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 computationThu, 01 Dec 2011 07:40:37 -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/01/t1322743266bszeh8kvi3hw5v9.htm/, Retrieved Sat, 20 Apr 2024 07:28:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149339, Retrieved Sat, 20 Apr 2024 07:28:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact200

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [Autocorrelation f...] [2011-12-01 11:43:29] [bc54fcbdb4f9c071218969745a8ec94b]
- RMP   [Spectral Analysis] [Spectral Analysis] [2011-12-01 11:58:37] [bc54fcbdb4f9c071218969745a8ec94b]
- R P     [Spectral Analysis] [Spectral Analysis] [2011-12-01 12:01:59] [bc54fcbdb4f9c071218969745a8ec94b]
-   P       [Spectral Analysis] [Spectral Analysis...] [2011-12-01 12:05:16] [bc54fcbdb4f9c071218969745a8ec94b]
- RMP           [ARIMA Backward Selection] [ARIMA backward se...] [2011-12-01 12:40:37] [f59ea4acf51788e47c4ec521df29536b] [Current]
-   P             [ARIMA Backward Selection] [Model 1] [2011-12-05 12:07:32] [c035d973aa8488be257660c2dc4ec375]
-   P             [ARIMA Backward Selection] [Model 2] [2011-12-05 12:10:23] [c035d973aa8488be257660c2dc4ec375]
- R P             [ARIMA Backward Selection] [Arima Backward Se...] [2011-12-07 18:31:25] [bc54fcbdb4f9c071218969745a8ec94b]
- RMPD            [Central Tendency] [Central Tendency] [2011-12-07 19:07:01] [bc54fcbdb4f9c071218969745a8ec94b]
- RMPD            [Univariate Data Series] [Plot & Describe D...] [2011-12-07 19:09:44] [bc54fcbdb4f9c071218969745a8ec94b]
- RMPD            [Skewness and Kurtosis Test] [Skewness Kurtosis] [2011-12-07 19:17:09] [bc54fcbdb4f9c071218969745a8ec94b]
- R P             [ARIMA Backward Selection] [WS 9 ARIMA backwa...] [2012-11-29 14:24:20] [64c86865dff7d646747b84f713e71815]
- RMP             [ARIMA Backward Selection] [WS9 - ARIMA] [2012-11-30 12:36:36] [6f05baeb40d1ecbb8a22259f722de55b]
- R P             [ARIMA Backward Selection] [WS9 - ARIMA] [2012-11-30 12:50:19] [6f05baeb40d1ecbb8a22259f722de55b]
-                   [ARIMA Backward Selection] [WS9 ARIMA] [2012-11-30 12:53:45] [6f05baeb40d1ecbb8a22259f722de55b]
- RM D              [Skewness and Kurtosis Test] [WS9 Skewness] [2012-11-30 13:00:54] [6f05baeb40d1ecbb8a22259f722de55b]
- RM                [ARIMA Forecasting] [WS9 - ARIMA FOREC...] [2012-11-30 13:06:29] [6f05baeb40d1ecbb8a22259f722de55b]
- R P             [ARIMA Backward Selection] [ARIMA] [2013-12-04 11:37:56] [c6f8b1ebc3c8db7b9216fced5f615834]
- R  D            [ARIMA Backward Selection] [Workshop 9 - vraa...] [2013-12-04 15:13:39] [6e6101806504d86600f236fbba4f8aec]
- RMP             [ARIMA Forecasting] [ARIMA forecasting] [2013-12-04 19:56:04] [c6f8b1ebc3c8db7b9216fced5f615834]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149339&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149339&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149339&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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.60280.27530.3160.8204-0.5458-0.2538-0.9997
(p-val)(0.0132 )(0.0663 )(0.0379 )(0.0027 )(5e-04 )(0.2217 )(0.4701 )
Estimates ( 2 )-0.55790.2590.39210.7137-1.0154-0.53460
(p-val)(0.0274 )(0.0795 )(0.0048 )(0.0077 )(0 )(1e-04 )(NA )
Estimates ( 3 )-0.594700.26980.669-0.9954-0.5620
(p-val)(0.1005 )(NA )(0.0469 )(0.0431 )(0 )(0 )(NA )
Estimates ( 4 )000.25680.1591-0.9607-0.56370
(p-val)(NA )(NA )(0.0614 )(0.2226 )(0 )(0 )(NA )
Estimates ( 5 )000.25430-0.9626-0.51910
(p-val)(NA )(NA )(0.0614 )(NA )(0 )(1e-04 )(NA )
Estimates ( 6 )0000-0.9295-0.54550
(p-val)(NA )(NA )(NA )(NA )(0 )(0 )(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 ) & -0.6028 & 0.2753 & 0.316 & 0.8204 & -0.5458 & -0.2538 & -0.9997 \tabularnewline
