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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 computationSun, 27 Dec 2009 04:47:45 -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/27/t1261914657ntopoq7ow8hjy5i.htm/, Retrieved Thu, 02 May 2024 21:27:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70852, Retrieved Thu, 02 May 2024 21:27:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [Spectraalanalyse ...] [2008-12-11 17:29:14] [12d343c4448a5f9e527bb31caeac580b]
- RMPD    [ARIMA Backward Selection] [Paper ARIMA Backw...] [2009-12-27 11:47:45] [eba9f01697e64705b70041e6f338cb22] [Current]
- RM        [ARIMA Forecasting] [Paper ARIMA Forec...] [2009-12-27 13:08:29] [83058a88a37d754675a5cd22dab372fc]
- RMP       [ARIMA Forecasting] [Paper ARIMA Forec...] [2009-12-30 13:38:43] [83058a88a37d754675a5cd22dab372fc]
- R           [ARIMA Forecasting] [Paper ARIMA Forec...] [2009-12-30 14:04:28] [83058a88a37d754675a5cd22dab372fc]
-               [ARIMA Forecasting] [paper arima 33] [2010-12-18 14:48:34] [d87a19cd5db53e12ea62bda70b3bb267]
-   PD        [ARIMA Forecasting] [paper arima forec...] [2010-12-18 13:46:38] [d87a19cd5db53e12ea62bda70b3bb267]
-             [ARIMA Forecasting] [paper arima 26] [2010-12-18 14:40:42] [d87a19cd5db53e12ea62bda70b3bb267]
-   PD      [ARIMA Backward Selection] [paper ARIMA] [2010-12-18 13:16:25] [d87a19cd5db53e12ea62bda70b3bb267]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.21
100.36
100.62
100.78
100.93
100.70
100.00
100.20
99.68
99.56
100.06
100.50
99.30
99.37
99.20
98.11
97.60
97.76
98.06
98.25
98.50
97.39
98.09
97.78
98.12
97.50
97.30
97.64
96.88
97.40
98.27
97.94
98.61
98.72
98.62
98.56
98.06
97.40
97.76
97.05
97.85
97.40
97.27
97.93
98.60
98.70
98.88
98.27
97.85
97.70
96.97
97.72
97.66
99.00
98.86
99.56
100.19
100.37
100.01
99.68
99.78
99.36
99.21
99.26
99.26
100.43
101.50
102.27
102.69
103.47
104.02
103.55
103.77
104.19
103.64
103.68
105.39
106.61
108.12
109.22
110.17
110.31
111.06
111.14
111.39
112.51
111.28
112.22
113.19
114.32
115.34
116.61
117.83
117.70
118.51
118.82
119.49
119.57
120.00
121.96
121.45
123.41
124.44
126.25
127.41
127.63
129.19
129.82
130.45
132.02
132.72
132.96
135.06
137.04
137.83
139.17
140.35
141.01
141.89
143.28
142.90
143.37
145.03
146.05
147.39
149.58
151.02
153.57
155.60
157.18
158.77
159.95
161.34
161.95
163.36
165.00
166.65
168.65
170.29
172.70
173.79
176.45
177.58
179.19
181.01
184.08
185.63
188.51
190.18
192.19
193.47
196.73
200.39
203.24
205.53
208.21
208.88
212.85
216.41
216.23
219.27
222.02
224.89
230.37
232.29
235.53
236.92
242.37
242.75
244.19
247.94
248.80
250.18
251.55
254.40
255.72
257.69
258.37
258.22
258.59
257.45
257.45
256.73
258.82
257.99
262.85
262.58
261.55
261.25
259.78
256.26
254.29
248.50
241.88
238.53
232.24
232.46
225.79
221.63
219.62
215.94
211.81
205.57
201.25
194.70
187.94
185.61
181.15
186.50
183.21
182.61
187.09
189.10
191.25
190.74
190.79

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 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 & 14 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70852&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]14 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=70852&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70852&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 time14 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.03140.26960.2497-0.94031.1698-0.1824-0.9471
(p-val)(0.7182 )(0.0023 )(0.0027 )(0 )(0 )(0.0924 )(0 )
Estimates ( 2 )00.2820.2539-0.951.1747-0.1857-0.9524
(p-val)(NA )(5e-04 )(0.002 )(0 )(0 )(0.0531 )(0 )
Estimates ( 3 )00.2720.2514-1.04840.75070-0.5384
(p-val)(NA )(7e-04 )(0.0021 )(0 )(1e-04 )(NA )(0.0322 )
Estimates ( 4 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.0314 & 0.2696 & 0.2497 & -0.9403 & 1.1698 & -0.1824 & -0.9471 \tabularnewline
(p-val) & (0.7182 ) & (0.0023 ) & (0.0027 ) & (0 ) & (0 ) & (0.0924 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.282 & 0.2539 & -0.95 & 1.1747 & -0.1857 & -0.9524 \tabularnewline
