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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 25 Nov 2015 11:15:28 +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/25/t1448450144fe0zrr5ffbk4gig.htm/, Retrieved Wed, 22 Apr 2026 17:48:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284106, Retrieved Wed, 22 Apr 2026 17:48:18 +0000

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Estimated Impact

329

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-       [Classical Decomposition] [] [2015-11-25 11:15:28] [4b96c7bb02a36edde4d8c72e28fc1c90] [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=284106&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=284106&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284106&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	103.4	NA	NA	0.0416146	NA
2	103.49	NA	NA	0.176719	NA
3	103.51	NA	NA	0.253281	NA
4	103.27	NA	NA	-0.0107813	NA
5	103.35	NA	NA	-0.151823	NA
6	103.34	NA	NA	-0.284844	NA
7	103.07	102.978	103.246	-0.267448	0.0916146
8	103.08	103.032	103.226	-0.194219	0.0483854
9	103.1	103.173	103.193	-0.0196354	-0.0732813
10	103.13	103.232	103.138	0.0944271	-0.102344
11	103.13	103.183	103.07	0.113385	-0.0529688
12	103.18	103.251	103.002	0.249323	-0.0714063
13	103.2	102.999	102.957	0.0416146	0.201302
14	103.21	103.11	102.934	0.176719	0.0995313
15	103	103.164	102.91	0.253281	-0.163698
16	102.46	102.869	102.88	-0.0107813	-0.408802
17	102.52	102.692	102.844	-0.151823	-0.171927
18	102.55	102.523	102.808	-0.284844	0.0269271
19	102.78	102.508	102.775	-0.267448	0.272031
20	102.81	102.556	102.75	-0.194219	0.254219
21	102.81	102.727	102.747	-0.0196354	0.0829688
22	102.68	102.856	102.762	0.0944271	-0.176094
23	102.72	102.868	102.754	0.113385	-0.147552
24	102.73	102.933	102.683	0.249323	-0.202656
25	102.87	102.59	102.549	0.0416146	0.279635
26	102.93	102.56	102.384	0.176719	0.369531
27	103.2	102.469	102.215	0.253281	0.731302
28	102.62	102.049	102.06	-0.0107813	0.571198
29	102.18	101.739	101.891	-0.151823	0.440573
30	101.19	101.418	101.702	-0.284844	-0.227656
31	100.91	101.247	101.515	-0.267448	-0.337135
32	100.72	101.132	101.326	-0.194219	-0.411615
33	100.86	101.105	101.124	-0.0196354	-0.244531
34	100.89	101.019	100.925	0.0944271	-0.129427
35	100.47	100.841	100.727	0.113385	-0.370885
36	100.45	100.811	100.562	0.249323	-0.36099
37	100.64	100.488	100.447	0.0416146	0.151719
38	100.63	100.525	100.348	0.176719	0.105365
39	100.66	100.508	100.255	0.253281	0.152135
40	100.38	100.149	100.16	-0.0107813	0.230781
41	99.68	99.9319	100.084	-0.151823	-0.251927
42	99.71	99.7418	100.027	-0.284844	-0.0318229
43	99.63	99.6271	99.8946	-0.267448	0.00286458
