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*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Fri, 23 May 2008 06:33:46 -0600

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/May/23/t12115461005nyis6sm1waqz1x.htm/, Retrieved Wed, 11 Feb 2026 00:30:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=13045, Retrieved Wed, 11 Feb 2026 00:30:54 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

465

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

-       [Classical Decomposition] [Klassieke Decompo...] [2008-05-23 12:33:46] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

1,44
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,57
1,58
1,58
1,58
1,58
1,59
1,6
1,6
1,61
1,61
1,61
1,62
1,63
1,63
1,64
1,64
1,64
1,64
1,64
1,65
1,65
1,65
1,65

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13045&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]5 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=13045&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13045&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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1.44	NA	NA	1.00102301380294	NA
2	1.48	NA	NA	0.99975803105593	NA
3	1.48	NA	NA	0.997108398665167	NA
4	1.48	NA	NA	0.994349527044258	NA
5	1.48	NA	NA	0.99162114019264	NA
6	1.48	NA	NA	0.988922735614117	NA
7	1.48	1.45781575428464	1.47833333333333	0.986121141567964	1.0152174550523
8	1.48	1.49840428587298	1.48	1.01243532829255	0.987717409749494
9	1.48	1.49829496402544	1.48	1.01236146217935	0.98778947772988
10	1.48	1.49322171239263	1.48	1.00893358945448	0.991145512898118
11	1.48	1.48801711496336	1.48	1.00541696956984	0.994612215892722
12	1.48	1.48288402058992	1.48	1.00194866256075	0.998055127339785
13	1.48	1.48151406042836	1.48	1.00102301380294	0.998978031684749
14	1.48	1.47964188596278	1.48	0.99975803105593	1.00024202750721
15	1.48	1.47572043002445	1.48	0.997108398665167	1.00289998694094
16	1.48	1.4716373000255	1.48	0.994349527044258	1.00568258223297
17	1.48	1.46759928748511	1.48	0.99162114019264	1.00844965830976
18	1.48	1.46360564870889	1.48	0.988922735614117	1.01120134464196
19	1.48	1.45945928952059	1.48	0.986121141567964	1.01407419215247
20	1.48	1.49840428587298	1.48	1.01243532829255	0.987717409749494
21	1.48	1.49829496402544	1.48	1.01236146217935	0.98778947772988
22	1.48	1.49322171239263	1.48	1.00893358945448	0.991145512898118
23	1.48	1.48801711496336	1.48	1.00541696956984	0.994612215892722
24	1.48	1.48288402058992	1.48	1.00194866256075	0.998055127339785
25	1.48	1.48151406042836	1.48	1.00102301380294	0.998978031684749
26	1.48	1.48339097857924	1.48375	0.99975803105593	0.997714035862288
27	1.48	1.48735336134221	1.49166666666667	0.997108398665167	0.995056076428555
28	1.48	1.49152429056639	1.5	0.994349527044258	0.992273481136529
29	1.48	1.49569521979057	1.50833333333333	0.99162114019264	0.98950640505974
30	1.48	1.49986614901474	1.51666666666667	0.988922735614117	0.986754718727538
31	1.48	1.50424562470013	1.52541666666667	0.986121141567964	0.983881871217033
32	1.57	1.55408822892907	1.535	1.01243532829255	1.01023865362000
33	1.58	1.5640984590671	1.545	1.01236146217935	1.01016658563962
