Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationThu, 03 Dec 2009 08:58:00 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/03/t1259856508dnybowar6irznkk.htm/, Retrieved Thu, 28 Mar 2024 12:34:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62868, Retrieved Thu, 28 Mar 2024 12:34:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
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] [] [2009-11-27 14:40:44] [b98453cac15ba1066b407e146608df68]
- R  D    [Standard Deviation-Mean Plot] [] [2009-12-01 17:14:00] [b7349fb284cae6f1172638396d27b11f]
- RMP         [ARIMA Backward Selection] [] [2009-12-03 15:58:00] [6dfcce621b31349cab7f0d189e6f8a9d] [Current]
Feedback Forum

Post a new message
Dataseries X:
116222
110924
103753
99983
93302
91496
119321
139261
133739
123913
113438
109416
109406
105645
101328
97686
93093
91382
122257
139183
139887
131822
116805
113706
113012
110452
107005
102841
98173
98181
137277
147579
146571
138920
130340
128140
127059
122860
117702
113537
108366
111078
150739
159129
157928
147768
137507
136919
136151
133001
125554
119647
114158
116193
152803
161761
160942
149470
139208
134588
130322
126611
122401
117352
112135
112879
148729
157230
157221
146681
136524
132111




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62868&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62868&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62868&T=0

As an alternative you can also use a QR Code:  

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

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







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.1762-0.2361-0.21840.00360.3241-0.3556-0.2352
(p-val)(0.694 )(0.102 )(0.1616 )(0.9937 )(0.4041 )(0.045 )(0.581 )
Estimates ( 2 )-0.1729-0.2357-0.217700.3239-0.3556-0.2349
(p-val)(0.2135 )(0.083 )(0.0946 )(NA )(0.4041 )(0.0445 )(0.5814 )
Estimates ( 3 )-0.1678-0.2398-0.215900.126-0.33180
(p-val)(0.224 )(0.0787 )(0.0988 )(NA )(0.4306 )(0.07 )(NA )
Estimates ( 4 )-0.1741-0.2064-0.19200-0.32150
(p-val)(0.2062 )(0.1153 )(0.1316 )(NA )(NA )(0.0791 )(NA )
Estimates ( 5 )0-0.1729-0.163500-0.23810
(p-val)(NA )(0.1785 )(0.1973 )(NA )(NA )(0.1835 )(NA )
Estimates ( 6 )0-0.1563000-0.25220
(p-val)(NA )(0.2266 )(NA )(NA )(NA )(0.1544 )(NA )
Estimates ( 7 )00000-0.23510
(p-val)(NA )(NA )(NA )(NA )(NA )(0.1863 )(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 ) & -0.1762 & -0.2361 & -0.2184 & 0.0036 & 0.3241 & -0.3556 & -0.2352 \tabularnewline
(p-val) & (0.694 ) & (0.102 ) & (0.1616 ) & (0.9937 ) & (0.4041 ) & (0.045 ) & (0.581 ) \tabularnewline
Estimates ( 2 ) & -0.1729 & -0.2357 & -0.2177 & 0 & 0.3239 & -0.3556 & -0.2349 \tabularnewline
(p-val) & (0.2135 ) & (0.083 ) & (0.0946 ) & (NA ) & (0.4041 ) & (0.0445 ) & (0.5814 ) \tabularnewline
Estimates ( 3 ) & -0.1678 & -0.2398 & -0.2159 & 0 & 0.126 & -0.3318 & 0 \tabularnewline
(p-val) & (0.224 ) & (0.0787 ) & (0.0988 ) & (NA ) & (0.4306 ) & (0.07 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.1741 & -0.2064 & -0.192 & 0 & 0 & -0.3215 & 0 \tabularnewline
(p-val) & (0.2062 ) & (0.1153 ) & (0.1316 ) & (NA ) & (NA ) & (0.0791 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & -0.1729 & -0.1635 & 0 & 0 & -0.2381 & 0 \tabularnewline
(p-val) & (NA ) & (0.1785 ) & (0.1973 ) & (NA ) & (NA ) & (0.1835 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & -0.1563 & 0 & 0 & 0 & -0.2522 & 0 \tabularnewline
(p-val) & (NA ) & (0.2266 ) & (NA ) & (NA ) & (NA ) & (0.1544 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & -0.2351 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.1863 ) & (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=62868&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]-0.1762[/C][C]-0.2361[/C][C]-0.2184[/C][C]0.0036[/C][C]0.3241[/C][C]-0.3556[/C][C]-0.2352[/C][/ROW]
[ROW][C](p-val)[/C][C](0.694 )[/C][C](0.102 )[/C][C](0.1616 )[/C][C](0.9937 )[/C][C](0.4041 )[/C][C](0.045 )[/C][C](0.581 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.1729[/C][C]-0.2357[/C][C]-0.2177[/C][C]0[/C][C]0.3239[/C][C]-0.3556[/C][C]-0.2349[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2135 )[/C][C](0.083 )[/C][C](0.0946 )[/C][C](NA )[/C][C](0.4041 )[/C][C](0.0445 )[/C][C](0.5814 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.1678[/C][C]-0.2398[/C][C]-0.2159[/C][C]0[/C][C]0.126[/C][C]-0.3318[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.224 )[/C][C](0.0787 )[/C][C](0.0988 )[/C][C](NA )[/C][C](0.4306 )[/C][C](0.07 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.1741[/C][C]-0.2064[/C][C]-0.192[/C][C]0[/C][C]0[/C][C]-0.3215[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2062 )[/C][C](0.1153 )[/C][C](0.1316 )[/C][C](NA )[/C][C](NA )[/C][C](0.0791 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]-0.1729[/C][C]-0.1635[/C][C]0[/C][C]0[/C][C]-0.2381[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1785 )[/C][C](0.1973 )[/C][C](NA )[/C][C](NA )[/C][C](0.1835 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]-0.1563[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.2522[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.2266 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1544 )[/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]-0.2351[/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](0.1863 )[/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=62868&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62868&T=1

