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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 25 Apr 2016 18:04:15 +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/t14616043007ccezp760uchm05.htm/, Retrieved Sun, 05 May 2024 21:02:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294748, Retrieved Sun, 05 May 2024 21:02:49 +0000

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-       [Classical Decomposition] [] [2016-04-25 17:04:15] [ed8c98a61958118f8b1101b2c94f1953] [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 Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294748&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294748&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294748&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 Ronald Aylmer Fisher' @ fisher.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	96.67	NA	NA	1.0042	NA
2	96.67	NA	NA	1.00322	NA
3	96.67	NA	NA	1.00224	NA
4	96.67	NA	NA	0.998841	NA
5	96.67	NA	NA	0.993177	NA
6	96.67	NA	NA	0.987725	NA
7	96.67	96.3648	96.53	0.998289	1.00317
8	96.67	96.2302	96.49	0.997307	1.00457
9	96.67	96.096	96.45	0.996329	1.00597
10	96.19	97.1054	96.41	1.00721	0.990573
11	96.19	96.9694	96.37	1.00622	0.991963
12	96.19	96.8338	96.33	1.00523	0.993352
13	96.19	96.6948	96.29	1.0042	0.994779
14	96.19	96.5601	96.25	1.00322	0.996167
15	96.19	96.4259	96.21	1.00224	0.997553
16	96.19	96.2008	96.3125	0.998841	0.999887
17	96.19	95.8987	96.5575	0.993177	1.00304
18	96.19	95.6142	96.8025	0.987725	1.00602
19	96.19	96.8815	97.0475	0.998289	0.992863
20	96.19	97.0305	97.2925	0.997307	0.991338
21	96.19	97.1795	97.5375	0.996329	0.989818
22	99.13	98.4878	97.7825	1.00721	1.00652
23	99.13	98.6372	98.0275	1.00622	1.005
24	99.13	98.7864	98.2725	1.00523	1.00348
25	99.13	98.9317	98.5175	1.0042	1.002
26	99.13	99.0807	98.7625	1.00322	1.0005
27	99.13	99.2297	99.0075	1.00224	0.998995
28	99.13	99.0338	99.1487	0.998841	1.00097
29	99.13	98.5095	99.1862	0.993177	1.0063
30	99.13	98.0057	99.2238	0.987725	1.01147
31	99.13	99.0914	99.2612	0.998289	1.00039
32	99.13	99.0314	99.2988	0.997307	1.001
33	99.13	98.9716	99.3362	0.996329	1.0016
34	99.58	100.091	99.3738	1.00721	0.994899
35	99.58	100.03	99.4112	1.00622	0.995506
36	99.58	99.9688	99.4488	1.00523	0.99611
37	99.58	99.9045	99.4862	1.0042	0.996752
38	99.58	99.8444	99.5237	1.00322	0.997351
39	99.58	99.7847	99.5612	1.00224	0.997949
40	99.58	99.5349	99.6504	0.998841	1.00045
41	99.58	99.1104	99.7912	0.993177	1.00474
42	99.58	98.7054	99.9321	0.987725	1.00886
43	99.58	99.9009	100.072	0.998289	0.996788
44	99.58	99.9414	100.211	0.997307	0.996384
