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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationTue, 07 Dec 2010 15:33:10 +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/07/t1291735981qq6ale853wru80s.htm/, Retrieved Sun, 28 Apr 2024 19:49:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106427, Retrieved Sun, 28 Apr 2024 19:49:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact223

Tree of Dependent Computations

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] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
-    D              [ARIMA Backward Selection] [] [2010-12-16 15:02:15] [69c775ce4d55db2aa75a88e773e8d700]
- R PD              [ARIMA Backward Selection] [] [2011-12-03 13:58:43] [74be16979710d4c4e7c6647856088456]
-   P                 [ARIMA Backward Selection] [Paper: ARMA] [2011-12-17 16:17:34] [54b1f171ce7a12209ffa11b565e1dcf5]
-   PD                  [ARIMA Backward Selection] [Paper: ARMA] [2011-12-17 16:36:06] [54b1f171ce7a12209ffa11b565e1dcf5]
- R PD              [ARIMA Backward Selection] [ARIMA] [2011-12-04 14:15:32] [74be16979710d4c4e7c6647856088456]
- R P               [ARIMA Backward Selection] [Workshop 9 - ARIMA] [2011-12-06 18:06:12] [f0855b2dc4da686ca3c0ae4fedd71fda]
-   P                 [ARIMA Backward Selection] [] [2012-12-04 03:05:25] [74be16979710d4c4e7c6647856088456]
- R P               [ARIMA Backward Selection] [WS9 ARIMA Backwar...] [2012-11-29 14:42:51] [617a576b3e2f0c57f6da5ea5fef54049]
-   P                 [ARIMA Backward Selection] [Arima backward se...] [2012-12-07 09:43:48] [57fcaae991493f873bcb4ee93ca06ef0]
- R PD              [ARIMA Backward Selection] [WS 9 ARIMA Backwa...] [2012-12-03 19:03:37] [8c30f4dd45e15fd207e4faf2fdf6253e]
- R P               [ARIMA Backward Selection] [] [2012-12-04 16:26:05] [74be16979710d4c4e7c6647856088456]
- R P               [ARIMA Backward Selection] [workshop 9: AR MA...] [2012-12-04 18:23:48] [74be16979710d4c4e7c6647856088456]
- RMP               [ARIMA Backward Selection] [WS 9 - ARIMA] [2012-12-04 18:58:07] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time29 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 & 29 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106427&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]29 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=106427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106427&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 time29 seconds
R Server'George Udny Yule' @ 72.249.76.132






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.32870.1219-0.0838-10.35160.4831-0.9998
(p-val)(0.0086 )(0.3572 )(0.5005 )(0 )(0.026 )(0.0031 )(8e-04 )
Estimates ( 2 )0.31420.10410-10.33630.4959-0.9997
(p-val)(0.0147 )(0.4443 )(NA )(0 )(0.0723 )(0.0182 )(0.0018 )
Estimates ( 3 )0.349600-10.34880.4502-1.0003
(p-val)(0.0031 )(NA )(NA )(0 )(0.0335 )(0.0046 )(0.0202 )
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 ) & 0.3287 & 0.1219 & -0.0838 & -1 & 0.3516 & 0.4831 & -0.9998 \tabularnewline
(p-val) & (0.0086 ) & (0.3572 ) & (0.5005 ) & (0 ) & (0.026 ) & (0.0031 ) & (8e-04 ) \tabularnewline
Estimates ( 2 ) & 0.3142 & 0.1041 & 0 & -1 & 0.3363 & 0.4959 & -0.9997 \tabularnewline
(p-val) & (0.0147 ) & (0.4443 ) & (NA ) & (0 ) & (0.0723 ) & (0.0182 ) & (0.0018 ) \tabularnewline
Estimates ( 3 ) & 0.3496 & 0 & 0 & -1 & 0.3488 & 0.4502 & -1.0003 \tabularnewline
