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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 computationSun, 19 Dec 2010 10:52:23 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292755857u8iufups7l5izlg.htm/, Retrieved Sun, 05 May 2024 03:15:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112272, Retrieved Sun, 05 May 2024 03:15:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134

Tree of Dependent Computations

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   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
- R  D      [ARIMA Backward Selection] [] [2010-12-13 21:48:37] [74be16979710d4c4e7c6647856088456]
-   PD          [ARIMA Backward Selection] [ARIMA berekening ...] [2010-12-19 10:52:23] [109f5cd2d2b7c934778912c55604f6f1] [Current]
-   P             [ARIMA Backward Selection] [ARIMA berekening ...] [2010-12-19 11:06:05] [46df8573ee32a55e1a6edcfb6691f406]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
126.64
126.81
125.84
126.77
124.34
124.4
120.48
118.54
117.66
116.97
120.11
119.16
116.9
116.11
114.98
113.65
115.82
117.59
118.57
118.07
114.98
114.04
115.02
114.28
115.04
116.7
119.21
118.39
116.5
115.46
117.59
117.33
116.2
116.83
118.99
118.62
121.09
122.4
123.76
125.33
123.23
122.52
123.64
124.67
124.71
122.53
124.4
125.45
125.35
124.3
127.03
128.51
128.1
128.94
129.67
129.87
131.12
132.68
132.24
133.63
129.91
127.93
131.17
130.86
133.48
134.08
136.02
132.8
132.37
133.05
132.57
130.7
130.5
129.67
127.8
126.82
126.85
128.28
128.3
126.82
125.08
128.53
130.34
131.52
132.59
131.17
132.72
133.36
132.82
132.9
130.9
129.41
128.67
129.28
130.91
131.06
130.84
131.41
133.22
132.06
132.48
134.38
135.22
134.89
136.09
136.33
136.32
137.48
136.53
136.8
138.03
137.39
137.55
136.08
134.78
133.28
133.57
134.84
133.02
133.49
133.77
134.34
134.5
134.03
135.51
136.53
135.95
134.32
132.44
133.61
131.02
130.05
128.21
129.03
130.34
131.57
132.63
132.06
134.44
134.1
132.49
134.23
134.92
135.61
134.53
133.86
133.89
135.33
135.86
136.22
137.38
137.31
136.89
138.01
136.72
135.77
137.52
135.61
132.94
134.12
132.55
134.11
134.19
135.57
135.05
134.32
133.61
134.75
133.1
133.26
131.63
132.47
132.45
133.33
133.57
134.13
133.92
132.62
132.3
133.26
132.6
134.38
134.17
135.46
135.09
134.96
133.85
132.59
131.15
130.91
131.07
130.78
129.95
131.41
131.21
130.68
130.46
131.12
132.99
133.02
133.39
134.07
135.6
135.66
135.53
135.82
136.9
137.97
138.09
136.91
134.76
135.13
134.66
132.95
132.25
134.3
134.3
134.76
134.81
134.51
135.11
134.32
133.51
134.02
132.76
133.39
132.05
131.87
133.03
132.57
132.1
130.7
129.2
129.77
131.02
131.55
133.17
133.08
133.24
130.74
129.91
130.03
131.13
129.55
130.22
130.61
129.27
129.68
130.1
130.83
130.95
131.73
131.86
132.44
132.35
133.16
133.62
132.54
132.69
133.5
133.36
134.23
132.41
133.02
132.88
130.76
130.33
129.79
128.65
129.14
127.35
127.74
126.31
125.95
126.36
126.15
125.6
126.2
126.73
125.68
122.49
122.07
123.4
123.01
123.03
122.33
122.42
122.68
124.69
123.3
124.17
124.38
123.19
122.16
120.66
120.92
120.67
120.68
121.1
120.86
121.48
123.48
121.72
123.16
123.84
124.57
124.3
124.22
124.43
123.33
122.86
121.25
122.16
122.62
123.44
124
124.75
124.8
125.93
126.28
126.04
125.04
123.76
125.34
126.99
126.34
127.42
126.18
125.3
123.5
125.32
124.65
124.03
125.11
125.46
124.7
124.48
124.76
125.81
124.95
123.66
122.66
119.34
117.84
120.97
117.38
118.06
116.99
115.55
114.17
115.32
112.49
111.93
112.08
111.63
109.53
111.35
110.79
113.06
112.62
110.65
112.36
113.74
111.73
109.86
109.32
109.99
109.84
111.13
112.43
111.77
112.15
112.89
112.12
113.1
111.09
110.76
109.59
109.99
110.25
108.31
108.79
108.14
109.88
109.93
110.46
109.56
111.49
111.85
111.35
110.95
112.49
113.11
112.54
112.84
111.5
111.52
111.57
112.48
112.31
113.79
114.01
113.64
112.62
113.27
113.51
112.92
113.66
113.14
113.48
113.23
110.56
109.5
109.78
109.49
109.66
109.93
109.82
108.54
108.23
106.19
106.49
107.15
107.74
107.54
107.07
107.54
107.81
108.38
108.42
106.86
106.41
106.46
106.84
107.69
107.04
111.04
111.93
111.98
112.07
112.05
113.14
112.49
113.2
113.52
113.22
113.85
113.68
114.26
114.1
114.8
114.98
115.1
114.21
114.24
113.35
114.23
114.43
114.28
113
113.16
112.59
113.65
113.18
113.21
113.11
112.78
112.57
111.87
111.94
113.18
113.67
115.15
114.41
112.88
112.44
113.48
112.78
112.59
113.31
113.21
112.5
113.72
114.09
113.97
112.5
111.28
111.35
110.92
110.73
109

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112272&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112272&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.0051-0.0097-0.0033-0.0049-0.0096-0.0622-0.009
(p-val)(0.9997 )(0.9488 )(0.9828 )(0.9997 )(0.9836 )(0.1937 )(0.9846 )
Estimates ( 2 )-0.0102-0.0097-0.00330-0.0096-0.0621-0.009
(p-val)(0.8225 )(0.833 )(0.9424 )(NA )(0.9834 )(0.1944 )(0.9846 )