45	99.58	99.9821	100.35	0.996329	0.995979
46	101.27	101.214	100.49	1.00721	1.00055
47	101.27	101.255	100.629	1.00622	1.00015
48	101.27	101.295	100.768	1.00523	0.999754
49	101.25	101.331	100.907	1.0042	0.999198
50	101.25	101.372	101.046	1.00322	0.998798
51	101.25	101.412	101.185	1.00224	0.998398
52	101.25	101.191	101.308	0.998841	1.00058
53	101.25	100.723	101.415	0.993177	1.00523
54	101.25	100.275	101.522	0.987725	1.00972
55	101.25	101.455	101.629	0.998289	0.997977
56	101.25	101.464	101.738	0.997307	0.997895
57	101.25	101.472	101.846	0.996329	0.997812
58	102.55	102.69	101.954	1.00721	0.998641
59	102.55	102.697	102.062	1.00622	0.998566
60	102.55	102.705	102.171	1.00523	0.998489
61	102.55	102.709	102.279	1.0042	0.99845
62	102.55	102.717	102.388	1.00322	0.99837
63	102.55	102.726	102.496	1.00224	0.998288
64	102.55	103.661	103.781	0.998841	0.989287
65	102.55	105.518	106.242	0.993177	0.971876
66	102.55	107.37	108.704	0.987725	0.95511
67	102.55	NA	NA	0.998289	NA
68	102.55	NA	NA	0.997307	NA
69	102.55	NA	NA	0.996329	NA
70	132.09	NA	NA	1.00721	NA
71	132.09	NA	NA	1.00622	NA
72	132.09	NA	NA	1.00523	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 96.67 & NA & NA & 1.0042 & NA \tabularnewline
2 & 96.67 & NA & NA & 1.00322 & NA \tabularnewline
3 & 96.67 & NA & NA & 1.00224 & NA \tabularnewline
4 & 96.67 & NA & NA & 0.998841 & NA \tabularnewline
5 & 96.67 & NA & NA & 0.993177 & NA \tabularnewline
6 & 96.67 & NA & NA & 0.987725 & NA \tabularnewline
7 & 96.67 & 96.3648 & 96.53 & 0.998289 & 1.00317 \tabularnewline
8 & 96.67 & 96.2302 & 96.49 & 0.997307 & 1.00457 \tabularnewline
9 & 96.67 & 96.096 & 96.45 & 0.996329 & 1.00597 \tabularnewline
10 & 96.19 & 97.1054 & 96.41 & 1.00721 & 0.990573 \tabularnewline
11 & 96.19 & 96.9694 & 96.37 & 1.00622 & 0.991963 \tabularnewline
12 & 96.19 & 96.8338 & 96.33 & 1.00523 & 0.993352 \tabularnewline
13 & 96.19 & 96.6948 & 96.29 & 1.0042 & 0.994779 \tabularnewline
14 & 96.19 & 96.5601 & 96.25 & 1.00322 & 0.996167 \tabularnewline
15 & 96.19 & 96.4259 & 96.21 & 1.00224 & 0.997553 \tabularnewline
16 & 96.19 & 96.2008 & 96.3125 & 0.998841 & 0.999887 \tabularnewline
17 & 96.19 & 95.8987 & 96.5575 & 0.993177 & 1.00304 \tabularnewline
18 & 96.19 & 95.6142 & 96.8025 & 0.987725 & 1.00602 \tabularnewline
19 & 96.19 & 96.8815 & 97.0475 & 0.998289 & 0.992863 \tabularnewline
20 & 96.19 & 97.0305 & 97.2925 & 0.997307 & 0.991338 \tabularnewline
21 & 96.19 & 97.1795 & 97.5375 & 0.996329 & 0.989818 \tabularnewline
22 & 99.13 & 98.4878 & 97.7825 & 1.00721 & 1.00652 \tabularnewline
