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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 09:26:29 -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/t1259857856jb8x0k61vq8ep7m.htm/, Retrieved Thu, 28 Mar 2024 14:24:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62882, Retrieved Thu, 28 Mar 2024 14:24:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
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] [] [2009-12-03 16:26:29] [faa1ded5041cd5a0e2be04844f08502a] [Current]
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Dataseries X:
24
22
25
24
29
26
26
21
23
22
21
16
19
16
25
27
23
22
23
20
24
23
20
21
22
17
21
19
23
22
15
23
21
18
18
18
18
10
13
10
9
9
6
11
9
10
9
16
10
7
7
14
11
10
6
8
13
12
15
16
16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62882&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]7 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=62882&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62882&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.3579-0.1713-0.1547-0.1315-0.03680.09870.3096
(p-val)(0.5124 )(0.5436 )(0.3236 )(0.8098 )(0.9926 )(0.9306 )(0.9378 )
Estimates ( 2 )-0.3576-0.1712-0.1547-0.131900.08820.273
(p-val)(0.5123 )(0.5435 )(0.3229 )(0.8092 )(NA )(0.6113 )(0.0537 )
Estimates ( 3 )-0.4847-0.2285-0.1724000.08930.2709
(p-val)(4e-04 )(0.1117 )(0.1782 )(NA )(NA )(0.6054 )(0.0554 )
Estimates ( 4 )-0.4895-0.2449-0.1810000.2626
(p-val)(3e-04 )(0.0799 )(0.1533 )(NA )(NA )(NA )(0.0554 )
Estimates ( 5 )-0.4572-0.160300000.2623
(p-val)(7e-04 )(0.2093 )(NA )(NA )(NA )(NA )(0.0557 )
Estimates ( 6 )-0.3918000000.2456
(p-val)(0.0015 )(NA )(NA )(NA )(NA )(NA )(0.061 )
Estimates ( 7 )-0.3805000000
(p-val)(0.0021 )(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 ) & -0.3579 & -0.1713 & -0.1547 & -0.1315 & -0.0368 & 0.0987 & 0.3096 \tabularnewline
(p-val) & (0.5124 ) & (0.5436 ) & (0.3236 ) & (0.8098 ) & (0.9926 ) & (0.9306 ) & (0.9378 ) \tabularnewline
Estimates ( 2 ) & -0.3576 & -0.1712 & -0.1547 & -0.1319 & 0 & 0.0882 & 0.273 \tabularnewline
(p-val) & (0.5123 ) & (0.5435 ) & (0.3229 ) & (0.8092 ) & (NA ) & (0.6113 ) & (0.0537 ) \tabularnewline
Estimates ( 3 ) & -0.4847 & -0.2285 & -0.1724 & 0 & 0 & 0.0893 & 0.2709 \tabularnewline
(p-val) & (4e-04 ) & (0.1117 ) & (0.1782 ) & (NA ) & (NA ) & (0.6054 ) & (0.0554 ) \tabularnewline
Estimates ( 4 ) & -0.4895 & -0.2449 & -0.181 & 0 & 0 & 0 & 0.2626 \tabularnewline
(p-val) & (3e-04 ) & (0.0799 ) & (0.1533 ) & (NA ) & (NA ) & (NA ) & (0.0554 ) \tabularnewline
Estimates ( 5 ) & -0.4572 & -0.1603 & 0 & 0 & 0 & 0 & 0.2623 \tabularnewline
(p-val) & (7e-04 ) & (0.2093 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0557 ) \tabularnewline
Estimates ( 6 ) & -0.3918 & 0 & 0 & 0 & 0 & 0 & 0.2456 \tabularnewline
(p-val) & (0.0015 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.061 ) \tabularnewline
