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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 computationThu, 03 Dec 2009 03:22:18 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/03/t1259835878tugqhl29eobr5mg.htm/, Retrieved Fri, 29 Mar 2024 12:00:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62668, Retrieved Fri, 29 Mar 2024 12:00:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160
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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-    D      [ARIMA Backward Selection] [] [2009-12-03 10:22:18] [5858ea01c9bd81debbf921a11363ad90] [Current]
-             [ARIMA Backward Selection] [] [2009-12-03 10:28:04] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-             [ARIMA Backward Selection] [] [2009-12-03 10:41:57] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-   P           [ARIMA Backward Selection] [] [2009-12-03 16:28:59] [2f674a53c3d7aaa1bcf80e66074d3c9b]
- RMP             [Spectral Analysis] [] [2009-12-03 16:51:13] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-                 [ARIMA Backward Selection] [] [2009-12-11 11:55:24] [ff47dd0689925b5f8d992b55e66ceb45]
-             [ARIMA Backward Selection] [] [2009-12-03 10:45:33] [2f674a53c3d7aaa1bcf80e66074d3c9b]
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Dataseries X:
21790
13253
37702
30364
32609
30212
29965
28352
25814
22414
20506
28806
22228
13971
36845
35338
35022
34777
26887
23970
22780
17351
21382
24561
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367
31124
26551
30651
25859
25100
25778
20418
18688
20424
24776
19814
12738
31566
30111
30019
31934
25826
26835
20205
17789
20520
22518
15572
11509
25447
24090
27786
26195
20516
22759
19028
16971
20036
22485
18730




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62668&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62668&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62668&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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.14180.37660.20180.07930.1453-0.1132-0.9999
(p-val)(0.7976 )(0.0425 )(0.3982 )(0.8885 )(0.246 )(0.3562 )(0 )
Estimates ( 2 )0.21850.35460.17100.1462-0.1119-0.9999
(p-val)(0.0548 )(4e-04 )(0.1453 )(NA )(0.2443 )(0.3615 )(0 )
Estimates ( 3 )0.18230.35530.199500.14260-0.9999
(p-val)(0.0793 )(3e-04 )(0.0702 )(NA )(0.2565 )(NA )(0 )
Estimates ( 4 )0.14980.36460.2504000-0.9997
(p-val)(0.13 )(2e-04 )(0.0133 )(NA )(NA )(NA )(0.0028 )
Estimates ( 5 )00.40140.3094000-0.9702
(p-val)(NA )(0 )(0.0014 )(NA )(NA )(NA )(0.1929 )
Estimates ( 6 )00.39780.12760000
(p-val)(NA )(1e-04 )(0.1966 )(NA )(NA )(NA )(NA )
Estimates ( 7 )00.450600000
(p-val)(NA )(0 )(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 ) & 0.1418 & 0.3766 & 0.2018 & 0.0793 & 0.1453 & -0.1132 & -0.9999 \tabularnewline
(p-val) & (0.7976 ) & (0.0425 ) & (0.3982 ) & (0.8885 ) & (0.246 ) & (0.3562 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.2185 & 0.3546 & 0.171 & 0 & 0.1462 & -0.1119 & -0.9999 \tabularnewline
(p-val) & (0.0548 ) & (4e-04 ) & (0.1453 ) & (NA ) & (0.2443 ) & (0.3615 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.1823 & 0.3553 & 0.1995 & 0 & 0.1426 & 0 & -0.9999 \tabularnewline
(p-val) & (0.0793 ) & (3e-04 ) & (0.0702 ) & (NA ) & (0.2565 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0.1498 & 0.3646 & 0.2504 & 0 & 0 & 0 & -0.9997 \tabularnewline
(p-val) & (0.13 ) & (2e-04 ) & (0.0133 ) & (NA ) & (NA ) & (NA ) & (0.0028 ) \tabularnewline
Estimates ( 5 ) & 0 & 0.4014 & 0.3094 & 0 & 0 & 0 & -0.9702 \tabularnewline
(p-val) & (NA ) & (0 ) & (0.0014 ) & (NA ) & (NA ) & (NA ) & (0.1929 ) \tabularnewline
