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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Thu, 26 Nov 2015 08:57:35 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Nov/26/t1448528295z09uh08tz5i0hyr.htm/, Retrieved Tue, 14 May 2024 21:18:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284170, Retrieved Tue, 14 May 2024 21:18:52 +0000

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Estimated Impact

122

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-       [Classical Decomposition] [OPDRACHT 9 OEF 2] [2015-11-26 08:57:35] [48da048a5e5e3f4e8c34faa6148f9354] [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	'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284170&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284170&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284170&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	78.46	NA	NA	1.00169	NA
2	78.59	NA	NA	1.00634	NA
3	81.37	NA	NA	1.01665	NA
4	83.61	NA	NA	1.00698	NA
5	84.65	NA	NA	0.997398	NA
6	84.56	NA	NA	0.994964	NA
7	83.85	83.8328	84.2946	0.994521	1.00021
8	84.08	84.9146	85.3583	0.994802	0.990171
9	85.41	86.7993	86.525	1.00317	0.983994
10	85.75	87.1813	87.6942	0.994152	0.983582
11	86.38	88.1572	88.8325	0.992398	0.97984
12	88.87	89.7476	90.0237	0.996932	0.990222
13	90.37	91.4341	91.2796	1.00169	0.988362
14	92.21	93.1169	92.53	1.00634	0.990261
15	95.75	95.3478	93.7863	1.01665	1.00422
16	97.29	95.6942	95.0312	1.00698	1.01668
17	98.29	96.0245	96.275	0.997398	1.02359
18	99.51	96.9398	97.4304	0.994964	1.02651
19	99.04	97.9405	98.48	0.994521	1.01123
20	98.9	98.9927	99.51	0.994802	0.999063
21	100.74	100.753	100.435	1.00317	0.999871
22	100.3	100.642	101.234	0.994152	0.9966
23	101.68	101.072	101.846	0.992398	1.00602
24	101.3	101.861	102.175	0.996932	0.994491
25	103.13	102.559	102.385	1.00169	1.00557
26	104.17	103.387	102.735	1.00634	1.00757
27	105.98	104.881	103.164	1.01665	1.01047
28	106.25	104.269	103.546	1.00698	1.019
29	104.01	103.521	103.791	0.997398	1.00473
30	101.68	103.385	103.908	0.994964	0.983508
31	101.93	103.377	103.946	0.994521	0.986005
32	104.41	103.293	103.833	0.994802	1.01082
33	105.51	103.874	103.545	1.00317	1.01575
34	104.71	102.538	103.141	0.994152	1.02118
35	103.14	101.954	102.735	0.992398	1.01164
36	102.66	102.129	102.443	0.996932	1.0052
37	102.68	102.434	102.26	1.00169	1.00241
38	101.89	102.609	101.962	1.00634	0.99299
39	101.37	103.207	101.517	1.01665	0.982202
40	101.16	101.725	101.02	1.00698	0.994448
41	99.34	100.282	100.543	0.997398	0.990609
42	99.35	99.6606	100.165	0.994964	0.996884
43	99.88	99.2586	99.8054	0.994521	1.00626
44	99.31	98.9492	99.4663	0.994802	1.00365
45	99.91	99.5041	99.1896	1.00317	1.00408
46	98.39	98.1609	98.7383	0.994152	1.00233
47	98.02	97.4159	98.1621	0.992398	1.0062
48	98.7	97.3583	97.6579	0.996932	1.01378
49	98.01	97.294	97.1296	1.00169	1.00736
50	98.42	97.1691	96.5567	1.00634	1.01287
51	98.2	97.5365	95.9392	1.01665	1.0068
52	93.5	96.0383	95.3729	1.00698	0.97357
53	93.17	94.6111	94.8579	0.997398	0.984768
54	93.42	93.7206	94.195	0.994964	0.996792
