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Description of Statistical Computation

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, 09 Dec 2009 14:40:34 -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/09/t12603949517v0kavq33bo40or.htm/, Retrieved Mon, 29 Apr 2024 12:35:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65213, Retrieved Mon, 29 Apr 2024 12:35:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132

Tree of Dependent Computations

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]
-   PD    [ARIMA Backward Selection] [WS 10 AR] [2009-12-09 21:40:34] [2e4ef2c1b76db9b31c0a03b96e94ad77] [Current]
-    D      [ARIMA Backward Selection] [ws 10 review] [2009-12-12 15:12:03] [6e4e01d7eb22a9f33d58ebb35753a195]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
87,28
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
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
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
50
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
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time98 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 & 98 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65213&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]98 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=65213&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65213&T=0

As an alternative you can also use a QR Code:  
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 time98 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11ma1sar1sar2sma1
Estimates ( 1 )-0.34860.0630.116-0.2085-0.22360.03480.15260.0011-0.04040.02680.11180.46760.21430.0608-0.26
(p-val)(0.4588 )(0.4275 )(0.0414 )(0.0091 )(0.0977 )(0.6583 )(0.0399 )(0.99 )(0.4922 )(0.6603 )(0.0459 )(0.3212 )(0.6924 )(0.318 )(0.6293 )
Estimates ( 2 )-0.34390.06240.1159-0.2091-0.22240.03540.15210-0.04040.0270.11170.46280.21370.0609-0.2601
(p-val)(0.29 )(0.3667 )(0.0354 )(7e-04 )(0.0262 )(0.5865 )(0.0101 )(NA )(0.493 )(0.6387 )(0.0447 )(0.1592 )(0.6906 )(0.3123 )(0.6286 )
Estimates ( 3 )-0.34690.06270.1174-0.2099-0.22370.0360.15410-0.04150.02610.11230.465600.0475-0.0472
(p-val)(0.2756 )(0.3609 )(0.0329 )(6e-04 )(0.0236 )(0.5794 )(0.0088 )(NA )(0.4802 )(0.6505 )(0.0432 )(0.148 )(NA )(0.3985 )(0.442 )
Estimates ( 4 )-0.36540.06560.1198-0.2079-0.23340.02840.15660-0.051400.10650.484200.0442-0.0407
(p-val)(0.2103 )(0.3287 )(0.0296 )(6e-04 )(0.012 )(0.6478 )(0.0067 )(NA )(0.3661 )(NA )(0.0468 )(0.1004 )(NA )(0.4275 )(0.4964 )
Estimates ( 5 )-0.42130.06640.1188-0.2009-0.253500.14840-0.054200.10350.53700.0462-0.0383
(p-val)(0.0679 )(0.3072 )(0.034 )(6e-04 )(0.0012 )(NA )(0.0052 )(NA )(0.3264 )(NA )(0.0508 )(0.0222 )(NA )(0.4055 )(0.5167 )
Estimates ( 6 )-0.44610.07210.1197-0.2004-0.262400.15710-0.058800.11670.563500.04330
(p-val)(0.0228 )(0.2474 )(0.0338 )(6e-04 )(2e-04 )(NA )(0.0019 )(NA )(0.2675 )(NA )(0.0159 )(0.0046 )(NA )(0.4332 )(NA )
Estimates ( 7 )-0.45460.07440.1186-0.2024-0.269700.1610-0.057200.11590.571000
(p-val)(0.0174 )(0.2312 )(0.0357 )(5e-04 )(1e-04 )(NA )(0.0014 )(NA )(0.2796 )(NA )(0.0167 )(0.0033 )(NA )(NA )(NA )
Estimates ( 8 )-0.37240.0570.1147-0.2015-0.235800.14790000.10810.4867000
(p-val)(0.0622 )(0.3406 )(0.0376 )(5e-04 )(4e-04 )(NA )(0.0032 )(NA )(NA )(NA )(0.0286 )(0.0171 )(NA )(NA )(NA )
Estimates ( 9 )-0.298700.1053-0.2083-0.212500.14360000.10960.3978000
(p-val)(0.179 )(NA )(0.0589 )(3e-04 )(0.0021 )(NA )(0.0051 )(NA )(NA )(NA )(0.0306 )(0.0615 )(NA )(NA )(NA )
Estimates ( 10 )000.1261-0.2383-0.139100.14110000.08770.1155000
(p-val)(NA )(NA )(0.015 )(0 )(0.006 )(NA )(0.0084 )(NA )(NA )(NA )(0.0963 )(0.0252 )(NA )(NA )(NA )
Estimates ( 11 )000.1231-0.2311-0.133700.119800000.1103000
(p-val)(NA )(NA )(0.018 )(0 )(0.0084 )(NA )(0.0216 )(NA )(NA )(NA )(NA )(0.0326 )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 22 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 23 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 24 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 25 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 26 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 27 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 28 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 29 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(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 & ma1 & sar1 & sar2 & sma1 \tabularnewline Estimates ( 1 ) & -0.3486 & 0.063 & 0.116 & -0.2085 & -0.2236 & 0.0348 & 0.1526 & 0.0011 & -0.0404 & 0.0268 & 0.1118 & 0.4676 & 0.2143 & 0.0608 & -0.26 \tabularnewline (p-val) & (0.4588 ) & (0.4275 ) & (0.0414 ) & (0.0091 ) & (0.0977 ) & (0.6583 ) & (0.0399 ) & (0.99 ) & (0.4922 ) & (0.6603 ) & (0.0459 ) & (0.3212 ) & (0.6924 ) & (0.318 ) & (0.6293 ) \tabularnewline Estimates ( 2 ) & -0.3439 & 0.0624 & 0.1159 & -0.2091 & -0.2224 & 0.0354 & 0.1521 & 0 & -0.0404 & 0.027 & 0.1117 & 0.4628 & 0.2137 & 0.0609 & -0.2601 \tabularnewline (p-val) & (0.29 ) & (0.3667 ) & (0.0354 ) & (7e-04 ) & (0.0262 ) & (0.5865 ) & (0.0101 ) & (NA ) & (0.493 ) & (0.6387 ) & (0.0447 ) & (0.1592 ) & (0.6906 ) & (0.3123 ) & (0.6286 ) \tabularnewline Estimates ( 3 ) & -0.3469 & 0.0627 & 0.1174 & -0.2099 & -0.2237 & 0.036 & 0.1541 & 0 & -0.0415 & 0.0261 & 0.1123 & 0.4656 & 0 & 0.0475 & -0.0472 \tabularnewline (p-val) & (0.2756 ) & (0.3609 ) & (0.0329 ) & (6e-04 ) & (0.0236 ) & (0.5794 ) & (0.0088 ) & (NA ) & (0.4802 ) & (0.6505 ) & (0.0432 ) & (0.148 ) & (NA ) & (0.3985 ) & (0.442 ) \tabularnewline Estimates ( 4 ) & -0.3654 & 0.0656 & 0.1198 & -0.2079 & -0.2334 & 0.0284 & 0.1566 & 0 & -0.0514 & 0 & 0.1065 & 0.4842 & 0 & 0.0442 & -0.0407 \tabularnewline (p-val) & (0.2103 ) & (0.3287 ) & (0.0296 ) & (6e-04 ) & (0.012 ) & (0.6478 ) & (0.0067 ) & (NA ) & (0.3661 ) & (NA ) & (0.0468 ) & (0.1004 ) & (NA ) & (0.4275 ) & (0.4964 ) \tabularnewline Estimates ( 5 ) & -0.4213 & 0.0664 & 0.1188 & -0.2009 & -0.2535 & 0 & 0.1484 & 0 & -0.0542 & 0 & 0.1035 & 0.537 & 0 & 0.0462 & -0.0383 \tabularnewline (p-val) & (0.0679 ) & (0.3072 ) & (0.034 ) & (6e-04 ) & (0.0012 ) & (NA ) & (0.0052 ) & (NA ) & (0.3264 ) & (NA ) & (0.0508 ) & (0.0222 ) & (NA ) & (0.4055 ) & (0.5167 ) \tabularnewline Estimates ( 6 ) & -0.4461 & 0.0721 & 0.1197 & -0.2004 & -0.2624 & 0 & 0.1571 & 0 & -0.0588 & 0 & 0.1167 & 0.5635 & 0 & 0.0433 & 0 \tabularnewline (p-val) & (0.0228 ) & (0.2474 ) & (0.0338 ) & (6e-04 ) & (2e-04 ) & (NA ) & (0.0019 ) & (NA ) & (0.2675 ) & (NA ) & (0.0159 ) & (0.0046 ) & (NA ) & (0.4332 ) & (NA ) \tabularnewline Estimates ( 7 ) & -0.4546 & 0.0744 & 0.1186 & -0.2024 & -0.2697 & 0 & 0.161 & 0 & -0.0572 & 0 & 0.1159 & 0.571 & 0 & 0 & 0 \tabularnewline (p-val) & (0.0174 ) & (0.2312 ) & (0.0357 ) & (5e-04 ) & (1e-04 ) & (NA ) & (0.0014 ) & (NA ) & (0.2796 ) & (NA ) & (0.0167 ) & (0.0033 ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 8 ) & -0.3724 & 0.057 & 0.1147 & -0.2015 & -0.2358 & 0 & 0.1479 & 0 & 0 & 0 & 0.1081 & 0.4867 & 0 & 0 & 0 \tabularnewline (p-val) & (0.0622 ) & (0.3406 ) & (0.0376 ) & (5e-04 ) & (4e-04 ) & (NA ) & (0.0032 ) & (NA ) & (NA ) & (NA ) & (0.0286 ) & (0.0171 ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 9 ) & -0.2987 & 0 & 0.1053 & -0.2083 & -0.2125 & 0 & 0.1436 & 0 & 0 & 0 & 0.1096 & 0.3978 & 0 & 0 & 0 \tabularnewline (p-val) & (0.179 ) & (NA ) & (0.0589 ) & (3e-04 ) & (0.0021 ) & (NA ) & (0.0051 ) & (NA ) & (NA ) & (NA ) & (0.0306 ) & (0.0615 ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 10 ) & 0 & 0 & 0.1261 & -0.2383 & -0.1391 & 0 & 0.1411 & 0 & 0 & 0 & 0.0877 & 0.1155 & 0 & 0 & 0 \tabularnewline (p-val) & (NA ) & (NA ) & (0.015 ) & (0 ) & (0.006 ) & (NA ) & (0.0084 ) & (NA ) & (NA ) & (NA ) & (0.0963 ) & (0.0252 ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 11 ) & 0 & 0 & 0.1231 & -0.2311 & -0.1337 & 0 & 0.1198 & 0 & 0 & 0 & 0 & 0.1103 & 0 & 0 & 0 \tabularnewline (p-val) & (NA ) & (NA ) & (0.018 ) & (0 ) & (0.0084 ) & (NA ) & (0.0216 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0326 ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 12 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 13 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 14 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 15 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 16 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 17 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 18 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 19 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 20 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 21 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 22 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 23 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 24 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 25 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 26 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 27 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 28 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline Estimates ( 29 ) & NA & NA & NA & NA & 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 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=65213&T=1

[TABLE]
[ROW]
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][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW] [ROW][C]Estimates ( 1 )[/C][C]-0.3486[/C][C]0.063[/C][C]0.116[/C][C]-0.2085[/C][C]-0.2236[/C][C]0.0348[/C][C]0.1526[/C][C]0.0011[/C][C]-0.0404[/C][C]0.0268[/C][C]0.1118[/C][C]0.4676[/C][C]0.2143[/C][C]0.0608[/C][C]-0.26[/C][/ROW] [ROW][C](p-val)[/C][C](0.4588 )[/C][C](0.4275 )[/C][C](0.0414 )[/C][C](0.0091 )[/C][C](0.0977 )[/C][C](0.6583 )[/C][C](0.0399 )[/C][C](0.99 )[/C][C](0.4922 )[/C][C](0.6603 )[/C][C](0.0459 )[/C][C](0.3212 )[/C][C](0.6924 )[/C][C](0.318 )[/C][C](0.6293 )[/C][/ROW] [ROW][C]Estimates ( 2 )[/C][C]-0.3439[/C][C]0.0624[/C][C]0.1159[/C][C]-0.2091[/C][C]-0.2224[/C][C]0.0354[/C][C]0.1521[/C][C]0[/C][C]-0.0404[/C][C]0.027[/C][C]0.1117[/C][C]0.4628[/C][C]0.2137[/C][C]0.0609[/C][C]-0.2601[/C][/ROW] [ROW][C](p-val)[/C][C](0.29 )[/C][C](0.3667 )[/C][C](0.0354 )[/C][C](7e-04 )[/C][C](0.0262 )[/C][C](0.5865 )[/C][C](0.0101 )[/C][C](NA )[/C][C](0.493 )[/C][C](0.6387 )[/C][C](0.0447 )[/C][C](0.1592 )[/C][C](0.6906 )[/C][C](0.3123 )[/C][C](0.6286 )[/C][/ROW] [ROW][C]Estimates ( 3 )[/C][C]-0.3469[/C][C]0.0627[/C][C]0.1174[/C][C]-0.2099[/C][C]-0.2237[/C][C]0.036[/C][C]0.1541[/C][C]0[/C][C]-0.0415[/C][C]0.0261[/C][C]0.1123[/C][C]0.4656[/C][C]0[/C][C]0.0475[/C][C]-0.0472[/C][/ROW] [ROW][C](p-val)[/C][C](0.2756 )[/C][C](0.3609 )[/C][C](0.0329 )[/C][C](6e-04 )[/C][C](0.0236 )[/C][C](0.5794 )[/C][C](0.0088 )[/C][C](NA )[/C][C](0.4802 )[/C][C](0.6505 )[/C][C](0.0432 )[/C][C](0.148 )[/C][C](NA )[/C][C](0.3985 )[/C][C](0.442 )[/C][/ROW] [ROW][C]Estimates ( 4 )[/C][C]-0.3654[/C][C]0.0656[/C][C]0.1198[/C][C]-0.2079[/C][C]-0.2334[/C][C]0.0284[/C][C]0.1566[/C][C]0[/C][C]-0.0514[/C][C]0[/C][C]0.1065[/C][C]0.4842[/C][C]0[/C][C]0.0442[/C][C]-0.0407[/C][/ROW] [ROW][C](p-val)[/C][C](0.2103 )[/C][C](0.3287 )[/C][C](0.0296 )[/C][C](6e-04 )[/C][C](0.012 )[/C][C](0.6478 )[/C][C](0.0067 )[/C][C](NA )[/C][C](0.3661 )[/C][C](NA )[/C][C](0.0468 )[/C][C](0.1004 )[/C][C](NA )[/C][C](0.4275 )[/C][C](0.4964 )[/C][/ROW] [ROW][C]Estimates ( 5 )[/C][C]-0.4213[/C][C]0.0664[/C][C]0.1188[/C][C]-0.2009[/C][C]-0.2535[/C][C]0[/C][C]0.1484[/C][C]0[/C][C]-0.0542[/C][C]0[/C][C]0.1035[/C][C]0.537[/C][C]0[/C][C]0.0462[/C][C]-0.0383[/C][/ROW] [ROW][C](p-val)[/C][C](0.0679 )[/C][C](0.3072 )[/C][C](0.034 )[/C][C](6e-04 )[/C][C](0.0012 )[/C][C](NA )[/C][C](0.0052 )[/C][C](NA )[/C][C](0.3264 )[/C][C](NA )[/C][C](0.0508 )[/C][C](0.0222 )[/C][C](NA )[/C][C](0.4055 )[/C][C](0.5167 )[/C][/ROW] [ROW][C]Estimates ( 6 )[/C][C]-0.4461[/C][C]0.0721[/C][C]0.1197[/C][C]-0.2004[/C][C]-0.2624[/C][C]0[/C][C]0.1571[/C][C]0[/C][C]-0.0588[/C][C]0[/C][C]0.1167[/C][C]0.5635[/C][C]0[/C][C]0.0433[/C][C]0[/C][/ROW] [ROW][C](p-val)[/C][C](0.0228 )[/C][C](0.2474 )[/C][C](0.0338 )[/C][C](6e-04 )[/C][C](2e-04 )[/C][C](NA )[/C][C](0.0019 )[/C][C](NA )[/C][C](0.2675 )[/C][C](NA )[/C][C](0.0159 )[/C][C](0.0046 )[/C][C](NA )[/C][C](0.4332 )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 7 )[/C][C]-0.4546[/C][C]0.0744[/C][C]0.1186[/C][C]-0.2024[/C][C]-0.2697[/C][C]0[/C][C]0.161[/C][C]0[/C][C]-0.0572[/C][C]0[/C][C]0.1159[/C][C]0.571[/C][C]0[/C][C]0[/C][C]0[/C][/ROW] [ROW][C](p-val)[/C][C](0.0174 )[/C][C](0.2312 )[/C][C](0.0357 )[/C][C](5e-04 )[/C][C](1e-04 )[/C][C](NA )[/C][C](0.0014 )[/C][C](NA )[/C][C](0.2796 )[/C][C](NA )[/C][C](0.0167 )[/C][C](0.0033 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 8 )[/C][C]-0.3724[/C][C]0.057[/C][C]0.1147[/C][C]-0.2015[/C][C]-0.2358[/C][C]0[/C][C]0.1479[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1081[/C][C]0.4867[/C][C]0[/C][C]0[/C][C]0[/C][/ROW] [ROW][C](p-val)[/C][C](0.0622 )[/C][C](0.3406 )[/C][C](0.0376 )[/C][C](5e-04 )[/C][C](4e-04 )[/C][C](NA )[/C][C](0.0032 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0286 )[/C][C](0.0171 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 9 )[/C][C]-0.2987[/C][C]0[/C][C]0.1053[/C][C]-0.2083[/C][C]-0.2125[/C][C]0[/C][C]0.1436[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1096[/C][C]0.3978[/C][C]0[/C][C]0[/C][C]0[/C][/ROW] [ROW][C](p-val)[/C][C](0.179 )[/C][C](NA )[/C][C](0.0589 )[/C][C](3e-04 )[/C][C](0.0021 )[/C][C](NA )[/C][C](0.0051 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0306 )[/C][C](0.0615 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 10 )[/C][C]0[/C][C]0[/C][C]0.1261[/C][C]-0.2383[/C][C]-0.1391[/C][C]0[/C][C]0.1411[/C][C]0[/C][C]0[/C][C]0[/C][C]0.0877[/C][C]0.1155[/C][C]0[/C][C]0[/C][C]0[/C][/ROW] [ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.015 )[/C][C](0 )[/C][C](0.006 )[/C][C](NA )[/C][C](0.0084 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0963 )[/C][C](0.0252 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 11 )[/C][C]0[/C][C]0[/C][C]0.1231[/C][C]-0.2311[/C][C]-0.1337[/C][C]0[/C][C]0.1198[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1103[/C][C]0[/C][C]0[/C][C]0[/C][/ROW] [ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.018 )[/C][C](0 )[/C][C](0.0084 )[/C][C](NA )[/C][C](0.0216 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0326 )[/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][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][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][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][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][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][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][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][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][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][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][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][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][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][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][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][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][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][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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 22 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 23 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 24 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 25 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 26 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 27 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 28 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [ROW][C]Estimates ( 29 )[/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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=65213&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65213&T=1

