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rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Sun, 22 Dec 2013 18:47:40 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/22/t13877560834e3lko18pyv9z5g.htm/, Retrieved Fri, 19 Apr 2024 20:51:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232577, Retrieved Fri, 19 Apr 2024 20:51:41 +0000

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-       [Classical Decomposition] [] [2013-12-22 23:47:40] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232577&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232577&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232577&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	85.73	NA	NA	0.996756	NA
2	85.73	NA	NA	0.994258	NA
3	85.74	NA	NA	0.992891	NA
4	86.32	NA	NA	0.997389	NA
5	87.59	NA	NA	1.0045	NA
6	87.81	NA	NA	1.00386	NA
7	87.87	87.7349	87.3217	1.00473	1.00154
8	87.94	87.8385	87.5117	1.00374	1.00116
9	87.96	87.9654	87.7254	1.00274	0.999939
10	88.01	88.0852	87.9738	1.00127	0.999146
11	88.01	88.1453	88.1883	0.999512	0.998465
12	88.01	88.2085	88.3533	0.99836	0.99775
13	88.01	88.2299	88.5171	0.996756	0.997507
14	88.01	88.1728	88.6821	0.994258	0.998153
15	88.59	88.2126	88.8442	0.992891	1.00428
16	89.43	88.7718	89.0042	0.997389	1.00741
17	89.63	89.5619	89.1604	1.0045	1.00076
18	89.73	89.6681	89.3233	1.00386	1.00069
19	89.88	89.9227	89.4992	1.00473	0.999525
20	89.89	90.0141	89.6792	1.00374	0.998621
21	89.9	90.0853	89.8396	1.00274	0.997943
22	89.91	90.084	89.97	1.00127	0.998068
23	89.86	90.0868	90.1308	0.999512	0.997482
24	90.07	90.1823	90.3304	0.99836	0.998755
25	90.17	90.2305	90.5242	0.996756	0.99933
26	90.17	90.1953	90.7162	0.994258	0.999719
27	90.28	90.265	90.9112	0.992891	1.00017
28	90.87	90.8704	91.1083	0.997389	0.999995
29	92.05	91.72	91.3087	1.0045	1.0036
30	92.1	91.8557	91.5025	1.00386	1.00266
31	92.16	92.1172	91.6833	1.00473	1.00046
32	92.22	92.2031	91.86	1.00374	1.00018
33	92.25	92.2696	92.0179	1.00274	0.999787
34	92.29	92.2438	92.1271	1.00127	1.0005
35	92.29	92.1263	92.1712	0.999512	1.00178
36	92.29	92.0334	92.1846	0.99836	1.00279
37	92.29	91.9013	92.2004	0.996756	1.00423
38	92.29	91.6855	92.215	0.994258	1.00659
39	91.95	91.5789	92.2346	0.992891	1.00405
40	91.82	92.0141	92.255	0.997389	0.99789
41	92.16	92.6893	92.2737	1.0045	0.99429
42	92.31	92.6525	92.2962	1.00386	0.996303
43	92.33	92.7573	92.3204	1.00473	0.995393
44	92.4	92.6816	92.3367	1.00374	0.996962
45	92.54	92.6093	92.3567	1.00274	0.999252
46	92.49	92.5634	92.4462	1.00127	0.999207
47	92.54	92.6144	92.6596	0.999512	0.999197
48	92.58	92.8001	92.9525	0.99836	0.997628
49	92.58	92.9653	93.2679	0.996756	0.995855
50	92.39	93.0923	93.63	0.994258	0.992455
51	92.33	93.4112	94.08	0.992891	0.988425
52	93.59	94.3808	94.6279	0.997389	0.991621
53	95.51	95.6752	95.2463	1.0045	0.998273
54	95.99	96.253	95.8829	1.00386	0.997268
55	96.22	NA	NA	1.00473	NA
56	97.2	NA	NA	1.00374	NA
57	98.54	NA	NA	1.00274	NA
58	99.64	NA	NA	1.00127	NA
59	100.23	NA	NA	0.999512	NA
