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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 computationThu, 03 Dec 2009 10:42:49 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/03/t1259862206ht7j3xyreuo4p2r.htm/, Retrieved Fri, 29 Mar 2024 07:30:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62974, Retrieved Fri, 29 Mar 2024 07:30:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
- R PD      [ARIMA Backward Selection] [opdracht 5] [2009-12-03 17:42:49] [0f1f1142419956a95ff6f880845f2408] [Current]
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Dataseries X:
115.47
103.34
102.60
100.69
105.67
123.61
113.08
106.46
123.38
109.87
95.74
123.06
123.39
120.28
115.33
110.4
114.49
132.03
123.16
118.82
128.32
112.24
104.53
132.57
122.52
131.8
124.55
120.96
122.6
145.52
118.57
134.25
136.7
121.37
111.63
134.42
137.65
137.86
119.77
130.69
128.28
147.45
128.42
136.9
143.95
135.64
122.48
136.83
153.04
142.71
123.46
144.37
146.15
147.61
158.51
147.4
165.05
154.64
126.2
157.36
154.15
123.21
113.07
110.45
113.57
122.44
114.93
111.85
126.04
121.34




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62974&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62974&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62974&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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.5041-0.0210.3671-0.9992-0.0681-0.3931-0.039
(p-val)(8e-04 )(0.8892 )(0.0054 )(0 )(0.9518 )(0.036 )(0.9782 )
Estimates ( 2 )-0.5033-0.02060.368-0.9933-0.1025-0.39310
(p-val)(8e-04 )(0.8923 )(0.0056 )(0 )(0.4908 )(0.0289 )(NA )
Estimates ( 3 )-0.492400.3784-1.005-0.1013-0.39560
(p-val)(1e-04 )(NA )(5e-04 )(0 )(0.4935 )(0.0261 )(NA )
Estimates ( 4 )-0.496200.3753-1.0050-0.39330
(p-val)(1e-04 )(NA )(6e-04 )(0 )(NA )(0.027 )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.5041 & -0.021 & 0.3671 & -0.9992 & -0.0681 & -0.3931 & -0.039 \tabularnewline
(p-val) & (8e-04 ) & (0.8892 ) & (0.0054 ) & (0 ) & (0.9518 ) & (0.036 ) & (0.9782 ) \tabularnewline
Estimates ( 2 ) & -0.5033 & -0.0206 & 0.368 & -0.9933 & -0.1025 & -0.3931 & 0 \tabularnewline
(p-val) & (8e-04 ) & (0.8923 ) & (0.0056 ) & (0 ) & (0.4908 ) & (0.0289 ) & (NA ) \tabularnewline
Estimates ( 3 ) & -0.4924 & 0 & 0.3784 & -1.005 & -0.1013 & -0.3956 & 0 \tabularnewline
(p-val) & (1e-04 ) & (NA ) & (5e-04 ) & (0 ) & (0.4935 ) & (0.0261 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.4962 & 0 & 0.3753 & -1.005 & 0 & -0.3933 & 0 \tabularnewline
(p-val) & (1e-04 ) & (NA ) & (6e-04 ) & (0 ) & (NA ) & (0.027 ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62974&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]-0.5041[/C][C]-0.021[/C][C]0.3671[/C][C]-0.9992[/C][C]-0.0681[/C][C]-0.3931[/C][C]-0.039[/C][/ROW]
[ROW][C](p-val)[/C][C](8e-04 )[/C][C](0.8892 )[/C][C](0.0054 )[/C][C](0 )[/C][C](0.9518 )[/C][C](0.036 )[/C][C](0.9782 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.5033[/C][C]-0.0206[/C][C]0.368[/C][C]-0.9933[/C][C]-0.1025[/C][C]-0.3931[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](8e-04 )[/C][C](0.8923 )[/C][C](0.0056 )[/C][C](0 )[/C][C](0.4908 )[/C][C](0.0289 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.4924[/C][C]0[/C][C]0.3784[/C][C]-1.005[/C][C]-0.1013[/C][C]-0.3956[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](1e-04 )[/C][C](NA )[/C][C](5e-04 )[/C][C](0 )[/C][C](0.4935 )[/C][C](0.0261 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.4962[/C][C]0[/C][C]0.3753[/C][C]-1.005[/C][C]0[/C][C]-0.3933[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](1e-04 )[/C][C](NA )[/C][C](6e-04 )[/C][C](0 )[/C][C](NA )[/C][C](0.027 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/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 ( 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=62974&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62974&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 )-0.5041-0.0210.3671-0.9992-0.0681-0.3931-0.039
