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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Fri, 04 Dec 2009 02:04:33 -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/t12599175396vx5yy6yrlcglgk.htm/, Retrieved Sat, 27 Apr 2024 15:53:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63191, Retrieved Sat, 27 Apr 2024 15:53:00 +0000

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IsPrivate?

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Estimated Impact

135

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] [decomposition] [2009-12-04 09:04:33] [87085ce7f5378f281469a8b1f0969170] [Current]
-             [Classical Decomposition] [Workshop 9-6] [2009-12-04 22:01:57] [aba88da643e3763d32ff92bd8f92a385] 
-             [Classical Decomposition] [Workshop 9] [2009-12-05 14:12:57] [b6394cb5c2dcec6d17418d3cdf42d699] 

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Dataseries X:

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Histogram

Boxplots

5.7
6.1
6
5.9
5.8
5.7
5.6
5.4
5.4
5.5
5.6
5.7
5.9
6.1
6
5.8
5.8
5.7
5.5
5.3
5.2
5.2
5
5.1
5.1
5.2
4.9
4.8
4.5
4.5
4.4
4.4
4.2
4.1
3.9
3.8
3.9
4.2
4.1
3.8
3.6
3.7
3.5
3.4
3.1
3.1
3.1
3.2
3.3
3.5
3.6
3.5
3.3
3.2
3.1
3.2
3
3
3.1
3.4

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=63191&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=63191&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63191&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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	5.7	NA	NA	1.01069177570925	NA
2	6.1	NA	NA	1.07078568749111	NA
3	6	NA	NA	1.06408636874514	NA
4	5.9	NA	NA	1.03482005530154	NA
5	5.8	NA	NA	1.00184825661367	NA
6	5.7	NA	NA	1.00770313813477	NA
7	5.6	5.65555196028078	5.70833333333333	0.990753628078384	0.990177446751277
8	5.4	5.58868361762068	5.71666666666667	0.977612294627524	0.96623827174153
9	5.4	5.43737320839893	5.71666666666667	0.9511440014692	0.99312660599769
10	5.5	5.48614752866397	5.7125	0.96037593499588	1.00252499067217
11	5.6	5.44618391246845	5.70833333333333	0.954076013863086	1.02824291100038
12	5.7	5.57192040670635	5.70833333333333	0.976102844970456	1.02298661573476
13	5.9	5.76515433727484	5.70416666666667	1.01069177570925	1.02338977498890
14	6.1	6.09901681166809	5.69583333333333	1.07078568749111	1.00016120439774
15	6	6.04755752903486	5.68333333333333	1.06408636874514	0.99213607662159
16	5.8	5.85966856314499	5.6625	1.03482005530154	0.989817075402475
17	5.8	5.63539644345188	5.625	1.00184825661367	1.02920886901211
18	5.7	5.61794499510132	5.575	1.00770313813477	1.01460587545272
19	5.5	5.46565751489909	5.51666666666667	0.990753628078384	1.00628332181577
20	5.3	5.32391362115906	5.44583333333333	0.977612294627524	0.995508262744156
21	5.2	5.10050970787859	5.3625	0.9511440014692	1.01950595093815
22	5.2	5.06598305710327	5.275	0.96037593499588	1.02645428170330
23	5	4.9413186884659	5.17916666666667	0.954076013863086	1.01187563790837
24	5.1	4.95372193822506	5.075	0.976102844970456	1.02952892059730
25	5.1	5.03240279988563	4.97916666666667	1.01069177570925	1.01343239060989
26	5.2	5.2423882616752	4.89583333333333	1.07078568749111	0.991914322335664
