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Author's title

Author*Unverified author*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationWed, 17 Dec 2008 03:56:10 -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/2008/Dec/17/t1229511430qmp4xcupqjwqwud.htm/, Retrieved Tue, 14 May 2024 18:34:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34298, Retrieved Tue, 14 May 2024 18:34:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [arima backward se...] [2008-12-17 10:56:10] [e1dd70d3b1099218056e8ae5041dcc2f] [Current]
- RMPD    [Central Tendency] [central tendency ...] [2008-12-23 10:31:31] [74be16979710d4c4e7c6647856088456]
-  M D      [Central Tendency] [paper centraltend...] [2009-12-30 14:57:55] [db72903d7941c8279d5ce0e4e873d517]
- RMPD      [ARIMA Forecasting] [paper arima forec...] [2009-12-30 15:31:43] [db72903d7941c8279d5ce0e4e873d517]
- RMPD        [Multiple Regression] [paper multiple re...] [2009-12-30 16:08:31] [db72903d7941c8279d5ce0e4e873d517]
- RMPD        [Multiple Regression] [paper multiple re...] [2009-12-30 16:26:00] [db72903d7941c8279d5ce0e4e873d517]
- RMPD        [Multiple Regression] [paper multiple re...] [2009-12-30 16:37:41] [db72903d7941c8279d5ce0e4e873d517]
- RMPD        [(Partial) Autocorrelation Function] [paper pacf export] [2009-12-30 16:59:53] [fd59abe368d8219a006d49608e51987e]
- RMPD        [ARIMA Backward Selection] [paper arima backw...] [2009-12-30 17:31:23] [fd59abe368d8219a006d49608e51987e]
- RMPD        [Central Tendency] [paper robustnessc...] [2009-12-30 17:38:16] [fd59abe368d8219a006d49608e51987e]
-   PD        [ARIMA Forecasting] [paper arima forec...] [2009-12-30 18:07:08] [fd59abe368d8219a006d49608e51987e]
-  MPD    [ARIMA Backward Selection] [paper arimaWLH] [2009-12-30 14:11:16] [db72903d7941c8279d5ce0e4e873d517]
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Dataseries X:
12.5
14.8
15.9
14.8
12.9
14.3
14.2
15.9
15.3
15.5
15.1
15
12.1
15.8
16.9
15.1
13.7
14.8
14.7
16
15.4
15
15.5
15.1
11.7
16.3
16.7
15
14.9
14.6
15.3
17.9
16.4
15.4
17.9
15.9
13.9
17.8
17.9
17.4
16.7
16
16.6
19.1
17.8
17.2
18.6
16.3
15.1
19.2
17.7
19.1
18
17.5
17.8
21.1
17.2
19.4
19.8
17.6
16.2
19.5
19.9
20
17.3
18.9
18.6
21.4
18.6
19.8
20.8
19.6
17.7
19.8
22.2
20.7
17.9
21.2
21.4
21.7
23.2
21.5
22.9
23.2
18.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34298&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]9 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=34298&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34298&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ma1sar1sar2sma1
Estimates ( 1 )0.57930.4206-0.58320.4897-0.4594-0.9761
(p-val)(0 )(0.0018 )(0 )(0.002 )(0.0011 )(0 )
Estimates ( 2 )0.55710.4385-0.63020-0.4268-0.2366
(p-val)(0 )(8e-04 )(0 )(NA )(0.0036 )(0.3679 )
Estimates ( 3 )0.57170.4195-0.6120-0.36210
(p-val)(0 )(0.0014 )(0 )(NA )(0.0132 )(NA )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.5793 & 0.4206 & -0.5832 & 0.4897 & -0.4594 & -0.9761 \tabularnewline
(p-val) & (0 ) & (0.0018 ) & (0 ) & (0.002 ) & (0.0011 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.5571 & 0.4385 & -0.6302 & 0 & -0.4268 & -0.2366 \tabularnewline
(p-val) & (0 ) & (8e-04 ) & (0 ) & (NA ) & (0.0036 ) & (0.3679 ) \tabularnewline
Estimates ( 3 ) & 0.5717 & 0.4195 & -0.612 & 0 & -0.3621 & 0 \tabularnewline
