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R Software Module

rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 23 Nov 2015 18:17:18 +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/2015/Nov/23/t1448302815znin7di326cj0b3.htm/, Retrieved Tue, 14 May 2024 11:33:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=283951, Retrieved Tue, 14 May 2024 11:33:31 +0000

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-       [Classical Decomposition] [] [2015-11-23 18:17:18] [64c14b596f7fde091cf1a84a44b2a252] [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	2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283951&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283951&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283951&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	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	91.16	NA	NA	0.994666	NA
2	91.17	NA	NA	0.99442	NA
3	91.17	NA	NA	0.996693	NA
4	91.38	NA	NA	0.997878	NA
5	92.68	NA	NA	1.00282	NA
6	92.72	NA	NA	1.00405	NA
7	92.79	92.547	92.3287	1.00236	1.00263
8	92.81	92.7026	92.4662	1.00256	1.00116
9	92.81	92.9856	92.6037	1.00412	0.998111
10	92.81	92.9476	92.735	1.00229	0.998519
11	92.81	92.8669	92.8267	1.00043	0.999388
12	92.81	92.6917	92.9046	0.997708	1.00128
13	92.81	92.5118	93.0079	0.994666	1.00322
14	92.82	92.5999	93.1196	0.99442	1.00238
15	92.82	92.9304	93.2387	0.996693	0.998812
16	92.88	93.1652	93.3633	0.997878	0.996938
17	93.38	93.7536	93.4904	1.00282	0.996015
18	93.89	93.9997	93.6204	1.00405	0.998833
19	94.1	93.9728	93.7512	1.00236	1.00135
20	94.18	94.1267	93.8867	1.00256	1.00057
21	94.3	94.4174	94.0296	1.00412	0.998757
22	94.31	94.4022	94.1862	1.00229	0.999023
23	94.36	94.4496	94.4088	1.00043	0.999051
24	94.38	94.4838	94.7008	0.997708	0.998901
25	94.38	94.5049	95.0117	0.994666	0.998679
26	94.5	94.7951	95.3271	0.99442	0.996887
27	94.57	95.3395	95.6558	0.996693	0.991929
28	94.89	95.798	96.0017	0.997878	0.990522
29	96.71	96.6187	96.3475	1.00282	1.00094
30	97.57	97.08	96.6883	1.00405	1.00505
31	97.88	97.2585	97.0292	1.00236	1.00639
32	97.97	97.6198	97.3708	1.00256	1.00359
33	98.4	98.1125	97.7096	1.00412	1.00293
34	98.51	98.2614	98.0367	1.00229	1.00253
35	98.46	98.3392	98.2967	1.00043	1.00123
36	98.46	98.2398	98.4654	0.997708	1.00224
37	98.48	98.0666	98.5925	0.994666	1.00422
38	98.6	98.1927	98.7437	0.99442	1.00415
39	98.6	98.6323	98.9596	0.996693	0.999673
40	98.71	99.0086	99.2192	0.997878	0.996984
41	99.13	99.7943	99.5142	1.00282	0.993343
42	99.2	100.241	99.8367	1.00405	0.989614
43	99.3	100.398	100.161	1.00236	0.989064
44	100.18	100.772	100.515	1.00256	0.994122
45	101.37	101.407	100.99	1.00412	0.999636
46	101.77	101.835	101.602	1.00229	0.999361
47	102.28	102.304	102.26	1.00043	0.999767
48	102.38	102.687	102.922	0.997708	0.997014
49	102.35	103.039	103.591	0.994666	0.993316
50	103.23	103.642	104.224	0.99442	0.996023
51	105.37	104.428	104.775	0.996693	1.00902
52	106.62	105.045	105.269	0.997878	1.01499
53	107	106.032	105.734	1.00282	1.00913
54	107.24	106.607	106.177	1.00405	1.00594
