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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 computationMon, 22 Dec 2008 03:23:57 -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/2008/Dec/22/t1229941508qenjq9kv3nqmvv0.htm/, Retrieved Sun, 12 May 2024 13:12:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35979, Retrieved Sun, 12 May 2024 13:12:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Standard Deviation-Mean Plot] [] [2008-12-12 12:13:32] [fad8a251ac01c156a8ae23a83577546f]
- RMPD  [(Partial) Autocorrelation Function] [Consumptiegoederen] [2008-12-12 13:39:25] [fad8a251ac01c156a8ae23a83577546f]
-   P     [(Partial) Autocorrelation Function] [auto corr cons] [2008-12-19 10:53:37] [fad8a251ac01c156a8ae23a83577546f]
-   P       [(Partial) Autocorrelation Function] [autocorr cons D] [2008-12-21 18:04:22] [fad8a251ac01c156a8ae23a83577546f]
- RMPD          [ARIMA Backward Selection] [Arima backw sel n...] [2008-12-22 10:23:57] [fa8b44cd657c07c6ee11bb2476ca3f8d] [Current]
-    D            [ARIMA Backward Selection] [arima backw sel cons] [2008-12-22 10:27:01] [fad8a251ac01c156a8ae23a83577546f]
-    D            [ARIMA Backward Selection] [arima backw sel d...] [2008-12-22 10:29:20] [fad8a251ac01c156a8ae23a83577546f]
- RMPD              [ARIMA Forecasting] [] [2008-12-22 19:10:36] [b98453cac15ba1066b407e146608df68]
-                     [ARIMA Forecasting] [forecasting duur ...] [2008-12-22 19:52:23] [fad8a251ac01c156a8ae23a83577546f]
-   PD            [ARIMA Backward Selection] [arima backw sel inv] [2008-12-22 10:34:37] [fad8a251ac01c156a8ae23a83577546f]
-   P               [ARIMA Backward Selection] [foutmelding arima...] [2008-12-22 10:39:41] [fad8a251ac01c156a8ae23a83577546f]
-   PD                [ARIMA Backward Selection] [arima backw sel inv] [2008-12-22 12:07:05] [fad8a251ac01c156a8ae23a83577546f]
- RMPD            [ARIMA Forecasting] [forecast inv] [2008-12-22 14:22:41] [fad8a251ac01c156a8ae23a83577546f]
- RMP             [ARIMA Forecasting] [forecast niet-duu...] [2008-12-22 14:29:00] [fad8a251ac01c156a8ae23a83577546f]
- RMPD            [ARIMA Forecasting] [forecast consumpt...] [2008-12-22 14:31:21] [fad8a251ac01c156a8ae23a83577546f]
- RMPD            [ARIMA Forecasting] [forecasting duur ...] [2008-12-22 16:42:36] [fad8a251ac01c156a8ae23a83577546f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
95,9
95,3
100,4
97,3
82,3
97,0
93,5
90,9
107,8
110,9
98,1
106,5
93,4
95,7
109,0
97,6
92,7
107,5
91,7
95,7
111,4
106,0
104,8
108,7
97,3
97,1
106,1
98,6
98,5
105,5
86,2
98,3
111,3
105,0
105,7
103,5
96,9
98,1
111,7
94,7
104,2
109,7
91,3
102,6
114,2
115,8
113,5
107,1
104,5
101,9
116,0
102,0
108,1
112,9
104,5
109,1
113,4
123,9
117,7
108,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 17 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35979&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35979&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35979&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 time17 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.0970.1330.56040.05920.9655-0.1587-0.9998
(p-val)(0.7374 )(0.4036 )(2e-04 )(0.875 )(8e-04 )(0.4796 )(0.2486 )
Estimates ( 2 )0.13840.11990.549200.9655-0.1512-0.9989
