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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationThu, 10 Dec 2009 09:30:27 -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/10/t1260462676bd4gzbxki891rsz.htm/, Retrieved Fri, 29 Mar 2024 10:50:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65562, Retrieved Fri, 29 Mar 2024 10:50:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Backward Selection] [prijsindex van de...] [2009-12-04 19:29:11] [7773f496f69461f4a67891f0ef752622]
-   P         [ARIMA Backward Selection] [review] [2009-12-10 16:30:27] [94ba0ef70f5b330d175ff4daa1c9cd40] [Current]
-               [ARIMA Backward Selection] [ARIMA Appelen Jon...] [2009-12-19 09:37:49] [7773f496f69461f4a67891f0ef752622]
-    D            [ARIMA Backward Selection] [arima backward ba...] [2010-12-16 12:47:35] [ff7c1e95cf99a1dae07ec89975494dde]
-   PD              [ARIMA Backward Selection] [Arima backward se...] [2010-12-19 11:59:26] [ff7c1e95cf99a1dae07ec89975494dde]
-    D            [ARIMA Backward Selection] [arima backward brood] [2010-12-16 12:49:55] [ff7c1e95cf99a1dae07ec89975494dde]
-   PD            [ARIMA Backward Selection] [Arima backward model] [2010-12-18 10:17:56] [717f3d787904f94c39256c5c1fc72d4c]
-   PD              [ARIMA Backward Selection] [Arima backward model] [2010-12-18 10:34:57] [717f3d787904f94c39256c5c1fc72d4c]
- R  D            [ARIMA Backward Selection] [arima backward] [2010-12-21 13:39:03] [3df61981e9f4dafed65341be376c4457]
-   PD            [ARIMA Backward Selection] [ARIMABWKoffie] [2010-12-22 21:42:55] [3fb95cad3bbcce10c72dbbcc5bec5662]
-   PD            [ARIMA Backward Selection] [ARIMABWKoffie2] [2010-12-22 21:57:12] [3fb95cad3bbcce10c72dbbcc5bec5662]
-   PD            [ARIMA Backward Selection] [ARIMABWKoffie2] [2010-12-24 12:22:33] [3fb95cad3bbcce10c72dbbcc5bec5662]
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Dataseries X:
226.9
235.9
216.2
226.2
198.3
176.7
166.2
157.6
163.4
159.7
191.0
239.4
321.9
362.7
413.6
407.1
383.2
347.7
333.8
312.3
295.4
283.3
287.6
265.7
250.2
234.7
244.0
231.2
223.8
223.5
210.5
201.6
190.7
207.5
198.8
196.6
204.2
227.4
229.7
217.9
221.4
216.3
197.0
193.8
196.8
180.5
174.8
181.6
190.0
190.6
179.0
174.1
161.1
168.6
169.4
152.2
148.3
137.7
145.0
153.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65562&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]9 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=65562&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65562&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.63710.2731-0.3478-1-0.0498-0.3548-0.0772
(p-val)(0 )(0.064 )(0.0091 )(0 )(0.9066 )(0.0425 )(0.8659 )
Estimates ( 2 )0.63860.2728-0.3497-10-0.3509-0.1274
(p-val)(0 )(0.0642 )(0.0083 )(0 )(NA )(0.0436 )(0.4263 )
Estimates ( 3 )0.62390.2757-0.3237-10-0.34480
(p-val)(0 )(0.063 )(0.0124 )(0 )(NA )(0.0474 )(NA )
Estimates ( 4 )-0.5960-0.12170.40590-0.30090
(p-val)(0.0736 )(NA )(0.4512 )(0.2336 )(NA )(0.0842 )(NA )
Estimates ( 5 )-0.7979000.5940-0.30730
(p-val)(0 )(NA )(NA )(0.0048 )(NA )(0.073 )(NA )
Estimates ( 6 )-0.7689000.5136000
(p-val)(1e-04 )(NA )(NA )(0.0247 )(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 ) & 0.6371 & 0.2731 & -0.3478 & -1 & -0.0498 & -0.3548 & -0.0772 \tabularnewline