(p-val) & (NA ) & (5e-04 ) & (0.002 ) & (0 ) & (0 ) & (0.0531 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.272 & 0.2514 & -1.0484 & 0.7507 & 0 & -0.5384 \tabularnewline
(p-val) & (NA ) & (7e-04 ) & (0.0021 ) & (0 ) & (1e-04 ) & (NA ) & (0.0322 ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70852&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]-0.0314[/C][C]0.2696[/C][C]0.2497[/C][C]-0.9403[/C][C]1.1698[/C][C]-0.1824[/C][C]-0.9471[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7182 )[/C][C](0.0023 )[/C][C](0.0027 )[/C][C](0 )[/C][C](0 )[/C][C](0.0924 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.282[/C][C]0.2539[/C][C]-0.95[/C][C]1.1747[/C][C]-0.1857[/C][C]-0.9524[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](5e-04 )[/C][C](0.002 )[/C][C](0 )[/C][C](0 )[/C][C](0.0531 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.272[/C][C]0.2514[/C][C]-1.0484[/C][C]0.7507[/C][C]0[/C][C]-0.5384[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](7e-04 )[/C][C](0.0021 )[/C][C](0 )[/C][C](1e-04 )[/C][C](NA )[/C][C](0.0322 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/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 ( 5 )[/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 ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70852&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70852&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 )-0.03140.26960.2497-0.94031.1698-0.1824-0.9471
(p-val)(0.7182 )(0.0023 )(0.0027 )(0 )(0 )(0.0924 )(0 )
Estimates ( 2 )00.2820.2539-0.951.1747-0.1857-0.9524
(p-val)(NA )(5e-04 )(0.002 )(0 )(0 )(0.0531 )(0 )
Estimates ( 3 )00.2720.2514-1.04840.75070-0.5384
(p-val)(NA )(7e-04 )(0.0021 )(0 )(1e-04 )(NA )(0.0322 )
Estimates ( 4 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
-1.34215903055762e-05
-7.24224985378705e-06
2.80204123465409e-06
4.81983994353419e-06
3.87577948336289e-05
7.61182578294985e-05
-2.34151967109661e-05
2.46964671608796e-05
-1.75360799694432e-06
-5.77161607291801e-05
-5.43614470049045e-05
0.000129968286736676
1.51187187137238e-05
-2.95758473354595e-06
8.20274667195701e-05
4.18112032044294e-05
-7.13585962891744e-05
-0.000100934272781908
-2.33617355408844e-05
-1.98548651922339e-05
0.000127864821937757
-4.9434614930895e-05
1.93848457182844e-05
-8.1969768073358e-05
6.85365599537795e-05
2.16944243849730e-05
-6.91934481788719e-05
4.34543219658194e-05
-4.19131144938879e-05
-9.03357482852257e-05
4.09244850785254e-05
-2.64931008667419e-05
-2.76119795770872e-05
4.41545490032594e-05
3.09745814826761e-05
5.44798651692514e-05
4.20040478025201e-05
-6.20346289724574e-05
4.79099403631117e-05
-0.000110535967180537
4.98291848147382e-05
4.22354206579328e-05
-6.79325870741827e-05
-7.72512751865444e-05
3.22374592246877e-06
2.41063941185364e-05
8.19694080509168e-05
3.17725293467970e-05
-2.03637414864481e-05
5.99635635831252e-05
-0.000109734187727383
-1.10183937066855e-06
-0.000139408665513955
3.93860951911116e-05
-1.13188128807241e-05
-3.90178898737998e-06
-2.21666531556996e-06
7.8223442338e-05
3.79775072829764e-05
-3.4318411505228e-05
1.86699891247647e-05
1.99890203983757e-06
7.21043486249589e-06
-9.626837796989e-06
-8.30136333874514e-05
-0.000101875111271675
-1.78235177353569e-05
4.29927676677119e-05
-2.13154513222867e-05
-2.33318371750069e-05
7.07428986107188e-05
1.56593544749776e-05
-4.25099689884518e-05
5.30554582414161e-05
2.40861787247831e-05
-0.000149759126869695
-7.0552314215347e-05
-4.20685241877179e-05
1.12360495800796e-05
4.80843773586681e-06
6.66834519260541e-05
5.5342609946322e-06
9.49450278224986e-06
3.50209959137374e-06
-5.53342323511449e-05
0.000110661781125523
-3.454286176483e-05
-2.85494410521161e-05
-3.61355307551169e-05
5.61846241080155e-06
-2.23622366581067e-05
-1.97618034913475e-05
5.7891062121636e-05
1.13606213656387e-05
1.84248579598982e-06
-2.60153004873349e-05
4.29665839454965e-05
-2.40002623031818e-05
-9.27261322253579e-05
8.98552720120887e-05
-3.3109513841197e-05
-8.68820061170922e-06
