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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 30 Apr 2017 20:06:21 +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/t1493579225uav9l9fgmy0tfyq.htm/, Retrieved Mon, 13 May 2024 14:48:51 +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 14:48:51 +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	3 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 & 3 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]3 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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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	101.16	NA	NA	-0.0837639	NA
2	100.81	NA	NA	-0.348931	NA
3	100.94	NA	NA	-0.279847	NA
4	101.13	NA	NA	-0.169181	NA
5	101.29	NA	NA	-0.0470139	NA
6	101.34	NA	NA	-0.0110972	NA
7	101.35	101.424	101.701	-0.277514	-0.0737361
8	101.7	101.731	101.824	-0.0929306	-0.0308194
9	102.05	101.994	101.934	0.0596528	0.0561806
10	102.48	102.359	102.039	0.320069	0.120764
11	102.66	102.603	102.142	0.460653	0.0568472
12	102.72	102.717	102.247	0.469903	0.00301389
13	102.73	102.269	102.353	-0.0837639	0.460847
14	102.18	102.111	102.46	-0.348931	0.0689306
15	102.22	102.281	102.561	-0.279847	-0.0609861
16	102.37	102.482	102.651	-0.169181	-0.111653
17	102.53	102.69	102.737	-0.0470139	-0.160069
18	102.61	102.811	102.822	-0.0110972	-0.200986
19	102.62	102.455	102.732	-0.277514	0.165014
20	103	102.379	102.472	-0.0929306	0.620847
21	103.17	102.275	102.215	0.0596528	0.895347
22	103.52	102.281	101.961	0.320069	1.23868
23	103.69	102.174	101.714	0.460653	1.5156
24	103.73	101.938	101.468	0.469903	1.79176
25	99.57	101.144	101.227	-0.0837639	-1.57374
26	99.09	100.642	100.991	-0.348931	-1.55232
27	99.14	100.479	100.759	-0.279847	-1.33932
28	99.36	100.367	100.536	-0.169181	-1.00707
29	99.6	100.273	100.32	-0.0470139	-0.672986
30	99.65	100.096	100.107	-0.0110972	-0.445569
31	99.8	99.7908	100.068	-0.277514	0.00918056
32	100.15	100.127	100.22	-0.0929306	0.0225139
33	100.45	100.448	100.389	0.0596528	0.00159722
34	100.89	100.866	100.546	0.320069	0.0236806
35	101.13	101.146	100.686	0.460653	-0.0164861
36	101.17	101.285	100.815	0.469903	-0.114903
37	101.21	100.84	100.924	-0.0837639	0.369597
38	101.1	100.661	101.01	-0.348931	0.438514
39	101.17	100.802	101.082	-0.279847	0.367764
40	101.11	100.96	101.129	-0.169181	0.150014
41	101.2	101.108	101.155	-0.0470139	0.0920139
42	101.15	101.166	101.177	-0.0110972	-0.0155694
43	100.92	100.941	101.218	-0.277514	-0.0208194
44	101.1	101.19	101.283	-0.0929306	-0.0904028
45	101.22	101.414	101.354	0.0596528	-0.193819
46	101.25	101.751	101.431	0.320069	-0.500903
47	101.39	101.969	101.508	0.460653	-0.578986
48	101.43	102.058	101.588	0.469903	-0.627819
49	101.95	101.576	101.66	-0.0837639	0.374181
50	101.92	101.354	101.703	-0.348931	0.566014
51	102.05	101.444	101.724	-0.279847	0.605681
