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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationWed, 22 Dec 2010 14:14:05 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t12930271545kgg7r9or8beqvn.htm/, Retrieved Mon, 06 May 2024 03:01:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114228, Retrieved Mon, 06 May 2024 03:01:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-08 19:22:39] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [ARIMA Backward Selection] [] [2010-12-14 14:12:58] [abe7df3fc544bbb0ed435b4e9982bc91]
-   PD      [ARIMA Backward Selection] [] [2010-12-22 14:14:05] [29eeba0e6ce2cd83aa315a4a7ff8c4aa] [Current]
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Dataseries X:
377
370
358
357
349
348
369
381
368
361
351
351
358
354
347
345
343
340
362
370
373
371
354
357
363
364
363
358
357
357
380
378
376
380
379
384
392
394
392
396
392
396
419
421
420
418
410
418
426
428
430
424
423
427
441
449
452
462
455
461
461
463
462
456
455
456
472
472
471
465
459
465
468
467
463
460
462
461
476
476
471
453
443
442
444
438
427
424
416
406
431
434
418
412
404
409
412
406
398
397
385
390
413
413
401
397
397
409
419
424
428
430
424
433
456
459
446
441
439
454
460
457
451
444
437
443
471
469
454
444
436




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time22 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 22 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114228&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]22 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114228&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114228&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 time22 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.9403-0.160.1208-0.76310.2278-0.0973-0.7889
(p-val)(0 )(0.2542 )(0.2465 )(0 )(0.3121 )(0.5155 )(0.0026 )
Estimates ( 2 )0.9297-0.13660.1048-0.75630.30720-1.1083
(p-val)(0 )(0.3129 )(0.3015 )(0 )(0.0822 )(NA )(0.0016 )
Estimates ( 3 )0.84200.0444-0.73350.3090-1.1697
(p-val)(0 )(NA )(0.6088 )(0 )(0.1041 )(NA )(1e-04 )
Estimates ( 4 )0.903700-0.77790.32440-0.8733
(p-val)(0 )(NA )(NA )(0 )(0.0798 )(NA )(2e-04 )
Estimates ( 5 )0.920400-0.783400-0.5815
(p-val)(0 )(NA )(NA )(0 )(NA )(NA )(0 )
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 ) & 0.9403 & -0.16 & 0.1208 & -0.7631 & 0.2278 & -0.0973 & -0.7889 \tabularnewline
(p-val) & (0 ) & (0.2542 ) & (0.2465 ) & (0 ) & (0.3121 ) & (0.5155 ) & (0.0026 ) \tabularnewline
Estimates ( 2 ) & 0.9297 & -0.1366 & 0.1048 & -0.7563 & 0.3072 & 0 & -1.1083 \tabularnewline
(p-val) & (0 ) & (0.3129 ) & (0.3015 ) & (0 ) & (0.0822 ) & (NA ) & (0.0016 ) \tabularnewline
Estimates ( 3 ) & 0.842 & 0 & 0.0444 & -0.7335 & 0.309 & 0 & -1.1697 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.6088 ) & (0 ) & (0.1041 ) & (NA ) & (1e-04 ) \tabularnewline
Estimates ( 4 ) & 0.9037 & 0 & 0 & -0.7779 & 0.3244 & 0 & -0.8733 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0 ) & (0.0798 ) & (NA ) & (2e-04 ) \tabularnewline
Estimates ( 5 ) & 0.9204 & 0 & 0 & -0.7834 & 0 & 0 & -0.5815 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) \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=114228&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]0.9403[/C][C]-0.16[/C][C]0.1208[/C][C]-0.7631[/C][C]0.2278[/C][C]-0.0973[/C][C]-0.7889[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.2542 )[/C][C](0.2465 )[/C][C](0 )[/C][C](0.3121 )[/C][C](0.5155 )[/C][C](0.0026 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.9297[/C][C]-0.1366[/C][C]0.1048[/C][C]-0.7563[/C][C]0.3072[/C][C]0[/C][C]-1.1083[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.3129 )[/C][C](0.3015 )[/C][C](0 )[/C][C](0.0822 )[/C][C](NA )[/C][C](0.0016 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.842[/C][C]0[/C][C]0.0444[/C][C]-0.7335[/C][C]0.309[/C][C]0[/C][C]-1.1697[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.6088 )[/C][C](0 )[/C][C](0.1041 )[/C][C](NA )[/C][C](1e-04 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.9037[/C][C]0[/C][C]0[/C][C]-0.7779[/C][C]0.3244[/C][C]0[/C][C]-0.8733[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0798 )[/C][C](NA )[/C][C](2e-04 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.9204[/C][C]0[/C][C]0[/C][C]-0.7834[/C][C]0[/C][C]0[/C][C]-0.5815[/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 )[/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=114228&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114228&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 )0.9403-0.160.1208-0.76310.2278-0.0973-0.7889
(p-val)(0 )(0.2542 )(0.2465 )(0 )(0.3121 )(0.5155 )(0.0026 )
Estimates ( 2 )0.9297-0.13660.1048-0.75630.30720-1.1083
(p-val)(0 )(0.3129 )(0.3015 )(0 )(0.0822 )(NA )(0.0016 )
Estimates ( 3 )0.84200.0444-0.73350.3090-1.1697
(p-val)(0 )(NA )(0.6088 )(0 )(0.1041 )(NA )(1e-04 )
Estimates ( 4 )0.903700-0.77790.32440-0.8733
(p-val)(0 )(NA )(NA )(0 )(0.0798 )(NA )(2e-04 )
Estimates ( 5 )0.920400-0.783400-0.5815
(p-val)(0 )(NA )(NA )(0 )(NA )(NA )(0 )
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
-1.33637994496789
2.5505584203868
3.91139332320353
-1.72087535312636
4.6700342146398
-2.74560813167806
0.326296880205094
-3.95983655966329
13.9806125046014
2.84155802303687
-7.68634400319816
2.15264292155107
-1.43475956691175
4.85327038368555
5.85404155783924
-4.78451161856814
1.79459512550481
1.13097420831175
0.212698244887548
-11.2613492818601
0.884709959910375
7.5493977753196
12.2017172013728
1.00669518127208
-0.0505951316463223
1.83913193673422
0.75606541654781
5.474791619457
-3.20641686971057
3.00302926116211
-1.04427114272206
-2.30097725975074
1.81688275065872
-3.1359277204423
-2.40662112725277
4.33194703783824
0.136366221225754
1.82359867318673
5.32514733763154
-8.15726029775783
2.77425209298365
1.81863951378772
-8.7864674630133
4.31717833652532
5.67063832923709
11.0323981441236
-0.338179072295962
-1.04053646803794
-8.47745286756994
1.73993428617053
0.361681870929897
-2.81938704416345
1.45062994631549
-1.21049841755996
-1.84021827216848
-6.32534584068303
0.54396399529051
-8.87363683805637
3.93718263933716
2.46335612312886
0.534046746125432
-0.605284676904308
-0.626984335514032
1.66644220659255
4.6095808304258
-2.01981008429551
-3.02575014250352
-2.42027030171848
-2.28425118078024
-13.7862741620806
0.0741015237380737
-3.47605720035217
0.403481665378031
-2.76566145339386
-4.63712601275771
2.60995153144162
-5.12446069169316
-7.17491042323247
10.7912203878153
2.10462399350504
-10.9315725479551
5.66496311204764
2.60915417006924
4.26332146939414
-0.122739609193282
-2.28144355865498
-0.126376890929133
2.45706121761559
-6.70777845698055
10.4666442843204
0.871244794766226
-3.91663051353796
-2.70108990168285
1.31518057667294
8.45467900418721
6.90722966696228
4.53643454061959
6.60407933392522
7.62832667542597
0.575342759344939
-1.79991327208584
4.63332328802241
-1.19992164751353
-1.50573860393361
-6.71064128869366
-1.86890123529865
1.83179152191894
6.5504184855184
-2.49000592269727
-5.15908316424443
-5.28822745646887
-6.16191145498065
-0.701217116339119
3.01999550664862
7.04208294885268
-5.44006789066294
-5.30109394189252
-4.38306929660935
-1.70389193648474

