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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 computationTue, 04 Dec 2012 15:17:22 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t1354652271ovyvuz6s7mrb5yb.htm/, Retrieved Thu, 28 Mar 2024 18:44:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196586, Retrieved Thu, 28 Mar 2024 18:44:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [Workshop 9 ] [2012-12-04 19:22:06] [a4b60d76ea6b846adbf54f7861413bce]
- RMP     [ARIMA Backward Selection] [Workshop 9] [2012-12-04 20:17:22] [ab4290de075ebbfc5b460761b0110080] [Current]
-   P       [ARIMA Backward Selection] [Workshop 9] [2012-12-04 20:20:33] [a4b60d76ea6b846adbf54f7861413bce]
- RMP       [ARIMA Forecasting] [Workshop 9] [2012-12-04 20:28:22] [a4b60d76ea6b846adbf54f7861413bce]
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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 time15 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 15 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196586&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196586&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196586&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 time15 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.76060.0826-0.2427-0.6443-0.0658-0.083-0.9999
(p-val)(0.0306 )(0.6264 )(0.0878 )(0.0582 )(0.7075 )(0.6447 )(0.0104 )
Estimates ( 2 )0.75010.0776-0.2311-0.64380-0.0532-1
(p-val)(0.0406 )(0.6444 )(0.0917 )(0.0717 )(NA )(0.7478 )(0.0012 )
Estimates ( 3 )0.73390.079-0.2323-0.628900-1
(p-val)(0.0398 )(0.6332 )(0.0876 )(0.0706 )(NA )(NA )(7e-04 )
Estimates ( 4 )0.8130-0.1975-0.674900-1.0001
(p-val)(0.003 )(NA )(0.0752 )(0.0156 )(NA )(NA )(0.001 )
Estimates ( 5 )0.393300-0.260900-1.0002
(p-val)(0.3689 )(NA )(NA )(0.5578 )(NA )(NA )(0.0026 )
Estimates ( 6 )0.124600000-1
(p-val)(0.3358 )(NA )(NA )(NA )(NA )(NA )(0.0043 )
Estimates ( 7 )000000-0.9999
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0132 )
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 ) & 0.7606 & 0.0826 & -0.2427 & -0.6443 & -0.0658 & -0.083 & -0.9999 \tabularnewline
(p-val) & (0.0306 ) & (0.6264 ) & (0.0878 ) & (0.0582 ) & (0.7075 ) & (0.6447 ) & (0.0104 ) \tabularnewline
Estimates ( 2 ) & 0.7501 & 0.0776 & -0.2311 & -0.6438 & 0 & -0.0532 & -1 \tabularnewline
(p-val) & (0.0406 ) & (0.6444 ) & (0.0917 ) & (0.0717 ) & (NA ) & (0.7478 ) & (0.0012 ) \tabularnewline
Estimates ( 3 ) & 0.7339 & 0.079 & -0.2323 & -0.6289 & 0 & 0 & -1 \tabularnewline
(p-val) & (0.0398 ) & (0.6332 ) & (0.0876 ) & (0.0706 ) & (NA ) & (NA ) & (7e-04 ) \tabularnewline
Estimates ( 4 ) & 0.813 & 0 & -0.1975 & -0.6749 & 0 & 0 & -1.0001 \tabularnewline
(p-val) & (0.003 ) & (NA ) & (0.0752 ) & (0.0156 ) & (NA ) & (NA ) & (0.001 ) \tabularnewline
Estimates ( 5 ) & 0.3933 & 0 & 0 & -0.2609 & 0 & 0 & -1.0002 \tabularnewline
(p-val) & (0.3689 ) & (NA ) & (NA ) & (0.5578 ) & (NA ) & (NA ) & (0.0026 ) \tabularnewline
Estimates ( 6 ) & 0.1246 & 0 & 0 & 0 & 0 & 0 & -1 \tabularnewline
