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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 28 Nov 2016 20:18:01 +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/2016/Nov/28/t1480364478dm8p9a923juuo54.htm/, Retrieved Sat, 04 May 2024 16:19:25 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 16:19:25 +0200

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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	'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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

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

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	98.98	NA	NA	0.99778	NA
2	98.97	NA	NA	0.99751	NA
3	98.91	NA	NA	0.997785	NA
4	98.98	NA	NA	0.997625	NA
5	98.95	NA	NA	0.997818	NA
6	98.96	NA	NA	0.99764	NA
7	98.96	99.1164	99.3404	0.997745	0.998422
8	99.04	99.2344	99.4458	0.997874	0.998041
9	99.33	99.5938	99.5638	1.0003	0.997351
10	100.04	100.222	99.6938	1.0053	0.998185
11	100.14	100.46	99.825	1.00636	0.996818
12	100.21	100.579	99.9529	1.00627	0.996329
13	100.21	99.8566	100.079	0.99778	1.00354
14	100.27	99.9526	100.202	0.99751	1.00318
15	100.44	100.11	100.332	0.997785	1.0033
16	100.57	100.229	100.467	0.997625	1.00341
17	100.51	100.376	100.595	0.997818	1.00134
18	100.47	100.484	100.722	0.99764	0.999857
19	100.47	100.622	100.85	0.997745	0.998488
20	100.49	100.759	100.974	0.997874	0.997325
21	101	101.121	101.09	1.0003	0.998804
22	101.61	101.734	101.198	1.0053	0.998781
23	101.65	101.955	101.311	1.00636	0.997009
24	101.74	102.075	101.439	1.00627	0.99672
25	101.74	101.349	101.574	0.99778	1.00386
26	101.73	101.455	101.708	0.99751	1.00271
27	101.77	101.608	101.833	0.997785	1.0016
28	101.82	101.699	101.941	0.997625	1.00119
29	101.97	101.825	102.048	0.997818	1.00142
30	102.09	101.921	102.162	0.99764	1.00166
31	102.09	101.88	102.11	0.997745	1.00206
32	102.08	101.68	101.897	0.997874	1.00393
33	102.42	101.716	101.685	1.0003	1.00692
34	102.78	102.012	101.475	1.0053	1.00753
35	103.04	101.909	101.265	1.00636	1.0111
36	103.08	101.686	101.052	1.00627	1.01371
37	99.16	100.631	100.855	0.99778	0.985381
38	99.19	100.434	100.685	0.99751	0.987611
39	99.23	100.299	100.521	0.997785	0.989346
40	99.31	100.13	100.368	0.997625	0.991811
41	99.46	100.005	100.224	0.997818	0.994546
42	99.49	99.8389	100.075	0.99764	0.996506
43	99.95	99.8526	100.078	0.997745	1.00098
44	100.14	100.024	100.237	0.997874	1.00116
45	100.43	100.424	100.394	1.0003	1.00006
46	101.1	101.073	100.54	1.0053	1.00027
47	101.26	101.314	100.674	1.00636	0.999469
48	101.28	101.432	100.801	1.00627	0.998497
49	101.04	100.691	100.915	0.99778	1.00347
50	101.12	100.761	101.013	0.99751	1.00356
51	101.07	100.873	101.097	0.997785	1.00196
52	100.97	100.923	101.163	0.997625	1.00047
53	101.01	101.004	101.225	0.997818	1.00006
54	100.99	101.053	101.292	0.99764	0.999376
55	101.19	101.148	101.377	0.997745	1.00041
56	101.25	101.26	101.476	0.997874	0.9999
57	101.33	101.612	101.581	1.0003	0.997226