55	93.13	NA	NA	0.994521	NA
56	92.31	NA	NA	0.994802	NA
57	92.09	NA	NA	1.00317	NA
58	92.62	NA	NA	0.994152	NA
59	91.43	NA	NA	0.992398	NA
60	89.38	NA	NA	0.996932	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 78.46 & NA & NA & 1.00169 & NA \tabularnewline
2 & 78.59 & NA & NA & 1.00634 & NA \tabularnewline
3 & 81.37 & NA & NA & 1.01665 & NA \tabularnewline
4 & 83.61 & NA & NA & 1.00698 & NA \tabularnewline
5 & 84.65 & NA & NA & 0.997398 & NA \tabularnewline
6 & 84.56 & NA & NA & 0.994964 & NA \tabularnewline
7 & 83.85 & 83.8328 & 84.2946 & 0.994521 & 1.00021 \tabularnewline
8 & 84.08 & 84.9146 & 85.3583 & 0.994802 & 0.990171 \tabularnewline
9 & 85.41 & 86.7993 & 86.525 & 1.00317 & 0.983994 \tabularnewline
10 & 85.75 & 87.1813 & 87.6942 & 0.994152 & 0.983582 \tabularnewline
11 & 86.38 & 88.1572 & 88.8325 & 0.992398 & 0.97984 \tabularnewline
12 & 88.87 & 89.7476 & 90.0237 & 0.996932 & 0.990222 \tabularnewline
13 & 90.37 & 91.4341 & 91.2796 & 1.00169 & 0.988362 \tabularnewline
14 & 92.21 & 93.1169 & 92.53 & 1.00634 & 0.990261 \tabularnewline
15 & 95.75 & 95.3478 & 93.7863 & 1.01665 & 1.00422 \tabularnewline
16 & 97.29 & 95.6942 & 95.0312 & 1.00698 & 1.01668 \tabularnewline
17 & 98.29 & 96.0245 & 96.275 & 0.997398 & 1.02359 \tabularnewline
18 & 99.51 & 96.9398 & 97.4304 & 0.994964 & 1.02651 \tabularnewline
19 & 99.04 & 97.9405 & 98.48 & 0.994521 & 1.01123 \tabularnewline
20 & 98.9 & 98.9927 & 99.51 & 0.994802 & 0.999063 \tabularnewline
21 & 100.74 & 100.753 & 100.435 & 1.00317 & 0.999871 \tabularnewline
22 & 100.3 & 100.642 & 101.234 & 0.994152 & 0.9966 \tabularnewline
23 & 101.68 & 101.072 & 101.846 & 0.992398 & 1.00602 \tabularnewline
24 & 101.3 & 101.861 & 102.175 & 0.996932 & 0.994491 \tabularnewline
25 & 103.13 & 102.559 & 102.385 & 1.00169 & 1.00557 \tabularnewline
26 & 104.17 & 103.387 & 102.735 & 1.00634 & 1.00757 \tabularnewline
27 & 105.98 & 104.881 & 103.164 & 1.01665 & 1.01047 \tabularnewline
28 & 106.25 & 104.269 & 103.546 & 1.00698 & 1.019 \tabularnewline
29 & 104.01 & 103.521 & 103.791 & 0.997398 & 1.00473 \tabularnewline
30 & 101.68 & 103.385 & 103.908 & 0.994964 & 0.983508 \tabularnewline
31 & 101.93 & 103.377 & 103.946 & 0.994521 & 0.986005 \tabularnewline
32 & 104.41 & 103.293 & 103.833 & 0.994802 & 1.01082 \tabularnewline
33 & 105.51 & 103.874 & 103.545 & 1.00317 & 1.01575 \tabularnewline
34 & 104.71 & 102.538 & 103.141 & 0.994152 & 1.02118 \tabularnewline
35 & 103.14 & 101.954 & 102.735 & 0.992398 & 1.01164 \tabularnewline
36 & 102.66 & 102.129 & 102.443 & 0.996932 & 1.0052 \tabularnewline
37 & 102.68 & 102.434 & 102.26 & 1.00169 & 1.00241 \tabularnewline
38 & 101.89 & 102.609 & 101.962 & 1.00634 & 0.99299 \tabularnewline
39 & 101.37 & 103.207 & 101.517 & 1.01665 & 0.982202 \tabularnewline
40 & 101.16 & 101.725 & 101.02 & 1.00698 & 0.994448 \tabularnewline
41 & 99.34 & 100.282 & 100.543 & 0.997398 & 0.990609 \tabularnewline
42 & 99.35 & 99.6606 & 100.165 & 0.994964 & 0.996884 \tabularnewline
43 & 99.88 & 99.2586 & 99.8054 & 0.994521 & 1.00626 \tabularnewline
