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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Thu, 22 May 2014 18:36:52 -0400

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/May/22/t1400798237zg5gt98h7w3gm0k.htm/, Retrieved Wed, 15 May 2024 15:06:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=235157, Retrieved Wed, 15 May 2024 15:06:30 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Classical Decomposition] [] [2014-05-22 19:38:25] [74be16979710d4c4e7c6647856088456]
- R PD    [Classical Decomposition] [] [2014-05-22 22:36:52] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- RM        [Classical Decomposition] [] [2014-05-22 23:12:56] [db363657be53a1294332fdf107f4512c] 

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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	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=235157&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]3 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=235157&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=235157&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	3 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	0.978	NA	NA	1.00745	NA
2	0.973	NA	NA	1.01109	NA
3	0.96	NA	NA	1.02385	NA
4	0.978	NA	NA	1.02266	NA
5	0.985	NA	NA	1.00284	NA
6	1.035	NA	NA	0.988898	NA
7	1.015	1.0066	1.01767	0.989126	1.00834
8	1.05	1.02171	1.02783	0.994038	1.02769
9	1.022	1.02729	1.04046	0.98734	0.994854
10	1.042	1.04641	1.05642	0.99053	0.995783
11	1.058	1.06622	1.07342	0.993299	0.992287
12	1.056	1.07631	1.08842	0.988875	0.981132
13	1.098	1.1095	1.10129	1.00745	0.989636
14	1.097	1.12492	1.11258	1.01109	0.97518
15	1.139	1.15077	1.12396	1.02385	0.989776
16	1.182	1.16209	1.13633	1.02266	1.01714
17	1.189	1.15143	1.14817	1.00284	1.03263
18	1.191	1.14931	1.16221	0.988898	1.03628
19	1.168	1.16544	1.17825	0.989126	1.0022
20	1.168	1.18804	1.19517	0.994038	0.983131
21	1.177	1.19814	1.2135	0.98734	0.982358
22	1.184	1.2193	1.23096	0.99053	0.971048
23	1.2	1.23711	1.24546	0.993299	0.970001
24	1.251	1.2438	1.25779	0.988875	1.00579
25	1.288	1.28072	1.27125	1.00745	1.00568
26	1.313	1.29963	1.28538	1.01109	1.01029
27	1.363	1.33015	1.29917	1.02385	1.02469
28	1.377	1.34348	1.31371	1.02266	1.02495
29	1.342	1.3329	1.32912	1.00284	1.00683
30	1.334	1.32764	1.34254	0.988898	1.00479
31	1.348	1.33874	1.35346	0.989126	1.00692
32	1.327	1.35583	1.36396	0.994038	0.978739
33	1.349	1.35545	1.37283	0.98734	0.995239
34	1.361	1.36685	1.37992	0.99053	0.995721
35	1.393	1.37737	1.38667	0.993299	1.01134
36	1.38	1.3768	1.39229	0.988875	1.00232