58	101.79	102.24	101.701	1.0053	0.995598
59	102.06	102.473	101.826	1.00636	0.995968
60	102.09	102.593	101.954	1.00627	0.995101
61	102.27	101.851	102.078	0.99778	1.00411
62	102.26	101.923	102.178	0.99751	1.0033
63	102.46	102.035	102.262	0.997785	1.00416
64	102.46	102.104	102.347	0.997625	1.00349
65	102.51	102.204	102.427	0.997818	1.003
66	102.56	102.257	102.499	0.99764	1.00296
67	102.59	NA	NA	0.997745	NA
68	102.26	NA	NA	0.997874	NA
69	102.33	NA	NA	1.0003	NA
70	102.84	NA	NA	1.0053	NA
71	102.93	NA	NA	1.00636	NA
72	102.95	NA	NA	1.00627	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 98.98 & NA & NA & 0.99778 & NA \tabularnewline
2 & 98.97 & NA & NA & 0.99751 & NA \tabularnewline
3 & 98.91 & NA & NA & 0.997785 & NA \tabularnewline
4 & 98.98 & NA & NA & 0.997625 & NA \tabularnewline
5 & 98.95 & NA & NA & 0.997818 & NA \tabularnewline
6 & 98.96 & NA & NA & 0.99764 & NA \tabularnewline
7 & 98.96 & 99.1164 & 99.3404 & 0.997745 & 0.998422 \tabularnewline
8 & 99.04 & 99.2344 & 99.4458 & 0.997874 & 0.998041 \tabularnewline
9 & 99.33 & 99.5938 & 99.5638 & 1.0003 & 0.997351 \tabularnewline
10 & 100.04 & 100.222 & 99.6938 & 1.0053 & 0.998185 \tabularnewline
11 & 100.14 & 100.46 & 99.825 & 1.00636 & 0.996818 \tabularnewline
12 & 100.21 & 100.579 & 99.9529 & 1.00627 & 0.996329 \tabularnewline
13 & 100.21 & 99.8566 & 100.079 & 0.99778 & 1.00354 \tabularnewline
14 & 100.27 & 99.9526 & 100.202 & 0.99751 & 1.00318 \tabularnewline
15 & 100.44 & 100.11 & 100.332 & 0.997785 & 1.0033 \tabularnewline
16 & 100.57 & 100.229 & 100.467 & 0.997625 & 1.00341 \tabularnewline
17 & 100.51 & 100.376 & 100.595 & 0.997818 & 1.00134 \tabularnewline
18 & 100.47 & 100.484 & 100.722 & 0.99764 & 0.999857 \tabularnewline
19 & 100.47 & 100.622 & 100.85 & 0.997745 & 0.998488 \tabularnewline
20 & 100.49 & 100.759 & 100.974 & 0.997874 & 0.997325 \tabularnewline
21 & 101 & 101.121 & 101.09 & 1.0003 & 0.998804 \tabularnewline
22 & 101.61 & 101.734 & 101.198 & 1.0053 & 0.998781 \tabularnewline
23 & 101.65 & 101.955 & 101.311 & 1.00636 & 0.997009 \tabularnewline
24 & 101.74 & 102.075 & 101.439 & 1.00627 & 0.99672 \tabularnewline
25 & 101.74 & 101.349 & 101.574 & 0.99778 & 1.00386 \tabularnewline
26 & 101.73 & 101.455 & 101.708 & 0.99751 & 1.00271 \tabularnewline
27 & 101.77 & 101.608 & 101.833 & 0.997785 & 1.0016 \tabularnewline
28 & 101.82 & 101.699 & 101.941 & 0.997625 & 1.00119 \tabularnewline
29 & 101.97 & 101.825 & 102.048 & 0.997818 & 1.00142 \tabularnewline
30 & 102.09 & 101.921 & 102.162 & 0.99764 & 1.00166 \tabularnewline
31 & 102.09 & 101.88 & 102.11 & 0.997745 & 1.00206 \tabularnewline
32 & 102.08 & 101.68 & 101.897 & 0.997874 & 1.00393 \tabularnewline
33 & 102.42 & 101.716 & 101.685 & 1.0003 & 1.00692 \tabularnewline
34 & 102.78 & 102.012 & 101.475 & 1.0053 & 1.00753 \tabularnewline
35 & 103.04 & 101.909 & 101.265 & 1.00636 & 1.0111 \tabularnewline
36 & 103.08 & 101.686 & 101.052 & 1.00627 & 1.01371 \tabularnewline
