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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 computationTue, 06 Dec 2011 15:20:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/06/t1323202883rlh490wyauh214l.htm/, Retrieved Sun, 28 Apr 2024 16:29:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=151894, Retrieved Sun, 28 Apr 2024 16:29:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact380

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-14 11:54:22] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP     [ARIMA Backward Selection] [] [2011-12-06 20:20:50] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
- RMP       [ARIMA Backward Selection] [Paper deel 2j] [2011-12-19 23:23:48] [3346191a817a30d0b56421a47a7031a4]
- RMP       [ARIMA Backward Selection] [arima backward se...] [2011-12-21 11:48:28] [baac05fe722f73c103cc2d713fa5bd78]
- R P       [ARIMA Backward Selection] [Deel 2 - tijdreek...] [2011-12-23 00:46:32] [43a132f5d1d3e2c258a569e3803c6f06]
-   P       [ARIMA Backward Selection] [WS9 - ARIMA estim...] [2012-11-23 15:01:52] [fe055a25191a04e375a94ef97fddf389]
-   P       [ARIMA Backward Selection] [ARIMA backward] [2012-11-26 22:00:04] [dbdfdab7c884aa7a69290945f2923e51]
- RMP       [ARIMA Backward Selection] [] [2012-12-07 19:57:10] [ec67509cb0a58a77552cc42e4bdf733e]
- R P       [ARIMA Backward Selection] [PR 1] [2012-12-11 19:28:42] [15980d50f043b229c552ae7b9d1fee1e]
-   P       [ARIMA Backward Selection] [Paper] [2012-12-12 18:24:08] [9d44b52ac7f20a3e9be7c3c8470fe2cd]
-   P       [ARIMA Backward Selection] [arima backward se...] [2012-12-17 13:39:37] [68a04a2519ca62ddd145df1e643e729d]
- R PD        [ARIMA Backward Selection] [] [2012-12-18 20:37:06] [74be16979710d4c4e7c6647856088456]
-               [ARIMA Backward Selection] [] [2012-12-18 20:41:31] [74be16979710d4c4e7c6647856088456]
- RMPD      [Central Tendency] [central tendency] [2012-12-17 13:42:05] [68a04a2519ca62ddd145df1e643e729d]
- RMPD      [Skewness and Kurtosis Test] [skewness and kurt...] [2012-12-17 13:48:06] [68a04a2519ca62ddd145df1e643e729d]
- RMPD      [(Partial) Autocorrelation Function] [] [2012-12-19 12:50:18] [4f34f32e808f328a79844d0e6ce51f0b]
- R           [(Partial) Autocorrelation Function] [] [2012-12-19 12:58:24] [4f34f32e808f328a79844d0e6ce51f0b]
-   P       [ARIMA Backward Selection] [paper] [2012-12-19 17:50:29] [fa543719fe3f8358943b948de15add90]
- RMP       [ARIMA Backward Selection] [Paper Deel 4 Arima 4] [2012-12-21 14:59:36] [2972d443360a042d7188069add54b356]
-   PD      [ARIMA Backward Selection] [] [2012-12-21 16:50:06] [4acb2b89f689cf743a6b65cec03f73b4]
- RMP       [ARIMA Forecasting] [] [2013-11-22 17:46:21] [0307e7a6407eb638caabc417e3a6b260]
- RM          [ARIMA Forecasting] [WW] [2013-12-04 09:46:19] [a33425df4f50c4c8e40ef78287b40411]
- RM          [ARIMA Forecasting] [ws 9] [2013-12-04 09:47:07] [a33425df4f50c4c8e40ef78287b40411]
- RMP         [ARIMA Forecasting] [] [2014-11-26 11:39:04] [69bf0eb8b9b38defaaf4848d8c317571]
- RM          [ARIMA Forecasting] [WS9 - arima forecast] [2014-11-26 13:46:31] [81f624c2f0b20a2549c93e7c3dccf981]
- RMP         [ARIMA Forecasting] [] [2014-11-26 19:41:39] [2b9d0c54c8c845c625e475ed5f1f3af1]
- RMP         [ARIMA Forecasting] [] [2014-11-26 19:43:09] [2b9d0c54c8c845c625e475ed5f1f3af1]
- RMP         [ARIMA Forecasting] [] [2014-11-26 19:44:28] [2b9d0c54c8c845c625e475ed5f1f3af1]
-  MP         [ARIMA Forecasting] [] [2014-11-27 09:15:03] [3cc57788b191749bdc089f5fad42e0f8]
