FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationFri, 12 Dec 2008 12:09:25 -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/2008/Dec/12/t12291093094t7n1hz2xza1t28.htm/, Retrieved Fri, 17 May 2024 13:50:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=32873, Retrieved Fri, 17 May 2024 13:50:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact234

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Standard Deviation-Mean Plot] [vraag 5] [2008-11-29 13:37:39] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMPD    [(Partial) Autocorrelation Function] [ACF] [2008-12-12 14:04:37] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMP         [ARIMA Backward Selection] [ABSM] [2008-12-12 19:09:25] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
- RMP           [ARIMA Forecasting] [] [2008-12-12 22:17:43] [a4ee3bef49b119f4bd2e925060c84f5e]
- RMP           [ARIMA Forecasting] [] [2008-12-12 22:20:54] [a4ee3bef49b119f4bd2e925060c84f5e]
-   PD            [ARIMA Forecasting] [ARIMA Forecast To...] [2008-12-15 10:23:36] [b635de6fc42b001d22cbe6e730fec936]
-   P           [ARIMA Backward Selection] [] [2008-12-12 22:24:32] [a4ee3bef49b119f4bd2e925060c84f5e]
F RMP           [ARIMA Forecasting] [ARIMA forecasting] [2008-12-13 15:15:45] [c45c87b96bbf32ffc2144fc37d767b2e]
-   PD            [ARIMA Forecasting] [ARIMA forecasting] [2008-12-16 23:08:55] [c45c87b96bbf32ffc2144fc37d767b2e]
-   PD            [ARIMA Forecasting] [ARIMA forecasting] [2008-12-16 23:08:55] [c45c87b96bbf32ffc2144fc37d767b2e]
-   PD            [ARIMA Forecasting] [ARIMA forecasting] [2008-12-16 23:08:55] [c45c87b96bbf32ffc2144fc37d767b2e]
-   PD            [ARIMA Forecasting] [ARIMA forecasting] [2008-12-16 23:08:55] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMPD          [] [ABSM] [-0001-11-30 00:00:00] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMPD            [ARIMA Backward Selection] [ABSM] [2008-12-16 11:46:07] [c45c87b96bbf32ffc2144fc37d767b2e]
-    D              [ARIMA Backward Selection] [ABSM] [2008-12-16 23:05:32] [c45c87b96bbf32ffc2144fc37d767b2e]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
493
481
462
457
442
439
488
521
501
485
464
460
467
460
448
443
436
431
484
510
513
503
471
471
476
475
470
461
455
456
517
525
523
519
509
512
519
517
510
509
501
507
569
580
578
565
547
555
562
561
555
544
537
543
594
611
613
611
594
595
591
589
584
573
567
569
621
629
628
612
595
597
593
590
580
574
573
573
620
626
620
588
566
557
561
549
532
526
511
499
555
565
542
527
510
514
517
508
493
490
469
478

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time28 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 28 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32873&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]28 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32873&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32873&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 time28 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.77850.08660.0921-0.82460.1403-0.1755-0.9999
(p-val)(0 )(0.5399 )(0.4421 )(0 )(0.3528 )(0.3076 )(0.0011 )
Estimates ( 2 )0.82200.1331-0.82610.1531-0.1851-1
(p-val)(0 )(NA )(0.177 )(0 )(0.3094 )(0.2789 )(0.0023 )
