## Free Statistics

of Irreproducible Research!

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, 12 Dec 2013 04:00:21 -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/2013/Dec/12/t1386838850f5xvcd3dwfu248c.htm/, Retrieved Tue, 07 Dec 2021 12:34:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232222, Retrieved Tue, 07 Dec 2021 12:34:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [] [2013-12-12 09:00:21] [9e6a405f514733ea23d87e4507d39d29] [Current]
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Dataseries X:
164
96
73
49
39
59
169
169
210
278
298
245
200
188
90
79
78
91
167
169
289
247
275
203
223
104
107
85
75
99
135
211
335
488
326
346
261
224
141
148
145
223
272
445
560
612
467
404
518
404
300
210
196
186
247
343
464
680
711
610
513
292
273
322
189
257
324
404
677
858
895
664
628
308
324
248
272

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 33 seconds R Server 'George Udny Yule' @ yule.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 & 33 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232222&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]33 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232222&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232222&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 33 seconds R Server 'George Udny Yule' @ yule.wessa.net

 ARIMA Parameter Estimation and Backward Selection Iteration ar1 ar2 ar3 ma1 sar1 sar2 sma1 Estimates ( 1 ) -0.0531 0.7653 0.1439 0.6104 -0.6178 -0.2584 -0.0143 (p-val) (0.8667 ) (0 ) (0.4413 ) (0.0364 ) (0.34 ) (0.4512 ) (0.9843 ) Estimates ( 2 ) -0.053 0.7647 0.1434 0.6097 -0.6301 -0.2642 0 (p-val) (0.8674 ) (0 ) (0.443 ) (0.0371 ) (0 ) (0.1346 ) (NA ) Estimates ( 3 ) 0 0.7427 0.1175 0.5646 -0.6306 -0.2657 0 (p-val) (NA ) (0 ) (0.2082 ) (0 ) (0 ) (0.1347 ) (NA ) Estimates ( 4 ) 0 0.7877 0 0.6104 -0.6126 -0.2329 0 (p-val) (NA ) (0 ) (NA ) (0 ) (1e-04 ) (0.2038 ) (NA ) Estimates ( 5 ) 0 0.7244 0 0.6044 -0.4914 0 0 (p-val) (NA ) (0 ) (NA ) (0 ) (1e-04 ) (NA ) (NA ) Estimates ( 6 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 7 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 8 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 9 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 10 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 11 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 12 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 13 ) NA NA NA NA NA NA NA (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 ) & -0.0531 & 0.7653 & 0.1439 & 0.6104 & -0.6178 & -0.2584 & -0.0143 \tabularnewline
(p-val) & (0.8667 ) & (0 ) & (0.4413 ) & (0.0364 ) & (0.34 ) & (0.4512 ) & (0.9843 ) \tabularnewline
Estimates ( 2 ) & -0.053 & 0.7647 & 0.1434 & 0.6097 & -0.6301 & -0.2642 & 0 \tabularnewline
(p-val) & (0.8674 ) & (0 ) & (0.443 ) & (0.0371 ) & (0 ) & (0.1346 ) & (NA ) \tabularnewline
Estimates ( 3 ) & 0 & 0.7427 & 0.1175 & 0.5646 & -0.6306 & -0.2657 & 0 \tabularnewline
(p-val) & (NA ) & (0 ) & (0.2082 ) & (0 ) & (0 ) & (0.1347 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & 0.7877 & 0 & 0.6104 & -0.6126 & -0.2329 & 0 \tabularnewline
(p-val) & (NA ) & (0 ) & (NA ) & (0 ) & (1e-04 ) & (0.2038 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0.7244 & 0 & 0.6044 & -0.4914 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (0 ) & (NA ) & (0 ) & (1e-04 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232222&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]-0.0531[/C][C]0.7653[/C][C]0.1439[/C][C]0.6104[/C][C]-0.6178[/C][C]-0.2584[/C][C]-0.0143[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8667 )[/C][C](0 )[/C][C](0.4413 )[/C][C](0.0364 )[/C][C](0.34 )[/C][C](0.4512 )[/C][C](0.9843 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.053[/C][C]0.7647[/C][C]0.1434[/C][C]0.6097[/C][C]-0.6301[/C][C]-0.2642[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8674 )[/C][C](0 )[/C][C](0.443 )[/C][C](0.0371 )[/C][C](0 )[/C][C](0.1346 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.7427[/C][C]0.1175[/C][C]0.5646[/C][C]-0.6306[/C][C]-0.2657[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](0.2082 )[/C][C](0 )[/C][C](0 )[/C][C](0.1347 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.7877[/C][C]0[/C][C]0.6104[/C][C]-0.6126[/C][C]-0.2329[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][C](1e-04 )[/C][C](0.2038 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0.7244[/C][C]0[/C][C]0.6044[/C][C]-0.4914[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][C](1e-04 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232222&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232222&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 Iteration ar1 ar2 ar3 ma1 sar1 sar2 sma1 Estimates ( 1 ) -0.0531 0.7653 0.1439 0.6104 -0.6178 -0.2584 -0.0143 (p-val) (0.8667 ) (0 ) (0.4413 ) (0.0364 ) (0.34 ) (0.4512 ) (0.9843 ) Estimates ( 2 ) -0.053 0.7647 0.1434 0.6097 -0.6301 -0.2642 0 (p-val) (0.8674 ) (0 ) (0.443 ) (0.0371 ) (0 ) (0.1346 ) (NA ) Estimates ( 3 ) 0 0.7427 0.1175 0.5646 -0.6306 -0.2657 0 (p-val) (NA ) (0 ) (0.2082 ) (0 ) (0 ) (0.1347 ) (NA ) Estimates ( 4 ) 0 0.7877 0 0.6104 -0.6126 -0.2329 0 (p-val) (NA ) (0 ) (NA ) (0 ) (1e-04 ) (0.2038 ) (NA ) Estimates ( 5 ) 0 0.7244 0 0.6044 -0.4914 0 0 (p-val) (NA ) (0 ) (NA ) (0 ) (1e-04 ) (NA ) (NA ) Estimates ( 6 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 7 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 8 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 9 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 10 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 11 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 12 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 13 ) NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA )

 Estimated ARIMA Residuals Value 0.00550118759611825 0.100183846454512 0.407011937175347 -0.16737394013665 0.0874322043971727 0.411921594926703 -0.16303809760263 -0.347349364549994 -0.0296049307932174 0.323806680916916 -0.239140348405293 -0.0990421431826477 0.0467509732678099 0.163478509066741 -0.350096927982722 0.32436525705737 0.311383604987517 -0.0960371667588798 0.124548267674515 -0.511624147113553 0.314584415989742 0.287739894396906 0.282204368241592 -0.25961081368395 0.139880423671516 0.0468251561703096 0.141297672568196 0.131779095699905 0.188261365013337 0.34246868819404 0.195820162553774 -0.179557156234377 0.232001180091104 0.0900659140956214 -0.1338451790818 -0.00791389868766919 -0.0425554884595751 0.482693233899655 0.282396103736813 0.156155004245922 -0.114835337644616 0.00659340969595179 -0.224997452667842 -0.127774776710956 0.0620755029470993 -0.0998584806098916 0.267864642498592 0.389744472117138 0.0761067268738662 -0.135349114066045 -0.199236081602781 0.202107333568291 0.47797312151875 -0.330634318524646 -0.00413610310225685 0.14024473328936 -0.223728014463631 0.223086730239358 0.073422994370519 0.225451904074953 -0.0398059712998328 -0.0698047210357811 -0.259657758635374 0.167444170403979 -0.0135427590292624 0.192334314572137

