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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 11:06:05 +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/t12927566655brf630j6oxs1cz.htm/, Retrieved Sun, 05 May 2024 00:44:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112283, Retrieved Sun, 05 May 2024 00:44:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

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] [46df8573ee32a55e1a6edcfb6691f406]
-   P             [ARIMA Backward Selection] [ARIMA berekening ...] [2010-12-19 11:06:05] [109f5cd2d2b7c934778912c55604f6f1] [Current]
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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 time6 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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112283&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112283&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112283&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.007-0.0123-0.0073-0.002-0.0481-0.05860.0295
(p-val)(0.9992 )(0.8789 )(0.953 )(0.9998 )(0.9194 )(0.2188 )(0.9505 )
Estimates ( 2 )-0.0091-0.0123-0.00730-0.0481-0.05860.0294
(p-val)(0.8415 )(0.7888 )(0.8721 )(NA )(0.9189 )(0.2186 )(0.9502 )
Estimates ( 3 )-0.0091-0.0122-0.00710-0.0187-0.0580
(p-val)(0.8419 )(0.7905 )(0.8754 )(NA )(0.6801 )(0.2186 )(NA )
Estimates ( 4 )-0.009-0.012100-0.0191-0.05840
(p-val)(0.8429 )(0.7924 )(NA )(NA )(0.6743 )(0.2149 )(NA )
Estimates ( 5 )0-0.012100-0.0189-0.05760
(p-val)(NA )(0.7915 )(NA )(NA )(0.677 )(0.2198 )(NA )
Estimates ( 6 )0000-0.0187-0.05960
(p-val)(NA )(NA )(NA )(NA )(0.6804 )(0.1977 )(NA )
Estimates ( 7 )00000-0.05910
(p-val)(NA )(NA )(NA )(NA )(NA )(0.2014 )(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 ) & -0.007 & -0.0123 & -0.0073 & -0.002 & -0.0481 & -0.0586 & 0.0295 \tabularnewline
(p-val) & (0.9992 ) & (0.8789 ) & (0.953 ) & (0.9998 ) & (0.9194 ) & (0.2188 ) & (0.9505 ) \tabularnewline
Estimates ( 2 ) & -0.0091 & -0.0123 & -0.0073 & 0 & -0.0481 & -0.0586 & 0.0294 \tabularnewline
(p-val) & (0.8415 ) & (0.7888 ) & (0.8721 ) & (NA ) & (0.9189 ) & (0.2186 ) & (0.9502 ) \tabularnewline
Estimates ( 3 ) & -0.0091 & -0.0122 & -0.0071 & 0 & -0.0187 & -0.058 & 0 \tabularnewline
(p-val) & (0.8419 ) & (0.7905 ) & (0.8754 ) & (NA ) & (0.6801 ) & (0.2186 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.009 & -0.0121 & 0 & 0 & -0.0191 & -0.0584 & 0 \tabularnewline
(p-val) & (0.8429 ) & (0.7924 ) & (NA ) & (NA ) & (0.6743 ) & (0.2149 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & -0.0121 & 0 & 0 & -0.0189 & -0.0576 & 0 \tabularnewline
(p-val) & (NA ) & (0.7915 ) & (NA ) & (NA ) & (0.677 ) & (0.2198 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & -0.0187 & -0.0596 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0.6804 ) & (0.1977 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & -0.0591 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.2014 ) & (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=112283&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]-0.007[/C][C]-0.0123[/C][C]-0.0073[/C][C]-0.002[/C][C]-0.0481[/C][C]-0.0586[/C][C]0.0295[/C][/ROW]
