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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Fri, 04 Dec 2009 13:06:02 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t1259957346bf3557g6lbhuu0q.htm/, Retrieved Fri, 13 Feb 2026 11:59:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64121, Retrieved Fri, 13 Feb 2026 11:59:53 +0000

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Estimated Impact

295

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Classical Decomposition] [] [2009-11-27 14:58:37] [b98453cac15ba1066b407e146608df68]
-   PD      [Classical Decomposition] [WS9] [2009-12-04 20:06:02] [b8ce264f75295a954feffaf60221d1b0] [Current]

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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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64121&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64121&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64121&T=0

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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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	14.3	NA	NA	0.953737785912845	NA
2	14.2	NA	NA	0.972138643471631	NA
3	15.9	NA	NA	1.11095936239077	NA
4	15.3	NA	NA	1.00151336390141	NA
5	15.5	NA	NA	0.995588510420574	NA
6	15.1	NA	NA	1.06227485121756	NA
7	15	14.5352108058679	14.9291666666667	0.97361166436179	1.03197677696869
8	12.1	12.2433340907121	14.9708333333333	0.817812463615614	0.98829288740713
9	15.8	15.9894581459219	14.9958333333333	1.06626005974472	0.988151059016958
10	16.9	16.0068803170713	15.0041666666667	1.06682901307890	1.05579598680301
11	15.1	15.2503098693384	14.9875	1.01753527068146	0.990143815396129
12	13.7	14.4100561845210	14.9833333333333	0.961739011202734	0.950724953780285
13	14.8	14.3100406961340	15.0041666666667	0.953737785912845	1.03423884769233
14	14.7	14.5739784967122	14.9916666666667	0.972138643471631	1.00864702135496
15	16	16.6597614385182	14.9958333333333	1.11095936239077	0.960397905999254
16	15.4	15.0310464032203	15.0083333333333	1.00151336390141	1.02454610190683
17	15	14.9296793708485	14.9958333333333	0.995588510420574	1.00471012319855
18	15.5	15.9783842203974	15.0416666666667	1.06227485121756	0.970060538424984
19	15.1	14.6853092707903	15.0833333333333	0.97361166436179	1.02823847435304
20	11.7	12.3489682005958	15.1	0.817812463615614	0.947447576991536
21	16.3	16.2115956583687	15.2041666666667	1.06626005974472	1.00545315485868
22	16.7	16.3491546254341	15.325	1.06682901307890	1.02145954225793
23	15	15.6530842473165	15.3833333333333	1.01753527068146	0.958277599673146
24	14.9	14.9069546736424	15.5	0.961739011202734	0.999533461139808
25	14.6	14.9101007197708	15.6333333333333	0.953737785912845	0.979201970154392
26	15.3	15.3192847900405	15.7583333333333	0.972138643471631	0.998741142925093
27	17.9	17.6781408540431	15.9125	1.11095936239077	1.01254991391848
28	16.4	16.0492516565201	16.025	1.00151336390141	1.02185449832718
29	15.4	16.1036441560528	16.175	0.995588510420574	0.95630528411867
30	17.9	17.3681938174071	16.35	1.06227485121756	1.03061954444911
31	15.9	16.0483656008968	16.4833333333333	0.97361166436179	0.990755095902815
