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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 computationTue, 01 Dec 2009 03:21:46 -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/01/t12596629776d4xmyrx7gjdwk2.htm/, Retrieved Thu, 28 Mar 2024 22:00:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61973, Retrieved Thu, 28 Mar 2024 22:00:26 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact204
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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-    D      [ARIMA Backward Selection] [] [2009-12-01 10:21:46] [2b679e8ec54382eeb0ec0b6bb527570a] [Current]
- RMPD        [Harrell-Davis Quantiles] [Bouwvergunningen] [2009-12-03 19:04:23] [11ac052cc87d77b9933b02bea117068e]
-   PD          [Harrell-Davis Quantiles] [] [2009-12-04 17:27:27] [bb3c50fa849023ee18f70dac946932de]
-   PD            [Harrell-Davis Quantiles] [] [2009-12-08 10:43:21] [bb3c50fa849023ee18f70dac946932de]
-    D              [Harrell-Davis Quantiles] [] [2009-12-10 10:47:14] [4b0ddbda2a8eb8bbc60159112cb39d44]
- R  D          [Harrell-Davis Quantiles] [residu's voor het...] [2009-12-13 22:00:38] [82d27727e9ba70a4d0e9e253f76836cf]
-    D        [ARIMA Backward Selection] [Arima backward] [2009-12-04 12:29:28] [74be16979710d4c4e7c6647856088456]
-   PD        [ARIMA Backward Selection] [] [2009-12-20 13:31:48] [5d885a68c2332cc44f6191ec94766bfa]
-   PD          [ARIMA Backward Selection] [Apple Inc - AR MA ] [2010-12-16 12:58:09] [afe9379cca749d06b3d6872e02cc47ed]
-   PD            [ARIMA Backward Selection] [Paper - C&S ARIMA...] [2010-12-21 15:10:09] [18fa53e8b37a5effc0c5f8a5122cdd2d]
-   P               [ARIMA Backward Selection] [Paper - C&S ARIMA...] [2010-12-21 15:42:46] [18fa53e8b37a5effc0c5f8a5122cdd2d]
-   P             [ARIMA Backward Selection] [Apple Inc - AR MA ] [2010-12-21 15:53:53] [afe9379cca749d06b3d6872e02cc47ed]
- R PD            [ARIMA Backward Selection] [] [2012-12-20 13:41:33] [d1865ed705b6ad9ba3d459a02c528b22]
- RMPD        [Harrell-Davis Quantiles] [] [2009-12-20 13:37:24] [5d885a68c2332cc44f6191ec94766bfa]
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Dataseries X:
100.03
100.25
99.6
100.16
100.49
99.72
100.14
98.48
100.38
101.45
98.42
98.6
100.06
98.62
100.84
100.02
97.95
98.32
98.27
97.22
99.28
100.38
99.02
100.32
99.81
100.6
101.19
100.47
101.77
102.32
102.39
101.16
100.63
101.48
101.44
100.09
100.7
100.78
99.81
98.45
98.49
97.48
97.91
96.94
98.53
96.82
95.76
95.27
97.32
96.68
97.87
97.42
97.94
99.52
100.99
99.92
101.97
101.58
99.54
100.83




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61973&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]8 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=61973&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61973&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.418-0.06450.22480.2844-0.4047-0.3928-0.4288
(p-val)(0.1852 )(0.7006 )(0.1488 )(0.3456 )(0.4734 )(0.2311 )(0.5979 )
Estimates ( 2 )-0.370400.24870.2608-0.4273-0.42-0.3906
(p-val)(0.1977 )(NA )(0.0757 )(0.3998 )(0.4375 )(0.1755 )(0.6154 )
Estimates ( 3 )-0.371300.26090.2564-0.7089-0.53970