(p-val)(0.2715 )(0.3835 )(1e-04 )(NA )(7e-04 )(0.4944 )(0.2374 )
Estimates ( 3 )0.13660.0950.54870-0.137100.2095
(p-val)(0.2821 )(0.4542 )(1e-04 )(NA )(0.9431 )(NA )(0.9115 )
Estimates ( 4 )0.13860.09350.5470000.0743
(p-val)(0.2624 )(0.4589 )(0 )(NA )(NA )(NA )(0.6864 )
Estimates ( 5 )0.12940.10820.55430000
(p-val)(0.2848 )(0.3672 )(0 )(NA )(NA )(NA )(NA )
Estimates ( 6 )0.166500.58520000
(p-val)(0.1484 )(NA )(0 )(NA )(NA )(NA )(NA )
Estimates ( 7 )000.65330000
(p-val)(NA )(NA )(0 )(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 ) & 0.097 & 0.133 & 0.5604 & 0.0592 & 0.9655 & -0.1587 & -0.9998 \tabularnewline
(p-val) & (0.7374 ) & (0.4036 ) & (2e-04 ) & (0.875 ) & (8e-04 ) & (0.4796 ) & (0.2486 ) \tabularnewline
Estimates ( 2 ) & 0.1384 & 0.1199 & 0.5492 & 0 & 0.9655 & -0.1512 & -0.9989 \tabularnewline
(p-val) & (0.2715 ) & (0.3835 ) & (1e-04 ) & (NA ) & (7e-04 ) & (0.4944 ) & (0.2374 ) \tabularnewline
Estimates ( 3 ) & 0.1366 & 0.095 & 0.5487 & 0 & -0.1371 & 0 & 0.2095 \tabularnewline
(p-val) & (0.2821 ) & (0.4542 ) & (1e-04 ) & (NA ) & (0.9431 ) & (NA ) & (0.9115 ) \tabularnewline
Estimates ( 4 ) & 0.1386 & 0.0935 & 0.547 & 0 & 0 & 0 & 0.0743 \tabularnewline
(p-val) & (0.2624 ) & (0.4589 ) & (0 ) & (NA ) & (NA ) & (NA ) & (0.6864 ) \tabularnewline
Estimates ( 5 ) & 0.1294 & 0.1082 & 0.5543 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.2848 ) & (0.3672 ) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0.1665 & 0 & 0.5852 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.1484 ) & (NA ) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0.6533 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0 ) & (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=35979&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]0.097[/C][C]0.133[/C][C]0.5604[/C][C]0.0592[/C][C]0.9655[/C][C]-0.1587[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7374 )[/C][C](0.4036 )[/C][C](2e-04 )[/C][C](0.875 )[/C][C](8e-04 )[/C][C](0.4796 )[/C][C](0.2486 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1384[/C][C]0.1199[/C][C]0.5492[/C][C]0[/C][C]0.9655[/C][C]-0.1512[/C][C]-0.9989[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2715 )[/C][C](0.3835 )[/C][C](1e-04 )[/C][C](NA )[/C][C](7e-04 )[/C][C](0.4944 )[/C][C](0.2374 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1366[/C][C]0.095[/C][C]0.5487[/C][C]0[/C][C]-0.1371[/C][C]0[/C][C]0.2095[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2821 )[/C][C](0.4542 )[/C][C](1e-04 )[/C][C](NA )[/C][C](0.9431 )[/C][C](NA )[/C][C](0.9115 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1386[/C][C]0.0935[/C][C]0.547[/C][C]0[/C][C]0[/C][C]0[/C][C]0.0743[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2624 )[/C][C](0.4589 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.6864 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.1294[/C][C]0.1082[/C][C]0.5543[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2848 )[/C][C](0.3672 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.1665[/C][C]0[/C][C]0.5852[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1484 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0.6533[/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](0 )[/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=35979&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35979&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 )0.0970.1330.56040.05920.9655-0.1587-0.9998