[ROW][C](p-val)[/C][C](0.9992 )[/C][C](0.8789 )[/C][C](0.953 )[/C][C](0.9998 )[/C][C](0.9194 )[/C][C](0.2188 )[/C][C](0.9505 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.0091[/C][C]-0.0123[/C][C]-0.0073[/C][C]0[/C][C]-0.0481[/C][C]-0.0586[/C][C]0.0294[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8415 )[/C][C](0.7888 )[/C][C](0.8721 )[/C][C](NA )[/C][C](0.9189 )[/C][C](0.2186 )[/C][C](0.9502 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.0091[/C][C]-0.0122[/C][C]-0.0071[/C][C]0[/C][C]-0.0187[/C][C]-0.058[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8419 )[/C][C](0.7905 )[/C][C](0.8754 )[/C][C](NA )[/C][C](0.6801 )[/C][C](0.2186 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.009[/C][C]-0.0121[/C][C]0[/C][C]0[/C][C]-0.0191[/C][C]-0.0584[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8429 )[/C][C](0.7924 )[/C][C](NA )[/C][C](NA )[/C][C](0.6743 )[/C][C](0.2149 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]-0.0121[/C][C]0[/C][C]0[/C][C]-0.0189[/C][C]-0.0576[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.7915 )[/C][C](NA )[/C][C](NA )[/C][C](0.677 )[/C][C](0.2198 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0187[/C][C]-0.0596[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.6804 )[/C][C](0.1977 )[/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]-0.0591[/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](0.2014 )[/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=112283&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112283&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 )-0.007-0.0123-0.0073-0.002-0.0481-0.05860.0295
(p-val)(0.9992 )(0.8789 )(0.953 )(0.9998 )(0.9194 )(0.2188 )(0.9505 )
Estimates ( 2 )-0.0091-0.0123-0.00730-0.0481-0.05860.0294
(p-val)(0.8415 )(0.7888 )(0.8721 )(NA )(0.9189 )(0.2186 )(0.9502 )
Estimates ( 3 )-0.0091-0.0122-0.00710-0.0187-0.0580
(p-val)(0.8419 )(0.7905 )(0.8754 )(NA )(0.6801 )(0.2186 )(NA )
Estimates ( 4 )-0.009-0.012100-0.0191-0.05840
(p-val)(0.8429 )(0.7924 )(NA )(NA )(0.6743 )(0.2149 )(NA )
Estimates ( 5 )0-0.012100-0.0189-0.05760
(p-val)(NA )(0.7915 )(NA )(NA )(0.677 )(0.2198 )(NA )
Estimates ( 6 )0000-0.0187-0.05960
(p-val)(NA )(NA )(NA )(NA )(0.6804 )(0.1977 )(NA )
Estimates ( 7 )00000-0.05910
(p-val)(NA )(NA )(NA )(NA )(NA )(0.2014 )(NA )
Estimates ( 8 )0000000
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
0.126639936458067
0.169702797175283
-0.968304195647203
0.928374125723601
-2.42575174785845
0.0598951048853964
-3.91314685251242
-1.93660839129441
-0.878461538319123
-0.68879370618203
3.13451799270478
-0.939952083256485
-2.31733223083065
-0.735031984873704
-1.27362610403969
-1.32645367644346
1.93830686097299
1.65533553833869
0.927987254504133
-0.54078272090016
-2.90440906720796
-0.996150122978479
0.846421812703814
-0.786693260161048
0.693210906351917
1.58138982783012
2.63825870196136
-0.71538345508219
-1.83207671524325
-1.06955269630447
1.94736433683842
-0.315559069052397
-1.07207671524325
0.586262009469389
2.20492009838278
-0.27188504826917
2.61835453544841
1.26153357806068
1.24829080796913
1.50853039168671
-1.97410551374298
-0.72536740207833
1.05321090635192
1.06723639734363
0.167667648035276
-2.2018689952653
2.01599031974406
1.12742806431769
-0.0196166660518668
-0.95720453360398
2.60587867552126
1.43803517124765
-0.343801960277997
0.9008785543872
0.732364215704346
0.071150244112555
1.3605270841787
1.62206066223938
-0.445910539260891
1.32793933776061
-3.55864227817763
-1.89252401893878
3.21576678903032
-0.260351470208474
2.66314693660449
0.611821078521809
2.01388174076116
-3.12779558753007
-0.456006372747935
0.762156495726416
-0.699872060505229
-1.98702867736568
-0.0084985279470639
-0.848322671708779
-1.7151438713646
-0.944536764434646
0.144664461661321
1.23968063579926
-0.00541531882182909
-1.43980833302595
-1.76837058845228
