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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 computationMon, 15 Dec 2014 07:32:57 +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/2014/Dec/15/t141862895173e9qs854oforge.htm/, Retrieved Thu, 31 Oct 2024 23:10:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267948, Retrieved Thu, 31 Oct 2024 23:10:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [] [2014-12-15 07:32:57] [0a6fc2c777821367d2239c664b701a36] [Current]
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Dataseries X:
1.894
1.757
3.582
5.321
5.561
5.907
4.944
4.966
3.258
1.964
1.743
1.262
2.086
1.793
3.548
5.672
6.084
4.914
4.990
5.139
3.218
2.179
2.238
1.442
2.205
2.025
3.531
4.977
7.998
4.880
5.231
5.202
3.303
2.683
2.202
1.376
2.422
1.997
3.163
5.964
5.657
6.415
6.208
4.500
2.939
2.702
2.090
1.504
2.549
1.931
3.013
6.204
5.788
5.611
5.594
4.647
3.490
2.487
1.992
1.507
2.306
2.002
3.075
5.331
5.589
5.813
4.876
4.665
3.601
2.192
2.111
1.580
2.288
1.993
3.228
5.000
5.480
5.770
4.962
4.685
3.607
2.222
2.467
1.594
2.228
1.910
3.157
4.809
6.249
4.607
4.975
4.784
3.028
2.461
2.218
1.351
2.070
1.887
3.024
4.596
6.398
4.459
5.382
4.359
2.687
2.249
2.154
1.169
2.429
1.762
2.846
5.627
5.749
4.502
5.720
4.403
2.867
2.635
2.059
1.511
2.359
1.741
2.917
6.249
5.760
6.250
5.134
4.831
3.695
2.462
2.146
1.579




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267948&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267948&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267948&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 time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.22920.06650.30710.20150.3725-0.2166-0.9504
(p-val)(0.2545 )(0.4717 )(0.0022 )(0.3184 )(0.0072 )(0.0702 )(0.0506 )
Estimates ( 2 )-0.255900.29020.2140.3847-0.2156-1
(p-val)(0.2166 )(NA )(0.0031 )(0.2797 )(8e-04 )(0.0661 )(0.1072 )
Estimates ( 3 )-0.049400.287800.3877-0.2134-0.9593
(p-val)(0.5893 )(NA )(0.005 )(NA )(0.0051 )(0.0761 )(0.1013 )
Estimates ( 4 )000.281300.3835-0.2217-0.9962
(p-val)(NA )(NA )(0.0052 )(NA )(7e-04 )(0.0583 )(0.1673 )
Estimates ( 5 )000.36430-0.2848-0.26870
(p-val)(NA )(NA )(1e-04 )(NA )(0.0085 )(0.0123 )(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 ) & -0.2292 & 0.0665 & 0.3071 & 0.2015 & 0.3725 & -0.2166 & -0.9504 \tabularnewline
(p-val) & (0.2545 ) & (0.4717 ) & (0.0022 ) & (0.3184 ) & (0.0072 ) & (0.0702 ) & (0.0506 ) \tabularnewline
Estimates ( 2 ) & -0.2559 & 0 & 0.2902 & 0.214 & 0.3847 & -0.2156 & -1 \tabularnewline
(p-val) & (0.2166 ) & (NA ) & (0.0031 ) & (0.2797 ) & (8e-04 ) & (0.0661 ) & (0.1072 ) \tabularnewline
Estimates ( 3 ) & -0.0494 & 0 & 0.2878 & 0 & 0.3877 & -0.2134 & -0.9593 \tabularnewline
(p-val) & (0.5893 ) & (NA ) & (0.005 ) & (NA ) & (0.0051 ) & (0.0761 ) & (0.1013 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0.2813 & 0 & 0.3835 & -0.2217 & -0.9962 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0052 ) & (NA ) & (7e-04 ) & (0.0583 ) & (0.1673 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0.3643 & 0 & -0.2848 & -0.2687 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (1e-04 ) & (NA ) & (0.0085 ) & (0.0123 ) & (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=267948&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]-0.2292[/C][C]0.0665[/C][C]0.3071[/C][C]0.2015[/C][C]0.3725[/C][C]-0.2166[/C][C]-0.9504[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2545 )[/C][C](0.4717 )[/C][C](0.0022 )[/C][C](0.3184 )[/C][C](0.0072 )[/C][C](0.0702 )[/C][C](0.0506 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.2559[/C][C]0[/C][C]0.2902[/C][C]0.214[/C][C]0.3847[/C][C]-0.2156[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2166 )[/C][C](NA )[/C][C](0.0031 )[/C][C](0.2797 )[/C][C](8e-04 )[/C][C](0.0661 )[/C][C](0.1072 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.0494[/C][C]0[/C][C]0.2878[/C][C]0[/C][C]0.3877[/C][C]-0.2134[/C][C]-0.9593[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5893 )[/C][C](NA )[/C][C](0.005 )[/C][C](NA )[/C][C](0.0051 )[/C][C](0.0761 )[/C][C](0.1013 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0.2813[/C][C]0[/C][C]0.3835[/C][C]-0.2217[/C][C]-0.9962[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0052 )[/C][C](NA )[/C][C](7e-04 )[/C][C](0.0583 )[/C][C](0.1673 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0.3643[/C][C]0[/C][C]-0.2848[/C][C]-0.2687[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](1e-04 )[/C][C](NA )[/C][C](0.0085 )[/C][C](0.0123 )[/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=267948&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267948&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 )-0.22920.06650.30710.20150.3725-0.2166-0.9504
(p-val)(0.2545 )(0.4717 )(0.0022 )(0.3184 )(0.0072 )(0.0702 )(0.0506 )
Estimates ( 2 )-0.255900.29020.2140.3847-0.2156-1
(p-val)(0.2166 )(NA )(0.0031 )(0.2797 )(8e-04 )(0.0661 )(0.1072 )
Estimates ( 3 )-0.049400.287800.3877-0.2134-0.9593
(p-val)(0.5893 )(NA )(0.005 )(NA )(0.0051 )(0.0761 )(0.1013 )
Estimates ( 4 )000.281300.3835-0.2217-0.9962
(p-val)(NA )(NA )(0.0052 )(NA )(7e-04 )(0.0583 )(0.1673 )
Estimates ( 5 )000.36430-0.2848-0.26870
(p-val)(NA )(NA )(1e-04 )(NA )(0.0085 )(0.0123 )(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
0.000932570451110904
-0.0179350912823047
-0.00389748590878778
0.00148645858051572
-0.00392751698943547
-0.0114663956025524
0.0259553424630215
0.000929185042780768
-0.00152130367827873
-0.0053362261282204
-0.0204564021147283
-0.0473018674625519
-0.0294967858047081
-0.00870674267251415
-0.0116632876515646
0.00908187458883646
0.021274503562833
-0.0325544368479944
0.00605816506637759
-0.0127075981251901
0.00808418610595929
-0.00576084950379804
-0.0427794963255517
-0.0067172143934619
0.00571838300673297
-0.0170958649899139
-0.00353143143803517
0.019341342694593
-0.0181455179570385
0.0367663086751663
-0.0376258500548689
-0.0220124470932463
0.0108397766141001
0.0301412577329144
-0.0139225153312025
-0.0134304560764801
-0.0341208403104449
-0.0217593722674654
-0.00194578645017292
0.0250580771465898
-0.00265662260572021
-0.00466926346713422
0.008687893562463
0.00538080836344976
0.00234085907680236
-0.029581987334701
-0.0103387903535513
0.000798151323300493
-0.00384329866826504
0.000731007305010229
-0.0137853407990613
0.0142151045540162
0.012394430891894
0.0180655584151956
-0.0159595441189612
0.00894544372696984
0.00348053750809631
-0.00536491148749547
0.00865871431502457
-0.0158007827449837
