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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 computationWed, 07 Dec 2011 13:32:39 -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/2011/Dec/07/t1323282830memq2v0xbquljgc.htm/, Retrieved Thu, 31 Oct 2024 23:38:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152607, Retrieved Thu, 31 Oct 2024 23:38:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
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]
-    D        [ARIMA Backward Selection] [arima backward se...] [2011-12-07 18:32:39] [5363b79245edacd2d961915f77b3b63a] [Current]
Feedback Forum

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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152607&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152607&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152607&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.12780.16640.2475-1-0.6699-0.39010.0853
(p-val)(0.3408 )(0.2108 )(0.0666 )(0 )(0.2389 )(0.1584 )(0.8931 )
Estimates ( 2 )-0.12530.16850.2421-1-0.5953-0.35840
(p-val)(0.3458 )(0.2018 )(0.0604 )(0 )(0 )(0.0302 )(NA )
Estimates ( 3 )00.18080.2315-1-0.6021-0.39790
(p-val)(NA )(0.1802 )(0.0778 )(0 )(0 )(0.0115 )(NA )
Estimates ( 4 )000.2026-1-0.6168-0.44590
(p-val)(NA )(NA )(0.1171 )(0 )(0 )(0.0027 )(NA )
Estimates ( 5 )000-1.0258-0.6204-0.4440
(p-val)(NA )(NA )(NA )(0 )(0 )(0.0029 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.1278 & 0.1664 & 0.2475 & -1 & -0.6699 & -0.3901 & 0.0853 \tabularnewline
(p-val) & (0.3408 ) & (0.2108 ) & (0.0666 ) & (0 ) & (0.2389 ) & (0.1584 ) & (0.8931 ) \tabularnewline
Estimates ( 2 ) & -0.1253 & 0.1685 & 0.2421 & -1 & -0.5953 & -0.3584 & 0 \tabularnewline
(p-val) & (0.3458 ) & (0.2018 ) & (0.0604 ) & (0 ) & (0 ) & (0.0302 ) & (NA ) \tabularnewline
Estimates ( 3 ) & 0 & 0.1808 & 0.2315 & -1 & -0.6021 & -0.3979 & 0 \tabularnewline
(p-val) & (NA ) & (0.1802 ) & (0.0778 ) & (0 ) & (0 ) & (0.0115 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0.2026 & -1 & -0.6168 & -0.4459 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1171 ) & (0 ) & (0 ) & (0.0027 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -1.0258 & -0.6204 & -0.444 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0 ) & (0.0029 ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152607&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]-0.1278[/C][C]0.1664[/C][C]0.2475[/C][C]-1[/C][C]-0.6699[/C][C]-0.3901[/C][C]0.0853[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3408 )[/C][C](0.2108 )[/C][C](0.0666 )[/C][C](0 )[/C][C](0.2389 )[/C][C](0.1584 )[/C][C](0.8931 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.1253[/C][C]0.1685[/C][C]0.2421[/C][C]-1[/C][C]-0.5953[/C][C]-0.3584[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3458 )[/C][C](0.2018 )[/C][C](0.0604 )[/C][C](0 )[/C][C](0 )[/C][C](0.0302 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.1808[/C][C]0.2315[/C][C]-1[/C][C]-0.6021[/C][C]-0.3979[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1802 )[/C][C](0.0778 )[/C][C](0 )[/C][C](0 )[/C][C](0.0115 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0.2026[/C][C]-1[/C][C]-0.6168[/C][C]-0.4459[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1171 )[/C][C](0 )[/C][C](0 )[/C][C](0.0027 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1.0258[/C][C]-0.6204[/C][C]-0.444[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0.0029 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152607&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152607&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 )-0.12780.16640.2475-1-0.6699-0.39010.0853
