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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 11:49:27 -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/t125986626789ar89wx177dgcs.htm/, Retrieved Fri, 29 Mar 2024 05:35:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63061, Retrieved Fri, 29 Mar 2024 05:35:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
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 18:49:27] [2b548c9d2e9bba6e1eaf65bd4d551f41] [Current]
-   P         [ARIMA Backward Selection] [Workshop 9] [2009-12-10 21:56:05] [786e067c4f7cec17385c4742b96b6dfa]
-   P         [ARIMA Backward Selection] [Workshop 9] [2009-12-10 21:56:05] [786e067c4f7cec17385c4742b96b6dfa]
-   P         [ARIMA Backward Selection] [blog 15] [2009-12-10 22:11:39] [42ad1186d39724f834063794eac7cea3]
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Dataseries X:
8.00
8.10
7.70
7.50
7.60
7.80
7.80
7.80
7.50
7.50
7.10
7.50
7.50
7.60
7.70
7.70
7.90
8.10
8.20
8.20
8.20
7.90
7.30
6.90
6.60
6.70
6.90
7.00
7.10
7.20
7.10
6.90
7.00
6.80
6.40
6.70
6.60
6.40
6.30
6.20
6.50
6.80
6.80
6.40
6.10
5.80
6.10
7.20
7.30
6.90
6.10
5.80
6.20
7.10
7.70
7.90
7.70
7.40
7.50
8.00




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=63061&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=63061&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63061&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 )0.4427-0.0819-0.4582-0.953-0.20050.33450.852
(p-val)(0.0013 )(0.5522 )(6e-04 )(0 )(0.8304 )(0.5666 )(0.4341 )
Estimates ( 2 )0.446-0.0811-0.4548-0.955700.21890.6434
(p-val)(0.0011 )(0.556 )(6e-04 )(0 )(NA )(0.224 )(0.0024 )
Estimates ( 3 )0.40920-0.4933-0.961300.2130.6829
(p-val)(6e-04 )(NA )(0 )(0 )(NA )(0.2394 )(0.0016 )
Estimates ( 4 )0.3730-0.4811-0.9562001.499
(p-val)(0.0012 )(NA )(0 )(0 )(NA )(NA )(0.0027 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(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 ) & 0.4427 & -0.0819 & -0.4582 & -0.953 & -0.2005 & 0.3345 & 0.852 \tabularnewline
(p-val) & (0.0013 ) & (0.5522 ) & (6e-04 ) & (0 ) & (0.8304 ) & (0.5666 ) & (0.4341 ) \tabularnewline
Estimates ( 2 ) & 0.446 & -0.0811 & -0.4548 & -0.9557 & 0 & 0.2189 & 0.6434 \tabularnewline
(p-val) & (0.0011 ) & (0.556 ) & (6e-04 ) & (0 ) & (NA ) & (0.224 ) & (0.0024 ) \tabularnewline
Estimates ( 3 ) & 0.4092 & 0 & -0.4933 & -0.9613 & 0 & 0.213 & 0.6829 \tabularnewline
(p-val) & (6e-04 ) & (NA ) & (0 ) & (0 ) & (NA ) & (0.2394 ) & (0.0016 ) \tabularnewline
Estimates ( 4 ) & 0.373 & 0 & -0.4811 & -0.9562 & 0 & 0 & 1.499 \tabularnewline
(p-val) & (0.0012 ) & (NA ) & (0 ) & (0 ) & (NA ) & (NA ) & (0.0027 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (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=63061&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]0.4427[/C][C]-0.0819[/C][C]-0.4582[/C][C]-0.953[/C][C]-0.2005[/C][C]0.3345[/C][C]0.852[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0013 )[/C][C](0.5522 )[/C][C](6e-04 )[/C][C](0 )[/C][C](0.8304 )[/C][C](0.5666 )[/C][C](0.4341 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.446[/C][C]-0.0811[/C][C]-0.4548[/C][C]-0.9557[/C][C]0[/C][C]0.2189[/C][C]0.6434[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0011 )[/C][C](0.556 )[/C][C](6e-04 )[/C][C](0 )[/C][C](NA )[/C][C](0.224 )[/C][C](0.0024 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4092[/C][C]0[/C][C]-0.4933[/C][C]-0.9613[/C][C]0[/C][C]0.213[/C][C]0.6829[/C][/ROW]
[ROW][C](p-val)[/C][C](6e-04 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0.2394 )[/C][C](0.0016 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.373[/C][C]0[/C][C]-0.4811[/C][C]-0.9562[/C][C]0[/C][C]0[/C][C]1.499[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0012 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0027 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/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 ( 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=63061&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63061&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 )0.4427-0.0819-0.4582-0.953-0.20050.33450.852