-0.0223926512957871
-0.0138538278979174
-0.00173993240737163
0.0104427592345406
0.012832977941783
0.0137822228583592
-0.00350760460669922
-0.000623559608613227
0.00111186364905794
-0.0123322004972064
-7.05988341411071e-05
-0.0392913649246548
-0.015086259947064
0.000745236175104745
0.013336865378545
0.0154752950053277
0.0164305292002313
-0.0119027812334734
0.031819425204697
0.0030903232133295
0.00351833624037564
0.0121125212447711
-0.0167143573344598
0.000457474179278293
0.0173806737371167
0.0195119339701125
0.00154892150929775
0.00727930425706111
0.0182258567973806
-0.00497368722918339
0.017848645839057
-0.0135863773172936
0.0195625772695037
0.0244995534764494
0.0169843556228311
-0.0146786806114507
0.0339196624179369
-0.0311993667551201
0.0215953454737422
0.010624519902557
-0.00923377895219689
0.0050091648571991
0.0177664464119756
-0.00697898727629811
0.00433340031478951
0.00516146830006451
-0.030641863677301
0.0024755686177235
-0.0379495940897352
0.0090291172129512
0.0130883883073365
0.0220067888597666
-0.017223688952656
-0.00272502499311128
-0.0366825688104698
0.0138975181214635
-0.0061607377471806
-0.020087561804305
-0.000734905246005602
-0.00623846542602318
0.000479414300358259

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.000932570451110904 \tabularnewline
-0.0179350912823047 \tabularnewline
-0.00389748590878778 \tabularnewline
0.00148645858051572 \tabularnewline
-0.00392751698943547 \tabularnewline
-0.0114663956025524 \tabularnewline
0.0259553424630215 \tabularnewline
0.000929185042780768 \tabularnewline
-0.00152130367827873 \tabularnewline
-0.0053362261282204 \tabularnewline
-0.0204564021147283 \tabularnewline
-0.0473018674625519 \tabularnewline
-0.0294967858047081 \tabularnewline
-0.00870674267251415 \tabularnewline
-0.0116632876515646 \tabularnewline
0.00908187458883646 \tabularnewline
0.021274503562833 \tabularnewline
-0.0325544368479944 \tabularnewline
0.00605816506637759 \tabularnewline
-0.0127075981251901 \tabularnewline
0.00808418610595929 \tabularnewline
-0.00576084950379804 \tabularnewline
-0.0427794963255517 \tabularnewline
-0.0067172143934619 \tabularnewline
0.00571838300673297 \tabularnewline
-0.0170958649899139 \tabularnewline
-0.00353143143803517 \tabularnewline
0.019341342694593 \tabularnewline
-0.0181455179570385 \tabularnewline
0.0367663086751663 \tabularnewline
-0.0376258500548689 \tabularnewline
-0.0220124470932463 \tabularnewline
0.0108397766141001 \tabularnewline
0.0301412577329144 \tabularnewline
-0.0139225153312025 \tabularnewline
-0.0134304560764801 \tabularnewline
-0.0341208403104449 \tabularnewline
-0.0217593722674654 \tabularnewline
-0.00194578645017292 \tabularnewline
0.0250580771465898 \tabularnewline
-0.00265662260572021 \tabularnewline
-0.00466926346713422 \tabularnewline
0.008687893562463 \tabularnewline
0.00538080836344976 \tabularnewline
0.00234085907680236 \tabularnewline
-0.029581987334701 \tabularnewline
-0.0103387903535513 \tabularnewline
0.000798151323300493 \tabularnewline
-0.00384329866826504 \tabularnewline
0.000731007305010229 \tabularnewline
-0.0137853407990613 \tabularnewline
0.0142151045540162 \tabularnewline
0.012394430891894 \tabularnewline
0.0180655584151956 \tabularnewline
-0.0159595441189612 \tabularnewline