23 & 99.13 & 98.6372 & 98.0275 & 1.00622 & 1.005 \tabularnewline
24 & 99.13 & 98.7864 & 98.2725 & 1.00523 & 1.00348 \tabularnewline
25 & 99.13 & 98.9317 & 98.5175 & 1.0042 & 1.002 \tabularnewline
26 & 99.13 & 99.0807 & 98.7625 & 1.00322 & 1.0005 \tabularnewline
27 & 99.13 & 99.2297 & 99.0075 & 1.00224 & 0.998995 \tabularnewline
28 & 99.13 & 99.0338 & 99.1487 & 0.998841 & 1.00097 \tabularnewline
29 & 99.13 & 98.5095 & 99.1862 & 0.993177 & 1.0063 \tabularnewline
30 & 99.13 & 98.0057 & 99.2238 & 0.987725 & 1.01147 \tabularnewline
31 & 99.13 & 99.0914 & 99.2612 & 0.998289 & 1.00039 \tabularnewline
32 & 99.13 & 99.0314 & 99.2988 & 0.997307 & 1.001 \tabularnewline
33 & 99.13 & 98.9716 & 99.3362 & 0.996329 & 1.0016 \tabularnewline
34 & 99.58 & 100.091 & 99.3738 & 1.00721 & 0.994899 \tabularnewline
35 & 99.58 & 100.03 & 99.4112 & 1.00622 & 0.995506 \tabularnewline
36 & 99.58 & 99.9688 & 99.4488 & 1.00523 & 0.99611 \tabularnewline
37 & 99.58 & 99.9045 & 99.4862 & 1.0042 & 0.996752 \tabularnewline
38 & 99.58 & 99.8444 & 99.5237 & 1.00322 & 0.997351 \tabularnewline
39 & 99.58 & 99.7847 & 99.5612 & 1.00224 & 0.997949 \tabularnewline
40 & 99.58 & 99.5349 & 99.6504 & 0.998841 & 1.00045 \tabularnewline
41 & 99.58 & 99.1104 & 99.7912 & 0.993177 & 1.00474 \tabularnewline
42 & 99.58 & 98.7054 & 99.9321 & 0.987725 & 1.00886 \tabularnewline
43 & 99.58 & 99.9009 & 100.072 & 0.998289 & 0.996788 \tabularnewline
44 & 99.58 & 99.9414 & 100.211 & 0.997307 & 0.996384 \tabularnewline
45 & 99.58 & 99.9821 & 100.35 & 0.996329 & 0.995979 \tabularnewline
46 & 101.27 & 101.214 & 100.49 & 1.00721 & 1.00055 \tabularnewline
47 & 101.27 & 101.255 & 100.629 & 1.00622 & 1.00015 \tabularnewline
48 & 101.27 & 101.295 & 100.768 & 1.00523 & 0.999754 \tabularnewline
49 & 101.25 & 101.331 & 100.907 & 1.0042 & 0.999198 \tabularnewline
50 & 101.25 & 101.372 & 101.046 & 1.00322 & 0.998798 \tabularnewline
51 & 101.25 & 101.412 & 101.185 & 1.00224 & 0.998398 \tabularnewline
52 & 101.25 & 101.191 & 101.308 & 0.998841 & 1.00058 \tabularnewline
53 & 101.25 & 100.723 & 101.415 & 0.993177 & 1.00523 \tabularnewline
54 & 101.25 & 100.275 & 101.522 & 0.987725 & 1.00972 \tabularnewline
55 & 101.25 & 101.455 & 101.629 & 0.998289 & 0.997977 \tabularnewline
56 & 101.25 & 101.464 & 101.738 & 0.997307 & 0.997895 \tabularnewline
57 & 101.25 & 101.472 & 101.846 & 0.996329 & 0.997812 \tabularnewline
58 & 102.55 & 102.69 & 101.954 & 1.00721 & 0.998641 \tabularnewline
59 & 102.55 & 102.697 & 102.062 & 1.00622 & 0.998566 \tabularnewline
60 & 102.55 & 102.705 & 102.171 & 1.00523 & 0.998489 \tabularnewline
