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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 computationSat, 22 Dec 2012 14:00:50 -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/22/t13562028836goeoupq2xi25r0.htm/, Retrieved Fri, 29 Mar 2024 01:27:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204592, Retrieved Fri, 29 Mar 2024 01:27:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
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]
- R PD        [ARIMA Backward Selection] [arimabackwardsele...] [2012-12-22 19:00:50] [081b45eff66f9ee50ac0b17603ac2bbc] [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 time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204592&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204592&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204592&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 time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.39760.07540.20110.1714-0.706-0.39320.1477
(p-val)(0.4482 )(0.6763 )(0.1141 )(0.7473 )(0.2508 )(0.1628 )(0.826 )
Estimates ( 2 )-0.4050.07920.19640.1828-0.5728-0.33710
(p-val)(0.4542 )(0.6611 )(0.1191 )(0.7385 )(0 )(0.037 )(NA )
Estimates ( 3 )-0.22940.11740.18250-0.5663-0.33360
(p-val)(0.0764 )(0.3664 )(0.1455 )(NA )(0 )(0.0389 )(NA )
Estimates ( 4 )-0.250400.15740-0.5771-0.35190
(p-val)(0.0522 )(NA )(0.201 )(NA )(0 )(0.0269 )(NA )
Estimates ( 5 )-0.2308000-0.5816-0.35130
(p-val)(0.0729 )(NA )(NA )(NA )(0 )(0.0258 )(NA )
Estimates ( 6 )0000-0.599-0.40340
(p-val)(NA )(NA )(NA )(NA )(0 )(0.008 )(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 ) & -0.3976 & 0.0754 & 0.2011 & 0.1714 & -0.706 & -0.3932 & 0.1477 \tabularnewline
(p-val) & (0.4482 ) & (0.6763 ) & (0.1141 ) & (0.7473 ) & (0.2508 ) & (0.1628 ) & (0.826 ) \tabularnewline
Estimates ( 2 ) & -0.405 & 0.0792 & 0.1964 & 0.1828 & -0.5728 & -0.3371 & 0 \tabularnewline
(p-val) & (0.4542 ) & (0.6611 ) & (0.1191 ) & (0.7385 ) & (0 ) & (0.037 ) & (NA ) \tabularnewline
Estimates ( 3 ) & -0.2294 & 0.1174 & 0.1825 & 0 & -0.5663 & -0.3336 & 0 \tabularnewline
(p-val) & (0.0764 ) & (0.3664 ) & (0.1455 ) & (NA ) & (0 ) & (0.0389 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.2504 & 0 & 0.1574 & 0 & -0.5771 & -0.3519 & 0 \tabularnewline
(p-val) & (0.0522 ) & (NA ) & (0.201 ) & (NA ) & (0 ) & (0.0269 ) & (NA ) \tabularnewline
Estimates ( 5 ) & -0.2308 & 0 & 0 & 0 & -0.5816 & -0.3513 & 0 \tabularnewline
(p-val) & (0.0729 ) & (NA ) & (NA ) & (NA ) & (0 ) & (0.0258 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & -0.599 & -0.4034 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) & (0.008 ) & (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=204592&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]-0.3976[/C][C]0.0754[/C][C]0.2011[/C][C]0.1714[/C][C]-0.706[/C][C]-0.3932[/C][C]0.1477[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4482 )[/C][C](0.6763 )[/C][C](0.1141 )[/C][C](0.7473 )[/C][C](0.2508 )[/C][C](0.1628 )[/C][C](0.826 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.405[/C][C]0.0792[/C][C]0.1964[/C][C]0.1828[/C][C]-0.5728[/C][C]-0.3371[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4542 )[/C][C](0.6611 )[/C][C](0.1191 )[/C][C](0.7385 )[/C][C](0 )[/C][C](0.037 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.2294[/C][C]0.1174[/C][C]0.1825[/C][C]0[/C][C]-0.5663[/C][C]-0.3336[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0764 )[/C][C](0.3664 )[/C][C](0.1455 )[/C][C](NA )[/C][C](0 )[/C][C](0.0389 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.2504[/C][C]0[/C][C]0.1574[/C][C]0[/C][C]-0.5771[/C][C]-0.3519[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0522 )[/C][C](NA )[/C][C](0.201 )[/C][C](NA )[/C][C](0 )[/C][C](0.0269 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.2308[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.5816[/C][C]-0.3513[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0729 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0258 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.599[/C][C]-0.4034[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.008 )[/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=204592&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204592&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 )-0.39760.07540.20110.1714-0.706-0.39320.1477
