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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, 19 Dec 2012 09:56:17 -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/19/t1355929014e7lfnc9f4860x8j.htm/, Retrieved Thu, 31 Oct 2024 23:35:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202033, Retrieved Thu, 31 Oct 2024 23:35:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
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]
-             [ARIMA Backward Selection] [Workshop 9: ARIMA...] [2012-12-03 20:03:07] [7bed0d115000febeef05f886574a3dac]
- R               [ARIMA Backward Selection] [paper: arima back...] [2012-12-19 14:56:17] [292b44c97cfd231f70174a072f53fc18] [Current]
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Dataseries X:
37
30
47
35
30
43
82
40
47
19
52
136
80
42
54
66
81
63
137
72
107
58
36
52
79
77
54
84
48
96
83
66
61
53
30
74
69
59
42
65
70
100
63
105
82
81
75
102
121
98
76
77
63
37
35
23
40
29
37
51
20
28
13
22
25
13
16
13
16
17
9
17
25
14
8
7
10
7
10
3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 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 & 20 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202033&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]20 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=202033&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202033&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 time20 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.1052-0.0694-0.2205-0.4445-0.70850.22290.8862
(p-val)(0.6994 )(0.6851 )(0.088 )(0.1044 )(0.3882 )(0.1712 )(0.3582 )
Estimates ( 2 )0-0.0244-0.1931-0.536-0.74840.220.9319
(p-val)(NA )(0.8455 )(0.0916 )(0 )(0.1369 )(0.1385 )(0.1677 )
Estimates ( 3 )00-0.1901-0.5448-0.69440.22180.8714
(p-val)(NA )(NA )(0.0952 )(0 )(0.54 )(0.2206 )(0.5012 )
Estimates ( 4 )00-0.1864-0.552400.11820.1598
(p-val)(NA )(NA )(0.1011 )(0 )(NA )(0.3899 )(0.1966 )
Estimates ( 5 )00-0.1853-0.5554000.1462
(p-val)(NA )(NA )(0.1019 )(0 )(NA )(NA )(0.2046 )
Estimates ( 6 )00-0.1824-0.5766000
(p-val)(NA )(NA )(0.1087 )(0 )(NA )(NA )(NA )
Estimates ( 7 )000-0.6263000
(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 ) & -0.1052 & -0.0694 & -0.2205 & -0.4445 & -0.7085 & 0.2229 & 0.8862 \tabularnewline
(p-val) & (0.6994 ) & (0.6851 ) & (0.088 ) & (0.1044 ) & (0.3882 ) & (0.1712 ) & (0.3582 ) \tabularnewline
Estimates ( 2 ) & 0 & -0.0244 & -0.1931 & -0.536 & -0.7484 & 0.22 & 0.9319 \tabularnewline
(p-val) & (NA ) & (0.8455 ) & (0.0916 ) & (0 ) & (0.1369 ) & (0.1385 ) & (0.1677 ) \tabularnewline
Estimates ( 3 ) & 0 & 0 & -0.1901 & -0.5448 & -0.6944 & 0.2218 & 0.8714 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0952 ) & (0 ) & (0.54 ) & (0.2206 ) & (0.5012 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & -0.1864 & -0.5524 & 0 & 0.1182 & 0.1598 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1011 ) & (0 ) & (NA ) & (0.3899 ) & (0.1966 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & -0.1853 & -0.5554 & 0 & 0 & 0.1462 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1019 ) & (0 ) & (NA ) & (NA ) & (0.2046 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & -0.1824 & -0.5766 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1087 ) & (0 ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & -0.6263 & 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=202033&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]-0.1052[/C][C]-0.0694[/C][C]-0.2205[/C][C]-0.4445[/C][C]-0.7085[/C][C]0.2229[/C][C]0.8862[/C][/ROW]