61 & 102.55 & 102.709 & 102.279 & 1.0042 & 0.99845 \tabularnewline
62 & 102.55 & 102.717 & 102.388 & 1.00322 & 0.99837 \tabularnewline
63 & 102.55 & 102.726 & 102.496 & 1.00224 & 0.998288 \tabularnewline
64 & 102.55 & 103.661 & 103.781 & 0.998841 & 0.989287 \tabularnewline
65 & 102.55 & 105.518 & 106.242 & 0.993177 & 0.971876 \tabularnewline
66 & 102.55 & 107.37 & 108.704 & 0.987725 & 0.95511 \tabularnewline
67 & 102.55 & NA & NA & 0.998289 & NA \tabularnewline
68 & 102.55 & NA & NA & 0.997307 & NA \tabularnewline
69 & 102.55 & NA & NA & 0.996329 & NA \tabularnewline
70 & 132.09 & NA & NA & 1.00721 & NA \tabularnewline
71 & 132.09 & NA & NA & 1.00622 & NA \tabularnewline
72 & 132.09 & NA & NA & 1.00523 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294748&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]96.67[/C][C]NA[/C][C]NA[/C][C]1.0042[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]96.67[/C][C]NA[/C][C]NA[/C][C]1.00322[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]96.67[/C][C]NA[/C][C]NA[/C][C]1.00224[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]96.67[/C][C]NA[/C][C]NA[/C][C]0.998841[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]96.67[/C][C]NA[/C][C]NA[/C][C]0.993177[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]96.67[/C][C]NA[/C][C]NA[/C][C]0.987725[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]96.67[/C][C]96.3648[/C][C]96.53[/C][C]0.998289[/C][C]1.00317[/C][/ROW]
[ROW][C]8[/C][C]96.67[/C][C]96.2302[/C][C]96.49[/C][C]0.997307[/C][C]1.00457[/C][/ROW]
[ROW][C]9[/C][C]96.67[/C][C]96.096[/C][C]96.45[/C][C]0.996329[/C][C]1.00597[/C][/ROW]
[ROW][C]10[/C][C]96.19[/C][C]97.1054[/C][C]96.41[/C][C]1.00721[/C][C]0.990573[/C][/ROW]
[ROW][C]11[/C][C]96.19[/C][C]96.9694[/C][C]96.37[/C][C]1.00622[/C][C]0.991963[/C][/ROW]
[ROW][C]12[/C][C]96.19[/C][C]96.8338[/C][C]96.33[/C][C]1.00523[/C][C]0.993352[/C][/ROW]
[ROW][C]13[/C][C]96.19[/C][C]96.6948[/C][C]96.29[/C][C]1.0042[/C][C]0.994779[/C][/ROW]
[ROW][C]14[/C][C]96.19[/C][C]96.5601[/C][C]96.25[/C][C]1.00322[/C][C]0.996167[/C][/ROW]
[ROW][C]15[/C][C]96.19[/C][C]96.4259[/C][C]96.21[/C][C]1.00224[/C][C]0.997553[/C][/ROW]
[ROW][C]16[/C][C]96.19[/C][C]96.2008[/C][C]96.3125[/C][C]0.998841[/C][C]0.999887[/C][/ROW]
[ROW][C]17[/C][C]96.19[/C][C]95.8987[/C][C]96.5575[/C][C]0.993177[/C][C]1.00304[/C][/ROW]
[ROW][C]18[/C][C]96.19[/C][C]95.6142[/C][C]96.8025[/C][C]0.987725[/C][C]1.00602[/C][/ROW]
[ROW][C]19[/C][C]96.19[/C][C]96.8815[/C][C]97.0475[/C][C]0.998289[/C][C]0.992863[/C][/ROW]
[ROW][C]20[/C][C]96.19[/C][C]97.0305[/C][C]97.2925[/C][C]0.997307[/C][C]0.991338[/C][/ROW]
