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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 11:41:43 -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/t1260470907ssvvqcsi3dinfhw.htm/, Retrieved Thu, 28 Mar 2024 18:58:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65717, Retrieved Thu, 28 Mar 2024 18:58:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-    D    [ARIMA Backward Selection] [BBWS9-Arimabackward1] [2009-12-01 20:26:03] [408e92805dcb18620260f240a7fb9d53]
-    D      [ARIMA Backward Selection] [shw-ws9] [2009-12-04 13:12:35] [2663058f2a5dda519058ac6b2228468f]
-   PD        [ARIMA Backward Selection] [ws 9 arima] [2009-12-04 19:09:46] [134dc66689e3d457a82860db6471d419]
-   PD            [ARIMA Backward Selection] [arima backward se...] [2009-12-10 18:41:43] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
-   PD              [ARIMA Backward Selection] [ws 10 deel 2 arim...] [2009-12-12 09:24:44] [134dc66689e3d457a82860db6471d419]
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Dataseries X:
100.00
102.04
102.51
102.71
103.00
103.39
102.32
103.88
104.65
104.46
104.65
104.36
102.71
104.55
104.76
105.72
106.20
106.50
105.14
106.50
106.69
106.50
106.50
106.39
105.43
107.18
107.37
107.46
107.66
107.37
106.30
107.85
107.95
107.85
107.66
107.76
106.69
108.92
109.22
109.02
108.62
109.02
107.76
109.60
109.80
109.41
109.60
109.60
108.15
110.18
110.27
110.87
111.25
111.15
109.99
111.83
111.73
112.31
112.12
111.73
110.27
112.71
113.38
113.57
113.77
114.15
112.99
115.03
115.03
114.84
114.75
114.84
113.32
115.92
115.84
116.49
116.90
116.99
115.74
117.73
117.17
116.83
117.08
117.23
115.25
117.98
117.97
118.56
118.42
118.51
117.25
119.08
118.85
119.41
120.43
120.87
119.31
122.24
123.14
123.39
124.46
125.33
124.17
125.48
125.35
125.15
124.31
124.14
121.81
124.62
123.93
124.29
124.16
124.02
122.00
124.58
124.06




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 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 & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65717&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65717&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65717&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.31260.125-0.02990.4909-0.7407-0.4291-0.3054
(p-val)(0.7581 )(0.5386 )(0.7973 )(0.6271 )(9e-04 )(0.0097 )(0.2062 )
Estimates ( 2 )-0.46290.158100.638-0.7354-0.4276-0.3063
(p-val)(0.5331 )(0.2735 )(NA )(0.3908 )(9e-04 )(0.0095 )(0.2015 )
Estimates ( 3 )00.069900.1763-0.7352-0.4267-0.3044
(p-val)(NA )(0.4938 )(NA )(0.0892 )(9e-04 )(0.0097 )(0.2026 )
Estimates ( 4 )0000.1627-0.7294-0.4152-0.3206
(p-val)(NA )(NA )(NA )(0.0898 )(0.0012 )(0.0146 )(0.1878 )
Estimates ( 5 )0000.1934-0.9698-0.55520
(p-val)(NA )(NA )(NA )(0.0341 )(0 )(0 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.3126 & 0.125 & -0.0299 & 0.4909 & -0.7407 & -0.4291 & -0.3054 \tabularnewline
(p-val) & (0.7581 ) & (0.5386 ) & (0.7973 ) & (0.6271 ) & (9e-04 ) & (0.0097 ) & (0.2062 ) \tabularnewline
Estimates ( 2 ) & -0.4629 & 0.1581 & 0 & 0.638 & -0.7354 & -0.4276 & -0.3063 \tabularnewline
