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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, 01 Dec 2011 12:57:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/01/t13227622603pfk5iu2njpn3ph.htm/, Retrieved Fri, 29 Mar 2024 00:16:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149911, Retrieved Fri, 29 Mar 2024 00:16:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2011-12-01 17:43:10] [a9dc51245fb8ca00f931d89893d090c8]
-         [ARIMA Backward Selection] [] [2011-12-01 17:57:28] [8a7469f165590e0a048f07fe1c69d604] [Current]
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Dataseries X:
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149911&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149911&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149911&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.77740.0679-0.2379-0.6727-0.0417-0.0597-0.9999
(p-val)(0.0152 )(0.6887 )(0.0963 )(0.027 )(0.8148 )(0.7418 )(0.0147 )
Estimates ( 2 )0.77040.065-0.2299-0.67240-0.0407-1
(p-val)(0.0185 )(0.6994 )(0.0951 )(0.0318 )(NA )(0.8046 )(0.0035 )
Estimates ( 3 )0.75730.0662-0.2304-0.661300-1
(p-val)(0.018 )(0.6906 )(0.0926 )(0.0311 )(NA )(NA )(0.0024 )
Estimates ( 4 )0.81610-0.1999-0.691400-1.0003
(p-val)(0.0017 )(NA )(0.0733 )(0.0089 )(NA )(NA )(0.0032 )
Estimates ( 5 )0.37300-0.247300-0.9973
(p-val)(0.4183 )(NA )(NA )(0.5963 )(NA )(NA )(0.0083 )
Estimates ( 6 )0.117600000-0.9991
(p-val)(0.3646 )(NA )(NA )(NA )(NA )(NA )(0.0144 )
Estimates ( 7 )000000-1
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0577 )
Estimates ( 8 )0000000
(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 ) & 0.7774 & 0.0679 & -0.2379 & -0.6727 & -0.0417 & -0.0597 & -0.9999 \tabularnewline
(p-val) & (0.0152 ) & (0.6887 ) & (0.0963 ) & (0.027 ) & (0.8148 ) & (0.7418 ) & (0.0147 ) \tabularnewline
Estimates ( 2 ) & 0.7704 & 0.065 & -0.2299 & -0.6724 & 0 & -0.0407 & -1 \tabularnewline
(p-val) & (0.0185 ) & (0.6994 ) & (0.0951 ) & (0.0318 ) & (NA ) & (0.8046 ) & (0.0035 ) \tabularnewline
Estimates ( 3 ) & 0.7573 & 0.0662 & -0.2304 & -0.6613 & 0 & 0 & -1 \tabularnewline
(p-val) & (0.018 ) & (0.6906 ) & (0.0926 ) & (0.0311 ) & (NA ) & (NA ) & (0.0024 ) \tabularnewline
Estimates ( 4 ) & 0.8161 & 0 & -0.1999 & -0.6914 & 0 & 0 & -1.0003 \tabularnewline
(p-val) & (0.0017 ) & (NA ) & (0.0733 ) & (0.0089 ) & (NA ) & (NA ) & (0.0032 ) \tabularnewline
Estimates ( 5 ) & 0.373 & 0 & 0 & -0.2473 & 0 & 0 & -0.9973 \tabularnewline
(p-val) & (0.4183 ) & (NA ) & (NA ) & (0.5963 ) & (NA ) & (NA ) & (0.0083 ) \tabularnewline
Estimates ( 6 ) & 0.1176 & 0 & 0 & 0 & 0 & 0 & -0.9991 \tabularnewline
(p-val) & (0.3646 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0144 ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & 0 & -1 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0577 ) \tabularnewline
