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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 computationWed, 17 Dec 2008 08:08:32 -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/17/t122952661943xscr22mwudhyz.htm/, Retrieved Sun, 19 May 2024 07:57:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34391, Retrieved Sun, 19 May 2024 07:57:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165

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] [Import uit Amerika] [2008-10-13 18:55:55] [b943bd7078334192ff8343563ee31113]
- RMPD  [Histogram] [Paper Analyse (1)] [2008-12-13 13:37:46] [b943bd7078334192ff8343563ee31113]
- RMP     [Tukey lambda PPCC Plot] [Paper Analyse (2)] [2008-12-13 14:19:33] [b943bd7078334192ff8343563ee31113]
- RM        [Central Tendency] [Paper Analyse (3)] [2008-12-13 14:48:04] [b943bd7078334192ff8343563ee31113]
- RMP         [Mean Plot] [Paper Analyse (4)] [2008-12-13 16:59:19] [b943bd7078334192ff8343563ee31113]
- RMPD          [Pearson Correlation] [Paper Analyse (5)] [2008-12-13 17:21:52] [b943bd7078334192ff8343563ee31113]
-    D            [Pearson Correlation] [Paper Analyse (6)] [2008-12-13 17:43:27] [b943bd7078334192ff8343563ee31113]
- RM D              [Partial Correlation] [Paper Analyse (7)] [2008-12-13 19:17:34] [b943bd7078334192ff8343563ee31113]
- RMPD                [Standard Deviation-Mean Plot] [Paper Analyse (8)] [2008-12-13 20:10:46] [b943bd7078334192ff8343563ee31113]
- RM                    [Variance Reduction Matrix] [Paper Analyse (9)] [2008-12-13 20:12:45] [b943bd7078334192ff8343563ee31113]
- RMP                     [(Partial) Autocorrelation Function] [Paper Analyse (10)] [2008-12-14 09:58:13] [b943bd7078334192ff8343563ee31113]
-   P                       [(Partial) Autocorrelation Function] [Paper Analyse (11)] [2008-12-14 09:59:43] [b943bd7078334192ff8343563ee31113]
- RMP                           [ARIMA Backward Selection] [ARIMA Backward Mo...] [2008-12-17 15:08:32] [620b6ad5c4696049e39cb73ce029682c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1593
1477.9
1733.7
1569.7
1843.7
1950.3
1657.5
1772.1
1568.3
1809.8
1646.7
1808.5
1763.9
1625.5
1538.8
1342.4
1645.1
1619.9
1338.1
1505.5
1529.1
1511.9
1656.7
1694.4
1662.3
1588.7
1483.3
1585.6
1658.9
1584.4
1470.6
1618.7
1407.6
1473.9
1515.3
1485.4
1496.1
1493.5
1298.4
1375.3
1507.9
1455.3
1363.3
1392.8
1348.8
1880.3
1669.2
1543.6
1701.2
1516.5
1466.8
1484.1
1577.2
1684.5
1414.7
1674.5
1598.7
1739.1
1674.6
1671.8
1802
1526.8
1580.9
1634.8
1610.3
1712
1678.8
1708.1
1680.6
2056
1624
2021.4
1861.1
1750.8
1767.5
1710.3
2151.5
2047.9
1915.4
1984.7
1896.5
2170.8
2139.9
2330.5
2121.8
2226.8
1857.9
2155.9
2341.7
2290.2
2006.5
2111.9
1731.3
1762.2
1863.2
1943.5
1975.2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 15 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34391&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34391&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34391&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 time15 seconds
R Server'George Udny Yule' @ 72.249.76.132






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-1.3744-0.6708-0.09220.81170.102-0.0717-1
(p-val)(0 )(0.0022 )(0.4952 )(0 )(0.5194 )(0.6586 )(2e-04 )
