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Author's title

Author*Unverified author*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationTue, 18 Dec 2012 15:41:31 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/18/t13558633018zjqkbuqbq2hl43.htm/, Retrieved Thu, 28 Mar 2024 19:38:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201644, Retrieved Thu, 28 Mar 2024 19:38:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-14 11:54:22] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [ARIMA Backward Selection] [] [2011-12-06 20:20:50] [b98453cac15ba1066b407e146608df68]
-   P     [ARIMA Backward Selection] [arima backward se...] [2012-12-17 13:39:37] [68a04a2519ca62ddd145df1e643e729d]
- R PD      [ARIMA Backward Selection] [] [2012-12-18 20:37:06] [74be16979710d4c4e7c6647856088456]
-               [ARIMA Backward Selection] [] [2012-12-18 20:41:31] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Post a new message
Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 13 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201644&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201644&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201644&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 time13 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-1.0256-0.6136-0.1521-0.9983-0.7658-0.40720.2762
(p-val)(0 )(8e-04 )(0.2388 )(0 )(0.1818 )(0.0904 )(0.6575 )
Estimates ( 2 )-1.018-0.5969-0.1499-0.9983-0.5143-0.30810
(p-val)(0 )(8e-04 )(0.245 )(0 )(2e-04 )(0.0732 )(NA )
Estimates ( 3 )-0.9402-0.44610-1.0016-0.5369-0.31520
(p-val)(0 )(2e-04 )(NA )(0 )(1e-04 )(0.0658 )(NA )
Estimates ( 4 )-0.9742-0.44680-1.0014-0.461800
(p-val)(0 )(2e-04 )(NA )(0 )(3e-04 )(NA )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -1.0256 & -0.6136 & -0.1521 & -0.9983 & -0.7658 & -0.4072 & 0.2762 \tabularnewline
(p-val) & (0 ) & (8e-04 ) & (0.2388 ) & (0 ) & (0.1818 ) & (0.0904 ) & (0.6575 ) \tabularnewline
Estimates ( 2 ) & -1.018 & -0.5969 & -0.1499 & -0.9983 & -0.5143 & -0.3081 & 0 \tabularnewline
(p-val) & (0 ) & (8e-04 ) & (0.245 ) & (0 ) & (2e-04 ) & (0.0732 ) & (NA ) \tabularnewline
Estimates ( 3 ) & -0.9402 & -0.4461 & 0 & -1.0016 & -0.5369 & -0.3152 & 0 \tabularnewline
(p-val) & (0 ) & (2e-04 ) & (NA ) & (0 ) & (1e-04 ) & (0.0658 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.9742 & -0.4468 & 0 & -1.0014 & -0.4618 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (2e-04 ) & (NA ) & (0 ) & (3e-04 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201644&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-1.0256[/C][C]-0.6136[/C][C]-0.1521[/C][C]-0.9983[/C][C]-0.7658[/C][C]-0.4072[/C][C]0.2762[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](8e-04 )[/C][C](0.2388 )[/C][C](0 )[/C][C](0.1818 )[/C][C](0.0904 )[/C][C](0.6575 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-1.018[/C][C]-0.5969[/C][C]-0.1499[/C][C]-0.9983[/C][C]-0.5143[/C][C]-0.3081[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](8e-04 )[/C][C](0.245 )[/C][C](0 )[/C][C](2e-04 )[/C][C](0.0732 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.9402[/C][C]-0.4461[/C][C]0[/C][C]-1.0016[/C][C]-0.5369[/C][C]-0.3152[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](2e-04 )[/C][C](NA )[/C][C](0 )[/C][C](1e-04 )[/C][C](0.0658 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.9742[/C][C]-0.4468[/C][C]0[/C][C]-1.0014[/C][C]-0.4618[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](2e-04 )[/C][C](NA )[/C][C](0 )[/C][C](3e-04 )[/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][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201644&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201644&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 )-1.0256-0.6136-0.1521-0.9983-0.7658-0.40720.2762
