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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 computationFri, 20 Dec 2013 11:16:51 -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/20/t1387556268rstqglyhp05l5gt.htm/, Retrieved Fri, 29 Mar 2024 00:19:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232469, Retrieved Fri, 29 Mar 2024 00:19:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [] [2013-12-20 16:16:51] [9e6a405f514733ea23d87e4507d39d29] [Current]
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Dataseries X:
56
55
54
52
72
71
56
46
47
47
48
50
44
38
33
33
52
54
39
22
31
31
38
42
41
31
36
34
51
47
31
19
30
33
36
40
32
25
28
29
55
55
40
38
44
41
49
59
61
47
43
39
66
68
63
68
67
59
68
78
82
70
62
68
94
102
100
104
103
93
110
114
120
102
95
103
122
139
135
135
137
130
148
148
145
128
131
133
146
163
151
157
152
149
172
167
160
150
160
165
171
179
171
176
170
169
194
196
188
174
186
191
197
206
197
204
201
190
213
213




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time30 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 30 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232469&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]30 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232469&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232469&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 time30 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.2652-0.0708-0.1464-0.39390.737-0.1256-0.7307
(p-val)(0.4007 )(0.5281 )(0.1863 )(0.2059 )(0.0916 )(0.2871 )(0.1088 )
Estimates ( 2 )0.3170-0.154-0.46510.7337-0.1513-0.7047
(p-val)(0.2599 )(NA )(0.158 )(0.0891 )(0.0717 )(0.1584 )(0.0958 )
Estimates ( 3 )00-0.1715-0.15260.7168-0.1527-0.699
(p-val)(NA )(NA )(0.0874 )(0.1582 )(0.061 )(0.1573 )(0.0779 )
Estimates ( 4 )00-0.141700.6441-0.1456-0.6353
(p-val)(NA )(NA )(0.1506 )(NA )(0.0907 )(0.1723 )(0.1032 )
Estimates ( 5 )00-0.15610-0.217800.2482
(p-val)(NA )(NA )(0.1098 )(NA )(0.8273 )(NA )(0.8022 )
Estimates ( 6 )00-0.15590000.0234
(p-val)(NA )(NA )(0.11 )(NA )(NA )(NA )(0.8274 )
Estimates ( 7 )00-0.1560000
(p-val)(NA )(NA )(0.1097 )(NA )(NA )(NA )(NA )
Estimates ( 8 )0000000
(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 ) & 0.2652 & -0.0708 & -0.1464 & -0.3939 & 0.737 & -0.1256 & -0.7307 \tabularnewline
(p-val) & (0.4007 ) & (0.5281 ) & (0.1863 ) & (0.2059 ) & (0.0916 ) & (0.2871 ) & (0.1088 ) \tabularnewline
Estimates ( 2 ) & 0.317 & 0 & -0.154 & -0.4651 & 0.7337 & -0.1513 & -0.7047 \tabularnewline
(p-val) & (0.2599 ) & (NA ) & (0.158 ) & (0.0891 ) & (0.0717 ) & (0.1584 ) & (0.0958 ) \tabularnewline
Estimates ( 3 ) & 0 & 0 & -0.1715 & -0.1526 & 0.7168 & -0.1527 & -0.699 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0874 ) & (0.1582 ) & (0.061 ) & (0.1573 ) & (0.0779 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & -0.1417 & 0 & 0.6441 & -0.1456 & -0.6353 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1506 ) & (NA ) & (0.0907 ) & (0.1723 ) & (0.1032 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & -0.1561 & 0 & -0.2178 & 0 & 0.2482 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1098 ) & (NA ) & (0.8273 ) & (NA ) & (0.8022 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & -0.1559 & 0 & 0 & 0 & 0.0234 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.11 ) & (NA ) & (NA ) & (NA ) & (0.8274 ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & -0.156 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1097 