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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 13 Nov 2013 07:33:06 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/13/t1384346411x67m5ovluzwl6f9.htm/, Retrieved Mon, 29 Apr 2024 04:53:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=224696, Retrieved Mon, 29 Apr 2024 04:53:33 +0000

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Original text written by user:

Howard Van den Brande

IsPrivate?

No (this computation is public)

User-defined keywords

Howard Van den Branden

Estimated Impact

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

-       [Classical Decomposition] [WS8: Additieve kl...] [2013-11-13 12:33:06] [c48df00dfd28bb130a7db97d228aa375] [Current]

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Dataseries X:

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6,02
5,62
4,87
4,24
4,02
3,74
3,45
3,34
3,21
3,12
3,04
2,97
2,93
2,95
2,92
2,9
2,95
2,91
2,89
2,84
2,82
2,78
2,86
2,87
2,94
3,04
3,12
3,19
3,27
3,34
3,4
3,55
3,64
3,76
3,78
3,77
3,81
3,81
3,82
3,96
3,86
3,84
3,68
3,56
3,48
3,4
3,42
3,2
3,11
3,1
2,99
3,1
3
3,05
3,1
3,2
3,1
3,3
3,13
3,14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=224696&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]3 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=224696&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=224696&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	3 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	6.02	NA	NA	-0.0294792	NA
2	5.62	NA	NA	0.003125	NA
3	4.87	NA	NA	-0.00677083	NA
4	4.24	NA	NA	0.0675	NA
5	4.02	NA	NA	0.0471875	NA
6	3.74	NA	NA	0.0594792	NA
7	3.45	3.78427	3.84125	-0.0569792	-0.334271
8	3.34	3.56833	3.60125	-0.0329167	-0.228333
9	3.21	3.38667	3.40875	-0.0220833	-0.176667
10	3.12	3.25854	3.27167	-0.013125	-0.138542
11	3.04	3.19062	3.17125	0.019375	-0.150625
12	2.97	3.05677	3.09208	-0.0353125	-0.0867708
13	2.93	3.00469	3.03417	-0.0294792	-0.0746875
14	2.95	2.99312	2.99	0.003125	-0.043125
15	2.92	2.94615	2.95292	-0.00677083	-0.0261458
16	2.9	2.99	2.9225	0.0675	-0.09
17	2.95	2.94802	2.90083	0.0471875	0.00197917
18	2.91	2.94865	2.88917	0.0594792	-0.0386458
19	2.89	2.82844	2.88542	-0.0569792	0.0615625
20	2.84	2.85667	2.88958	-0.0329167	-0.0166667
21	2.82	2.87958	2.90167	-0.0220833	-0.0595833
22	2.78	2.90896	2.92208	-0.013125	-0.128958
23	2.86	2.96688	2.9475	0.019375	-0.106875
24	2.87	2.94344	2.97875	-0.0353125	-0.0734375
25	2.94	2.98844	3.01792	-0.0294792	-0.0484375
26	3.04	3.07188	3.06875	0.003125	-0.031875
27	3.12	3.12573	3.1325	-0.00677083	-0.00572917
28	3.19	3.275	3.2075	0.0675	-0.085
29	3.27	3.33385	3.28667	0.0471875	-0.0638542
30	3.34	3.42198	3.3625	0.0594792	-0.0819792