Estimates ( 6 ) & 0 & 0.3978 & 0.1276 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (1e-04 ) & (0.1966 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0.4506 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (0 ) & (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=62668&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]0.1418[/C][C]0.3766[/C][C]0.2018[/C][C]0.0793[/C][C]0.1453[/C][C]-0.1132[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7976 )[/C][C](0.0425 )[/C][C](0.3982 )[/C][C](0.8885 )[/C][C](0.246 )[/C][C](0.3562 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.2185[/C][C]0.3546[/C][C]0.171[/C][C]0[/C][C]0.1462[/C][C]-0.1119[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0548 )[/C][C](4e-04 )[/C][C](0.1453 )[/C][C](NA )[/C][C](0.2443 )[/C][C](0.3615 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1823[/C][C]0.3553[/C][C]0.1995[/C][C]0[/C][C]0.1426[/C][C]0[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0793 )[/C][C](3e-04 )[/C][C](0.0702 )[/C][C](NA )[/C][C](0.2565 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1498[/C][C]0.3646[/C][C]0.2504[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.9997[/C][/ROW]
[ROW][C](p-val)[/C][C](0.13 )[/C][C](2e-04 )[/C][C](0.0133 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0028 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0.4014[/C][C]0.3094[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.9702[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](0.0014 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1929 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0.3978[/C][C]0.1276[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](1e-04 )[/C][C](0.1966 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0.4506[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/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=62668&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62668&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 )0.14180.37660.20180.07930.1453-0.1132-0.9999
(p-val)(0.7976 )(0.0425 )(0.3982 )(0.8885 )(0.246 )(0.3562 )(0 )
Estimates ( 2 )0.21850.35460.17100.1462-0.1119-0.9999
(p-val)(0.0548 )(4e-04 )(0.1453 )(NA )(0.2443 )(0.3615 )(0 )
Estimates ( 3 )0.18230.35530.199500.14260-0.9999
(p-val)(0.0793 )(3e-04 )(0.0702 )(NA )(0.2565 )(NA )(0 )
Estimates ( 4 )0.14980.36460.2504000-0.9997
(p-val)(0.13 )(2e-04 )(0.0133 )(NA )(NA )(NA )(0.0028 )
Estimates ( 5 )00.40140.3094000-0.9702
(p-val)(NA )(0 )(0.0014 )(NA )(NA )(NA )(0.1929 )
Estimates ( 6 )00.39780.12760000
(p-val)(NA )(1e-04 )(0.1966 )(NA )(NA )(NA )(NA )
Estimates ( 7 )00.450600000
(p-val)(NA )(0 )(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
5.89192959504912e-06
-6.06250851177332e-05
-0.000199877315751040
9.75427489107244e-05
-0.000320661919501023
-0.00018901056156856
-0.000231706413866418
0.000452449375026747
0.000700362250531803
0.000323420951608120
0.000664276470986052
-0.000370651602066251
7.47310076531623e-05
0.000812787010311063
0.000683018408899884
1.42636259033377e-05
0.0003052223893087
0.000204355206695696
0.000471239844878407
0.000294716450620965
-0.000373356308826137
-0.000175521577101700
-0.000237755554831028
3.43470362801062e-05
-5.43005601426564e-05
-8.36330748689778e-05
-0.00127434392900585
0.00037740028238226
0.00028109619319787
-0.000556692106469921
-0.000548350643429367
0.000223106762744771
0.000250763033607168
0.000758475416370231
0.000146766612449761
-0.000188977727928553
0.000209259246509525
-0.000334562191432188
0.000346621275357574
-0.000101559799981404
0.000259484000795248
0.000630694761734907
0.000414110496108162
-0.000260943245935719
-0.000633170857856212
-8.02403013891615e-05
-0.00011767255320215
0.000159873834010740