3.33947291582131
1.79817892147822
1.1309425241346
0.959472915821299
-1.47792328475677
1.55177316177828
0.724520711430782
-0.538817892147841
-0.00747598106120162
-2.10284338313953
-1.28608639549921
-0.63301923937785
0.679744363278549
1.69324277009155
0.0660703424953274
-0.12838664145616
0.607827451269708
1.77808308799118
-1.15527156859128
0.301789214782132
1.8119329650127
0.796262009469396
-0.293945710508566
1.29634178995257
0.248865808891349
-0.0230031863739834
1.19369007378709
-0.84301923937783
0.201437744573654
1.25482426489574
-0.527699754043052
0.209648529791525
-1.48950477956095
-1.2290735288693
-1.48581470577386
0.289408946073902
1.33856225542637
-1.87615012297847
0.48595845600441
0.352699632908982
0.532172548730279
0.169456862817427
-0.556884927135123
1.40316298960838
0.931341911086619
-0.562859436143424
-1.55493615138666
-1.98757181454824
1.19777953452621
-2.5734504900695
-0.93630992621291
-1.83054313718258
0.792220465473797
1.39747598106121
1.2902875004611
1.02571887228682
-0.666341789952545
2.26888186189521
-0.270846690647557
-1.76308296685711
1.68266776916932
0.58124607759957
0.738466421939347
-1.00257193568232
-0.597300367091007
0.0926517161654363
1.40630992621294
0.670670834409249
0.33990416651295
1.06484031789962
0.0328433831395399
-0.379217279099856
1.16078272090017
-1.35383382401763
-0.98960061304797
1.75177316177827
-1.82488823464314
-2.63867414191728
1.20127794133922
-1.50143774457364
1.55586262251738
0.0551757351042335
1.44619803972199
-0.596245956465498
-0.786150122978499
-0.606565562934356
1.02710870011693
-1.80781139826584
0.229744363278532
-1.72279546639601
0.932204412469931
-0.015271568591297
0.961565441800344
0.209265195843339
0.516853063395484
-0.251964828752345
-1.23261985242581
-0.417523897804724
0.969456862817408
-0.756341789952549
1.8296485297915
-0.211182107852187
1.34201274549588
-0.355814705773863
-0.0969009801389958
-1.12241213244789
-1.3368370103916
-1.45891372563485
-0.18325882309544
0.120990440878104
-0.184792401156101
-0.842412132447888
1.53624595646553
-0.221868995265292
-0.537683701039162
-0.285606985795909
0.585527205312748
1.78488823464315
0.0158147057738579
0.379456862817404
0.662859436143418
1.48094252413459
0.146293873209037
-0.14182107852178
0.25867414191726
1.06699681362605
1.10900955912189
0.2305270841787
-1.17822683822174
-2.1281310047347
0.410191666974076
-0.379568749308339
-1.70645367644347
-0.707683701039149
2.0671405638566
0.0638338240176427
0.523242770091532
0.057092647113083
-0.369744363278546
0.472923405890828
-0.768131004734717
-0.837779534526198
0.408929778638752
-1.30137377482627
0.751166054848298
-1.33999999999997
-0.152811519399901
1.16295526963044
-0.477731617782687
-0.434536764434641
-1.44669326016106
-1.54787536801323
0.600143750230576
1.17552720531275
0.567236397343625
1.54079877390401
-0.100638970669582
0.228562255426353
-2.52718848060011
-0.857779534526208
0.0372524503475054
1.0113419110866
-1.54630992621289
0.743881740761147
0.421325858082747
-1.24424926397354
0.404680514665195
0.429456862817416
0.582236518477699
0.0709425241345654
0.787092647113073
0.195015931869844
0.486613479677879
-0.0503993869520226
0.833051103117485
0.380798773904045
-1.05576678903035
0.174824264895755
0.85314693660452
-0.132907352886916
0.91610220623494
-1.81231629896083
0.644281127713191
-0.145319485334818
-2.07212463198677
-0.402811519399871
-0.603833824017663
-1.13113419110865
0.537875368013212
-1.79827475496524
0.441421691569766
-1.53757181454824
-0.323945710508553
0.401725245034746
-0.335303432330921