44	99.63	99.4916	99.6858	-0.194219	0.138385
45	99.71	99.4458	99.4654	-0.0196354	0.264219
46	99.77	99.3328	99.2383	0.0944271	0.43724
47	99.76	99.1592	99.0458	0.113385	0.600781
48	99.79	99.1256	98.8762	0.249323	0.664427
49	98.13	98.7333	98.6917	0.0416146	-0.603281
50	98.13	98.6751	98.4983	0.176719	-0.545052
51	97.87	98.5604	98.3071	0.253281	-0.690365
52	97.72	98.0838	98.0946	-0.0107813	-0.363802
53	97.72	97.7073	97.8592	-0.151823	0.0126563
54	97.6	97.3381	97.6229	-0.284844	0.261927
55	97.31	NA	NA	-0.267448	NA
56	97.31	NA	NA	-0.194219	NA
57	97.44	NA	NA	-0.0196354	NA
58	96.94	NA	NA	0.0944271	NA
59	96.94	NA	NA	0.113385	NA
60	96.94	NA	NA	0.249323	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 103.4 & NA & NA & 0.0416146 & NA \tabularnewline
2 & 103.49 & NA & NA & 0.176719 & NA \tabularnewline
3 & 103.51 & NA & NA & 0.253281 & NA \tabularnewline
4 & 103.27 & NA & NA & -0.0107813 & NA \tabularnewline
5 & 103.35 & NA & NA & -0.151823 & NA \tabularnewline
6 & 103.34 & NA & NA & -0.284844 & NA \tabularnewline
7 & 103.07 & 102.978 & 103.246 & -0.267448 & 0.0916146 \tabularnewline
8 & 103.08 & 103.032 & 103.226 & -0.194219 & 0.0483854 \tabularnewline
9 & 103.1 & 103.173 & 103.193 & -0.0196354 & -0.0732813 \tabularnewline
10 & 103.13 & 103.232 & 103.138 & 0.0944271 & -0.102344 \tabularnewline
11 & 103.13 & 103.183 & 103.07 & 0.113385 & -0.0529688 \tabularnewline
12 & 103.18 & 103.251 & 103.002 & 0.249323 & -0.0714063 \tabularnewline
13 & 103.2 & 102.999 & 102.957 & 0.0416146 & 0.201302 \tabularnewline
14 & 103.21 & 103.11 & 102.934 & 0.176719 & 0.0995313 \tabularnewline
15 & 103 & 103.164 & 102.91 & 0.253281 & -0.163698 \tabularnewline
16 & 102.46 & 102.869 & 102.88 & -0.0107813 & -0.408802 \tabularnewline
17 & 102.52 & 102.692 & 102.844 & -0.151823 & -0.171927 \tabularnewline
18 & 102.55 & 102.523 & 102.808 & -0.284844 & 0.0269271 \tabularnewline
19 & 102.78 & 102.508 & 102.775 & -0.267448 & 0.272031 \tabularnewline
20 & 102.81 & 102.556 & 102.75 & -0.194219 & 0.254219 \tabularnewline
21 & 102.81 & 102.727 & 102.747 & -0.0196354 & 0.0829688 \tabularnewline
22 & 102.68 & 102.856 & 102.762 & 0.0944271 & -0.176094 \tabularnewline
23 & 102.72 & 102.868 & 102.754 & 0.113385 & -0.147552 \tabularnewline
24 & 102.73 & 102.933 & 102.683 & 0.249323 & -0.202656 \tabularnewline
25 & 102.87 & 102.59 & 102.549 & 0.0416146 & 0.279635 \tabularnewline
26 & 102.93 & 102.56 & 102.384 & 0.176719 & 0.369531 \tabularnewline
27 & 103.2 & 102.469 & 102.215 & 0.253281 & 0.731302 \tabularnewline
28 & 102.62 & 102.049 & 102.06 & -0.0107813 & 0.571198 \tabularnewline
29 & 102.18 & 101.739 & 101.891 & -0.151823 & 0.440573 \tabularnewline