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-1.33637994496789 \tabularnewline
2.5505584203868 \tabularnewline
3.91139332320353 \tabularnewline
-1.72087535312636 \tabularnewline
4.6700342146398 \tabularnewline
-2.74560813167806 \tabularnewline
0.326296880205094 \tabularnewline
-3.95983655966329 \tabularnewline
13.9806125046014 \tabularnewline
2.84155802303687 \tabularnewline
-7.68634400319816 \tabularnewline
2.15264292155107 \tabularnewline
-1.43475956691175 \tabularnewline
4.85327038368555 \tabularnewline
5.85404155783924 \tabularnewline
-4.78451161856814 \tabularnewline
1.79459512550481 \tabularnewline
1.13097420831175 \tabularnewline
0.212698244887548 \tabularnewline
-11.2613492818601 \tabularnewline
0.884709959910375 \tabularnewline
7.5493977753196 \tabularnewline
12.2017172013728 \tabularnewline
1.00669518127208 \tabularnewline
-0.0505951316463223 \tabularnewline
1.83913193673422 \tabularnewline
0.75606541654781 \tabularnewline
5.474791619457 \tabularnewline
-3.20641686971057 \tabularnewline
3.00302926116211 \tabularnewline
-1.04427114272206 \tabularnewline
-2.30097725975074 \tabularnewline
1.81688275065872 \tabularnewline
-3.1359277204423 \tabularnewline
-2.40662112725277 \tabularnewline
4.33194703783824 \tabularnewline
0.136366221225754 \tabularnewline
1.82359867318673 \tabularnewline
5.32514733763154 \tabularnewline
-8.15726029775783 \tabularnewline
2.77425209298365 \tabularnewline
1.81863951378772 \tabularnewline
-8.7864674630133 \tabularnewline
4.31717833652532 \tabularnewline
5.67063832923709 \tabularnewline
11.0323981441236 \tabularnewline
-0.338179072295962 \tabularnewline
-1.04053646803794 \tabularnewline
-8.47745286756994 \tabularnewline
1.73993428617053 \tabularnewline
0.361681870929897 \tabularnewline
-2.81938704416345 \tabularnewline
1.45062994631549 \tabularnewline
-1.21049841755996 \tabularnewline
-1.84021827216848 \tabularnewline
-6.32534584068303 \tabularnewline
0.54396399529051 \tabularnewline
-8.87363683805637 \tabularnewline
3.93718263933716 \tabularnewline
2.46335612312886 \tabularnewline
0.534046746125432 \tabularnewline
-0.605284676904308 \tabularnewline
-0.626984335514032 \tabularnewline
1.66644220659255 \tabularnewline
4.6095808304258 \tabularnewline
-2.01981008429551 \tabularnewline
-3.02575014250352 \tabularnewline
-2.42027030171848 \tabularnewline
-2.28425118078024 \tabularnewline
-13.7862741620806 \tabularnewline
0.0741015237380737 \tabularnewline
-3.47605720035217 \tabularnewline
0.403481665378031 \tabularnewline
-2.76566145339386 \tabularnewline
-4.63712601275771 \tabularnewline
2.60995153144162 \tabularnewline
-5.12446069169316 \tabularnewline
-7.17491042323247 \tabularnewline
10.7912203878153 \tabularnewline
2.10462399350504 \tabularnewline
-10.9315725479551 \tabularnewline
5.66496311204764 \tabularnewline
2.60915417006924 \tabularnewline
4.26332146939414 \tabularnewline
-0.122739609193282 \tabularnewline
-2.28144355865498 \tabularnewline
-0.126376890929133 \tabularnewline
2.45706121761559 \tabularnewline
-6.70777845698055 \tabularnewline
10.4666442843204 \tabularnewline
0.871244794766226 \tabularnewline
-3.91663051353796 \tabularnewline
-2.70108990168285 \tabularnewline
1.31518057667294 \tabularnewline
8.45467900418721 \tabularnewline
6.90722966696228 \tabularnewline
4.53643454061959 \tabularnewline
6.60407933392522 \tabularnewline
7.62832667542597 \tabularnewline
0.575342759344939 \tabularnewline
-1.79991327208584 \tabularnewline
4.63332328802241 \tabularnewline
-1.19992164751353 \tabularnewline
-1.50573860393361 \tabularnewline
-6.71064128869366 \tabularnewline
