FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationFri, 16 Dec 2016 15:51:40 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/16/t1481899927t1qsfc6p0rpihoj.htm/, Retrieved Thu, 20 Feb 2025 19:08:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300332, Retrieved Thu, 20 Feb 2025 19:08:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2016-12-16 13:36:55] [683f400e1b95307fc738e729f07c4fce]
-    D  [ARIMA Backward Selection] [] [2016-12-16 14:17:56] [683f400e1b95307fc738e729f07c4fce]
- R  D      [ARIMA Backward Selection] [] [2016-12-16 14:51:40] [404ac5ee4f7301873f6a96ef36861981] [Current]
- RM          [ARIMA Forecasting] [] [2016-12-16 14:54:52] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Univariate Data Series] [] [2016-12-16 15:07:18] [683f400e1b95307fc738e729f07c4fce]
- RM D        [(Partial) Autocorrelation Function] [] [2016-12-16 15:11:19] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Variance Reduction Matrix] [] [2016-12-16 15:12:05] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Spectral Analysis] [] [2016-12-16 15:14:16] [683f400e1b95307fc738e729f07c4fce]
- R  D        [ARIMA Backward Selection] [] [2016-12-16 15:17:08] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Exponential Smoothing] [] [2016-12-16 15:22:42] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Univariate Data Series] [] [2016-12-16 15:24:03] [683f400e1b95307fc738e729f07c4fce]
- RM D        [(Partial) Autocorrelation Function] [] [2016-12-16 15:30:48] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Spectral Analysis] [] [2016-12-16 15:34:39] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Variance Reduction Matrix] [] [2016-12-16 15:35:21] [683f400e1b95307fc738e729f07c4fce]
- RM D        [(Partial) Autocorrelation Function] [] [2016-12-16 15:37:16] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Classical Decomposition] [] [2016-12-16 15:38:17] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Decomposition by Loess] [] [2016-12-16 15:40:09] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Structural Time Series Models] [] [2016-12-16 15:41:08] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Exponential Smoothing] [] [2016-12-16 15:42:09] [683f400e1b95307fc738e729f07c4fce]
- R  D        [ARIMA Backward Selection] [] [2016-12-16 15:45:16] [683f400e1b95307fc738e729f07c4fce]
- RM D        [ARIMA Forecasting] [] [2016-12-16 15:46:48] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Univariate Data Series] [] [2016-12-16 16:17:41] [683f400e1b95307fc738e729f07c4fce]
- RM D        [(Partial) Autocorrelation Function] [] [2016-12-16 16:19:49] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Variance Reduction Matrix] [] [2016-12-16 16:20:43] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Spectral Analysis] [] [2016-12-16 16:24:22] [683f400e1b95307fc738e729f07c4fce]
- RM D        [ARIMA Forecasting] [] [2016-12-16 16:28:43] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Exponential Smoothing] [] [2016-12-16 16:31:32] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Univariate Data Series] [] [2016-12-16 16:37:41] [683f400e1b95307fc738e729f07c4fce]
- RM D        [(Partial) Autocorrelation Function] [] [2016-12-16 16:39:43] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Variance Reduction Matrix] [] [2016-12-16 16:41:31] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Spectral Analysis] [] [2016-12-16 16:43:10] [683f400e1b95307fc738e729f07c4fce]
- R  D        [ARIMA Backward Selection] [] [2016-12-16 16:45:23] [683f400e1b95307fc738e729f07c4fce]
- RM D        [ARIMA Forecasting] [] [2016-12-16 16:46:28] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Exponential Smoothing] [] [2016-12-16 16:47:13] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Univariate Data Series] [] [2016-12-16 16:49:29] [683f400e1b95307fc738e729f07c4fce]
- RM D        [(Partial) Autocorrelation Function] [] [2016-12-16 16:51:41] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Variance Reduction Matrix] [] [2016-12-16 16:52:32] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Spectral Analysis] [] [2016-12-16 16:54:26] [683f400e1b95307fc738e729f07c4fce]