(p-val)(0.3408 )(0.2108 )(0.0666 )(0 )(0.2389 )(0.1584 )(0.8931 )
Estimates ( 2 )-0.12530.16850.2421-1-0.5953-0.35840
(p-val)(0.3458 )(0.2018 )(0.0604 )(0 )(0 )(0.0302 )(NA )
Estimates ( 3 )00.18080.2315-1-0.6021-0.39790
(p-val)(NA )(0.1802 )(0.0778 )(0 )(0 )(0.0115 )(NA )
Estimates ( 4 )000.2026-1-0.6168-0.44590
(p-val)(NA )(NA )(0.1171 )(0 )(0 )(0.0027 )(NA )
Estimates ( 5 )000-1.0258-0.6204-0.4440
(p-val)(NA )(NA )(NA )(0 )(0 )(0.0029 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
-3.98983386487333
13.0279292273047
-6.22707532669883
-3.727308955297
1.55851412721614
1.32306700344172
63.0688988477587
-64.3516189661354
-3.3247075563255
9.97002410841291
11.1034522804254
-47.1140737637922
-10.4457621650474
-27.0251168226883
23.8034695979385
31.382946065211
-74.851888028175
50.382723555312
-60.9814102745866
68.4263209648509
42.3846755230026
35.1134442723221
21.8672875993756
36.9164687423742
7.7296075117894
-18.1147924331061
-37.8850452722331
-25.0333984428524
-53.6331100839019
-30.3418411339127
-34.6622703233401
22.121127260943
18.7782020912031
2.51088279990446
-19.3279077050329
-9.03224059593809
-30.3369467496862
33.168095054609
20.0070596547124
-70.1042307561964
25.3910268750564
-18.3824730780169
-26.2596050173113
55.7677823009751
14.7805196495053
7.59857958057215
-8.1218891681502
0.791849768511198
15.6739602710521
-28.350016915815
-36.6588000592886
-15.6523413390125
-11.8292331213895
-15.9848279228253
-12.027476703628
21.8290069041768
-11.6530957490167
-4.44706572791047
-22.6175657625597
-13.5066921272696

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-3.98983386487333 \tabularnewline
13.0279292273047 \tabularnewline
-6.22707532669883 \tabularnewline
-3.727308955297 \tabularnewline
1.55851412721614 \tabularnewline
1.32306700344172 \tabularnewline
63.0688988477587 \tabularnewline
-64.3516189661354 \tabularnewline
-3.3247075563255 \tabularnewline
9.97002410841291 \tabularnewline
11.1034522804254 \tabularnewline
-47.1140737637922 \tabularnewline
-10.4457621650474 \tabularnewline
-27.0251168226883 \tabularnewline
23.8034695979385 \tabularnewline
31.382946065211 \tabularnewline
-74.851888028175 \tabularnewline
50.382723555312 \tabularnewline
-60.9814102745866 \tabularnewline
68.4263209648509 \tabularnewline
42.3846755230026 \tabularnewline
35.1134442723221 \tabularnewline
21.8672875993756 \tabularnewline
36.9164687423742 \tabularnewline
7.7296075117894 \tabularnewline
-18.1147924331061 \tabularnewline
-37.8850452722331 \tabularnewline
-25.0333984428524 \tabularnewline
-53.6331100839019 \tabularnewline
-30.3418411339127 \tabularnewline
-34.6622703233401 \tabularnewline
22.121127260943 \tabularnewline
18.7782020912031 \tabularnewline
2.51088279990446 \tabularnewline
-19.3279077050329 \tabularnewline
-9.03224059593809 \tabularnewline
-30.3369467496862 \tabularnewline
33.168095054609 \tabularnewline
20.0070596547124 \tabularnewline
-70.1042307561964 \tabularnewline
25.3910268750564 \tabularnewline
-18.3824730780169 \tabularnewline
-26.2596050173113 \tabularnewline
55.7677823009751 \tabularnewline
14.7805196495053 \tabularnewline
7.59857958057215 \tabularnewline
-8.1218891681502 \tabularnewline
0.791849768511198 \tabularnewline
15.6739602710521 \tabularnewline
-28.350016915815 \tabularnewline
-36.6588000592886 \tabularnewline
-15.6523413390125 \tabularnewline
-11.8292331213895 \tabularnewline
-15.9848279228253 \tabularnewline
-12.027476703628 \tabularnewline
21.8290069041768 \tabularnewline
-11.6530957490167 \tabularnewline
-4.44706572791047 \tabularnewline
