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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 25 Apr 2016 20:41:19 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Apr/25/t1461613406jhpvkqz8odqdwth.htm/, Retrieved Sun, 05 May 2024 21:52:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294776, Retrieved Sun, 05 May 2024 21:52:14 +0000

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User-defined keywords

benzine Classical Decomposition (additief decompositie model)

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

-       [Classical Decomposition] [opgave 9 stap 2 o...] [2016-04-25 19:41:19] [8fd6d867e46a5221be3e0a22eb2f8c7a] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294776&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294776&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294776&T=0

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The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	93.41	NA	NA	0.0426562	NA
2	93	NA	NA	-0.305737	NA
3	96.61	96.5219	96.6325	-0.110647	0.0881473
4	99.69	98.5862	98.2125	0.373728	1.10377
5	101.05	98.9689	98.9262	0.0426562	2.08109
6	98	98.4768	98.7825	-0.305737	-0.476763
7	97.32	98.2544	98.365	-0.110647	-0.934353
8	97.83	98.5075	98.1337	0.373728	-0.677478
9	99.57	98.0502	98.0075	0.0426562	1.51984
10	97.63	97.428	97.7338	-0.305737	0.201987
11	96.68	97.4594	97.57	-0.110647	-0.779353
12	96.28	98.4487	98.075	0.373728	-2.16873
13	99.81	99.7064	99.6638	0.0426562	0.103594
14	101.43	102.044	102.35	-0.305737	-0.614263
15	105.59	104.337	104.448	-0.110647	1.25315
16	108.86	105.411	105.038	0.373728	3.44877
17	104.01	104.636	104.594	0.0426562	-0.626406
18	101.95	103.373	103.679	-0.305737	-1.42301
19	101.52	103.714	103.825	-0.110647	-2.19435
20	105.61	105.2	104.826	0.373728	0.410022
21	108.43	105.141	105.099	0.0426562	3.28859
22	105.54	103.907	104.212	-0.305737	1.63324
23	100.11	102.323	102.434	-0.110647	-2.2131
24	99.93	101.385	101.011	0.373728	-1.45498
25	99.88	100.923	100.88	0.0426562	-1.04266
26	102.71	101.047	101.352	-0.305737	1.66324
27	101.89	101.443	101.554	-0.110647	0.446897
28	101.93	101.524	101.15	0.373728	0.406272
29	99.49	100.643	100.6	0.0426562	-1.15266
30	99.87	100.046	100.351	-0.305737	-0.175513
31	100.33	100.537	100.648	-0.110647	-0.206853
32	101.5	101.017	100.644	0.373728	0.482522
33	102.29	99.7552	99.7125	0.0426562	2.53484
34	97.04	98.313	98.6188	-0.305737	-1.27301
35	95.71	97.2694	97.38	-0.110647	-1.55935
36	97.37	96.9425	96.5688	0.373728	0.427522
37	96.51	96.6689	96.6262	0.0426562	-0.158906
38	96.33	96.4943	96.8	-0.305737	-0.164263
39	96.88	97.0231	97.1337	-0.110647	-0.143103
40	97.59	98.2637	97.89	0.373728	-0.673728