34	1.58	1.56931212059732	1.55541666666667	1.00893358945448	1.00681055047138
35	1.58	1.57473432858876	1.56625	1.00541696956984	1.00334384747678
36	1.58	1.58015653658019	1.57708333333333	1.00194866256075	0.999900936029713
37	1.59	1.58995822025701	1.58833333333333	1.00102301380294	1.00002627725839
38	1.6	1.59628032291930	1.59666666666667	0.99975803105593	1.00233021545608
39	1.6	1.59661982336260	1.60125	0.997108398665167	1.00211708296987
40	1.61	1.59675961551190	1.60583333333333	0.994349527044258	1.00829203366585
41	1.61	1.59733638666031	1.61083333333333	0.99162114019264	1.00792795646894
42	1.61	1.59793432029648	1.61583333333333	0.988922735614117	1.00755079827141
43	1.62	1.59792713314909	1.62041666666667	0.986121141567964	1.01381343766747
44	1.63	1.64436371236849	1.62416666666667	1.01243532829255	0.99126488120575
45	1.63	1.64804009697280	1.62791666666667	1.01236146217935	0.989053605548833
46	1.64	1.64624330679323	1.63166666666667	1.00893358945448	0.99620754309678
47	1.64	1.64385674524668	1.635	1.00541696956984	0.99765384346426
48	1.64	1.64152589216204	1.63833333333333	1.00194866256075	0.999070442830466
49	1.64	NA	NA	NA	NA
50	1.64	NA	NA	NA	NA
51	1.65	NA	NA	NA	NA
52	1.65	NA	NA	NA	NA
53	1.65	NA	NA	NA	NA
54	1.65	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1.44 & NA & NA & 1.00102301380294 & NA \tabularnewline
2 & 1.48 & NA & NA & 0.99975803105593 & NA \tabularnewline
3 & 1.48 & NA & NA & 0.997108398665167 & NA \tabularnewline
4 & 1.48 & NA & NA & 0.994349527044258 & NA \tabularnewline
5 & 1.48 & NA & NA & 0.99162114019264 & NA \tabularnewline
6 & 1.48 & NA & NA & 0.988922735614117 & NA \tabularnewline
7 & 1.48 & 1.45781575428464 & 1.47833333333333 & 0.986121141567964 & 1.0152174550523 \tabularnewline
8 & 1.48 & 1.49840428587298 & 1.48 & 1.01243532829255 & 0.987717409749494 \tabularnewline
9 & 1.48 & 1.49829496402544 & 1.48 & 1.01236146217935 & 0.98778947772988 \tabularnewline
10 & 1.48 & 1.49322171239263 & 1.48 & 1.00893358945448 & 0.991145512898118 \tabularnewline
11 & 1.48 & 1.48801711496336 & 1.48 & 1.00541696956984 & 0.994612215892722 \tabularnewline
12 & 1.48 & 1.48288402058992 & 1.48 & 1.00194866256075 & 0.998055127339785 \tabularnewline
13 & 1.48 & 1.48151406042836 & 1.48 & 1.00102301380294 & 0.998978031684749 \tabularnewline
14 & 1.48 & 1.47964188596278 & 1.48 & 0.99975803105593 & 1.00024202750721 \tabularnewline
15 & 1.48 & 1.47572043002445 & 1.48 & 0.997108398665167 & 1.00289998694094 \tabularnewline
16 & 1.48 & 1.4716373000255 & 1.48 & 0.994349527044258 & 1.00568258223297 \tabularnewline
17 & 1.48 & 1.46759928748511 & 1.48 & 0.99162114019264 & 1.00844965830976 \tabularnewline
18 & 1.48 & 1.46360564870889 & 1.48 & 0.988922735614117 & 1.01120134464196 \tabularnewline
19 & 1.48 & 1.45945928952059 & 1.48 & 0.986121141567964 & 1.01407419215247 \tabularnewline
20 & 1.48 & 1.49840428587298 & 1.48 & 1.01243532829255 & 0.987717409749494 \tabularnewline
21 & 1.48 & 1.49829496402544 & 1.48 & 1.01236146217935 & 0.98778947772988 \tabularnewline
22 & 1.48 & 1.49322171239263 & 1.48 & 1.00893358945448 & 0.991145512898118 \tabularnewline