As an alternative you can also use a QR Code:  

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

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.1762-0.2361-0.21840.00360.3241-0.3556-0.2352
(p-val)(0.694 )(0.102 )(0.1616 )(0.9937 )(0.4041 )(0.045 )(0.581 )
Estimates ( 2 )-0.1729-0.2357-0.217700.3239-0.3556-0.2349
(p-val)(0.2135 )(0.083 )(0.0946 )(NA )(0.4041 )(0.0445 )(0.5814 )
Estimates ( 3 )-0.1678-0.2398-0.215900.126-0.33180
(p-val)(0.224 )(0.0787 )(0.0988 )(NA )(0.4306 )(0.07 )(NA )
Estimates ( 4 )-0.1741-0.2064-0.19200-0.32150
(p-val)(0.2062 )(0.1153 )(0.1316 )(NA )(NA )(0.0791 )(NA )
Estimates ( 5 )0-0.1729-0.163500-0.23810
(p-val)(NA )(0.1785 )(0.1973 )(NA )(NA )(0.1835 )(NA )
Estimates ( 6 )0-0.1563000-0.25220
(p-val)(NA )(0.2266 )(NA )(NA )(NA )(0.1544 )(NA )
Estimates ( 7 )00000-0.23510
(p-val)(NA )(NA )(NA )(NA )(NA )(0.1863 )(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
-420.649133951776
1493.92274183504
2774.01138897383
124.412564198775
2029.47995100395
92.337449987095
2964.51812754658
-2929.52709355973
6051.50487278124
1711.64472835091
-4414.7020772296
897.131222941034
-664.859416996131
1167.28027546593
845.528827920077
-507.484532118172
-73.0384302424731
1670.65748197797
7990.40509893685
-6438.56678886943
-1664.27191816248
402.108697664594
6256.27353218498
873.459425024459
-376.555218629995
-1277.66381065858
-1040.04783077962
29.0917581492218
-12.1281957359557
2726.33372678289
1282.03017373853
-2620.56686651182
1270.68192199044
-2095.00323412028
-2748.78722929728
1828.98978683146
152.197167596489
1331.34532421081
-2084.47008154595
-1864.71795107242
-335.631889521886
-272.877092158029
-1118.30981653924
-989.248482574389
-20.4772648199578
-1214.67196983911
1512.28630469981
-3820.65241759748
-3588.98054993301
-946.315559018636
2834.75782704033
857.764908139725
153.748794273080
-655.31160977036
-627.17309893496
-906.49563687836
764.627270963625
342.1545225273
-290.189417150890
585.968078790757