41	98.96	98.9402	98.8975	0.0426562	0.0198438
42	99.93	99.2055	99.5112	-0.305737	0.724487
43	101.34	99.4069	99.5175	-0.110647	1.93315
44	98.04	99.4412	99.0675	0.373728	-1.40123
45	98.56	97.5877	97.545	0.0426562	0.972344
46	96.73	94.8468	95.1525	-0.305737	1.88324
47	92.36	91.4319	91.5425	-0.110647	0.928147
48	87.88	87.8487	87.475	0.373728	0.0312723
49	79.84	85.2177	85.175	0.0426562	-5.37766
50	82.91	84.4818	84.7875	-0.305737	-1.57176
51	87.78	86.3644	86.475	-0.110647	1.41565
52	89.36	89.5475	89.1738	0.373728	-0.187478
53	91.86	91.1152	91.0725	0.0426562	0.744844
54	92.48	91.5455	91.8513	-0.305737	0.934487
55	93.4	90.8294	90.94	-0.110647	2.57065
56	89.97	89.1112	88.7375	0.373728	0.858772
57	83.96	86.2614	86.2187	0.0426562	-2.30141
58	82.76	83.4968	83.8025	-0.305737	-0.736763
59	82.97	NA	NA	-0.110647	NA
60	81.07	NA	NA	0.373728	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 93.41 & NA & NA & 0.0426562 & NA \tabularnewline
2 & 93 & NA & NA & -0.305737 & NA \tabularnewline
3 & 96.61 & 96.5219 & 96.6325 & -0.110647 & 0.0881473 \tabularnewline
4 & 99.69 & 98.5862 & 98.2125 & 0.373728 & 1.10377 \tabularnewline
5 & 101.05 & 98.9689 & 98.9262 & 0.0426562 & 2.08109 \tabularnewline
6 & 98 & 98.4768 & 98.7825 & -0.305737 & -0.476763 \tabularnewline
7 & 97.32 & 98.2544 & 98.365 & -0.110647 & -0.934353 \tabularnewline
8 & 97.83 & 98.5075 & 98.1337 & 0.373728 & -0.677478 \tabularnewline
9 & 99.57 & 98.0502 & 98.0075 & 0.0426562 & 1.51984 \tabularnewline
10 & 97.63 & 97.428 & 97.7338 & -0.305737 & 0.201987 \tabularnewline
11 & 96.68 & 97.4594 & 97.57 & -0.110647 & -0.779353 \tabularnewline
12 & 96.28 & 98.4487 & 98.075 & 0.373728 & -2.16873 \tabularnewline
13 & 99.81 & 99.7064 & 99.6638 & 0.0426562 & 0.103594 \tabularnewline
14 & 101.43 & 102.044 & 102.35 & -0.305737 & -0.614263 \tabularnewline
15 & 105.59 & 104.337 & 104.448 & -0.110647 & 1.25315 \tabularnewline
16 & 108.86 & 105.411 & 105.038 & 0.373728 & 3.44877 \tabularnewline
17 & 104.01 & 104.636 & 104.594 & 0.0426562 & -0.626406 \tabularnewline
18 & 101.95 & 103.373 & 103.679 & -0.305737 & -1.42301 \tabularnewline
19 & 101.52 & 103.714 & 103.825 & -0.110647 & -2.19435 \tabularnewline
20 & 105.61 & 105.2 & 104.826 & 0.373728 & 0.410022 \tabularnewline
21 & 108.43 & 105.141 & 105.099 & 0.0426562 & 3.28859 \tabularnewline
22 & 105.54 & 103.907 & 104.212 & -0.305737 & 1.63324 \tabularnewline
23 & 100.11 & 102.323 & 102.434 & -0.110647 & -2.2131 \tabularnewline
24 & 99.93 & 101.385 & 101.011 & 0.373728 & -1.45498 \tabularnewline
25 & 99.88 & 100.923 & 100.88 & 0.0426562 & -1.04266 \tabularnewline
26 & 102.71 & 101.047 & 101.352 & -0.305737 & 1.66324 \tabularnewline
27 & 101.89 & 101.443 & 101.554 & -0.110647 & 0.446897 \tabularnewline