-5.05705345435648e-05
1.49293470484712e-05
3.59943221099345e-05
-2.33235274361622e-05
-3.3411148318324e-06
1.21658753136748e-05
-4.38821189475452e-05
-2.52909544553623e-06
7.28271379945954e-05
-6.60042963177308e-05
-3.47492540541444e-05
3.56682127133268e-05
3.87131267707249e-05
7.2753025921088e-06
-1.09553936915625e-06
2.14031384046755e-05
-2.94451808260875e-05
5.34995561554334e-05
3.36445257178204e-05
-5.36293231239796e-05
-2.75551661216276e-05
1.10197224101631e-05
-6.36249467988572e-06
-4.55728377324943e-06
-3.45422786998778e-05
-1.0555400441079e-05
-4.54556947359289e-06
1.46033727707589e-05
1.78363293924480e-05
-1.46441188966236e-05
2.34491206659728e-05
4.24173309388562e-06
-5.41641926396179e-06
-2.39880826887899e-06
1.54176422989373e-05
1.10409520759310e-05
-3.02320819065717e-06
3.17405766564422e-05
-3.64687437250802e-05
1.94123472520437e-05
-2.04793011319055e-06
-8.35450560844626e-06
-5.29041098984516e-05
8.32175242691538e-06
-5.63956635105705e-06
2.92611154790037e-05
3.04818699121046e-05
3.27593774778695e-05
-1.95926957005104e-05
-4.42930271043147e-05
-1.35109207733182e-05
1.76087039235709e-05
-8.92079723416734e-08
3.66662156723497e-05
-2.57200569905784e-05
-3.52771185559797e-05
7.03396462008726e-05
1.03450298867846e-05
5.42588758328228e-06
-1.39987191086921e-05
-3.049913028068e-05
3.56456689612682e-05
5.16924242018243e-06
3.32261997039747e-05
-5.2791149569268e-05
2.41573017780291e-05
3.90781235793754e-05
-8.95453429176817e-06
1.00759576754094e-05
3.05165720216384e-05
4.08505811473137e-05
-5.28095437860549e-06
3.29750954506058e-05
7.2280245574875e-06
1.53308752622027e-05
2.44426674676085e-05
2.53453440626779e-05
1.61098642400321e-05
6.5199847768673e-06
2.16798337988795e-05
-2.40947940722628e-05
2.40950781068301e-05
-3.97488628826355e-05
3.20100635689357e-05
5.306070796364e-05
3.35620756498815e-05
1.13017378487904e-05
4.57699604928432e-05
1.85004688872849e-05
5.71780177420779e-05
8.28350844419898e-05
1.40171443948919e-05
6.19336126278373e-05
-5.56132102026676e-05
0.000103298448826039
4.66948654349823e-05
-5.86490168106219e-06
5.61570207473574e-06
3.07579359935519e-05
8.04972226488014e-05
2.83519612253913e-05
5.49940580585887e-05
7.1041083983168e-05
-4.33852423523521e-05
1.04494318265660e-06
-0.000234352327894885
2.57925956060314e-05
5.66151259192688e-06
-0.000127000426430103
-9.83605216207806e-05
-5.2940130013637e-05
3.15929290644253e-05
7.03344239458356e-06

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-1.34215903055762e-05 \tabularnewline
-7.24224985378705e-06 \tabularnewline
2.80204123465409e-06 \tabularnewline
4.81983994353419e-06 \tabularnewline
3.87577948336289e-05 \tabularnewline
7.61182578294985e-05 \tabularnewline
-2.34151967109661e-05 \tabularnewline
2.46964671608796e-05 \tabularnewline
-1.75360799694432e-06 \tabularnewline
-5.77161607291801e-05 \tabularnewline
-5.43614470049045e-05 \tabularnewline
0.000129968286736676 \tabularnewline
1.51187187137238e-05 \tabularnewline
-2.95758473354595e-06 \tabularnewline
8.20274667195701e-05 \tabularnewline
4.18112032044294e-05 \tabularnewline
-7.13585962891744e-05 \tabularnewline
-0.000100934272781908 \tabularnewline
-2.33617355408844e-05 \tabularnewline
-1.98548651922339e-05 \tabularnewline
0.000127864821937757 \tabularnewline
-4.9434614930895e-05 \tabularnewline
1.93848457182844e-05 \tabularnewline
-8.1969768073358e-05 \tabularnewline
6.85365599537795e-05 \tabularnewline
2.16944243849730e-05 \tabularnewline
-6.91934481788719e-05 \tabularnewline
4.34543219658194e-05 \tabularnewline
-4.19131144938879e-05 \tabularnewline
-9.03357482852257e-05 \tabularnewline
4.09244850785254e-05 \tabularnewline
-2.64931008667419e-05 \tabularnewline
-2.76119795770872e-05 \tabularnewline
4.41545490032594e-05 \tabularnewline
3.09745814826761e-05 \tabularnewline
5.44798651692514e-05 \tabularnewline
4.20040478025201e-05 \tabularnewline
-6.20346289724574e-05 \tabularnewline
4.79099403631117e-05 \tabularnewline
-0.000110535967180537 \tabularnewline
4.98291848147382e-05 \tabularnewline
