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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Mon, 28 May 2012 17:29:08 -0400

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/28/t1338240882s25va7o5v15w6xt.htm/, Retrieved Thu, 02 May 2024 03:13:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=167885, Retrieved Thu, 02 May 2024 03:13:12 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

KEYWORD: KDGP2W92

Estimated Impact

113

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

-       [Classical Decomposition] [Multiplicatieve d...] [2012-05-28 21:29:08] [be417f314f65e9d8a38b0902dfa3287c] [Current]

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Dataseries X:

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Boxplots

1,26
1,26
1,28
1,34
1,39
1,47
1,57
1,63
1,72
1,43
1,35
1,41
1,44
1,43
1,43
1,42
1,45
1,51
1,48
1,48
1,45
1,38
1,46
1,45
1,41
1,45
1,47
1,47
1,53
1,56
1,66
1,79
1,78
1,46
1,41
1,43
1,43
1,45
1,35
1,35
1,29
1,29
1,26
1,3
1,3
1,16
1,24
1,15
1,21
1,22
1,17
1,13
1,15
1,2
1,23
1,25
1,38
1,28
1,26
1,25

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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=167885&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=167885&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167885&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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1.26	NA	NA	0.97773777195226	NA
2	1.26	NA	NA	0.99379775056412	NA
3	1.28	NA	NA	0.974499903860109	NA
4	1.34	NA	NA	0.968348003040046	NA
5	1.39	NA	NA	0.978056385546839	NA
6	1.47	NA	NA	1.00539633398255	NA
7	1.57	1.49517340757968	1.43333333333333	1.04314423784629	1.05004542753435
8	1.63	1.56848203081638	1.44791666666667	1.08326816516814	1.03922134138292
9	1.72	1.59755656637644	1.46125	1.09328079820458	1.07664419288845
10	1.43	1.40323539400455	1.47083333333333	0.954041061079579	1.01907349694129
11	1.35	1.42603768884635	1.47666666666667	0.965714010505432	0.946679046815468
12	1.41	1.42562131879195	1.48083333333333	0.962715578250048	0.989042448660081
13	1.44	1.4458297302744	1.47875	0.97773777195226	0.995967899848554
14	1.43	1.45964044614105	1.46875	0.99379775056412	0.97969332364048
15	1.43	1.41424298547698	1.45125	0.974499903860109	1.01114166001516
16	1.42	1.39240373270467	1.43791666666667	0.968348003040046	1.01981915636044
17	1.45	1.40880871868143	1.44041666666667	0.978056385546839	1.02923837762527
18	1.51	1.45447336316143	1.44666666666667	1.00539633398255	1.03817645495953
19	1.48	1.50951664085007	1.44708333333333	1.04314423784629	0.980446296482397
20	1.48	1.56712794560992	1.44666666666667	1.08326816516814	0.944402787370366
21	1.45	1.5843460900648	1.44916666666667	1.09328079820458	0.915204076364839
22	1.38	1.38614215832687	1.45291666666667	0.954041061079579	0.995568882823472
23	1.46	1.40833293198709	1.45833333333333	0.965714010505432	1.03668668596708
24	1.45	1.40917492766351	1.46375	0.962715578250048	1.02897090455915
25	1.41	1.44053365067633	1.47333333333333	0.97773777195226	0.978803930986274
26	1.45	1.48448538990515	1.49375	0.99379775056412	0.976769464934002
27	1.47	1.48164589549397	1.52041666666667	0.974499903860109	0.992139892852002
