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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, 12 Dec 2013 03:49:51 -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/t1386838230kkyjdav824pyn80.htm/, Retrieved Sun, 05 Dec 2021 17:23:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232220, Retrieved Sun, 05 Dec 2021 17:23:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [] [2013-12-12 08:49:51] [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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time31 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 31 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232220&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]31 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232220&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232220&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 time31 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.55040.2224-0.2904-0.88810.87070.1087-0.7424
(p-val)(3e-04 )(0.0921 )(0.0227 )(0 )(0.0015 )(0.6272 )(0.0459 )
Estimates ( 2 )0.54760.2393-0.2981-0.8910.99220-0.8518
(p-val)(3e-04 )(0.0591 )(0.0183 )(0 )(0 )(NA )(0 )
Estimates ( 3 )0.62090-0.2059-0.8720.99530-0.8839
(p-val)(3e-04 )(NA )(0.0673 )(0 )(0 )(NA )(0 )
Estimates ( 4 )-0.7521000.51420.98440-0.7662
(p-val)(0 )(NA )(NA )(0.0137 )(0 )(NA )(5e-04 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.5504 & 0.2224 & -0.2904 & -0.8881 & 0.8707 & 0.1087 & -0.7424 \tabularnewline
(p-val) & (3e-04 ) & (0.0921 ) & (0.0227 ) & (0 ) & (0.0015 ) & (0.6272 ) & (0.0459 ) \tabularnewline
Estimates ( 2 ) & 0.5476 & 0.2393 & -0.2981 & -0.891 & 0.9922 & 0 & -0.8518 \tabularnewline
(p-val) & (3e-04 ) & (0.0591 ) & (0.0183 ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.6209 & 0 & -0.2059 & -0.872 & 0.9953 & 0 & -0.8839 \tabularnewline
(p-val) & (3e-04 ) & (NA ) & (0.0673 ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & -0.7521 & 0 & 0 & 0.5142 & 0.9844 & 0 & -0.7662 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0.0137 ) & (0 ) & (NA ) & (5e-04 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (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=232220&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]0.5504[/C][C]0.2224[/C][C]-0.2904[/C][C]-0.8881[/C][C]0.8707[/C][C]0.1087[/C][C]-0.7424[/C][/ROW]
[ROW][C](p-val)[/C][C](3e-04 )[/C][C](0.0921 )[/C][C](0.0227 )[/C][C](0 )[/C][C](0.0015 )[/C][C](0.6272 )[/C][C](0.0459 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.5476[/C][C]0.2393[/C][C]-0.2981[/C][C]-0.891[/C][C]0.9922[/C][C]0[/C][C]-0.8518[/C][/ROW]
[ROW][C](p-val)[/C][C](3e-04 )[/C][C](0.0591 )[/C][C](0.0183 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.6209[/C][C]0[/C][C]-0.2059[/C][C]-0.872[/C][C]0.9953[/C][C]0[/C][C]-0.8839[/C][/ROW]
[ROW][C](p-val)[/C][C](3e-04 )[/C][C](NA )[/C][C](0.0673 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.7521[/C][C]0[/C][C]0[/C][C]0.5142[/C][C]0.9844[/C][C]0[/C][C]-0.7662[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0137 )[/C][C](0 )[/C][C](NA )[/C][C](5e-04 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/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 ( 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=232220&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232220&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 )0.55040.2224-0.2904-0.88810.87070.1087-0.7424