Estimates ( 3 )-0.0102-0.0097-0.00340-0.0187-0.06220
(p-val)(0.8226 )(0.8321 )(0.9409 )(NA )(0.681 )(0.186 )(NA )
Estimates ( 4 )-0.0102-0.009700-0.0188-0.06240
(p-val)(0.8228 )(0.833 )(NA )(NA )(0.6784 )(0.1843 )(NA )
Estimates ( 5 )-0.0102000-0.0186-0.06390
(p-val)(0.822 )(NA )(NA )(NA )(0.6808 )(0.1682 )(NA )
Estimates ( 6 )0000-0.0184-0.06290
(p-val)(NA )(NA )(NA )(NA )(0.6852 )(0.1732 )(NA )
Estimates ( 7 )00000-0.06240
(p-val)(NA )(NA )(NA )(NA )(NA )(0.1763 )(NA )
Estimates ( 8 )0000000
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.0051 & -0.0097 & -0.0033 & -0.0049 & -0.0096 & -0.0622 & -0.009 \tabularnewline
(p-val) & (0.9997 ) & (0.9488 ) & (0.9828 ) & (0.9997 ) & (0.9836 ) & (0.1937 ) & (0.9846 ) \tabularnewline
Estimates ( 2 ) & -0.0102 & -0.0097 & -0.0033 & 0 & -0.0096 & -0.0621 & -0.009 \tabularnewline
(p-val) & (0.8225 ) & (0.833 ) & (0.9424 ) & (NA ) & (0.9834 ) & (0.1944 ) & (0.9846 ) \tabularnewline
Estimates ( 3 ) & -0.0102 & -0.0097 & -0.0034 & 0 & -0.0187 & -0.0622 & 0 \tabularnewline
(p-val) & (0.8226 ) & (0.8321 ) & (0.9409 ) & (NA ) & (0.681 ) & (0.186 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.0102 & -0.0097 & 0 & 0 & -0.0188 & -0.0624 & 0 \tabularnewline
(p-val) & (0.8228 ) & (0.833 ) & (NA ) & (NA ) & (0.6784 ) & (0.1843 ) & (NA ) \tabularnewline
Estimates ( 5 ) & -0.0102 & 0 & 0 & 0 & -0.0186 & -0.0639 & 0 \tabularnewline
(p-val) & (0.822 ) & (NA ) & (NA ) & (NA ) & (0.6808 ) & (0.1682 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & -0.0184 & -0.0629 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0.6852 ) & (0.1732 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & -0.0624 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.1763 ) & (NA ) \tabularnewline
Estimates ( 8 ) & 0 & 0 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112272&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]-0.0051[/C][C]-0.0097[/C][C]-0.0033[/C][C]-0.0049[/C][C]-0.0096[/C][C]-0.0622[/C][C]-0.009[/C][/ROW]
[ROW][C](p-val)[/C][C](0.9997 )[/C][C](0.9488 )[/C][C](0.9828 )[/C][C](0.9997 )[/C][C](0.9836 )[/C][C](0.1937 )[/C][C](0.9846 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.0102[/C][C]-0.0097[/C][C]-0.0033[/C][C]0[/C][C]-0.0096[/C][C]-0.0621[/C][C]-0.009[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8225 )[/C][C](0.833 )[/C][C](0.9424 )[/C][C](NA )[/C][C](0.9834 )[/C][C](0.1944 )[/C][C](0.9846 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.0102[/C][C]-0.0097[/C][C]-0.0034[/C][C]0[/C][C]-0.0187[/C][C]-0.0622[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8226 )[/C][C](0.8321 )[/C][C](0.9409 )[/C][C](NA )[/C][C](0.681 )[/C][C](0.186 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.0102[/C][C]-0.0097[/C][C]0[/C][C]0[/C][C]-0.0188[/C][C]-0.0624[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8228 )[/C][C](0.833 )[/C][C](NA )[/C][C](NA )[/C][C](0.6784 )[/C][C](0.1843 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.0102[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0186[/C][C]-0.0639[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.822 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.6808 )[/C][C](0.1682 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0184[/C][C]-0.0629[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.6852 )[/C][C](0.1732 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0624[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1763 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112272&T=1

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


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

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.0051-0.0097-0.0033-0.0049-0.0096-0.0622-0.009
(p-val)(0.9997 )(0.9488 )(0.9828 )(0.9997 )(0.9836 )(0.1937 )(0.9846 )
Estimates ( 2 )-0.0102-0.0097-0.00330-0.0096-0.0621-0.009
(p-val)(0.8225 )(0.833 )(0.9424 )(NA )(0.9834 )(0.1944 )(0.9846 )
Estimates ( 3 )-0.0102-0.0097-0.00340-0.0187-0.06220
(p-val)(0.8226 )(0.8321 )(0.9409 )(NA )(0.681 )(0.186 )(NA )
Estimates ( 4 )-0.0102-0.009700-0.0188-0.06240
(p-val)(0.8228 )(0.833 )(NA )(NA )(0.6784 )(0.1843 )(NA )
Estimates ( 5 )-0.0102000-0.0186-0.06390
(p-val)(0.822 )(NA )(NA )(NA )(0.6808 )(0.1682 )(NA )
Estimates ( 6 )0000-0.0184-0.06290
(p-val)(NA )(NA )(NA )(NA )(0.6852 )(0.1732 )(NA )
Estimates ( 7 )00000-0.06240
(p-val)(NA )(NA )(NA )(NA )(NA )(0.1763 )(NA )
Estimates ( 8 )0000000
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
9.8829021514408
25.1727721362117
-143.224749290191
137.299009879993
-356.830797785976
8.73577374074307
-562.757695748934
-272.502931586513
-122.29636417686
-95.3176400264975
437.838155990306
-132.254586972353
-323.481254152358
-100.079169834866
-176.594062029674
-179.298516652076
259.208413889482
226.799993088809