28	1.47	1.48883505467407	1.5375	0.968348003040046	0.98734913272297
29	1.53	1.5049842632602	1.53875	0.978056385546839	1.01662192579051
30	1.56	1.54412120294154	1.53583333333333	1.00539633398255	1.01028338774716
31	1.66	1.60209569195893	1.53583333333333	1.04314423784629	1.03614285234752
32	1.79	1.66462208047505	1.53666666666667	1.08326816516814	1.07531914961093
33	1.78	1.67454175591668	1.53166666666667	1.09328079820458	1.06297737498077
34	1.46	1.45173248127609	1.52166666666667	0.954041061079579	1.00569493266186
35	1.41	1.45500910916152	1.50666666666667	0.965714010505432	0.969066098020888
36	1.43	1.43003376519226	1.48541666666667	0.962715578250048	0.999976388534956
37	1.43	1.42505280262042	1.4575	0.97773777195226	1.00347158882147
38	1.45	1.41160688819712	1.42041666666667	0.99379775056412	1.02719816127556
39	1.35	1.34480986732695	1.38	0.974499903860109	1.00385938027311
40	1.35	1.30484893409646	1.3475	0.968348003040046	1.0346025234981
41	1.29	1.29877737530741	1.32791666666667	0.978056385546839	0.993241816900815
42	1.29	1.31623136723883	1.30916666666667	1.00539633398255	0.980070853885017
43	1.26	1.34391749309197	1.28833333333333	1.04314423784629	0.93755755578499
44	1.3	1.37529920802806	1.26958333333333	1.08326816516814	0.945248853785045
45	1.3	1.36933419975123	1.2525	1.09328079820458	0.949366487915201
46	1.16	1.17903574465085	1.23583333333333	0.954041061079579	0.983854819722634
47	1.24	1.17897585449205	1.22083333333333	0.965714010505432	1.05176030134582
48	1.15	1.16608924415537	1.21125	0.962715578250048	0.986202390395065
49	1.21	1.17939618741741	1.20625	0.97773777195226	1.02594871249296
50	1.22	1.19545587744942	1.20291666666667	0.99379775056412	1.02053118229921
51	1.17	1.17346030089821	1.20416666666667	0.974499903860109	0.997051199008977
52	1.13	1.17412195368606	1.2125	0.968348003040046	0.962421319567751
53	1.15	1.19159869639123	1.21833333333333	0.978056385546839	0.965090011832663
54	1.2	1.22993484857199	1.22333333333333	1.00539633398255	0.975661435557545
55	1.23	NA	NA	1.04314423784629	NA
56	1.25	NA	NA	1.08326816516814	NA
57	1.38	NA	NA	1.09328079820458	NA
58	1.28	NA	NA	0.954041061079579	NA
59	1.26	NA	NA	0.965714010505432	NA
60	1.25	NA	NA	0.962715578250048	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1.26 & NA & NA & 0.97773777195226 & NA \tabularnewline
2 & 1.26 & NA & NA & 0.99379775056412 & NA \tabularnewline
3 & 1.28 & NA & NA & 0.974499903860109 & NA \tabularnewline
4 & 1.34 & NA & NA & 0.968348003040046 & NA \tabularnewline
5 & 1.39 & NA & NA & 0.978056385546839 & NA \tabularnewline
6 & 1.47 & NA & NA & 1.00539633398255 & NA \tabularnewline
7 & 1.57 & 1.49517340757968 & 1.43333333333333 & 1.04314423784629 & 1.05004542753435 \tabularnewline
8 & 1.63 & 1.56848203081638 & 1.44791666666667 & 1.08326816516814 & 1.03922134138292 \tabularnewline
9 & 1.72 & 1.59755656637644 & 1.46125 & 1.09328079820458 & 1.07664419288845 \tabularnewline
10 & 1.43 & 1.40323539400455 & 1.47083333333333 & 0.954041061079579 & 1.01907349694129 \tabularnewline
11 & 1.35 & 1.42603768884635 & 1.47666666666667 & 0.965714010505432 & 0.946679046815468 \tabularnewline
12 & 1.41 & 1.42562131879195 & 1.48083333333333 & 0.962715578250048 & 0.989042448660081 \tabularnewline
