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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 28 Nov 2016 21:33:54 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Nov/28/t1480368854gtassix7nsqx34a.htm/, Retrieved Sat, 04 May 2024 15:59:15 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 15:59:15 +0200

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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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	94.94	NA	NA	-0.074566	NA
2	96.24	NA	NA	0.981476	NA
3	95.77	NA	NA	0.290955	NA
4	94.41	NA	NA	-0.201649	NA
5	95.09	NA	NA	0.279392	NA
6	95.37	NA	NA	0.263976	NA
7	95.17	95.3448	95.4883	-0.143524	-0.174809
8	95.05	95.2816	95.6754	-0.393837	-0.23158
9	95.33	95.506	95.8733	-0.367378	-0.175955
10	95.42	95.7313	96.0929	-0.361649	-0.311267
11	95.95	96.1695	96.3558	-0.186337	-0.219497
12	96.12	96.5481	96.635	-0.0868576	-0.428142
13	96.94	96.8392	96.9137	-0.074566	0.100816
14	98.73	98.1677	97.1862	0.981476	0.562274
15	98.03	97.7343	97.4433	0.290955	0.295712
16	97.42	97.4846	97.6862	-0.201649	-0.0646007
17	98.39	98.1977	97.9183	0.279392	0.192274
18	98.77	98.4127	98.1487	0.263976	0.357274
19	98.46	98.2023	98.3458	-0.143524	0.257691
20	98.3	98.0932	98.4871	-0.393837	0.206753
21	98.25	98.301	98.6683	-0.367378	-0.0509549
22	98.33	98.4846	98.8463	-0.361649	-0.154601
23	98.61	98.7795	98.9658	-0.186337	-0.169497
24	98.99	99.0077	99.0946	-0.0868576	-0.0177257
25	98.8	99.1567	99.2312	-0.074566	-0.356684
26	100.26	100.342	99.3608	0.981476	-0.082309
27	100.85	99.7776	99.4867	0.290955	1.07238
28	98.87	99.4138	99.6154	-0.201649	-0.543767
29	99.81	100.042	99.7629	0.279392	-0.232309
30	100.44	100.187	99.9229	0.263976	0.253108
31	100.07	99.9511	100.095	-0.143524	0.118941
32	99.8	99.8378	100.232	-0.393837	-0.0378299
33	99.77	99.9326	100.3	-0.367378	-0.162622
34	99.9	100.058	100.419	-0.361649	-0.157517
35	100.58	100.392	100.579	-0.186337	0.187587
36	100.86	100.582	100.669	-0.0868576	0.278108
37	101.05	100.649	100.723	-0.074566	0.401233
38	101.3	101.778	100.797	0.981476	-0.478142
39	101.45	101.189	100.898	0.290955	0.260712
40	101.13	100.816	101.017	-0.201649	0.314149
41	101.38	101.381	101.102	0.279392	-0.00105903
42	101.03	101.405	101.141	0.263976	-0.374809
43	100.79	101.01	101.154	-0.143524	-0.220226
44	100.84	100.796	101.19	-0.393837	0.0442535
45	101.17	100.799	101.166	-0.367378	0.371128
46	101.36	100.755	101.117	-0.361649	0.604983
47	101.14	100.957	101.143	-0.186337	0.183003
48	101.24	101.091	101.178	-0.0868576	0.149358
49	100.98	101.144	101.218	-0.074566	-0.163767
50	102.23	102.25	101.269	0.981476	-0.0202257
51	99.96	101.607	101.316	0.290955	-1.6472
52	101.43	101.154	101.356	-0.201649	0.275816
53	101.72	101.697	101.418	0.279392	0.022691
54	101.51	101.764	101.5	0.263976	-0.253976
55	101.29	NA	NA	-0.143524	NA
56	101.55	NA	NA	-0.393837	NA
57	101.6	NA	NA	-0.367378	NA
58	101.88	NA	NA	-0.361649	NA
59	102.11	NA	NA	-0.186337	NA
60	102.24	NA	NA	-0.0868576	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 94.94 & NA & NA & -0.074566 & NA \tabularnewline