(p-val) & (0.5331 ) & (0.2735 ) & (NA ) & (0.3908 ) & (9e-04 ) & (0.0095 ) & (0.2015 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.0699 & 0 & 0.1763 & -0.7352 & -0.4267 & -0.3044 \tabularnewline
(p-val) & (NA ) & (0.4938 ) & (NA ) & (0.0892 ) & (9e-04 ) & (0.0097 ) & (0.2026 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0 & 0.1627 & -0.7294 & -0.4152 & -0.3206 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0.0898 ) & (0.0012 ) & (0.0146 ) & (0.1878 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & 0.1934 & -0.9698 & -0.5552 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0.0341 ) & (0 ) & (0 ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65717&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.3126[/C][C]0.125[/C][C]-0.0299[/C][C]0.4909[/C][C]-0.7407[/C][C]-0.4291[/C][C]-0.3054[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7581 )[/C][C](0.5386 )[/C][C](0.7973 )[/C][C](0.6271 )[/C][C](9e-04 )[/C][C](0.0097 )[/C][C](0.2062 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.4629[/C][C]0.1581[/C][C]0[/C][C]0.638[/C][C]-0.7354[/C][C]-0.4276[/C][C]-0.3063[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5331 )[/C][C](0.2735 )[/C][C](NA )[/C][C](0.3908 )[/C][C](9e-04 )[/C][C](0.0095 )[/C][C](0.2015 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.0699[/C][C]0[/C][C]0.1763[/C][C]-0.7352[/C][C]-0.4267[/C][C]-0.3044[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.4938 )[/C][C](NA )[/C][C](0.0892 )[/C][C](9e-04 )[/C][C](0.0097 )[/C][C](0.2026 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1627[/C][C]-0.7294[/C][C]-0.4152[/C][C]-0.3206[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0898 )[/C][C](0.0012 )[/C][C](0.0146 )[/C][C](0.1878 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1934[/C][C]-0.9698[/C][C]-0.5552[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0341 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65717&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65717&T=1

As an alternative you can also use a QR Code:  

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

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.31260.125-0.02990.4909-0.7407-0.4291-0.3054
(p-val)(0.7581 )(0.5386 )(0.7973 )(0.6271 )(9e-04 )(0.0097 )(0.2062 )
Estimates ( 2 )-0.46290.158100.638-0.7354-0.4276-0.3063
(p-val)(0.5331 )(0.2735 )(NA )(0.3908 )(9e-04 )(0.0095 )(0.2015 )
Estimates ( 3 )00.069900.1763-0.7352-0.4267-0.3044
(p-val)(NA )(0.4938 )(NA )(0.0892 )(9e-04 )(0.0097 )(0.2026 )
Estimates ( 4 )0000.1627-0.7294-0.4152-0.3206
(p-val)(NA )(NA )(NA )(0.0898 )(0.0012 )(0.0146 )(0.1878 )
Estimates ( 5 )0000.1934-0.9698-0.55520
(p-val)(NA )(NA )(NA )(0.0341 )(0 )(0 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
-5.53187442531606e-06
4.3071522270902e-06
3.01477468337252e-06
-1.00530785141566e-05
-4.93762219762133e-07
1.55425693538121e-06
2.43878249856451e-06
3.37962064707032e-06
7.14145997516872e-06
-1.29391437709342e-06
2.65550031868344e-06
-2.85784985370317e-06
-9.10406444587485e-06
7.75880103112249e-06
2.11566610308387e-06
6.39023001190256e-06
1.87717339932466e-06
1.00106161756063e-05
-4.55812973658567e-06
1.32118770825757e-06
7.18534092216022e-06
-2.72483418813664e-06
