Free Statistics

of Irreproducible Research!

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, 04 Dec 2013 06:25:59 -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/2013/Dec/04/t1386156483nuidw9xbhi3abk8.htm/, Retrieved Thu, 28 Mar 2024 23:48:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230518, Retrieved Thu, 28 Mar 2024 23:48:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [WS9 - ARIMA Foor...] [2013-12-04 11:25:59] [38144ba7d4215ff1336c69b1a02252e0] [Current]
- RM      [ARIMA Forecasting] [WS9 - ARIMA] [2013-12-04 12:05:46] [31634b7e94db88df109226f71dc63e83]
-         [ARIMA Backward Selection] [WS9 - ARIMA] [2013-12-04 12:09:06] [31634b7e94db88df109226f71dc63e83]
- RM      [ARIMA Forecasting] [WS 9 - Forecasting] [2013-12-04 12:13:55] [31634b7e94db88df109226f71dc63e83]
- RM      [ARIMA Forecasting] [WS9 - Foorcasting] [2013-12-04 12:14:17] [31634b7e94db88df109226f71dc63e83]
Feedback Forum

Post a new message
Dataseries X:
37
30
47
35
30
43
82
40
47
19
52
136
80
42
54
66
81
63
137
72
107
58
36
52
79
77
54
84
48
96
83
66
61
53
30
74
69
59
42
65
70
100
63
105
82
81
75
102
121
98
76
77
63
37
35
23
40
29
37
51
20
28
13
22
25
13
16
13
16
17
9
17
25
14
8
7
10
7
10
3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230518&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]9 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=230518&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230518&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.2291-0.2435-0.1831-0.1685-0.4626-0.0676-0.1685
(p-val)(0.8974 )(0.8296 )(0.8345 )(0.9431 )(0.8128 )(0.9227 )(0.9431 )
Estimates ( 2 )0.2095-0.237-0.19050-0.5286-0.0988-0.2514
(p-val)(0.9095 )(0.841 )(0.8389 )(NA )(0.7577 )(0.8367 )(0.7745 )
Estimates ( 3 )0-0.1538-0.29390-0.5722-0.21220.0013
(p-val)(NA )(0.5641 )(0.0945 )(NA )(0.5667 )(0.6371 )(0.999 )
Estimates ( 4 )0-0.1536-0.29380-0.571-0.21170
(p-val)(NA )(0.4786 )(0.0621 )(NA )(0 )(0.3539 )(NA )
Estimates ( 5 )00-0.3520-0.5621-0.33460
(p-val)(NA )(NA )(0.0029 )(NA )(0 )(0.0053 )(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 ) & 0.2291 & -0.2435 & -0.1831 & -0.1685 & -0.4626 & -0.0676 & -0.1685 \tabularnewline
(p-val) & (0.8974 ) & (0.8296 ) & (0.8345 ) & (0.9431 ) & (0.8128 ) & (0.9227 ) & (0.9431 ) \tabularnewline
Estimates ( 2 ) & 0.2095 & -0.237 & -0.1905 & 0 & -0.5286 & -0.0988 & -0.2514 \tabularnewline
(p-val) & (0.9095 ) & (0.841 ) & (0.8389 ) & (NA ) & (0.7577 ) & (0.8367 ) & (0.7745 ) \tabularnewline
Estimates ( 3 ) & 0 & -0.1538 & -0.2939 & 0 & -0.5722 & -0.2122 & 0.0013 \tabularnewline
(p-val) & (NA ) & (0.5641 ) & (0.0945 ) & (NA ) & (0.5667 ) & (0.6371 ) & (0.999 ) \tabularnewline
Estimates ( 4 ) & 0 & -0.1536 & -0.2938 & 0 & -0.571 & -0.2117 & 0 \tabularnewline
(p-val) & (NA ) & (0.4786 ) & (0.0621 ) & (NA ) & (0 ) & (0.3539 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & -0.352 & 0 & -0.5621 & -0.3346 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0029 ) & (NA ) & (0 ) & (0.0053 ) & (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=230518&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]0.2291[/C][C]-0.2435[/C][C]-0.1831[/C][C]-0.1685[/C][C]-0.4626[/C][C]-0.0676[/C][C]-0.1685[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8974 )[/C][C](0.8296 )[/C][C](0.8345 )[/C][C](0.9431 )[/C][C](0.8128 )[/C][C](0.9227 )[/C][C](0.9431 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.2095[/C][C]-0.237[/C][C]-0.1905[/C][C]0[/C][C]-0.5286[/C][C]-0.0988[/C][C]-0.2514[/C][/ROW]