Estimates ( 2 )-1.3952-0.693-0.1010.82450.1270-1.0009
(p-val)(0 )(0.001 )(0.4447 )(0 )(0.4027 )(NA )(0 )
Estimates ( 3 )-1.2942-0.552600.76250.12330-1
(p-val)(0 )(0 )(NA )(0 )(0.4125 )(NA )(1e-04 )
Estimates ( 4 )-1.269-0.546400.725700-1
(p-val)(0 )(0 )(NA )(0.0016 )(NA )(NA )(0.0047 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
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 ) & -1.3744 & -0.6708 & -0.0922 & 0.8117 & 0.102 & -0.0717 & -1 \tabularnewline
(p-val) & (0 ) & (0.0022 ) & (0.4952 ) & (0 ) & (0.5194 ) & (0.6586 ) & (2e-04 ) \tabularnewline
Estimates ( 2 ) & -1.3952 & -0.693 & -0.101 & 0.8245 & 0.127 & 0 & -1.0009 \tabularnewline
(p-val) & (0 ) & (0.001 ) & (0.4447 ) & (0 ) & (0.4027 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & -1.2942 & -0.5526 & 0 & 0.7625 & 0.1233 & 0 & -1 \tabularnewline
(p-val) & (0 ) & (0 ) & (NA ) & (0 ) & (0.4125 ) & (NA ) & (1e-04 ) \tabularnewline
Estimates ( 4 ) & -1.269 & -0.5464 & 0 & 0.7257 & 0 & 0 & -1 \tabularnewline
(p-val) & (0 ) & (0 ) & (NA ) & (0.0016 ) & (NA ) & (NA ) & (0.0047 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \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=34391&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]-1.3744[/C][C]-0.6708[/C][C]-0.0922[/C][C]0.8117[/C][C]0.102[/C][C]-0.0717[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0022 )[/C][C](0.4952 )[/C][C](0 )[/C][C](0.5194 )[/C][C](0.6586 )[/C][C](2e-04 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-1.3952[/C][C]-0.693[/C][C]-0.101[/C][C]0.8245[/C][C]0.127[/C][C]0[/C][C]-1.0009[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.001 )[/C][C](0.4447 )[/C][C](0 )[/C][C](0.4027 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-1.2942[/C][C]-0.5526[/C][C]0[/C][C]0.7625[/C][C]0.1233[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][C](0.4125 )[/C][C](NA )[/C][C](1e-04 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-1.269[/C][C]-0.5464[/C][C]0[/C][C]0.7257[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0.0016 )[/C][C](NA )[/C][C](NA )[/C][C](0.0047 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/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 ( 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=34391&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34391&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 )-1.3744-0.6708-0.09220.81170.102-0.0717-1
(p-val)(0 )(0.0022 )(0.4952 )(0 )(0.5194 )(0.6586 )(2e-04 )
Estimates ( 2 )-1.3952-0.693-0.1010.82450.1270-1.0009
(p-val)(0 )(0.001 )(0.4447 )(0 )(0.4027 )(NA )(0 )
Estimates ( 3 )-1.2942-0.552600.76250.12330-1
(p-val)(0 )(0 )(NA )(0 )(0.4125 )(NA )(1e-04 )
Estimates ( 4 )-1.269-0.546400.725700-1
(p-val)(0 )(0 )(NA )(0.0016 )(NA )(NA )(0.0047 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
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
-5.32829736390014
-14.6389973594297
-254.108189598783
-175.707681866108
-20.9800401892224
-67.5893385702342
-56.4020818544546
37.7384579423218
199.395475134974
-106.600328121849
158.550307511084
-23.8523815991239
32.4669461613135
-8.0535133512713
-65.9349621941162
130.115360327534
-51.0820334324816
-152.818417200216
45.4656026250712
104.388209219209