[ROW][C](p-val)[/C][C](0.6994 )[/C][C](0.6851 )[/C][C](0.088 )[/C][C](0.1044 )[/C][C](0.3882 )[/C][C](0.1712 )[/C][C](0.3582 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]-0.0244[/C][C]-0.1931[/C][C]-0.536[/C][C]-0.7484[/C][C]0.22[/C][C]0.9319[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.8455 )[/C][C](0.0916 )[/C][C](0 )[/C][C](0.1369 )[/C][C](0.1385 )[/C][C](0.1677 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0[/C][C]-0.1901[/C][C]-0.5448[/C][C]-0.6944[/C][C]0.2218[/C][C]0.8714[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0952 )[/C][C](0 )[/C][C](0.54 )[/C][C](0.2206 )[/C][C](0.5012 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]-0.1864[/C][C]-0.5524[/C][C]0[/C][C]0.1182[/C][C]0.1598[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1011 )[/C][C](0 )[/C][C](NA )[/C][C](0.3899 )[/C][C](0.1966 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]-0.1853[/C][C]-0.5554[/C][C]0[/C][C]0[/C][C]0.1462[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1019 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.2046 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]-0.1824[/C][C]-0.5766[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1087 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.6263[/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=202033&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202033&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 )-0.1052-0.0694-0.2205-0.4445-0.70850.22290.8862
(p-val)(0.6994 )(0.6851 )(0.088 )(0.1044 )(0.3882 )(0.1712 )(0.3582 )
Estimates ( 2 )0-0.0244-0.1931-0.536-0.74840.220.9319
(p-val)(NA )(0.8455 )(0.0916 )(0 )(0.1369 )(0.1385 )(0.1677 )
Estimates ( 3 )00-0.1901-0.5448-0.69440.22180.8714
(p-val)(NA )(NA )(0.0952 )(0 )(0.54 )(0.2206 )(0.5012 )
Estimates ( 4 )00-0.1864-0.552400.11820.1598
(p-val)(NA )(NA )(0.1011 )(0 )(NA )(0.3899 )(0.1966 )
Estimates ( 5 )00-0.1853-0.5554000.1462
(p-val)(NA )(NA )(0.1019 )(0 )(NA )(NA )(0.2046 )
Estimates ( 6 )00-0.1824-0.5766000
(p-val)(NA )(NA )(0.1087 )(0 )(NA )(NA )(NA )
Estimates ( 7 )000-0.6263000
(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
0.0369999745003413
-5.9623186537317
13.199750510756
-4.38134104653015
-8.55664024772041
11.1858500834747
43.1600828172584
-18.0555098078549
-1.03390376859901
-21.480207773057
12.9526883745974
92.7450087068449
-7.62959923006792
-36.3793071886167
6.34670392141556
5.4438142475064
11.2068476762212
-9.34880084666512
70.7983973254214
-21.4398345761841
19.3537016552739
-24.3407859893616
-47.8930389401323
-5.23120399526416
15.0447379573563
2.66174086946239
-18.5463802136914
24.2312685058628
-22.3926123716306
30.8921837808626
10.2858183676532
-17.6363037027495
-6.4130297615578
-14.0694124848462
-34.2139388425848
23.3594254521777
7.01011297221759
-10.153611430772
-14.8280599663193
13.537714424612
10.981865403099
33.2311335292979
-13.6424737570068
35.0456012872994
2.68076271897523
-6.2039576547908
-1.91546067977486
21.6997226809524
31.3300726113285
-6.0290002401709
-20.550957597228
-7.38401440956276
-22.453555756565
-42.9605320118509
-26.5894843205177
-29.8859887428515
-4.97592443186665
-14.2340692287293
-2.3967562794556
15.7192136412636
-23.9426635182183
-4.34641981100157
-14.9522778744557
-5.27697234321827
1.41659439929679
-13.9195439755118
-3.38446142046753
-4.40427257572364
-1.7286971613264
0.550474509876316
-8.22986146756033
3.80177197561401
10.3746038348396
-6.47719720961919
-8.27547947953864
-4.31240532620612
-1.49329925845406
-4.95561884498003
-0.0399352246639766
-6.47575127776272

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0369999745003413 \tabularnewline
-5.9623186537317 \tabularnewline
