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Author's title

Author*Unverified author*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationThu, 18 Dec 2008 05:14:52 -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/18/t1229602550w0idndccbc37d44.htm/, Retrieved Sat, 11 May 2024 15:58:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34701, Retrieved Sat, 11 May 2024 15:58:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSeverijns Britt
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [paper ARIMA] [2008-12-18 12:14:52] [78308c9f3efc33d1da821bcd963df161] [Current]
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Dataseries X:
492865
480961
461935
456608
441977
439148
488180
520564
501492
485025
464196
460170
467037
460070
447988
442867
436087
431328
484015
509673
512927
502831
470984
471067
476049
474605
470439
461251
454724
455626
516847
525192
522975
518585
509239
512238
519164
517009
509933
509127
500857
506971
569323
579714
577992
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379
533590




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time19 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 & 19 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=34701&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]19 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=34701&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34701&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time19 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.2196-0.1884-0.0719-0.8080.0509-0.1814-0.7047
(p-val)(0.1078 )(0.1615 )(0.5648 )(0 )(0.8697 )(0.4223 )(0.0532 )
Estimates ( 2 )-0.225-0.1944-0.0677-0.80510-0.2078-0.6513
(p-val)(0.09 )(0.1351 )(0.5809 )(0 )(NA )(0.1817 )(1e-04 )
Estimates ( 3 )-0.1956-0.16470-0.8270-0.2085-0.6509
(p-val)(0.1035 )(0.1611 )(NA )(0 )(NA )(0.1798 )(1e-04 )
Estimates ( 4 )-0.198-0.14110-0.83700-0.7012
(p-val)(0.0966 )(0.2213 )(NA )(0 )(NA )(NA )(0 )
Estimates ( 5 )-0.15100-1.155500-0.6986
(p-val)(0.1791 )(NA )(NA )(0 )(NA )(NA )(1e-04 )
Estimates ( 6 )000-1.122800-0.6971
(p-val)(NA )(NA )(NA )(0 )(NA )(NA )(2e-04 )
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 ) & -0.2196 & -0.1884 & -0.0719 & -0.808 & 0.0509 & -0.1814 & -0.7047 \tabularnewline
(p-val) & (0.1078 ) & (0.1615 ) & (0.5648 ) & (0 ) & (0.8697 ) & (0.4223 ) & (0.0532 ) \tabularnewline
Estimates ( 2 ) & -0.225 & -0.1944 & -0.0677 & -0.8051 & 0 & -0.2078 & -0.6513 \tabularnewline
(p-val) & (0.09 ) & (0.1351 ) & (0.5809 ) & (0 ) & (NA ) & (0.1817 ) & (1e-04 ) \tabularnewline
Estimates ( 3 ) & -0.1956 & -0.1647 & 0 & -0.827 & 0 & -0.2085 & -0.6509 \tabularnewline
(p-val) & (0.1035 ) & (0.1611 ) & (NA ) & (0 ) & (NA ) & (0.1798 ) & (1e-04 ) \tabularnewline
Estimates ( 4 ) & -0.198 & -0.1411 & 0 & -0.837 & 0 & 0 & -0.7012 \tabularnewline
(p-val) & (0.0966 ) & (0.2213 ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & -0.151 & 0 & 0 & -1.1555 & 0 & 0 & -0.6986 \tabularnewline
(p-val) & (0.1791 ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (1e-04 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & -1.1228 & 0 & 0 & -0.6971 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (2e-04 ) \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=34701&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]-0.2196[/C][C]-0.1884[/C][C]-0.0719[/C][C]-0.808[/C][C]0.0509[/C][C]-0.1814[/C][C]-0.7047[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1078 )[/C][C](0.1615 )[/C][C](0.5648 )[/C][C](0 )[/C][C](0.8697 )[/C][C](0.4223 )[/C][C](0.0532 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.225[/C][C]-0.1944[/C][C]-0.0677[/C][C]-0.8051[/C][C]0[/C][C]-0.2078[/C][C]-0.6513[/C][/ROW]