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & 0 & 0 & 0 & 0 & 0 & 0 & 0 \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=232469&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]0.2652[/C][C]-0.0708[/C][C]-0.1464[/C][C]-0.3939[/C][C]0.737[/C][C]-0.1256[/C][C]-0.7307[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4007 )[/C][C](0.5281 )[/C][C](0.1863 )[/C][C](0.2059 )[/C][C](0.0916 )[/C][C](0.2871 )[/C][C](0.1088 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.317[/C][C]0[/C][C]-0.154[/C][C]-0.4651[/C][C]0.7337[/C][C]-0.1513[/C][C]-0.7047[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2599 )[/C][C](NA )[/C][C](0.158 )[/C][C](0.0891 )[/C][C](0.0717 )[/C][C](0.1584 )[/C][C](0.0958 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0[/C][C]-0.1715[/C][C]-0.1526[/C][C]0.7168[/C][C]-0.1527[/C][C]-0.699[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0874 )[/C][C](0.1582 )[/C][C](0.061 )[/C][C](0.1573 )[/C][C](0.0779 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]-0.1417[/C][C]0[/C][C]0.6441[/C][C]-0.1456[/C][C]-0.6353[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1506 )[/C][C](NA )[/C][C](0.0907 )[/C][C](0.1723 )[/C][C](0.1032 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]-0.1561[/C][C]0[/C][C]-0.2178[/C][C]0[/C][C]0.2482[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1098 )[/C][C](NA )[/C][C](0.8273 )[/C][C](NA )[/C][C](0.8022 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]-0.1559[/C][C]0[/C][C]0[/C][C]0[/C][C]0.0234[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.11 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.8274 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]-0.156[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1097 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/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=232469&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232469&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 )0.2652-0.0708-0.1464-0.39390.737-0.1256-0.7307
(p-val)(0.4007 )(0.5281 )(0.1863 )(0.2059 )(0.0916 )(0.2871 )(0.1088 )
Estimates ( 2 )0.3170-0.154-0.46510.7337-0.1513-0.7047
(p-val)(0.2599 )(NA )(0.158 )(0.0891 )(0.0717 )(0.1584 )(0.0958 )
Estimates ( 3 )00-0.1715-0.15260.7168-0.1527-0.699
(p-val)(NA )(NA )(0.0874 )(0.1582 )(0.061 )(0.1573 )(0.0779 )
Estimates ( 4 )00-0.141700.6441-0.1456-0.6353
(p-val)(NA )(NA )(0.1506 )(NA )(0.0907 )(0.1723 )(0.1032 )
Estimates ( 5 )00-0.15610-0.217800.2482
(p-val)(NA )(NA )(0.1098 )(NA )(0.8273 )(NA )(0.8022 )
Estimates ( 6 )00-0.15590000.0234
(p-val)(NA )(NA )(0.11 )(NA )(NA )(NA )(0.8274 )
Estimates ( 7 )00-0.1560000
(p-val)(NA )(NA )(0.1097 )(NA )(NA )(NA )(NA )
Estimates ( 8 )0000000
(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
-0.236493005027621
-4.93891480965441
-3.95116239084637
1.9753875025203
-1.7798512823075
2.37611897412902
0.311940513005736
-7.15597025619578
8.46791076910932
-1.41127366738798e-10
4.90820820490978
3.24776205176132
4.99999999991935
-3.06417846121247
10.3119405129169
-1.22014871767994
-2.62388102585579
-4.4402974353625
-1.31194051292749
4.6880594870725
1.06417846121749
2.84402974353625
-3.22014871768125
0.311940512927502
-6.53208923060875
2.376118974145
-2
1.90820820475374
9.46791076939125
3.6880594870725
1.46791076939125
11.4037323081738
-4.37611897414499
-5.84402974353625
6.55970256463751
5.22014871768124
9.06417846121749
-6.22014871768125
-6.06417846121749
-3.44029743536249
-0.0917917952462566
0.908208204753743
9.22014871768125
