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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Sat, 05 Dec 2009 03:47:34 -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/05/t1260010089eq5imiuggegk9en.htm/, Retrieved Mon, 29 Apr 2024 03:30:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64226, Retrieved Mon, 29 Apr 2024 03:30:08 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

152

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]
-    D    [Classical Decomposition] [SHWWS9klasmeth1] [2009-12-01 19:48:34] [a66d3a79ef9e5308cd94a469bc5ca464]
-    D        [Classical Decomposition] [SHWWS9review1] [2009-12-05 10:47:34] [db49399df1e4a3dbe31268849cebfd7f] [Current]

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Dataseries X:

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Histogram

Boxplots

7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
8.1

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' @ 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64226&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64226&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64226&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' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	7.8	NA	NA	1.05481536151932	NA
2	7.8	NA	NA	1.05476429314202	NA
3	7.8	NA	NA	1.02580815307596	NA
4	7.5	NA	NA	1.01989441724888	NA
5	7.5	NA	NA	0.98832610253764	NA
6	7.1	NA	NA	0.96412743911143	NA
7	7.5	7.80619074180304	7.62916666666667	1.02320359259024	0.960775908259152
8	7.5	7.74807266049795	7.65833333333333	1.01171786644152	0.967982662093155
9	7.6	7.58538513046524	7.69166666666667	0.98618224881455	1.00192671423842
10	7.7	7.36273995606765	7.7375	0.951565745533784	1.04580632291032
11	7.7	7.30935078186014	7.78333333333333	0.939102884179032	1.05344513210521
12	7.9	7.65600755308225	7.80833333333333	0.980491895805625	1.03186941042391
13	8.1	8.218769691838	7.79166666666667	1.05481536151932	0.985548969457563
14	8.2	8.15244901574355	7.72916666666667	1.05476429314202	1.00583272390476
15	8.2	7.85170657166894	7.65416666666667	1.02580815307596	1.04435894606502
16	8.2	7.734199330804	7.58333333333333	1.01989441724888	1.06022610088943
17	7.9	7.4330358961685	7.52083333333333	0.98832610253764	1.06282279681606
18	7.3	7.19078381670608	7.45833333333333	0.96412743911143	1.01518835582850
19	6.9	7.5589165402604	7.3875	1.02320359259024	0.912829234619688
20	6.6	7.38975591613325	7.30416666666667	1.01171786644152	0.893128281218456
21	6.7	7.10462128416815	7.20416666666667	0.98618224881455	0.943048155843324
22	6.9	6.75611679328986	7.1	0.951565745533784	1.02129673170438
23	7	6.5776331179373	7.00416666666667	0.939102884179032	1.06421259357122
24	7.1	6.78582099555476	6.92083333333333	0.980491895805625	1.04629933572534
25	7.2	7.2518556104453	6.875	1.05481536151932	0.992849332194286
26	7.1	7.24271481290856	6.86666666666667	1.05476429314202	0.980295397983337
27	6.9	7.03106004920817	6.85416666666667	1.02580815307596	0.981359844989102
28	7	6.95228027757986	6.81666666666667	1.01989441724888	1.00686389508404
29	6.8	6.67943724298355	6.75833333333333	0.98832610253764	1.01804983752832
30	6.4	6.45965384204658	6.7	0.96412743911143	0.990765164278884
31	6.7	6.81283058733002	6.65833333333333	1.02320359259024	0.983438515623763