Estimates ( 8 ) & 0 & 0 & 0 & 0 & 0 & 0 & 0 \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=149911&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]0.7774[/C][C]0.0679[/C][C]-0.2379[/C][C]-0.6727[/C][C]-0.0417[/C][C]-0.0597[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0152 )[/C][C](0.6887 )[/C][C](0.0963 )[/C][C](0.027 )[/C][C](0.8148 )[/C][C](0.7418 )[/C][C](0.0147 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.7704[/C][C]0.065[/C][C]-0.2299[/C][C]-0.6724[/C][C]0[/C][C]-0.0407[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0185 )[/C][C](0.6994 )[/C][C](0.0951 )[/C][C](0.0318 )[/C][C](NA )[/C][C](0.8046 )[/C][C](0.0035 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.7573[/C][C]0.0662[/C][C]-0.2304[/C][C]-0.6613[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.018 )[/C][C](0.6906 )[/C][C](0.0926 )[/C][C](0.0311 )[/C][C](NA )[/C][C](NA )[/C][C](0.0024 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.8161[/C][C]0[/C][C]-0.1999[/C][C]-0.6914[/C][C]0[/C][C]0[/C][C]-1.0003[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0017 )[/C][C](NA )[/C][C](0.0733 )[/C][C](0.0089 )[/C][C](NA )[/C][C](NA )[/C][C](0.0032 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.373[/C][C]0[/C][C]0[/C][C]-0.2473[/C][C]0[/C][C]0[/C][C]-0.9973[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4183 )[/C][C](NA )[/C][C](NA )[/C][C](0.5963 )[/C][C](NA )[/C][C](NA )[/C][C](0.0083 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.1176[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.9991[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3646 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0144 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-1[/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](0.0577 )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0[/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](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=149911&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149911&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 )0.77740.0679-0.2379-0.6727-0.0417-0.0597-0.9999
(p-val)(0.0152 )(0.6887 )(0.0963 )(0.027 )(0.8148 )(0.7418 )(0.0147 )
Estimates ( 2 )0.77040.065-0.2299-0.67240-0.0407-1
(p-val)(0.0185 )(0.6994 )(0.0951 )(0.0318 )(NA )(0.8046 )(0.0035 )
Estimates ( 3 )0.75730.0662-0.2304-0.661300-1
(p-val)(0.018 )(0.6906 )(0.0926 )(0.0311 )(NA )(NA )(0.0024 )
Estimates ( 4 )0.81610-0.1999-0.691400-1.0003
(p-val)(0.0017 )(NA )(0.0733 )(0.0089 )(NA )(NA )(0.0032 )
Estimates ( 5 )0.37300-0.247300-0.9973
(p-val)(0.4183 )(NA )(NA )(0.5963 )(NA )(NA )(0.0083 )
Estimates ( 6 )0.117600000-0.9991
(p-val)(0.3646 )(NA )(NA )(NA )(NA )(NA )(0.0144 )
Estimates ( 7 )000000-1
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0577 )
Estimates ( 8 )0000000
(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
0.0589999409995141
-1.41417450501306
-18.3846475755861
4.24266776527226
-9.89940750475467
-1.41416601970523
-4.94968147844429
8.48527118362683
5.65684722004823
2.12135156036077
-12.7278137906086
-1.41417238368611
-5.65678640867546
5.71546709301464
11.430917448217
4.0824915417843
-5.71542749248443
-4.89893277693375
5.30722585896545
-8.98140023237252
-0.816480018948223
-3.67419274460486
2.44950292718234
-7.34841447478369
-12.2473676641251
13.5676795115537
-7.50550178552387
-10.1035605510469
4.61879420118808