(p-val)(0.1875 )(NA )(0.0573 )(0.3956 )(0 )(0.001 )(NA )
Estimates ( 4 )-0.160300.24480-0.7165-0.51710
(p-val)(0.2942 )(NA )(0.1031 )(NA )(0 )(0.0023 )(NA )
Estimates ( 5 )000.24060-0.6943-0.57010
(p-val)(NA )(NA )(0.1162 )(NA )(0 )(2e-04 )(NA )
Estimates ( 6 )0000-0.7299-0.62050
(p-val)(NA )(NA )(NA )(NA )(0 )(0 )(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 ) & -0.418 & -0.0645 & 0.2248 & 0.2844 & -0.4047 & -0.3928 & -0.4288 \tabularnewline
(p-val) & (0.1852 ) & (0.7006 ) & (0.1488 ) & (0.3456 ) & (0.4734 ) & (0.2311 ) & (0.5979 ) \tabularnewline
Estimates ( 2 ) & -0.3704 & 0 & 0.2487 & 0.2608 & -0.4273 & -0.42 & -0.3906 \tabularnewline
(p-val) & (0.1977 ) & (NA ) & (0.0757 ) & (0.3998 ) & (0.4375 ) & (0.1755 ) & (0.6154 ) \tabularnewline
Estimates ( 3 ) & -0.3713 & 0 & 0.2609 & 0.2564 & -0.7089 & -0.5397 & 0 \tabularnewline
(p-val) & (0.1875 ) & (NA ) & (0.0573 ) & (0.3956 ) & (0 ) & (0.001 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.1603 & 0 & 0.2448 & 0 & -0.7165 & -0.5171 & 0 \tabularnewline
(p-val) & (0.2942 ) & (NA ) & (0.1031 ) & (NA ) & (0 ) & (0.0023 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0.2406 & 0 & -0.6943 & -0.5701 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1162 ) & (NA ) & (0 ) & (2e-04 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & -0.7299 & -0.6205 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) & (0 ) & (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=61973&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]-0.418[/C][C]-0.0645[/C][C]0.2248[/C][C]0.2844[/C][C]-0.4047[/C][C]-0.3928[/C][C]-0.4288[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1852 )[/C][C](0.7006 )[/C][C](0.1488 )[/C][C](0.3456 )[/C][C](0.4734 )[/C][C](0.2311 )[/C][C](0.5979 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.3704[/C][C]0[/C][C]0.2487[/C][C]0.2608[/C][C]-0.4273[/C][C]-0.42[/C][C]-0.3906[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1977 )[/C][C](NA )[/C][C](0.0757 )[/C][C](0.3998 )[/C][C](0.4375 )[/C][C](0.1755 )[/C][C](0.6154 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.3713[/C][C]0[/C][C]0.2609[/C][C]0.2564[/C][C]-0.7089[/C][C]-0.5397[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1875 )[/C][C](NA )[/C][C](0.0573 )[/C][C](0.3956 )[/C][C](0 )[/C][C](0.001 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.1603[/C][C]0[/C][C]0.2448[/C][C]0[/C][C]-0.7165[/C][C]-0.5171[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2942 )[/C][C](NA )[/C][C](0.1031 )[/C][C](NA )[/C][C](0 )[/C][C](0.0023 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0.2406[/C][C]0[/C][C]-0.6943[/C][C]-0.5701[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1162 )[/C][C](NA )[/C][C](0 )[/C][C](2e-04 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.7299[/C][C]-0.6205[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/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=61973&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61973&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 )-0.418-0.06450.22480.2844-0.4047-0.3928-0.4288
(p-val)(0.1852 )(0.7006 )(0.1488 )(0.3456 )(0.4734 )(0.2311 )(0.5979 )