37 & 99.16 & 100.631 & 100.855 & 0.99778 & 0.985381 \tabularnewline
38 & 99.19 & 100.434 & 100.685 & 0.99751 & 0.987611 \tabularnewline
39 & 99.23 & 100.299 & 100.521 & 0.997785 & 0.989346 \tabularnewline
40 & 99.31 & 100.13 & 100.368 & 0.997625 & 0.991811 \tabularnewline
41 & 99.46 & 100.005 & 100.224 & 0.997818 & 0.994546 \tabularnewline
42 & 99.49 & 99.8389 & 100.075 & 0.99764 & 0.996506 \tabularnewline
43 & 99.95 & 99.8526 & 100.078 & 0.997745 & 1.00098 \tabularnewline
44 & 100.14 & 100.024 & 100.237 & 0.997874 & 1.00116 \tabularnewline
45 & 100.43 & 100.424 & 100.394 & 1.0003 & 1.00006 \tabularnewline
46 & 101.1 & 101.073 & 100.54 & 1.0053 & 1.00027 \tabularnewline
47 & 101.26 & 101.314 & 100.674 & 1.00636 & 0.999469 \tabularnewline
48 & 101.28 & 101.432 & 100.801 & 1.00627 & 0.998497 \tabularnewline
49 & 101.04 & 100.691 & 100.915 & 0.99778 & 1.00347 \tabularnewline
50 & 101.12 & 100.761 & 101.013 & 0.99751 & 1.00356 \tabularnewline
51 & 101.07 & 100.873 & 101.097 & 0.997785 & 1.00196 \tabularnewline
52 & 100.97 & 100.923 & 101.163 & 0.997625 & 1.00047 \tabularnewline
53 & 101.01 & 101.004 & 101.225 & 0.997818 & 1.00006 \tabularnewline
54 & 100.99 & 101.053 & 101.292 & 0.99764 & 0.999376 \tabularnewline
55 & 101.19 & 101.148 & 101.377 & 0.997745 & 1.00041 \tabularnewline
56 & 101.25 & 101.26 & 101.476 & 0.997874 & 0.9999 \tabularnewline
57 & 101.33 & 101.612 & 101.581 & 1.0003 & 0.997226 \tabularnewline
58 & 101.79 & 102.24 & 101.701 & 1.0053 & 0.995598 \tabularnewline
59 & 102.06 & 102.473 & 101.826 & 1.00636 & 0.995968 \tabularnewline
60 & 102.09 & 102.593 & 101.954 & 1.00627 & 0.995101 \tabularnewline
61 & 102.27 & 101.851 & 102.078 & 0.99778 & 1.00411 \tabularnewline
62 & 102.26 & 101.923 & 102.178 & 0.99751 & 1.0033 \tabularnewline
63 & 102.46 & 102.035 & 102.262 & 0.997785 & 1.00416 \tabularnewline
64 & 102.46 & 102.104 & 102.347 & 0.997625 & 1.00349 \tabularnewline
65 & 102.51 & 102.204 & 102.427 & 0.997818 & 1.003 \tabularnewline
66 & 102.56 & 102.257 & 102.499 & 0.99764 & 1.00296 \tabularnewline
67 & 102.59 & NA & NA & 0.997745 & NA \tabularnewline
68 & 102.26 & NA & NA & 0.997874 & NA \tabularnewline
69 & 102.33 & NA & NA & 1.0003 & NA \tabularnewline
70 & 102.84 & NA & NA & 1.0053 & NA \tabularnewline
71 & 102.93 & NA & NA & 1.00636 & NA \tabularnewline
72 & 102.95 & NA & NA & 1.00627 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]98.98[/C][C]NA[/C][C]NA[/C][C]0.99778[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]98.97[/C][C]NA[/C][C]NA[/C][C]0.99751[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]98.91[/C][C]NA[/C][C]NA[/C][C]0.997785[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]98.98[/C][C]NA[/C][C]NA[/C][C]0.997625[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]98.95[/C][C]NA[/C][C]NA[/C][C]0.997818[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]98.96[/C][C]NA[/C][C]NA[/C][C]0.99764[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]98.96[/C][C]99.1164[/C][C]99.3404[/C][C]0.997745[/C][C]0.998422[/C][/ROW]