5.2345052207674e-06
-5.98182109401112e-06
-4.83218441594069e-06
2.86448194747561e-07
1.78710603670990e-06
1.29759567401428e-05
1.20883641341437e-05
-2.80452786336476e-06
-2.36236704327991e-07
-3.65092862716824e-06
6.94209184365124e-06
2.14041721646211e-06
-1.83089572921258e-06
-3.55347758402198e-06
3.92915496346237e-08
6.55966117521306e-07
3.20129792307587e-06
5.79037723460469e-07
8.85962913010199e-07
3.9497317539452e-06
-4.17079545640654e-06
-3.16582246087927e-06
7.24848521962577e-06
-1.38891028369960e-05
4.79523680577655e-06
4.31134056038357e-06
3.22662064060977e-06
-5.23674991270104e-06
-5.55967717908351e-06
5.41570527699629e-07
-1.99395371173913e-06
-4.95945441097089e-06
-2.48038164334267e-06
-2.63047150035067e-06
4.00709303434761e-06
-1.99892187188109e-06
1.30788918980134e-06
-1.03305065513826e-06
2.60456318090578e-06
-3.61073190859021e-06
5.51616325392656e-06
-8.44465399796794e-06
-5.58234421851054e-06
1.97817211067140e-06
-3.15071790893165e-06
1.91121580350830e-06
1.02238705560034e-05
2.46069966283706e-06
-3.87973283624162e-06
-3.59489808331319e-06
6.51404391773307e-06
-4.4640397571819e-06
6.25570393681444e-06
-4.14500440703753e-06
5.95806219696702e-06
-3.62718052189907e-07
-1.58164451977862e-06
5.35009829263459e-06
2.87994215715276e-06
-5.58997883040844e-06
-1.55883026883347e-05
-6.71499192791076e-06
-1.17985711349191e-06
-5.15584604424072e-07
-6.20169204988256e-06
3.47279380715960e-06
-1.02909890042799e-05
-6.10452700212328e-06
-3.42328429887995e-06
1.57710857941440e-05
-3.25361216871793e-06
8.01845290211076e-07
1.01093265297895e-05
6.24560543617429e-07
4.78001133307674e-06
2.82632368275932e-06
9.35623419675438e-06
2.34897876291511e-06
4.01047628021827e-06
2.75500014881646e-06
7.38387183204985e-06
-5.15009608882732e-06
2.44345429701566e-06

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-5.53187442531606e-06 \tabularnewline
4.3071522270902e-06 \tabularnewline
3.01477468337252e-06 \tabularnewline
-1.00530785141566e-05 \tabularnewline
-4.93762219762133e-07 \tabularnewline
1.55425693538121e-06 \tabularnewline
2.43878249856451e-06 \tabularnewline
3.37962064707032e-06 \tabularnewline
7.14145997516872e-06 \tabularnewline
-1.29391437709342e-06 \tabularnewline
2.65550031868344e-06 \tabularnewline
-2.85784985370317e-06 \tabularnewline
-9.10406444587485e-06 \tabularnewline
7.75880103112249e-06 \tabularnewline
2.11566610308387e-06 \tabularnewline
6.39023001190256e-06 \tabularnewline
1.87717339932466e-06 \tabularnewline
1.00106161756063e-05 \tabularnewline
-4.55812973658567e-06 \tabularnewline
1.32118770825757e-06 \tabularnewline
7.18534092216022e-06 \tabularnewline
-2.72483418813664e-06 \tabularnewline
5.2345052207674e-06 \tabularnewline
-5.98182109401112e-06 \tabularnewline
-4.83218441594069e-06 \tabularnewline
2.86448194747561e-07 \tabularnewline
1.78710603670990e-06 \tabularnewline
1.29759567401428e-05 \tabularnewline
1.20883641341437e-05 \tabularnewline
-2.80452786336476e-06 \tabularnewline
-2.36236704327991e-07 \tabularnewline
-3.65092862716824e-06 \tabularnewline
6.94209184365124e-06 \tabularnewline
2.14041721646211e-06 \tabularnewline
-1.83089572921258e-06 \tabularnewline