(p-val)(8e-04 )(0.8892 )(0.0054 )(0 )(0.9518 )(0.036 )(0.9782 )
Estimates ( 2 )-0.5033-0.02060.368-0.9933-0.1025-0.39310
(p-val)(8e-04 )(0.8923 )(0.0056 )(0 )(0.4908 )(0.0289 )(NA )
Estimates ( 3 )-0.492400.3784-1.005-0.1013-0.39560
(p-val)(1e-04 )(NA )(5e-04 )(0 )(0.4935 )(0.0261 )(NA )
Estimates ( 4 )-0.496200.3753-1.0050-0.39330
(p-val)(1e-04 )(NA )(6e-04 )(0 )(NA )(0.027 )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.620282822114019
-5.57067507776883
-4.47674083640012
-4.85432175971457
1.54782642524642
2.83477974449843
3.15302004145725
-5.36333267206240
-5.5639821820507
4.77057694770017
6.5462070469089
-8.08511236861657
5.15442903373964
3.48992955785942
3.50546105704443
-6.5598534612707
5.05826301154617
-14.3618648887049
11.5452325522477
1.01663550532770
2.44743601528085
-7.56074470274566
-1.82776297363082
7.93470705445287
0.416356267891476
-12.4013625262674
4.61562796976781
4.14768329120676
-0.384700472946797
0.444589958499682
1.20481243959401
0.469164257130139
4.21073272262601
3.54382912494267
-9.3913775622035
3.92737952087005
-0.884446326169087
-2.79744020756
6.77270740032368
11.0479986850687
-13.4254132817537
11.1218235828383
-2.04210304146195
7.8983089792073
-6.24518516499537
-12.2731294957813
2.76913348939139
-5.43279522330749
-24.7208008410411
-11.845420908154
-8.189374377502
2.91365742052276
3.79638657785642
-2.49963614602460
-1.48422415140645
0.855348334304045
13.8341268292740

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.620282822114019 \tabularnewline
-5.57067507776883 \tabularnewline
-4.47674083640012 \tabularnewline
-4.85432175971457 \tabularnewline
1.54782642524642 \tabularnewline
2.83477974449843 \tabularnewline
3.15302004145725 \tabularnewline
-5.36333267206240 \tabularnewline
-5.5639821820507 \tabularnewline
4.77057694770017 \tabularnewline
6.5462070469089 \tabularnewline
-8.08511236861657 \tabularnewline
5.15442903373964 \tabularnewline
3.48992955785942 \tabularnewline
3.50546105704443 \tabularnewline
-6.5598534612707 \tabularnewline
5.05826301154617 \tabularnewline
-14.3618648887049 \tabularnewline
11.5452325522477 \tabularnewline
1.01663550532770 \tabularnewline
2.44743601528085 \tabularnewline
-7.56074470274566 \tabularnewline
-1.82776297363082 \tabularnewline
7.93470705445287 \tabularnewline
0.416356267891476 \tabularnewline
-12.4013625262674 \tabularnewline
4.61562796976781 \tabularnewline
4.14768329120676 \tabularnewline
-0.384700472946797 \tabularnewline
0.444589958499682 \tabularnewline
1.20481243959401 \tabularnewline
0.469164257130139 \tabularnewline
4.21073272262601 \tabularnewline
3.54382912494267 \tabularnewline
-9.3913775622035 \tabularnewline
3.92737952087005 \tabularnewline
-0.884446326169087 \tabularnewline
-2.79744020756 \tabularnewline
6.77270740032368 \tabularnewline
11.0479986850687 \tabularnewline
-13.4254132817537 \tabularnewline
11.1218235828383 \tabularnewline
-2.04210304146195 \tabularnewline
7.8983089792073 \tabularnewline
-6.24518516499537 \tabularnewline
-12.2731294957813 \tabularnewline
2.76913348939139 \tabularnewline
-5.43279522330749 \tabularnewline
-24.7208008410411 \tabularnewline
-11.845420908154 \tabularnewline
