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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 computationTue, 20 Dec 2011 12:11:29 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/20/t1324401151xzk4vcuu79n5s6w.htm/, Retrieved Mon, 29 Apr 2024 18:57:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158074, Retrieved Mon, 29 Apr 2024 18:57:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

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] [9717cb857c153ca3061376906953b329]
-                       [ARIMA Backward Selection] [Paper Arima backw...] [2011-12-20 17:11:29] [c98b04636162cea751932dfe577607eb] [Current]
- 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 time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158074&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]9 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=158074&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158074&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






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=158074&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=158074&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158074&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
-706.026542300685
-59.4405596548676
-969.833746613734
1994.5690860138
102.064817486532
1282.18415594707
-307.754630247536
225.069471260843
-862.471359634918
-2850.16181210868
-3875.76353612714
2996.98137267597
1931.51761452713
488.179283473324
-601.581667482475
-3495.10366003445
265.163833706658
640.313301733885
-3418.4510574428
2072.03404656756
2662.99849038346
-1064.81721847798
-9.57991656352343
-2208.88683058998
-4275.21509127468
381.837236296814
1914.99180128805
1019.08611182693
884.745418119049
-1702.08990277241
-2064.71651478338
-2452.89212280113
1313.39947216102
-569.737651112737
-187.917104209882
-128.334995682566
-2601.1483915317
581.657313532775
-299.792740763143
2868.57386101258
2723.0125115592
-1846.18424154383
-6526.92699834498
-3652.78153946976
2790.57298908843
-7703.28379219637
-1373.4254360951
-2976.48826606303
5585.19486009147
-2568.07442941032
-4750.48277036158
1324.51875257136
-1689.96836200234
-7068.09990824395
1855.55522946586
2269.5616610076
-4661.24502123733
3260.97179216006
2160.39827396123
4793.18322344231
-2209.06423236155
-2213.50516221846
-3114.14403114371
3411.46077018857
-4693.82009052388
5920.52060014337
-2077.91676973543
-3642.83864360219
332.648517080619
2734.67424108874
7903.13077566713
5491.06214959447
4354.8302967856
1619.51910031159
5543.22958996006
-366.009044132314
-3459.03185919305
1134.72885550765
-2525.56378016236
-926.76615891251
-2956.34222567584
-885.143376828708

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-706.026542300685 \tabularnewline
-59.4405596548676 \tabularnewline
-969.833746613734 \tabularnewline
1994.5690860138 \tabularnewline
102.064817486532 \tabularnewline
1282.18415594707 \tabularnewline
-307.754630247536 \tabularnewline
225.069471260843 \tabularnewline
-862.471359634918 \tabularnewline
-2850.16181210868 \tabularnewline
-3875.76353612714 \tabularnewline
2996.98137267597 \tabularnewline
1931.51761452713 \tabularnewline
488.179283473324 \tabularnewline
-601.581667482475 \tabularnewline
-3495.10366003445 \tabularnewline
265.163833706658 \tabularnewline
640.313301733885 \tabularnewline
-3418.4510574428 \tabularnewline