- RMP         [Skewness and Kurtosis Test] [] [2014-11-27 09:22:05] [3cc57788b191749bdc089f5fad42e0f8]
- RM          [ARIMA Forecasting] [WS9 A2] [2014-11-27 09:39:02] [46c7ebd23dbdec306a09830d8b7528e7]
- RMP         [ARIMA Forecasting] [] [2014-11-27 09:41:05] [3d5212c89039da1a3a24d8e18d23c716]
- RM          [ARIMA Forecasting] [WS9 SHW] [2014-11-27 09:52:28] [cac6c5fb035463be46c296b46e439cb5]
- RMP         [ARIMA Forecasting] [w9] [2014-11-27 10:34:52] [673773038936aef3a5778d7e6bda5c1e]
- RM          [ARIMA Forecasting] [Ws9] [2014-11-27 12:08:35] [006b54b8ce76f482b86cd20c6480b526]
- RMP         [ARIMA Forecasting] [ws9 8] [2014-11-27 15:31:21] [9378e2688aa9dcfd1390615d31e9d404]
- RMP         [ARIMA Forecasting] [qsdqs] [2014-11-27 15:35:39] [9378e2688aa9dcfd1390615d31e9d404]
- RM          [ARIMA Forecasting] [] [2014-11-27 23:10:42] [7b949ef3605c038fc6e10efeab34f433]
- RMPD        [ARIMA Forecasting] [] [2014-12-17 16:00:58] [1651e47f7f65f3a10bbbb444d4b26be7]
- R P           [ARIMA Forecasting] [] [2014-12-17 18:55:33] [bcf5edf18529a33bd1494456d2c6cb9a]
- RMP       [ARIMA Backward Selection] [WS 9] [2013-12-04 09:43:33] [a33425df4f50c4c8e40ef78287b40411]
- RM        [ARIMA Backward Selection] [] [2014-11-26 11:14:51] [69bf0eb8b9b38defaaf4848d8c317571]
- RM D        [Central Tendency] [] [2014-11-26 11:36:03] [69bf0eb8b9b38defaaf4848d8c317571]
- RM D        [Skewness and Kurtosis Test] [] [2014-11-26 11:38:14] [69bf0eb8b9b38defaaf4848d8c317571]
- RM        [ARIMA Backward Selection] [] [2014-11-26 12:55:37] [7b949ef3605c038fc6e10efeab34f433]
- RMP       [ARIMA Backward Selection] [dddsasdf] [2014-11-26 13:08:22] [a21a52637ee232286cdd7eb4387e60d4]
- RMP       [ARIMA Backward Selection] [123456] [2014-11-26 13:32:23] [388e7fc45813588c973a58470d39020f]
- RMP       [ARIMA Backward Selection] [] [2014-11-27 09:06:15] [3cc57788b191749bdc089f5fad42e0f8]
- RMP       [ARIMA Backward Selection] [WS9 ARIMA] [2014-11-27 09:10:46] [46c7ebd23dbdec306a09830d8b7528e7]
- RMP       [ARIMA Backward Selection] [W9 arima] [2014-11-27 10:23:18] [673773038936aef3a5778d7e6bda5c1e]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'AstonUniversity' @ aston.wessa.net

\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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151894&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151894&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151894&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 time8 seconds
R Server'AstonUniversity' @ aston.wessa.net






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.77740.0679-0.2379-0.6727-0.0417-0.0597-0.9999
(p-val)(0.0152 )(0.6887 )(0.0963 )(0.027 )(0.8148 )(0.7418 )(0.0147 )
Estimates ( 2 )0.77040.065-0.2299-0.67240-0.0407-1
(p-val)(0.0185 )(0.6994 )(0.0951 )(0.0318 )(NA )(0.8046 )(0.0035 )
Estimates ( 3 )0.75730.0662-0.2304-0.661300-1
(p-val)(0.018 )(0.6906 )(0.0926 )(0.0311 )(NA )(NA )(0.0024 )
Estimates ( 4 )0.81610-0.1999-0.691400-1.0003
(p-val)(0.0017 )(NA )(0.0733 )(0.0089 )(NA )(NA )(0.0032 )
Estimates ( 5 )0.37300-0.247300-0.9973
(p-val)(0.4183 )(NA )(NA )(0.5963 )(NA )(NA )(0.0083 )
Estimates ( 6 )0.117600000-0.9991
(p-val)(0.3646 )(NA )(NA )(NA )(NA )(NA )(0.0144 )
Estimates ( 7 )000000-1
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0577 )
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 ) & 0.7774 & 0.0679 & -0.2379 & -0.6727 & -0.0417 & -0.0597 & -0.9999 \tabularnewline
(p-val) & (0.0152 ) & (0.6887 ) & (0.0963 ) & (0.027 ) & (0.8148 ) & (0.7418 ) & (0.0147 ) \tabularnewline