27	4.9	5.12534934278908	4.81666666666667	1.06408636874514	0.956032393556524
28	4.8	4.89383651153022	4.72916666666667	1.03482005530154	0.98082557287945
29	4.5	4.64607129004589	4.6375	1.00184825661367	0.968560256413017
30	4.5	4.5724529892865	4.5375	1.00770313813477	0.984154459443048
31	4.4	4.39234108448084	4.43333333333333	0.990753628078384	1.00174369780758
32	4.4	4.24446671250783	4.34166666666667	0.977612294627524	1.03664377600932
33	4.2	4.05821440626859	4.26666666666667	0.9511440014692	1.03493792578145
34	4.1	4.02557579419107	4.19166666666667	0.96037593499588	1.01848784114718
35	3.9	3.92363760701194	4.1125	0.954076013863086	0.993975588629872
36	3.8	3.94508233175559	4.04166666666667	0.976102844970456	0.963224511035482
37	3.9	4.01328859271214	3.97083333333333	1.01069177570925	0.971771630647777
38	4.2	4.16714096715289	3.89166666666667	1.07078568749111	1.0078852702863
39	4.1	4.04796189443462	3.80416666666667	1.06408636874514	1.01285538424582
40	3.8	3.84608120553740	3.71666666666667	1.03482005530154	0.98801866027398
41	3.6	3.64839740116811	3.64166666666667	1.00184825661367	0.986734613627174
42	3.7	3.61093624498291	3.58333333333333	1.00770313813477	1.02466500347128
43	3.5	3.50066281921029	3.53333333333333	0.990753628078384	0.9998106589396
44	3.4	3.40127610839159	3.47916666666667	0.977612294627524	0.999624814819225
45	3.1	3.26163130503813	3.42916666666667	0.9511440014692	0.950444642596952
46	3.1	3.26127661259018	3.39583333333333	0.96037593499588	0.950548011791589
47	3.1	3.21603123006349	3.37083333333333	0.954076013863086	0.963920987775608
48	3.2	3.25774324508890	3.3375	0.976102844970456	0.98227507794669
49	3.3	3.33528285984052	3.3	1.01069177570925	0.989421329067662
50	3.5	3.50682312653337	3.275	1.07078568749111	0.998054328294534
51	3.6	3.47158177803101	3.2625	1.06408636874514	1.03699127089030
52	3.5	3.36747692996044	3.25416666666667	1.03482005530154	1.03935381675833
53	3.3	3.25600683399442	3.25	1.00184825661367	1.01351138626193
54	3.2	3.28343272508911	3.25833333333333	1.00770313813477	0.974589786947182
55	3.1	NA	NA	0.990753628078384	NA
56	3.2	NA	NA	0.977612294627524	NA
57	3	NA	NA	0.9511440014692	NA
58	3	NA	NA	0.96037593499588	NA
59	3.1	NA	NA	0.954076013863086	NA
60	3.4	NA	NA	0.976102844970456	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 5.7 & NA & NA & 1.01069177570925 & NA \tabularnewline
2 & 6.1 & NA & NA & 1.07078568749111 & NA \tabularnewline
3 & 6 & NA & NA & 1.06408636874514 & NA \tabularnewline
4 & 5.9 & NA & NA & 1.03482005530154 & NA \tabularnewline
5 & 5.8 & NA & NA & 1.00184825661367 & NA \tabularnewline
6 & 5.7 & NA & NA & 1.00770313813477 & NA \tabularnewline
7 & 5.6 & 5.65555196028078 & 5.70833333333333 & 0.990753628078384 & 0.990177446751277 \tabularnewline
8 & 5.4 & 5.58868361762068 & 5.71666666666667 & 0.977612294627524 & 0.96623827174153 \tabularnewline
9 & 5.4 & 5.43737320839893 & 5.71666666666667 & 0.9511440014692 & 0.99312660599769 \tabularnewline
10 & 5.5 & 5.48614752866397 & 5.7125 & 0.96037593499588 & 1.00252499067217 \tabularnewline
11 & 5.6 & 5.44618391246845 & 5.70833333333333 & 0.954076013863086 & 1.02824291100038 \tabularnewline
