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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 30 Apr 2017 19:41:43 +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/2017/Apr/30/t14935778063m3qg4qzeq2gezl.htm/, Retrieved Mon, 13 May 2024 11:00:18 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Mon, 13 May 2024 11:00:18 +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	'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.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]'Herman Ole Andreas Wold' @ wold.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	'Herman Ole Andreas Wold' @ wold.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	101.16	NA	NA	0.999169	NA
2	100.81	NA	NA	0.996558	NA
3	100.94	NA	NA	0.997234	NA
4	101.13	NA	NA	0.99832	NA
5	101.29	NA	NA	0.999527	NA
6	101.34	NA	NA	0.999885	NA
7	101.35	101.424	101.701	0.997276	0.999269
8	101.7	101.731	101.824	0.999089	0.999695
9	102.05	101.995	101.934	1.00059	1.00054
10	102.48	102.361	102.039	1.00316	1.00116
11	102.66	102.607	102.142	1.00455	1.00052
12	102.72	102.722	102.247	1.00465	0.999979
13	102.73	102.268	102.353	0.999169	1.00452
14	102.18	102.107	102.46	0.996558	1.00071
15	102.22	102.277	102.561	0.997234	0.999441
16	102.37	102.478	102.651	0.99832	0.998942
17	102.53	102.688	102.737	0.999527	0.998457
18	102.61	102.81	102.822	0.999885	0.998052
19	102.62	102.453	102.732	0.997276	1.00163
20	103	102.379	102.472	0.999089	1.00607
21	103.17	102.276	102.215	1.00059	1.00875
22	103.52	102.283	101.961	1.00316	1.01209
23	103.69	102.176	101.714	1.00455	1.01482
24	103.73	101.94	101.468	1.00465	1.01756
25	99.57	101.143	101.227	0.999169	0.984444
26	99.09	100.644	100.991	0.996558	0.984563
27	99.14	100.48	100.759	0.997234	0.986659
28	99.36	100.367	100.536	0.99832	0.989963
29	99.6	100.273	100.32	0.999527	0.993293
30	99.65	100.095	100.107	0.999885	0.995553
31	99.8	99.7957	100.068	0.997276	1.00004
32	100.15	100.129	100.22	0.999089	1.00021
33	100.45	100.448	100.389	1.00059	1.00002
34	100.89	100.864	100.546	1.00316	1.00026
35	101.13	101.144	100.686	1.00455	0.999865
36	101.17	101.283	100.815	1.00465	0.99888
37	101.21	100.84	100.924	0.999169	1.00367
38	101.1	100.663	101.01	0.996558	1.00434
39	101.17	100.803	101.082	0.997234	1.00365
40	101.11	100.959	101.129	0.99832	1.00149
41	101.2	101.107	101.155	0.999527	1.00092
42	101.15	101.165	101.177	0.999885	0.999852
43	100.92	100.943	101.218	0.997276	0.999776
44	101.1	101.191	101.283	0.999089	0.9991
45	101.22	101.414	101.354	1.00059	0.998085
46	101.25	101.751	101.431	1.00316	0.995076
47	101.39	101.97	101.508	1.00455	0.994313
48	101.43	102.06	101.588	1.00465	0.993828
49	101.95	101.575	101.66	0.999169	1.00369
50	101.92	101.353	101.703	0.996558	1.0056
51	102.05	101.443	101.724	0.997234	1.00599
52	102.07	101.575	101.746	0.99832	1.00487
53	102.1	101.726	101.775	0.999527	1.00367
54	102.16	101.789	101.801	0.999885	1.00364
55	101.63	101.566	101.843	0.997276	1.00063
56	101.43	101.808	101.901	0.999089	0.996283