37	1.421	1.40724	1.39683	1.00745	1.00978
38	1.432	1.42037	1.40479	1.01109	1.00819
39	1.457	1.44922	1.41546	1.02385	1.00537
40	1.453	1.4584	1.42608	1.02266	0.996295
41	1.428	1.43903	1.43496	1.00284	0.992332
42	1.383	1.42517	1.44117	0.988898	0.970413
43	1.408	1.42945	1.44517	0.989126	0.984993
44	1.458	1.43945	1.44808	0.994038	1.01289
45	1.474	1.43107	1.44942	0.98734	1.03
46	1.491	1.43379	1.4475	0.99053	1.0399
47	1.476	1.43465	1.44433	0.993299	1.02882
48	1.446	1.42715	1.44321	0.988875	1.01321
49	1.451	1.45484	1.44408	1.00745	0.997357
50	1.472	1.45917	1.44317	1.01109	1.00879
51	1.449	1.47464	1.44029	1.02385	0.982611
52	1.415	1.46773	1.43521	1.02266	0.964071
53	1.39	1.43201	1.42796	1.00284	0.970661
54	1.394	1.40671	1.4225	0.988898	0.990967
55	1.418	NA	NA	0.989126	NA
56	1.426	NA	NA	0.994038	NA
57	1.437	NA	NA	0.98734	NA
58	1.406	NA	NA	0.99053	NA
59	1.387	NA	NA	0.993299	NA
60	1.404	NA	NA	0.988875	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 0.978 & NA & NA & 1.00745 & NA \tabularnewline
2 & 0.973 & NA & NA & 1.01109 & NA \tabularnewline
3 & 0.96 & NA & NA & 1.02385 & NA \tabularnewline
4 & 0.978 & NA & NA & 1.02266 & NA \tabularnewline
5 & 0.985 & NA & NA & 1.00284 & NA \tabularnewline
6 & 1.035 & NA & NA & 0.988898 & NA \tabularnewline
7 & 1.015 & 1.0066 & 1.01767 & 0.989126 & 1.00834 \tabularnewline
8 & 1.05 & 1.02171 & 1.02783 & 0.994038 & 1.02769 \tabularnewline
9 & 1.022 & 1.02729 & 1.04046 & 0.98734 & 0.994854 \tabularnewline
10 & 1.042 & 1.04641 & 1.05642 & 0.99053 & 0.995783 \tabularnewline
11 & 1.058 & 1.06622 & 1.07342 & 0.993299 & 0.992287 \tabularnewline
12 & 1.056 & 1.07631 & 1.08842 & 0.988875 & 0.981132 \tabularnewline
13 & 1.098 & 1.1095 & 1.10129 & 1.00745 & 0.989636 \tabularnewline
14 & 1.097 & 1.12492 & 1.11258 & 1.01109 & 0.97518 \tabularnewline
15 & 1.139 & 1.15077 & 1.12396 & 1.02385 & 0.989776 \tabularnewline
16 & 1.182 & 1.16209 & 1.13633 & 1.02266 & 1.01714 \tabularnewline
17 & 1.189 & 1.15143 & 1.14817 & 1.00284 & 1.03263 \tabularnewline
18 & 1.191 & 1.14931 & 1.16221 & 0.988898 & 1.03628 \tabularnewline
19 & 1.168 & 1.16544 & 1.17825 & 0.989126 & 1.0022 \tabularnewline
20 & 1.168 & 1.18804 & 1.19517 & 0.994038 & 0.983131 \tabularnewline
21 & 1.177 & 1.19814 & 1.2135 & 0.98734 & 0.982358 \tabularnewline
22 & 1.184 & 1.2193 & 1.23096 & 0.99053 & 0.971048 \tabularnewline
23 & 1.2 & 1.23711 & 1.24546 & 0.993299 & 0.970001 \tabularnewline
24 & 1.251 & 1.2438 & 1.25779 & 0.988875 & 1.00579 \tabularnewline
25 & 1.288 & 1.28072 & 1.27125 & 1.00745 & 1.00568 \tabularnewline
26 & 1.313 & 1.29963 & 1.28538 & 1.01109 & 1.01029 \tabularnewline