(p-val)(0 )(8e-04 )(0.2388 )(0 )(0.1818 )(0.0904 )(0.6575 )
Estimates ( 2 )-1.018-0.5969-0.1499-0.9983-0.5143-0.30810
(p-val)(0 )(8e-04 )(0.245 )(0 )(2e-04 )(0.0732 )(NA )
Estimates ( 3 )-0.9402-0.44610-1.0016-0.5369-0.31520
(p-val)(0 )(2e-04 )(NA )(0 )(1e-04 )(0.0658 )(NA )
Estimates ( 4 )-0.9742-0.44680-1.0014-0.461800
(p-val)(0 )(2e-04 )(NA )(0 )(3e-04 )(NA )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
5541.25053195015
-26717.260621681
-11096.8995735751
14084.4713071586
13010.9141006813
223945.421685309
-251634.390156234
-119098.393573196
50755.4117005483
85286.7639273275
-118295.384207686
-27949.6169838939
-8831.61572099072
81876.795969459
111213.468547414
-209439.280135481
102709.35360341
-142057.246740276
224215.098086219
159977.095953346
50329.5813766909
-88286.4185123061
-2243.517929397
-45436.8138777233
-95396.0218038732
-110290.880174063
-51662.9308260122
-76675.0273778484
-36681.3374322764
-46055.6155638292
161247.520356547
127420.005869212
12952.7218660898
-67934.4838934765
-4593.49234002268
-22020.9501598857
92522.2769875278
69956.3245385834
-198425.822439898
73598.8656184062
30371.4262572801
-61250.6717786809
147406.180860233
69233.3864912577
-42776.0531525183
-77570.6439940812
-16129.2531069031
23547.7291806268
-59135.8552761928
-75223.8017962516
18080.1317060512
5755.78464193631
4421.24465098542
-9909.98494129632
105634.764542099
4954.20213418691
-15609.1097335735
-37191.8650252877
-11.6993832152771

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
5541.25053195015 \tabularnewline
-26717.260621681 \tabularnewline
-11096.8995735751 \tabularnewline
14084.4713071586 \tabularnewline
13010.9141006813 \tabularnewline
223945.421685309 \tabularnewline
-251634.390156234 \tabularnewline
-119098.393573196 \tabularnewline
50755.4117005483 \tabularnewline
85286.7639273275 \tabularnewline
-118295.384207686 \tabularnewline
-27949.6169838939 \tabularnewline
-8831.61572099072 \tabularnewline
81876.795969459 \tabularnewline
111213.468547414 \tabularnewline
-209439.280135481 \tabularnewline
102709.35360341 \tabularnewline
-142057.246740276 \tabularnewline
224215.098086219 \tabularnewline
159977.095953346 \tabularnewline
50329.5813766909 \tabularnewline
-88286.4185123061 \tabularnewline
-2243.517929397 \tabularnewline
-45436.8138777233 \tabularnewline
-95396.0218038732 \tabularnewline
-110290.880174063 \tabularnewline
-51662.9308260122 \tabularnewline
-76675.0273778484 \tabularnewline
-36681.3374322764 \tabularnewline
-46055.6155638292 \tabularnewline
161247.520356547 \tabularnewline
127420.005869212 \tabularnewline
12952.7218660898 \tabularnewline
-67934.4838934765 \tabularnewline
-4593.49234002268 \tabularnewline
-22020.9501598857 \tabularnewline
92522.2769875278 \tabularnewline
69956.3245385834 \tabularnewline
-198425.822439898 \tabularnewline
73598.8656184062 \tabularnewline
30371.4262572801 \tabularnewline
-61250.6717786809 \tabularnewline
147406.180860233 \tabularnewline
69233.3864912577 \tabularnewline
-42776.0531525183 \tabularnewline
-77570.6439940812 \tabularnewline
-16129.2531069031 \tabularnewline
23547.7291806268 \tabularnewline
-59135.8552761928 \tabularnewline
-75223.8017962516 \tabularnewline
18080.1317060512 \tabularnewline
5755.78464193631 \tabularnewline
4421.24465098542 \tabularnewline
-9909.98494129632 \tabularnewline
105634.764542099 \tabularnewline
4954.20213418691 \tabularnewline