30 & 101.19 & 101.418 & 101.702 & -0.284844 & -0.227656 \tabularnewline
31 & 100.91 & 101.247 & 101.515 & -0.267448 & -0.337135 \tabularnewline
32 & 100.72 & 101.132 & 101.326 & -0.194219 & -0.411615 \tabularnewline
33 & 100.86 & 101.105 & 101.124 & -0.0196354 & -0.244531 \tabularnewline
34 & 100.89 & 101.019 & 100.925 & 0.0944271 & -0.129427 \tabularnewline
35 & 100.47 & 100.841 & 100.727 & 0.113385 & -0.370885 \tabularnewline
36 & 100.45 & 100.811 & 100.562 & 0.249323 & -0.36099 \tabularnewline
37 & 100.64 & 100.488 & 100.447 & 0.0416146 & 0.151719 \tabularnewline
38 & 100.63 & 100.525 & 100.348 & 0.176719 & 0.105365 \tabularnewline
39 & 100.66 & 100.508 & 100.255 & 0.253281 & 0.152135 \tabularnewline
40 & 100.38 & 100.149 & 100.16 & -0.0107813 & 0.230781 \tabularnewline
41 & 99.68 & 99.9319 & 100.084 & -0.151823 & -0.251927 \tabularnewline
42 & 99.71 & 99.7418 & 100.027 & -0.284844 & -0.0318229 \tabularnewline
43 & 99.63 & 99.6271 & 99.8946 & -0.267448 & 0.00286458 \tabularnewline
44 & 99.63 & 99.4916 & 99.6858 & -0.194219 & 0.138385 \tabularnewline
45 & 99.71 & 99.4458 & 99.4654 & -0.0196354 & 0.264219 \tabularnewline
46 & 99.77 & 99.3328 & 99.2383 & 0.0944271 & 0.43724 \tabularnewline
47 & 99.76 & 99.1592 & 99.0458 & 0.113385 & 0.600781 \tabularnewline
48 & 99.79 & 99.1256 & 98.8762 & 0.249323 & 0.664427 \tabularnewline
49 & 98.13 & 98.7333 & 98.6917 & 0.0416146 & -0.603281 \tabularnewline
50 & 98.13 & 98.6751 & 98.4983 & 0.176719 & -0.545052 \tabularnewline
51 & 97.87 & 98.5604 & 98.3071 & 0.253281 & -0.690365 \tabularnewline
52 & 97.72 & 98.0838 & 98.0946 & -0.0107813 & -0.363802 \tabularnewline
53 & 97.72 & 97.7073 & 97.8592 & -0.151823 & 0.0126563 \tabularnewline
54 & 97.6 & 97.3381 & 97.6229 & -0.284844 & 0.261927 \tabularnewline
55 & 97.31 & NA & NA & -0.267448 & NA \tabularnewline
56 & 97.31 & NA & NA & -0.194219 & NA \tabularnewline
57 & 97.44 & NA & NA & -0.0196354 & NA \tabularnewline
58 & 96.94 & NA & NA & 0.0944271 & NA \tabularnewline
59 & 96.94 & NA & NA & 0.113385 & NA \tabularnewline
60 & 96.94 & NA & NA & 0.249323 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284106&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]103.4[/C][C]NA[/C][C]NA[/C][C]0.0416146[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]103.49[/C][C]NA[/C][C]NA[/C][C]0.176719[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]103.51[/C][C]NA[/C][C]NA[/C][C]0.253281[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]103.27[/C][C]NA[/C][C]NA[/C][C]-0.0107813[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]103.35[/C][C]NA[/C][C]NA[/C][C]-0.151823[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]103.34[/C][C]NA[/C][C]NA[/C][C]-0.284844[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]103.07[/C][C]102.978[/C][C]103.246[/C][C]-0.267448[/C][C]0.0916146[/C][/ROW]
