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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, 20 Dec 2012 16:17:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t1356038425vkiqd5f2u2eb4n5.htm/, Retrieved Fri, 29 Mar 2024 08:33:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203141, Retrieved Fri, 29 Mar 2024 08:33:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
-   PD        [ARIMA Backward Selection] [] [2012-12-20 21:17:18] [647590d21113774a1754266cc86dbc25] [Current]
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Dataseries X:
41
39
50
40
43
38
44
35
39
35
29
49
50
59
63
32
39
47
53
60
57
52
70
90
74
62
55
84
94
70
108
139
120
97
126
149
158
124
140
109
114
77
120
133
110
92
97
78
99
107
112
90
98
125
155
190
236
189
174
178
136
161
171
149
184
155
276
224
213
279
268
287
238
213
257
293
212
246
353
339
308
247
257
322
298
273
312
249
286
279
309
401
309
328
353
354
327
324
285
243
241
287
355
460
364
487
452
391
500
451
375
372
302
316
398
394
431
431




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203141&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203141&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203141&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sma1
Estimates ( 1 )0.52730.03980.1837-0.945-0.0193-0.9282
(p-val)(0 )(0.7298 )(0.0865 )(0 )(0.8878 )(0.0192 )
Estimates ( 2 )0.52760.04190.1833-0.94650-0.9755
(p-val)(0 )(0.713 )(0.0863 )(0 )(NA )(0.3065 )
Estimates ( 3 )0.537300.1961-0.93890-1.0584
(p-val)(0 )(NA )(0.0558 )(0 )(NA )(0.0222 )
Estimates ( 4 )0.306700-0.70480-1
(p-val)(0.2085 )(NA )(NA )(5e-04 )(NA )(0.0036 )
Estimates ( 5 )000-0.45130-0.9998
(p-val)(NA )(NA )(NA )(0 )(NA )(5e-04 )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.5273 & 0.0398 & 0.1837 & -0.945 & -0.0193 & -0.9282 \tabularnewline
(p-val) & (0 ) & (0.7298 ) & (0.0865 ) & (0 ) & (0.8878 ) & (0.0192 ) \tabularnewline
Estimates ( 2 ) & 0.5276 & 0.0419 & 0.1833 & -0.9465 & 0 & -0.9755 \tabularnewline
(p-val) & (0 ) & (0.713 ) & (0.0863 ) & (0 ) & (NA ) & (0.3065 ) \tabularnewline
Estimates ( 3 ) & 0.5373 & 0 & 0.1961 & -0.9389 & 0 & -1.0584 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.0558 ) & (0 ) & (NA ) & (0.0222 ) \tabularnewline
Estimates ( 4 ) & 0.3067 & 0 & 0 & -0.7048 & 0 & -1 \tabularnewline
(p-val) & (0.2085 ) & (NA ) & (NA ) & (5e-04 ) & (NA ) & (0.0036 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -0.4513 & 0 & -0.9998 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (NA ) & (5e-04 ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203141&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]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.5273[/C][C]0.0398[/C][C]0.1837[/C][C]-0.945[/C][C]-0.0193[/C][C]-0.9282[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.7298 )[/C][C](0.0865 )[/C][C](0 )[/C][C](0.8878 )[/C][C](0.0192 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.5276[/C][C]0.0419[/C][C]0.1833[/C][C]-0.9465[/C][C]0[/C][C]-0.9755[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.713 )[/C][C](0.0863 )[/C][C](0 )[/C][C](NA )[/C][C](0.3065 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.5373[/C][C]0[/C][C]0.1961[/C][C]-0.9389[/C][C]0[/C][C]-1.0584[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.0558 )[/C][C](0 )[/C][C](NA )[/C][C](0.0222 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.3067[/C][C]0[/C][C]0[/C][C]-0.7048[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2085 )[/C][C](NA )[/C][C](NA )[/C][C](5e-04 )[/C][C](NA )[/C][C](0.0036 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.4513[/C][C]0[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](5e-04 )[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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=203141&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203141&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
Iterationar1ar2ar3ma1sar1sma1
Estimates ( 1 )0.52730.03980.1837-0.945-0.0193-0.9282
(p-val)(0 )(0.7298 )(0.0865 )(0 )(0.8878 )(0.0192 )
Estimates ( 2 )0.52760.04190.1833-0.94650-0.9755
(p-val)(0 )(0.713 )(0.0863 )(0 )(NA )(0.3065 )
Estimates ( 3 )0.537300.1961-0.93890-1.0584
(p-val)(0 )(NA )(0.0558 )(0 )(NA )(0.0222 )
Estimates ( 4 )0.306700-0.70480-1
(p-val)(0.2085 )(NA )(NA )(5e-04 )(NA )(0.0036 )
Estimates ( 5 )000-0.45130-0.9998
(p-val)(NA )(NA )(NA )(0 )(NA )(5e-04 )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
-0.0122003930820727
0.140570140030647
-0.081687371460278
-0.33148021449181
-0.0421067992238456
0.162037257478376
0.0276969800717812
0.274481045390924
0.00391064089903841
0.0489193389225946
0.373841103863353
-0.0353884701938093
-0.122819048106779
-0.222102099766771
-0.318255624011111
0.558014654896738
0.154003407911521
-0.152283532677478
0.219556878764722
0.328186522661151
0.011657842585771
-0.0399272375976897
0.16905154344859
-0.112707272210367
0.0955464197158949
-0.161530173471549
-0.00499783909533175
-0.0974129305501768
-0.115785732513526
-0.33230878779966
0.0316729847093224
0.012970068730942
-0.143477247002782
-0.0940121174890362
-0.115816006563835
-0.521915185771235
0.019883468478864
0.0773450670452239
-0.0175644556594336
-0.0361578996883562
-0.0347474348321432
0.338630394003095
0.0655905740340771
0.1917818501113
0.352996362410882
0.103786608078505
-0.0749451668563249
-0.142195154697443
-0.323630791455062
0.0512004413201224
-0.0348316851809399
0.0258315193970391
0.102753454021247
-0.0450000530650043
0.273516807895291
-0.166145637732761
-0.0676554680858569
0.357798760493849
0.0319682948894001
-0.0214148264960299
-0.137490500198384
-0.14545485455592
0.0352597062575204
0.279921884574932
-0.302507787462133
0.135925459333494
0.062985544504495
-0.0418412324288907
-0.0776711616229153
-0.153351364408429
-0.0808773068784941
0.0284283826810392
-0.0262891044448826
-0.0713572928254906
0.0114966104228055
-0.0888895119497099
0.0391692889671642
0.0350027996441418
-0.196216875439795
0.142997879992815
-0.181418184731052
0.0962918459576303
0.0425904911303657
-0.113845441178872
-0.0579772448530563
-0.0131154991339906
-0.224169346020814
-0.10539028683769
-0.14198965893898
0.136977662798654
-0.0517733456466863
0.178197144694991
-0.0978171357098639
0.339281375675446
0.0103723203927647
-0.215955571397826
0.217642500272203
-0.00577832644385305
-0.222392305069071
0.0502722007057091
-0.259134088690089
-0.0350640801476952
-0.102564391729931
-0.140912407543961
0.0864318115533719
0.0556591919369319

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0122003930820727 \tabularnewline
0.140570140030647 \tabularnewline
-0.081687371460278 \tabularnewline
-0.33148021449181 \tabularnewline