(p-val) & (0.0031 ) & (NA ) & (NA ) & (0 ) & (0.0335 ) & (0.0046 ) & (0.0202 ) \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=106427&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]0.3287[/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](0.3572 )[/C][C](0.5005 )[/C][C](0 )[/C][C](0.026 )[/C][C](0.0031 )[/C][C](8e-04 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.3142[/C][C]0.1041[/C][C]0[/C][C]-1[/C][C]0.3363[/C][C]0.4959[/C][C]-0.9997[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0147 )[/C][C](0.4443 )[/C][C](NA )[/C][C](0 )[/C][C](0.0723 )[/C][C](0.0182 )[/C][C](0.0018 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.3496[/C][C]0[/C][C]0[/C][C]-1[/C][C]0.3488[/C][C]0.4502[/C][C]-1.0003[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0031 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0335 )[/C][C](0.0046 )[/C][C](0.0202 )[/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=106427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106427&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 )0.32870.1219-0.0838-10.35160.4831-0.9998
(p-val)(0.0086 )(0.3572 )(0.5005 )(0 )(0.026 )(0.0031 )(8e-04 )
Estimates ( 2 )0.31420.10410-10.33630.4959-0.9997
(p-val)(0.0147 )(0.4443 )(NA )(0 )(0.0723 )(0.0182 )(0.0018 )
Estimates ( 3 )0.349600-10.34880.4502-1.0003
(p-val)(0.0031 )(NA )(NA )(0 )(0.0335 )(0.0046 )(0.0202 )
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
-38.2805909879409
777.600354370594
1062.03187313405
405.062039256935
652.643111779269
77.2748166402063
119.840567305103
119.027040655907
87.6208128048723
-238.306414602437
-157.075335025361
551.663933233048
143.279032741731
267.333303200384
332.419015598376
239.292807320751
-91.1509204293838
-97.2482032660786
263.007251126209
48.5548274958255
93.9425706301934
401.443269018874
-280.951927861857
561.900402568793
-787.605640581041
404.99639767696
341.003170652816
348.792928303481
-386.76660610041
40.0936701892881
59.924404382818
313.090072426748
314.339257420917
185.970008298672
96.4816123456803
684.889634914603
-645.205946242765
-374.768396469973
-241.179308246527
-379.697749528688
-157.103136174338
-53.2474942374579
-148.312922325738
-445.765905822265
3.04284329717006
-37.1752401245885
-136.647718229172
-362.001457800104
66.0343763913002
737.0059849215
1012.33785375636
-720.863490692973
102.169081127092
-14.6526785274693
-628.689875921194
-533.955859563544
-132.801725898951
35.686049399637
-229.542320014106
-379.599491285187
-308.055085759243
-37.3811210487568
223.234415416605
11.1345184558395
20.9995848195592
121.481288500443
1172.91477566297
-377.147852871748
-43.2775389333166
-296.842642641704
-33.2895039226744
-553.829286270648
-216.063805785670
359.769420370368
-778.036200409918
390.379739097070
-270.827110460206
-139.153679163221
-192.237885910035
175.891884219968
176.139262399222
181.573647142424
718.630312817827
-676.18236506685

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-38.2805909879409 \tabularnewline
777.600354370594 \tabularnewline
1062.03187313405 \tabularnewline
405.062039256935 \tabularnewline
652.643111779269 \tabularnewline
77.2748166402063 \tabularnewline
119.840567305103 \tabularnewline
119.027040655907 \tabularnewline
87.6208128048723 \tabularnewline
-238.306414602437 \tabularnewline
-157.075335025361 \tabularnewline
551.663933233048 \tabularnewline
143.279032741731 \tabularnewline
267.333303200384 \tabularnewline
332.419015598376 \tabularnewline
239.292807320751 \tabularnewline
-91.1509204293838 \tabularnewline
-97.2482032660786 \tabularnewline
263.007251126209 \tabularnewline