Estimates ( 2 ) & 0.7704 & 0.065 & -0.2299 & -0.6724 & 0 & -0.0407 & -1 \tabularnewline
(p-val) & (0.0185 ) & (0.6994 ) & (0.0951 ) & (0.0318 ) & (NA ) & (0.8046 ) & (0.0035 ) \tabularnewline
Estimates ( 3 ) & 0.7573 & 0.0662 & -0.2304 & -0.6613 & 0 & 0 & -1 \tabularnewline
(p-val) & (0.018 ) & (0.6906 ) & (0.0926 ) & (0.0311 ) & (NA ) & (NA ) & (0.0024 ) \tabularnewline
Estimates ( 4 ) & 0.8161 & 0 & -0.1999 & -0.6914 & 0 & 0 & -1.0003 \tabularnewline
(p-val) & (0.0017 ) & (NA ) & (0.0733 ) & (0.0089 ) & (NA ) & (NA ) & (0.0032 ) \tabularnewline
Estimates ( 5 ) & 0.373 & 0 & 0 & -0.2473 & 0 & 0 & -0.9973 \tabularnewline
(p-val) & (0.4183 ) & (NA ) & (NA ) & (0.5963 ) & (NA ) & (NA ) & (0.0083 ) \tabularnewline
Estimates ( 6 ) & 0.1176 & 0 & 0 & 0 & 0 & 0 & -0.9991 \tabularnewline
(p-val) & (0.3646 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0144 ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & 0 & -1 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0577 ) \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=151894&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]0.7774[/C][C]0.0679[/C][C]-0.2379[/C][C]-0.6727[/C][C]-0.0417[/C][C]-0.0597[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0152 )[/C][C](0.6887 )[/C][C](0.0963 )[/C][C](0.027 )[/C][C](0.8148 )[/C][C](0.7418 )[/C][C](0.0147 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.7704[/C][C]0.065[/C][C]-0.2299[/C][C]-0.6724[/C][C]0[/C][C]-0.0407[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0185 )[/C][C](0.6994 )[/C][C](0.0951 )[/C][C](0.0318 )[/C][C](NA )[/C][C](0.8046 )[/C][C](0.0035 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.7573[/C][C]0.0662[/C][C]-0.2304[/C][C]-0.6613[/C][C]0[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.018 )[/C][C](0.6906 )[/C][C](0.0926 )[/C][C](0.0311 )[/C][C](NA )[/C][C](NA )[/C][C](0.0024 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.8161[/C][C]0[/C][C]-0.1999[/C][C]-0.6914[/C][C]0[/C][C]0[/C][C]-1.0003[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0017 )[/C][C](NA )[/C][C](0.0733 )[/C][C](0.0089 )[/C][C](NA )[/C][C](NA )[/C][C](0.0032 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.373[/C][C]0[/C][C]0[/C][C]-0.2473[/C][C]0[/C][C]0[/C][C]-0.9973[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4183 )[/C][C](NA )[/C][C](NA )[/C][C](0.5963 )[/C][C](NA )[/C][C](NA )[/C][C](0.0083 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.1176[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.9991[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3646 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0144 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-1[/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](0.0577 )[/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=151894&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151894&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 )0.77740.0679-0.2379-0.6727-0.0417-0.0597-0.9999
(p-val)(0.0152 )(0.6887 )(0.0963 )(0.027 )(0.8148 )(0.7418 )(0.0147 )
Estimates ( 2 )0.77040.065-0.2299-0.67240-0.0407-1
(p-val)(0.0185 )(0.6994 )(0.0951 )(0.0318 )(NA )(0.8046 )(0.0035 )
Estimates ( 3 )0.75730.0662-0.2304-0.661300-1
(p-val)(0.018 )(0.6906 )(0.0926 )(0.0311 )(NA )(NA )(0.0024 )
Estimates ( 4 )0.81610-0.1999-0.691400-1.0003
(p-val)(0.0017 )(NA )(0.0733 )(0.0089 )(NA )(NA )(0.0032 )
Estimates ( 5 )0.37300-0.247300-0.9973
(p-val)(0.4183 )(NA )(NA )(0.5963 )(NA )(NA )(0.0083 )