2072.03404656756 \tabularnewline
2662.99849038346 \tabularnewline
-1064.81721847798 \tabularnewline
-9.57991656352343 \tabularnewline
-2208.88683058998 \tabularnewline
-4275.21509127468 \tabularnewline
381.837236296814 \tabularnewline
1914.99180128805 \tabularnewline
1019.08611182693 \tabularnewline
884.745418119049 \tabularnewline
-1702.08990277241 \tabularnewline
-2064.71651478338 \tabularnewline
-2452.89212280113 \tabularnewline
1313.39947216102 \tabularnewline
-569.737651112737 \tabularnewline
-187.917104209882 \tabularnewline
-128.334995682566 \tabularnewline
-2601.1483915317 \tabularnewline
581.657313532775 \tabularnewline
-299.792740763143 \tabularnewline
2868.57386101258 \tabularnewline
2723.0125115592 \tabularnewline
-1846.18424154383 \tabularnewline
-6526.92699834498 \tabularnewline
-3652.78153946976 \tabularnewline
2790.57298908843 \tabularnewline
-7703.28379219637 \tabularnewline
-1373.4254360951 \tabularnewline
-2976.48826606303 \tabularnewline
5585.19486009147 \tabularnewline
-2568.07442941032 \tabularnewline
-4750.48277036158 \tabularnewline
1324.51875257136 \tabularnewline
-1689.96836200234 \tabularnewline
-7068.09990824395 \tabularnewline
1855.55522946586 \tabularnewline
2269.5616610076 \tabularnewline
-4661.24502123733 \tabularnewline
3260.97179216006 \tabularnewline
2160.39827396123 \tabularnewline
4793.18322344231 \tabularnewline
-2209.06423236155 \tabularnewline
-2213.50516221846 \tabularnewline
-3114.14403114371 \tabularnewline
3411.46077018857 \tabularnewline
-4693.82009052388 \tabularnewline
5920.52060014337 \tabularnewline
-2077.91676973543 \tabularnewline
-3642.83864360219 \tabularnewline
332.648517080619 \tabularnewline
2734.67424108874 \tabularnewline
7903.13077566713 \tabularnewline
5491.06214959447 \tabularnewline
4354.8302967856 \tabularnewline
1619.51910031159 \tabularnewline
5543.22958996006 \tabularnewline
-366.009044132314 \tabularnewline
-3459.03185919305 \tabularnewline
1134.72885550765 \tabularnewline
-2525.56378016236 \tabularnewline
-926.76615891251 \tabularnewline
-2956.34222567584 \tabularnewline
-885.143376828708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158074&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-706.026542300685[/C][/ROW]
[ROW][C]-59.4405596548676[/C][/ROW]
[ROW][C]-969.833746613734[/C][/ROW]
[ROW][C]1994.5690860138[/C][/ROW]
[ROW][C]102.064817486532[/C][/ROW]
[ROW][C]1282.18415594707[/C][/ROW]
[ROW][C]-307.754630247536[/C][/ROW]
[ROW][C]225.069471260843[/C][/ROW]
[ROW][C]-862.471359634918[/C][/ROW]
[ROW][C]-2850.16181210868[/C][/ROW]
[ROW][C]-3875.76353612714[/C][/ROW]
[ROW][C]2996.98137267597[/C][/ROW]
[ROW][C]1931.51761452713[/C][/ROW]
[ROW][C]488.179283473324[/C][/ROW]
[ROW][C]-601.581667482475[/C][/ROW]
[ROW][C]-3495.10366003445[/C][/ROW]
[ROW][C]265.163833706658[/C][/ROW]
[ROW][C]640.313301733885[/C][/ROW]
[ROW][C]-3418.4510574428[/C][/ROW]
[ROW][C]2072.03404656756[/C][/ROW]
[ROW][C]2662.99849038346[/C][/ROW]
[ROW][C]-1064.81721847798[/C][/ROW]
[ROW][C]-9.57991656352343[/C][/ROW]
[ROW][C]-2208.88683058998[/C][/ROW]
[ROW][C]-4275.21509127468[/C][/ROW]
[ROW][C]381.837236296814[/C][/ROW]
[ROW][C]1914.99180128805[/C][/ROW]
[ROW][C]1019.08611182693[/C][/ROW]
[ROW][C]884.745418119049[/C][/ROW]
[ROW][C]-1702.08990277241[/C][/ROW]
[ROW][C]-2064.71651478338[/C][/ROW]
[ROW][C]-2452.89212280113[/C][/ROW]
[ROW][C]1313.39947216102[/C][/ROW]