(p-val)(0.0013 )(0.5522 )(6e-04 )(0 )(0.8304 )(0.5666 )(0.4341 )
Estimates ( 2 )0.446-0.0811-0.4548-0.955700.21890.6434
(p-val)(0.0011 )(0.556 )(6e-04 )(0 )(NA )(0.224 )(0.0024 )
Estimates ( 3 )0.40920-0.4933-0.961300.2130.6829
(p-val)(6e-04 )(NA )(0 )(0 )(NA )(0.2394 )(0.0016 )
Estimates ( 4 )0.3730-0.4811-0.9562001.499
(p-val)(0.0012 )(NA )(0 )(0 )(NA )(NA )(0.0027 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(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
-0.0105094411229491
-0.292875316885611
0.0815353875974915
0.183636697347257
-0.0694144915052029
-0.134915955008600
0.072322508827671
-0.116410692904101
0.114598739657068
-0.309530201804394
0.31786799771638
-0.107918912492362
-0.069537565451419
0.395813842603827
-0.0479969166674879
0.123745152551289
0.186128609701185
0.102992196021295
0.0068521767310685
0.181188234724193
-0.332818506955053
-0.249088412613647
-0.355020181300507
-0.171288284834226
0.0141064445454023
-0.206369182500390
-0.0687373460419016
0.0117988235258745
0.0721242483777001
-0.0975747018728779
-0.0993166191232005
0.179844850150904
-0.0762944013195243
-0.135152803298301
0.694532661420575
-0.148815940426830
-0.310375213707181
0.254022535625485
-0.0148150655238473
0.222058173858014
0.076189167308921
-0.0813475247355664
-0.162387594894083
-0.0878786964900622
-0.0308955416469721
0.460925697732489
0.421622800212491
-0.339855360030150
-0.0549837921535103
-0.236015173818032
0.138965252545501
0.170994068365176
0.263299904961978
0.153717128264007
0.27655221355965
0.157093686721356
0.140757248391873
0.0724250144948403
-0.0744195360928353

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0105094411229491 \tabularnewline
-0.292875316885611 \tabularnewline
0.0815353875974915 \tabularnewline
0.183636697347257 \tabularnewline
-0.0694144915052029 \tabularnewline
-0.134915955008600 \tabularnewline
0.072322508827671 \tabularnewline
-0.116410692904101 \tabularnewline
0.114598739657068 \tabularnewline
-0.309530201804394 \tabularnewline
0.31786799771638 \tabularnewline
-0.107918912492362 \tabularnewline
-0.069537565451419 \tabularnewline
0.395813842603827 \tabularnewline
-0.0479969166674879 \tabularnewline
0.123745152551289 \tabularnewline
0.186128609701185 \tabularnewline
0.102992196021295 \tabularnewline
0.0068521767310685 \tabularnewline
0.181188234724193 \tabularnewline
-0.332818506955053 \tabularnewline
-0.249088412613647 \tabularnewline
-0.355020181300507 \tabularnewline
-0.171288284834226 \tabularnewline
0.0141064445454023 \tabularnewline
-0.206369182500390 \tabularnewline
-0.0687373460419016 \tabularnewline
0.0117988235258745 \tabularnewline
0.0721242483777001 \tabularnewline
-0.0975747018728779 \tabularnewline
-0.0993166191232005 \tabularnewline
0.179844850150904 \tabularnewline
-0.0762944013195243 \tabularnewline
-0.135152803298301 \tabularnewline
0.694532661420575 \tabularnewline
-0.148815940426830 \tabularnewline
-0.310375213707181 \tabularnewline
0.254022535625485 \tabularnewline
-0.0148150655238473 \tabularnewline
0.222058173858014 \tabularnewline
0.076189167308921 \tabularnewline
-0.0813475247355664 \tabularnewline
-0.162387594894083 \tabularnewline
-0.0878786964900622 \tabularnewline
-0.0308955416469721 \tabularnewline
0.460925697732489 \tabularnewline
0.421622800212491 \tabularnewline
-0.339855360030150 \tabularnewline
-0.0549837921535103 \tabularnewline
-0.236015173818032 \tabularnewline
0.138965252545501 \tabularnewline
0.170994068365176 \tabularnewline
0.263299904961978 \tabularnewline
0.153717128264007 \tabularnewline