-0.575415318821849
0.568083087991186
0.462619852425824
-1.02103835762162
-3.29579865276999
-0.39694889688252
1.24547928856925
-0.411277941339215
0.0442332109696562
-0.712412132447877
0.0574920340650925
0.295463235565362
2.04132585808272
-1.45206066223937
0.681453797577524
0.185175735104244
-1.11138982783012
-1.05305110311748
-1.49881789214782
0.218626225173756
-0.244680514665196
0.0253674020783266
0.538801839143932
-0.322156495726404
0.671421691569772
2.01241213244787
-1.83033541720463
1.3791214456128
0.591341911086615
0.745367402078311
-0.284776348152228
-0.0794089460739087
0.234824264895757
-1.11418529422615
-0.433354656582463
-1.49178921478214
0.805974509008283
0.545111765356864
0.860191666974064
0.603146936604519
0.73404154399559
0.045271568591283
1.14241213244788
0.284984068130173
-0.267779534526191
-1.09515968210037
-1.22621409272588
1.60718848060011
1.69846642193931
-0.616900980138992
1.12432904445669
-1.23704473036955
-0.813210906351921
-1.77931311258687
1.80581470577385
-0.729105392608915
-0.695654902539432
1.1733865203221
0.447523897804725
-0.798418505195793
-0.156166175982357
0.206709313164931
0.99798725450414
-0.96638970669607
-1.18242818545176
-1.03960061304798
-3.35664534341753
-1.43616617598236
3.15068688741312
-3.63492009838279
0.666996813626043
-1.05345049006951
-1.37793933776062
-1.43083063764367
1.07375404353447
-2.88910539260893
-0.756229903461628
0.0613419110865999
-0.265000121134059
-2.31218835946605
1.86019166697406
-0.623242770091541
2.18488823464314
-0.521565441800318
-1.90202879849973
1.54273173891673
1.346900980139
-2.00113419110865
-1.89659742667402
-0.664121324478754
0.77757181454825
-0.18309901986099
1.42416924122226
1.27399362725208
-0.776437623439598
0.481070221361276
0.821565441800315
-0.88880183914394
0.869472915821295
-2.04191691200881
-0.290399386952017
-1.17886580889134
0.476245956465508
0.336837010391612
-1.97900955912189
0.502460049191397
-0.6062620094694
1.69448884769112
0.10792328475676
0.411198160856043
-0.919504779560938
1.86084669064755
0.38364215704357
-0.484632597921679
-0.514664461661311
1.56837058845228
0.581581494804201
-0.467156616860459
0.302955269630444
-1.30867414191727
-0.0331948533480384
0.164073407735227
0.931277941339225
-0.199552696304466
1.45635784295643
0.311022304617747
-0.333354656582469
-1.05369007378708
0.66773161778267
0.160798773904046
-0.588817892147825
0.742955269630441
-0.466214092725871
0.329952083256486
-0.162524018938787
-2.65699681362604
-1.08186899526531
0.219712499538895
-0.251581494804204
0.184185294226145
0.235127818360743
-0.0662620094694074
-1.31073480415663
-0.289904166512967
-2.05477634815224
0.14218860173416
0.597348283834547
0.606549509930489
-0.217140563856578
-0.459952083256495
0.485958456004424
0.263498406813013
0.494345097460566
0.0216773282912385
-1.68057500092222
-0.432268382217325
0.08900955912189
0.414872181639276
0.83817892147821
-0.677779534526188
4.0277795345262
0.905958456004411
0.0836900737870856
0.0923642157043467
-0.112204412469923
1.06340257332599
-0.64704473036956
0.732460049191401
0.370239583717581
-0.3384185051958
0.866421570435706
-0.117396200578042
0.582955269630445
-0.154680514665208
0.698817892147825
0.244424877943738
0.081581494804187
-0.848035171247661
0.0489137256348576
-0.907731617782679
0.917236397343634
0.189952083256486
-0.115718872286828
-1.28945686281743
0.201373774826246
-0.559361029330386
1.06709264711307
-0.522603799421945
0.0317731617782548
-0.15260379942194
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-0.198178921478222