60	100.17	NA	NA	0.99836	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 85.73 & NA & NA & 0.996756 & NA \tabularnewline
2 & 85.73 & NA & NA & 0.994258 & NA \tabularnewline
3 & 85.74 & NA & NA & 0.992891 & NA \tabularnewline
4 & 86.32 & NA & NA & 0.997389 & NA \tabularnewline
5 & 87.59 & NA & NA & 1.0045 & NA \tabularnewline
6 & 87.81 & NA & NA & 1.00386 & NA \tabularnewline
7 & 87.87 & 87.7349 & 87.3217 & 1.00473 & 1.00154 \tabularnewline
8 & 87.94 & 87.8385 & 87.5117 & 1.00374 & 1.00116 \tabularnewline
9 & 87.96 & 87.9654 & 87.7254 & 1.00274 & 0.999939 \tabularnewline
10 & 88.01 & 88.0852 & 87.9738 & 1.00127 & 0.999146 \tabularnewline
11 & 88.01 & 88.1453 & 88.1883 & 0.999512 & 0.998465 \tabularnewline
12 & 88.01 & 88.2085 & 88.3533 & 0.99836 & 0.99775 \tabularnewline
13 & 88.01 & 88.2299 & 88.5171 & 0.996756 & 0.997507 \tabularnewline
14 & 88.01 & 88.1728 & 88.6821 & 0.994258 & 0.998153 \tabularnewline
15 & 88.59 & 88.2126 & 88.8442 & 0.992891 & 1.00428 \tabularnewline
16 & 89.43 & 88.7718 & 89.0042 & 0.997389 & 1.00741 \tabularnewline
17 & 89.63 & 89.5619 & 89.1604 & 1.0045 & 1.00076 \tabularnewline
18 & 89.73 & 89.6681 & 89.3233 & 1.00386 & 1.00069 \tabularnewline
19 & 89.88 & 89.9227 & 89.4992 & 1.00473 & 0.999525 \tabularnewline
20 & 89.89 & 90.0141 & 89.6792 & 1.00374 & 0.998621 \tabularnewline
21 & 89.9 & 90.0853 & 89.8396 & 1.00274 & 0.997943 \tabularnewline
22 & 89.91 & 90.084 & 89.97 & 1.00127 & 0.998068 \tabularnewline
23 & 89.86 & 90.0868 & 90.1308 & 0.999512 & 0.997482 \tabularnewline
24 & 90.07 & 90.1823 & 90.3304 & 0.99836 & 0.998755 \tabularnewline
25 & 90.17 & 90.2305 & 90.5242 & 0.996756 & 0.99933 \tabularnewline
26 & 90.17 & 90.1953 & 90.7162 & 0.994258 & 0.999719 \tabularnewline
27 & 90.28 & 90.265 & 90.9112 & 0.992891 & 1.00017 \tabularnewline
28 & 90.87 & 90.8704 & 91.1083 & 0.997389 & 0.999995 \tabularnewline
29 & 92.05 & 91.72 & 91.3087 & 1.0045 & 1.0036 \tabularnewline
30 & 92.1 & 91.8557 & 91.5025 & 1.00386 & 1.00266 \tabularnewline
31 & 92.16 & 92.1172 & 91.6833 & 1.00473 & 1.00046 \tabularnewline
32 & 92.22 & 92.2031 & 91.86 & 1.00374 & 1.00018 \tabularnewline
33 & 92.25 & 92.2696 & 92.0179 & 1.00274 & 0.999787 \tabularnewline
34 & 92.29 & 92.2438 & 92.1271 & 1.00127 & 1.0005 \tabularnewline
35 & 92.29 & 92.1263 & 92.1712 & 0.999512 & 1.00178 \tabularnewline
36 & 92.29 & 92.0334 & 92.1846 & 0.99836 & 1.00279 \tabularnewline
37 & 92.29 & 91.9013 & 92.2004 & 0.996756 & 1.00423 \tabularnewline
38 & 92.29 & 91.6855 & 92.215 & 0.994258 & 1.00659 \tabularnewline
39 & 91.95 & 91.5789 & 92.2346 & 0.992891 & 1.00405 \tabularnewline
40 & 91.82 & 92.0141 & 92.255 & 0.997389 & 0.99789 \tabularnewline
41 & 92.16 & 92.6893 & 92.2737 & 1.0045 & 0.99429 \tabularnewline
42 & 92.31 & 92.6525 & 92.2962 & 1.00386 & 0.996303 \tabularnewline
43 & 92.33 & 92.7573 & 92.3204 & 1.00473 & 0.995393 \tabularnewline
44 & 92.4 & 92.6816 & 92.3367 & 1.00374 & 0.996962 \tabularnewline
45 & 92.54 & 92.6093 & 92.3567 & 1.00274 & 0.999252 \tabularnewline