23 & 1.48 & 1.48801711496336 & 1.48 & 1.00541696956984 & 0.994612215892722 \tabularnewline
24 & 1.48 & 1.48288402058992 & 1.48 & 1.00194866256075 & 0.998055127339785 \tabularnewline
25 & 1.48 & 1.48151406042836 & 1.48 & 1.00102301380294 & 0.998978031684749 \tabularnewline
26 & 1.48 & 1.48339097857924 & 1.48375 & 0.99975803105593 & 0.997714035862288 \tabularnewline
27 & 1.48 & 1.48735336134221 & 1.49166666666667 & 0.997108398665167 & 0.995056076428555 \tabularnewline
28 & 1.48 & 1.49152429056639 & 1.5 & 0.994349527044258 & 0.992273481136529 \tabularnewline
29 & 1.48 & 1.49569521979057 & 1.50833333333333 & 0.99162114019264 & 0.98950640505974 \tabularnewline
30 & 1.48 & 1.49986614901474 & 1.51666666666667 & 0.988922735614117 & 0.986754718727538 \tabularnewline
31 & 1.48 & 1.50424562470013 & 1.52541666666667 & 0.986121141567964 & 0.983881871217033 \tabularnewline
32 & 1.57 & 1.55408822892907 & 1.535 & 1.01243532829255 & 1.01023865362000 \tabularnewline
33 & 1.58 & 1.5640984590671 & 1.545 & 1.01236146217935 & 1.01016658563962 \tabularnewline
34 & 1.58 & 1.56931212059732 & 1.55541666666667 & 1.00893358945448 & 1.00681055047138 \tabularnewline
35 & 1.58 & 1.57473432858876 & 1.56625 & 1.00541696956984 & 1.00334384747678 \tabularnewline
36 & 1.58 & 1.58015653658019 & 1.57708333333333 & 1.00194866256075 & 0.999900936029713 \tabularnewline
37 & 1.59 & 1.58995822025701 & 1.58833333333333 & 1.00102301380294 & 1.00002627725839 \tabularnewline
38 & 1.6 & 1.59628032291930 & 1.59666666666667 & 0.99975803105593 & 1.00233021545608 \tabularnewline
39 & 1.6 & 1.59661982336260 & 1.60125 & 0.997108398665167 & 1.00211708296987 \tabularnewline
40 & 1.61 & 1.59675961551190 & 1.60583333333333 & 0.994349527044258 & 1.00829203366585 \tabularnewline
41 & 1.61 & 1.59733638666031 & 1.61083333333333 & 0.99162114019264 & 1.00792795646894 \tabularnewline
42 & 1.61 & 1.59793432029648 & 1.61583333333333 & 0.988922735614117 & 1.00755079827141 \tabularnewline
43 & 1.62 & 1.59792713314909 & 1.62041666666667 & 0.986121141567964 & 1.01381343766747 \tabularnewline
44 & 1.63 & 1.64436371236849 & 1.62416666666667 & 1.01243532829255 & 0.99126488120575 \tabularnewline
45 & 1.63 & 1.64804009697280 & 1.62791666666667 & 1.01236146217935 & 0.989053605548833 \tabularnewline
46 & 1.64 & 1.64624330679323 & 1.63166666666667 & 1.00893358945448 & 0.99620754309678 \tabularnewline
47 & 1.64 & 1.64385674524668 & 1.635 & 1.00541696956984 & 0.99765384346426 \tabularnewline
48 & 1.64 & 1.64152589216204 & 1.63833333333333 & 1.00194866256075 & 0.999070442830466 \tabularnewline
49 & 1.64 & NA & NA & NA & NA \tabularnewline
50 & 1.64 & NA & NA & NA & NA \tabularnewline
51 & 1.65 & NA & NA & NA & NA \tabularnewline
52 & 1.65 & NA & NA & NA & NA \tabularnewline
53 & 1.65 & NA & NA & NA & NA \tabularnewline