128.791030920731
-75.6997889519106
-397.71661374814
-135.656282554545
113.110882164298
-107.110152644543
93.4184352410888
215.989295398737
367.49191918076
-99.5704527956987
-253.342460781329
-146.825360095868
266.496580798974
-43.9724811325479
-147.459808383219
80.4019595096013
306.764124861866
-37.6159412038419
370.319918666718
180.305099205004
180.201012846463
220.430237603478
-287.860479852892
-104.702616971652
152.139640922538
155.432244311505
24.5936265213595
-319.525768753207
292.762459327067
165.490368802721
-2.4296621857451
-139.366000536308
382.875693997246
214.813595399582
-51.409237816454
135.577207012978
110.653533109465
10.5727686811715
207.328313919524
249.536183538232
-68.8505522993543
205.70812050831
-546.560582649917
-284.530257845247
486.493995486841
-39.514435192027
410.597562237765
95.3586953849259
316.674019285569
-488.753594991385
-70.6856897501727
118.603772077774
-109.964071256035
-305.717629160136
0.115301514530859
-129.034983865266
-256.201217427831
-140.16739519337
23.4774801700292
181.986606561765
-1.14688089370861
-214.317082570723
-261.368948315224
494.227696421889
271.799391495076
172.420674139113
147.18351899653
-227.487416513597
238.750743547959
112.522081902066
-83.542188957932
-1.40094421723321
-323.6358360407
-194.475664867237
-94.5102648946245
103.199730862031
257.896633237473
9.29723697670344
-18.7339648516091
93.3973324879904
273.951390710062
-178.542788372859
45.5624372844611
281.14764520602
124.785996132636
-46.1086467414106
204.53699473908
39.4282679932035
-3.68264802188257
189.933585661661
-133.77135182877
31.6836970725595
200.328014634149
-83.9002975681264
33.7654171453529
-236.899608355485
-192.956325298887
-231.688050852507
44.9687775655947
209.918116261126
-293.238260511046
75.6329590069107
55.8247276748507
82.5708222621747
26.625936782599
-88.0382571673429
219.306342291242
146.677269702649
-89.0322024154151
-243.848107413192
-309.78704160029
185.887533612943
-396.76579781618
-142.249549809934
-276.086300597571
118.710311140091
212.923670269894
198.0277602953
157.524635253794
-103.928056694542
351.199179035417
-41.8212848385921
-274.917107671602
261.062409817666
90.7323006793104
116.25522701185
-157.314097182527
-92.931114187884
14.8658110170979
220.083760509983
106.627120813591
53.6153411802572
168.767020765514
5.70402231245653
-60.1416885363711
185.548577057796
-216.385078600751
-156.973185142943
278.151688835967
-289.025501957786
-412.095129412563
187.061948192986
-232.331584627925
241.580601841515
8.31862180781488
227.721424091698
-94.682128239864
-123.799510075575
-93.3976145744179
159.146612275674
-283.329965580411
36.2772912172818
-266.855014899779
144.450891812383
-2.30812555226413
149.779965623523
32.195864374609
80.1359072224209
-39.6789559981208
-190.702488966218
-65.4640856242308
150.089597532049
-117.930816689242
284.815276599592
-33.0149902109166
210.856741275588
-55.888908287107
-14.9733905149322
-175.685358263032
-208.136505187009
-224.519145699523
-27.4233322327779
18.0758748523778
-27.0343571689969
-128.369846870053
235.316351677703
-34.27093333877
-82.260441504908
-44.3676818431904
88.5385147178837
274.100969603567
2.35873273819514
58.9411910250174
103.12792277612
232.146734911865
23.3616427872956
-22.4104199658228
40.6964521023383
169.064604762783
177.062982707857
37.1972526227794
-188.12156172169
-335.965303169582
64.6985392817552
-58.7833131756977
-265.834125285238
-109.45782497938
321.114648782128
10.6825777816471
82.677986527417
9.04156200899286
-58.7694176838751
72.9215808929548
-120.212728710027
-130.898548536657
62.8100997472963
-202.516780366414
117.538200512134
-207.253068388893
-23.2014601893583
179.574015836321
-74.1192628381963
-66.629435109048
-222.323071658185
-235.519758662713
91.1816264217832
178.063831976795
87.2713707249523
237.012429752848
-15.6868518440433
35.9978166763836
-389.194109341018
-130.811616121024
4.81989561785278
153.454501852376
-235.043887086269
113.508467156955
64.442727566805
-187.741671958057
61.1449605816768
65.2611073058485
87.1647787516664
10.4474898235405
120.647107221573
30.4449160538532
74.3539034811562
-7.54605350236868
129.015502334244
58.7784304274092
-163.576874023971
27.1599644977489
132.537479900087
-20.61352549257
142.999466329536
-281.390882951229
99.9206567807762
-22.5539671647098
-318.069841080452
-61.0641849109393
-92.462903064181
-170.682445898258
81.6570457604835
-269.795177896094
66.6585561379122
-230.253454397422
-47.2970991913563
59.2295228156541
-51.3925870429309
-85.1973014927171
83.3782113080257
67.7446602448548
-150.599879494797
-481.152420915677
-56.7100071148916
178.4540889762
-59.7330876936225
6.67031476961107
-102.8048428167
7.87732594755434
42.9524857920783
296.664644693889
-211.910718297252
97.3479361384955
26.8476745656295
-160.896610157745
