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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationTue, 28 Dec 2010 22:54:06 +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/28/t1293576772pxthl13out4wri9.htm/, Retrieved Sat, 04 May 2024 23:30:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116575, Retrieved Sat, 04 May 2024 23:30:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [arima] [2010-12-28 22:54:06] [a7b2a0c11f64eef3aa1d49ba29ffb331] [Current]
-         [ARIMA Backward Selection] [Arima Backward OPJV] [2010-12-29 22:24:13] [17d39bb3ec485d4ce196f61215d11ba1]
- RMP     [Spectral Analysis] [ARIMA BW opjb] [2010-12-29 22:51:15] [442b6d00ecbe55ac6a674160c9c5510a]
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Dataseries X:
20503
22885
26217
26583
27751
28158
27373
28367
26851
26733
26849
26733
27951
29781
32914
33488
35652
36488
35387
35676
34844
32447
31068
29010
29812
30951
32974
32936
34012
32946
31948
30599
27691
25073
23406
22248
22896
25317
26558
26471
27543
26198
24725
25005
23462
20780
19815
19761
21454
23899
24939
23580
24562
24696
23785
23812
21917
19713
19282
18788
21453
24482
27474
27264
27349
30632
29429
30084
26290
24379
23335
21346
21106
24514
28353
30805
31348
34556
33855
34787
32529
29998
29257
28155
30466
35704
39327
39351
42234
43630
43722
43121
37985
37135
34646
33026
35087
38846
42013
43908
42868
44423
44167
43636
44382
42142
43452
36912
42413
45344
44873
47510
49554
47369
45998
48140
48441
44928
40454
38661
37246
36843
36424
37594
38144
38737
34560
36080
33508
35462
33374
32110
35533
35532
37903
36763
40399
44164
44496
43110
43880
43930
44327




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time21 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 21 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116575&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]21 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116575&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116575&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 time21 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.56650.1750.0214-0.65490.1438-0.0416-0.9999
(p-val)(0.0846 )(0.0982 )(0.8496 )(0.0353 )(0.1722 )(0.6903 )(0 )
Estimates ( 2 )0.60380.18410-0.68940.1423-0.0401-0.9998
(p-val)(0.011 )(0.0543 )(NA )(0.0021 )(0.1753 )(0.6993 )(0 )
Estimates ( 3 )0.6160.18060-0.69930.1440-1.0001
(p-val)(0.008 )(0.0582 )(NA )(0.0013 )(0.171 )(NA )(0 )
Estimates ( 4 )0.69590.13430-0.742600-1.0008
(p-val)(0.0029 )(0.1476 )(NA )(8e-04 )(NA )(NA )(0.0158 )
Estimates ( 5 )-0.6587000.600500-1.0001
(p-val)(0.1179 )(NA )(NA )(0.1737 )(NA )(NA )(7e-04 )
Estimates ( 6 )-0.037400000-1
(p-val)(0.6704 )(NA )(NA )(NA )(NA )(NA )(0 )
Estimates ( 7 )000000-1
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0 )
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 ) & 0.5665 & 0.175 & 0.0214 & -0.6549 & 0.1438 & -0.0416 & -0.9999 \tabularnewline
(p-val) & (0.0846 ) & (0.0982 ) & (0.8496 ) & (0.0353 ) & (0.1722 ) & (0.6903 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.6038 & 0.1841 & 0 & -0.6894 & 0.1423 & -0.0401 & -0.9998 \tabularnewline
(p-val) & (0.011 ) & (0.0543 ) & (NA ) & (0.0021 ) & (0.1753 ) & (0.6993 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.616 & 0.1806 & 0 & -0.6993 & 0.144 & 0 & -1.0001 \tabularnewline