4.22354206579328e-05 \tabularnewline
-6.79325870741827e-05 \tabularnewline
-7.72512751865444e-05 \tabularnewline
3.22374592246877e-06 \tabularnewline
2.41063941185364e-05 \tabularnewline
8.19694080509168e-05 \tabularnewline
3.17725293467970e-05 \tabularnewline
-2.03637414864481e-05 \tabularnewline
5.99635635831252e-05 \tabularnewline
-0.000109734187727383 \tabularnewline
-1.10183937066855e-06 \tabularnewline
-0.000139408665513955 \tabularnewline
3.93860951911116e-05 \tabularnewline
-1.13188128807241e-05 \tabularnewline
-3.90178898737998e-06 \tabularnewline
-2.21666531556996e-06 \tabularnewline
7.8223442338e-05 \tabularnewline
3.79775072829764e-05 \tabularnewline
-3.4318411505228e-05 \tabularnewline
1.86699891247647e-05 \tabularnewline
1.99890203983757e-06 \tabularnewline
7.21043486249589e-06 \tabularnewline
-9.626837796989e-06 \tabularnewline
-8.30136333874514e-05 \tabularnewline
-0.000101875111271675 \tabularnewline
-1.78235177353569e-05 \tabularnewline
4.29927676677119e-05 \tabularnewline
-2.13154513222867e-05 \tabularnewline
-2.33318371750069e-05 \tabularnewline
7.07428986107188e-05 \tabularnewline
1.56593544749776e-05 \tabularnewline
-4.25099689884518e-05 \tabularnewline
5.30554582414161e-05 \tabularnewline
2.40861787247831e-05 \tabularnewline
-0.000149759126869695 \tabularnewline
-7.0552314215347e-05 \tabularnewline
-4.20685241877179e-05 \tabularnewline
1.12360495800796e-05 \tabularnewline
4.80843773586681e-06 \tabularnewline
6.66834519260541e-05 \tabularnewline
5.5342609946322e-06 \tabularnewline
9.49450278224986e-06 \tabularnewline
3.50209959137374e-06 \tabularnewline
-5.53342323511449e-05 \tabularnewline
0.000110661781125523 \tabularnewline
-3.454286176483e-05 \tabularnewline
-2.85494410521161e-05 \tabularnewline
-3.61355307551169e-05 \tabularnewline
5.61846241080155e-06 \tabularnewline
-2.23622366581067e-05 \tabularnewline
-1.97618034913475e-05 \tabularnewline
5.7891062121636e-05 \tabularnewline
1.13606213656387e-05 \tabularnewline
1.84248579598982e-06 \tabularnewline
-2.60153004873349e-05 \tabularnewline
4.29665839454965e-05 \tabularnewline
-2.40002623031818e-05 \tabularnewline
-9.27261322253579e-05 \tabularnewline
8.98552720120887e-05 \tabularnewline
-3.3109513841197e-05 \tabularnewline
-8.68820061170922e-06 \tabularnewline
-5.05705345435648e-05 \tabularnewline
1.49293470484712e-05 \tabularnewline
3.59943221099345e-05 \tabularnewline
-2.33235274361622e-05 \tabularnewline
-3.3411148318324e-06 \tabularnewline
1.21658753136748e-05 \tabularnewline
-4.38821189475452e-05 \tabularnewline
-2.52909544553623e-06 \tabularnewline
7.28271379945954e-05 \tabularnewline
-6.60042963177308e-05 \tabularnewline
-3.47492540541444e-05 \tabularnewline
3.56682127133268e-05 \tabularnewline
3.87131267707249e-05 \tabularnewline
7.2753025921088e-06 \tabularnewline
-1.09553936915625e-06 \tabularnewline
2.14031384046755e-05 \tabularnewline
-2.94451808260875e-05 \tabularnewline
5.34995561554334e-05 \tabularnewline
3.36445257178204e-05 \tabularnewline
-5.36293231239796e-05 \tabularnewline
-2.75551661216276e-05 \tabularnewline
1.10197224101631e-05 \tabularnewline
-6.36249467988572e-06 \tabularnewline
-4.55728377324943e-06 \tabularnewline
-3.45422786998778e-05 \tabularnewline
-1.0555400441079e-05 \tabularnewline
-4.54556947359289e-06 \tabularnewline
1.46033727707589e-05 \tabularnewline
1.78363293924480e-05 \tabularnewline
-1.46441188966236e-05 \tabularnewline
2.34491206659728e-05 \tabularnewline
4.24173309388562e-06 \tabularnewline
-5.41641926396179e-06 \tabularnewline
-2.39880826887899e-06 \tabularnewline
1.54176422989373e-05 \tabularnewline
1.10409520759310e-05 \tabularnewline
-3.02320819065717e-06 \tabularnewline
3.17405766564422e-05 \tabularnewline
-3.64687437250802e-05 \tabularnewline
1.94123472520437e-05 \tabularnewline
-2.04793011319055e-06 \tabularnewline
-8.35450560844626e-06 \tabularnewline
-5.29041098984516e-05 \tabularnewline
8.32175242691538e-06 \tabularnewline
-5.63956635105705e-06 \tabularnewline
2.92611154790037e-05 \tabularnewline
