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 computationFri, 23 Dec 2011 09:17:11 -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/23/t13246500272s6lteedyt8uxn3.htm/, Retrieved Thu, 31 Oct 2024 23:25:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160444, Retrieved Thu, 31 Oct 2024 23:25:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Histogram] [Workshop 1 - Task 1] [2011-10-03 18:33:04] [fbaf17a8836493f6de0f4e0e997711e1]
- RMPD  [Univariate Data Series] [Paper run sequenc...] [2011-12-15 17:43:06] [abc1cbe561c2c4615f632bb3153b1275]
- RMP     [Univariate Explorative Data Analysis] [Paper Univariate Eda] [2011-12-16 15:55:55] [abc1cbe561c2c4615f632bb3153b1275]
- RMP         [ARIMA Backward Selection] [Paper ARIMA Backw...] [2011-12-23 14:17:11] [c98b04636162cea751932dfe577607eb] [Current]
Feedback Forum

Post a new message
Dataseries X:
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
286445
297584
300431
298522
292213
285383
277537
277891
302686
300653
296369
287224
279998
283495
285775
282329
277799
271980
266730
262433
285378
286692
282917
277686
274371




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160444&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160444&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160444&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 time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.41050.19280.0804-0.28510.2359-0.0567-0.9994
(p-val)(0.7495 )(0.4359 )(0.8515 )(0.8257 )(0.0933 )(0.748 )(0.0164 )
Estimates ( 2 )0.6230.16360-0.49680.2294-0.0739-0.9989
(p-val)(0.0476 )(0.3193 )(NA )(0.1013 )(0.0942 )(0.6303 )(0.0179 )
Estimates ( 3 )0.63440.16590-0.5050.24390-0.9998
(p-val)(0.0253 )(0.2966 )(NA )(0.0607 )(0.0728 )(NA )(0.0015 )
Estimates ( 4 )0.870700-0.66670.21210-0.9999
(p-val)(0 )(NA )(NA )(0 )(0.1074 )(NA )(4e-04 )
Estimates ( 5 )0.875800-0.670200-1.3808
(p-val)(0 )(NA )(NA )(0 )(NA )(NA )(1e-04 )
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 ) & 0.4105 & 0.1928 & 0.0804 & -0.2851 & 0.2359 & -0.0567 & -0.9994 \tabularnewline
(p-val) & (0.7495 ) & (0.4359 ) & (0.8515 ) & (0.8257 ) & (0.0933 ) & (0.748 ) & (0.0164 ) \tabularnewline
Estimates ( 2 ) & 0.623 & 0.1636 & 0 & -0.4968 & 0.2294 & -0.0739 & -0.9989 \tabularnewline
(p-val) & (0.0476 ) & (0.3193 ) & (NA ) & (0.1013 ) & (0.0942 ) & (0.6303 ) & (0.0179 ) \tabularnewline
Estimates ( 3 ) & 0.6344 & 0.1659 & 0 & -0.505 & 0.2439 & 0 & -0.9998 \tabularnewline
(p-val) & (0.0253 ) & (0.2966 ) & (NA ) & (0.0607 ) & (0.0728 ) & (NA ) & (0.0015 ) \tabularnewline
Estimates ( 4 ) & 0.8707 & 0 & 0 & -0.6667 & 0.2121 & 0 & -0.9999 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0 ) & (0.1074 ) & (NA ) & (4e-04 ) \tabularnewline
Estimates ( 5 ) & 0.8758 & 0 & 0 & -0.6702 & 0 & 0 & -1.3808 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (1e-04 ) \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=160444&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]0.4105[/C][C]0.1928[/C][C]0.0804[/C][C]-0.2851[/C][C]0.2359[/C][C]-0.0567[/C][C]-0.9994[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7495 )[/C][C](0.4359 )[/C][C](0.8515 )[/C][C](0.8257 )[/C][C](0.0933 )[/C][C](0.748 )[/C][C](0.0164 