55	107.31	NA	NA	1.00236	NA
56	107.35	NA	NA	1.00256	NA
57	107.42	NA	NA	1.00412	NA
58	107.58	NA	NA	1.00229	NA
59	107.64	NA	NA	1.00043	NA
60	107.64	NA	NA	0.997708	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 91.16 & NA & NA & 0.994666 & NA \tabularnewline
2 & 91.17 & NA & NA & 0.99442 & NA \tabularnewline
3 & 91.17 & NA & NA & 0.996693 & NA \tabularnewline
4 & 91.38 & NA & NA & 0.997878 & NA \tabularnewline
5 & 92.68 & NA & NA & 1.00282 & NA \tabularnewline
6 & 92.72 & NA & NA & 1.00405 & NA \tabularnewline
7 & 92.79 & 92.547 & 92.3287 & 1.00236 & 1.00263 \tabularnewline
8 & 92.81 & 92.7026 & 92.4662 & 1.00256 & 1.00116 \tabularnewline
9 & 92.81 & 92.9856 & 92.6037 & 1.00412 & 0.998111 \tabularnewline
10 & 92.81 & 92.9476 & 92.735 & 1.00229 & 0.998519 \tabularnewline
11 & 92.81 & 92.8669 & 92.8267 & 1.00043 & 0.999388 \tabularnewline
12 & 92.81 & 92.6917 & 92.9046 & 0.997708 & 1.00128 \tabularnewline
13 & 92.81 & 92.5118 & 93.0079 & 0.994666 & 1.00322 \tabularnewline
14 & 92.82 & 92.5999 & 93.1196 & 0.99442 & 1.00238 \tabularnewline
15 & 92.82 & 92.9304 & 93.2387 & 0.996693 & 0.998812 \tabularnewline
16 & 92.88 & 93.1652 & 93.3633 & 0.997878 & 0.996938 \tabularnewline
17 & 93.38 & 93.7536 & 93.4904 & 1.00282 & 0.996015 \tabularnewline
18 & 93.89 & 93.9997 & 93.6204 & 1.00405 & 0.998833 \tabularnewline
19 & 94.1 & 93.9728 & 93.7512 & 1.00236 & 1.00135 \tabularnewline
20 & 94.18 & 94.1267 & 93.8867 & 1.00256 & 1.00057 \tabularnewline
21 & 94.3 & 94.4174 & 94.0296 & 1.00412 & 0.998757 \tabularnewline
22 & 94.31 & 94.4022 & 94.1862 & 1.00229 & 0.999023 \tabularnewline
23 & 94.36 & 94.4496 & 94.4088 & 1.00043 & 0.999051 \tabularnewline
24 & 94.38 & 94.4838 & 94.7008 & 0.997708 & 0.998901 \tabularnewline
25 & 94.38 & 94.5049 & 95.0117 & 0.994666 & 0.998679 \tabularnewline
26 & 94.5 & 94.7951 & 95.3271 & 0.99442 & 0.996887 \tabularnewline
27 & 94.57 & 95.3395 & 95.6558 & 0.996693 & 0.991929 \tabularnewline
28 & 94.89 & 95.798 & 96.0017 & 0.997878 & 0.990522 \tabularnewline
29 & 96.71 & 96.6187 & 96.3475 & 1.00282 & 1.00094 \tabularnewline
30 & 97.57 & 97.08 & 96.6883 & 1.00405 & 1.00505 \tabularnewline
31 & 97.88 & 97.2585 & 97.0292 & 1.00236 & 1.00639 \tabularnewline
32 & 97.97 & 97.6198 & 97.3708 & 1.00256 & 1.00359 \tabularnewline
33 & 98.4 & 98.1125 & 97.7096 & 1.00412 & 1.00293 \tabularnewline
34 & 98.51 & 98.2614 & 98.0367 & 1.00229 & 1.00253 \tabularnewline
35 & 98.46 & 98.3392 & 98.2967 & 1.00043 & 1.00123 \tabularnewline
36 & 98.46 & 98.2398 & 98.4654 & 0.997708 & 1.00224 \tabularnewline
37 & 98.48 & 98.0666 & 98.5925 & 0.994666 & 1.00422 \tabularnewline
38 & 98.6 & 98.1927 & 98.7437 & 0.99442 & 1.00415 \tabularnewline
39 & 98.6 & 98.6323 & 98.9596 & 0.996693 & 0.999673 \tabularnewline
40 & 98.71 & 99.0086 & 99.2192 & 0.997878 & 0.996984 \tabularnewline
41 & 99.13 & 99.7943 & 99.5142 & 1.00282 & 0.993343 \tabularnewline
42 & 99.2 & 100.241 & 99.8367 & 1.00405 & 0.989614 \tabularnewline
43 & 99.3 & 100.398 & 100.161 & 1.00236 & 0.989064 \tabularnewline