(p-val) & (0.0132 ) & (0.0663 ) & (0.0379 ) & (0.0027 ) & (5e-04 ) & (0.2217 ) & (0.4701 ) \tabularnewline
Estimates ( 2 ) & -0.5579 & 0.259 & 0.3921 & 0.7137 & -1.0154 & -0.5346 & 0 \tabularnewline
(p-val) & (0.0274 ) & (0.0795 ) & (0.0048 ) & (0.0077 ) & (0 ) & (1e-04 ) & (NA ) \tabularnewline
Estimates ( 3 ) & -0.5947 & 0 & 0.2698 & 0.669 & -0.9954 & -0.562 & 0 \tabularnewline
(p-val) & (0.1005 ) & (NA ) & (0.0469 ) & (0.0431 ) & (0 ) & (0 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0.2568 & 0.1591 & -0.9607 & -0.5637 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0614 ) & (0.2226 ) & (0 ) & (0 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0.2543 & 0 & -0.9626 & -0.5191 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0614 ) & (NA ) & (0 ) & (1e-04 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & -0.9295 & -0.5455 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) & (0 ) & (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=149339&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]-0.6028[/C][C]0.2753[/C][C]0.316[/C][C]0.8204[/C][C]-0.5458[/C][C]-0.2538[/C][C]-0.9997[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0132 )[/C][C](0.0663 )[/C][C](0.0379 )[/C][C](0.0027 )[/C][C](5e-04 )[/C][C](0.2217 )[/C][C](0.4701 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.5579[/C][C]0.259[/C][C]0.3921[/C][C]0.7137[/C][C]-1.0154[/C][C]-0.5346[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0274 )[/C][C](0.0795 )[/C][C](0.0048 )[/C][C](0.0077 )[/C][C](0 )[/C][C](1e-04 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.5947[/C][C]0[/C][C]0.2698[/C][C]0.669[/C][C]-0.9954[/C][C]-0.562[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1005 )[/C][C](NA )[/C][C](0.0469 )[/C][C](0.0431 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0.2568[/C][C]0.1591[/C][C]-0.9607[/C][C]-0.5637[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0614 )[/C][C](0.2226 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0.2543[/C][C]0[/C][C]-0.9626[/C][C]-0.5191[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0614 )[/C][C](NA )[/C][C](0 )[/C][C](1e-04 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.9295[/C][C]-0.5455[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/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=149339&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149339&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 )-0.60280.27530.3160.8204-0.5458-0.2538-0.9997
(p-val)(0.0132 )(0.0663 )(0.0379 )(0.0027 )(5e-04 )(0.2217 )(0.4701 )
Estimates ( 2 )-0.55790.2590.39210.7137-1.0154-0.53460
(p-val)(0.0274 )(0.0795 )(0.0048 )(0.0077 )(0 )(1e-04 )(NA )
Estimates ( 3 )-0.594700.26980.669-0.9954-0.5620
(p-val)(0.1005 )(NA )(0.0469 )(0.0431 )(0 )(0 )(NA )
Estimates ( 4 )000.25680.1591-0.9607-0.56370
(p-val)(NA )(NA )(0.0614 )(0.2226 )(0 )(0 )(NA )
Estimates ( 5 )000.25430-0.9626-0.51910
(p-val)(NA )(NA )(0.0614 )(NA )(0 )(1e-04 )(NA )
Estimates ( 6 )0000-0.9295-0.54550
(p-val)(NA )(NA )(NA )(NA )(0 )(0 )(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
1.23681260257777e-12
7.72096832223397e-12
-5.19161293902823e-11
-4.80801525179924e-13
-1.90092093395401e-12
5.52522269413485e-12
-5.67754131404674e-12
-1.78298167372504e-11
2.07121988380858e-11
-2.10461616791291e-12
-3.66904238552535e-11
-2.86193522851985e-11
1.54008583848729e-10
1.9883572338324e-11
8.52916572254774e-11
-6.63842766491144e-11
-4.09989103357053e-11
6.88463824752294e-11
-5.42172117528046e-11
2.17585839879982e-11
-3.93545814756323e-11
-7.08693860541561e-11
-6.14281097197127e-11