54 & 1.65 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13045&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]1.44[/C][C]NA[/C][C]NA[/C][C]1.00102301380294[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1.48[/C][C]NA[/C][C]NA[/C][C]0.99975803105593[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.48[/C][C]NA[/C][C]NA[/C][C]0.997108398665167[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.48[/C][C]NA[/C][C]NA[/C][C]0.994349527044258[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1.48[/C][C]NA[/C][C]NA[/C][C]0.99162114019264[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.48[/C][C]NA[/C][C]NA[/C][C]0.988922735614117[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.48[/C][C]1.45781575428464[/C][C]1.47833333333333[/C][C]0.986121141567964[/C][C]1.0152174550523[/C][/ROW]
[ROW][C]8[/C][C]1.48[/C][C]1.49840428587298[/C][C]1.48[/C][C]1.01243532829255[/C][C]0.987717409749494[/C][/ROW]
[ROW][C]9[/C][C]1.48[/C][C]1.49829496402544[/C][C]1.48[/C][C]1.01236146217935[/C][C]0.98778947772988[/C][/ROW]
[ROW][C]10[/C][C]1.48[/C][C]1.49322171239263[/C][C]1.48[/C][C]1.00893358945448[/C][C]0.991145512898118[/C][/ROW]
[ROW][C]11[/C][C]1.48[/C][C]1.48801711496336[/C][C]1.48[/C][C]1.00541696956984[/C][C]0.994612215892722[/C][/ROW]
[ROW][C]12[/C][C]1.48[/C][C]1.48288402058992[/C][C]1.48[/C][C]1.00194866256075[/C][C]0.998055127339785[/C][/ROW]
[ROW][C]13[/C][C]1.48[/C][C]1.48151406042836[/C][C]1.48[/C][C]1.00102301380294[/C][C]0.998978031684749[/C][/ROW]
[ROW][C]14[/C][C]1.48[/C][C]1.47964188596278[/C][C]1.48[/C][C]0.99975803105593[/C][C]1.00024202750721[/C][/ROW]
[ROW][C]15[/C][C]1.48[/C][C]1.47572043002445[/C][C]1.48[/C][C]0.997108398665167[/C][C]1.00289998694094[/C][/ROW]
[ROW][C]16[/C][C]1.48[/C][C]1.4716373000255[/C][C]1.48[/C][C]0.994349527044258[/C][C]1.00568258223297[/C][/ROW]
[ROW][C]17[/C][C]1.48[/C][C]1.46759928748511[/C][C]1.48[/C][C]0.99162114019264[/C][C]1.00844965830976[/C][/ROW]
[ROW][C]18[/C][C]1.48[/C][C]1.46360564870889[/C][C]1.48[/C][C]0.988922735614117[/C][C]1.01120134464196[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.45945928952059[/C][C]1.48[/C][C]0.986121141567964[/C][C]1.01407419215247[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.49840428587298[/C][C]1.48[/C][C]1.01243532829255[/C][C]0.987717409749494[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.49829496402544[/C][C]1.48[/C][C]1.01236146217935[/C][C]0.98778947772988[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.49322171239263[/C][C]1.48[/C][C]1.00893358945448[/C][C]0.991145512898118[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.48801711496336[/C][C]1.48[/C][C]1.00541696956984[/C][C]0.994612215892722[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.48288402058992[/C][C]1.48[/C][C]1.00194866256075[/C][C]0.998055127339785[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.48151406042836[/C][C]1.48[/C][C]1.00102301380294[/C][C]0.998978031684749[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.48339097857924[/C][C]1.48375[/C][C]0.99975803105593[/C][C]0.997714035862288[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.48735336134221[/C][C]1.49166666666667[/C][C]0.997108398665167[/C][C]0.995056076428555[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.49152429056639[/C][C]1.5[/C][C]0.994349527044258[/C][C]0.992273481136529[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.49569521979057[/C][C]1.50833333333333[/C][C]0.99162114019264[/C][C]0.98950640505974[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.49986614901474[/C][C]1.51666666666667[/C][C]0.988922735614117[/C][C]0.986754718727538[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.50424562470013[/C][C]1.52541666666667[/C][C]0.986121141567964[/C][C]0.983881871217033[/C][/ROW]