[ROW][C](p-val)[/C][C](0.09 )[/C][C](0.1351 )[/C][C](0.5809 )[/C][C](0 )[/C][C](NA )[/C][C](0.1817 )[/C][C](1e-04 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.1956[/C][C]-0.1647[/C][C]0[/C][C]-0.827[/C][C]0[/C][C]-0.2085[/C][C]-0.6509[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1035 )[/C][C](0.1611 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0.1798 )[/C][C](1e-04 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.198[/C][C]-0.1411[/C][C]0[/C][C]-0.837[/C][C]0[/C][C]0[/C][C]-0.7012[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0966 )[/C][C](0.2213 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.151[/C][C]0[/C][C]0[/C][C]-1.1555[/C][C]0[/C][C]0[/C][C]-0.6986[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1791 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](1e-04 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1.1228[/C][C]0[/C][C]0[/C][C]-0.6971[/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](2e-04 )[/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=34701&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34701&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.2196-0.1884-0.0719-0.8080.0509-0.1814-0.7047
(p-val)(0.1078 )(0.1615 )(0.5648 )(0 )(0.8697 )(0.4223 )(0.0532 )
Estimates ( 2 )-0.225-0.1944-0.0677-0.80510-0.2078-0.6513
(p-val)(0.09 )(0.1351 )(0.5809 )(0 )(NA )(0.1817 )(1e-04 )
Estimates ( 3 )-0.1956-0.16470-0.8270-0.2085-0.6509
(p-val)(0.1035 )(0.1611 )(NA )(0 )(NA )(0.1798 )(1e-04 )
Estimates ( 4 )-0.198-0.14110-0.83700-0.7012
(p-val)(0.0966 )(0.2213 )(NA )(0 )(NA )(NA )(0 )
Estimates ( 5 )-0.15100-1.155500-0.6986
(p-val)(0.1791 )(NA )(NA )(0 )(NA )(NA )(1e-04 )
Estimates ( 6 )000-1.122800-0.6971
(p-val)(NA )(NA )(NA )(0 )(NA )(NA )(2e-04 )
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
1799.32828380744
1054.27910768024
-3339.24072954432
2043.41281764486
-4246.02667198436
-521.936858521343
-6996.76722735506
13339.6526183320
3057.83442188941
-11316.9242519428
-802.137917052747
-3398.90841006694
2558.58898789809
5370.8613045119
-7096.27077089627
-1389.39541964095
433.268883470358
4823.11669565652
-19715.9945520014
-160.532117921332
5886.64140511381
12404.0202628094
1723.97159745004
-3175.17515770552
-1819.15722275014
-1490.02671983889
519.14601726174
-3741.05490959207
2334.79988503376
2292.29276615435
-12809.4607235724
-1711.83598967205
-5095.11384226847
-1390.55646067455
4159.53474429553
-889.083236204865
-506.60778079539
351.964476211544
-8985.71491403314
-1697.26735298481
2627.68807707304
-7493.19073133879
-2151.16080110249
3930.16689822224
7090.44646962746
361.825820498467
-3502.04873077395
-12029.3998890574
-582.235073002984
2094.29494022634
-4624.24021296194
869.81323112614
-1299.04190340433
-3919.04572685972
-8312.11792286856
1128.63719308590
-6229.56890036393
2190.19212323596
1274.62515690534
-4704.00009930976
406.198658469257
-1194.91888559545
3594.45287359274
6593.24680741407
-760.072850231871
-6492.38990057247
-6761.51688399923
-2562.22075699314
-17060.4273493410
-1455.02746874078
-4595.94425684117
6218.91922229038
-2570.88743808947
-4025.31351531136
5757.03206874261
-3195.81041727777
-8202.7798476413
8503.842173822
3386.5448509476
-13011.9118395354
5828.02404854122
7552.46609970321
9616.90423031134
4803.01719836162
-548.798590064884
-1442.66562810894
6154.25582610272
-8334.85162540564
10776.1536112461
755.272999805302
-3944.57944516594

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
1799.32828380744 \tabularnewline
1054.27910768024 \tabularnewline
-3339.24072954432 \tabularnewline
2043.41281764486 \tabularnewline
-4246.02667198436 \tabularnewline
-521.936858521343 \tabularnewline
-6996.76722735506 \tabularnewline
13339.6526183320 \tabularnewline
3057.83442188941 \tabularnewline
-11316.9242519428 \tabularnewline
-802.137917052747 \tabularnewline
-3398.90841006694 \tabularnewline
2558.58898789809 \tabularnewline
5370.8613045119 \tabularnewline
-7096.27077089627 \tabularnewline