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11ma1sar1sar2sma1
Estimates ( 1 )-0.34860.0630.116-0.2085-0.22360.03480.15260.0011-0.04040.02680.11180.46760.21430.0608-0.26
(p-val)(0.4588 )(0.4275 )(0.0414 )(0.0091 )(0.0977 )(0.6583 )(0.0399 )(0.99 )(0.4922 )(0.6603 )(0.0459 )(0.3212 )(0.6924 )(0.318 )(0.6293 )
Estimates ( 2 )-0.34390.06240.1159-0.2091-0.22240.03540.15210-0.04040.0270.11170.46280.21370.0609-0.2601
(p-val)(0.29 )(0.3667 )(0.0354 )(7e-04 )(0.0262 )(0.5865 )(0.0101 )(NA )(0.493 )(0.6387 )(0.0447 )(0.1592 )(0.6906 )(0.3123 )(0.6286 )
Estimates ( 3 )-0.34690.06270.1174-0.2099-0.22370.0360.15410-0.04150.02610.11230.465600.0475-0.0472
(p-val)(0.2756 )(0.3609 )(0.0329 )(6e-04 )(0.0236 )(0.5794 )(0.0088 )(NA )(0.4802 )(0.6505 )(0.0432 )(0.148 )(NA )(0.3985 )(0.442 )
Estimates ( 4 )-0.36540.06560.1198-0.2079-0.23340.02840.15660-0.051400.10650.484200.0442-0.0407
(p-val)(0.2103 )(0.3287 )(0.0296 )(6e-04 )(0.012 )(0.6478 )(0.0067 )(NA )(0.3661 )(NA )(0.0468 )(0.1004 )(NA )(0.4275 )(0.4964 )
Estimates ( 5 )-0.42130.06640.1188-0.2009-0.253500.14840-0.054200.10350.53700.0462-0.0383
(p-val)(0.0679 )(0.3072 )(0.034 )(6e-04 )(0.0012 )(NA )(0.0052 )(NA )(0.3264 )(NA )(0.0508 )(0.0222 )(NA )(0.4055 )(0.5167 )
Estimates ( 6 )-0.44610.07210.1197-0.2004-0.262400.15710-0.058800.11670.563500.04330
(p-val)(0.0228 )(0.2474 )(0.0338 )(6e-04 )(2e-04 )(NA )(0.0019 )(NA )(0.2675 )(NA )(0.0159 )(0.0046 )(NA )(0.4332 )(NA )
Estimates ( 7 )-0.45460.07440.1186-0.2024-0.269700.1610-0.057200.11590.571000
(p-val)(0.0174 )(0.2312 )(0.0357 )(5e-04 )(1e-04 )(NA )(0.0014 )(NA )(0.2796 )(NA )(0.0167 )(0.0033 )(NA )(NA )(NA )
Estimates ( 8 )-0.37240.0570.1147-0.2015-0.235800.14790000.10810.4867000
(p-val)(0.0622 )(0.3406 )(0.0376 )(5e-04 )(4e-04 )(NA )(0.0032 )(NA )(NA )(NA )(0.0286 )(0.0171 )(NA )(NA )(NA )
Estimates ( 9 )-0.298700.1053-0.2083-0.212500.14360000.10960.3978000
(p-val)(0.179 )(NA )(0.0589 )(3e-04 )(0.0021 )(NA )(0.0051 )(NA )(NA )(NA )(0.0306 )(0.0615 )(NA )(NA )(NA )
Estimates ( 10 )000.1261-0.2383-0.139100.14110000.08770.1155000
(p-val)(NA )(NA )(0.015 )(0 )(0.006 )(NA )(0.0084 )(NA )(NA )(NA )(0.0963 )(0.0252 )(NA )(NA )(NA )
Estimates ( 11 )000.1231-0.2311-0.133700.119800000.1103000
(p-val)(NA )(NA )(0.018 )(0 )(0.0084 )(NA )(0.0216 )(NA )(NA )(NA )(NA )(0.0326 )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 22 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 23 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 24 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 25 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 26 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 27 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 28 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 29 )NANANANANANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
0.087279950826086
-1.10718968573808e-05
-0.180279492456848
-0.139270973590557
0.651742668667524
2.99308781367018
-0.391988280877749
-0.425167068474264
-0.962350776376153
0.331531322193706
-0.055729098486627
-0.627489311492403
-1.27532857323276
-0.264646378110857
-0.456238275617647
-0.399940149392666
-0.731373482126053
-0.289052367813334
-0.256112917008735
-0.232429401549595
-0.710169190276886
-1.01299129631511
-0.739587766859387
-0.589111845142767
-0.651914676264013
-1.71778065355126
-9.37996009543748
0.304034150781348
1.37071180244631
4.58346388816506
-0.917723863792318
0.624335314570502
3.75307519729502
3.34136351327896
1.78040290727643
0.689356468195285
0.651905424755782
0.219924455957809
-0.459363096176432
2.55554836654697
-1.72145827365233
-1.47806006645096
-1.49320276913080
-0.334355421761302
-0.582550601499591
-1.41634523584612
-1.66172459124024
2.93051126942244
-0.719279943527567
-0.9944554841679
-1.60288188043680
0.255117212798083
-0.139506687530016
-0.913272920750899
-1.40327629586542
-0.894194985012575
-0.804676269694468
3.88025622784896
1.60628927614746
-1.07613476171598
-1.66553058826295
-0.111498485267148
4.78438732779823
-0.109860582108311
-1.75179759005643
-1.99349940019864
0.203465926710805
-0.29836307292841
-1.20818858336537
5.11309362151566
-2.40313298894966
-1.34499112974326
-1.97935769636834
0.301155931561169
-0.697501438149416
1.53930329810585
-1.63195518889007
-1.17838416223496
-1.63718684600745
-0.809311063020331
2.67582330026079
0.354063528657093
-0.837292762093838
-2.07888254084411
0.56191546718047
-0.642775400979389
-0.633950860399949
-0.650625548730574
-0.317738816914357
-0.709957253054426
-1.58966168873859
1.39722060561158
0.531569943057988
-1.31798131148858
-1.13276165778505
1.04345102299486
-0.828411428480337
-1.50495958634491