(p-val) & (0 ) & (0.064 ) & (0.0091 ) & (0 ) & (0.9066 ) & (0.0425 ) & (0.8659 ) \tabularnewline
Estimates ( 2 ) & 0.6386 & 0.2728 & -0.3497 & -1 & 0 & -0.3509 & -0.1274 \tabularnewline
(p-val) & (0 ) & (0.0642 ) & (0.0083 ) & (0 ) & (NA ) & (0.0436 ) & (0.4263 ) \tabularnewline
Estimates ( 3 ) & 0.6239 & 0.2757 & -0.3237 & -1 & 0 & -0.3448 & 0 \tabularnewline
(p-val) & (0 ) & (0.063 ) & (0.0124 ) & (0 ) & (NA ) & (0.0474 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.596 & 0 & -0.1217 & 0.4059 & 0 & -0.3009 & 0 \tabularnewline
(p-val) & (0.0736 ) & (NA ) & (0.4512 ) & (0.2336 ) & (NA ) & (0.0842 ) & (NA ) \tabularnewline
Estimates ( 5 ) & -0.7979 & 0 & 0 & 0.594 & 0 & -0.3073 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0.0048 ) & (NA ) & (0.073 ) & (NA ) \tabularnewline
Estimates ( 6 ) & -0.7689 & 0 & 0 & 0.5136 & 0 & 0 & 0 \tabularnewline
(p-val) & (1e-04 ) & (NA ) & (NA ) & (0.0247 ) & (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=65562&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]0.6371[/C][C]0.2731[/C][C]-0.3478[/C][C]-1[/C][C]-0.0498[/C][C]-0.3548[/C][C]-0.0772[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.064 )[/C][C](0.0091 )[/C][C](0 )[/C][C](0.9066 )[/C][C](0.0425 )[/C][C](0.8659 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.6386[/C][C]0.2728[/C][C]-0.3497[/C][C]-1[/C][C]0[/C][C]-0.3509[/C][C]-0.1274[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0642 )[/C][C](0.0083 )[/C][C](0 )[/C][C](NA )[/C][C](0.0436 )[/C][C](0.4263 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.6239[/C][C]0.2757[/C][C]-0.3237[/C][C]-1[/C][C]0[/C][C]-0.3448[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.063 )[/C][C](0.0124 )[/C][C](0 )[/C][C](NA )[/C][C](0.0474 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.596[/C][C]0[/C][C]-0.1217[/C][C]0.4059[/C][C]0[/C][C]-0.3009[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0736 )[/C][C](NA )[/C][C](0.4512 )[/C][C](0.2336 )[/C][C](NA )[/C][C](0.0842 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.7979[/C][C]0[/C][C]0[/C][C]0.594[/C][C]0[/C][C]-0.3073[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.0048 )[/C][C](NA )[/C][C](0.073 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]-0.7689[/C][C]0[/C][C]0[/C][C]0.5136[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](1e-04 )[/C][C](NA )[/C][C](NA )[/C][C](0.0247 )[/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=65562&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65562&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.63710.2731-0.3478-1-0.0498-0.3548-0.0772
(p-val)(0 )(0.064 )(0.0091 )(0 )(0.9066 )(0.0425 )(0.8659 )
Estimates ( 2 )0.63860.2728-0.3497-10-0.3509-0.1274
(p-val)(0 )(0.0642 )(0.0083 )(0 )(NA )(0.0436 )(0.4263 )
Estimates ( 3 )0.62390.2757-0.3237-10-0.34480
(p-val)(0 )(0.063 )(0.0124 )(0 )(NA )(0.0474 )(NA )
Estimates ( 4 )-0.5960-0.12170.40590-0.30090
(p-val)(0.0736 )(NA )(0.4512 )(0.2336 )(NA )(0.0842 )(NA )
Estimates ( 5 )-0.7979000.5940-0.30730
(p-val)(0 )(NA )(NA )(0.0048 )(NA )(0.073 )(NA )
Estimates ( 6 )-0.7689000.5136000
(p-val)(1e-04 )(NA )(NA )(0.0247 )(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
-0.284292870631464
-25.8836310508726
20.6828536476618
