Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationThu, 20 Dec 2007 08:28:14 -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/2007/Dec/20/t1198163887iz4iomdkuedboc9.htm/, Retrieved Thu, 31 Oct 2024 23:18:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=4739, Retrieved Thu, 31 Oct 2024 23:18:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact321
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [ARMA backward sel...] [2007-12-20 15:28:14] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- RMPD    [ARIMA Forecasting] [forecasting] [2008-01-07 19:49:31] [74be16979710d4c4e7c6647856088456]
-   PD      [ARIMA Forecasting] [invoer] [2008-12-14 17:54:54] [5e74953d94072114d25d7276793b561e]
-   PD      [ARIMA Forecasting] [werkloosheid] [2008-12-14 18:02:13] [5e74953d94072114d25d7276793b561e]
- RMPD      [Pearson Correlation] [correlatie tussen...] [2008-12-14 18:10:42] [5e74953d94072114d25d7276793b561e]
- RMPD      [Linear Regression Graphical Model Validation] [Simple Linear Reg...] [2008-12-14 18:25:57] [5e74953d94072114d25d7276793b561e]
- RMPD    [Spectral Analysis] [] [2008-01-07 20:25:18] [74be16979710d4c4e7c6647856088456]
- RMPD    [ARIMA Forecasting] [] [2008-01-07 20:23:46] [74be16979710d4c4e7c6647856088456]
- RMPD    [Spectral Analysis] [] [2008-01-07 20:25:18] [74be16979710d4c4e7c6647856088456]
- RMPD    [Spectral Analysis] [] [2008-01-07 20:25:18] [74be16979710d4c4e7c6647856088456]
- RMPD    [ARIMA Forecasting] [] [2008-01-07 20:32:36] [74be16979710d4c4e7c6647856088456]
-   PD      [ARIMA Forecasting] [werkloosheid/invoer] [2008-12-17 23:06:44] [5e74953d94072114d25d7276793b561e]
-   P         [ARIMA Forecasting] [werkloosheid/invoer] [2008-12-19 10:13:06] [5e74953d94072114d25d7276793b561e]
-   PD      [ARIMA Forecasting] [werkloosheid/invoer] [2008-12-17 23:09:12] [5e74953d94072114d25d7276793b561e]
- RMPD      [Notched Boxplots] [de productie] [2008-12-17 23:17:54] [5e74953d94072114d25d7276793b561e]
- RMPD      [Multiple Regression] [verband tussen in...] [2008-12-17 23:33:00] [5e74953d94072114d25d7276793b561e]
- RMPD      [Multiple Regression] [verband tussen in...] [2008-12-17 23:36:56] [5e74953d94072114d25d7276793b561e]
- RMPD      [Multiple Regression] [verband tussen in...] [2008-12-17 23:40:07] [5e74953d94072114d25d7276793b561e]
- RMPD    [ARIMA Forecasting] [] [2008-01-07 20:37:09] [74be16979710d4c4e7c6647856088456]
- RMPD    [Central Tendency] [] [2008-01-07 20:57:08] [74be16979710d4c4e7c6647856088456]
Feedback Forum

Post a new message
Dataseries X:
104.2
103.2
112.7
106.4
102.6
110.6
95.2
89
112.5
116.8
107.2
113.6
101.8
102.6
122.7
110.3
110.5
121.6
100.3
100.7
123.4
127.1
124.1
131.2
111.6
114.2
130.1
125.9
119
133.8
107.5
113.5
134.4
126.8
135.6
139.9
129.8
131
153.1
134.1
144.1
155.9
123.3
128.1
144.3
153
149.9
150.9




Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 20 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4739&T=0