[ROW][C]8[/C][C]103.08[/C][C]103.032[/C][C]103.226[/C][C]-0.194219[/C][C]0.0483854[/C][/ROW]
[ROW][C]9[/C][C]103.1[/C][C]103.173[/C][C]103.193[/C][C]-0.0196354[/C][C]-0.0732813[/C][/ROW]
[ROW][C]10[/C][C]103.13[/C][C]103.232[/C][C]103.138[/C][C]0.0944271[/C][C]-0.102344[/C][/ROW]
[ROW][C]11[/C][C]103.13[/C][C]103.183[/C][C]103.07[/C][C]0.113385[/C][C]-0.0529688[/C][/ROW]
[ROW][C]12[/C][C]103.18[/C][C]103.251[/C][C]103.002[/C][C]0.249323[/C][C]-0.0714063[/C][/ROW]
[ROW][C]13[/C][C]103.2[/C][C]102.999[/C][C]102.957[/C][C]0.0416146[/C][C]0.201302[/C][/ROW]
[ROW][C]14[/C][C]103.21[/C][C]103.11[/C][C]102.934[/C][C]0.176719[/C][C]0.0995313[/C][/ROW]
[ROW][C]15[/C][C]103[/C][C]103.164[/C][C]102.91[/C][C]0.253281[/C][C]-0.163698[/C][/ROW]
[ROW][C]16[/C][C]102.46[/C][C]102.869[/C][C]102.88[/C][C]-0.0107813[/C][C]-0.408802[/C][/ROW]
[ROW][C]17[/C][C]102.52[/C][C]102.692[/C][C]102.844[/C][C]-0.151823[/C][C]-0.171927[/C][/ROW]
[ROW][C]18[/C][C]102.55[/C][C]102.523[/C][C]102.808[/C][C]-0.284844[/C][C]0.0269271[/C][/ROW]
[ROW][C]19[/C][C]102.78[/C][C]102.508[/C][C]102.775[/C][C]-0.267448[/C][C]0.272031[/C][/ROW]
[ROW][C]20[/C][C]102.81[/C][C]102.556[/C][C]102.75[/C][C]-0.194219[/C][C]0.254219[/C][/ROW]
[ROW][C]21[/C][C]102.81[/C][C]102.727[/C][C]102.747[/C][C]-0.0196354[/C][C]0.0829688[/C][/ROW]
[ROW][C]22[/C][C]102.68[/C][C]102.856[/C][C]102.762[/C][C]0.0944271[/C][C]-0.176094[/C][/ROW]
[ROW][C]23[/C][C]102.72[/C][C]102.868[/C][C]102.754[/C][C]0.113385[/C][C]-0.147552[/C][/ROW]
[ROW][C]24[/C][C]102.73[/C][C]102.933[/C][C]102.683[/C][C]0.249323[/C][C]-0.202656[/C][/ROW]
[ROW][C]25[/C][C]102.87[/C][C]102.59[/C][C]102.549[/C][C]0.0416146[/C][C]0.279635[/C][/ROW]
[ROW][C]26[/C][C]102.93[/C][C]102.56[/C][C]102.384[/C][C]0.176719[/C][C]0.369531[/C][/ROW]
[ROW][C]27[/C][C]103.2[/C][C]102.469[/C][C]102.215[/C][C]0.253281[/C][C]0.731302[/C][/ROW]
[ROW][C]28[/C][C]102.62[/C][C]102.049[/C][C]102.06[/C][C]-0.0107813[/C][C]0.571198[/C][/ROW]
[ROW][C]29[/C][C]102.18[/C][C]101.739[/C][C]101.891[/C][C]-0.151823[/C][C]0.440573[/C][/ROW]
[ROW][C]30[/C][C]101.19[/C][C]101.418[/C][C]101.702[/C][C]-0.284844[/C][C]-0.227656[/C][/ROW]
[ROW][C]31[/C][C]100.91[/C][C]101.247[/C][C]101.515[/C][C]-0.267448[/C][C]-0.337135[/C][/ROW]
[ROW][C]32[/C][C]100.72[/C][C]101.132[/C][C]101.326[/C][C]-0.194219[/C][C]-0.411615[/C][/ROW]
[ROW][C]33[/C][C]100.86[/C][C]101.105[/C][C]101.124[/C][C]-0.0196354[/C][C]-0.244531[/C][/ROW]