Estimates ( 6 )0.117600000-0.9991
(p-val)(0.3646 )(NA )(NA )(NA )(NA )(NA )(0.0144 )
Estimates ( 7 )000000-1
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0577 )
Estimates ( 8 )0000000
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
0.0589999409995141
-1.41417450501306
-18.3846475755861
4.24266776527226
-9.89940750475467
-1.41416601970523
-4.94968147844429
8.48527118362683
5.65684722004823
2.12135156036077
-12.7278137906086
-1.41417238368611
-5.65678640867546
5.71546709301464
11.430917448217
4.0824915417843
-5.71542749248443
-4.89893277693375
5.30722585896545
-8.98140023237252
-0.816480018948223
-3.67419274460486
2.44950292718234
-7.34841447478369
-12.2473676641251
13.5676795115537
-7.50550178552387
-10.1035605510469
4.61879420118808
-12.9903024874634
3.7527758629083
-14.1450008210891
-2.30938107887528
-11.2582591729112
-4.33008822361948
1.29908651985677e-05
16.454417696876
7.82621147962468
4.02491438422167
-8.72060773063855
-0.894411455568734
-0.223594253653449
-6.93176357788012
-2.01244265593132
-7.1553761680782
0.223617958497356
8.27342503551901
15.2051986751483
2.90688653528183
-12.7801213366766
-3.10373653589491
-7.1203466912372
-3.46888342810694
-8.3983624583384
2.55603701106735
0.182580385499283
10.5892565952768
15.7013138196074
6.75522353446613
5.11206123957597
8.76352852127385

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0589999409995141 \tabularnewline
-1.41417450501306 \tabularnewline
-18.3846475755861 \tabularnewline
4.24266776527226 \tabularnewline
-9.89940750475467 \tabularnewline
-1.41416601970523 \tabularnewline
-4.94968147844429 \tabularnewline
8.48527118362683 \tabularnewline
5.65684722004823 \tabularnewline
2.12135156036077 \tabularnewline
-12.7278137906086 \tabularnewline
-1.41417238368611 \tabularnewline
-5.65678640867546 \tabularnewline
5.71546709301464 \tabularnewline
11.430917448217 \tabularnewline
4.0824915417843 \tabularnewline
-5.71542749248443 \tabularnewline
-4.89893277693375 \tabularnewline
5.30722585896545 \tabularnewline
-8.98140023237252 \tabularnewline
-0.816480018948223 \tabularnewline
-3.67419274460486 \tabularnewline
2.44950292718234 \tabularnewline
-7.34841447478369 \tabularnewline
-12.2473676641251 \tabularnewline
13.5676795115537 \tabularnewline
-7.50550178552387 \tabularnewline
-10.1035605510469 \tabularnewline
4.61879420118808 \tabularnewline
-12.9903024874634 \tabularnewline
3.7527758629083 \tabularnewline
-14.1450008210891 \tabularnewline
-2.30938107887528 \tabularnewline
-11.2582591729112 \tabularnewline
-4.33008822361948 \tabularnewline
1.29908651985677e-05 \tabularnewline
16.454417696876 \tabularnewline
7.82621147962468 \tabularnewline
4.02491438422167 \tabularnewline
-8.72060773063855 \tabularnewline
-0.894411455568734 \tabularnewline
-0.223594253653449 \tabularnewline
-6.93176357788012 \tabularnewline
-2.01244265593132 \tabularnewline
-7.1553761680782 \tabularnewline
0.223617958497356 \tabularnewline
8.27342503551901 \tabularnewline
15.2051986751483 \tabularnewline
2.90688653528183 \tabularnewline
-12.7801213366766 \tabularnewline
-3.10373653589491 \tabularnewline
-7.1203466912372 \tabularnewline
-3.46888342810694 \tabularnewline
-8.3983624583384 \tabularnewline
2.55603701106735 \tabularnewline
0.182580385499283 \tabularnewline
10.5892565952768 \tabularnewline
15.7013138196074 \tabularnewline
6.75522353446613 \tabularnewline
5.11206123957597 \tabularnewline
8.76352852127385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151894&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0589999409995141[/C][/ROW]