46 & 92.49 & 92.5634 & 92.4462 & 1.00127 & 0.999207 \tabularnewline
47 & 92.54 & 92.6144 & 92.6596 & 0.999512 & 0.999197 \tabularnewline
48 & 92.58 & 92.8001 & 92.9525 & 0.99836 & 0.997628 \tabularnewline
49 & 92.58 & 92.9653 & 93.2679 & 0.996756 & 0.995855 \tabularnewline
50 & 92.39 & 93.0923 & 93.63 & 0.994258 & 0.992455 \tabularnewline
51 & 92.33 & 93.4112 & 94.08 & 0.992891 & 0.988425 \tabularnewline
52 & 93.59 & 94.3808 & 94.6279 & 0.997389 & 0.991621 \tabularnewline
53 & 95.51 & 95.6752 & 95.2463 & 1.0045 & 0.998273 \tabularnewline
54 & 95.99 & 96.253 & 95.8829 & 1.00386 & 0.997268 \tabularnewline
55 & 96.22 & NA & NA & 1.00473 & NA \tabularnewline
56 & 97.2 & NA & NA & 1.00374 & NA \tabularnewline
57 & 98.54 & NA & NA & 1.00274 & NA \tabularnewline
58 & 99.64 & NA & NA & 1.00127 & NA \tabularnewline
59 & 100.23 & NA & NA & 0.999512 & NA \tabularnewline
60 & 100.17 & NA & NA & 0.99836 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232577&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]85.73[/C][C]NA[/C][C]NA[/C][C]0.996756[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]85.73[/C][C]NA[/C][C]NA[/C][C]0.994258[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]85.74[/C][C]NA[/C][C]NA[/C][C]0.992891[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]86.32[/C][C]NA[/C][C]NA[/C][C]0.997389[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]87.59[/C][C]NA[/C][C]NA[/C][C]1.0045[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]87.81[/C][C]NA[/C][C]NA[/C][C]1.00386[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]87.87[/C][C]87.7349[/C][C]87.3217[/C][C]1.00473[/C][C]1.00154[/C][/ROW]
[ROW][C]8[/C][C]87.94[/C][C]87.8385[/C][C]87.5117[/C][C]1.00374[/C][C]1.00116[/C][/ROW]
[ROW][C]9[/C][C]87.96[/C][C]87.9654[/C][C]87.7254[/C][C]1.00274[/C][C]0.999939[/C][/ROW]
[ROW][C]10[/C][C]88.01[/C][C]88.0852[/C][C]87.9738[/C][C]1.00127[/C][C]0.999146[/C][/ROW]
[ROW][C]11[/C][C]88.01[/C][C]88.1453[/C][C]88.1883[/C][C]0.999512[/C][C]0.998465[/C][/ROW]
[ROW][C]12[/C][C]88.01[/C][C]88.2085[/C][C]88.3533[/C][C]0.99836[/C][C]0.99775[/C][/ROW]
[ROW][C]13[/C][C]88.01[/C][C]88.2299[/C][C]88.5171[/C][C]0.996756[/C][C]0.997507[/C][/ROW]
[ROW][C]14[/C][C]88.01[/C][C]88.1728[/C][C]88.6821[/C][C]0.994258[/C][C]0.998153[/C][/ROW]
[ROW][C]15[/C][C]88.59[/C][C]88.2126[/C][C]88.8442[/C][C]0.992891[/C][C]1.00428[/C][/ROW]
[ROW][C]16[/C][C]89.43[/C][C]88.7718[/C][C]89.0042[/C][C]0.997389[/C][C]1.00741[/C][/ROW]
[ROW][C]17[/C][C]89.63[/C][C]89.5619[/C][C]89.1604[/C][C]1.0045[/C][C]1.00076[/C][/ROW]
[ROW][C]18[/C][C]89.73[/C][C]89.6681[/C][C]89.3233[/C][C]1.00386[/C][C]1.00069[/C][/ROW]
[ROW][C]19[/C][C]89.88[/C][C]89.9227[/C][C]89.4992[/C][C]1.00473[/C][C]0.999525[/C][/ROW]
[ROW][C]20[/C][C]89.89[/C][C]90.0141[/C][C]89.6792[/C][C]1.00374[/C][C]0.998621[/C][/ROW]
[ROW][C]21[/C][C]89.9[/C][C]90.0853[/C][C]89.8396[/C][C]1.00274[/C][C]0.997943[/C][/ROW]
[ROW][C]22[/C][C]89.91[/C][C]90.084[/C][C]89.97[/C][C]1.00127[/C][C]0.998068[/C][/ROW]
