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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareARIMA Backward Selection
Date of computationThu, 20 Dec 2012 06:08:02 -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/t135600170041gx5x7sz1xdt3x.htm/, Retrieved Fri, 29 Mar 2024 13:39:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202614, Retrieved Fri, 29 Mar 2024 13:39:51 +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]
- RMPD    [ARIMA Backward Selection] [Backwards ARIMA] [2012-12-04 18:12:12] [f055db2f1c47e4197bf514e64f7886e5]
- RM          [ARIMA Backward Selection] [Paper2012: ARIMA] [2012-12-20 11:08:02] [86f0addf4b5362ca5a545029cdfac14b] [Current]
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Dataseries X:
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 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 & 13 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202614&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]13 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=202614&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202614&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 time13 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.10490.13-0.1371-0.9237-0.0147-0.0343-0.9995
(p-val)(0.4737 )(0.3622 )(0.3187 )(0 )(0.9345 )(0.8516 )(0.0209 )
Estimates ( 2 )0.10270.1277-0.1363-0.92440-0.0277-0.9998
(p-val)(0.4747 )(0.3611 )(0.3198 )(0 )(NA )(0.8672 )(0.0122 )
Estimates ( 3 )0.10030.1256-0.1396-0.923200-1
(p-val)(0.4817 )(0.3658 )(0.3018 )(0 )(NA )(NA )(0.0099 )
Estimates ( 4 )00.1169-0.143-1.117100-1
(p-val)(NA )(0.4047 )(0.2928 )(0 )(NA )(NA )(0.0231 )
Estimates ( 5 )00-0.1491-1.156100-1
(p-val)(NA )(NA )(0.2773 )(0 )(NA )(NA )(0.0779 )
Estimates ( 6 )000-1.113400-1.0001
(p-val)(NA )(NA )(NA )(0 )(NA )(NA )(0.0815 )
Estimates ( 7 )000-0.9829000
(p-val)(NA )(NA )(NA )(0 )(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 ) & 0.1049 & 0.13 & -0.1371 & -0.9237 & -0.0147 & -0.0343 & -0.9995 \tabularnewline
(p-val) & (0.4737 ) & (0.3622 ) & (0.3187 ) & (0 ) & (0.9345 ) & (0.8516 ) & (0.0209 ) \tabularnewline
Estimates ( 2 ) & 0.1027 & 0.1277 & -0.1363 & -0.9244 & 0 & -0.0277 & -0.9998 \tabularnewline
(p-val) & (0.4747 ) & (0.3611 ) & (0.3198 ) & (0 ) & (NA ) & (0.8672 ) & (0.0122 ) \tabularnewline
Estimates ( 3 ) & 0.1003 & 0.1256 & -0.1396 & -0.9232 & 0 & 0 & -1 \tabularnewline
(p-val) & (0.4817 ) & (0.3658 ) & (0.3018 ) & (0 ) & (NA ) & (NA ) & (0.0099 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.1169 & -0.143 & -1.1171 & 0 & 0 & -1 \tabularnewline
(p-val) & (NA ) & (0.4047 ) & (0.2928 ) & (0 ) & (NA ) & (NA ) & (0.0231 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & -0.1491 & -1.1561 & 0 & 0 & -1 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.2773 ) & (0 ) & (NA ) & (NA ) & (0.0779 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & -1.1134 & 0 & 0 & -1.0001 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (0.0815 ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & -0.9829 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (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=202614&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]0.1049[/C][C]0.13[/C][C]-0.1371[/C][C]-0.9237[/C][C]-0.0147[/C][C]-0.0343[/C][C]-0.9995[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4737 )[/C][C](0.3622 )[/C][C](0.3187 )[/C][C](0 )[/C][C](0.9345 )[/C][C](0.8516 )[/C][C](0.0209 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1027[/C][C]0.1277[/C][C]-0.1363[/C][C]-0.9244[/C][C]0[/C][C]-0.0277[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4747 )[/C][C](0.3611 )[/C][C](0.3198 )[/C][C](0 )[/C][C](NA )[/C][C](0.8672 )[/C][C](0.0122 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1003[/C][C]0.1256[/C][C]-0.1396[/C][C]-0.9232[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4817 )[/C][C](0.3658 )[/C][C](0.3018 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0099 