[ROW][C]-1.41417450501306[/C][/ROW]
[ROW][C]-18.3846475755861[/C][/ROW]
[ROW][C]4.24266776527226[/C][/ROW]
[ROW][C]-9.89940750475467[/C][/ROW]
[ROW][C]-1.41416601970523[/C][/ROW]
[ROW][C]-4.94968147844429[/C][/ROW]
[ROW][C]8.48527118362683[/C][/ROW]
[ROW][C]5.65684722004823[/C][/ROW]
[ROW][C]2.12135156036077[/C][/ROW]
[ROW][C]-12.7278137906086[/C][/ROW]
[ROW][C]-1.41417238368611[/C][/ROW]
[ROW][C]-5.65678640867546[/C][/ROW]
[ROW][C]5.71546709301464[/C][/ROW]
[ROW][C]11.430917448217[/C][/ROW]
[ROW][C]4.0824915417843[/C][/ROW]
[ROW][C]-5.71542749248443[/C][/ROW]
[ROW][C]-4.89893277693375[/C][/ROW]
[ROW][C]5.30722585896545[/C][/ROW]
[ROW][C]-8.98140023237252[/C][/ROW]
[ROW][C]-0.816480018948223[/C][/ROW]
[ROW][C]-3.67419274460486[/C][/ROW]
[ROW][C]2.44950292718234[/C][/ROW]
[ROW][C]-7.34841447478369[/C][/ROW]
[ROW][C]-12.2473676641251[/C][/ROW]
[ROW][C]13.5676795115537[/C][/ROW]
[ROW][C]-7.50550178552387[/C][/ROW]
[ROW][C]-10.1035605510469[/C][/ROW]
[ROW][C]4.61879420118808[/C][/ROW]
[ROW][C]-12.9903024874634[/C][/ROW]
[ROW][C]3.7527758629083[/C][/ROW]
[ROW][C]-14.1450008210891[/C][/ROW]
[ROW][C]-2.30938107887528[/C][/ROW]
[ROW][C]-11.2582591729112[/C][/ROW]
[ROW][C]-4.33008822361948[/C][/ROW]
[ROW][C]1.29908651985677e-05[/C][/ROW]
[ROW][C]16.454417696876[/C][/ROW]
[ROW][C]7.82621147962468[/C][/ROW]
[ROW][C]4.02491438422167[/C][/ROW]
[ROW][C]-8.72060773063855[/C][/ROW]
[ROW][C]-0.894411455568734[/C][/ROW]
[ROW][C]-0.223594253653449[/C][/ROW]
[ROW][C]-6.93176357788012[/C][/ROW]
[ROW][C]-2.01244265593132[/C][/ROW]
[ROW][C]-7.1553761680782[/C][/ROW]
[ROW][C]0.223617958497356[/C][/ROW]
[ROW][C]8.27342503551901[/C][/ROW]
[ROW][C]15.2051986751483[/C][/ROW]
[ROW][C]2.90688653528183[/C][/ROW]
[ROW][C]-12.7801213366766[/C][/ROW]
[ROW][C]-3.10373653589491[/C][/ROW]
[ROW][C]-7.1203466912372[/C][/ROW]
[ROW][C]-3.46888342810694[/C][/ROW]
[ROW][C]-8.3983624583384[/C][/ROW]
[ROW][C]2.55603701106735[/C][/ROW]
[ROW][C]0.182580385499283[/C][/ROW]
[ROW][C]10.5892565952768[/C][/ROW]
[ROW][C]15.7013138196074[/C][/ROW]
[ROW][C]6.75522353446613[/C][/ROW]
[ROW][C]5.11206123957597[/C][/ROW]
[ROW][C]8.76352852127385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151894&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151894&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
0.0589999409995141
-1.41417450501306
-18.3846475755861
4.24266776527226
-9.89940750475467
-1.41416601970523
-4.94968147844429
8.48527118362683
5.65684722004823
2.12135156036077
-12.7278137906086
-1.41417238368611
-5.65678640867546
5.71546709301464
11.430917448217
4.0824915417843
-5.71542749248443
-4.89893277693375
5.30722585896545
-8.98140023237252
-0.816480018948223
-3.67419274460486
2.44950292718234
-7.34841447478369
-12.2473676641251
13.5676795115537
-7.50550178552387
-10.1035605510469
4.61879420118808
-12.9903024874634
3.7527758629083
-14.1450008210891
-2.30938107887528
-11.2582591729112
-4.33008822361948
1.29908651985677e-05
16.454417696876
7.82621147962468
4.02491438422167
-8.72060773063855
-0.894411455568734
-0.223594253653449
-6.93176357788012
-2.01244265593132
-7.1553761680782
0.223617958497356
8.27342503551901
15.2051986751483
2.90688653528183
-12.7801213366766
-3.10373653589491
-7.1203466912372
-3.46888342810694
-8.3983624583384
2.55603701106735
0.182580385499283
10.5892565952768
15.7013138196074
6.75522353446613
5.11206123957597
8.76352852127385


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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')