0.00894544372696984 \tabularnewline
0.00348053750809631 \tabularnewline
-0.00536491148749547 \tabularnewline
0.00865871431502457 \tabularnewline
-0.0158007827449837 \tabularnewline
-0.0223926512957871 \tabularnewline
-0.0138538278979174 \tabularnewline
-0.00173993240737163 \tabularnewline
0.0104427592345406 \tabularnewline
0.012832977941783 \tabularnewline
0.0137822228583592 \tabularnewline
-0.00350760460669922 \tabularnewline
-0.000623559608613227 \tabularnewline
0.00111186364905794 \tabularnewline
-0.0123322004972064 \tabularnewline
-7.05988341411071e-05 \tabularnewline
-0.0392913649246548 \tabularnewline
-0.015086259947064 \tabularnewline
0.000745236175104745 \tabularnewline
0.013336865378545 \tabularnewline
0.0154752950053277 \tabularnewline
0.0164305292002313 \tabularnewline
-0.0119027812334734 \tabularnewline
0.031819425204697 \tabularnewline
0.0030903232133295 \tabularnewline
0.00351833624037564 \tabularnewline
0.0121125212447711 \tabularnewline
-0.0167143573344598 \tabularnewline
0.000457474179278293 \tabularnewline
0.0173806737371167 \tabularnewline
0.0195119339701125 \tabularnewline
0.00154892150929775 \tabularnewline
0.00727930425706111 \tabularnewline
0.0182258567973806 \tabularnewline
-0.00497368722918339 \tabularnewline
0.017848645839057 \tabularnewline
-0.0135863773172936 \tabularnewline
0.0195625772695037 \tabularnewline
0.0244995534764494 \tabularnewline
0.0169843556228311 \tabularnewline
-0.0146786806114507 \tabularnewline
0.0339196624179369 \tabularnewline
-0.0311993667551201 \tabularnewline
0.0215953454737422 \tabularnewline
0.010624519902557 \tabularnewline
-0.00923377895219689 \tabularnewline
0.0050091648571991 \tabularnewline
0.0177664464119756 \tabularnewline
-0.00697898727629811 \tabularnewline
0.00433340031478951 \tabularnewline
0.00516146830006451 \tabularnewline
-0.030641863677301 \tabularnewline
0.0024755686177235 \tabularnewline
-0.0379495940897352 \tabularnewline
0.0090291172129512 \tabularnewline
0.0130883883073365 \tabularnewline
0.0220067888597666 \tabularnewline
-0.017223688952656 \tabularnewline
-0.00272502499311128 \tabularnewline
-0.0366825688104698 \tabularnewline
0.0138975181214635 \tabularnewline
-0.0061607377471806 \tabularnewline
-0.020087561804305 \tabularnewline
-0.000734905246005602 \tabularnewline
-0.00623846542602318 \tabularnewline
0.000479414300358259 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267948&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.000932570451110904[/C][/ROW]
[ROW][C]-0.0179350912823047[/C][/ROW]
[ROW][C]-0.00389748590878778[/C][/ROW]
[ROW][C]0.00148645858051572[/C][/ROW]
[ROW][C]-0.00392751698943547[/C][/ROW]
[ROW][C]-0.0114663956025524[/C][/ROW]
[ROW][C]0.0259553424630215[/C][/ROW]
[ROW][C]0.000929185042780768[/C][/ROW]
[ROW][C]-0.00152130367827873[/C][/ROW]
[ROW][C]-0.0053362261282204[/C][/ROW]
[ROW][C]-0.0204564021147283[/C][/ROW]
[ROW][C]-0.0473018674625519[/C][/ROW]
[ROW][C]-0.0294967858047081[/C][/ROW]
[ROW][C]-0.00870674267251415[/C][/ROW]
[ROW][C]-0.0116632876515646[/C][/ROW]
[ROW][C]0.00908187458883646[/C][/ROW]
[ROW][C]0.021274503562833[/C][/ROW]
[ROW][C]-0.0325544368479944[/C][/ROW]
[ROW][C]0.00605816506637759[/C][/ROW]
[ROW][C]-0.0127075981251901[/C][/ROW]