27 & 1.363 & 1.33015 & 1.29917 & 1.02385 & 1.02469 \tabularnewline
28 & 1.377 & 1.34348 & 1.31371 & 1.02266 & 1.02495 \tabularnewline
29 & 1.342 & 1.3329 & 1.32912 & 1.00284 & 1.00683 \tabularnewline
30 & 1.334 & 1.32764 & 1.34254 & 0.988898 & 1.00479 \tabularnewline
31 & 1.348 & 1.33874 & 1.35346 & 0.989126 & 1.00692 \tabularnewline
32 & 1.327 & 1.35583 & 1.36396 & 0.994038 & 0.978739 \tabularnewline
33 & 1.349 & 1.35545 & 1.37283 & 0.98734 & 0.995239 \tabularnewline
34 & 1.361 & 1.36685 & 1.37992 & 0.99053 & 0.995721 \tabularnewline
35 & 1.393 & 1.37737 & 1.38667 & 0.993299 & 1.01134 \tabularnewline
36 & 1.38 & 1.3768 & 1.39229 & 0.988875 & 1.00232 \tabularnewline
37 & 1.421 & 1.40724 & 1.39683 & 1.00745 & 1.00978 \tabularnewline
38 & 1.432 & 1.42037 & 1.40479 & 1.01109 & 1.00819 \tabularnewline
39 & 1.457 & 1.44922 & 1.41546 & 1.02385 & 1.00537 \tabularnewline
40 & 1.453 & 1.4584 & 1.42608 & 1.02266 & 0.996295 \tabularnewline
41 & 1.428 & 1.43903 & 1.43496 & 1.00284 & 0.992332 \tabularnewline
42 & 1.383 & 1.42517 & 1.44117 & 0.988898 & 0.970413 \tabularnewline
43 & 1.408 & 1.42945 & 1.44517 & 0.989126 & 0.984993 \tabularnewline
44 & 1.458 & 1.43945 & 1.44808 & 0.994038 & 1.01289 \tabularnewline
45 & 1.474 & 1.43107 & 1.44942 & 0.98734 & 1.03 \tabularnewline
46 & 1.491 & 1.43379 & 1.4475 & 0.99053 & 1.0399 \tabularnewline
47 & 1.476 & 1.43465 & 1.44433 & 0.993299 & 1.02882 \tabularnewline
48 & 1.446 & 1.42715 & 1.44321 & 0.988875 & 1.01321 \tabularnewline
49 & 1.451 & 1.45484 & 1.44408 & 1.00745 & 0.997357 \tabularnewline
50 & 1.472 & 1.45917 & 1.44317 & 1.01109 & 1.00879 \tabularnewline
51 & 1.449 & 1.47464 & 1.44029 & 1.02385 & 0.982611 \tabularnewline
52 & 1.415 & 1.46773 & 1.43521 & 1.02266 & 0.964071 \tabularnewline
53 & 1.39 & 1.43201 & 1.42796 & 1.00284 & 0.970661 \tabularnewline
54 & 1.394 & 1.40671 & 1.4225 & 0.988898 & 0.990967 \tabularnewline
55 & 1.418 & NA & NA & 0.989126 & NA \tabularnewline
56 & 1.426 & NA & NA & 0.994038 & NA \tabularnewline
57 & 1.437 & NA & NA & 0.98734 & NA \tabularnewline
58 & 1.406 & NA & NA & 0.99053 & NA \tabularnewline
59 & 1.387 & NA & NA & 0.993299 & NA \tabularnewline
60 & 1.404 & NA & NA & 0.988875 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=235157&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]0.978[/C][C]NA[/C][C]NA[/C][C]1.00745[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]0.973[/C][C]NA[/C][C]NA[/C][C]1.01109[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]0.96[/C][C]NA[/C][C]NA[/C][C]1.02385[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]0.978[/C][C]NA[/C][C]NA[/C][C]1.02266[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]0.985[/C][C]NA[/C][C]NA[/C][C]1.00284[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.035[/C][C]NA[/C][C]NA[/C][C]0.988898[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.015[/C][C]1.0066[/C][C]1.01767[/C][C]0.989126[/C][C]1.00834[/C][/ROW]
