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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 computationThu, 10 Dec 2009 09:04:57 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t12604611674ptw0sq9y987xwj.htm/, Retrieved Thu, 28 Mar 2024 20:33:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65530, Retrieved Thu, 28 Mar 2024 20:33:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2009-12-07 09:20:41] [b98453cac15ba1066b407e146608df68]
-    D    [ARIMA Backward Selection] [workshop 10] [2009-12-10 16:04:57] [6c94b261890ba36343a04d1029691995] [Current]
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Dataseries X:
87.28
87.09
86.92
87.59
90.72
90.69
90.3
89.55
88.94
88.41
87.82
87.07
86.82
86.4
86.02
85.66
85.32
85.00
84.67
83.94
82.83
81.95
81.19
80.48
78.86
69.47
68.77
70.06
73.95
75.8
77.79
81.57
83.07
84.34
85.1
85.25
84.26
83.63
86.44
85.3
84.1
83.36
82.48
81.58
80.47
79.34
82.13
81.69
80.7
79.88
79.16
78.38
77.42
76.47
75.46
74.48
78.27
80.7
79.91
78.75
77.78
81.14
81.08
80.03
78.91
78.01
76.9
75.97
81.93
80.27
78.67
77.42
76.16
74.7
76.39
76.04
74.65
73.29
71.79
74.39
74.91
74.54
73.08
72.75
71.32
70.38
70.35
70.01
69.36
67.77
69.26
69.8
68.38
67.62
68.39
66.95
65.21
66.64
63.45
60.66
62.34
60.32
58.64
60.46
58.59
61.87
61.85
67.44
77.06
91.74
93.15
94.15
93.11
91.51
89.96
88.16
86.98
88.03
86.24
84.65
83.23
81.7
80.25
78.8
77.51
76.2
75.04
74.00
75.49
77.14
76.15
76.27
78.19
76.49
77.31
76.65
74.99
73.51
72.07
70.59
71.96
76.29
74.86
74.93
71.9
71.01
77.47
75.78
76.6
76.07
74.57
73.02
72.65
73.16
71.53
69.78
67.98
69.96
72.16
70.47
68.86
67.37
65.87
72.16
71.34
69.93
68.44
67.16
66.01
67.25
70.91
69.75
68.59
67.48
66.31
64.81
66.58
65.97
64.7
64.7
60.94
59.08
58.42
57.77
57.11
53.31
49.96
49.4
48.84
48.3
47.74
47.24
46.76
46.29
48.9
49.23
48.53
48.03
54.34
53.79
53.24
52.96
52.17
51.7
58.55
78.2
77.03
76.19
77.15
75.87
95.47
109.67
112.28
112.01
107.93
105.96
105.06
102.98
102.2
105.23
101.85
99.89
96.23
94.76
91.51
91.63
91.54
85.23
87.83
87.38
84.44
85.19
84.03
86.73
102.52
104.45
106.98
107.02
99.26
94.45
113.44
157.33
147.38
171.89
171.95
132.71
126.02
121.18
115.45
110.48
117.85
117.63
124.65
109.59
111.27
99.78
98.21
99.2
97.97
89.55
87.91
93.34
94.42
93.2
90.29
91.46
89.98
88.35
88.41
82.44
79.89
75.69
75.66
84.5
96.73
87.48
82.39
83.48
79.31
78.16
72.77
72.45
68.46
67.62
68.76
70.07
68.55
65.3
58.96
59.17
62.37
66.28
55.62
55.23
55.85
56.75
50.89
53.88
52.95
55.08
53.61
58.78
61.85
55.91
53.32
46.41
44.57
50.00
50.00
53.36
46.23
50.45
49.07
45.85
48.45
49.96
46.53
50.51
47.58
48.05
46.84
47.67
49.16
55.54
55.82
58.22
56.19
57.77
63.19
54.76
55.74
62.54
61.39
69.6
79.23
80.00
93.68
107.63
100.18
97.3
90.45
80.64
80.58
75.82
85.59
89.35
89.42
104.73
95.32
89.27
90.44
86.97
79.98
81.22
87.35
83.64
82.22
94.4
102.18




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65530&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65530&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65530&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )0.11650.0090.1192-0.2595-0.1240.07980.1344-0.0388-0.04560.03130.0971
(p-val)(0.0279 )(0.866 )(0.0256 )(0 )(0.0247 )(0.1485 )(0.015 )(0.4687 )(0.3912 )(0.5565 )(0.0681 )
Estimates ( 2 )0.117500.1202-0.2596-0.12270.07760.1333-0.0383-0.04450.03110.0968
(p-val)(0.0257 )(NA )(0.0236 )(0 )(0.0247 )(0.1485 )(0.0151 )(0.4737 )(0.3991 )(0.5588 )(0.0687 )
Estimates ( 3 )0.116200.1238-0.2578-0.12660.07020.1378-0.0381-0.041200.1003
(p-val)(0.0273 )(NA )(0.0191 )(0 )(0.0197 )(0.1788 )(0.0112 )(0.4761 )(0.4328 )(NA )(0.0579 )
Estimates ( 4 )0.111800.1278-0.2486-0.13210.07020.13310-0.045600.0965
(p-val)(0.0325 )(NA )(0.015 )(0 )(0.014 )(0.1796 )(0.0137 )(NA )(0.3827 )(NA )(0.0669 )
Estimates ( 5 )0.112100.1247-0.242-0.12170.06590.13190000.0945
(p-val)(0.0322 )(NA )(0.0174 )(0 )(0.0204 )(0.2061 )(0.0147 )(NA )(NA )(NA )(0.0727 )
Estimates ( 6 )0.103100.1314-0.243-0.115200.13970000.0861
(p-val)(0.0473 )(NA )(0.0121 )(0 )(0.0276 )(NA )(0.0094 )(NA )(NA )(NA )(0.0998 )
Estimates ( 7 )0.109500.1278-0.2355-0.109800.11750000
(p-val)(0.0353 )(NA )(0.0149 )(0 )(0.0362 )(NA )(0.0245 )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ar4 & ar5 & ar6 & ar7 & ar8 & ar9 & ar10 & ar11 \tabularnewline
Estimates ( 1 ) & 0.1165 & 0.009 & 0.1192 & -0.2595 & -0.124 & 0.0798 & 0.1344 & -0.0388 & -0.0456 & 0.0313 & 0.0971 \tabularnewline