Estimates ( 7 ) & -0.3805 & 0 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.0021 ) & (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=62882&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]-0.3579[/C][C]-0.1713[/C][C]-0.1547[/C][C]-0.1315[/C][C]-0.0368[/C][C]0.0987[/C][C]0.3096[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5124 )[/C][C](0.5436 )[/C][C](0.3236 )[/C][C](0.8098 )[/C][C](0.9926 )[/C][C](0.9306 )[/C][C](0.9378 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.3576[/C][C]-0.1712[/C][C]-0.1547[/C][C]-0.1319[/C][C]0[/C][C]0.0882[/C][C]0.273[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5123 )[/C][C](0.5435 )[/C][C](0.3229 )[/C][C](0.8092 )[/C][C](NA )[/C][C](0.6113 )[/C][C](0.0537 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.4847[/C][C]-0.2285[/C][C]-0.1724[/C][C]0[/C][C]0[/C][C]0.0893[/C][C]0.2709[/C][/ROW]
[ROW][C](p-val)[/C][C](4e-04 )[/C][C](0.1117 )[/C][C](0.1782 )[/C][C](NA )[/C][C](NA )[/C][C](0.6054 )[/C][C](0.0554 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.4895[/C][C]-0.2449[/C][C]-0.181[/C][C]0[/C][C]0[/C][C]0[/C][C]0.2626[/C][/ROW]
[ROW][C](p-val)[/C][C](3e-04 )[/C][C](0.0799 )[/C][C](0.1533 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0554 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.4572[/C][C]-0.1603[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.2623[/C][/ROW]
[ROW][C](p-val)[/C][C](7e-04 )[/C][C](0.2093 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0557 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]-0.3918[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.2456[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0015 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.061 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]-0.3805[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0021 )[/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=62882&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62882&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 )-0.3579-0.1713-0.1547-0.1315-0.03680.09870.3096
(p-val)(0.5124 )(0.5436 )(0.3236 )(0.8098 )(0.9926 )(0.9306 )(0.9378 )
Estimates ( 2 )-0.3576-0.1712-0.1547-0.131900.08820.273
(p-val)(0.5123 )(0.5435 )(0.3229 )(0.8092 )(NA )(0.6113 )(0.0537 )
Estimates ( 3 )-0.4847-0.2285-0.1724000.08930.2709
(p-val)(4e-04 )(0.1117 )(0.1782 )(NA )(NA )(0.6054 )(0.0554 )
Estimates ( 4 )-0.4895-0.2449-0.1810000.2626
(p-val)(3e-04 )(0.0799 )(0.1533 )(NA )(NA )(NA )(0.0554 )
Estimates ( 5 )-0.4572-0.160300000.2623
(p-val)(7e-04 )(0.2093 )(NA )(NA )(NA )(NA )(0.0557 )
Estimates ( 6 )-0.3918000000.2456
(p-val)(0.0015 )(NA )(NA )(NA )(NA )(NA )(0.061 )
Estimates ( 7 )-0.3805000000
(p-val)(0.0021 )(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
0.0239999849690112
-1.78699823521396
2.15247650685426
0.170270139321371
4.47513333829281
-1.01090547772665
-1.14191184539989
-4.85429801458053
0.0364745614254726
-0.201269415294633
-1.37454478510393
-5.18002808302682
0.878062571098002
-1.47444403418531
7.29875137067163
5.47589047929018
-4.27660162988766
-2.32193328106845
0.879068827574622
-1.44774608332873
2.81092363375008
0.614676306005461
-3.06036568604295
1.06235224343704
1.17300747384918
-4.24706027168539
0.251482432381181
-1.77544738644041
4.26465499743330
1.13628863587409
-7.60653382869547
5.61205911365498
0.444812806300252