-0.0421067992238456 \tabularnewline
0.162037257478376 \tabularnewline
0.0276969800717812 \tabularnewline
0.274481045390924 \tabularnewline
0.00391064089903841 \tabularnewline
0.0489193389225946 \tabularnewline
0.373841103863353 \tabularnewline
-0.0353884701938093 \tabularnewline
-0.122819048106779 \tabularnewline
-0.222102099766771 \tabularnewline
-0.318255624011111 \tabularnewline
0.558014654896738 \tabularnewline
0.154003407911521 \tabularnewline
-0.152283532677478 \tabularnewline
0.219556878764722 \tabularnewline
0.328186522661151 \tabularnewline
0.011657842585771 \tabularnewline
-0.0399272375976897 \tabularnewline
0.16905154344859 \tabularnewline
-0.112707272210367 \tabularnewline
0.0955464197158949 \tabularnewline
-0.161530173471549 \tabularnewline
-0.00499783909533175 \tabularnewline
-0.0974129305501768 \tabularnewline
-0.115785732513526 \tabularnewline
-0.33230878779966 \tabularnewline
0.0316729847093224 \tabularnewline
0.012970068730942 \tabularnewline
-0.143477247002782 \tabularnewline
-0.0940121174890362 \tabularnewline
-0.115816006563835 \tabularnewline
-0.521915185771235 \tabularnewline
0.019883468478864 \tabularnewline
0.0773450670452239 \tabularnewline
-0.0175644556594336 \tabularnewline
-0.0361578996883562 \tabularnewline
-0.0347474348321432 \tabularnewline
0.338630394003095 \tabularnewline
0.0655905740340771 \tabularnewline
0.1917818501113 \tabularnewline
0.352996362410882 \tabularnewline
0.103786608078505 \tabularnewline
-0.0749451668563249 \tabularnewline
-0.142195154697443 \tabularnewline
-0.323630791455062 \tabularnewline
0.0512004413201224 \tabularnewline
-0.0348316851809399 \tabularnewline
0.0258315193970391 \tabularnewline
0.102753454021247 \tabularnewline
-0.0450000530650043 \tabularnewline
0.273516807895291 \tabularnewline
-0.166145637732761 \tabularnewline
-0.0676554680858569 \tabularnewline
0.357798760493849 \tabularnewline
0.0319682948894001 \tabularnewline
-0.0214148264960299 \tabularnewline
-0.137490500198384 \tabularnewline
-0.14545485455592 \tabularnewline
0.0352597062575204 \tabularnewline
0.279921884574932 \tabularnewline
-0.302507787462133 \tabularnewline
0.135925459333494 \tabularnewline
0.062985544504495 \tabularnewline
-0.0418412324288907 \tabularnewline
-0.0776711616229153 \tabularnewline
-0.153351364408429 \tabularnewline
-0.0808773068784941 \tabularnewline
0.0284283826810392 \tabularnewline
-0.0262891044448826 \tabularnewline
-0.0713572928254906 \tabularnewline
0.0114966104228055 \tabularnewline
-0.0888895119497099 \tabularnewline
0.0391692889671642 \tabularnewline
0.0350027996441418 \tabularnewline
-0.196216875439795 \tabularnewline
0.142997879992815 \tabularnewline
-0.181418184731052 \tabularnewline
0.0962918459576303 \tabularnewline
0.0425904911303657 \tabularnewline
-0.113845441178872 \tabularnewline
-0.0579772448530563 \tabularnewline
-0.0131154991339906 \tabularnewline
-0.224169346020814 \tabularnewline
-0.10539028683769 \tabularnewline
-0.14198965893898 \tabularnewline
0.136977662798654 \tabularnewline
-0.0517733456466863 \tabularnewline
0.178197144694991 \tabularnewline
-0.0978171357098639 \tabularnewline
0.339281375675446 \tabularnewline
0.0103723203927647 \tabularnewline
-0.215955571397826 \tabularnewline
0.217642500272203 \tabularnewline
-0.00577832644385305 \tabularnewline