52	102.07	101.577	101.746	-0.169181	0.492931
53	102.1	101.728	101.775	-0.0470139	0.372431
54	102.16	101.79	101.801	-0.0110972	0.370264
55	101.63	101.566	101.843	-0.277514	0.0641806
56	101.43	101.808	101.901	-0.0929306	-0.378319
57	101.4	102.015	101.956	0.0596528	-0.615486
58	101.6	102.338	102.018	0.320069	-0.738403
59	101.72	102.553	102.092	0.460653	-0.833153
60	101.73	102.638	102.168	0.469903	-0.908236
61	102.67	102.157	102.241	-0.0837639	0.512931
62	102.59	101.967	102.316	-0.348931	0.622681
63	102.69	102.119	102.399	-0.279847	0.570681
64	102.93	102.31	102.48	-0.169181	0.619597
65	103.02	102.508	102.555	-0.0470139	0.512431
66	103.06	102.624	102.635	-0.0110972	0.435681
67	102.47	NA	NA	-0.277514	NA
68	102.4	NA	NA	-0.0929306	NA
69	102.42	NA	NA	0.0596528	NA
70	102.51	NA	NA	0.320069	NA
71	102.61	NA	NA	0.460653	NA
72	102.78	NA	NA	0.469903	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 101.16 & NA & NA & -0.0837639 & NA \tabularnewline
2 & 100.81 & NA & NA & -0.348931 & NA \tabularnewline
3 & 100.94 & NA & NA & -0.279847 & NA \tabularnewline
4 & 101.13 & NA & NA & -0.169181 & NA \tabularnewline
5 & 101.29 & NA & NA & -0.0470139 & NA \tabularnewline
6 & 101.34 & NA & NA & -0.0110972 & NA \tabularnewline
7 & 101.35 & 101.424 & 101.701 & -0.277514 & -0.0737361 \tabularnewline
8 & 101.7 & 101.731 & 101.824 & -0.0929306 & -0.0308194 \tabularnewline
9 & 102.05 & 101.994 & 101.934 & 0.0596528 & 0.0561806 \tabularnewline
10 & 102.48 & 102.359 & 102.039 & 0.320069 & 0.120764 \tabularnewline
11 & 102.66 & 102.603 & 102.142 & 0.460653 & 0.0568472 \tabularnewline
12 & 102.72 & 102.717 & 102.247 & 0.469903 & 0.00301389 \tabularnewline
13 & 102.73 & 102.269 & 102.353 & -0.0837639 & 0.460847 \tabularnewline
14 & 102.18 & 102.111 & 102.46 & -0.348931 & 0.0689306 \tabularnewline
15 & 102.22 & 102.281 & 102.561 & -0.279847 & -0.0609861 \tabularnewline
16 & 102.37 & 102.482 & 102.651 & -0.169181 & -0.111653 \tabularnewline
17 & 102.53 & 102.69 & 102.737 & -0.0470139 & -0.160069 \tabularnewline
18 & 102.61 & 102.811 & 102.822 & -0.0110972 & -0.200986 \tabularnewline
19 & 102.62 & 102.455 & 102.732 & -0.277514 & 0.165014 \tabularnewline
20 & 103 & 102.379 & 102.472 & -0.0929306 & 0.620847 \tabularnewline
21 & 103.17 & 102.275 & 102.215 & 0.0596528 & 0.895347 \tabularnewline
22 & 103.52 & 102.281 & 101.961 & 0.320069 & 1.23868 \tabularnewline
23 & 103.69 & 102.174 & 101.714 & 0.460653 & 1.5156 \tabularnewline
24 & 103.73 & 101.938 & 101.468 & 0.469903 & 1.79176 \tabularnewline
25 & 99.57 & 101.144 & 101.227 & -0.0837639 & -1.57374 \tabularnewline
26 & 99.09 & 100.642 & 100.991 & -0.348931 & -1.55232 \tabularnewline
27 & 99.14 & 100.479 & 100.759 & -0.279847 & -1.33932 \tabularnewline
28 & 99.36 & 100.367 & 100.536 & -0.169181 & -1.00707 \tabularnewline
29 & 99.6 & 100.273 & 100.32 & -0.0470139 & -0.672986 \tabularnewline