44 & 100.18 & 100.772 & 100.515 & 1.00256 & 0.994122 \tabularnewline
45 & 101.37 & 101.407 & 100.99 & 1.00412 & 0.999636 \tabularnewline
46 & 101.77 & 101.835 & 101.602 & 1.00229 & 0.999361 \tabularnewline
47 & 102.28 & 102.304 & 102.26 & 1.00043 & 0.999767 \tabularnewline
48 & 102.38 & 102.687 & 102.922 & 0.997708 & 0.997014 \tabularnewline
49 & 102.35 & 103.039 & 103.591 & 0.994666 & 0.993316 \tabularnewline
50 & 103.23 & 103.642 & 104.224 & 0.99442 & 0.996023 \tabularnewline
51 & 105.37 & 104.428 & 104.775 & 0.996693 & 1.00902 \tabularnewline
52 & 106.62 & 105.045 & 105.269 & 0.997878 & 1.01499 \tabularnewline
53 & 107 & 106.032 & 105.734 & 1.00282 & 1.00913 \tabularnewline
54 & 107.24 & 106.607 & 106.177 & 1.00405 & 1.00594 \tabularnewline
55 & 107.31 & NA & NA & 1.00236 & NA \tabularnewline
56 & 107.35 & NA & NA & 1.00256 & NA \tabularnewline
57 & 107.42 & NA & NA & 1.00412 & NA \tabularnewline
58 & 107.58 & NA & NA & 1.00229 & NA \tabularnewline
59 & 107.64 & NA & NA & 1.00043 & NA \tabularnewline
60 & 107.64 & NA & NA & 0.997708 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283951&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]91.16[/C][C]NA[/C][C]NA[/C][C]0.994666[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]91.17[/C][C]NA[/C][C]NA[/C][C]0.99442[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]91.17[/C][C]NA[/C][C]NA[/C][C]0.996693[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]91.38[/C][C]NA[/C][C]NA[/C][C]0.997878[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]92.68[/C][C]NA[/C][C]NA[/C][C]1.00282[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]92.72[/C][C]NA[/C][C]NA[/C][C]1.00405[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]92.79[/C][C]92.547[/C][C]92.3287[/C][C]1.00236[/C][C]1.00263[/C][/ROW]
[ROW][C]8[/C][C]92.81[/C][C]92.7026[/C][C]92.4662[/C][C]1.00256[/C][C]1.00116[/C][/ROW]
[ROW][C]9[/C][C]92.81[/C][C]92.9856[/C][C]92.6037[/C][C]1.00412[/C][C]0.998111[/C][/ROW]
[ROW][C]10[/C][C]92.81[/C][C]92.9476[/C][C]92.735[/C][C]1.00229[/C][C]0.998519[/C][/ROW]
[ROW][C]11[/C][C]92.81[/C][C]92.8669[/C][C]92.8267[/C][C]1.00043[/C][C]0.999388[/C][/ROW]
[ROW][C]12[/C][C]92.81[/C][C]92.6917[/C][C]92.9046[/C][C]0.997708[/C][C]1.00128[/C][/ROW]
[ROW][C]13[/C][C]92.81[/C][C]92.5118[/C][C]93.0079[/C][C]0.994666[/C][C]1.00322[/C][/ROW]
[ROW][C]14[/C][C]92.82[/C][C]92.5999[/C][C]93.1196[/C][C]0.99442[/C][C]1.00238[/C][/ROW]
[ROW][C]15[/C][C]92.82[/C][C]92.9304[/C][C]93.2387[/C][C]0.996693[/C][C]0.998812[/C][/ROW]
[ROW][C]16[/C][C]92.88[/C][C]93.1652[/C][C]93.3633[/C][C]0.997878[/C][C]0.996938[/C][/ROW]
[ROW][C]17[/C][C]93.38[/C][C]93.7536[/C][C]93.4904[/C][C]1.00282[/C][C]0.996015[/C][/ROW]
[ROW][C]18[/C][C]93.89[/C][C]93.9997[/C][C]93.6204[/C][C]1.00405[/C][C]0.998833[/C][/ROW]
[ROW][C]19[/C][C]94.1[/C][C]93.9728[/C][C]93.7512[/C][C]1.00236[/C][C]1.00135[/C][/ROW]
[ROW][C]20[/C][C]94.18[/C][C]94.1267[/C][C]93.8867[/C][C]1.00256[/C][C]1.00057[/C][/ROW]
[ROW][C]21[/C][C]94.3[/C][C]94.4174[/C][C]94.0296[/C][C]1.00412[/C][C]0.998757[/C][/ROW]