-22.6175657625597 \tabularnewline
-13.5066921272696 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152607&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-3.98983386487333[/C][/ROW]
[ROW][C]13.0279292273047[/C][/ROW]
[ROW][C]-6.22707532669883[/C][/ROW]
[ROW][C]-3.727308955297[/C][/ROW]
[ROW][C]1.55851412721614[/C][/ROW]
[ROW][C]1.32306700344172[/C][/ROW]
[ROW][C]63.0688988477587[/C][/ROW]
[ROW][C]-64.3516189661354[/C][/ROW]
[ROW][C]-3.3247075563255[/C][/ROW]
[ROW][C]9.97002410841291[/C][/ROW]
[ROW][C]11.1034522804254[/C][/ROW]
[ROW][C]-47.1140737637922[/C][/ROW]
[ROW][C]-10.4457621650474[/C][/ROW]
[ROW][C]-27.0251168226883[/C][/ROW]
[ROW][C]23.8034695979385[/C][/ROW]
[ROW][C]31.382946065211[/C][/ROW]
[ROW][C]-74.851888028175[/C][/ROW]
[ROW][C]50.382723555312[/C][/ROW]
[ROW][C]-60.9814102745866[/C][/ROW]
[ROW][C]68.4263209648509[/C][/ROW]
[ROW][C]42.3846755230026[/C][/ROW]
[ROW][C]35.1134442723221[/C][/ROW]
[ROW][C]21.8672875993756[/C][/ROW]
[ROW][C]36.9164687423742[/C][/ROW]
[ROW][C]7.7296075117894[/C][/ROW]
[ROW][C]-18.1147924331061[/C][/ROW]
[ROW][C]-37.8850452722331[/C][/ROW]
[ROW][C]-25.0333984428524[/C][/ROW]
[ROW][C]-53.6331100839019[/C][/ROW]
[ROW][C]-30.3418411339127[/C][/ROW]
[ROW][C]-34.6622703233401[/C][/ROW]
[ROW][C]22.121127260943[/C][/ROW]
[ROW][C]18.7782020912031[/C][/ROW]
[ROW][C]2.51088279990446[/C][/ROW]
[ROW][C]-19.3279077050329[/C][/ROW]
[ROW][C]-9.03224059593809[/C][/ROW]
[ROW][C]-30.3369467496862[/C][/ROW]
[ROW][C]33.168095054609[/C][/ROW]
[ROW][C]20.0070596547124[/C][/ROW]
[ROW][C]-70.1042307561964[/C][/ROW]
[ROW][C]25.3910268750564[/C][/ROW]
[ROW][C]-18.3824730780169[/C][/ROW]
[ROW][C]-26.2596050173113[/C][/ROW]
[ROW][C]55.7677823009751[/C][/ROW]
[ROW][C]14.7805196495053[/C][/ROW]
[ROW][C]7.59857958057215[/C][/ROW]
[ROW][C]-8.1218891681502[/C][/ROW]
[ROW][C]0.791849768511198[/C][/ROW]
[ROW][C]15.6739602710521[/C][/ROW]
[ROW][C]-28.350016915815[/C][/ROW]
[ROW][C]-36.6588000592886[/C][/ROW]
[ROW][C]-15.6523413390125[/C][/ROW]
[ROW][C]-11.8292331213895[/C][/ROW]
[ROW][C]-15.9848279228253[/C][/ROW]
[ROW][C]-12.027476703628[/C][/ROW]
[ROW][C]21.8290069041768[/C][/ROW]
[ROW][C]-11.6530957490167[/C][/ROW]
[ROW][C]-4.44706572791047[/C][/ROW]
[ROW][C]-22.6175657625597[/C][/ROW]
[ROW][C]-13.5066921272696[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152607&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152607&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
-3.98983386487333
13.0279292273047
-6.22707532669883
-3.727308955297
1.55851412721614
1.32306700344172
63.0688988477587
-64.3516189661354
-3.3247075563255
9.97002410841291
11.1034522804254
-47.1140737637922
-10.4457621650474
-27.0251168226883
23.8034695979385
31.382946065211
-74.851888028175
50.382723555312
-60.9814102745866
68.4263209648509
42.3846755230026
35.1134442723221
21.8672875993756
36.9164687423742
7.7296075117894
-18.1147924331061
-37.8850452722331
-25.0333984428524
-53.6331100839019
-30.3418411339127
-34.6622703233401
22.121127260943
18.7782020912031
2.51088279990446
-19.3279077050329
-9.03224059593809
-30.3369467496862
33.168095054609
20.0070596547124
-70.1042307561964
25.3910268750564
-18.3824730780169
-26.2596050173113
55.7677823009751
14.7805196495053
7.59857958057215
-8.1218891681502
0.791849768511198
15.6739602710521
-28.350016915815
-36.6588000592886
-15.6523413390125
-11.8292331213895
-15.9848279228253
-12.027476703628
21.8290069041768
-11.6530957490167
-4.44706572791047
-22.6175657625597
-13.5066921272696



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 = 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')