-105.932403638206
-91.7282720835214
5.84033818409394
-88.7660837969062
-9.77042049268336
96.7643852789302
-112.784815774767
21.7796844473159
-17.9026155964183
-32.8163853088855
48.5394492748602
-23.2055982465109
39.9958738075651
406.967613985411
43.5438068021814
-222.896089182944
21.9462789944869
-1.28213165375556
-44.2415721905159
19.814772400539
-46.3063245376778
57.7453343553438
-28.3591497412972
121.158161669450
73.5696489496465
-54.810486930229
-66.9929869459536
6.18286997516334
83.071525099732
-99.8621136709047
13.6151808517476
85.90024780663
-99.0158174637503
-40.5386155961765
193.543044962604
-8.61979193779809
15.0245318368591
180.273844587915
-218.188525189578
167.669713626682
-56.1011849419748
28.6266798494758
-54.7491223614195
46.3619481785393
217.367792489861
63.2182307800993
-28.4746020954581
-55.8056323165388
4.03020041474288
-5.4556058523007
159.341290226410
111.850479052760
-115.331660119714
112.024610651281
-240.387797931569
171.835465566627
46.0095983247066
48.3902130517606
-217.963792596845
-1.00092631708847
-333.612706809927
-294.465779940636
9.56076470630294
96.9055173659052
70.1334846486532

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-5.32829736390014 \tabularnewline
-14.6389973594297 \tabularnewline
-254.108189598783 \tabularnewline
-175.707681866108 \tabularnewline
-20.9800401892224 \tabularnewline
-67.5893385702342 \tabularnewline
-56.4020818544546 \tabularnewline
37.7384579423218 \tabularnewline
199.395475134974 \tabularnewline
-106.600328121849 \tabularnewline
158.550307511084 \tabularnewline
-23.8523815991239 \tabularnewline
32.4669461613135 \tabularnewline
-8.0535133512713 \tabularnewline
-65.9349621941162 \tabularnewline
130.115360327534 \tabularnewline
-51.0820334324816 \tabularnewline
-152.818417200216 \tabularnewline
45.4656026250712 \tabularnewline
104.388209219209 \tabularnewline
-105.932403638206 \tabularnewline
-91.7282720835214 \tabularnewline
5.84033818409394 \tabularnewline
-88.7660837969062 \tabularnewline
-9.77042049268336 \tabularnewline
96.7643852789302 \tabularnewline
-112.784815774767 \tabularnewline
21.7796844473159 \tabularnewline
-17.9026155964183 \tabularnewline
-32.8163853088855 \tabularnewline
48.5394492748602 \tabularnewline
-23.2055982465109 \tabularnewline
39.9958738075651 \tabularnewline
406.967613985411 \tabularnewline
43.5438068021814 \tabularnewline
-222.896089182944 \tabularnewline
21.9462789944869 \tabularnewline
-1.28213165375556 \tabularnewline
-44.2415721905159 \tabularnewline
19.814772400539 \tabularnewline
-46.3063245376778 \tabularnewline
57.7453343553438 \tabularnewline
-28.3591497412972 \tabularnewline
121.158161669450 \tabularnewline
73.5696489496465 \tabularnewline
-54.810486930229 \tabularnewline
-66.9929869459536 \tabularnewline
6.18286997516334 \tabularnewline
83.071525099732 \tabularnewline
-99.8621136709047 \tabularnewline
13.6151808517476 \tabularnewline
85.90024780663 \tabularnewline
-99.0158174637503 \tabularnewline
-40.5386155961765 \tabularnewline
193.543044962604 \tabularnewline
-8.61979193779809 \tabularnewline
15.0245318368591 \tabularnewline
180.273844587915 \tabularnewline
-218.188525189578 \tabularnewline
167.669713626682 \tabularnewline
-56.1011849419748 \tabularnewline
