Free Statistics

of Irreproducible Research!

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, 17 Dec 2011 11:17:34 -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/17/t1324138694b60clu3yc6hc3wl.htm/, Retrieved Thu, 25 Apr 2024 07:22:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156441, Retrieved Thu, 25 Apr 2024 07:22:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Monthly US soldie...] [2010-11-02 12:07:39] [b98453cac15ba1066b407e146608df68]
- RMP   [Variance Reduction Matrix] [Soldiers] [2010-11-29 09:51:25] [b98453cac15ba1066b407e146608df68]
- RM      [Standard Deviation-Mean Plot] [Soldiers] [2010-11-29 11:02:42] [b98453cac15ba1066b407e146608df68]
- RMP       [ARIMA Backward Selection] [Soldiers] [2010-11-29 17:56:11] [b98453cac15ba1066b407e146608df68]
- R PD        [ARIMA Backward Selection] [WS9 4 Foutmelding] [2010-12-07 15:26:08] [afe9379cca749d06b3d6872e02cc47ed]
-   P           [ARIMA Backward Selection] [WS9 4 AR MA] [2010-12-07 15:33:10] [afe9379cca749d06b3d6872e02cc47ed]
- R PD            [ARIMA Backward Selection] [] [2011-12-03 13:58:43] [74be16979710d4c4e7c6647856088456]
-   P                 [ARIMA Backward Selection] [Paper: ARMA] [2011-12-17 16:17:34] [70041e5e9044b1d424b6896a10522877] [Current]
-   PD                  [ARIMA Backward Selection] [Paper: ARMA] [2011-12-17 16:36:06] [54b1f171ce7a12209ffa11b565e1dcf5]
Feedback Forum

Post a new message
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 time11 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156441&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156441&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156441&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'Gertrude Mary Cox' @ cox.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.11720.058-0.0582-11.011-0.0114-0.9638
(p-val)(0.3331 )(0.6495 )(0.6331 )(0 )(0 )(0.9403 )(0 )
Estimates ( 2 )0.11680.0548-0.0583-10.99510-0.884
(p-val)(0.3332 )(0.667 )(0.6387 )(0 )(0 )(NA )(0 )
Estimates ( 3 )0.11750-0.0558-10.99560-0.8934
(p-val)(0.3292 )(NA )(0.65 )(0 )(0 )(NA )(0 )
Estimates ( 4 )0.120800-10.99540-0.8944
(p-val)(0.317 )(NA )(NA )(0 )(0 )(NA )(0 )
Estimates ( 5 )000-10.99450-0.8846
(p-val)(NA )(NA )(NA )(0 )(0 )(NA )(0 )
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 ) & 0.1172 & 0.058 & -0.0582 & -1 & 1.011 & -0.0114 & -0.9638 \tabularnewline
(p-val) & (0.3331 ) & (0.6495 ) & (0.6331 ) & (0 ) & (0 ) & (0.9403 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.1168 & 0.0548 & -0.0583 & -1 & 0.9951 & 0 & -0.884 \tabularnewline
(p-val) & (0.3332 ) & (0.667 ) & (0.6387 ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.1175 & 0 & -0.0558 & -1 & 0.9956 & 0 & -0.8934 \tabularnewline
(p-val) & (0.3292 ) & (NA ) & (0.65 ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0.1208 & 0 & 0 & -1 & 0.9954 & 0 & -0.8944 \tabularnewline