31	3.4	3.37927	3.43625	-0.0569792	0.0207292
32	3.55	3.47167	3.50458	-0.0329167	0.0783333
33	3.64	3.54375	3.56583	-0.0220833	0.09625
34	3.76	3.61396	3.62708	-0.013125	0.146042
35	3.78	3.70312	3.68375	0.019375	0.076875
36	3.77	3.69385	3.72917	-0.0353125	0.0761458
37	3.81	3.73219	3.76167	-0.0294792	0.0778125
38	3.81	3.77688	3.77375	0.003125	0.033125
39	3.82	3.76073	3.7675	-0.00677083	0.0592708
40	3.96	3.81333	3.74583	0.0675	0.146667
41	3.86	3.76302	3.71583	0.0471875	0.0969792
42	3.84	3.73656	3.67708	0.0594792	0.103437
43	3.68	3.56719	3.62417	-0.0569792	0.112812
44	3.56	3.5325	3.56542	-0.0329167	0.0275
45	3.48	3.47917	3.50125	-0.0220833	0.000833333
46	3.4	3.41771	3.43083	-0.013125	-0.0177083
47	3.42	3.37854	3.35917	0.019375	0.0414583
48	3.2	3.2551	3.29042	-0.0353125	-0.0551042
49	3.11	3.20385	3.23333	-0.0294792	-0.0938542
50	3.1	3.19729	3.19417	0.003125	-0.0972917
51	2.99	3.15656	3.16333	-0.00677083	-0.166562
52	3.1	3.21083	3.14333	0.0675	-0.110833
53	3	3.17427	3.12708	0.0471875	-0.174271
54	3.05	3.17198	3.1125	0.0594792	-0.121979
55	3.1	NA	NA	-0.0569792	NA
56	3.2	NA	NA	-0.0329167	NA
57	3.1	NA	NA	-0.0220833	NA
58	3.3	NA	NA	-0.013125	NA
59	3.13	NA	NA	0.019375	NA
60	3.14	NA	NA	-0.0353125	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 6.02 & NA & NA & -0.0294792 & NA \tabularnewline
2 & 5.62 & NA & NA & 0.003125 & NA \tabularnewline
3 & 4.87 & NA & NA & -0.00677083 & NA \tabularnewline
4 & 4.24 & NA & NA & 0.0675 & NA \tabularnewline
5 & 4.02 & NA & NA & 0.0471875 & NA \tabularnewline
6 & 3.74 & NA & NA & 0.0594792 & NA \tabularnewline
7 & 3.45 & 3.78427 & 3.84125 & -0.0569792 & -0.334271 \tabularnewline
8 & 3.34 & 3.56833 & 3.60125 & -0.0329167 & -0.228333 \tabularnewline
9 & 3.21 & 3.38667 & 3.40875 & -0.0220833 & -0.176667 \tabularnewline
10 & 3.12 & 3.25854 & 3.27167 & -0.013125 & -0.138542 \tabularnewline
11 & 3.04 & 3.19062 & 3.17125 & 0.019375 & -0.150625 \tabularnewline
12 & 2.97 & 3.05677 & 3.09208 & -0.0353125 & -0.0867708 \tabularnewline
13 & 2.93 & 3.00469 & 3.03417 & -0.0294792 & -0.0746875 \tabularnewline
14 & 2.95 & 2.99312 & 2.99 & 0.003125 & -0.043125 \tabularnewline
15 & 2.92 & 2.94615 & 2.95292 & -0.00677083 & -0.0261458 \tabularnewline
16 & 2.9 & 2.99 & 2.9225 & 0.0675 & -0.09 \tabularnewline
17 & 2.95 & 2.94802 & 2.90083 & 0.0471875 & 0.00197917 \tabularnewline
18 & 2.91 & 2.94865 & 2.88917 & 0.0594792 & -0.0386458 \tabularnewline
19 & 2.89 & 2.82844 & 2.88542 & -0.0569792 & 0.0615625 \tabularnewline
20 & 2.84 & 2.85667 & 2.88958 & -0.0329167 & -0.0166667 \tabularnewline
21 & 2.82 & 2.87958 & 2.90167 & -0.0220833 & -0.0595833 \tabularnewline
22 & 2.78 & 2.90896 & 2.92208 & -0.013125 & -0.128958 \tabularnewline