7.15597025646375
-6.6880594870725
-3.44029743536249
2.09179179524626
-1.09179179524626
1.22014871768125
2.15597025646375
-4
10.3119405129275
-0.688059487072498
5.376118974145
4.55970256463751
-1.15597025646375
0.935821538782506
-1.53208923060875
7.84402974353625
-6
1.6880594870725
-4.75223794828999
0.0641784612174945
2.3119405129275
-7.93582153878251
9.15597025646375
-1.6880594870725
-5.09179179524626
4.40373230817376
2.6880594870725
0.376118974144996
-3.53208923060875
-8.53208923060875
1.15597025646375
9.376118974145
-7.40373230817376
-5.84402974353625
1.55970256463751
-8.93582153878251
5.06417846121749
-7
2.75223794828999
5.93582153878251
-6.09179179524626
-3.376118974145
7.77985128231875
6.22014871768125
2.376118974145
-5.90820820475374
-7.90820820475374
4.46791076939125
-2.09179179524626
-2.40373230817376
2.623881025855
1.84402974353625
6.84402974353625
-0.688059487072498
-3.6880594870725
3.09179179524626
-0.155970256463751
-0.623881025855004
1.3119405129275
-1
2
3.15597025646375
-10.1559702564638
-1.6880594870725
-1.53208923060875

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.236493005027621 \tabularnewline
-4.93891480965441 \tabularnewline
-3.95116239084637 \tabularnewline
1.9753875025203 \tabularnewline
-1.7798512823075 \tabularnewline
2.37611897412902 \tabularnewline
0.311940513005736 \tabularnewline
-7.15597025619578 \tabularnewline
8.46791076910932 \tabularnewline
-1.41127366738798e-10 \tabularnewline
4.90820820490978 \tabularnewline
3.24776205176132 \tabularnewline
4.99999999991935 \tabularnewline
-3.06417846121247 \tabularnewline
10.3119405129169 \tabularnewline
-1.22014871767994 \tabularnewline
-2.62388102585579 \tabularnewline
-4.4402974353625 \tabularnewline
-1.31194051292749 \tabularnewline
4.6880594870725 \tabularnewline
1.06417846121749 \tabularnewline
2.84402974353625 \tabularnewline
-3.22014871768125 \tabularnewline
0.311940512927502 \tabularnewline
-6.53208923060875 \tabularnewline
2.376118974145 \tabularnewline
-2 \tabularnewline
1.90820820475374 \tabularnewline
9.46791076939125 \tabularnewline
3.6880594870725 \tabularnewline
1.46791076939125 \tabularnewline
11.4037323081738 \tabularnewline
-4.37611897414499 \tabularnewline
-5.84402974353625 \tabularnewline
6.55970256463751 \tabularnewline
5.22014871768124 \tabularnewline
9.06417846121749 \tabularnewline
-6.22014871768125 \tabularnewline
-6.06417846121749 \tabularnewline
-3.44029743536249 \tabularnewline
-0.0917917952462566 \tabularnewline
0.908208204753743 \tabularnewline
9.22014871768125 \tabularnewline
7.15597025646375 \tabularnewline
-6.6880594870725 \tabularnewline
-3.44029743536249 \tabularnewline
2.09179179524626 \tabularnewline
-1.09179179524626 \tabularnewline
1.22014871768125 \tabularnewline
2.15597025646375 \tabularnewline
-4 \tabularnewline
10.3119405129275 \tabularnewline
-0.688059487072498 \tabularnewline
5.376118974145 \tabularnewline
4.55970256463751 \tabularnewline
-1.15597025646375 \tabularnewline
0.935821538782506 \tabularnewline
-1.53208923060875 \tabularnewline
7.84402974353625 \tabularnewline
-6 \tabularnewline
1.6880594870725 \tabularnewline
-4.75223794828999 \tabularnewline
0.0641784612174945 \tabularnewline
2.3119405129275 \tabularnewline
-7.93582153878251 \tabularnewline
9.15597025646375 \tabularnewline
-1.6880594870725 \tabularnewline
-5.09179179524626 \tabularnewline
4.40373230817376 \tabularnewline
2.6880594870725 \tabularnewline
0.376118974144996 \tabularnewline
-3.53208923060875 \tabularnewline
