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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, 03 Dec 2009 09:09:10 -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/03/t1259856634vt12v5frt8xo4sr.htm/, Retrieved Fri, 29 Mar 2024 07:41:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62872, Retrieved Fri, 29 Mar 2024 07:41:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157
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]
- R PD      [ARIMA Backward Selection] [] [2009-12-03 16:09:10] [791a4a78a0a7ca497fb8791b982a539e] [Current]
- R PD        [ARIMA Backward Selection] [Backward ARIMA Es...] [2009-12-04 15:46:32] [fa71ec4c741ffec745cb91dcbd756720]
-   PD          [ARIMA Backward Selection] [arima backward] [2009-12-20 11:21:32] [fa71ec4c741ffec745cb91dcbd756720]
-   PD          [ARIMA Backward Selection] [arima backward] [2009-12-20 11:26:17] [fa71ec4c741ffec745cb91dcbd756720]
- R PD        [ARIMA Backward Selection] [] [2009-12-04 17:56:59] [eba9b8a72d680086d9ebbb043233c887]
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Dataseries X:
785.8
819.3
849.4
880.4
900.1
937.2
948.9
952.6
947.3
974.2
1000.8
1032.8
1050.7
1057.3
1075.4
1118.4
1179.8
1227
1257.8
1251.5
1236.3
1170.6
1213.1
1265.5
1300.8
1348.4
1371.9
1403.3
1451.8
1474.2
1438.2
1513.6
1562.2
1546.2
1527.5
1418.7
1448.5
1492.1
1395.4
1403.7
1316.6
1274.5
1264.4
1323.9
1332.1
1250.2
1096.7
1080.8
1039.2
792
746.6
688.8
715.8
672.9
629.5
681.2
755.4
760.6
765.9
836.8
904.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62872&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62872&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62872&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.7253-0.21110.241-0.44520.7521-0.0753-0.9607
(p-val)(0.0133 )(0.2435 )(0.0787 )(0.1067 )(0.1889 )(0.7351 )(0.6526 )
Estimates ( 2 )0.7305-0.21030.2378-0.4390.76010-0.9998
(p-val)(0.0143 )(0.2489 )(0.082 )(0.1195 )(3e-04 )(NA )(0.0855 )
Estimates ( 3 )0.493600.1753-0.25140.83940-0.9997
(p-val)(0.159 )(NA )(0.1762 )(0.5679 )(0 )(NA )(0.0837 )
Estimates ( 4 )0.299800.190400.8230-0.9999
(p-val)(0.0159 )(NA )(0.1212 )(NA )(0 )(NA )(0.089 )
Estimates ( 5 )0.31980000.81180-1.0005
(p-val)(0.0118 )(NA )(NA )(NA )(1e-04 )(NA )(0.0412 )
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 ) & 0.7253 & -0.2111 & 0.241 & -0.4452 & 0.7521 & -0.0753 & -0.9607 \tabularnewline
(p-val) & (0.0133 ) & (0.2435 ) & (0.0787 ) & (0.1067 ) & (0.1889 ) & (0.7351 ) & (0.6526 ) \tabularnewline
Estimates ( 2 ) & 0.7305 & -0.2103 & 0.2378 & -0.439 & 0.7601 & 0 & -0.9998 \tabularnewline
(p-val) & (0.0143 ) & (0.2489 ) & (0.082 ) & (0.1195 ) & (3e-04 ) & (NA ) & (0.0855 ) \tabularnewline
Estimates ( 3 ) & 0.4936 & 0 & 0.1753 & -0.2514 & 0.8394 & 0 & -0.9997 \tabularnewline
(p-val) & (0.159 ) & (NA ) & (0.1762 ) & (0.5679 ) & (0 ) & (NA ) & (0.0837 ) \tabularnewline
Estimates ( 4 ) & 0.2998 & 0 & 0.1904 & 0 & 0.823 & 0 & -0.9999 \tabularnewline
(p-val) & (0.0159 ) & (NA ) & (0.1212 ) & (NA ) & (0 ) & (NA ) & (0.089 ) \tabularnewline