(p-val) & (0.008 ) & (0.0582 ) & (NA ) & (0.0013 ) & (0.171 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0.6959 & 0.1343 & 0 & -0.7426 & 0 & 0 & -1.0008 \tabularnewline
(p-val) & (0.0029 ) & (0.1476 ) & (NA ) & (8e-04 ) & (NA ) & (NA ) & (0.0158 ) \tabularnewline
Estimates ( 5 ) & -0.6587 & 0 & 0 & 0.6005 & 0 & 0 & -1.0001 \tabularnewline
(p-val) & (0.1179 ) & (NA ) & (NA ) & (0.1737 ) & (NA ) & (NA ) & (7e-04 ) \tabularnewline
Estimates ( 6 ) & -0.0374 & 0 & 0 & 0 & 0 & 0 & -1 \tabularnewline
(p-val) & (0.6704 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & 0 & -1 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) \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=116575&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]0.5665[/C][C]0.175[/C][C]0.0214[/C][C]-0.6549[/C][C]0.1438[/C][C]-0.0416[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0846 )[/C][C](0.0982 )[/C][C](0.8496 )[/C][C](0.0353 )[/C][C](0.1722 )[/C][C](0.6903 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.6038[/C][C]0.1841[/C][C]0[/C][C]-0.6894[/C][C]0.1423[/C][C]-0.0401[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.011 )[/C][C](0.0543 )[/C][C](NA )[/C][C](0.0021 )[/C][C](0.1753 )[/C][C](0.6993 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.616[/C][C]0.1806[/C][C]0[/C][C]-0.6993[/C][C]0.144[/C][C]0[/C][C]-1.0001[/C][/ROW]
[ROW][C](p-val)[/C][C](0.008 )[/C][C](0.0582 )[/C][C](NA )[/C][C](0.0013 )[/C][C](0.171 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.6959[/C][C]0.1343[/C][C]0[/C][C]-0.7426[/C][C]0[/C][C]0[/C][C]-1.0008[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0029 )[/C][C](0.1476 )[/C][C](NA )[/C][C](8e-04 )[/C][C](NA )[/C][C](NA )[/C][C](0.0158 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.6587[/C][C]0[/C][C]0[/C][C]0.6005[/C][C]0[/C][C]0[/C][C]-1.0001[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1179 )[/C][C](NA )[/C][C](NA )[/C][C](0.1737 )[/C][C](NA )[/C][C](NA )[/C][C](7e-04 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]-0.0374[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.6704 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-1[/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](0 )[/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=116575&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116575&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 )0.56650.1750.0214-0.65490.1438-0.0416-0.9999
(p-val)(0.0846 )(0.0982 )(0.8496 )(0.0353 )(0.1722 )(0.6903 )(0 )
Estimates ( 2 )0.60380.18410-0.68940.1423-0.0401-0.9998
(p-val)(0.011 )(0.0543 )(NA )(0.0021 )(0.1753 )(0.6993 )(0 )
Estimates ( 3 )0.6160.18060-0.69930.1440-1.0001
(p-val)(0.008 )(0.0582 )(NA )(0.0013 )(0.171 )(NA )(0 )
Estimates ( 4 )0.69590.13430-0.742600-1.0008
(p-val)(0.0029 )(0.1476 )(NA )(8e-04 )(NA )(NA )(0.0158 )
Estimates ( 5 )-0.6587000.600500-1.0001
(p-val)(0.1179 )(NA )(NA )(0.1737 )(NA )(NA )(7e-04 )
Estimates ( 6 )-0.037400000-1
(p-val)(0.6704 )(NA )(NA )(NA )(NA )(NA )(0 )
Estimates ( 7 )000000-1
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0 )
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
-62.321381126758
-390.088749557945
-155.337368874004
141.78084661056
709.741418516009
329.620568764402
-212.189416541707
-506.969604366085
464.927791945498
-1593.57790647935
-1117.52936416192
-1413.19078384949
-338.45472739476
-798.74038234253
-1017.11307755271
-451.737909199677
-497.288012586387
-1395.92759094214
-96.4758675263218
-1627.0215503915
-1476.67701271934
