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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 computationSun, 19 Dec 2010 17:42:04 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292780431gjuasjmmdz04k8j.htm/, Retrieved Sat, 04 May 2024 23:51:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112662, Retrieved Sat, 04 May 2024 23:51:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145

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   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
-    D      [ARIMA Backward Selection] [WS 9 ARMA Parameters] [2010-12-03 21:54:01] [8081b8996d5947580de3eb171e82db4f]
-   PD        [ARIMA Backward Selection] [Workshop 9, ARIMA] [2010-12-05 19:24:43] [3635fb7041b1998c5a1332cf9de22bce]
-   P           [ARIMA Backward Selection] [Workshop 9, ARIMA] [2010-12-06 22:46:35] [3635fb7041b1998c5a1332cf9de22bce]
-   PD              [ARIMA Backward Selection] [Paper ARIMA] [2010-12-19 17:42:04] [23a9b79f355c69a75648521a893cf584] [Current]
-   PD                [ARIMA Backward Selection] [Paper ARIMA 2] [2010-12-19 21:44:21] [3635fb7041b1998c5a1332cf9de22bce]
-   P                   [ARIMA Backward Selection] [ARIMA Backward Se...] [2010-12-22 09:04:25] [8081b8996d5947580de3eb171e82db4f]
-   P                   [ARIMA Backward Selection] [ARIMA Backward Se...] [2010-12-22 09:04:25] [8081b8996d5947580de3eb171e82db4f]
-   P                   [ARIMA Backward Selection] [ARIMA Backward Se...] [2010-12-22 09:04:25] [8081b8996d5947580de3eb171e82db4f]
-   P                   [ARIMA Backward Selection] [ARIMA Backward Se...] [2010-12-22 09:04:25] [8081b8996d5947580de3eb171e82db4f]
-                       [ARIMA Backward Selection] [Paper ARIMA] [2010-12-22 14:24:57] [d946de7cca328fbcf207448a112523ab]
-   PD                  [ARIMA Backward Selection] [paper] [2011-12-20 14:00:47] [43239ed98a62e091c70785d80176537f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
631923
654294
671833
586840
600969
625568
558110
630577
628654
603184
656255
600730
670326
678423
641502
625311
628177
589767
582471
636248
599885
621694
637406
595994
696308
674201
648861
649605
672392
598396
613177
638104
615632
634465
638686
604243
706669
677185
644328
664825
605707
600136
612166
599659
634210
618234
613576
627200
668973
651479
619661
644260
579936
601752
595376
588902
634341
594305
606200
610926
633685
639696
659451
593248
606677
599434
569578
629873
613438
604172
658328
612633
707372
739770
777535
685030
730234
714154
630872
719492
677023
679272
718317
645672

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 15 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112662&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112662&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112662&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 time15 seconds
R Server'George Udny Yule' @ 72.249.76.132






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.4161-0.01020.5584-0.36570.2238-0.4937-0.9999
(p-val)(0.0396 )(0.9575 )(1e-04 )(0.0986 )(0.1715 )(1e-04 )(3e-04 )
Estimates ( 2 )-0.407200.5642-0.37480.2236-0.4937-0.9999
(p-val)(2e-04 )(NA )(0 )(0.0057 )(0.1721 )(1e-04 )(3e-04 )
Estimates ( 3 )-0.399200.5969-0.41620-0.5171-1.0001
(p-val)(1e-04 )(NA )(0 )(0.0011 )(NA )(0 )(0.0077 )
Estimates ( 4 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
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 ) & -0.4161 & -0.0102 & 0.5584 & -0.3657 & 0.2238 & -0.4937 & -0.9999 \tabularnewline
(p-val) & (0.0396 ) & (0.9575 ) & (1e-04 ) & (0.0986 ) & (0.1715 ) & (1e-04 ) & (3e-04 ) \tabularnewline
Estimates ( 2 ) & -0.4072 & 0 & 0.5642 & -0.3748 & 0.2236 & -0.4937 & -0.9999 \tabularnewline