[ROW][C]21[/C][C]96.19[/C][C]97.1795[/C][C]97.5375[/C][C]0.996329[/C][C]0.989818[/C][/ROW]
[ROW][C]22[/C][C]99.13[/C][C]98.4878[/C][C]97.7825[/C][C]1.00721[/C][C]1.00652[/C][/ROW]
[ROW][C]23[/C][C]99.13[/C][C]98.6372[/C][C]98.0275[/C][C]1.00622[/C][C]1.005[/C][/ROW]
[ROW][C]24[/C][C]99.13[/C][C]98.7864[/C][C]98.2725[/C][C]1.00523[/C][C]1.00348[/C][/ROW]
[ROW][C]25[/C][C]99.13[/C][C]98.9317[/C][C]98.5175[/C][C]1.0042[/C][C]1.002[/C][/ROW]
[ROW][C]26[/C][C]99.13[/C][C]99.0807[/C][C]98.7625[/C][C]1.00322[/C][C]1.0005[/C][/ROW]
[ROW][C]27[/C][C]99.13[/C][C]99.2297[/C][C]99.0075[/C][C]1.00224[/C][C]0.998995[/C][/ROW]
[ROW][C]28[/C][C]99.13[/C][C]99.0338[/C][C]99.1487[/C][C]0.998841[/C][C]1.00097[/C][/ROW]
[ROW][C]29[/C][C]99.13[/C][C]98.5095[/C][C]99.1862[/C][C]0.993177[/C][C]1.0063[/C][/ROW]
[ROW][C]30[/C][C]99.13[/C][C]98.0057[/C][C]99.2238[/C][C]0.987725[/C][C]1.01147[/C][/ROW]
[ROW][C]31[/C][C]99.13[/C][C]99.0914[/C][C]99.2612[/C][C]0.998289[/C][C]1.00039[/C][/ROW]
[ROW][C]32[/C][C]99.13[/C][C]99.0314[/C][C]99.2988[/C][C]0.997307[/C][C]1.001[/C][/ROW]
[ROW][C]33[/C][C]99.13[/C][C]98.9716[/C][C]99.3362[/C][C]0.996329[/C][C]1.0016[/C][/ROW]
[ROW][C]34[/C][C]99.58[/C][C]100.091[/C][C]99.3738[/C][C]1.00721[/C][C]0.994899[/C][/ROW]
[ROW][C]35[/C][C]99.58[/C][C]100.03[/C][C]99.4112[/C][C]1.00622[/C][C]0.995506[/C][/ROW]
[ROW][C]36[/C][C]99.58[/C][C]99.9688[/C][C]99.4488[/C][C]1.00523[/C][C]0.99611[/C][/ROW]
[ROW][C]37[/C][C]99.58[/C][C]99.9045[/C][C]99.4862[/C][C]1.0042[/C][C]0.996752[/C][/ROW]
[ROW][C]38[/C][C]99.58[/C][C]99.8444[/C][C]99.5237[/C][C]1.00322[/C][C]0.997351[/C][/ROW]
[ROW][C]39[/C][C]99.58[/C][C]99.7847[/C][C]99.5612[/C][C]1.00224[/C][C]0.997949[/C][/ROW]
[ROW][C]40[/C][C]99.58[/C][C]99.5349[/C][C]99.6504[/C][C]0.998841[/C][C]1.00045[/C][/ROW]
[ROW][C]41[/C][C]99.58[/C][C]99.1104[/C][C]99.7912[/C][C]0.993177[/C][C]1.00474[/C][/ROW]
[ROW][C]42[/C][C]99.58[/C][C]98.7054[/C][C]99.9321[/C][C]0.987725[/C][C]1.00886[/C][/ROW]
[ROW][C]43[/C][C]99.58[/C][C]99.9009[/C][C]100.072[/C][C]0.998289[/C][C]0.996788[/C][/ROW]
[ROW][C]44[/C][C]99.58[/C][C]99.9414[/C][C]100.211[/C][C]0.997307[/C][C]0.996384[/C][/ROW]
[ROW][C]45[/C][C]99.58[/C][C]99.9821[/C][C]100.35[/C][C]0.996329[/C][C]0.995979[/C][/ROW]
[ROW][C]46[/C][C]101.27[/C][C]101.214[/C][C]100.49[/C][C]1.00721[/C][C]1.00055[/C][/ROW]
[ROW][C]47[/C][C]101.27[/C][C]101.255[/C][C]100.629[/C][C]1.00622[/C][C]1.00015[/C][/ROW]
[ROW][C]48[/C][C]101.27[/C][C]101.295[/C][C]100.768[/C][C]1.00523[/C][C]0.999754[/C][/ROW]
[ROW][C]49[/C][C]101.25[/C][C]101.331[/C][C]100.907[/C][C]1.0042[/C][C]0.999198[/C][/ROW]