-151.933721068383
-213.94776153755
30.6494531304762
-34.717685737759
3.75583048259215
77.9349473034278
-46.7728292049959
96.233745987631
289.678279678548
-264.247813145797
197.868836404557
85.209534698684
108.671566279121
-41.6235675102775
-11.5725498525807
34.35260529218
-162.031058474254
-62.4167831575483
-212.968729548336
114.369135101023
79.0707084732375
124.613901759207
87.9474785743971
106.954930514092
6.58758865247771
167.942955927099
41.7188934016035
-39.7043702853742
-161.528330825655
-178.642132759716
234.919710674531
251.220898037525
-91.320958908944
167.238840123492
-183.613183133463
-119.284293339932
-259.415238414914
263.365933583769
-107.356912197852
-102.083233454655
172.183625600811
66.6143577697381
-117.45516996951
-22.1330436474654
29.4319777662165
146.096733193449
-142.766386139072
-171.52161926893
-150.730101590288
-478.138932327139
-199.799030443454
443.374425759606
-510.935214060447
92.4058259170807
-145.784701736162
-188.090205972288
-195.293858665036
144.29140714511
-390.466345562635
-103.958258144308
6.82916225309702
-32.1975249719658
-307.056576040527
244.474962904346
-83.0451806805802
288.830961002997
-70.5029289344419
-251.020708763124
202.299933347073
180.098554414015
-267.169156603378
-249.57008406883
-87.499025447688
102.158609116072
-24.1845667693807
187.964390497348
168.605543764332
-103.963447036411
64.5427596671194
110.179565402546
-119.388417370059
115.392920435408
-271.427811867371
-37.9837065497413
-154.265213342602
62.7162606865224
44.7467212439024
-257.380665482884
65.100463597948
-77.6783748631023
219.02952445611
14.6856799876608
52.6716167271928
-120.277525060799
243.625365108596
50.9203006481278
-64.0422233039901
-68.4615369800063
207.848715521541
77.6043363811025
-62.1013481338016
40.4544615956784
-173.806476816936
-4.6931563989365
22.4152173474262
123.805941192717
-26.7694577706602
194.842148006418
42.3828540779969
-44.6688267878656
-141.392836532752
89.4450152319165
21.0987516252915
-78.9244950680549
99.668976979859
-62.2670372541404
44.1984113796206
-21.1835419268396
-352.304472193776
-141.600927210998
27.937495152024
-32.3398269104383
24.1388060898737
30.2717462974955
-8.15846095366446
-170.42737644392
-37.1097262970396
-262.466370653915
15.9061844418859
75.3186867263258
77.728374472026
-27.9760120958033
-58.654579590975
62.2331400661732
33.6997394202275
62.9263944381416
2.66248401700575
-216.077830946214
-54.7854800503329
11.5805617978099
52.9820108751535
106.9397401315
-86.8168896934736
522.11562102648
119.821803081602
11.2097316656699
12.2721463846916
-15.128333130577
141.826781984961
-86.459735517564
97.908462080307
49.7197875813875
-45.4492831060915
117.019703838967
-15.5443648602552
78.6296758660052
-20.8666157884524
94.5900045256564
33.5246735705698
10.898221499247
-114.746623146289
6.7341671513744
-122.367571903719
123.78748937198
25.6190053423944
-15.4079195700951
-173.512568709149
27.339066495731
-74.6759842278556
143.004299551815
-70.635142718731
4.27387551033689
-20.8797232235005
-36.7453465715736
-26.341512288475
-94.3588798466038
-1.45965224601878
166.693522266067
61.0381367391743
209.139584795657
-104.36886234208
-205.546514969949
-59.5580463895182
136.374390927212
-95.5210929227566
-31.1724435640487
96.8922765821132
-3.08929330578903
-90.7966815700197
175.904639830942
43.6022744944424
-29.0347932189484
-200.749913303746
-153.133867227745
3.38901553561396
-58.2705148202512
-18.9738628248714
-226.558351063969

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
9.8829021514408 \tabularnewline
25.1727721362117 \tabularnewline
-143.224749290191 \tabularnewline
137.299009879993 \tabularnewline
-356.830797785976 \tabularnewline
8.73577374074307 \tabularnewline
-562.757695748934 \tabularnewline
-272.502931586513 \tabularnewline
-122.29636417686 \tabularnewline
-95.3176400264975 \tabularnewline
437.838155990306 \tabularnewline
-132.254586972353 \tabularnewline
-323.481254152358 \tabularnewline
-100.079169834866 \tabularnewline
-176.594062029674 \tabularnewline
-179.298516652076 \tabularnewline
259.208413889482 \tabularnewline
226.799993088809 \tabularnewline
128.791030920731 \tabularnewline
-75.6997889519106 \tabularnewline
-397.71661374814 \tabularnewline
-135.656282554545 \tabularnewline
113.110882164298 \tabularnewline
-107.110152644543 \tabularnewline
93.4184352410888 \tabularnewline
215.989295398737 \tabularnewline
367.49191918076 \tabularnewline
-99.5704527956987 \tabularnewline
-253.342460781329 \tabularnewline
-146.825360095868 \tabularnewline
266.496580798974 \tabularnewline
-43.9724811325479 \tabularnewline
-147.459808383219 \tabularnewline
80.4019595096013 \tabularnewline
306.764124861866 \tabularnewline
-37.6159412038419 \tabularnewline
370.319918666718 \tabularnewline
180.305099205004 \tabularnewline