-12.9903024874634
3.7527758629083
-14.1450008210891
-2.30938107887528
-11.2582591729112
-4.33008822361948
1.29908651985677e-05
16.454417696876
7.82621147962468
4.02491438422167
-8.72060773063855
-0.894411455568734
-0.223594253653449
-6.93176357788012
-2.01244265593132
-7.1553761680782
0.223617958497356
8.27342503551901
15.2051986751483
2.90688653528183
-12.7801213366766
-3.10373653589491
-7.12034669123719
-3.46888342810694
-8.3983624583384
2.55603701106735
0.182580385499283
10.5892565952768
15.7013138196074
6.75522353446613
5.11206123957597
8.76352852127385

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0589999409995141 \tabularnewline
-1.41417450501306 \tabularnewline
-18.3846475755861 \tabularnewline
4.24266776527226 \tabularnewline
-9.89940750475467 \tabularnewline
-1.41416601970523 \tabularnewline
-4.94968147844429 \tabularnewline
8.48527118362683 \tabularnewline
5.65684722004823 \tabularnewline
2.12135156036077 \tabularnewline
-12.7278137906086 \tabularnewline
-1.41417238368611 \tabularnewline
-5.65678640867546 \tabularnewline
5.71546709301464 \tabularnewline
11.430917448217 \tabularnewline
4.0824915417843 \tabularnewline
-5.71542749248443 \tabularnewline
-4.89893277693375 \tabularnewline
5.30722585896545 \tabularnewline
-8.98140023237252 \tabularnewline
-0.816480018948223 \tabularnewline
-3.67419274460486 \tabularnewline
2.44950292718234 \tabularnewline
-7.34841447478369 \tabularnewline
-12.2473676641251 \tabularnewline
13.5676795115537 \tabularnewline
-7.50550178552387 \tabularnewline
-10.1035605510469 \tabularnewline
4.61879420118808 \tabularnewline
-12.9903024874634 \tabularnewline
3.7527758629083 \tabularnewline
-14.1450008210891 \tabularnewline
-2.30938107887528 \tabularnewline
-11.2582591729112 \tabularnewline
-4.33008822361948 \tabularnewline
1.29908651985677e-05 \tabularnewline
16.454417696876 \tabularnewline
7.82621147962468 \tabularnewline
4.02491438422167 \tabularnewline
-8.72060773063855 \tabularnewline
-0.894411455568734 \tabularnewline
-0.223594253653449 \tabularnewline
-6.93176357788012 \tabularnewline
-2.01244265593132 \tabularnewline
-7.1553761680782 \tabularnewline
0.223617958497356 \tabularnewline
8.27342503551901 \tabularnewline
15.2051986751483 \tabularnewline
2.90688653528183 \tabularnewline
-12.7801213366766 \tabularnewline
-3.10373653589491 \tabularnewline
-7.12034669123719 \tabularnewline
-3.46888342810694 \tabularnewline
-8.3983624583384 \tabularnewline
2.55603701106735 \tabularnewline
0.182580385499283 \tabularnewline
10.5892565952768 \tabularnewline
15.7013138196074 \tabularnewline
6.75522353446613 \tabularnewline
5.11206123957597 \tabularnewline
8.76352852127385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149911&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0589999409995141[/C][/ROW]
[ROW][C]-1.41417450501306[/C][/ROW]
[ROW][C]-18.3846475755861[/C][/ROW]
[ROW][C]4.24266776527226[/C][/ROW]
[ROW][C]-9.89940750475467[/C][/ROW]
[ROW][C]-1.41416601970523[/C][/ROW]
[ROW][C]-4.94968147844429[/C][/ROW]
[ROW][C]8.48527118362683[/C][/ROW]
[ROW][C]5.65684722004823[/C][/ROW]
[ROW][C]2.12135156036077[/C][/ROW]