-1.2341400284042e-10
-1.68520963124308e-10
-2.00944410063371e-10
3.59457145888544e-11
5.29161188621583e-11
4.65425480045796e-11
8.73316520570799e-11
6.41308383152694e-12
2.18657560985924e-11
-4.14013279891876e-11
-6.55762205720878e-11
-2.2012526485132e-11
-6.25101742734698e-12
-9.06593058829902e-11
1.21550143926051e-10
-1.35452562160999e-10
-1.11768913896942e-11
7.8322678100561e-11
-2.75009489319772e-12
4.73852043813881e-11
-1.12070153994773e-11
-1.99246698740461e-11
-2.96936139453833e-11
5.23707377338071e-12
1.6306521729374e-11
-1.67411215735618e-11
-3.73235850767022e-11
3.70700075030095e-11
6.77186943451588e-11
5.44954345076424e-11
-2.20029360911284e-11
1.54075228307279e-11
-7.49439887370209e-12
-8.31377772800236e-12
1.77666697689224e-11
7.29305417191286e-12
5.62159180088656e-11
8.37966502220244e-11

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
1.23681260257777e-12 \tabularnewline
7.72096832223397e-12 \tabularnewline
-5.19161293902823e-11 \tabularnewline
-4.80801525179924e-13 \tabularnewline
-1.90092093395401e-12 \tabularnewline
5.52522269413485e-12 \tabularnewline
-5.67754131404674e-12 \tabularnewline
-1.78298167372504e-11 \tabularnewline
2.07121988380858e-11 \tabularnewline
-2.10461616791291e-12 \tabularnewline
-3.66904238552535e-11 \tabularnewline
-2.86193522851985e-11 \tabularnewline
1.54008583848729e-10 \tabularnewline
1.9883572338324e-11 \tabularnewline
8.52916572254774e-11 \tabularnewline
-6.63842766491144e-11 \tabularnewline
-4.09989103357053e-11 \tabularnewline
6.88463824752294e-11 \tabularnewline
-5.42172117528046e-11 \tabularnewline
2.17585839879982e-11 \tabularnewline
-3.93545814756323e-11 \tabularnewline
-7.08693860541561e-11 \tabularnewline
-6.14281097197127e-11 \tabularnewline
-1.2341400284042e-10 \tabularnewline
-1.68520963124308e-10 \tabularnewline
-2.00944410063371e-10 \tabularnewline
3.59457145888544e-11 \tabularnewline
5.29161188621583e-11 \tabularnewline
4.65425480045796e-11 \tabularnewline
8.73316520570799e-11 \tabularnewline
6.41308383152694e-12 \tabularnewline
2.18657560985924e-11 \tabularnewline
-4.14013279891876e-11 \tabularnewline
-6.55762205720878e-11 \tabularnewline
-2.2012526485132e-11 \tabularnewline
-6.25101742734698e-12 \tabularnewline
-9.06593058829902e-11 \tabularnewline
1.21550143926051e-10 \tabularnewline
-1.35452562160999e-10 \tabularnewline
-1.11768913896942e-11 \tabularnewline
7.8322678100561e-11 \tabularnewline
-2.75009489319772e-12 \tabularnewline
4.73852043813881e-11 \tabularnewline
-1.12070153994773e-11 \tabularnewline
-1.99246698740461e-11 \tabularnewline
-2.96936139453833e-11 \tabularnewline
5.23707377338071e-12 \tabularnewline
1.6306521729374e-11 \tabularnewline
-1.67411215735618e-11 \tabularnewline
-3.73235850767022e-11 \tabularnewline
3.70700075030095e-11 \tabularnewline
6.77186943451588e-11 \tabularnewline
5.44954345076424e-11 \tabularnewline
-2.20029360911284e-11 \tabularnewline
1.54075228307279e-11 \tabularnewline
-7.49439887370209e-12 \tabularnewline
-8.31377772800236e-12 \tabularnewline
1.77666697689224e-11 \tabularnewline
7.29305417191286e-12 \tabularnewline
5.62159180088656e-11 \tabularnewline
8.37966502220244e-11 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149339&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]1.23681260257777e-12[/C][/ROW]
[ROW][C]7.72096832223397e-12[/C][/ROW]
[ROW][C]-5.19161293902823e-11[/C][/ROW]
[ROW][C]-4.80801525179924e-13[/C][/ROW]
[ROW][C]-1.90092093395401e-12[/C][/ROW]
[ROW][C]5.52522269413485e-12[/C][/ROW]
[ROW][C]-5.67754131404674e-12[/C][/ROW]
[ROW][C]-1.78298167372504e-11[/C][/ROW]
[ROW][C]2.07121988380858e-11[/C][/ROW]
[ROW][C]-2.10461616791291e-12[/C][/ROW]