180.201012846463 \tabularnewline
220.430237603478 \tabularnewline
-287.860479852892 \tabularnewline
-104.702616971652 \tabularnewline
152.139640922538 \tabularnewline
155.432244311505 \tabularnewline
24.5936265213595 \tabularnewline
-319.525768753207 \tabularnewline
292.762459327067 \tabularnewline
165.490368802721 \tabularnewline
-2.4296621857451 \tabularnewline
-139.366000536308 \tabularnewline
382.875693997246 \tabularnewline
214.813595399582 \tabularnewline
-51.409237816454 \tabularnewline
135.577207012978 \tabularnewline
110.653533109465 \tabularnewline
10.5727686811715 \tabularnewline
207.328313919524 \tabularnewline
249.536183538232 \tabularnewline
-68.8505522993543 \tabularnewline
205.70812050831 \tabularnewline
-546.560582649917 \tabularnewline
-284.530257845247 \tabularnewline
486.493995486841 \tabularnewline
-39.514435192027 \tabularnewline
410.597562237765 \tabularnewline
95.3586953849259 \tabularnewline
316.674019285569 \tabularnewline
-488.753594991385 \tabularnewline
-70.6856897501727 \tabularnewline
118.603772077774 \tabularnewline
-109.964071256035 \tabularnewline
-305.717629160136 \tabularnewline
0.115301514530859 \tabularnewline
-129.034983865266 \tabularnewline
-256.201217427831 \tabularnewline
-140.16739519337 \tabularnewline
23.4774801700292 \tabularnewline
181.986606561765 \tabularnewline
-1.14688089370861 \tabularnewline
-214.317082570723 \tabularnewline
-261.368948315224 \tabularnewline
494.227696421889 \tabularnewline
271.799391495076 \tabularnewline
172.420674139113 \tabularnewline
147.18351899653 \tabularnewline
-227.487416513597 \tabularnewline
238.750743547959 \tabularnewline
112.522081902066 \tabularnewline
-83.542188957932 \tabularnewline
-1.40094421723321 \tabularnewline
-323.6358360407 \tabularnewline
-194.475664867237 \tabularnewline
-94.5102648946245 \tabularnewline
103.199730862031 \tabularnewline
257.896633237473 \tabularnewline
9.29723697670344 \tabularnewline
-18.7339648516091 \tabularnewline
93.3973324879904 \tabularnewline
273.951390710062 \tabularnewline
-178.542788372859 \tabularnewline
45.5624372844611 \tabularnewline
281.14764520602 \tabularnewline
124.785996132636 \tabularnewline
-46.1086467414106 \tabularnewline
204.53699473908 \tabularnewline
39.4282679932035 \tabularnewline
-3.68264802188257 \tabularnewline
189.933585661661 \tabularnewline
-133.77135182877 \tabularnewline
31.6836970725595 \tabularnewline
200.328014634149 \tabularnewline
-83.9002975681264 \tabularnewline
33.7654171453529 \tabularnewline
-236.899608355485 \tabularnewline
-192.956325298887 \tabularnewline
-231.688050852507 \tabularnewline
44.9687775655947 \tabularnewline
209.918116261126 \tabularnewline
-293.238260511046 \tabularnewline
75.6329590069107 \tabularnewline
55.8247276748507 \tabularnewline
82.5708222621747 \tabularnewline
26.625936782599 \tabularnewline
-88.0382571673429 \tabularnewline
219.306342291242 \tabularnewline
146.677269702649 \tabularnewline
-89.0322024154151 \tabularnewline
-243.848107413192 \tabularnewline
-309.78704160029 \tabularnewline
185.887533612943 \tabularnewline
-396.76579781618 \tabularnewline
-142.249549809934 \tabularnewline
-276.086300597571 \tabularnewline
118.710311140091 \tabularnewline
212.923670269894 \tabularnewline
198.0277602953 \tabularnewline
157.524635253794 \tabularnewline
-103.928056694542 \tabularnewline
351.199179035417 \tabularnewline
-41.8212848385921 \tabularnewline
-274.917107671602 \tabularnewline
261.062409817666 \tabularnewline
90.7323006793104 \tabularnewline
116.25522701185 \tabularnewline
-157.314097182527 \tabularnewline
-92.931114187884 \tabularnewline
14.8658110170979 \tabularnewline
220.083760509983 \tabularnewline
106.627120813591 \tabularnewline
53.6153411802572 \tabularnewline
168.767020765514 \tabularnewline
5.70402231245653 \tabularnewline
-60.1416885363711 \tabularnewline
185.548577057796 \tabularnewline
-216.385078600751 \tabularnewline
-156.973185142943 \tabularnewline
278.151688835967 \tabularnewline
-289.025501957786 \tabularnewline
-412.095129412563 \tabularnewline
187.061948192986 \tabularnewline
-232.331584627925 \tabularnewline
241.580601841515 \tabularnewline
8.31862180781488 \tabularnewline
227.721424091698 \tabularnewline
-94.682128239864 \tabularnewline
-123.799510075575 \tabularnewline
-93.3976145744179 \tabularnewline
159.146612275674 \tabularnewline
-283.329965580411 \tabularnewline
36.2772912172818 \tabularnewline
-266.855014899779 \tabularnewline
144.450891812383 \tabularnewline
-2.30812555226413 \tabularnewline
149.779965623523 \tabularnewline
32.195864374609 \tabularnewline
80.1359072224209 \tabularnewline
-39.6789559981208 \tabularnewline
-190.702488966218 \tabularnewline
-65.4640856242308 \tabularnewline
150.089597532049 \tabularnewline
-117.930816689242 \tabularnewline