Estimates ( 5 ) & 0.3198 & 0 & 0 & 0 & 0.8118 & 0 & -1.0005 \tabularnewline
(p-val) & (0.0118 ) & (NA ) & (NA ) & (NA ) & (1e-04 ) & (NA ) & (0.0412 ) \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=62872&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]0.7253[/C][C]-0.2111[/C][C]0.241[/C][C]-0.4452[/C][C]0.7521[/C][C]-0.0753[/C][C]-0.9607[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0133 )[/C][C](0.2435 )[/C][C](0.0787 )[/C][C](0.1067 )[/C][C](0.1889 )[/C][C](0.7351 )[/C][C](0.6526 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.7305[/C][C]-0.2103[/C][C]0.2378[/C][C]-0.439[/C][C]0.7601[/C][C]0[/C][C]-0.9998[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0143 )[/C][C](0.2489 )[/C][C](0.082 )[/C][C](0.1195 )[/C][C](3e-04 )[/C][C](NA )[/C][C](0.0855 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4936[/C][C]0[/C][C]0.1753[/C][C]-0.2514[/C][C]0.8394[/C][C]0[/C][C]-0.9997[/C][/ROW]
[ROW][C](p-val)[/C][C](0.159 )[/C][C](NA )[/C][C](0.1762 )[/C][C](0.5679 )[/C][C](0 )[/C][C](NA )[/C][C](0.0837 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.2998[/C][C]0[/C][C]0.1904[/C][C]0[/C][C]0.823[/C][C]0[/C][C]-0.9999[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0159 )[/C][C](NA )[/C][C](0.1212 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0.089 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.3198[/C][C]0[/C][C]0[/C][C]0[/C][C]0.8118[/C][C]0[/C][C]-1.0005[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0118 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](1e-04 )[/C][C](NA )[/C][C](0.0412 )[/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=62872&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62872&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 )0.7253-0.21110.241-0.44520.7521-0.0753-0.9607
(p-val)(0.0133 )(0.2435 )(0.0787 )(0.1067 )(0.1889 )(0.7351 )(0.6526 )
Estimates ( 2 )0.7305-0.21030.2378-0.4390.76010-0.9998
(p-val)(0.0143 )(0.2489 )(0.082 )(0.1195 )(3e-04 )(NA )(0.0855 )
Estimates ( 3 )0.493600.1753-0.25140.83940-0.9997
(p-val)(0.159 )(NA )(0.1762 )(0.5679 )(0 )(NA )(0.0837 )
Estimates ( 4 )0.299800.190400.8230-0.9999
(p-val)(0.0159 )(NA )(0.1212 )(NA )(0 )(NA )(0.089 )
Estimates ( 5 )0.31980000.81180-1.0005
(p-val)(0.0118 )(NA )(NA )(NA )(1e-04 )(NA )(0.0412 )
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
0.78579949675862
29.6000993558862
17.8338192351336
18.4716051833255
3.90648687993891
24.3932871456449
-4.95553922705324
-3.21202494826693
-12.6378934367398
25.4493842786775
17.6483299691466
24.5089163942132
4.06443062523229
-1.30034547513347
11.2129250966395
34.4132296750920
45.6936644466284
26.5366165315687
7.78383950713139
-26.2190467555896
-22.2976968395259
-61.6449963837953
62.8581857419326
43.50953367376
32.0445018040293
29.7302871260665
1.73542416124453
21.6591752059623
33.5980475248494
7.66194823734239
-46.3420076320515
71.4074662418884
18.018730217235
-26.5484591060413
-19.3532407123808
-101.700435649166
67.1581729484485