[ROW][C]23[/C][C]89.86[/C][C]90.0868[/C][C]90.1308[/C][C]0.999512[/C][C]0.997482[/C][/ROW]
[ROW][C]24[/C][C]90.07[/C][C]90.1823[/C][C]90.3304[/C][C]0.99836[/C][C]0.998755[/C][/ROW]
[ROW][C]25[/C][C]90.17[/C][C]90.2305[/C][C]90.5242[/C][C]0.996756[/C][C]0.99933[/C][/ROW]
[ROW][C]26[/C][C]90.17[/C][C]90.1953[/C][C]90.7162[/C][C]0.994258[/C][C]0.999719[/C][/ROW]
[ROW][C]27[/C][C]90.28[/C][C]90.265[/C][C]90.9112[/C][C]0.992891[/C][C]1.00017[/C][/ROW]
[ROW][C]28[/C][C]90.87[/C][C]90.8704[/C][C]91.1083[/C][C]0.997389[/C][C]0.999995[/C][/ROW]
[ROW][C]29[/C][C]92.05[/C][C]91.72[/C][C]91.3087[/C][C]1.0045[/C][C]1.0036[/C][/ROW]
[ROW][C]30[/C][C]92.1[/C][C]91.8557[/C][C]91.5025[/C][C]1.00386[/C][C]1.00266[/C][/ROW]
[ROW][C]31[/C][C]92.16[/C][C]92.1172[/C][C]91.6833[/C][C]1.00473[/C][C]1.00046[/C][/ROW]
[ROW][C]32[/C][C]92.22[/C][C]92.2031[/C][C]91.86[/C][C]1.00374[/C][C]1.00018[/C][/ROW]
[ROW][C]33[/C][C]92.25[/C][C]92.2696[/C][C]92.0179[/C][C]1.00274[/C][C]0.999787[/C][/ROW]
[ROW][C]34[/C][C]92.29[/C][C]92.2438[/C][C]92.1271[/C][C]1.00127[/C][C]1.0005[/C][/ROW]
[ROW][C]35[/C][C]92.29[/C][C]92.1263[/C][C]92.1712[/C][C]0.999512[/C][C]1.00178[/C][/ROW]
[ROW][C]36[/C][C]92.29[/C][C]92.0334[/C][C]92.1846[/C][C]0.99836[/C][C]1.00279[/C][/ROW]
[ROW][C]37[/C][C]92.29[/C][C]91.9013[/C][C]92.2004[/C][C]0.996756[/C][C]1.00423[/C][/ROW]
[ROW][C]38[/C][C]92.29[/C][C]91.6855[/C][C]92.215[/C][C]0.994258[/C][C]1.00659[/C][/ROW]
[ROW][C]39[/C][C]91.95[/C][C]91.5789[/C][C]92.2346[/C][C]0.992891[/C][C]1.00405[/C][/ROW]
[ROW][C]40[/C][C]91.82[/C][C]92.0141[/C][C]92.255[/C][C]0.997389[/C][C]0.99789[/C][/ROW]
[ROW][C]41[/C][C]92.16[/C][C]92.6893[/C][C]92.2737[/C][C]1.0045[/C][C]0.99429[/C][/ROW]
[ROW][C]42[/C][C]92.31[/C][C]92.6525[/C][C]92.2962[/C][C]1.00386[/C][C]0.996303[/C][/ROW]
[ROW][C]43[/C][C]92.33[/C][C]92.7573[/C][C]92.3204[/C][C]1.00473[/C][C]0.995393[/C][/ROW]
[ROW][C]44[/C][C]92.4[/C][C]92.6816[/C][C]92.3367[/C][C]1.00374[/C][C]0.996962[/C][/ROW]
[ROW][C]45[/C][C]92.54[/C][C]92.6093[/C][C]92.3567[/C][C]1.00274[/C][C]0.999252[/C][/ROW]
[ROW][C]46[/C][C]92.49[/C][C]92.5634[/C][C]92.4462[/C][C]1.00127[/C][C]0.999207[/C][/ROW]
[ROW][C]47[/C][C]92.54[/C][C]92.6144[/C][C]92.6596[/C][C]0.999512[/C][C]0.999197[/C][/ROW]
[ROW][C]48[/C][C]92.58[/C][C]92.8001[/C][C]92.9525[/C][C]0.99836[/C][C]0.997628[/C][/ROW]
[ROW][C]49[/C][C]92.58[/C][C]92.9653[/C][C]93.2679[/C][C]0.996756[/C][C]0.995855[/C][/ROW]
[ROW][C]50[/C][C]92.39[/C][C]93.0923[/C][C]93.63[/C][C]0.994258[/C][C]0.992455[/C][/ROW]
[ROW][C]51[/C][C]92.33[/C][C]93.4112[/C][C]94.08[/C][C]0.992891[/C][C]0.988425[/C][/ROW]
[ROW][C]52[/C][C]93.59[/C][C]94.3808[/C][C]94.6279[/C][C]0.997389[/C][C]0.991621[/C][/ROW]
[ROW][C]53[/C][C]95.51[/C][C]95.6752[/C][C]95.2463[/C][C]1.0045[/C][C]0.998273[/C][/ROW]
[ROW][C]54[/C][C]95.99[/C][C]96.253[/C][C]95.8829[/C][C]1.00386[/C][C]0.997268[/C][/ROW]