Estimates ( 3 )0.794500.1551-0.80460-0.2472-0.7394
(p-val)(0 )(NA )(0.1112 )(0 )(NA )(0.1117 )(0.0015 )
Estimates ( 4 )0.818100.1394-0.825800-0.8233
(p-val)(0 )(NA )(0.1474 )(0 )(NA )(NA )(0.0062 )
Estimates ( 5 )0.970100-0.878200-0.8305
(p-val)(0 )(NA )(NA )(0 )(NA )(NA )(0.0217 )
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 ) & 0.7785 & 0.0866 & 0.0921 & -0.8246 & 0.1403 & -0.1755 & -0.9999 \tabularnewline
(p-val) & (0 ) & (0.5399 ) & (0.4421 ) & (0 ) & (0.3528 ) & (0.3076 ) & (0.0011 ) \tabularnewline
Estimates ( 2 ) & 0.822 & 0 & 0.1331 & -0.8261 & 0.1531 & -0.1851 & -1 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.177 ) & (0 ) & (0.3094 ) & (0.2789 ) & (0.0023 ) \tabularnewline
Estimates ( 3 ) & 0.7945 & 0 & 0.1551 & -0.8046 & 0 & -0.2472 & -0.7394 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.1112 ) & (0 ) & (NA ) & (0.1117 ) & (0.0015 ) \tabularnewline
Estimates ( 4 ) & 0.8181 & 0 & 0.1394 & -0.8258 & 0 & 0 & -0.8233 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.1474 ) & (0 ) & (NA ) & (NA ) & (0.0062 ) \tabularnewline
Estimates ( 5 ) & 0.9701 & 0 & 0 & -0.8782 & 0 & 0 & -0.8305 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (0.0217 ) \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=32873&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]0.7785[/C][C]0.0866[/C][C]0.0921[/C][C]-0.8246[/C][C]0.1403[/C][C]-0.1755[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.5399 )[/C][C](0.4421 )[/C][C](0 )[/C][C](0.3528 )[/C][C](0.3076 )[/C][C](0.0011 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.822[/C][C]0[/C][C]0.1331[/C][C]-0.8261[/C][C]0.1531[/C][C]-0.1851[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.177 )[/C][C](0 )[/C][C](0.3094 )[/C][C](0.2789 )[/C][C](0.0023 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.7945[/C][C]0[/C][C]0.1551[/C][C]-0.8046[/C][C]0[/C][C]-0.2472[/C][C]-0.7394[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.1112 )[/C][C](0 )[/C][C](NA )[/C][C](0.1117 )[/C][C](0.0015 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.8181[/C][C]0[/C][C]0.1394[/C][C]-0.8258[/C][C]0[/C][C]0[/C][C]-0.8233[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.1474 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0062 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.9701[/C][C]0[/C][C]0[/C][C]-0.8782[/C][C]0[/C][C]0[/C][C]-0.8305[/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](0.0217 )[/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=32873&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32873&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 )0.77850.08660.0921-0.82460.1403-0.1755-0.9999
(p-val)(0 )(0.5399 )(0.4421 )(0 )(0.3528 )(0.3076 )(0.0011 )
Estimates ( 2 )0.82200.1331-0.82610.1531-0.1851-1
(p-val)(0 )(NA )(0.177 )(0 )(0.3094 )(0.2789 )(0.0023 )
Estimates ( 3 )0.794500.1551-0.80460-0.2472-0.7394
(p-val)(0 )(NA )(0.1112 )(0 )(NA )(0.1117 )(0.0015 )
Estimates ( 4 )0.818100.1394-0.825800-0.8233
(p-val)(0 )(NA )(0.1474 )(0 )(NA )(NA )(0.0062 )
Estimates ( 5 )0.970100-0.878200-0.8305
(p-val)(0 )(NA )(NA )(0 )(NA )(NA )(0.0217 )
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
-39.3764444415393
132.947368409575
186.895161286371