32	6.6	6.70684635628522	6.62916666666667	1.01171786644152	0.984069061581366
33	6.4	6.50469374947263	6.59583333333333	0.98618224881455	0.98390489183582
34	6.3	6.22086106142711	6.5375	0.951565745533784	1.01272154092359
35	6.2	6.06503946032292	6.45833333333333	0.939102884179032	1.02225221131041
36	6.5	6.27923351605519	6.40416666666667	0.980491895805625	1.03515818983007
37	6.8	6.76400350574261	6.4125	1.05481536151932	1.00532177344776
38	6.8	6.81641424443032	6.4625	1.05476429314202	0.99759195321151
39	6.4	6.68057559690722	6.5125	1.02580815307596	0.958001284045478
40	6.1	6.65481107254894	6.525	1.01989441724888	0.916630079126133
41	5.8	6.42411966649466	6.5	0.98832610253764	0.902847440755223
42	6.1	6.23870797058354	6.47083333333333	0.96412743911143	0.977766554992224
43	7.2	6.62097991371935	6.47083333333333	1.02320359259024	1.08745232485615
44	7.3	6.59724358742072	6.52083333333333	1.01171786644152	1.10652273229978
45	6.9	6.52934830569299	6.62083333333333	0.98618224881455	1.05676702742046
46	6.1	6.42306878235304	6.75	0.951565745533784	0.94970180247164
47	5.8	6.464158186099	6.88333333333333	0.939102884179032	0.897255270217975
48	6.2	6.87161403643775	7.00833333333333	0.980491895805625	0.902262549544195
49	7.1	NA	7.1	NA	NA
50	7.7	NA	7.16666666666667	NA	NA
51	7.9	NA	NA	NA	NA
52	7.7	NA	NA	NA	NA
53	7.4	NA	NA	NA	NA
54	7.5	NA	NA	NA	NA
55	8	NA	NA	NA	NA
56	8.1	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 7.8 & NA & NA & 1.05481536151932 & NA \tabularnewline
2 & 7.8 & NA & NA & 1.05476429314202 & NA \tabularnewline
3 & 7.8 & NA & NA & 1.02580815307596 & NA \tabularnewline
4 & 7.5 & NA & NA & 1.01989441724888 & NA \tabularnewline
5 & 7.5 & NA & NA & 0.98832610253764 & NA \tabularnewline
6 & 7.1 & NA & NA & 0.96412743911143 & NA \tabularnewline
7 & 7.5 & 7.80619074180304 & 7.62916666666667 & 1.02320359259024 & 0.960775908259152 \tabularnewline
8 & 7.5 & 7.74807266049795 & 7.65833333333333 & 1.01171786644152 & 0.967982662093155 \tabularnewline
9 & 7.6 & 7.58538513046524 & 7.69166666666667 & 0.98618224881455 & 1.00192671423842 \tabularnewline
10 & 7.7 & 7.36273995606765 & 7.7375 & 0.951565745533784 & 1.04580632291032 \tabularnewline
11 & 7.7 & 7.30935078186014 & 7.78333333333333 & 0.939102884179032 & 1.05344513210521 \tabularnewline
12 & 7.9 & 7.65600755308225 & 7.80833333333333 & 0.980491895805625 & 1.03186941042391 \tabularnewline
13 & 8.1 & 8.218769691838 & 7.79166666666667 & 1.05481536151932 & 0.985548969457563 \tabularnewline
14 & 8.2 & 8.15244901574355 & 7.72916666666667 & 1.05476429314202 & 1.00583272390476 \tabularnewline
15 & 8.2 & 7.85170657166894 & 7.65416666666667 & 1.02580815307596 & 1.04435894606502 \tabularnewline
16 & 8.2 & 7.734199330804 & 7.58333333333333 & 1.01989441724888 & 1.06022610088943 \tabularnewline
17 & 7.9 & 7.4330358961685 & 7.52083333333333 & 0.98832610253764 & 1.06282279681606 \tabularnewline
18 & 7.3 & 7.19078381670608 & 7.45833333333333 & 0.96412743911143 & 1.01518835582850 \tabularnewline
19 & 6.9 & 7.5589165402604 & 7.3875 & 1.02320359259024 & 0.912829234619688 \tabularnewline
20 & 6.6 & 7.38975591613325 & 7.30416666666667 & 1.01171786644152 & 0.893128281218456 \tabularnewline