2 & 96.24 & NA & NA & 0.981476 & NA \tabularnewline
3 & 95.77 & NA & NA & 0.290955 & NA \tabularnewline
4 & 94.41 & NA & NA & -0.201649 & NA \tabularnewline
5 & 95.09 & NA & NA & 0.279392 & NA \tabularnewline
6 & 95.37 & NA & NA & 0.263976 & NA \tabularnewline
7 & 95.17 & 95.3448 & 95.4883 & -0.143524 & -0.174809 \tabularnewline
8 & 95.05 & 95.2816 & 95.6754 & -0.393837 & -0.23158 \tabularnewline
9 & 95.33 & 95.506 & 95.8733 & -0.367378 & -0.175955 \tabularnewline
10 & 95.42 & 95.7313 & 96.0929 & -0.361649 & -0.311267 \tabularnewline
11 & 95.95 & 96.1695 & 96.3558 & -0.186337 & -0.219497 \tabularnewline
12 & 96.12 & 96.5481 & 96.635 & -0.0868576 & -0.428142 \tabularnewline
13 & 96.94 & 96.8392 & 96.9137 & -0.074566 & 0.100816 \tabularnewline
14 & 98.73 & 98.1677 & 97.1862 & 0.981476 & 0.562274 \tabularnewline
15 & 98.03 & 97.7343 & 97.4433 & 0.290955 & 0.295712 \tabularnewline
16 & 97.42 & 97.4846 & 97.6862 & -0.201649 & -0.0646007 \tabularnewline
17 & 98.39 & 98.1977 & 97.9183 & 0.279392 & 0.192274 \tabularnewline
18 & 98.77 & 98.4127 & 98.1487 & 0.263976 & 0.357274 \tabularnewline
19 & 98.46 & 98.2023 & 98.3458 & -0.143524 & 0.257691 \tabularnewline
20 & 98.3 & 98.0932 & 98.4871 & -0.393837 & 0.206753 \tabularnewline
21 & 98.25 & 98.301 & 98.6683 & -0.367378 & -0.0509549 \tabularnewline
22 & 98.33 & 98.4846 & 98.8463 & -0.361649 & -0.154601 \tabularnewline
23 & 98.61 & 98.7795 & 98.9658 & -0.186337 & -0.169497 \tabularnewline
24 & 98.99 & 99.0077 & 99.0946 & -0.0868576 & -0.0177257 \tabularnewline
25 & 98.8 & 99.1567 & 99.2312 & -0.074566 & -0.356684 \tabularnewline
26 & 100.26 & 100.342 & 99.3608 & 0.981476 & -0.082309 \tabularnewline
27 & 100.85 & 99.7776 & 99.4867 & 0.290955 & 1.07238 \tabularnewline
28 & 98.87 & 99.4138 & 99.6154 & -0.201649 & -0.543767 \tabularnewline
29 & 99.81 & 100.042 & 99.7629 & 0.279392 & -0.232309 \tabularnewline
30 & 100.44 & 100.187 & 99.9229 & 0.263976 & 0.253108 \tabularnewline
31 & 100.07 & 99.9511 & 100.095 & -0.143524 & 0.118941 \tabularnewline
32 & 99.8 & 99.8378 & 100.232 & -0.393837 & -0.0378299 \tabularnewline
33 & 99.77 & 99.9326 & 100.3 & -0.367378 & -0.162622 \tabularnewline
34 & 99.9 & 100.058 & 100.419 & -0.361649 & -0.157517 \tabularnewline
35 & 100.58 & 100.392 & 100.579 & -0.186337 & 0.187587 \tabularnewline
36 & 100.86 & 100.582 & 100.669 & -0.0868576 & 0.278108 \tabularnewline
37 & 101.05 & 100.649 & 100.723 & -0.074566 & 0.401233 \tabularnewline
38 & 101.3 & 101.778 & 100.797 & 0.981476 & -0.478142 \tabularnewline
39 & 101.45 & 101.189 & 100.898 & 0.290955 & 0.260712 \tabularnewline
40 & 101.13 & 100.816 & 101.017 & -0.201649 & 0.314149 \tabularnewline
41 & 101.38 & 101.381 & 101.102 & 0.279392 & -0.00105903 \tabularnewline
42 & 101.03 & 101.405 & 101.141 & 0.263976 & -0.374809 \tabularnewline
43 & 100.79 & 101.01 & 101.154 & -0.143524 & -0.220226 \tabularnewline
44 & 100.84 & 100.796 & 101.19 & -0.393837 & 0.0442535 \tabularnewline
45 & 101.17 & 100.799 & 101.166 & -0.367378 & 0.371128 \tabularnewline
46 & 101.36 & 100.755 & 101.117 & -0.361649 & 0.604983 \tabularnewline