[ROW][C]50[/C][C]101.25[/C][C]101.372[/C][C]101.046[/C][C]1.00322[/C][C]0.998798[/C][/ROW]
[ROW][C]51[/C][C]101.25[/C][C]101.412[/C][C]101.185[/C][C]1.00224[/C][C]0.998398[/C][/ROW]
[ROW][C]52[/C][C]101.25[/C][C]101.191[/C][C]101.308[/C][C]0.998841[/C][C]1.00058[/C][/ROW]
[ROW][C]53[/C][C]101.25[/C][C]100.723[/C][C]101.415[/C][C]0.993177[/C][C]1.00523[/C][/ROW]
[ROW][C]54[/C][C]101.25[/C][C]100.275[/C][C]101.522[/C][C]0.987725[/C][C]1.00972[/C][/ROW]
[ROW][C]55[/C][C]101.25[/C][C]101.455[/C][C]101.629[/C][C]0.998289[/C][C]0.997977[/C][/ROW]
[ROW][C]56[/C][C]101.25[/C][C]101.464[/C][C]101.738[/C][C]0.997307[/C][C]0.997895[/C][/ROW]
[ROW][C]57[/C][C]101.25[/C][C]101.472[/C][C]101.846[/C][C]0.996329[/C][C]0.997812[/C][/ROW]
[ROW][C]58[/C][C]102.55[/C][C]102.69[/C][C]101.954[/C][C]1.00721[/C][C]0.998641[/C][/ROW]
[ROW][C]59[/C][C]102.55[/C][C]102.697[/C][C]102.062[/C][C]1.00622[/C][C]0.998566[/C][/ROW]
[ROW][C]60[/C][C]102.55[/C][C]102.705[/C][C]102.171[/C][C]1.00523[/C][C]0.998489[/C][/ROW]
[ROW][C]61[/C][C]102.55[/C][C]102.709[/C][C]102.279[/C][C]1.0042[/C][C]0.99845[/C][/ROW]
[ROW][C]62[/C][C]102.55[/C][C]102.717[/C][C]102.388[/C][C]1.00322[/C][C]0.99837[/C][/ROW]
[ROW][C]63[/C][C]102.55[/C][C]102.726[/C][C]102.496[/C][C]1.00224[/C][C]0.998288[/C][/ROW]
[ROW][C]64[/C][C]102.55[/C][C]103.661[/C][C]103.781[/C][C]0.998841[/C][C]0.989287[/C][/ROW]
[ROW][C]65[/C][C]102.55[/C][C]105.518[/C][C]106.242[/C][C]0.993177[/C][C]0.971876[/C][/ROW]
[ROW][C]66[/C][C]102.55[/C][C]107.37[/C][C]108.704[/C][C]0.987725[/C][C]0.95511[/C][/ROW]
[ROW][C]67[/C][C]102.55[/C][C]NA[/C][C]NA[/C][C]0.998289[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.55[/C][C]NA[/C][C]NA[/C][C]0.997307[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]102.55[/C][C]NA[/C][C]NA[/C][C]0.996329[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]132.09[/C][C]NA[/C][C]NA[/C][C]1.00721[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]132.09[/C][C]NA[/C][C]NA[/C][C]1.00622[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]132.09[/C][C]NA[/C][C]NA[/C][C]1.00523[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294748&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294748&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	96.67	NA	NA	1.0042	NA
2	96.67	NA	NA	1.00322	NA
3	96.67	NA	NA	1.00224	NA
4	96.67	NA	NA	0.998841	NA
5	96.67	NA	NA	0.993177	NA
6	96.67	NA	NA	0.987725	NA
7	96.67	96.3648	96.53	0.998289	1.00317
8	96.67	96.2302	96.49	0.997307	1.00457
9	96.67	96.096	96.45	0.996329	1.00597
10	96.19	97.1054	96.41	1.00721	0.990573
