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Description of Statistical Computation

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 computationWed, 02 Dec 2009 18:42:16 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/03/t1259804703s6osk8ji8g0lzwn.htm/, Retrieved Tue, 16 Apr 2024 18:41:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62635, Retrieved Tue, 16 Apr 2024 18:41:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsARIMA Backward Selection
Estimated Impact214

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [(Partial) Autocorrelation Function] [Identifying Integ...] [2009-11-22 12:16:10] [b98453cac15ba1066b407e146608df68]
-    D        [(Partial) Autocorrelation Function] [ACF van Y(t) (d=0...] [2009-11-26 00:58:58] [9717cb857c153ca3061376906953b329]
-   P           [(Partial) Autocorrelation Function] [ACF van Y(t) (d=1...] [2009-11-26 17:32:24] [9717cb857c153ca3061376906953b329]
-   P             [(Partial) Autocorrelation Function] [ACF van Y(t) (d=1...] [2009-11-26 17:41:56] [9717cb857c153ca3061376906953b329]
- RMP                 [ARIMA Backward Selection] [ARIMA Backward Se...] [2009-12-03 01:42:16] [52b85b290d6f50b0921ad6729b8a5af2] [Current]
-                       [ARIMA Backward Selection] [Paper Arima backw...] [2011-12-20 17:11:29] [abc1cbe561c2c4615f632bb3153b1275]
- RMP                     [ARIMA Forecasting] [Paper Arima forec...] [2011-12-22 18:40:24] [abc1cbe561c2c4615f632bb3153b1275]
- R PD                      [ARIMA Forecasting] [Arima Forecasting...] [2011-12-23 19:41:16] [7156a20ff7d97880b6dc50f7239ba03b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
220206
220115
218444
214912
210705
209673
237041
242081
241878
242621
238545
240337
244752
244576
241572
240541
236089
236997
264579
270349
269645
267037
258113
262813
267413
267366
264777
258863
254844
254868
277267
285351
286602
283042
276687
277915
277128
277103
275037
270150
267140
264993
287259
291186
292300
288186
281477
282656
280190
280408
276836
275216
274352
271311
289802
290726
292300
278506
269826
265861
269034
264176
255198
253353
246057
235372
258556
260993
254663
250643
243422
247105
248541
245039
237080
237085
225554
226839
247934
248333
246969
245098
246263
255765
264319
268347
273046
273963
267430
271993
292710
295881
293299
288576

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62635&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62635&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62635&T=0

As an alternative you can also use a QR Code:  
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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.09770.25040.2386-0.02840.2781-0.1043-0.9997
(p-val)(0.7769 )(0.0324 )(0.1023 )(0.935 )(0.0523 )(0.4862 )(0.0395 )
Estimates ( 2 )0.0710.25430.24600.2774-0.1053-0.9966
(p-val)(0.5154 )(0.0173 )(0.0273 )(NA )(0.0523 )(0.4808 )(0.0351 )
Estimates ( 3 )00.26510.264500.292-0.1142-0.9998
(p-val)(NA )(0.0122 )(0.0141 )(NA )(0.0402 )(0.4418 )(0.0442 )
Estimates ( 4 )00.25480.283800.31350-0.9999
(p-val)(NA )(0.0146 )(0.0067 )(NA )(0.0294 )(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 ) & 0.0977 & 0.2504 & 0.2386 & -0.0284 & 0.2781 & -0.1043 & -0.9997 \tabularnewline
(p-val) & (0.7769 ) & (0.0324 ) & (0.1023 ) & (0.935 ) & (0.0523 ) & (0.4862 ) & (0.0395 ) \tabularnewline
Estimates ( 2 ) & 0.071 & 0.2543 & 0.246 & 0 & 0.2774 & -0.1053 & -0.9966 \tabularnewline
(p-val) & (0.5154 ) & (0.0173 ) & (0.0273 ) & (NA ) & (0.0523 ) & (0.4808 ) & (0.0351 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.2651 & 0.2645 & 0 & 0.292 & -0.1142 & -0.9998 \tabularnewline
(p-val) & (NA ) & (0.0122 ) & (0.0141 ) & (NA ) & (0.0402 ) & (0.4418 ) & (0.0442 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.2548 & 0.2838 & 0 & 0.3135 & 0 & -0.9999 \tabularnewline
(p-val) & (NA ) & (0.0146 ) & (0.0067 ) & (NA ) & (0.0294 ) & (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=62635&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]0.0977[/C][C]0.2504[/C][C]0.2386[/C][C]-0.0284[/C][C]0.2781[/C][C]-0.1043[/C][C]-0.9997[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7769 )[/C][C](0.0324 )[/C][C](0.1023 )[/C][C](0.935 )[/C][C](0.0523 )[/C][C](0.4862 )[/C][C](0.0395 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.071[/C][C]0.2543[/C][C]0.246[/C][C]0[/C][C]0.2774[/C][C]-0.1053[/C][C]-0.9966[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5154 )[/C][C](0.0173 )[/C][C](0.0273 )[/C][C](NA )[/C][C](0.0523 )[/C][C](0.4808 )[/C][C](0.0351 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.2651[/C][C]0.2645[/C][C]0[/C][C]0.292[/C][C]-0.1142[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0122 )[/C][C](0.0141 )[/C][C](NA )[/C][C](0.0402 )[/C][C](0.4418 )[/C][C](0.0442 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.2548[/C][C]0.2838[/C][C]0[/C][C]0.3135[/C][C]0[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0146 )[/C][C](0.0067 )[/C][C](NA )[/C][C](0.0294 )[/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=62635&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62635&T=1