30 & 99.65 & 100.096 & 100.107 & -0.0110972 & -0.445569 \tabularnewline
31 & 99.8 & 99.7908 & 100.068 & -0.277514 & 0.00918056 \tabularnewline
32 & 100.15 & 100.127 & 100.22 & -0.0929306 & 0.0225139 \tabularnewline
33 & 100.45 & 100.448 & 100.389 & 0.0596528 & 0.00159722 \tabularnewline
34 & 100.89 & 100.866 & 100.546 & 0.320069 & 0.0236806 \tabularnewline
35 & 101.13 & 101.146 & 100.686 & 0.460653 & -0.0164861 \tabularnewline
36 & 101.17 & 101.285 & 100.815 & 0.469903 & -0.114903 \tabularnewline
37 & 101.21 & 100.84 & 100.924 & -0.0837639 & 0.369597 \tabularnewline
38 & 101.1 & 100.661 & 101.01 & -0.348931 & 0.438514 \tabularnewline
39 & 101.17 & 100.802 & 101.082 & -0.279847 & 0.367764 \tabularnewline
40 & 101.11 & 100.96 & 101.129 & -0.169181 & 0.150014 \tabularnewline
41 & 101.2 & 101.108 & 101.155 & -0.0470139 & 0.0920139 \tabularnewline
42 & 101.15 & 101.166 & 101.177 & -0.0110972 & -0.0155694 \tabularnewline
43 & 100.92 & 100.941 & 101.218 & -0.277514 & -0.0208194 \tabularnewline
44 & 101.1 & 101.19 & 101.283 & -0.0929306 & -0.0904028 \tabularnewline
45 & 101.22 & 101.414 & 101.354 & 0.0596528 & -0.193819 \tabularnewline
46 & 101.25 & 101.751 & 101.431 & 0.320069 & -0.500903 \tabularnewline
47 & 101.39 & 101.969 & 101.508 & 0.460653 & -0.578986 \tabularnewline
48 & 101.43 & 102.058 & 101.588 & 0.469903 & -0.627819 \tabularnewline
49 & 101.95 & 101.576 & 101.66 & -0.0837639 & 0.374181 \tabularnewline
50 & 101.92 & 101.354 & 101.703 & -0.348931 & 0.566014 \tabularnewline
51 & 102.05 & 101.444 & 101.724 & -0.279847 & 0.605681 \tabularnewline
52 & 102.07 & 101.577 & 101.746 & -0.169181 & 0.492931 \tabularnewline
53 & 102.1 & 101.728 & 101.775 & -0.0470139 & 0.372431 \tabularnewline
54 & 102.16 & 101.79 & 101.801 & -0.0110972 & 0.370264 \tabularnewline
55 & 101.63 & 101.566 & 101.843 & -0.277514 & 0.0641806 \tabularnewline
56 & 101.43 & 101.808 & 101.901 & -0.0929306 & -0.378319 \tabularnewline
57 & 101.4 & 102.015 & 101.956 & 0.0596528 & -0.615486 \tabularnewline
58 & 101.6 & 102.338 & 102.018 & 0.320069 & -0.738403 \tabularnewline
59 & 101.72 & 102.553 & 102.092 & 0.460653 & -0.833153 \tabularnewline
60 & 101.73 & 102.638 & 102.168 & 0.469903 & -0.908236 \tabularnewline
61 & 102.67 & 102.157 & 102.241 & -0.0837639 & 0.512931 \tabularnewline
62 & 102.59 & 101.967 & 102.316 & -0.348931 & 0.622681 \tabularnewline
63 & 102.69 & 102.119 & 102.399 & -0.279847 & 0.570681 \tabularnewline
64 & 102.93 & 102.31 & 102.48 & -0.169181 & 0.619597 \tabularnewline
65 & 103.02 & 102.508 & 102.555 & -0.0470139 & 0.512431 \tabularnewline
66 & 103.06 & 102.624 & 102.635 & -0.0110972 & 0.435681 \tabularnewline
67 & 102.47 & NA & NA & -0.277514 & NA \tabularnewline
68 & 102.4 & NA & NA & -0.0929306 & NA \tabularnewline
69 & 102.42 & NA & NA & 0.0596528 & NA \tabularnewline