(p-val) & (2e-04 ) & (NA ) & (0 ) & (0.0057 ) & (0.1721 ) & (1e-04 ) & (3e-04 ) \tabularnewline
Estimates ( 3 ) & -0.3992 & 0 & 0.5969 & -0.4162 & 0 & -0.5171 & -1.0001 \tabularnewline
(p-val) & (1e-04 ) & (NA ) & (0 ) & (0.0011 ) & (NA ) & (0 ) & (0.0077 ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \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=112662&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]-0.4161[/C][C]-0.0102[/C][C]0.5584[/C][C]-0.3657[/C][C]0.2238[/C][C]-0.4937[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0396 )[/C][C](0.9575 )[/C][C](1e-04 )[/C][C](0.0986 )[/C][C](0.1715 )[/C][C](1e-04 )[/C][C](3e-04 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.4072[/C][C]0[/C][C]0.5642[/C][C]-0.3748[/C][C]0.2236[/C][C]-0.4937[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](2e-04 )[/C][C](NA )[/C][C](0 )[/C][C](0.0057 )[/C][C](0.1721 )[/C][C](1e-04 )[/C][C](3e-04 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.3992[/C][C]0[/C][C]0.5969[/C][C]-0.4162[/C][C]0[/C][C]-0.5171[/C][C]-1.0001[/C][/ROW]
[ROW][C](p-val)[/C][C](1e-04 )[/C][C](NA )[/C][C](0 )[/C][C](0.0011 )[/C][C](NA )[/C][C](0 )[/C][C](0.0077 )[/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][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 ( 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=112662&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112662&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 )-0.4161-0.01020.5584-0.36570.2238-0.4937-0.9999
(p-val)(0.0396 )(0.9575 )(1e-04 )(0.0986 )(0.1715 )(1e-04 )(3e-04 )
Estimates ( 2 )-0.407200.5642-0.37480.2236-0.4937-0.9999
(p-val)(2e-04 )(NA )(0 )(0.0057 )(0.1721 )(1e-04 )(3e-04 )
Estimates ( 3 )-0.399200.5969-0.41620-0.5171-1.0001
(p-val)(1e-04 )(NA )(0 )(0.0011 )(NA )(0 )(0.0077 )
Estimates ( 4 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
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
-5.558000359199e-13
1.95931257376669e-12
9.46546249760426e-12
-2.99260401777115e-12
-6.89027865147169e-12
9.02442116506426e-12
2.77125032680272e-12
-2.05615255554143e-12
1.55812882885906e-12
1.95480952654987e-12
4.94566853812078e-13
-3.41924606412419e-12
-1.88532108400407e-12
-3.3731174927397e-12
1.52888901501105e-12
-1.03895688774215e-12
-1.195294137468e-11
5.77287279748399e-12
1.21546975904495e-12
6.61634127828851e-12
3.46164451372234e-13
2.24909860744035e-12
-1.72060875189789e-12
3.49096935844113e-12
-2.90196968186375e-12
-1.22312235006374e-12
8.11995584080992e-12
-4.94725598673468e-12
1.10673882779332e-11
3.33520030706452e-12
-3.73036430692832e-12
-3.22004514395948e-12
-1.13296978650904e-12
3.264341815037e-12
6.19156029555229e-13
-3.7288264300141e-12
2.16292426103231e-13
1.65621723228509e-12
1.40649886576634e-11
-1.04397889776018e-11
-1.59018823195425e-12
-2.79665160204746e-12
-4.74786465081958e-13
2.35441827375593e-12
-5.1577308052717e-13
6.73776581857727e-12
-4.36275157441204e-13
2.05037812268400e-12
1.15078281711075e-12
-9.77283785182315e-13
-1.10767537205835e-11
4.98431671531946e-12
1.93041084012458e-12
3.8134013153917e-12
-4.11886151337701e-12
-5.34319715194517e-12
8.83000080221834e-13
-8.8493137631535e-13
-5.4728086728112e-12
-4.60770486477802e-12
-6.62805111319549e-12
-7.74325011321645e-12
-1.53208599109928e-11
-4.21081000590805e-13
1.34948891255903e-12
-4.46558156345255e-12
9.19695929038302e-12
7.35706266807698e-12
8.16225848687121e-12
-2.97064830026404e-12
2.46159823228308e-12
5.74482680732056e-12

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-5.558000359199e-13 \tabularnewline
1.95931257376669e-12 \tabularnewline
9.46546249760426e-12 \tabularnewline
-2.99260401777115e-12 \tabularnewline