-1163.88991495856
-887.117993106668
-90.0297585792944
-288.777124314963
541.805694615512
-1354.95211521141
-387.185162063238
-356.690746325884
-1228.83119339613
-488.629150431658
244.927189740032
190.721546569678
-834.231039133104
-21.3998797954502
915.245084873905
734.892196440462
474.348403097308
-1228.52339788642
-1444.37258434554
-399.325711806788
368.031300966376
173.644742349217
-17.7832498657755
-175.573335200881
-230.415951632902
477.042329487877
333.276374799065
1423.11562786152
951.41089493545
798.81241383719
-63.7938458274309
-1105.6998173208
3144.58426304238
-17.3153498277729
548.812716307257
-1855.50529376333
14.5364933302269
-160.101020806881
-1113.53354231872
-1542.18558810317
1055.9714552549
1472.42594719982
2439.99283135841
-418.314562338369
2604.0728028311
447.542735410159
737.664657904132
-136.506964243455
-508.573000981219
123.75941666081
-109.361109060897
1088.19226038051
2714.66002559656
1137.10032443033
-165.697908710922
1741.74358709365
642.013120029304
1066.01901205731
-767.456203879512
-2864.01506178994
1031.38762916915
-1469.18175242878
-640.327364234324
688.639977374673
990.382705271232
520.76688434506
1601.8337818069
-2096.70844693665
577.838920094179
617.638940820678
-623.100786016039
3022.3794703219
-193.615312331436
2237.12769581985
-5069.52576688584
3676.77832698638
220.495358021104
-3014.97082465778
2007.63669552485
1076.77295455738
-2921.94706175993
-638.018696315082
1939.02826294356
2375.92642063677
-1396.6599647477
-3531.51722516781
-235.950815922162
-3104.36658127538
-3224.81311959252
-2796.40367905345
419.065665356569
-502.82148504883
-47.4721813737582
-3153.56825794044
1060.82806983594
-612.429750508933
3846.9693220275
-724.48814475154
375.977746223589
1824.89896869593
-2386.30119418338
132.919160569482
-1729.29339251285
2412.99733907329
3104.15432366185
1551.87877963368
-1652.55815795726
2536.71401949951
1808.42341461426
1649.75041923491

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-62.321381126758 \tabularnewline
-390.088749557945 \tabularnewline
-155.337368874004 \tabularnewline
141.78084661056 \tabularnewline
709.741418516009 \tabularnewline
329.620568764402 \tabularnewline
-212.189416541707 \tabularnewline
-506.969604366085 \tabularnewline
464.927791945498 \tabularnewline
-1593.57790647935 \tabularnewline
-1117.52936416192 \tabularnewline
-1413.19078384949 \tabularnewline
-338.45472739476 \tabularnewline
-798.74038234253 \tabularnewline
-1017.11307755271 \tabularnewline
-451.737909199677 \tabularnewline
-497.288012586387 \tabularnewline
-1395.92759094214 \tabularnewline
-96.4758675263218 \tabularnewline
-1627.0215503915 \tabularnewline
-1476.67701271934 \tabularnewline
-1163.88991495856 \tabularnewline
-887.117993106668 \tabularnewline
-90.0297585792944 \tabularnewline
-288.777124314963 \tabularnewline
541.805694615512 \tabularnewline
-1354.95211521141 \tabularnewline
-387.185162063238 \tabularnewline
-356.690746325884 \tabularnewline
-1228.83119339613 \tabularnewline
-488.629150431658 \tabularnewline
244.927189740032 \tabularnewline
190.721546569678 \tabularnewline
-834.231039133104 \tabularnewline
-21.3998797954502 \tabularnewline
915.245084873905 \tabularnewline
734.892196440462 \tabularnewline
474.348403097308 \tabularnewline
-1228.52339788642 \tabularnewline
-1444.37258434554 \tabularnewline
-399.325711806788 \tabularnewline
368.031300966376 \tabularnewline
173.644742349217 \tabularnewline