70 & 102.51 & NA & NA & 0.320069 & NA \tabularnewline
71 & 102.61 & NA & NA & 0.460653 & NA \tabularnewline
72 & 102.78 & NA & NA & 0.469903 & 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]-0.0837639[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]100.81[/C][C]NA[/C][C]NA[/C][C]-0.348931[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.94[/C][C]NA[/C][C]NA[/C][C]-0.279847[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]101.13[/C][C]NA[/C][C]NA[/C][C]-0.169181[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]101.29[/C][C]NA[/C][C]NA[/C][C]-0.0470139[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]101.34[/C][C]NA[/C][C]NA[/C][C]-0.0110972[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]101.35[/C][C]101.424[/C][C]101.701[/C][C]-0.277514[/C][C]-0.0737361[/C][/ROW]
[ROW][C]8[/C][C]101.7[/C][C]101.731[/C][C]101.824[/C][C]-0.0929306[/C][C]-0.0308194[/C][/ROW]
[ROW][C]9[/C][C]102.05[/C][C]101.994[/C][C]101.934[/C][C]0.0596528[/C][C]0.0561806[/C][/ROW]
[ROW][C]10[/C][C]102.48[/C][C]102.359[/C][C]102.039[/C][C]0.320069[/C][C]0.120764[/C][/ROW]
[ROW][C]11[/C][C]102.66[/C][C]102.603[/C][C]102.142[/C][C]0.460653[/C][C]0.0568472[/C][/ROW]
[ROW][C]12[/C][C]102.72[/C][C]102.717[/C][C]102.247[/C][C]0.469903[/C][C]0.00301389[/C][/ROW]
[ROW][C]13[/C][C]102.73[/C][C]102.269[/C][C]102.353[/C][C]-0.0837639[/C][C]0.460847[/C][/ROW]
[ROW][C]14[/C][C]102.18[/C][C]102.111[/C][C]102.46[/C][C]-0.348931[/C][C]0.0689306[/C][/ROW]
[ROW][C]15[/C][C]102.22[/C][C]102.281[/C][C]102.561[/C][C]-0.279847[/C][C]-0.0609861[/C][/ROW]
[ROW][C]16[/C][C]102.37[/C][C]102.482[/C][C]102.651[/C][C]-0.169181[/C][C]-0.111653[/C][/ROW]
[ROW][C]17[/C][C]102.53[/C][C]102.69[/C][C]102.737[/C][C]-0.0470139[/C][C]-0.160069[/C][/ROW]
[ROW][C]18[/C][C]102.61[/C][C]102.811[/C][C]102.822[/C][C]-0.0110972[/C][C]-0.200986[/C][/ROW]
[ROW][C]19[/C][C]102.62[/C][C]102.455[/C][C]102.732[/C][C]-0.277514[/C][C]0.165014[/C][/ROW]
[ROW][C]20[/C][C]103[/C][C]102.379[/C][C]102.472[/C][C]-0.0929306[/C][C]0.620847[/C][/ROW]
[ROW][C]21[/C][C]103.17[/C][C]102.275[/C][C]102.215[/C][C]0.0596528[/C][C]0.895347[/C][/ROW]
[ROW][C]22[/C][C]103.52[/C][C]102.281[/C][C]101.961[/C][C]0.320069[/C][C]1.23868[/C][/ROW]
[ROW][C]23[/C][C]103.69[/C][C]102.174[/C][C]101.714[/C][C]0.460653[/C][C]1.5156[/C][/ROW]
[ROW][C]24[/C][C]103.73[/C][C]101.938[/C][C]101.468[/C][C]0.469903[/C][C]1.79176[/C][/ROW]
[ROW][C]25[/C][C]99.57[/C][C]101.144[/C][C]101.227[/C][C]-0.0837639[/C][C]-1.57374[/C][/ROW]
[ROW][C]26[/C][C]99.09[/C][C]100.642[/C][C]100.991[/C][C]-0.348931[/C][C]-1.55232[/C][/ROW]
[ROW][C]27[/C][C]99.14[/C][C]100.479[/C][C]100.759[/C][C]-0.279847[/C][C]-1.33932[/C][/ROW]
[ROW][C]28[/C][C]99.36[/C][C]100.367[/C][C]100.536[/C][C]-0.169181[/C][C]-1.00707[/C][/ROW]
[ROW][C]29[/C][C]99.6[/C][C]100.273[/C][C]100.32[/C][C]-0.0470139[/C][C]-0.672986[/C][/ROW]