21 & 6.7 & 7.10462128416815 & 7.20416666666667 & 0.98618224881455 & 0.943048155843324 \tabularnewline
22 & 6.9 & 6.75611679328986 & 7.1 & 0.951565745533784 & 1.02129673170438 \tabularnewline
23 & 7 & 6.5776331179373 & 7.00416666666667 & 0.939102884179032 & 1.06421259357122 \tabularnewline
24 & 7.1 & 6.78582099555476 & 6.92083333333333 & 0.980491895805625 & 1.04629933572534 \tabularnewline
25 & 7.2 & 7.2518556104453 & 6.875 & 1.05481536151932 & 0.992849332194286 \tabularnewline
26 & 7.1 & 7.24271481290856 & 6.86666666666667 & 1.05476429314202 & 0.980295397983337 \tabularnewline
27 & 6.9 & 7.03106004920817 & 6.85416666666667 & 1.02580815307596 & 0.981359844989102 \tabularnewline
28 & 7 & 6.95228027757986 & 6.81666666666667 & 1.01989441724888 & 1.00686389508404 \tabularnewline
29 & 6.8 & 6.67943724298355 & 6.75833333333333 & 0.98832610253764 & 1.01804983752832 \tabularnewline
30 & 6.4 & 6.45965384204658 & 6.7 & 0.96412743911143 & 0.990765164278884 \tabularnewline
31 & 6.7 & 6.81283058733002 & 6.65833333333333 & 1.02320359259024 & 0.983438515623763 \tabularnewline
32 & 6.6 & 6.70684635628522 & 6.62916666666667 & 1.01171786644152 & 0.984069061581366 \tabularnewline
33 & 6.4 & 6.50469374947263 & 6.59583333333333 & 0.98618224881455 & 0.98390489183582 \tabularnewline
34 & 6.3 & 6.22086106142711 & 6.5375 & 0.951565745533784 & 1.01272154092359 \tabularnewline
35 & 6.2 & 6.06503946032292 & 6.45833333333333 & 0.939102884179032 & 1.02225221131041 \tabularnewline
36 & 6.5 & 6.27923351605519 & 6.40416666666667 & 0.980491895805625 & 1.03515818983007 \tabularnewline
37 & 6.8 & 6.76400350574261 & 6.4125 & 1.05481536151932 & 1.00532177344776 \tabularnewline
38 & 6.8 & 6.81641424443032 & 6.4625 & 1.05476429314202 & 0.99759195321151 \tabularnewline
39 & 6.4 & 6.68057559690722 & 6.5125 & 1.02580815307596 & 0.958001284045478 \tabularnewline
40 & 6.1 & 6.65481107254894 & 6.525 & 1.01989441724888 & 0.916630079126133 \tabularnewline
41 & 5.8 & 6.42411966649466 & 6.5 & 0.98832610253764 & 0.902847440755223 \tabularnewline
42 & 6.1 & 6.23870797058354 & 6.47083333333333 & 0.96412743911143 & 0.977766554992224 \tabularnewline
43 & 7.2 & 6.62097991371935 & 6.47083333333333 & 1.02320359259024 & 1.08745232485615 \tabularnewline
44 & 7.3 & 6.59724358742072 & 6.52083333333333 & 1.01171786644152 & 1.10652273229978 \tabularnewline
45 & 6.9 & 6.52934830569299 & 6.62083333333333 & 0.98618224881455 & 1.05676702742046 \tabularnewline
46 & 6.1 & 6.42306878235304 & 6.75 & 0.951565745533784 & 0.94970180247164 \tabularnewline
47 & 5.8 & 6.464158186099 & 6.88333333333333 & 0.939102884179032 & 0.897255270217975 \tabularnewline
48 & 6.2 & 6.87161403643775 & 7.00833333333333 & 0.980491895805625 & 0.902262549544195 \tabularnewline
49 & 7.1 & NA & 7.1 & NA & NA \tabularnewline
50 & 7.7 & NA & 7.16666666666667 & NA & NA \tabularnewline
51 & 7.9 & NA & NA & NA & NA \tabularnewline
52 & 7.7 & NA & NA & NA & NA \tabularnewline
53 & 7.4 & NA & NA & NA & NA \tabularnewline
54 & 7.5 & NA & NA & NA & NA \tabularnewline
55 & 8 & NA & NA & NA & NA \tabularnewline