-17.7832498657755 \tabularnewline
-175.573335200881 \tabularnewline
-230.415951632902 \tabularnewline
477.042329487877 \tabularnewline
333.276374799065 \tabularnewline
1423.11562786152 \tabularnewline
951.41089493545 \tabularnewline
798.81241383719 \tabularnewline
-63.7938458274309 \tabularnewline
-1105.6998173208 \tabularnewline
3144.58426304238 \tabularnewline
-17.3153498277729 \tabularnewline
548.812716307257 \tabularnewline
-1855.50529376333 \tabularnewline
14.5364933302269 \tabularnewline
-160.101020806881 \tabularnewline
-1113.53354231872 \tabularnewline
-1542.18558810317 \tabularnewline
1055.9714552549 \tabularnewline
1472.42594719982 \tabularnewline
2439.99283135841 \tabularnewline
-418.314562338369 \tabularnewline
2604.0728028311 \tabularnewline
447.542735410159 \tabularnewline
737.664657904132 \tabularnewline
-136.506964243455 \tabularnewline
-508.573000981219 \tabularnewline
123.75941666081 \tabularnewline
-109.361109060897 \tabularnewline
1088.19226038051 \tabularnewline
2714.66002559656 \tabularnewline
1137.10032443033 \tabularnewline
-165.697908710922 \tabularnewline
1741.74358709365 \tabularnewline
642.013120029304 \tabularnewline
1066.01901205731 \tabularnewline
-767.456203879512 \tabularnewline
-2864.01506178994 \tabularnewline
1031.38762916915 \tabularnewline
-1469.18175242878 \tabularnewline
-640.327364234324 \tabularnewline
688.639977374673 \tabularnewline
990.382705271232 \tabularnewline
520.76688434506 \tabularnewline
1601.8337818069 \tabularnewline
-2096.70844693665 \tabularnewline
577.838920094179 \tabularnewline
617.638940820678 \tabularnewline
-623.100786016039 \tabularnewline
3022.3794703219 \tabularnewline
-193.615312331436 \tabularnewline
2237.12769581985 \tabularnewline
-5069.52576688584 \tabularnewline
3676.77832698638 \tabularnewline
220.495358021104 \tabularnewline
-3014.97082465778 \tabularnewline
2007.63669552485 \tabularnewline
1076.77295455738 \tabularnewline
-2921.94706175993 \tabularnewline
-638.018696315082 \tabularnewline
1939.02826294356 \tabularnewline
2375.92642063677 \tabularnewline
-1396.6599647477 \tabularnewline
-3531.51722516781 \tabularnewline
-235.950815922162 \tabularnewline
-3104.36658127538 \tabularnewline
-3224.81311959252 \tabularnewline
-2796.40367905345 \tabularnewline
419.065665356569 \tabularnewline
-502.82148504883 \tabularnewline
-47.4721813737582 \tabularnewline
-3153.56825794044 \tabularnewline
1060.82806983594 \tabularnewline
-612.429750508933 \tabularnewline
3846.9693220275 \tabularnewline
-724.48814475154 \tabularnewline
375.977746223589 \tabularnewline
1824.89896869593 \tabularnewline
-2386.30119418338 \tabularnewline
132.919160569482 \tabularnewline
-1729.29339251285 \tabularnewline
2412.99733907329 \tabularnewline
3104.15432366185 \tabularnewline
1551.87877963368 \tabularnewline
-1652.55815795726 \tabularnewline
2536.71401949951 \tabularnewline
1808.42341461426 \tabularnewline
1649.75041923491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116575&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-62.321381126758[/C][/ROW]
[ROW][C]-390.088749557945[/C][/ROW]
[ROW][C]-155.337368874004[/C][/ROW]
[ROW][C]141.78084661056[/C][/ROW]
[ROW][C]709.741418516009[/C][/ROW]
[ROW][C]329.620568764402[/C][/ROW]
[ROW][C]-212.189416541707[/C][/ROW]
[ROW][C]-506.969604366085[/C][/ROW]
[ROW][C]464.927791945498[/C][/ROW]
[ROW][C]-1593.57790647935[/C][/ROW]
[ROW][C]-1117.52936416192[/C][/ROW]