-6.89027865147169e-12 \tabularnewline
9.02442116506426e-12 \tabularnewline
2.77125032680272e-12 \tabularnewline
-2.05615255554143e-12 \tabularnewline
1.55812882885906e-12 \tabularnewline
1.95480952654987e-12 \tabularnewline
4.94566853812078e-13 \tabularnewline
-3.41924606412419e-12 \tabularnewline
-1.88532108400407e-12 \tabularnewline
-3.3731174927397e-12 \tabularnewline
1.52888901501105e-12 \tabularnewline
-1.03895688774215e-12 \tabularnewline
-1.195294137468e-11 \tabularnewline
5.77287279748399e-12 \tabularnewline
1.21546975904495e-12 \tabularnewline
6.61634127828851e-12 \tabularnewline
3.46164451372234e-13 \tabularnewline
2.24909860744035e-12 \tabularnewline
-1.72060875189789e-12 \tabularnewline
3.49096935844113e-12 \tabularnewline
-2.90196968186375e-12 \tabularnewline
-1.22312235006374e-12 \tabularnewline
8.11995584080992e-12 \tabularnewline
-4.94725598673468e-12 \tabularnewline
1.10673882779332e-11 \tabularnewline
3.33520030706452e-12 \tabularnewline
-3.73036430692832e-12 \tabularnewline
-3.22004514395948e-12 \tabularnewline
-1.13296978650904e-12 \tabularnewline
3.264341815037e-12 \tabularnewline
6.19156029555229e-13 \tabularnewline
-3.7288264300141e-12 \tabularnewline
2.16292426103231e-13 \tabularnewline
1.65621723228509e-12 \tabularnewline
1.40649886576634e-11 \tabularnewline
-1.04397889776018e-11 \tabularnewline
-1.59018823195425e-12 \tabularnewline
-2.79665160204746e-12 \tabularnewline
-4.74786465081958e-13 \tabularnewline
2.35441827375593e-12 \tabularnewline
-5.1577308052717e-13 \tabularnewline
6.73776581857727e-12 \tabularnewline
-4.36275157441204e-13 \tabularnewline
2.05037812268400e-12 \tabularnewline
1.15078281711075e-12 \tabularnewline
-9.77283785182315e-13 \tabularnewline
-1.10767537205835e-11 \tabularnewline
4.98431671531946e-12 \tabularnewline
1.93041084012458e-12 \tabularnewline
3.8134013153917e-12 \tabularnewline
-4.11886151337701e-12 \tabularnewline
-5.34319715194517e-12 \tabularnewline
8.83000080221834e-13 \tabularnewline
-8.8493137631535e-13 \tabularnewline
-5.4728086728112e-12 \tabularnewline
-4.60770486477802e-12 \tabularnewline
-6.62805111319549e-12 \tabularnewline
-7.74325011321645e-12 \tabularnewline
-1.53208599109928e-11 \tabularnewline
-4.21081000590805e-13 \tabularnewline
1.34948891255903e-12 \tabularnewline
-4.46558156345255e-12 \tabularnewline
9.19695929038302e-12 \tabularnewline
7.35706266807698e-12 \tabularnewline
8.16225848687121e-12 \tabularnewline
-2.97064830026404e-12 \tabularnewline
2.46159823228308e-12 \tabularnewline
5.74482680732056e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112662&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-5.558000359199e-13[/C][/ROW]
[ROW][C]1.95931257376669e-12[/C][/ROW]
[ROW][C]9.46546249760426e-12[/C][/ROW]
[ROW][C]-2.99260401777115e-12[/C][/ROW]
[ROW][C]-6.89027865147169e-12[/C][/ROW]
[ROW][C]9.02442116506426e-12[/C][/ROW]
[ROW][C]2.77125032680272e-12[/C][/ROW]
[ROW][C]-2.05615255554143e-12[/C][/ROW]
[ROW][C]1.55812882885906e-12[/C][/ROW]
[ROW][C]1.95480952654987e-12[/C][/ROW]
[ROW][C]4.94566853812078e-13[/C][/ROW]
[ROW][C]-3.41924606412419e-12[/C][/ROW]
[ROW][C]-1.88532108400407e-12[/C][/ROW]
[ROW][C]-3.3731174927397e-12[/C][/ROW]
[ROW][C]1.52888901501105e-12[/C][/ROW]
[ROW][C]-1.03895688774215e-12[/C][/ROW]
[ROW][C]-1.195294137468e-11[/C][/ROW]
[ROW][C]5.77287279748399e-12[/C][/ROW]
[ROW][C]1.21546975904495e-12[/C][/ROW]
[ROW][C]6.61634127828851e-12[/C][/ROW]
[ROW][C]3.46164451372234e-13[/C][/ROW]
[ROW][C]2.24909860744035e-12[/C][/ROW]
[ROW][C]-1.72060875189789e-12[/C][/ROW]
[ROW][C]3.49096935844113e-12[/C][/ROW]
[ROW][C]-2.90196968186375e-12[/C][/ROW]
[ROW][C]-1.22312235006374e-12[/C][/ROW]