-8.53208923060875 \tabularnewline
1.15597025646375 \tabularnewline
9.376118974145 \tabularnewline
-7.40373230817376 \tabularnewline
-5.84402974353625 \tabularnewline
1.55970256463751 \tabularnewline
-8.93582153878251 \tabularnewline
5.06417846121749 \tabularnewline
-7 \tabularnewline
2.75223794828999 \tabularnewline
5.93582153878251 \tabularnewline
-6.09179179524626 \tabularnewline
-3.376118974145 \tabularnewline
7.77985128231875 \tabularnewline
6.22014871768125 \tabularnewline
2.376118974145 \tabularnewline
-5.90820820475374 \tabularnewline
-7.90820820475374 \tabularnewline
4.46791076939125 \tabularnewline
-2.09179179524626 \tabularnewline
-2.40373230817376 \tabularnewline
2.623881025855 \tabularnewline
1.84402974353625 \tabularnewline
6.84402974353625 \tabularnewline
-0.688059487072498 \tabularnewline
-3.6880594870725 \tabularnewline
3.09179179524626 \tabularnewline
-0.155970256463751 \tabularnewline
-0.623881025855004 \tabularnewline
1.3119405129275 \tabularnewline
-1 \tabularnewline
2 \tabularnewline
3.15597025646375 \tabularnewline
-10.1559702564638 \tabularnewline
-1.6880594870725 \tabularnewline
-1.53208923060875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232469&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.236493005027621[/C][/ROW]
[ROW][C]-4.93891480965441[/C][/ROW]
[ROW][C]-3.95116239084637[/C][/ROW]
[ROW][C]1.9753875025203[/C][/ROW]
[ROW][C]-1.7798512823075[/C][/ROW]
[ROW][C]2.37611897412902[/C][/ROW]
[ROW][C]0.311940513005736[/C][/ROW]
[ROW][C]-7.15597025619578[/C][/ROW]
[ROW][C]8.46791076910932[/C][/ROW]
[ROW][C]-1.41127366738798e-10[/C][/ROW]
[ROW][C]4.90820820490978[/C][/ROW]
[ROW][C]3.24776205176132[/C][/ROW]
[ROW][C]4.99999999991935[/C][/ROW]
[ROW][C]-3.06417846121247[/C][/ROW]
[ROW][C]10.3119405129169[/C][/ROW]
[ROW][C]-1.22014871767994[/C][/ROW]
[ROW][C]-2.62388102585579[/C][/ROW]
[ROW][C]-4.4402974353625[/C][/ROW]
[ROW][C]-1.31194051292749[/C][/ROW]
[ROW][C]4.6880594870725[/C][/ROW]
[ROW][C]1.06417846121749[/C][/ROW]
[ROW][C]2.84402974353625[/C][/ROW]
[ROW][C]-3.22014871768125[/C][/ROW]
[ROW][C]0.311940512927502[/C][/ROW]
[ROW][C]-6.53208923060875[/C][/ROW]
[ROW][C]2.376118974145[/C][/ROW]
[ROW][C]-2[/C][/ROW]
[ROW][C]1.90820820475374[/C][/ROW]
[ROW][C]9.46791076939125[/C][/ROW]
[ROW][C]3.6880594870725[/C][/ROW]
[ROW][C]1.46791076939125[/C][/ROW]
[ROW][C]11.4037323081738[/C][/ROW]
[ROW][C]-4.37611897414499[/C][/ROW]
[ROW][C]-5.84402974353625[/C][/ROW]
[ROW][C]6.55970256463751[/C][/ROW]
[ROW][C]5.22014871768124[/C][/ROW]
[ROW][C]9.06417846121749[/C][/ROW]
[ROW][C]-6.22014871768125[/C][/ROW]
[ROW][C]-6.06417846121749[/C][/ROW]
[ROW][C]-3.44029743536249[/C][/ROW]
[ROW][C]-0.0917917952462566[/C][/ROW]
[ROW][C]0.908208204753743[/C][/ROW]
[ROW][C]9.22014871768125[/C][/ROW]
[ROW][C]7.15597025646375[/C][/ROW]
[ROW][C]-6.6880594870725[/C][/ROW]
[ROW][C]-3.44029743536249[/C][/ROW]
[ROW][C]2.09179179524626[/C][/ROW]
[ROW][C]-1.09179179524626[/C][/ROW]
[ROW][C]1.22014871768125[/C][/ROW]
[ROW][C]2.15597025646375[/C][/ROW]
[ROW][C]-4[/C][/ROW]
[ROW][C]10.3119405129275[/C][/ROW]
[ROW][C]-0.688059487072498[/C][/ROW]
[ROW][C]5.376118974145[/C][/ROW]
[ROW][C]4.55970256463751[/C][/ROW]
[ROW][C]-1.15597025646375[/C][/ROW]
[ROW][C]0.935821538782506[/C][/ROW]
[ROW][C]-1.53208923060875[/C][/ROW]
[ROW][C]7.84402974353625[/C][/ROW]
[ROW][C]-6[/C][/ROW]
[ROW][C]1.6880594870725[/C][/ROW]
[ROW][C]-4.75223794828999[/C][/ROW]
[ROW][C]0.0641784612174945[/C][/ROW]
[ROW][C]2.3119405129275[/C][/ROW]
[ROW][C]-7.93582153878251[/C][/ROW]