(p-val)(0.7374 )(0.4036 )(2e-04 )(0.875 )(8e-04 )(0.4796 )(0.2486 )
Estimates ( 2 )0.13840.11990.549200.9655-0.1512-0.9989
(p-val)(0.2715 )(0.3835 )(1e-04 )(NA )(7e-04 )(0.4944 )(0.2374 )
Estimates ( 3 )0.13660.0950.54870-0.137100.2095
(p-val)(0.2821 )(0.4542 )(1e-04 )(NA )(0.9431 )(NA )(0.9115 )
Estimates ( 4 )0.13860.09350.5470000.0743
(p-val)(0.2624 )(0.4589 )(0 )(NA )(NA )(NA )(0.6864 )
Estimates ( 5 )0.12940.10820.55430000
(p-val)(0.2848 )(0.3672 )(0 )(NA )(NA )(NA )(NA )
Estimates ( 6 )0.166500.58520000
(p-val)(0.1484 )(NA )(0 )(NA )(NA )(NA )(NA )
Estimates ( 7 )000.65330000
(p-val)(NA )(NA )(0 )(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
0.106499420519601
-1.91464464239575
0.916355239548587
7.19159436875044
0.331404284756631
10.1159756854497
3.73593037887457
-3.72347240528179
-0.986543611301506
-3.3437505128232
-4.44590500188473
4.70668247743139
-1.02208906720202
6.40130094374571
-3.17013126476929
-4.42051684026007
-0.799563535323571
4.81423827702767
-1.26840250303655
-5.75227499895291
0.121352895790622
0.637604580593617
2.23530239983697
-0.455078745340899
-5.29129979144859
1.05084142420435
0.539897918254596
8.47662504974453
-4.59813428391978
5.7640133632662
-0.0260417422959165
6.68315570854207
0.115317781433873
-0.273692995698497
7.33267248161879
3.48574685868613
0.604443699783843
0.680447522928517
-2.02979236208731
1.56066685407983
2.13659260623524
0.460988373944716
0.0343730162187512
8.3952699563277
2.02030876376699
-3.75471150855813
0.508400501816453
-0.952252989416877
0.96900434559933

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.106499420519601 \tabularnewline
-1.91464464239575 \tabularnewline
0.916355239548587 \tabularnewline
7.19159436875044 \tabularnewline
0.331404284756631 \tabularnewline
10.1159756854497 \tabularnewline
3.73593037887457 \tabularnewline
-3.72347240528179 \tabularnewline
-0.986543611301506 \tabularnewline
-3.3437505128232 \tabularnewline
-4.44590500188473 \tabularnewline
4.70668247743139 \tabularnewline
-1.02208906720202 \tabularnewline
6.40130094374571 \tabularnewline
-3.17013126476929 \tabularnewline
-4.42051684026007 \tabularnewline
-0.799563535323571 \tabularnewline
4.81423827702767 \tabularnewline
-1.26840250303655 \tabularnewline
-5.75227499895291 \tabularnewline
0.121352895790622 \tabularnewline
0.637604580593617 \tabularnewline
2.23530239983697 \tabularnewline
-0.455078745340899 \tabularnewline
-5.29129979144859 \tabularnewline
1.05084142420435 \tabularnewline
0.539897918254596 \tabularnewline
8.47662504974453 \tabularnewline
-4.59813428391978 \tabularnewline
5.7640133632662 \tabularnewline
-0.0260417422959165 \tabularnewline
6.68315570854207 \tabularnewline
0.115317781433873 \tabularnewline
-0.273692995698497 \tabularnewline
7.33267248161879 \tabularnewline
3.48574685868613 \tabularnewline
0.604443699783843 \tabularnewline
0.680447522928517 \tabularnewline
-2.02979236208731 \tabularnewline
1.56066685407983 \tabularnewline
2.13659260623524 \tabularnewline
0.460988373944716 \tabularnewline