48.5548274958255 \tabularnewline
93.9425706301934 \tabularnewline
401.443269018874 \tabularnewline
-280.951927861857 \tabularnewline
561.900402568793 \tabularnewline
-787.605640581041 \tabularnewline
404.99639767696 \tabularnewline
341.003170652816 \tabularnewline
348.792928303481 \tabularnewline
-386.76660610041 \tabularnewline
40.0936701892881 \tabularnewline
59.924404382818 \tabularnewline
313.090072426748 \tabularnewline
314.339257420917 \tabularnewline
185.970008298672 \tabularnewline
96.4816123456803 \tabularnewline
684.889634914603 \tabularnewline
-645.205946242765 \tabularnewline
-374.768396469973 \tabularnewline
-241.179308246527 \tabularnewline
-379.697749528688 \tabularnewline
-157.103136174338 \tabularnewline
-53.2474942374579 \tabularnewline
-148.312922325738 \tabularnewline
-445.765905822265 \tabularnewline
3.04284329717006 \tabularnewline
-37.1752401245885 \tabularnewline
-136.647718229172 \tabularnewline
-362.001457800104 \tabularnewline
66.0343763913002 \tabularnewline
737.0059849215 \tabularnewline
1012.33785375636 \tabularnewline
-720.863490692973 \tabularnewline
102.169081127092 \tabularnewline
-14.6526785274693 \tabularnewline
-628.689875921194 \tabularnewline
-533.955859563544 \tabularnewline
-132.801725898951 \tabularnewline
35.686049399637 \tabularnewline
-229.542320014106 \tabularnewline
-379.599491285187 \tabularnewline
-308.055085759243 \tabularnewline
-37.3811210487568 \tabularnewline
223.234415416605 \tabularnewline
11.1345184558395 \tabularnewline
20.9995848195592 \tabularnewline
121.481288500443 \tabularnewline
1172.91477566297 \tabularnewline
-377.147852871748 \tabularnewline
-43.2775389333166 \tabularnewline
-296.842642641704 \tabularnewline
-33.2895039226744 \tabularnewline
-553.829286270648 \tabularnewline
-216.063805785670 \tabularnewline
359.769420370368 \tabularnewline
-778.036200409918 \tabularnewline
390.379739097070 \tabularnewline
-270.827110460206 \tabularnewline
-139.153679163221 \tabularnewline
-192.237885910035 \tabularnewline
175.891884219968 \tabularnewline
176.139262399222 \tabularnewline
181.573647142424 \tabularnewline
718.630312817827 \tabularnewline
-676.18236506685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106427&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-38.2805909879409[/C][/ROW]
[ROW][C]777.600354370594[/C][/ROW]
[ROW][C]1062.03187313405[/C][/ROW]
[ROW][C]405.062039256935[/C][/ROW]
[ROW][C]652.643111779269[/C][/ROW]
[ROW][C]77.2748166402063[/C][/ROW]
[ROW][C]119.840567305103[/C][/ROW]
[ROW][C]119.027040655907[/C][/ROW]
[ROW][C]87.6208128048723[/C][/ROW]
[ROW][C]-238.306414602437[/C][/ROW]
[ROW][C]-157.075335025361[/C][/ROW]
[ROW][C]551.663933233048[/C][/ROW]
[ROW][C]143.279032741731[/C][/ROW]
[ROW][C]267.333303200384[/C][/ROW]
[ROW][C]332.419015598376[/C][/ROW]
[ROW][C]239.292807320751[/C][/ROW]
[ROW][C]-91.1509204293838[/C][/ROW]
[ROW][C]-97.2482032660786[/C][/ROW]
[ROW][C]263.007251126209[/C][/ROW]
[ROW][C]48.5548274958255[/C][/ROW]
[ROW][C]93.9425706301934[/C][/ROW]
[ROW][C]401.443269018874[/C][/ROW]
[ROW][C]-280.951927861857[/C][/ROW]
[ROW][C]561.900402568793[/C][/ROW]
[ROW][C]-787.605640581041[/C][/ROW]
[ROW][C]404.99639767696[/C][/ROW]
[ROW][C]341.003170652816[/C][/ROW]
[ROW][C]348.792928303481[/C][/ROW]
[ROW][C]-386.76660610041[/C][/ROW]
[ROW][C]40.0936701892881[/C][/ROW]
[ROW][C]59.924404382818[/C][/ROW]
[ROW][C]313.090072426748[/C][/ROW]
[ROW][C]314.339257420917[/C][/ROW]
[ROW][C]185.970008298672[/C][/ROW]
[ROW][C]96.4816123456803[/C][/ROW]