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.623[/C][C]0.1636[/C][C]0[/C][C]-0.4968[/C][C]0.2294[/C][C]-0.0739[/C][C]-0.9989[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0476 )[/C][C](0.3193 )[/C][C](NA )[/C][C](0.1013 )[/C][C](0.0942 )[/C][C](0.6303 )[/C][C](0.0179 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.6344[/C][C]0.1659[/C][C]0[/C][C]-0.505[/C][C]0.2439[/C][C]0[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0253 )[/C][C](0.2966 )[/C][C](NA )[/C][C](0.0607 )[/C][C](0.0728 )[/C][C](NA )[/C][C](0.0015 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.8707[/C][C]0[/C][C]0[/C][C]-0.6667[/C][C]0.2121[/C][C]0[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.1074 )[/C][C](NA )[/C][C](4e-04 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.8758[/C][C]0[/C][C]0[/C][C]-0.6702[/C][C]0[/C][C]0[/C][C]-1.3808[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](1e-04 )[/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=160444&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160444&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 )0.41050.19280.0804-0.28510.2359-0.0567-0.9994
(p-val)(0.7495 )(0.4359 )(0.8515 )(0.8257 )(0.0933 )(0.748 )(0.0164 )
Estimates ( 2 )0.6230.16360-0.49680.2294-0.0739-0.9989
(p-val)(0.0476 )(0.3193 )(NA )(0.1013 )(0.0942 )(0.6303 )(0.0179 )
Estimates ( 3 )0.63440.16590-0.5050.24390-0.9998
(p-val)(0.0253 )(0.2966 )(NA )(0.0607 )(0.0728 )(NA )(0.0015 )
Estimates ( 4 )0.870700-0.66670.21210-0.9999
(p-val)(0 )(NA )(NA )(0 )(0.1074 )(NA )(4e-04 )
Estimates ( 5 )0.875800-0.670200-1.3808
(p-val)(0 )(NA )(NA )(0 )(NA )(NA )(1e-04 )
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
-961.062336466214
-32.2499684960441
-1274.54763247662
481.075673062073
-1027.1677212379
2875.77110914691
1380.32047625722
-1233.64752099747
-3176.85727409745
-1928.84284674272
1083.80182331192
-7184.39335698442
152.17860582808
-2908.4330004837
6073.6533466079
-3855.97112979341
-4002.25358008941
2633.80754486109
-3785.4767725616
-5219.11858588425
5095.67763907064
1280.15643402994
-5987.01450115966
6700.69164276812
608.315137149883
4488.3605609924
-328.322480676965
-1722.24389209223
-2169.8020037371
2536.71815666273
-6442.15290537358
7917.94264843534
-1462.68395573439
-2264.79976106487
1075.25287109231
3697.83134846704
6682.51694311459
5314.65543646124
4364.960236883
2375.33619457744
6342.48060975902
-1903.45641630643
-2818.90107611816
3929.19493850866
-3267.15127606506
-326.307319335248
-2697.37838007676
-586.712905944903
1021.53419867313
5844.42890827957
-2376.20291995631
-3177.73284911017
-4722.62895774764
-4597.34871236639
-178.320553690719
2106.63303522272
4258.42189419234
-4281.09315709218
-1609.11687521233
-2421.41547852411
-1430.35001526295
-433.26024988137
1412.47776648795
-1207.47718876129
1019.19399715675
-1425.82108954053
2048.75422360478
-2560.02399450822
1253.86370694336
1078.22893285539
-1013.27587049321
1983.06048156333
2199.27456179842

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-961.062336466214 \tabularnewline
-32.2499684960441 \tabularnewline
-1274.54763247662 \tabularnewline
481.075673062073 \tabularnewline
-1027.1677212379 \tabularnewline
2875.77110914691 \tabularnewline