12 & 5.7 & 5.57192040670635 & 5.70833333333333 & 0.976102844970456 & 1.02298661573476 \tabularnewline
13 & 5.9 & 5.76515433727484 & 5.70416666666667 & 1.01069177570925 & 1.02338977498890 \tabularnewline
14 & 6.1 & 6.09901681166809 & 5.69583333333333 & 1.07078568749111 & 1.00016120439774 \tabularnewline
15 & 6 & 6.04755752903486 & 5.68333333333333 & 1.06408636874514 & 0.99213607662159 \tabularnewline
16 & 5.8 & 5.85966856314499 & 5.6625 & 1.03482005530154 & 0.989817075402475 \tabularnewline
17 & 5.8 & 5.63539644345188 & 5.625 & 1.00184825661367 & 1.02920886901211 \tabularnewline
18 & 5.7 & 5.61794499510132 & 5.575 & 1.00770313813477 & 1.01460587545272 \tabularnewline
19 & 5.5 & 5.46565751489909 & 5.51666666666667 & 0.990753628078384 & 1.00628332181577 \tabularnewline
20 & 5.3 & 5.32391362115906 & 5.44583333333333 & 0.977612294627524 & 0.995508262744156 \tabularnewline
21 & 5.2 & 5.10050970787859 & 5.3625 & 0.9511440014692 & 1.01950595093815 \tabularnewline
22 & 5.2 & 5.06598305710327 & 5.275 & 0.96037593499588 & 1.02645428170330 \tabularnewline
23 & 5 & 4.9413186884659 & 5.17916666666667 & 0.954076013863086 & 1.01187563790837 \tabularnewline
24 & 5.1 & 4.95372193822506 & 5.075 & 0.976102844970456 & 1.02952892059730 \tabularnewline
25 & 5.1 & 5.03240279988563 & 4.97916666666667 & 1.01069177570925 & 1.01343239060989 \tabularnewline
26 & 5.2 & 5.2423882616752 & 4.89583333333333 & 1.07078568749111 & 0.991914322335664 \tabularnewline
27 & 4.9 & 5.12534934278908 & 4.81666666666667 & 1.06408636874514 & 0.956032393556524 \tabularnewline
28 & 4.8 & 4.89383651153022 & 4.72916666666667 & 1.03482005530154 & 0.98082557287945 \tabularnewline
29 & 4.5 & 4.64607129004589 & 4.6375 & 1.00184825661367 & 0.968560256413017 \tabularnewline
30 & 4.5 & 4.5724529892865 & 4.5375 & 1.00770313813477 & 0.984154459443048 \tabularnewline
31 & 4.4 & 4.39234108448084 & 4.43333333333333 & 0.990753628078384 & 1.00174369780758 \tabularnewline
32 & 4.4 & 4.24446671250783 & 4.34166666666667 & 0.977612294627524 & 1.03664377600932 \tabularnewline
33 & 4.2 & 4.05821440626859 & 4.26666666666667 & 0.9511440014692 & 1.03493792578145 \tabularnewline
34 & 4.1 & 4.02557579419107 & 4.19166666666667 & 0.96037593499588 & 1.01848784114718 \tabularnewline
35 & 3.9 & 3.92363760701194 & 4.1125 & 0.954076013863086 & 0.993975588629872 \tabularnewline
36 & 3.8 & 3.94508233175559 & 4.04166666666667 & 0.976102844970456 & 0.963224511035482 \tabularnewline
37 & 3.9 & 4.01328859271214 & 3.97083333333333 & 1.01069177570925 & 0.971771630647777 \tabularnewline
38 & 4.2 & 4.16714096715289 & 3.89166666666667 & 1.07078568749111 & 1.0078852702863 \tabularnewline
39 & 4.1 & 4.04796189443462 & 3.80416666666667 & 1.06408636874514 & 1.01285538424582 \tabularnewline
40 & 3.8 & 3.84608120553740 & 3.71666666666667 & 1.03482005530154 & 0.98801866027398 \tabularnewline
41 & 3.6 & 3.64839740116811 & 3.64166666666667 & 1.00184825661367 & 0.986734613627174 \tabularnewline
42 & 3.7 & 3.61093624498291 & 3.58333333333333 & 1.00770313813477 & 1.02466500347128 \tabularnewline
43 & 3.5 & 3.50066281921029 & 3.53333333333333 & 0.990753628078384 & 0.9998106589396 \tabularnewline