[ROW][C](p-val)[/C][C](0.9095 )[/C][C](0.841 )[/C][C](0.8389 )[/C][C](NA )[/C][C](0.7577 )[/C][C](0.8367 )[/C][C](0.7745 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]-0.1538[/C][C]-0.2939[/C][C]0[/C][C]-0.5722[/C][C]-0.2122[/C][C]0.0013[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.5641 )[/C][C](0.0945 )[/C][C](NA )[/C][C](0.5667 )[/C][C](0.6371 )[/C][C](0.999 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]-0.1536[/C][C]-0.2938[/C][C]0[/C][C]-0.571[/C][C]-0.2117[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.4786 )[/C][C](0.0621 )[/C][C](NA )[/C][C](0 )[/C][C](0.3539 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]-0.352[/C][C]0[/C][C]-0.5621[/C][C]-0.3346[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0029 )[/C][C](NA )[/C][C](0 )[/C][C](0.0053 )[/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=230518&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230518&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 )0.2291-0.2435-0.1831-0.1685-0.4626-0.0676-0.1685
(p-val)(0.8974 )(0.8296 )(0.8345 )(0.9431 )(0.8128 )(0.9227 )(0.9431 )
Estimates ( 2 )0.2095-0.237-0.19050-0.5286-0.0988-0.2514
(p-val)(0.9095 )(0.841 )(0.8389 )(NA )(0.7577 )(0.8367 )(0.7745 )
Estimates ( 3 )0-0.1538-0.29390-0.5722-0.21220.0013
(p-val)(NA )(0.5641 )(0.0945 )(NA )(0.5667 )(0.6371 )(0.999 )
Estimates ( 4 )0-0.1536-0.29380-0.571-0.21170
(p-val)(NA )(0.4786 )(0.0621 )(NA )(0 )(0.3539 )(NA )
Estimates ( 5 )00-0.3520-0.5621-0.33460
(p-val)(NA )(NA )(0.0029 )(NA )(0 )(0.0053 )(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
0.0369999746409443
-5.9788260993911
13.2326659864855
-4.89065154715986
-7.97861500135054
11.0260606011445
42.9875575979118
-18.2360876713008
0.476982029759466
-22.1726558030614
12.1641290931707
89.2987570123368
-7.87443816646138
-31.8727562463738
6.76292754382407
2.48086558048848
5.7449527914586
-11.5666027421916
73.8201747316708
-20.4486113099643
21.8000329634804
-27.1977267950616
-48.2917822747903
-9.52421336883806
12.371569562996
3.23042137259639
-15.6279991403241
28.2744358519461
-21.632148610225
30.9061449217619
7.97241765941931
-16.0458996741479
-6.48652341340767
-14.6501557007084
-35.4992359320581
21.8235643006317
6.61106463471603
-7.4705717456512
-12.8528350204583
15.1146083603297
9.84246586256988
32.4568221662862
-13.2949213950463
37.2890706584655
1.34396922716284
-6.58786087590239
-4.49305969442355
20.5442897339218
29.8490417370505
-6.20790809521093
-19.154621626737
-7.67992578941498
-24.7563643668653
-45.4475664211461
-27.4153272882316
-29.1492373741064
-3.24447746401397
-12.5178451240732
1.33304750721963
18.5080647676684
-21.6219836198498
-2.68037238124473
-15.4964830420381
-5.16879640457591
0.373593855453976
-13.0480568148802
-1.82901048420911
-3.65623673766761
-1.03500562243322
0.544842602214881
-7.62316019452506
4.52764171140171
10.4414344035459
-6.17509080948551
-7.84638535701176
-4.28717150170863
-1.85990259183404
-5.6471838307484
0.115527225084431
-5.81171159140988

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0369999746409443 \tabularnewline
-5.9788260993911 \tabularnewline
13.2326659864855 \tabularnewline
-4.89065154715986 \tabularnewline
-7.97861500135054 \tabularnewline
11.0260606011445 \tabularnewline
42.9875575979118 \tabularnewline
-18.2360876713008 \tabularnewline
0.476982029759466 \tabularnewline
-22.1726558030614 \tabularnewline
12.1641290931707 \tabularnewline
89.2987570123368 \tabularnewline