0.000580455484507022
0.000116921481471482
0.000949909816499088
-0.000319304996236724
-0.000907150034484098
-0.000545598513728826
0.000119191825335595
-0.000319580459727470
0.000508150051734644
0.000196184519662971
0.000196417311722505
0.000246103151822164
-0.000318303315276883
7.86609983162634e-05
0.000710584409762925
9.8686951592094e-06
2.20480071715899e-05
0.000214218754293794
1.63657168358016e-05
2.5224022888886e-05
-0.000370669917796593
-0.000520302508259609
-0.000199837253512991
-0.000211657545981314
-0.000382378567164824
-0.000280008062898401
-0.00120102293254119
0.000221401786965688
0.000272773353495361
0.000263498821745580
-0.000468728302097863
-6.53966649963745e-05
0.000116192069955239
0.000151795080093800
0.000243254022415853
-1.52138948691675e-05
0.000233596506482969
0.000892646525912088
0.000339478787375828
0.000238819415437245
0.000380570894517093
-8.61288955488083e-05
0.000230414837026887
0.000581725220750692
0.000263311212618933
-0.000161982623000182
-0.00012680350600897
-6.83315799530756e-05
-9.3499177176202e-05
-0.00076286711690782

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
5.89192959504912e-06 \tabularnewline
-6.06250851177332e-05 \tabularnewline
-0.000199877315751040 \tabularnewline
9.75427489107244e-05 \tabularnewline
-0.000320661919501023 \tabularnewline
-0.00018901056156856 \tabularnewline
-0.000231706413866418 \tabularnewline
0.000452449375026747 \tabularnewline
0.000700362250531803 \tabularnewline
0.000323420951608120 \tabularnewline
0.000664276470986052 \tabularnewline
-0.000370651602066251 \tabularnewline
7.47310076531623e-05 \tabularnewline
0.000812787010311063 \tabularnewline
0.000683018408899884 \tabularnewline
1.42636259033377e-05 \tabularnewline
0.0003052223893087 \tabularnewline
0.000204355206695696 \tabularnewline
0.000471239844878407 \tabularnewline
0.000294716450620965 \tabularnewline
-0.000373356308826137 \tabularnewline
-0.000175521577101700 \tabularnewline
-0.000237755554831028 \tabularnewline
3.43470362801062e-05 \tabularnewline
-5.43005601426564e-05 \tabularnewline
-8.36330748689778e-05 \tabularnewline
-0.00127434392900585 \tabularnewline
0.00037740028238226 \tabularnewline
0.00028109619319787 \tabularnewline
-0.000556692106469921 \tabularnewline
-0.000548350643429367 \tabularnewline
0.000223106762744771 \tabularnewline
0.000250763033607168 \tabularnewline
0.000758475416370231 \tabularnewline
0.000146766612449761 \tabularnewline
-0.000188977727928553 \tabularnewline
0.000209259246509525 \tabularnewline
-0.000334562191432188 \tabularnewline
0.000346621275357574 \tabularnewline
-0.000101559799981404 \tabularnewline
0.000259484000795248 \tabularnewline
0.000630694761734907 \tabularnewline
0.000414110496108162 \tabularnewline
-0.000260943245935719 \tabularnewline
-0.000633170857856212 \tabularnewline
-8.02403013891615e-05 \tabularnewline
-0.00011767255320215 \tabularnewline
0.000159873834010740 \tabularnewline
0.000580455484507022 \tabularnewline
0.000116921481471482 \tabularnewline
0.000949909816499088 \tabularnewline
-0.000319304996236724 \tabularnewline
-0.000907150034484098 \tabularnewline
-0.000545598513728826 \tabularnewline
0.000119191825335595 \tabularnewline
-0.000319580459727470 \tabularnewline
0.000508150051734644 \tabularnewline
0.000196184519662971 \tabularnewline
0.000196417311722505 \tabularnewline
0.000246103151822164 \tabularnewline
-0.000318303315276883 \tabularnewline
7.86609983162634e-05 \tabularnewline
0.000710584409762925 \tabularnewline
9.8686951592094e-06 \tabularnewline
2.20480071715899e-05 \tabularnewline
0.000214218754293794 \tabularnewline
1.63657168358016e-05 \tabularnewline
2.5224022888886e-05 \tabularnewline
-0.000370669917796593 \tabularnewline
