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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, 10 Dec 2009 04:46:40 -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/10/t1260445661y0nqws17bikpw37.htm/, Retrieved Fri, 29 Mar 2024 12:41:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65292, Retrieved Fri, 29 Mar 2024 12:41:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
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] [ARIMA ] [2009-12-08 19:25:36] [03557919bc1ce1475f4920f6a43c36b0]
-   P         [ARIMA Backward Selection] [verbetering] [2009-12-10 11:46:40] [9be6fbb216efe5bb8ca600257c6e1971] [Current]
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Dataseries X:
130.7
117.2
110.8
111.4
108.2
108.8
110.2
109.5
109.5
116
111.2
112.1
114
119.1
114.1
115.1
115.4
110.8
116
119.2
126.5
127.8
131.3
140.3
137.3
143
134.5
139.9
159.3
170.4
175
175.8
180.9
180.3
169.6
172.3
184.8
177.7
184.6
211.4
215.3
215.9
244.7
259.3
289
310.9
321
315.1
333.2
314.1
284.7
273.9
216
196.4
190.9
206.4
196.3
199.5
198.9
214.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65292&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]6 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=65292&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65292&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.12870.23490.1380.1557-0.3517-0.16510.4119
(p-val)(0.8044 )(0.2629 )(0.4397 )(0.7613 )(0.6148 )(0.4058 )(0.5697 )
Estimates ( 2 )00.27540.15990.2801-0.3521-0.16520.4129
(p-val)(NA )(0.0619 )(0.2445 )(0.0388 )(0.6125 )(0.4052 )(0.5671 )
Estimates ( 3 )00.26380.16240.28510-0.16640.0593
(p-val)(NA )(0.0668 )(0.2387 )(0.0349 )(NA )(0.3637 )(0.7718 )
Estimates ( 4 )00.24760.17780.27990-0.16080
(p-val)(NA )(0.0601 )(0.1623 )(0.0348 )(NA )(0.3787 )(NA )
Estimates ( 5 )00.24230.18450.284000
(p-val)(NA )(0.0649 )(0.1454 )(0.0319 )(NA )(NA )(NA )
Estimates ( 6 )00.260200.3171000
(p-val)(NA )(0.0456 )(NA )(0.0305 )(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 ) & 0.1287 & 0.2349 & 0.138 & 0.1557 & -0.3517 & -0.1651 & 0.4119 \tabularnewline
(p-val) & (0.8044 ) & (0.2629 ) & (0.4397 ) & (0.7613 ) & (0.6148 ) & (0.4058 ) & (0.5697 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.2754 & 0.1599 & 0.2801 & -0.3521 & -0.1652 & 0.4129 \tabularnewline
(p-val) & (NA ) & (0.0619 ) & (0.2445 ) & (0.0388 ) & (0.6125 ) & (0.4052 ) & (0.5671 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.2638 & 0.1624 & 0.2851 & 0 & -0.1664 & 0.0593 \tabularnewline
(p-val) & (NA ) & (0.0668 ) & (0.2387 ) & (0.0349 ) & (NA ) & (0.3637 ) & (0.7718 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.2476 & 0.1778 & 0.2799 & 0 & -0.1608 & 0 \tabularnewline
(p-val) & (NA ) & (0.0601 ) & (0.1623 ) & (0.0348 ) & (NA ) & (0.3787 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0.2423 & 0.1845 & 0.284 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (0.0649 ) & (0.1454 ) & (0.0319 ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0.2602 & 0 & 0.3171 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (0.0456 ) & (NA ) & (0.0305 ) & (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=65292&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]0.1287[/C][C]0.2349[/C][C]0.138[/C][C]0.1557[/C][C]-0.3517[/C][C]-0.1651[/C][C]0.4119[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8044 )[/C][C](0.2629 )[/C][C](0.4397 )[/C][C](0.7613 )[/C][C](0.6148 )[/C][C](0.4058 )[/C][C](0.5697 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.2754[/C][C]0.1599[/C][C]0.2801[/C][C]-0.3521[/C][C]-0.1652[/C][C]0.4129[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0619 )[/C][C](0.2445 )[/C][C](0.0388 )[/C][C](0.6125 )[/C][C](0.4052 )[/C][C](0.5671 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.2638[/C][C]0.1624[/C][C]0.2851[/C][C]0[/C][C]-0.1664[/C][C]0.0593[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0668 )[/C][C](0.2387 )[/C][C](0.0349 )[/C][C](NA )[/C][C](0.3637 )[/C][C](0.7718 