-0.708865808891328
-0.0056549025394344
1.24945686281744
0.456309926212907
1.54265171616547
-0.767779534526205
-1.52822683822173
-0.44591053926089
1.02049522043906
-0.712412132447878
-0.231373774826246
0.724137377482623
-0.0267093131649376
-0.68103835762162
1.30747598106121
0.326262009469397
-0.210431250691664
-1.49600637274793
-1.15853039168671
0.0286262251737436
-0.441230024595689
-0.14744411732157
-1.7359105392609

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.126639936458067 \tabularnewline
0.169702797175283 \tabularnewline
-0.968304195647203 \tabularnewline
0.928374125723601 \tabularnewline
-2.42575174785845 \tabularnewline
0.0598951048853964 \tabularnewline
-3.91314685251242 \tabularnewline
-1.93660839129441 \tabularnewline
-0.878461538319123 \tabularnewline
-0.68879370618203 \tabularnewline
3.13451799270478 \tabularnewline
-0.939952083256485 \tabularnewline
-2.31733223083065 \tabularnewline
-0.735031984873704 \tabularnewline
-1.27362610403969 \tabularnewline
-1.32645367644346 \tabularnewline
1.93830686097299 \tabularnewline
1.65533553833869 \tabularnewline
0.927987254504133 \tabularnewline
-0.54078272090016 \tabularnewline
-2.90440906720796 \tabularnewline
-0.996150122978479 \tabularnewline
0.846421812703814 \tabularnewline
-0.786693260161048 \tabularnewline
0.693210906351917 \tabularnewline
1.58138982783012 \tabularnewline
2.63825870196136 \tabularnewline
-0.71538345508219 \tabularnewline
-1.83207671524325 \tabularnewline
-1.06955269630447 \tabularnewline
1.94736433683842 \tabularnewline
-0.315559069052397 \tabularnewline
-1.07207671524325 \tabularnewline
0.586262009469389 \tabularnewline
2.20492009838278 \tabularnewline
-0.27188504826917 \tabularnewline
2.61835453544841 \tabularnewline
1.26153357806068 \tabularnewline
1.24829080796913 \tabularnewline
1.50853039168671 \tabularnewline
-1.97410551374298 \tabularnewline
-0.72536740207833 \tabularnewline
1.05321090635192 \tabularnewline
1.06723639734363 \tabularnewline
0.167667648035276 \tabularnewline
-2.2018689952653 \tabularnewline
2.01599031974406 \tabularnewline
1.12742806431769 \tabularnewline
-0.0196166660518668 \tabularnewline
-0.95720453360398 \tabularnewline
2.60587867552126 \tabularnewline
1.43803517124765 \tabularnewline
-0.343801960277997 \tabularnewline
0.9008785543872 \tabularnewline
0.732364215704346 \tabularnewline
0.071150244112555 \tabularnewline
1.3605270841787 \tabularnewline
1.62206066223938 \tabularnewline
-0.445910539260891 \tabularnewline
1.32793933776061 \tabularnewline
-3.55864227817763 \tabularnewline
-1.89252401893878 \tabularnewline
3.21576678903032 \tabularnewline
-0.260351470208474 \tabularnewline
2.66314693660449 \tabularnewline
0.611821078521809 \tabularnewline
2.01388174076116 \tabularnewline
-3.12779558753007 \tabularnewline
-0.456006372747935 \tabularnewline
0.762156495726416 \tabularnewline
-0.699872060505229 \tabularnewline
-1.98702867736568 \tabularnewline
-0.0084985279470639 \tabularnewline
-0.848322671708779 \tabularnewline
-1.7151438713646 \tabularnewline
-0.944536764434646 \tabularnewline
0.144664461661321 \tabularnewline
1.23968063579926 \tabularnewline
-0.00541531882182909 \tabularnewline
-1.43980833302595 \tabularnewline
-1.76837058845228 \tabularnewline
3.33947291582131 \tabularnewline
1.79817892147822 \tabularnewline
1.1309425241346 \tabularnewline
0.959472915821299 \tabularnewline
-1.47792328475677 \tabularnewline
1.55177316177828 \tabularnewline
0.724520711430782 \tabularnewline
-0.538817892147841 \tabularnewline
-0.00747598106120162 \tabularnewline