[ROW][C]-1413.19078384949[/C][/ROW]
[ROW][C]-338.45472739476[/C][/ROW]
[ROW][C]-798.74038234253[/C][/ROW]
[ROW][C]-1017.11307755271[/C][/ROW]
[ROW][C]-451.737909199677[/C][/ROW]
[ROW][C]-497.288012586387[/C][/ROW]
[ROW][C]-1395.92759094214[/C][/ROW]
[ROW][C]-96.4758675263218[/C][/ROW]
[ROW][C]-1627.0215503915[/C][/ROW]
[ROW][C]-1476.67701271934[/C][/ROW]
[ROW][C]-1163.88991495856[/C][/ROW]
[ROW][C]-887.117993106668[/C][/ROW]
[ROW][C]-90.0297585792944[/C][/ROW]
[ROW][C]-288.777124314963[/C][/ROW]
[ROW][C]541.805694615512[/C][/ROW]
[ROW][C]-1354.95211521141[/C][/ROW]
[ROW][C]-387.185162063238[/C][/ROW]
[ROW][C]-356.690746325884[/C][/ROW]
[ROW][C]-1228.83119339613[/C][/ROW]
[ROW][C]-488.629150431658[/C][/ROW]
[ROW][C]244.927189740032[/C][/ROW]
[ROW][C]190.721546569678[/C][/ROW]
[ROW][C]-834.231039133104[/C][/ROW]
[ROW][C]-21.3998797954502[/C][/ROW]
[ROW][C]915.245084873905[/C][/ROW]
[ROW][C]734.892196440462[/C][/ROW]
[ROW][C]474.348403097308[/C][/ROW]
[ROW][C]-1228.52339788642[/C][/ROW]
[ROW][C]-1444.37258434554[/C][/ROW]
[ROW][C]-399.325711806788[/C][/ROW]
[ROW][C]368.031300966376[/C][/ROW]
[ROW][C]173.644742349217[/C][/ROW]
[ROW][C]-17.7832498657755[/C][/ROW]
[ROW][C]-175.573335200881[/C][/ROW]
[ROW][C]-230.415951632902[/C][/ROW]
[ROW][C]477.042329487877[/C][/ROW]
[ROW][C]333.276374799065[/C][/ROW]
[ROW][C]1423.11562786152[/C][/ROW]
[ROW][C]951.41089493545[/C][/ROW]
[ROW][C]798.81241383719[/C][/ROW]
[ROW][C]-63.7938458274309[/C][/ROW]
[ROW][C]-1105.6998173208[/C][/ROW]
[ROW][C]3144.58426304238[/C][/ROW]
[ROW][C]-17.3153498277729[/C][/ROW]
[ROW][C]548.812716307257[/C][/ROW]
[ROW][C]-1855.50529376333[/C][/ROW]
[ROW][C]14.5364933302269[/C][/ROW]
[ROW][C]-160.101020806881[/C][/ROW]
[ROW][C]-1113.53354231872[/C][/ROW]
[ROW][C]-1542.18558810317[/C][/ROW]
[ROW][C]1055.9714552549[/C][/ROW]
[ROW][C]1472.42594719982[/C][/ROW]
[ROW][C]2439.99283135841[/C][/ROW]
[ROW][C]-418.314562338369[/C][/ROW]
[ROW][C]2604.0728028311[/C][/ROW]
[ROW][C]447.542735410159[/C][/ROW]
[ROW][C]737.664657904132[/C][/ROW]
[ROW][C]-136.506964243455[/C][/ROW]
[ROW][C]-508.573000981219[/C][/ROW]
[ROW][C]123.75941666081[/C][/ROW]
[ROW][C]-109.361109060897[/C][/ROW]
[ROW][C]1088.19226038051[/C][/ROW]
[ROW][C]2714.66002559656[/C][/ROW]
[ROW][C]1137.10032443033[/C][/ROW]
[ROW][C]-165.697908710922[/C][/ROW]
[ROW][C]1741.74358709365[/C][/ROW]
[ROW][C]642.013120029304[/C][/ROW]
[ROW][C]1066.01901205731[/C][/ROW]
[ROW][C]-767.456203879512[/C][/ROW]
[ROW][C]-2864.01506178994[/C][/ROW]
[ROW][C]1031.38762916915[/C][/ROW]
[ROW][C]-1469.18175242878[/C][/ROW]
[ROW][C]-640.327364234324[/C][/ROW]
[ROW][C]688.639977374673[/C][/ROW]
[ROW][C]990.382705271232[/C][/ROW]
[ROW][C]520.76688434506[/C][/ROW]
[ROW][C]1601.8337818069[/C][/ROW]
[ROW][C]-2096.70844693665[/C][/ROW]
[ROW][C]577.838920094179[/C][/ROW]
[ROW][C]617.638940820678[/C][/ROW]
[ROW][C]-623.100786016039[/C][/ROW]
[ROW][C]3022.3794703219[/C][/ROW]
[ROW][C]-193.615312331436[/C][/ROW]
[ROW][C]2237.12769581985[/C][/ROW]
[ROW][C]-5069.52576688584[/C][/ROW]
[ROW][C]3676.77832698638[/C][/ROW]
[ROW][C]220.495358021104[/C][/ROW]
[ROW][C]-3014.97082465778[/C][/ROW]
[ROW][C]2007.63669552485[/C][/ROW]
[ROW][C]1076.77295455738[/C][/ROW]
[ROW][C]-2921.94706175993[/C][/ROW]
[ROW][C]-638.018696315082[/C][/ROW]
[ROW][C]1939.02826294356[/C][/ROW]
[ROW][C]2375.92642063677[/C][/ROW]
[ROW][C]-1396.6599647477[/C][/ROW]
[ROW][C]-3531.51722516781[/C][/ROW]