-8.189374377502 \tabularnewline
2.91365742052276 \tabularnewline
3.79638657785642 \tabularnewline
-2.49963614602460 \tabularnewline
-1.48422415140645 \tabularnewline
0.855348334304045 \tabularnewline
13.8341268292740 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62974&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.620282822114019[/C][/ROW]
[ROW][C]-5.57067507776883[/C][/ROW]
[ROW][C]-4.47674083640012[/C][/ROW]
[ROW][C]-4.85432175971457[/C][/ROW]
[ROW][C]1.54782642524642[/C][/ROW]
[ROW][C]2.83477974449843[/C][/ROW]
[ROW][C]3.15302004145725[/C][/ROW]
[ROW][C]-5.36333267206240[/C][/ROW]
[ROW][C]-5.5639821820507[/C][/ROW]
[ROW][C]4.77057694770017[/C][/ROW]
[ROW][C]6.5462070469089[/C][/ROW]
[ROW][C]-8.08511236861657[/C][/ROW]
[ROW][C]5.15442903373964[/C][/ROW]
[ROW][C]3.48992955785942[/C][/ROW]
[ROW][C]3.50546105704443[/C][/ROW]
[ROW][C]-6.5598534612707[/C][/ROW]
[ROW][C]5.05826301154617[/C][/ROW]
[ROW][C]-14.3618648887049[/C][/ROW]
[ROW][C]11.5452325522477[/C][/ROW]
[ROW][C]1.01663550532770[/C][/ROW]
[ROW][C]2.44743601528085[/C][/ROW]
[ROW][C]-7.56074470274566[/C][/ROW]
[ROW][C]-1.82776297363082[/C][/ROW]
[ROW][C]7.93470705445287[/C][/ROW]
[ROW][C]0.416356267891476[/C][/ROW]
[ROW][C]-12.4013625262674[/C][/ROW]
[ROW][C]4.61562796976781[/C][/ROW]
[ROW][C]4.14768329120676[/C][/ROW]
[ROW][C]-0.384700472946797[/C][/ROW]
[ROW][C]0.444589958499682[/C][/ROW]
[ROW][C]1.20481243959401[/C][/ROW]
[ROW][C]0.469164257130139[/C][/ROW]
[ROW][C]4.21073272262601[/C][/ROW]
[ROW][C]3.54382912494267[/C][/ROW]
[ROW][C]-9.3913775622035[/C][/ROW]
[ROW][C]3.92737952087005[/C][/ROW]
[ROW][C]-0.884446326169087[/C][/ROW]
[ROW][C]-2.79744020756[/C][/ROW]
[ROW][C]6.77270740032368[/C][/ROW]
[ROW][C]11.0479986850687[/C][/ROW]
[ROW][C]-13.4254132817537[/C][/ROW]
[ROW][C]11.1218235828383[/C][/ROW]
[ROW][C]-2.04210304146195[/C][/ROW]
[ROW][C]7.8983089792073[/C][/ROW]
[ROW][C]-6.24518516499537[/C][/ROW]
[ROW][C]-12.2731294957813[/C][/ROW]
[ROW][C]2.76913348939139[/C][/ROW]
[ROW][C]-5.43279522330749[/C][/ROW]
[ROW][C]-24.7208008410411[/C][/ROW]
[ROW][C]-11.845420908154[/C][/ROW]
[ROW][C]-8.189374377502[/C][/ROW]
[ROW][C]2.91365742052276[/C][/ROW]
[ROW][C]3.79638657785642[/C][/ROW]
[ROW][C]-2.49963614602460[/C][/ROW]
[ROW][C]-1.48422415140645[/C][/ROW]
[ROW][C]0.855348334304045[/C][/ROW]
[ROW][C]13.8341268292740[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62974&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62974&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
0.620282822114019
-5.57067507776883
-4.47674083640012
-4.85432175971457
1.54782642524642
2.83477974449843
3.15302004145725
-5.36333267206240
-5.5639821820507
4.77057694770017
6.5462070469089
-8.08511236861657
5.15442903373964
3.48992955785942
3.50546105704443
-6.5598534612707
5.05826301154617
-14.3618648887049
11.5452325522477
1.01663550532770
2.44743601528085
-7.56074470274566
-1.82776297363082
7.93470705445287
0.416356267891476
-12.4013625262674
4.61562796976781
4.14768329120676
-0.384700472946797
0.444589958499682
1.20481243959401
0.469164257130139
4.21073272262601
3.54382912494267
-9.3913775622035
3.92737952087005
-0.884446326169087
-2.79744020756
6.77270740032368
11.0479986850687
-13.4254132817537
11.1218235828383
-2.04210304146195
7.8983089792073
-6.24518516499537
-12.2731294957813
2.76913348939139
-5.43279522330749
-24.7208008410411
-11.845420908154
-8.189374377502
2.91365742052276
3.79638657785642
-2.49963614602460
-1.48422415140645
0.855348334304045
13.8341268292740



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