32	13.9	13.5722793440875	16.5958333333333	0.817812463615614	1.02414632410696
33	17.8	17.8065429977368	16.7	1.06626005974472	0.999632550925935
34	17.9	17.9316176615011	16.8083333333333	1.06682901307890	0.998236764685821
35	17.4	17.2387433774618	16.9416666666667	1.01753527068146	1.00935431423320
36	16.7	16.3936428951266	17.0458333333333	0.961739011202734	1.01868755509884
37	16	16.3009683242270	17.0916666666667	0.953737785912845	0.981536782463424
38	16.6	16.6802788909007	17.1583333333333	0.972138643471631	0.99518719732291
39	19.1	19.1825649906139	17.2666666666667	1.11095936239077	0.99569583157131
40	17.8	17.3428730848927	17.3166666666667	1.00151336390141	1.02635819987090
41	17.2	17.3024986540176	17.3791666666667	0.995588510420574	0.994076077908334
42	18.6	18.5942360415207	17.5041666666667	1.06227485121756	1.00030998630255
43	16.3	17.1558488691084	17.6208333333333	0.97361166436179	0.950113289313858
44	15.1	14.5025410214502	17.7333333333333	0.817812463615614	1.04119684803277
45	19.2	19.050513067439	17.8666666666667	1.06626005974472	1.00784687173683
46	17.7	19.1229100594392	17.925	1.06682901307890	0.925591342791635
47	19.1	18.3071554116773	17.9916666666667	1.01753527068146	1.04330790723593
48	18	17.4395340698096	18.1333333333333	0.961739011202734	1.03213766651947
49	17.5	17.3937928705855	18.2375	0.953737785912845	1.00610603622825
50	17.8	17.8265923746610	18.3375	0.972138643471631	0.998508274935437
51	21.1	20.4370232706468	18.3958333333333	1.11095936239077	1.03243998504936
52	17.2	18.5279972321761	18.5	1.00151336390141	0.92832483643349
53	19.4	18.5469842920433	18.6291666666667	0.995588510420574	1.04599215131285
54	19.8	19.7981475395672	18.6375	1.06227485121756	1.00009356736175
55	17.6	NA	NA	0.97361166436179	NA
56	16.2	NA	NA	0.817812463615614	NA
57	19.5	NA	NA	1.06626005974472	NA
58	19.9	NA	NA	1.06682901307890	NA
59	20	NA	NA	1.01753527068146	NA
60	17.3	NA	NA	0.961739011202734	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 14.3 & NA & NA & 0.953737785912845 & NA \tabularnewline
2 & 14.2 & NA & NA & 0.972138643471631 & NA \tabularnewline
3 & 15.9 & NA & NA & 1.11095936239077 & NA \tabularnewline
4 & 15.3 & NA & NA & 1.00151336390141 & NA \tabularnewline
5 & 15.5 & NA & NA & 0.995588510420574 & NA \tabularnewline
6 & 15.1 & NA & NA & 1.06227485121756 & NA \tabularnewline
7 & 15 & 14.5352108058679 & 14.9291666666667 & 0.97361166436179 & 1.03197677696869 \tabularnewline
8 & 12.1 & 12.2433340907121 & 14.9708333333333 & 0.817812463615614 & 0.98829288740713 \tabularnewline
9 & 15.8 & 15.9894581459219 & 14.9958333333333 & 1.06626005974472 & 0.988151059016958 \tabularnewline
10 & 16.9 & 16.0068803170713 & 15.0041666666667 & 1.06682901307890 & 1.05579598680301 \tabularnewline
11 & 15.1 & 15.2503098693384 & 14.9875 & 1.01753527068146 & 0.990143815396129 \tabularnewline
12 & 13.7 & 14.4100561845210 & 14.9833333333333 & 0.961739011202734 & 0.950724953780285 \tabularnewline
13 & 14.8 & 14.3100406961340 & 15.0041666666667 & 0.953737785912845 & 1.03423884769233 \tabularnewline
14 & 14.7 & 14.5739784967122 & 14.9916666666667 & 0.972138643471631 & 1.00864702135496 \tabularnewline