Estimates ( 2 )-0.370400.24870.2608-0.4273-0.42-0.3906
(p-val)(0.1977 )(NA )(0.0757 )(0.3998 )(0.4375 )(0.1755 )(0.6154 )
Estimates ( 3 )-0.371300.26090.2564-0.7089-0.53970
(p-val)(0.1875 )(NA )(0.0573 )(0.3956 )(0 )(0.001 )(NA )
Estimates ( 4 )-0.160300.24480-0.7165-0.51710
(p-val)(0.2942 )(NA )(0.1031 )(NA )(0 )(0.0023 )(NA )
Estimates ( 5 )000.24060-0.6943-0.57010
(p-val)(NA )(NA )(0.1162 )(NA )(0 )(2e-04 )(NA )
Estimates ( 6 )0000-0.7299-0.62050
(p-val)(NA )(NA )(NA )(NA )(0 )(0 )(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
-0.0349270413186066
-0.0596001053081093
0.102927485463034
-0.0493605422998952
-0.0744486964901621
0.0172122166243359
-0.00547655137538673
0.0420595639658103
-0.00126898252030444
0.00417814534569686
0.0502324245318664
0.0506330593271085
-0.078701461734425
0.0492049563319333
-0.0261690888058081
-0.00393879381828204
0.07973936826098
0.0319961742697832
0.00091067709659756
-0.019220682787933
-0.107979686333248
-0.0107667247517434
0.0806227637205843
-0.0529491860051703
0.00528114849116934
-0.0214345304008214
-0.0369393145483388
-0.0680735468724003
-0.0129276272272485
-0.0270496644483431
0.0254460905207515
0.0269982152250543
0.0308225338043865
-0.138807729471492
0.0357776400010795
-0.0228794714726368
0.0894287310745218
-0.0073993180101393
0.0123959579161959
0.0122806920509619
0.0763511379220961
0.079124533029784
0.0615362876121939
-0.019030572914577
0.00196369350314640
-0.0458122032417219
-0.0460091449745193
0.0381733992230888

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0349270413186066 \tabularnewline
-0.0596001053081093 \tabularnewline
0.102927485463034 \tabularnewline
-0.0493605422998952 \tabularnewline
-0.0744486964901621 \tabularnewline
0.0172122166243359 \tabularnewline
-0.00547655137538673 \tabularnewline
0.0420595639658103 \tabularnewline
-0.00126898252030444 \tabularnewline
0.00417814534569686 \tabularnewline
0.0502324245318664 \tabularnewline
0.0506330593271085 \tabularnewline
-0.078701461734425 \tabularnewline
0.0492049563319333 \tabularnewline
-0.0261690888058081 \tabularnewline
-0.00393879381828204 \tabularnewline
0.07973936826098 \tabularnewline
0.0319961742697832 \tabularnewline
0.00091067709659756 \tabularnewline
-0.019220682787933 \tabularnewline
-0.107979686333248 \tabularnewline
-0.0107667247517434 \tabularnewline
0.0806227637205843 \tabularnewline
-0.0529491860051703 \tabularnewline
0.00528114849116934 \tabularnewline
-0.0214345304008214 \tabularnewline
-0.0369393145483388 \tabularnewline
-0.0680735468724003 \tabularnewline
-0.0129276272272485 \tabularnewline
-0.0270496644483431 \tabularnewline
0.0254460905207515 \tabularnewline
0.0269982152250543 \tabularnewline
0.0308225338043865 \tabularnewline
-0.138807729471492 \tabularnewline
0.0357776400010795 \tabularnewline
-0.0228794714726368 \tabularnewline
0.0894287310745218 \tabularnewline
-0.0073993180101393 \tabularnewline
0.0123959579161959 \tabularnewline
0.0122806920509619 \tabularnewline
0.0763511379220961 \tabularnewline
0.079124533029784 \tabularnewline
0.0615362876121939 \tabularnewline
-0.019030572914577 \tabularnewline
0.00196369350314640 \tabularnewline
-0.0458122032417219 \tabularnewline