(p-val)(0.4482 )(0.6763 )(0.1141 )(0.7473 )(0.2508 )(0.1628 )(0.826 )
Estimates ( 2 )-0.4050.07920.19640.1828-0.5728-0.33710
(p-val)(0.4542 )(0.6611 )(0.1191 )(0.7385 )(0 )(0.037 )(NA )
Estimates ( 3 )-0.22940.11740.18250-0.5663-0.33360
(p-val)(0.0764 )(0.3664 )(0.1455 )(NA )(0 )(0.0389 )(NA )
Estimates ( 4 )-0.250400.15740-0.5771-0.35190
(p-val)(0.0522 )(NA )(0.201 )(NA )(0 )(0.0269 )(NA )
Estimates ( 5 )-0.2308000-0.5816-0.35130
(p-val)(0.0729 )(NA )(NA )(NA )(0 )(0.0258 )(NA )
Estimates ( 6 )0000-0.599-0.40340
(p-val)(NA )(NA )(NA )(NA )(0 )(0.008 )(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
221.114422853728
-538.308881293121
7737.06071284906
1937.35298327409
3.3416323640987
5094.27126551264
4139.5962415987
53754.5807036735
-24955.117438707
-7277.45376137881
16096.6963779443
6048.17419872984
-17573.9045200179
-4356.52061010803
-11531.0027015825
5421.91009710594
21384.0313858023
-40246.7377728038
20054.0546382438
-33216.6945003095
26493.722401323
38842.6286130917
23781.6303349412
22996.6548677136
30652.6472591777
18776.243910306
6552.15426462882
-4697.16825731282
-5604.45507440801
-23416.3900091153
-21407.1575712903
-30483.4615292691
6330.4647839063
10143.227004815
-500.081486848103
-6990.71042611177
-2025.75273848114
-11356.2998902176
12938.8762693161
14022.3081839885
-37986.0587384995
13264.3328184866
-3084.88259123966
-23842.4653510761
32125.1962166807
13478.256984451
1838.17224723342
2340.96100448108
40.154400905124
6568.47239037984
-10323.4115619447
-18029.7240692072
-6745.95280524122
-11315.0864881102
-11366.6204686721
-11408.6231856564
10262.6357383565
-4353.44744077624
-4285.53909794557
-7649.75252442461
-7651.35050747628

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
221.114422853728 \tabularnewline
-538.308881293121 \tabularnewline
7737.06071284906 \tabularnewline
1937.35298327409 \tabularnewline
3.3416323640987 \tabularnewline
5094.27126551264 \tabularnewline
4139.5962415987 \tabularnewline
53754.5807036735 \tabularnewline
-24955.117438707 \tabularnewline
-7277.45376137881 \tabularnewline
16096.6963779443 \tabularnewline
6048.17419872984 \tabularnewline
-17573.9045200179 \tabularnewline
-4356.52061010803 \tabularnewline
-11531.0027015825 \tabularnewline
5421.91009710594 \tabularnewline
21384.0313858023 \tabularnewline
-40246.7377728038 \tabularnewline
20054.0546382438 \tabularnewline
-33216.6945003095 \tabularnewline
26493.722401323 \tabularnewline
38842.6286130917 \tabularnewline
23781.6303349412 \tabularnewline
22996.6548677136 \tabularnewline
30652.6472591777 \tabularnewline
18776.243910306 \tabularnewline
6552.15426462882 \tabularnewline
-4697.16825731282 \tabularnewline
-5604.45507440801 \tabularnewline
-23416.3900091153 \tabularnewline
-21407.1575712903 \tabularnewline
-30483.4615292691 \tabularnewline
6330.4647839063 \tabularnewline
10143.227004815 \tabularnewline
-500.081486848103 \tabularnewline
-6990.71042611177 \tabularnewline
-2025.75273848114 \tabularnewline
-11356.2998902176 \tabularnewline
12938.8762693161 \tabularnewline
14022.3081839885 \tabularnewline
-37986.0587384995 \tabularnewline
13264.3328184866 \tabularnewline
-3084.88259123966 \tabularnewline
-23842.4653510761 \tabularnewline
32125.1962166807 \tabularnewline
13478.256984451 \tabularnewline
1838.17224723342 \tabularnewline
2340.96100448108 \tabularnewline
40.154400905124 \tabularnewline
6568.47239037984 \tabularnewline
-10323.4115619447 \tabularnewline
-18029.7240692072 \tabularnewline