[ROW][C]8[/C][C]99.04[/C][C]99.2344[/C][C]99.4458[/C][C]0.997874[/C][C]0.998041[/C][/ROW]
[ROW][C]9[/C][C]99.33[/C][C]99.5938[/C][C]99.5638[/C][C]1.0003[/C][C]0.997351[/C][/ROW]
[ROW][C]10[/C][C]100.04[/C][C]100.222[/C][C]99.6938[/C][C]1.0053[/C][C]0.998185[/C][/ROW]
[ROW][C]11[/C][C]100.14[/C][C]100.46[/C][C]99.825[/C][C]1.00636[/C][C]0.996818[/C][/ROW]
[ROW][C]12[/C][C]100.21[/C][C]100.579[/C][C]99.9529[/C][C]1.00627[/C][C]0.996329[/C][/ROW]
[ROW][C]13[/C][C]100.21[/C][C]99.8566[/C][C]100.079[/C][C]0.99778[/C][C]1.00354[/C][/ROW]
[ROW][C]14[/C][C]100.27[/C][C]99.9526[/C][C]100.202[/C][C]0.99751[/C][C]1.00318[/C][/ROW]
[ROW][C]15[/C][C]100.44[/C][C]100.11[/C][C]100.332[/C][C]0.997785[/C][C]1.0033[/C][/ROW]
[ROW][C]16[/C][C]100.57[/C][C]100.229[/C][C]100.467[/C][C]0.997625[/C][C]1.00341[/C][/ROW]
[ROW][C]17[/C][C]100.51[/C][C]100.376[/C][C]100.595[/C][C]0.997818[/C][C]1.00134[/C][/ROW]
[ROW][C]18[/C][C]100.47[/C][C]100.484[/C][C]100.722[/C][C]0.99764[/C][C]0.999857[/C][/ROW]
[ROW][C]19[/C][C]100.47[/C][C]100.622[/C][C]100.85[/C][C]0.997745[/C][C]0.998488[/C][/ROW]
[ROW][C]20[/C][C]100.49[/C][C]100.759[/C][C]100.974[/C][C]0.997874[/C][C]0.997325[/C][/ROW]
[ROW][C]21[/C][C]101[/C][C]101.121[/C][C]101.09[/C][C]1.0003[/C][C]0.998804[/C][/ROW]
[ROW][C]22[/C][C]101.61[/C][C]101.734[/C][C]101.198[/C][C]1.0053[/C][C]0.998781[/C][/ROW]
[ROW][C]23[/C][C]101.65[/C][C]101.955[/C][C]101.311[/C][C]1.00636[/C][C]0.997009[/C][/ROW]
[ROW][C]24[/C][C]101.74[/C][C]102.075[/C][C]101.439[/C][C]1.00627[/C][C]0.99672[/C][/ROW]
[ROW][C]25[/C][C]101.74[/C][C]101.349[/C][C]101.574[/C][C]0.99778[/C][C]1.00386[/C][/ROW]
[ROW][C]26[/C][C]101.73[/C][C]101.455[/C][C]101.708[/C][C]0.99751[/C][C]1.00271[/C][/ROW]
[ROW][C]27[/C][C]101.77[/C][C]101.608[/C][C]101.833[/C][C]0.997785[/C][C]1.0016[/C][/ROW]
[ROW][C]28[/C][C]101.82[/C][C]101.699[/C][C]101.941[/C][C]0.997625[/C][C]1.00119[/C][/ROW]
[ROW][C]29[/C][C]101.97[/C][C]101.825[/C][C]102.048[/C][C]0.997818[/C][C]1.00142[/C][/ROW]
[ROW][C]30[/C][C]102.09[/C][C]101.921[/C][C]102.162[/C][C]0.99764[/C][C]1.00166[/C][/ROW]
[ROW][C]31[/C][C]102.09[/C][C]101.88[/C][C]102.11[/C][C]0.997745[/C][C]1.00206[/C][/ROW]
[ROW][C]32[/C][C]102.08[/C][C]101.68[/C][C]101.897[/C][C]0.997874[/C][C]1.00393[/C][/ROW]
[ROW][C]33[/C][C]102.42[/C][C]101.716[/C][C]101.685[/C][C]1.0003[/C][C]1.00692[/C][/ROW]
[ROW][C]34[/C][C]102.78[/C][C]102.012[/C][C]101.475[/C][C]1.0053[/C][C]1.00753[/C][/ROW]
[ROW][C]35[/C][C]103.04[/C][C]101.909[/C][C]101.265[/C][C]1.00636[/C][C]1.0111[/C][/ROW]
[ROW][C]36[/C][C]103.08[/C][C]101.686[/C][C]101.052[/C][C]1.00627[/C][C]1.01371[/C][/ROW]
[ROW][C]37[/C][C]99.16[/C][C]100.631[/C][C]100.855[/C][C]0.99778[/C][C]0.985381[/C][/ROW]
[ROW][C]38[/C][C]99.19[/C][C]100.434[/C][C]100.685[/C][C]0.99751[/C][C]0.987611[/C][/ROW]
[ROW][C]39[/C][C]99.23[/C][C]100.299[/C][C]100.521[/C][C]0.997785[/C][C]0.989346[/C][/ROW]
[ROW][C]40[/C][C]99.31[/C][C]100.13[/C][C]100.368[/C][C]0.997625[/C][C]0.991811[/C][/ROW]