-25.4461435821366
-7.72480992400053
19.8938266730234
-1.55065085883711
16.0381737215915
-7.59875592122157
30.5647167106079
24.7450305081263
30.6679208077089
-31.9255085655699
-3.19032612762646
-44.9297016193334
-33.6145894104079
-4.07691611016741
13.9141571428294
1.22178247070260
-2.52041814401527
10.0654518815917
12.6416832171014
-19.2284206760855
-3.33321888349981
8.07242432492435
13.9133487600180
-8.43546264474914
-11.5883724002874
10.9296486751830
-8.56991082880853
2.36245730683399
4.75881499231337
23.8887333984224
-9.16286646556955
5.4328018800317
26.430303298105
3.26598867872077
-17.5134990067615
-35.5346225889043
5.73763072659126
-7.63130673675063
-12.7353716604394
15.2955740705059
9.51030100064732
-17.3995485371303
11.7528274560557
9.94623648090485
1.20822230954599
-5.67191259147824
-7.43352569004669
0.670842413799376
-6.91181891938166
21.6485936190775
-5.36472040429606
-22.0130493874981
12.4048255476895
4.56478916640407
8.79825891688432
5.90109775466922

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.284292870631464 \tabularnewline
-25.8836310508726 \tabularnewline
20.6828536476618 \tabularnewline
-25.4461435821366 \tabularnewline
-7.72480992400053 \tabularnewline
19.8938266730234 \tabularnewline
-1.55065085883711 \tabularnewline
16.0381737215915 \tabularnewline
-7.59875592122157 \tabularnewline
30.5647167106079 \tabularnewline
24.7450305081263 \tabularnewline
30.6679208077089 \tabularnewline
-31.9255085655699 \tabularnewline
-3.19032612762646 \tabularnewline
-44.9297016193334 \tabularnewline
-33.6145894104079 \tabularnewline
-4.07691611016741 \tabularnewline
13.9141571428294 \tabularnewline
1.22178247070260 \tabularnewline
-2.52041814401527 \tabularnewline
10.0654518815917 \tabularnewline
12.6416832171014 \tabularnewline
-19.2284206760855 \tabularnewline
-3.33321888349981 \tabularnewline
8.07242432492435 \tabularnewline
13.9133487600180 \tabularnewline
-8.43546264474914 \tabularnewline
-11.5883724002874 \tabularnewline
10.9296486751830 \tabularnewline
-8.56991082880853 \tabularnewline
2.36245730683399 \tabularnewline
4.75881499231337 \tabularnewline
23.8887333984224 \tabularnewline
-9.16286646556955 \tabularnewline
5.4328018800317 \tabularnewline
26.430303298105 \tabularnewline
3.26598867872077 \tabularnewline
-17.5134990067615 \tabularnewline
-35.5346225889043 \tabularnewline
5.73763072659126 \tabularnewline
-7.63130673675063 \tabularnewline
-12.7353716604394 \tabularnewline
15.2955740705059 \tabularnewline
9.51030100064732 \tabularnewline
-17.3995485371303 \tabularnewline
11.7528274560557 \tabularnewline
9.94623648090485 \tabularnewline
1.20822230954599 \tabularnewline
-5.67191259147824 \tabularnewline
-7.43352569004669 \tabularnewline
0.670842413799376 \tabularnewline
-6.91181891938166 \tabularnewline
21.6485936190775 \tabularnewline
-5.36472040429606 \tabularnewline
-22.0130493874981 \tabularnewline
12.4048255476895 \tabularnewline
4.56478916640407 \tabularnewline
8.79825891688432 \tabularnewline
5.90109775466922 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65562&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.284292870631464[/C][/ROW]
[ROW][C]-25.8836310508726[/C][/ROW]
[ROW][C]20.6828536476618[/C][/ROW]
[ROW][C]-25.4461435821366[/C][/ROW]