15 & 16 & 16.6597614385182 & 14.9958333333333 & 1.11095936239077 & 0.960397905999254 \tabularnewline
16 & 15.4 & 15.0310464032203 & 15.0083333333333 & 1.00151336390141 & 1.02454610190683 \tabularnewline
17 & 15 & 14.9296793708485 & 14.9958333333333 & 0.995588510420574 & 1.00471012319855 \tabularnewline
18 & 15.5 & 15.9783842203974 & 15.0416666666667 & 1.06227485121756 & 0.970060538424984 \tabularnewline
19 & 15.1 & 14.6853092707903 & 15.0833333333333 & 0.97361166436179 & 1.02823847435304 \tabularnewline
20 & 11.7 & 12.3489682005958 & 15.1 & 0.817812463615614 & 0.947447576991536 \tabularnewline
21 & 16.3 & 16.2115956583687 & 15.2041666666667 & 1.06626005974472 & 1.00545315485868 \tabularnewline
22 & 16.7 & 16.3491546254341 & 15.325 & 1.06682901307890 & 1.02145954225793 \tabularnewline
23 & 15 & 15.6530842473165 & 15.3833333333333 & 1.01753527068146 & 0.958277599673146 \tabularnewline
24 & 14.9 & 14.9069546736424 & 15.5 & 0.961739011202734 & 0.999533461139808 \tabularnewline
25 & 14.6 & 14.9101007197708 & 15.6333333333333 & 0.953737785912845 & 0.979201970154392 \tabularnewline
26 & 15.3 & 15.3192847900405 & 15.7583333333333 & 0.972138643471631 & 0.998741142925093 \tabularnewline
27 & 17.9 & 17.6781408540431 & 15.9125 & 1.11095936239077 & 1.01254991391848 \tabularnewline
28 & 16.4 & 16.0492516565201 & 16.025 & 1.00151336390141 & 1.02185449832718 \tabularnewline
29 & 15.4 & 16.1036441560528 & 16.175 & 0.995588510420574 & 0.95630528411867 \tabularnewline
30 & 17.9 & 17.3681938174071 & 16.35 & 1.06227485121756 & 1.03061954444911 \tabularnewline
31 & 15.9 & 16.0483656008968 & 16.4833333333333 & 0.97361166436179 & 0.990755095902815 \tabularnewline
32 & 13.9 & 13.5722793440875 & 16.5958333333333 & 0.817812463615614 & 1.02414632410696 \tabularnewline
33 & 17.8 & 17.8065429977368 & 16.7 & 1.06626005974472 & 0.999632550925935 \tabularnewline
34 & 17.9 & 17.9316176615011 & 16.8083333333333 & 1.06682901307890 & 0.998236764685821 \tabularnewline
35 & 17.4 & 17.2387433774618 & 16.9416666666667 & 1.01753527068146 & 1.00935431423320 \tabularnewline
36 & 16.7 & 16.3936428951266 & 17.0458333333333 & 0.961739011202734 & 1.01868755509884 \tabularnewline
37 & 16 & 16.3009683242270 & 17.0916666666667 & 0.953737785912845 & 0.981536782463424 \tabularnewline
38 & 16.6 & 16.6802788909007 & 17.1583333333333 & 0.972138643471631 & 0.99518719732291 \tabularnewline
39 & 19.1 & 19.1825649906139 & 17.2666666666667 & 1.11095936239077 & 0.99569583157131 \tabularnewline
40 & 17.8 & 17.3428730848927 & 17.3166666666667 & 1.00151336390141 & 1.02635819987090 \tabularnewline
41 & 17.2 & 17.3024986540176 & 17.3791666666667 & 0.995588510420574 & 0.994076077908334 \tabularnewline
42 & 18.6 & 18.5942360415207 & 17.5041666666667 & 1.06227485121756 & 1.00030998630255 \tabularnewline
43 & 16.3 & 17.1558488691084 & 17.6208333333333 & 0.97361166436179 & 0.950113289313858 \tabularnewline
44 & 15.1 & 14.5025410214502 & 17.7333333333333 & 0.817812463615614 & 1.04119684803277 \tabularnewline
45 & 19.2 & 19.050513067439 & 17.8666666666667 & 1.06626005974472 & 1.00784687173683 \tabularnewline
46 & 17.7 & 19.1229100594392 & 17.925 & 1.06682901307890 & 0.925591342791635 \tabularnewline