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-420.649133951776 \tabularnewline
1493.92274183504 \tabularnewline
2774.01138897383 \tabularnewline
124.412564198775 \tabularnewline
2029.47995100395 \tabularnewline
92.337449987095 \tabularnewline
2964.51812754658 \tabularnewline
-2929.52709355973 \tabularnewline
6051.50487278124 \tabularnewline
1711.64472835091 \tabularnewline
-4414.7020772296 \tabularnewline
897.131222941034 \tabularnewline
-664.859416996131 \tabularnewline
1167.28027546593 \tabularnewline
845.528827920077 \tabularnewline
-507.484532118172 \tabularnewline
-73.0384302424731 \tabularnewline
1670.65748197797 \tabularnewline
7990.40509893685 \tabularnewline
-6438.56678886943 \tabularnewline
-1664.27191816248 \tabularnewline
402.108697664594 \tabularnewline
6256.27353218498 \tabularnewline
873.459425024459 \tabularnewline
-376.555218629995 \tabularnewline
-1277.66381065858 \tabularnewline
-1040.04783077962 \tabularnewline
29.0917581492218 \tabularnewline
-12.1281957359557 \tabularnewline
2726.33372678289 \tabularnewline
1282.03017373853 \tabularnewline
-2620.56686651182 \tabularnewline
1270.68192199044 \tabularnewline
-2095.00323412028 \tabularnewline
-2748.78722929728 \tabularnewline
1828.98978683146 \tabularnewline
152.197167596489 \tabularnewline
1331.34532421081 \tabularnewline
-2084.47008154595 \tabularnewline
-1864.71795107242 \tabularnewline
-335.631889521886 \tabularnewline
-272.877092158029 \tabularnewline
-1118.30981653924 \tabularnewline
-989.248482574389 \tabularnewline
-20.4772648199578 \tabularnewline
-1214.67196983911 \tabularnewline
1512.28630469981 \tabularnewline
-3820.65241759748 \tabularnewline
-3588.98054993301 \tabularnewline
-946.315559018636 \tabularnewline
2834.75782704033 \tabularnewline
857.764908139725 \tabularnewline
153.748794273080 \tabularnewline
-655.31160977036 \tabularnewline
-627.17309893496 \tabularnewline
-906.49563687836 \tabularnewline
764.627270963625 \tabularnewline
342.1545225273 \tabularnewline
-290.189417150890 \tabularnewline
585.968078790757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62868&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-420.649133951776[/C][/ROW]
[ROW][C]1493.92274183504[/C][/ROW]
[ROW][C]2774.01138897383[/C][/ROW]
[ROW][C]124.412564198775[/C][/ROW]
[ROW][C]2029.47995100395[/C][/ROW]
[ROW][C]92.337449987095[/C][/ROW]
[ROW][C]2964.51812754658[/C][/ROW]
[ROW][C]-2929.52709355973[/C][/ROW]
[ROW][C]6051.50487278124[/C][/ROW]
[ROW][C]1711.64472835091[/C][/ROW]
[ROW][C]-4414.7020772296[/C][/ROW]
[ROW][C]897.131222941034[/C][/ROW]
[ROW][C]-664.859416996131[/C][/ROW]
[ROW][C]1167.28027546593[/C][/ROW]
[ROW][C]845.528827920077[/C][/ROW]
[ROW][C]-507.484532118172[/C][/ROW]
[ROW][C]-73.0384302424731[/C][/ROW]
[ROW][C]1670.65748197797[/C][/ROW]
[ROW][C]7990.40509893685[/C][/ROW]
[ROW][C]-6438.56678886943[/C][/ROW]
[ROW][C]-1664.27191816248[/C][/ROW]
[ROW][C]402.108697664594[/C][/ROW]
[ROW][C]6256.27353218498[/C][/ROW]
[ROW][C]873.459425024459[/C][/ROW]
[ROW][C]-376.555218629995[/C][/ROW]
[ROW][C]-1277.66381065858[/C][/ROW]
[ROW][C]-1040.04783077962[/C][/ROW]
[ROW][C]29.0917581492218[/C][/ROW]
[ROW][C]-12.1281957359557[/C][/ROW]
[ROW][C]2726.33372678289[/C][/ROW]
[ROW][C]1282.03017373853[/C][/ROW]
[ROW][C]-2620.56686651182[/C][/ROW]
[ROW][C]1270.68192199044[/C][/ROW]
[ROW][C]-2095.00323412028[/C][/ROW]
[ROW][C]-2748.78722929728[/C][/ROW]
[ROW][C]1828.98978683146[/C][/ROW]
[ROW][C]152.197167596489[/C][/ROW]
[ROW][C]1331.34532421081[/C][/ROW]
[ROW][C]-2084.47008154595[/C][/ROW]
[ROW][C]-1864.71795107242[/C][/ROW]
[ROW][C]-335.631889521886[/C][/ROW]
[ROW][C]-272.877092158029[/C][/ROW]
[ROW][C]-1118.30981653924[/C][/ROW]
[ROW][C]-989.248482574389[/C][/ROW]
[ROW][C]-20.4772648199578[/C][/ROW]
[ROW][C]-1214.67196983911[/C][/ROW]
[ROW][C]1512.28630469981[/C][/ROW]
[ROW][C]-3820.65241759748[/C][/ROW]
[ROW][C]-3588.98054993301[/C][/ROW]
[ROW][C]-946.315559018636[/C][/ROW]
[ROW][C]2834.75782704033[/C][/ROW]
[ROW][C]857.764908139725[/C][/ROW]
[ROW][C]153.748794273080[/C][/ROW]
[ROW][C]-655.31160977036[/C][/ROW]
[ROW][C]-627.17309893496[/C][/ROW]
[ROW][C]-906.49563687836[/C][/ROW]
[ROW][C]764.627270963625[/C][/ROW]
[ROW][C]342.1545225273[/C][/ROW]
[ROW][C]-290.189417150890[/C][/ROW]
[ROW][C]585.968078790757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62868&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62868&T=2