(p-val) & (0.317 ) & (NA ) & (NA ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -1 & 0.9945 & 0 & -0.8846 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0 ) & (NA ) & (0 ) \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=156441&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]0.1172[/C][C]0.058[/C][C]-0.0582[/C][C]-1[/C][C]1.011[/C][C]-0.0114[/C][C]-0.9638[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3331 )[/C][C](0.6495 )[/C][C](0.6331 )[/C][C](0 )[/C][C](0 )[/C][C](0.9403 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1168[/C][C]0.0548[/C][C]-0.0583[/C][C]-1[/C][C]0.9951[/C][C]0[/C][C]-0.884[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3332 )[/C][C](0.667 )[/C][C](0.6387 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1175[/C][C]0[/C][C]-0.0558[/C][C]-1[/C][C]0.9956[/C][C]0[/C][C]-0.8934[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3292 )[/C][C](NA )[/C][C](0.65 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1208[/C][C]0[/C][C]0[/C][C]-1[/C][C]0.9954[/C][C]0[/C][C]-0.8944[/C][/ROW]
[ROW][C](p-val)[/C][C](0.317 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1[/C][C]0.9945[/C][C]0[/C][C]-0.8846[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/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=156441&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156441&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 )0.11720.058-0.0582-11.011-0.0114-0.9638
(p-val)(0.3331 )(0.6495 )(0.6331 )(0 )(0 )(0.9403 )(0 )
Estimates ( 2 )0.11680.0548-0.0583-10.99510-0.884
(p-val)(0.3332 )(0.667 )(0.6387 )(0 )(0 )(NA )(0 )
Estimates ( 3 )0.11750-0.0558-10.99560-0.8934
(p-val)(0.3292 )(NA )(0.65 )(0 )(0 )(NA )(0 )
Estimates ( 4 )0.120800-10.99540-0.8944
(p-val)(0.317 )(NA )(NA )(0 )(0 )(NA )(0 )
Estimates ( 5 )000-10.99450-0.8846
(p-val)(NA )(NA )(NA )(0 )(0 )(NA )(0 )
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
0.0459999128668554
8.22041661121988
6.31154132834228
0.10897985674582
-0.00967284319322342
1.9557974214979
-11.3137114981803
-17.30692470327
5.86513146149396
10.6523692379567
-5.00768966154267
4.0124656055512
-5.04006777786846
-18.2905297167277
11.7722892848891
-10.9134174785553
1.21516602554633
-3.05833624426531
6.07578228388479
-3.36284813616973
4.51071867997217
-9.5910844400472
-1.955179931877
-4.02826047831199
4.91466735590813
11.161870453175
6.91784071833429
-7.57247817120838
-3.64018728198219
7.46325326637786
-12.2412212011555
-4.87355870603965
-1.62119441859778
5.06861444782093
-9.7795512444026
-11.6016563821556
15.101739248419
-9.49049002200983
-6.4819728958656
5.48181385030572
-13.9116232168617
7.07877067416942
-17.4015964881357
-4.37314436123641
-10.0527086725406
-1.29286840876335
-0.751660956691809
17.839762907708
5.99009469615001
3.33404006063394
-7.25122099094357
-0.221558905853446
0.0184512140637023
-6.0074981414376
-3.07559056765406
-10.3777451693433
2.23253983015863
10.1891233339388
13.9846779261512
1.76820018288527
-14.0095898615814
-1.53282375331227
-5.07659553784073
-3.05926128211667
-8.2754639599869
4.83437045036958
-1.68744502877068
8.48232041516736
16.108298931006
6.40330245342871
3.75315848494867
9.08986815078307

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0459999128668554 \tabularnewline
8.22041661121988 \tabularnewline
6.31154132834228 \tabularnewline
0.10897985674582 \tabularnewline
-0.00967284319322342 \tabularnewline
1.9557974214979 \tabularnewline