44 & 3.4 & 3.40127610839159 & 3.47916666666667 & 0.977612294627524 & 0.999624814819225 \tabularnewline
45 & 3.1 & 3.26163130503813 & 3.42916666666667 & 0.9511440014692 & 0.950444642596952 \tabularnewline
46 & 3.1 & 3.26127661259018 & 3.39583333333333 & 0.96037593499588 & 0.950548011791589 \tabularnewline
47 & 3.1 & 3.21603123006349 & 3.37083333333333 & 0.954076013863086 & 0.963920987775608 \tabularnewline
48 & 3.2 & 3.25774324508890 & 3.3375 & 0.976102844970456 & 0.98227507794669 \tabularnewline
49 & 3.3 & 3.33528285984052 & 3.3 & 1.01069177570925 & 0.989421329067662 \tabularnewline
50 & 3.5 & 3.50682312653337 & 3.275 & 1.07078568749111 & 0.998054328294534 \tabularnewline
51 & 3.6 & 3.47158177803101 & 3.2625 & 1.06408636874514 & 1.03699127089030 \tabularnewline
52 & 3.5 & 3.36747692996044 & 3.25416666666667 & 1.03482005530154 & 1.03935381675833 \tabularnewline
53 & 3.3 & 3.25600683399442 & 3.25 & 1.00184825661367 & 1.01351138626193 \tabularnewline
54 & 3.2 & 3.28343272508911 & 3.25833333333333 & 1.00770313813477 & 0.974589786947182 \tabularnewline
55 & 3.1 & NA & NA & 0.990753628078384 & NA \tabularnewline
56 & 3.2 & NA & NA & 0.977612294627524 & NA \tabularnewline
57 & 3 & NA & NA & 0.9511440014692 & NA \tabularnewline
58 & 3 & NA & NA & 0.96037593499588 & NA \tabularnewline
59 & 3.1 & NA & NA & 0.954076013863086 & NA \tabularnewline
60 & 3.4 & NA & NA & 0.976102844970456 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63191&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]5.7[/C][C]NA[/C][C]NA[/C][C]1.01069177570925[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]6.1[/C][C]NA[/C][C]NA[/C][C]1.07078568749111[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]NA[/C][C]NA[/C][C]1.06408636874514[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]5.9[/C][C]NA[/C][C]NA[/C][C]1.03482005530154[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]5.8[/C][C]NA[/C][C]NA[/C][C]1.00184825661367[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]1.00770313813477[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]5.6[/C][C]5.65555196028078[/C][C]5.70833333333333[/C][C]0.990753628078384[/C][C]0.990177446751277[/C][/ROW]
[ROW][C]8[/C][C]5.4[/C][C]5.58868361762068[/C][C]5.71666666666667[/C][C]0.977612294627524[/C][C]0.96623827174153[/C][/ROW]
[ROW][C]9[/C][C]5.4[/C][C]5.43737320839893[/C][C]5.71666666666667[/C][C]0.9511440014692[/C][C]0.99312660599769[/C][/ROW]
[ROW][C]10[/C][C]5.5[/C][C]5.48614752866397[/C][C]5.7125[/C][C]0.96037593499588[/C][C]1.00252499067217[/C][/ROW]
[ROW][C]11[/C][C]5.6[/C][C]5.44618391246845[/C][C]5.70833333333333[/C][C]0.954076013863086[/C][C]1.02824291100038[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]5.57192040670635[/C][C]5.70833333333333[/C][C]0.976102844970456[/C][C]1.02298661573476[/C][/ROW]
[ROW][C]13[/C][C]5.9[/C][C]5.76515433727484[/C][C]5.70416666666667[/C][C]1.01069177570925[/C][C]1.02338977498890[/C][/ROW]
[ROW][C]14[/C][C]6.1[/C][C]6.09901681166809[/C][C]5.69583333333333[/C][C]1.07078568749111[/C][C]1.00016120439774[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]6.04755752903486[/C][C]5.68333333333333[/C][C]1.06408636874514[/C][C]0.99213607662159[/C][/ROW]