28 & 101.93 & 101.524 & 101.15 & 0.373728 & 0.406272 \tabularnewline
29 & 99.49 & 100.643 & 100.6 & 0.0426562 & -1.15266 \tabularnewline
30 & 99.87 & 100.046 & 100.351 & -0.305737 & -0.175513 \tabularnewline
31 & 100.33 & 100.537 & 100.648 & -0.110647 & -0.206853 \tabularnewline
32 & 101.5 & 101.017 & 100.644 & 0.373728 & 0.482522 \tabularnewline
33 & 102.29 & 99.7552 & 99.7125 & 0.0426562 & 2.53484 \tabularnewline
34 & 97.04 & 98.313 & 98.6188 & -0.305737 & -1.27301 \tabularnewline
35 & 95.71 & 97.2694 & 97.38 & -0.110647 & -1.55935 \tabularnewline
36 & 97.37 & 96.9425 & 96.5688 & 0.373728 & 0.427522 \tabularnewline
37 & 96.51 & 96.6689 & 96.6262 & 0.0426562 & -0.158906 \tabularnewline
38 & 96.33 & 96.4943 & 96.8 & -0.305737 & -0.164263 \tabularnewline
39 & 96.88 & 97.0231 & 97.1337 & -0.110647 & -0.143103 \tabularnewline
40 & 97.59 & 98.2637 & 97.89 & 0.373728 & -0.673728 \tabularnewline
41 & 98.96 & 98.9402 & 98.8975 & 0.0426562 & 0.0198438 \tabularnewline
42 & 99.93 & 99.2055 & 99.5112 & -0.305737 & 0.724487 \tabularnewline
43 & 101.34 & 99.4069 & 99.5175 & -0.110647 & 1.93315 \tabularnewline
44 & 98.04 & 99.4412 & 99.0675 & 0.373728 & -1.40123 \tabularnewline
45 & 98.56 & 97.5877 & 97.545 & 0.0426562 & 0.972344 \tabularnewline
46 & 96.73 & 94.8468 & 95.1525 & -0.305737 & 1.88324 \tabularnewline
47 & 92.36 & 91.4319 & 91.5425 & -0.110647 & 0.928147 \tabularnewline
48 & 87.88 & 87.8487 & 87.475 & 0.373728 & 0.0312723 \tabularnewline
49 & 79.84 & 85.2177 & 85.175 & 0.0426562 & -5.37766 \tabularnewline
50 & 82.91 & 84.4818 & 84.7875 & -0.305737 & -1.57176 \tabularnewline
51 & 87.78 & 86.3644 & 86.475 & -0.110647 & 1.41565 \tabularnewline
52 & 89.36 & 89.5475 & 89.1738 & 0.373728 & -0.187478 \tabularnewline
53 & 91.86 & 91.1152 & 91.0725 & 0.0426562 & 0.744844 \tabularnewline
54 & 92.48 & 91.5455 & 91.8513 & -0.305737 & 0.934487 \tabularnewline
55 & 93.4 & 90.8294 & 90.94 & -0.110647 & 2.57065 \tabularnewline
56 & 89.97 & 89.1112 & 88.7375 & 0.373728 & 0.858772 \tabularnewline
57 & 83.96 & 86.2614 & 86.2187 & 0.0426562 & -2.30141 \tabularnewline
58 & 82.76 & 83.4968 & 83.8025 & -0.305737 & -0.736763 \tabularnewline
59 & 82.97 & NA & NA & -0.110647 & NA \tabularnewline
60 & 81.07 & NA & NA & 0.373728 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294776&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]93.41[/C][C]NA[/C][C]NA[/C][C]0.0426562[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]93[/C][C]NA[/C][C]NA[/C][C]-0.305737[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]96.61[/C][C]96.5219[/C][C]96.6325[/C][C]-0.110647[/C][C]0.0881473[/C][/ROW]