[ROW][C]8[/C][C]1.05[/C][C]1.02171[/C][C]1.02783[/C][C]0.994038[/C][C]1.02769[/C][/ROW]
[ROW][C]9[/C][C]1.022[/C][C]1.02729[/C][C]1.04046[/C][C]0.98734[/C][C]0.994854[/C][/ROW]
[ROW][C]10[/C][C]1.042[/C][C]1.04641[/C][C]1.05642[/C][C]0.99053[/C][C]0.995783[/C][/ROW]
[ROW][C]11[/C][C]1.058[/C][C]1.06622[/C][C]1.07342[/C][C]0.993299[/C][C]0.992287[/C][/ROW]
[ROW][C]12[/C][C]1.056[/C][C]1.07631[/C][C]1.08842[/C][C]0.988875[/C][C]0.981132[/C][/ROW]
[ROW][C]13[/C][C]1.098[/C][C]1.1095[/C][C]1.10129[/C][C]1.00745[/C][C]0.989636[/C][/ROW]
[ROW][C]14[/C][C]1.097[/C][C]1.12492[/C][C]1.11258[/C][C]1.01109[/C][C]0.97518[/C][/ROW]
[ROW][C]15[/C][C]1.139[/C][C]1.15077[/C][C]1.12396[/C][C]1.02385[/C][C]0.989776[/C][/ROW]
[ROW][C]16[/C][C]1.182[/C][C]1.16209[/C][C]1.13633[/C][C]1.02266[/C][C]1.01714[/C][/ROW]
[ROW][C]17[/C][C]1.189[/C][C]1.15143[/C][C]1.14817[/C][C]1.00284[/C][C]1.03263[/C][/ROW]
[ROW][C]18[/C][C]1.191[/C][C]1.14931[/C][C]1.16221[/C][C]0.988898[/C][C]1.03628[/C][/ROW]
[ROW][C]19[/C][C]1.168[/C][C]1.16544[/C][C]1.17825[/C][C]0.989126[/C][C]1.0022[/C][/ROW]
[ROW][C]20[/C][C]1.168[/C][C]1.18804[/C][C]1.19517[/C][C]0.994038[/C][C]0.983131[/C][/ROW]
[ROW][C]21[/C][C]1.177[/C][C]1.19814[/C][C]1.2135[/C][C]0.98734[/C][C]0.982358[/C][/ROW]
[ROW][C]22[/C][C]1.184[/C][C]1.2193[/C][C]1.23096[/C][C]0.99053[/C][C]0.971048[/C][/ROW]
[ROW][C]23[/C][C]1.2[/C][C]1.23711[/C][C]1.24546[/C][C]0.993299[/C][C]0.970001[/C][/ROW]
[ROW][C]24[/C][C]1.251[/C][C]1.2438[/C][C]1.25779[/C][C]0.988875[/C][C]1.00579[/C][/ROW]
[ROW][C]25[/C][C]1.288[/C][C]1.28072[/C][C]1.27125[/C][C]1.00745[/C][C]1.00568[/C][/ROW]
[ROW][C]26[/C][C]1.313[/C][C]1.29963[/C][C]1.28538[/C][C]1.01109[/C][C]1.01029[/C][/ROW]
[ROW][C]27[/C][C]1.363[/C][C]1.33015[/C][C]1.29917[/C][C]1.02385[/C][C]1.02469[/C][/ROW]
[ROW][C]28[/C][C]1.377[/C][C]1.34348[/C][C]1.31371[/C][C]1.02266[/C][C]1.02495[/C][/ROW]
[ROW][C]29[/C][C]1.342[/C][C]1.3329[/C][C]1.32912[/C][C]1.00284[/C][C]1.00683[/C][/ROW]
[ROW][C]30[/C][C]1.334[/C][C]1.32764[/C][C]1.34254[/C][C]0.988898[/C][C]1.00479[/C][/ROW]
[ROW][C]31[/C][C]1.348[/C][C]1.33874[/C][C]1.35346[/C][C]0.989126[/C][C]1.00692[/C][/ROW]
[ROW][C]32[/C][C]1.327[/C][C]1.35583[/C][C]1.36396[/C][C]0.994038[/C][C]0.978739[/C][/ROW]