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.1169[/C][C]-0.143[/C][C]-1.1171[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.4047 )[/C][C](0.2928 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0231 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]-0.1491[/C][C]-1.1561[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.2773 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0779 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1.1134[/C][C]0[/C][C]0[/C][C]-1.0001[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0815 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.9829[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=202614&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202614&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 )0.10490.13-0.1371-0.9237-0.0147-0.0343-0.9995
(p-val)(0.4737 )(0.3622 )(0.3187 )(0 )(0.9345 )(0.8516 )(0.0209 )
Estimates ( 2 )0.10270.1277-0.1363-0.92440-0.0277-0.9998
(p-val)(0.4747 )(0.3611 )(0.3198 )(0 )(NA )(0.8672 )(0.0122 )
Estimates ( 3 )0.10030.1256-0.1396-0.923200-1
(p-val)(0.4817 )(0.3658 )(0.3018 )(0 )(NA )(NA )(0.0099 )
Estimates ( 4 )00.1169-0.143-1.117100-1
(p-val)(NA )(0.4047 )(0.2928 )(0 )(NA )(NA )(0.0231 )
Estimates ( 5 )00-0.1491-1.156100-1
(p-val)(NA )(NA )(0.2773 )(0 )(NA )(NA )(0.0779 )
Estimates ( 6 )000-1.113400-1.0001
(p-val)(NA )(NA )(NA )(0 )(NA )(NA )(0.0815 )
Estimates ( 7 )000-0.9829000
(p-val)(NA )(NA )(NA )(0 )(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
-0.201495772702999
-11.3400019538982
10.9272190204048
-3.89041163700983
4.16303048580663
0.308512324249218
11.9002003733679
7.56898181074824
3.37192241904051
-10.2039781851694
1.12804873835857
-2.7819503761412
9.1174596776653
12.032432344505
3.48743145216592
-5.6181992475455
-4.06707647204309
5.49743401016974
-7.87173065032561
0.357281462351981
-2.22959857105356
3.49027700807077
-5.65501619479299
-9.40912850395061
15.1777470152335
-5.56507754122207
-7.09288463341003
6.86764100614091
-9.62591150434642
6.42928395856747
-10.2784590823146
1.45285281491172
-6.70989216699535
0.221097549992618
4.0753079174218
18.3862275184539
9.2109718020317
4.67608518705448
-7.2280732259349
0.593820317341087
1.12499451500879
-4.99617437748183
-0.0475904079090846
-4.64707725343993
2.46003681709183
9.41472617930485
14.6408474195279
2.08078749515246
-11.7958440510021
-1.79328633431859
-5.17578265609024
-1.33587506040935
-5.60037039128776
4.81246722113017
2.17250906904651
11.2708346785196
14.6714044287092
5.11558606821106
3.109869072465
6.0646574440848

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.201495772702999 \tabularnewline
-11.3400019538982 \tabularnewline
10.9272190204048 \tabularnewline
-3.89041163700983 \tabularnewline
4.16303048580663 \tabularnewline
0.308512324249218 \tabularnewline
11.9002003733679 \tabularnewline
7.56898181074824 \tabularnewline
3.37192241904051 \tabularnewline
-10.2039781851694 \tabularnewline
1.12804873835857 \tabularnewline
-2.7819503761412 \tabularnewline
9.1174596776653 \tabularnewline
12.032432344505 \tabularnewline
3.48743145216592 \tabularnewline
-5.6181992475455 \tabularnewline
-4.06707647204309 \tabularnewline
5.49743401016974 \tabularnewline
-7.87173065032561 \tabularnewline
0.357281462351981 \tabularnewline
-2.22959857105356 \tabularnewline
3.49027700807077 \tabularnewline
-5.65501619479299 \tabularnewline
-9.40912850395061 \tabularnewline
15.1777470152335 \tabularnewline
-5.56507754122207 \tabularnewline
-7.09288463341003 \tabularnewline
6.86764100614091 \tabularnewline
-9.62591150434642 \tabularnewline
6.42928395856747 \tabularnewline
-10.2784590823146 \tabularnewline
1.45285281491172 \tabularnewline
-6.70989216699535 \tabularnewline
0.221097549992618 \tabularnewline
4.0753079174218 \tabularnewline
18.3862275184539 \tabularnewline
9.2109718020317 \tabularnewline
4.67608518705448 \tabularnewline
-7.2280732259349 \tabularnewline
0.593820317341087 \tabularnewline
1.12499451500879 \tabularnewline
-4.99617437748183 \tabularnewline
-0.0475904079090846 \tabularnewline
-4.64707725343993 \tabularnewline