47 & 19.1 & 18.3071554116773 & 17.9916666666667 & 1.01753527068146 & 1.04330790723593 \tabularnewline
48 & 18 & 17.4395340698096 & 18.1333333333333 & 0.961739011202734 & 1.03213766651947 \tabularnewline
49 & 17.5 & 17.3937928705855 & 18.2375 & 0.953737785912845 & 1.00610603622825 \tabularnewline
50 & 17.8 & 17.8265923746610 & 18.3375 & 0.972138643471631 & 0.998508274935437 \tabularnewline
51 & 21.1 & 20.4370232706468 & 18.3958333333333 & 1.11095936239077 & 1.03243998504936 \tabularnewline
52 & 17.2 & 18.5279972321761 & 18.5 & 1.00151336390141 & 0.92832483643349 \tabularnewline
53 & 19.4 & 18.5469842920433 & 18.6291666666667 & 0.995588510420574 & 1.04599215131285 \tabularnewline
54 & 19.8 & 19.7981475395672 & 18.6375 & 1.06227485121756 & 1.00009356736175 \tabularnewline
55 & 17.6 & NA & NA & 0.97361166436179 & NA \tabularnewline
56 & 16.2 & NA & NA & 0.817812463615614 & NA \tabularnewline
57 & 19.5 & NA & NA & 1.06626005974472 & NA \tabularnewline
58 & 19.9 & NA & NA & 1.06682901307890 & NA \tabularnewline
59 & 20 & NA & NA & 1.01753527068146 & NA \tabularnewline
60 & 17.3 & NA & NA & 0.961739011202734 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64121&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]14.3[/C][C]NA[/C][C]NA[/C][C]0.953737785912845[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]14.2[/C][C]NA[/C][C]NA[/C][C]0.972138643471631[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]15.9[/C][C]NA[/C][C]NA[/C][C]1.11095936239077[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]15.3[/C][C]NA[/C][C]NA[/C][C]1.00151336390141[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]15.5[/C][C]NA[/C][C]NA[/C][C]0.995588510420574[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]15.1[/C][C]NA[/C][C]NA[/C][C]1.06227485121756[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]14.5352108058679[/C][C]14.9291666666667[/C][C]0.97361166436179[/C][C]1.03197677696869[/C][/ROW]
[ROW][C]8[/C][C]12.1[/C][C]12.2433340907121[/C][C]14.9708333333333[/C][C]0.817812463615614[/C][C]0.98829288740713[/C][/ROW]
[ROW][C]9[/C][C]15.8[/C][C]15.9894581459219[/C][C]14.9958333333333[/C][C]1.06626005974472[/C][C]0.988151059016958[/C][/ROW]
[ROW][C]10[/C][C]16.9[/C][C]16.0068803170713[/C][C]15.0041666666667[/C][C]1.06682901307890[/C][C]1.05579598680301[/C][/ROW]
[ROW][C]11[/C][C]15.1[/C][C]15.2503098693384[/C][C]14.9875[/C][C]1.01753527068146[/C][C]0.990143815396129[/C][/ROW]
[ROW][C]12[/C][C]13.7[/C][C]14.4100561845210[/C][C]14.9833333333333[/C][C]0.961739011202734[/C][C]0.950724953780285[/C][/ROW]
[ROW][C]13[/C][C]14.8[/C][C]14.3100406961340[/C][C]15.0041666666667[/C][C]0.953737785912845[/C][C]1.03423884769233[/C][/ROW]
[ROW][C]14[/C][C]14.7[/C][C]14.5739784967122[/C][C]14.9916666666667[/C][C]0.972138643471631[/C][C]1.00864702135496[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]16.6597614385182[/C][C]14.9958333333333[/C][C]1.11095936239077[/C][C]0.960397905999254[/C][/ROW]
[ROW][C]16[/C][C]15.4[/C][C]15.0310464032203[/C][C]15.0083333333333[/C][C]1.00151336390141[/C][C]1.02454610190683[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.9296793708485[/C][C]14.9958333333333[/C][C]0.995588510420574[/C][C]1.00471012319855[/C][/ROW]