-0.000520302508259609 \tabularnewline
-0.000199837253512991 \tabularnewline
-0.000211657545981314 \tabularnewline
-0.000382378567164824 \tabularnewline
-0.000280008062898401 \tabularnewline
-0.00120102293254119 \tabularnewline
0.000221401786965688 \tabularnewline
0.000272773353495361 \tabularnewline
0.000263498821745580 \tabularnewline
-0.000468728302097863 \tabularnewline
-6.53966649963745e-05 \tabularnewline
0.000116192069955239 \tabularnewline
0.000151795080093800 \tabularnewline
0.000243254022415853 \tabularnewline
-1.52138948691675e-05 \tabularnewline
0.000233596506482969 \tabularnewline
0.000892646525912088 \tabularnewline
0.000339478787375828 \tabularnewline
0.000238819415437245 \tabularnewline
0.000380570894517093 \tabularnewline
-8.61288955488083e-05 \tabularnewline
0.000230414837026887 \tabularnewline
0.000581725220750692 \tabularnewline
0.000263311212618933 \tabularnewline
-0.000161982623000182 \tabularnewline
-0.00012680350600897 \tabularnewline
-6.83315799530756e-05 \tabularnewline
-9.3499177176202e-05 \tabularnewline
-0.00076286711690782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62668&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]5.89192959504912e-06[/C][/ROW]
[ROW][C]-6.06250851177332e-05[/C][/ROW]
[ROW][C]-0.000199877315751040[/C][/ROW]
[ROW][C]9.75427489107244e-05[/C][/ROW]
[ROW][C]-0.000320661919501023[/C][/ROW]
[ROW][C]-0.00018901056156856[/C][/ROW]
[ROW][C]-0.000231706413866418[/C][/ROW]
[ROW][C]0.000452449375026747[/C][/ROW]
[ROW][C]0.000700362250531803[/C][/ROW]
[ROW][C]0.000323420951608120[/C][/ROW]
[ROW][C]0.000664276470986052[/C][/ROW]
[ROW][C]-0.000370651602066251[/C][/ROW]
[ROW][C]7.47310076531623e-05[/C][/ROW]
[ROW][C]0.000812787010311063[/C][/ROW]
[ROW][C]0.000683018408899884[/C][/ROW]
[ROW][C]1.42636259033377e-05[/C][/ROW]
[ROW][C]0.0003052223893087[/C][/ROW]
[ROW][C]0.000204355206695696[/C][/ROW]
[ROW][C]0.000471239844878407[/C][/ROW]
[ROW][C]0.000294716450620965[/C][/ROW]
[ROW][C]-0.000373356308826137[/C][/ROW]
[ROW][C]-0.000175521577101700[/C][/ROW]
[ROW][C]-0.000237755554831028[/C][/ROW]
[ROW][C]3.43470362801062e-05[/C][/ROW]
[ROW][C]-5.43005601426564e-05[/C][/ROW]
[ROW][C]-8.36330748689778e-05[/C][/ROW]
[ROW][C]-0.00127434392900585[/C][/ROW]
[ROW][C]0.00037740028238226[/C][/ROW]
[ROW][C]0.00028109619319787[/C][/ROW]
[ROW][C]-0.000556692106469921[/C][/ROW]
[ROW][C]-0.000548350643429367[/C][/ROW]
[ROW][C]0.000223106762744771[/C][/ROW]
[ROW][C]0.000250763033607168[/C][/ROW]
[ROW][C]0.000758475416370231[/C][/ROW]
[ROW][C]0.000146766612449761[/C][/ROW]
[ROW][C]-0.000188977727928553[/C][/ROW]
[ROW][C]0.000209259246509525[/C][/ROW]
[ROW][C]-0.000334562191432188[/C][/ROW]
[ROW][C]0.000346621275357574[/C][/ROW]
[ROW][C]-0.000101559799981404[/C][/ROW]
[ROW][C]0.000259484000795248[/C][/ROW]
[ROW][C]0.000630694761734907[/C][/ROW]
[ROW][C]0.000414110496108162[/C][/ROW]
[ROW][C]-0.000260943245935719[/C][/ROW]
[ROW][C]-0.000633170857856212[/C][/ROW]
[ROW][C]-8.02403013891615e-05[/C][/ROW]
[ROW][C]-0.00011767255320215[/C][/ROW]
[ROW][C]0.000159873834010740[/C][/ROW]
[ROW][C]0.000580455484507022[/C][/ROW]
[ROW][C]0.000116921481471482[/C][/ROW]
[ROW][C]0.000949909816499088[/C][/ROW]
[ROW][C]-0.000319304996236724[/C][/ROW]
[ROW][C]-0.000907150034484098[/C][/ROW]
[ROW][C]-0.000545598513728826[/C][/ROW]
[ROW][C]0.000119191825335595[/C][/ROW]
[ROW][C]-0.000319580459727470[/C][/ROW]
[ROW][C]0.000508150051734644[/C][/ROW]
[ROW][C]0.000196184519662971[/C][/ROW]
[ROW][C]0.000196417311722505[/C][/ROW]
[ROW][C]0.000246103151822164[/C][/ROW]
[ROW][C]-0.000318303315276883[/C][/ROW]
[ROW][C]7.86609983162634e-05[/C][/ROW]
[ROW][C]0.000710584409762925[/C][/ROW]
[ROW][C]9.8686951592094e-06[/C][/ROW]