[ROW][C]-569.737651112737[/C][/ROW]
[ROW][C]-187.917104209882[/C][/ROW]
[ROW][C]-128.334995682566[/C][/ROW]
[ROW][C]-2601.1483915317[/C][/ROW]
[ROW][C]581.657313532775[/C][/ROW]
[ROW][C]-299.792740763143[/C][/ROW]
[ROW][C]2868.57386101258[/C][/ROW]
[ROW][C]2723.0125115592[/C][/ROW]
[ROW][C]-1846.18424154383[/C][/ROW]
[ROW][C]-6526.92699834498[/C][/ROW]
[ROW][C]-3652.78153946976[/C][/ROW]
[ROW][C]2790.57298908843[/C][/ROW]
[ROW][C]-7703.28379219637[/C][/ROW]
[ROW][C]-1373.4254360951[/C][/ROW]
[ROW][C]-2976.48826606303[/C][/ROW]
[ROW][C]5585.19486009147[/C][/ROW]
[ROW][C]-2568.07442941032[/C][/ROW]
[ROW][C]-4750.48277036158[/C][/ROW]
[ROW][C]1324.51875257136[/C][/ROW]
[ROW][C]-1689.96836200234[/C][/ROW]
[ROW][C]-7068.09990824395[/C][/ROW]
[ROW][C]1855.55522946586[/C][/ROW]
[ROW][C]2269.5616610076[/C][/ROW]
[ROW][C]-4661.24502123733[/C][/ROW]
[ROW][C]3260.97179216006[/C][/ROW]
[ROW][C]2160.39827396123[/C][/ROW]
[ROW][C]4793.18322344231[/C][/ROW]
[ROW][C]-2209.06423236155[/C][/ROW]
[ROW][C]-2213.50516221846[/C][/ROW]
[ROW][C]-3114.14403114371[/C][/ROW]
[ROW][C]3411.46077018857[/C][/ROW]
[ROW][C]-4693.82009052388[/C][/ROW]
[ROW][C]5920.52060014337[/C][/ROW]
[ROW][C]-2077.91676973543[/C][/ROW]
[ROW][C]-3642.83864360219[/C][/ROW]
[ROW][C]332.648517080619[/C][/ROW]
[ROW][C]2734.67424108874[/C][/ROW]
[ROW][C]7903.13077566713[/C][/ROW]
[ROW][C]5491.06214959447[/C][/ROW]
[ROW][C]4354.8302967856[/C][/ROW]
[ROW][C]1619.51910031159[/C][/ROW]
[ROW][C]5543.22958996006[/C][/ROW]
[ROW][C]-366.009044132314[/C][/ROW]
[ROW][C]-3459.03185919305[/C][/ROW]
[ROW][C]1134.72885550765[/C][/ROW]
[ROW][C]-2525.56378016236[/C][/ROW]
[ROW][C]-926.76615891251[/C][/ROW]
[ROW][C]-2956.34222567584[/C][/ROW]
[ROW][C]-885.143376828708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158074&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158074&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
-706.026542300685
-59.4405596548676
-969.833746613734
1994.5690860138
102.064817486532
1282.18415594707
-307.754630247536
225.069471260843
-862.471359634918
-2850.16181210868
-3875.76353612714
2996.98137267597
1931.51761452713
488.179283473324
-601.581667482475
-3495.10366003445
265.163833706658
640.313301733885
-3418.4510574428
2072.03404656756
2662.99849038346
-1064.81721847798
-9.57991656352343
-2208.88683058998
-4275.21509127468
381.837236296814
1914.99180128805
1019.08611182693
884.745418119049
-1702.08990277241
-2064.71651478338
-2452.89212280113
1313.39947216102
-569.737651112737
-187.917104209882
-128.334995682566
-2601.1483915317
581.657313532775
-299.792740763143
2868.57386101258
2723.0125115592
-1846.18424154383
-6526.92699834498
-3652.78153946976
2790.57298908843
-7703.28379219637
-1373.4254360951
-2976.48826606303
5585.19486009147
-2568.07442941032
-4750.48277036158
1324.51875257136
-1689.96836200234
-7068.09990824395
1855.55522946586
2269.5616610076
-4661.24502123733
3260.97179216006
2160.39827396123
4793.18322344231
-2209.06423236155
-2213.50516221846
-3114.14403114371
3411.46077018857
-4693.82009052388
5920.52060014337
-2077.91676973543
-3642.83864360219
332.648517080619
2734.67424108874
7903.13077566713
5491.06214959447
4354.8302967856
1619.51910031159
5543.22958996006
-366.009044132314
-3459.03185919305
1134.72885550765
-2525.56378016236
-926.76615891251
-2956.34222567584
-885.143376828708


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')