(p-val) & (0.3358 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0043 ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & 0 & -0.9999 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0132 ) \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=196586&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]0.7606[/C][C]0.0826[/C][C]-0.2427[/C][C]-0.6443[/C][C]-0.0658[/C][C]-0.083[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0306 )[/C][C](0.6264 )[/C][C](0.0878 )[/C][C](0.0582 )[/C][C](0.7075 )[/C][C](0.6447 )[/C][C](0.0104 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.7501[/C][C]0.0776[/C][C]-0.2311[/C][C]-0.6438[/C][C]0[/C][C]-0.0532[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0406 )[/C][C](0.6444 )[/C][C](0.0917 )[/C][C](0.0717 )[/C][C](NA )[/C][C](0.7478 )[/C][C](0.0012 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.7339[/C][C]0.079[/C][C]-0.2323[/C][C]-0.6289[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0398 )[/C][C](0.6332 )[/C][C](0.0876 )[/C][C](0.0706 )[/C][C](NA )[/C][C](NA )[/C][C](7e-04 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.813[/C][C]0[/C][C]-0.1975[/C][C]-0.6749[/C][C]0[/C][C]0[/C][C]-1.0001[/C][/ROW]
[ROW][C](p-val)[/C][C](0.003 )[/C][C](NA )[/C][C](0.0752 )[/C][C](0.0156 )[/C][C](NA )[/C][C](NA )[/C][C](0.001 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.3933[/C][C]0[/C][C]0[/C][C]-0.2609[/C][C]0[/C][C]0[/C][C]-1.0002[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3689 )[/C][C](NA )[/C][C](NA )[/C][C](0.5578 )[/C][C](NA )[/C][C](NA )[/C][C](0.0026 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.1246[/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](0.3358 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0043 )[/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]-0.9999[/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](0.0132 )[/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=196586&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196586&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 )0.76060.0826-0.2427-0.6443-0.0658-0.083-0.9999
(p-val)(0.0306 )(0.6264 )(0.0878 )(0.0582 )(0.7075 )(0.6447 )(0.0104 )
Estimates ( 2 )0.75010.0776-0.2311-0.64380-0.0532-1
(p-val)(0.0406 )(0.6444 )(0.0917 )(0.0717 )(NA )(0.7478 )(0.0012 )
Estimates ( 3 )0.73390.079-0.2323-0.628900-1
(p-val)(0.0398 )(0.6332 )(0.0876 )(0.0706 )(NA )(NA )(7e-04 )
Estimates ( 4 )0.8130-0.1975-0.674900-1.0001
(p-val)(0.003 )(NA )(0.0752 )(0.0156 )(NA )(NA )(0.001 )
Estimates ( 5 )0.393300-0.260900-1.0002
(p-val)(0.3689 )(NA )(NA )(0.5578 )(NA )(NA )(0.0026 )
Estimates ( 6 )0.124600000-1
(p-val)(0.3358 )(NA )(NA )(NA )(NA )(NA )(0.0043 )
Estimates ( 7 )000000-0.9999
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0132 )
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
0.00768113687760075
-0.104592134777486
-1.31200887542424
0.420603396293161
-0.719818900070618
-0.00791410035115873
-0.314855932186422
0.6608374354283
0.431792309960076
0.0740662670210689
-0.834311819571925
-0.00104387257555639
-0.376155549453245
0.452148895986607
0.765504037683065
0.141264352893143
-0.427498418954355
-0.283724491307605
0.384993482290251
-0.729994849770346
0.0212333706636007
-0.234311426884929
0.202686442184777
-0.57844900830232
-0.813982885534429
1.02247773842172
-0.601496335626237
-0.565235992188182
0.410295504159069
-0.984489654829495
0.360400862286453