41.4851950797125
-83.7879808310883
36.5982094561617
-87.0806355670562
6.75200230716907
-3.41830362876037
81.4446704466919
-2.10655166000896
-85.2800435395474
-130.808066690370
22.3950644851693
-10.0954946038630
-190.532545574048
23.4231405773398
-26.0734922567823
85.0441099453305
-36.5939365478563
-22.8575921151788
70.5298480883146
63.9316414888038
-22.5135039036823
-16.4098604982532
51.4749832978399
51.6012890979182

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.78579949675862 \tabularnewline
29.6000993558862 \tabularnewline
17.8338192351336 \tabularnewline
18.4716051833255 \tabularnewline
3.90648687993891 \tabularnewline
24.3932871456449 \tabularnewline
-4.95553922705324 \tabularnewline
-3.21202494826693 \tabularnewline
-12.6378934367398 \tabularnewline
25.4493842786775 \tabularnewline
17.6483299691466 \tabularnewline
24.5089163942132 \tabularnewline
4.06443062523229 \tabularnewline
-1.30034547513347 \tabularnewline
11.2129250966395 \tabularnewline
34.4132296750920 \tabularnewline
45.6936644466284 \tabularnewline
26.5366165315687 \tabularnewline
7.78383950713139 \tabularnewline
-26.2190467555896 \tabularnewline
-22.2976968395259 \tabularnewline
-61.6449963837953 \tabularnewline
62.8581857419326 \tabularnewline
43.50953367376 \tabularnewline
32.0445018040293 \tabularnewline
29.7302871260665 \tabularnewline
1.73542416124453 \tabularnewline
21.6591752059623 \tabularnewline
33.5980475248494 \tabularnewline
7.66194823734239 \tabularnewline
-46.3420076320515 \tabularnewline
71.4074662418884 \tabularnewline
18.018730217235 \tabularnewline
-26.5484591060413 \tabularnewline
-19.3532407123808 \tabularnewline
-101.700435649166 \tabularnewline
67.1581729484485 \tabularnewline
41.4851950797125 \tabularnewline
-83.7879808310883 \tabularnewline
36.5982094561617 \tabularnewline
-87.0806355670562 \tabularnewline
6.75200230716907 \tabularnewline
-3.41830362876037 \tabularnewline
81.4446704466919 \tabularnewline
-2.10655166000896 \tabularnewline
-85.2800435395474 \tabularnewline
-130.808066690370 \tabularnewline
22.3950644851693 \tabularnewline
-10.0954946038630 \tabularnewline
-190.532545574048 \tabularnewline
23.4231405773398 \tabularnewline
-26.0734922567823 \tabularnewline
85.0441099453305 \tabularnewline
-36.5939365478563 \tabularnewline
-22.8575921151788 \tabularnewline
70.5298480883146 \tabularnewline
63.9316414888038 \tabularnewline
-22.5135039036823 \tabularnewline
-16.4098604982532 \tabularnewline
51.4749832978399 \tabularnewline
51.6012890979182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62872&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.78579949675862[/C][/ROW]
[ROW][C]29.6000993558862[/C][/ROW]
[ROW][C]17.8338192351336[/C][/ROW]
[ROW][C]18.4716051833255[/C][/ROW]
[ROW][C]3.90648687993891[/C][/ROW]
[ROW][C]24.3932871456449[/C][/ROW]
[ROW][C]-4.95553922705324[/C][/ROW]
[ROW][C]-3.21202494826693[/C][/ROW]
[ROW][C]-12.6378934367398[/C][/ROW]
[ROW][C]25.4493842786775[/C][/ROW]
[ROW][C]17.6483299691466[/C][/ROW]
[ROW][C]24.5089163942132[/C][/ROW]