[ROW][C]4[/C][C]99.69[/C][C]98.5862[/C][C]98.2125[/C][C]0.373728[/C][C]1.10377[/C][/ROW]
[ROW][C]5[/C][C]101.05[/C][C]98.9689[/C][C]98.9262[/C][C]0.0426562[/C][C]2.08109[/C][/ROW]
[ROW][C]6[/C][C]98[/C][C]98.4768[/C][C]98.7825[/C][C]-0.305737[/C][C]-0.476763[/C][/ROW]
[ROW][C]7[/C][C]97.32[/C][C]98.2544[/C][C]98.365[/C][C]-0.110647[/C][C]-0.934353[/C][/ROW]
[ROW][C]8[/C][C]97.83[/C][C]98.5075[/C][C]98.1337[/C][C]0.373728[/C][C]-0.677478[/C][/ROW]
[ROW][C]9[/C][C]99.57[/C][C]98.0502[/C][C]98.0075[/C][C]0.0426562[/C][C]1.51984[/C][/ROW]
[ROW][C]10[/C][C]97.63[/C][C]97.428[/C][C]97.7338[/C][C]-0.305737[/C][C]0.201987[/C][/ROW]
[ROW][C]11[/C][C]96.68[/C][C]97.4594[/C][C]97.57[/C][C]-0.110647[/C][C]-0.779353[/C][/ROW]
[ROW][C]12[/C][C]96.28[/C][C]98.4487[/C][C]98.075[/C][C]0.373728[/C][C]-2.16873[/C][/ROW]
[ROW][C]13[/C][C]99.81[/C][C]99.7064[/C][C]99.6638[/C][C]0.0426562[/C][C]0.103594[/C][/ROW]
[ROW][C]14[/C][C]101.43[/C][C]102.044[/C][C]102.35[/C][C]-0.305737[/C][C]-0.614263[/C][/ROW]
[ROW][C]15[/C][C]105.59[/C][C]104.337[/C][C]104.448[/C][C]-0.110647[/C][C]1.25315[/C][/ROW]
[ROW][C]16[/C][C]108.86[/C][C]105.411[/C][C]105.038[/C][C]0.373728[/C][C]3.44877[/C][/ROW]
[ROW][C]17[/C][C]104.01[/C][C]104.636[/C][C]104.594[/C][C]0.0426562[/C][C]-0.626406[/C][/ROW]
[ROW][C]18[/C][C]101.95[/C][C]103.373[/C][C]103.679[/C][C]-0.305737[/C][C]-1.42301[/C][/ROW]
[ROW][C]19[/C][C]101.52[/C][C]103.714[/C][C]103.825[/C][C]-0.110647[/C][C]-2.19435[/C][/ROW]
[ROW][C]20[/C][C]105.61[/C][C]105.2[/C][C]104.826[/C][C]0.373728[/C][C]0.410022[/C][/ROW]
[ROW][C]21[/C][C]108.43[/C][C]105.141[/C][C]105.099[/C][C]0.0426562[/C][C]3.28859[/C][/ROW]
[ROW][C]22[/C][C]105.54[/C][C]103.907[/C][C]104.212[/C][C]-0.305737[/C][C]1.63324[/C][/ROW]
[ROW][C]23[/C][C]100.11[/C][C]102.323[/C][C]102.434[/C][C]-0.110647[/C][C]-2.2131[/C][/ROW]
[ROW][C]24[/C][C]99.93[/C][C]101.385[/C][C]101.011[/C][C]0.373728[/C][C]-1.45498[/C][/ROW]
[ROW][C]25[/C][C]99.88[/C][C]100.923[/C][C]100.88[/C][C]0.0426562[/C][C]-1.04266[/C][/ROW]
[ROW][C]26[/C][C]102.71[/C][C]101.047[/C][C]101.352[/C][C]-0.305737[/C][C]1.66324[/C][/ROW]
[ROW][C]27[/C][C]101.89[/C][C]101.443[/C][C]101.554[/C][C]-0.110647[/C][C]0.446897[/C][/ROW]
[ROW][C]28[/C][C]101.93[/C][C]101.524[/C][C]101.15[/C][C]0.373728[/C][C]0.406272[/C][/ROW]
[ROW][C]29[/C][C]99.49[/C][C]100.643[/C][C]100.6[/C][C]0.0426562[/C][C]-1.15266[/C][/ROW]
[ROW][C]30[/C][C]99.87[/C][C]100.046[/C][C]100.351[/C][C]-0.305737[/C][C]-0.175513[/C][/ROW]
[ROW][C]31[/C][C]100.33[/C][C]100.537[/C][C]100.648[/C][C]-0.110647[/C][C]-0.206853[/C][/ROW]
[ROW][C]32[/C][C]101.5[/C][C]101.017[/C][C]100.644[/C][C]0.373728[/C][C]0.482522[/C][/ROW]
[ROW][C]33[/C][C]102.29[/C][C]99.7552[/C][C]99.7125[/C][C]0.0426562[/C][C]2.53484[/C][/ROW]