0.920499615928449
-3.08333555889846
-2.19306601136037
1.38462278649115
-1.91816017348142
-1.51186512684360
1.04411857917848
-1.85878608811484
3.84155544709277
-0.854758773323617
6.0400445599867
8.47588121982967
14.7415280104230
-0.558459730834784
1.29683547435572
-0.257204433659226
3.23810607702335
-0.620094705550528
-2.35602306439390
-3.17329914130811
0.888953164750617
-2.88865504004096
-2.44859844358834
-2.86230581715486
-0.79251535920855
-1.27206137984204
-1.49419873387782
-1.49185201524789
-1.12849257748202
-1.02300509969271
-1.00257355901573
1.38574923924750
1.50612444806565
-1.14748899399437
-0.0381032211892744
2.24555990091481
-0.943314297311915
1.18130991670714
-1.24488045419932
-0.945556075461013
-1.37080839039271
-1.16529914844730
-1.58069008563071
1.34709858686544
3.7435484235324
-2.14214608519070
-0.342456346483715
-3.05823399592553
0.997193503152687
6.86429755754786
-2.33072021210614
0.00811132188520958
-1.65135816291458
0.439182565452143
-0.900751276171277
-0.492844627430372
-0.0420986216082184
-1.62848878487749
-1.94330443277889
-1.79086766778747
2.10782811020778
2.22664563926870
-2.38357893242247
-2.28225622212591
-0.921059252154947
0.00196255126887479
6.6826020416386
-2.34672135377917
-1.69623602439141
-2.26029986470367
0.759480683992052
-0.343565018917843
1.03634380514099
2.41132027638999
-1.5489311688644
-1.26003989525501
-0.948926926420995
-0.240301147111524
-0.859030057172987
1.52011843680241
-1.44960529091823
-1.07070901220585
-0.355292853726439
-3.38106081381278
-1.36415821809195
-0.576594342958018
-0.433854189549933
-1.08782471050148
-4.27564320338428
-3.05868057870907
0.00514658358090259
-0.0131066050028537
-0.90875947039779
-1.61955985324906
-0.419064013718163
0.124323226840090
-0.0898310601480574
2.61086807610246
0.0287873246766992
-0.418682951407561
-0.586939019476546
7.01230404664568
-0.713138755866254
-0.411799927444783
-1.56396180487535
0.891227458297344
0.384052494283438
6.74513069679756
17.7083047086035
-3.33466767076067
-1.40175299178514
0.29355629357849
4.02698241982202
21.810237005005
10.278594377957
-1.05157029810599
-2.55781137333834
-0.923851166098089
3.18179159201640
-0.178023802377979
-3.90863860580605
-3.01944282112485
2.00291347088141
-3.68721130451449
-3.19906879671508
-5.1147283781885
0.058733602309502
-3.0762838208196
0.467428340264846
-1.35810463527709
-6.04690078418666
2.76328135729611
-0.596646782816705
-2.13817521436212
-0.0921826301500062
-1.19604430811725
3.79699440766707
15.5127694216497
-0.0277322065582126
2.07356180404722
-1.28649255278700
-3.26957873411014
-2.15916361224419
19.7642135749195
40.9774633496404
-16.2579308506083
21.5117904508134
-4.34732735775913
-24.6726327698014
-2.68706634549977
-2.98225793024717
-3.20752282079467
-11.0131495041549
-0.836697779145155
-3.15769839527213
7.6615002537789
-17.0398618456471
3.27487829057586
-10.9776964983740
7.38052169275262
-3.13945619496958
-0.657658176173513
-11.1381117224568
0.109409163185759
4.70690793761396
3.08327208189785
-3.94052712307779
-3.5213855116027
2.53243968032425
1.58921176626210
-1.21797249489566
-1.66282481425353
-5.76186154286053
-0.95853318642014
-4.13682799318877
0.347305380741034
7.82150617706397
10.7550824804626
-11.5973671765565
-4.7171897853967
2.68392668786255
1.56598354421693
-1.19249137055159
-8.61319950711366
-0.749414126997308
-2.92718987852547
0.0445344816400848
-1.19774335108795
0.641724873944455
-1.51029932245869
-2.76796788741115
-6.08114682943526
2.50330880138542
3.36016327172492
3.64735999702571
-13.2273216840482
0.466452075332029
1.39677613021681
4.25454172181946
-8.44271720202604
1.99284596305305
-1.44695491840296
5.39661885097563
-3.70492690366815
5.24445835219672
1.92022424920140
-3.83707181736683
-3.24046031464411
-5.81871276713451
0.65266125289066
5.41412711703318
-2.18877745653225
1.48653597682892
-8.73503277735468
6.76104373692173
-1.30767575501015
-1.37858459495811
0.75017279740149
1.83803684352853
-2.84619170756732
4.1887823854807
-4.50409253548352
2.33906768503670
-2.42964256272455
2.20955577467453
0.448104150386548
6.79009857112795
-1.11114011963509
2.55528568842934
-2.85804446975667
4.07364540692805
5.13506695293322
-8.10964802493108
0.626344106912796
6.20465063224119
0.29649345430218
6.95331771535716
6.24771665917734
1.16078864102192
14.1612974208190
12.9355559915732
-6.70237939485872
-2.62098597816922
-5.35938326131674
-4.46874397970305
0.280293209423718
-7.48018912114298
7.15079039464865
-0.141934466581262
-0.353470328837375
12.7432146773969
-9.52934523186984
-2.04150090250192
0.938513269946014
0.488289648501834
-6.06604593551292
-0.962172331705062
4.37326401247
-3.52641074318979
-2.79341418649884

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.087279950826086 \tabularnewline
-1.10718968573808e-05 \tabularnewline
-0.180279492456848 \tabularnewline
-0.139270973590557 \tabularnewline
0.651742668667524 \tabularnewline
2.99308781367018 \tabularnewline
-0.391988280877749 \tabularnewline
-0.425167068474264 \tabularnewline
-0.962350776376153 \tabularnewline
0.331531322193706 \tabularnewline
-0.055729098486627 \tabularnewline
-0.627489311492403 \tabularnewline