-2.10284338313953 \tabularnewline
-1.28608639549921 \tabularnewline
-0.63301923937785 \tabularnewline
0.679744363278549 \tabularnewline
1.69324277009155 \tabularnewline
0.0660703424953274 \tabularnewline
-0.12838664145616 \tabularnewline
0.607827451269708 \tabularnewline
1.77808308799118 \tabularnewline
-1.15527156859128 \tabularnewline
0.301789214782132 \tabularnewline
1.8119329650127 \tabularnewline
0.796262009469396 \tabularnewline
-0.293945710508566 \tabularnewline
1.29634178995257 \tabularnewline
0.248865808891349 \tabularnewline
-0.0230031863739834 \tabularnewline
1.19369007378709 \tabularnewline
-0.84301923937783 \tabularnewline
0.201437744573654 \tabularnewline
1.25482426489574 \tabularnewline
-0.527699754043052 \tabularnewline
0.209648529791525 \tabularnewline
-1.48950477956095 \tabularnewline
-1.2290735288693 \tabularnewline
-1.48581470577386 \tabularnewline
0.289408946073902 \tabularnewline
1.33856225542637 \tabularnewline
-1.87615012297847 \tabularnewline
0.48595845600441 \tabularnewline
0.352699632908982 \tabularnewline
0.532172548730279 \tabularnewline
0.169456862817427 \tabularnewline
-0.556884927135123 \tabularnewline
1.40316298960838 \tabularnewline
0.931341911086619 \tabularnewline
-0.562859436143424 \tabularnewline
-1.55493615138666 \tabularnewline
-1.98757181454824 \tabularnewline
1.19777953452621 \tabularnewline
-2.5734504900695 \tabularnewline
-0.93630992621291 \tabularnewline
-1.83054313718258 \tabularnewline
0.792220465473797 \tabularnewline
1.39747598106121 \tabularnewline
1.2902875004611 \tabularnewline
1.02571887228682 \tabularnewline
-0.666341789952545 \tabularnewline
2.26888186189521 \tabularnewline
-0.270846690647557 \tabularnewline
-1.76308296685711 \tabularnewline
1.68266776916932 \tabularnewline
0.58124607759957 \tabularnewline
0.738466421939347 \tabularnewline
-1.00257193568232 \tabularnewline
-0.597300367091007 \tabularnewline
0.0926517161654363 \tabularnewline
1.40630992621294 \tabularnewline
0.670670834409249 \tabularnewline
0.33990416651295 \tabularnewline
1.06484031789962 \tabularnewline
0.0328433831395399 \tabularnewline
-0.379217279099856 \tabularnewline
1.16078272090017 \tabularnewline
-1.35383382401763 \tabularnewline
-0.98960061304797 \tabularnewline
1.75177316177827 \tabularnewline
-1.82488823464314 \tabularnewline
-2.63867414191728 \tabularnewline
1.20127794133922 \tabularnewline
-1.50143774457364 \tabularnewline
1.55586262251738 \tabularnewline
0.0551757351042335 \tabularnewline
1.44619803972199 \tabularnewline
-0.596245956465498 \tabularnewline
-0.786150122978499 \tabularnewline
-0.606565562934356 \tabularnewline
1.02710870011693 \tabularnewline
-1.80781139826584 \tabularnewline
0.229744363278532 \tabularnewline
-1.72279546639601 \tabularnewline
0.932204412469931 \tabularnewline
-0.015271568591297 \tabularnewline
0.961565441800344 \tabularnewline
0.209265195843339 \tabularnewline
0.516853063395484 \tabularnewline
-0.251964828752345 \tabularnewline
-1.23261985242581 \tabularnewline
-0.417523897804724 \tabularnewline
0.969456862817408 \tabularnewline
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\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112283&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.126639936458067[/C][/ROW]
[ROW][C]0.169702797175283[/C][/ROW]
[ROW][C]-0.968304195647203[/C][/ROW]
[ROW][C]0.928374125723601[/C][/ROW]
[ROW][C]-2.42575174785845[/C][/ROW]
[ROW][C]0.0598951048853964[/C][/ROW]
[ROW][C]-3.91314685251242[/C][/ROW]
[ROW][C]-1.93660839129441[/C][/ROW]