[TABLE]
[ROW][C]Summary of compuational 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]20 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4739&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4739&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.44830.83330.588310.31170.3673-0.9999
(p-val)(0.0016 )(0 )(2e-04 )(0 )(0.4698 )(0.3409 )(0.0703 )
Estimates ( 2 )0.31010.35370.3251-0.072300.1572-0.6494
(p-val)(0.7286 )(0.3929 )(0.5417 )(0.9405 )(NA )(0.5798 )(0.1998 )
Estimates ( 3 )0.24520.38250.3617000.1573-0.6757
(p-val)(0.1434 )(0.0149 )(0.0443 )(NA )(NA )(0.5805 )(0.0856 )
Estimates ( 4 )0.23420.39230.3678000-0.7062
(p-val)(0.1564 )(0.0112 )(0.0376 )(NA )(NA )(NA )(0.1117 )
Estimates ( 5 )00.4910.5057000-0.755
(p-val)(NA )(6e-04 )(4e-04 )(NA )(NA )(NA )(0.0178 )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.4483 & 0.8333 & 0.5883 & 1 & 0.3117 & 0.3673 & -0.9999 \tabularnewline
(p-val) & (0.0016 ) & (0 ) & (2e-04 ) & (0 ) & (0.4698 ) & (0.3409 ) & (0.0703 ) \tabularnewline
Estimates ( 2 ) & 0.3101 & 0.3537 & 0.3251 & -0.0723 & 0 & 0.1572 & -0.6494 \tabularnewline
(p-val) & (0.7286 ) & (0.3929 ) & (0.5417 ) & (0.9405 ) & (NA ) & (0.5798 ) & (0.1998 ) \tabularnewline
Estimates ( 3 ) & 0.2452 & 0.3825 & 0.3617 & 0 & 0 & 0.1573 & -0.6757 \tabularnewline
(p-val) & (0.1434 ) & (0.0149 ) & (0.0443 ) & (NA ) & (NA ) & (0.5805 ) & (0.0856 ) \tabularnewline
Estimates ( 4 ) & 0.2342 & 0.3923 & 0.3678 & 0 & 0 & 0 & -0.7062 \tabularnewline
(p-val) & (0.1564 ) & (0.0112 ) & (0.0376 ) & (NA ) & (NA ) & (NA ) & (0.1117 ) \tabularnewline
Estimates ( 5 ) & 0 & 0.491 & 0.5057 & 0 & 0 & 0 & -0.755 \tabularnewline
(p-val) & (NA ) & (6e-04 ) & (4e-04 ) & (NA ) & (NA ) & (NA ) & (0.0178 ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4739&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]-0.4483[/C][C]0.8333[/C][C]0.5883[/C][C]1[/C][C]0.3117[/C][C]0.3673[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0016 )[/C][C](0 )[/C][C](2e-04 )[/C][C](0 )[/C][C](0.4698 )[/C][C](0.3409 )[/C][C](0.0703 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.3101[/C][C]0.3537[/C][C]0.3251[/C][C]-0.0723[/C][C]0[/C][C]0.1572[/C][C]-0.6494[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7286 )[/C][C](0.3929 )[/C][C](0.5417 )[/C][C](0.9405 )[/C][C](NA )[/C][C](0.5798 )[/C][C](0.1998 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.2452[/C][C]0.3825[/C][C]0.3617[/C][C]0[/C][C]0[/C][C]0.1573[/C][C]-0.6757[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1434 )[/C][C](0.0149 )[/C][C](0.0443 )[/C][C](NA )[/C][C](NA )[/C][C](0.5805 )[/C][C](0.0856 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.2342[/C][C]0.3923[/C][C]0.3678[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.7062[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1564 )[/C][C](0.0112 )[/C][C](0.0376 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1117 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0.491[/C][C]0.5057[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.755[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](6e-04 )[/C][C](4e-04 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0178 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4739&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4739&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 )-0.44830.83330.588310.31170.3673-0.9999
(p-val)(0.0016 )(0 )(2e-04 )(0 )(0.4698 )(0.3409 )(0.0703 )
Estimates ( 2 )0.31010.35370.3251-0.072300.1572-0.6494
(p-val)(0.7286 )(0.3929 )(0.5417 )(0.9405 )(NA )(0.5798 )(0.1998 )
Estimates ( 3 )0.24520.38250.3617000.1573-0.6757
(p-val)(0.1434 )(0.0149 )(0.0443 )(NA )(NA )(0.5805 )(0.0856 )
Estimates ( 4 )0.23420.39230.3678000-0.7062
(p-val)(0.1564 )(0.0112 )(0.0376 )(NA )(NA )(NA )(0.1117 )
Estimates ( 5 )00.4910.5057000-0.755
(p-val)(NA )(6e-04 )(4e-04 )(NA )(NA )(NA )(0.0178 )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.113594566200521
-0.970421983644229
0.866824768341399
8.82996371967137
2.31406113774513
2.80321867491826
3.44517442069126
-1.41910874274129
2.78284420598674
2.18258515701947
1.22508680266925
4.81867349295226
5.97558570865055
-4.98688856366749
-2.68789120002633
-0.0400778102306022
6.64082133956298
-0.545887000343177
3.20695293758174
-5.05855577809736
4.52029815261354
1.90597668721327
-9.11155587071804
4.94683168446554
5.22551776038647
8.27794369983137
3.02066547196251
8.33579381744882
-5.84761367228622
7.42682093033261
6.49661540701886
-5.27932740485275
-3.84693004162808
-6.31080358544585
6.23739070150029
2.11162045429064
-2.65323681858853