[ROW][C]9.15597025646375[/C][/ROW]
[ROW][C]-1.6880594870725[/C][/ROW]
[ROW][C]-5.09179179524626[/C][/ROW]
[ROW][C]4.40373230817376[/C][/ROW]
[ROW][C]2.6880594870725[/C][/ROW]
[ROW][C]0.376118974144996[/C][/ROW]
[ROW][C]-3.53208923060875[/C][/ROW]
[ROW][C]-8.53208923060875[/C][/ROW]
[ROW][C]1.15597025646375[/C][/ROW]
[ROW][C]9.376118974145[/C][/ROW]
[ROW][C]-7.40373230817376[/C][/ROW]
[ROW][C]-5.84402974353625[/C][/ROW]
[ROW][C]1.55970256463751[/C][/ROW]
[ROW][C]-8.93582153878251[/C][/ROW]
[ROW][C]5.06417846121749[/C][/ROW]
[ROW][C]-7[/C][/ROW]
[ROW][C]2.75223794828999[/C][/ROW]
[ROW][C]5.93582153878251[/C][/ROW]
[ROW][C]-6.09179179524626[/C][/ROW]
[ROW][C]-3.376118974145[/C][/ROW]
[ROW][C]7.77985128231875[/C][/ROW]
[ROW][C]6.22014871768125[/C][/ROW]
[ROW][C]2.376118974145[/C][/ROW]
[ROW][C]-5.90820820475374[/C][/ROW]
[ROW][C]-7.90820820475374[/C][/ROW]
[ROW][C]4.46791076939125[/C][/ROW]
[ROW][C]-2.09179179524626[/C][/ROW]
[ROW][C]-2.40373230817376[/C][/ROW]
[ROW][C]2.623881025855[/C][/ROW]
[ROW][C]1.84402974353625[/C][/ROW]
[ROW][C]6.84402974353625[/C][/ROW]
[ROW][C]-0.688059487072498[/C][/ROW]
[ROW][C]-3.6880594870725[/C][/ROW]
[ROW][C]3.09179179524626[/C][/ROW]
[ROW][C]-0.155970256463751[/C][/ROW]
[ROW][C]-0.623881025855004[/C][/ROW]
[ROW][C]1.3119405129275[/C][/ROW]
[ROW][C]-1[/C][/ROW]
[ROW][C]2[/C][/ROW]
[ROW][C]3.15597025646375[/C][/ROW]
[ROW][C]-10.1559702564638[/C][/ROW]
[ROW][C]-1.6880594870725[/C][/ROW]
[ROW][C]-1.53208923060875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232469&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232469&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
-0.236493005027621
-4.93891480965441
-3.95116239084637
1.9753875025203
-1.7798512823075
2.37611897412902
0.311940513005736
-7.15597025619578
8.46791076910932
-1.41127366738798e-10
4.90820820490978
3.24776205176132
4.99999999991935
-3.06417846121247
10.3119405129169
-1.22014871767994
-2.62388102585579
-4.4402974353625
-1.31194051292749
4.6880594870725
1.06417846121749
2.84402974353625
-3.22014871768125
0.311940512927502
-6.53208923060875
2.376118974145
-2
1.90820820475374
9.46791076939125
3.6880594870725
1.46791076939125
11.4037323081738
-4.37611897414499
-5.84402974353625
6.55970256463751
5.22014871768124
9.06417846121749
-6.22014871768125
-6.06417846121749
-3.44029743536249
-0.0917917952462566
0.908208204753743
9.22014871768125
7.15597025646375
-6.6880594870725
-3.44029743536249
2.09179179524626
-1.09179179524626
1.22014871768125
2.15597025646375
-4
10.3119405129275
-0.688059487072498
5.376118974145
4.55970256463751
-1.15597025646375
0.935821538782506
-1.53208923060875
7.84402974353625
-6
1.6880594870725
-4.75223794828999
0.0641784612174945
2.3119405129275
-7.93582153878251
9.15597025646375
-1.6880594870725
-5.09179179524626
4.40373230817376
2.6880594870725
0.376118974144996
-3.53208923060875
-8.53208923060875
1.15597025646375
9.376118974145
-7.40373230817376
-5.84402974353625
1.55970256463751
-8.93582153878251
5.06417846121749
-7
2.75223794828999
5.93582153878251
-6.09179179524626
-3.376118974145
7.77985128231875
6.22014871768125
2.376118974145
-5.90820820475374
-7.90820820475374
4.46791076939125
-2.09179179524626
-2.40373230817376
2.623881025855
1.84402974353625
6.84402974353625
-0.688059487072498
-3.6880594870725
3.09179179524626
-0.155970256463751
-0.623881025855004
1.3119405129275
-1
2
3.15597025646375
-10.1559702564638
-1.6880594870725
-1.53208923060875



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