-1.86890123529865 \tabularnewline
1.83179152191894 \tabularnewline
6.5504184855184 \tabularnewline
-2.49000592269727 \tabularnewline
-5.15908316424443 \tabularnewline
-5.28822745646887 \tabularnewline
-6.16191145498065 \tabularnewline
-0.701217116339119 \tabularnewline
3.01999550664862 \tabularnewline
7.04208294885268 \tabularnewline
-5.44006789066294 \tabularnewline
-5.30109394189252 \tabularnewline
-4.38306929660935 \tabularnewline
-1.70389193648474 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114228&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-1.33637994496789[/C][/ROW]
[ROW][C]2.5505584203868[/C][/ROW]
[ROW][C]3.91139332320353[/C][/ROW]
[ROW][C]-1.72087535312636[/C][/ROW]
[ROW][C]4.6700342146398[/C][/ROW]
[ROW][C]-2.74560813167806[/C][/ROW]
[ROW][C]0.326296880205094[/C][/ROW]
[ROW][C]-3.95983655966329[/C][/ROW]
[ROW][C]13.9806125046014[/C][/ROW]
[ROW][C]2.84155802303687[/C][/ROW]
[ROW][C]-7.68634400319816[/C][/ROW]
[ROW][C]2.15264292155107[/C][/ROW]
[ROW][C]-1.43475956691175[/C][/ROW]
[ROW][C]4.85327038368555[/C][/ROW]
[ROW][C]5.85404155783924[/C][/ROW]
[ROW][C]-4.78451161856814[/C][/ROW]
[ROW][C]1.79459512550481[/C][/ROW]
[ROW][C]1.13097420831175[/C][/ROW]
[ROW][C]0.212698244887548[/C][/ROW]
[ROW][C]-11.2613492818601[/C][/ROW]
[ROW][C]0.884709959910375[/C][/ROW]
[ROW][C]7.5493977753196[/C][/ROW]
[ROW][C]12.2017172013728[/C][/ROW]
[ROW][C]1.00669518127208[/C][/ROW]
[ROW][C]-0.0505951316463223[/C][/ROW]
[ROW][C]1.83913193673422[/C][/ROW]
[ROW][C]0.75606541654781[/C][/ROW]
[ROW][C]5.474791619457[/C][/ROW]
[ROW][C]-3.20641686971057[/C][/ROW]
[ROW][C]3.00302926116211[/C][/ROW]
[ROW][C]-1.04427114272206[/C][/ROW]
[ROW][C]-2.30097725975074[/C][/ROW]
[ROW][C]1.81688275065872[/C][/ROW]
[ROW][C]-3.1359277204423[/C][/ROW]
[ROW][C]-2.40662112725277[/C][/ROW]
[ROW][C]4.33194703783824[/C][/ROW]
[ROW][C]0.136366221225754[/C][/ROW]
[ROW][C]1.82359867318673[/C][/ROW]
[ROW][C]5.32514733763154[/C][/ROW]
[ROW][C]-8.15726029775783[/C][/ROW]
[ROW][C]2.77425209298365[/C][/ROW]
[ROW][C]1.81863951378772[/C][/ROW]
[ROW][C]-8.7864674630133[/C][/ROW]
[ROW][C]4.31717833652532[/C][/ROW]
[ROW][C]5.67063832923709[/C][/ROW]
[ROW][C]11.0323981441236[/C][/ROW]
[ROW][C]-0.338179072295962[/C][/ROW]
[ROW][C]-1.04053646803794[/C][/ROW]
[ROW][C]-8.47745286756994[/C][/ROW]
[ROW][C]1.73993428617053[/C][/ROW]
[ROW][C]0.361681870929897[/C][/ROW]
[ROW][C]-2.81938704416345[/C][/ROW]
[ROW][C]1.45062994631549[/C][/ROW]
[ROW][C]-1.21049841755996[/C][/ROW]
[ROW][C]-1.84021827216848[/C][/ROW]
[ROW][C]-6.32534584068303[/C][/ROW]
[ROW][C]0.54396399529051[/C][/ROW]
[ROW][C]-8.87363683805637[/C][/ROW]
[ROW][C]3.93718263933716[/C][/ROW]
[ROW][C]2.46335612312886[/C][/ROW]
[ROW][C]0.534046746125432[/C][/ROW]
[ROW][C]-0.605284676904308[/C][/ROW]
[ROW][C]-0.626984335514032[/C][/ROW]
[ROW][C]1.66644220659255[/C][/ROW]
[ROW][C]4.6095808304258[/C][/ROW]
[ROW][C]-2.01981008429551[/C][/ROW]
[ROW][C]-3.02575014250352[/C][/ROW]
[ROW][C]-2.42027030171848[/C][/ROW]
[ROW][C]-2.28425118078024[/C][/ROW]
[ROW][C]-13.7862741620806[/C][/ROW]
[ROW][C]0.0741015237380737[/C][/ROW]
[ROW][C]-3.47605720035217[/C][/ROW]
[ROW][C]0.403481665378031[/C][/ROW]
[ROW][C]-2.76566145339386[/C][/ROW]
[ROW][C]-4.63712601275771[/C][/ROW]
[ROW][C]2.60995153144162[/C][/ROW]
[ROW][C]-5.12446069169316[/C][/ROW]
[ROW][C]-7.17491042323247[/C][/ROW]
[ROW][C]10.7912203878153[/C][/ROW]
[ROW][C]2.10462399350504[/C][/ROW]
[ROW][C]-10.9315725479551[/C][/ROW]
[ROW][C]5.66496311204764[/C][/ROW]
[ROW][C]2.60915417006924[/C][/ROW]
[ROW][C]4.26332146939414[/C][/ROW]
[ROW][C]-0.122739609193282[/C][/ROW]