-1389.39541964095 \tabularnewline
433.268883470358 \tabularnewline
4823.11669565652 \tabularnewline
-19715.9945520014 \tabularnewline
-160.532117921332 \tabularnewline
5886.64140511381 \tabularnewline
12404.0202628094 \tabularnewline
1723.97159745004 \tabularnewline
-3175.17515770552 \tabularnewline
-1819.15722275014 \tabularnewline
-1490.02671983889 \tabularnewline
519.14601726174 \tabularnewline
-3741.05490959207 \tabularnewline
2334.79988503376 \tabularnewline
2292.29276615435 \tabularnewline
-12809.4607235724 \tabularnewline
-1711.83598967205 \tabularnewline
-5095.11384226847 \tabularnewline
-1390.55646067455 \tabularnewline
4159.53474429553 \tabularnewline
-889.083236204865 \tabularnewline
-506.60778079539 \tabularnewline
351.964476211544 \tabularnewline
-8985.71491403314 \tabularnewline
-1697.26735298481 \tabularnewline
2627.68807707304 \tabularnewline
-7493.19073133879 \tabularnewline
-2151.16080110249 \tabularnewline
3930.16689822224 \tabularnewline
7090.44646962746 \tabularnewline
361.825820498467 \tabularnewline
-3502.04873077395 \tabularnewline
-12029.3998890574 \tabularnewline
-582.235073002984 \tabularnewline
2094.29494022634 \tabularnewline
-4624.24021296194 \tabularnewline
869.81323112614 \tabularnewline
-1299.04190340433 \tabularnewline
-3919.04572685972 \tabularnewline
-8312.11792286856 \tabularnewline
1128.63719308590 \tabularnewline
-6229.56890036393 \tabularnewline
2190.19212323596 \tabularnewline
1274.62515690534 \tabularnewline
-4704.00009930976 \tabularnewline
406.198658469257 \tabularnewline
-1194.91888559545 \tabularnewline
3594.45287359274 \tabularnewline
6593.24680741407 \tabularnewline
-760.072850231871 \tabularnewline
-6492.38990057247 \tabularnewline
-6761.51688399923 \tabularnewline
-2562.22075699314 \tabularnewline
-17060.4273493410 \tabularnewline
-1455.02746874078 \tabularnewline
-4595.94425684117 \tabularnewline
6218.91922229038 \tabularnewline
-2570.88743808947 \tabularnewline
-4025.31351531136 \tabularnewline
5757.03206874261 \tabularnewline
-3195.81041727777 \tabularnewline
-8202.7798476413 \tabularnewline
8503.842173822 \tabularnewline
3386.5448509476 \tabularnewline
-13011.9118395354 \tabularnewline
5828.02404854122 \tabularnewline
7552.46609970321 \tabularnewline
9616.90423031134 \tabularnewline
4803.01719836162 \tabularnewline
-548.798590064884 \tabularnewline
-1442.66562810894 \tabularnewline
6154.25582610272 \tabularnewline
-8334.85162540564 \tabularnewline
10776.1536112461 \tabularnewline
755.272999805302 \tabularnewline
-3944.57944516594 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34701&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]1799.32828380744[/C][/ROW]
[ROW][C]1054.27910768024[/C][/ROW]
[ROW][C]-3339.24072954432[/C][/ROW]
[ROW][C]2043.41281764486[/C][/ROW]
[ROW][C]-4246.02667198436[/C][/ROW]
[ROW][C]-521.936858521343[/C][/ROW]
[ROW][C]-6996.76722735506[/C][/ROW]
[ROW][C]13339.6526183320[/C][/ROW]
[ROW][C]3057.83442188941[/C][/ROW]
[ROW][C]-11316.9242519428[/C][/ROW]
[ROW][C]-802.137917052747[/C][/ROW]
[ROW][C]-3398.90841006694[/C][/ROW]
[ROW][C]2558.58898789809[/C][/ROW]
[ROW][C]5370.8613045119[/C][/ROW]
[ROW][C]-7096.27077089627[/C][/ROW]
[ROW][C]-1389.39541964095[/C][/ROW]
[ROW][C]433.268883470358[/C][/ROW]
[ROW][C]4823.11669565652[/C][/ROW]
[ROW][C]-19715.9945520014[/C][/ROW]
[ROW][C]-160.532117921332[/C][/ROW]
[ROW][C]5886.64140511381[/C][/ROW]
[ROW][C]12404.0202628094[/C][/ROW]
[ROW][C]1723.97159745004[/C][/ROW]
[ROW][C]-3175.17515770552[/C][/ROW]
[ROW][C]-1819.15722275014[/C][/ROW]
[ROW][C]-1490.02671983889[/C][/ROW]
[ROW][C]519.14601726174[/C][/ROW]
[ROW][C]-3741.05490959207[/C][/ROW]
[ROW][C]2334.79988503376[/C][/ROW]
[ROW][C]2292.29276615435[/C][/ROW]
[ROW][C]-12809.4607235724[/C][/ROW]
[ROW][C]-1711.83598967205[/C][/ROW]
[ROW][C]-5095.11384226847[/C][/ROW]
[ROW][C]-1390.55646067455[/C][/ROW]