- R  D        [ARIMA Backward Selection] [] [2016-12-16 16:56:15] [683f400e1b95307fc738e729f07c4fce]
- RM D        [ARIMA Forecasting] [] [2016-12-16 16:57:22] [683f400e1b95307fc738e729f07c4fce]
- RM D        [Exponential Smoothing] [] [2016-12-16 16:58:05] [683f400e1b95307fc738e729f07c4fce]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5100
5100
5050
5150
5150
5050
4800
4750
4900
4950
5050
4900
4950
4850
5100
5200
5450
5150
5150
5000
5200
5350
5600
5600
5650
5550
5700
5750
5850
5750
5700
5500
5750
5750
5750
5500
5750
5750
5900
6000
6150
5950
5900
5750
5750
5800
5800
5450
5400
5600
5600
5800
5650
5700
5550
5350
5800
5700
5950
5450
5400
5400
5450
5700
5850
5850
5700
5450
5800
5600
5700
5800
5750
5850
6250
6450
6550
6500
6150
6100
6300
6350
6250
6200
6250
6450
6050
6500
6600
6450

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300332&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=300332&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300332&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1
Estimates ( 1 )-0.19640.08560.03-0.1092-0.4985
(p-val)(0.8725 )(0.825 )(0.8154 )(0.9285 )(0 )
Estimates ( 2 )-0.30560.0520.03350-0.4985
(p-val)(0.0086 )(0.6647 )(0.7724 )(NA )(0 )
Estimates ( 3 )-0.30370.04200-0.5
(p-val)(0.0089 )(0.7148 )(NA )(NA )(0 )
Estimates ( 4 )-0.3169000-0.4981
(p-val)(0.0042 )(NA )(NA )(NA )(0 )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 \tabularnewline
Estimates ( 1 ) & -0.1964 & 0.0856 & 0.03 & -0.1092 & -0.4985 \tabularnewline
(p-val) & (0.8725 ) & (0.825 ) & (0.8154 ) & (0.9285 ) & (0 ) \tabularnewline
Estimates ( 2 ) & -0.3056 & 0.052 & 0.0335 & 0 & -0.4985 \tabularnewline
(p-val) & (0.0086 ) & (0.6647 ) & (0.7724 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & -0.3037 & 0.042 & 0 & 0 & -0.5 \tabularnewline
(p-val) & (0.0089 ) & (0.7148 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & -0.3169 & 0 & 0 & 0 & -0.4981 \tabularnewline
(p-val) & (0.0042 ) & (NA ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300332&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][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.1964[/C][C]0.0856[/C][C]0.03[/C][C]-0.1092[/C][C]-0.4985[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8725 )[/C][C](0.825 )[/C][C](0.8154 )[/C][C](0.9285 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.3056[/C][C]0.052[/C][C]0.0335[/C][C]0[/C][C]-0.4985[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0086 )[/C][C](0.6647 )[/C][C](0.7724 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.3037[/C][C]0.042[/C][C]0[/C][C]0[/C][C]-0.5[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0089 )[/C][C](0.7148 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.3169[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.4981[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0042 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/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][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[ROW][C]Estimates ( 8 )[/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][/ROW]
[ROW][C]Estimates ( 9 )[/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300332&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300332&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
Iterationar1ar2ar3ma1sar1
Estimates ( 1 )-0.19640.08560.03-0.1092-0.4985
(p-val)(0.8725 )(0.825 )(0.8154 )(0.9285 )(0 )
Estimates ( 2 )-0.30560.0520.03350-0.4985
(p-val)(0.0086 )(0.6647 )(0.7724 )(NA )(0 )
Estimates ( 3 )-0.30370.04200-0.5
(p-val)(0.0089 )(0.7148 )(NA )(NA )(0 )
Estimates ( 4 )-0.3169000-0.4981
(p-val)(0.0042 )(NA )(NA )(NA )(0 )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
-18.036947590563
-82.090038260857
232.153248096109
82.5175308806827
205.599857365907
-107.505108268725
154.877540547335
-13.7816485802828
8.33049195931419
102.292900395385
157.040193469252
159.326275801631
49.6728168536981
-48.9168546333784
33.9620173125067
-32.7192328737336
-42.2875978799199
94.5106199713466
106.412342871683
-81.4242722320197
41.4841763892498
-73.0285540692159
-208.519296062306
-223.945536170782
154.207020842721
168.081212797938
-28.0302363447158
