Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareARIMA Backward Selection
Date of computationTue, 04 Dec 2012 13:58:07 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t13546475411j63rjk57z2wtyt.htm/, Retrieved Fri, 29 Mar 2024 11:27:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196515, Retrieved Fri, 29 Mar 2024 11:27:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Monthly US soldie...] [2010-11-02 12:07:39] [b98453cac15ba1066b407e146608df68]
- RMP   [Variance Reduction Matrix] [Soldiers] [2010-11-29 09:51:25] [b98453cac15ba1066b407e146608df68]
- RM      [Standard Deviation-Mean Plot] [Soldiers] [2010-11-29 11:02:42] [b98453cac15ba1066b407e146608df68]
- RMP       [ARIMA Backward Selection] [Soldiers] [2010-11-29 17:56:11] [b98453cac15ba1066b407e146608df68]
- R PD        [ARIMA Backward Selection] [WS9 4 Foutmelding] [2010-12-07 15:26:08] [afe9379cca749d06b3d6872e02cc47ed]
-   P           [ARIMA Backward Selection] [WS9 4 AR MA] [2010-12-07 15:33:10] [afe9379cca749d06b3d6872e02cc47ed]
- RMP               [ARIMA Backward Selection] [WS 9 - ARIMA] [2012-12-04 18:58:07] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

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Dataseries X:
12008
9169
8788
8417
8247
8197
8236
8253
7733
8366
8626
8863
10102
8463
9114
8563
8872
8301
8301
8278
7736
7973
8268
9476
11100
8962
9173
8738
8459
8078
8411
8291
7810
8616
8312
9692
9911
8915
9452
9112
8472
8230
8384
8625
8221
8649
8625
10443
10357
8586
8892
8329
8101
7922
8120
7838
7735
8406
8209
9451
10041
9411
10405
8467
8464
8102
7627
7513
7510
8291
8064
9383
9706
8579
9474
8318
8213
8059
9111
7708
7680
8014
8007
8718
9486
9113
9025
8476
7952
7759
7835
7600
7651
8319
8812
8630




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time18 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 18 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196515&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]18 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196515&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196515&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 time18 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.32860.1219-0.0838-10.35160.4831-0.9998
(p-val)(0.0086 )(0.3568 )(0.5005 )(0 )(0.0272 )(0.0032 )(8e-04 )
Estimates ( 2 )0.31410.10390-1.00010.33660.4959-1.0001
(p-val)(0.0092 )(0.393 )(NA )(0 )(0.025 )(0.0019 )(0.0022 )
Estimates ( 3 )0.349600-10.34910.4505-0.9995
(p-val)(0.003 )(NA )(NA )(0 )(0.0334 )(0.0045 )(0.0176 )
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 ) & 0.3286 & 0.1219 & -0.0838 & -1 & 0.3516 & 0.4831 & -0.9998 \tabularnewline
(p-val) & (0.0086 ) & (0.3568 ) & (0.5005 ) & (0 ) & (0.0272 ) & (0.0032 ) & (8e-04 ) \tabularnewline
Estimates ( 2 ) & 0.3141 & 0.1039 & 0 & -1.0001 & 0.3366 & 0.4959 & -1.0001 \tabularnewline
(p-val) & (0.0092 ) & (0.393 ) & (NA ) & (0 ) & (0.025 ) & (0.0019 ) & (0.0022 ) \tabularnewline
Estimates ( 3 ) & 0.3496 & 0 & 0 & -1 & 0.3491 & 0.4505 & -0.9995 \tabularnewline
