058ac6b2228468f]
-   PD          [ARIMA Backward Selection] [ws 9 arima] [2009-12-04 19:09:46] [134dc66689e3d457a82860db6471d419]
-   PD            [ARIMA Backward Selection] [ws9 arma] [2009-12-04 20:18:49] [95cead3ebb75668735f848316249436a]
-   PD            [ARIMA Backward Selection] [arima backward se...] [2009-12-10 18:41:43] [134dc66689e3d457a82860db6471d419]
-   PD              [ARIMA Backward Selection] [ws 10 deel 2 arim...] [2009-12-12 09:24:44] [134dc66689e3d457a82860db6471d419]
-   P             [ARIMA Backward Selection] [Paper ARIMA B IGP] [2009-12-14 21:01:27] [134dc66689e3d457a82860db6471d419]
-   PD            [ARIMA Backward Selection] [Paper ARIMA B ICP] [2009-12-14 21:23:05] [134dc66689e3d457a82860db6471d419]
-   P               [ARIMA Backward Selection] [Paper ARIMA B ICP] [2009-12-15 19:58:21] [134dc66689e3d457a82860db6471d419]
-   PD                [ARIMA Backward Selection] [xavier blog] [2009-12-20 14:38:48] [134dc66689e3d457a82860db6471d419]
-   P           [ARIMA Backward Selection] [review ws 10 arim...] [2009-12-14 09:13:12] [12f02da0296cb21dc23d82ae014a8b71]
-   P           [ARIMA Backward Selection] [Arima] [2009-12-29 16:04:30] [2663058f2a5dda519058ac6b2228468f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
96,8
114,1
110,3
103,9
101,6
94,6
95,9
104,7
102,8
98,1
113,9
80,9
95,7
113,2
105,9
108,8
102,3
99
100,7
115,5
100,7
109,9
114,6
85,4
100,5
114,8
116,5
112,9
102
106
105,3
118,8
106,1
109,3
117,2
92,5
104,2
112,5
122,4
113,3
100
110,7
112,8
109,8
117,3
109,1
115,9
96
99,8
116,8
115,7
99,4
94,3
91
93,2
103,1
94,1
91,8
102,7
82,6

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'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=62245&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=62245&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62245&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'Gwilym Jenkins' @ 72.249.127.135