56 & 8.1 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64226&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]7.8[/C][C]NA[/C][C]NA[/C][C]1.05481536151932[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]7.8[/C][C]NA[/C][C]NA[/C][C]1.05476429314202[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]7.8[/C][C]NA[/C][C]NA[/C][C]1.02580815307596[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]7.5[/C][C]NA[/C][C]NA[/C][C]1.01989441724888[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]7.5[/C][C]NA[/C][C]NA[/C][C]0.98832610253764[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]7.1[/C][C]NA[/C][C]NA[/C][C]0.96412743911143[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]7.5[/C][C]7.80619074180304[/C][C]7.62916666666667[/C][C]1.02320359259024[/C][C]0.960775908259152[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]7.74807266049795[/C][C]7.65833333333333[/C][C]1.01171786644152[/C][C]0.967982662093155[/C][/ROW]
[ROW][C]9[/C][C]7.6[/C][C]7.58538513046524[/C][C]7.69166666666667[/C][C]0.98618224881455[/C][C]1.00192671423842[/C][/ROW]
[ROW][C]10[/C][C]7.7[/C][C]7.36273995606765[/C][C]7.7375[/C][C]0.951565745533784[/C][C]1.04580632291032[/C][/ROW]
[ROW][C]11[/C][C]7.7[/C][C]7.30935078186014[/C][C]7.78333333333333[/C][C]0.939102884179032[/C][C]1.05344513210521[/C][/ROW]
[ROW][C]12[/C][C]7.9[/C][C]7.65600755308225[/C][C]7.80833333333333[/C][C]0.980491895805625[/C][C]1.03186941042391[/C][/ROW]
[ROW][C]13[/C][C]8.1[/C][C]8.218769691838[/C][C]7.79166666666667[/C][C]1.05481536151932[/C][C]0.985548969457563[/C][/ROW]
[ROW][C]14[/C][C]8.2[/C][C]8.15244901574355[/C][C]7.72916666666667[/C][C]1.05476429314202[/C][C]1.00583272390476[/C][/ROW]
[ROW][C]15[/C][C]8.2[/C][C]7.85170657166894[/C][C]7.65416666666667[/C][C]1.02580815307596[/C][C]1.04435894606502[/C][/ROW]
[ROW][C]16[/C][C]8.2[/C][C]7.734199330804[/C][C]7.58333333333333[/C][C]1.01989441724888[/C][C]1.06022610088943[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]7.4330358961685[/C][C]7.52083333333333[/C][C]0.98832610253764[/C][C]1.06282279681606[/C][/ROW]
[ROW][C]18[/C][C]7.3[/C][C]7.19078381670608[/C][C]7.45833333333333[/C][C]0.96412743911143[/C][C]1.01518835582850[/C][/ROW]
[ROW][C]19[/C][C]6.9[/C][C]7.5589165402604[/C][C]7.3875[/C][C]1.02320359259024[/C][C]0.912829234619688[/C][/ROW]
[ROW][C]20[/C][C]6.6[/C][C]7.38975591613325[/C][C]7.30416666666667[/C][C]1.01171786644152[/C][C]0.893128281218456[/C][/ROW]
[ROW][C]21[/C][C]6.7[/C][C]7.10462128416815[/C][C]7.20416666666667[/C][C]0.98618224881455[/C][C]0.943048155843324[/C][/ROW]
[ROW][C]22[/C][C]6.9[/C][C]6.75611679328986[/C][C]7.1[/C][C]0.951565745533784[/C][C]1.02129673170438[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]6.5776331179373[/C][C]7.00416666666667[/C][C]0.939102884179032[/C][C]1.06421259357122[/C][/ROW]
[ROW][C]24[/C][C]7.1[/C][C]6.78582099555476[/C][C]6.92083333333333[/C][C]0.980491895805625[/C][C]1.04629933572534[/C][/ROW]
[ROW][C]25[/C][C]7.2[/C][C]7.2518556104453[/C][C]6.875[/C][C]1.05481536151932[/C][C]0.992849332194286[/C][/ROW]
[ROW][C]26[/C][C]7.1[/C][C]7.24271481290856[/C][C]6.86666666666667[/C][C]1.05476429314202[/C][C]0.980295397983337[/C][/ROW]
[ROW][C]27[/C][C]6.9[/C][C]7.03106004920817[/C][C]6.85416666666667[/C][C]1.02580815307596[/C][C]0.981359844989102[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]6.95228027757986[/C][C]6.81666666666667[/C][C]1.01989441724888[/C][C]1.00686389508404[/C][/ROW]