[ROW][C]33[/C][C]1.349[/C][C]1.35545[/C][C]1.37283[/C][C]0.98734[/C][C]0.995239[/C][/ROW]
[ROW][C]34[/C][C]1.361[/C][C]1.36685[/C][C]1.37992[/C][C]0.99053[/C][C]0.995721[/C][/ROW]
[ROW][C]35[/C][C]1.393[/C][C]1.37737[/C][C]1.38667[/C][C]0.993299[/C][C]1.01134[/C][/ROW]
[ROW][C]36[/C][C]1.38[/C][C]1.3768[/C][C]1.39229[/C][C]0.988875[/C][C]1.00232[/C][/ROW]
[ROW][C]37[/C][C]1.421[/C][C]1.40724[/C][C]1.39683[/C][C]1.00745[/C][C]1.00978[/C][/ROW]
[ROW][C]38[/C][C]1.432[/C][C]1.42037[/C][C]1.40479[/C][C]1.01109[/C][C]1.00819[/C][/ROW]
[ROW][C]39[/C][C]1.457[/C][C]1.44922[/C][C]1.41546[/C][C]1.02385[/C][C]1.00537[/C][/ROW]
[ROW][C]40[/C][C]1.453[/C][C]1.4584[/C][C]1.42608[/C][C]1.02266[/C][C]0.996295[/C][/ROW]
[ROW][C]41[/C][C]1.428[/C][C]1.43903[/C][C]1.43496[/C][C]1.00284[/C][C]0.992332[/C][/ROW]
[ROW][C]42[/C][C]1.383[/C][C]1.42517[/C][C]1.44117[/C][C]0.988898[/C][C]0.970413[/C][/ROW]
[ROW][C]43[/C][C]1.408[/C][C]1.42945[/C][C]1.44517[/C][C]0.989126[/C][C]0.984993[/C][/ROW]
[ROW][C]44[/C][C]1.458[/C][C]1.43945[/C][C]1.44808[/C][C]0.994038[/C][C]1.01289[/C][/ROW]
[ROW][C]45[/C][C]1.474[/C][C]1.43107[/C][C]1.44942[/C][C]0.98734[/C][C]1.03[/C][/ROW]
[ROW][C]46[/C][C]1.491[/C][C]1.43379[/C][C]1.4475[/C][C]0.99053[/C][C]1.0399[/C][/ROW]
[ROW][C]47[/C][C]1.476[/C][C]1.43465[/C][C]1.44433[/C][C]0.993299[/C][C]1.02882[/C][/ROW]
[ROW][C]48[/C][C]1.446[/C][C]1.42715[/C][C]1.44321[/C][C]0.988875[/C][C]1.01321[/C][/ROW]
[ROW][C]49[/C][C]1.451[/C][C]1.45484[/C][C]1.44408[/C][C]1.00745[/C][C]0.997357[/C][/ROW]
[ROW][C]50[/C][C]1.472[/C][C]1.45917[/C][C]1.44317[/C][C]1.01109[/C][C]1.00879[/C][/ROW]
[ROW][C]51[/C][C]1.449[/C][C]1.47464[/C][C]1.44029[/C][C]1.02385[/C][C]0.982611[/C][/ROW]
[ROW][C]52[/C][C]1.415[/C][C]1.46773[/C][C]1.43521[/C][C]1.02266[/C][C]0.964071[/C][/ROW]
[ROW][C]53[/C][C]1.39[/C][C]1.43201[/C][C]1.42796[/C][C]1.00284[/C][C]0.970661[/C][/ROW]
[ROW][C]54[/C][C]1.394[/C][C]1.40671[/C][C]1.4225[/C][C]0.988898[/C][C]0.990967[/C][/ROW]
[ROW][C]55[/C][C]1.418[/C][C]NA[/C][C]NA[/C][C]0.989126[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]1.426[/C][C]NA[/C][C]NA[/C][C]0.994038[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]1.437[/C][C]NA[/C][C]NA[/C][C]0.98734[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]1.406[/C][C]NA[/C][C]NA[/C][C]0.99053[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]1.387[/C][C]NA[/C][C]NA[/C][C]0.993299[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]1.404[/C][C]NA[/C][C]NA[/C][C]0.988875[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=235157&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=235157&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	0.978	NA	NA	1.00745	NA