28.6266798494758 \tabularnewline
-54.7491223614195 \tabularnewline
46.3619481785393 \tabularnewline
217.367792489861 \tabularnewline
63.2182307800993 \tabularnewline
-28.4746020954581 \tabularnewline
-55.8056323165388 \tabularnewline
4.03020041474288 \tabularnewline
-5.4556058523007 \tabularnewline
159.341290226410 \tabularnewline
111.850479052760 \tabularnewline
-115.331660119714 \tabularnewline
112.024610651281 \tabularnewline
-240.387797931569 \tabularnewline
171.835465566627 \tabularnewline
46.0095983247066 \tabularnewline
48.3902130517606 \tabularnewline
-217.963792596845 \tabularnewline
-1.00092631708847 \tabularnewline
-333.612706809927 \tabularnewline
-294.465779940636 \tabularnewline
9.56076470630294 \tabularnewline
96.9055173659052 \tabularnewline
70.1334846486532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34391&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-5.32829736390014[/C][/ROW]
[ROW][C]-14.6389973594297[/C][/ROW]
[ROW][C]-254.108189598783[/C][/ROW]
[ROW][C]-175.707681866108[/C][/ROW]
[ROW][C]-20.9800401892224[/C][/ROW]
[ROW][C]-67.5893385702342[/C][/ROW]
[ROW][C]-56.4020818544546[/C][/ROW]
[ROW][C]37.7384579423218[/C][/ROW]
[ROW][C]199.395475134974[/C][/ROW]
[ROW][C]-106.600328121849[/C][/ROW]
[ROW][C]158.550307511084[/C][/ROW]
[ROW][C]-23.8523815991239[/C][/ROW]
[ROW][C]32.4669461613135[/C][/ROW]
[ROW][C]-8.0535133512713[/C][/ROW]
[ROW][C]-65.9349621941162[/C][/ROW]
[ROW][C]130.115360327534[/C][/ROW]
[ROW][C]-51.0820334324816[/C][/ROW]
[ROW][C]-152.818417200216[/C][/ROW]
[ROW][C]45.4656026250712[/C][/ROW]
[ROW][C]104.388209219209[/C][/ROW]
[ROW][C]-105.932403638206[/C][/ROW]
[ROW][C]-91.7282720835214[/C][/ROW]
[ROW][C]5.84033818409394[/C][/ROW]
[ROW][C]-88.7660837969062[/C][/ROW]
[ROW][C]-9.77042049268336[/C][/ROW]
[ROW][C]96.7643852789302[/C][/ROW]
[ROW][C]-112.784815774767[/C][/ROW]
[ROW][C]21.7796844473159[/C][/ROW]
[ROW][C]-17.9026155964183[/C][/ROW]
[ROW][C]-32.8163853088855[/C][/ROW]
[ROW][C]48.5394492748602[/C][/ROW]
[ROW][C]-23.2055982465109[/C][/ROW]
[ROW][C]39.9958738075651[/C][/ROW]
[ROW][C]406.967613985411[/C][/ROW]
[ROW][C]43.5438068021814[/C][/ROW]
[ROW][C]-222.896089182944[/C][/ROW]
[ROW][C]21.9462789944869[/C][/ROW]
[ROW][C]-1.28213165375556[/C][/ROW]
[ROW][C]-44.2415721905159[/C][/ROW]
[ROW][C]19.814772400539[/C][/ROW]
[ROW][C]-46.3063245376778[/C][/ROW]
[ROW][C]57.7453343553438[/C][/ROW]
[ROW][C]-28.3591497412972[/C][/ROW]
[ROW][C]121.158161669450[/C][/ROW]
[ROW][C]73.5696489496465[/C][/ROW]
[ROW][C]-54.810486930229[/C][/ROW]
[ROW][C]-66.9929869459536[/C][/ROW]
[ROW][C]6.18286997516334[/C][/ROW]
[ROW][C]83.071525099732[/C][/ROW]
[ROW][C]-99.8621136709047[/C][/ROW]
[ROW][C]13.6151808517476[/C][/ROW]
[ROW][C]85.90024780663[/C][/ROW]
[ROW][C]-99.0158174637503[/C][/ROW]
[ROW][C]-40.5386155961765[/C][/ROW]
[ROW][C]193.543044962604[/C][/ROW]
[ROW][C]-8.61979193779809[/C][/ROW]
[ROW][C]15.0245318368591[/C][/ROW]
[ROW][C]180.273844587915[/C][/ROW]
[ROW][C]-218.188525189578[/C][/ROW]
[ROW][C]167.669713626682[/C][/ROW]
[ROW][C]-56.1011849419748[/C][/ROW]
[ROW][C]28.6266798494758[/C][/ROW]
[ROW][C]-54.7491223614195[/C][/ROW]
[ROW][C]46.3619481785393[/C][/ROW]
[ROW][C]217.367792489861[/C][/ROW]
[ROW][C]63.2182307800993[/C][/ROW]