[ROW][C]18[/C][C]15.5[/C][C]15.9783842203974[/C][C]15.0416666666667[/C][C]1.06227485121756[/C][C]0.970060538424984[/C][/ROW]
[ROW][C]19[/C][C]15.1[/C][C]14.6853092707903[/C][C]15.0833333333333[/C][C]0.97361166436179[/C][C]1.02823847435304[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]12.3489682005958[/C][C]15.1[/C][C]0.817812463615614[/C][C]0.947447576991536[/C][/ROW]
[ROW][C]21[/C][C]16.3[/C][C]16.2115956583687[/C][C]15.2041666666667[/C][C]1.06626005974472[/C][C]1.00545315485868[/C][/ROW]
[ROW][C]22[/C][C]16.7[/C][C]16.3491546254341[/C][C]15.325[/C][C]1.06682901307890[/C][C]1.02145954225793[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]15.6530842473165[/C][C]15.3833333333333[/C][C]1.01753527068146[/C][C]0.958277599673146[/C][/ROW]
[ROW][C]24[/C][C]14.9[/C][C]14.9069546736424[/C][C]15.5[/C][C]0.961739011202734[/C][C]0.999533461139808[/C][/ROW]
[ROW][C]25[/C][C]14.6[/C][C]14.9101007197708[/C][C]15.6333333333333[/C][C]0.953737785912845[/C][C]0.979201970154392[/C][/ROW]
[ROW][C]26[/C][C]15.3[/C][C]15.3192847900405[/C][C]15.7583333333333[/C][C]0.972138643471631[/C][C]0.998741142925093[/C][/ROW]
[ROW][C]27[/C][C]17.9[/C][C]17.6781408540431[/C][C]15.9125[/C][C]1.11095936239077[/C][C]1.01254991391848[/C][/ROW]
[ROW][C]28[/C][C]16.4[/C][C]16.0492516565201[/C][C]16.025[/C][C]1.00151336390141[/C][C]1.02185449832718[/C][/ROW]
[ROW][C]29[/C][C]15.4[/C][C]16.1036441560528[/C][C]16.175[/C][C]0.995588510420574[/C][C]0.95630528411867[/C][/ROW]
[ROW][C]30[/C][C]17.9[/C][C]17.3681938174071[/C][C]16.35[/C][C]1.06227485121756[/C][C]1.03061954444911[/C][/ROW]
[ROW][C]31[/C][C]15.9[/C][C]16.0483656008968[/C][C]16.4833333333333[/C][C]0.97361166436179[/C][C]0.990755095902815[/C][/ROW]
[ROW][C]32[/C][C]13.9[/C][C]13.5722793440875[/C][C]16.5958333333333[/C][C]0.817812463615614[/C][C]1.02414632410696[/C][/ROW]
[ROW][C]33[/C][C]17.8[/C][C]17.8065429977368[/C][C]16.7[/C][C]1.06626005974472[/C][C]0.999632550925935[/C][/ROW]
[ROW][C]34[/C][C]17.9[/C][C]17.9316176615011[/C][C]16.8083333333333[/C][C]1.06682901307890[/C][C]0.998236764685821[/C][/ROW]
[ROW][C]35[/C][C]17.4[/C][C]17.2387433774618[/C][C]16.9416666666667[/C][C]1.01753527068146[/C][C]1.00935431423320[/C][/ROW]
[ROW][C]36[/C][C]16.7[/C][C]16.3936428951266[/C][C]17.0458333333333[/C][C]0.961739011202734[/C][C]1.01868755509884[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]16.3009683242270[/C][C]17.0916666666667[/C][C]0.953737785912845[/C][C]0.981536782463424[/C][/ROW]
[ROW][C]38[/C][C]16.6[/C][C]16.6802788909007[/C][C]17.1583333333333[/C][C]0.972138643471631[/C][C]0.99518719732291[/C][/ROW]
[ROW][C]39[/C][C]19.1[/C][C]19.1825649906139[/C][C]17.2666666666667[/C][C]1.11095936239077[/C][C]0.99569583157131[/C][/ROW]
[ROW][C]40[/C][C]17.8[/C][C]17.3428730848927[/C][C]17.3166666666667[/C][C]1.00151336390141[/C][C]1.02635819987090[/C][/ROW]
[ROW][C]41[/C][C]17.2[/C][C]17.3024986540176[/C][C]17.3791666666667[/C][C]0.995588510420574[/C][C]0.994076077908334[/C][/ROW]
[ROW][C]42[/C][C]18.6[/C][C]18.5942360415207[/C][C]17.5041666666667[/C][C]1.06227485121756[/C][C]1.00030998630255[/C][/ROW]