[ROW][C]29[/C][C]6.8[/C][C]6.67943724298355[/C][C]6.75833333333333[/C][C]0.98832610253764[/C][C]1.01804983752832[/C][/ROW]
[ROW][C]30[/C][C]6.4[/C][C]6.45965384204658[/C][C]6.7[/C][C]0.96412743911143[/C][C]0.990765164278884[/C][/ROW]
[ROW][C]31[/C][C]6.7[/C][C]6.81283058733002[/C][C]6.65833333333333[/C][C]1.02320359259024[/C][C]0.983438515623763[/C][/ROW]
[ROW][C]32[/C][C]6.6[/C][C]6.70684635628522[/C][C]6.62916666666667[/C][C]1.01171786644152[/C][C]0.984069061581366[/C][/ROW]
[ROW][C]33[/C][C]6.4[/C][C]6.50469374947263[/C][C]6.59583333333333[/C][C]0.98618224881455[/C][C]0.98390489183582[/C][/ROW]
[ROW][C]34[/C][C]6.3[/C][C]6.22086106142711[/C][C]6.5375[/C][C]0.951565745533784[/C][C]1.01272154092359[/C][/ROW]
[ROW][C]35[/C][C]6.2[/C][C]6.06503946032292[/C][C]6.45833333333333[/C][C]0.939102884179032[/C][C]1.02225221131041[/C][/ROW]
[ROW][C]36[/C][C]6.5[/C][C]6.27923351605519[/C][C]6.40416666666667[/C][C]0.980491895805625[/C][C]1.03515818983007[/C][/ROW]
[ROW][C]37[/C][C]6.8[/C][C]6.76400350574261[/C][C]6.4125[/C][C]1.05481536151932[/C][C]1.00532177344776[/C][/ROW]
[ROW][C]38[/C][C]6.8[/C][C]6.81641424443032[/C][C]6.4625[/C][C]1.05476429314202[/C][C]0.99759195321151[/C][/ROW]
[ROW][C]39[/C][C]6.4[/C][C]6.68057559690722[/C][C]6.5125[/C][C]1.02580815307596[/C][C]0.958001284045478[/C][/ROW]
[ROW][C]40[/C][C]6.1[/C][C]6.65481107254894[/C][C]6.525[/C][C]1.01989441724888[/C][C]0.916630079126133[/C][/ROW]
[ROW][C]41[/C][C]5.8[/C][C]6.42411966649466[/C][C]6.5[/C][C]0.98832610253764[/C][C]0.902847440755223[/C][/ROW]
[ROW][C]42[/C][C]6.1[/C][C]6.23870797058354[/C][C]6.47083333333333[/C][C]0.96412743911143[/C][C]0.977766554992224[/C][/ROW]
[ROW][C]43[/C][C]7.2[/C][C]6.62097991371935[/C][C]6.47083333333333[/C][C]1.02320359259024[/C][C]1.08745232485615[/C][/ROW]
[ROW][C]44[/C][C]7.3[/C][C]6.59724358742072[/C][C]6.52083333333333[/C][C]1.01171786644152[/C][C]1.10652273229978[/C][/ROW]
[ROW][C]45[/C][C]6.9[/C][C]6.52934830569299[/C][C]6.62083333333333[/C][C]0.98618224881455[/C][C]1.05676702742046[/C][/ROW]
[ROW][C]46[/C][C]6.1[/C][C]6.42306878235304[/C][C]6.75[/C][C]0.951565745533784[/C][C]0.94970180247164[/C][/ROW]
[ROW][C]47[/C][C]5.8[/C][C]6.464158186099[/C][C]6.88333333333333[/C][C]0.939102884179032[/C][C]0.897255270217975[/C][/ROW]
[ROW][C]48[/C][C]6.2[/C][C]6.87161403643775[/C][C]7.00833333333333[/C][C]0.980491895805625[/C][C]0.902262549544195[/C][/ROW]
[ROW][C]49[/C][C]7.1[/C][C]NA[/C][C]7.1[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]50[/C][C]7.7[/C][C]NA[/C][C]7.16666666666667[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]51[/C][C]7.9[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]53[/C][C]7.4[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]54[/C][C]7.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64226&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64226&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	7.8	NA	NA	1.05481536151932	NA
2	7.8	NA	NA	1.05476429314202	NA
3	7.8	NA	NA	1.02580815307596	NA
4	7.5	NA	NA	1.01989441724888	NA
5	7.5	NA	NA	0.98832610253764	NA
6	7.1	NA	NA	0.96412743911143	NA
7	7.5	7.80619074180304	7.62916666666667	1.02320359259024	0.960775908259152
8	7.5	7.74807266049795	7.65833333333333	1.01171786644152	0.967982662093155