[ROW][C]16[/C][C]5.8[/C][C]5.85966856314499[/C][C]5.6625[/C][C]1.03482005530154[/C][C]0.989817075402475[/C][/ROW]
[ROW][C]17[/C][C]5.8[/C][C]5.63539644345188[/C][C]5.625[/C][C]1.00184825661367[/C][C]1.02920886901211[/C][/ROW]
[ROW][C]18[/C][C]5.7[/C][C]5.61794499510132[/C][C]5.575[/C][C]1.00770313813477[/C][C]1.01460587545272[/C][/ROW]
[ROW][C]19[/C][C]5.5[/C][C]5.46565751489909[/C][C]5.51666666666667[/C][C]0.990753628078384[/C][C]1.00628332181577[/C][/ROW]
[ROW][C]20[/C][C]5.3[/C][C]5.32391362115906[/C][C]5.44583333333333[/C][C]0.977612294627524[/C][C]0.995508262744156[/C][/ROW]
[ROW][C]21[/C][C]5.2[/C][C]5.10050970787859[/C][C]5.3625[/C][C]0.9511440014692[/C][C]1.01950595093815[/C][/ROW]
[ROW][C]22[/C][C]5.2[/C][C]5.06598305710327[/C][C]5.275[/C][C]0.96037593499588[/C][C]1.02645428170330[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]4.9413186884659[/C][C]5.17916666666667[/C][C]0.954076013863086[/C][C]1.01187563790837[/C][/ROW]
[ROW][C]24[/C][C]5.1[/C][C]4.95372193822506[/C][C]5.075[/C][C]0.976102844970456[/C][C]1.02952892059730[/C][/ROW]
[ROW][C]25[/C][C]5.1[/C][C]5.03240279988563[/C][C]4.97916666666667[/C][C]1.01069177570925[/C][C]1.01343239060989[/C][/ROW]
[ROW][C]26[/C][C]5.2[/C][C]5.2423882616752[/C][C]4.89583333333333[/C][C]1.07078568749111[/C][C]0.991914322335664[/C][/ROW]
[ROW][C]27[/C][C]4.9[/C][C]5.12534934278908[/C][C]4.81666666666667[/C][C]1.06408636874514[/C][C]0.956032393556524[/C][/ROW]
[ROW][C]28[/C][C]4.8[/C][C]4.89383651153022[/C][C]4.72916666666667[/C][C]1.03482005530154[/C][C]0.98082557287945[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]4.64607129004589[/C][C]4.6375[/C][C]1.00184825661367[/C][C]0.968560256413017[/C][/ROW]
[ROW][C]30[/C][C]4.5[/C][C]4.5724529892865[/C][C]4.5375[/C][C]1.00770313813477[/C][C]0.984154459443048[/C][/ROW]
[ROW][C]31[/C][C]4.4[/C][C]4.39234108448084[/C][C]4.43333333333333[/C][C]0.990753628078384[/C][C]1.00174369780758[/C][/ROW]
[ROW][C]32[/C][C]4.4[/C][C]4.24446671250783[/C][C]4.34166666666667[/C][C]0.977612294627524[/C][C]1.03664377600932[/C][/ROW]
[ROW][C]33[/C][C]4.2[/C][C]4.05821440626859[/C][C]4.26666666666667[/C][C]0.9511440014692[/C][C]1.03493792578145[/C][/ROW]
[ROW][C]34[/C][C]4.1[/C][C]4.02557579419107[/C][C]4.19166666666667[/C][C]0.96037593499588[/C][C]1.01848784114718[/C][/ROW]
[ROW][C]35[/C][C]3.9[/C][C]3.92363760701194[/C][C]4.1125[/C][C]0.954076013863086[/C][C]0.993975588629872[/C][/ROW]
[ROW][C]36[/C][C]3.8[/C][C]3.94508233175559[/C][C]4.04166666666667[/C][C]0.976102844970456[/C][C]0.963224511035482[/C][/ROW]
[ROW][C]37[/C][C]3.9[/C][C]4.01328859271214[/C][C]3.97083333333333[/C][C]1.01069177570925[/C][C]0.971771630647777[/C][/ROW]
[ROW][C]38[/C][C]4.2[/C][C]4.16714096715289[/C][C]3.89166666666667[/C][C]1.07078568749111[/C][C]1.0078852702863[/C][/ROW]
[ROW][C]39[/C][C]4.1[/C][C]4.04796189443462[/C][C]3.80416666666667[/C][C]1.06408636874514[/C][C]1.01285538424582[/C][/ROW]
[ROW][C]40[/C][C]3.8[/C][C]3.84608120553740[/C][C]3.71666666666667[/C][C]1.03482005530154[/C][C]0.98801866027398[/C][/ROW]