(p-val) & (0.0279 ) & (0.866 ) & (0.0256 ) & (0 ) & (0.0247 ) & (0.1485 ) & (0.015 ) & (0.4687 ) & (0.3912 ) & (0.5565 ) & (0.0681 ) \tabularnewline
Estimates ( 2 ) & 0.1175 & 0 & 0.1202 & -0.2596 & -0.1227 & 0.0776 & 0.1333 & -0.0383 & -0.0445 & 0.0311 & 0.0968 \tabularnewline
(p-val) & (0.0257 ) & (NA ) & (0.0236 ) & (0 ) & (0.0247 ) & (0.1485 ) & (0.0151 ) & (0.4737 ) & (0.3991 ) & (0.5588 ) & (0.0687 ) \tabularnewline
Estimates ( 3 ) & 0.1162 & 0 & 0.1238 & -0.2578 & -0.1266 & 0.0702 & 0.1378 & -0.0381 & -0.0412 & 0 & 0.1003 \tabularnewline
(p-val) & (0.0273 ) & (NA ) & (0.0191 ) & (0 ) & (0.0197 ) & (0.1788 ) & (0.0112 ) & (0.4761 ) & (0.4328 ) & (NA ) & (0.0579 ) \tabularnewline
Estimates ( 4 ) & 0.1118 & 0 & 0.1278 & -0.2486 & -0.1321 & 0.0702 & 0.1331 & 0 & -0.0456 & 0 & 0.0965 \tabularnewline
(p-val) & (0.0325 ) & (NA ) & (0.015 ) & (0 ) & (0.014 ) & (0.1796 ) & (0.0137 ) & (NA ) & (0.3827 ) & (NA ) & (0.0669 ) \tabularnewline
Estimates ( 5 ) & 0.1121 & 0 & 0.1247 & -0.242 & -0.1217 & 0.0659 & 0.1319 & 0 & 0 & 0 & 0.0945 \tabularnewline
(p-val) & (0.0322 ) & (NA ) & (0.0174 ) & (0 ) & (0.0204 ) & (0.2061 ) & (0.0147 ) & (NA ) & (NA ) & (NA ) & (0.0727 ) \tabularnewline
Estimates ( 6 ) & 0.1031 & 0 & 0.1314 & -0.243 & -0.1152 & 0 & 0.1397 & 0 & 0 & 0 & 0.0861 \tabularnewline
(p-val) & (0.0473 ) & (NA ) & (0.0121 ) & (0 ) & (0.0276 ) & (NA ) & (0.0094 ) & (NA ) & (NA ) & (NA ) & (0.0998 ) \tabularnewline
Estimates ( 7 ) & 0.1095 & 0 & 0.1278 & -0.2355 & -0.1098 & 0 & 0.1175 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.0353 ) & (NA ) & (0.0149 ) & (0 ) & (0.0362 ) & (NA ) & (0.0245 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 14 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 15 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 16 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 17 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 18 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 19 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 20 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 21 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65530&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]ar4[/C][C]ar5[/C][C]ar6[/C][C]ar7[/C][C]ar8[/C][C]ar9[/C][C]ar10[/C][C]ar11[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.1165[/C][C]0.009[/C][C]0.1192[/C][C]-0.2595[/C][C]-0.124[/C][C]0.0798[/C][C]0.1344[/C][C]-0.0388[/C][C]-0.0456[/C][C]0.0313[/C][C]0.0971[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0279 )[/C][C](0.866 )[/C][C](0.0256 )[/C][C](0 )[/C][C](0.0247 )[/C][C](0.1485 )[/C][C](0.015 )[/C][C](0.4687 )[/C][C](0.3912 )[/C][C](0.5565 )[/C][C](0.0681 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1175[/C][C]0[/C][C]0.1202[/C][C]-0.2596[/C][C]-0.1227[/C][C]0.0776[/C][C]0.1333[/C][C]-0.0383[/C][C]-0.0445[/C][C]0.0311[/C][C]0.0968[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0257 )[/C][C](NA )[/C][C](0.0236 )[/C][C](0 )[/C][C](0.0247 )[/C][C](0.1485 )[/C][C](0.0151 )[/C][C](0.4737 )[/C][C](0.3991 )[/C][C](0.5588 )[/C][C](0.0687 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1162[/C][C]0[/C][C]0.1238[/C][C]-0.2578[/C][C]-0.1266[/C][C]0.0702[/C][C]0.1378[/C][C]-0.0381[/C][C]-0.0412[/C][C]0[/C][C]0.1003[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0273 )[/C][C](NA )[/C][C](0.0191 )[/C][C](0 )[/C][C](0.0197 )[/C][C](0.1788 )[/C][C](0.0112 )[/C][C](0.4761 )[/C][C](0.4328 )[/C][C](NA )[/C][C](0.0579 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1118[/C][C]0[/C][C]0.1278[/C][C]-0.2486[/C][C]-0.1321[/C][C]0.0702[/C][C]0.1331[/C][C]0[/C][C]-0.0456[/C][C]0[/C][C]0.0965[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0325 )[/C][C](NA )[/C][C](0.015 )[/C][C](0 )[/C][C](0.014 )[/C][C](0.1796 )[/C][C](0.0137 )[/C][C](NA )[/C][C](0.3827 )[/C][C](NA )[/C][C](0.0669 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.1121[/C][C]0[/C][C]0.1247[/C][C]-0.242[/C][C]-0.1217[/C][C]0.0659[/C][C]0.1319[/C][C]0[/C][C]0[/C][C]0[/C][C]0.0945[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0322 )[/C][C](NA )[/C][C](0.0174 )[/C][C](0 )[/C][C](0.0204 )[/C][C](0.2061 )[/C][C](0.0147 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0727 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.1031[/C][C]0[/C][C]0.1314[/C][C]-0.243[/C][C]-0.1152[/C][C]0[/C][C]0.1397[/C][C]0[/C][C]0[/C][C]0[/C][C]0.0861[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0473 )[/C][C](NA )[/C][C](0.0121 )[/C][C](0 )[/C][C](0.0276 )[/C][C](NA )[/C][C](0.0094 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0998 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0.1095[/C][C]0[/C][C]0.1278[/C][C]-0.2355[/C][C]-0.1098[/C][C]0[/C][C]0.1175[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0353 )[/C][C](NA )[/C][C](0.0149 )[/C][C](0 )[/C][C](0.0362 )[/C][C](NA )[/C][C](0.0245 )[/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][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][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][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][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][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][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][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][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][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][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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 14 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 15 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 16 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 17 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 18 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 19 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 20 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 21 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65530&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65530&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
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )0.11650.0090.1192-0.2595-0.1240.07980.1344-0.0388-0.04560.03130.0971
(p-val)(0.0279 )(0.866 )(0.0256 )(0 )(0.0247 )(0.1485 )(0.015 )(0.4687 )(0.3912 )(0.5565 )(0.0681 )
Estimates ( 2 )0.117500.1202-0.2596-0.12270.07760.1333-0.0383-0.04450.03110.0968
(p-val)(0.0257 )(NA )(0.0236 )(0 )(0.0247 )(0.1485 )(0.0151 )(0.4737 )(0.3991 )(0.5588 )(0.0687 )
Estimates ( 3 )0.116200.1238-0.2578-0.12660.07020.1378-0.0381-0.041200.1003
(p-val)(0.0273 )(NA )(0.0191 )(0 )(0.0197 )(0.1788 )(0.0112 )(0.4761 )(0.4328 )(NA )(0.0579 )
Estimates ( 4 )0.111800.1278-0.2486-0.13210.07020.13310-0.045600.0965
(p-val)(0.0325 )(NA )(0.015 )(0 )(0.014 )(0.1796 )(0.0137 )(NA )(0.3827 )(NA )(0.0669 )
Estimates ( 5 )0.112100.1247-0.242-0.12170.06590.13190000.0945
(p-val)(0.0322 )(NA )(0.0174 )(0 )(0.0204 )(0.2061 )(0.0147 )(NA )(NA )(NA )(0.0727 )
Estimates ( 6 )0.103100.1314-0.243-0.115200.13970000.0861
(p-val)(0.0473 )(NA )(0.0121 )(0 )(0.0276 )(NA )(0.0094 )(NA )(NA )(NA )(0.0998 )
Estimates ( 7 )0.109500.1278-0.2355-0.109800.11750000
(p-val)(0.0353 )(NA )(0.0149 )(0 )(0.0362 )(NA )(0.0245 )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.087279951072447
-0.179450212427608
-0.141429241795526
0.652704563393097
2.92300166361407
-0.447547159020687
-0.466790559808276
-0.898150772423025
0.307903995652118
-0.0233120054575684
-0.56797655086488
-1.20400604385635
-0.317117722320133
-0.446627369851342
-0.395515233839504
-0.722617537998133
-0.318234747804738
-0.249854089951299
-0.221047424833117
-0.695085008634805
-1.01246967518138
-0.735247972267473
-0.575519007492105
-0.632154295593452
-1.70409799420594
-9.38603268653345
0.207980375772863
1.49932053372052
4.66617543206253
-0.792447285068548
0.539911129680092
3.61839334497267
3.34862761116398
1.91483044616869
0.7099653708987
0.618400374610118
0.178108174221109
-0.364141430284761
2.54702186124226
-1.72013810641209
-1.55976009316474
-1.53035827744155
-0.390352538251420
-0.595753108305871
-1.3643068725209
-1.67602509200992
2.87199114160659
-0.648803480661954
-1.01189318110941
-1.60622614060682
0.193957441304860
-0.102703430463791
-0.841480038776439
-1.38367655299851
-0.940002745589112
-0.788297018698273
3.90462437692604
1.69093602216000
-1.11963157079340
-1.71175318481259
-0.158509760022000
4.79399671701279
0.0382949420608298
-1.73594368415371
-2.08030906520436
0.125297308780631
-0.260233150370439
-1.12144180526163
5.10230757248115
-2.39963554014952
-1.43348088397846
-1.98220260304917
0.264196818382828
-0.676278313251629
1.64510208360224
-1.58273189671193
-1.30279251545453
-1.61962189319095
-0.816704551731135
2.70951357873497
0.39968263967306
-0.815296886792737