[ROW][C]41[/C][C]99.46[/C][C]100.005[/C][C]100.224[/C][C]0.997818[/C][C]0.994546[/C][/ROW]
[ROW][C]42[/C][C]99.49[/C][C]99.8389[/C][C]100.075[/C][C]0.99764[/C][C]0.996506[/C][/ROW]
[ROW][C]43[/C][C]99.95[/C][C]99.8526[/C][C]100.078[/C][C]0.997745[/C][C]1.00098[/C][/ROW]
[ROW][C]44[/C][C]100.14[/C][C]100.024[/C][C]100.237[/C][C]0.997874[/C][C]1.00116[/C][/ROW]
[ROW][C]45[/C][C]100.43[/C][C]100.424[/C][C]100.394[/C][C]1.0003[/C][C]1.00006[/C][/ROW]
[ROW][C]46[/C][C]101.1[/C][C]101.073[/C][C]100.54[/C][C]1.0053[/C][C]1.00027[/C][/ROW]
[ROW][C]47[/C][C]101.26[/C][C]101.314[/C][C]100.674[/C][C]1.00636[/C][C]0.999469[/C][/ROW]
[ROW][C]48[/C][C]101.28[/C][C]101.432[/C][C]100.801[/C][C]1.00627[/C][C]0.998497[/C][/ROW]
[ROW][C]49[/C][C]101.04[/C][C]100.691[/C][C]100.915[/C][C]0.99778[/C][C]1.00347[/C][/ROW]
[ROW][C]50[/C][C]101.12[/C][C]100.761[/C][C]101.013[/C][C]0.99751[/C][C]1.00356[/C][/ROW]
[ROW][C]51[/C][C]101.07[/C][C]100.873[/C][C]101.097[/C][C]0.997785[/C][C]1.00196[/C][/ROW]
[ROW][C]52[/C][C]100.97[/C][C]100.923[/C][C]101.163[/C][C]0.997625[/C][C]1.00047[/C][/ROW]
[ROW][C]53[/C][C]101.01[/C][C]101.004[/C][C]101.225[/C][C]0.997818[/C][C]1.00006[/C][/ROW]
[ROW][C]54[/C][C]100.99[/C][C]101.053[/C][C]101.292[/C][C]0.99764[/C][C]0.999376[/C][/ROW]
[ROW][C]55[/C][C]101.19[/C][C]101.148[/C][C]101.377[/C][C]0.997745[/C][C]1.00041[/C][/ROW]
[ROW][C]56[/C][C]101.25[/C][C]101.26[/C][C]101.476[/C][C]0.997874[/C][C]0.9999[/C][/ROW]
[ROW][C]57[/C][C]101.33[/C][C]101.612[/C][C]101.581[/C][C]1.0003[/C][C]0.997226[/C][/ROW]
[ROW][C]58[/C][C]101.79[/C][C]102.24[/C][C]101.701[/C][C]1.0053[/C][C]0.995598[/C][/ROW]
[ROW][C]59[/C][C]102.06[/C][C]102.473[/C][C]101.826[/C][C]1.00636[/C][C]0.995968[/C][/ROW]
[ROW][C]60[/C][C]102.09[/C][C]102.593[/C][C]101.954[/C][C]1.00627[/C][C]0.995101[/C][/ROW]
[ROW][C]61[/C][C]102.27[/C][C]101.851[/C][C]102.078[/C][C]0.99778[/C][C]1.00411[/C][/ROW]
[ROW][C]62[/C][C]102.26[/C][C]101.923[/C][C]102.178[/C][C]0.99751[/C][C]1.0033[/C][/ROW]
[ROW][C]63[/C][C]102.46[/C][C]102.035[/C][C]102.262[/C][C]0.997785[/C][C]1.00416[/C][/ROW]
[ROW][C]64[/C][C]102.46[/C][C]102.104[/C][C]102.347[/C][C]0.997625[/C][C]1.00349[/C][/ROW]
[ROW][C]65[/C][C]102.51[/C][C]102.204[/C][C]102.427[/C][C]0.997818[/C][C]1.003[/C][/ROW]
[ROW][C]66[/C][C]102.56[/C][C]102.257[/C][C]102.499[/C][C]0.99764[/C][C]1.00296[/C][/ROW]
[ROW][C]67[/C][C]102.59[/C][C]NA[/C][C]NA[/C][C]0.997745[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.26[/C][C]NA[/C][C]NA[/C][C]0.997874[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]102.33[/C][C]NA[/C][C]NA[/C][C]1.0003[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]102.84[/C][C]NA[/C][C]NA[/C][C]1.0053[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.93[/C][C]NA[/C][C]NA[/C][C]1.00636[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]102.95[/C][C]NA[/C][C]NA[/C][C]1.00627[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	98.98	NA	NA	0.99778	NA