13.199750510756 \tabularnewline
-4.38134104653015 \tabularnewline
-8.55664024772041 \tabularnewline
11.1858500834747 \tabularnewline
43.1600828172584 \tabularnewline
-18.0555098078549 \tabularnewline
-1.03390376859901 \tabularnewline
-21.480207773057 \tabularnewline
12.9526883745974 \tabularnewline
92.7450087068449 \tabularnewline
-7.62959923006792 \tabularnewline
-36.3793071886167 \tabularnewline
6.34670392141556 \tabularnewline
5.4438142475064 \tabularnewline
11.2068476762212 \tabularnewline
-9.34880084666512 \tabularnewline
70.7983973254214 \tabularnewline
-21.4398345761841 \tabularnewline
19.3537016552739 \tabularnewline
-24.3407859893616 \tabularnewline
-47.8930389401323 \tabularnewline
-5.23120399526416 \tabularnewline
15.0447379573563 \tabularnewline
2.66174086946239 \tabularnewline
-18.5463802136914 \tabularnewline
24.2312685058628 \tabularnewline
-22.3926123716306 \tabularnewline
30.8921837808626 \tabularnewline
10.2858183676532 \tabularnewline
-17.6363037027495 \tabularnewline
-6.4130297615578 \tabularnewline
-14.0694124848462 \tabularnewline
-34.2139388425848 \tabularnewline
23.3594254521777 \tabularnewline
7.01011297221759 \tabularnewline
-10.153611430772 \tabularnewline
-14.8280599663193 \tabularnewline
13.537714424612 \tabularnewline
10.981865403099 \tabularnewline
33.2311335292979 \tabularnewline
-13.6424737570068 \tabularnewline
35.0456012872994 \tabularnewline
2.68076271897523 \tabularnewline
-6.2039576547908 \tabularnewline
-1.91546067977486 \tabularnewline
21.6997226809524 \tabularnewline
31.3300726113285 \tabularnewline
-6.0290002401709 \tabularnewline
-20.550957597228 \tabularnewline
-7.38401440956276 \tabularnewline
-22.453555756565 \tabularnewline
-42.9605320118509 \tabularnewline
-26.5894843205177 \tabularnewline
-29.8859887428515 \tabularnewline
-4.97592443186665 \tabularnewline
-14.2340692287293 \tabularnewline
-2.3967562794556 \tabularnewline
15.7192136412636 \tabularnewline
-23.9426635182183 \tabularnewline
-4.34641981100157 \tabularnewline
-14.9522778744557 \tabularnewline
-5.27697234321827 \tabularnewline
1.41659439929679 \tabularnewline
-13.9195439755118 \tabularnewline
-3.38446142046753 \tabularnewline
-4.40427257572364 \tabularnewline
-1.7286971613264 \tabularnewline
0.550474509876316 \tabularnewline
-8.22986146756033 \tabularnewline
3.80177197561401 \tabularnewline
10.3746038348396 \tabularnewline
-6.47719720961919 \tabularnewline
-8.27547947953864 \tabularnewline
-4.31240532620612 \tabularnewline
-1.49329925845406 \tabularnewline
-4.95561884498003 \tabularnewline
-0.0399352246639766 \tabularnewline
-6.47575127776272 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202033&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0369999745003413[/C][/ROW]
[ROW][C]-5.9623186537317[/C][/ROW]
[ROW][C]13.199750510756[/C][/ROW]
[ROW][C]-4.38134104653015[/C][/ROW]
[ROW][C]-8.55664024772041[/C][/ROW]
[ROW][C]11.1858500834747[/C][/ROW]
[ROW][C]43.1600828172584[/C][/ROW]
[ROW][C]-18.0555098078549[/C][/ROW]
[ROW][C]-1.03390376859901[/C][/ROW]
[ROW][C]-21.480207773057[/C][/ROW]
[ROW][C]12.9526883745974[/C][/ROW]
[ROW][C]92.7450087068449[/C][/ROW]
[ROW][C]-7.62959923006792[/C][/ROW]
[ROW][C]-36.3793071886167[/C][/ROW]
[ROW][C]6.34670392141556[/C][/ROW]
[ROW][C]5.4438142475064[/C][/ROW]
[ROW][C]11.2068476762212[/C][/ROW]
[ROW][C]-9.34880084666512[/C][/ROW]
[ROW][C]70.7983973254214[/C][/ROW]
[ROW][C]-21.4398345761841[/C][/ROW]
[ROW][C]19.3537016552739[/C][/ROW]
[ROW][C]-24.3407859893616[/C][/ROW]
[ROW][C]-47.8930389401323[/C][/ROW]
[ROW][C]-5.23120399526416[/C][/ROW]
[ROW][C]15.0447379573563[/C][/ROW]