-11.3137114981803 \tabularnewline
-17.30692470327 \tabularnewline
5.86513146149396 \tabularnewline
10.6523692379567 \tabularnewline
-5.00768966154267 \tabularnewline
4.0124656055512 \tabularnewline
-5.04006777786846 \tabularnewline
-18.2905297167277 \tabularnewline
11.7722892848891 \tabularnewline
-10.9134174785553 \tabularnewline
1.21516602554633 \tabularnewline
-3.05833624426531 \tabularnewline
6.07578228388479 \tabularnewline
-3.36284813616973 \tabularnewline
4.51071867997217 \tabularnewline
-9.5910844400472 \tabularnewline
-1.955179931877 \tabularnewline
-4.02826047831199 \tabularnewline
4.91466735590813 \tabularnewline
11.161870453175 \tabularnewline
6.91784071833429 \tabularnewline
-7.57247817120838 \tabularnewline
-3.64018728198219 \tabularnewline
7.46325326637786 \tabularnewline
-12.2412212011555 \tabularnewline
-4.87355870603965 \tabularnewline
-1.62119441859778 \tabularnewline
5.06861444782093 \tabularnewline
-9.7795512444026 \tabularnewline
-11.6016563821556 \tabularnewline
15.101739248419 \tabularnewline
-9.49049002200983 \tabularnewline
-6.4819728958656 \tabularnewline
5.48181385030572 \tabularnewline
-13.9116232168617 \tabularnewline
7.07877067416942 \tabularnewline
-17.4015964881357 \tabularnewline
-4.37314436123641 \tabularnewline
-10.0527086725406 \tabularnewline
-1.29286840876335 \tabularnewline
-0.751660956691809 \tabularnewline
17.839762907708 \tabularnewline
5.99009469615001 \tabularnewline
3.33404006063394 \tabularnewline
-7.25122099094357 \tabularnewline
-0.221558905853446 \tabularnewline
0.0184512140637023 \tabularnewline
-6.0074981414376 \tabularnewline
-3.07559056765406 \tabularnewline
-10.3777451693433 \tabularnewline
2.23253983015863 \tabularnewline
10.1891233339388 \tabularnewline
13.9846779261512 \tabularnewline
1.76820018288527 \tabularnewline
-14.0095898615814 \tabularnewline
-1.53282375331227 \tabularnewline
-5.07659553784073 \tabularnewline
-3.05926128211667 \tabularnewline
-8.2754639599869 \tabularnewline
4.83437045036958 \tabularnewline
-1.68744502877068 \tabularnewline
8.48232041516736 \tabularnewline
16.108298931006 \tabularnewline
6.40330245342871 \tabularnewline
3.75315848494867 \tabularnewline
9.08986815078307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156441&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0459999128668554[/C][/ROW]
[ROW][C]8.22041661121988[/C][/ROW]
[ROW][C]6.31154132834228[/C][/ROW]
[ROW][C]0.10897985674582[/C][/ROW]
[ROW][C]-0.00967284319322342[/C][/ROW]
[ROW][C]1.9557974214979[/C][/ROW]
[ROW][C]-11.3137114981803[/C][/ROW]
[ROW][C]-17.30692470327[/C][/ROW]
[ROW][C]5.86513146149396[/C][/ROW]
[ROW][C]10.6523692379567[/C][/ROW]
[ROW][C]-5.00768966154267[/C][/ROW]
[ROW][C]4.0124656055512[/C][/ROW]
[ROW][C]-5.04006777786846[/C][/ROW]
[ROW][C]-18.2905297167277[/C][/ROW]
[ROW][C]11.7722892848891[/C][/ROW]
[ROW][C]-10.9134174785553[/C][/ROW]
[ROW][C]1.21516602554633[/C][/ROW]
[ROW][C]-3.05833624426531[/C][/ROW]
[ROW][C]6.07578228388479[/C][/ROW]
[ROW][C]-3.36284813616973[/C][/ROW]
[ROW][C]4.51071867997217[/C][/ROW]
[ROW][C]-9.5910844400472[/C][/ROW]
[ROW][C]-1.955179931877[/C][/ROW]
[ROW][C]-4.02826047831199[/C][/ROW]
[ROW][C]4.91466735590813[/C][/ROW]