[ROW][C]34[/C][C]97.04[/C][C]98.313[/C][C]98.6188[/C][C]-0.305737[/C][C]-1.27301[/C][/ROW]
[ROW][C]35[/C][C]95.71[/C][C]97.2694[/C][C]97.38[/C][C]-0.110647[/C][C]-1.55935[/C][/ROW]
[ROW][C]36[/C][C]97.37[/C][C]96.9425[/C][C]96.5688[/C][C]0.373728[/C][C]0.427522[/C][/ROW]
[ROW][C]37[/C][C]96.51[/C][C]96.6689[/C][C]96.6262[/C][C]0.0426562[/C][C]-0.158906[/C][/ROW]
[ROW][C]38[/C][C]96.33[/C][C]96.4943[/C][C]96.8[/C][C]-0.305737[/C][C]-0.164263[/C][/ROW]
[ROW][C]39[/C][C]96.88[/C][C]97.0231[/C][C]97.1337[/C][C]-0.110647[/C][C]-0.143103[/C][/ROW]
[ROW][C]40[/C][C]97.59[/C][C]98.2637[/C][C]97.89[/C][C]0.373728[/C][C]-0.673728[/C][/ROW]
[ROW][C]41[/C][C]98.96[/C][C]98.9402[/C][C]98.8975[/C][C]0.0426562[/C][C]0.0198438[/C][/ROW]
[ROW][C]42[/C][C]99.93[/C][C]99.2055[/C][C]99.5112[/C][C]-0.305737[/C][C]0.724487[/C][/ROW]
[ROW][C]43[/C][C]101.34[/C][C]99.4069[/C][C]99.5175[/C][C]-0.110647[/C][C]1.93315[/C][/ROW]
[ROW][C]44[/C][C]98.04[/C][C]99.4412[/C][C]99.0675[/C][C]0.373728[/C][C]-1.40123[/C][/ROW]
[ROW][C]45[/C][C]98.56[/C][C]97.5877[/C][C]97.545[/C][C]0.0426562[/C][C]0.972344[/C][/ROW]
[ROW][C]46[/C][C]96.73[/C][C]94.8468[/C][C]95.1525[/C][C]-0.305737[/C][C]1.88324[/C][/ROW]
[ROW][C]47[/C][C]92.36[/C][C]91.4319[/C][C]91.5425[/C][C]-0.110647[/C][C]0.928147[/C][/ROW]
[ROW][C]48[/C][C]87.88[/C][C]87.8487[/C][C]87.475[/C][C]0.373728[/C][C]0.0312723[/C][/ROW]
[ROW][C]49[/C][C]79.84[/C][C]85.2177[/C][C]85.175[/C][C]0.0426562[/C][C]-5.37766[/C][/ROW]
[ROW][C]50[/C][C]82.91[/C][C]84.4818[/C][C]84.7875[/C][C]-0.305737[/C][C]-1.57176[/C][/ROW]
[ROW][C]51[/C][C]87.78[/C][C]86.3644[/C][C]86.475[/C][C]-0.110647[/C][C]1.41565[/C][/ROW]
[ROW][C]52[/C][C]89.36[/C][C]89.5475[/C][C]89.1738[/C][C]0.373728[/C][C]-0.187478[/C][/ROW]
[ROW][C]53[/C][C]91.86[/C][C]91.1152[/C][C]91.0725[/C][C]0.0426562[/C][C]0.744844[/C][/ROW]
[ROW][C]54[/C][C]92.48[/C][C]91.5455[/C][C]91.8513[/C][C]-0.305737[/C][C]0.934487[/C][/ROW]
[ROW][C]55[/C][C]93.4[/C][C]90.8294[/C][C]90.94[/C][C]-0.110647[/C][C]2.57065[/C][/ROW]
[ROW][C]56[/C][C]89.97[/C][C]89.1112[/C][C]88.7375[/C][C]0.373728[/C][C]0.858772[/C][/ROW]
[ROW][C]57[/C][C]83.96[/C][C]86.2614[/C][C]86.2187[/C][C]0.0426562[/C][C]-2.30141[/C][/ROW]
[ROW][C]58[/C][C]82.76[/C][C]83.4968[/C][C]83.8025[/C][C]-0.305737[/C][C]-0.736763[/C][/ROW]
[ROW][C]59[/C][C]82.97[/C][C]NA[/C][C]NA[/C][C]-0.110647[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]81.07[/C][C]NA[/C][C]NA[/C][C]0.373728[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294776&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294776&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	93.41	NA	NA	0.0426562	NA
