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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationSun, 14 Dec 2008 01:46:35 -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/14/t1229244510kg0737x9njql282.htm/, Retrieved Thu, 09 May 2024 14:16:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33242, Retrieved Thu, 09 May 2024 14:16:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsARIMA backward selection- paper
Estimated Impact268

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMP   [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-06 10:27:24] [c94d7012e41b73cfa20d93e879679ede]
-   PD      [ARIMA Backward Selection] [ARIMA backward se...] [2008-12-14 08:46:35] [0cdfeda4aa2f9e551c2e529c44a404df] [Current]
-  M D        [ARIMA Backward Selection] [] [2009-12-11 16:14:09] [cf890101a20378422561610e0d41fd9c]
-   P           [ARIMA Backward Selection] [] [2009-12-17 10:01:15] [cf890101a20378422561610e0d41fd9c]
-   PD            [ARIMA Backward Selection] [Arima backward se...] [2009-12-30 18:59:08] [acdebb2ecda2ddb208f4e14f4a68b9e7]
-   PD            [ARIMA Backward Selection] [Arima backward se...] [2009-12-30 19:10:07] [acdebb2ecda2ddb208f4e14f4a68b9e7]
-   PD            [ARIMA Backward Selection] [] [2010-12-21 13:47:21] [4f85667043e8913570b3eb8f368f82b2]
-    D            [ARIMA Backward Selection] [] [2010-12-22 10:27:26] [4f85667043e8913570b3eb8f368f82b2]
- RM D        [ARIMA Backward Selection] [ARIMA BACKWARD SE...] [2009-12-12 16:32:49] [517ac0676608e46c618c738721d88e41]
- RMPD        [Spectral Analysis] [periodogram] [2009-12-12 17:00:55] [517ac0676608e46c618c738721d88e41]
- R P           [Spectral Analysis] [periodogram 12] [2009-12-12 18:18:40] [517ac0676608e46c618c738721d88e41]
- RMPD        [Spectral Analysis] [periodeogram geen...] [2009-12-12 17:05:50] [517ac0676608e46c618c738721d88e41]
- R P           [Spectral Analysis] [periodogram 12] [2009-12-12 18:21:03] [517ac0676608e46c618c738721d88e41]
-  MPD        [ARIMA Backward Selection] [armica backward] [2009-12-15 19:35:10] [ba905ddf7cdf9ecb063c35348c4dab2e]
-   PD          [ARIMA Backward Selection] [backward] [2009-12-24 18:00:11] [ba905ddf7cdf9ecb063c35348c4dab2e]
-  MPD        [ARIMA Backward Selection] [PAPER 21] [2009-12-20 13:38:41] [4a2be4899cba879e4eea9daa25281df8]
-   PD          [ARIMA Backward Selection] [paper 9] [2009-12-20 16:23:04] [4a2be4899cba879e4eea9daa25281df8]
-  M D        [ARIMA Backward Selection] [] [2009-12-20 19:21:39] [25d480487237d24b5bee738546d96a8b]
-  M D        [ARIMA Backward Selection] [Arima Backward JD...] [2009-12-21 16:26:35] [74be16979710d4c4e7c6647856088456]
-  MPD        [ARIMA Backward Selection] [Paper arima backw...] [2009-12-30 12:54:27] [9b3063011151cbe1c3c2955cd3d2c958]
- RMPD        [Harrell-Davis Quantiles] [Paper Harrell Davis] [2009-12-30 13:10:46] [9b3063011151cbe1c3c2955cd3d2c958]
- RMPD        [ARIMA Forecasting] [Paper Forecasting] [2009-12-30 13:28:42] [9b3063011151cbe1c3c2955cd3d2c958]
- RMPD        [ARIMA Backward Selection] [arima geboortes] [2010-12-03 14:12:36] [0ed07bc2d995651e4b58fa9f9f0e94d1]
- RMPD        [ARIMA Backward Selection] [arima geboortes] [2010-12-03 14:12:36] [4eaa304e6a28c475ba490fccf4c01ad3]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14929387,5
14717825,3
15826281,2
16301309,6
15033016,9
16998460,6
14066462,7
13328937,3
17319718,2
17586426,8
15887037,4
17935679,1
15869489
15892510,9
17556558,1
16791643
15953688,5
18144913,6
14390881
13885708,7
17332571,5