9	7.6	7.58538513046524	7.69166666666667	0.98618224881455	1.00192671423842
10	7.7	7.36273995606765	7.7375	0.951565745533784	1.04580632291032
11	7.7	7.30935078186014	7.78333333333333	0.939102884179032	1.05344513210521
12	7.9	7.65600755308225	7.80833333333333	0.980491895805625	1.03186941042391
13	8.1	8.218769691838	7.79166666666667	1.05481536151932	0.985548969457563
14	8.2	8.15244901574355	7.72916666666667	1.05476429314202	1.00583272390476
15	8.2	7.85170657166894	7.65416666666667	1.02580815307596	1.04435894606502
16	8.2	7.734199330804	7.58333333333333	1.01989441724888	1.06022610088943
17	7.9	7.4330358961685	7.52083333333333	0.98832610253764	1.06282279681606
18	7.3	7.19078381670608	7.45833333333333	0.96412743911143	1.01518835582850
19	6.9	7.5589165402604	7.3875	1.02320359259024	0.912829234619688
20	6.6	7.38975591613325	7.30416666666667	1.01171786644152	0.893128281218456
21	6.7	7.10462128416815	7.20416666666667	0.98618224881455	0.943048155843324
22	6.9	6.75611679328986	7.1	0.951565745533784	1.02129673170438
23	7	6.5776331179373	7.00416666666667	0.939102884179032	1.06421259357122
24	7.1	6.78582099555476	6.92083333333333	0.980491895805625	1.04629933572534
25	7.2	7.2518556104453	6.875	1.05481536151932	0.992849332194286
26	7.1	7.24271481290856	6.86666666666667	1.05476429314202	0.980295397983337
27	6.9	7.03106004920817	6.85416666666667	1.02580815307596	0.981359844989102
28	7	6.95228027757986	6.81666666666667	1.01989441724888	1.00686389508404
29	6.8	6.67943724298355	6.75833333333333	0.98832610253764	1.01804983752832
30	6.4	6.45965384204658	6.7	0.96412743911143	0.990765164278884
31	6.7	6.81283058733002	6.65833333333333	1.02320359259024	0.983438515623763
32	6.6	6.70684635628522	6.62916666666667	1.01171786644152	0.984069061581366
33	6.4	6.50469374947263	6.59583333333333	0.98618224881455	0.98390489183582
34	6.3	6.22086106142711	6.5375	0.951565745533784	1.01272154092359
35	6.2	6.06503946032292	6.45833333333333	0.939102884179032	1.02225221131041
36	6.5	6.27923351605519	6.40416666666667	0.980491895805625	1.03515818983007
37	6.8	6.76400350574261	6.4125	1.05481536151932	1.00532177344776
38	6.8	6.81641424443032	6.4625	1.05476429314202	0.99759195321151
39	6.4	6.68057559690722	6.5125	1.02580815307596	0.958001284045478
40	6.1	6.65481107254894	6.525	1.01989441724888	0.916630079126133
41	5.8	6.42411966649466	6.5	0.98832610253764	0.902847440755223
42	6.1	6.23870797058354	6.47083333333333	0.96412743911143	0.977766554992224
43	7.2	6.62097991371935	6.47083333333333	1.02320359259024	1.08745232485615
44	7.3	6.59724358742072	6.52083333333333	1.01171786644152	1.10652273229978
45	6.9	6.52934830569299	6.62083333333333	0.98618224881455	1.05676702742046
46	6.1	6.42306878235304	6.75	0.951565745533784	0.94970180247164
47	5.8	6.464158186099	6.88333333333333	0.939102884179032	0.897255270217975
48	6.2	6.87161403643775	7.00833333333333	0.980491895805625	0.902262549544195
49	7.1	NA	7.1	NA	NA
50	7.7	NA	7.16666666666667	NA	NA
51	7.9	NA	NA	NA	NA
52	7.7	NA	NA	NA	NA
53	7.4	NA	NA	NA	NA
54	7.5	NA	NA	NA	NA
55	8	NA	NA	NA	NA
56	8.1	NA	NA	NA	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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$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