[ROW][C]11.161870453175[/C][/ROW]
[ROW][C]6.91784071833429[/C][/ROW]
[ROW][C]-7.57247817120838[/C][/ROW]
[ROW][C]-3.64018728198219[/C][/ROW]
[ROW][C]7.46325326637786[/C][/ROW]
[ROW][C]-12.2412212011555[/C][/ROW]
[ROW][C]-4.87355870603965[/C][/ROW]
[ROW][C]-1.62119441859778[/C][/ROW]
[ROW][C]5.06861444782093[/C][/ROW]
[ROW][C]-9.7795512444026[/C][/ROW]
[ROW][C]-11.6016563821556[/C][/ROW]
[ROW][C]15.101739248419[/C][/ROW]
[ROW][C]-9.49049002200983[/C][/ROW]
[ROW][C]-6.4819728958656[/C][/ROW]
[ROW][C]5.48181385030572[/C][/ROW]
[ROW][C]-13.9116232168617[/C][/ROW]
[ROW][C]7.07877067416942[/C][/ROW]
[ROW][C]-17.4015964881357[/C][/ROW]
[ROW][C]-4.37314436123641[/C][/ROW]
[ROW][C]-10.0527086725406[/C][/ROW]
[ROW][C]-1.29286840876335[/C][/ROW]
[ROW][C]-0.751660956691809[/C][/ROW]
[ROW][C]17.839762907708[/C][/ROW]
[ROW][C]5.99009469615001[/C][/ROW]
[ROW][C]3.33404006063394[/C][/ROW]
[ROW][C]-7.25122099094357[/C][/ROW]
[ROW][C]-0.221558905853446[/C][/ROW]
[ROW][C]0.0184512140637023[/C][/ROW]
[ROW][C]-6.0074981414376[/C][/ROW]
[ROW][C]-3.07559056765406[/C][/ROW]
[ROW][C]-10.3777451693433[/C][/ROW]
[ROW][C]2.23253983015863[/C][/ROW]
[ROW][C]10.1891233339388[/C][/ROW]
[ROW][C]13.9846779261512[/C][/ROW]
[ROW][C]1.76820018288527[/C][/ROW]
[ROW][C]-14.0095898615814[/C][/ROW]
[ROW][C]-1.53282375331227[/C][/ROW]
[ROW][C]-5.07659553784073[/C][/ROW]
[ROW][C]-3.05926128211667[/C][/ROW]
[ROW][C]-8.2754639599869[/C][/ROW]
[ROW][C]4.83437045036958[/C][/ROW]
[ROW][C]-1.68744502877068[/C][/ROW]
[ROW][C]8.48232041516736[/C][/ROW]
[ROW][C]16.108298931006[/C][/ROW]
[ROW][C]6.40330245342871[/C][/ROW]
[ROW][C]3.75315848494867[/C][/ROW]
[ROW][C]9.08986815078307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156441&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156441&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
0.0459999128668554
8.22041661121988
6.31154132834228
0.10897985674582
-0.00967284319322342
1.9557974214979
-11.3137114981803
-17.30692470327
5.86513146149396
10.6523692379567
-5.00768966154267
4.0124656055512
-5.04006777786846
-18.2905297167277
11.7722892848891
-10.9134174785553
1.21516602554633
-3.05833624426531
6.07578228388479
-3.36284813616973
4.51071867997217
-9.5910844400472
-1.955179931877
-4.02826047831199
4.91466735590813
11.161870453175
6.91784071833429
-7.57247817120838
-3.64018728198219
7.46325326637786
-12.2412212011555
-4.87355870603965
-1.62119441859778
5.06861444782093
-9.7795512444026
-11.6016563821556
15.101739248419
-9.49049002200983
-6.4819728958656
5.48181385030572
-13.9116232168617
7.07877067416942
-17.4015964881357
-4.37314436123641
-10.0527086725406
-1.29286840876335
-0.751660956691809
17.839762907708
5.99009469615001
3.33404006063394
-7.25122099094357
-0.221558905853446
0.0184512140637023
-6.0074981414376
-3.07559056765406
-10.3777451693433
2.23253983015863
10.1891233339388
13.9846779261512
1.76820018288527
-14.0095898615814
-1.53282375331227
-5.07659553784073
-3.05926128211667
-8.2754639599869
4.83437045036958
-1.68744502877068
8.48232041516736
16.108298931006
6.40330245342871
3.75315848494867
9.08986815078307



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