[ROW][C]34[/C][C]100.89[/C][C]101.019[/C][C]100.925[/C][C]0.0944271[/C][C]-0.129427[/C][/ROW]
[ROW][C]35[/C][C]100.47[/C][C]100.841[/C][C]100.727[/C][C]0.113385[/C][C]-0.370885[/C][/ROW]
[ROW][C]36[/C][C]100.45[/C][C]100.811[/C][C]100.562[/C][C]0.249323[/C][C]-0.36099[/C][/ROW]
[ROW][C]37[/C][C]100.64[/C][C]100.488[/C][C]100.447[/C][C]0.0416146[/C][C]0.151719[/C][/ROW]
[ROW][C]38[/C][C]100.63[/C][C]100.525[/C][C]100.348[/C][C]0.176719[/C][C]0.105365[/C][/ROW]
[ROW][C]39[/C][C]100.66[/C][C]100.508[/C][C]100.255[/C][C]0.253281[/C][C]0.152135[/C][/ROW]
[ROW][C]40[/C][C]100.38[/C][C]100.149[/C][C]100.16[/C][C]-0.0107813[/C][C]0.230781[/C][/ROW]
[ROW][C]41[/C][C]99.68[/C][C]99.9319[/C][C]100.084[/C][C]-0.151823[/C][C]-0.251927[/C][/ROW]
[ROW][C]42[/C][C]99.71[/C][C]99.7418[/C][C]100.027[/C][C]-0.284844[/C][C]-0.0318229[/C][/ROW]
[ROW][C]43[/C][C]99.63[/C][C]99.6271[/C][C]99.8946[/C][C]-0.267448[/C][C]0.00286458[/C][/ROW]
[ROW][C]44[/C][C]99.63[/C][C]99.4916[/C][C]99.6858[/C][C]-0.194219[/C][C]0.138385[/C][/ROW]
[ROW][C]45[/C][C]99.71[/C][C]99.4458[/C][C]99.4654[/C][C]-0.0196354[/C][C]0.264219[/C][/ROW]
[ROW][C]46[/C][C]99.77[/C][C]99.3328[/C][C]99.2383[/C][C]0.0944271[/C][C]0.43724[/C][/ROW]
[ROW][C]47[/C][C]99.76[/C][C]99.1592[/C][C]99.0458[/C][C]0.113385[/C][C]0.600781[/C][/ROW]
[ROW][C]48[/C][C]99.79[/C][C]99.1256[/C][C]98.8762[/C][C]0.249323[/C][C]0.664427[/C][/ROW]
[ROW][C]49[/C][C]98.13[/C][C]98.7333[/C][C]98.6917[/C][C]0.0416146[/C][C]-0.603281[/C][/ROW]
[ROW][C]50[/C][C]98.13[/C][C]98.6751[/C][C]98.4983[/C][C]0.176719[/C][C]-0.545052[/C][/ROW]
[ROW][C]51[/C][C]97.87[/C][C]98.5604[/C][C]98.3071[/C][C]0.253281[/C][C]-0.690365[/C][/ROW]
[ROW][C]52[/C][C]97.72[/C][C]98.0838[/C][C]98.0946[/C][C]-0.0107813[/C][C]-0.363802[/C][/ROW]
[ROW][C]53[/C][C]97.72[/C][C]97.7073[/C][C]97.8592[/C][C]-0.151823[/C][C]0.0126563[/C][/ROW]
[ROW][C]54[/C][C]97.6[/C][C]97.3381[/C][C]97.6229[/C][C]-0.284844[/C][C]0.261927[/C][/ROW]
[ROW][C]55[/C][C]97.31[/C][C]NA[/C][C]NA[/C][C]-0.267448[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]97.31[/C][C]NA[/C][C]NA[/C][C]-0.194219[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]97.44[/C][C]NA[/C][C]NA[/C][C]-0.0196354[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]96.94[/C][C]NA[/C][C]NA[/C][C]0.0944271[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]96.94[/C][C]NA[/C][C]NA[/C][C]0.113385[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]96.94[/C][C]NA[/C][C]NA[/C][C]0.249323[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284106&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284106&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	103.4	NA	NA	0.0416146	NA