57	101.4	102.016	101.956	1.00059	0.99396
58	101.6	102.34	102.018	1.00316	0.992765
59	101.72	102.557	102.092	1.00455	0.991842
60	101.73	102.643	102.168	1.00465	0.991105
61	102.67	102.156	102.241	0.999169	1.00503
62	102.59	101.964	102.316	0.996558	1.00614
63	102.69	102.116	102.399	0.997234	1.00562
64	102.93	102.307	102.48	0.99832	1.00609
65	103.02	102.506	102.555	0.999527	1.00501
66	103.06	102.624	102.635	0.999885	1.00425
67	102.47	NA	NA	0.997276	NA
68	102.4	NA	NA	0.999089	NA
69	102.42	NA	NA	1.00059	NA
70	102.51	NA	NA	1.00316	NA
71	102.61	NA	NA	1.00455	NA
72	102.78	NA	NA	1.00465	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 101.16 & NA & NA & 0.999169 & NA \tabularnewline
2 & 100.81 & NA & NA & 0.996558 & NA \tabularnewline
3 & 100.94 & NA & NA & 0.997234 & NA \tabularnewline
4 & 101.13 & NA & NA & 0.99832 & NA \tabularnewline
5 & 101.29 & NA & NA & 0.999527 & NA \tabularnewline
6 & 101.34 & NA & NA & 0.999885 & NA \tabularnewline
7 & 101.35 & 101.424 & 101.701 & 0.997276 & 0.999269 \tabularnewline
8 & 101.7 & 101.731 & 101.824 & 0.999089 & 0.999695 \tabularnewline
9 & 102.05 & 101.995 & 101.934 & 1.00059 & 1.00054 \tabularnewline
10 & 102.48 & 102.361 & 102.039 & 1.00316 & 1.00116 \tabularnewline
11 & 102.66 & 102.607 & 102.142 & 1.00455 & 1.00052 \tabularnewline
12 & 102.72 & 102.722 & 102.247 & 1.00465 & 0.999979 \tabularnewline
13 & 102.73 & 102.268 & 102.353 & 0.999169 & 1.00452 \tabularnewline
14 & 102.18 & 102.107 & 102.46 & 0.996558 & 1.00071 \tabularnewline
15 & 102.22 & 102.277 & 102.561 & 0.997234 & 0.999441 \tabularnewline
16 & 102.37 & 102.478 & 102.651 & 0.99832 & 0.998942 \tabularnewline
17 & 102.53 & 102.688 & 102.737 & 0.999527 & 0.998457 \tabularnewline
18 & 102.61 & 102.81 & 102.822 & 0.999885 & 0.998052 \tabularnewline
19 & 102.62 & 102.453 & 102.732 & 0.997276 & 1.00163 \tabularnewline
20 & 103 & 102.379 & 102.472 & 0.999089 & 1.00607 \tabularnewline
21 & 103.17 & 102.276 & 102.215 & 1.00059 & 1.00875 \tabularnewline
22 & 103.52 & 102.283 & 101.961 & 1.00316 & 1.01209 \tabularnewline
23 & 103.69 & 102.176 & 101.714 & 1.00455 & 1.01482 \tabularnewline
24 & 103.73 & 101.94 & 101.468 & 1.00465 & 1.01756 \tabularnewline
25 & 99.57 & 101.143 & 101.227 & 0.999169 & 0.984444 \tabularnewline
26 & 99.09 & 100.644 & 100.991 & 0.996558 & 0.984563 \tabularnewline
27 & 99.14 & 100.48 & 100.759 & 0.997234 & 0.986659 \tabularnewline
28 & 99.36 & 100.367 & 100.536 & 0.99832 & 0.989963 \tabularnewline
29 & 99.6 & 100.273 & 100.32 & 0.999527 & 0.993293 \tabularnewline
30 & 99.65 & 100.095 & 100.107 & 0.999885 & 0.995553 \tabularnewline
31 & 99.8 & 99.7957 & 100.068 & 0.997276 & 1.00004 \tabularnewline
32 & 100.15 & 100.129 & 100.22 & 0.999089 & 1.00021 \tabularnewline
33 & 100.45 & 100.448 & 100.389 & 1.00059 & 1.00002 \tabularnewline
34 & 100.89 & 100.864 & 100.546 & 1.00316 & 1.00026 \tabularnewline
35 & 101.13 & 101.144 & 100.686 & 1.00455 & 0.999865 \tabularnewline
36 & 101.17 & 101.283 & 100.815 & 1.00465 & 0.99888 \tabularnewline