[ROW][C]55[/C][C]96.22[/C][C]NA[/C][C]NA[/C][C]1.00473[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]97.2[/C][C]NA[/C][C]NA[/C][C]1.00374[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]98.54[/C][C]NA[/C][C]NA[/C][C]1.00274[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]99.64[/C][C]NA[/C][C]NA[/C][C]1.00127[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]100.23[/C][C]NA[/C][C]NA[/C][C]0.999512[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]100.17[/C][C]NA[/C][C]NA[/C][C]0.99836[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232577&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232577&T=1

As an alternative you can also use a QR Code:

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

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	85.73	NA	NA	0.996756	NA
2	85.73	NA	NA	0.994258	NA
3	85.74	NA	NA	0.992891	NA
4	86.32	NA	NA	0.997389	NA
5	87.59	NA	NA	1.0045	NA
6	87.81	NA	NA	1.00386	NA
7	87.87	87.7349	87.3217	1.00473	1.00154
8	87.94	87.8385	87.5117	1.00374	1.00116
9	87.96	87.9654	87.7254	1.00274	0.999939
10	88.01	88.0852	87.9738	1.00127	0.999146
11	88.01	88.1453	88.1883	0.999512	0.998465
12	88.01	88.2085	88.3533	0.99836	0.99775
13	88.01	88.2299	88.5171	0.996756	0.997507
14	88.01	88.1728	88.6821	0.994258	0.998153
15	88.59	88.2126	88.8442	0.992891	1.00428
16	89.43	88.7718	89.0042	0.997389	1.00741
17	89.63	89.5619	89.1604	1.0045	1.00076
18	89.73	89.6681	89.3233	1.00386	1.00069
19	89.88	89.9227	89.4992	1.00473	0.999525
20	89.89	90.0141	89.6792	1.00374	0.998621
21	89.9	90.0853	89.8396	1.00274	0.997943
22	89.91	90.084	89.97	1.00127	0.998068
23	89.86	90.0868	90.1308	0.999512	0.997482
24	90.07	90.1823	90.3304	0.99836	0.998755
25	90.17	90.2305	90.5242	0.996756	0.99933
26	90.17	90.1953	90.7162	0.994258	0.999719
27	90.28	90.265	90.9112	0.992891	1.00017
28	90.87	90.8704	91.1083	0.997389	0.999995
29	92.05	91.72	91.3087	1.0045	1.0036
30	92.1	91.8557	91.5025	1.00386	1.00266
31	92.16	92.1172	91.6833	1.00473	1.00046
32	92.22	92.2031	91.86	1.00374	1.00018
33	92.25	92.2696	92.0179	1.00274	0.999787
34	92.29	92.2438	92.1271	1.00127	1.0005
35	92.29	92.1263	92.1712	0.999512	1.00178
36	92.29	92.0334	92.1846	0.99836	1.00279
37	92.29	91.9013	92.2004	0.996756	1.00423
38	92.29	91.6855	92.215	0.994258	1.00659
39	91.95	91.5789	92.2346	0.992891	1.00405
40	91.82	92.0141	92.255	0.997389	0.99789
41	92.16	92.6893	92.2737	1.0045	0.99429
42	92.31	92.6525	92.2962	1.00386	0.996303
43	92.33	92.7573	92.3204	1.00473	0.995393
44	92.4	92.6816	92.3367	1.00374	0.996962
45	92.54	92.6093	92.3567	1.00274	0.999252
46	92.49	92.5634	92.4462	1.00127	0.999207
47	92.54	92.6144	92.6596	0.999512	0.999197
48	92.58	92.8001	92.9525	0.99836	0.997628
49	92.58	92.9653	93.2679	0.996756	0.995855
50	92.39	93.0923	93.63	0.994258	0.992455
51	92.33	93.4112	94.08	0.992891	0.988425
52	93.59	94.3808	94.6279	0.997389	0.991621
53	95.51	95.6752	95.2463	1.0045	0.998273
54	95.99	96.253	95.8829	1.00386	0.997268
55	96.22	NA	NA	1.00473	NA
56	97.2	NA	NA	1.00374	NA
57	98.54	NA	NA	1.00274	NA
58	99.64	NA	NA	1.00127	NA
59	100.23	NA	NA	0.999512	NA
60	100.17	NA	NA	0.99836	NA

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code