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.21860.18080.5005-0.6370.375-0.0899-0.9991
(p-val)(0.4453 )(0.4747 )(0.0038 )(0.0387 )(0.1356 )(0.7418 )(0.4099 )
Estimates ( 2 )-0.22330.1880.509-0.64060.40470-0.9995
(p-val)(0.4257 )(0.4498 )(0.0026 )(0.0346 )(0.0893 )(NA )(0.1387 )
Estimates ( 3 )-0.407800.408-0.44680.40250-0.9998
(p-val)(0.0095 )(NA )(9e-04 )(0.0051 )(0.0928 )(NA )(0.1277 )
Estimates ( 4 )-0.451800.4162-0.4372-0.291600
(p-val)(0.002 )(NA )(5e-04 )(0.0037 )(0.098 )(NA )(NA )
Estimates ( 5 )-0.444200.4149-0.4664000
(p-val)(0.0023 )(NA )(6e-04 )(0.0017 )(NA )(NA )(NA )
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 ) & -0.2186 & 0.1808 & 0.5005 & -0.637 & 0.375 & -0.0899 & -0.9991 \tabularnewline
(p-val) & (0.4453 ) & (0.4747 ) & (0.0038 ) & (0.0387 ) & (0.1356 ) & (0.7418 ) & (0.4099 ) \tabularnewline
Estimates ( 2 ) & -0.2233 & 0.188 & 0.509 & -0.6406 & 0.4047 & 0 & -0.9995 \tabularnewline
(p-val) & (0.4257 ) & (0.4498 ) & (0.0026 ) & (0.0346 ) & (0.0893 ) & (NA ) & (0.1387 ) \tabularnewline
Estimates ( 3 ) & -0.4078 & 0 & 0.408 & -0.4468 & 0.4025 & 0 & -0.9998 \tabularnewline
(p-val) & (0.0095 ) & (NA ) & (9e-04 ) & (0.0051 ) & (0.0928 ) & (NA ) & (0.1277 ) \tabularnewline
Estimates ( 4 ) & -0.4518 & 0 & 0.4162 & -0.4372 & -0.2916 & 0 & 0 \tabularnewline
(p-val) & (0.002 ) & (NA ) & (5e-04 ) & (0.0037 ) & (0.098 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & -0.4442 & 0 & 0.4149 & -0.4664 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.0023 ) & (NA ) & (6e-04 ) & (0.0017 ) & (NA ) & (NA ) & (NA ) \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=62245&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]-0.2186[/C][C]0.1808[/C][C]0.5005[/C][C]-0.637[/C][C]0.375[/C][C]-0.0899[/C][C]-0.9991[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4453 )[/C][C](0.4747 )[/C][C](0.0038 )[/C][C](0.0387 )[/C][C](0.1356 )[/C][C](0.7418 )[/C][C](0.4099 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.2233[/C][C]0.188[/C][C]0.509[/C][C]-0.6406[/C][C]0.4047[/C][C]0[/C][C]-0.9995[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4257 )[/C][C](0.4498 )[/C][C](0.0026 )[/C][C](0.0346 )[/C][C](0.0893 )[/C][C](NA )[/C][C](0.1387 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.4078[/C][C]0[/C][C]0.408[/C][C]-0.4468[/C][C]0.4025[/C][C]0[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0095 )[/C][C](NA )[/C][C](9e-04 )[/C][C](0.0051 )[/C][C](0.0928 )[/C][C](NA )[/C][C](0.1277 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.4518[/C][C]0[/C][C]0.4162[/C][C]-0.4372[/C][C]-0.2916[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.002 )[/C][C](NA )[/C][C](5e-04 )[/C][C](0.0037 )[/C][C](0.098 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.4442[/C][C]0[/C][C]0.4149[/C][C]-0.4664[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0023 )[/C][C](NA )[/C][C](6e-04 )[/C][C](0.0017 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/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=62245&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62245&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:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.21860.18080.5005-0.6370.375-0.0899-0.9991
(p-val)(0.4453 )(0.4747 )(0.0038 )(0.0387 )(0.1356 )(0.7418 )(0.4099 )
Estimates ( 2 )-0.22330.1880.509-0.64060.40470-0.9995
(p-val)(0.4257 )(0.4498 )(0.0026 )(0.0346 )(0.0893 )(NA )(0.1387 )
Estimates ( 3 )-0.407800.408-0.44680.40250-0.9998
(p-val)(0.0095 )(NA )(9e-04 )(0.0051 )(0.0928 )(NA )(0.1277 )
Estimates ( 4 )-0.451800.4162-0.4372-0.291600
(p-val)(0.002 )(NA )(5e-04 )(0.0037 )(0.098 )(NA )(NA )
Estimates ( 5 )-0.444200.4149-0.4664000
(p-val)(0.0023 )(NA )(6e-04 )(0.0017 )(NA )(NA )(NA )
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
-0.0349102355942728
0.00807721497901564
-0.121638609033658
0.271140172031791
0.115362521983019
0.208637719955046
0.00736333218421053
0.364107986611877
-0.401950824320542
0.208115628227064
-0.231184614403349
0.0886065933683892
-0.115016757667903
0.0105278025279736
0.238703049625312
0.0833760938194638
-0.244263809620781
0.0300469072378363
0.160888921911825
0.136128315686798
-0.178471109158176
-0.156191416101216
-0.123271475512903
0.287855689322172
0.114229814421261
-0.373596176543768
0.0534370488169519
-0.0094243656563928
-0.196264153183377
0.0544183083541583
0.458801613285757
-0.481978233278928
0.231921304283570
-0.120496663818209
-0.00860173725973467
-0.101427264965322
-0.096296702560096
0.0893328589991427
-0.338915896651276
-0.579111335739363
-0.240498095063384
-0.379196305811148
-0.198420345132734
0.206304690993777
-0.00161455539305067
-0.152996595141273
0.0272720847132301
0.322114095042851