37 & 101.21 & 100.84 & 100.924 & 0.999169 & 1.00367 \tabularnewline
38 & 101.1 & 100.663 & 101.01 & 0.996558 & 1.00434 \tabularnewline
39 & 101.17 & 100.803 & 101.082 & 0.997234 & 1.00365 \tabularnewline
40 & 101.11 & 100.959 & 101.129 & 0.99832 & 1.00149 \tabularnewline
41 & 101.2 & 101.107 & 101.155 & 0.999527 & 1.00092 \tabularnewline
42 & 101.15 & 101.165 & 101.177 & 0.999885 & 0.999852 \tabularnewline
43 & 100.92 & 100.943 & 101.218 & 0.997276 & 0.999776 \tabularnewline
44 & 101.1 & 101.191 & 101.283 & 0.999089 & 0.9991 \tabularnewline
45 & 101.22 & 101.414 & 101.354 & 1.00059 & 0.998085 \tabularnewline
46 & 101.25 & 101.751 & 101.431 & 1.00316 & 0.995076 \tabularnewline
47 & 101.39 & 101.97 & 101.508 & 1.00455 & 0.994313 \tabularnewline
48 & 101.43 & 102.06 & 101.588 & 1.00465 & 0.993828 \tabularnewline
49 & 101.95 & 101.575 & 101.66 & 0.999169 & 1.00369 \tabularnewline
50 & 101.92 & 101.353 & 101.703 & 0.996558 & 1.0056 \tabularnewline
51 & 102.05 & 101.443 & 101.724 & 0.997234 & 1.00599 \tabularnewline
52 & 102.07 & 101.575 & 101.746 & 0.99832 & 1.00487 \tabularnewline
53 & 102.1 & 101.726 & 101.775 & 0.999527 & 1.00367 \tabularnewline
54 & 102.16 & 101.789 & 101.801 & 0.999885 & 1.00364 \tabularnewline
55 & 101.63 & 101.566 & 101.843 & 0.997276 & 1.00063 \tabularnewline
56 & 101.43 & 101.808 & 101.901 & 0.999089 & 0.996283 \tabularnewline
57 & 101.4 & 102.016 & 101.956 & 1.00059 & 0.99396 \tabularnewline
58 & 101.6 & 102.34 & 102.018 & 1.00316 & 0.992765 \tabularnewline
59 & 101.72 & 102.557 & 102.092 & 1.00455 & 0.991842 \tabularnewline
60 & 101.73 & 102.643 & 102.168 & 1.00465 & 0.991105 \tabularnewline
61 & 102.67 & 102.156 & 102.241 & 0.999169 & 1.00503 \tabularnewline
62 & 102.59 & 101.964 & 102.316 & 0.996558 & 1.00614 \tabularnewline
63 & 102.69 & 102.116 & 102.399 & 0.997234 & 1.00562 \tabularnewline
64 & 102.93 & 102.307 & 102.48 & 0.99832 & 1.00609 \tabularnewline
65 & 103.02 & 102.506 & 102.555 & 0.999527 & 1.00501 \tabularnewline
66 & 103.06 & 102.624 & 102.635 & 0.999885 & 1.00425 \tabularnewline
67 & 102.47 & NA & NA & 0.997276 & NA \tabularnewline
68 & 102.4 & NA & NA & 0.999089 & NA \tabularnewline
69 & 102.42 & NA & NA & 1.00059 & NA \tabularnewline
70 & 102.51 & NA & NA & 1.00316 & NA \tabularnewline
71 & 102.61 & NA & NA & 1.00455 & NA \tabularnewline
72 & 102.78 & NA & NA & 1.00465 & 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]101.16[/C][C]NA[/C][C]NA[/C][C]0.999169[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]100.81[/C][C]NA[/C][C]NA[/C][C]0.996558[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.94[/C][C]NA[/C][C]NA[/C][C]0.997234[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]101.13[/C][C]NA[/C][C]NA[/C][C]0.99832[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]101.29[/C][C]NA[/C][C]NA[/C][C]0.999527[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]101.34[/C][C]NA[/C][C]NA[/C][C]0.999885[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]101.35[/C][C]101.424[/C][C]101.701[/C][C]0.997276[/C][C]0.999269[/C][/ROW]