[ROW][C]22[/C][C]94.31[/C][C]94.4022[/C][C]94.1862[/C][C]1.00229[/C][C]0.999023[/C][/ROW]
[ROW][C]23[/C][C]94.36[/C][C]94.4496[/C][C]94.4088[/C][C]1.00043[/C][C]0.999051[/C][/ROW]
[ROW][C]24[/C][C]94.38[/C][C]94.4838[/C][C]94.7008[/C][C]0.997708[/C][C]0.998901[/C][/ROW]
[ROW][C]25[/C][C]94.38[/C][C]94.5049[/C][C]95.0117[/C][C]0.994666[/C][C]0.998679[/C][/ROW]
[ROW][C]26[/C][C]94.5[/C][C]94.7951[/C][C]95.3271[/C][C]0.99442[/C][C]0.996887[/C][/ROW]
[ROW][C]27[/C][C]94.57[/C][C]95.3395[/C][C]95.6558[/C][C]0.996693[/C][C]0.991929[/C][/ROW]
[ROW][C]28[/C][C]94.89[/C][C]95.798[/C][C]96.0017[/C][C]0.997878[/C][C]0.990522[/C][/ROW]
[ROW][C]29[/C][C]96.71[/C][C]96.6187[/C][C]96.3475[/C][C]1.00282[/C][C]1.00094[/C][/ROW]
[ROW][C]30[/C][C]97.57[/C][C]97.08[/C][C]96.6883[/C][C]1.00405[/C][C]1.00505[/C][/ROW]
[ROW][C]31[/C][C]97.88[/C][C]97.2585[/C][C]97.0292[/C][C]1.00236[/C][C]1.00639[/C][/ROW]
[ROW][C]32[/C][C]97.97[/C][C]97.6198[/C][C]97.3708[/C][C]1.00256[/C][C]1.00359[/C][/ROW]
[ROW][C]33[/C][C]98.4[/C][C]98.1125[/C][C]97.7096[/C][C]1.00412[/C][C]1.00293[/C][/ROW]
[ROW][C]34[/C][C]98.51[/C][C]98.2614[/C][C]98.0367[/C][C]1.00229[/C][C]1.00253[/C][/ROW]
[ROW][C]35[/C][C]98.46[/C][C]98.3392[/C][C]98.2967[/C][C]1.00043[/C][C]1.00123[/C][/ROW]
[ROW][C]36[/C][C]98.46[/C][C]98.2398[/C][C]98.4654[/C][C]0.997708[/C][C]1.00224[/C][/ROW]
[ROW][C]37[/C][C]98.48[/C][C]98.0666[/C][C]98.5925[/C][C]0.994666[/C][C]1.00422[/C][/ROW]
[ROW][C]38[/C][C]98.6[/C][C]98.1927[/C][C]98.7437[/C][C]0.99442[/C][C]1.00415[/C][/ROW]
[ROW][C]39[/C][C]98.6[/C][C]98.6323[/C][C]98.9596[/C][C]0.996693[/C][C]0.999673[/C][/ROW]
[ROW][C]40[/C][C]98.71[/C][C]99.0086[/C][C]99.2192[/C][C]0.997878[/C][C]0.996984[/C][/ROW]
[ROW][C]41[/C][C]99.13[/C][C]99.7943[/C][C]99.5142[/C][C]1.00282[/C][C]0.993343[/C][/ROW]
[ROW][C]42[/C][C]99.2[/C][C]100.241[/C][C]99.8367[/C][C]1.00405[/C][C]0.989614[/C][/ROW]
[ROW][C]43[/C][C]99.3[/C][C]100.398[/C][C]100.161[/C][C]1.00236[/C][C]0.989064[/C][/ROW]
[ROW][C]44[/C][C]100.18[/C][C]100.772[/C][C]100.515[/C][C]1.00256[/C][C]0.994122[/C][/ROW]
[ROW][C]45[/C][C]101.37[/C][C]101.407[/C][C]100.99[/C][C]1.00412[/C][C]0.999636[/C][/ROW]
[ROW][C]46[/C][C]101.77[/C][C]101.835[/C][C]101.602[/C][C]1.00229[/C][C]0.999361[/C][/ROW]
[ROW][C]47[/C][C]102.28[/C][C]102.304[/C][C]102.26[/C][C]1.00043[/C][C]0.999767[/C][/ROW]
[ROW][C]48[/C][C]102.38[/C][C]102.687[/C][C]102.922[/C][C]0.997708[/C][C]0.997014[/C][/ROW]
[ROW][C]49[/C][C]102.35[/C][C]103.039[/C][C]103.591[/C][C]0.994666[/C][C]0.993316[/C][/ROW]
[ROW][C]50[/C][C]103.23[/C][C]103.642[/C][C]104.224[/C][C]0.99442[/C][C]0.996023[/C][/ROW]
[ROW][C]51[/C][C]105.37[/C][C]104.428[/C][C]104.775[/C][C]0.996693[/C][C]1.00902[/C][/ROW]
[ROW][C]52[/C][C]106.62[/C][C]105.045[/C][C]105.269[/C][C]0.997878[/C][C]1.01499[/C][/ROW]
[ROW][C]53[/C][C]107[/C][C]106.032[/C][C]105.734[/C][C]1.00282[/C][C]1.00913[/C][/ROW]
[ROW][C]54[/C][C]107.24[/C][C]106.607[/C][C]106.177[/C][C]1.00405[/C][C]1.00594[/C][/ROW]