-1.27532857323276 \tabularnewline
-0.264646378110857 \tabularnewline
-0.456238275617647 \tabularnewline
-0.399940149392666 \tabularnewline
-0.731373482126053 \tabularnewline
-0.289052367813334 \tabularnewline
-0.256112917008735 \tabularnewline
-0.232429401549595 \tabularnewline
-0.710169190276886 \tabularnewline
-1.01299129631511 \tabularnewline
-0.739587766859387 \tabularnewline
-0.589111845142767 \tabularnewline
-0.651914676264013 \tabularnewline
-1.71778065355126 \tabularnewline
-9.37996009543748 \tabularnewline
0.304034150781348 \tabularnewline
1.37071180244631 \tabularnewline
4.58346388816506 \tabularnewline
-0.917723863792318 \tabularnewline
0.624335314570502 \tabularnewline
3.75307519729502 \tabularnewline
3.34136351327896 \tabularnewline
1.78040290727643 \tabularnewline
0.689356468195285 \tabularnewline
0.651905424755782 \tabularnewline
0.219924455957809 \tabularnewline
-0.459363096176432 \tabularnewline
2.55554836654697 \tabularnewline
-1.72145827365233 \tabularnewline
-1.47806006645096 \tabularnewline
-1.49320276913080 \tabularnewline
-0.334355421761302 \tabularnewline
-0.582550601499591 \tabularnewline
-1.41634523584612 \tabularnewline
-1.66172459124024 \tabularnewline
2.93051126942244 \tabularnewline
-0.719279943527567 \tabularnewline
-0.9944554841679 \tabularnewline
-1.60288188043680 \tabularnewline
0.255117212798083 \tabularnewline
-0.139506687530016 \tabularnewline
-0.913272920750899 \tabularnewline
-1.40327629586542 \tabularnewline
-0.894194985012575 \tabularnewline
-0.804676269694468 \tabularnewline
3.88025622784896 \tabularnewline
1.60628927614746 \tabularnewline
-1.07613476171598 \tabularnewline
-1.66553058826295 \tabularnewline
-0.111498485267148 \tabularnewline
4.78438732779823 \tabularnewline
-0.109860582108311 \tabularnewline
-1.75179759005643 \tabularnewline
-1.99349940019864 \tabularnewline
0.203465926710805 \tabularnewline
-0.29836307292841 \tabularnewline
-1.20818858336537 \tabularnewline
5.11309362151566 \tabularnewline
-2.40313298894966 \tabularnewline
-1.34499112974326 \tabularnewline
-1.97935769636834 \tabularnewline
0.301155931561169 \tabularnewline
-0.697501438149416 \tabularnewline
1.53930329810585 \tabularnewline
-1.63195518889007 \tabularnewline
-1.17838416223496 \tabularnewline
-1.63718684600745 \tabularnewline
-0.809311063020331 \tabularnewline
2.67582330026079 \tabularnewline
0.354063528657093 \tabularnewline
-0.837292762093838 \tabularnewline
-2.07888254084411 \tabularnewline
0.56191546718047 \tabularnewline
-0.642775400979389 \tabularnewline
-0.633950860399949 \tabularnewline
-0.650625548730574 \tabularnewline
-0.317738816914357 \tabularnewline
-0.709957253054426 \tabularnewline
-1.58966168873859 \tabularnewline
1.39722060561158 \tabularnewline
0.531569943057988 \tabularnewline
-1.31798131148858 \tabularnewline
-1.13276165778505 \tabularnewline
1.04345102299486 \tabularnewline
-0.828411428480337 \tabularnewline
-1.50495958634491 \tabularnewline
0.920499615928449 \tabularnewline
-3.08333555889846 \tabularnewline
-2.19306601136037 \tabularnewline
1.38462278649115 \tabularnewline
-1.91816017348142 \tabularnewline
-1.51186512684360 \tabularnewline
1.04411857917848 \tabularnewline
-1.85878608811484 \tabularnewline
3.84155544709277 \tabularnewline
-0.854758773323617 \tabularnewline
6.0400445599867 \tabularnewline
8.47588121982967 \tabularnewline
14.7415280104230 \tabularnewline
-0.558459730834784 \tabularnewline
1.29683547435572 \tabularnewline
-0.257204433659226 \tabularnewline
3.23810607702335 \tabularnewline
-0.620094705550528 \tabularnewline
-2.35602306439390 \tabularnewline
-3.17329914130811 \tabularnewline
0.888953164750617 \tabularnewline
-2.88865504004096 \tabularnewline
-2.44859844358834 \tabularnewline
-2.86230581715486 \tabularnewline
-0.79251535920855 \tabularnewline
-1.27206137984204 \tabularnewline
-1.49419873387782 \tabularnewline
-1.49185201524789 \tabularnewline
-1.12849257748202 \tabularnewline
-1.02300509969271 \tabularnewline
-1.00257355901573 \tabularnewline
1.38574923924750 \tabularnewline
1.50612444806565 \tabularnewline
-1.14748899399437 \tabularnewline
-0.0381032211892744 \tabularnewline
2.24555990091481 \tabularnewline
-0.943314297311915 \tabularnewline
1.18130991670714 \tabularnewline
-1.24488045419932 \tabularnewline
-0.945556075461013 \tabularnewline
-1.37080839039271 \tabularnewline
-1.16529914844730 \tabularnewline
-1.58069008563071 \tabularnewline
1.34709858686544 \tabularnewline
3.7435484235324 \tabularnewline
-2.14214608519070 \tabularnewline
-0.342456346483715 \tabularnewline
-3.05823399592553 \tabularnewline
0.997193503152687 \tabularnewline
6.86429755754786 \tabularnewline
-2.33072021210614 \tabularnewline
0.00811132188520958 \tabularnewline
-1.65135816291458 \tabularnewline
0.439182565452143 \tabularnewline
-0.900751276171277 \tabularnewline
-0.492844627430372 \tabularnewline
-0.0420986216082184 \tabularnewline
-1.62848878487749 \tabularnewline
-1.94330443277889 \tabularnewline
-1.79086766778747 \tabularnewline
2.10782811020778 \tabularnewline
2.22664563926870 \tabularnewline
-2.38357893242247 \tabularnewline
-2.28225622212591 \tabularnewline
-0.921059252154947 \tabularnewline