-15609.1097335735 \tabularnewline
-37191.8650252877 \tabularnewline
-11.6993832152771 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201644&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]5541.25053195015[/C][/ROW]
[ROW][C]-26717.260621681[/C][/ROW]
[ROW][C]-11096.8995735751[/C][/ROW]
[ROW][C]14084.4713071586[/C][/ROW]
[ROW][C]13010.9141006813[/C][/ROW]
[ROW][C]223945.421685309[/C][/ROW]
[ROW][C]-251634.390156234[/C][/ROW]
[ROW][C]-119098.393573196[/C][/ROW]
[ROW][C]50755.4117005483[/C][/ROW]
[ROW][C]85286.7639273275[/C][/ROW]
[ROW][C]-118295.384207686[/C][/ROW]
[ROW][C]-27949.6169838939[/C][/ROW]
[ROW][C]-8831.61572099072[/C][/ROW]
[ROW][C]81876.795969459[/C][/ROW]
[ROW][C]111213.468547414[/C][/ROW]
[ROW][C]-209439.280135481[/C][/ROW]
[ROW][C]102709.35360341[/C][/ROW]
[ROW][C]-142057.246740276[/C][/ROW]
[ROW][C]224215.098086219[/C][/ROW]
[ROW][C]159977.095953346[/C][/ROW]
[ROW][C]50329.5813766909[/C][/ROW]
[ROW][C]-88286.4185123061[/C][/ROW]
[ROW][C]-2243.517929397[/C][/ROW]
[ROW][C]-45436.8138777233[/C][/ROW]
[ROW][C]-95396.0218038732[/C][/ROW]
[ROW][C]-110290.880174063[/C][/ROW]
[ROW][C]-51662.9308260122[/C][/ROW]
[ROW][C]-76675.0273778484[/C][/ROW]
[ROW][C]-36681.3374322764[/C][/ROW]
[ROW][C]-46055.6155638292[/C][/ROW]
[ROW][C]161247.520356547[/C][/ROW]
[ROW][C]127420.005869212[/C][/ROW]
[ROW][C]12952.7218660898[/C][/ROW]
[ROW][C]-67934.4838934765[/C][/ROW]
[ROW][C]-4593.49234002268[/C][/ROW]
[ROW][C]-22020.9501598857[/C][/ROW]
[ROW][C]92522.2769875278[/C][/ROW]
[ROW][C]69956.3245385834[/C][/ROW]
[ROW][C]-198425.822439898[/C][/ROW]
[ROW][C]73598.8656184062[/C][/ROW]
[ROW][C]30371.4262572801[/C][/ROW]
[ROW][C]-61250.6717786809[/C][/ROW]
[ROW][C]147406.180860233[/C][/ROW]
[ROW][C]69233.3864912577[/C][/ROW]
[ROW][C]-42776.0531525183[/C][/ROW]
[ROW][C]-77570.6439940812[/C][/ROW]
[ROW][C]-16129.2531069031[/C][/ROW]
[ROW][C]23547.7291806268[/C][/ROW]
[ROW][C]-59135.8552761928[/C][/ROW]
[ROW][C]-75223.8017962516[/C][/ROW]
[ROW][C]18080.1317060512[/C][/ROW]
[ROW][C]5755.78464193631[/C][/ROW]
[ROW][C]4421.24465098542[/C][/ROW]
[ROW][C]-9909.98494129632[/C][/ROW]
[ROW][C]105634.764542099[/C][/ROW]
[ROW][C]4954.20213418691[/C][/ROW]
[ROW][C]-15609.1097335735[/C][/ROW]
[ROW][C]-37191.8650252877[/C][/ROW]
[ROW][C]-11.6993832152771[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201644&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201644&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
5541.25053195015
-26717.260621681
-11096.8995735751
14084.4713071586
13010.9141006813
223945.421685309
-251634.390156234
-119098.393573196
50755.4117005483
85286.7639273275
-118295.384207686
-27949.6169838939
-8831.61572099072
81876.795969459
111213.468547414
-209439.280135481
102709.35360341
-142057.246740276
224215.098086219
159977.095953346
50329.5813766909
-88286.4185123061
-2243.517929397
-45436.8138777233
-95396.0218038732
-110290.880174063
-51662.9308260122
-76675.0273778484
-36681.3374322764
-46055.6155638292
161247.520356547
127420.005869212
12952.7218660898
-67934.4838934765
-4593.49234002268
-22020.9501598857
92522.2769875278
69956.3245385834
-198425.822439898
73598.8656184062
30371.4262572801
-61250.6717786809
147406.180860233
69233.3864912577
-42776.0531525183
-77570.6439940812
-16129.2531069031
23547.7291806268
-59135.8552761928
-75223.8017962516
18080.1317060512
5755.78464193631
4421.24465098542
-9909.98494129632
105634.764542099
4954.20213418691
-15609.1097335735
-37191.8650252877
-11.6993832152771



Parameters (Session):
par1 = 12 ;
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')