(p-val) & (0 ) & (0.0014 ) & (0 ) & (NA ) & (0.0132 ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34298&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.5793[/C][C]0.4206[/C][C]-0.5832[/C][C]0.4897[/C][C]-0.4594[/C][C]-0.9761[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0018 )[/C][C](0 )[/C][C](0.002 )[/C][C](0.0011 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.5571[/C][C]0.4385[/C][C]-0.6302[/C][C]0[/C][C]-0.4268[/C][C]-0.2366[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](8e-04 )[/C][C](0 )[/C][C](NA )[/C][C](0.0036 )[/C][C](0.3679 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.5717[/C][C]0.4195[/C][C]-0.612[/C][C]0[/C][C]-0.3621[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0014 )[/C][C](0 )[/C][C](NA )[/C][C](0.0132 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/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][/ROW]
[ROW][C]Estimates ( 5 )[/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][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[ROW][C]Estimates ( 8 )[/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][/ROW]
[ROW][C]Estimates ( 9 )[/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][/ROW]
[ROW][C]Estimates ( 10 )[/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][/ROW]
[ROW][C]Estimates ( 11 )[/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34298&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34298&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
Iterationar1ar2ma1sar1sar2sma1
Estimates ( 1 )0.57930.4206-0.58320.4897-0.4594-0.9761
(p-val)(0 )(0.0018 )(0 )(0.002 )(0.0011 )(0 )
Estimates ( 2 )0.55710.4385-0.63020-0.4268-0.2366
(p-val)(0 )(8e-04 )(0 )(NA )(0.0036 )(0.3679 )
Estimates ( 3 )0.57170.4195-0.6120-0.36210
(p-val)(0 )(0.0014 )(0 )(NA )(0.0132 )(NA )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.0149994435710854
-0.187272889456763
0.783610541316898
0.820253359437909
-0.128480187064758
0.0973604964959972
-0.000555424984621835
-0.108798129324484
-0.411259536814343
-0.412967982493527
-0.775180072815647
0.0513699485461512
0.171091997313326
-0.559987647777577
0.350298858460566
0.0248441904146359
-0.244826657610183
1.06340098337485
-0.064430759914597
0.109951554465647
1.50429932801390
0.63402991841865
-0.505156450025503
1.22453590536772
0.334342185134797
0.387623702787292
0.609800320816726
-0.00684756070541829
0.710259001142361
0.717745165814745
-0.38479617444722
-0.532103579474728
-0.34112865179578
-0.193930196585549
-0.0736614923676532
-0.367047391683380
-1.05336791990604
-0.237345748394650
0.776921090951805
-1.23329471813023
0.500914642312414
1.39368409436530
0.359974890816273
0.0321277650951418
1.39795292628765
-1.49091899243776
0.306194045706259
1.09684993392529
-0.138783333463160
0.161299798351927
-0.594652246674679
0.512191518507154
0.626101389245217
-1.54333588056056
0.0206384486198507
0.179085634154539
-0.37975488705151
0.15080216195892
0.0890102756063935
0.0419478410965998
0.765059089089874
0.774437669297186
-0.85186048667728
0.505328816512842
0.188557358188863
-0.949944839369926
1.30814280572219
2.03149903031633
-0.8170352751667
1.82633654903848
0.862775217520398
-0.222242407311244
1.57724133026930
-1.02736312888576

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0149994435710854 \tabularnewline
-0.187272889456763 \tabularnewline
0.783610541316898 \tabularnewline
0.820253359437909 \tabularnewline
-0.128480187064758 \tabularnewline
0.0973604964959972 \tabularnewline