[ROW][C]4159.53474429553[/C][/ROW]
[ROW][C]-889.083236204865[/C][/ROW]
[ROW][C]-506.60778079539[/C][/ROW]
[ROW][C]351.964476211544[/C][/ROW]
[ROW][C]-8985.71491403314[/C][/ROW]
[ROW][C]-1697.26735298481[/C][/ROW]
[ROW][C]2627.68807707304[/C][/ROW]
[ROW][C]-7493.19073133879[/C][/ROW]
[ROW][C]-2151.16080110249[/C][/ROW]
[ROW][C]3930.16689822224[/C][/ROW]
[ROW][C]7090.44646962746[/C][/ROW]
[ROW][C]361.825820498467[/C][/ROW]
[ROW][C]-3502.04873077395[/C][/ROW]
[ROW][C]-12029.3998890574[/C][/ROW]
[ROW][C]-582.235073002984[/C][/ROW]
[ROW][C]2094.29494022634[/C][/ROW]
[ROW][C]-4624.24021296194[/C][/ROW]
[ROW][C]869.81323112614[/C][/ROW]
[ROW][C]-1299.04190340433[/C][/ROW]
[ROW][C]-3919.04572685972[/C][/ROW]
[ROW][C]-8312.11792286856[/C][/ROW]
[ROW][C]1128.63719308590[/C][/ROW]
[ROW][C]-6229.56890036393[/C][/ROW]
[ROW][C]2190.19212323596[/C][/ROW]
[ROW][C]1274.62515690534[/C][/ROW]
[ROW][C]-4704.00009930976[/C][/ROW]
[ROW][C]406.198658469257[/C][/ROW]
[ROW][C]-1194.91888559545[/C][/ROW]
[ROW][C]3594.45287359274[/C][/ROW]
[ROW][C]6593.24680741407[/C][/ROW]
[ROW][C]-760.072850231871[/C][/ROW]
[ROW][C]-6492.38990057247[/C][/ROW]
[ROW][C]-6761.51688399923[/C][/ROW]
[ROW][C]-2562.22075699314[/C][/ROW]
[ROW][C]-17060.4273493410[/C][/ROW]
[ROW][C]-1455.02746874078[/C][/ROW]
[ROW][C]-4595.94425684117[/C][/ROW]
[ROW][C]6218.91922229038[/C][/ROW]
[ROW][C]-2570.88743808947[/C][/ROW]
[ROW][C]-4025.31351531136[/C][/ROW]
[ROW][C]5757.03206874261[/C][/ROW]
[ROW][C]-3195.81041727777[/C][/ROW]
[ROW][C]-8202.7798476413[/C][/ROW]
[ROW][C]8503.842173822[/C][/ROW]
[ROW][C]3386.5448509476[/C][/ROW]
[ROW][C]-13011.9118395354[/C][/ROW]
[ROW][C]5828.02404854122[/C][/ROW]
[ROW][C]7552.46609970321[/C][/ROW]
[ROW][C]9616.90423031134[/C][/ROW]
[ROW][C]4803.01719836162[/C][/ROW]
[ROW][C]-548.798590064884[/C][/ROW]
[ROW][C]-1442.66562810894[/C][/ROW]
[ROW][C]6154.25582610272[/C][/ROW]
[ROW][C]-8334.85162540564[/C][/ROW]
[ROW][C]10776.1536112461[/C][/ROW]
[ROW][C]755.272999805302[/C][/ROW]
[ROW][C]-3944.57944516594[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34701&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34701&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
1799.32828380744
1054.27910768024
-3339.24072954432
2043.41281764486
-4246.02667198436
-521.936858521343
-6996.76722735506
13339.6526183320
3057.83442188941
-11316.9242519428
-802.137917052747
-3398.90841006694
2558.58898789809
5370.8613045119
-7096.27077089627
-1389.39541964095
433.268883470358
4823.11669565652
-19715.9945520014
-160.532117921332
5886.64140511381
12404.0202628094
1723.97159745004
-3175.17515770552
-1819.15722275014
-1490.02671983889
519.14601726174
-3741.05490959207
2334.79988503376
2292.29276615435
-12809.4607235724
-1711.83598967205
-5095.11384226847
-1390.55646067455
4159.53474429553
-889.083236204865
-506.60778079539
351.964476211544
-8985.71491403314
-1697.26735298481
2627.68807707304
-7493.19073133879
-2151.16080110249
3930.16689822224
7090.44646962746
361.825820498467
-3502.04873077395
-12029.3998890574
-582.235073002984
2094.29494022634
-4624.24021296194
869.81323112614
-1299.04190340433
-3919.04572685972
-8312.11792286856
1128.63719308590
-6229.56890036393
2190.19212323596
1274.62515690534
-4704.00009930976
406.198658469257
-1194.91888559545
3594.45287359274
6593.24680741407
-760.072850231871
-6492.38990057247
-6761.51688399923
-2562.22075699314
-17060.4273493410
-1455.02746874078
-4595.94425684117
6218.91922229038
-2570.88743808947
-4025.31351531136
5757.03206874261
-3195.81041727777
-8202.7798476413
8503.842173822
3386.5448509476
-13011.9118395354
5828.02404854122
7552.46609970321
9616.90423031134
4803.01719836162
-548.798590064884
-1442.66562810894
6154.25582610272
-8334.85162540564
10776.1536112461
755.272999805302
-3944.57944516594



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