[ROW][C]55[/C][C]107.31[/C][C]NA[/C][C]NA[/C][C]1.00236[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]107.35[/C][C]NA[/C][C]NA[/C][C]1.00256[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]107.42[/C][C]NA[/C][C]NA[/C][C]1.00412[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]107.58[/C][C]NA[/C][C]NA[/C][C]1.00229[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]107.64[/C][C]NA[/C][C]NA[/C][C]1.00043[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]107.64[/C][C]NA[/C][C]NA[/C][C]0.997708[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283951&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283951&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	91.16	NA	NA	0.994666	NA
2	91.17	NA	NA	0.99442	NA
3	91.17	NA	NA	0.996693	NA
4	91.38	NA	NA	0.997878	NA
5	92.68	NA	NA	1.00282	NA
6	92.72	NA	NA	1.00405	NA
7	92.79	92.547	92.3287	1.00236	1.00263
8	92.81	92.7026	92.4662	1.00256	1.00116
9	92.81	92.9856	92.6037	1.00412	0.998111
10	92.81	92.9476	92.735	1.00229	0.998519
11	92.81	92.8669	92.8267	1.00043	0.999388
12	92.81	92.6917	92.9046	0.997708	1.00128
13	92.81	92.5118	93.0079	0.994666	1.00322
14	92.82	92.5999	93.1196	0.99442	1.00238
15	92.82	92.9304	93.2387	0.996693	0.998812
16	92.88	93.1652	93.3633	0.997878	0.996938
17	93.38	93.7536	93.4904	1.00282	0.996015
18	93.89	93.9997	93.6204	1.00405	0.998833
19	94.1	93.9728	93.7512	1.00236	1.00135
20	94.18	94.1267	93.8867	1.00256	1.00057
21	94.3	94.4174	94.0296	1.00412	0.998757
22	94.31	94.4022	94.1862	1.00229	0.999023
23	94.36	94.4496	94.4088	1.00043	0.999051
24	94.38	94.4838	94.7008	0.997708	0.998901
25	94.38	94.5049	95.0117	0.994666	0.998679
26	94.5	94.7951	95.3271	0.99442	0.996887
27	94.57	95.3395	95.6558	0.996693	0.991929
28	94.89	95.798	96.0017	0.997878	0.990522
29	96.71	96.6187	96.3475	1.00282	1.00094
30	97.57	97.08	96.6883	1.00405	1.00505
31	97.88	97.2585	97.0292	1.00236	1.00639
32	97.97	97.6198	97.3708	1.00256	1.00359
33	98.4	98.1125	97.7096	1.00412	1.00293
34	98.51	98.2614	98.0367	1.00229	1.00253
35	98.46	98.3392	98.2967	1.00043	1.00123
36	98.46	98.2398	98.4654	0.997708	1.00224
37	98.48	98.0666	98.5925	0.994666	1.00422
38	98.6	98.1927	98.7437	0.99442	1.00415
39	98.6	98.6323	98.9596	0.996693	0.999673
40	98.71	99.0086	99.2192	0.997878	0.996984
41	99.13	99.7943	99.5142	1.00282	0.993343
42	99.2	100.241	99.8367	1.00405	0.989614
43	99.3	100.398	100.161	1.00236	0.989064
44	100.18	100.772	100.515	1.00256	0.994122
45	101.37	101.407	100.99	1.00412	0.999636
46	101.77	101.835	101.602	1.00229	0.999361
47	102.28	102.304	102.26	1.00043	0.999767
48	102.38	102.687	102.922	0.997708	0.997014
49	102.35	103.039	103.591	0.994666	0.993316
50	103.23	103.642	104.224	0.99442	0.996023
51	105.37	104.428	104.775	0.996693	1.00902
52	106.62	105.045	105.269	0.997878	1.01499
53	107	106.032	105.734	1.00282	1.00913
54	107.24	106.607	106.177	1.00405	1.00594
55	107.31	NA	NA	1.00236	NA
56	107.35	NA	NA	1.00256	NA
57	107.42	NA	NA	1.00412	NA
58	107.58	NA	NA	1.00229	NA
59	107.64	NA	NA	1.00043	NA
60	107.64	NA	NA	0.997708	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