[ROW][C]41[/C][C]3.6[/C][C]3.64839740116811[/C][C]3.64166666666667[/C][C]1.00184825661367[/C][C]0.986734613627174[/C][/ROW]
[ROW][C]42[/C][C]3.7[/C][C]3.61093624498291[/C][C]3.58333333333333[/C][C]1.00770313813477[/C][C]1.02466500347128[/C][/ROW]
[ROW][C]43[/C][C]3.5[/C][C]3.50066281921029[/C][C]3.53333333333333[/C][C]0.990753628078384[/C][C]0.9998106589396[/C][/ROW]
[ROW][C]44[/C][C]3.4[/C][C]3.40127610839159[/C][C]3.47916666666667[/C][C]0.977612294627524[/C][C]0.999624814819225[/C][/ROW]
[ROW][C]45[/C][C]3.1[/C][C]3.26163130503813[/C][C]3.42916666666667[/C][C]0.9511440014692[/C][C]0.950444642596952[/C][/ROW]
[ROW][C]46[/C][C]3.1[/C][C]3.26127661259018[/C][C]3.39583333333333[/C][C]0.96037593499588[/C][C]0.950548011791589[/C][/ROW]
[ROW][C]47[/C][C]3.1[/C][C]3.21603123006349[/C][C]3.37083333333333[/C][C]0.954076013863086[/C][C]0.963920987775608[/C][/ROW]
[ROW][C]48[/C][C]3.2[/C][C]3.25774324508890[/C][C]3.3375[/C][C]0.976102844970456[/C][C]0.98227507794669[/C][/ROW]
[ROW][C]49[/C][C]3.3[/C][C]3.33528285984052[/C][C]3.3[/C][C]1.01069177570925[/C][C]0.989421329067662[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]3.50682312653337[/C][C]3.275[/C][C]1.07078568749111[/C][C]0.998054328294534[/C][/ROW]
[ROW][C]51[/C][C]3.6[/C][C]3.47158177803101[/C][C]3.2625[/C][C]1.06408636874514[/C][C]1.03699127089030[/C][/ROW]
[ROW][C]52[/C][C]3.5[/C][C]3.36747692996044[/C][C]3.25416666666667[/C][C]1.03482005530154[/C][C]1.03935381675833[/C][/ROW]
[ROW][C]53[/C][C]3.3[/C][C]3.25600683399442[/C][C]3.25[/C][C]1.00184825661367[/C][C]1.01351138626193[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]3.28343272508911[/C][C]3.25833333333333[/C][C]1.00770313813477[/C][C]0.974589786947182[/C][/ROW]
[ROW][C]55[/C][C]3.1[/C][C]NA[/C][C]NA[/C][C]0.990753628078384[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]3.2[/C][C]NA[/C][C]NA[/C][C]0.977612294627524[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]NA[/C][C]NA[/C][C]0.9511440014692[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]NA[/C][C]NA[/C][C]0.96037593499588[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]3.1[/C][C]NA[/C][C]NA[/C][C]0.954076013863086[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]NA[/C][C]NA[/C][C]0.976102844970456[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63191&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63191&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	5.7	NA	NA	1.01069177570925	NA
2	6.1	NA	NA	1.07078568749111	NA
3	6	NA	NA	1.06408636874514	NA
4	5.9	NA	NA	1.03482005530154	NA
5	5.8	NA	NA	1.00184825661367	NA
6	5.7	NA	NA	1.00770313813477	NA
7	5.6	5.65555196028078	5.70833333333333	0.990753628078384	0.990177446751277
8	5.4	5.58868361762068	5.71666666666667	0.977612294627524	0.96623827174153
9	5.4	5.43737320839893	5.71666666666667	0.9511440014692	0.99312660599769
10	5.5	5.48614752866397	5.7125	0.96037593499588	1.00252499067217
11	5.6	5.44618391246845	5.70833333333333	0.954076013863086	1.02824291100038
12	5.7	5.57192040670635	5.70833333333333	0.976102844970456	1.02298661573476
13	5.9	5.76515433727484	5.70416666666667	1.01069177570925	1.02338977498890
14	6.1	6.09901681166809	5.69583333333333	1.07078568749111	1.00016120439774