-0.000555424984621835 \tabularnewline
-0.108798129324484 \tabularnewline
-0.411259536814343 \tabularnewline
-0.412967982493527 \tabularnewline
-0.775180072815647 \tabularnewline
0.0513699485461512 \tabularnewline
0.171091997313326 \tabularnewline
-0.559987647777577 \tabularnewline
0.350298858460566 \tabularnewline
0.0248441904146359 \tabularnewline
-0.244826657610183 \tabularnewline
1.06340098337485 \tabularnewline
-0.064430759914597 \tabularnewline
0.109951554465647 \tabularnewline
1.50429932801390 \tabularnewline
0.63402991841865 \tabularnewline
-0.505156450025503 \tabularnewline
1.22453590536772 \tabularnewline
0.334342185134797 \tabularnewline
0.387623702787292 \tabularnewline
0.609800320816726 \tabularnewline
-0.00684756070541829 \tabularnewline
0.710259001142361 \tabularnewline
0.717745165814745 \tabularnewline
-0.38479617444722 \tabularnewline
-0.532103579474728 \tabularnewline
-0.34112865179578 \tabularnewline
-0.193930196585549 \tabularnewline
-0.0736614923676532 \tabularnewline
-0.367047391683380 \tabularnewline
-1.05336791990604 \tabularnewline
-0.237345748394650 \tabularnewline
0.776921090951805 \tabularnewline
-1.23329471813023 \tabularnewline
0.500914642312414 \tabularnewline
1.39368409436530 \tabularnewline
0.359974890816273 \tabularnewline
0.0321277650951418 \tabularnewline
1.39795292628765 \tabularnewline
-1.49091899243776 \tabularnewline
0.306194045706259 \tabularnewline
1.09684993392529 \tabularnewline
-0.138783333463160 \tabularnewline
0.161299798351927 \tabularnewline
-0.594652246674679 \tabularnewline
0.512191518507154 \tabularnewline
0.626101389245217 \tabularnewline
-1.54333588056056 \tabularnewline
0.0206384486198507 \tabularnewline
0.179085634154539 \tabularnewline
-0.37975488705151 \tabularnewline
0.15080216195892 \tabularnewline
0.0890102756063935 \tabularnewline
0.0419478410965998 \tabularnewline
0.765059089089874 \tabularnewline
0.774437669297186 \tabularnewline
-0.85186048667728 \tabularnewline
0.505328816512842 \tabularnewline
0.188557358188863 \tabularnewline
-0.949944839369926 \tabularnewline
1.30814280572219 \tabularnewline
2.03149903031633 \tabularnewline
-0.8170352751667 \tabularnewline
1.82633654903848 \tabularnewline
0.862775217520398 \tabularnewline
-0.222242407311244 \tabularnewline
1.57724133026930 \tabularnewline
-1.02736312888576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34298&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0149994435710854[/C][/ROW]
[ROW][C]-0.187272889456763[/C][/ROW]
[ROW][C]0.783610541316898[/C][/ROW]
[ROW][C]0.820253359437909[/C][/ROW]
[ROW][C]-0.128480187064758[/C][/ROW]
[ROW][C]0.0973604964959972[/C][/ROW]
[ROW][C]-0.000555424984621835[/C][/ROW]
[ROW][C]-0.108798129324484[/C][/ROW]
[ROW][C]-0.411259536814343[/C][/ROW]
[ROW][C]-0.412967982493527[/C][/ROW]
[ROW][C]-0.775180072815647[/C][/ROW]
[ROW][C]0.0513699485461512[/C][/ROW]
[ROW][C]0.171091997313326[/C][/ROW]
[ROW][C]-0.559987647777577[/C][/ROW]
[ROW][C]0.350298858460566[/C][/ROW]
[ROW][C]0.0248441904146359[/C][/ROW]
[ROW][C]-0.244826657610183[/C][/ROW]
[ROW][C]1.06340098337485[/C][/ROW]
[ROW][C]-0.064430759914597[/C][/ROW]
[ROW][C]0.109951554465647[/C][/ROW]
[ROW][C]1.50429932801390[/C][/ROW]
[ROW][C]0.63402991841865[/C][/ROW]
[ROW][C]-0.505156450025503[/C][/ROW]
[ROW][C]1.22453590536772[/C][/ROW]
[ROW][C]0.334342185134797[/C][/ROW]
[ROW][C]0.387623702787292[/C][/ROW]
[ROW][C]0.609800320816726[/C][/ROW]
[ROW][C]-0.00684756070541829[/C][/ROW]
