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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, 22 Dec 2010 19:29:00 +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/t12930460561x7maclm1t74g15.htm/, Retrieved Sun, 05 May 2024 23:17:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114523, Retrieved Sun, 05 May 2024 23:17:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [Unemployment] [2010-11-29 09:29:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Variance Reduction Matrix] [WS9 - Variance Re...] [2010-12-04 11:04:59] [8ef49741e164ec6343c90c7935194465]
-   P     [Variance Reduction Matrix] [WS 9 VRM] [2010-12-05 14:01:21] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD      [(Partial) Autocorrelation Function] [paper ACF] [2010-12-10 10:47:04] [8214fe6d084e5ad7598b249a26cc9f06]
-   P         [(Partial) Autocorrelation Function] [paper acf met D=1] [2010-12-10 11:19:24] [8214fe6d084e5ad7598b249a26cc9f06]
- RMP           [Spectral Analysis] [paper - cum perio...] [2010-12-10 11:22:34] [8214fe6d084e5ad7598b249a26cc9f06]
-   P             [Spectral Analysis] [paper - cum perio...] [2010-12-10 11:27:22] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD              [Spectral Analysis] [cum periodogram 2 ] [2010-12-20 20:28:39] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                [Spectral Analysis] [cum per 2 paper] [2010-12-22 13:46:56] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                  [Spectral Analysis] [cum per 1 middeng...] [2010-12-22 19:06:59] [8214fe6d084e5ad7598b249a26cc9f06]
-   P                     [Spectral Analysis] [cum per 2 middeng...] [2010-12-22 19:08:52] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                      [Spectral Analysis] [cum per 1 hoogges...] [2010-12-22 19:10:38] [8214fe6d084e5ad7598b249a26cc9f06]
-   P                         [Spectral Analysis] [cum per 2 hoogges...] [2010-12-22 19:12:39] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD                          [Standard Deviation-Mean Plot] [sdmp laaggeschoolden] [2010-12-22 19:15:16] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                            [Standard Deviation-Mean Plot] [sdmp middengescho...] [2010-12-22 19:17:33] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD                                [ARIMA Backward Selection] [arima backward se...] [2010-12-22 19:29:00] [b47314d83d48c7bf812ec2bcd743b159] [Current]
-   PD                                  [ARIMA Backward Selection] [arima backward se...] [2010-12-22 19:34:28] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                                    [ARIMA Backward Selection] [arima backward se...] [2010-12-22 22:09:56] [8214fe6d084e5ad7598b249a26cc9f06]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
104708
101817
97898
95559
92822
90848
101141
105841
93647
90923
89130
90212
93196
91861
90593
89895
88819
87924
96906
101217
98709
98139
95529
98577
100772
100180
99200
96251
94514
93780
105192
107682
99687
99436
102049
102673
105813
105056
103916
103513
101893
102503
113149
116696
108500
107800
105941
108742
111680
111270
110698
108517
107127
107088
116321
125045
116779
122887
120162
123198
123610
122293
121289
119393
117494
116693
125062
127281
120195
119804
117113
119240
115823
116281
113816
114632
112987
111633
116721
114850
112797
105368
102524
101327
102612
98873
95993
93244
90403
88539
98106
96963
90781
89253
87794
89810
90864
89025
87621
87718
83433
84535
92223
91052
88456
88706
89137
94066
99258
100673
102269
100833
99314
101764
108242
108148
104761
103772
103737
111043
109906
109335
107247
105690
102755
102280
110590
109122
102803
101424
99138