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.2476[/C][C]0.1778[/C][C]0.2799[/C][C]0[/C][C]-0.1608[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0601 )[/C][C](0.1623 )[/C][C](0.0348 )[/C][C](NA )[/C][C](0.3787 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0.2423[/C][C]0.1845[/C][C]0.284[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0649 )[/C][C](0.1454 )[/C][C](0.0319 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0.2602[/C][C]0[/C][C]0.3171[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0456 )[/C][C](NA )[/C][C](0.0305 )[/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=65292&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65292&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 )0.12870.23490.1380.1557-0.3517-0.16510.4119
(p-val)(0.8044 )(0.2629 )(0.4397 )(0.7613 )(0.6148 )(0.4058 )(0.5697 )
Estimates ( 2 )00.27540.15990.2801-0.3521-0.16520.4129
(p-val)(NA )(0.0619 )(0.2445 )(0.0388 )(0.6125 )(0.4052 )(0.5671 )
Estimates ( 3 )00.26380.16240.28510-0.16640.0593
(p-val)(NA )(0.0668 )(0.2387 )(0.0349 )(NA )(0.3637 )(0.7718 )
Estimates ( 4 )00.24760.17780.27990-0.16080
(p-val)(NA )(0.0601 )(0.1623 )(0.0348 )(NA )(0.3787 )(NA )
Estimates ( 5 )00.24230.18450.284000
(p-val)(NA )(0.0649 )(0.1454 )(0.0319 )(NA )(NA )(NA )
Estimates ( 6 )00.260200.3171000
(p-val)(NA )(0.0456 )(NA )(0.0305 )(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
0.130699919140578
-12.1364887309292
-1.24787230445048
5.15835040122827
-0.609405250900776
1.80814983142586
1.5513316136869
-0.695546919660416
-0.252443409480876
6.48302828684895
-6.5117904570965
1.17397173500963
1.53062718505994
5.33281487754849
-7.14077905394967
1.44130904386824
0.161464324828529
-3.96572987876074
6.06892851260254
2.53601510803466
6.16841688316805
-2.18638864491071
1.76149408938947
6.83800392492336
-6.02971518767563
4.58554885125585
-10.7355439235198
7.62068722973135
18.2442088486457
6.17893161990335
-2.85197773606623
-4.6590778027325
3.26046107158808
-2.56836029359377
-11.3541453035585
5.12864499405299
13.7472580485146
-9.68394458290337
6.12265614918056
24.4758057478398
-3.41238102839864
-6.19836794513358
24.6707054572896
6.72955395979702
20.6993374839478
7.17089854434084
-1.82693484676486
-16.1675293883205
16.2031354942601
-24.1346846860005
-25.8443086575283
-2.17200497247626
-46.6350561637573
1.68370684898122
10.0451395690352
28.0791220102501
-13.1246063257120
4.1855041701522
-2.2006027941664
17.2127830023156

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.130699919140578 \tabularnewline
-12.1364887309292 \tabularnewline
-1.24787230445048 \tabularnewline
5.15835040122827 \tabularnewline
-0.609405250900776 \tabularnewline
1.80814983142586 \tabularnewline
1.5513316136869 \tabularnewline
-0.695546919660416 \tabularnewline
-0.252443409480876 \tabularnewline
6.48302828684895 \tabularnewline
-6.5117904570965 \tabularnewline
1.17397173500963 \tabularnewline
1.53062718505994 \tabularnewline
5.33281487754849 \tabularnewline
-7.14077905394967 \tabularnewline
1.44130904386824 \tabularnewline
0.161464324828529 \tabularnewline
-3.96572987876074 \tabularnewline
6.06892851260254 \tabularnewline
2.53601510803466 \tabularnewline
6.16841688316805 \tabularnewline
-2.18638864491071 \tabularnewline
1.76149408938947 \tabularnewline
6.83800392492336 \tabularnewline
-6.02971518767563 \tabularnewline
4.58554885125585 \tabularnewline
-10.7355439235198 \tabularnewline
7.62068722973135 \tabularnewline
18.2442088486457 \tabularnewline
6.17893161990335 \tabularnewline
-2.85197773606623 \tabularnewline
-4.6590778027325 \tabularnewline
3.26046107158808 \tabularnewline
-2.56836029359377 \tabularnewline
-11.3541453035585 \tabularnewline
5.12864499405299 \tabularnewline
13.7472580485146 \tabularnewline
-9.68394458290337 \tabularnewline
6.12265614918056 \tabularnewline
24.4758057478398 \tabularnewline
-3.41238102839864 \tabularnewline
-6.19836794513358 \tabularnewline
24.6707054572896 \tabularnewline
6.72955395979702 \tabularnewline
20.6993374839478 \tabularnewline