[ROW][C]684.889634914603[/C][/ROW]
[ROW][C]-645.205946242765[/C][/ROW]
[ROW][C]-374.768396469973[/C][/ROW]
[ROW][C]-241.179308246527[/C][/ROW]
[ROW][C]-379.697749528688[/C][/ROW]
[ROW][C]-157.103136174338[/C][/ROW]
[ROW][C]-53.2474942374579[/C][/ROW]
[ROW][C]-148.312922325738[/C][/ROW]
[ROW][C]-445.765905822265[/C][/ROW]
[ROW][C]3.04284329717006[/C][/ROW]
[ROW][C]-37.1752401245885[/C][/ROW]
[ROW][C]-136.647718229172[/C][/ROW]
[ROW][C]-362.001457800104[/C][/ROW]
[ROW][C]66.0343763913002[/C][/ROW]
[ROW][C]737.0059849215[/C][/ROW]
[ROW][C]1012.33785375636[/C][/ROW]
[ROW][C]-720.863490692973[/C][/ROW]
[ROW][C]102.169081127092[/C][/ROW]
[ROW][C]-14.6526785274693[/C][/ROW]
[ROW][C]-628.689875921194[/C][/ROW]
[ROW][C]-533.955859563544[/C][/ROW]
[ROW][C]-132.801725898951[/C][/ROW]
[ROW][C]35.686049399637[/C][/ROW]
[ROW][C]-229.542320014106[/C][/ROW]
[ROW][C]-379.599491285187[/C][/ROW]
[ROW][C]-308.055085759243[/C][/ROW]
[ROW][C]-37.3811210487568[/C][/ROW]
[ROW][C]223.234415416605[/C][/ROW]
[ROW][C]11.1345184558395[/C][/ROW]
[ROW][C]20.9995848195592[/C][/ROW]
[ROW][C]121.481288500443[/C][/ROW]
[ROW][C]1172.91477566297[/C][/ROW]
[ROW][C]-377.147852871748[/C][/ROW]
[ROW][C]-43.2775389333166[/C][/ROW]
[ROW][C]-296.842642641704[/C][/ROW]
[ROW][C]-33.2895039226744[/C][/ROW]
[ROW][C]-553.829286270648[/C][/ROW]
[ROW][C]-216.063805785670[/C][/ROW]
[ROW][C]359.769420370368[/C][/ROW]
[ROW][C]-778.036200409918[/C][/ROW]
[ROW][C]390.379739097070[/C][/ROW]
[ROW][C]-270.827110460206[/C][/ROW]
[ROW][C]-139.153679163221[/C][/ROW]
[ROW][C]-192.237885910035[/C][/ROW]
[ROW][C]175.891884219968[/C][/ROW]
[ROW][C]176.139262399222[/C][/ROW]
[ROW][C]181.573647142424[/C][/ROW]
[ROW][C]718.630312817827[/C][/ROW]
[ROW][C]-676.18236506685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106427&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
-38.2805909879409
777.600354370594
1062.03187313405
405.062039256935
652.643111779269
77.2748166402063
119.840567305103
119.027040655907
87.6208128048723
-238.306414602437
-157.075335025361
551.663933233048
143.279032741731
267.333303200384
332.419015598376
239.292807320751
-91.1509204293838
-97.2482032660786
263.007251126209
48.5548274958255
93.9425706301934
401.443269018874
-280.951927861857
561.900402568793
-787.605640581041
404.99639767696
341.003170652816
348.792928303481
-386.76660610041
40.0936701892881
59.924404382818
313.090072426748
314.339257420917
185.970008298672
96.4816123456803
684.889634914603
-645.205946242765
-374.768396469973
-241.179308246527
-379.697749528688
-157.103136174338
-53.2474942374579
-148.312922325738
-445.765905822265
3.04284329717006
-37.1752401245885
-136.647718229172
-362.001457800104
66.0343763913002
737.0059849215
1012.33785375636
-720.863490692973
102.169081127092
-14.6526785274693
-628.689875921194
-533.955859563544
-132.801725898951
35.686049399637
-229.542320014106
-379.599491285187
-308.055085759243
-37.3811210487568
223.234415416605
11.1345184558395
20.9995848195592
121.481288500443
1172.91477566297
-377.147852871748
-43.2775389333166
-296.842642641704
-33.2895039226744
-553.829286270648
-216.063805785670
359.769420370368
-778.036200409918
390.379739097070
-270.827110460206
-139.153679163221
-192.237885910035
175.891884219968
176.139262399222
181.573647142424
718.630312817827
-676.18236506685


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; 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 ;
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')