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114523&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]5 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=114523&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114523&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sma1
Estimates ( 1 )0.60380.3066-0.0351-0.7024-0.679
(p-val)(0.0022 )(0.0037 )(0.7732 )(0 )(0 )
Estimates ( 2 )0.56460.29490-0.6692-0.6803
(p-val)(4e-04 )(0.0022 )(NA )(0 )(0 )
Estimates ( 3 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.6038 & 0.3066 & -0.0351 & -0.7024 & -0.679 \tabularnewline
(p-val) & (0.0022 ) & (0.0037 ) & (0.7732 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.5646 & 0.2949 & 0 & -0.6692 & -0.6803 \tabularnewline
(p-val) & (4e-04 ) & (0.0022 ) & (NA ) & (0 ) & (0 ) \tabularnewline
Estimates ( 3 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114523&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]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.6038[/C][C]0.3066[/C][C]-0.0351[/C][C]-0.7024[/C][C]-0.679[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0022 )[/C][C](0.0037 )[/C][C](0.7732 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.5646[/C][C]0.2949[/C][C]0[/C][C]-0.6692[/C][C]-0.6803[/C][/ROW]
[ROW][C](p-val)[/C][C](4e-04 )[/C][C](0.0022 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/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][/ROW]
[ROW][C]Estimates ( 4 )[/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][/ROW]
[ROW][C]Estimates ( 5 )[/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][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[ROW][C]Estimates ( 8 )[/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][/ROW]
[ROW][C]Estimates ( 9 )[/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114523&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114523&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
Iterationar1ar2ar3ma1sma1
Estimates ( 1 )0.60380.3066-0.0351-0.7024-0.679
(p-val)(0.0022 )(0.0037 )(0.7732 )(0 )(0 )
Estimates ( 2 )0.56460.29490-0.6692-0.6803
(p-val)(4e-04 )(0.0022 )(NA )(0 )(0 )
Estimates ( 3 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
-379.757988900563
1241.0354595946
2241.50124874447
1207.12750954307
821.501947904502
362.067376367431
-1688.1640021554
-1028.57589657243
7967.08506436016
2774.83637679673
-2148.36485860075
422.610713627592
-987.7104818259
308.268874772065
957.949704601282
-1969.86981160254
-794.546789404751
511.65981359214
1652.61234073001
-1875.30105033668
-1565.59554750845
1382.16124174848
5037.60832003173
-906.916748461226
-557.880261121985
361.074686033048
309.649666259166
1237.38339065811
-101.991599877773
1066.6345788555
68.1121571259719
-739.230384624971
-1205.54101814999
30.0180481219233
-1463.01192130587
1097.11339666832
667.193679796504
599.658115605079
945.585919428443
-876.455305095726
-120.755823396225
437.029604429193
-1277.08881883574
4751.41226144998
143.284325409644
5825.73057197777
-1583.8815621231
-1038.18653134978
-2935.75719297185
-1462.83102319817
204.232148201908
-150.806436534166
-352.977466818907
-454.094653552572
-1611.43709678373
-3102.86333173426
1004.85539502573
-698.096928743572
-927.942907158746
493.047091189936
-4623.11281574118
1495.64682996392
483.924489353112
3050.54846304544
1105.24106895635
-947.489595329479
-4458.20885510661
-6384.17924652785
6127.26955120536
-5556.44485157359
-1794.46900460235
-1383.99058102624
2172.87781107783
-1289.20319728664
-727.098776810152
-538.000773933657
-144.424999054607
21.3295634071992
2472.18501771356
-2323.52721310319
-407.789944477305
1362.16334205956
1497.47714471805
1389.98545003513
718.119845368922
-299.207845468879
422.980376317561
1641.88120590551
-2235.0023733686
1528.87807706302
-386.388020062517