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00550118759611825 \tabularnewline
0.100183846454512 \tabularnewline
0.407011937175347 \tabularnewline
-0.16737394013665 \tabularnewline
0.0874322043971727 \tabularnewline
0.411921594926703 \tabularnewline
-0.16303809760263 \tabularnewline
-0.347349364549994 \tabularnewline
-0.0296049307932174 \tabularnewline
0.323806680916916 \tabularnewline
-0.239140348405293 \tabularnewline
-0.0990421431826477 \tabularnewline
0.0467509732678099 \tabularnewline
0.163478509066741 \tabularnewline
-0.350096927982722 \tabularnewline
0.32436525705737 \tabularnewline
0.311383604987517 \tabularnewline
-0.0960371667588798 \tabularnewline
0.124548267674515 \tabularnewline
-0.511624147113553 \tabularnewline
0.314584415989742 \tabularnewline
0.287739894396906 \tabularnewline
0.282204368241592 \tabularnewline
-0.25961081368395 \tabularnewline
0.139880423671516 \tabularnewline
0.0468251561703096 \tabularnewline
0.141297672568196 \tabularnewline
0.131779095699905 \tabularnewline
0.188261365013337 \tabularnewline
0.34246868819404 \tabularnewline
0.195820162553774 \tabularnewline
-0.179557156234377 \tabularnewline
0.232001180091104 \tabularnewline
0.0900659140956214 \tabularnewline
-0.1338451790818 \tabularnewline
-0.00791389868766919 \tabularnewline
-0.0425554884595751 \tabularnewline
0.482693233899655 \tabularnewline
0.282396103736813 \tabularnewline
0.156155004245922 \tabularnewline
-0.114835337644616 \tabularnewline
0.00659340969595179 \tabularnewline
-0.224997452667842 \tabularnewline
-0.127774776710956 \tabularnewline
0.0620755029470993 \tabularnewline
-0.0998584806098916 \tabularnewline
0.267864642498592 \tabularnewline
0.389744472117138 \tabularnewline
0.0761067268738662 \tabularnewline
-0.135349114066045 \tabularnewline
-0.199236081602781 \tabularnewline
0.202107333568291 \tabularnewline
0.47797312151875 \tabularnewline
-0.330634318524646 \tabularnewline
-0.00413610310225685 \tabularnewline
0.14024473328936 \tabularnewline
-0.223728014463631 \tabularnewline
0.223086730239358 \tabularnewline
0.073422994370519 \tabularnewline
0.225451904074953 \tabularnewline
-0.0398059712998328 \tabularnewline
-0.0698047210357811 \tabularnewline
-0.259657758635374 \tabularnewline
0.167444170403979 \tabularnewline
-0.0135427590292624 \tabularnewline
0.192334314572137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232222&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00550118759611825[/C][/ROW]
[ROW][C]0.100183846454512[/C][/ROW]
[ROW][C]0.407011937175347[/C][/ROW]
[ROW][C]-0.16737394013665[/C][/ROW]
[ROW][C]0.0874322043971727[/C][/ROW]
[ROW][C]0.411921594926703[/C][/ROW]
[ROW][C]-0.16303809760263[/C][/ROW]
[ROW][C]-0.347349364549994[/C][/ROW]
[ROW][C]-0.0296049307932174[/C][/ROW]
[ROW][C]0.323806680916916[/C][/ROW]
[ROW][C]-0.239140348405293[/C][/ROW]
[ROW][C]-0.0990421431826477[/C][/ROW]
[ROW][C]0.0467509732678099[/C][/ROW]
[ROW][C]0.163478509066741[/C][/ROW]
[ROW][C]-0.350096927982722[/C][/ROW]
[ROW][C]0.32436525705737[/C][/ROW]
[ROW][C]0.311383604987517[/C][/ROW]
[ROW][C]-0.0960371667588798[/C][/ROW]
[ROW][C]0.124548267674515[/C][/ROW]
[ROW][C]-0.511624147113553[/C][/ROW]
[ROW][C]0.314584415989742[/C][/ROW]
[ROW][C]0.287739894396906[/C][/ROW]
[ROW][C]0.282204368241592[/C][/ROW]
[ROW][C]-0.25961081368395[/C][/ROW]
[ROW][C]0.139880423671516[/C][/ROW]
[ROW][C]0.0468251561703096[/C][/ROW]
[ROW][C]0.141297672568196[/C][/ROW]
[ROW][C]0.131779095699905[/C][/ROW]
[ROW][C]0.188261365013337[/C][/ROW]
[ROW][C]0.34246868819404[/C][/ROW]
[ROW][C]0.195820162553774[/C][/ROW]
[ROW][C]-0.179557156234377[/C][/ROW]
[ROW][C]0.232001180091104[/C][/ROW]
[ROW][C]0.0900659140956214[/C][/ROW]
[ROW][C]-0.1338451790818[/C][/ROW]
[ROW][C]-0.00791389868766919[/C][/ROW]
[ROW][C]-0.0425554884595751[/C][/ROW]
[ROW][C]0.482693233899655[/C][/ROW]
[ROW][C]0.282396103736813[/C][/ROW]
[ROW][C]0.156155004245922[/C][/ROW]
[ROW][C]-0.114835337644616[/C][/ROW]
[ROW][C]0.00659340969595179[/C][/ROW]
[ROW][C]-0.224997452667842[/C][/ROW]
[ROW][C]-0.127774776710956[/C][/ROW]
[ROW][C]0.0620755029470993[/C][/ROW]
[ROW][C]-0.0998584806098916[/C][/ROW]
[ROW][C]0.267864642498592[/C][/ROW]
[ROW][C]0.389744472117138[/C][/ROW]
[ROW][C]0.0761067268738662[/C][/ROW]
[ROW][C]-0.135349114066045[/C][/ROW]
[ROW][C]-0.199236081602781[/C][/ROW]
[ROW][C]0.202107333568291[/C][/ROW]
[ROW][C]0.47797312151875[/C][/ROW]
[ROW][C]-0.330634318524646[/C][/ROW]
[ROW][C]-0.00413610310225685[/C][/ROW]
[ROW][C]0.14024473328936[/C][/ROW]
[ROW][C]-0.223728014463631[/C][/ROW]
[ROW][C]0.223086730239358[/C][/ROW]
[ROW][C]0.073422994370519[/C][/ROW]
[ROW][C]0.225451904074953[/C][/ROW]
[ROW][C]-0.0398059712998328[/C][/ROW]
[ROW][C]-0.0698047210357811[/C][/ROW]
[ROW][C]-0.259657758635374[/C][/ROW]
[ROW][C]0.167444170403979[/C][/ROW]
[ROW][C]-0.0135427590292624[/C][/ROW]
[ROW][C]0.192334314572137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232222&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232222&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 0.00550118759611825 0.100183846454512 0.407011937175347 -0.16737394013665 0.0874322043971727 0.411921594926703 -0.16303809760263 -0.347349364549994 -0.0296049307932174 0.323806680916916 -0.239140348405293 -0.0990421431826477 0.0467509732678099 0.163478509066741 -0.350096927982722 0.32436525705737 0.311383604987517 -0.0960371667588798 0.124548267674515 -0.511624147113553 0.314584415989742 0.287739894396906 0.282204368241592 -0.25961081368395 0.139880423671516 0.0468251561703096 0.141297672568196 0.131779095699905 0.188261365013337 0.34246868819404 0.195820162553774 -0.179557156234377 0.232001180091104 0.0900659140956214 -0.1338451790818 -0.00791389868766919 -0.0425554884595751 0.482693233899655 0.282396103736813 0.156155004245922 -0.114835337644616 0.00659340969595179 -0.224997452667842 -0.127774776710956 0.0620755029470993 -0.0998584806098916 0.267864642498592 0.389744472117138 0.0761067268738662 -0.135349114066045 -0.199236081602781 0.202107333568291 0.47797312151875 -0.330634318524646 -0.00413610310225685 0.14024473328936 -0.223728014463631 0.223086730239358 0.073422994370519 0.225451904074953 -0.0398059712998328 -0.0698047210357811 -0.259657758635374 0.167444170403979 -0.0135427590292624 0.192334314572137