1380.32047625722 \tabularnewline
-1233.64752099747 \tabularnewline
-3176.85727409745 \tabularnewline
-1928.84284674272 \tabularnewline
1083.80182331192 \tabularnewline
-7184.39335698442 \tabularnewline
152.17860582808 \tabularnewline
-2908.4330004837 \tabularnewline
6073.6533466079 \tabularnewline
-3855.97112979341 \tabularnewline
-4002.25358008941 \tabularnewline
2633.80754486109 \tabularnewline
-3785.4767725616 \tabularnewline
-5219.11858588425 \tabularnewline
5095.67763907064 \tabularnewline
1280.15643402994 \tabularnewline
-5987.01450115966 \tabularnewline
6700.69164276812 \tabularnewline
608.315137149883 \tabularnewline
4488.3605609924 \tabularnewline
-328.322480676965 \tabularnewline
-1722.24389209223 \tabularnewline
-2169.8020037371 \tabularnewline
2536.71815666273 \tabularnewline
-6442.15290537358 \tabularnewline
7917.94264843534 \tabularnewline
-1462.68395573439 \tabularnewline
-2264.79976106487 \tabularnewline
1075.25287109231 \tabularnewline
3697.83134846704 \tabularnewline
6682.51694311459 \tabularnewline
5314.65543646124 \tabularnewline
4364.960236883 \tabularnewline
2375.33619457744 \tabularnewline
6342.48060975902 \tabularnewline
-1903.45641630643 \tabularnewline
-2818.90107611816 \tabularnewline
3929.19493850866 \tabularnewline
-3267.15127606506 \tabularnewline
-326.307319335248 \tabularnewline
-2697.37838007676 \tabularnewline
-586.712905944903 \tabularnewline
1021.53419867313 \tabularnewline
5844.42890827957 \tabularnewline
-2376.20291995631 \tabularnewline
-3177.73284911017 \tabularnewline
-4722.62895774764 \tabularnewline
-4597.34871236639 \tabularnewline
-178.320553690719 \tabularnewline
2106.63303522272 \tabularnewline
4258.42189419234 \tabularnewline
-4281.09315709218 \tabularnewline
-1609.11687521233 \tabularnewline
-2421.41547852411 \tabularnewline
-1430.35001526295 \tabularnewline
-433.26024988137 \tabularnewline
1412.47776648795 \tabularnewline
-1207.47718876129 \tabularnewline
1019.19399715675 \tabularnewline
-1425.82108954053 \tabularnewline
2048.75422360478 \tabularnewline
-2560.02399450822 \tabularnewline
1253.86370694336 \tabularnewline
1078.22893285539 \tabularnewline
-1013.27587049321 \tabularnewline
1983.06048156333 \tabularnewline
2199.27456179842 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160444&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-961.062336466214[/C][/ROW]
[ROW][C]-32.2499684960441[/C][/ROW]
[ROW][C]-1274.54763247662[/C][/ROW]
[ROW][C]481.075673062073[/C][/ROW]
[ROW][C]-1027.1677212379[/C][/ROW]
[ROW][C]2875.77110914691[/C][/ROW]
[ROW][C]1380.32047625722[/C][/ROW]
[ROW][C]-1233.64752099747[/C][/ROW]
[ROW][C]-3176.85727409745[/C][/ROW]
[ROW][C]-1928.84284674272[/C][/ROW]
[ROW][C]1083.80182331192[/C][/ROW]
[ROW][C]-7184.39335698442[/C][/ROW]
[ROW][C]152.17860582808[/C][/ROW]
[ROW][C]-2908.4330004837[/C][/ROW]
[ROW][C]6073.6533466079[/C][/ROW]
[ROW][C]-3855.97112979341[/C][/ROW]
[ROW][C]-4002.25358008941[/C][/ROW]
[ROW][C]2633.80754486109[/C][/ROW]
[ROW][C]-3785.4767725616[/C][/ROW]
[ROW][C]-5219.11858588425[/C][/ROW]
[ROW][C]5095.67763907064[/C][/ROW]
[ROW][C]1280.15643402994[/C][/ROW]
[ROW][C]-5987.01450115966[/C][/ROW]
[ROW][C]6700.69164276812[/C][/ROW]
[ROW][C]608.315137149883[/C][/ROW]