-2.12813291878668
0.513745257103139
-0.605695772029719
-0.56678558538222
-0.620174300811811
-0.350268899203428
-0.708044342628838
-1.57904480510358
1.40506866927767
0.540764107205078
-1.30061020964501
-1.14071772809095
1.03211266657257
-0.815868668820329
-1.47142116892144
0.95430477311406
-3.09475255626639
-2.23936516604136
1.43406825416307
-1.86286497254131
-1.56082920869329
1.09232414736957
-1.83965587629147
3.77556459422739
-0.724348188374933
6.0016689777119
8.52716994673781
14.7821781482687
-0.478393435492194
1.06281552720156
-0.375059969453744
3.14458387567136
-0.420231523430814
-2.28091607763021
-3.25496810479031
0.671489134549574
-2.84386785630579
-2.54984145633765
-2.92936485615846
-0.934193417668268
-1.23199630053990
-1.45207346918468
-1.47654415079833
-1.13826289087606
-0.985839832041066
-0.970268524533097
1.41212400219602
1.53873050721209
-1.11661801135629
-0.0575919821279314
2.24779391323638
-0.90823475773989
1.19919584133271
-1.17875823266691
-1.00587951618786
-1.37028839077479
-1.12453748895362
-1.57589157279888
1.33299362183112
3.79793589883084
-2.12028275680508
-0.421690452957066
-3.09071883931539
0.950626022974689
6.95757221157614
-2.15368857402026
-0.0943745388389345
-1.70502030311229
0.361483356722815
-0.864241127166963
-0.384779878958369
-0.068265944138048
-1.67432514731360
-1.93633632926952
-1.80444567422551
2.11403163703687
2.25076450856139
-2.31212936547503
-2.36060271196224
-0.982586443847339
0.0163169093746234
6.78235980573531
-2.17885629095871
-1.84279044368655
-2.32060665851569
0.71675113006647
-0.269729208451764
1.13728553648282
2.44285718816657
-1.61549137164688
-1.30497574825579
-0.965300856907916
-0.234080924972091
-0.855832881776536
1.60321760491037
-1.42492174455101
-1.14985338090592
-0.339871470793383
-3.37436591944078
-1.40172880564418
-0.537689853043439
-0.381492109514419
-1.08129692925114
-4.25217787721281
-3.11839175442684
0.0107818625692104
0.0742419860081043
-0.839749335715922
-1.5917241663428
-0.474616326373607
0.132985840467732
-0.0178276540464637
2.66009310024248
0.0731764420605145
-0.443678352439449
-0.573540957274368
7.01624971448337
-0.612118977138323
-0.447487786798639
-1.57107660855161
0.7836426336671
0.416221914601365
6.84856979679098
17.8101460322846
-3.30915727344207
-1.68773271917088
0.156534607479358
3.90517866385414
21.9350884688933
10.8054457041769
-1.26962956641742
-3.08372167733302
-1.14578638861093
3.09167793162659
0.0947428762548839
-3.85314929087448
-3.24045577535577
1.83262506620270
-3.71657915828587
-3.23677276956356
-5.23348047480877
-0.101393543684637
-2.99922368147683
0.530734407093775
-1.27783497052097
-6.10276795499772
2.72850396130549
-0.472949897385675
-2.12807896078705
-0.0870192053088203
-1.12136730404428
3.72402809723953
15.6551035806891
0.215402061863102
1.83335220104912
-1.35505068677807
-3.43136401377109
-2.11648216640413
19.9791341591133
41.301443218093
-16.0562093676209
20.7235039064207
-4.41242728670451
-25.3628917473692
-2.72206541420201
-2.22038931526845
-3.37032601498565
-10.9690128403387
-0.637836439319145
-3.81761853775684
7.44652848835855
-16.8280727919440
3.04367763182559
-10.9947367505490
7.34837831319766
-2.37249708964482
-0.701297776731252
-11.1720874759248
-0.0757000535372612
4.95071640717988
3.06619886297044
-3.68873543918889
-3.70738590930789
2.48543332249426
1.61385615064790
-0.902675464602254
-1.61720753762116
-5.8775960477741
-1.04951590121939
-3.96385194443751
0.382978937273805
7.84830614596294
10.8963032078140
-11.5784429090899
-5.05647589025416
2.63541438757531
1.65047951132179
-0.89065295973333
-8.4377960264893
-1.02688590418786
-3.03949041755018
0.233322293604843
-1.08729699882228
0.54704349434337
-1.59341486137551
-2.71553382159473
-6.04621347314352
2.42941221971500
3.60350826844413
3.75372111121022
-13.1608762284212
0.164682995061050
1.47440787853356
4.34320443826062
-8.18324666288748
1.87339985635641
-1.51694332516031
5.32139635433345
-3.36618126156841
5.13274145719151
1.91322962965259
-3.91588462013976
-3.15311111646057
-5.88324912029366
0.619286887181133
5.58066412543414
-1.94498033271047
1.27573800995368
-8.78681964593298
6.550586218571
-1.11095430483945
-1.33180864489729
0.785193287041935
1.85036290793828
-2.88561713480346
4.20497159381276
-4.33519397707424
2.08196363797278
-2.28054191542287
2.16261964588148
0.514872695597788
6.76010527569139
-1.00502666061649
2.42289786120126