-3.55347758402198e-06 \tabularnewline
3.92915496346237e-08 \tabularnewline
6.55966117521306e-07 \tabularnewline
3.20129792307587e-06 \tabularnewline
5.79037723460469e-07 \tabularnewline
8.85962913010199e-07 \tabularnewline
3.9497317539452e-06 \tabularnewline
-4.17079545640654e-06 \tabularnewline
-3.16582246087927e-06 \tabularnewline
7.24848521962577e-06 \tabularnewline
-1.38891028369960e-05 \tabularnewline
4.79523680577655e-06 \tabularnewline
4.31134056038357e-06 \tabularnewline
3.22662064060977e-06 \tabularnewline
-5.23674991270104e-06 \tabularnewline
-5.55967717908351e-06 \tabularnewline
5.41570527699629e-07 \tabularnewline
-1.99395371173913e-06 \tabularnewline
-4.95945441097089e-06 \tabularnewline
-2.48038164334267e-06 \tabularnewline
-2.63047150035067e-06 \tabularnewline
4.00709303434761e-06 \tabularnewline
-1.99892187188109e-06 \tabularnewline
1.30788918980134e-06 \tabularnewline
-1.03305065513826e-06 \tabularnewline
2.60456318090578e-06 \tabularnewline
-3.61073190859021e-06 \tabularnewline
5.51616325392656e-06 \tabularnewline
-8.44465399796794e-06 \tabularnewline
-5.58234421851054e-06 \tabularnewline
1.97817211067140e-06 \tabularnewline
-3.15071790893165e-06 \tabularnewline
1.91121580350830e-06 \tabularnewline
1.02238705560034e-05 \tabularnewline
2.46069966283706e-06 \tabularnewline
-3.87973283624162e-06 \tabularnewline
-3.59489808331319e-06 \tabularnewline
6.51404391773307e-06 \tabularnewline
-4.4640397571819e-06 \tabularnewline
6.25570393681444e-06 \tabularnewline
-4.14500440703753e-06 \tabularnewline
5.95806219696702e-06 \tabularnewline
-3.62718052189907e-07 \tabularnewline
-1.58164451977862e-06 \tabularnewline
5.35009829263459e-06 \tabularnewline
2.87994215715276e-06 \tabularnewline
-5.58997883040844e-06 \tabularnewline
-1.55883026883347e-05 \tabularnewline
-6.71499192791076e-06 \tabularnewline
-1.17985711349191e-06 \tabularnewline
-5.15584604424072e-07 \tabularnewline
-6.20169204988256e-06 \tabularnewline
3.47279380715960e-06 \tabularnewline
-1.02909890042799e-05 \tabularnewline
-6.10452700212328e-06 \tabularnewline
-3.42328429887995e-06 \tabularnewline
1.57710857941440e-05 \tabularnewline
-3.25361216871793e-06 \tabularnewline
8.01845290211076e-07 \tabularnewline
1.01093265297895e-05 \tabularnewline
6.24560543617429e-07 \tabularnewline
4.78001133307674e-06 \tabularnewline
2.82632368275932e-06 \tabularnewline
9.35623419675438e-06 \tabularnewline
2.34897876291511e-06 \tabularnewline
4.01047628021827e-06 \tabularnewline
2.75500014881646e-06 \tabularnewline
7.38387183204985e-06 \tabularnewline
-5.15009608882732e-06 \tabularnewline
2.44345429701566e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65717&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-5.53187442531606e-06[/C][/ROW]
[ROW][C]4.3071522270902e-06[/C][/ROW]
[ROW][C]3.01477468337252e-06[/C][/ROW]
[ROW][C]-1.00530785141566e-05[/C][/ROW]
[ROW][C]-4.93762219762133e-07[/C][/ROW]
[ROW][C]1.55425693538121e-06[/C][/ROW]
[ROW][C]2.43878249856451e-06[/C][/ROW]
[ROW][C]3.37962064707032e-06[/C][/ROW]
[ROW][C]7.14145997516872e-06[/C][/ROW]
[ROW][C]-1.29391437709342e-06[/C][/ROW]
[ROW][C]2.65550031868344e-06[/C][/ROW]