[ROW][C]-12.7278137906086[/C][/ROW]
[ROW][C]-1.41417238368611[/C][/ROW]
[ROW][C]-5.65678640867546[/C][/ROW]
[ROW][C]5.71546709301464[/C][/ROW]
[ROW][C]11.430917448217[/C][/ROW]
[ROW][C]4.0824915417843[/C][/ROW]
[ROW][C]-5.71542749248443[/C][/ROW]
[ROW][C]-4.89893277693375[/C][/ROW]
[ROW][C]5.30722585896545[/C][/ROW]
[ROW][C]-8.98140023237252[/C][/ROW]
[ROW][C]-0.816480018948223[/C][/ROW]
[ROW][C]-3.67419274460486[/C][/ROW]
[ROW][C]2.44950292718234[/C][/ROW]
[ROW][C]-7.34841447478369[/C][/ROW]
[ROW][C]-12.2473676641251[/C][/ROW]
[ROW][C]13.5676795115537[/C][/ROW]
[ROW][C]-7.50550178552387[/C][/ROW]
[ROW][C]-10.1035605510469[/C][/ROW]
[ROW][C]4.61879420118808[/C][/ROW]
[ROW][C]-12.9903024874634[/C][/ROW]
[ROW][C]3.7527758629083[/C][/ROW]
[ROW][C]-14.1450008210891[/C][/ROW]
[ROW][C]-2.30938107887528[/C][/ROW]
[ROW][C]-11.2582591729112[/C][/ROW]
[ROW][C]-4.33008822361948[/C][/ROW]
[ROW][C]1.29908651985677e-05[/C][/ROW]
[ROW][C]16.454417696876[/C][/ROW]
[ROW][C]7.82621147962468[/C][/ROW]
[ROW][C]4.02491438422167[/C][/ROW]
[ROW][C]-8.72060773063855[/C][/ROW]
[ROW][C]-0.894411455568734[/C][/ROW]
[ROW][C]-0.223594253653449[/C][/ROW]
[ROW][C]-6.93176357788012[/C][/ROW]
[ROW][C]-2.01244265593132[/C][/ROW]
[ROW][C]-7.1553761680782[/C][/ROW]
[ROW][C]0.223617958497356[/C][/ROW]
[ROW][C]8.27342503551901[/C][/ROW]
[ROW][C]15.2051986751483[/C][/ROW]
[ROW][C]2.90688653528183[/C][/ROW]
[ROW][C]-12.7801213366766[/C][/ROW]
[ROW][C]-3.10373653589491[/C][/ROW]
[ROW][C]-7.12034669123719[/C][/ROW]
[ROW][C]-3.46888342810694[/C][/ROW]
[ROW][C]-8.3983624583384[/C][/ROW]
[ROW][C]2.55603701106735[/C][/ROW]
[ROW][C]0.182580385499283[/C][/ROW]
[ROW][C]10.5892565952768[/C][/ROW]
[ROW][C]15.7013138196074[/C][/ROW]
[ROW][C]6.75522353446613[/C][/ROW]
[ROW][C]5.11206123957597[/C][/ROW]
[ROW][C]8.76352852127385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149911&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149911&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
0.0589999409995141
-1.41417450501306
-18.3846475755861
4.24266776527226
-9.89940750475467
-1.41416601970523
-4.94968147844429
8.48527118362683
5.65684722004823
2.12135156036077
-12.7278137906086
-1.41417238368611
-5.65678640867546
5.71546709301464
11.430917448217
4.0824915417843
-5.71542749248443
-4.89893277693375
5.30722585896545
-8.98140023237252
-0.816480018948223
-3.67419274460486
2.44950292718234
-7.34841447478369
-12.2473676641251
13.5676795115537
-7.50550178552387
-10.1035605510469
4.61879420118808
-12.9903024874634
3.7527758629083
-14.1450008210891
-2.30938107887528
-11.2582591729112
-4.33008822361948
1.29908651985677e-05
16.454417696876
7.82621147962468
4.02491438422167
-8.72060773063855
-0.894411455568734
-0.223594253653449
-6.93176357788012
-2.01244265593132
-7.1553761680782
0.223617958497356
8.27342503551901
15.2051986751483
2.90688653528183
-12.7801213366766
-3.10373653589491
-7.12034669123719
-3.46888342810694
-8.3983624583384
2.55603701106735
0.182580385499283
10.5892565952768
15.7013138196074
6.75522353446613
5.11206123957597
8.76352852127385



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