[ROW][C]30[/C][C]99.65[/C][C]100.096[/C][C]100.107[/C][C]-0.0110972[/C][C]-0.445569[/C][/ROW]
[ROW][C]31[/C][C]99.8[/C][C]99.7908[/C][C]100.068[/C][C]-0.277514[/C][C]0.00918056[/C][/ROW]
[ROW][C]32[/C][C]100.15[/C][C]100.127[/C][C]100.22[/C][C]-0.0929306[/C][C]0.0225139[/C][/ROW]
[ROW][C]33[/C][C]100.45[/C][C]100.448[/C][C]100.389[/C][C]0.0596528[/C][C]0.00159722[/C][/ROW]
[ROW][C]34[/C][C]100.89[/C][C]100.866[/C][C]100.546[/C][C]0.320069[/C][C]0.0236806[/C][/ROW]
[ROW][C]35[/C][C]101.13[/C][C]101.146[/C][C]100.686[/C][C]0.460653[/C][C]-0.0164861[/C][/ROW]
[ROW][C]36[/C][C]101.17[/C][C]101.285[/C][C]100.815[/C][C]0.469903[/C][C]-0.114903[/C][/ROW]
[ROW][C]37[/C][C]101.21[/C][C]100.84[/C][C]100.924[/C][C]-0.0837639[/C][C]0.369597[/C][/ROW]
[ROW][C]38[/C][C]101.1[/C][C]100.661[/C][C]101.01[/C][C]-0.348931[/C][C]0.438514[/C][/ROW]
[ROW][C]39[/C][C]101.17[/C][C]100.802[/C][C]101.082[/C][C]-0.279847[/C][C]0.367764[/C][/ROW]
[ROW][C]40[/C][C]101.11[/C][C]100.96[/C][C]101.129[/C][C]-0.169181[/C][C]0.150014[/C][/ROW]
[ROW][C]41[/C][C]101.2[/C][C]101.108[/C][C]101.155[/C][C]-0.0470139[/C][C]0.0920139[/C][/ROW]
[ROW][C]42[/C][C]101.15[/C][C]101.166[/C][C]101.177[/C][C]-0.0110972[/C][C]-0.0155694[/C][/ROW]
[ROW][C]43[/C][C]100.92[/C][C]100.941[/C][C]101.218[/C][C]-0.277514[/C][C]-0.0208194[/C][/ROW]
[ROW][C]44[/C][C]101.1[/C][C]101.19[/C][C]101.283[/C][C]-0.0929306[/C][C]-0.0904028[/C][/ROW]
[ROW][C]45[/C][C]101.22[/C][C]101.414[/C][C]101.354[/C][C]0.0596528[/C][C]-0.193819[/C][/ROW]
[ROW][C]46[/C][C]101.25[/C][C]101.751[/C][C]101.431[/C][C]0.320069[/C][C]-0.500903[/C][/ROW]
[ROW][C]47[/C][C]101.39[/C][C]101.969[/C][C]101.508[/C][C]0.460653[/C][C]-0.578986[/C][/ROW]
[ROW][C]48[/C][C]101.43[/C][C]102.058[/C][C]101.588[/C][C]0.469903[/C][C]-0.627819[/C][/ROW]
[ROW][C]49[/C][C]101.95[/C][C]101.576[/C][C]101.66[/C][C]-0.0837639[/C][C]0.374181[/C][/ROW]
[ROW][C]50[/C][C]101.92[/C][C]101.354[/C][C]101.703[/C][C]-0.348931[/C][C]0.566014[/C][/ROW]
[ROW][C]51[/C][C]102.05[/C][C]101.444[/C][C]101.724[/C][C]-0.279847[/C][C]0.605681[/C][/ROW]
[ROW][C]52[/C][C]102.07[/C][C]101.577[/C][C]101.746[/C][C]-0.169181[/C][C]0.492931[/C][/ROW]
[ROW][C]53[/C][C]102.1[/C][C]101.728[/C][C]101.775[/C][C]-0.0470139[/C][C]0.372431[/C][/ROW]
[ROW][C]54[/C][C]102.16[/C][C]101.79[/C][C]101.801[/C][C]-0.0110972[/C][C]0.370264[/C][/ROW]
[ROW][C]55[/C][C]101.63[/C][C]101.566[/C][C]101.843[/C][C]-0.277514[/C][C]0.0641806[/C][/ROW]
[ROW][C]56[/C][C]101.43[/C][C]101.808[/C][C]101.901[/C][C]-0.0929306[/C][C]-0.378319[/C][/ROW]
[ROW][C]57[/C][C]101.4[/C][C]102.015[/C][C]101.956[/C][C]0.0596528[/C][C]-0.615486[/C][/ROW]
[ROW][C]58[/C][C]101.6[/C][C]102.338[/C][C]102.018[/C][C]0.320069[/C][C]-0.738403[/C][/ROW]
[ROW][C]59[/C][C]101.72[/C][C]102.553[/C][C]102.092[/C][C]0.460653[/C][C]-0.833153[/C][/ROW]