[ROW][C]43[/C][C]16.3[/C][C]17.1558488691084[/C][C]17.6208333333333[/C][C]0.97361166436179[/C][C]0.950113289313858[/C][/ROW]
[ROW][C]44[/C][C]15.1[/C][C]14.5025410214502[/C][C]17.7333333333333[/C][C]0.817812463615614[/C][C]1.04119684803277[/C][/ROW]
[ROW][C]45[/C][C]19.2[/C][C]19.050513067439[/C][C]17.8666666666667[/C][C]1.06626005974472[/C][C]1.00784687173683[/C][/ROW]
[ROW][C]46[/C][C]17.7[/C][C]19.1229100594392[/C][C]17.925[/C][C]1.06682901307890[/C][C]0.925591342791635[/C][/ROW]
[ROW][C]47[/C][C]19.1[/C][C]18.3071554116773[/C][C]17.9916666666667[/C][C]1.01753527068146[/C][C]1.04330790723593[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]17.4395340698096[/C][C]18.1333333333333[/C][C]0.961739011202734[/C][C]1.03213766651947[/C][/ROW]
[ROW][C]49[/C][C]17.5[/C][C]17.3937928705855[/C][C]18.2375[/C][C]0.953737785912845[/C][C]1.00610603622825[/C][/ROW]
[ROW][C]50[/C][C]17.8[/C][C]17.8265923746610[/C][C]18.3375[/C][C]0.972138643471631[/C][C]0.998508274935437[/C][/ROW]
[ROW][C]51[/C][C]21.1[/C][C]20.4370232706468[/C][C]18.3958333333333[/C][C]1.11095936239077[/C][C]1.03243998504936[/C][/ROW]
[ROW][C]52[/C][C]17.2[/C][C]18.5279972321761[/C][C]18.5[/C][C]1.00151336390141[/C][C]0.92832483643349[/C][/ROW]
[ROW][C]53[/C][C]19.4[/C][C]18.5469842920433[/C][C]18.6291666666667[/C][C]0.995588510420574[/C][C]1.04599215131285[/C][/ROW]
[ROW][C]54[/C][C]19.8[/C][C]19.7981475395672[/C][C]18.6375[/C][C]1.06227485121756[/C][C]1.00009356736175[/C][/ROW]
[ROW][C]55[/C][C]17.6[/C][C]NA[/C][C]NA[/C][C]0.97361166436179[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]16.2[/C][C]NA[/C][C]NA[/C][C]0.817812463615614[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]19.5[/C][C]NA[/C][C]NA[/C][C]1.06626005974472[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]19.9[/C][C]NA[/C][C]NA[/C][C]1.06682901307890[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]20[/C][C]NA[/C][C]NA[/C][C]1.01753527068146[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]17.3[/C][C]NA[/C][C]NA[/C][C]0.961739011202734[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64121&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64121&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	14.3	NA	NA	0.953737785912845	NA
2	14.2	NA	NA	0.972138643471631	NA
3	15.9	NA	NA	1.11095936239077	NA
4	15.3	NA	NA	1.00151336390141	NA
5	15.5	NA	NA	0.995588510420574	NA
6	15.1	NA	NA	1.06227485121756	NA
7	15	14.5352108058679	14.9291666666667	0.97361166436179	1.03197677696869
8	12.1	12.2433340907121	14.9708333333333	0.817812463615614	0.98829288740713
9	15.8	15.9894581459219	14.9958333333333	1.06626005974472	0.988151059016958
10	16.9	16.0068803170713	15.0041666666667	1.06682901307890	1.05579598680301
11	15.1	15.2503098693384	14.9875	1.01753527068146	0.990143815396129
12	13.7	14.4100561845210	14.9833333333333	0.961739011202734	0.950724953780285
13	14.8	14.3100406961340	15.0041666666667	0.953737785912845	1.03423884769233
14	14.7	14.5739784967122	14.9916666666667	0.972138643471631	1.00864702135496
15	16	16.6597614385182	14.9958333333333	1.11095936239077	0.960397905999254
16	15.4	15.0310464032203	15.0083333333333	1.00151336390141	1.02454610190683