13 & 1.44 & 1.4458297302744 & 1.47875 & 0.97773777195226 & 0.995967899848554 \tabularnewline
14 & 1.43 & 1.45964044614105 & 1.46875 & 0.99379775056412 & 0.97969332364048 \tabularnewline
15 & 1.43 & 1.41424298547698 & 1.45125 & 0.974499903860109 & 1.01114166001516 \tabularnewline
16 & 1.42 & 1.39240373270467 & 1.43791666666667 & 0.968348003040046 & 1.01981915636044 \tabularnewline
17 & 1.45 & 1.40880871868143 & 1.44041666666667 & 0.978056385546839 & 1.02923837762527 \tabularnewline
18 & 1.51 & 1.45447336316143 & 1.44666666666667 & 1.00539633398255 & 1.03817645495953 \tabularnewline
19 & 1.48 & 1.50951664085007 & 1.44708333333333 & 1.04314423784629 & 0.980446296482397 \tabularnewline
20 & 1.48 & 1.56712794560992 & 1.44666666666667 & 1.08326816516814 & 0.944402787370366 \tabularnewline
21 & 1.45 & 1.5843460900648 & 1.44916666666667 & 1.09328079820458 & 0.915204076364839 \tabularnewline
22 & 1.38 & 1.38614215832687 & 1.45291666666667 & 0.954041061079579 & 0.995568882823472 \tabularnewline
23 & 1.46 & 1.40833293198709 & 1.45833333333333 & 0.965714010505432 & 1.03668668596708 \tabularnewline
24 & 1.45 & 1.40917492766351 & 1.46375 & 0.962715578250048 & 1.02897090455915 \tabularnewline
25 & 1.41 & 1.44053365067633 & 1.47333333333333 & 0.97773777195226 & 0.978803930986274 \tabularnewline
26 & 1.45 & 1.48448538990515 & 1.49375 & 0.99379775056412 & 0.976769464934002 \tabularnewline
27 & 1.47 & 1.48164589549397 & 1.52041666666667 & 0.974499903860109 & 0.992139892852002 \tabularnewline
28 & 1.47 & 1.48883505467407 & 1.5375 & 0.968348003040046 & 0.98734913272297 \tabularnewline
29 & 1.53 & 1.5049842632602 & 1.53875 & 0.978056385546839 & 1.01662192579051 \tabularnewline
30 & 1.56 & 1.54412120294154 & 1.53583333333333 & 1.00539633398255 & 1.01028338774716 \tabularnewline
31 & 1.66 & 1.60209569195893 & 1.53583333333333 & 1.04314423784629 & 1.03614285234752 \tabularnewline
32 & 1.79 & 1.66462208047505 & 1.53666666666667 & 1.08326816516814 & 1.07531914961093 \tabularnewline
33 & 1.78 & 1.67454175591668 & 1.53166666666667 & 1.09328079820458 & 1.06297737498077 \tabularnewline
34 & 1.46 & 1.45173248127609 & 1.52166666666667 & 0.954041061079579 & 1.00569493266186 \tabularnewline
35 & 1.41 & 1.45500910916152 & 1.50666666666667 & 0.965714010505432 & 0.969066098020888 \tabularnewline
36 & 1.43 & 1.43003376519226 & 1.48541666666667 & 0.962715578250048 & 0.999976388534956 \tabularnewline
37 & 1.43 & 1.42505280262042 & 1.4575 & 0.97773777195226 & 1.00347158882147 \tabularnewline
38 & 1.45 & 1.41160688819712 & 1.42041666666667 & 0.99379775056412 & 1.02719816127556 \tabularnewline
39 & 1.35 & 1.34480986732695 & 1.38 & 0.974499903860109 & 1.00385938027311 \tabularnewline
40 & 1.35 & 1.30484893409646 & 1.3475 & 0.968348003040046 & 1.0346025234981 \tabularnewline
41 & 1.29 & 1.29877737530741 & 1.32791666666667 & 0.978056385546839 & 0.993241816900815 \tabularnewline
42 & 1.29 & 1.31623136723883 & 1.30916666666667 & 1.00539633398255 & 0.980070853885017 \tabularnewline
43 & 1.26 & 1.34391749309197 & 1.28833333333333 & 1.04314423784629 & 0.93755755578499 \tabularnewline
44 & 1.3 & 1.37529920802806 & 1.26958333333333 & 1.08326816516814 & 0.945248853785045 \tabularnewline