[ROW][C]-235.950815922162[/C][/ROW]
[ROW][C]-3104.36658127538[/C][/ROW]
[ROW][C]-3224.81311959252[/C][/ROW]
[ROW][C]-2796.40367905345[/C][/ROW]
[ROW][C]419.065665356569[/C][/ROW]
[ROW][C]-502.82148504883[/C][/ROW]
[ROW][C]-47.4721813737582[/C][/ROW]
[ROW][C]-3153.56825794044[/C][/ROW]
[ROW][C]1060.82806983594[/C][/ROW]
[ROW][C]-612.429750508933[/C][/ROW]
[ROW][C]3846.9693220275[/C][/ROW]
[ROW][C]-724.48814475154[/C][/ROW]
[ROW][C]375.977746223589[/C][/ROW]
[ROW][C]1824.89896869593[/C][/ROW]
[ROW][C]-2386.30119418338[/C][/ROW]
[ROW][C]132.919160569482[/C][/ROW]
[ROW][C]-1729.29339251285[/C][/ROW]
[ROW][C]2412.99733907329[/C][/ROW]
[ROW][C]3104.15432366185[/C][/ROW]
[ROW][C]1551.87877963368[/C][/ROW]
[ROW][C]-1652.55815795726[/C][/ROW]
[ROW][C]2536.71401949951[/C][/ROW]
[ROW][C]1808.42341461426[/C][/ROW]
[ROW][C]1649.75041923491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116575&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116575&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
-62.321381126758
-390.088749557945
-155.337368874004
141.78084661056
709.741418516009
329.620568764402
-212.189416541707
-506.969604366085
464.927791945498
-1593.57790647935
-1117.52936416192
-1413.19078384949
-338.45472739476
-798.74038234253
-1017.11307755271
-451.737909199677
-497.288012586387
-1395.92759094214
-96.4758675263218
-1627.0215503915
-1476.67701271934
-1163.88991495856
-887.117993106668
-90.0297585792944
-288.777124314963
541.805694615512
-1354.95211521141
-387.185162063238
-356.690746325884
-1228.83119339613
-488.629150431658
244.927189740032
190.721546569678
-834.231039133104
-21.3998797954502
915.245084873905
734.892196440462
474.348403097308
-1228.52339788642
-1444.37258434554
-399.325711806788
368.031300966376
173.644742349217
-17.7832498657755
-175.573335200881
-230.415951632902
477.042329487877
333.276374799065
1423.11562786152
951.41089493545
798.81241383719
-63.7938458274309
-1105.6998173208
3144.58426304238
-17.3153498277729
548.812716307257
-1855.50529376333
14.5364933302269
-160.101020806881
-1113.53354231872
-1542.18558810317
1055.9714552549
1472.42594719982
2439.99283135841
-418.314562338369
2604.0728028311
447.542735410159
737.664657904132
-136.506964243455
-508.573000981219
123.75941666081
-109.361109060897
1088.19226038051
2714.66002559656
1137.10032443033
-165.697908710922
1741.74358709365
642.013120029304
1066.01901205731
-767.456203879512
-2864.01506178994
1031.38762916915
-1469.18175242878
-640.327364234324
688.639977374673
990.382705271232
520.76688434506
1601.8337818069
-2096.70844693665
577.838920094179
617.638940820678
-623.100786016039
3022.3794703219
-193.615312331436
2237.12769581985
-5069.52576688584
3676.77832698638
220.495358021104
-3014.97082465778
2007.63669552485
1076.77295455738
-2921.94706175993
-638.018696315082
1939.02826294356
2375.92642063677
-1396.6599647477
-3531.51722516781
-235.950815922162
-3104.36658127538
-3224.81311959252
-2796.40367905345
419.065665356569
-502.82148504883
-47.4721813737582
-3153.56825794044
1060.82806983594
-612.429750508933
3846.9693220275
-724.48814475154
375.977746223589
1824.89896869593
-2386.30119418338
132.919160569482
-1729.29339251285
2412.99733907329
3104.15432366185
1551.87877963368
-1652.55815795726
2536.71401949951
1808.42341461426
1649.75041923491



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 ;
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')