2	98.97	NA	NA	0.99751	NA
3	98.91	NA	NA	0.997785	NA
4	98.98	NA	NA	0.997625	NA
5	98.95	NA	NA	0.997818	NA
6	98.96	NA	NA	0.99764	NA
7	98.96	99.1164	99.3404	0.997745	0.998422
8	99.04	99.2344	99.4458	0.997874	0.998041
9	99.33	99.5938	99.5638	1.0003	0.997351
10	100.04	100.222	99.6938	1.0053	0.998185
11	100.14	100.46	99.825	1.00636	0.996818
12	100.21	100.579	99.9529	1.00627	0.996329
13	100.21	99.8566	100.079	0.99778	1.00354
14	100.27	99.9526	100.202	0.99751	1.00318
15	100.44	100.11	100.332	0.997785	1.0033
16	100.57	100.229	100.467	0.997625	1.00341
17	100.51	100.376	100.595	0.997818	1.00134
18	100.47	100.484	100.722	0.99764	0.999857
19	100.47	100.622	100.85	0.997745	0.998488
20	100.49	100.759	100.974	0.997874	0.997325
21	101	101.121	101.09	1.0003	0.998804
22	101.61	101.734	101.198	1.0053	0.998781
23	101.65	101.955	101.311	1.00636	0.997009
24	101.74	102.075	101.439	1.00627	0.99672
25	101.74	101.349	101.574	0.99778	1.00386
26	101.73	101.455	101.708	0.99751	1.00271
27	101.77	101.608	101.833	0.997785	1.0016
28	101.82	101.699	101.941	0.997625	1.00119
29	101.97	101.825	102.048	0.997818	1.00142
30	102.09	101.921	102.162	0.99764	1.00166
31	102.09	101.88	102.11	0.997745	1.00206
32	102.08	101.68	101.897	0.997874	1.00393
33	102.42	101.716	101.685	1.0003	1.00692
34	102.78	102.012	101.475	1.0053	1.00753
35	103.04	101.909	101.265	1.00636	1.0111
36	103.08	101.686	101.052	1.00627	1.01371
37	99.16	100.631	100.855	0.99778	0.985381
38	99.19	100.434	100.685	0.99751	0.987611
39	99.23	100.299	100.521	0.997785	0.989346
40	99.31	100.13	100.368	0.997625	0.991811
41	99.46	100.005	100.224	0.997818	0.994546
42	99.49	99.8389	100.075	0.99764	0.996506
43	99.95	99.8526	100.078	0.997745	1.00098
44	100.14	100.024	100.237	0.997874	1.00116
45	100.43	100.424	100.394	1.0003	1.00006
46	101.1	101.073	100.54	1.0053	1.00027
47	101.26	101.314	100.674	1.00636	0.999469
48	101.28	101.432	100.801	1.00627	0.998497
49	101.04	100.691	100.915	0.99778	1.00347
50	101.12	100.761	101.013	0.99751	1.00356
51	101.07	100.873	101.097	0.997785	1.00196
52	100.97	100.923	101.163	0.997625	1.00047
53	101.01	101.004	101.225	0.997818	1.00006
54	100.99	101.053	101.292	0.99764	0.999376
55	101.19	101.148	101.377	0.997745	1.00041
56	101.25	101.26	101.476	0.997874	0.9999
57	101.33	101.612	101.581	1.0003	0.997226
58	101.79	102.24	101.701	1.0053	0.995598
59	102.06	102.473	101.826	1.00636	0.995968
60	102.09	102.593	101.954	1.00627	0.995101
61	102.27	101.851	102.078	0.99778	1.00411
62	102.26	101.923	102.178	0.99751	1.0033
63	102.46	102.035	102.262	0.997785	1.00416
64	102.46	102.104	102.347	0.997625	1.00349
65	102.51	102.204	102.427	0.997818	1.003
66	102.56	102.257	102.499	0.99764	1.00296
67	102.59	NA	NA	0.997745	NA
68	102.26	NA	NA	0.997874	NA
69	102.33	NA	NA	1.0003	NA
70	102.84	NA	NA	1.0053	NA
71	102.93	NA	NA	1.00636	NA
72	102.95	NA	NA	1.00627	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 <- '12'
par1 <- 'multiplicative'
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