12.8018271624785
194.017084385034
-69.8591760207362
76.4885871921543
-224.344830990588
586.383518760918
147.783688918412
-254.656516299659
38.9584673043891
-125.662628529561
212.206595617221
262.010797610690
-135.012525799088
90.1421326668725
67.1480631346385
297.936195288903
-675.578947573836
128.858484131389
176.885098879208
508.22941850765
156.062361471218
7.64016467539135
32.5733798672872
31.0041775362576
74.6666152798628
-63.6028678563526
171.149505766976
274.324406849361
-381.07779537029
38.2609650683431
-243.919588450413
0.813278357501768
214.442011522837
9.2228930684103
88.6808473361873
52.6163115038628
-280.805208880139
-27.7215007088427
125.349162060130
-77.6268272931156
-33.8225145151693
192.372765877571
263.084476739942
40.362147421421
-72.8962877422762
-424.79009775264
-16.2498213986072
63.440625289154
-179.51740815623
50.6634342904111
-5.82599812036612
43.3592536448348
-325.317375364469
69.4851589665336
-295.428633793097
79.3154296605485
34.5370118616858
-210.805507246645
49.17401135354
-52.6340476481807
96.4220465550775
272.332057065973
-1.99409597127087
-108.854786249939
-328.097734712005
-118.078489764824
-773.566407615625
-69.8106138487607
-310.324520194048
234.076852034612
-149.973406817819
-118.108923622758
169.843798923738
-141.284902387650
-282.077882776651
210.796090852750
10.9718254679075
-499.162972819431
145.245928766969
234.172903638161
351.76931907763
184.731298313361
-2.93734416799206
-67.7902198091946
199.563027294919
-342.321337813712
408.70358906745

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-39.3764444415393 \tabularnewline
132.947368409575 \tabularnewline
186.895161286371 \tabularnewline
12.8018271624785 \tabularnewline
194.017084385034 \tabularnewline
-69.8591760207362 \tabularnewline
76.4885871921543 \tabularnewline
-224.344830990588 \tabularnewline
586.383518760918 \tabularnewline
147.783688918412 \tabularnewline
-254.656516299659 \tabularnewline
38.9584673043891 \tabularnewline
-125.662628529561 \tabularnewline
212.206595617221 \tabularnewline
262.010797610690 \tabularnewline
-135.012525799088 \tabularnewline
90.1421326668725 \tabularnewline
67.1480631346385 \tabularnewline
297.936195288903 \tabularnewline
-675.578947573836 \tabularnewline
128.858484131389 \tabularnewline
176.885098879208 \tabularnewline
508.22941850765 \tabularnewline
156.062361471218 \tabularnewline
7.64016467539135 \tabularnewline
32.5733798672872 \tabularnewline
31.0041775362576 \tabularnewline
74.6666152798628 \tabularnewline
-63.6028678563526 \tabularnewline
171.149505766976 \tabularnewline
274.324406849361 \tabularnewline
-381.07779537029 \tabularnewline
38.2609650683431 \tabularnewline
-243.919588450413 \tabularnewline
0.813278357501768 \tabularnewline
214.442011522837 \tabularnewline
9.2228930684103 \tabularnewline
88.6808473361873 \tabularnewline
52.6163115038628 \tabularnewline
-280.805208880139 \tabularnewline
-27.7215007088427 \tabularnewline
125.349162060130 \tabularnewline
-77.6268272931156 \tabularnewline
-33.8225145151693 \tabularnewline
192.372765877571 \tabularnewline
263.084476739942 \tabularnewline
40.362147421421 \tabularnewline
-72.8962877422762 \tabularnewline
-424.79009775264 \tabularnewline
-16.2498213986072 \tabularnewline