[ROW][C]-2.28144355865498[/C][/ROW]
[ROW][C]-0.126376890929133[/C][/ROW]
[ROW][C]2.45706121761559[/C][/ROW]
[ROW][C]-6.70777845698055[/C][/ROW]
[ROW][C]10.4666442843204[/C][/ROW]
[ROW][C]0.871244794766226[/C][/ROW]
[ROW][C]-3.91663051353796[/C][/ROW]
[ROW][C]-2.70108990168285[/C][/ROW]
[ROW][C]1.31518057667294[/C][/ROW]
[ROW][C]8.45467900418721[/C][/ROW]
[ROW][C]6.90722966696228[/C][/ROW]
[ROW][C]4.53643454061959[/C][/ROW]
[ROW][C]6.60407933392522[/C][/ROW]
[ROW][C]7.62832667542597[/C][/ROW]
[ROW][C]0.575342759344939[/C][/ROW]
[ROW][C]-1.79991327208584[/C][/ROW]
[ROW][C]4.63332328802241[/C][/ROW]
[ROW][C]-1.19992164751353[/C][/ROW]
[ROW][C]-1.50573860393361[/C][/ROW]
[ROW][C]-6.71064128869366[/C][/ROW]
[ROW][C]-1.86890123529865[/C][/ROW]
[ROW][C]1.83179152191894[/C][/ROW]
[ROW][C]6.5504184855184[/C][/ROW]
[ROW][C]-2.49000592269727[/C][/ROW]
[ROW][C]-5.15908316424443[/C][/ROW]
[ROW][C]-5.28822745646887[/C][/ROW]
[ROW][C]-6.16191145498065[/C][/ROW]
[ROW][C]-0.701217116339119[/C][/ROW]
[ROW][C]3.01999550664862[/C][/ROW]
[ROW][C]7.04208294885268[/C][/ROW]
[ROW][C]-5.44006789066294[/C][/ROW]
[ROW][C]-5.30109394189252[/C][/ROW]
[ROW][C]-4.38306929660935[/C][/ROW]
[ROW][C]-1.70389193648474[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114228&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114228&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
-1.33637994496789
2.5505584203868
3.91139332320353
-1.72087535312636
4.6700342146398
-2.74560813167806
0.326296880205094
-3.95983655966329
13.9806125046014
2.84155802303687
-7.68634400319816
2.15264292155107
-1.43475956691175
4.85327038368555
5.85404155783924
-4.78451161856814
1.79459512550481
1.13097420831175
0.212698244887548
-11.2613492818601
0.884709959910375
7.5493977753196
12.2017172013728
1.00669518127208
-0.0505951316463223
1.83913193673422
0.75606541654781
5.474791619457
-3.20641686971057
3.00302926116211
-1.04427114272206
-2.30097725975074
1.81688275065872
-3.1359277204423
-2.40662112725277
4.33194703783824
0.136366221225754
1.82359867318673
5.32514733763154
-8.15726029775783
2.77425209298365
1.81863951378772
-8.7864674630133
4.31717833652532
5.67063832923709
11.0323981441236
-0.338179072295962
-1.04053646803794
-8.47745286756994
1.73993428617053
0.361681870929897
-2.81938704416345
1.45062994631549
-1.21049841755996
-1.84021827216848
-6.32534584068303
0.54396399529051
-8.87363683805637
3.93718263933716
2.46335612312886
0.534046746125432
-0.605284676904308
-0.626984335514032
1.66644220659255
4.6095808304258
-2.01981008429551
-3.02575014250352
-2.42027030171848
-2.28425118078024
-13.7862741620806
0.0741015237380737
-3.47605720035217
0.403481665378031
-2.76566145339386
-4.63712601275771
2.60995153144162
-5.12446069169316
-7.17491042323247
10.7912203878153
2.10462399350504
-10.9315725479551
5.66496311204764
2.60915417006924
4.26332146939414
-0.122739609193282
-2.28144355865498
-0.126376890929133
2.45706121761559
-6.70777845698055
10.4666442843204
0.871244794766226
-3.91663051353796
-2.70108990168285
1.31518057667294
8.45467900418721
6.90722966696228
4.53643454061959
6.60407933392522
7.62832667542597
0.575342759344939
-1.79991327208584
4.63332328802241
-1.19992164751353
-1.50573860393361
-6.71064128869366
-1.86890123529865
1.83179152191894
6.5504184855184
-2.49000592269727
-5.15908316424443
-5.28822745646887
-6.16191145498065
-0.701217116339119
3.01999550664862
7.04208294885268
-5.44006789066294
-5.30109394189252
-4.38306929660935
-1.70389193648474



Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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')