3.04818699121046e-05 \tabularnewline
3.27593774778695e-05 \tabularnewline
-1.95926957005104e-05 \tabularnewline
-4.42930271043147e-05 \tabularnewline
-1.35109207733182e-05 \tabularnewline
1.76087039235709e-05 \tabularnewline
-8.92079723416734e-08 \tabularnewline
3.66662156723497e-05 \tabularnewline
-2.57200569905784e-05 \tabularnewline
-3.52771185559797e-05 \tabularnewline
7.03396462008726e-05 \tabularnewline
1.03450298867846e-05 \tabularnewline
5.42588758328228e-06 \tabularnewline
-1.39987191086921e-05 \tabularnewline
-3.049913028068e-05 \tabularnewline
3.56456689612682e-05 \tabularnewline
5.16924242018243e-06 \tabularnewline
3.32261997039747e-05 \tabularnewline
-5.2791149569268e-05 \tabularnewline
2.41573017780291e-05 \tabularnewline
3.90781235793754e-05 \tabularnewline
-8.95453429176817e-06 \tabularnewline
1.00759576754094e-05 \tabularnewline
3.05165720216384e-05 \tabularnewline
4.08505811473137e-05 \tabularnewline
-5.28095437860549e-06 \tabularnewline
3.29750954506058e-05 \tabularnewline
7.2280245574875e-06 \tabularnewline
1.53308752622027e-05 \tabularnewline
2.44426674676085e-05 \tabularnewline
2.53453440626779e-05 \tabularnewline
1.61098642400321e-05 \tabularnewline
6.5199847768673e-06 \tabularnewline
2.16798337988795e-05 \tabularnewline
-2.40947940722628e-05 \tabularnewline
2.40950781068301e-05 \tabularnewline
-3.97488628826355e-05 \tabularnewline
3.20100635689357e-05 \tabularnewline
5.306070796364e-05 \tabularnewline
3.35620756498815e-05 \tabularnewline
1.13017378487904e-05 \tabularnewline
4.57699604928432e-05 \tabularnewline
1.85004688872849e-05 \tabularnewline
5.71780177420779e-05 \tabularnewline
8.28350844419898e-05 \tabularnewline
1.40171443948919e-05 \tabularnewline
6.19336126278373e-05 \tabularnewline
-5.56132102026676e-05 \tabularnewline
0.000103298448826039 \tabularnewline
4.66948654349823e-05 \tabularnewline
-5.86490168106219e-06 \tabularnewline
5.61570207473574e-06 \tabularnewline
3.07579359935519e-05 \tabularnewline
8.04972226488014e-05 \tabularnewline
2.83519612253913e-05 \tabularnewline
5.49940580585887e-05 \tabularnewline
7.1041083983168e-05 \tabularnewline
-4.33852423523521e-05 \tabularnewline
1.04494318265660e-06 \tabularnewline
-0.000234352327894885 \tabularnewline
2.57925956060314e-05 \tabularnewline
5.66151259192688e-06 \tabularnewline
-0.000127000426430103 \tabularnewline
-9.83605216207806e-05 \tabularnewline
-5.2940130013637e-05 \tabularnewline
3.15929290644253e-05 \tabularnewline
7.03344239458356e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70852&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-1.34215903055762e-05[/C][/ROW]
[ROW][C]-7.24224985378705e-06[/C][/ROW]
[ROW][C]2.80204123465409e-06[/C][/ROW]
[ROW][C]4.81983994353419e-06[/C][/ROW]
[ROW][C]3.87577948336289e-05[/C][/ROW]
[ROW][C]7.61182578294985e-05[/C][/ROW]
[ROW][C]-2.34151967109661e-05[/C][/ROW]
[ROW][C]2.46964671608796e-05[/C][/ROW]
[ROW][C]-1.75360799694432e-06[/C][/ROW]
[ROW][C]-5.77161607291801e-05[/C][/ROW]
[ROW][C]-5.43614470049045e-05[/C][/ROW]
[ROW][C]0.000129968286736676[/C][/ROW]
[ROW][C]1.51187187137238e-05[/C][/ROW]
[ROW][C]-2.95758473354595e-06[/C][/ROW]
[ROW][C]8.20274667195701e-05[/C][/ROW]
[ROW][C]4.18112032044294e-05[/C][/ROW]
[ROW][C]-7.13585962891744e-05[/C][/ROW]
[ROW][C]-0.000100934272781908[/C][/ROW]
[ROW][C]-2.33617355408844e-05[/C][/ROW]
[ROW][C]-1.98548651922339e-05[/C][/ROW]
[ROW][C]0.000127864821937757[/C][/ROW]
[ROW][C]-4.9434614930895e-05[/C][/ROW]
[ROW][C]1.93848457182844e-05[/C][/ROW]
[ROW][C]-8.1969768073358e-05[/C][/ROW]
[ROW][C]6.85365599537795e-05[/C][/ROW]
[ROW][C]2.16944243849730e-05[/C][/ROW]
[ROW][C]-6.91934481788719e-05[/C][/ROW]
[ROW][C]4.34543219658194e-05[/C][/ROW]
[ROW][C]-4.19131144938879e-05[/C][/ROW]
[ROW][C]-9.03357482852257e-05[/C][/ROW]
[ROW][C]4.09244850785254e-05[/C][/ROW]
[ROW][C]-2.64931008667419e-05[/C][/ROW]
[ROW][C]-2.76119795770872e-05[/C][/ROW]
[ROW][C]4.41545490032594e-05[/C][/ROW]
[ROW][C]3.09745814826761e-05[/C][/ROW]
[ROW][C]5.44798651692514e-05[/C][/ROW]