284.815276599592 \tabularnewline
-33.0149902109166 \tabularnewline
210.856741275588 \tabularnewline
-55.888908287107 \tabularnewline
-14.9733905149322 \tabularnewline
-175.685358263032 \tabularnewline
-208.136505187009 \tabularnewline
-224.519145699523 \tabularnewline
-27.4233322327779 \tabularnewline
18.0758748523778 \tabularnewline
-27.0343571689969 \tabularnewline
-128.369846870053 \tabularnewline
235.316351677703 \tabularnewline
-34.27093333877 \tabularnewline
-82.260441504908 \tabularnewline
-44.3676818431904 \tabularnewline
88.5385147178837 \tabularnewline
274.100969603567 \tabularnewline
2.35873273819514 \tabularnewline
58.9411910250174 \tabularnewline
103.12792277612 \tabularnewline
232.146734911865 \tabularnewline
23.3616427872956 \tabularnewline
-22.4104199658228 \tabularnewline
40.6964521023383 \tabularnewline
169.064604762783 \tabularnewline
177.062982707857 \tabularnewline
37.1972526227794 \tabularnewline
-188.12156172169 \tabularnewline
-335.965303169582 \tabularnewline
64.6985392817552 \tabularnewline
-58.7833131756977 \tabularnewline
-265.834125285238 \tabularnewline
-109.45782497938 \tabularnewline
321.114648782128 \tabularnewline
10.6825777816471 \tabularnewline
82.677986527417 \tabularnewline
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202.299933347073 \tabularnewline
180.098554414015 \tabularnewline
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110.179565402546 \tabularnewline
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115.392920435408 \tabularnewline
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33.6997394202275 \tabularnewline
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2.66248401700575 \tabularnewline
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\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112272&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]9.8829021514408[/C][/ROW]
[ROW][C]25.1727721362117[/C][/ROW]
[ROW][C]-143.224749290191[/C][/ROW]
[ROW][C]137.299009879993[/C][/ROW]
[ROW][C]-356.830797785976[/C][/ROW]
[ROW][C]8.73577374074307[/C][/ROW]
[ROW][C]-562.757695748934[/C][/ROW]
[ROW][C]-272.502931586513[/C][/ROW]
[ROW][C]-122.29636417686[/C][/ROW]
[ROW][C]-95.3176400264975[/C][/ROW]
[ROW][C]437.838155990306[/C][/ROW]
[ROW][C]-132.254586972353[/C][/ROW]
[ROW][C]-323.481254152358[/C][/ROW]
[ROW][C]-100.079169834866[/C][/ROW]
[ROW][C]-176.594062029674[/C][/ROW]
[ROW][C]-179.298516652076[/C][/ROW]
[ROW][C]259.208413889482[/C][/ROW]
[ROW][C]226.799993088809[/C][/ROW]
[ROW][C]128.791030920731[/C][/ROW]
[ROW][C]-75.6997889519106[/C][/ROW]
[ROW][C]-397.71661374814[/C][/ROW]
[ROW][C]-135.656282554545[/C][/ROW]
[ROW][C]113.110882164298[/C][/ROW]
[ROW][C]-107.110152644543[/C][/ROW]
[ROW][C]93.4184352410888[/C][/ROW]
[ROW][C]215.989295398737[/C][/ROW]
[ROW][C]367.49191918076[/C][/ROW]
[ROW][C]-99.5704527956987[/C][/ROW]
[ROW][C]-253.342460781329[/C][/ROW]
[ROW][C]-146.825360095868[/C][/ROW]
[ROW][C]266.496580798974[/C][/ROW]
[ROW][C]-43.9724811325479[/C][/ROW]
[ROW][C]-147.459808383219[/C][/ROW]
[ROW][C]80.4019595096013[/C][/ROW]
[ROW][C]306.764124861866[/C][/ROW]
[ROW][C]-37.6159412038419[/C][/ROW]
[ROW][C]370.319918666718[/C][/ROW]
[ROW][C]180.305099205004[/C][/ROW]
[ROW][C]180.201012846463[/C][/ROW]
[ROW][C]220.430237603478[/C][/ROW]
[ROW][C]-287.860479852892[/C][/ROW]
[ROW][C]-104.702616971652[/C][/ROW]
[ROW][C]152.139640922538[/C][/ROW]
[ROW][C]155.432244311505[/C][/ROW]
[ROW][C]24.5936265213595[/C][/ROW]
[ROW][C]-319.525768753207[/C][/ROW]
[ROW][C]292.762459327067[/C][/ROW]
[ROW][C]165.490368802721[/C][/ROW]
[ROW][C]-2.4296621857451[/C][/ROW]
[ROW][C]-139.366000536308[/C][/ROW]
[ROW][C]382.875693997246[/C][/ROW]
[ROW][C]214.813595399582[/C][/ROW]
[ROW][C]-51.409237816454[/C][/ROW]
[ROW][C]135.577207012978[/C][/ROW]
[ROW][C]110.653533109465[/C][/ROW]
[ROW][C]10.5727686811715[/C][/ROW]
[ROW][C]207.328313919524[/C][/ROW]
[ROW][C]249.536183538232[/C][/ROW]
[ROW][C]-68.8505522993543[/C][/ROW]
[ROW][C]205.70812050831[/C][/ROW]
[ROW][C]-546.560582649917[/C][/ROW]
[ROW][C]-284.530257845247[/C][/ROW]
[ROW][C]486.493995486841[/C][/ROW]
[ROW][C]-39.514435192027[/C][/ROW]
[ROW][C]410.597562237765[/C][/ROW]
[ROW][C]95.3586953849259[/C][/ROW]
[ROW][C]316.674019285569[/C][/ROW]
[ROW][C]-488.753594991385[/C][/ROW]
[ROW][C]-70.6856897501727[/C][/ROW]
[ROW][C]118.603772077774[/C][/ROW]
[ROW][C]-109.964071256035[/C][/ROW]
[ROW][C]-305.717629160136[/C][/ROW]
[ROW][C]0.115301514530859[/C][/ROW]
[ROW][C]-129.034983865266[/C][/ROW]
[ROW][C]-256.201217427831[/C][/ROW]
[ROW][C]-140.16739519337[/C][/ROW]
[ROW][C]23.4774801700292[/C][/ROW]
[ROW][C]181.986606561765[/C][/ROW]
[ROW][C]-1.14688089370861[/C][/ROW]
[ROW][C]-214.317082570723[/C][/ROW]
[ROW][C]-261.368948315224[/C][/ROW]
[ROW][C]494.227696421889[/C][/ROW]