[ROW][C]8.11995584080992e-12[/C][/ROW]
[ROW][C]-4.94725598673468e-12[/C][/ROW]
[ROW][C]1.10673882779332e-11[/C][/ROW]
[ROW][C]3.33520030706452e-12[/C][/ROW]
[ROW][C]-3.73036430692832e-12[/C][/ROW]
[ROW][C]-3.22004514395948e-12[/C][/ROW]
[ROW][C]-1.13296978650904e-12[/C][/ROW]
[ROW][C]3.264341815037e-12[/C][/ROW]
[ROW][C]6.19156029555229e-13[/C][/ROW]
[ROW][C]-3.7288264300141e-12[/C][/ROW]
[ROW][C]2.16292426103231e-13[/C][/ROW]
[ROW][C]1.65621723228509e-12[/C][/ROW]
[ROW][C]1.40649886576634e-11[/C][/ROW]
[ROW][C]-1.04397889776018e-11[/C][/ROW]
[ROW][C]-1.59018823195425e-12[/C][/ROW]
[ROW][C]-2.79665160204746e-12[/C][/ROW]
[ROW][C]-4.74786465081958e-13[/C][/ROW]
[ROW][C]2.35441827375593e-12[/C][/ROW]
[ROW][C]-5.1577308052717e-13[/C][/ROW]
[ROW][C]6.73776581857727e-12[/C][/ROW]
[ROW][C]-4.36275157441204e-13[/C][/ROW]
[ROW][C]2.05037812268400e-12[/C][/ROW]
[ROW][C]1.15078281711075e-12[/C][/ROW]
[ROW][C]-9.77283785182315e-13[/C][/ROW]
[ROW][C]-1.10767537205835e-11[/C][/ROW]
[ROW][C]4.98431671531946e-12[/C][/ROW]
[ROW][C]1.93041084012458e-12[/C][/ROW]
[ROW][C]3.8134013153917e-12[/C][/ROW]
[ROW][C]-4.11886151337701e-12[/C][/ROW]
[ROW][C]-5.34319715194517e-12[/C][/ROW]
[ROW][C]8.83000080221834e-13[/C][/ROW]
[ROW][C]-8.8493137631535e-13[/C][/ROW]
[ROW][C]-5.4728086728112e-12[/C][/ROW]
[ROW][C]-4.60770486477802e-12[/C][/ROW]
[ROW][C]-6.62805111319549e-12[/C][/ROW]
[ROW][C]-7.74325011321645e-12[/C][/ROW]
[ROW][C]-1.53208599109928e-11[/C][/ROW]
[ROW][C]-4.21081000590805e-13[/C][/ROW]
[ROW][C]1.34948891255903e-12[/C][/ROW]
[ROW][C]-4.46558156345255e-12[/C][/ROW]
[ROW][C]9.19695929038302e-12[/C][/ROW]
[ROW][C]7.35706266807698e-12[/C][/ROW]
[ROW][C]8.16225848687121e-12[/C][/ROW]
[ROW][C]-2.97064830026404e-12[/C][/ROW]
[ROW][C]2.46159823228308e-12[/C][/ROW]
[ROW][C]5.74482680732056e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112662&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112662&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
-5.558000359199e-13
1.95931257376669e-12
9.46546249760426e-12
-2.99260401777115e-12
-6.89027865147169e-12
9.02442116506426e-12
2.77125032680272e-12
-2.05615255554143e-12
1.55812882885906e-12
1.95480952654987e-12
4.94566853812078e-13
-3.41924606412419e-12
-1.88532108400407e-12
-3.3731174927397e-12
1.52888901501105e-12
-1.03895688774215e-12
-1.195294137468e-11
5.77287279748399e-12
1.21546975904495e-12
6.61634127828851e-12
3.46164451372234e-13
2.24909860744035e-12
-1.72060875189789e-12
3.49096935844113e-12
-2.90196968186375e-12
-1.22312235006374e-12
8.11995584080992e-12
-4.94725598673468e-12
1.10673882779332e-11
3.33520030706452e-12
-3.73036430692832e-12
-3.22004514395948e-12
-1.13296978650904e-12
3.264341815037e-12
6.19156029555229e-13
-3.7288264300141e-12
2.16292426103231e-13
1.65621723228509e-12
1.40649886576634e-11
-1.04397889776018e-11
-1.59018823195425e-12
-2.79665160204746e-12
-4.74786465081958e-13
2.35441827375593e-12
-5.1577308052717e-13
6.73776581857727e-12
-4.36275157441204e-13
2.05037812268400e-12
1.15078281711075e-12
-9.77283785182315e-13
-1.10767537205835e-11
4.98431671531946e-12
1.93041084012458e-12
3.8134013153917e-12
-4.11886151337701e-12
-5.34319715194517e-12
8.83000080221834e-13
-8.8493137631535e-13
-5.4728086728112e-12
-4.60770486477802e-12
-6.62805111319549e-12
-7.74325011321645e-12
-1.53208599109928e-11
-4.21081000590805e-13
1.34948891255903e-12
-4.46558156345255e-12
9.19695929038302e-12
7.35706266807698e-12
8.16225848687121e-12
-2.97064830026404e-12
2.46159823228308e-12
5.74482680732056e-12


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = -1.7 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = -1.7 ; 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')