-1.2123520357661
-0.0590644682585004
-0.758773933168738
-0.169979099836638
0.041896932896722
1.10149217258008
0.407815185986203
0.248702303769742
-0.582707738860573
0.0163998118184203
0.00654076457744412
-0.455799182171053
-0.0658082554025117
-0.682225563214727
0.11253242984826
0.520016586222892
0.984573473665068
0.10834906355571
-0.957306799194905
-0.0673786071494449
-0.420790423187482
-0.183258450398332
-0.5732872085908
0.245174612293277
0.0268010819033026
0.916297835219885
0.881909456158398
0.302899377931581
0.32366515197866
0.55209888435608

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00768113687760075 \tabularnewline
-0.104592134777486 \tabularnewline
-1.31200887542424 \tabularnewline
0.420603396293161 \tabularnewline
-0.719818900070618 \tabularnewline
-0.00791410035115873 \tabularnewline
-0.314855932186422 \tabularnewline
0.6608374354283 \tabularnewline
0.431792309960076 \tabularnewline
0.0740662670210689 \tabularnewline
-0.834311819571925 \tabularnewline
-0.00104387257555639 \tabularnewline
-0.376155549453245 \tabularnewline
0.452148895986607 \tabularnewline
0.765504037683065 \tabularnewline
0.141264352893143 \tabularnewline
-0.427498418954355 \tabularnewline
-0.283724491307605 \tabularnewline
0.384993482290251 \tabularnewline
-0.729994849770346 \tabularnewline
0.0212333706636007 \tabularnewline
-0.234311426884929 \tabularnewline
0.202686442184777 \tabularnewline
-0.57844900830232 \tabularnewline
-0.813982885534429 \tabularnewline
1.02247773842172 \tabularnewline
-0.601496335626237 \tabularnewline
-0.565235992188182 \tabularnewline
0.410295504159069 \tabularnewline
-0.984489654829495 \tabularnewline
0.360400862286453 \tabularnewline
-1.2123520357661 \tabularnewline
-0.0590644682585004 \tabularnewline
-0.758773933168738 \tabularnewline
-0.169979099836638 \tabularnewline
0.041896932896722 \tabularnewline
1.10149217258008 \tabularnewline
0.407815185986203 \tabularnewline
0.248702303769742 \tabularnewline
-0.582707738860573 \tabularnewline
0.0163998118184203 \tabularnewline
0.00654076457744412 \tabularnewline
-0.455799182171053 \tabularnewline
-0.0658082554025117 \tabularnewline
-0.682225563214727 \tabularnewline
0.11253242984826 \tabularnewline
0.520016586222892 \tabularnewline
0.984573473665068 \tabularnewline
0.10834906355571 \tabularnewline
-0.957306799194905 \tabularnewline
-0.0673786071494449 \tabularnewline
-0.420790423187482 \tabularnewline
-0.183258450398332 \tabularnewline
-0.5732872085908 \tabularnewline
0.245174612293277 \tabularnewline
0.0268010819033026 \tabularnewline
0.916297835219885 \tabularnewline
0.881909456158398 \tabularnewline
0.302899377931581 \tabularnewline
0.32366515197866 \tabularnewline
0.55209888435608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196586&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00768113687760075[/C][/ROW]
[ROW][C]-0.104592134777486[/C][/ROW]
[ROW][C]-1.31200887542424[/C][/ROW]
[ROW][C]0.420603396293161[/C][/ROW]
[ROW][C]-0.719818900070618[/C][/ROW]
[ROW][C]-0.00791410035115873[/C][/ROW]
[ROW][C]-0.314855932186422[/C][/ROW]
[ROW][C]0.6608374354283[/C][/ROW]
[ROW][C]0.431792309960076[/C][/ROW]
[ROW][C]0.0740662670210689[/C][/ROW]
[ROW][C]-0.834311819571925[/C][/ROW]