-2.85389110563092
3.93875074409947
5.28605740239332
-8.06214197240379
0.492701890048131
6.20182835986286
0.349239134969352
6.93118779761686
6.38670955679105
0.912511618182862
13.9966094453603
13.1748259901133
-6.78918034407975
-2.91968621554351
-5.39462473035108
-4.58904348505072
0.433288963193107
-7.22365211586595
6.89762197646839
-0.200660028827969
-0.500814702687819
12.6337583525738
-9.48796938939024
-2.39998710172843
1.14473714361193
0.599319333260482
-6.03979697448187
-0.752144022459774
4.31679281115913
-3.65857046272377
-2.77753795789698
10.8471790111290
7.81038893833232

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.087279951072447 \tabularnewline
-0.179450212427608 \tabularnewline
-0.141429241795526 \tabularnewline
0.652704563393097 \tabularnewline
2.92300166361407 \tabularnewline
-0.447547159020687 \tabularnewline
-0.466790559808276 \tabularnewline
-0.898150772423025 \tabularnewline
0.307903995652118 \tabularnewline
-0.0233120054575684 \tabularnewline
-0.56797655086488 \tabularnewline
-1.20400604385635 \tabularnewline
-0.317117722320133 \tabularnewline
-0.446627369851342 \tabularnewline
-0.395515233839504 \tabularnewline
-0.722617537998133 \tabularnewline
-0.318234747804738 \tabularnewline
-0.249854089951299 \tabularnewline
-0.221047424833117 \tabularnewline
-0.695085008634805 \tabularnewline
-1.01246967518138 \tabularnewline
-0.735247972267473 \tabularnewline
-0.575519007492105 \tabularnewline
-0.632154295593452 \tabularnewline
-1.70409799420594 \tabularnewline
-9.38603268653345 \tabularnewline
0.207980375772863 \tabularnewline
1.49932053372052 \tabularnewline
4.66617543206253 \tabularnewline
-0.792447285068548 \tabularnewline
0.539911129680092 \tabularnewline
3.61839334497267 \tabularnewline
3.34862761116398 \tabularnewline
1.91483044616869 \tabularnewline
0.7099653708987 \tabularnewline
0.618400374610118 \tabularnewline
0.178108174221109 \tabularnewline
-0.364141430284761 \tabularnewline
2.54702186124226 \tabularnewline
-1.72013810641209 \tabularnewline
-1.55976009316474 \tabularnewline
-1.53035827744155 \tabularnewline
-0.390352538251420 \tabularnewline
-0.595753108305871 \tabularnewline
-1.3643068725209 \tabularnewline
-1.67602509200992 \tabularnewline
2.87199114160659 \tabularnewline
-0.648803480661954 \tabularnewline
-1.01189318110941 \tabularnewline
-1.60622614060682 \tabularnewline
0.193957441304860 \tabularnewline
-0.102703430463791 \tabularnewline
-0.841480038776439 \tabularnewline
-1.38367655299851 \tabularnewline
-0.940002745589112 \tabularnewline
-0.788297018698273 \tabularnewline
3.90462437692604 \tabularnewline
1.69093602216000 \tabularnewline
-1.11963157079340 \tabularnewline
-1.71175318481259 \tabularnewline
-0.158509760022000 \tabularnewline
4.79399671701279 \tabularnewline
0.0382949420608298 \tabularnewline
-1.73594368415371 \tabularnewline
-2.08030906520436 \tabularnewline
0.125297308780631 \tabularnewline
-0.260233150370439 \tabularnewline
-1.12144180526163 \tabularnewline
5.10230757248115 \tabularnewline
-2.39963554014952 \tabularnewline
-1.43348088397846 \tabularnewline
-1.98220260304917 \tabularnewline
0.264196818382828 \tabularnewline
-0.676278313251629 \tabularnewline
1.64510208360224 \tabularnewline
-1.58273189671193 \tabularnewline
-1.30279251545453 \tabularnewline
-1.61962189319095 \tabularnewline
-0.816704551731135 \tabularnewline
2.70951357873497 \tabularnewline
0.39968263967306 \tabularnewline
-0.815296886792737 \tabularnewline
-2.12813291878668 \tabularnewline
0.513745257103139 \tabularnewline
-0.605695772029719 \tabularnewline
-0.56678558538222 \tabularnewline
-0.620174300811811 \tabularnewline
-0.350268899203428 \tabularnewline
-0.708044342628838 \tabularnewline
-1.57904480510358 \tabularnewline
1.40506866927767 \tabularnewline
0.540764107205078 \tabularnewline
-1.30061020964501 \tabularnewline
-1.14071772809095 \tabularnewline
1.03211266657257 \tabularnewline
-0.815868668820329 \tabularnewline
-1.47142116892144 \tabularnewline
0.95430477311406 \tabularnewline
-3.09475255626639 \tabularnewline
-2.23936516604136 \tabularnewline
1.43406825416307 \tabularnewline
-1.86286497254131 \tabularnewline
-1.56082920869329 \tabularnewline
1.09232414736957 \tabularnewline
-1.83965587629147 \tabularnewline
3.77556459422739 \tabularnewline
-0.724348188374933 \tabularnewline
6.0016689777119 \tabularnewline
8.52716994673781 \tabularnewline