(p-val) & (0.003 ) & (NA ) & (NA ) & (0 ) & (0.0334 ) & (0.0045 ) & (0.0176 ) \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=196515&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]0.3286[/C][C]0.1219[/C][C]-0.0838[/C][C]-1[/C][C]0.3516[/C][C]0.4831[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0086 )[/C][C](0.3568 )[/C][C](0.5005 )[/C][C](0 )[/C][C](0.0272 )[/C][C](0.0032 )[/C][C](8e-04 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.3141[/C][C]0.1039[/C][C]0[/C][C]-1.0001[/C][C]0.3366[/C][C]0.4959[/C][C]-1.0001[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0092 )[/C][C](0.393 )[/C][C](NA )[/C][C](0 )[/C][C](0.025 )[/C][C](0.0019 )[/C][C](0.0022 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.3496[/C][C]0[/C][C]0[/C][C]-1[/C][C]0.3491[/C][C]0.4505[/C][C]-0.9995[/C][/ROW]
[ROW][C](p-val)[/C][C](0.003 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0334 )[/C][C](0.0045 )[/C][C](0.0176 )[/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=196515&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196515&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 )0.32860.1219-0.0838-10.35160.4831-0.9998
(p-val)(0.0086 )(0.3568 )(0.5005 )(0 )(0.0272 )(0.0032 )(8e-04 )
Estimates ( 2 )0.31410.10390-1.00010.33660.4959-1.0001
(p-val)(0.0092 )(0.393 )(NA )(0 )(0.025 )(0.0019 )(0.0022 )
Estimates ( 3 )0.349600-10.34910.4505-0.9995
(p-val)(0.003 )(NA )(NA )(0 )(0.0334 )(0.0045 )(0.0176 )
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
-38.2805910380235
777.505350462662
1061.84589538549
405.105603545765
652.752791058235
77.4042746480946
119.985956267112
119.094246151143
87.6992125357233
-238.198889869428
-156.971230954284
551.540961161193
143.241556857497
267.224949825606
332.209873467933
239.247470746765
-91.160240190191
-97.1898209219373
262.91586807927
48.5165895066445
93.9436667923795
401.420428952458
-280.814792745847
561.723148372828
-787.526147034464
404.896712603186
340.808043988964
348.750765427474
-386.544293240681
40.1534247718902
59.81431288849
313.014849163822
314.290878771059
185.936114831155
96.6210468038424
684.77622533899
-644.737856222727
-374.673398506
-241.355118204965
-379.823993644635
-157.178138648107
-53.3997539192356
-148.395892713347
-445.817227306197
2.86682292036768
-37.307523381813
-136.69081174371
-362.145328035104
66.0032259836581
736.80630099335
1012.19933207122
-720.416784643495
102.370824861145
-14.6768747306916
-628.531576732233
-533.824690182997
-132.969609814253
35.4702854243875
-229.541811382881
-379.545510494863
-308.052273463815
-37.7273001391964
222.795609481481
11.1774116731748
20.9934124316241
121.444872599491
1172.78774462833
-376.860497154435
-43.0510447499842
-296.795689535833
-33.2523640678701
-553.792967060671
-215.993623071733
359.552283351927
-777.980127352455
390.239732296415
-270.892981274191
-139.157508072443
-192.534857032478
175.872115095657
176.039253792518
181.59722429896
718.554402301482
-675.815739954088

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-38.2805910380235 \tabularnewline
777.505350462662 \tabularnewline
1061.84589538549 \tabularnewline
405.105603545765 \tabularnewline
652.752791058235 \tabularnewline
77.4042746480946 \tabularnewline
119.985956267112 \tabularnewline
119.094246151143 \tabularnewline
87.6992125357233 \tabularnewline
-238.198889869428 \tabularnewline
-156.971230954284 \tabularnewline
551.540961161193 \tabularnewline
143.241556857497 \tabularnewline
267.224949825606 \tabularnewline
332.209873467933 \tabularnewline
239.247470746765 \tabularnewline
-91.160240190191 \tabularnewline
-97.1898209219373 \tabularnewline
262.91586807927 \tabularnewline
48.5165895066445 \tabularnewline
93.9436667923795 \tabularnewline
401.420428952458 \tabularnewline
-280.814792745847 \tabularnewline