-0.0460091449745193 \tabularnewline
0.0381733992230888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61973&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0349270413186066[/C][/ROW]
[ROW][C]-0.0596001053081093[/C][/ROW]
[ROW][C]0.102927485463034[/C][/ROW]
[ROW][C]-0.0493605422998952[/C][/ROW]
[ROW][C]-0.0744486964901621[/C][/ROW]
[ROW][C]0.0172122166243359[/C][/ROW]
[ROW][C]-0.00547655137538673[/C][/ROW]
[ROW][C]0.0420595639658103[/C][/ROW]
[ROW][C]-0.00126898252030444[/C][/ROW]
[ROW][C]0.00417814534569686[/C][/ROW]
[ROW][C]0.0502324245318664[/C][/ROW]
[ROW][C]0.0506330593271085[/C][/ROW]
[ROW][C]-0.078701461734425[/C][/ROW]
[ROW][C]0.0492049563319333[/C][/ROW]
[ROW][C]-0.0261690888058081[/C][/ROW]
[ROW][C]-0.00393879381828204[/C][/ROW]
[ROW][C]0.07973936826098[/C][/ROW]
[ROW][C]0.0319961742697832[/C][/ROW]
[ROW][C]0.00091067709659756[/C][/ROW]
[ROW][C]-0.019220682787933[/C][/ROW]
[ROW][C]-0.107979686333248[/C][/ROW]
[ROW][C]-0.0107667247517434[/C][/ROW]
[ROW][C]0.0806227637205843[/C][/ROW]
[ROW][C]-0.0529491860051703[/C][/ROW]
[ROW][C]0.00528114849116934[/C][/ROW]
[ROW][C]-0.0214345304008214[/C][/ROW]
[ROW][C]-0.0369393145483388[/C][/ROW]
[ROW][C]-0.0680735468724003[/C][/ROW]
[ROW][C]-0.0129276272272485[/C][/ROW]
[ROW][C]-0.0270496644483431[/C][/ROW]
[ROW][C]0.0254460905207515[/C][/ROW]
[ROW][C]0.0269982152250543[/C][/ROW]
[ROW][C]0.0308225338043865[/C][/ROW]
[ROW][C]-0.138807729471492[/C][/ROW]
[ROW][C]0.0357776400010795[/C][/ROW]
[ROW][C]-0.0228794714726368[/C][/ROW]
[ROW][C]0.0894287310745218[/C][/ROW]
[ROW][C]-0.0073993180101393[/C][/ROW]
[ROW][C]0.0123959579161959[/C][/ROW]
[ROW][C]0.0122806920509619[/C][/ROW]
[ROW][C]0.0763511379220961[/C][/ROW]
[ROW][C]0.079124533029784[/C][/ROW]
[ROW][C]0.0615362876121939[/C][/ROW]
[ROW][C]-0.019030572914577[/C][/ROW]
[ROW][C]0.00196369350314640[/C][/ROW]
[ROW][C]-0.0458122032417219[/C][/ROW]
[ROW][C]-0.0460091449745193[/C][/ROW]
[ROW][C]0.0381733992230888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61973&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61973&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
-0.0349270413186066
-0.0596001053081093
0.102927485463034
-0.0493605422998952
-0.0744486964901621
0.0172122166243359
-0.00547655137538673
0.0420595639658103
-0.00126898252030444
0.00417814534569686
0.0502324245318664
0.0506330593271085
-0.078701461734425
0.0492049563319333
-0.0261690888058081
-0.00393879381828204
0.07973936826098
0.0319961742697832
0.00091067709659756
-0.019220682787933
-0.107979686333248
-0.0107667247517434
0.0806227637205843
-0.0529491860051703
0.00528114849116934
-0.0214345304008214
-0.0369393145483388
-0.0680735468724003
-0.0129276272272485
-0.0270496644483431
0.0254460905207515
0.0269982152250543
0.0308225338043865
-0.138807729471492
0.0357776400010795
-0.0228794714726368
0.0894287310745218
-0.0073993180101393
0.0123959579161959
0.0122806920509619
0.0763511379220961
0.079124533029784
0.0615362876121939
-0.019030572914577
0.00196369350314640
-0.0458122032417219
-0.0460091449745193
0.0381733992230888



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; 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')