As an alternative you can also use a QR Code:  

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

Estimated ARIMA Residuals
Value
-420.649133951776
1493.92274183504
2774.01138897383
124.412564198775
2029.47995100395
92.337449987095
2964.51812754658
-2929.52709355973
6051.50487278124
1711.64472835091
-4414.7020772296
897.131222941034
-664.859416996131
1167.28027546593
845.528827920077
-507.484532118172
-73.0384302424731
1670.65748197797
7990.40509893685
-6438.56678886943
-1664.27191816248
402.108697664594
6256.27353218498
873.459425024459
-376.555218629995
-1277.66381065858
-1040.04783077962
29.0917581492218
-12.1281957359557
2726.33372678289
1282.03017373853
-2620.56686651182
1270.68192199044
-2095.00323412028
-2748.78722929728
1828.98978683146
152.197167596489
1331.34532421081
-2084.47008154595
-1864.71795107242
-335.631889521886
-272.877092158029
-1118.30981653924
-989.248482574389
-20.4772648199578
-1214.67196983911
1512.28630469981
-3820.65241759748
-3588.98054993301
-946.315559018636
2834.75782704033
857.764908139725
153.748794273080
-655.31160977036
-627.17309893496
-906.49563687836
764.627270963625
342.1545225273
-290.189417150890
585.968078790757



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