14.7821781482687 \tabularnewline
-0.478393435492194 \tabularnewline
1.06281552720156 \tabularnewline
-0.375059969453744 \tabularnewline
3.14458387567136 \tabularnewline
-0.420231523430814 \tabularnewline
-2.28091607763021 \tabularnewline
-3.25496810479031 \tabularnewline
0.671489134549574 \tabularnewline
-2.84386785630579 \tabularnewline
-2.54984145633765 \tabularnewline
-2.92936485615846 \tabularnewline
-0.934193417668268 \tabularnewline
-1.23199630053990 \tabularnewline
-1.45207346918468 \tabularnewline
-1.47654415079833 \tabularnewline
-1.13826289087606 \tabularnewline
-0.985839832041066 \tabularnewline
-0.970268524533097 \tabularnewline
1.41212400219602 \tabularnewline
1.53873050721209 \tabularnewline
-1.11661801135629 \tabularnewline
-0.0575919821279314 \tabularnewline
2.24779391323638 \tabularnewline
-0.90823475773989 \tabularnewline
1.19919584133271 \tabularnewline
-1.17875823266691 \tabularnewline
-1.00587951618786 \tabularnewline
-1.37028839077479 \tabularnewline
-1.12453748895362 \tabularnewline
-1.57589157279888 \tabularnewline
1.33299362183112 \tabularnewline
3.79793589883084 \tabularnewline
-2.12028275680508 \tabularnewline
-0.421690452957066 \tabularnewline
-3.09071883931539 \tabularnewline
0.950626022974689 \tabularnewline
6.95757221157614 \tabularnewline
-2.15368857402026 \tabularnewline
-0.0943745388389345 \tabularnewline
-1.70502030311229 \tabularnewline
0.361483356722815 \tabularnewline
-0.864241127166963 \tabularnewline
-0.384779878958369 \tabularnewline
-0.068265944138048 \tabularnewline
-1.67432514731360 \tabularnewline
-1.93633632926952 \tabularnewline
-1.80444567422551 \tabularnewline
2.11403163703687 \tabularnewline
2.25076450856139 \tabularnewline
-2.31212936547503 \tabularnewline
-2.36060271196224 \tabularnewline
-0.982586443847339 \tabularnewline
0.0163169093746234 \tabularnewline
6.78235980573531 \tabularnewline
-2.17885629095871 \tabularnewline
-1.84279044368655 \tabularnewline
-2.32060665851569 \tabularnewline
0.71675113006647 \tabularnewline
-0.269729208451764 \tabularnewline
1.13728553648282 \tabularnewline
2.44285718816657 \tabularnewline
-1.61549137164688 \tabularnewline
-1.30497574825579 \tabularnewline
-0.965300856907916 \tabularnewline
-0.234080924972091 \tabularnewline
-0.855832881776536 \tabularnewline
1.60321760491037 \tabularnewline
-1.42492174455101 \tabularnewline
-1.14985338090592 \tabularnewline
-0.339871470793383 \tabularnewline
-3.37436591944078 \tabularnewline
-1.40172880564418 \tabularnewline
-0.537689853043439 \tabularnewline
-0.381492109514419 \tabularnewline
-1.08129692925114 \tabularnewline
-4.25217787721281 \tabularnewline
-3.11839175442684 \tabularnewline
0.0107818625692104 \tabularnewline
0.0742419860081043 \tabularnewline
-0.839749335715922 \tabularnewline
-1.5917241663428 \tabularnewline
-0.474616326373607 \tabularnewline
0.132985840467732 \tabularnewline
-0.0178276540464637 \tabularnewline
2.66009310024248 \tabularnewline
0.0731764420605145 \tabularnewline
-0.443678352439449 \tabularnewline
-0.573540957274368 \tabularnewline
7.01624971448337 \tabularnewline
-0.612118977138323 \tabularnewline
-0.447487786798639 \tabularnewline
-1.57107660855161 \tabularnewline
0.7836426336671 \tabularnewline
0.416221914601365 \tabularnewline
6.84856979679098 \tabularnewline
17.8101460322846 \tabularnewline
-3.30915727344207 \tabularnewline
-1.68773271917088 \tabularnewline
0.156534607479358 \tabularnewline
3.90517866385414 \tabularnewline
21.9350884688933 \tabularnewline
10.8054457041769 \tabularnewline
-1.26962956641742 \tabularnewline
-3.08372167733302 \tabularnewline
-1.14578638861093 \tabularnewline
3.09167793162659 \tabularnewline
0.0947428762548839 \tabularnewline
-3.85314929087448 \tabularnewline
-3.24045577535577 \tabularnewline
1.83262506620270 \tabularnewline
-3.71657915828587 \tabularnewline
-3.23677276956356 \tabularnewline
-5.23348047480877 \tabularnewline
-0.101393543684637 \tabularnewline
-2.99922368147683 \tabularnewline
0.530734407093775 \tabularnewline
-1.27783497052097 \tabularnewline
-6.10276795499772 \tabularnewline
2.72850396130549 \tabularnewline
-0.472949897385675 \tabularnewline
-2.12807896078705 \tabularnewline
-0.0870192053088203 \tabularnewline
-1.12136730404428 \tabularnewline
3.72402809723953 \tabularnewline
15.6551035806891 \tabularnewline
0.215402061863102 \tabularnewline
1.83335220104912 \tabularnewline
-1.35505068677807 \tabularnewline