-3.93377675166844
-0.425027203507864
-0.259990097514153
-0.288763967264017
-6.95704312341412
-0.195889185606664
-1.38865038436180
-3.22266101825299
-0.670829809882402
-1.13185712844669
2.44638736472166
-0.150416478633420
1.18258822799360
-0.503862556589598
6.67208851495682
-3.18681512302015
-3.64180362508050
-1.12718298119487
7.34107764579385
0.533911363346562
-2.01052769911527
-4.11375754117513
-0.167945382771309
5.82047490383876
0.668361955615914
2.73199162927287
0.536499369881571
1.1745084189292

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0239999849690112 \tabularnewline
-1.78699823521396 \tabularnewline
2.15247650685426 \tabularnewline
0.170270139321371 \tabularnewline
4.47513333829281 \tabularnewline
-1.01090547772665 \tabularnewline
-1.14191184539989 \tabularnewline
-4.85429801458053 \tabularnewline
0.0364745614254726 \tabularnewline
-0.201269415294633 \tabularnewline
-1.37454478510393 \tabularnewline
-5.18002808302682 \tabularnewline
0.878062571098002 \tabularnewline
-1.47444403418531 \tabularnewline
7.29875137067163 \tabularnewline
5.47589047929018 \tabularnewline
-4.27660162988766 \tabularnewline
-2.32193328106845 \tabularnewline
0.879068827574622 \tabularnewline
-1.44774608332873 \tabularnewline
2.81092363375008 \tabularnewline
0.614676306005461 \tabularnewline
-3.06036568604295 \tabularnewline
1.06235224343704 \tabularnewline
1.17300747384918 \tabularnewline
-4.24706027168539 \tabularnewline
0.251482432381181 \tabularnewline
-1.77544738644041 \tabularnewline
4.26465499743330 \tabularnewline
1.13628863587409 \tabularnewline
-7.60653382869547 \tabularnewline
5.61205911365498 \tabularnewline
0.444812806300252 \tabularnewline
-3.93377675166844 \tabularnewline
-0.425027203507864 \tabularnewline
-0.259990097514153 \tabularnewline
-0.288763967264017 \tabularnewline
-6.95704312341412 \tabularnewline
-0.195889185606664 \tabularnewline
-1.38865038436180 \tabularnewline
-3.22266101825299 \tabularnewline
-0.670829809882402 \tabularnewline
-1.13185712844669 \tabularnewline
2.44638736472166 \tabularnewline
-0.150416478633420 \tabularnewline
1.18258822799360 \tabularnewline
-0.503862556589598 \tabularnewline
6.67208851495682 \tabularnewline
-3.18681512302015 \tabularnewline
-3.64180362508050 \tabularnewline
-1.12718298119487 \tabularnewline
7.34107764579385 \tabularnewline
0.533911363346562 \tabularnewline
-2.01052769911527 \tabularnewline
-4.11375754117513 \tabularnewline
-0.167945382771309 \tabularnewline
5.82047490383876 \tabularnewline
0.668361955615914 \tabularnewline
2.73199162927287 \tabularnewline
0.536499369881571 \tabularnewline
1.1745084189292 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62882&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0239999849690112[/C][/ROW]
[ROW][C]-1.78699823521396[/C][/ROW]
[ROW][C]2.15247650685426[/C][/ROW]
[ROW][C]0.170270139321371[/C][/ROW]
[ROW][C]4.47513333829281[/C][/ROW]
[ROW][C]-1.01090547772665[/C][/ROW]
[ROW][C]-1.14191184539989[/C][/ROW]
[ROW][C]-4.85429801458053[/C][/ROW]
[ROW][C]0.0364745614254726[/C][/ROW]
[ROW][C]-0.201269415294633[/C][/ROW]
[ROW][C]-1.37454478510393[/C][/ROW]