0.27655221355965 \tabularnewline
0.157093686721356 \tabularnewline
0.140757248391873 \tabularnewline
0.0724250144948403 \tabularnewline
-0.0744195360928353 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63061&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0105094411229491[/C][/ROW]
[ROW][C]-0.292875316885611[/C][/ROW]
[ROW][C]0.0815353875974915[/C][/ROW]
[ROW][C]0.183636697347257[/C][/ROW]
[ROW][C]-0.0694144915052029[/C][/ROW]
[ROW][C]-0.134915955008600[/C][/ROW]
[ROW][C]0.072322508827671[/C][/ROW]
[ROW][C]-0.116410692904101[/C][/ROW]
[ROW][C]0.114598739657068[/C][/ROW]
[ROW][C]-0.309530201804394[/C][/ROW]
[ROW][C]0.31786799771638[/C][/ROW]
[ROW][C]-0.107918912492362[/C][/ROW]
[ROW][C]-0.069537565451419[/C][/ROW]
[ROW][C]0.395813842603827[/C][/ROW]
[ROW][C]-0.0479969166674879[/C][/ROW]
[ROW][C]0.123745152551289[/C][/ROW]
[ROW][C]0.186128609701185[/C][/ROW]
[ROW][C]0.102992196021295[/C][/ROW]
[ROW][C]0.0068521767310685[/C][/ROW]
[ROW][C]0.181188234724193[/C][/ROW]
[ROW][C]-0.332818506955053[/C][/ROW]
[ROW][C]-0.249088412613647[/C][/ROW]
[ROW][C]-0.355020181300507[/C][/ROW]
[ROW][C]-0.171288284834226[/C][/ROW]
[ROW][C]0.0141064445454023[/C][/ROW]
[ROW][C]-0.206369182500390[/C][/ROW]
[ROW][C]-0.0687373460419016[/C][/ROW]
[ROW][C]0.0117988235258745[/C][/ROW]
[ROW][C]0.0721242483777001[/C][/ROW]
[ROW][C]-0.0975747018728779[/C][/ROW]
[ROW][C]-0.0993166191232005[/C][/ROW]
[ROW][C]0.179844850150904[/C][/ROW]
[ROW][C]-0.0762944013195243[/C][/ROW]
[ROW][C]-0.135152803298301[/C][/ROW]
[ROW][C]0.694532661420575[/C][/ROW]
[ROW][C]-0.148815940426830[/C][/ROW]
[ROW][C]-0.310375213707181[/C][/ROW]
[ROW][C]0.254022535625485[/C][/ROW]
[ROW][C]-0.0148150655238473[/C][/ROW]
[ROW][C]0.222058173858014[/C][/ROW]
[ROW][C]0.076189167308921[/C][/ROW]
[ROW][C]-0.0813475247355664[/C][/ROW]
[ROW][C]-0.162387594894083[/C][/ROW]
[ROW][C]-0.0878786964900622[/C][/ROW]
[ROW][C]-0.0308955416469721[/C][/ROW]
[ROW][C]0.460925697732489[/C][/ROW]
[ROW][C]0.421622800212491[/C][/ROW]
[ROW][C]-0.339855360030150[/C][/ROW]
[ROW][C]-0.0549837921535103[/C][/ROW]
[ROW][C]-0.236015173818032[/C][/ROW]
[ROW][C]0.138965252545501[/C][/ROW]
[ROW][C]0.170994068365176[/C][/ROW]
[ROW][C]0.263299904961978[/C][/ROW]
[ROW][C]0.153717128264007[/C][/ROW]
[ROW][C]0.27655221355965[/C][/ROW]
[ROW][C]0.157093686721356[/C][/ROW]
[ROW][C]0.140757248391873[/C][/ROW]
[ROW][C]0.0724250144948403[/C][/ROW]
[ROW][C]-0.0744195360928353[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63061&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63061&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
-0.0105094411229491
-0.292875316885611
0.0815353875974915
0.183636697347257
-0.0694144915052029
-0.134915955008600
0.072322508827671
-0.116410692904101
0.114598739657068
-0.309530201804394
0.31786799771638
-0.107918912492362
-0.069537565451419
0.395813842603827
-0.0479969166674879
0.123745152551289
0.186128609701185
0.102992196021295
0.0068521767310685
0.181188234724193
-0.332818506955053
-0.249088412613647
-0.355020181300507
-0.171288284834226
0.0141064445454023
-0.206369182500390
-0.0687373460419016
0.0117988235258745
0.0721242483777001
-0.0975747018728779
-0.0993166191232005
0.179844850150904
-0.0762944013195243
-0.135152803298301
0.694532661420575
-0.148815940426830
-0.310375213707181
0.254022535625485
-0.0148150655238473
0.222058173858014
0.076189167308921
-0.0813475247355664
-0.162387594894083
-0.0878786964900622
-0.0308955416469721
0.460925697732489
0.421622800212491
-0.339855360030150
-0.0549837921535103
-0.236015173818032
0.138965252545501
0.170994068365176
0.263299904961978
0.153717128264007
0.27655221355965
0.157093686721356
0.140757248391873
0.0724250144948403
-0.0744195360928353



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 2 ; 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')