23 & 2.86 & 2.96688 & 2.9475 & 0.019375 & -0.106875 \tabularnewline
24 & 2.87 & 2.94344 & 2.97875 & -0.0353125 & -0.0734375 \tabularnewline
25 & 2.94 & 2.98844 & 3.01792 & -0.0294792 & -0.0484375 \tabularnewline
26 & 3.04 & 3.07188 & 3.06875 & 0.003125 & -0.031875 \tabularnewline
27 & 3.12 & 3.12573 & 3.1325 & -0.00677083 & -0.00572917 \tabularnewline
28 & 3.19 & 3.275 & 3.2075 & 0.0675 & -0.085 \tabularnewline
29 & 3.27 & 3.33385 & 3.28667 & 0.0471875 & -0.0638542 \tabularnewline
30 & 3.34 & 3.42198 & 3.3625 & 0.0594792 & -0.0819792 \tabularnewline
31 & 3.4 & 3.37927 & 3.43625 & -0.0569792 & 0.0207292 \tabularnewline
32 & 3.55 & 3.47167 & 3.50458 & -0.0329167 & 0.0783333 \tabularnewline
33 & 3.64 & 3.54375 & 3.56583 & -0.0220833 & 0.09625 \tabularnewline
34 & 3.76 & 3.61396 & 3.62708 & -0.013125 & 0.146042 \tabularnewline
35 & 3.78 & 3.70312 & 3.68375 & 0.019375 & 0.076875 \tabularnewline
36 & 3.77 & 3.69385 & 3.72917 & -0.0353125 & 0.0761458 \tabularnewline
37 & 3.81 & 3.73219 & 3.76167 & -0.0294792 & 0.0778125 \tabularnewline
38 & 3.81 & 3.77688 & 3.77375 & 0.003125 & 0.033125 \tabularnewline
39 & 3.82 & 3.76073 & 3.7675 & -0.00677083 & 0.0592708 \tabularnewline
40 & 3.96 & 3.81333 & 3.74583 & 0.0675 & 0.146667 \tabularnewline
41 & 3.86 & 3.76302 & 3.71583 & 0.0471875 & 0.0969792 \tabularnewline
42 & 3.84 & 3.73656 & 3.67708 & 0.0594792 & 0.103437 \tabularnewline
43 & 3.68 & 3.56719 & 3.62417 & -0.0569792 & 0.112812 \tabularnewline
44 & 3.56 & 3.5325 & 3.56542 & -0.0329167 & 0.0275 \tabularnewline
45 & 3.48 & 3.47917 & 3.50125 & -0.0220833 & 0.000833333 \tabularnewline
46 & 3.4 & 3.41771 & 3.43083 & -0.013125 & -0.0177083 \tabularnewline
47 & 3.42 & 3.37854 & 3.35917 & 0.019375 & 0.0414583 \tabularnewline
48 & 3.2 & 3.2551 & 3.29042 & -0.0353125 & -0.0551042 \tabularnewline
49 & 3.11 & 3.20385 & 3.23333 & -0.0294792 & -0.0938542 \tabularnewline
50 & 3.1 & 3.19729 & 3.19417 & 0.003125 & -0.0972917 \tabularnewline
51 & 2.99 & 3.15656 & 3.16333 & -0.00677083 & -0.166562 \tabularnewline
52 & 3.1 & 3.21083 & 3.14333 & 0.0675 & -0.110833 \tabularnewline
53 & 3 & 3.17427 & 3.12708 & 0.0471875 & -0.174271 \tabularnewline
54 & 3.05 & 3.17198 & 3.1125 & 0.0594792 & -0.121979 \tabularnewline
55 & 3.1 & NA & NA & -0.0569792 & NA \tabularnewline
56 & 3.2 & NA & NA & -0.0329167 & NA \tabularnewline
57 & 3.1 & NA & NA & -0.0220833 & NA \tabularnewline
58 & 3.3 & NA & NA & -0.013125 & NA \tabularnewline
59 & 3.13 & NA & NA & 0.019375 & NA \tabularnewline