0.00196255126887479 \tabularnewline
6.6826020416386 \tabularnewline
-2.34672135377917 \tabularnewline
-1.69623602439141 \tabularnewline
-2.26029986470367 \tabularnewline
0.759480683992052 \tabularnewline
-0.343565018917843 \tabularnewline
1.03634380514099 \tabularnewline
2.41132027638999 \tabularnewline
-1.5489311688644 \tabularnewline
-1.26003989525501 \tabularnewline
-0.948926926420995 \tabularnewline
-0.240301147111524 \tabularnewline
-0.859030057172987 \tabularnewline
1.52011843680241 \tabularnewline
-1.44960529091823 \tabularnewline
-1.07070901220585 \tabularnewline
-0.355292853726439 \tabularnewline
-3.38106081381278 \tabularnewline
-1.36415821809195 \tabularnewline
-0.576594342958018 \tabularnewline
-0.433854189549933 \tabularnewline
-1.08782471050148 \tabularnewline
-4.27564320338428 \tabularnewline
-3.05868057870907 \tabularnewline
0.00514658358090259 \tabularnewline
-0.0131066050028537 \tabularnewline
-0.90875947039779 \tabularnewline
-1.61955985324906 \tabularnewline
-0.419064013718163 \tabularnewline
0.124323226840090 \tabularnewline
-0.0898310601480574 \tabularnewline
2.61086807610246 \tabularnewline
0.0287873246766992 \tabularnewline
-0.418682951407561 \tabularnewline
-0.586939019476546 \tabularnewline
7.01230404664568 \tabularnewline
-0.713138755866254 \tabularnewline
-0.411799927444783 \tabularnewline
-1.56396180487535 \tabularnewline
0.891227458297344 \tabularnewline
0.384052494283438 \tabularnewline
6.74513069679756 \tabularnewline
17.7083047086035 \tabularnewline
-3.33466767076067 \tabularnewline
-1.40175299178514 \tabularnewline
0.29355629357849 \tabularnewline
4.02698241982202 \tabularnewline
21.810237005005 \tabularnewline
10.278594377957 \tabularnewline
-1.05157029810599 \tabularnewline
-2.55781137333834 \tabularnewline
-0.923851166098089 \tabularnewline
3.18179159201640 \tabularnewline
-0.178023802377979 \tabularnewline
-3.90863860580605 \tabularnewline
-3.01944282112485 \tabularnewline
2.00291347088141 \tabularnewline
-3.68721130451449 \tabularnewline
-3.19906879671508 \tabularnewline
-5.1147283781885 \tabularnewline
0.058733602309502 \tabularnewline
-3.0762838208196 \tabularnewline
0.467428340264846 \tabularnewline
-1.35810463527709 \tabularnewline
-6.04690078418666 \tabularnewline
2.76328135729611 \tabularnewline
-0.596646782816705 \tabularnewline
-2.13817521436212 \tabularnewline
-0.0921826301500062 \tabularnewline
-1.19604430811725 \tabularnewline
3.79699440766707 \tabularnewline
15.5127694216497 \tabularnewline
-0.0277322065582126 \tabularnewline
2.07356180404722 \tabularnewline
-1.28649255278700 \tabularnewline
-3.26957873411014 \tabularnewline
-2.15916361224419 \tabularnewline
19.7642135749195 \tabularnewline
40.9774633496404 \tabularnewline
-16.2579308506083 \tabularnewline
21.5117904508134 \tabularnewline
-4.34732735775913 \tabularnewline
-24.6726327698014 \tabularnewline
-2.68706634549977 \tabularnewline
-2.98225793024717 \tabularnewline
-3.20752282079467 \tabularnewline
-11.0131495041549 \tabularnewline
-0.836697779145155 \tabularnewline
-3.15769839527213 \tabularnewline
7.6615002537789 \tabularnewline
-17.0398618456471 \tabularnewline
3.27487829057586 \tabularnewline
-10.9776964983740 \tabularnewline
7.38052169275262 \tabularnewline
-3.13945619496958 \tabularnewline
-0.657658176173513 \tabularnewline
-11.1381117224568 \tabularnewline
0.109409163185759 \tabularnewline
4.70690793761396 \tabularnewline
3.08327208189785 \tabularnewline
-3.94052712307779 \tabularnewline
-3.5213855116027 \tabularnewline
2.53243968032425 \tabularnewline
1.58921176626210 \tabularnewline
-1.21797249489566 \tabularnewline
-1.66282481425353 \tabularnewline
-5.76186154286053 \tabularnewline
-0.95853318642014 \tabularnewline
-4.13682799318877 \tabularnewline
0.347305380741034 \tabularnewline
7.82150617706397 \tabularnewline
10.7550824804626 \tabularnewline
-11.5973671765565 \tabularnewline
-4.7171897853967 \tabularnewline
2.68392668786255 \tabularnewline
1.56598354421693 \tabularnewline
-1.19249137055159 \tabularnewline
-8.61319950711366 \tabularnewline
-0.749414126997308 \tabularnewline
-2.92718987852547 \tabularnewline
0.0445344816400848 \tabularnewline
-1.19774335108795 \tabularnewline
0.641724873944455 \tabularnewline
-1.51029932245869 \tabularnewline
-2.76796788741115 \tabularnewline
-6.08114682943526 \tabularnewline
2.50330880138542 \tabularnewline
3.36016327172492 \tabularnewline
3.64735999702571 \tabularnewline
-13.2273216840482 \tabularnewline
0.466452075332029 \tabularnewline
1.39677613021681 \tabularnewline
4.25454172181946 \tabularnewline
-8.44271720202604 \tabularnewline
1.99284596305305 \tabularnewline
-1.44695491840296 \tabularnewline
5.39661885097563 \tabularnewline
-3.70492690366815 \tabularnewline
5.24445835219672 \tabularnewline
1.92022424920140 \tabularnewline
-3.83707181736683 \tabularnewline
-3.24046031464411 \tabularnewline
-5.81871276713451 \tabularnewline
0.65266125289066 \tabularnewline
5.41412711703318 \tabularnewline
-2.18877745653225 \tabularnewline
1.48653597682892 \tabularnewline
-8.73503277735468 \tabularnewline
6.76104373692173 \tabularnewline
-1.30767575501015 \tabularnewline