2	0.973	NA	NA	1.01109	NA
3	0.96	NA	NA	1.02385	NA
4	0.978	NA	NA	1.02266	NA
5	0.985	NA	NA	1.00284	NA
6	1.035	NA	NA	0.988898	NA
7	1.015	1.0066	1.01767	0.989126	1.00834
8	1.05	1.02171	1.02783	0.994038	1.02769
9	1.022	1.02729	1.04046	0.98734	0.994854
10	1.042	1.04641	1.05642	0.99053	0.995783
11	1.058	1.06622	1.07342	0.993299	0.992287
12	1.056	1.07631	1.08842	0.988875	0.981132
13	1.098	1.1095	1.10129	1.00745	0.989636
14	1.097	1.12492	1.11258	1.01109	0.97518
15	1.139	1.15077	1.12396	1.02385	0.989776
16	1.182	1.16209	1.13633	1.02266	1.01714
17	1.189	1.15143	1.14817	1.00284	1.03263
18	1.191	1.14931	1.16221	0.988898	1.03628
19	1.168	1.16544	1.17825	0.989126	1.0022
20	1.168	1.18804	1.19517	0.994038	0.983131
21	1.177	1.19814	1.2135	0.98734	0.982358
22	1.184	1.2193	1.23096	0.99053	0.971048
23	1.2	1.23711	1.24546	0.993299	0.970001
24	1.251	1.2438	1.25779	0.988875	1.00579
25	1.288	1.28072	1.27125	1.00745	1.00568
26	1.313	1.29963	1.28538	1.01109	1.01029
27	1.363	1.33015	1.29917	1.02385	1.02469
28	1.377	1.34348	1.31371	1.02266	1.02495
29	1.342	1.3329	1.32912	1.00284	1.00683
30	1.334	1.32764	1.34254	0.988898	1.00479
31	1.348	1.33874	1.35346	0.989126	1.00692
32	1.327	1.35583	1.36396	0.994038	0.978739
33	1.349	1.35545	1.37283	0.98734	0.995239
34	1.361	1.36685	1.37992	0.99053	0.995721
35	1.393	1.37737	1.38667	0.993299	1.01134
36	1.38	1.3768	1.39229	0.988875	1.00232
37	1.421	1.40724	1.39683	1.00745	1.00978
38	1.432	1.42037	1.40479	1.01109	1.00819
39	1.457	1.44922	1.41546	1.02385	1.00537
40	1.453	1.4584	1.42608	1.02266	0.996295
41	1.428	1.43903	1.43496	1.00284	0.992332
42	1.383	1.42517	1.44117	0.988898	0.970413
43	1.408	1.42945	1.44517	0.989126	0.984993
44	1.458	1.43945	1.44808	0.994038	1.01289
45	1.474	1.43107	1.44942	0.98734	1.03
46	1.491	1.43379	1.4475	0.99053	1.0399
47	1.476	1.43465	1.44433	0.993299	1.02882
48	1.446	1.42715	1.44321	0.988875	1.01321
49	1.451	1.45484	1.44408	1.00745	0.997357
50	1.472	1.45917	1.44317	1.01109	1.00879
51	1.449	1.47464	1.44029	1.02385	0.982611
52	1.415	1.46773	1.43521	1.02266	0.964071
53	1.39	1.43201	1.42796	1.00284	0.970661
54	1.394	1.40671	1.4225	0.988898	0.990967
55	1.418	NA	NA	0.989126	NA
56	1.426	NA	NA	0.994038	NA
57	1.437	NA	NA	0.98734	NA
58	1.406	NA	NA	0.99053	NA
59	1.387	NA	NA	0.993299	NA
60	1.404	NA	NA	0.988875	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 <- '12'
par1 <- 'multiplicative'
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