[ROW][C]4.20040478025201e-05[/C][/ROW]
[ROW][C]-6.20346289724574e-05[/C][/ROW]
[ROW][C]4.79099403631117e-05[/C][/ROW]
[ROW][C]-0.000110535967180537[/C][/ROW]
[ROW][C]4.98291848147382e-05[/C][/ROW]
[ROW][C]4.22354206579328e-05[/C][/ROW]
[ROW][C]-6.79325870741827e-05[/C][/ROW]
[ROW][C]-7.72512751865444e-05[/C][/ROW]
[ROW][C]3.22374592246877e-06[/C][/ROW]
[ROW][C]2.41063941185364e-05[/C][/ROW]
[ROW][C]8.19694080509168e-05[/C][/ROW]
[ROW][C]3.17725293467970e-05[/C][/ROW]
[ROW][C]-2.03637414864481e-05[/C][/ROW]
[ROW][C]5.99635635831252e-05[/C][/ROW]
[ROW][C]-0.000109734187727383[/C][/ROW]
[ROW][C]-1.10183937066855e-06[/C][/ROW]
[ROW][C]-0.000139408665513955[/C][/ROW]
[ROW][C]3.93860951911116e-05[/C][/ROW]
[ROW][C]-1.13188128807241e-05[/C][/ROW]
[ROW][C]-3.90178898737998e-06[/C][/ROW]
[ROW][C]-2.21666531556996e-06[/C][/ROW]
[ROW][C]7.8223442338e-05[/C][/ROW]
[ROW][C]3.79775072829764e-05[/C][/ROW]
[ROW][C]-3.4318411505228e-05[/C][/ROW]
[ROW][C]1.86699891247647e-05[/C][/ROW]
[ROW][C]1.99890203983757e-06[/C][/ROW]
[ROW][C]7.21043486249589e-06[/C][/ROW]
[ROW][C]-9.626837796989e-06[/C][/ROW]
[ROW][C]-8.30136333874514e-05[/C][/ROW]
[ROW][C]-0.000101875111271675[/C][/ROW]
[ROW][C]-1.78235177353569e-05[/C][/ROW]
[ROW][C]4.29927676677119e-05[/C][/ROW]
[ROW][C]-2.13154513222867e-05[/C][/ROW]
[ROW][C]-2.33318371750069e-05[/C][/ROW]
[ROW][C]7.07428986107188e-05[/C][/ROW]
[ROW][C]1.56593544749776e-05[/C][/ROW]
[ROW][C]-4.25099689884518e-05[/C][/ROW]
[ROW][C]5.30554582414161e-05[/C][/ROW]
[ROW][C]2.40861787247831e-05[/C][/ROW]
[ROW][C]-0.000149759126869695[/C][/ROW]
[ROW][C]-7.0552314215347e-05[/C][/ROW]
[ROW][C]-4.20685241877179e-05[/C][/ROW]
[ROW][C]1.12360495800796e-05[/C][/ROW]
[ROW][C]4.80843773586681e-06[/C][/ROW]
[ROW][C]6.66834519260541e-05[/C][/ROW]
[ROW][C]5.5342609946322e-06[/C][/ROW]
[ROW][C]9.49450278224986e-06[/C][/ROW]
[ROW][C]3.50209959137374e-06[/C][/ROW]
[ROW][C]-5.53342323511449e-05[/C][/ROW]
[ROW][C]0.000110661781125523[/C][/ROW]
[ROW][C]-3.454286176483e-05[/C][/ROW]
[ROW][C]-2.85494410521161e-05[/C][/ROW]
[ROW][C]-3.61355307551169e-05[/C][/ROW]
[ROW][C]5.61846241080155e-06[/C][/ROW]
[ROW][C]-2.23622366581067e-05[/C][/ROW]
[ROW][C]-1.97618034913475e-05[/C][/ROW]
[ROW][C]5.7891062121636e-05[/C][/ROW]
[ROW][C]1.13606213656387e-05[/C][/ROW]
[ROW][C]1.84248579598982e-06[/C][/ROW]
[ROW][C]-2.60153004873349e-05[/C][/ROW]
[ROW][C]4.29665839454965e-05[/C][/ROW]
[ROW][C]-2.40002623031818e-05[/C][/ROW]
[ROW][C]-9.27261322253579e-05[/C][/ROW]
[ROW][C]8.98552720120887e-05[/C][/ROW]
[ROW][C]-3.3109513841197e-05[/C][/ROW]
[ROW][C]-8.68820061170922e-06[/C][/ROW]
[ROW][C]-5.05705345435648e-05[/C][/ROW]
[ROW][C]1.49293470484712e-05[/C][/ROW]
[ROW][C]3.59943221099345e-05[/C][/ROW]
[ROW][C]-2.33235274361622e-05[/C][/ROW]
[ROW][C]-3.3411148318324e-06[/C][/ROW]
[ROW][C]1.21658753136748e-05[/C][/ROW]
[ROW][C]-4.38821189475452e-05[/C][/ROW]
[ROW][C]-2.52909544553623e-06[/C][/ROW]
[ROW][C]7.28271379945954e-05[/C][/ROW]
[ROW][C]-6.60042963177308e-05[/C][/ROW]
[ROW][C]-3.47492540541444e-05[/C][/ROW]
[ROW][C]3.56682127133268e-05[/C][/ROW]
[ROW][C]3.87131267707249e-05[/C][/ROW]
[ROW][C]7.2753025921088e-06[/C][/ROW]
[ROW][C]-1.09553936915625e-06[/C][/ROW]
[ROW][C]2.14031384046755e-05[/C][/ROW]
[ROW][C]-2.94451808260875e-05[/C][/ROW]
[ROW][C]5.34995561554334e-05[/C][/ROW]
[ROW][C]3.36445257178204e-05[/C][/ROW]
[ROW][C]-5.36293231239796e-05[/C][/ROW]
[ROW][C]-2.75551661216276e-05[/C][/ROW]
[ROW][C]1.10197224101631e-05[/C][/ROW]
[ROW][C]-6.36249467988572e-06[/C][/ROW]
[ROW][C]-4.55728377324943e-06[/C][/ROW]
[ROW][C]-3.45422786998778e-05[/C][/ROW]
[ROW][C]-1.0555400441079e-05[/C][/ROW]
[ROW][C]-4.54556947359289e-06[/C][/ROW]
[ROW][C]1.46033727707589e-05[/C][/ROW]
[ROW][C]1.78363293924480e-05[/C][/ROW]
[ROW][C]-1.46441188966236e-05[/C][/ROW]
[ROW][C]2.34491206659728e-05[/C][/ROW]
[ROW][C]4.24173309388562e-06[/C][/ROW]
[ROW][C]-5.41641926396179e-06[/C][/ROW]
[ROW][C]-2.39880826887899e-06[/C][/ROW]
[ROW][C]1.54176422989373e-05[/C][/ROW]
[ROW][C]1.10409520759310e-05[/C][/ROW]