60 & 3.14 & NA & NA & -0.0353125 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=224696&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]6.02[/C][C]NA[/C][C]NA[/C][C]-0.0294792[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]5.62[/C][C]NA[/C][C]NA[/C][C]0.003125[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]4.87[/C][C]NA[/C][C]NA[/C][C]-0.00677083[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]4.24[/C][C]NA[/C][C]NA[/C][C]0.0675[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]4.02[/C][C]NA[/C][C]NA[/C][C]0.0471875[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]3.74[/C][C]NA[/C][C]NA[/C][C]0.0594792[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]3.45[/C][C]3.78427[/C][C]3.84125[/C][C]-0.0569792[/C][C]-0.334271[/C][/ROW]
[ROW][C]8[/C][C]3.34[/C][C]3.56833[/C][C]3.60125[/C][C]-0.0329167[/C][C]-0.228333[/C][/ROW]
[ROW][C]9[/C][C]3.21[/C][C]3.38667[/C][C]3.40875[/C][C]-0.0220833[/C][C]-0.176667[/C][/ROW]
[ROW][C]10[/C][C]3.12[/C][C]3.25854[/C][C]3.27167[/C][C]-0.013125[/C][C]-0.138542[/C][/ROW]
[ROW][C]11[/C][C]3.04[/C][C]3.19062[/C][C]3.17125[/C][C]0.019375[/C][C]-0.150625[/C][/ROW]
[ROW][C]12[/C][C]2.97[/C][C]3.05677[/C][C]3.09208[/C][C]-0.0353125[/C][C]-0.0867708[/C][/ROW]
[ROW][C]13[/C][C]2.93[/C][C]3.00469[/C][C]3.03417[/C][C]-0.0294792[/C][C]-0.0746875[/C][/ROW]
[ROW][C]14[/C][C]2.95[/C][C]2.99312[/C][C]2.99[/C][C]0.003125[/C][C]-0.043125[/C][/ROW]
[ROW][C]15[/C][C]2.92[/C][C]2.94615[/C][C]2.95292[/C][C]-0.00677083[/C][C]-0.0261458[/C][/ROW]
[ROW][C]16[/C][C]2.9[/C][C]2.99[/C][C]2.9225[/C][C]0.0675[/C][C]-0.09[/C][/ROW]
[ROW][C]17[/C][C]2.95[/C][C]2.94802[/C][C]2.90083[/C][C]0.0471875[/C][C]0.00197917[/C][/ROW]
[ROW][C]18[/C][C]2.91[/C][C]2.94865[/C][C]2.88917[/C][C]0.0594792[/C][C]-0.0386458[/C][/ROW]
[ROW][C]19[/C][C]2.89[/C][C]2.82844[/C][C]2.88542[/C][C]-0.0569792[/C][C]0.0615625[/C][/ROW]
[ROW][C]20[/C][C]2.84[/C][C]2.85667[/C][C]2.88958[/C][C]-0.0329167[/C][C]-0.0166667[/C][/ROW]
[ROW][C]21[/C][C]2.82[/C][C]2.87958[/C][C]2.90167[/C][C]-0.0220833[/C][C]-0.0595833[/C][/ROW]
[ROW][C]22[/C][C]2.78[/C][C]2.90896[/C][C]2.92208[/C][C]-0.013125[/C][C]-0.128958[/C][/ROW]
[ROW][C]23[/C][C]2.86[/C][C]2.96688[/C][C]2.9475[/C][C]0.019375[/C][C]-0.106875[/C][/ROW]
[ROW][C]24[/C][C]2.87[/C][C]2.94344[/C][C]2.97875[/C][C]-0.0353125[/C][C]-0.0734375[/C][/ROW]
[ROW][C]25[/C][C]2.94[/C][C]2.98844[/C][C]3.01792[/C][C]-0.0294792[/C][C]-0.0484375[/C][/ROW]
[ROW][C]26[/C][C]3.04[/C][C]3.07188[/C][C]3.06875[/C][C]0.003125[/C][C]-0.031875[/C][/ROW]
[ROW][C]27[/C][C]3.12[/C][C]3.12573[/C][C]3.1325[/C][C]-0.00677083[/C][C]-0.00572917[/C][/ROW]
[ROW][C]28[/C][C]3.19[/C][C]3.275[/C][C]3.2075[/C][C]0.0675[/C][C]-0.085[/C][/ROW]
[ROW][C]29[/C][C]3.27[/C][C]3.33385[/C][C]3.28667[/C][C]0.0471875[/C][C]-0.0638542[/C][/ROW]
[ROW][C]30[/C][C]3.34[/C][C]3.42198[/C][C]3.3625[/C][C]0.0594792[/C][C]-0.0819792[/C][/ROW]