0.0343730162187512 \tabularnewline
8.3952699563277 \tabularnewline
2.02030876376699 \tabularnewline
-3.75471150855813 \tabularnewline
0.508400501816453 \tabularnewline
-0.952252989416877 \tabularnewline
0.96900434559933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35979&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.106499420519601[/C][/ROW]
[ROW][C]-1.91464464239575[/C][/ROW]
[ROW][C]0.916355239548587[/C][/ROW]
[ROW][C]7.19159436875044[/C][/ROW]
[ROW][C]0.331404284756631[/C][/ROW]
[ROW][C]10.1159756854497[/C][/ROW]
[ROW][C]3.73593037887457[/C][/ROW]
[ROW][C]-3.72347240528179[/C][/ROW]
[ROW][C]-0.986543611301506[/C][/ROW]
[ROW][C]-3.3437505128232[/C][/ROW]
[ROW][C]-4.44590500188473[/C][/ROW]
[ROW][C]4.70668247743139[/C][/ROW]
[ROW][C]-1.02208906720202[/C][/ROW]
[ROW][C]6.40130094374571[/C][/ROW]
[ROW][C]-3.17013126476929[/C][/ROW]
[ROW][C]-4.42051684026007[/C][/ROW]
[ROW][C]-0.799563535323571[/C][/ROW]
[ROW][C]4.81423827702767[/C][/ROW]
[ROW][C]-1.26840250303655[/C][/ROW]
[ROW][C]-5.75227499895291[/C][/ROW]
[ROW][C]0.121352895790622[/C][/ROW]
[ROW][C]0.637604580593617[/C][/ROW]
[ROW][C]2.23530239983697[/C][/ROW]
[ROW][C]-0.455078745340899[/C][/ROW]
[ROW][C]-5.29129979144859[/C][/ROW]
[ROW][C]1.05084142420435[/C][/ROW]
[ROW][C]0.539897918254596[/C][/ROW]
[ROW][C]8.47662504974453[/C][/ROW]
[ROW][C]-4.59813428391978[/C][/ROW]
[ROW][C]5.7640133632662[/C][/ROW]
[ROW][C]-0.0260417422959165[/C][/ROW]
[ROW][C]6.68315570854207[/C][/ROW]
[ROW][C]0.115317781433873[/C][/ROW]
[ROW][C]-0.273692995698497[/C][/ROW]
[ROW][C]7.33267248161879[/C][/ROW]
[ROW][C]3.48574685868613[/C][/ROW]
[ROW][C]0.604443699783843[/C][/ROW]
[ROW][C]0.680447522928517[/C][/ROW]
[ROW][C]-2.02979236208731[/C][/ROW]
[ROW][C]1.56066685407983[/C][/ROW]
[ROW][C]2.13659260623524[/C][/ROW]
[ROW][C]0.460988373944716[/C][/ROW]
[ROW][C]0.0343730162187512[/C][/ROW]
[ROW][C]8.3952699563277[/C][/ROW]
[ROW][C]2.02030876376699[/C][/ROW]
[ROW][C]-3.75471150855813[/C][/ROW]
[ROW][C]0.508400501816453[/C][/ROW]
[ROW][C]-0.952252989416877[/C][/ROW]
[ROW][C]0.96900434559933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35979&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35979&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
0.106499420519601
-1.91464464239575
0.916355239548587
7.19159436875044
0.331404284756631
10.1159756854497
3.73593037887457
-3.72347240528179
-0.986543611301506
-3.3437505128232
-4.44590500188473
4.70668247743139
-1.02208906720202
6.40130094374571
-3.17013126476929
-4.42051684026007
-0.799563535323571
4.81423827702767
-1.26840250303655
-5.75227499895291
0.121352895790622
0.637604580593617
2.23530239983697
-0.455078745340899
-5.29129979144859
1.05084142420435
0.539897918254596
8.47662504974453
-4.59813428391978
5.7640133632662
-0.0260417422959165
6.68315570854207
0.115317781433873
-0.273692995698497
7.33267248161879
3.48574685868613
0.604443699783843
0.680447522928517
-2.02979236208731
1.56066685407983
2.13659260623524
0.460988373944716
0.0343730162187512
8.3952699563277
2.02030876376699
-3.75471150855813
0.508400501816453
-0.952252989416877
0.96900434559933


Figures (Output of Computation)

Input Parameters & R Code

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