-7.87443816646138 \tabularnewline
-31.8727562463738 \tabularnewline
6.76292754382407 \tabularnewline
2.48086558048848 \tabularnewline
5.7449527914586 \tabularnewline
-11.5666027421916 \tabularnewline
73.8201747316708 \tabularnewline
-20.4486113099643 \tabularnewline
21.8000329634804 \tabularnewline
-27.1977267950616 \tabularnewline
-48.2917822747903 \tabularnewline
-9.52421336883806 \tabularnewline
12.371569562996 \tabularnewline
3.23042137259639 \tabularnewline
-15.6279991403241 \tabularnewline
28.2744358519461 \tabularnewline
-21.632148610225 \tabularnewline
30.9061449217619 \tabularnewline
7.97241765941931 \tabularnewline
-16.0458996741479 \tabularnewline
-6.48652341340767 \tabularnewline
-14.6501557007084 \tabularnewline
-35.4992359320581 \tabularnewline
21.8235643006317 \tabularnewline
6.61106463471603 \tabularnewline
-7.4705717456512 \tabularnewline
-12.8528350204583 \tabularnewline
15.1146083603297 \tabularnewline
9.84246586256988 \tabularnewline
32.4568221662862 \tabularnewline
-13.2949213950463 \tabularnewline
37.2890706584655 \tabularnewline
1.34396922716284 \tabularnewline
-6.58786087590239 \tabularnewline
-4.49305969442355 \tabularnewline
20.5442897339218 \tabularnewline
29.8490417370505 \tabularnewline
-6.20790809521093 \tabularnewline
-19.154621626737 \tabularnewline
-7.67992578941498 \tabularnewline
-24.7563643668653 \tabularnewline
-45.4475664211461 \tabularnewline
-27.4153272882316 \tabularnewline
-29.1492373741064 \tabularnewline
-3.24447746401397 \tabularnewline
-12.5178451240732 \tabularnewline
1.33304750721963 \tabularnewline
18.5080647676684 \tabularnewline
-21.6219836198498 \tabularnewline
-2.68037238124473 \tabularnewline
-15.4964830420381 \tabularnewline
-5.16879640457591 \tabularnewline
0.373593855453976 \tabularnewline
-13.0480568148802 \tabularnewline
-1.82901048420911 \tabularnewline
-3.65623673766761 \tabularnewline
-1.03500562243322 \tabularnewline
0.544842602214881 \tabularnewline
-7.62316019452506 \tabularnewline
4.52764171140171 \tabularnewline
10.4414344035459 \tabularnewline
-6.17509080948551 \tabularnewline
-7.84638535701176 \tabularnewline
-4.28717150170863 \tabularnewline
-1.85990259183404 \tabularnewline
-5.6471838307484 \tabularnewline
0.115527225084431 \tabularnewline
-5.81171159140988 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230518&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0369999746409443[/C][/ROW]
[ROW][C]-5.9788260993911[/C][/ROW]
[ROW][C]13.2326659864855[/C][/ROW]
[ROW][C]-4.89065154715986[/C][/ROW]
[ROW][C]-7.97861500135054[/C][/ROW]
[ROW][C]11.0260606011445[/C][/ROW]
[ROW][C]42.9875575979118[/C][/ROW]
[ROW][C]-18.2360876713008[/C][/ROW]
[ROW][C]0.476982029759466[/C][/ROW]
[ROW][C]-22.1726558030614[/C][/ROW]
[ROW][C]12.1641290931707[/C][/ROW]
[ROW][C]89.2987570123368[/C][/ROW]
[ROW][C]-7.87443816646138[/C][/ROW]
[ROW][C]-31.8727562463738[/C][/ROW]
[ROW][C]6.76292754382407[/C][/ROW]
[ROW][C]2.48086558048848[/C][/ROW]
[ROW][C]5.7449527914586[/C][/ROW]
[ROW][C]-11.5666027421916[/C][/ROW]
[ROW][C]73.8201747316708[/C][/ROW]
[ROW][C]-20.4486113099643[/C][/ROW]
[ROW][C]21.8000329634804[/C][/ROW]
[ROW][C]-27.1977267950616[/C][/ROW]
[ROW][C]-48.2917822747903[/C][/ROW]
[ROW][C]-9.52421336883806[/C][/ROW]
[ROW][C]12.371569562996[/C][/ROW]
[ROW][C]3.23042137259639[/C][/ROW]
[ROW][C]-15.6279991403241[/C][/ROW]
[ROW][C]28.2744358519461[/C][/ROW]
[ROW][C]-21.632148610225[/C][/ROW]
[ROW][C]30.9061449217619[/C][/ROW]
[ROW][C]7.97241765941931[/C][/ROW]