[ROW][C]31[/C][C]3.4[/C][C]3.37927[/C][C]3.43625[/C][C]-0.0569792[/C][C]0.0207292[/C][/ROW]
[ROW][C]32[/C][C]3.55[/C][C]3.47167[/C][C]3.50458[/C][C]-0.0329167[/C][C]0.0783333[/C][/ROW]
[ROW][C]33[/C][C]3.64[/C][C]3.54375[/C][C]3.56583[/C][C]-0.0220833[/C][C]0.09625[/C][/ROW]
[ROW][C]34[/C][C]3.76[/C][C]3.61396[/C][C]3.62708[/C][C]-0.013125[/C][C]0.146042[/C][/ROW]
[ROW][C]35[/C][C]3.78[/C][C]3.70312[/C][C]3.68375[/C][C]0.019375[/C][C]0.076875[/C][/ROW]
[ROW][C]36[/C][C]3.77[/C][C]3.69385[/C][C]3.72917[/C][C]-0.0353125[/C][C]0.0761458[/C][/ROW]
[ROW][C]37[/C][C]3.81[/C][C]3.73219[/C][C]3.76167[/C][C]-0.0294792[/C][C]0.0778125[/C][/ROW]
[ROW][C]38[/C][C]3.81[/C][C]3.77688[/C][C]3.77375[/C][C]0.003125[/C][C]0.033125[/C][/ROW]
[ROW][C]39[/C][C]3.82[/C][C]3.76073[/C][C]3.7675[/C][C]-0.00677083[/C][C]0.0592708[/C][/ROW]
[ROW][C]40[/C][C]3.96[/C][C]3.81333[/C][C]3.74583[/C][C]0.0675[/C][C]0.146667[/C][/ROW]
[ROW][C]41[/C][C]3.86[/C][C]3.76302[/C][C]3.71583[/C][C]0.0471875[/C][C]0.0969792[/C][/ROW]
[ROW][C]42[/C][C]3.84[/C][C]3.73656[/C][C]3.67708[/C][C]0.0594792[/C][C]0.103437[/C][/ROW]
[ROW][C]43[/C][C]3.68[/C][C]3.56719[/C][C]3.62417[/C][C]-0.0569792[/C][C]0.112812[/C][/ROW]
[ROW][C]44[/C][C]3.56[/C][C]3.5325[/C][C]3.56542[/C][C]-0.0329167[/C][C]0.0275[/C][/ROW]
[ROW][C]45[/C][C]3.48[/C][C]3.47917[/C][C]3.50125[/C][C]-0.0220833[/C][C]0.000833333[/C][/ROW]
[ROW][C]46[/C][C]3.4[/C][C]3.41771[/C][C]3.43083[/C][C]-0.013125[/C][C]-0.0177083[/C][/ROW]
[ROW][C]47[/C][C]3.42[/C][C]3.37854[/C][C]3.35917[/C][C]0.019375[/C][C]0.0414583[/C][/ROW]
[ROW][C]48[/C][C]3.2[/C][C]3.2551[/C][C]3.29042[/C][C]-0.0353125[/C][C]-0.0551042[/C][/ROW]
[ROW][C]49[/C][C]3.11[/C][C]3.20385[/C][C]3.23333[/C][C]-0.0294792[/C][C]-0.0938542[/C][/ROW]
[ROW][C]50[/C][C]3.1[/C][C]3.19729[/C][C]3.19417[/C][C]0.003125[/C][C]-0.0972917[/C][/ROW]
[ROW][C]51[/C][C]2.99[/C][C]3.15656[/C][C]3.16333[/C][C]-0.00677083[/C][C]-0.166562[/C][/ROW]
[ROW][C]52[/C][C]3.1[/C][C]3.21083[/C][C]3.14333[/C][C]0.0675[/C][C]-0.110833[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]3.17427[/C][C]3.12708[/C][C]0.0471875[/C][C]-0.174271[/C][/ROW]
[ROW][C]54[/C][C]3.05[/C][C]3.17198[/C][C]3.1125[/C][C]0.0594792[/C][C]-0.121979[/C][/ROW]
[ROW][C]55[/C][C]3.1[/C][C]NA[/C][C]NA[/C][C]-0.0569792[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]3.2[/C][C]NA[/C][C]NA[/C][C]-0.0329167[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]3.1[/C][C]NA[/C][C]NA[/C][C]-0.0220833[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]3.3[/C][C]NA[/C][C]NA[/C][C]-0.013125[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]3.13[/C][C]NA[/C][C]NA[/C][C]0.019375[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]3.14[/C][C]NA[/C][C]NA[/C][C]-0.0353125[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=224696&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=224696&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	6.02	NA	NA	-0.0294792	NA