[ROW][C]4.06443062523229[/C][/ROW]
[ROW][C]-1.30034547513347[/C][/ROW]
[ROW][C]11.2129250966395[/C][/ROW]
[ROW][C]34.4132296750920[/C][/ROW]
[ROW][C]45.6936644466284[/C][/ROW]
[ROW][C]26.5366165315687[/C][/ROW]
[ROW][C]7.78383950713139[/C][/ROW]
[ROW][C]-26.2190467555896[/C][/ROW]
[ROW][C]-22.2976968395259[/C][/ROW]
[ROW][C]-61.6449963837953[/C][/ROW]
[ROW][C]62.8581857419326[/C][/ROW]
[ROW][C]43.50953367376[/C][/ROW]
[ROW][C]32.0445018040293[/C][/ROW]
[ROW][C]29.7302871260665[/C][/ROW]
[ROW][C]1.73542416124453[/C][/ROW]
[ROW][C]21.6591752059623[/C][/ROW]
[ROW][C]33.5980475248494[/C][/ROW]
[ROW][C]7.66194823734239[/C][/ROW]
[ROW][C]-46.3420076320515[/C][/ROW]
[ROW][C]71.4074662418884[/C][/ROW]
[ROW][C]18.018730217235[/C][/ROW]
[ROW][C]-26.5484591060413[/C][/ROW]
[ROW][C]-19.3532407123808[/C][/ROW]
[ROW][C]-101.700435649166[/C][/ROW]
[ROW][C]67.1581729484485[/C][/ROW]
[ROW][C]41.4851950797125[/C][/ROW]
[ROW][C]-83.7879808310883[/C][/ROW]
[ROW][C]36.5982094561617[/C][/ROW]
[ROW][C]-87.0806355670562[/C][/ROW]
[ROW][C]6.75200230716907[/C][/ROW]
[ROW][C]-3.41830362876037[/C][/ROW]
[ROW][C]81.4446704466919[/C][/ROW]
[ROW][C]-2.10655166000896[/C][/ROW]
[ROW][C]-85.2800435395474[/C][/ROW]
[ROW][C]-130.808066690370[/C][/ROW]
[ROW][C]22.3950644851693[/C][/ROW]
[ROW][C]-10.0954946038630[/C][/ROW]
[ROW][C]-190.532545574048[/C][/ROW]
[ROW][C]23.4231405773398[/C][/ROW]
[ROW][C]-26.0734922567823[/C][/ROW]
[ROW][C]85.0441099453305[/C][/ROW]
[ROW][C]-36.5939365478563[/C][/ROW]
[ROW][C]-22.8575921151788[/C][/ROW]
[ROW][C]70.5298480883146[/C][/ROW]
[ROW][C]63.9316414888038[/C][/ROW]
[ROW][C]-22.5135039036823[/C][/ROW]
[ROW][C]-16.4098604982532[/C][/ROW]
[ROW][C]51.4749832978399[/C][/ROW]
[ROW][C]51.6012890979182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62872&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62872&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
0.78579949675862
29.6000993558862
17.8338192351336
18.4716051833255
3.90648687993891
24.3932871456449
-4.95553922705324
-3.21202494826693
-12.6378934367398
25.4493842786775
17.6483299691466
24.5089163942132
4.06443062523229
-1.30034547513347
11.2129250966395
34.4132296750920
45.6936644466284
26.5366165315687
7.78383950713139
-26.2190467555896
-22.2976968395259
-61.6449963837953
62.8581857419326
43.50953367376
32.0445018040293
29.7302871260665
1.73542416124453
21.6591752059623
33.5980475248494
7.66194823734239
-46.3420076320515
71.4074662418884
18.018730217235
-26.5484591060413
-19.3532407123808
-101.700435649166
67.1581729484485
41.4851950797125
-83.7879808310883
36.5982094561617
-87.0806355670562
6.75200230716907
-3.41830362876037
81.4446704466919
-2.10655166000896
-85.2800435395474
-130.808066690370
22.3950644851693
-10.0954946038630
-190.532545574048
23.4231405773398
-26.0734922567823
85.0441099453305
-36.5939365478563
-22.8575921151788
70.5298480883146
63.9316414888038
-22.5135039036823
-16.4098604982532
51.4749832978399
51.6012890979182



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