[ROW][C]-16.0458996741479[/C][/ROW]
[ROW][C]-6.48652341340767[/C][/ROW]
[ROW][C]-14.6501557007084[/C][/ROW]
[ROW][C]-35.4992359320581[/C][/ROW]
[ROW][C]21.8235643006317[/C][/ROW]
[ROW][C]6.61106463471603[/C][/ROW]
[ROW][C]-7.4705717456512[/C][/ROW]
[ROW][C]-12.8528350204583[/C][/ROW]
[ROW][C]15.1146083603297[/C][/ROW]
[ROW][C]9.84246586256988[/C][/ROW]
[ROW][C]32.4568221662862[/C][/ROW]
[ROW][C]-13.2949213950463[/C][/ROW]
[ROW][C]37.2890706584655[/C][/ROW]
[ROW][C]1.34396922716284[/C][/ROW]
[ROW][C]-6.58786087590239[/C][/ROW]
[ROW][C]-4.49305969442355[/C][/ROW]
[ROW][C]20.5442897339218[/C][/ROW]
[ROW][C]29.8490417370505[/C][/ROW]
[ROW][C]-6.20790809521093[/C][/ROW]
[ROW][C]-19.154621626737[/C][/ROW]
[ROW][C]-7.67992578941498[/C][/ROW]
[ROW][C]-24.7563643668653[/C][/ROW]
[ROW][C]-45.4475664211461[/C][/ROW]
[ROW][C]-27.4153272882316[/C][/ROW]
[ROW][C]-29.1492373741064[/C][/ROW]
[ROW][C]-3.24447746401397[/C][/ROW]
[ROW][C]-12.5178451240732[/C][/ROW]
[ROW][C]1.33304750721963[/C][/ROW]
[ROW][C]18.5080647676684[/C][/ROW]
[ROW][C]-21.6219836198498[/C][/ROW]
[ROW][C]-2.68037238124473[/C][/ROW]
[ROW][C]-15.4964830420381[/C][/ROW]
[ROW][C]-5.16879640457591[/C][/ROW]
[ROW][C]0.373593855453976[/C][/ROW]
[ROW][C]-13.0480568148802[/C][/ROW]
[ROW][C]-1.82901048420911[/C][/ROW]
[ROW][C]-3.65623673766761[/C][/ROW]
[ROW][C]-1.03500562243322[/C][/ROW]
[ROW][C]0.544842602214881[/C][/ROW]
[ROW][C]-7.62316019452506[/C][/ROW]
[ROW][C]4.52764171140171[/C][/ROW]
[ROW][C]10.4414344035459[/C][/ROW]
[ROW][C]-6.17509080948551[/C][/ROW]
[ROW][C]-7.84638535701176[/C][/ROW]
[ROW][C]-4.28717150170863[/C][/ROW]
[ROW][C]-1.85990259183404[/C][/ROW]
[ROW][C]-5.6471838307484[/C][/ROW]
[ROW][C]0.115527225084431[/C][/ROW]
[ROW][C]-5.81171159140988[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230518&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230518&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
0.0369999746409443
-5.9788260993911
13.2326659864855
-4.89065154715986
-7.97861500135054
11.0260606011445
42.9875575979118
-18.2360876713008
0.476982029759466
-22.1726558030614
12.1641290931707
89.2987570123368
-7.87443816646138
-31.8727562463738
6.76292754382407
2.48086558048848
5.7449527914586
-11.5666027421916
73.8201747316708
-20.4486113099643
21.8000329634804
-27.1977267950616
-48.2917822747903
-9.52421336883806
12.371569562996
3.23042137259639
-15.6279991403241
28.2744358519461
-21.632148610225
30.9061449217619
7.97241765941931
-16.0458996741479
-6.48652341340767
-14.6501557007084
-35.4992359320581
21.8235643006317
6.61106463471603
-7.4705717456512
-12.8528350204583
15.1146083603297
9.84246586256988
32.4568221662862
-13.2949213950463
37.2890706584655
1.34396922716284
-6.58786087590239
-4.49305969442355
20.5442897339218
29.8490417370505
-6.20790809521093
-19.154621626737
-7.67992578941498
-24.7563643668653
-45.4475664211461
-27.4153272882316
-29.1492373741064
-3.24447746401397
-12.5178451240732
1.33304750721963
18.5080647676684
-21.6219836198498
-2.68037238124473
-15.4964830420381
-5.16879640457591
0.373593855453976
-13.0480568148802
-1.82901048420911
-3.65623673766761
-1.03500562243322
0.544842602214881
-7.62316019452506
4.52764171140171
10.4414344035459
-6.17509080948551
-7.84638535701176
-4.28717150170863
-1.85990259183404
-5.6471838307484
0.115527225084431
-5.81171159140988



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 1 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 1 ; 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')