[ROW][C]-3.66904238552535e-11[/C][/ROW]
[ROW][C]-2.86193522851985e-11[/C][/ROW]
[ROW][C]1.54008583848729e-10[/C][/ROW]
[ROW][C]1.9883572338324e-11[/C][/ROW]
[ROW][C]8.52916572254774e-11[/C][/ROW]
[ROW][C]-6.63842766491144e-11[/C][/ROW]
[ROW][C]-4.09989103357053e-11[/C][/ROW]
[ROW][C]6.88463824752294e-11[/C][/ROW]
[ROW][C]-5.42172117528046e-11[/C][/ROW]
[ROW][C]2.17585839879982e-11[/C][/ROW]
[ROW][C]-3.93545814756323e-11[/C][/ROW]
[ROW][C]-7.08693860541561e-11[/C][/ROW]
[ROW][C]-6.14281097197127e-11[/C][/ROW]
[ROW][C]-1.2341400284042e-10[/C][/ROW]
[ROW][C]-1.68520963124308e-10[/C][/ROW]
[ROW][C]-2.00944410063371e-10[/C][/ROW]
[ROW][C]3.59457145888544e-11[/C][/ROW]
[ROW][C]5.29161188621583e-11[/C][/ROW]
[ROW][C]4.65425480045796e-11[/C][/ROW]
[ROW][C]8.73316520570799e-11[/C][/ROW]
[ROW][C]6.41308383152694e-12[/C][/ROW]
[ROW][C]2.18657560985924e-11[/C][/ROW]
[ROW][C]-4.14013279891876e-11[/C][/ROW]
[ROW][C]-6.55762205720878e-11[/C][/ROW]
[ROW][C]-2.2012526485132e-11[/C][/ROW]
[ROW][C]-6.25101742734698e-12[/C][/ROW]
[ROW][C]-9.06593058829902e-11[/C][/ROW]
[ROW][C]1.21550143926051e-10[/C][/ROW]
[ROW][C]-1.35452562160999e-10[/C][/ROW]
[ROW][C]-1.11768913896942e-11[/C][/ROW]
[ROW][C]7.8322678100561e-11[/C][/ROW]
[ROW][C]-2.75009489319772e-12[/C][/ROW]
[ROW][C]4.73852043813881e-11[/C][/ROW]
[ROW][C]-1.12070153994773e-11[/C][/ROW]
[ROW][C]-1.99246698740461e-11[/C][/ROW]
[ROW][C]-2.96936139453833e-11[/C][/ROW]
[ROW][C]5.23707377338071e-12[/C][/ROW]
[ROW][C]1.6306521729374e-11[/C][/ROW]
[ROW][C]-1.67411215735618e-11[/C][/ROW]
[ROW][C]-3.73235850767022e-11[/C][/ROW]
[ROW][C]3.70700075030095e-11[/C][/ROW]
[ROW][C]6.77186943451588e-11[/C][/ROW]
[ROW][C]5.44954345076424e-11[/C][/ROW]
[ROW][C]-2.20029360911284e-11[/C][/ROW]
[ROW][C]1.54075228307279e-11[/C][/ROW]
[ROW][C]-7.49439887370209e-12[/C][/ROW]
[ROW][C]-8.31377772800236e-12[/C][/ROW]
[ROW][C]1.77666697689224e-11[/C][/ROW]
[ROW][C]7.29305417191286e-12[/C][/ROW]
[ROW][C]5.62159180088656e-11[/C][/ROW]
[ROW][C]8.37966502220244e-11[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149339&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149339&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
1.23681260257777e-12
7.72096832223397e-12
-5.19161293902823e-11
-4.80801525179924e-13
-1.90092093395401e-12
5.52522269413485e-12
-5.67754131404674e-12
-1.78298167372504e-11
2.07121988380858e-11
-2.10461616791291e-12
-3.66904238552535e-11
-2.86193522851985e-11
1.54008583848729e-10
1.9883572338324e-11
8.52916572254774e-11
-6.63842766491144e-11
-4.09989103357053e-11
6.88463824752294e-11
-5.42172117528046e-11
2.17585839879982e-11
-3.93545814756323e-11
-7.08693860541561e-11
-6.14281097197127e-11
-1.2341400284042e-10
-1.68520963124308e-10
-2.00944410063371e-10
3.59457145888544e-11
5.29161188621583e-11
4.65425480045796e-11
8.73316520570799e-11
6.41308383152694e-12
2.18657560985924e-11
-4.14013279891876e-11
-6.55762205720878e-11
-2.2012526485132e-11
-6.25101742734698e-12
-9.06593058829902e-11
1.21550143926051e-10
-1.35452562160999e-10
-1.11768913896942e-11
7.8322678100561e-11
-2.75009489319772e-12
4.73852043813881e-11
-1.12070153994773e-11
-1.99246698740461e-11
-2.96936139453833e-11
5.23707377338071e-12
1.6306521729374e-11
-1.67411215735618e-11
-3.73235850767022e-11
3.70700075030095e-11
6.77186943451588e-11
5.44954345076424e-11
-2.20029360911284e-11
1.54075228307279e-11
-7.49439887370209e-12
-8.31377772800236e-12
1.77666697689224e-11
7.29305417191286e-12
5.62159180088656e-11
8.37966502220244e-11


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = -1.5 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = -1.5 ; par3 = 0 ; 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')