As an alternative you can also use a QR Code:  
QR Code


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

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.09770.25040.2386-0.02840.2781-0.1043-0.9997
(p-val)(0.7769 )(0.0324 )(0.1023 )(0.935 )(0.0523 )(0.4862 )(0.0395 )
Estimates ( 2 )0.0710.25430.24600.2774-0.1053-0.9966
(p-val)(0.5154 )(0.0173 )(0.0273 )(NA )(0.0523 )(0.4808 )(0.0351 )
Estimates ( 3 )00.26510.264500.292-0.1142-0.9998
(p-val)(NA )(0.0122 )(0.0141 )(NA )(0.0402 )(0.4418 )(0.0442 )
Estimates ( 4 )00.25480.283800.31350-0.9999
(p-val)(NA )(0.0146 )(0.0067 )(NA )(0.0294 )(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
-706.026542296181
-59.4405579520267
-969.833720383066
1994.56903063601
102.064814516947
1282.18411025837
-307.754606959943
225.069461418321
-862.471322666482
-2850.16172979440
-3875.76341994283
2996.98129088558
1931.51753614196
488.179241950267
-601.581627830989
-3495.10356420977
265.163825175723
640.313274555833
-3418.45097379814
2072.03398243007
2662.9984145817
-1064.81719671349
-9.57987333730985
-2208.8867746199
-4275.21498681124
381.837227886395
1914.99171248840
1019.08609621188
884.745394297871
-1702.08983953302
-2064.71644375347
-2452.89205796152
1313.39939683182
-569.737683944086
-187.9171774845
-128.334928612711
-2601.14827891639
581.657297679053
-299.792761503996
2868.57371158154
2723.0124492194
-1846.18418996859
-6526.92686969168
-3652.78139505582
2790.5729463211
-7703.28365973859
-1373.42544129638
-2976.48819823291
5585.1945937738
-2568.07437379205
-4750.48266905558
1324.51875012340
-1689.96832952026
-7068.09981170815
1855.55513995599
2269.56153308754
-4661.24493442205
3260.97170059017
2160.39821896397
4793.18303923311
-2209.06420471032
-2213.50510931699
-3114.14394163762
3411.46075067066
-4693.81993673823
5920.52037235391
-2077.91680939984
-3642.83868232441
332.648590721953
2734.67402718683
7903.1305482291
5491.06198558059
4354.83029956606
1619.51905250655
5543.22938817069
-366.008933224093
-3459.03179080274
1134.72876550400
-2525.56367971036
-926.76612281528
-2956.34221718809
-885.143314774538