[ROW][C]32[/C][C]1.57[/C][C]1.55408822892907[/C][C]1.535[/C][C]1.01243532829255[/C][C]1.01023865362000[/C][/ROW]
[ROW][C]33[/C][C]1.58[/C][C]1.5640984590671[/C][C]1.545[/C][C]1.01236146217935[/C][C]1.01016658563962[/C][/ROW]
[ROW][C]34[/C][C]1.58[/C][C]1.56931212059732[/C][C]1.55541666666667[/C][C]1.00893358945448[/C][C]1.00681055047138[/C][/ROW]
[ROW][C]35[/C][C]1.58[/C][C]1.57473432858876[/C][C]1.56625[/C][C]1.00541696956984[/C][C]1.00334384747678[/C][/ROW]
[ROW][C]36[/C][C]1.58[/C][C]1.58015653658019[/C][C]1.57708333333333[/C][C]1.00194866256075[/C][C]0.999900936029713[/C][/ROW]
[ROW][C]37[/C][C]1.59[/C][C]1.58995822025701[/C][C]1.58833333333333[/C][C]1.00102301380294[/C][C]1.00002627725839[/C][/ROW]
[ROW][C]38[/C][C]1.6[/C][C]1.59628032291930[/C][C]1.59666666666667[/C][C]0.99975803105593[/C][C]1.00233021545608[/C][/ROW]
[ROW][C]39[/C][C]1.6[/C][C]1.59661982336260[/C][C]1.60125[/C][C]0.997108398665167[/C][C]1.00211708296987[/C][/ROW]
[ROW][C]40[/C][C]1.61[/C][C]1.59675961551190[/C][C]1.60583333333333[/C][C]0.994349527044258[/C][C]1.00829203366585[/C][/ROW]
[ROW][C]41[/C][C]1.61[/C][C]1.59733638666031[/C][C]1.61083333333333[/C][C]0.99162114019264[/C][C]1.00792795646894[/C][/ROW]
[ROW][C]42[/C][C]1.61[/C][C]1.59793432029648[/C][C]1.61583333333333[/C][C]0.988922735614117[/C][C]1.00755079827141[/C][/ROW]
[ROW][C]43[/C][C]1.62[/C][C]1.59792713314909[/C][C]1.62041666666667[/C][C]0.986121141567964[/C][C]1.01381343766747[/C][/ROW]
[ROW][C]44[/C][C]1.63[/C][C]1.64436371236849[/C][C]1.62416666666667[/C][C]1.01243532829255[/C][C]0.99126488120575[/C][/ROW]
[ROW][C]45[/C][C]1.63[/C][C]1.64804009697280[/C][C]1.62791666666667[/C][C]1.01236146217935[/C][C]0.989053605548833[/C][/ROW]
[ROW][C]46[/C][C]1.64[/C][C]1.64624330679323[/C][C]1.63166666666667[/C][C]1.00893358945448[/C][C]0.99620754309678[/C][/ROW]
[ROW][C]47[/C][C]1.64[/C][C]1.64385674524668[/C][C]1.635[/C][C]1.00541696956984[/C][C]0.99765384346426[/C][/ROW]
[ROW][C]48[/C][C]1.64[/C][C]1.64152589216204[/C][C]1.63833333333333[/C][C]1.00194866256075[/C][C]0.999070442830466[/C][/ROW]
[ROW][C]49[/C][C]1.64[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]50[/C][C]1.64[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]51[/C][C]1.65[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]52[/C][C]1.65[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]53[/C][C]1.65[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]54[/C][C]1.65[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13045&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13045&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	1.44	NA	NA	1.00102301380294	NA
2	1.48	NA	NA	0.99975803105593	NA
3	1.48	NA	NA	0.997108398665167	NA
4	1.48	NA	NA	0.994349527044258	NA
5	1.48	NA	NA	0.99162114019264	NA