[ROW][C]0.00808418610595929[/C][/ROW]
[ROW][C]-0.00576084950379804[/C][/ROW]
[ROW][C]-0.0427794963255517[/C][/ROW]
[ROW][C]-0.0067172143934619[/C][/ROW]
[ROW][C]0.00571838300673297[/C][/ROW]
[ROW][C]-0.0170958649899139[/C][/ROW]
[ROW][C]-0.00353143143803517[/C][/ROW]
[ROW][C]0.019341342694593[/C][/ROW]
[ROW][C]-0.0181455179570385[/C][/ROW]
[ROW][C]0.0367663086751663[/C][/ROW]
[ROW][C]-0.0376258500548689[/C][/ROW]
[ROW][C]-0.0220124470932463[/C][/ROW]
[ROW][C]0.0108397766141001[/C][/ROW]
[ROW][C]0.0301412577329144[/C][/ROW]
[ROW][C]-0.0139225153312025[/C][/ROW]
[ROW][C]-0.0134304560764801[/C][/ROW]
[ROW][C]-0.0341208403104449[/C][/ROW]
[ROW][C]-0.0217593722674654[/C][/ROW]
[ROW][C]-0.00194578645017292[/C][/ROW]
[ROW][C]0.0250580771465898[/C][/ROW]
[ROW][C]-0.00265662260572021[/C][/ROW]
[ROW][C]-0.00466926346713422[/C][/ROW]
[ROW][C]0.008687893562463[/C][/ROW]
[ROW][C]0.00538080836344976[/C][/ROW]
[ROW][C]0.00234085907680236[/C][/ROW]
[ROW][C]-0.029581987334701[/C][/ROW]
[ROW][C]-0.0103387903535513[/C][/ROW]
[ROW][C]0.000798151323300493[/C][/ROW]
[ROW][C]-0.00384329866826504[/C][/ROW]
[ROW][C]0.000731007305010229[/C][/ROW]
[ROW][C]-0.0137853407990613[/C][/ROW]
[ROW][C]0.0142151045540162[/C][/ROW]
[ROW][C]0.012394430891894[/C][/ROW]
[ROW][C]0.0180655584151956[/C][/ROW]
[ROW][C]-0.0159595441189612[/C][/ROW]
[ROW][C]0.00894544372696984[/C][/ROW]
[ROW][C]0.00348053750809631[/C][/ROW]
[ROW][C]-0.00536491148749547[/C][/ROW]
[ROW][C]0.00865871431502457[/C][/ROW]
[ROW][C]-0.0158007827449837[/C][/ROW]
[ROW][C]-0.0223926512957871[/C][/ROW]
[ROW][C]-0.0138538278979174[/C][/ROW]
[ROW][C]-0.00173993240737163[/C][/ROW]
[ROW][C]0.0104427592345406[/C][/ROW]
[ROW][C]0.012832977941783[/C][/ROW]
[ROW][C]0.0137822228583592[/C][/ROW]
[ROW][C]-0.00350760460669922[/C][/ROW]
[ROW][C]-0.000623559608613227[/C][/ROW]
[ROW][C]0.00111186364905794[/C][/ROW]
[ROW][C]-0.0123322004972064[/C][/ROW]
[ROW][C]-7.05988341411071e-05[/C][/ROW]
[ROW][C]-0.0392913649246548[/C][/ROW]
[ROW][C]-0.015086259947064[/C][/ROW]
[ROW][C]0.000745236175104745[/C][/ROW]
[ROW][C]0.013336865378545[/C][/ROW]
[ROW][C]0.0154752950053277[/C][/ROW]
[ROW][C]0.0164305292002313[/C][/ROW]
[ROW][C]-0.0119027812334734[/C][/ROW]
[ROW][C]0.031819425204697[/C][/ROW]
[ROW][C]0.0030903232133295[/C][/ROW]
[ROW][C]0.00351833624037564[/C][/ROW]
[ROW][C]0.0121125212447711[/C][/ROW]
[ROW][C]-0.0167143573344598[/C][/ROW]
[ROW][C]0.000457474179278293[/C][/ROW]
[ROW][C]0.0173806737371167[/C][/ROW]
[ROW][C]0.0195119339701125[/C][/ROW]
[ROW][C]0.00154892150929775[/C][/ROW]
[ROW][C]0.00727930425706111[/C][/ROW]
[ROW][C]0.0182258567973806[/C][/ROW]
[ROW][C]-0.00497368722918339[/C][/ROW]
[ROW][C]0.017848645839057[/C][/ROW]
[ROW][C]-0.0135863773172936[/C][/ROW]
[ROW][C]0.0195625772695037[/C][/ROW]
[ROW][C]0.0244995534764494[/C][/ROW]
[ROW][C]0.0169843556228311[/C][/ROW]
[ROW][C]-0.0146786806114507[/C][/ROW]
[ROW][C]0.0339196624179369[/C][/ROW]
[ROW][C]-0.0311993667551201[/C][/ROW]
[ROW][C]0.0215953454737422[/C][/ROW]
[ROW][C]0.010624519902557[/C][/ROW]
[ROW][C]-0.00923377895219689[/C][/ROW]
[ROW][C]0.0050091648571991[/C][/ROW]
[ROW][C]0.0177664464119756[/C][/ROW]
[ROW][C]-0.00697898727629811[/C][/ROW]
[ROW][C]0.00433340031478951[/C][/ROW]
[ROW][C]0.00516146830006451[/C][/ROW]