17152595,8
16003877,1
16841467,1
14783398,1
14667847,5
17714362,2
16282088
15014866,2
17722582,4
13876509,4
15495489,6
17799521,1
17920079,1
17248022,4
18813782,4
16249688,3
17823358,5
20424438,3
17814218,7
19699959,6
19776328,1
15679833,1
17119266,5
20092613
20863688,3
20925203,1
21032593
20664684,3
19711511,4
22553293,4
19498332,9
20722827,8
21321275
17960847,7
17789654,9
20003708,5
21169851,7
20422839,4
19810562,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 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 & 14 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=33242&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]14 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=33242&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33242&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 time14 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.11340.22830.3466-0.6569-0.8919-0.53080.527
(p-val)(0.8483 )(0.6196 )(0.1012 )(0.2703 )(0.7021 )(0.4388 )(0.8699 )
Estimates ( 2 )-0.11350.2520.3446-0.6663-0.4606-0.39770
(p-val)(0.835 )(0.5267 )(0.0847 )(0.2073 )(0.0154 )(0.0758 )(NA )
Estimates ( 3 )00.31190.3509-0.7561-0.4447-0.3780
(p-val)(NA )(0.0672 )(0.0293 )(0 )(0.0134 )(0.0758 )(NA )
Estimates ( 4 )00.22770.283-0.6309-0.299900
(p-val)(NA )(0.1776 )(0.0722 )(1e-04 )(0.0494 )(NA )(NA )
Estimates ( 5 )000.2325-0.5376-0.287900
(p-val)(NA )(NA )(0.146 )(0 )(0.0624 )(NA )(NA )
Estimates ( 6 )000-0.485-0.261400
(p-val)(NA )(NA )(NA )(0 )(0.0916 )(NA )(NA )
Estimates ( 7 )000-0.4689000
(p-val)(NA )(NA )(NA )(0 )(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 ) & -0.1134 & 0.2283 & 0.3466 & -0.6569 & -0.8919 & -0.5308 & 0.527 \tabularnewline
(p-val) & (0.8483 ) & (0.6196 ) & (0.1012 ) & (0.2703 ) & (0.7021 ) & (0.4388 ) & (0.8699 ) \tabularnewline
Estimates ( 2 ) & -0.1135 & 0.252 & 0.3446 & -0.6663 & -0.4606 & -0.3977 & 0 \tabularnewline
(p-val) & (0.835 ) & (0.5267 ) & (0.0847 ) & (0.2073 ) & (0.0154 ) & (0.0758 ) & (NA ) \tabularnewline
Estimates ( 3 ) & 0 & 0.3119 & 0.3509 & -0.7561 & -0.4447 & -0.378 & 0 \tabularnewline
(p-val) & (NA ) & (0.0672 ) & (0.0293 ) & (0 ) & (0.0134 ) & (0.0758 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & 0.2277 & 0.283 & -0.6309 & -0.2999 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (0.1776 ) & (0.0722 ) & (1e-04 ) & (0.0494 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0.2325 & -0.5376 & -0.2879 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.146 ) & (0 ) & (0.0624 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & -0.485 & -0.2614 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0.0916 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & -0.4689 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (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=33242&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]-0.1134[/C][C]0.2283[/C][C]0.3466[/C][C]-0.6569[/C][C]-0.8919[/C][C]-0.5308[/C][C]0.527[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8483 )[/C][C](0.6196 )[/C][C](0.1012 )[/C][C](0.2703 )[/C][C](0.7021 )[/C][C](0.4388 )[/C][C](0.8699 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.1135[/C][C]0.252[/C][C]0.3446[/C][C]-0.6663[/C][C]-0.4606[/C][C]-0.3977[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.835 )[/C][C](0.5267 )[/C][C](0.0847 )[/C][C](0.2073 )[/C][C](0.0154 )[/C][C](0.0758 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.3119[/C][C]0.3509[/C][C]-0.7561[/C][C]-0.4447[/C][C]-0.378[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0672 )[/C][C](0.0293 )[/C][C](0 )[/C][C](0.0134 )[/C][C](0.0758 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.2277[/C][C]0.283[/C][C]-0.6309[/C][C]-0.2999[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1776 )[/C][C](0.0722 )[/C][C](1e-04 )[/C][C](0.0494 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0.2325[/C][C]-0.5376[/C][C]-0.2879[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.146 )[/C][C](0 )[/C][C](0.0624 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.485[/C][C]-0.2614[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0916 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.4689[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=33242&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33242&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 )-0.11340.22830.3466-0.6569-0.8919-0.53080.527
(p-val)(0.8483 )(0.6196 )(0.1012 )(0.2703 )(0.7021 )(0.4388 )(0.8699 )
Estimates ( 2 )-0.11350.2520.3446-0.6663-0.4606-0.39770
(p-val)(0.835 )(0.5267 )(0.0847 )(0.2073 )(0.0154 )(0.0758 )(NA )
Estimates ( 3 )00.31190.3509-0.7561-0.4447-0.3780
(p-val)(NA )(0.0672 )(0.0293 )(0 )(0.0134 )(0.0758 )(NA )
Estimates ( 4 )00.22770.283-0.6309-0.299900
(p-val)(NA )(0.1776 )(0.0722 )(1e-04 )(0.0494 )(NA )(NA )
Estimates ( 5 )000.2325-0.5376-0.287900
(p-val)(NA )(NA )(0.146 )(0 )(0.0624 )(NA )(NA )
Estimates ( 6 )000-0.485-0.261400
(p-val)(NA )(NA )(NA )(0 )(0.0916 )(NA )(NA )
Estimates ( 7 )000-0.4689000
(p-val)(NA )(NA )(NA )(0 )(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
-52689.331819243
203631.758593518
611587.896847872
-902089.573250663
-19957.6205744159
208199.485764945
-692462.223227589
-111576.808911902
-579131.922684595
-712061.077833008
186141.049524042
-1078673.54150015
-550433.289921653
-334949.361118760
1364258.56507618
-330606.866974391
-477042.776419796
344139.263003584
-139997.035706512
2116982.59649611
-258202.65134442
58537.6350906933
648995.228878698
726395.206609673
-151577.096495270
1579479.33439936
682028.895162118
-1021581.21433957
2545255.91657187
-1261809.74960906
-886499.195357612
-54292.4320307618
344255.129386795
896049.122247959
1292779.71739268
-640992.655356377
1753012.57379218
-1235027.38243139
-474758.964772899
-982919.190946382
-313836.309376668
-317953.759351939
516391.662451975
-1407091.49734177
-1266824.69263592
-49343.5033457391
-640711.011958387
-1411638.01945440

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-52689.331819243 \tabularnewline
203631.758593518 \tabularnewline
611587.896847872 \tabularnewline
-902089.573250663 \tabularnewline
-19957.6205744159 \tabularnewline
208199.485764945 \tabularnewline
-692462.223227589 \tabularnewline
-111576.808911902 \tabularnewline
-579131.922684595 \tabularnewline
-712061.077833008 \tabularnewline
186141.049524042 \tabularnewline
-1078673.54150015 \tabularnewline
-550433.289921653 \tabularnewline
-334949.361118760 \tabularnewline
1364258.56507618 \tabularnewline
-330606.866974391 \tabularnewline
-477042.776419796 \tabularnewline
344139.263003584 \tabularnewline
-139997.035706512 \tabularnewline
2116982.59649611 \tabularnewline
-258202.65134442 \tabularnewline
58537.6350906933 \tabularnewline
648995.228878698 \tabularnewline
726395.206609673 \tabularnewline
-151577.096495270 \tabularnewline
1579479.33439936 \tabularnewline
682028.895162118 \tabularnewline
-1021581.21433957 \tabularnewline
2545255.91657187 \tabularnewline
-1261809.74960906 \tabularnewline
-886499.195357612 \tabularnewline