-3.43136401377109 \tabularnewline
-2.11648216640413 \tabularnewline
19.9791341591133 \tabularnewline
41.301443218093 \tabularnewline
-16.0562093676209 \tabularnewline
20.7235039064207 \tabularnewline
-4.41242728670451 \tabularnewline
-25.3628917473692 \tabularnewline
-2.72206541420201 \tabularnewline
-2.22038931526845 \tabularnewline
-3.37032601498565 \tabularnewline
-10.9690128403387 \tabularnewline
-0.637836439319145 \tabularnewline
-3.81761853775684 \tabularnewline
7.44652848835855 \tabularnewline
-16.8280727919440 \tabularnewline
3.04367763182559 \tabularnewline
-10.9947367505490 \tabularnewline
7.34837831319766 \tabularnewline
-2.37249708964482 \tabularnewline
-0.701297776731252 \tabularnewline
-11.1720874759248 \tabularnewline
-0.0757000535372612 \tabularnewline
4.95071640717988 \tabularnewline
3.06619886297044 \tabularnewline
-3.68873543918889 \tabularnewline
-3.70738590930789 \tabularnewline
2.48543332249426 \tabularnewline
1.61385615064790 \tabularnewline
-0.902675464602254 \tabularnewline
-1.61720753762116 \tabularnewline
-5.8775960477741 \tabularnewline
-1.04951590121939 \tabularnewline
-3.96385194443751 \tabularnewline
0.382978937273805 \tabularnewline
7.84830614596294 \tabularnewline
10.8963032078140 \tabularnewline
-11.5784429090899 \tabularnewline
-5.05647589025416 \tabularnewline
2.63541438757531 \tabularnewline
1.65047951132179 \tabularnewline
-0.89065295973333 \tabularnewline
-8.4377960264893 \tabularnewline
-1.02688590418786 \tabularnewline
-3.03949041755018 \tabularnewline
0.233322293604843 \tabularnewline
-1.08729699882228 \tabularnewline
0.54704349434337 \tabularnewline
-1.59341486137551 \tabularnewline
-2.71553382159473 \tabularnewline
-6.04621347314352 \tabularnewline
2.42941221971500 \tabularnewline
3.60350826844413 \tabularnewline
3.75372111121022 \tabularnewline
-13.1608762284212 \tabularnewline
0.164682995061050 \tabularnewline
1.47440787853356 \tabularnewline
4.34320443826062 \tabularnewline
-8.18324666288748 \tabularnewline
1.87339985635641 \tabularnewline
-1.51694332516031 \tabularnewline
5.32139635433345 \tabularnewline
-3.36618126156841 \tabularnewline
5.13274145719151 \tabularnewline
1.91322962965259 \tabularnewline
-3.91588462013976 \tabularnewline
-3.15311111646057 \tabularnewline
-5.88324912029366 \tabularnewline
0.619286887181133 \tabularnewline
5.58066412543414 \tabularnewline
-1.94498033271047 \tabularnewline
1.27573800995368 \tabularnewline
-8.78681964593298 \tabularnewline
6.550586218571 \tabularnewline
-1.11095430483945 \tabularnewline
-1.33180864489729 \tabularnewline
0.785193287041935 \tabularnewline
1.85036290793828 \tabularnewline
-2.88561713480346 \tabularnewline
4.20497159381276 \tabularnewline
-4.33519397707424 \tabularnewline
2.08196363797278 \tabularnewline
-2.28054191542287 \tabularnewline
2.16261964588148 \tabularnewline
0.514872695597788 \tabularnewline
6.76010527569139 \tabularnewline
-1.00502666061649 \tabularnewline
2.42289786120126 \tabularnewline
-2.85389110563092 \tabularnewline
3.93875074409947 \tabularnewline
5.28605740239332 \tabularnewline
-8.06214197240379 \tabularnewline
0.492701890048131 \tabularnewline
6.20182835986286 \tabularnewline
0.349239134969352 \tabularnewline
6.93118779761686 \tabularnewline
6.38670955679105 \tabularnewline
0.912511618182862 \tabularnewline
13.9966094453603 \tabularnewline
13.1748259901133 \tabularnewline
-6.78918034407975 \tabularnewline
-2.91968621554351 \tabularnewline
-5.39462473035108 \tabularnewline
-4.58904348505072 \tabularnewline
0.433288963193107 \tabularnewline
-7.22365211586595 \tabularnewline
6.89762197646839 \tabularnewline
-0.200660028827969 \tabularnewline
-0.500814702687819 \tabularnewline
12.6337583525738 \tabularnewline
-9.48796938939024 \tabularnewline
-2.39998710172843 \tabularnewline
1.14473714361193 \tabularnewline
0.599319333260482 \tabularnewline
-6.03979697448187 \tabularnewline
-0.752144022459774 \tabularnewline
4.31679281115913 \tabularnewline
-3.65857046272377 \tabularnewline
-2.77753795789698 \tabularnewline
10.8471790111290 \tabularnewline
7.81038893833232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65530&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.087279951072447[/C][/ROW]
[ROW][C]-0.179450212427608[/C][/ROW]
[ROW][C]-0.141429241795526[/C][/ROW]
[ROW][C]0.652704563393097[/C][/ROW]
[ROW][C]2.92300166361407[/C][/ROW]
[ROW][C]-0.447547159020687[/C][/ROW]