[ROW][C]-3.02320819065717e-06[/C][/ROW]
[ROW][C]3.17405766564422e-05[/C][/ROW]
[ROW][C]-3.64687437250802e-05[/C][/ROW]
[ROW][C]1.94123472520437e-05[/C][/ROW]
[ROW][C]-2.04793011319055e-06[/C][/ROW]
[ROW][C]-8.35450560844626e-06[/C][/ROW]
[ROW][C]-5.29041098984516e-05[/C][/ROW]
[ROW][C]8.32175242691538e-06[/C][/ROW]
[ROW][C]-5.63956635105705e-06[/C][/ROW]
[ROW][C]2.92611154790037e-05[/C][/ROW]
[ROW][C]3.04818699121046e-05[/C][/ROW]
[ROW][C]3.27593774778695e-05[/C][/ROW]
[ROW][C]-1.95926957005104e-05[/C][/ROW]
[ROW][C]-4.42930271043147e-05[/C][/ROW]
[ROW][C]-1.35109207733182e-05[/C][/ROW]
[ROW][C]1.76087039235709e-05[/C][/ROW]
[ROW][C]-8.92079723416734e-08[/C][/ROW]
[ROW][C]3.66662156723497e-05[/C][/ROW]
[ROW][C]-2.57200569905784e-05[/C][/ROW]
[ROW][C]-3.52771185559797e-05[/C][/ROW]
[ROW][C]7.03396462008726e-05[/C][/ROW]
[ROW][C]1.03450298867846e-05[/C][/ROW]
[ROW][C]5.42588758328228e-06[/C][/ROW]
[ROW][C]-1.39987191086921e-05[/C][/ROW]
[ROW][C]-3.049913028068e-05[/C][/ROW]
[ROW][C]3.56456689612682e-05[/C][/ROW]
[ROW][C]5.16924242018243e-06[/C][/ROW]
[ROW][C]3.32261997039747e-05[/C][/ROW]
[ROW][C]-5.2791149569268e-05[/C][/ROW]
[ROW][C]2.41573017780291e-05[/C][/ROW]
[ROW][C]3.90781235793754e-05[/C][/ROW]
[ROW][C]-8.95453429176817e-06[/C][/ROW]
[ROW][C]1.00759576754094e-05[/C][/ROW]
[ROW][C]3.05165720216384e-05[/C][/ROW]
[ROW][C]4.08505811473137e-05[/C][/ROW]
[ROW][C]-5.28095437860549e-06[/C][/ROW]
[ROW][C]3.29750954506058e-05[/C][/ROW]
[ROW][C]7.2280245574875e-06[/C][/ROW]
[ROW][C]1.53308752622027e-05[/C][/ROW]
[ROW][C]2.44426674676085e-05[/C][/ROW]
[ROW][C]2.53453440626779e-05[/C][/ROW]
[ROW][C]1.61098642400321e-05[/C][/ROW]
[ROW][C]6.5199847768673e-06[/C][/ROW]
[ROW][C]2.16798337988795e-05[/C][/ROW]
[ROW][C]-2.40947940722628e-05[/C][/ROW]
[ROW][C]2.40950781068301e-05[/C][/ROW]
[ROW][C]-3.97488628826355e-05[/C][/ROW]
[ROW][C]3.20100635689357e-05[/C][/ROW]
[ROW][C]5.306070796364e-05[/C][/ROW]
[ROW][C]3.35620756498815e-05[/C][/ROW]
[ROW][C]1.13017378487904e-05[/C][/ROW]
[ROW][C]4.57699604928432e-05[/C][/ROW]
[ROW][C]1.85004688872849e-05[/C][/ROW]
[ROW][C]5.71780177420779e-05[/C][/ROW]
[ROW][C]8.28350844419898e-05[/C][/ROW]
[ROW][C]1.40171443948919e-05[/C][/ROW]
[ROW][C]6.19336126278373e-05[/C][/ROW]
[ROW][C]-5.56132102026676e-05[/C][/ROW]
[ROW][C]0.000103298448826039[/C][/ROW]
[ROW][C]4.66948654349823e-05[/C][/ROW]
[ROW][C]-5.86490168106219e-06[/C][/ROW]
[ROW][C]5.61570207473574e-06[/C][/ROW]
[ROW][C]3.07579359935519e-05[/C][/ROW]
[ROW][C]8.04972226488014e-05[/C][/ROW]
[ROW][C]2.83519612253913e-05[/C][/ROW]
[ROW][C]5.49940580585887e-05[/C][/ROW]
[ROW][C]7.1041083983168e-05[/C][/ROW]
[ROW][C]-4.33852423523521e-05[/C][/ROW]
[ROW][C]1.04494318265660e-06[/C][/ROW]
[ROW][C]-0.000234352327894885[/C][/ROW]
[ROW][C]2.57925956060314e-05[/C][/ROW]
[ROW][C]5.66151259192688e-06[/C][/ROW]
[ROW][C]-0.000127000426430103[/C][/ROW]
[ROW][C]-9.83605216207806e-05[/C][/ROW]
[ROW][C]-5.2940130013637e-05[/C][/ROW]
[ROW][C]3.15929290644253e-05[/C][/ROW]
[ROW][C]7.03344239458356e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70852&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70852&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
-1.34215903055762e-05
-7.24224985378705e-06
2.80204123465409e-06
4.81983994353419e-06
3.87577948336289e-05
7.61182578294985e-05
-2.34151967109661e-05
2.46964671608796e-05
-1.75360799694432e-06
-5.77161607291801e-05
-5.43614470049045e-05
0.000129968286736676
1.51187187137238e-05
-2.95758473354595e-06
8.20274667195701e-05
4.18112032044294e-05
-7.13585962891744e-05
-0.000100934272781908
-2.33617355408844e-05
-1.98548651922339e-05
0.000127864821937757
-4.9434614930895e-05
1.93848457182844e-05
-8.1969768073358e-05
6.85365599537795e-05
2.16944243849730e-05
-6.91934481788719e-05
4.34543219658194e-05
-4.19131144938879e-05
-9.03357482852257e-05
4.09244850785254e-05
-2.64931008667419e-05
-2.76119795770872e-05
4.41545490032594e-05
3.09745814826761e-05
5.44798651692514e-05
4.20040478025201e-05
-6.20346289724574e-05