-6745.95280524122 \tabularnewline
-11315.0864881102 \tabularnewline
-11366.6204686721 \tabularnewline
-11408.6231856564 \tabularnewline
10262.6357383565 \tabularnewline
-4353.44744077624 \tabularnewline
-4285.53909794557 \tabularnewline
-7649.75252442461 \tabularnewline
-7651.35050747628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204592&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]221.114422853728[/C][/ROW]
[ROW][C]-538.308881293121[/C][/ROW]
[ROW][C]7737.06071284906[/C][/ROW]
[ROW][C]1937.35298327409[/C][/ROW]
[ROW][C]3.3416323640987[/C][/ROW]
[ROW][C]5094.27126551264[/C][/ROW]
[ROW][C]4139.5962415987[/C][/ROW]
[ROW][C]53754.5807036735[/C][/ROW]
[ROW][C]-24955.117438707[/C][/ROW]
[ROW][C]-7277.45376137881[/C][/ROW]
[ROW][C]16096.6963779443[/C][/ROW]
[ROW][C]6048.17419872984[/C][/ROW]
[ROW][C]-17573.9045200179[/C][/ROW]
[ROW][C]-4356.52061010803[/C][/ROW]
[ROW][C]-11531.0027015825[/C][/ROW]
[ROW][C]5421.91009710594[/C][/ROW]
[ROW][C]21384.0313858023[/C][/ROW]
[ROW][C]-40246.7377728038[/C][/ROW]
[ROW][C]20054.0546382438[/C][/ROW]
[ROW][C]-33216.6945003095[/C][/ROW]
[ROW][C]26493.722401323[/C][/ROW]
[ROW][C]38842.6286130917[/C][/ROW]
[ROW][C]23781.6303349412[/C][/ROW]
[ROW][C]22996.6548677136[/C][/ROW]
[ROW][C]30652.6472591777[/C][/ROW]
[ROW][C]18776.243910306[/C][/ROW]
[ROW][C]6552.15426462882[/C][/ROW]
[ROW][C]-4697.16825731282[/C][/ROW]
[ROW][C]-5604.45507440801[/C][/ROW]
[ROW][C]-23416.3900091153[/C][/ROW]
[ROW][C]-21407.1575712903[/C][/ROW]
[ROW][C]-30483.4615292691[/C][/ROW]
[ROW][C]6330.4647839063[/C][/ROW]
[ROW][C]10143.227004815[/C][/ROW]
[ROW][C]-500.081486848103[/C][/ROW]
[ROW][C]-6990.71042611177[/C][/ROW]
[ROW][C]-2025.75273848114[/C][/ROW]
[ROW][C]-11356.2998902176[/C][/ROW]
[ROW][C]12938.8762693161[/C][/ROW]
[ROW][C]14022.3081839885[/C][/ROW]
[ROW][C]-37986.0587384995[/C][/ROW]
[ROW][C]13264.3328184866[/C][/ROW]
[ROW][C]-3084.88259123966[/C][/ROW]
[ROW][C]-23842.4653510761[/C][/ROW]
[ROW][C]32125.1962166807[/C][/ROW]
[ROW][C]13478.256984451[/C][/ROW]
[ROW][C]1838.17224723342[/C][/ROW]
[ROW][C]2340.96100448108[/C][/ROW]
[ROW][C]40.154400905124[/C][/ROW]
[ROW][C]6568.47239037984[/C][/ROW]
[ROW][C]-10323.4115619447[/C][/ROW]
[ROW][C]-18029.7240692072[/C][/ROW]
[ROW][C]-6745.95280524122[/C][/ROW]
[ROW][C]-11315.0864881102[/C][/ROW]
[ROW][C]-11366.6204686721[/C][/ROW]
[ROW][C]-11408.6231856564[/C][/ROW]
[ROW][C]10262.6357383565[/C][/ROW]
[ROW][C]-4353.44744077624[/C][/ROW]
[ROW][C]-4285.53909794557[/C][/ROW]
[ROW][C]-7649.75252442461[/C][/ROW]
[ROW][C]-7651.35050747628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204592&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204592&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
221.114422853728
-538.308881293121
7737.06071284906
1937.35298327409
3.3416323640987
5094.27126551264
4139.5962415987
53754.5807036735
-24955.117438707
-7277.45376137881
16096.6963779443
6048.17419872984
-17573.9045200179
-4356.52061010803
-11531.0027015825
5421.91009710594
21384.0313858023
-40246.7377728038
20054.0546382438
-33216.6945003095
26493.722401323
38842.6286130917
23781.6303349412
22996.6548677136
30652.6472591777
18776.243910306
6552.15426462882
-4697.16825731282
-5604.45507440801
-23416.3900091153
-21407.1575712903
-30483.4615292691
6330.4647839063
10143.227004815
-500.081486848103
-6990.71042611177
-2025.75273848114
-11356.2998902176
12938.8762693161
14022.3081839885
-37986.0587384995
13264.3328184866
-3084.88259123966
-23842.4653510761
32125.1962166807
13478.256984451
1838.17224723342
2340.96100448108
40.154400905124
6568.47239037984
-10323.4115619447
-18029.7240692072
-6745.95280524122
-11315.0864881102
-11366.6204686721
-11408.6231856564
10262.6357383565
-4353.44744077624
-4285.53909794557
-7649.75252442461
-7651.35050747628



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.9 ; 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')