5.6193034756825
-15.3055215389959
-8.64457602631592
-23.9505382635471
17.4099602667047
-216.35939607995
-94.3710406286337
-123.137136682195
-261.901378736387
-263.077660162461
198.712383499236
-65.6864552360948
68.9516428569314
-230.744717575766
111.24567248814
-27.7190043268965
-63.7658058531361
321.612516256482
-25.2594575359017
198.394172160137
-118.833342211648
-221.223734887609
-137.153133530329
-49.0657592289545
96.6067496769456
181.421419797261
116.346880853504
-33.523981261581
-93.330669576555
104.315957935024
-133.892582488539
-83.3937198538224
524.758521130852
160.47352161357
-22.0420626702735
375.000220349117
88.871746055961
76.6548504357188
-43.5847874805086
-226.973326616521
117.418014065915
-138.458290720325
131.921549991469
-205.865488197028
58.0829840077904
157.093358157006
174.065797526484
-583.657752300111
28.9113009407838
69.5708793285803
-142.038557952217

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-18.036947590563 \tabularnewline
-82.090038260857 \tabularnewline
232.153248096109 \tabularnewline
82.5175308806827 \tabularnewline
205.599857365907 \tabularnewline
-107.505108268725 \tabularnewline
154.877540547335 \tabularnewline
-13.7816485802828 \tabularnewline
8.33049195931419 \tabularnewline
102.292900395385 \tabularnewline
157.040193469252 \tabularnewline
159.326275801631 \tabularnewline
49.6728168536981 \tabularnewline
-48.9168546333784 \tabularnewline
33.9620173125067 \tabularnewline
-32.7192328737336 \tabularnewline
-42.2875978799199 \tabularnewline
94.5106199713466 \tabularnewline
106.412342871683 \tabularnewline
-81.4242722320197 \tabularnewline
41.4841763892498 \tabularnewline
-73.0285540692159 \tabularnewline
-208.519296062306 \tabularnewline
-223.945536170782 \tabularnewline
154.207020842721 \tabularnewline
168.081212797938 \tabularnewline
-28.0302363447158 \tabularnewline
5.6193034756825 \tabularnewline
-15.3055215389959 \tabularnewline
-8.64457602631592 \tabularnewline
-23.9505382635471 \tabularnewline
17.4099602667047 \tabularnewline
-216.35939607995 \tabularnewline
-94.3710406286337 \tabularnewline
-123.137136682195 \tabularnewline
-261.901378736387 \tabularnewline
-263.077660162461 \tabularnewline
198.712383499236 \tabularnewline
-65.6864552360948 \tabularnewline
68.9516428569314 \tabularnewline
-230.744717575766 \tabularnewline
111.24567248814 \tabularnewline
-27.7190043268965 \tabularnewline
-63.7658058531361 \tabularnewline
321.612516256482 \tabularnewline
-25.2594575359017 \tabularnewline
198.394172160137 \tabularnewline
-118.833342211648 \tabularnewline
-221.223734887609 \tabularnewline
-137.153133530329 \tabularnewline
-49.0657592289545 \tabularnewline
96.6067496769456 \tabularnewline
181.421419797261 \tabularnewline
116.346880853504 \tabularnewline
-33.523981261581 \tabularnewline
-93.330669576555 \tabularnewline
104.315957935024 \tabularnewline
-133.892582488539 \tabularnewline
-83.3937198538224 \tabularnewline
524.758521130852 \tabularnewline
160.47352161357 \tabularnewline
-22.0420626702735 \tabularnewline
375.000220349117 \tabularnewline
88.871746055961 \tabularnewline
76.6548504357188 \tabularnewline
-43.5847874805086 \tabularnewline
-226.973326616521 \tabularnewline
117.418014065915 \tabularnewline
-138.458290720325 \tabularnewline
131.921549991469 \tabularnewline
-205.865488197028 \tabularnewline
58.0829840077904 \tabularnewline
157.093358157006 \tabularnewline
174.065797526484 \tabularnewline
-583.657752300111 \tabularnewline
28.9113009407838 \tabularnewline
69.5708793285803 \tabularnewline
-142.038557952217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300332&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-18.036947590563[/C][/ROW]
[ROW][C]-82.090038260857[/C][/ROW]
[ROW][C]232.153248096109[/C][/ROW]
[ROW][C]82.5175308806827[/C][/ROW]
[ROW][C]205.599857365907[/C][/ROW]
[ROW][C]-107.505108268725[/C][/ROW]
[ROW][C]154.877540547335[/C][/ROW]
[ROW][C]-13.7816485802828[/C][/ROW]
[ROW][C]8.33049195931419[/C][/ROW]
[ROW][C]102.292900395385[/C][/ROW]
[ROW][C]157.040193469252[/C][/ROW]
[ROW][C]159.326275801631[/C][/ROW]
[ROW][C]49.6728168536981[/C][/ROW]
[ROW][C]-48.9168546333784[/C][/ROW]
[ROW][C]33.9620173125067[/C][/ROW]