[ROW][C]-2.85784985370317e-06[/C][/ROW]
[ROW][C]-9.10406444587485e-06[/C][/ROW]
[ROW][C]7.75880103112249e-06[/C][/ROW]
[ROW][C]2.11566610308387e-06[/C][/ROW]
[ROW][C]6.39023001190256e-06[/C][/ROW]
[ROW][C]1.87717339932466e-06[/C][/ROW]
[ROW][C]1.00106161756063e-05[/C][/ROW]
[ROW][C]-4.55812973658567e-06[/C][/ROW]
[ROW][C]1.32118770825757e-06[/C][/ROW]
[ROW][C]7.18534092216022e-06[/C][/ROW]
[ROW][C]-2.72483418813664e-06[/C][/ROW]
[ROW][C]5.2345052207674e-06[/C][/ROW]
[ROW][C]-5.98182109401112e-06[/C][/ROW]
[ROW][C]-4.83218441594069e-06[/C][/ROW]
[ROW][C]2.86448194747561e-07[/C][/ROW]
[ROW][C]1.78710603670990e-06[/C][/ROW]
[ROW][C]1.29759567401428e-05[/C][/ROW]
[ROW][C]1.20883641341437e-05[/C][/ROW]
[ROW][C]-2.80452786336476e-06[/C][/ROW]
[ROW][C]-2.36236704327991e-07[/C][/ROW]
[ROW][C]-3.65092862716824e-06[/C][/ROW]
[ROW][C]6.94209184365124e-06[/C][/ROW]
[ROW][C]2.14041721646211e-06[/C][/ROW]
[ROW][C]-1.83089572921258e-06[/C][/ROW]
[ROW][C]-3.55347758402198e-06[/C][/ROW]
[ROW][C]3.92915496346237e-08[/C][/ROW]
[ROW][C]6.55966117521306e-07[/C][/ROW]
[ROW][C]3.20129792307587e-06[/C][/ROW]
[ROW][C]5.79037723460469e-07[/C][/ROW]
[ROW][C]8.85962913010199e-07[/C][/ROW]
[ROW][C]3.9497317539452e-06[/C][/ROW]
[ROW][C]-4.17079545640654e-06[/C][/ROW]
[ROW][C]-3.16582246087927e-06[/C][/ROW]
[ROW][C]7.24848521962577e-06[/C][/ROW]
[ROW][C]-1.38891028369960e-05[/C][/ROW]
[ROW][C]4.79523680577655e-06[/C][/ROW]
[ROW][C]4.31134056038357e-06[/C][/ROW]
[ROW][C]3.22662064060977e-06[/C][/ROW]
[ROW][C]-5.23674991270104e-06[/C][/ROW]
[ROW][C]-5.55967717908351e-06[/C][/ROW]
[ROW][C]5.41570527699629e-07[/C][/ROW]
[ROW][C]-1.99395371173913e-06[/C][/ROW]
[ROW][C]-4.95945441097089e-06[/C][/ROW]
[ROW][C]-2.48038164334267e-06[/C][/ROW]
[ROW][C]-2.63047150035067e-06[/C][/ROW]
[ROW][C]4.00709303434761e-06[/C][/ROW]
[ROW][C]-1.99892187188109e-06[/C][/ROW]
[ROW][C]1.30788918980134e-06[/C][/ROW]
[ROW][C]-1.03305065513826e-06[/C][/ROW]
[ROW][C]2.60456318090578e-06[/C][/ROW]
[ROW][C]-3.61073190859021e-06[/C][/ROW]
[ROW][C]5.51616325392656e-06[/C][/ROW]
[ROW][C]-8.44465399796794e-06[/C][/ROW]
[ROW][C]-5.58234421851054e-06[/C][/ROW]
[ROW][C]1.97817211067140e-06[/C][/ROW]
[ROW][C]-3.15071790893165e-06[/C][/ROW]
[ROW][C]1.91121580350830e-06[/C][/ROW]
[ROW][C]1.02238705560034e-05[/C][/ROW]
[ROW][C]2.46069966283706e-06[/C][/ROW]
[ROW][C]-3.87973283624162e-06[/C][/ROW]
[ROW][C]-3.59489808331319e-06[/C][/ROW]
[ROW][C]6.51404391773307e-06[/C][/ROW]
[ROW][C]-4.4640397571819e-06[/C][/ROW]
[ROW][C]6.25570393681444e-06[/C][/ROW]
[ROW][C]-4.14500440703753e-06[/C][/ROW]
[ROW][C]5.95806219696702e-06[/C][/ROW]
[ROW][C]-3.62718052189907e-07[/C][/ROW]
[ROW][C]-1.58164451977862e-06[/C][/ROW]
[ROW][C]5.35009829263459e-06[/C][/ROW]
[ROW][C]2.87994215715276e-06[/C][/ROW]
[ROW][C]-5.58997883040844e-06[/C][/ROW]
[ROW][C]-1.55883026883347e-05[/C][/ROW]
[ROW][C]-6.71499192791076e-06[/C][/ROW]
[ROW][C]-1.17985711349191e-06[/C][/ROW]
[ROW][C]-5.15584604424072e-07[/C][/ROW]
[ROW][C]-6.20169204988256e-06[/C][/ROW]
[ROW][C]3.47279380715960e-06[/C][/ROW]
[ROW][C]-1.02909890042799e-05[/C][/ROW]
[ROW][C]-6.10452700212328e-06[/C][/ROW]
[ROW][C]-3.42328429887995e-06[/C][/ROW]