[ROW][C]0.710259001142361[/C][/ROW]
[ROW][C]0.717745165814745[/C][/ROW]
[ROW][C]-0.38479617444722[/C][/ROW]
[ROW][C]-0.532103579474728[/C][/ROW]
[ROW][C]-0.34112865179578[/C][/ROW]
[ROW][C]-0.193930196585549[/C][/ROW]
[ROW][C]-0.0736614923676532[/C][/ROW]
[ROW][C]-0.367047391683380[/C][/ROW]
[ROW][C]-1.05336791990604[/C][/ROW]
[ROW][C]-0.237345748394650[/C][/ROW]
[ROW][C]0.776921090951805[/C][/ROW]
[ROW][C]-1.23329471813023[/C][/ROW]
[ROW][C]0.500914642312414[/C][/ROW]
[ROW][C]1.39368409436530[/C][/ROW]
[ROW][C]0.359974890816273[/C][/ROW]
[ROW][C]0.0321277650951418[/C][/ROW]
[ROW][C]1.39795292628765[/C][/ROW]
[ROW][C]-1.49091899243776[/C][/ROW]
[ROW][C]0.306194045706259[/C][/ROW]
[ROW][C]1.09684993392529[/C][/ROW]
[ROW][C]-0.138783333463160[/C][/ROW]
[ROW][C]0.161299798351927[/C][/ROW]
[ROW][C]-0.594652246674679[/C][/ROW]
[ROW][C]0.512191518507154[/C][/ROW]
[ROW][C]0.626101389245217[/C][/ROW]
[ROW][C]-1.54333588056056[/C][/ROW]
[ROW][C]0.0206384486198507[/C][/ROW]
[ROW][C]0.179085634154539[/C][/ROW]
[ROW][C]-0.37975488705151[/C][/ROW]
[ROW][C]0.15080216195892[/C][/ROW]
[ROW][C]0.0890102756063935[/C][/ROW]
[ROW][C]0.0419478410965998[/C][/ROW]
[ROW][C]0.765059089089874[/C][/ROW]
[ROW][C]0.774437669297186[/C][/ROW]
[ROW][C]-0.85186048667728[/C][/ROW]
[ROW][C]0.505328816512842[/C][/ROW]
[ROW][C]0.188557358188863[/C][/ROW]
[ROW][C]-0.949944839369926[/C][/ROW]
[ROW][C]1.30814280572219[/C][/ROW]
[ROW][C]2.03149903031633[/C][/ROW]
[ROW][C]-0.8170352751667[/C][/ROW]
[ROW][C]1.82633654903848[/C][/ROW]
[ROW][C]0.862775217520398[/C][/ROW]
[ROW][C]-0.222242407311244[/C][/ROW]
[ROW][C]1.57724133026930[/C][/ROW]
[ROW][C]-1.02736312888576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34298&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34298&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
0.0149994435710854
-0.187272889456763
0.783610541316898
0.820253359437909
-0.128480187064758
0.0973604964959972
-0.000555424984621835
-0.108798129324484
-0.411259536814343
-0.412967982493527
-0.775180072815647
0.0513699485461512
0.171091997313326
-0.559987647777577
0.350298858460566
0.0248441904146359
-0.244826657610183
1.06340098337485
-0.064430759914597
0.109951554465647
1.50429932801390
0.63402991841865
-0.505156450025503
1.22453590536772
0.334342185134797
0.387623702787292
0.609800320816726
-0.00684756070541829
0.710259001142361
0.717745165814745
-0.38479617444722
-0.532103579474728
-0.34112865179578
-0.193930196585549
-0.0736614923676532
-0.367047391683380
-1.05336791990604
-0.237345748394650
0.776921090951805
-1.23329471813023
0.500914642312414
1.39368409436530
0.359974890816273
0.0321277650951418
1.39795292628765
-1.49091899243776
0.306194045706259
1.09684993392529
-0.138783333463160
0.161299798351927
-0.594652246674679
0.512191518507154
0.626101389245217
-1.54333588056056
0.0206384486198507
0.179085634154539
-0.37975488705151
0.15080216195892
0.0890102756063935
0.0419478410965998
0.765059089089874
0.774437669297186
-0.85186048667728
0.505328816512842
0.188557358188863
-0.949944839369926
1.30814280572219
2.03149903031633
-0.8170352751667
1.82633654903848
0.862775217520398
-0.222242407311244
1.57724133026930
-1.02736312888576



Parameters (Session):
par1 = FALSE ; par2 = 1.0 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1.0 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; 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')