45 & 1.3 & 1.36933419975123 & 1.2525 & 1.09328079820458 & 0.949366487915201 \tabularnewline
46 & 1.16 & 1.17903574465085 & 1.23583333333333 & 0.954041061079579 & 0.983854819722634 \tabularnewline
47 & 1.24 & 1.17897585449205 & 1.22083333333333 & 0.965714010505432 & 1.05176030134582 \tabularnewline
48 & 1.15 & 1.16608924415537 & 1.21125 & 0.962715578250048 & 0.986202390395065 \tabularnewline
49 & 1.21 & 1.17939618741741 & 1.20625 & 0.97773777195226 & 1.02594871249296 \tabularnewline
50 & 1.22 & 1.19545587744942 & 1.20291666666667 & 0.99379775056412 & 1.02053118229921 \tabularnewline
51 & 1.17 & 1.17346030089821 & 1.20416666666667 & 0.974499903860109 & 0.997051199008977 \tabularnewline
52 & 1.13 & 1.17412195368606 & 1.2125 & 0.968348003040046 & 0.962421319567751 \tabularnewline
53 & 1.15 & 1.19159869639123 & 1.21833333333333 & 0.978056385546839 & 0.965090011832663 \tabularnewline
54 & 1.2 & 1.22993484857199 & 1.22333333333333 & 1.00539633398255 & 0.975661435557545 \tabularnewline
55 & 1.23 & NA & NA & 1.04314423784629 & NA \tabularnewline
56 & 1.25 & NA & NA & 1.08326816516814 & NA \tabularnewline
57 & 1.38 & NA & NA & 1.09328079820458 & NA \tabularnewline
58 & 1.28 & NA & NA & 0.954041061079579 & NA \tabularnewline
59 & 1.26 & NA & NA & 0.965714010505432 & NA \tabularnewline
60 & 1.25 & NA & NA & 0.962715578250048 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=167885&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]1.26[/C][C]NA[/C][C]NA[/C][C]0.97773777195226[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1.26[/C][C]NA[/C][C]NA[/C][C]0.99379775056412[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.28[/C][C]NA[/C][C]NA[/C][C]0.974499903860109[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.34[/C][C]NA[/C][C]NA[/C][C]0.968348003040046[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1.39[/C][C]NA[/C][C]NA[/C][C]0.978056385546839[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.47[/C][C]NA[/C][C]NA[/C][C]1.00539633398255[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.57[/C][C]1.49517340757968[/C][C]1.43333333333333[/C][C]1.04314423784629[/C][C]1.05004542753435[/C][/ROW]
[ROW][C]8[/C][C]1.63[/C][C]1.56848203081638[/C][C]1.44791666666667[/C][C]1.08326816516814[/C][C]1.03922134138292[/C][/ROW]
[ROW][C]9[/C][C]1.72[/C][C]1.59755656637644[/C][C]1.46125[/C][C]1.09328079820458[/C][C]1.07664419288845[/C][/ROW]
[ROW][C]10[/C][C]1.43[/C][C]1.40323539400455[/C][C]1.47083333333333[/C][C]0.954041061079579[/C][C]1.01907349694129[/C][/ROW]
[ROW][C]11[/C][C]1.35[/C][C]1.42603768884635[/C][C]1.47666666666667[/C][C]0.965714010505432[/C][C]0.946679046815468[/C][/ROW]
[ROW][C]12[/C][C]1.41[/C][C]1.42562131879195[/C][C]1.48083333333333[/C][C]0.962715578250048[/C][C]0.989042448660081[/C][/ROW]
[ROW][C]13[/C][C]1.44[/C][C]1.4458297302744[/C][C]1.47875[/C][C]0.97773777195226[/C][C]0.995967899848554[/C][/ROW]
[ROW][C]14[/C][C]1.43[/C][C]1.45964044614105[/C][C]1.46875[/C][C]0.99379775056412[/C][C]0.97969332364048[/C][/ROW]
[ROW][C]15[/C][C]1.43[/C][C]1.41424298547698[/C][C]1.45125[/C][C]0.974499903860109[/C][C]1.01114166001516[/C][/ROW]