[ROW][C]8[/C][C]101.7[/C][C]101.731[/C][C]101.824[/C][C]0.999089[/C][C]0.999695[/C][/ROW]
[ROW][C]9[/C][C]102.05[/C][C]101.995[/C][C]101.934[/C][C]1.00059[/C][C]1.00054[/C][/ROW]
[ROW][C]10[/C][C]102.48[/C][C]102.361[/C][C]102.039[/C][C]1.00316[/C][C]1.00116[/C][/ROW]
[ROW][C]11[/C][C]102.66[/C][C]102.607[/C][C]102.142[/C][C]1.00455[/C][C]1.00052[/C][/ROW]
[ROW][C]12[/C][C]102.72[/C][C]102.722[/C][C]102.247[/C][C]1.00465[/C][C]0.999979[/C][/ROW]
[ROW][C]13[/C][C]102.73[/C][C]102.268[/C][C]102.353[/C][C]0.999169[/C][C]1.00452[/C][/ROW]
[ROW][C]14[/C][C]102.18[/C][C]102.107[/C][C]102.46[/C][C]0.996558[/C][C]1.00071[/C][/ROW]
[ROW][C]15[/C][C]102.22[/C][C]102.277[/C][C]102.561[/C][C]0.997234[/C][C]0.999441[/C][/ROW]
[ROW][C]16[/C][C]102.37[/C][C]102.478[/C][C]102.651[/C][C]0.99832[/C][C]0.998942[/C][/ROW]
[ROW][C]17[/C][C]102.53[/C][C]102.688[/C][C]102.737[/C][C]0.999527[/C][C]0.998457[/C][/ROW]
[ROW][C]18[/C][C]102.61[/C][C]102.81[/C][C]102.822[/C][C]0.999885[/C][C]0.998052[/C][/ROW]
[ROW][C]19[/C][C]102.62[/C][C]102.453[/C][C]102.732[/C][C]0.997276[/C][C]1.00163[/C][/ROW]
[ROW][C]20[/C][C]103[/C][C]102.379[/C][C]102.472[/C][C]0.999089[/C][C]1.00607[/C][/ROW]
[ROW][C]21[/C][C]103.17[/C][C]102.276[/C][C]102.215[/C][C]1.00059[/C][C]1.00875[/C][/ROW]
[ROW][C]22[/C][C]103.52[/C][C]102.283[/C][C]101.961[/C][C]1.00316[/C][C]1.01209[/C][/ROW]
[ROW][C]23[/C][C]103.69[/C][C]102.176[/C][C]101.714[/C][C]1.00455[/C][C]1.01482[/C][/ROW]
[ROW][C]24[/C][C]103.73[/C][C]101.94[/C][C]101.468[/C][C]1.00465[/C][C]1.01756[/C][/ROW]
[ROW][C]25[/C][C]99.57[/C][C]101.143[/C][C]101.227[/C][C]0.999169[/C][C]0.984444[/C][/ROW]
[ROW][C]26[/C][C]99.09[/C][C]100.644[/C][C]100.991[/C][C]0.996558[/C][C]0.984563[/C][/ROW]
[ROW][C]27[/C][C]99.14[/C][C]100.48[/C][C]100.759[/C][C]0.997234[/C][C]0.986659[/C][/ROW]
[ROW][C]28[/C][C]99.36[/C][C]100.367[/C][C]100.536[/C][C]0.99832[/C][C]0.989963[/C][/ROW]
[ROW][C]29[/C][C]99.6[/C][C]100.273[/C][C]100.32[/C][C]0.999527[/C][C]0.993293[/C][/ROW]
[ROW][C]30[/C][C]99.65[/C][C]100.095[/C][C]100.107[/C][C]0.999885[/C][C]0.995553[/C][/ROW]
[ROW][C]31[/C][C]99.8[/C][C]99.7957[/C][C]100.068[/C][C]0.997276[/C][C]1.00004[/C][/ROW]
[ROW][C]32[/C][C]100.15[/C][C]100.129[/C][C]100.22[/C][C]0.999089[/C][C]1.00021[/C][/ROW]
[ROW][C]33[/C][C]100.45[/C][C]100.448[/C][C]100.389[/C][C]1.00059[/C][C]1.00002[/C][/ROW]
[ROW][C]34[/C][C]100.89[/C][C]100.864[/C][C]100.546[/C][C]1.00316[/C][C]1.00026[/C][/ROW]
[ROW][C]35[/C][C]101.13[/C][C]101.144[/C][C]100.686[/C][C]1.00455[/C][C]0.999865[/C][/ROW]
[ROW][C]36[/C][C]101.17[/C][C]101.283[/C][C]100.815[/C][C]1.00465[/C][C]0.99888[/C][/ROW]
[ROW][C]37[/C][C]101.21[/C][C]100.84[/C][C]100.924[/C][C]0.999169[/C][C]1.00367[/C][/ROW]
[ROW][C]38[/C][C]101.1[/C][C]100.663[/C][C]101.01[/C][C]0.996558[/C][C]1.00434[/C][/ROW]
[ROW][C]39[/C][C]101.17[/C][C]100.803[/C][C]101.082[/C][C]0.997234[/C][C]1.00365[/C][/ROW]