4.79099403631117e-05
-0.000110535967180537
4.98291848147382e-05
4.22354206579328e-05
-6.79325870741827e-05
-7.72512751865444e-05
3.22374592246877e-06
2.41063941185364e-05
8.19694080509168e-05
3.17725293467970e-05
-2.03637414864481e-05
5.99635635831252e-05
-0.000109734187727383
-1.10183937066855e-06
-0.000139408665513955
3.93860951911116e-05
-1.13188128807241e-05
-3.90178898737998e-06
-2.21666531556996e-06
7.8223442338e-05
3.79775072829764e-05
-3.4318411505228e-05
1.86699891247647e-05
1.99890203983757e-06
7.21043486249589e-06
-9.626837796989e-06
-8.30136333874514e-05
-0.000101875111271675
-1.78235177353569e-05
4.29927676677119e-05
-2.13154513222867e-05
-2.33318371750069e-05
7.07428986107188e-05
1.56593544749776e-05
-4.25099689884518e-05
5.30554582414161e-05
2.40861787247831e-05
-0.000149759126869695
-7.0552314215347e-05
-4.20685241877179e-05
1.12360495800796e-05
4.80843773586681e-06
6.66834519260541e-05
5.5342609946322e-06
9.49450278224986e-06
3.50209959137374e-06
-5.53342323511449e-05
0.000110661781125523
-3.454286176483e-05
-2.85494410521161e-05
-3.61355307551169e-05
5.61846241080155e-06
-2.23622366581067e-05
-1.97618034913475e-05
5.7891062121636e-05
1.13606213656387e-05
1.84248579598982e-06
-2.60153004873349e-05
4.29665839454965e-05
-2.40002623031818e-05
-9.27261322253579e-05
8.98552720120887e-05
-3.3109513841197e-05
-8.68820061170922e-06
-5.05705345435648e-05
1.49293470484712e-05
3.59943221099345e-05
-2.33235274361622e-05
-3.3411148318324e-06
1.21658753136748e-05
-4.38821189475452e-05
-2.52909544553623e-06
7.28271379945954e-05
-6.60042963177308e-05
-3.47492540541444e-05
3.56682127133268e-05
3.87131267707249e-05
7.2753025921088e-06
-1.09553936915625e-06
2.14031384046755e-05
-2.94451808260875e-05
5.34995561554334e-05
3.36445257178204e-05
-5.36293231239796e-05
-2.75551661216276e-05
1.10197224101631e-05
-6.36249467988572e-06
-4.55728377324943e-06
-3.45422786998778e-05
-1.0555400441079e-05
-4.54556947359289e-06
1.46033727707589e-05
1.78363293924480e-05
-1.46441188966236e-05
2.34491206659728e-05
4.24173309388562e-06
-5.41641926396179e-06
-2.39880826887899e-06
1.54176422989373e-05
1.10409520759310e-05
-3.02320819065717e-06
3.17405766564422e-05
-3.64687437250802e-05
1.94123472520437e-05
-2.04793011319055e-06
-8.35450560844626e-06
-5.29041098984516e-05
8.32175242691538e-06
-5.63956635105705e-06
2.92611154790037e-05
3.04818699121046e-05
3.27593774778695e-05
-1.95926957005104e-05
-4.42930271043147e-05
-1.35109207733182e-05
1.76087039235709e-05
-8.92079723416734e-08
3.66662156723497e-05
-2.57200569905784e-05
-3.52771185559797e-05
7.03396462008726e-05
1.03450298867846e-05
5.42588758328228e-06
-1.39987191086921e-05
-3.049913028068e-05
3.56456689612682e-05
5.16924242018243e-06
3.32261997039747e-05
-5.2791149569268e-05
2.41573017780291e-05
3.90781235793754e-05
-8.95453429176817e-06
1.00759576754094e-05
3.05165720216384e-05
4.08505811473137e-05
-5.28095437860549e-06
3.29750954506058e-05
7.2280245574875e-06
1.53308752622027e-05
2.44426674676085e-05
2.53453440626779e-05
1.61098642400321e-05
6.5199847768673e-06
2.16798337988795e-05
-2.40947940722628e-05
2.40950781068301e-05
-3.97488628826355e-05
3.20100635689357e-05
5.306070796364e-05
3.35620756498815e-05
1.13017378487904e-05
4.57699604928432e-05
1.85004688872849e-05
5.71780177420779e-05
8.28350844419898e-05
1.40171443948919e-05
6.19336126278373e-05
-5.56132102026676e-05
0.000103298448826039
4.66948654349823e-05
-5.86490168106219e-06
5.61570207473574e-06
3.07579359935519e-05
8.04972226488014e-05
2.83519612253913e-05
5.49940580585887e-05
7.1041083983168e-05
-4.33852423523521e-05
1.04494318265660e-06
-0.000234352327894885
2.57925956060314e-05
5.66151259192688e-06
-0.000127000426430103
-9.83605216207806e-05
-5.2940130013637e-05
3.15929290644253e-05
7.03344239458356e-06


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = Default ; par2 = -1.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = FALSE ; par2 = -1.0 ; par3 = 2 ; par4 = 0 ; 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')