[ROW][C]-0.030641863677301[/C][/ROW]
[ROW][C]0.0024755686177235[/C][/ROW]
[ROW][C]-0.0379495940897352[/C][/ROW]
[ROW][C]0.0090291172129512[/C][/ROW]
[ROW][C]0.0130883883073365[/C][/ROW]
[ROW][C]0.0220067888597666[/C][/ROW]
[ROW][C]-0.017223688952656[/C][/ROW]
[ROW][C]-0.00272502499311128[/C][/ROW]
[ROW][C]-0.0366825688104698[/C][/ROW]
[ROW][C]0.0138975181214635[/C][/ROW]
[ROW][C]-0.0061607377471806[/C][/ROW]
[ROW][C]-0.020087561804305[/C][/ROW]
[ROW][C]-0.000734905246005602[/C][/ROW]
[ROW][C]-0.00623846542602318[/C][/ROW]
[ROW][C]0.000479414300358259[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267948&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267948&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
0.000932570451110904
-0.0179350912823047
-0.00389748590878778
0.00148645858051572
-0.00392751698943547
-0.0114663956025524
0.0259553424630215
0.000929185042780768
-0.00152130367827873
-0.0053362261282204
-0.0204564021147283
-0.0473018674625519
-0.0294967858047081
-0.00870674267251415
-0.0116632876515646
0.00908187458883646
0.021274503562833
-0.0325544368479944
0.00605816506637759
-0.0127075981251901
0.00808418610595929
-0.00576084950379804
-0.0427794963255517
-0.0067172143934619
0.00571838300673297
-0.0170958649899139
-0.00353143143803517
0.019341342694593
-0.0181455179570385
0.0367663086751663
-0.0376258500548689
-0.0220124470932463
0.0108397766141001
0.0301412577329144
-0.0139225153312025
-0.0134304560764801
-0.0341208403104449
-0.0217593722674654
-0.00194578645017292
0.0250580771465898
-0.00265662260572021
-0.00466926346713422
0.008687893562463
0.00538080836344976
0.00234085907680236
-0.029581987334701
-0.0103387903535513
0.000798151323300493
-0.00384329866826504
0.000731007305010229
-0.0137853407990613
0.0142151045540162
0.012394430891894
0.0180655584151956
-0.0159595441189612
0.00894544372696984
0.00348053750809631
-0.00536491148749547
0.00865871431502457
-0.0158007827449837
-0.0223926512957871
-0.0138538278979174
-0.00173993240737163
0.0104427592345406
0.012832977941783
0.0137822228583592
-0.00350760460669922
-0.000623559608613227
0.00111186364905794
-0.0123322004972064
-7.05988341411071e-05
-0.0392913649246548
-0.015086259947064
0.000745236175104745
0.013336865378545
0.0154752950053277
0.0164305292002313
-0.0119027812334734
0.031819425204697
0.0030903232133295
0.00351833624037564
0.0121125212447711
-0.0167143573344598
0.000457474179278293
0.0173806737371167
0.0195119339701125
0.00154892150929775
0.00727930425706111
0.0182258567973806
-0.00497368722918339
0.017848645839057
-0.0135863773172936
0.0195625772695037
0.0244995534764494
0.0169843556228311
-0.0146786806114507
0.0339196624179369
-0.0311993667551201
0.0215953454737422
0.010624519902557
-0.00923377895219689
0.0050091648571991
0.0177664464119756
-0.00697898727629811
0.00433340031478951
0.00516146830006451
-0.030641863677301
0.0024755686177235
-0.0379495940897352
0.0090291172129512
0.0130883883073365
0.0220067888597666
-0.017223688952656
-0.00272502499311128
-0.0366825688104698
0.0138975181214635
-0.0061607377471806
-0.020087561804305
-0.000734905246005602
-0.00623846542602318
0.000479414300358259



Parameters (Session):
par1 = FALSE ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')