[ROW][C]-0.466790559808276[/C][/ROW]
[ROW][C]-0.898150772423025[/C][/ROW]
[ROW][C]0.307903995652118[/C][/ROW]
[ROW][C]-0.0233120054575684[/C][/ROW]
[ROW][C]-0.56797655086488[/C][/ROW]
[ROW][C]-1.20400604385635[/C][/ROW]
[ROW][C]-0.317117722320133[/C][/ROW]
[ROW][C]-0.446627369851342[/C][/ROW]
[ROW][C]-0.395515233839504[/C][/ROW]
[ROW][C]-0.722617537998133[/C][/ROW]
[ROW][C]-0.318234747804738[/C][/ROW]
[ROW][C]-0.249854089951299[/C][/ROW]
[ROW][C]-0.221047424833117[/C][/ROW]
[ROW][C]-0.695085008634805[/C][/ROW]
[ROW][C]-1.01246967518138[/C][/ROW]
[ROW][C]-0.735247972267473[/C][/ROW]
[ROW][C]-0.575519007492105[/C][/ROW]
[ROW][C]-0.632154295593452[/C][/ROW]
[ROW][C]-1.70409799420594[/C][/ROW]
[ROW][C]-9.38603268653345[/C][/ROW]
[ROW][C]0.207980375772863[/C][/ROW]
[ROW][C]1.49932053372052[/C][/ROW]
[ROW][C]4.66617543206253[/C][/ROW]
[ROW][C]-0.792447285068548[/C][/ROW]
[ROW][C]0.539911129680092[/C][/ROW]
[ROW][C]3.61839334497267[/C][/ROW]
[ROW][C]3.34862761116398[/C][/ROW]
[ROW][C]1.91483044616869[/C][/ROW]
[ROW][C]0.7099653708987[/C][/ROW]
[ROW][C]0.618400374610118[/C][/ROW]
[ROW][C]0.178108174221109[/C][/ROW]
[ROW][C]-0.364141430284761[/C][/ROW]
[ROW][C]2.54702186124226[/C][/ROW]
[ROW][C]-1.72013810641209[/C][/ROW]
[ROW][C]-1.55976009316474[/C][/ROW]
[ROW][C]-1.53035827744155[/C][/ROW]
[ROW][C]-0.390352538251420[/C][/ROW]
[ROW][C]-0.595753108305871[/C][/ROW]
[ROW][C]-1.3643068725209[/C][/ROW]
[ROW][C]-1.67602509200992[/C][/ROW]
[ROW][C]2.87199114160659[/C][/ROW]
[ROW][C]-0.648803480661954[/C][/ROW]
[ROW][C]-1.01189318110941[/C][/ROW]
[ROW][C]-1.60622614060682[/C][/ROW]
[ROW][C]0.193957441304860[/C][/ROW]
[ROW][C]-0.102703430463791[/C][/ROW]
[ROW][C]-0.841480038776439[/C][/ROW]
[ROW][C]-1.38367655299851[/C][/ROW]
[ROW][C]-0.940002745589112[/C][/ROW]
[ROW][C]-0.788297018698273[/C][/ROW]
[ROW][C]3.90462437692604[/C][/ROW]
[ROW][C]1.69093602216000[/C][/ROW]
[ROW][C]-1.11963157079340[/C][/ROW]
[ROW][C]-1.71175318481259[/C][/ROW]
[ROW][C]-0.158509760022000[/C][/ROW]
[ROW][C]4.79399671701279[/C][/ROW]
[ROW][C]0.0382949420608298[/C][/ROW]
[ROW][C]-1.73594368415371[/C][/ROW]
[ROW][C]-2.08030906520436[/C][/ROW]
[ROW][C]0.125297308780631[/C][/ROW]
[ROW][C]-0.260233150370439[/C][/ROW]
[ROW][C]-1.12144180526163[/C][/ROW]
[ROW][C]5.10230757248115[/C][/ROW]
[ROW][C]-2.39963554014952[/C][/ROW]
[ROW][C]-1.43348088397846[/C][/ROW]
[ROW][C]-1.98220260304917[/C][/ROW]
[ROW][C]0.264196818382828[/C][/ROW]
[ROW][C]-0.676278313251629[/C][/ROW]
[ROW][C]1.64510208360224[/C][/ROW]
[ROW][C]-1.58273189671193[/C][/ROW]
[ROW][C]-1.30279251545453[/C][/ROW]
[ROW][C]-1.61962189319095[/C][/ROW]
[ROW][C]-0.816704551731135[/C][/ROW]
[ROW][C]2.70951357873497[/C][/ROW]
[ROW][C]0.39968263967306[/C][/ROW]
[ROW][C]-0.815296886792737[/C][/ROW]
[ROW][C]-2.12813291878668[/C][/ROW]
[ROW][C]0.513745257103139[/C][/ROW]
[ROW][C]-0.605695772029719[/C][/ROW]
[ROW][C]-0.56678558538222[/C][/ROW]
[ROW][C]-0.620174300811811[/C][/ROW]
[ROW][C]-0.350268899203428[/C][/ROW]
[ROW][C]-0.708044342628838[/C][/ROW]
[ROW][C]-1.57904480510358[/C][/ROW]
[ROW][C]1.40506866927767[/C][/ROW]
[ROW][C]0.540764107205078[/C][/ROW]
[ROW][C]-1.30061020964501[/C][/ROW]
[ROW][C]-1.14071772809095[/C][/ROW]
[ROW][C]1.03211266657257[/C][/ROW]
[ROW][C]-0.815868668820329[/C][/ROW]
[ROW][C]-1.47142116892144[/C][/ROW]
[ROW][C]0.95430477311406[/C][/ROW]
[ROW][C]-3.09475255626639[/C][/ROW]
[ROW][C]-2.23936516604136[/C][/ROW]
[ROW][C]1.43406825416307[/C][/ROW]
[ROW][C]-1.86286497254131[/C][/ROW]
[ROW][C]-1.56082920869329[/C][/ROW]
[ROW][C]1.09232414736957[/C][/ROW]
[ROW][C]-1.83965587629147[/C][/ROW]
[ROW][C]3.77556459422739[/C][/ROW]
[ROW][C]-0.724348188374933[/C][/ROW]
[ROW][C]6.0016689777119[/C][/ROW]
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[ROW][C]4.31679281115913[/C][/ROW]
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[ROW][C]10.8471790111290[/C][/ROW]
[ROW][C]7.81038893833232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65530&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65530&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
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2.54702186124226
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Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
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
par6 <- 11
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')