-1.37858459495811 \tabularnewline
0.75017279740149 \tabularnewline
1.83803684352853 \tabularnewline
-2.84619170756732 \tabularnewline
4.1887823854807 \tabularnewline
-4.50409253548352 \tabularnewline
2.33906768503670 \tabularnewline
-2.42964256272455 \tabularnewline
2.20955577467453 \tabularnewline
0.448104150386548 \tabularnewline
6.79009857112795 \tabularnewline
-1.11114011963509 \tabularnewline
2.55528568842934 \tabularnewline
-2.85804446975667 \tabularnewline
4.07364540692805 \tabularnewline
5.13506695293322 \tabularnewline
-8.10964802493108 \tabularnewline
0.626344106912796 \tabularnewline
6.20465063224119 \tabularnewline
0.29649345430218 \tabularnewline
6.95331771535716 \tabularnewline
6.24771665917734 \tabularnewline
1.16078864102192 \tabularnewline
14.1612974208190 \tabularnewline
12.9355559915732 \tabularnewline
-6.70237939485872 \tabularnewline
-2.62098597816922 \tabularnewline
-5.35938326131674 \tabularnewline
-4.46874397970305 \tabularnewline
0.280293209423718 \tabularnewline
-7.48018912114298 \tabularnewline
7.15079039464865 \tabularnewline
-0.141934466581262 \tabularnewline
-0.353470328837375 \tabularnewline
12.7432146773969 \tabularnewline
-9.52934523186984 \tabularnewline
-2.04150090250192 \tabularnewline
0.938513269946014 \tabularnewline
0.488289648501834 \tabularnewline
-6.06604593551292 \tabularnewline
-0.962172331705062 \tabularnewline
4.37326401247 \tabularnewline
-3.52641074318979 \tabularnewline
-2.79341418649884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65213&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.087279950826086[/C][/ROW]
[ROW][C]-1.10718968573808e-05[/C][/ROW]
[ROW][C]-0.180279492456848[/C][/ROW]
[ROW][C]-0.139270973590557[/C][/ROW]
[ROW][C]0.651742668667524[/C][/ROW]
[ROW][C]2.99308781367018[/C][/ROW]
[ROW][C]-0.391988280877749[/C][/ROW]
[ROW][C]-0.425167068474264[/C][/ROW]
[ROW][C]-0.962350776376153[/C][/ROW]
[ROW][C]0.331531322193706[/C][/ROW]
[ROW][C]-0.055729098486627[/C][/ROW]
[ROW][C]-0.627489311492403[/C][/ROW]
[ROW][C]-1.27532857323276[/C][/ROW]
[ROW][C]-0.264646378110857[/C][/ROW]
[ROW][C]-0.456238275617647[/C][/ROW]
[ROW][C]-0.399940149392666[/C][/ROW]
[ROW][C]-0.731373482126053[/C][/ROW]
[ROW][C]-0.289052367813334[/C][/ROW]
[ROW][C]-0.256112917008735[/C][/ROW]
[ROW][C]-0.232429401549595[/C][/ROW]
[ROW][C]-0.710169190276886[/C][/ROW]
[ROW][C]-1.01299129631511[/C][/ROW]
[ROW][C]-0.739587766859387[/C][/ROW]
[ROW][C]-0.589111845142767[/C][/ROW]
[ROW][C]-0.651914676264013[/C][/ROW]
[ROW][C]-1.71778065355126[/C][/ROW]
[ROW][C]-9.37996009543748[/C][/ROW]
[ROW][C]0.304034150781348[/C][/ROW]
[ROW][C]1.37071180244631[/C][/ROW]
[ROW][C]4.58346388816506[/C][/ROW]
[ROW][C]-0.917723863792318[/C][/ROW]
[ROW][C]0.624335314570502[/C][/ROW]
[ROW][C]3.75307519729502[/C][/ROW]
[ROW][C]3.34136351327896[/C][/ROW]
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[ROW][C]0.938513269946014[/C][/ROW]
[ROW][C]0.488289648501834[/C][/ROW]
[ROW][C]-6.06604593551292[/C][/ROW]
[ROW][C]-0.962172331705062[/C][/ROW]
[ROW][C]4.37326401247[/C][/ROW]
[ROW][C]-3.52641074318979[/C][/ROW]
[ROW][C]-2.79341418649884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65213&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65213&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
0.087279950826086
-1.10718968573808e-05
-0.180279492456848
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0.651742668667524
2.99308781367018
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0.331531322193706
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0.304034150781348
1.37071180244631
4.58346388816506
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0.624335314570502
3.75307519729502
3.34136351327896
1.78040290727643
0.689356468195285
0.651905424755782
0.219924455957809
-0.459363096176432
2.55554836654697
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-1.66172459124024
2.93051126942244
-0.719279943527567
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0.255117212798083
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-1.40327629586542
-0.894194985012575
-0.804676269694468
3.88025622784896
1.60628927614746
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4.78438732779823
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5.11309362151566
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0.301155931561169
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1.53930329810585
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2.67582330026079
0.354063528657093
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0.56191546718047
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1.39722060561158
0.531569943057988
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1.04345102299486
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1.04411857917848
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3.84155544709277
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6.0400445599867
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Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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
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')