2.46003681709183 \tabularnewline
9.41472617930485 \tabularnewline
14.6408474195279 \tabularnewline
2.08078749515246 \tabularnewline
-11.7958440510021 \tabularnewline
-1.79328633431859 \tabularnewline
-5.17578265609024 \tabularnewline
-1.33587506040935 \tabularnewline
-5.60037039128776 \tabularnewline
4.81246722113017 \tabularnewline
2.17250906904651 \tabularnewline
11.2708346785196 \tabularnewline
14.6714044287092 \tabularnewline
5.11558606821106 \tabularnewline
3.109869072465 \tabularnewline
6.0646574440848 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202614&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.201495772702999[/C][/ROW]
[ROW][C]-11.3400019538982[/C][/ROW]
[ROW][C]10.9272190204048[/C][/ROW]
[ROW][C]-3.89041163700983[/C][/ROW]
[ROW][C]4.16303048580663[/C][/ROW]
[ROW][C]0.308512324249218[/C][/ROW]
[ROW][C]11.9002003733679[/C][/ROW]
[ROW][C]7.56898181074824[/C][/ROW]
[ROW][C]3.37192241904051[/C][/ROW]
[ROW][C]-10.2039781851694[/C][/ROW]
[ROW][C]1.12804873835857[/C][/ROW]
[ROW][C]-2.7819503761412[/C][/ROW]
[ROW][C]9.1174596776653[/C][/ROW]
[ROW][C]12.032432344505[/C][/ROW]
[ROW][C]3.48743145216592[/C][/ROW]
[ROW][C]-5.6181992475455[/C][/ROW]
[ROW][C]-4.06707647204309[/C][/ROW]
[ROW][C]5.49743401016974[/C][/ROW]
[ROW][C]-7.87173065032561[/C][/ROW]
[ROW][C]0.357281462351981[/C][/ROW]
[ROW][C]-2.22959857105356[/C][/ROW]
[ROW][C]3.49027700807077[/C][/ROW]
[ROW][C]-5.65501619479299[/C][/ROW]
[ROW][C]-9.40912850395061[/C][/ROW]
[ROW][C]15.1777470152335[/C][/ROW]
[ROW][C]-5.56507754122207[/C][/ROW]
[ROW][C]-7.09288463341003[/C][/ROW]
[ROW][C]6.86764100614091[/C][/ROW]
[ROW][C]-9.62591150434642[/C][/ROW]
[ROW][C]6.42928395856747[/C][/ROW]
[ROW][C]-10.2784590823146[/C][/ROW]
[ROW][C]1.45285281491172[/C][/ROW]
[ROW][C]-6.70989216699535[/C][/ROW]
[ROW][C]0.221097549992618[/C][/ROW]
[ROW][C]4.0753079174218[/C][/ROW]
[ROW][C]18.3862275184539[/C][/ROW]
[ROW][C]9.2109718020317[/C][/ROW]
[ROW][C]4.67608518705448[/C][/ROW]
[ROW][C]-7.2280732259349[/C][/ROW]
[ROW][C]0.593820317341087[/C][/ROW]
[ROW][C]1.12499451500879[/C][/ROW]
[ROW][C]-4.99617437748183[/C][/ROW]
[ROW][C]-0.0475904079090846[/C][/ROW]
[ROW][C]-4.64707725343993[/C][/ROW]
[ROW][C]2.46003681709183[/C][/ROW]
[ROW][C]9.41472617930485[/C][/ROW]
[ROW][C]14.6408474195279[/C][/ROW]
[ROW][C]2.08078749515246[/C][/ROW]
[ROW][C]-11.7958440510021[/C][/ROW]
[ROW][C]-1.79328633431859[/C][/ROW]
[ROW][C]-5.17578265609024[/C][/ROW]
[ROW][C]-1.33587506040935[/C][/ROW]
[ROW][C]-5.60037039128776[/C][/ROW]
[ROW][C]4.81246722113017[/C][/ROW]
[ROW][C]2.17250906904651[/C][/ROW]
[ROW][C]11.2708346785196[/C][/ROW]
[ROW][C]14.6714044287092[/C][/ROW]
[ROW][C]5.11558606821106[/C][/ROW]
[ROW][C]3.109869072465[/C][/ROW]
[ROW][C]6.0646574440848[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202614&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202614&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
-0.201495772702999
-11.3400019538982
10.9272190204048
-3.89041163700983
4.16303048580663
0.308512324249218
11.9002003733679
7.56898181074824
3.37192241904051
-10.2039781851694
1.12804873835857
-2.7819503761412
9.1174596776653
12.032432344505
3.48743145216592
-5.6181992475455
-4.06707647204309
5.49743401016974
-7.87173065032561
0.357281462351981
-2.22959857105356
3.49027700807077
-5.65501619479299
-9.40912850395061
15.1777470152335
-5.56507754122207
-7.09288463341003
6.86764100614091
-9.62591150434642
6.42928395856747
-10.2784590823146
1.45285281491172
-6.70989216699535
0.221097549992618
4.0753079174218
18.3862275184539
9.2109718020317
4.67608518705448
-7.2280732259349
0.593820317341087
1.12499451500879
-4.99617437748183
-0.0475904079090846
-4.64707725343993
2.46003681709183
9.41472617930485
14.6408474195279
2.08078749515246
-11.7958440510021
-1.79328633431859
-5.17578265609024
-1.33587506040935
-5.60037039128776
4.81246722113017
2.17250906904651
11.2708346785196
14.6714044287092
5.11558606821106
3.109869072465
6.0646574440848



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')