17	15	14.9296793708485	14.9958333333333	0.995588510420574	1.00471012319855
18	15.5	15.9783842203974	15.0416666666667	1.06227485121756	0.970060538424984
19	15.1	14.6853092707903	15.0833333333333	0.97361166436179	1.02823847435304
20	11.7	12.3489682005958	15.1	0.817812463615614	0.947447576991536
21	16.3	16.2115956583687	15.2041666666667	1.06626005974472	1.00545315485868
22	16.7	16.3491546254341	15.325	1.06682901307890	1.02145954225793
23	15	15.6530842473165	15.3833333333333	1.01753527068146	0.958277599673146
24	14.9	14.9069546736424	15.5	0.961739011202734	0.999533461139808
25	14.6	14.9101007197708	15.6333333333333	0.953737785912845	0.979201970154392
26	15.3	15.3192847900405	15.7583333333333	0.972138643471631	0.998741142925093
27	17.9	17.6781408540431	15.9125	1.11095936239077	1.01254991391848
28	16.4	16.0492516565201	16.025	1.00151336390141	1.02185449832718
29	15.4	16.1036441560528	16.175	0.995588510420574	0.95630528411867
30	17.9	17.3681938174071	16.35	1.06227485121756	1.03061954444911
31	15.9	16.0483656008968	16.4833333333333	0.97361166436179	0.990755095902815
32	13.9	13.5722793440875	16.5958333333333	0.817812463615614	1.02414632410696
33	17.8	17.8065429977368	16.7	1.06626005974472	0.999632550925935
34	17.9	17.9316176615011	16.8083333333333	1.06682901307890	0.998236764685821
35	17.4	17.2387433774618	16.9416666666667	1.01753527068146	1.00935431423320
36	16.7	16.3936428951266	17.0458333333333	0.961739011202734	1.01868755509884
37	16	16.3009683242270	17.0916666666667	0.953737785912845	0.981536782463424
38	16.6	16.6802788909007	17.1583333333333	0.972138643471631	0.99518719732291
39	19.1	19.1825649906139	17.2666666666667	1.11095936239077	0.99569583157131
40	17.8	17.3428730848927	17.3166666666667	1.00151336390141	1.02635819987090
41	17.2	17.3024986540176	17.3791666666667	0.995588510420574	0.994076077908334
42	18.6	18.5942360415207	17.5041666666667	1.06227485121756	1.00030998630255
43	16.3	17.1558488691084	17.6208333333333	0.97361166436179	0.950113289313858
44	15.1	14.5025410214502	17.7333333333333	0.817812463615614	1.04119684803277
45	19.2	19.050513067439	17.8666666666667	1.06626005974472	1.00784687173683
46	17.7	19.1229100594392	17.925	1.06682901307890	0.925591342791635
47	19.1	18.3071554116773	17.9916666666667	1.01753527068146	1.04330790723593
48	18	17.4395340698096	18.1333333333333	0.961739011202734	1.03213766651947
49	17.5	17.3937928705855	18.2375	0.953737785912845	1.00610603622825
50	17.8	17.8265923746610	18.3375	0.972138643471631	0.998508274935437
51	21.1	20.4370232706468	18.3958333333333	1.11095936239077	1.03243998504936
52	17.2	18.5279972321761	18.5	1.00151336390141	0.92832483643349
53	19.4	18.5469842920433	18.6291666666667	0.995588510420574	1.04599215131285
54	19.8	19.7981475395672	18.6375	1.06227485121756	1.00009356736175
55	17.6	NA	NA	0.97361166436179	NA
56	16.2	NA	NA	0.817812463615614	NA
57	19.5	NA	NA	1.06626005974472	NA
58	19.9	NA	NA	1.06682901307890	NA
59	20	NA	NA	1.01753527068146	NA
60	17.3	NA	NA	0.961739011202734	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 = 0.2 ; par2 = 1 ; par3 = 1 ; par4 = 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
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