[ROW][C]16[/C][C]1.42[/C][C]1.39240373270467[/C][C]1.43791666666667[/C][C]0.968348003040046[/C][C]1.01981915636044[/C][/ROW]
[ROW][C]17[/C][C]1.45[/C][C]1.40880871868143[/C][C]1.44041666666667[/C][C]0.978056385546839[/C][C]1.02923837762527[/C][/ROW]
[ROW][C]18[/C][C]1.51[/C][C]1.45447336316143[/C][C]1.44666666666667[/C][C]1.00539633398255[/C][C]1.03817645495953[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.50951664085007[/C][C]1.44708333333333[/C][C]1.04314423784629[/C][C]0.980446296482397[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.56712794560992[/C][C]1.44666666666667[/C][C]1.08326816516814[/C][C]0.944402787370366[/C][/ROW]
[ROW][C]21[/C][C]1.45[/C][C]1.5843460900648[/C][C]1.44916666666667[/C][C]1.09328079820458[/C][C]0.915204076364839[/C][/ROW]
[ROW][C]22[/C][C]1.38[/C][C]1.38614215832687[/C][C]1.45291666666667[/C][C]0.954041061079579[/C][C]0.995568882823472[/C][/ROW]
[ROW][C]23[/C][C]1.46[/C][C]1.40833293198709[/C][C]1.45833333333333[/C][C]0.965714010505432[/C][C]1.03668668596708[/C][/ROW]
[ROW][C]24[/C][C]1.45[/C][C]1.40917492766351[/C][C]1.46375[/C][C]0.962715578250048[/C][C]1.02897090455915[/C][/ROW]
[ROW][C]25[/C][C]1.41[/C][C]1.44053365067633[/C][C]1.47333333333333[/C][C]0.97773777195226[/C][C]0.978803930986274[/C][/ROW]
[ROW][C]26[/C][C]1.45[/C][C]1.48448538990515[/C][C]1.49375[/C][C]0.99379775056412[/C][C]0.976769464934002[/C][/ROW]
[ROW][C]27[/C][C]1.47[/C][C]1.48164589549397[/C][C]1.52041666666667[/C][C]0.974499903860109[/C][C]0.992139892852002[/C][/ROW]
[ROW][C]28[/C][C]1.47[/C][C]1.48883505467407[/C][C]1.5375[/C][C]0.968348003040046[/C][C]0.98734913272297[/C][/ROW]
[ROW][C]29[/C][C]1.53[/C][C]1.5049842632602[/C][C]1.53875[/C][C]0.978056385546839[/C][C]1.01662192579051[/C][/ROW]
[ROW][C]30[/C][C]1.56[/C][C]1.54412120294154[/C][C]1.53583333333333[/C][C]1.00539633398255[/C][C]1.01028338774716[/C][/ROW]
[ROW][C]31[/C][C]1.66[/C][C]1.60209569195893[/C][C]1.53583333333333[/C][C]1.04314423784629[/C][C]1.03614285234752[/C][/ROW]
[ROW][C]32[/C][C]1.79[/C][C]1.66462208047505[/C][C]1.53666666666667[/C][C]1.08326816516814[/C][C]1.07531914961093[/C][/ROW]
[ROW][C]33[/C][C]1.78[/C][C]1.67454175591668[/C][C]1.53166666666667[/C][C]1.09328079820458[/C][C]1.06297737498077[/C][/ROW]
[ROW][C]34[/C][C]1.46[/C][C]1.45173248127609[/C][C]1.52166666666667[/C][C]0.954041061079579[/C][C]1.00569493266186[/C][/ROW]
[ROW][C]35[/C][C]1.41[/C][C]1.45500910916152[/C][C]1.50666666666667[/C][C]0.965714010505432[/C][C]0.969066098020888[/C][/ROW]
[ROW][C]36[/C][C]1.43[/C][C]1.43003376519226[/C][C]1.48541666666667[/C][C]0.962715578250048[/C][C]0.999976388534956[/C][/ROW]
[ROW][C]37[/C][C]1.43[/C][C]1.42505280262042[/C][C]1.4575[/C][C]0.97773777195226[/C][C]1.00347158882147[/C][/ROW]
[ROW][C]38[/C][C]1.45[/C][C]1.41160688819712[/C][C]1.42041666666667[/C][C]0.99379775056412[/C][C]1.02719816127556[/C][/ROW]
[ROW][C]39[/C][C]1.35[/C][C]1.34480986732695[/C][C]1.38[/C][C]0.974499903860109[/C][C]1.00385938027311[/C][/ROW]
[ROW][C]40[/C][C]1.35[/C][C]1.30484893409646[/C][C]1.3475[/C][C]0.968348003040046[/C][C]1.0346025234981[/C][/ROW]
[ROW][C]41[/C][C]1.29[/C][C]1.29877737530741[/C][C]1.32791666666667[/C][C]0.978056385546839[/C][C]0.993241816900815[/C][/ROW]