library(lattice)if (par1 == 'TRUE') par1 <- TRUEif (par1 == 'FALSE') par1 <- FALSEpar2 <- as.numeric(par2) #Box-Cox lambda transformation parameterpar3 <- as.numeric(par3) #degree of non-seasonal differencingpar4 <- as.numeric(par4) #degree of seasonal differencingpar5 <- as.numeric(par5) #seasonal periodpar6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomialpar7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomialpar8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomialpar9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomialarmaGR <- function(arima.out, names, n){try1 <- arima.out$coeftry2 <- sqrt(diag(arima.out$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] <- try1for(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])] <- 0maxi <- which.max(try.data.frame[,4])continue <- max(try.data.frame[,4],na.rm=TRUE) > .05vector[maxi] <- 0list(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  <- NULLif(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.outlast.arma <- armaGR(arima.out, names, length(series))mystop <- FALSEi <- 1coeff[i,] <- last.arma[[1]][,1]pval [i,] <- last.arma[[1]][,4]i <- 2aic <- arima.out$aicwhile(!mystop){mylist[[i]] <- arima.outarima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)aic <- c(aic, arima.out$aic)last.arma <- armaGR(arima.out, names, length(series))mystop <- !last.arma$continuecoeff[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]]$coef)coeff <- arimaSelect.out[[1]]k <- min(which(is.na(coeff[,1])))-1coeff <- coeff[1:k,]pval <- arimaSelect.out[[2]][1:k,]aic <- arimaSelect.out$aic[1:k]coeff[coeff==0] <- NAn <- ncol(coeff)if(missing(choix)) choix <- klayout(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]]$respar(mar=c(1,2,4,1))acf(residus,main='')title('acf',line=.5)par(mar=c(1,2,4,1))pacf(residus,main='')title('pacf',line=.5)par(mar=c(2,2,4,1))qqnorm(residus,main='')title('qq-norm',line=.5)qqline(residus)residus}if (par2 == 0) x <- log(x)if (par2 != 0) x <- x^par2(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))bitmap(file='test1.png')resid <- arimaSelectplot(selection)dev.off()residbitmap(file='test2.png')acf(resid,length(resid)/2, main='Residual Autocorrelation Function')dev.off()bitmap(file='test3.png')pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')dev.off()bitmap(file='test4.png')cpgram(resid, main='Residual Cumulative Periodogram')dev.off()bitmap(file='test5.png')hist(resid, main='Residual Histogram', xlab='values of Residuals')dev.off()bitmap(file='test6.png')densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')dev.off()bitmap(file='test7.png')qqnorm(resid, main='Residual Normal Q-Q Plot')qqline(resid)dev.off()ncols <- length(selection[[1]][1,])nrows <- length(selection[[2]][,1])-1load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Iteration', header=TRUE)for (i in 1:ncols) {a<-table.element(a,names(selection[[3]][[1]]$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')