2	103.49	NA	NA	0.176719	NA
3	103.51	NA	NA	0.253281	NA
4	103.27	NA	NA	-0.0107813	NA
5	103.35	NA	NA	-0.151823	NA
6	103.34	NA	NA	-0.284844	NA
7	103.07	102.978	103.246	-0.267448	0.0916146
8	103.08	103.032	103.226	-0.194219	0.0483854
9	103.1	103.173	103.193	-0.0196354	-0.0732813
10	103.13	103.232	103.138	0.0944271	-0.102344
11	103.13	103.183	103.07	0.113385	-0.0529688
12	103.18	103.251	103.002	0.249323	-0.0714063
13	103.2	102.999	102.957	0.0416146	0.201302
14	103.21	103.11	102.934	0.176719	0.0995313
15	103	103.164	102.91	0.253281	-0.163698
16	102.46	102.869	102.88	-0.0107813	-0.408802
17	102.52	102.692	102.844	-0.151823	-0.171927
18	102.55	102.523	102.808	-0.284844	0.0269271
19	102.78	102.508	102.775	-0.267448	0.272031
20	102.81	102.556	102.75	-0.194219	0.254219
21	102.81	102.727	102.747	-0.0196354	0.0829688
22	102.68	102.856	102.762	0.0944271	-0.176094
23	102.72	102.868	102.754	0.113385	-0.147552
24	102.73	102.933	102.683	0.249323	-0.202656
25	102.87	102.59	102.549	0.0416146	0.279635
26	102.93	102.56	102.384	0.176719	0.369531
27	103.2	102.469	102.215	0.253281	0.731302
28	102.62	102.049	102.06	-0.0107813	0.571198
29	102.18	101.739	101.891	-0.151823	0.440573
30	101.19	101.418	101.702	-0.284844	-0.227656
31	100.91	101.247	101.515	-0.267448	-0.337135
32	100.72	101.132	101.326	-0.194219	-0.411615
33	100.86	101.105	101.124	-0.0196354	-0.244531
34	100.89	101.019	100.925	0.0944271	-0.129427
35	100.47	100.841	100.727	0.113385	-0.370885
36	100.45	100.811	100.562	0.249323	-0.36099
37	100.64	100.488	100.447	0.0416146	0.151719
38	100.63	100.525	100.348	0.176719	0.105365
39	100.66	100.508	100.255	0.253281	0.152135
40	100.38	100.149	100.16	-0.0107813	0.230781
41	99.68	99.9319	100.084	-0.151823	-0.251927
42	99.71	99.7418	100.027	-0.284844	-0.0318229
43	99.63	99.6271	99.8946	-0.267448	0.00286458
44	99.63	99.4916	99.6858	-0.194219	0.138385
45	99.71	99.4458	99.4654	-0.0196354	0.264219
46	99.77	99.3328	99.2383	0.0944271	0.43724
47	99.76	99.1592	99.0458	0.113385	0.600781
48	99.79	99.1256	98.8762	0.249323	0.664427
49	98.13	98.7333	98.6917	0.0416146	-0.603281
50	98.13	98.6751	98.4983	0.176719	-0.545052
51	97.87	98.5604	98.3071	0.253281	-0.690365
52	97.72	98.0838	98.0946	-0.0107813	-0.363802
53	97.72	97.7073	97.8592	-0.151823	0.0126563
54	97.6	97.3381	97.6229	-0.284844	0.261927
55	97.31	NA	NA	-0.267448	NA
56	97.31	NA	NA	-0.194219	NA
57	97.44	NA	NA	-0.0196354	NA
58	96.94	NA	NA	0.0944271	NA
59	96.94	NA	NA	0.113385	NA
60	96.94	NA	NA	0.249323	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 <- '12'
par1 <- 'additive'
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