[ROW][C]42[/C][C]1.29[/C][C]1.31623136723883[/C][C]1.30916666666667[/C][C]1.00539633398255[/C][C]0.980070853885017[/C][/ROW]
[ROW][C]43[/C][C]1.26[/C][C]1.34391749309197[/C][C]1.28833333333333[/C][C]1.04314423784629[/C][C]0.93755755578499[/C][/ROW]
[ROW][C]44[/C][C]1.3[/C][C]1.37529920802806[/C][C]1.26958333333333[/C][C]1.08326816516814[/C][C]0.945248853785045[/C][/ROW]
[ROW][C]45[/C][C]1.3[/C][C]1.36933419975123[/C][C]1.2525[/C][C]1.09328079820458[/C][C]0.949366487915201[/C][/ROW]
[ROW][C]46[/C][C]1.16[/C][C]1.17903574465085[/C][C]1.23583333333333[/C][C]0.954041061079579[/C][C]0.983854819722634[/C][/ROW]
[ROW][C]47[/C][C]1.24[/C][C]1.17897585449205[/C][C]1.22083333333333[/C][C]0.965714010505432[/C][C]1.05176030134582[/C][/ROW]
[ROW][C]48[/C][C]1.15[/C][C]1.16608924415537[/C][C]1.21125[/C][C]0.962715578250048[/C][C]0.986202390395065[/C][/ROW]
[ROW][C]49[/C][C]1.21[/C][C]1.17939618741741[/C][C]1.20625[/C][C]0.97773777195226[/C][C]1.02594871249296[/C][/ROW]
[ROW][C]50[/C][C]1.22[/C][C]1.19545587744942[/C][C]1.20291666666667[/C][C]0.99379775056412[/C][C]1.02053118229921[/C][/ROW]
[ROW][C]51[/C][C]1.17[/C][C]1.17346030089821[/C][C]1.20416666666667[/C][C]0.974499903860109[/C][C]0.997051199008977[/C][/ROW]
[ROW][C]52[/C][C]1.13[/C][C]1.17412195368606[/C][C]1.2125[/C][C]0.968348003040046[/C][C]0.962421319567751[/C][/ROW]
[ROW][C]53[/C][C]1.15[/C][C]1.19159869639123[/C][C]1.21833333333333[/C][C]0.978056385546839[/C][C]0.965090011832663[/C][/ROW]
[ROW][C]54[/C][C]1.2[/C][C]1.22993484857199[/C][C]1.22333333333333[/C][C]1.00539633398255[/C][C]0.975661435557545[/C][/ROW]
[ROW][C]55[/C][C]1.23[/C][C]NA[/C][C]NA[/C][C]1.04314423784629[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]1.25[/C][C]NA[/C][C]NA[/C][C]1.08326816516814[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]1.38[/C][C]NA[/C][C]NA[/C][C]1.09328079820458[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]1.28[/C][C]NA[/C][C]NA[/C][C]0.954041061079579[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]1.26[/C][C]NA[/C][C]NA[/C][C]0.965714010505432[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]1.25[/C][C]NA[/C][C]NA[/C][C]0.962715578250048[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=167885&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167885&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	1.26	NA	NA	0.97773777195226	NA
2	1.26	NA	NA	0.99379775056412	NA
3	1.28	NA	NA	0.974499903860109	NA
4	1.34	NA	NA	0.968348003040046	NA
5	1.39	NA	NA	0.978056385546839	NA
6	1.47	NA	NA	1.00539633398255	NA
7	1.57	1.49517340757968	1.43333333333333	1.04314423784629	1.05004542753435
8	1.63	1.56848203081638	1.44791666666667	1.08326816516814	1.03922134138292
9	1.72	1.59755656637644	1.46125	1.09328079820458	1.07664419288845
10	1.43	1.40323539400455	1.47083333333333	0.954041061079579	1.01907349694129
11	1.35	1.42603768884635	1.47666666666667	0.965714010505432	0.946679046815468
12	1.41	1.42562131879195	1.48083333333333	0.962715578250048	0.989042448660081
13	1.44	1.4458297302744	1.47875	0.97773777195226	0.995967899848554
14	1.43	1.45964044614105	1.46875	0.99379775056412	0.97969332364048
15	1.43	1.41424298547698	1.45125	0.974499903860109	1.01114166001516