-0.222392305069071 \tabularnewline
0.0502722007057091 \tabularnewline
-0.259134088690089 \tabularnewline
-0.0350640801476952 \tabularnewline
-0.102564391729931 \tabularnewline
-0.140912407543961 \tabularnewline
0.0864318115533719 \tabularnewline
0.0556591919369319 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203141&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0122003930820727[/C][/ROW]
[ROW][C]0.140570140030647[/C][/ROW]
[ROW][C]-0.081687371460278[/C][/ROW]
[ROW][C]-0.33148021449181[/C][/ROW]
[ROW][C]-0.0421067992238456[/C][/ROW]
[ROW][C]0.162037257478376[/C][/ROW]
[ROW][C]0.0276969800717812[/C][/ROW]
[ROW][C]0.274481045390924[/C][/ROW]
[ROW][C]0.00391064089903841[/C][/ROW]
[ROW][C]0.0489193389225946[/C][/ROW]
[ROW][C]0.373841103863353[/C][/ROW]
[ROW][C]-0.0353884701938093[/C][/ROW]
[ROW][C]-0.122819048106779[/C][/ROW]
[ROW][C]-0.222102099766771[/C][/ROW]
[ROW][C]-0.318255624011111[/C][/ROW]
[ROW][C]0.558014654896738[/C][/ROW]
[ROW][C]0.154003407911521[/C][/ROW]
[ROW][C]-0.152283532677478[/C][/ROW]
[ROW][C]0.219556878764722[/C][/ROW]
[ROW][C]0.328186522661151[/C][/ROW]
[ROW][C]0.011657842585771[/C][/ROW]
[ROW][C]-0.0399272375976897[/C][/ROW]
[ROW][C]0.16905154344859[/C][/ROW]
[ROW][C]-0.112707272210367[/C][/ROW]
[ROW][C]0.0955464197158949[/C][/ROW]
[ROW][C]-0.161530173471549[/C][/ROW]
[ROW][C]-0.00499783909533175[/C][/ROW]
[ROW][C]-0.0974129305501768[/C][/ROW]
[ROW][C]-0.115785732513526[/C][/ROW]
[ROW][C]-0.33230878779966[/C][/ROW]
[ROW][C]0.0316729847093224[/C][/ROW]
[ROW][C]0.012970068730942[/C][/ROW]
[ROW][C]-0.143477247002782[/C][/ROW]
[ROW][C]-0.0940121174890362[/C][/ROW]
[ROW][C]-0.115816006563835[/C][/ROW]
[ROW][C]-0.521915185771235[/C][/ROW]
[ROW][C]0.019883468478864[/C][/ROW]
[ROW][C]0.0773450670452239[/C][/ROW]
[ROW][C]-0.0175644556594336[/C][/ROW]
[ROW][C]-0.0361578996883562[/C][/ROW]
[ROW][C]-0.0347474348321432[/C][/ROW]
[ROW][C]0.338630394003095[/C][/ROW]
[ROW][C]0.0655905740340771[/C][/ROW]
[ROW][C]0.1917818501113[/C][/ROW]
[ROW][C]0.352996362410882[/C][/ROW]
[ROW][C]0.103786608078505[/C][/ROW]
[ROW][C]-0.0749451668563249[/C][/ROW]
[ROW][C]-0.142195154697443[/C][/ROW]
[ROW][C]-0.323630791455062[/C][/ROW]
[ROW][C]0.0512004413201224[/C][/ROW]
[ROW][C]-0.0348316851809399[/C][/ROW]
[ROW][C]0.0258315193970391[/C][/ROW]
[ROW][C]0.102753454021247[/C][/ROW]
[ROW][C]-0.0450000530650043[/C][/ROW]
[ROW][C]0.273516807895291[/C][/ROW]
[ROW][C]-0.166145637732761[/C][/ROW]
[ROW][C]-0.0676554680858569[/C][/ROW]
[ROW][C]0.357798760493849[/C][/ROW]
[ROW][C]0.0319682948894001[/C][/ROW]
[ROW][C]-0.0214148264960299[/C][/ROW]
[ROW][C]-0.137490500198384[/C][/ROW]
[ROW][C]-0.14545485455592[/C][/ROW]
[ROW][C]0.0352597062575204[/C][/ROW]
[ROW][C]0.279921884574932[/C][/ROW]
[ROW][C]-0.302507787462133[/C][/ROW]
[ROW][C]0.135925459333494[/C][/ROW]
[ROW][C]0.062985544504495[/C][/ROW]
[ROW][C]-0.0418412324288907[/C][/ROW]
[ROW][C]-0.0776711616229153[/C][/ROW]
[ROW][C]-0.153351364408429[/C][/ROW]
[ROW][C]-0.0808773068784941[/C][/ROW]
[ROW][C]0.0284283826810392[/C][/ROW]
[ROW][C]-0.0262891044448826[/C][/ROW]
[ROW][C]-0.0713572928254906[/C][/ROW]
[ROW][C]0.0114966104228055[/C][/ROW]
[ROW][C]-0.0888895119497099[/C][/ROW]
[ROW][C]0.0391692889671642[/C][/ROW]
[ROW][C]0.0350027996441418[/C][/ROW]
[ROW][C]-0.196216875439795[/C][/ROW]
[ROW][C]0.142997879992815[/C][/ROW]