[ROW][C]2.20480071715899e-05[/C][/ROW]
[ROW][C]0.000214218754293794[/C][/ROW]
[ROW][C]1.63657168358016e-05[/C][/ROW]
[ROW][C]2.5224022888886e-05[/C][/ROW]
[ROW][C]-0.000370669917796593[/C][/ROW]
[ROW][C]-0.000520302508259609[/C][/ROW]
[ROW][C]-0.000199837253512991[/C][/ROW]
[ROW][C]-0.000211657545981314[/C][/ROW]
[ROW][C]-0.000382378567164824[/C][/ROW]
[ROW][C]-0.000280008062898401[/C][/ROW]
[ROW][C]-0.00120102293254119[/C][/ROW]
[ROW][C]0.000221401786965688[/C][/ROW]
[ROW][C]0.000272773353495361[/C][/ROW]
[ROW][C]0.000263498821745580[/C][/ROW]
[ROW][C]-0.000468728302097863[/C][/ROW]
[ROW][C]-6.53966649963745e-05[/C][/ROW]
[ROW][C]0.000116192069955239[/C][/ROW]
[ROW][C]0.000151795080093800[/C][/ROW]
[ROW][C]0.000243254022415853[/C][/ROW]
[ROW][C]-1.52138948691675e-05[/C][/ROW]
[ROW][C]0.000233596506482969[/C][/ROW]
[ROW][C]0.000892646525912088[/C][/ROW]
[ROW][C]0.000339478787375828[/C][/ROW]
[ROW][C]0.000238819415437245[/C][/ROW]
[ROW][C]0.000380570894517093[/C][/ROW]
[ROW][C]-8.61288955488083e-05[/C][/ROW]
[ROW][C]0.000230414837026887[/C][/ROW]
[ROW][C]0.000581725220750692[/C][/ROW]
[ROW][C]0.000263311212618933[/C][/ROW]
[ROW][C]-0.000161982623000182[/C][/ROW]
[ROW][C]-0.00012680350600897[/C][/ROW]
[ROW][C]-6.83315799530756e-05[/C][/ROW]
[ROW][C]-9.3499177176202e-05[/C][/ROW]
[ROW][C]-0.00076286711690782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62668&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62668&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
5.89192959504912e-06
-6.06250851177332e-05
-0.000199877315751040
9.75427489107244e-05
-0.000320661919501023
-0.00018901056156856
-0.000231706413866418
0.000452449375026747
0.000700362250531803
0.000323420951608120
0.000664276470986052
-0.000370651602066251
7.47310076531623e-05
0.000812787010311063
0.000683018408899884
1.42636259033377e-05
0.0003052223893087
0.000204355206695696
0.000471239844878407
0.000294716450620965
-0.000373356308826137
-0.000175521577101700
-0.000237755554831028
3.43470362801062e-05
-5.43005601426564e-05
-8.36330748689778e-05
-0.00127434392900585
0.00037740028238226
0.00028109619319787
-0.000556692106469921
-0.000548350643429367
0.000223106762744771
0.000250763033607168
0.000758475416370231
0.000146766612449761
-0.000188977727928553
0.000209259246509525
-0.000334562191432188
0.000346621275357574
-0.000101559799981404
0.000259484000795248
0.000630694761734907
0.000414110496108162
-0.000260943245935719
-0.000633170857856212
-8.02403013891615e-05
-0.00011767255320215
0.000159873834010740
0.000580455484507022
0.000116921481471482
0.000949909816499088
-0.000319304996236724
-0.000907150034484098
-0.000545598513728826
0.000119191825335595
-0.000319580459727470
0.000508150051734644
0.000196184519662971
0.000196417311722505
0.000246103151822164
-0.000318303315276883
7.86609983162634e-05
0.000710584409762925
9.8686951592094e-06
2.20480071715899e-05
0.000214218754293794
1.63657168358016e-05
2.5224022888886e-05
-0.000370669917796593
-0.000520302508259609
-0.000199837253512991
-0.000211657545981314
-0.000382378567164824
-0.000280008062898401
-0.00120102293254119
0.000221401786965688
0.000272773353495361
0.000263498821745580
-0.000468728302097863
-6.53966649963745e-05
0.000116192069955239
0.000151795080093800
0.000243254022415853
-1.52138948691675e-05
0.000233596506482969
0.000892646525912088
0.000339478787375828
0.000238819415437245
0.000380570894517093
-8.61288955488083e-05
0.000230414837026887
0.000581725220750692
0.000263311212618933
-0.000161982623000182
-0.00012680350600897
-6.83315799530756e-05
-9.3499177176202e-05
-0.00076286711690782



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = -0.5 ; par3 = 0 ; 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')