[ROW][C]-7.72480992400053[/C][/ROW]
[ROW][C]19.8938266730234[/C][/ROW]
[ROW][C]-1.55065085883711[/C][/ROW]
[ROW][C]16.0381737215915[/C][/ROW]
[ROW][C]-7.59875592122157[/C][/ROW]
[ROW][C]30.5647167106079[/C][/ROW]
[ROW][C]24.7450305081263[/C][/ROW]
[ROW][C]30.6679208077089[/C][/ROW]
[ROW][C]-31.9255085655699[/C][/ROW]
[ROW][C]-3.19032612762646[/C][/ROW]
[ROW][C]-44.9297016193334[/C][/ROW]
[ROW][C]-33.6145894104079[/C][/ROW]
[ROW][C]-4.07691611016741[/C][/ROW]
[ROW][C]13.9141571428294[/C][/ROW]
[ROW][C]1.22178247070260[/C][/ROW]
[ROW][C]-2.52041814401527[/C][/ROW]
[ROW][C]10.0654518815917[/C][/ROW]
[ROW][C]12.6416832171014[/C][/ROW]
[ROW][C]-19.2284206760855[/C][/ROW]
[ROW][C]-3.33321888349981[/C][/ROW]
[ROW][C]8.07242432492435[/C][/ROW]
[ROW][C]13.9133487600180[/C][/ROW]
[ROW][C]-8.43546264474914[/C][/ROW]
[ROW][C]-11.5883724002874[/C][/ROW]
[ROW][C]10.9296486751830[/C][/ROW]
[ROW][C]-8.56991082880853[/C][/ROW]
[ROW][C]2.36245730683399[/C][/ROW]
[ROW][C]4.75881499231337[/C][/ROW]
[ROW][C]23.8887333984224[/C][/ROW]
[ROW][C]-9.16286646556955[/C][/ROW]
[ROW][C]5.4328018800317[/C][/ROW]
[ROW][C]26.430303298105[/C][/ROW]
[ROW][C]3.26598867872077[/C][/ROW]
[ROW][C]-17.5134990067615[/C][/ROW]
[ROW][C]-35.5346225889043[/C][/ROW]
[ROW][C]5.73763072659126[/C][/ROW]
[ROW][C]-7.63130673675063[/C][/ROW]
[ROW][C]-12.7353716604394[/C][/ROW]
[ROW][C]15.2955740705059[/C][/ROW]
[ROW][C]9.51030100064732[/C][/ROW]
[ROW][C]-17.3995485371303[/C][/ROW]
[ROW][C]11.7528274560557[/C][/ROW]
[ROW][C]9.94623648090485[/C][/ROW]
[ROW][C]1.20822230954599[/C][/ROW]
[ROW][C]-5.67191259147824[/C][/ROW]
[ROW][C]-7.43352569004669[/C][/ROW]
[ROW][C]0.670842413799376[/C][/ROW]
[ROW][C]-6.91181891938166[/C][/ROW]
[ROW][C]21.6485936190775[/C][/ROW]
[ROW][C]-5.36472040429606[/C][/ROW]
[ROW][C]-22.0130493874981[/C][/ROW]
[ROW][C]12.4048255476895[/C][/ROW]
[ROW][C]4.56478916640407[/C][/ROW]
[ROW][C]8.79825891688432[/C][/ROW]
[ROW][C]5.90109775466922[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65562&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65562&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
-0.284292870631464
-25.8836310508726
20.6828536476618
-25.4461435821366
-7.72480992400053
19.8938266730234
-1.55065085883711
16.0381737215915
-7.59875592122157
30.5647167106079
24.7450305081263
30.6679208077089
-31.9255085655699
-3.19032612762646
-44.9297016193334
-33.6145894104079
-4.07691611016741
13.9141571428294
1.22178247070260
-2.52041814401527
10.0654518815917
12.6416832171014
-19.2284206760855
-3.33321888349981
8.07242432492435
13.9133487600180
-8.43546264474914
-11.5883724002874
10.9296486751830
-8.56991082880853
2.36245730683399
4.75881499231337
23.8887333984224
-9.16286646556955
5.4328018800317
26.430303298105
3.26598867872077
-17.5134990067615
-35.5346225889043
5.73763072659126
-7.63130673675063
-12.7353716604394
15.2955740705059
9.51030100064732
-17.3995485371303
11.7528274560557
9.94623648090485
1.20822230954599
-5.67191259147824
-7.43352569004669
0.670842413799376
-6.91181891938166
21.6485936190775
-5.36472040429606
-22.0130493874981
12.4048255476895
4.56478916640407
8.79825891688432
5.90109775466922



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; 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')