[ROW][C]-0.181418184731052[/C][/ROW]
[ROW][C]0.0962918459576303[/C][/ROW]
[ROW][C]0.0425904911303657[/C][/ROW]
[ROW][C]-0.113845441178872[/C][/ROW]
[ROW][C]-0.0579772448530563[/C][/ROW]
[ROW][C]-0.0131154991339906[/C][/ROW]
[ROW][C]-0.224169346020814[/C][/ROW]
[ROW][C]-0.10539028683769[/C][/ROW]
[ROW][C]-0.14198965893898[/C][/ROW]
[ROW][C]0.136977662798654[/C][/ROW]
[ROW][C]-0.0517733456466863[/C][/ROW]
[ROW][C]0.178197144694991[/C][/ROW]
[ROW][C]-0.0978171357098639[/C][/ROW]
[ROW][C]0.339281375675446[/C][/ROW]
[ROW][C]0.0103723203927647[/C][/ROW]
[ROW][C]-0.215955571397826[/C][/ROW]
[ROW][C]0.217642500272203[/C][/ROW]
[ROW][C]-0.00577832644385305[/C][/ROW]
[ROW][C]-0.222392305069071[/C][/ROW]
[ROW][C]0.0502722007057091[/C][/ROW]
[ROW][C]-0.259134088690089[/C][/ROW]
[ROW][C]-0.0350640801476952[/C][/ROW]
[ROW][C]-0.102564391729931[/C][/ROW]
[ROW][C]-0.140912407543961[/C][/ROW]
[ROW][C]0.0864318115533719[/C][/ROW]
[ROW][C]0.0556591919369319[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203141&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203141&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
-0.0122003930820727
0.140570140030647
-0.081687371460278
-0.33148021449181
-0.0421067992238456
0.162037257478376
0.0276969800717812
0.274481045390924
0.00391064089903841
0.0489193389225946
0.373841103863353
-0.0353884701938093
-0.122819048106779
-0.222102099766771
-0.318255624011111
0.558014654896738
0.154003407911521
-0.152283532677478
0.219556878764722
0.328186522661151
0.011657842585771
-0.0399272375976897
0.16905154344859
-0.112707272210367
0.0955464197158949
-0.161530173471549
-0.00499783909533175
-0.0974129305501768
-0.115785732513526
-0.33230878779966
0.0316729847093224
0.012970068730942
-0.143477247002782
-0.0940121174890362
-0.115816006563835
-0.521915185771235
0.019883468478864
0.0773450670452239
-0.0175644556594336
-0.0361578996883562
-0.0347474348321432
0.338630394003095
0.0655905740340771
0.1917818501113
0.352996362410882
0.103786608078505
-0.0749451668563249
-0.142195154697443
-0.323630791455062
0.0512004413201224
-0.0348316851809399
0.0258315193970391
0.102753454021247
-0.0450000530650043
0.273516807895291
-0.166145637732761
-0.0676554680858569
0.357798760493849
0.0319682948894001
-0.0214148264960299
-0.137490500198384
-0.14545485455592
0.0352597062575204
0.279921884574932
-0.302507787462133
0.135925459333494
0.062985544504495
-0.0418412324288907
-0.0776711616229153
-0.153351364408429
-0.0808773068784941
0.0284283826810392
-0.0262891044448826
-0.0713572928254906
0.0114966104228055
-0.0888895119497099
0.0391692889671642
0.0350027996441418
-0.196216875439795
0.142997879992815
-0.181418184731052
0.0962918459576303
0.0425904911303657
-0.113845441178872
-0.0579772448530563
-0.0131154991339906
-0.224169346020814
-0.10539028683769
-0.14198965893898
0.136977662798654
-0.0517733456466863
0.178197144694991
-0.0978171357098639
0.339281375675446
0.0103723203927647
-0.215955571397826
0.217642500272203
-0.00577832644385305
-0.222392305069071
0.0502722007057091
-0.259134088690089
-0.0350640801476952
-0.102564391729931
-0.140912407543961
0.0864318115533719
0.0556591919369319



Parameters (Session):
par1 = FALSE ; par2 = 0.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; 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')