47 & 101.14 & 100.957 & 101.143 & -0.186337 & 0.183003 \tabularnewline
48 & 101.24 & 101.091 & 101.178 & -0.0868576 & 0.149358 \tabularnewline
49 & 100.98 & 101.144 & 101.218 & -0.074566 & -0.163767 \tabularnewline
50 & 102.23 & 102.25 & 101.269 & 0.981476 & -0.0202257 \tabularnewline
51 & 99.96 & 101.607 & 101.316 & 0.290955 & -1.6472 \tabularnewline
52 & 101.43 & 101.154 & 101.356 & -0.201649 & 0.275816 \tabularnewline
53 & 101.72 & 101.697 & 101.418 & 0.279392 & 0.022691 \tabularnewline
54 & 101.51 & 101.764 & 101.5 & 0.263976 & -0.253976 \tabularnewline
55 & 101.29 & NA & NA & -0.143524 & NA \tabularnewline
56 & 101.55 & NA & NA & -0.393837 & NA \tabularnewline
57 & 101.6 & NA & NA & -0.367378 & NA \tabularnewline
58 & 101.88 & NA & NA & -0.361649 & NA \tabularnewline
59 & 102.11 & NA & NA & -0.186337 & NA \tabularnewline
60 & 102.24 & NA & NA & -0.0868576 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]94.94[/C][C]NA[/C][C]NA[/C][C]-0.074566[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]96.24[/C][C]NA[/C][C]NA[/C][C]0.981476[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]95.77[/C][C]NA[/C][C]NA[/C][C]0.290955[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]94.41[/C][C]NA[/C][C]NA[/C][C]-0.201649[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]95.09[/C][C]NA[/C][C]NA[/C][C]0.279392[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]95.37[/C][C]NA[/C][C]NA[/C][C]0.263976[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]95.17[/C][C]95.3448[/C][C]95.4883[/C][C]-0.143524[/C][C]-0.174809[/C][/ROW]
[ROW][C]8[/C][C]95.05[/C][C]95.2816[/C][C]95.6754[/C][C]-0.393837[/C][C]-0.23158[/C][/ROW]
[ROW][C]9[/C][C]95.33[/C][C]95.506[/C][C]95.8733[/C][C]-0.367378[/C][C]-0.175955[/C][/ROW]
[ROW][C]10[/C][C]95.42[/C][C]95.7313[/C][C]96.0929[/C][C]-0.361649[/C][C]-0.311267[/C][/ROW]
[ROW][C]11[/C][C]95.95[/C][C]96.1695[/C][C]96.3558[/C][C]-0.186337[/C][C]-0.219497[/C][/ROW]
[ROW][C]12[/C][C]96.12[/C][C]96.5481[/C][C]96.635[/C][C]-0.0868576[/C][C]-0.428142[/C][/ROW]
[ROW][C]13[/C][C]96.94[/C][C]96.8392[/C][C]96.9137[/C][C]-0.074566[/C][C]0.100816[/C][/ROW]
[ROW][C]14[/C][C]98.73[/C][C]98.1677[/C][C]97.1862[/C][C]0.981476[/C][C]0.562274[/C][/ROW]
[ROW][C]15[/C][C]98.03[/C][C]97.7343[/C][C]97.4433[/C][C]0.290955[/C][C]0.295712[/C][/ROW]
[ROW][C]16[/C][C]97.42[/C][C]97.4846[/C][C]97.6862[/C][C]-0.201649[/C][C]-0.0646007[/C][/ROW]
[ROW][C]17[/C][C]98.39[/C][C]98.1977[/C][C]97.9183[/C][C]0.279392[/C][C]0.192274[/C][/ROW]
[ROW][C]18[/C][C]98.77[/C][C]98.4127[/C][C]98.1487[/C][C]0.263976[/C][C]0.357274[/C][/ROW]
[ROW][C]19[/C][C]98.46[/C][C]98.2023[/C][C]98.3458[/C][C]-0.143524[/C][C]0.257691[/C][/ROW]
[ROW][C]20[/C][C]98.3[/C][C]98.0932[/C][C]98.4871[/C][C]-0.393837[/C][C]0.206753[/C][/ROW]
[ROW][C]21[/C][C]98.25[/C][C]98.301[/C][C]98.6683[/C][C]-0.367378[/C][C]-0.0509549[/C][/ROW]
[ROW][C]22[/C][C]98.33[/C][C]98.4846[/C][C]98.8463[/C][C]-0.361649[/C][C]-0.154601[/C][/ROW]
[ROW][C]23[/C][C]98.61[/C][C]98.7795[/C][C]98.9658[/C][C]-0.186337[/C][C]-0.169497[/C][/ROW]