44 & 99.31 & 98.9492 & 99.4663 & 0.994802 & 1.00365 \tabularnewline
45 & 99.91 & 99.5041 & 99.1896 & 1.00317 & 1.00408 \tabularnewline
46 & 98.39 & 98.1609 & 98.7383 & 0.994152 & 1.00233 \tabularnewline
47 & 98.02 & 97.4159 & 98.1621 & 0.992398 & 1.0062 \tabularnewline
48 & 98.7 & 97.3583 & 97.6579 & 0.996932 & 1.01378 \tabularnewline
49 & 98.01 & 97.294 & 97.1296 & 1.00169 & 1.00736 \tabularnewline
50 & 98.42 & 97.1691 & 96.5567 & 1.00634 & 1.01287 \tabularnewline
51 & 98.2 & 97.5365 & 95.9392 & 1.01665 & 1.0068 \tabularnewline
52 & 93.5 & 96.0383 & 95.3729 & 1.00698 & 0.97357 \tabularnewline
53 & 93.17 & 94.6111 & 94.8579 & 0.997398 & 0.984768 \tabularnewline
54 & 93.42 & 93.7206 & 94.195 & 0.994964 & 0.996792 \tabularnewline
55 & 93.13 & NA & NA & 0.994521 & NA \tabularnewline
56 & 92.31 & NA & NA & 0.994802 & NA \tabularnewline
57 & 92.09 & NA & NA & 1.00317 & NA \tabularnewline
58 & 92.62 & NA & NA & 0.994152 & NA \tabularnewline
59 & 91.43 & NA & NA & 0.992398 & NA \tabularnewline
60 & 89.38 & NA & NA & 0.996932 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284170&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]78.46[/C][C]NA[/C][C]NA[/C][C]1.00169[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]78.59[/C][C]NA[/C][C]NA[/C][C]1.00634[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]81.37[/C][C]NA[/C][C]NA[/C][C]1.01665[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]83.61[/C][C]NA[/C][C]NA[/C][C]1.00698[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]84.65[/C][C]NA[/C][C]NA[/C][C]0.997398[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]84.56[/C][C]NA[/C][C]NA[/C][C]0.994964[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]83.85[/C][C]83.8328[/C][C]84.2946[/C][C]0.994521[/C][C]1.00021[/C][/ROW]
[ROW][C]8[/C][C]84.08[/C][C]84.9146[/C][C]85.3583[/C][C]0.994802[/C][C]0.990171[/C][/ROW]
[ROW][C]9[/C][C]85.41[/C][C]86.7993[/C][C]86.525[/C][C]1.00317[/C][C]0.983994[/C][/ROW]
[ROW][C]10[/C][C]85.75[/C][C]87.1813[/C][C]87.6942[/C][C]0.994152[/C][C]0.983582[/C][/ROW]
[ROW][C]11[/C][C]86.38[/C][C]88.1572[/C][C]88.8325[/C][C]0.992398[/C][C]0.97984[/C][/ROW]
[ROW][C]12[/C][C]88.87[/C][C]89.7476[/C][C]90.0237[/C][C]0.996932[/C][C]0.990222[/C][/ROW]
[ROW][C]13[/C][C]90.37[/C][C]91.4341[/C][C]91.2796[/C][C]1.00169[/C][C]0.988362[/C][/ROW]
[ROW][C]14[/C][C]92.21[/C][C]93.1169[/C][C]92.53[/C][C]1.00634[/C][C]0.990261[/C][/ROW]
[ROW][C]15[/C][C]95.75[/C][C]95.3478[/C][C]93.7863[/C][C]1.01665[/C][C]1.00422[/C][/ROW]
[ROW][C]16[/C][C]97.29[/C][C]95.6942[/C][C]95.0312[/C][C]1.00698[/C][C]1.01668[/C][/ROW]
[ROW][C]17[/C][C]98.29[/C][C]96.0245[/C][C]96.275[/C][C]0.997398[/C][C]1.02359[/C][/ROW]
[ROW][C]18[/C][C]99.51[/C][C]96.9398[/C][C]97.4304[/C][C]0.994964[/C][C]1.02651[/C][/ROW]
[ROW][C]19[/C][C]99.04[/C][C]97.9405[/C][C]98.48[/C][C]0.994521[/C][C]1.01123[/C][/ROW]
[ROW][C]20[/C][C]98.9[/C][C]98.9927[/C][C]99.51[/C][C]0.994802[/C][C]0.999063[/C][/ROW]
[ROW][C]21[/C][C]100.74[/C][C]100.753[/C][C]100.435[/C][C]1.00317[/C][C]0.999871[/C][/ROW]