6	1.48	NA	NA	0.988922735614117	NA
7	1.48	1.45781575428464	1.47833333333333	0.986121141567964	1.0152174550523
8	1.48	1.49840428587298	1.48	1.01243532829255	0.987717409749494
9	1.48	1.49829496402544	1.48	1.01236146217935	0.98778947772988
10	1.48	1.49322171239263	1.48	1.00893358945448	0.991145512898118
11	1.48	1.48801711496336	1.48	1.00541696956984	0.994612215892722
12	1.48	1.48288402058992	1.48	1.00194866256075	0.998055127339785
13	1.48	1.48151406042836	1.48	1.00102301380294	0.998978031684749
14	1.48	1.47964188596278	1.48	0.99975803105593	1.00024202750721
15	1.48	1.47572043002445	1.48	0.997108398665167	1.00289998694094
16	1.48	1.4716373000255	1.48	0.994349527044258	1.00568258223297
17	1.48	1.46759928748511	1.48	0.99162114019264	1.00844965830976
18	1.48	1.46360564870889	1.48	0.988922735614117	1.01120134464196
19	1.48	1.45945928952059	1.48	0.986121141567964	1.01407419215247
20	1.48	1.49840428587298	1.48	1.01243532829255	0.987717409749494
21	1.48	1.49829496402544	1.48	1.01236146217935	0.98778947772988
22	1.48	1.49322171239263	1.48	1.00893358945448	0.991145512898118
23	1.48	1.48801711496336	1.48	1.00541696956984	0.994612215892722
24	1.48	1.48288402058992	1.48	1.00194866256075	0.998055127339785
25	1.48	1.48151406042836	1.48	1.00102301380294	0.998978031684749
26	1.48	1.48339097857924	1.48375	0.99975803105593	0.997714035862288
27	1.48	1.48735336134221	1.49166666666667	0.997108398665167	0.995056076428555
28	1.48	1.49152429056639	1.5	0.994349527044258	0.992273481136529
29	1.48	1.49569521979057	1.50833333333333	0.99162114019264	0.98950640505974
30	1.48	1.49986614901474	1.51666666666667	0.988922735614117	0.986754718727538
31	1.48	1.50424562470013	1.52541666666667	0.986121141567964	0.983881871217033
32	1.57	1.55408822892907	1.535	1.01243532829255	1.01023865362000
33	1.58	1.5640984590671	1.545	1.01236146217935	1.01016658563962
34	1.58	1.56931212059732	1.55541666666667	1.00893358945448	1.00681055047138
35	1.58	1.57473432858876	1.56625	1.00541696956984	1.00334384747678
36	1.58	1.58015653658019	1.57708333333333	1.00194866256075	0.999900936029713
37	1.59	1.58995822025701	1.58833333333333	1.00102301380294	1.00002627725839
38	1.6	1.59628032291930	1.59666666666667	0.99975803105593	1.00233021545608
39	1.6	1.59661982336260	1.60125	0.997108398665167	1.00211708296987
40	1.61	1.59675961551190	1.60583333333333	0.994349527044258	1.00829203366585
41	1.61	1.59733638666031	1.61083333333333	0.99162114019264	1.00792795646894
42	1.61	1.59793432029648	1.61583333333333	0.988922735614117	1.00755079827141
43	1.62	1.59792713314909	1.62041666666667	0.986121141567964	1.01381343766747
44	1.63	1.64436371236849	1.62416666666667	1.01243532829255	0.99126488120575
45	1.63	1.64804009697280	1.62791666666667	1.01236146217935	0.989053605548833
46	1.64	1.64624330679323	1.63166666666667	1.00893358945448	0.99620754309678
47	1.64	1.64385674524668	1.635	1.00541696956984	0.99765384346426
48	1.64	1.64152589216204	1.63833333333333	1.00194866256075	0.999070442830466
49	1.64	NA	NA	NA	NA
50	1.64	NA	NA	NA	NA
51	1.65	NA	NA	NA	NA
52	1.65	NA	NA	NA	NA
53	1.65	NA	NA	NA	NA
54	1.65	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