[ROW][C]40[/C][C]101.11[/C][C]100.959[/C][C]101.129[/C][C]0.99832[/C][C]1.00149[/C][/ROW]
[ROW][C]41[/C][C]101.2[/C][C]101.107[/C][C]101.155[/C][C]0.999527[/C][C]1.00092[/C][/ROW]
[ROW][C]42[/C][C]101.15[/C][C]101.165[/C][C]101.177[/C][C]0.999885[/C][C]0.999852[/C][/ROW]
[ROW][C]43[/C][C]100.92[/C][C]100.943[/C][C]101.218[/C][C]0.997276[/C][C]0.999776[/C][/ROW]
[ROW][C]44[/C][C]101.1[/C][C]101.191[/C][C]101.283[/C][C]0.999089[/C][C]0.9991[/C][/ROW]
[ROW][C]45[/C][C]101.22[/C][C]101.414[/C][C]101.354[/C][C]1.00059[/C][C]0.998085[/C][/ROW]
[ROW][C]46[/C][C]101.25[/C][C]101.751[/C][C]101.431[/C][C]1.00316[/C][C]0.995076[/C][/ROW]
[ROW][C]47[/C][C]101.39[/C][C]101.97[/C][C]101.508[/C][C]1.00455[/C][C]0.994313[/C][/ROW]
[ROW][C]48[/C][C]101.43[/C][C]102.06[/C][C]101.588[/C][C]1.00465[/C][C]0.993828[/C][/ROW]
[ROW][C]49[/C][C]101.95[/C][C]101.575[/C][C]101.66[/C][C]0.999169[/C][C]1.00369[/C][/ROW]
[ROW][C]50[/C][C]101.92[/C][C]101.353[/C][C]101.703[/C][C]0.996558[/C][C]1.0056[/C][/ROW]
[ROW][C]51[/C][C]102.05[/C][C]101.443[/C][C]101.724[/C][C]0.997234[/C][C]1.00599[/C][/ROW]
[ROW][C]52[/C][C]102.07[/C][C]101.575[/C][C]101.746[/C][C]0.99832[/C][C]1.00487[/C][/ROW]
[ROW][C]53[/C][C]102.1[/C][C]101.726[/C][C]101.775[/C][C]0.999527[/C][C]1.00367[/C][/ROW]
[ROW][C]54[/C][C]102.16[/C][C]101.789[/C][C]101.801[/C][C]0.999885[/C][C]1.00364[/C][/ROW]
[ROW][C]55[/C][C]101.63[/C][C]101.566[/C][C]101.843[/C][C]0.997276[/C][C]1.00063[/C][/ROW]
[ROW][C]56[/C][C]101.43[/C][C]101.808[/C][C]101.901[/C][C]0.999089[/C][C]0.996283[/C][/ROW]
[ROW][C]57[/C][C]101.4[/C][C]102.016[/C][C]101.956[/C][C]1.00059[/C][C]0.99396[/C][/ROW]
[ROW][C]58[/C][C]101.6[/C][C]102.34[/C][C]102.018[/C][C]1.00316[/C][C]0.992765[/C][/ROW]
[ROW][C]59[/C][C]101.72[/C][C]102.557[/C][C]102.092[/C][C]1.00455[/C][C]0.991842[/C][/ROW]
[ROW][C]60[/C][C]101.73[/C][C]102.643[/C][C]102.168[/C][C]1.00465[/C][C]0.991105[/C][/ROW]
[ROW][C]61[/C][C]102.67[/C][C]102.156[/C][C]102.241[/C][C]0.999169[/C][C]1.00503[/C][/ROW]
[ROW][C]62[/C][C]102.59[/C][C]101.964[/C][C]102.316[/C][C]0.996558[/C][C]1.00614[/C][/ROW]
[ROW][C]63[/C][C]102.69[/C][C]102.116[/C][C]102.399[/C][C]0.997234[/C][C]1.00562[/C][/ROW]
[ROW][C]64[/C][C]102.93[/C][C]102.307[/C][C]102.48[/C][C]0.99832[/C][C]1.00609[/C][/ROW]
[ROW][C]65[/C][C]103.02[/C][C]102.506[/C][C]102.555[/C][C]0.999527[/C][C]1.00501[/C][/ROW]
[ROW][C]66[/C][C]103.06[/C][C]102.624[/C][C]102.635[/C][C]0.999885[/C][C]1.00425[/C][/ROW]
[ROW][C]67[/C][C]102.47[/C][C]NA[/C][C]NA[/C][C]0.997276[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.4[/C][C]NA[/C][C]NA[/C][C]0.999089[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]102.42[/C][C]NA[/C][C]NA[/C][C]1.00059[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]102.51[/C][C]NA[/C][C]NA[/C][C]1.00316[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.61[/C][C]NA[/C][C]NA[/C][C]1.00455[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]102.78[/C][C]NA[/C][C]NA[/C][C]1.00465[/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	101.16	NA	NA	0.999169	NA