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-706.026542296181 \tabularnewline
-59.4405579520267 \tabularnewline
-969.833720383066 \tabularnewline
1994.56903063601 \tabularnewline
102.064814516947 \tabularnewline
1282.18411025837 \tabularnewline
-307.754606959943 \tabularnewline
225.069461418321 \tabularnewline
-862.471322666482 \tabularnewline
-2850.16172979440 \tabularnewline
-3875.76341994283 \tabularnewline
2996.98129088558 \tabularnewline
1931.51753614196 \tabularnewline
488.179241950267 \tabularnewline
-601.581627830989 \tabularnewline
-3495.10356420977 \tabularnewline
265.163825175723 \tabularnewline
640.313274555833 \tabularnewline
-3418.45097379814 \tabularnewline
2072.03398243007 \tabularnewline
2662.9984145817 \tabularnewline
-1064.81719671349 \tabularnewline
-9.57987333730985 \tabularnewline
-2208.8867746199 \tabularnewline
-4275.21498681124 \tabularnewline
381.837227886395 \tabularnewline
1914.99171248840 \tabularnewline
1019.08609621188 \tabularnewline
884.745394297871 \tabularnewline
-1702.08983953302 \tabularnewline
-2064.71644375347 \tabularnewline
-2452.89205796152 \tabularnewline
1313.39939683182 \tabularnewline
-569.737683944086 \tabularnewline
-187.9171774845 \tabularnewline
-128.334928612711 \tabularnewline
-2601.14827891639 \tabularnewline
581.657297679053 \tabularnewline
-299.792761503996 \tabularnewline
2868.57371158154 \tabularnewline
2723.0124492194 \tabularnewline
-1846.18418996859 \tabularnewline
-6526.92686969168 \tabularnewline
-3652.78139505582 \tabularnewline
2790.5729463211 \tabularnewline
-7703.28365973859 \tabularnewline
-1373.42544129638 \tabularnewline
-2976.48819823291 \tabularnewline
5585.1945937738 \tabularnewline
-2568.07437379205 \tabularnewline
-4750.48266905558 \tabularnewline
1324.51875012340 \tabularnewline
-1689.96832952026 \tabularnewline
-7068.09981170815 \tabularnewline
1855.55513995599 \tabularnewline
2269.56153308754 \tabularnewline
-4661.24493442205 \tabularnewline
3260.97170059017 \tabularnewline
2160.39821896397 \tabularnewline
4793.18303923311 \tabularnewline
-2209.06420471032 \tabularnewline
-2213.50510931699 \tabularnewline
-3114.14394163762 \tabularnewline
3411.46075067066 \tabularnewline
-4693.81993673823 \tabularnewline
5920.52037235391 \tabularnewline
-2077.91680939984 \tabularnewline
-3642.83868232441 \tabularnewline
332.648590721953 \tabularnewline
2734.67402718683 \tabularnewline
7903.1305482291 \tabularnewline
5491.06198558059 \tabularnewline
4354.83029956606 \tabularnewline
1619.51905250655 \tabularnewline
5543.22938817069 \tabularnewline
-366.008933224093 \tabularnewline
-3459.03179080274 \tabularnewline
1134.72876550400 \tabularnewline
-2525.56367971036 \tabularnewline
-926.76612281528 \tabularnewline
-2956.34221718809 \tabularnewline
-885.143314774538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62635&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-706.026542296181[/C][/ROW]
[ROW][C]-59.4405579520267[/C][/ROW]
[ROW][C]-969.833720383066[/C][/ROW]
[ROW][C]1994.56903063601[/C][/ROW]
[ROW][C]102.064814516947[/C][/ROW]
[ROW][C]1282.18411025837[/C][/ROW]
[ROW][C]-307.754606959943[/C][/ROW]
[ROW][C]225.069461418321[/C][/ROW]
[ROW][C]-862.471322666482[/C][/ROW]
[ROW][C]-2850.16172979440[/C][/ROW]
[ROW][C]-3875.76341994283[/C][/ROW]
[ROW][C]2996.98129088558[/C][/ROW]
[ROW][C]1931.51753614196[/C][/ROW]
[ROW][C]488.179241950267[/C][/ROW]
[ROW][C]-601.581627830989[/C][/ROW]
[ROW][C]-3495.10356420977[/C][/ROW]
[ROW][C]265.163825175723[/C][/ROW]
[ROW][C]640.313274555833[/C][/ROW]
[ROW][C]-3418.45097379814[/C][/ROW]
[ROW][C]2072.03398243007[/C][/ROW]
[ROW][C]2662.9984145817[/C][/ROW]
[ROW][C]-1064.81719671349[/C][/ROW]
[ROW][C]-9.57987333730985[/C][/ROW]
[ROW][C]-2208.8867746199[/C][/ROW]
[ROW][C]-4275.21498681124[/C][/ROW]