[ROW][C]-0.00104387257555639[/C][/ROW]
[ROW][C]-0.376155549453245[/C][/ROW]
[ROW][C]0.452148895986607[/C][/ROW]
[ROW][C]0.765504037683065[/C][/ROW]
[ROW][C]0.141264352893143[/C][/ROW]
[ROW][C]-0.427498418954355[/C][/ROW]
[ROW][C]-0.283724491307605[/C][/ROW]
[ROW][C]0.384993482290251[/C][/ROW]
[ROW][C]-0.729994849770346[/C][/ROW]
[ROW][C]0.0212333706636007[/C][/ROW]
[ROW][C]-0.234311426884929[/C][/ROW]
[ROW][C]0.202686442184777[/C][/ROW]
[ROW][C]-0.57844900830232[/C][/ROW]
[ROW][C]-0.813982885534429[/C][/ROW]
[ROW][C]1.02247773842172[/C][/ROW]
[ROW][C]-0.601496335626237[/C][/ROW]
[ROW][C]-0.565235992188182[/C][/ROW]
[ROW][C]0.410295504159069[/C][/ROW]
[ROW][C]-0.984489654829495[/C][/ROW]
[ROW][C]0.360400862286453[/C][/ROW]
[ROW][C]-1.2123520357661[/C][/ROW]
[ROW][C]-0.0590644682585004[/C][/ROW]
[ROW][C]-0.758773933168738[/C][/ROW]
[ROW][C]-0.169979099836638[/C][/ROW]
[ROW][C]0.041896932896722[/C][/ROW]
[ROW][C]1.10149217258008[/C][/ROW]
[ROW][C]0.407815185986203[/C][/ROW]
[ROW][C]0.248702303769742[/C][/ROW]
[ROW][C]-0.582707738860573[/C][/ROW]
[ROW][C]0.0163998118184203[/C][/ROW]
[ROW][C]0.00654076457744412[/C][/ROW]
[ROW][C]-0.455799182171053[/C][/ROW]
[ROW][C]-0.0658082554025117[/C][/ROW]
[ROW][C]-0.682225563214727[/C][/ROW]
[ROW][C]0.11253242984826[/C][/ROW]
[ROW][C]0.520016586222892[/C][/ROW]
[ROW][C]0.984573473665068[/C][/ROW]
[ROW][C]0.10834906355571[/C][/ROW]
[ROW][C]-0.957306799194905[/C][/ROW]
[ROW][C]-0.0673786071494449[/C][/ROW]
[ROW][C]-0.420790423187482[/C][/ROW]
[ROW][C]-0.183258450398332[/C][/ROW]
[ROW][C]-0.5732872085908[/C][/ROW]
[ROW][C]0.245174612293277[/C][/ROW]
[ROW][C]0.0268010819033026[/C][/ROW]
[ROW][C]0.916297835219885[/C][/ROW]
[ROW][C]0.881909456158398[/C][/ROW]
[ROW][C]0.302899377931581[/C][/ROW]
[ROW][C]0.32366515197866[/C][/ROW]
[ROW][C]0.55209888435608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196586&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196586&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
0.00768113687760075
-0.104592134777486
-1.31200887542424
0.420603396293161
-0.719818900070618
-0.00791410035115873
-0.314855932186422
0.6608374354283
0.431792309960076
0.0740662670210689
-0.834311819571925
-0.00104387257555639
-0.376155549453245
0.452148895986607
0.765504037683065
0.141264352893143
-0.427498418954355
-0.283724491307605
0.384993482290251
-0.729994849770346
0.0212333706636007
-0.234311426884929
0.202686442184777
-0.57844900830232
-0.813982885534429
1.02247773842172
-0.601496335626237
-0.565235992188182
0.410295504159069
-0.984489654829495
0.360400862286453
-1.2123520357661
-0.0590644682585004
-0.758773933168738
-0.169979099836638
0.041896932896722
1.10149217258008
0.407815185986203
0.248702303769742
-0.582707738860573
0.0163998118184203
0.00654076457744412
-0.455799182171053
-0.0658082554025117
-0.682225563214727
0.11253242984826
0.520016586222892
0.984573473665068
0.10834906355571
-0.957306799194905
-0.0673786071494449
-0.420790423187482
-0.183258450398332
-0.5732872085908
0.245174612293277
0.0268010819033026
0.916297835219885
0.881909456158398
0.302899377931581
0.32366515197866
0.55209888435608



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; 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')