[ROW][C]-32.7192328737336[/C][/ROW]
[ROW][C]-42.2875978799199[/C][/ROW]
[ROW][C]94.5106199713466[/C][/ROW]
[ROW][C]106.412342871683[/C][/ROW]
[ROW][C]-81.4242722320197[/C][/ROW]
[ROW][C]41.4841763892498[/C][/ROW]
[ROW][C]-73.0285540692159[/C][/ROW]
[ROW][C]-208.519296062306[/C][/ROW]
[ROW][C]-223.945536170782[/C][/ROW]
[ROW][C]154.207020842721[/C][/ROW]
[ROW][C]168.081212797938[/C][/ROW]
[ROW][C]-28.0302363447158[/C][/ROW]
[ROW][C]5.6193034756825[/C][/ROW]
[ROW][C]-15.3055215389959[/C][/ROW]
[ROW][C]-8.64457602631592[/C][/ROW]
[ROW][C]-23.9505382635471[/C][/ROW]
[ROW][C]17.4099602667047[/C][/ROW]
[ROW][C]-216.35939607995[/C][/ROW]
[ROW][C]-94.3710406286337[/C][/ROW]
[ROW][C]-123.137136682195[/C][/ROW]
[ROW][C]-261.901378736387[/C][/ROW]
[ROW][C]-263.077660162461[/C][/ROW]
[ROW][C]198.712383499236[/C][/ROW]
[ROW][C]-65.6864552360948[/C][/ROW]
[ROW][C]68.9516428569314[/C][/ROW]
[ROW][C]-230.744717575766[/C][/ROW]
[ROW][C]111.24567248814[/C][/ROW]
[ROW][C]-27.7190043268965[/C][/ROW]
[ROW][C]-63.7658058531361[/C][/ROW]
[ROW][C]321.612516256482[/C][/ROW]
[ROW][C]-25.2594575359017[/C][/ROW]
[ROW][C]198.394172160137[/C][/ROW]
[ROW][C]-118.833342211648[/C][/ROW]
[ROW][C]-221.223734887609[/C][/ROW]
[ROW][C]-137.153133530329[/C][/ROW]
[ROW][C]-49.0657592289545[/C][/ROW]
[ROW][C]96.6067496769456[/C][/ROW]
[ROW][C]181.421419797261[/C][/ROW]
[ROW][C]116.346880853504[/C][/ROW]
[ROW][C]-33.523981261581[/C][/ROW]
[ROW][C]-93.330669576555[/C][/ROW]
[ROW][C]104.315957935024[/C][/ROW]
[ROW][C]-133.892582488539[/C][/ROW]
[ROW][C]-83.3937198538224[/C][/ROW]
[ROW][C]524.758521130852[/C][/ROW]
[ROW][C]160.47352161357[/C][/ROW]
[ROW][C]-22.0420626702735[/C][/ROW]
[ROW][C]375.000220349117[/C][/ROW]
[ROW][C]88.871746055961[/C][/ROW]
[ROW][C]76.6548504357188[/C][/ROW]
[ROW][C]-43.5847874805086[/C][/ROW]
[ROW][C]-226.973326616521[/C][/ROW]
[ROW][C]117.418014065915[/C][/ROW]
[ROW][C]-138.458290720325[/C][/ROW]
[ROW][C]131.921549991469[/C][/ROW]
[ROW][C]-205.865488197028[/C][/ROW]
[ROW][C]58.0829840077904[/C][/ROW]
[ROW][C]157.093358157006[/C][/ROW]
[ROW][C]174.065797526484[/C][/ROW]
[ROW][C]-583.657752300111[/C][/ROW]
[ROW][C]28.9113009407838[/C][/ROW]
[ROW][C]69.5708793285803[/C][/ROW]
[ROW][C]-142.038557952217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300332&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300332&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
-18.036947590563
-82.090038260857
232.153248096109
82.5175308806827
205.599857365907
-107.505108268725
154.877540547335
-13.7816485802828
8.33049195931419
102.292900395385
157.040193469252
159.326275801631
49.6728168536981
-48.9168546333784
33.9620173125067
-32.7192328737336
-42.2875978799199
94.5106199713466
106.412342871683
-81.4242722320197
41.4841763892498
-73.0285540692159
-208.519296062306
-223.945536170782
154.207020842721
168.081212797938
-28.0302363447158
5.6193034756825
-15.3055215389959
-8.64457602631592
-23.9505382635471
17.4099602667047
-216.35939607995
-94.3710406286337
-123.137136682195
-261.901378736387
-263.077660162461
198.712383499236
-65.6864552360948
68.9516428569314
-230.744717575766
111.24567248814
-27.7190043268965
-63.7658058531361
321.612516256482
-25.2594575359017
198.394172160137
-118.833342211648
-221.223734887609
-137.153133530329
-49.0657592289545
96.6067496769456
181.421419797261
116.346880853504
-33.523981261581
-93.330669576555
104.315957935024
-133.892582488539
-83.3937198538224
524.758521130852
160.47352161357
-22.0420626702735
375.000220349117
88.871746055961
76.6548504357188
-43.5847874805086
-226.973326616521
117.418014065915
-138.458290720325
131.921549991469
-205.865488197028
58.0829840077904
157.093358157006
174.065797526484
-583.657752300111
28.9113009407838
69.5708793285803
-142.038557952217


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 1 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 0 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 0 ;
R code (references can be found in the software module):
par9 <- '0'
par8 <- '2'
par7 <- '1'
par6 <- '3'
par5 <- '12'
par4 <- '1'
par3 <- '1'
par2 <- '1'
par1 <- 'FALSE'
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')