561.723148372828 \tabularnewline
-787.526147034464 \tabularnewline
404.896712603186 \tabularnewline
340.808043988964 \tabularnewline
348.750765427474 \tabularnewline
-386.544293240681 \tabularnewline
40.1534247718902 \tabularnewline
59.81431288849 \tabularnewline
313.014849163822 \tabularnewline
314.290878771059 \tabularnewline
185.936114831155 \tabularnewline
96.6210468038424 \tabularnewline
684.77622533899 \tabularnewline
-644.737856222727 \tabularnewline
-374.673398506 \tabularnewline
-241.355118204965 \tabularnewline
-379.823993644635 \tabularnewline
-157.178138648107 \tabularnewline
-53.3997539192356 \tabularnewline
-148.395892713347 \tabularnewline
-445.817227306197 \tabularnewline
2.86682292036768 \tabularnewline
-37.307523381813 \tabularnewline
-136.69081174371 \tabularnewline
-362.145328035104 \tabularnewline
66.0032259836581 \tabularnewline
736.80630099335 \tabularnewline
1012.19933207122 \tabularnewline
-720.416784643495 \tabularnewline
102.370824861145 \tabularnewline
-14.6768747306916 \tabularnewline
-628.531576732233 \tabularnewline
-533.824690182997 \tabularnewline
-132.969609814253 \tabularnewline
35.4702854243875 \tabularnewline
-229.541811382881 \tabularnewline
-379.545510494863 \tabularnewline
-308.052273463815 \tabularnewline
-37.7273001391964 \tabularnewline
222.795609481481 \tabularnewline
11.1774116731748 \tabularnewline
20.9934124316241 \tabularnewline
121.444872599491 \tabularnewline
1172.78774462833 \tabularnewline
-376.860497154435 \tabularnewline
-43.0510447499842 \tabularnewline
-296.795689535833 \tabularnewline
-33.2523640678701 \tabularnewline
-553.792967060671 \tabularnewline
-215.993623071733 \tabularnewline
359.552283351927 \tabularnewline
-777.980127352455 \tabularnewline
390.239732296415 \tabularnewline
-270.892981274191 \tabularnewline
-139.157508072443 \tabularnewline
-192.534857032478 \tabularnewline
175.872115095657 \tabularnewline
176.039253792518 \tabularnewline
181.59722429896 \tabularnewline
718.554402301482 \tabularnewline
-675.815739954088 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196515&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-38.2805910380235[/C][/ROW]
[ROW][C]777.505350462662[/C][/ROW]
[ROW][C]1061.84589538549[/C][/ROW]
[ROW][C]405.105603545765[/C][/ROW]
[ROW][C]652.752791058235[/C][/ROW]
[ROW][C]77.4042746480946[/C][/ROW]
[ROW][C]119.985956267112[/C][/ROW]
[ROW][C]119.094246151143[/C][/ROW]
[ROW][C]87.6992125357233[/C][/ROW]
[ROW][C]-238.198889869428[/C][/ROW]
[ROW][C]-156.971230954284[/C][/ROW]
[ROW][C]551.540961161193[/C][/ROW]
[ROW][C]143.241556857497[/C][/ROW]
[ROW][C]267.224949825606[/C][/ROW]
[ROW][C]332.209873467933[/C][/ROW]
[ROW][C]239.247470746765[/C][/ROW]
[ROW][C]-91.160240190191[/C][/ROW]
[ROW][C]-97.1898209219373[/C][/ROW]
[ROW][C]262.91586807927[/C][/ROW]
[ROW][C]48.5165895066445[/C][/ROW]
[ROW][C]93.9436667923795[/C][/ROW]
[ROW][C]401.420428952458[/C][/ROW]
[ROW][C]-280.814792745847[/C][/ROW]
[ROW][C]561.723148372828[/C][/ROW]
[ROW][C]-787.526147034464[/C][/ROW]
[ROW][C]404.896712603186[/C][/ROW]
[ROW][C]340.808043988964[/C][/ROW]
[ROW][C]348.750765427474[/C][/ROW]
[ROW][C]-386.544293240681[/C][/ROW]
[ROW][C]40.1534247718902[/C][/ROW]
[ROW][C]59.81431288849[/C][/ROW]
[ROW][C]313.014849163822[/C][/ROW]
[ROW][C]314.290878771059[/C][/ROW]
[ROW][C]185.936114831155[/C][/ROW]
[ROW][C]96.6210468038424[/C][/ROW]
[ROW][C]684.77622533899[/C][/ROW]