[ROW][C]2.66174086946239[/C][/ROW]
[ROW][C]-18.5463802136914[/C][/ROW]
[ROW][C]24.2312685058628[/C][/ROW]
[ROW][C]-22.3926123716306[/C][/ROW]
[ROW][C]30.8921837808626[/C][/ROW]
[ROW][C]10.2858183676532[/C][/ROW]
[ROW][C]-17.6363037027495[/C][/ROW]
[ROW][C]-6.4130297615578[/C][/ROW]
[ROW][C]-14.0694124848462[/C][/ROW]
[ROW][C]-34.2139388425848[/C][/ROW]
[ROW][C]23.3594254521777[/C][/ROW]
[ROW][C]7.01011297221759[/C][/ROW]
[ROW][C]-10.153611430772[/C][/ROW]
[ROW][C]-14.8280599663193[/C][/ROW]
[ROW][C]13.537714424612[/C][/ROW]
[ROW][C]10.981865403099[/C][/ROW]
[ROW][C]33.2311335292979[/C][/ROW]
[ROW][C]-13.6424737570068[/C][/ROW]
[ROW][C]35.0456012872994[/C][/ROW]
[ROW][C]2.68076271897523[/C][/ROW]
[ROW][C]-6.2039576547908[/C][/ROW]
[ROW][C]-1.91546067977486[/C][/ROW]
[ROW][C]21.6997226809524[/C][/ROW]
[ROW][C]31.3300726113285[/C][/ROW]
[ROW][C]-6.0290002401709[/C][/ROW]
[ROW][C]-20.550957597228[/C][/ROW]
[ROW][C]-7.38401440956276[/C][/ROW]
[ROW][C]-22.453555756565[/C][/ROW]
[ROW][C]-42.9605320118509[/C][/ROW]
[ROW][C]-26.5894843205177[/C][/ROW]
[ROW][C]-29.8859887428515[/C][/ROW]
[ROW][C]-4.97592443186665[/C][/ROW]
[ROW][C]-14.2340692287293[/C][/ROW]
[ROW][C]-2.3967562794556[/C][/ROW]
[ROW][C]15.7192136412636[/C][/ROW]
[ROW][C]-23.9426635182183[/C][/ROW]
[ROW][C]-4.34641981100157[/C][/ROW]
[ROW][C]-14.9522778744557[/C][/ROW]
[ROW][C]-5.27697234321827[/C][/ROW]
[ROW][C]1.41659439929679[/C][/ROW]
[ROW][C]-13.9195439755118[/C][/ROW]
[ROW][C]-3.38446142046753[/C][/ROW]
[ROW][C]-4.40427257572364[/C][/ROW]
[ROW][C]-1.7286971613264[/C][/ROW]
[ROW][C]0.550474509876316[/C][/ROW]
[ROW][C]-8.22986146756033[/C][/ROW]
[ROW][C]3.80177197561401[/C][/ROW]
[ROW][C]10.3746038348396[/C][/ROW]
[ROW][C]-6.47719720961919[/C][/ROW]
[ROW][C]-8.27547947953864[/C][/ROW]
[ROW][C]-4.31240532620612[/C][/ROW]
[ROW][C]-1.49329925845406[/C][/ROW]
[ROW][C]-4.95561884498003[/C][/ROW]
[ROW][C]-0.0399352246639766[/C][/ROW]
[ROW][C]-6.47575127776272[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202033&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202033&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
0.0369999745003413
-5.9623186537317
13.199750510756
-4.38134104653015
-8.55664024772041
11.1858500834747
43.1600828172584
-18.0555098078549
-1.03390376859901
-21.480207773057
12.9526883745974
92.7450087068449
-7.62959923006792
-36.3793071886167
6.34670392141556
5.4438142475064
11.2068476762212
-9.34880084666512
70.7983973254214
-21.4398345761841
19.3537016552739
-24.3407859893616
-47.8930389401323
-5.23120399526416
15.0447379573563
2.66174086946239
-18.5463802136914
24.2312685058628
-22.3926123716306
30.8921837808626
10.2858183676532
-17.6363037027495
-6.4130297615578
-14.0694124848462
-34.2139388425848
23.3594254521777
7.01011297221759
-10.153611430772
-14.8280599663193
13.537714424612
10.981865403099
33.2311335292979
-13.6424737570068
35.0456012872994
2.68076271897523
-6.2039576547908
-1.91546067977486
21.6997226809524
31.3300726113285
-6.0290002401709
-20.550957597228
-7.38401440956276
-22.453555756565
-42.9605320118509
-26.5894843205177
-29.8859887428515
-4.97592443186665
-14.2340692287293
-2.3967562794556
15.7192136412636
-23.9426635182183
-4.34641981100157
-14.9522778744557
-5.27697234321827
1.41659439929679
-13.9195439755118
-3.38446142046753
-4.40427257572364
-1.7286971613264
0.550474509876316
-8.22986146756033
3.80177197561401
10.3746038348396
-6.47719720961919
-8.27547947953864
-4.31240532620612
-1.49329925845406
-4.95561884498003
-0.0399352246639766
-6.47575127776272



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')