-2876.61407157692
3090.12112195292
2843.53155052477
2079.87167310673
3041.80808791162
3822.68909275291
2069.43428355978
1752.19090198903
-2053.05340358904
-855.303041798431
1754.53646637946
-2683.30488631671
-2161.02560496658
926.539033709919
-26.2386751852243
599.991690195714
4503.91906175728
-3355.80016876026
-1683.90834678012
-1441.08906567313
-750.598785852534
-411.981077514924
-852.682982007366
771.192757988392
-1278.73106545025
-2005.49138149402
-2.36487528047451
-746.523472664195

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-379.757988900563 \tabularnewline
1241.0354595946 \tabularnewline
2241.50124874447 \tabularnewline
1207.12750954307 \tabularnewline
821.501947904502 \tabularnewline
362.067376367431 \tabularnewline
-1688.1640021554 \tabularnewline
-1028.57589657243 \tabularnewline
7967.08506436016 \tabularnewline
2774.83637679673 \tabularnewline
-2148.36485860075 \tabularnewline
422.610713627592 \tabularnewline
-987.7104818259 \tabularnewline
308.268874772065 \tabularnewline
957.949704601282 \tabularnewline
-1969.86981160254 \tabularnewline
-794.546789404751 \tabularnewline
511.65981359214 \tabularnewline
1652.61234073001 \tabularnewline
-1875.30105033668 \tabularnewline
-1565.59554750845 \tabularnewline
1382.16124174848 \tabularnewline
5037.60832003173 \tabularnewline
-906.916748461226 \tabularnewline
-557.880261121985 \tabularnewline
361.074686033048 \tabularnewline
309.649666259166 \tabularnewline
1237.38339065811 \tabularnewline
-101.991599877773 \tabularnewline
1066.6345788555 \tabularnewline
68.1121571259719 \tabularnewline
-739.230384624971 \tabularnewline
-1205.54101814999 \tabularnewline
30.0180481219233 \tabularnewline
-1463.01192130587 \tabularnewline
1097.11339666832 \tabularnewline
667.193679796504 \tabularnewline
599.658115605079 \tabularnewline
945.585919428443 \tabularnewline
-876.455305095726 \tabularnewline
-120.755823396225 \tabularnewline
437.029604429193 \tabularnewline
-1277.08881883574 \tabularnewline
4751.41226144998 \tabularnewline
143.284325409644 \tabularnewline
5825.73057197777 \tabularnewline
-1583.8815621231 \tabularnewline
-1038.18653134978 \tabularnewline
-2935.75719297185 \tabularnewline
-1462.83102319817 \tabularnewline
204.232148201908 \tabularnewline
-150.806436534166 \tabularnewline
-352.977466818907 \tabularnewline
-454.094653552572 \tabularnewline
-1611.43709678373 \tabularnewline
-3102.86333173426 \tabularnewline
1004.85539502573 \tabularnewline
-698.096928743572 \tabularnewline
-927.942907158746 \tabularnewline
493.047091189936 \tabularnewline
-4623.11281574118 \tabularnewline
1495.64682996392 \tabularnewline
483.924489353112 \tabularnewline
3050.54846304544 \tabularnewline
1105.24106895635 \tabularnewline
-947.489595329479 \tabularnewline
-4458.20885510661 \tabularnewline
-6384.17924652785 \tabularnewline
6127.26955120536 \tabularnewline
-5556.44485157359 \tabularnewline
-1794.46900460235 \tabularnewline
-1383.99058102624 \tabularnewline
2172.87781107783 \tabularnewline
-1289.20319728664 \tabularnewline
-727.098776810152 \tabularnewline
-538.000773933657 \tabularnewline
-144.424999054607 \tabularnewline
21.3295634071992 \tabularnewline
2472.18501771356 \tabularnewline
-2323.52721310319 \tabularnewline
-407.789944477305 \tabularnewline
1362.16334205956 \tabularnewline
1497.47714471805 \tabularnewline
1389.98545003513 \tabularnewline
718.119845368922 \tabularnewline
-299.207845468879 \tabularnewline
422.980376317561 \tabularnewline
1641.88120590551 \tabularnewline
-2235.0023733686 \tabularnewline
1528.87807706302 \tabularnewline
-386.388020062517 \tabularnewline
-2876.61407157692 \tabularnewline
3090.12112195292 \tabularnewline
2843.53155052477 \tabularnewline
2079.87167310673 \tabularnewline