[ROW][C]-0.878461538319123[/C][/ROW]
[ROW][C]-0.68879370618203[/C][/ROW]
[ROW][C]3.13451799270478[/C][/ROW]
[ROW][C]-0.939952083256485[/C][/ROW]
[ROW][C]-2.31733223083065[/C][/ROW]
[ROW][C]-0.735031984873704[/C][/ROW]
[ROW][C]-1.27362610403969[/C][/ROW]
[ROW][C]-1.32645367644346[/C][/ROW]
[ROW][C]1.93830686097299[/C][/ROW]
[ROW][C]1.65533553833869[/C][/ROW]
[ROW][C]0.927987254504133[/C][/ROW]
[ROW][C]-0.54078272090016[/C][/ROW]
[ROW][C]-2.90440906720796[/C][/ROW]
[ROW][C]-0.996150122978479[/C][/ROW]
[ROW][C]0.846421812703814[/C][/ROW]
[ROW][C]-0.786693260161048[/C][/ROW]
[ROW][C]0.693210906351917[/C][/ROW]
[ROW][C]1.58138982783012[/C][/ROW]
[ROW][C]2.63825870196136[/C][/ROW]
[ROW][C]-0.71538345508219[/C][/ROW]
[ROW][C]-1.83207671524325[/C][/ROW]
[ROW][C]-1.06955269630447[/C][/ROW]
[ROW][C]1.94736433683842[/C][/ROW]
[ROW][C]-0.315559069052397[/C][/ROW]
[ROW][C]-1.07207671524325[/C][/ROW]
[ROW][C]0.586262009469389[/C][/ROW]
[ROW][C]2.20492009838278[/C][/ROW]
[ROW][C]-0.27188504826917[/C][/ROW]
[ROW][C]2.61835453544841[/C][/ROW]
[ROW][C]1.26153357806068[/C][/ROW]
[ROW][C]1.24829080796913[/C][/ROW]
[ROW][C]1.50853039168671[/C][/ROW]
[ROW][C]-1.97410551374298[/C][/ROW]
[ROW][C]-0.72536740207833[/C][/ROW]
[ROW][C]1.05321090635192[/C][/ROW]
[ROW][C]1.06723639734363[/C][/ROW]
[ROW][C]0.167667648035276[/C][/ROW]
[ROW][C]-2.2018689952653[/C][/ROW]
[ROW][C]2.01599031974406[/C][/ROW]
[ROW][C]1.12742806431769[/C][/ROW]
[ROW][C]-0.0196166660518668[/C][/ROW]
[ROW][C]-0.95720453360398[/C][/ROW]
[ROW][C]2.60587867552126[/C][/ROW]
[ROW][C]1.43803517124765[/C][/ROW]
[ROW][C]-0.343801960277997[/C][/ROW]
[ROW][C]0.9008785543872[/C][/ROW]
[ROW][C]0.732364215704346[/C][/ROW]
[ROW][C]0.071150244112555[/C][/ROW]
[ROW][C]1.3605270841787[/C][/ROW]
[ROW][C]1.62206066223938[/C][/ROW]
[ROW][C]-0.445910539260891[/C][/ROW]
[ROW][C]1.32793933776061[/C][/ROW]
[ROW][C]-3.55864227817763[/C][/ROW]
[ROW][C]-1.89252401893878[/C][/ROW]
[ROW][C]3.21576678903032[/C][/ROW]
[ROW][C]-0.260351470208474[/C][/ROW]
[ROW][C]2.66314693660449[/C][/ROW]
[ROW][C]0.611821078521809[/C][/ROW]
[ROW][C]2.01388174076116[/C][/ROW]
[ROW][C]-3.12779558753007[/C][/ROW]
[ROW][C]-0.456006372747935[/C][/ROW]
[ROW][C]0.762156495726416[/C][/ROW]
[ROW][C]-0.699872060505229[/C][/ROW]
[ROW][C]-1.98702867736568[/C][/ROW]
[ROW][C]-0.0084985279470639[/C][/ROW]
[ROW][C]-0.848322671708779[/C][/ROW]
[ROW][C]-1.7151438713646[/C][/ROW]
[ROW][C]-0.944536764434646[/C][/ROW]
[ROW][C]0.144664461661321[/C][/ROW]
[ROW][C]1.23968063579926[/C][/ROW]
[ROW][C]-0.00541531882182909[/C][/ROW]
[ROW][C]-1.43980833302595[/C][/ROW]
[ROW][C]-1.76837058845228[/C][/ROW]
[ROW][C]3.33947291582131[/C][/ROW]
[ROW][C]1.79817892147822[/C][/ROW]
[ROW][C]1.1309425241346[/C][/ROW]
[ROW][C]0.959472915821299[/C][/ROW]
[ROW][C]-1.47792328475677[/C][/ROW]
[ROW][C]1.55177316177828[/C][/ROW]
[ROW][C]0.724520711430782[/C][/ROW]
[ROW][C]-0.538817892147841[/C][/ROW]
[ROW][C]-0.00747598106120162[/C][/ROW]
[ROW][C]-2.10284338313953[/C][/ROW]
[ROW][C]-1.28608639549921[/C][/ROW]
[ROW][C]-0.63301923937785[/C][/ROW]
[ROW][C]0.679744363278549[/C][/ROW]
[ROW][C]1.69324277009155[/C][/ROW]
[ROW][C]0.0660703424953274[/C][/ROW]
[ROW][C]-0.12838664145616[/C][/ROW]
[ROW][C]0.607827451269708[/C][/ROW]
[ROW][C]1.77808308799118[/C][/ROW]
[ROW][C]-1.15527156859128[/C][/ROW]
[ROW][C]0.301789214782132[/C][/ROW]
[ROW][C]1.8119329650127[/C][/ROW]
[ROW][C]0.796262009469396[/C][/ROW]
[ROW][C]-0.293945710508566[/C][/ROW]
[ROW][C]1.29634178995257[/C][/ROW]
[ROW][C]0.248865808891349[/C][/ROW]