[ROW][C]381.837227886395[/C][/ROW]
[ROW][C]1914.99171248840[/C][/ROW]
[ROW][C]1019.08609621188[/C][/ROW]
[ROW][C]884.745394297871[/C][/ROW]
[ROW][C]-1702.08983953302[/C][/ROW]
[ROW][C]-2064.71644375347[/C][/ROW]
[ROW][C]-2452.89205796152[/C][/ROW]
[ROW][C]1313.39939683182[/C][/ROW]
[ROW][C]-569.737683944086[/C][/ROW]
[ROW][C]-187.9171774845[/C][/ROW]
[ROW][C]-128.334928612711[/C][/ROW]
[ROW][C]-2601.14827891639[/C][/ROW]
[ROW][C]581.657297679053[/C][/ROW]
[ROW][C]-299.792761503996[/C][/ROW]
[ROW][C]2868.57371158154[/C][/ROW]
[ROW][C]2723.0124492194[/C][/ROW]
[ROW][C]-1846.18418996859[/C][/ROW]
[ROW][C]-6526.92686969168[/C][/ROW]
[ROW][C]-3652.78139505582[/C][/ROW]
[ROW][C]2790.5729463211[/C][/ROW]
[ROW][C]-7703.28365973859[/C][/ROW]
[ROW][C]-1373.42544129638[/C][/ROW]
[ROW][C]-2976.48819823291[/C][/ROW]
[ROW][C]5585.1945937738[/C][/ROW]
[ROW][C]-2568.07437379205[/C][/ROW]
[ROW][C]-4750.48266905558[/C][/ROW]
[ROW][C]1324.51875012340[/C][/ROW]
[ROW][C]-1689.96832952026[/C][/ROW]
[ROW][C]-7068.09981170815[/C][/ROW]
[ROW][C]1855.55513995599[/C][/ROW]
[ROW][C]2269.56153308754[/C][/ROW]
[ROW][C]-4661.24493442205[/C][/ROW]
[ROW][C]3260.97170059017[/C][/ROW]
[ROW][C]2160.39821896397[/C][/ROW]
[ROW][C]4793.18303923311[/C][/ROW]
[ROW][C]-2209.06420471032[/C][/ROW]
[ROW][C]-2213.50510931699[/C][/ROW]
[ROW][C]-3114.14394163762[/C][/ROW]
[ROW][C]3411.46075067066[/C][/ROW]
[ROW][C]-4693.81993673823[/C][/ROW]
[ROW][C]5920.52037235391[/C][/ROW]
[ROW][C]-2077.91680939984[/C][/ROW]
[ROW][C]-3642.83868232441[/C][/ROW]
[ROW][C]332.648590721953[/C][/ROW]
[ROW][C]2734.67402718683[/C][/ROW]
[ROW][C]7903.1305482291[/C][/ROW]
[ROW][C]5491.06198558059[/C][/ROW]
[ROW][C]4354.83029956606[/C][/ROW]
[ROW][C]1619.51905250655[/C][/ROW]
[ROW][C]5543.22938817069[/C][/ROW]
[ROW][C]-366.008933224093[/C][/ROW]
[ROW][C]-3459.03179080274[/C][/ROW]
[ROW][C]1134.72876550400[/C][/ROW]
[ROW][C]-2525.56367971036[/C][/ROW]
[ROW][C]-926.76612281528[/C][/ROW]
[ROW][C]-2956.34221718809[/C][/ROW]
[ROW][C]-885.143314774538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62635&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62635&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Estimated ARIMA Residuals
Value
-706.026542296181
-59.4405579520267
-969.833720383066
1994.56903063601
102.064814516947
1282.18411025837
-307.754606959943
225.069461418321
-862.471322666482
-2850.16172979440
-3875.76341994283
2996.98129088558
1931.51753614196
488.179241950267
-601.581627830989
-3495.10356420977
265.163825175723
640.313274555833
-3418.45097379814
2072.03398243007
2662.9984145817
-1064.81719671349
-9.57987333730985
-2208.8867746199
-4275.21498681124
381.837227886395
1914.99171248840
1019.08609621188
884.745394297871
-1702.08983953302
-2064.71644375347
-2452.89205796152
1313.39939683182
-569.737683944086
-187.9171774845
-128.334928612711
-2601.14827891639
581.657297679053
-299.792761503996
2868.57371158154
2723.0124492194
-1846.18418996859
-6526.92686969168
-3652.78139505582
2790.5729463211
-7703.28365973859
-1373.42544129638
-2976.48819823291
5585.1945937738
-2568.07437379205
-4750.48266905558
1324.51875012340
-1689.96832952026
-7068.09981170815
1855.55513995599
2269.56153308754
-4661.24493442205
3260.97170059017
2160.39821896397
4793.18303923311
-2209.06420471032
-2213.50510931699
-3114.14394163762
3411.46075067066
-4693.81993673823
5920.52037235391
-2077.91680939984
-3642.83868232441
332.648590721953
2734.67402718683
7903.1305482291
5491.06198558059
4354.83029956606
1619.51905250655
5543.22938817069
-366.008933224093
-3459.03179080274
1134.72876550400
-2525.56367971036
-926.76612281528
-2956.34221718809
-885.143314774538


Figures (Output of Computation)

Input Parameters & R Code

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