2	100.81	NA	NA	0.996558	NA
3	100.94	NA	NA	0.997234	NA
4	101.13	NA	NA	0.99832	NA
5	101.29	NA	NA	0.999527	NA
6	101.34	NA	NA	0.999885	NA
7	101.35	101.424	101.701	0.997276	0.999269
8	101.7	101.731	101.824	0.999089	0.999695
9	102.05	101.995	101.934	1.00059	1.00054
10	102.48	102.361	102.039	1.00316	1.00116
11	102.66	102.607	102.142	1.00455	1.00052
12	102.72	102.722	102.247	1.00465	0.999979
13	102.73	102.268	102.353	0.999169	1.00452
14	102.18	102.107	102.46	0.996558	1.00071
15	102.22	102.277	102.561	0.997234	0.999441
16	102.37	102.478	102.651	0.99832	0.998942
17	102.53	102.688	102.737	0.999527	0.998457
18	102.61	102.81	102.822	0.999885	0.998052
19	102.62	102.453	102.732	0.997276	1.00163
20	103	102.379	102.472	0.999089	1.00607
21	103.17	102.276	102.215	1.00059	1.00875
22	103.52	102.283	101.961	1.00316	1.01209
23	103.69	102.176	101.714	1.00455	1.01482
24	103.73	101.94	101.468	1.00465	1.01756
25	99.57	101.143	101.227	0.999169	0.984444
26	99.09	100.644	100.991	0.996558	0.984563
27	99.14	100.48	100.759	0.997234	0.986659
28	99.36	100.367	100.536	0.99832	0.989963
29	99.6	100.273	100.32	0.999527	0.993293
30	99.65	100.095	100.107	0.999885	0.995553
31	99.8	99.7957	100.068	0.997276	1.00004
32	100.15	100.129	100.22	0.999089	1.00021
33	100.45	100.448	100.389	1.00059	1.00002
34	100.89	100.864	100.546	1.00316	1.00026
35	101.13	101.144	100.686	1.00455	0.999865
36	101.17	101.283	100.815	1.00465	0.99888
37	101.21	100.84	100.924	0.999169	1.00367
38	101.1	100.663	101.01	0.996558	1.00434
39	101.17	100.803	101.082	0.997234	1.00365
40	101.11	100.959	101.129	0.99832	1.00149
41	101.2	101.107	101.155	0.999527	1.00092
42	101.15	101.165	101.177	0.999885	0.999852
43	100.92	100.943	101.218	0.997276	0.999776
44	101.1	101.191	101.283	0.999089	0.9991
45	101.22	101.414	101.354	1.00059	0.998085
46	101.25	101.751	101.431	1.00316	0.995076
47	101.39	101.97	101.508	1.00455	0.994313
48	101.43	102.06	101.588	1.00465	0.993828
49	101.95	101.575	101.66	0.999169	1.00369
50	101.92	101.353	101.703	0.996558	1.0056
51	102.05	101.443	101.724	0.997234	1.00599
52	102.07	101.575	101.746	0.99832	1.00487
53	102.1	101.726	101.775	0.999527	1.00367
54	102.16	101.789	101.801	0.999885	1.00364
55	101.63	101.566	101.843	0.997276	1.00063
56	101.43	101.808	101.901	0.999089	0.996283
57	101.4	102.016	101.956	1.00059	0.99396
58	101.6	102.34	102.018	1.00316	0.992765
59	101.72	102.557	102.092	1.00455	0.991842
60	101.73	102.643	102.168	1.00465	0.991105
61	102.67	102.156	102.241	0.999169	1.00503
62	102.59	101.964	102.316	0.996558	1.00614
63	102.69	102.116	102.399	0.997234	1.00562
64	102.93	102.307	102.48	0.99832	1.00609
65	103.02	102.506	102.555	0.999527	1.00501
66	103.06	102.624	102.635	0.999885	1.00425
67	102.47	NA	NA	0.997276	NA
68	102.4	NA	NA	0.999089	NA
69	102.42	NA	NA	1.00059	NA
70	102.51	NA	NA	1.00316	NA
71	102.61	NA	NA	1.00455	NA
72	102.78	NA	NA	1.00465	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