[ROW][C]-5.18002808302682[/C][/ROW]
[ROW][C]0.878062571098002[/C][/ROW]
[ROW][C]-1.47444403418531[/C][/ROW]
[ROW][C]7.29875137067163[/C][/ROW]
[ROW][C]5.47589047929018[/C][/ROW]
[ROW][C]-4.27660162988766[/C][/ROW]
[ROW][C]-2.32193328106845[/C][/ROW]
[ROW][C]0.879068827574622[/C][/ROW]
[ROW][C]-1.44774608332873[/C][/ROW]
[ROW][C]2.81092363375008[/C][/ROW]
[ROW][C]0.614676306005461[/C][/ROW]
[ROW][C]-3.06036568604295[/C][/ROW]
[ROW][C]1.06235224343704[/C][/ROW]
[ROW][C]1.17300747384918[/C][/ROW]
[ROW][C]-4.24706027168539[/C][/ROW]
[ROW][C]0.251482432381181[/C][/ROW]
[ROW][C]-1.77544738644041[/C][/ROW]
[ROW][C]4.26465499743330[/C][/ROW]
[ROW][C]1.13628863587409[/C][/ROW]
[ROW][C]-7.60653382869547[/C][/ROW]
[ROW][C]5.61205911365498[/C][/ROW]
[ROW][C]0.444812806300252[/C][/ROW]
[ROW][C]-3.93377675166844[/C][/ROW]
[ROW][C]-0.425027203507864[/C][/ROW]
[ROW][C]-0.259990097514153[/C][/ROW]
[ROW][C]-0.288763967264017[/C][/ROW]
[ROW][C]-6.95704312341412[/C][/ROW]
[ROW][C]-0.195889185606664[/C][/ROW]
[ROW][C]-1.38865038436180[/C][/ROW]
[ROW][C]-3.22266101825299[/C][/ROW]
[ROW][C]-0.670829809882402[/C][/ROW]
[ROW][C]-1.13185712844669[/C][/ROW]
[ROW][C]2.44638736472166[/C][/ROW]
[ROW][C]-0.150416478633420[/C][/ROW]
[ROW][C]1.18258822799360[/C][/ROW]
[ROW][C]-0.503862556589598[/C][/ROW]
[ROW][C]6.67208851495682[/C][/ROW]
[ROW][C]-3.18681512302015[/C][/ROW]
[ROW][C]-3.64180362508050[/C][/ROW]
[ROW][C]-1.12718298119487[/C][/ROW]
[ROW][C]7.34107764579385[/C][/ROW]
[ROW][C]0.533911363346562[/C][/ROW]
[ROW][C]-2.01052769911527[/C][/ROW]
[ROW][C]-4.11375754117513[/C][/ROW]
[ROW][C]-0.167945382771309[/C][/ROW]
[ROW][C]5.82047490383876[/C][/ROW]
[ROW][C]0.668361955615914[/C][/ROW]
[ROW][C]2.73199162927287[/C][/ROW]
[ROW][C]0.536499369881571[/C][/ROW]
[ROW][C]1.1745084189292[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62882&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62882&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
0.0239999849690112
-1.78699823521396
2.15247650685426
0.170270139321371
4.47513333829281
-1.01090547772665
-1.14191184539989
-4.85429801458053
0.0364745614254726
-0.201269415294633
-1.37454478510393
-5.18002808302682
0.878062571098002
-1.47444403418531
7.29875137067163
5.47589047929018
-4.27660162988766
-2.32193328106845
0.879068827574622
-1.44774608332873
2.81092363375008
0.614676306005461
-3.06036568604295
1.06235224343704
1.17300747384918
-4.24706027168539
0.251482432381181
-1.77544738644041
4.26465499743330
1.13628863587409
-7.60653382869547
5.61205911365498
0.444812806300252
-3.93377675166844
-0.425027203507864
-0.259990097514153
-0.288763967264017
-6.95704312341412
-0.195889185606664
-1.38865038436180
-3.22266101825299
-0.670829809882402
-1.13185712844669
2.44638736472166
-0.150416478633420
1.18258822799360
-0.503862556589598
6.67208851495682
-3.18681512302015
-3.64180362508050
-1.12718298119487
7.34107764579385
0.533911363346562
-2.01052769911527
-4.11375754117513
-0.167945382771309
5.82047490383876
0.668361955615914
2.73199162927287
0.536499369881571
1.1745084189292



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')