2	5.62	NA	NA	0.003125	NA
3	4.87	NA	NA	-0.00677083	NA
4	4.24	NA	NA	0.0675	NA
5	4.02	NA	NA	0.0471875	NA
6	3.74	NA	NA	0.0594792	NA
7	3.45	3.78427	3.84125	-0.0569792	-0.334271
8	3.34	3.56833	3.60125	-0.0329167	-0.228333
9	3.21	3.38667	3.40875	-0.0220833	-0.176667
10	3.12	3.25854	3.27167	-0.013125	-0.138542
11	3.04	3.19062	3.17125	0.019375	-0.150625
12	2.97	3.05677	3.09208	-0.0353125	-0.0867708
13	2.93	3.00469	3.03417	-0.0294792	-0.0746875
14	2.95	2.99312	2.99	0.003125	-0.043125
15	2.92	2.94615	2.95292	-0.00677083	-0.0261458
16	2.9	2.99	2.9225	0.0675	-0.09
17	2.95	2.94802	2.90083	0.0471875	0.00197917
18	2.91	2.94865	2.88917	0.0594792	-0.0386458
19	2.89	2.82844	2.88542	-0.0569792	0.0615625
20	2.84	2.85667	2.88958	-0.0329167	-0.0166667
21	2.82	2.87958	2.90167	-0.0220833	-0.0595833
22	2.78	2.90896	2.92208	-0.013125	-0.128958
23	2.86	2.96688	2.9475	0.019375	-0.106875
24	2.87	2.94344	2.97875	-0.0353125	-0.0734375
25	2.94	2.98844	3.01792	-0.0294792	-0.0484375
26	3.04	3.07188	3.06875	0.003125	-0.031875
27	3.12	3.12573	3.1325	-0.00677083	-0.00572917
28	3.19	3.275	3.2075	0.0675	-0.085
29	3.27	3.33385	3.28667	0.0471875	-0.0638542
30	3.34	3.42198	3.3625	0.0594792	-0.0819792
31	3.4	3.37927	3.43625	-0.0569792	0.0207292
32	3.55	3.47167	3.50458	-0.0329167	0.0783333
33	3.64	3.54375	3.56583	-0.0220833	0.09625
34	3.76	3.61396	3.62708	-0.013125	0.146042
35	3.78	3.70312	3.68375	0.019375	0.076875
36	3.77	3.69385	3.72917	-0.0353125	0.0761458
37	3.81	3.73219	3.76167	-0.0294792	0.0778125
38	3.81	3.77688	3.77375	0.003125	0.033125
39	3.82	3.76073	3.7675	-0.00677083	0.0592708
40	3.96	3.81333	3.74583	0.0675	0.146667
41	3.86	3.76302	3.71583	0.0471875	0.0969792
42	3.84	3.73656	3.67708	0.0594792	0.103437
43	3.68	3.56719	3.62417	-0.0569792	0.112812
44	3.56	3.5325	3.56542	-0.0329167	0.0275
45	3.48	3.47917	3.50125	-0.0220833	0.000833333
46	3.4	3.41771	3.43083	-0.013125	-0.0177083
47	3.42	3.37854	3.35917	0.019375	0.0414583
48	3.2	3.2551	3.29042	-0.0353125	-0.0551042
49	3.11	3.20385	3.23333	-0.0294792	-0.0938542
50	3.1	3.19729	3.19417	0.003125	-0.0972917
51	2.99	3.15656	3.16333	-0.00677083	-0.166562
52	3.1	3.21083	3.14333	0.0675	-0.110833
53	3	3.17427	3.12708	0.0471875	-0.174271
54	3.05	3.17198	3.1125	0.0594792	-0.121979
55	3.1	NA	NA	-0.0569792	NA
56	3.2	NA	NA	-0.0329167	NA
57	3.1	NA	NA	-0.0220833	NA
58	3.3	NA	NA	-0.013125	NA
59	3.13	NA	NA	0.019375	NA
60	3.14	NA	NA	-0.0353125	NA

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code