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0349102355942728 \tabularnewline
0.00807721497901564 \tabularnewline
-0.121638609033658 \tabularnewline
0.271140172031791 \tabularnewline
0.115362521983019 \tabularnewline
0.208637719955046 \tabularnewline
0.00736333218421053 \tabularnewline
0.364107986611877 \tabularnewline
-0.401950824320542 \tabularnewline
0.208115628227064 \tabularnewline
-0.231184614403349 \tabularnewline
0.0886065933683892 \tabularnewline
-0.115016757667903 \tabularnewline
0.0105278025279736 \tabularnewline
0.238703049625312 \tabularnewline
0.0833760938194638 \tabularnewline
-0.244263809620781 \tabularnewline
0.0300469072378363 \tabularnewline
0.160888921911825 \tabularnewline
0.136128315686798 \tabularnewline
-0.178471109158176 \tabularnewline
-0.156191416101216 \tabularnewline
-0.123271475512903 \tabularnewline
0.287855689322172 \tabularnewline
0.114229814421261 \tabularnewline
-0.373596176543768 \tabularnewline
0.0534370488169519 \tabularnewline
-0.0094243656563928 \tabularnewline
-0.196264153183377 \tabularnewline
0.0544183083541583 \tabularnewline
0.458801613285757 \tabularnewline
-0.481978233278928 \tabularnewline
0.231921304283570 \tabularnewline
-0.120496663818209 \tabularnewline
-0.00860173725973467 \tabularnewline
-0.101427264965322 \tabularnewline
-0.096296702560096 \tabularnewline
0.0893328589991427 \tabularnewline
-0.338915896651276 \tabularnewline
-0.579111335739363 \tabularnewline
-0.240498095063384 \tabularnewline
-0.379196305811148 \tabularnewline
-0.198420345132734 \tabularnewline
0.206304690993777 \tabularnewline
-0.00161455539305067 \tabularnewline
-0.152996595141273 \tabularnewline
0.0272720847132301 \tabularnewline
0.322114095042851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62245&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0349102355942728[/C][/ROW]
[ROW][C]0.00807721497901564[/C][/ROW]
[ROW][C]-0.121638609033658[/C][/ROW]
[ROW][C]0.271140172031791[/C][/ROW]
[ROW][C]0.115362521983019[/C][/ROW]
[ROW][C]0.208637719955046[/C][/ROW]
[ROW][C]0.00736333218421053[/C][/ROW]
[ROW][C]0.364107986611877[/C][/ROW]
[ROW][C]-0.401950824320542[/C][/ROW]
[ROW][C]0.208115628227064[/C][/ROW]
[ROW][C]-0.231184614403349[/C][/ROW]
[ROW][C]0.0886065933683892[/C][/ROW]
[ROW][C]-0.115016757667903[/C][/ROW]
[ROW][C]0.0105278025279736[/C][/ROW]
[ROW][C]0.238703049625312[/C][/ROW]
[ROW][C]0.0833760938194638[/C][/ROW]
[ROW][C]-0.244263809620781[/C][/ROW]
[ROW][C]0.0300469072378363[/C][/ROW]
[ROW][C]0.160888921911825[/C][/ROW]
[ROW][C]0.136128315686798[/C][/ROW]
[ROW][C]-0.178471109158176[/C][/ROW]
[ROW][C]-0.156191416101216[/C][/ROW]
[ROW][C]-0.123271475512903[/C][/ROW]
[ROW][C]0.287855689322172[/C][/ROW]
[ROW][C]0.114229814421261[/C][/ROW]
[ROW][C]-0.373596176543768[/C][/ROW]
[ROW][C]0.0534370488169519[/C][/ROW]
[ROW][C]-0.0094243656563928[/C][/ROW]
[ROW][C]-0.196264153183377[/C][/ROW]
[ROW][C]0.0544183083541583[/C][/ROW]
[ROW][C]0.458801613285757[/C][/ROW]
[ROW][C]-0.481978233278928[/C][/ROW]
[ROW][C]0.231921304283570[/C][/ROW]
[ROW][C]-0.120496663818209[/C][/ROW]
[ROW][C]-0.00860173725973467[/C][/ROW]
[ROW][C]-0.101427264965322[/C][/ROW]
[ROW][C]-0.096296702560096[/C][/ROW]
[ROW][C]0.0893328589991427[/C][/ROW]
[ROW][C]-0.338915896651276[/C][/ROW]
[ROW][C]-0.579111335739363[/C][/ROW]
[ROW][C]-0.240498095063384[/C][/ROW]
[ROW][C]-0.379196305811148[/C][/ROW]
[ROW][C]-0.198420345132734[/C][/ROW]
[ROW][C]0.206304690993777[/C][/ROW]
[ROW][C]-0.00161455539305067[/C][/ROW]
[ROW][C]-0.152996595141273[/C][/ROW]
[ROW][C]0.0272720847132301[/C][/ROW]
[ROW][C]0.322114095042851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62245&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62245&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:

Estimated ARIMA Residuals
Value
-0.0349102355942728
0.00807721497901564
-0.121638609033658
0.271140172031791
0.115362521983019
0.208637719955046
0.00736333218421053
0.364107986611877
-0.401950824320542
0.208115628227064
-0.231184614403349
0.0886065933683892
-0.115016757667903
0.0105278025279736
0.238703049625312
0.0833760938194638
-0.244263809620781
0.0300469072378363
0.160888921911825
0.136128315686798
-0.178471109158176
-0.156191416101216
-0.123271475512903
0.287855689322172
0.114229814421261
-0.373596176543768
0.0534370488169519
-0.0094243656563928
-0.196264153183377
0.0544183083541583
0.458801613285757
-0.481978233278928
0.231921304283570
-0.120496663818209
-0.00860173725973467
-0.101427264965322
-0.096296702560096
0.0893328589991427
-0.338915896651276
-0.579111335739363
-0.240498095063384
-0.379196305811148
-0.198420345132734
0.206304690993777
-0.00161455539305067
-0.152996595141273
0.0272720847132301
0.322114095042851


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; 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')