[ROW][C]24[/C][C]98.99[/C][C]99.0077[/C][C]99.0946[/C][C]-0.0868576[/C][C]-0.0177257[/C][/ROW]
[ROW][C]25[/C][C]98.8[/C][C]99.1567[/C][C]99.2312[/C][C]-0.074566[/C][C]-0.356684[/C][/ROW]
[ROW][C]26[/C][C]100.26[/C][C]100.342[/C][C]99.3608[/C][C]0.981476[/C][C]-0.082309[/C][/ROW]
[ROW][C]27[/C][C]100.85[/C][C]99.7776[/C][C]99.4867[/C][C]0.290955[/C][C]1.07238[/C][/ROW]
[ROW][C]28[/C][C]98.87[/C][C]99.4138[/C][C]99.6154[/C][C]-0.201649[/C][C]-0.543767[/C][/ROW]
[ROW][C]29[/C][C]99.81[/C][C]100.042[/C][C]99.7629[/C][C]0.279392[/C][C]-0.232309[/C][/ROW]
[ROW][C]30[/C][C]100.44[/C][C]100.187[/C][C]99.9229[/C][C]0.263976[/C][C]0.253108[/C][/ROW]
[ROW][C]31[/C][C]100.07[/C][C]99.9511[/C][C]100.095[/C][C]-0.143524[/C][C]0.118941[/C][/ROW]
[ROW][C]32[/C][C]99.8[/C][C]99.8378[/C][C]100.232[/C][C]-0.393837[/C][C]-0.0378299[/C][/ROW]
[ROW][C]33[/C][C]99.77[/C][C]99.9326[/C][C]100.3[/C][C]-0.367378[/C][C]-0.162622[/C][/ROW]
[ROW][C]34[/C][C]99.9[/C][C]100.058[/C][C]100.419[/C][C]-0.361649[/C][C]-0.157517[/C][/ROW]
[ROW][C]35[/C][C]100.58[/C][C]100.392[/C][C]100.579[/C][C]-0.186337[/C][C]0.187587[/C][/ROW]
[ROW][C]36[/C][C]100.86[/C][C]100.582[/C][C]100.669[/C][C]-0.0868576[/C][C]0.278108[/C][/ROW]
[ROW][C]37[/C][C]101.05[/C][C]100.649[/C][C]100.723[/C][C]-0.074566[/C][C]0.401233[/C][/ROW]
[ROW][C]38[/C][C]101.3[/C][C]101.778[/C][C]100.797[/C][C]0.981476[/C][C]-0.478142[/C][/ROW]
[ROW][C]39[/C][C]101.45[/C][C]101.189[/C][C]100.898[/C][C]0.290955[/C][C]0.260712[/C][/ROW]
[ROW][C]40[/C][C]101.13[/C][C]100.816[/C][C]101.017[/C][C]-0.201649[/C][C]0.314149[/C][/ROW]
[ROW][C]41[/C][C]101.38[/C][C]101.381[/C][C]101.102[/C][C]0.279392[/C][C]-0.00105903[/C][/ROW]
[ROW][C]42[/C][C]101.03[/C][C]101.405[/C][C]101.141[/C][C]0.263976[/C][C]-0.374809[/C][/ROW]
[ROW][C]43[/C][C]100.79[/C][C]101.01[/C][C]101.154[/C][C]-0.143524[/C][C]-0.220226[/C][/ROW]
[ROW][C]44[/C][C]100.84[/C][C]100.796[/C][C]101.19[/C][C]-0.393837[/C][C]0.0442535[/C][/ROW]
[ROW][C]45[/C][C]101.17[/C][C]100.799[/C][C]101.166[/C][C]-0.367378[/C][C]0.371128[/C][/ROW]
[ROW][C]46[/C][C]101.36[/C][C]100.755[/C][C]101.117[/C][C]-0.361649[/C][C]0.604983[/C][/ROW]
[ROW][C]47[/C][C]101.14[/C][C]100.957[/C][C]101.143[/C][C]-0.186337[/C][C]0.183003[/C][/ROW]
[ROW][C]48[/C][C]101.24[/C][C]101.091[/C][C]101.178[/C][C]-0.0868576[/C][C]0.149358[/C][/ROW]
[ROW][C]49[/C][C]100.98[/C][C]101.144[/C][C]101.218[/C][C]-0.074566[/C][C]-0.163767[/C][/ROW]
[ROW][C]50[/C][C]102.23[/C][C]102.25[/C][C]101.269[/C][C]0.981476[/C][C]-0.0202257[/C][/ROW]
[ROW][C]51[/C][C]99.96[/C][C]101.607[/C][C]101.316[/C][C]0.290955[/C][C]-1.6472[/C][/ROW]
[ROW][C]52[/C][C]101.43[/C][C]101.154[/C][C]101.356[/C][C]-0.201649[/C][C]0.275816[/C][/ROW]
[ROW][C]53[/C][C]101.72[/C][C]101.697[/C][C]101.418[/C][C]0.279392[/C][C]0.022691[/C][/ROW]
[ROW][C]54[/C][C]101.51[/C][C]101.764[/C][C]101.5[/C][C]0.263976[/C][C]-0.253976[/C][/ROW]