15	6	6.04755752903486	5.68333333333333	1.06408636874514	0.99213607662159
16	5.8	5.85966856314499	5.6625	1.03482005530154	0.989817075402475
17	5.8	5.63539644345188	5.625	1.00184825661367	1.02920886901211
18	5.7	5.61794499510132	5.575	1.00770313813477	1.01460587545272
19	5.5	5.46565751489909	5.51666666666667	0.990753628078384	1.00628332181577
20	5.3	5.32391362115906	5.44583333333333	0.977612294627524	0.995508262744156
21	5.2	5.10050970787859	5.3625	0.9511440014692	1.01950595093815
22	5.2	5.06598305710327	5.275	0.96037593499588	1.02645428170330
23	5	4.9413186884659	5.17916666666667	0.954076013863086	1.01187563790837
24	5.1	4.95372193822506	5.075	0.976102844970456	1.02952892059730
25	5.1	5.03240279988563	4.97916666666667	1.01069177570925	1.01343239060989
26	5.2	5.2423882616752	4.89583333333333	1.07078568749111	0.991914322335664
27	4.9	5.12534934278908	4.81666666666667	1.06408636874514	0.956032393556524
28	4.8	4.89383651153022	4.72916666666667	1.03482005530154	0.98082557287945
29	4.5	4.64607129004589	4.6375	1.00184825661367	0.968560256413017
30	4.5	4.5724529892865	4.5375	1.00770313813477	0.984154459443048
31	4.4	4.39234108448084	4.43333333333333	0.990753628078384	1.00174369780758
32	4.4	4.24446671250783	4.34166666666667	0.977612294627524	1.03664377600932
33	4.2	4.05821440626859	4.26666666666667	0.9511440014692	1.03493792578145
34	4.1	4.02557579419107	4.19166666666667	0.96037593499588	1.01848784114718
35	3.9	3.92363760701194	4.1125	0.954076013863086	0.993975588629872
36	3.8	3.94508233175559	4.04166666666667	0.976102844970456	0.963224511035482
37	3.9	4.01328859271214	3.97083333333333	1.01069177570925	0.971771630647777
38	4.2	4.16714096715289	3.89166666666667	1.07078568749111	1.0078852702863
39	4.1	4.04796189443462	3.80416666666667	1.06408636874514	1.01285538424582
40	3.8	3.84608120553740	3.71666666666667	1.03482005530154	0.98801866027398
41	3.6	3.64839740116811	3.64166666666667	1.00184825661367	0.986734613627174
42	3.7	3.61093624498291	3.58333333333333	1.00770313813477	1.02466500347128
43	3.5	3.50066281921029	3.53333333333333	0.990753628078384	0.9998106589396
44	3.4	3.40127610839159	3.47916666666667	0.977612294627524	0.999624814819225
45	3.1	3.26163130503813	3.42916666666667	0.9511440014692	0.950444642596952
46	3.1	3.26127661259018	3.39583333333333	0.96037593499588	0.950548011791589
47	3.1	3.21603123006349	3.37083333333333	0.954076013863086	0.963920987775608
48	3.2	3.25774324508890	3.3375	0.976102844970456	0.98227507794669
49	3.3	3.33528285984052	3.3	1.01069177570925	0.989421329067662
50	3.5	3.50682312653337	3.275	1.07078568749111	0.998054328294534
51	3.6	3.47158177803101	3.2625	1.06408636874514	1.03699127089030
52	3.5	3.36747692996044	3.25416666666667	1.03482005530154	1.03935381675833
53	3.3	3.25600683399442	3.25	1.00184825661367	1.01351138626193
54	3.2	3.28343272508911	3.25833333333333	1.00770313813477	0.974589786947182
55	3.1	NA	NA	0.990753628078384	NA
56	3.2	NA	NA	0.977612294627524	NA
57	3	NA	NA	0.9511440014692	NA
58	3	NA	NA	0.96037593499588	NA
59	3.1	NA	NA	0.954076013863086	NA
60	3.4	NA	NA	0.976102844970456	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