[ROW][C]22[/C][C]100.3[/C][C]100.642[/C][C]101.234[/C][C]0.994152[/C][C]0.9966[/C][/ROW]
[ROW][C]23[/C][C]101.68[/C][C]101.072[/C][C]101.846[/C][C]0.992398[/C][C]1.00602[/C][/ROW]
[ROW][C]24[/C][C]101.3[/C][C]101.861[/C][C]102.175[/C][C]0.996932[/C][C]0.994491[/C][/ROW]
[ROW][C]25[/C][C]103.13[/C][C]102.559[/C][C]102.385[/C][C]1.00169[/C][C]1.00557[/C][/ROW]
[ROW][C]26[/C][C]104.17[/C][C]103.387[/C][C]102.735[/C][C]1.00634[/C][C]1.00757[/C][/ROW]
[ROW][C]27[/C][C]105.98[/C][C]104.881[/C][C]103.164[/C][C]1.01665[/C][C]1.01047[/C][/ROW]
[ROW][C]28[/C][C]106.25[/C][C]104.269[/C][C]103.546[/C][C]1.00698[/C][C]1.019[/C][/ROW]
[ROW][C]29[/C][C]104.01[/C][C]103.521[/C][C]103.791[/C][C]0.997398[/C][C]1.00473[/C][/ROW]
[ROW][C]30[/C][C]101.68[/C][C]103.385[/C][C]103.908[/C][C]0.994964[/C][C]0.983508[/C][/ROW]
[ROW][C]31[/C][C]101.93[/C][C]103.377[/C][C]103.946[/C][C]0.994521[/C][C]0.986005[/C][/ROW]
[ROW][C]32[/C][C]104.41[/C][C]103.293[/C][C]103.833[/C][C]0.994802[/C][C]1.01082[/C][/ROW]
[ROW][C]33[/C][C]105.51[/C][C]103.874[/C][C]103.545[/C][C]1.00317[/C][C]1.01575[/C][/ROW]
[ROW][C]34[/C][C]104.71[/C][C]102.538[/C][C]103.141[/C][C]0.994152[/C][C]1.02118[/C][/ROW]
[ROW][C]35[/C][C]103.14[/C][C]101.954[/C][C]102.735[/C][C]0.992398[/C][C]1.01164[/C][/ROW]
[ROW][C]36[/C][C]102.66[/C][C]102.129[/C][C]102.443[/C][C]0.996932[/C][C]1.0052[/C][/ROW]
[ROW][C]37[/C][C]102.68[/C][C]102.434[/C][C]102.26[/C][C]1.00169[/C][C]1.00241[/C][/ROW]
[ROW][C]38[/C][C]101.89[/C][C]102.609[/C][C]101.962[/C][C]1.00634[/C][C]0.99299[/C][/ROW]
[ROW][C]39[/C][C]101.37[/C][C]103.207[/C][C]101.517[/C][C]1.01665[/C][C]0.982202[/C][/ROW]
[ROW][C]40[/C][C]101.16[/C][C]101.725[/C][C]101.02[/C][C]1.00698[/C][C]0.994448[/C][/ROW]
[ROW][C]41[/C][C]99.34[/C][C]100.282[/C][C]100.543[/C][C]0.997398[/C][C]0.990609[/C][/ROW]
[ROW][C]42[/C][C]99.35[/C][C]99.6606[/C][C]100.165[/C][C]0.994964[/C][C]0.996884[/C][/ROW]
[ROW][C]43[/C][C]99.88[/C][C]99.2586[/C][C]99.8054[/C][C]0.994521[/C][C]1.00626[/C][/ROW]
[ROW][C]44[/C][C]99.31[/C][C]98.9492[/C][C]99.4663[/C][C]0.994802[/C][C]1.00365[/C][/ROW]
[ROW][C]45[/C][C]99.91[/C][C]99.5041[/C][C]99.1896[/C][C]1.00317[/C][C]1.00408[/C][/ROW]
[ROW][C]46[/C][C]98.39[/C][C]98.1609[/C][C]98.7383[/C][C]0.994152[/C][C]1.00233[/C][/ROW]
[ROW][C]47[/C][C]98.02[/C][C]97.4159[/C][C]98.1621[/C][C]0.992398[/C][C]1.0062[/C][/ROW]
[ROW][C]48[/C][C]98.7[/C][C]97.3583[/C][C]97.6579[/C][C]0.996932[/C][C]1.01378[/C][/ROW]
[ROW][C]49[/C][C]98.01[/C][C]97.294[/C][C]97.1296[/C][C]1.00169[/C][C]1.00736[/C][/ROW]
[ROW][C]50[/C][C]98.42[/C][C]97.1691[/C][C]96.5567[/C][C]1.00634[/C][C]1.01287[/C][/ROW]
[ROW][C]51[/C][C]98.2[/C][C]97.5365[/C][C]95.9392[/C][C]1.01665[/C][C]1.0068[/C][/ROW]
[ROW][C]52[/C][C]93.5[/C][C]96.0383[/C][C]95.3729[/C][C]1.00698[/C][C]0.97357[/C][/ROW]
[ROW][C]53[/C][C]93.17[/C][C]94.6111[/C][C]94.8579[/C][C]0.997398[/C][C]0.984768[/C][/ROW]
[ROW][C]54[/C][C]93.42[/C][C]93.7206[/C][C]94.195[/C][C]0.994964[/C][C]0.996792[/C][/ROW]