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.113594566200521 \tabularnewline
-0.970421983644229 \tabularnewline
0.866824768341399 \tabularnewline
8.82996371967137 \tabularnewline
2.31406113774513 \tabularnewline
2.80321867491826 \tabularnewline
3.44517442069126 \tabularnewline
-1.41910874274129 \tabularnewline
2.78284420598674 \tabularnewline
2.18258515701947 \tabularnewline
1.22508680266925 \tabularnewline
4.81867349295226 \tabularnewline
5.97558570865055 \tabularnewline
-4.98688856366749 \tabularnewline
-2.68789120002633 \tabularnewline
-0.0400778102306022 \tabularnewline
6.64082133956298 \tabularnewline
-0.545887000343177 \tabularnewline
3.20695293758174 \tabularnewline
-5.05855577809736 \tabularnewline
4.52029815261354 \tabularnewline
1.90597668721327 \tabularnewline
-9.11155587071804 \tabularnewline
4.94683168446554 \tabularnewline
5.22551776038647 \tabularnewline
8.27794369983137 \tabularnewline
3.02066547196251 \tabularnewline
8.33579381744882 \tabularnewline
-5.84761367228622 \tabularnewline
7.42682093033261 \tabularnewline
6.49661540701886 \tabularnewline
-5.27932740485275 \tabularnewline
-3.84693004162808 \tabularnewline
-6.31080358544585 \tabularnewline
6.23739070150029 \tabularnewline
2.11162045429064 \tabularnewline
-2.65323681858853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4739&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.113594566200521[/C][/ROW]
[ROW][C]-0.970421983644229[/C][/ROW]
[ROW][C]0.866824768341399[/C][/ROW]
[ROW][C]8.82996371967137[/C][/ROW]
[ROW][C]2.31406113774513[/C][/ROW]
[ROW][C]2.80321867491826[/C][/ROW]
[ROW][C]3.44517442069126[/C][/ROW]
[ROW][C]-1.41910874274129[/C][/ROW]
[ROW][C]2.78284420598674[/C][/ROW]
[ROW][C]2.18258515701947[/C][/ROW]
[ROW][C]1.22508680266925[/C][/ROW]
[ROW][C]4.81867349295226[/C][/ROW]
[ROW][C]5.97558570865055[/C][/ROW]
[ROW][C]-4.98688856366749[/C][/ROW]
[ROW][C]-2.68789120002633[/C][/ROW]
[ROW][C]-0.0400778102306022[/C][/ROW]
[ROW][C]6.64082133956298[/C][/ROW]
[ROW][C]-0.545887000343177[/C][/ROW]
[ROW][C]3.20695293758174[/C][/ROW]
[ROW][C]-5.05855577809736[/C][/ROW]
[ROW][C]4.52029815261354[/C][/ROW]
[ROW][C]1.90597668721327[/C][/ROW]
[ROW][C]-9.11155587071804[/C][/ROW]
[ROW][C]4.94683168446554[/C][/ROW]
[ROW][C]5.22551776038647[/C][/ROW]
[ROW][C]8.27794369983137[/C][/ROW]
[ROW][C]3.02066547196251[/C][/ROW]
[ROW][C]8.33579381744882[/C][/ROW]
[ROW][C]-5.84761367228622[/C][/ROW]
[ROW][C]7.42682093033261[/C][/ROW]
[ROW][C]6.49661540701886[/C][/ROW]
[ROW][C]-5.27932740485275[/C][/ROW]
[ROW][C]-3.84693004162808[/C][/ROW]
[ROW][C]-6.31080358544585[/C][/ROW]
[ROW][C]6.23739070150029[/C][/ROW]
[ROW][C]2.11162045429064[/C][/ROW]
[ROW][C]-2.65323681858853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4739&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4739&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
0.113594566200521
-0.970421983644229
0.866824768341399
8.82996371967137
2.31406113774513
2.80321867491826
3.44517442069126
-1.41910874274129
2.78284420598674
2.18258515701947
1.22508680266925
4.81867349295226
5.97558570865055
-4.98688856366749
-2.68789120002633
-0.0400778102306022
6.64082133956298
-0.545887000343177
3.20695293758174
-5.05855577809736
4.52029815261354
1.90597668721327
-9.11155587071804
4.94683168446554
5.22551776038647
8.27794369983137
3.02066547196251
8.33579381744882
-5.84761367228622
7.42682093033261
6.49661540701886
-5.27932740485275
-3.84693004162808
-6.31080358544585
6.23739070150029
2.11162045429064
-2.65323681858853



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; 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]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$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')