(p-val)(3e-04 )(0.0921 )(0.0227 )(0 )(0.0015 )(0.6272 )(0.0459 )
Estimates ( 2 )0.54760.2393-0.2981-0.8910.99220-0.8518
(p-val)(3e-04 )(0.0591 )(0.0183 )(0 )(0 )(NA )(0 )
Estimates ( 3 )0.62090-0.2059-0.8720.99530-0.8839
(p-val)(3e-04 )(NA )(0.0673 )(0 )(0 )(NA )(0 )
Estimates ( 4 )-0.7521000.51420.98440-0.7662
(p-val)(0 )(NA )(NA )(0.0137 )(0 )(NA )(5e-04 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.00509985932295509
-0.320818222286888
-0.197117196687452
-0.273021303154159
-0.259307390005561
0.117630375827139
0.555831262688101
0.010351517675088
0.186684058005416
0.364862612399688
0.208424913402032
0.0647618673786489
0.0525051410399968
0.321269449652341
-0.350838864527021
0.0658651693688728
0.149554375196613
-0.114936607453198
-0.0612678864901738
-0.00290255501150305
0.299406072944713
-0.236384620475663
0.0015162047029825
-0.0938556758527673
0.124750000263356
-0.471783613685228
0.234820874083843
-0.0203883669827977
-0.129022235700457
0.041076861804919
-0.29004767860936
0.314346990835869
0.191624455541518
0.303417989902342
-0.268503945062622
0.289915006795012
-0.0448823511961261
0.213557197426895
-0.0820876439224611
0.236085417896762
0.173065057049405
0.251710933962102
-0.160389171911666
0.40817566275154
0.0820190953928902
0.0173582939155595
-0.0866614148161779
0.000536593053893895
0.329911858162196
0.115744305385573
0.0621722848647087
-0.0861085957245128
0.0753749365683205
-0.250523461798932
-0.228666727370203
0.0163675459851644
-0.129247437631836
0.0984376599509383
0.100778218291185
-0.0302527053025421
-0.1037533234522
-0.218990024090205
0.187279008866825
0.330481842656331
-0.412290770407357
0.0545177145540067
-0.122307642656818
-0.0956975808541104
0.143309956329961
0.0187572159841698
0.0916514301580276
-0.114220581652296
0.0100967146974866
-0.318283738127712
0.2145351886932
-0.141165358157766
0.131873070807267

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00509985932295509 \tabularnewline
-0.320818222286888 \tabularnewline
-0.197117196687452 \tabularnewline
-0.273021303154159 \tabularnewline
-0.259307390005561 \tabularnewline
0.117630375827139 \tabularnewline
0.555831262688101 \tabularnewline
0.010351517675088 \tabularnewline
0.186684058005416 \tabularnewline
0.364862612399688 \tabularnewline
0.208424913402032 \tabularnewline
0.0647618673786489 \tabularnewline
0.0525051410399968 \tabularnewline
0.321269449652341 \tabularnewline
-0.350838864527021 \tabularnewline
0.0658651693688728 \tabularnewline
0.149554375196613 \tabularnewline
-0.114936607453198 \tabularnewline
-0.0612678864901738 \tabularnewline
-0.00290255501150305 \tabularnewline
0.299406072944713 \tabularnewline
-0.236384620475663 \tabularnewline
0.0015162047029825 \tabularnewline
-0.0938556758527673 \tabularnewline
0.124750000263356 \tabularnewline
-0.471783613685228 \tabularnewline
0.234820874083843 \tabularnewline
-0.0203883669827977 \tabularnewline
-0.129022235700457 \tabularnewline