[ROW][C]-0.0230031863739834[/C][/ROW]
[ROW][C]1.19369007378709[/C][/ROW]
[ROW][C]-0.84301923937783[/C][/ROW]
[ROW][C]0.201437744573654[/C][/ROW]
[ROW][C]1.25482426489574[/C][/ROW]
[ROW][C]-0.527699754043052[/C][/ROW]
[ROW][C]0.209648529791525[/C][/ROW]
[ROW][C]-1.48950477956095[/C][/ROW]
[ROW][C]-1.2290735288693[/C][/ROW]
[ROW][C]-1.48581470577386[/C][/ROW]
[ROW][C]0.289408946073902[/C][/ROW]
[ROW][C]1.33856225542637[/C][/ROW]
[ROW][C]-1.87615012297847[/C][/ROW]
[ROW][C]0.48595845600441[/C][/ROW]
[ROW][C]0.352699632908982[/C][/ROW]
[ROW][C]0.532172548730279[/C][/ROW]
[ROW][C]0.169456862817427[/C][/ROW]
[ROW][C]-0.556884927135123[/C][/ROW]
[ROW][C]1.40316298960838[/C][/ROW]
[ROW][C]0.931341911086619[/C][/ROW]
[ROW][C]-0.562859436143424[/C][/ROW]
[ROW][C]-1.55493615138666[/C][/ROW]
[ROW][C]-1.98757181454824[/C][/ROW]
[ROW][C]1.19777953452621[/C][/ROW]
[ROW][C]-2.5734504900695[/C][/ROW]
[ROW][C]-0.93630992621291[/C][/ROW]
[ROW][C]-1.83054313718258[/C][/ROW]
[ROW][C]0.792220465473797[/C][/ROW]
[ROW][C]1.39747598106121[/C][/ROW]
[ROW][C]1.2902875004611[/C][/ROW]
[ROW][C]1.02571887228682[/C][/ROW]
[ROW][C]-0.666341789952545[/C][/ROW]
[ROW][C]2.26888186189521[/C][/ROW]
[ROW][C]-0.270846690647557[/C][/ROW]
[ROW][C]-1.76308296685711[/C][/ROW]
[ROW][C]1.68266776916932[/C][/ROW]
[ROW][C]0.58124607759957[/C][/ROW]
[ROW][C]0.738466421939347[/C][/ROW]
[ROW][C]-1.00257193568232[/C][/ROW]
[ROW][C]-0.597300367091007[/C][/ROW]
[ROW][C]0.0926517161654363[/C][/ROW]
[ROW][C]1.40630992621294[/C][/ROW]
[ROW][C]0.670670834409249[/C][/ROW]
[ROW][C]0.33990416651295[/C][/ROW]
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[ROW][C]-1.49600637274793[/C][/ROW]
[ROW][C]-1.15853039168671[/C][/ROW]
[ROW][C]0.0286262251737436[/C][/ROW]
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[ROW][C]-0.14744411732157[/C][/ROW]
[ROW][C]-1.7359105392609[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112283&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112283&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
0.126639936458067
0.169702797175283
-0.968304195647203
0.928374125723601
-2.42575174785845
0.0598951048853964
-3.91314685251242
-1.93660839129441
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-0.68879370618203
3.13451799270478
-0.939952083256485
-2.31733223083065
-0.735031984873704
-1.27362610403969
-1.32645367644346
1.93830686097299
1.65533553833869
0.927987254504133
-0.54078272090016
-2.90440906720796
-0.996150122978479
0.846421812703814
-0.786693260161048
0.693210906351917
1.58138982783012
2.63825870196136
-0.71538345508219
-1.83207671524325
-1.06955269630447
1.94736433683842
-0.315559069052397
-1.07207671524325
0.586262009469389
2.20492009838278
-0.27188504826917
2.61835453544841
1.26153357806068
1.24829080796913
1.50853039168671
-1.97410551374298
-0.72536740207833
1.05321090635192
1.06723639734363
0.167667648035276
-2.2018689952653
2.01599031974406
1.12742806431769
-0.0196166660518668
-0.95720453360398
2.60587867552126
1.43803517124765
-0.343801960277997
0.9008785543872
0.732364215704346
0.071150244112555
1.3605270841787
1.62206066223938
-0.445910539260891
1.32793933776061
-3.55864227817763
-1.89252401893878
3.21576678903032
-0.260351470208474
2.66314693660449
0.611821078521809
2.01388174076116
-3.12779558753007
-0.456006372747935
0.762156495726416
-0.699872060505229
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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 <- 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')