16	1.42	1.39240373270467	1.43791666666667	0.968348003040046	1.01981915636044
17	1.45	1.40880871868143	1.44041666666667	0.978056385546839	1.02923837762527
18	1.51	1.45447336316143	1.44666666666667	1.00539633398255	1.03817645495953
19	1.48	1.50951664085007	1.44708333333333	1.04314423784629	0.980446296482397
20	1.48	1.56712794560992	1.44666666666667	1.08326816516814	0.944402787370366
21	1.45	1.5843460900648	1.44916666666667	1.09328079820458	0.915204076364839
22	1.38	1.38614215832687	1.45291666666667	0.954041061079579	0.995568882823472
23	1.46	1.40833293198709	1.45833333333333	0.965714010505432	1.03668668596708
24	1.45	1.40917492766351	1.46375	0.962715578250048	1.02897090455915
25	1.41	1.44053365067633	1.47333333333333	0.97773777195226	0.978803930986274
26	1.45	1.48448538990515	1.49375	0.99379775056412	0.976769464934002
27	1.47	1.48164589549397	1.52041666666667	0.974499903860109	0.992139892852002
28	1.47	1.48883505467407	1.5375	0.968348003040046	0.98734913272297
29	1.53	1.5049842632602	1.53875	0.978056385546839	1.01662192579051
30	1.56	1.54412120294154	1.53583333333333	1.00539633398255	1.01028338774716
31	1.66	1.60209569195893	1.53583333333333	1.04314423784629	1.03614285234752
32	1.79	1.66462208047505	1.53666666666667	1.08326816516814	1.07531914961093
33	1.78	1.67454175591668	1.53166666666667	1.09328079820458	1.06297737498077
34	1.46	1.45173248127609	1.52166666666667	0.954041061079579	1.00569493266186
35	1.41	1.45500910916152	1.50666666666667	0.965714010505432	0.969066098020888
36	1.43	1.43003376519226	1.48541666666667	0.962715578250048	0.999976388534956
37	1.43	1.42505280262042	1.4575	0.97773777195226	1.00347158882147
38	1.45	1.41160688819712	1.42041666666667	0.99379775056412	1.02719816127556
39	1.35	1.34480986732695	1.38	0.974499903860109	1.00385938027311
40	1.35	1.30484893409646	1.3475	0.968348003040046	1.0346025234981
41	1.29	1.29877737530741	1.32791666666667	0.978056385546839	0.993241816900815
42	1.29	1.31623136723883	1.30916666666667	1.00539633398255	0.980070853885017
43	1.26	1.34391749309197	1.28833333333333	1.04314423784629	0.93755755578499
44	1.3	1.37529920802806	1.26958333333333	1.08326816516814	0.945248853785045
45	1.3	1.36933419975123	1.2525	1.09328079820458	0.949366487915201
46	1.16	1.17903574465085	1.23583333333333	0.954041061079579	0.983854819722634
47	1.24	1.17897585449205	1.22083333333333	0.965714010505432	1.05176030134582
48	1.15	1.16608924415537	1.21125	0.962715578250048	0.986202390395065
49	1.21	1.17939618741741	1.20625	0.97773777195226	1.02594871249296
50	1.22	1.19545587744942	1.20291666666667	0.99379775056412	1.02053118229921
51	1.17	1.17346030089821	1.20416666666667	0.974499903860109	0.997051199008977
52	1.13	1.17412195368606	1.2125	0.968348003040046	0.962421319567751
53	1.15	1.19159869639123	1.21833333333333	0.978056385546839	0.965090011832663
54	1.2	1.22993484857199	1.22333333333333	1.00539633398255	0.975661435557545
55	1.23	NA	NA	1.04314423784629	NA
56	1.25	NA	NA	1.08326816516814	NA
57	1.38	NA	NA	1.09328079820458	NA
58	1.28	NA	NA	0.954041061079579	NA
59	1.26	NA	NA	0.965714010505432	NA
60	1.25	NA	NA	0.962715578250048	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 = 750 ; 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