[ROW][C]4488.3605609924[/C][/ROW]
[ROW][C]-328.322480676965[/C][/ROW]
[ROW][C]-1722.24389209223[/C][/ROW]
[ROW][C]-2169.8020037371[/C][/ROW]
[ROW][C]2536.71815666273[/C][/ROW]
[ROW][C]-6442.15290537358[/C][/ROW]
[ROW][C]7917.94264843534[/C][/ROW]
[ROW][C]-1462.68395573439[/C][/ROW]
[ROW][C]-2264.79976106487[/C][/ROW]
[ROW][C]1075.25287109231[/C][/ROW]
[ROW][C]3697.83134846704[/C][/ROW]
[ROW][C]6682.51694311459[/C][/ROW]
[ROW][C]5314.65543646124[/C][/ROW]
[ROW][C]4364.960236883[/C][/ROW]
[ROW][C]2375.33619457744[/C][/ROW]
[ROW][C]6342.48060975902[/C][/ROW]
[ROW][C]-1903.45641630643[/C][/ROW]
[ROW][C]-2818.90107611816[/C][/ROW]
[ROW][C]3929.19493850866[/C][/ROW]
[ROW][C]-3267.15127606506[/C][/ROW]
[ROW][C]-326.307319335248[/C][/ROW]
[ROW][C]-2697.37838007676[/C][/ROW]
[ROW][C]-586.712905944903[/C][/ROW]
[ROW][C]1021.53419867313[/C][/ROW]
[ROW][C]5844.42890827957[/C][/ROW]
[ROW][C]-2376.20291995631[/C][/ROW]
[ROW][C]-3177.73284911017[/C][/ROW]
[ROW][C]-4722.62895774764[/C][/ROW]
[ROW][C]-4597.34871236639[/C][/ROW]
[ROW][C]-178.320553690719[/C][/ROW]
[ROW][C]2106.63303522272[/C][/ROW]
[ROW][C]4258.42189419234[/C][/ROW]
[ROW][C]-4281.09315709218[/C][/ROW]
[ROW][C]-1609.11687521233[/C][/ROW]
[ROW][C]-2421.41547852411[/C][/ROW]
[ROW][C]-1430.35001526295[/C][/ROW]
[ROW][C]-433.26024988137[/C][/ROW]
[ROW][C]1412.47776648795[/C][/ROW]
[ROW][C]-1207.47718876129[/C][/ROW]
[ROW][C]1019.19399715675[/C][/ROW]
[ROW][C]-1425.82108954053[/C][/ROW]
[ROW][C]2048.75422360478[/C][/ROW]
[ROW][C]-2560.02399450822[/C][/ROW]
[ROW][C]1253.86370694336[/C][/ROW]
[ROW][C]1078.22893285539[/C][/ROW]
[ROW][C]-1013.27587049321[/C][/ROW]
[ROW][C]1983.06048156333[/C][/ROW]
[ROW][C]2199.27456179842[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160444&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160444&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
-961.062336466214
-32.2499684960441
-1274.54763247662
481.075673062073
-1027.1677212379
2875.77110914691
1380.32047625722
-1233.64752099747
-3176.85727409745
-1928.84284674272
1083.80182331192
-7184.39335698442
152.17860582808
-2908.4330004837
6073.6533466079
-3855.97112979341
-4002.25358008941
2633.80754486109
-3785.4767725616
-5219.11858588425
5095.67763907064
1280.15643402994
-5987.01450115966
6700.69164276812
608.315137149883
4488.3605609924
-328.322480676965
-1722.24389209223
-2169.8020037371
2536.71815666273
-6442.15290537358
7917.94264843534
-1462.68395573439
-2264.79976106487
1075.25287109231
3697.83134846704
6682.51694311459
5314.65543646124
4364.960236883
2375.33619457744
6342.48060975902
-1903.45641630643
-2818.90107611816
3929.19493850866
-3267.15127606506
-326.307319335248
-2697.37838007676
-586.712905944903
1021.53419867313
5844.42890827957
-2376.20291995631
-3177.73284911017
-4722.62895774764
-4597.34871236639
-178.320553690719
2106.63303522272
4258.42189419234
-4281.09315709218
-1609.11687521233
-2421.41547852411
-1430.35001526295
-433.26024988137
1412.47776648795
-1207.47718876129
1019.19399715675
-1425.82108954053
2048.75422360478
-2560.02399450822
1253.86370694336
1078.22893285539
-1013.27587049321
1983.06048156333
2199.27456179842



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')