[ROW][C]-28.4746020954581[/C][/ROW]
[ROW][C]-55.8056323165388[/C][/ROW]
[ROW][C]4.03020041474288[/C][/ROW]
[ROW][C]-5.4556058523007[/C][/ROW]
[ROW][C]159.341290226410[/C][/ROW]
[ROW][C]111.850479052760[/C][/ROW]
[ROW][C]-115.331660119714[/C][/ROW]
[ROW][C]112.024610651281[/C][/ROW]
[ROW][C]-240.387797931569[/C][/ROW]
[ROW][C]171.835465566627[/C][/ROW]
[ROW][C]46.0095983247066[/C][/ROW]
[ROW][C]48.3902130517606[/C][/ROW]
[ROW][C]-217.963792596845[/C][/ROW]
[ROW][C]-1.00092631708847[/C][/ROW]
[ROW][C]-333.612706809927[/C][/ROW]
[ROW][C]-294.465779940636[/C][/ROW]
[ROW][C]9.56076470630294[/C][/ROW]
[ROW][C]96.9055173659052[/C][/ROW]
[ROW][C]70.1334846486532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34391&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34391&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
-5.32829736390014
-14.6389973594297
-254.108189598783
-175.707681866108
-20.9800401892224
-67.5893385702342
-56.4020818544546
37.7384579423218
199.395475134974
-106.600328121849
158.550307511084
-23.8523815991239
32.4669461613135
-8.0535133512713
-65.9349621941162
130.115360327534
-51.0820334324816
-152.818417200216
45.4656026250712
104.388209219209
-105.932403638206
-91.7282720835214
5.84033818409394
-88.7660837969062
-9.77042049268336
96.7643852789302
-112.784815774767
21.7796844473159
-17.9026155964183
-32.8163853088855
48.5394492748602
-23.2055982465109
39.9958738075651
406.967613985411
43.5438068021814
-222.896089182944
21.9462789944869
-1.28213165375556
-44.2415721905159
19.814772400539
-46.3063245376778
57.7453343553438
-28.3591497412972
121.158161669450
73.5696489496465
-54.810486930229
-66.9929869459536
6.18286997516334
83.071525099732
-99.8621136709047
13.6151808517476
85.90024780663
-99.0158174637503
-40.5386155961765
193.543044962604
-8.61979193779809
15.0245318368591
180.273844587915
-218.188525189578
167.669713626682
-56.1011849419748
28.6266798494758
-54.7491223614195
46.3619481785393
217.367792489861
63.2182307800993
-28.4746020954581
-55.8056323165388
4.03020041474288
-5.4556058523007
159.341290226410
111.850479052760
-115.331660119714
112.024610651281
-240.387797931569
171.835465566627
46.0095983247066
48.3902130517606
-217.963792596845
-1.00092631708847
-333.612706809927
-294.465779940636
9.56076470630294
96.9055173659052
70.1334846486532


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame  <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names  <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval  <- arimaSelect.out[[2]][1:k,]
aic   <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft  = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop   = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01)                            symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1)  & (pval[j,i]>=.05)) symb = 'red'
else                                         symb = 'black'
polygon(c(i+.5   ,i+.2   ,i+.5   ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix)  {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv  <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i   ,1+2*i   ,1+2*i-.5   ,1+2*i),
c(.4      ,.7      , .4        , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')