0.041076861804919 \tabularnewline
-0.29004767860936 \tabularnewline
0.314346990835869 \tabularnewline
0.191624455541518 \tabularnewline
0.303417989902342 \tabularnewline
-0.268503945062622 \tabularnewline
0.289915006795012 \tabularnewline
-0.0448823511961261 \tabularnewline
0.213557197426895 \tabularnewline
-0.0820876439224611 \tabularnewline
0.236085417896762 \tabularnewline
0.173065057049405 \tabularnewline
0.251710933962102 \tabularnewline
-0.160389171911666 \tabularnewline
0.40817566275154 \tabularnewline
0.0820190953928902 \tabularnewline
0.0173582939155595 \tabularnewline
-0.0866614148161779 \tabularnewline
0.000536593053893895 \tabularnewline
0.329911858162196 \tabularnewline
0.115744305385573 \tabularnewline
0.0621722848647087 \tabularnewline
-0.0861085957245128 \tabularnewline
0.0753749365683205 \tabularnewline
-0.250523461798932 \tabularnewline
-0.228666727370203 \tabularnewline
0.0163675459851644 \tabularnewline
-0.129247437631836 \tabularnewline
0.0984376599509383 \tabularnewline
0.100778218291185 \tabularnewline
-0.0302527053025421 \tabularnewline
-0.1037533234522 \tabularnewline
-0.218990024090205 \tabularnewline
0.187279008866825 \tabularnewline
0.330481842656331 \tabularnewline
-0.412290770407357 \tabularnewline
0.0545177145540067 \tabularnewline
-0.122307642656818 \tabularnewline
-0.0956975808541104 \tabularnewline
0.143309956329961 \tabularnewline
0.0187572159841698 \tabularnewline
0.0916514301580276 \tabularnewline
-0.114220581652296 \tabularnewline
0.0100967146974866 \tabularnewline
-0.318283738127712 \tabularnewline
0.2145351886932 \tabularnewline
-0.141165358157766 \tabularnewline
0.131873070807267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232220&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00509985932295509[/C][/ROW]
[ROW][C]-0.320818222286888[/C][/ROW]
[ROW][C]-0.197117196687452[/C][/ROW]
[ROW][C]-0.273021303154159[/C][/ROW]
[ROW][C]-0.259307390005561[/C][/ROW]
[ROW][C]0.117630375827139[/C][/ROW]
[ROW][C]0.555831262688101[/C][/ROW]
[ROW][C]0.010351517675088[/C][/ROW]
[ROW][C]0.186684058005416[/C][/ROW]
[ROW][C]0.364862612399688[/C][/ROW]
[ROW][C]0.208424913402032[/C][/ROW]
[ROW][C]0.0647618673786489[/C][/ROW]
[ROW][C]0.0525051410399968[/C][/ROW]
[ROW][C]0.321269449652341[/C][/ROW]
[ROW][C]-0.350838864527021[/C][/ROW]
[ROW][C]0.0658651693688728[/C][/ROW]
[ROW][C]0.149554375196613[/C][/ROW]
[ROW][C]-0.114936607453198[/C][/ROW]
[ROW][C]-0.0612678864901738[/C][/ROW]
[ROW][C]-0.00290255501150305[/C][/ROW]
[ROW][C]0.299406072944713[/C][/ROW]
[ROW][C]-0.236384620475663[/C][/ROW]
[ROW][C]0.0015162047029825[/C][/ROW]
[ROW][C]-0.0938556758527673[/C][/ROW]
[ROW][C]0.124750000263356[/C][/ROW]
[ROW][C]-0.471783613685228[/C][/ROW]
[ROW][C]0.234820874083843[/C][/ROW]
[ROW][C]-0.0203883669827977[/C][/ROW]
[ROW][C]-0.129022235700457[/C][/ROW]
[ROW][C]0.041076861804919[/C][/ROW]
[ROW][C]-0.29004767860936[/C][/ROW]
[ROW][C]0.314346990835869[/C][/ROW]
[ROW][C]0.191624455541518[/C][/ROW]
[ROW][C]0.303417989902342[/C][/ROW]
[ROW][C]-0.268503945062622[/C][/ROW]
[ROW][C]0.289915006795012[/C][/ROW]
[ROW][C]-0.0448823511961261[/C][/ROW]
[ROW][C]0.213557197426895[/C][/ROW]
[ROW][C]-0.0820876439224611[/C][/ROW]