[ROW][C]-644.737856222727[/C][/ROW]
[ROW][C]-374.673398506[/C][/ROW]
[ROW][C]-241.355118204965[/C][/ROW]
[ROW][C]-379.823993644635[/C][/ROW]
[ROW][C]-157.178138648107[/C][/ROW]
[ROW][C]-53.3997539192356[/C][/ROW]
[ROW][C]-148.395892713347[/C][/ROW]
[ROW][C]-445.817227306197[/C][/ROW]
[ROW][C]2.86682292036768[/C][/ROW]
[ROW][C]-37.307523381813[/C][/ROW]
[ROW][C]-136.69081174371[/C][/ROW]
[ROW][C]-362.145328035104[/C][/ROW]
[ROW][C]66.0032259836581[/C][/ROW]
[ROW][C]736.80630099335[/C][/ROW]
[ROW][C]1012.19933207122[/C][/ROW]
[ROW][C]-720.416784643495[/C][/ROW]
[ROW][C]102.370824861145[/C][/ROW]
[ROW][C]-14.6768747306916[/C][/ROW]
[ROW][C]-628.531576732233[/C][/ROW]
[ROW][C]-533.824690182997[/C][/ROW]
[ROW][C]-132.969609814253[/C][/ROW]
[ROW][C]35.4702854243875[/C][/ROW]
[ROW][C]-229.541811382881[/C][/ROW]
[ROW][C]-379.545510494863[/C][/ROW]
[ROW][C]-308.052273463815[/C][/ROW]
[ROW][C]-37.7273001391964[/C][/ROW]
[ROW][C]222.795609481481[/C][/ROW]
[ROW][C]11.1774116731748[/C][/ROW]
[ROW][C]20.9934124316241[/C][/ROW]
[ROW][C]121.444872599491[/C][/ROW]
[ROW][C]1172.78774462833[/C][/ROW]
[ROW][C]-376.860497154435[/C][/ROW]
[ROW][C]-43.0510447499842[/C][/ROW]
[ROW][C]-296.795689535833[/C][/ROW]
[ROW][C]-33.2523640678701[/C][/ROW]
[ROW][C]-553.792967060671[/C][/ROW]
[ROW][C]-215.993623071733[/C][/ROW]
[ROW][C]359.552283351927[/C][/ROW]
[ROW][C]-777.980127352455[/C][/ROW]
[ROW][C]390.239732296415[/C][/ROW]
[ROW][C]-270.892981274191[/C][/ROW]
[ROW][C]-139.157508072443[/C][/ROW]
[ROW][C]-192.534857032478[/C][/ROW]
[ROW][C]175.872115095657[/C][/ROW]
[ROW][C]176.039253792518[/C][/ROW]
[ROW][C]181.59722429896[/C][/ROW]
[ROW][C]718.554402301482[/C][/ROW]
[ROW][C]-675.815739954088[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196515&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196515&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
-38.2805910380235
777.505350462662
1061.84589538549
405.105603545765
652.752791058235
77.4042746480946
119.985956267112
119.094246151143
87.6992125357233
-238.198889869428
-156.971230954284
551.540961161193
143.241556857497
267.224949825606
332.209873467933
239.247470746765
-91.160240190191
-97.1898209219373
262.91586807927
48.5165895066445
93.9436667923795
401.420428952458
-280.814792745847
561.723148372828
-787.526147034464
404.896712603186
340.808043988964
348.750765427474
-386.544293240681
40.1534247718902
59.81431288849
313.014849163822
314.290878771059
185.936114831155
96.6210468038424
684.77622533899
-644.737856222727
-374.673398506
-241.355118204965
-379.823993644635
-157.178138648107
-53.3997539192356
-148.395892713347
-445.817227306197
2.86682292036768
-37.307523381813
-136.69081174371
-362.145328035104
66.0032259836581
736.80630099335
1012.19933207122
-720.416784643495
102.370824861145
-14.6768747306916
-628.531576732233
-533.824690182997
-132.969609814253
35.4702854243875
-229.541811382881
-379.545510494863
-308.052273463815
-37.7273001391964
222.795609481481
11.1774116731748
20.9934124316241
121.444872599491
1172.78774462833
-376.860497154435
-43.0510447499842
-296.795689535833
-33.2523640678701
-553.792967060671
-215.993623071733
359.552283351927
-777.980127352455
390.239732296415
-270.892981274191
-139.157508072443
-192.534857032478
175.872115095657
176.039253792518
181.59722429896
718.554402301482
-675.815739954088



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')