2	93	NA	NA	-0.305737	NA
3	96.61	96.5219	96.6325	-0.110647	0.0881473
4	99.69	98.5862	98.2125	0.373728	1.10377
5	101.05	98.9689	98.9262	0.0426562	2.08109
6	98	98.4768	98.7825	-0.305737	-0.476763
7	97.32	98.2544	98.365	-0.110647	-0.934353
8	97.83	98.5075	98.1337	0.373728	-0.677478
9	99.57	98.0502	98.0075	0.0426562	1.51984
10	97.63	97.428	97.7338	-0.305737	0.201987
11	96.68	97.4594	97.57	-0.110647	-0.779353
12	96.28	98.4487	98.075	0.373728	-2.16873
13	99.81	99.7064	99.6638	0.0426562	0.103594
14	101.43	102.044	102.35	-0.305737	-0.614263
15	105.59	104.337	104.448	-0.110647	1.25315
16	108.86	105.411	105.038	0.373728	3.44877
17	104.01	104.636	104.594	0.0426562	-0.626406
18	101.95	103.373	103.679	-0.305737	-1.42301
19	101.52	103.714	103.825	-0.110647	-2.19435
20	105.61	105.2	104.826	0.373728	0.410022
21	108.43	105.141	105.099	0.0426562	3.28859
22	105.54	103.907	104.212	-0.305737	1.63324
23	100.11	102.323	102.434	-0.110647	-2.2131
24	99.93	101.385	101.011	0.373728	-1.45498
25	99.88	100.923	100.88	0.0426562	-1.04266
26	102.71	101.047	101.352	-0.305737	1.66324
27	101.89	101.443	101.554	-0.110647	0.446897
28	101.93	101.524	101.15	0.373728	0.406272
29	99.49	100.643	100.6	0.0426562	-1.15266
30	99.87	100.046	100.351	-0.305737	-0.175513
31	100.33	100.537	100.648	-0.110647	-0.206853
32	101.5	101.017	100.644	0.373728	0.482522
33	102.29	99.7552	99.7125	0.0426562	2.53484
34	97.04	98.313	98.6188	-0.305737	-1.27301
35	95.71	97.2694	97.38	-0.110647	-1.55935
36	97.37	96.9425	96.5688	0.373728	0.427522
37	96.51	96.6689	96.6262	0.0426562	-0.158906
38	96.33	96.4943	96.8	-0.305737	-0.164263
39	96.88	97.0231	97.1337	-0.110647	-0.143103
40	97.59	98.2637	97.89	0.373728	-0.673728
41	98.96	98.9402	98.8975	0.0426562	0.0198438
42	99.93	99.2055	99.5112	-0.305737	0.724487
43	101.34	99.4069	99.5175	-0.110647	1.93315
44	98.04	99.4412	99.0675	0.373728	-1.40123
45	98.56	97.5877	97.545	0.0426562	0.972344
46	96.73	94.8468	95.1525	-0.305737	1.88324
47	92.36	91.4319	91.5425	-0.110647	0.928147
48	87.88	87.8487	87.475	0.373728	0.0312723
49	79.84	85.2177	85.175	0.0426562	-5.37766
50	82.91	84.4818	84.7875	-0.305737	-1.57176
51	87.78	86.3644	86.475	-0.110647	1.41565
52	89.36	89.5475	89.1738	0.373728	-0.187478
53	91.86	91.1152	91.0725	0.0426562	0.744844
54	92.48	91.5455	91.8513	-0.305737	0.934487
55	93.4	90.8294	90.94	-0.110647	2.57065
56	89.97	89.1112	88.7375	0.373728	0.858772
57	83.96	86.2614	86.2187	0.0426562	-2.30141
58	82.76	83.4968	83.8025	-0.305737	-0.736763
59	82.97	NA	NA	-0.110647	NA
60	81.07	NA	NA	0.373728	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 = 4 ;

Parameters (R input):

par1 = additive ; par2 = 4 ;

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