63.440625289154 \tabularnewline
-179.51740815623 \tabularnewline
50.6634342904111 \tabularnewline
-5.82599812036612 \tabularnewline
43.3592536448348 \tabularnewline
-325.317375364469 \tabularnewline
69.4851589665336 \tabularnewline
-295.428633793097 \tabularnewline
79.3154296605485 \tabularnewline
34.5370118616858 \tabularnewline
-210.805507246645 \tabularnewline
49.17401135354 \tabularnewline
-52.6340476481807 \tabularnewline
96.4220465550775 \tabularnewline
272.332057065973 \tabularnewline
-1.99409597127087 \tabularnewline
-108.854786249939 \tabularnewline
-328.097734712005 \tabularnewline
-118.078489764824 \tabularnewline
-773.566407615625 \tabularnewline
-69.8106138487607 \tabularnewline
-310.324520194048 \tabularnewline
234.076852034612 \tabularnewline
-149.973406817819 \tabularnewline
-118.108923622758 \tabularnewline
169.843798923738 \tabularnewline
-141.284902387650 \tabularnewline
-282.077882776651 \tabularnewline
210.796090852750 \tabularnewline
10.9718254679075 \tabularnewline
-499.162972819431 \tabularnewline
145.245928766969 \tabularnewline
234.172903638161 \tabularnewline
351.76931907763 \tabularnewline
184.731298313361 \tabularnewline
-2.93734416799206 \tabularnewline
-67.7902198091946 \tabularnewline
199.563027294919 \tabularnewline
-342.321337813712 \tabularnewline
408.70358906745 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32873&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-39.3764444415393[/C][/ROW]
[ROW][C]132.947368409575[/C][/ROW]
[ROW][C]186.895161286371[/C][/ROW]
[ROW][C]12.8018271624785[/C][/ROW]
[ROW][C]194.017084385034[/C][/ROW]
[ROW][C]-69.8591760207362[/C][/ROW]
[ROW][C]76.4885871921543[/C][/ROW]
[ROW][C]-224.344830990588[/C][/ROW]
[ROW][C]586.383518760918[/C][/ROW]
[ROW][C]147.783688918412[/C][/ROW]
[ROW][C]-254.656516299659[/C][/ROW]
[ROW][C]38.9584673043891[/C][/ROW]
[ROW][C]-125.662628529561[/C][/ROW]
[ROW][C]212.206595617221[/C][/ROW]
[ROW][C]262.010797610690[/C][/ROW]
[ROW][C]-135.012525799088[/C][/ROW]
[ROW][C]90.1421326668725[/C][/ROW]
[ROW][C]67.1480631346385[/C][/ROW]
[ROW][C]297.936195288903[/C][/ROW]
[ROW][C]-675.578947573836[/C][/ROW]
[ROW][C]128.858484131389[/C][/ROW]
[ROW][C]176.885098879208[/C][/ROW]
[ROW][C]508.22941850765[/C][/ROW]
[ROW][C]156.062361471218[/C][/ROW]
[ROW][C]7.64016467539135[/C][/ROW]
[ROW][C]32.5733798672872[/C][/ROW]
[ROW][C]31.0041775362576[/C][/ROW]
[ROW][C]74.6666152798628[/C][/ROW]
[ROW][C]-63.6028678563526[/C][/ROW]
[ROW][C]171.149505766976[/C][/ROW]
[ROW][C]274.324406849361[/C][/ROW]
[ROW][C]-381.07779537029[/C][/ROW]
[ROW][C]38.2609650683431[/C][/ROW]
[ROW][C]-243.919588450413[/C][/ROW]
[ROW][C]0.813278357501768[/C][/ROW]
[ROW][C]214.442011522837[/C][/ROW]
[ROW][C]9.2228930684103[/C][/ROW]
[ROW][C]88.6808473361873[/C][/ROW]
[ROW][C]52.6163115038628[/C][/ROW]
[ROW][C]-280.805208880139[/C][/ROW]
[ROW][C]-27.7215007088427[/C][/ROW]
[ROW][C]125.349162060130[/C][/ROW]
[ROW][C]-77.6268272931156[/C][/ROW]
[ROW][C]-33.8225145151693[/C][/ROW]
[ROW][C]192.372765877571[/C][/ROW]
[ROW][C]263.084476739942[/C][/ROW]
[ROW][C]40.362147421421[/C][/ROW]
[ROW][C]-72.8962877422762[/C][/ROW]
[ROW][C]-424.79009775264[/C][/ROW]
[ROW][C]-16.2498213986072[/C][/ROW]
[ROW][C]63.440625289154[/C][/ROW]