[ROW][C]1.57710857941440e-05[/C][/ROW]
[ROW][C]-3.25361216871793e-06[/C][/ROW]
[ROW][C]8.01845290211076e-07[/C][/ROW]
[ROW][C]1.01093265297895e-05[/C][/ROW]
[ROW][C]6.24560543617429e-07[/C][/ROW]
[ROW][C]4.78001133307674e-06[/C][/ROW]
[ROW][C]2.82632368275932e-06[/C][/ROW]
[ROW][C]9.35623419675438e-06[/C][/ROW]
[ROW][C]2.34897876291511e-06[/C][/ROW]
[ROW][C]4.01047628021827e-06[/C][/ROW]
[ROW][C]2.75500014881646e-06[/C][/ROW]
[ROW][C]7.38387183204985e-06[/C][/ROW]
[ROW][C]-5.15009608882732e-06[/C][/ROW]
[ROW][C]2.44345429701566e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65717&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65717&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
-5.53187442531606e-06
4.3071522270902e-06
3.01477468337252e-06
-1.00530785141566e-05
-4.93762219762133e-07
1.55425693538121e-06
2.43878249856451e-06
3.37962064707032e-06
7.14145997516872e-06
-1.29391437709342e-06
2.65550031868344e-06
-2.85784985370317e-06
-9.10406444587485e-06
7.75880103112249e-06
2.11566610308387e-06
6.39023001190256e-06
1.87717339932466e-06
1.00106161756063e-05
-4.55812973658567e-06
1.32118770825757e-06
7.18534092216022e-06
-2.72483418813664e-06
5.2345052207674e-06
-5.98182109401112e-06
-4.83218441594069e-06
2.86448194747561e-07
1.78710603670990e-06
1.29759567401428e-05
1.20883641341437e-05
-2.80452786336476e-06
-2.36236704327991e-07
-3.65092862716824e-06
6.94209184365124e-06
2.14041721646211e-06
-1.83089572921258e-06
-3.55347758402198e-06
3.92915496346237e-08
6.55966117521306e-07
3.20129792307587e-06
5.79037723460469e-07
8.85962913010199e-07
3.9497317539452e-06
-4.17079545640654e-06
-3.16582246087927e-06
7.24848521962577e-06
-1.38891028369960e-05
4.79523680577655e-06
4.31134056038357e-06
3.22662064060977e-06
-5.23674991270104e-06
-5.55967717908351e-06
5.41570527699629e-07
-1.99395371173913e-06
-4.95945441097089e-06
-2.48038164334267e-06
-2.63047150035067e-06
4.00709303434761e-06
-1.99892187188109e-06
1.30788918980134e-06
-1.03305065513826e-06
2.60456318090578e-06
-3.61073190859021e-06
5.51616325392656e-06
-8.44465399796794e-06
-5.58234421851054e-06
1.97817211067140e-06
-3.15071790893165e-06
1.91121580350830e-06
1.02238705560034e-05
2.46069966283706e-06
-3.87973283624162e-06
-3.59489808331319e-06
6.51404391773307e-06
-4.4640397571819e-06
6.25570393681444e-06
-4.14500440703753e-06
5.95806219696702e-06
-3.62718052189907e-07
-1.58164451977862e-06
5.35009829263459e-06
2.87994215715276e-06
-5.58997883040844e-06
-1.55883026883347e-05
-6.71499192791076e-06
-1.17985711349191e-06
-5.15584604424072e-07
-6.20169204988256e-06
3.47279380715960e-06
-1.02909890042799e-05
-6.10452700212328e-06
-3.42328429887995e-06
1.57710857941440e-05
-3.25361216871793e-06
8.01845290211076e-07
1.01093265297895e-05
6.24560543617429e-07
4.78001133307674e-06
2.82632368275932e-06
9.35623419675438e-06
2.34897876291511e-06
4.01047628021827e-06
2.75500014881646e-06
7.38387183204985e-06
-5.15009608882732e-06
2.44345429701566e-06



Parameters (Session):
par1 = FALSE ; par2 = -1.4 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = -1.4 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')