[ROW][C]55[/C][C]93.13[/C][C]NA[/C][C]NA[/C][C]0.994521[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]92.31[/C][C]NA[/C][C]NA[/C][C]0.994802[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]92.09[/C][C]NA[/C][C]NA[/C][C]1.00317[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]92.62[/C][C]NA[/C][C]NA[/C][C]0.994152[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]91.43[/C][C]NA[/C][C]NA[/C][C]0.992398[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]89.38[/C][C]NA[/C][C]NA[/C][C]0.996932[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284170&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284170&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	78.46	NA	NA	1.00169	NA
2	78.59	NA	NA	1.00634	NA
3	81.37	NA	NA	1.01665	NA
4	83.61	NA	NA	1.00698	NA
5	84.65	NA	NA	0.997398	NA
6	84.56	NA	NA	0.994964	NA
7	83.85	83.8328	84.2946	0.994521	1.00021
8	84.08	84.9146	85.3583	0.994802	0.990171
9	85.41	86.7993	86.525	1.00317	0.983994
10	85.75	87.1813	87.6942	0.994152	0.983582
11	86.38	88.1572	88.8325	0.992398	0.97984
12	88.87	89.7476	90.0237	0.996932	0.990222
13	90.37	91.4341	91.2796	1.00169	0.988362
14	92.21	93.1169	92.53	1.00634	0.990261
15	95.75	95.3478	93.7863	1.01665	1.00422
16	97.29	95.6942	95.0312	1.00698	1.01668
17	98.29	96.0245	96.275	0.997398	1.02359
18	99.51	96.9398	97.4304	0.994964	1.02651
19	99.04	97.9405	98.48	0.994521	1.01123
20	98.9	98.9927	99.51	0.994802	0.999063
21	100.74	100.753	100.435	1.00317	0.999871
22	100.3	100.642	101.234	0.994152	0.9966
23	101.68	101.072	101.846	0.992398	1.00602
24	101.3	101.861	102.175	0.996932	0.994491
25	103.13	102.559	102.385	1.00169	1.00557
26	104.17	103.387	102.735	1.00634	1.00757
27	105.98	104.881	103.164	1.01665	1.01047
28	106.25	104.269	103.546	1.00698	1.019
29	104.01	103.521	103.791	0.997398	1.00473
30	101.68	103.385	103.908	0.994964	0.983508
31	101.93	103.377	103.946	0.994521	0.986005
32	104.41	103.293	103.833	0.994802	1.01082
33	105.51	103.874	103.545	1.00317	1.01575
34	104.71	102.538	103.141	0.994152	1.02118
35	103.14	101.954	102.735	0.992398	1.01164
36	102.66	102.129	102.443	0.996932	1.0052
37	102.68	102.434	102.26	1.00169	1.00241
38	101.89	102.609	101.962	1.00634	0.99299
39	101.37	103.207	101.517	1.01665	0.982202
40	101.16	101.725	101.02	1.00698	0.994448
41	99.34	100.282	100.543	0.997398	0.990609
42	99.35	99.6606	100.165	0.994964	0.996884
43	99.88	99.2586	99.8054	0.994521	1.00626
44	99.31	98.9492	99.4663	0.994802	1.00365
45	99.91	99.5041	99.1896	1.00317	1.00408
46	98.39	98.1609	98.7383	0.994152	1.00233
47	98.02	97.4159	98.1621	0.992398	1.0062
48	98.7	97.3583	97.6579	0.996932	1.01378
49	98.01	97.294	97.1296	1.00169	1.00736
50	98.42	97.1691	96.5567	1.00634	1.01287
51	98.2	97.5365	95.9392	1.01665	1.0068
52	93.5	96.0383	95.3729	1.00698	0.97357
53	93.17	94.6111	94.8579	0.997398	0.984768
54	93.42	93.7206	94.195	0.994964	0.996792
55	93.13	NA	NA	0.994521	NA
56	92.31	NA	NA	0.994802	NA
57	92.09	NA	NA	1.00317	NA
58	92.62	NA	NA	0.994152	NA
59	91.43	NA	NA	0.992398	NA
60	89.38	NA	NA	0.996932	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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$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