3041.80808791162 \tabularnewline
3822.68909275291 \tabularnewline
2069.43428355978 \tabularnewline
1752.19090198903 \tabularnewline
-2053.05340358904 \tabularnewline
-855.303041798431 \tabularnewline
1754.53646637946 \tabularnewline
-2683.30488631671 \tabularnewline
-2161.02560496658 \tabularnewline
926.539033709919 \tabularnewline
-26.2386751852243 \tabularnewline
599.991690195714 \tabularnewline
4503.91906175728 \tabularnewline
-3355.80016876026 \tabularnewline
-1683.90834678012 \tabularnewline
-1441.08906567313 \tabularnewline
-750.598785852534 \tabularnewline
-411.981077514924 \tabularnewline
-852.682982007366 \tabularnewline
771.192757988392 \tabularnewline
-1278.73106545025 \tabularnewline
-2005.49138149402 \tabularnewline
-2.36487528047451 \tabularnewline
-746.523472664195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114523&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-379.757988900563[/C][/ROW]
[ROW][C]1241.0354595946[/C][/ROW]
[ROW][C]2241.50124874447[/C][/ROW]
[ROW][C]1207.12750954307[/C][/ROW]
[ROW][C]821.501947904502[/C][/ROW]
[ROW][C]362.067376367431[/C][/ROW]
[ROW][C]-1688.1640021554[/C][/ROW]
[ROW][C]-1028.57589657243[/C][/ROW]
[ROW][C]7967.08506436016[/C][/ROW]
[ROW][C]2774.83637679673[/C][/ROW]
[ROW][C]-2148.36485860075[/C][/ROW]
[ROW][C]422.610713627592[/C][/ROW]
[ROW][C]-987.7104818259[/C][/ROW]
[ROW][C]308.268874772065[/C][/ROW]
[ROW][C]957.949704601282[/C][/ROW]
[ROW][C]-1969.86981160254[/C][/ROW]
[ROW][C]-794.546789404751[/C][/ROW]
[ROW][C]511.65981359214[/C][/ROW]
[ROW][C]1652.61234073001[/C][/ROW]
[ROW][C]-1875.30105033668[/C][/ROW]
[ROW][C]-1565.59554750845[/C][/ROW]
[ROW][C]1382.16124174848[/C][/ROW]
[ROW][C]5037.60832003173[/C][/ROW]
[ROW][C]-906.916748461226[/C][/ROW]
[ROW][C]-557.880261121985[/C][/ROW]
[ROW][C]361.074686033048[/C][/ROW]
[ROW][C]309.649666259166[/C][/ROW]
[ROW][C]1237.38339065811[/C][/ROW]
[ROW][C]-101.991599877773[/C][/ROW]
[ROW][C]1066.6345788555[/C][/ROW]
[ROW][C]68.1121571259719[/C][/ROW]
[ROW][C]-739.230384624971[/C][/ROW]
[ROW][C]-1205.54101814999[/C][/ROW]
[ROW][C]30.0180481219233[/C][/ROW]
[ROW][C]-1463.01192130587[/C][/ROW]
[ROW][C]1097.11339666832[/C][/ROW]
[ROW][C]667.193679796504[/C][/ROW]
[ROW][C]599.658115605079[/C][/ROW]
[ROW][C]945.585919428443[/C][/ROW]
[ROW][C]-876.455305095726[/C][/ROW]
[ROW][C]-120.755823396225[/C][/ROW]
[ROW][C]437.029604429193[/C][/ROW]
[ROW][C]-1277.08881883574[/C][/ROW]
[ROW][C]4751.41226144998[/C][/ROW]
[ROW][C]143.284325409644[/C][/ROW]
[ROW][C]5825.73057197777[/C][/ROW]
[ROW][C]-1583.8815621231[/C][/ROW]
[ROW][C]-1038.18653134978[/C][/ROW]
[ROW][C]-2935.75719297185[/C][/ROW]
[ROW][C]-1462.83102319817[/C][/ROW]
[ROW][C]204.232148201908[/C][/ROW]
[ROW][C]-150.806436534166[/C][/ROW]
[ROW][C]-352.977466818907[/C][/ROW]
[ROW][C]-454.094653552572[/C][/ROW]
[ROW][C]-1611.43709678373[/C][/ROW]
[ROW][C]-3102.86333173426[/C][/ROW]
[ROW][C]1004.85539502573[/C][/ROW]
[ROW][C]-698.096928743572[/C][/ROW]
[ROW][C]-927.942907158746[/C][/ROW]
[ROW][C]493.047091189936[/C][/ROW]
[ROW][C]-4623.11281574118[/C][/ROW]
[ROW][C]1495.64682996392[/C][/ROW]
[ROW][C]483.924489353112[/C][/ROW]
[ROW][C]3050.54846304544[/C][/ROW]
[ROW][C]1105.24106895635[/C][/ROW]
[ROW][C]-947.489595329479[/C][/ROW]
[ROW][C]-4458.20885510661[/C][/ROW]
[ROW][C]-6384.17924652785[/C][/ROW]
[ROW][C]6127.26955120536[/C][/ROW]
[ROW][C]-5556.44485157359[/C][/ROW]
[ROW][C]-1794.46900460235[/C][/ROW]
[ROW][C]-1383.99058102624[/C][/ROW]
[ROW][C]2172.87781107783[/C][/ROW]
[ROW][C]-1289.20319728664[/C][/ROW]
[ROW][C]-727.098776810152[/C][/ROW]
[ROW][C]-538.000773933657[/C][/ROW]
[ROW][C]-144.424999054607[/C][/ROW]
[ROW][C]21.3295634071992[/C][/ROW]
[ROW][C]2472.18501771356[/C][/ROW]
[ROW][C]-2323.52721310319[/C][/ROW]