[ROW][C]271.799391495076[/C][/ROW]
[ROW][C]172.420674139113[/C][/ROW]
[ROW][C]147.18351899653[/C][/ROW]
[ROW][C]-227.487416513597[/C][/ROW]
[ROW][C]238.750743547959[/C][/ROW]
[ROW][C]112.522081902066[/C][/ROW]
[ROW][C]-83.542188957932[/C][/ROW]
[ROW][C]-1.40094421723321[/C][/ROW]
[ROW][C]-323.6358360407[/C][/ROW]
[ROW][C]-194.475664867237[/C][/ROW]
[ROW][C]-94.5102648946245[/C][/ROW]
[ROW][C]103.199730862031[/C][/ROW]
[ROW][C]257.896633237473[/C][/ROW]
[ROW][C]9.29723697670344[/C][/ROW]
[ROW][C]-18.7339648516091[/C][/ROW]
[ROW][C]93.3973324879904[/C][/ROW]
[ROW][C]273.951390710062[/C][/ROW]
[ROW][C]-178.542788372859[/C][/ROW]
[ROW][C]45.5624372844611[/C][/ROW]
[ROW][C]281.14764520602[/C][/ROW]
[ROW][C]124.785996132636[/C][/ROW]
[ROW][C]-46.1086467414106[/C][/ROW]
[ROW][C]204.53699473908[/C][/ROW]
[ROW][C]39.4282679932035[/C][/ROW]
[ROW][C]-3.68264802188257[/C][/ROW]
[ROW][C]189.933585661661[/C][/ROW]
[ROW][C]-133.77135182877[/C][/ROW]
[ROW][C]31.6836970725595[/C][/ROW]
[ROW][C]200.328014634149[/C][/ROW]
[ROW][C]-83.9002975681264[/C][/ROW]
[ROW][C]33.7654171453529[/C][/ROW]
[ROW][C]-236.899608355485[/C][/ROW]
[ROW][C]-192.956325298887[/C][/ROW]
[ROW][C]-231.688050852507[/C][/ROW]
[ROW][C]44.9687775655947[/C][/ROW]
[ROW][C]209.918116261126[/C][/ROW]
[ROW][C]-293.238260511046[/C][/ROW]
[ROW][C]75.6329590069107[/C][/ROW]
[ROW][C]55.8247276748507[/C][/ROW]
[ROW][C]82.5708222621747[/C][/ROW]
[ROW][C]26.625936782599[/C][/ROW]
[ROW][C]-88.0382571673429[/C][/ROW]
[ROW][C]219.306342291242[/C][/ROW]
[ROW][C]146.677269702649[/C][/ROW]
[ROW][C]-89.0322024154151[/C][/ROW]
[ROW][C]-243.848107413192[/C][/ROW]
[ROW][C]-309.78704160029[/C][/ROW]
[ROW][C]185.887533612943[/C][/ROW]
[ROW][C]-396.76579781618[/C][/ROW]
[ROW][C]-142.249549809934[/C][/ROW]
[ROW][C]-276.086300597571[/C][/ROW]
[ROW][C]118.710311140091[/C][/ROW]
[ROW][C]212.923670269894[/C][/ROW]
[ROW][C]198.0277602953[/C][/ROW]
[ROW][C]157.524635253794[/C][/ROW]
[ROW][C]-103.928056694542[/C][/ROW]
[ROW][C]351.199179035417[/C][/ROW]
[ROW][C]-41.8212848385921[/C][/ROW]
[ROW][C]-274.917107671602[/C][/ROW]
[ROW][C]261.062409817666[/C][/ROW]
[ROW][C]90.7323006793104[/C][/ROW]
[ROW][C]116.25522701185[/C][/ROW]
[ROW][C]-157.314097182527[/C][/ROW]
[ROW][C]-92.931114187884[/C][/ROW]
[ROW][C]14.8658110170979[/C][/ROW]
[ROW][C]220.083760509983[/C][/ROW]
[ROW][C]106.627120813591[/C][/ROW]
[ROW][C]53.6153411802572[/C][/ROW]
[ROW][C]168.767020765514[/C][/ROW]
[ROW][C]5.70402231245653[/C][/ROW]
[ROW][C]-60.1416885363711[/C][/ROW]
[ROW][C]185.548577057796[/C][/ROW]
[ROW][C]-216.385078600751[/C][/ROW]
[ROW][C]-156.973185142943[/C][/ROW]
[ROW][C]278.151688835967[/C][/ROW]
[ROW][C]-289.025501957786[/C][/ROW]
[ROW][C]-412.095129412563[/C][/ROW]
[ROW][C]187.061948192986[/C][/ROW]
[ROW][C]-232.331584627925[/C][/ROW]
[ROW][C]241.580601841515[/C][/ROW]
[ROW][C]8.31862180781488[/C][/ROW]
[ROW][C]227.721424091698[/C][/ROW]
[ROW][C]-94.682128239864[/C][/ROW]
[ROW][C]-123.799510075575[/C][/ROW]
[ROW][C]-93.3976145744179[/C][/ROW]
[ROW][C]159.146612275674[/C][/ROW]
[ROW][C]-283.329965580411[/C][/ROW]
[ROW][C]36.2772912172818[/C][/ROW]
[ROW][C]-266.855014899779[/C][/ROW]
[ROW][C]144.450891812383[/C][/ROW]
[ROW][C]-2.30812555226413[/C][/ROW]
[ROW][C]149.779965623523[/C][/ROW]
[ROW][C]32.195864374609[/C][/ROW]
[ROW][C]80.1359072224209[/C][/ROW]
[ROW][C]-39.6789559981208[/C][/ROW]
[ROW][C]-190.702488966218[/C][/ROW]
[ROW][C]-65.4640856242308[/C][/ROW]
[ROW][C]150.089597532049[/C][/ROW]
[ROW][C]-117.930816689242[/C][/ROW]
[ROW][C]284.815276599592[/C][/ROW]
[ROW][C]-33.0149902109166[/C][/ROW]
[ROW][C]210.856741275588[/C][/ROW]
[ROW][C]-55.888908287107[/C][/ROW]
[ROW][C]-14.9733905149322[/C][/ROW]
[ROW][C]-175.685358263032[/C][/ROW]
[ROW][C]-208.136505187009[/C][/ROW]
[ROW][C]-224.519145699523[/C][/ROW]
[ROW][C]-27.4233322327779[/C][/ROW]
[ROW][C]18.0758748523778[/C][/ROW]
[ROW][C]-27.0343571689969[/C][/ROW]
[ROW][C]-128.369846870053[/C][/ROW]
[ROW][C]235.316351677703[/C][/ROW]
[ROW][C]-34.27093333877[/C][/ROW]
[ROW][C]-82.260441504908[/C][/ROW]
[ROW][C]-44.3676818431904[/C][/ROW]
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[ROW][C]103.12792277612[/C][/ROW]
[ROW][C]232.146734911865[/C][/ROW]
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[ROW][C]-22.4104199658228[/C][/ROW]
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[ROW][C]179.574015836321[/C][/ROW]
[ROW][C]-74.1192628381963[/C][/ROW]
[ROW][C]-66.629435109048[/C][/ROW]
[ROW][C]-222.323071658185[/C][/ROW]
[ROW][C]-235.519758662713[/C][/ROW]
[ROW][C]91.1816264217832[/C][/ROW]
[ROW][C]178.063831976795[/C][/ROW]
[ROW][C]87.2713707249523[/C][/ROW]
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[ROW][C]21.0987516252915[/C][/ROW]
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[ROW][C]99.668976979859[/C][/ROW]