11	96.19	96.9694	96.37	1.00622	0.991963
12	96.19	96.8338	96.33	1.00523	0.993352
13	96.19	96.6948	96.29	1.0042	0.994779
14	96.19	96.5601	96.25	1.00322	0.996167
15	96.19	96.4259	96.21	1.00224	0.997553
16	96.19	96.2008	96.3125	0.998841	0.999887
17	96.19	95.8987	96.5575	0.993177	1.00304
18	96.19	95.6142	96.8025	0.987725	1.00602
19	96.19	96.8815	97.0475	0.998289	0.992863
20	96.19	97.0305	97.2925	0.997307	0.991338
21	96.19	97.1795	97.5375	0.996329	0.989818
22	99.13	98.4878	97.7825	1.00721	1.00652
23	99.13	98.6372	98.0275	1.00622	1.005
24	99.13	98.7864	98.2725	1.00523	1.00348
25	99.13	98.9317	98.5175	1.0042	1.002
26	99.13	99.0807	98.7625	1.00322	1.0005
27	99.13	99.2297	99.0075	1.00224	0.998995
28	99.13	99.0338	99.1487	0.998841	1.00097
29	99.13	98.5095	99.1862	0.993177	1.0063
30	99.13	98.0057	99.2238	0.987725	1.01147
31	99.13	99.0914	99.2612	0.998289	1.00039
32	99.13	99.0314	99.2988	0.997307	1.001
33	99.13	98.9716	99.3362	0.996329	1.0016
34	99.58	100.091	99.3738	1.00721	0.994899
35	99.58	100.03	99.4112	1.00622	0.995506
36	99.58	99.9688	99.4488	1.00523	0.99611
37	99.58	99.9045	99.4862	1.0042	0.996752
38	99.58	99.8444	99.5237	1.00322	0.997351
39	99.58	99.7847	99.5612	1.00224	0.997949
40	99.58	99.5349	99.6504	0.998841	1.00045
41	99.58	99.1104	99.7912	0.993177	1.00474
42	99.58	98.7054	99.9321	0.987725	1.00886
43	99.58	99.9009	100.072	0.998289	0.996788
44	99.58	99.9414	100.211	0.997307	0.996384
45	99.58	99.9821	100.35	0.996329	0.995979
46	101.27	101.214	100.49	1.00721	1.00055
47	101.27	101.255	100.629	1.00622	1.00015
48	101.27	101.295	100.768	1.00523	0.999754
49	101.25	101.331	100.907	1.0042	0.999198
50	101.25	101.372	101.046	1.00322	0.998798
51	101.25	101.412	101.185	1.00224	0.998398
52	101.25	101.191	101.308	0.998841	1.00058
53	101.25	100.723	101.415	0.993177	1.00523
54	101.25	100.275	101.522	0.987725	1.00972
55	101.25	101.455	101.629	0.998289	0.997977
56	101.25	101.464	101.738	0.997307	0.997895
57	101.25	101.472	101.846	0.996329	0.997812
58	102.55	102.69	101.954	1.00721	0.998641
59	102.55	102.697	102.062	1.00622	0.998566
60	102.55	102.705	102.171	1.00523	0.998489
61	102.55	102.709	102.279	1.0042	0.99845
62	102.55	102.717	102.388	1.00322	0.99837
63	102.55	102.726	102.496	1.00224	0.998288
64	102.55	103.661	103.781	0.998841	0.989287
65	102.55	105.518	106.242	0.993177	0.971876
66	102.55	107.37	108.704	0.987725	0.95511
67	102.55	NA	NA	0.998289	NA
68	102.55	NA	NA	0.997307	NA
69	102.55	NA	NA	0.996329	NA
70	132.09	NA	NA	1.00721	NA
71	132.09	NA	NA	1.00622	NA
72	132.09	NA	NA	1.00523	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