[ROW][C]-407.789944477305[/C][/ROW]
[ROW][C]1362.16334205956[/C][/ROW]
[ROW][C]1497.47714471805[/C][/ROW]
[ROW][C]1389.98545003513[/C][/ROW]
[ROW][C]718.119845368922[/C][/ROW]
[ROW][C]-299.207845468879[/C][/ROW]
[ROW][C]422.980376317561[/C][/ROW]
[ROW][C]1641.88120590551[/C][/ROW]
[ROW][C]-2235.0023733686[/C][/ROW]
[ROW][C]1528.87807706302[/C][/ROW]
[ROW][C]-386.388020062517[/C][/ROW]
[ROW][C]-2876.61407157692[/C][/ROW]
[ROW][C]3090.12112195292[/C][/ROW]
[ROW][C]2843.53155052477[/C][/ROW]
[ROW][C]2079.87167310673[/C][/ROW]
[ROW][C]3041.80808791162[/C][/ROW]
[ROW][C]3822.68909275291[/C][/ROW]
[ROW][C]2069.43428355978[/C][/ROW]
[ROW][C]1752.19090198903[/C][/ROW]
[ROW][C]-2053.05340358904[/C][/ROW]
[ROW][C]-855.303041798431[/C][/ROW]
[ROW][C]1754.53646637946[/C][/ROW]
[ROW][C]-2683.30488631671[/C][/ROW]
[ROW][C]-2161.02560496658[/C][/ROW]
[ROW][C]926.539033709919[/C][/ROW]
[ROW][C]-26.2386751852243[/C][/ROW]
[ROW][C]599.991690195714[/C][/ROW]
[ROW][C]4503.91906175728[/C][/ROW]
[ROW][C]-3355.80016876026[/C][/ROW]
[ROW][C]-1683.90834678012[/C][/ROW]
[ROW][C]-1441.08906567313[/C][/ROW]
[ROW][C]-750.598785852534[/C][/ROW]
[ROW][C]-411.981077514924[/C][/ROW]
[ROW][C]-852.682982007366[/C][/ROW]
[ROW][C]771.192757988392[/C][/ROW]
[ROW][C]-1278.73106545025[/C][/ROW]
[ROW][C]-2005.49138149402[/C][/ROW]
[ROW][C]-2.36487528047451[/C][/ROW]
[ROW][C]-746.523472664195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114523&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114523&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
-379.757988900563
1241.0354595946
2241.50124874447
1207.12750954307
821.501947904502
362.067376367431
-1688.1640021554
-1028.57589657243
7967.08506436016
2774.83637679673
-2148.36485860075
422.610713627592
-987.7104818259
308.268874772065
957.949704601282
-1969.86981160254
-794.546789404751
511.65981359214
1652.61234073001
-1875.30105033668
-1565.59554750845
1382.16124174848
5037.60832003173
-906.916748461226
-557.880261121985
361.074686033048
309.649666259166
1237.38339065811
-101.991599877773
1066.6345788555
68.1121571259719
-739.230384624971
-1205.54101814999
30.0180481219233
-1463.01192130587
1097.11339666832
667.193679796504
599.658115605079
945.585919428443
-876.455305095726
-120.755823396225
437.029604429193
-1277.08881883574
4751.41226144998
143.284325409644
5825.73057197777
-1583.8815621231
-1038.18653134978
-2935.75719297185
-1462.83102319817
204.232148201908
-150.806436534166
-352.977466818907
-454.094653552572
-1611.43709678373
-3102.86333173426
1004.85539502573
-698.096928743572
-927.942907158746
493.047091189936
-4623.11281574118
1495.64682996392
483.924489353112
3050.54846304544
1105.24106895635
-947.489595329479
-4458.20885510661
-6384.17924652785
6127.26955120536
-5556.44485157359
-1794.46900460235
-1383.99058102624
2172.87781107783
-1289.20319728664
-727.098776810152
-538.000773933657
-144.424999054607
21.3295634071992
2472.18501771356
-2323.52721310319
-407.789944477305
1362.16334205956
1497.47714471805
1389.98545003513
718.119845368922
-299.207845468879
422.980376317561
1641.88120590551
-2235.0023733686
1528.87807706302
-386.388020062517
-2876.61407157692
3090.12112195292
2843.53155052477
2079.87167310673
3041.80808791162
3822.68909275291
2069.43428355978
1752.19090198903
-2053.05340358904
-855.303041798431
1754.53646637946
-2683.30488631671
-2161.02560496658
926.539033709919
-26.2386751852243
599.991690195714
4503.91906175728
-3355.80016876026
-1683.90834678012
-1441.08906567313
-750.598785852534
-411.981077514924
-852.682982007366
771.192757988392
-1278.73106545025
-2005.49138149402
-2.36487528047451
-746.523472664195


Figures (Output of Computation)

Input Parameters & R Code

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