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[ROW][C]44.1984113796206[/C][/ROW]
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[ROW][C]-29.0347932189484[/C][/ROW]
[ROW][C]-200.749913303746[/C][/ROW]
[ROW][C]-153.133867227745[/C][/ROW]
[ROW][C]3.38901553561396[/C][/ROW]
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[ROW][C]-18.9738628248714[/C][/ROW]
[ROW][C]-226.558351063969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112272&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112272&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
9.8829021514408
25.1727721362117
-143.224749290191
137.299009879993
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8.73577374074307
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437.838155990306
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-176.594062029674
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259.208413889482
226.799993088809
128.791030920731
-75.6997889519106
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-135.656282554545
113.110882164298
-107.110152644543
93.4184352410888
215.989295398737
367.49191918076
-99.5704527956987
-253.342460781329
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266.496580798974
-43.9724811325479
-147.459808383219
80.4019595096013
306.764124861866
-37.6159412038419
370.319918666718
180.305099205004
180.201012846463
220.430237603478
-287.860479852892
-104.702616971652
152.139640922538
155.432244311505
24.5936265213595
-319.525768753207
292.762459327067
165.490368802721
-2.4296621857451
-139.366000536308
382.875693997246
214.813595399582
-51.409237816454
135.577207012978
110.653533109465
10.5727686811715
207.328313919524
249.536183538232
-68.8505522993543
205.70812050831
-546.560582649917
-284.530257845247
486.493995486841
-39.514435192027
410.597562237765
95.3586953849259
316.674019285569
-488.753594991385
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118.603772077774
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0.115301514530859
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181.986606561765
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494.227696421889
271.799391495076
172.420674139113
147.18351899653
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238.750743547959
112.522081902066
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103.199730862031
257.896633237473
9.29723697670344
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273.951390710062
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45.5624372844611
281.14764520602
124.785996132636
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204.53699473908
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189.933585661661
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26.625936782599
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219.306342291242
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185.887533612943
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118.710311140091
212.923670269894
198.0277602953
157.524635253794
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351.199179035417
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261.062409817666
90.7323006793104
116.25522701185
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14.8658110170979
220.083760509983
106.627120813591
53.6153411802572
168.767020765514
5.70402231245653
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185.548577057796
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278.151688835967
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187.061948192986
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241.580601841515
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159.146612275674
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36.2772912172818
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144.450891812383
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149.779965623523
32.195864374609
80.1359072224209
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-65.4640856242308
150.089597532049
-117.930816689242
284.815276599592
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210.856741275588
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235.316351677703
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-44.3676818431904
88.5385147178837
274.100969603567
2.35873273819514
58.9411910250174
103.12792277612
232.146734911865
23.3616427872956
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40.6964521023383
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Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1.9 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1.9 ; 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 <- 5 #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')