[ROW][C]-179.51740815623[/C][/ROW]
[ROW][C]50.6634342904111[/C][/ROW]
[ROW][C]-5.82599812036612[/C][/ROW]
[ROW][C]43.3592536448348[/C][/ROW]
[ROW][C]-325.317375364469[/C][/ROW]
[ROW][C]69.4851589665336[/C][/ROW]
[ROW][C]-295.428633793097[/C][/ROW]
[ROW][C]79.3154296605485[/C][/ROW]
[ROW][C]34.5370118616858[/C][/ROW]
[ROW][C]-210.805507246645[/C][/ROW]
[ROW][C]49.17401135354[/C][/ROW]
[ROW][C]-52.6340476481807[/C][/ROW]
[ROW][C]96.4220465550775[/C][/ROW]
[ROW][C]272.332057065973[/C][/ROW]
[ROW][C]-1.99409597127087[/C][/ROW]
[ROW][C]-108.854786249939[/C][/ROW]
[ROW][C]-328.097734712005[/C][/ROW]
[ROW][C]-118.078489764824[/C][/ROW]
[ROW][C]-773.566407615625[/C][/ROW]
[ROW][C]-69.8106138487607[/C][/ROW]
[ROW][C]-310.324520194048[/C][/ROW]
[ROW][C]234.076852034612[/C][/ROW]
[ROW][C]-149.973406817819[/C][/ROW]
[ROW][C]-118.108923622758[/C][/ROW]
[ROW][C]169.843798923738[/C][/ROW]
[ROW][C]-141.284902387650[/C][/ROW]
[ROW][C]-282.077882776651[/C][/ROW]
[ROW][C]210.796090852750[/C][/ROW]
[ROW][C]10.9718254679075[/C][/ROW]
[ROW][C]-499.162972819431[/C][/ROW]
[ROW][C]145.245928766969[/C][/ROW]
[ROW][C]234.172903638161[/C][/ROW]
[ROW][C]351.76931907763[/C][/ROW]
[ROW][C]184.731298313361[/C][/ROW]
[ROW][C]-2.93734416799206[/C][/ROW]
[ROW][C]-67.7902198091946[/C][/ROW]
[ROW][C]199.563027294919[/C][/ROW]
[ROW][C]-342.321337813712[/C][/ROW]
[ROW][C]408.70358906745[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32873&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32873&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
-39.3764444415393
132.947368409575
186.895161286371
12.8018271624785
194.017084385034
-69.8591760207362
76.4885871921543
-224.344830990588
586.383518760918
147.783688918412
-254.656516299659
38.9584673043891
-125.662628529561
212.206595617221
262.010797610690
-135.012525799088
90.1421326668725
67.1480631346385
297.936195288903
-675.578947573836
128.858484131389
176.885098879208
508.22941850765
156.062361471218
7.64016467539135
32.5733798672872
31.0041775362576
74.6666152798628
-63.6028678563526
171.149505766976
274.324406849361
-381.07779537029
38.2609650683431
-243.919588450413
0.813278357501768
214.442011522837
9.2228930684103
88.6808473361873
52.6163115038628
-280.805208880139
-27.7215007088427
125.349162060130
-77.6268272931156
-33.8225145151693
192.372765877571
263.084476739942
40.362147421421
-72.8962877422762
-424.79009775264
-16.2498213986072
63.440625289154
-179.51740815623
50.6634342904111
-5.82599812036612
43.3592536448348
-325.317375364469
69.4851589665336
-295.428633793097
79.3154296605485
34.5370118616858
-210.805507246645
49.17401135354
-52.6340476481807
96.4220465550775
272.332057065973
-1.99409597127087
-108.854786249939
-328.097734712005
-118.078489764824
-773.566407615625
-69.8106138487607
-310.324520194048
234.076852034612
-149.973406817819
-118.108923622758
169.843798923738
-141.284902387650
-282.077882776651
210.796090852750
10.9718254679075
-499.162972819431
145.245928766969
234.172903638161
351.76931907763
184.731298313361
-2.93734416799206
-67.7902198091946
199.563027294919
-342.321337813712
408.70358906745


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1.5 ; 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')