[ROW][C]0.236085417896762[/C][/ROW]
[ROW][C]0.173065057049405[/C][/ROW]
[ROW][C]0.251710933962102[/C][/ROW]
[ROW][C]-0.160389171911666[/C][/ROW]
[ROW][C]0.40817566275154[/C][/ROW]
[ROW][C]0.0820190953928902[/C][/ROW]
[ROW][C]0.0173582939155595[/C][/ROW]
[ROW][C]-0.0866614148161779[/C][/ROW]
[ROW][C]0.000536593053893895[/C][/ROW]
[ROW][C]0.329911858162196[/C][/ROW]
[ROW][C]0.115744305385573[/C][/ROW]
[ROW][C]0.0621722848647087[/C][/ROW]
[ROW][C]-0.0861085957245128[/C][/ROW]
[ROW][C]0.0753749365683205[/C][/ROW]
[ROW][C]-0.250523461798932[/C][/ROW]
[ROW][C]-0.228666727370203[/C][/ROW]
[ROW][C]0.0163675459851644[/C][/ROW]
[ROW][C]-0.129247437631836[/C][/ROW]
[ROW][C]0.0984376599509383[/C][/ROW]
[ROW][C]0.100778218291185[/C][/ROW]
[ROW][C]-0.0302527053025421[/C][/ROW]
[ROW][C]-0.1037533234522[/C][/ROW]
[ROW][C]-0.218990024090205[/C][/ROW]
[ROW][C]0.187279008866825[/C][/ROW]
[ROW][C]0.330481842656331[/C][/ROW]
[ROW][C]-0.412290770407357[/C][/ROW]
[ROW][C]0.0545177145540067[/C][/ROW]
[ROW][C]-0.122307642656818[/C][/ROW]
[ROW][C]-0.0956975808541104[/C][/ROW]
[ROW][C]0.143309956329961[/C][/ROW]
[ROW][C]0.0187572159841698[/C][/ROW]
[ROW][C]0.0916514301580276[/C][/ROW]
[ROW][C]-0.114220581652296[/C][/ROW]
[ROW][C]0.0100967146974866[/C][/ROW]
[ROW][C]-0.318283738127712[/C][/ROW]
[ROW][C]0.2145351886932[/C][/ROW]
[ROW][C]-0.141165358157766[/C][/ROW]
[ROW][C]0.131873070807267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232220&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232220&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
0.00509985932295509
-0.320818222286888
-0.197117196687452
-0.273021303154159
-0.259307390005561
0.117630375827139
0.555831262688101
0.010351517675088
0.186684058005416
0.364862612399688
0.208424913402032
0.0647618673786489
0.0525051410399968
0.321269449652341
-0.350838864527021
0.0658651693688728
0.149554375196613
-0.114936607453198
-0.0612678864901738
-0.00290255501150305
0.299406072944713
-0.236384620475663
0.0015162047029825
-0.0938556758527673
0.124750000263356
-0.471783613685228
0.234820874083843
-0.0203883669827977
-0.129022235700457
0.041076861804919
-0.29004767860936
0.314346990835869
0.191624455541518
0.303417989902342
-0.268503945062622
0.289915006795012
-0.0448823511961261
0.213557197426895
-0.0820876439224611
0.236085417896762
0.173065057049405
0.251710933962102
-0.160389171911666
0.40817566275154
0.0820190953928902
0.0173582939155595
-0.0866614148161779
0.000536593053893895
0.329911858162196
0.115744305385573
0.0621722848647087
-0.0861085957245128
0.0753749365683205
-0.250523461798932
-0.228666727370203
0.0163675459851644
-0.129247437631836
0.0984376599509383
0.100778218291185
-0.0302527053025421
-0.1037533234522
-0.218990024090205
0.187279008866825
0.330481842656331
-0.412290770407357
0.0545177145540067
-0.122307642656818
-0.0956975808541104
0.143309956329961
0.0187572159841698
0.0916514301580276
-0.114220581652296
0.0100967146974866
-0.318283738127712
0.2145351886932
-0.141165358157766
0.131873070807267



Parameters (Session):
par1 = FALSE ; par2 = 0.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.0 ; 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')