7.17089854434084 \tabularnewline
-1.82693484676486 \tabularnewline
-16.1675293883205 \tabularnewline
16.2031354942601 \tabularnewline
-24.1346846860005 \tabularnewline
-25.8443086575283 \tabularnewline
-2.17200497247626 \tabularnewline
-46.6350561637573 \tabularnewline
1.68370684898122 \tabularnewline
10.0451395690352 \tabularnewline
28.0791220102501 \tabularnewline
-13.1246063257120 \tabularnewline
4.1855041701522 \tabularnewline
-2.2006027941664 \tabularnewline
17.2127830023156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65292&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.130699919140578[/C][/ROW]
[ROW][C]-12.1364887309292[/C][/ROW]
[ROW][C]-1.24787230445048[/C][/ROW]
[ROW][C]5.15835040122827[/C][/ROW]
[ROW][C]-0.609405250900776[/C][/ROW]
[ROW][C]1.80814983142586[/C][/ROW]
[ROW][C]1.5513316136869[/C][/ROW]
[ROW][C]-0.695546919660416[/C][/ROW]
[ROW][C]-0.252443409480876[/C][/ROW]
[ROW][C]6.48302828684895[/C][/ROW]
[ROW][C]-6.5117904570965[/C][/ROW]
[ROW][C]1.17397173500963[/C][/ROW]
[ROW][C]1.53062718505994[/C][/ROW]
[ROW][C]5.33281487754849[/C][/ROW]
[ROW][C]-7.14077905394967[/C][/ROW]
[ROW][C]1.44130904386824[/C][/ROW]
[ROW][C]0.161464324828529[/C][/ROW]
[ROW][C]-3.96572987876074[/C][/ROW]
[ROW][C]6.06892851260254[/C][/ROW]
[ROW][C]2.53601510803466[/C][/ROW]
[ROW][C]6.16841688316805[/C][/ROW]
[ROW][C]-2.18638864491071[/C][/ROW]
[ROW][C]1.76149408938947[/C][/ROW]
[ROW][C]6.83800392492336[/C][/ROW]
[ROW][C]-6.02971518767563[/C][/ROW]
[ROW][C]4.58554885125585[/C][/ROW]
[ROW][C]-10.7355439235198[/C][/ROW]
[ROW][C]7.62068722973135[/C][/ROW]
[ROW][C]18.2442088486457[/C][/ROW]
[ROW][C]6.17893161990335[/C][/ROW]
[ROW][C]-2.85197773606623[/C][/ROW]
[ROW][C]-4.6590778027325[/C][/ROW]
[ROW][C]3.26046107158808[/C][/ROW]
[ROW][C]-2.56836029359377[/C][/ROW]
[ROW][C]-11.3541453035585[/C][/ROW]
[ROW][C]5.12864499405299[/C][/ROW]
[ROW][C]13.7472580485146[/C][/ROW]
[ROW][C]-9.68394458290337[/C][/ROW]
[ROW][C]6.12265614918056[/C][/ROW]
[ROW][C]24.4758057478398[/C][/ROW]
[ROW][C]-3.41238102839864[/C][/ROW]
[ROW][C]-6.19836794513358[/C][/ROW]
[ROW][C]24.6707054572896[/C][/ROW]
[ROW][C]6.72955395979702[/C][/ROW]
[ROW][C]20.6993374839478[/C][/ROW]
[ROW][C]7.17089854434084[/C][/ROW]
[ROW][C]-1.82693484676486[/C][/ROW]
[ROW][C]-16.1675293883205[/C][/ROW]
[ROW][C]16.2031354942601[/C][/ROW]
[ROW][C]-24.1346846860005[/C][/ROW]
[ROW][C]-25.8443086575283[/C][/ROW]
[ROW][C]-2.17200497247626[/C][/ROW]
[ROW][C]-46.6350561637573[/C][/ROW]
[ROW][C]1.68370684898122[/C][/ROW]
[ROW][C]10.0451395690352[/C][/ROW]
[ROW][C]28.0791220102501[/C][/ROW]
[ROW][C]-13.1246063257120[/C][/ROW]
[ROW][C]4.1855041701522[/C][/ROW]
[ROW][C]-2.2006027941664[/C][/ROW]
[ROW][C]17.2127830023156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65292&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65292&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
0.130699919140578
-12.1364887309292
-1.24787230445048
5.15835040122827
-0.609405250900776
1.80814983142586
1.5513316136869
-0.695546919660416
-0.252443409480876
6.48302828684895
-6.5117904570965
1.17397173500963
1.53062718505994
5.33281487754849
-7.14077905394967
1.44130904386824
0.161464324828529
-3.96572987876074
6.06892851260254
2.53601510803466
6.16841688316805
-2.18638864491071
1.76149408938947
6.83800392492336
-6.02971518767563
4.58554885125585
-10.7355439235198
7.62068722973135
18.2442088486457
6.17893161990335
-2.85197773606623
-4.6590778027325
3.26046107158808
-2.56836029359377
-11.3541453035585
5.12864499405299
13.7472580485146
-9.68394458290337
6.12265614918056
24.4758057478398
-3.41238102839864
-6.19836794513358
24.6707054572896
6.72955395979702
20.6993374839478
7.17089854434084
-1.82693484676486
-16.1675293883205
16.2031354942601
-24.1346846860005
-25.8443086575283
-2.17200497247626
-46.6350561637573
1.68370684898122
10.0451395690352
28.0791220102501
-13.1246063257120
4.1855041701522
-2.2006027941664
17.2127830023156



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')