-54292.4320307618 \tabularnewline
344255.129386795 \tabularnewline
896049.122247959 \tabularnewline
1292779.71739268 \tabularnewline
-640992.655356377 \tabularnewline
1753012.57379218 \tabularnewline
-1235027.38243139 \tabularnewline
-474758.964772899 \tabularnewline
-982919.190946382 \tabularnewline
-313836.309376668 \tabularnewline
-317953.759351939 \tabularnewline
516391.662451975 \tabularnewline
-1407091.49734177 \tabularnewline
-1266824.69263592 \tabularnewline
-49343.5033457391 \tabularnewline
-640711.011958387 \tabularnewline
-1411638.01945440 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33242&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-52689.331819243[/C][/ROW]
[ROW][C]203631.758593518[/C][/ROW]
[ROW][C]611587.896847872[/C][/ROW]
[ROW][C]-902089.573250663[/C][/ROW]
[ROW][C]-19957.6205744159[/C][/ROW]
[ROW][C]208199.485764945[/C][/ROW]
[ROW][C]-692462.223227589[/C][/ROW]
[ROW][C]-111576.808911902[/C][/ROW]
[ROW][C]-579131.922684595[/C][/ROW]
[ROW][C]-712061.077833008[/C][/ROW]
[ROW][C]186141.049524042[/C][/ROW]
[ROW][C]-1078673.54150015[/C][/ROW]
[ROW][C]-550433.289921653[/C][/ROW]
[ROW][C]-334949.361118760[/C][/ROW]
[ROW][C]1364258.56507618[/C][/ROW]
[ROW][C]-330606.866974391[/C][/ROW]
[ROW][C]-477042.776419796[/C][/ROW]
[ROW][C]344139.263003584[/C][/ROW]
[ROW][C]-139997.035706512[/C][/ROW]
[ROW][C]2116982.59649611[/C][/ROW]
[ROW][C]-258202.65134442[/C][/ROW]
[ROW][C]58537.6350906933[/C][/ROW]
[ROW][C]648995.228878698[/C][/ROW]
[ROW][C]726395.206609673[/C][/ROW]
[ROW][C]-151577.096495270[/C][/ROW]
[ROW][C]1579479.33439936[/C][/ROW]
[ROW][C]682028.895162118[/C][/ROW]
[ROW][C]-1021581.21433957[/C][/ROW]
[ROW][C]2545255.91657187[/C][/ROW]
[ROW][C]-1261809.74960906[/C][/ROW]
[ROW][C]-886499.195357612[/C][/ROW]
[ROW][C]-54292.4320307618[/C][/ROW]
[ROW][C]344255.129386795[/C][/ROW]
[ROW][C]896049.122247959[/C][/ROW]
[ROW][C]1292779.71739268[/C][/ROW]
[ROW][C]-640992.655356377[/C][/ROW]
[ROW][C]1753012.57379218[/C][/ROW]
[ROW][C]-1235027.38243139[/C][/ROW]
[ROW][C]-474758.964772899[/C][/ROW]
[ROW][C]-982919.190946382[/C][/ROW]
[ROW][C]-313836.309376668[/C][/ROW]
[ROW][C]-317953.759351939[/C][/ROW]
[ROW][C]516391.662451975[/C][/ROW]
[ROW][C]-1407091.49734177[/C][/ROW]
[ROW][C]-1266824.69263592[/C][/ROW]
[ROW][C]-49343.5033457391[/C][/ROW]
[ROW][C]-640711.011958387[/C][/ROW]
[ROW][C]-1411638.01945440[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33242&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33242&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
-52689.331819243
203631.758593518
611587.896847872
-902089.573250663
-19957.6205744159
208199.485764945
-692462.223227589
-111576.808911902
-579131.922684595
-712061.077833008
186141.049524042
-1078673.54150015
-550433.289921653
-334949.361118760
1364258.56507618
-330606.866974391
-477042.776419796
344139.263003584
-139997.035706512
2116982.59649611
-258202.65134442
58537.6350906933
648995.228878698
726395.206609673
-151577.096495270
1579479.33439936
682028.895162118
-1021581.21433957
2545255.91657187
-1261809.74960906
-886499.195357612
-54292.4320307618
344255.129386795
896049.122247959
1292779.71739268
-640992.655356377
1753012.57379218
-1235027.38243139
-474758.964772899
-982919.190946382
-313836.309376668
-317953.759351939
516391.662451975
-1407091.49734177
-1266824.69263592
-49343.5033457391
-640711.011958387
-1411638.01945440


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1.0 ; 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')