[ROW][C]55[/C][C]101.29[/C][C]NA[/C][C]NA[/C][C]-0.143524[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]101.55[/C][C]NA[/C][C]NA[/C][C]-0.393837[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]101.6[/C][C]NA[/C][C]NA[/C][C]-0.367378[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]101.88[/C][C]NA[/C][C]NA[/C][C]-0.361649[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]102.11[/C][C]NA[/C][C]NA[/C][C]-0.186337[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]102.24[/C][C]NA[/C][C]NA[/C][C]-0.0868576[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	94.94	NA	NA	-0.074566	NA
2	96.24	NA	NA	0.981476	NA
3	95.77	NA	NA	0.290955	NA
4	94.41	NA	NA	-0.201649	NA
5	95.09	NA	NA	0.279392	NA
6	95.37	NA	NA	0.263976	NA
7	95.17	95.3448	95.4883	-0.143524	-0.174809
8	95.05	95.2816	95.6754	-0.393837	-0.23158
9	95.33	95.506	95.8733	-0.367378	-0.175955
10	95.42	95.7313	96.0929	-0.361649	-0.311267
11	95.95	96.1695	96.3558	-0.186337	-0.219497
12	96.12	96.5481	96.635	-0.0868576	-0.428142
13	96.94	96.8392	96.9137	-0.074566	0.100816
14	98.73	98.1677	97.1862	0.981476	0.562274
15	98.03	97.7343	97.4433	0.290955	0.295712
16	97.42	97.4846	97.6862	-0.201649	-0.0646007
17	98.39	98.1977	97.9183	0.279392	0.192274
18	98.77	98.4127	98.1487	0.263976	0.357274
19	98.46	98.2023	98.3458	-0.143524	0.257691
20	98.3	98.0932	98.4871	-0.393837	0.206753
21	98.25	98.301	98.6683	-0.367378	-0.0509549
22	98.33	98.4846	98.8463	-0.361649	-0.154601
23	98.61	98.7795	98.9658	-0.186337	-0.169497
24	98.99	99.0077	99.0946	-0.0868576	-0.0177257
25	98.8	99.1567	99.2312	-0.074566	-0.356684
26	100.26	100.342	99.3608	0.981476	-0.082309
27	100.85	99.7776	99.4867	0.290955	1.07238
28	98.87	99.4138	99.6154	-0.201649	-0.543767
29	99.81	100.042	99.7629	0.279392	-0.232309
30	100.44	100.187	99.9229	0.263976	0.253108
31	100.07	99.9511	100.095	-0.143524	0.118941
32	99.8	99.8378	100.232	-0.393837	-0.0378299
33	99.77	99.9326	100.3	-0.367378	-0.162622
34	99.9	100.058	100.419	-0.361649	-0.157517
35	100.58	100.392	100.579	-0.186337	0.187587
36	100.86	100.582	100.669	-0.0868576	0.278108
37	101.05	100.649	100.723	-0.074566	0.401233
38	101.3	101.778	100.797	0.981476	-0.478142
39	101.45	101.189	100.898	0.290955	0.260712
40	101.13	100.816	101.017	-0.201649	0.314149
41	101.38	101.381	101.102	0.279392	-0.00105903
42	101.03	101.405	101.141	0.263976	-0.374809
43	100.79	101.01	101.154	-0.143524	-0.220226
44	100.84	100.796	101.19	-0.393837	0.0442535
45	101.17	100.799	101.166	-0.367378	0.371128
46	101.36	100.755	101.117	-0.361649	0.604983
47	101.14	100.957	101.143	-0.186337	0.183003
48	101.24	101.091	101.178	-0.0868576	0.149358
49	100.98	101.144	101.218	-0.074566	-0.163767
50	102.23	102.25	101.269	0.981476	-0.0202257
51	99.96	101.607	101.316	0.290955	-1.6472
52	101.43	101.154	101.356	-0.201649	0.275816
53	101.72	101.697	101.418	0.279392	0.022691
54	101.51	101.764	101.5	0.263976	-0.253976
55	101.29	NA	NA	-0.143524	NA
56	101.55	NA	NA	-0.393837	NA
57	101.6	NA	NA	-0.367378	NA
58	101.88	NA	NA	-0.361649	NA
59	102.11	NA	NA	-0.186337	NA
60	102.24	NA	NA	-0.0868576	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 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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