[ROW][C]60[/C][C]101.73[/C][C]102.638[/C][C]102.168[/C][C]0.469903[/C][C]-0.908236[/C][/ROW]
[ROW][C]61[/C][C]102.67[/C][C]102.157[/C][C]102.241[/C][C]-0.0837639[/C][C]0.512931[/C][/ROW]
[ROW][C]62[/C][C]102.59[/C][C]101.967[/C][C]102.316[/C][C]-0.348931[/C][C]0.622681[/C][/ROW]
[ROW][C]63[/C][C]102.69[/C][C]102.119[/C][C]102.399[/C][C]-0.279847[/C][C]0.570681[/C][/ROW]
[ROW][C]64[/C][C]102.93[/C][C]102.31[/C][C]102.48[/C][C]-0.169181[/C][C]0.619597[/C][/ROW]
[ROW][C]65[/C][C]103.02[/C][C]102.508[/C][C]102.555[/C][C]-0.0470139[/C][C]0.512431[/C][/ROW]
[ROW][C]66[/C][C]103.06[/C][C]102.624[/C][C]102.635[/C][C]-0.0110972[/C][C]0.435681[/C][/ROW]
[ROW][C]67[/C][C]102.47[/C][C]NA[/C][C]NA[/C][C]-0.277514[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.4[/C][C]NA[/C][C]NA[/C][C]-0.0929306[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]102.42[/C][C]NA[/C][C]NA[/C][C]0.0596528[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]102.51[/C][C]NA[/C][C]NA[/C][C]0.320069[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.61[/C][C]NA[/C][C]NA[/C][C]0.460653[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]102.78[/C][C]NA[/C][C]NA[/C][C]0.469903[/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	-0.0837639	NA
2	100.81	NA	NA	-0.348931	NA
3	100.94	NA	NA	-0.279847	NA
4	101.13	NA	NA	-0.169181	NA
5	101.29	NA	NA	-0.0470139	NA
6	101.34	NA	NA	-0.0110972	NA
7	101.35	101.424	101.701	-0.277514	-0.0737361
8	101.7	101.731	101.824	-0.0929306	-0.0308194
9	102.05	101.994	101.934	0.0596528	0.0561806
10	102.48	102.359	102.039	0.320069	0.120764
11	102.66	102.603	102.142	0.460653	0.0568472
12	102.72	102.717	102.247	0.469903	0.00301389
13	102.73	102.269	102.353	-0.0837639	0.460847
14	102.18	102.111	102.46	-0.348931	0.0689306
15	102.22	102.281	102.561	-0.279847	-0.0609861
16	102.37	102.482	102.651	-0.169181	-0.111653
17	102.53	102.69	102.737	-0.0470139	-0.160069
18	102.61	102.811	102.822	-0.0110972	-0.200986
19	102.62	102.455	102.732	-0.277514	0.165014
20	103	102.379	102.472	-0.0929306	0.620847
21	103.17	102.275	102.215	0.0596528	0.895347
22	103.52	102.281	101.961	0.320069	1.23868
23	103.69	102.174	101.714	0.460653	1.5156
24	103.73	101.938	101.468	0.469903	1.79176
25	99.57	101.144	101.227	-0.0837639	-1.57374
26	99.09	100.642	100.991	-0.348931	-1.55232
27	99.14	100.479	100.759	-0.279847	-1.33932
28	99.36	100.367	100.536	-0.169181	-1.00707
29	99.6	100.273	100.32	-0.0470139	-0.672986
30	99.65	100.096	100.107	-0.0110972	-0.445569
31	99.8	99.7908	100.068	-0.277514	0.00918056
32	100.15	100.127	100.22	-0.0929306	0.0225139
33	100.45	100.448	100.389	0.0596528	0.00159722
34	100.89	100.866	100.546	0.320069	0.0236806
35	101.13	101.146	100.686	0.460653	-0.0164861
36	101.17	101.285	100.815	0.469903	-0.114903
37	101.21	100.84	100.924	-0.0837639	0.369597
38	101.1	100.661	101.01	-0.348931	0.438514
39	101.17	100.802	101.082	-0.279847	0.367764
40	101.11	100.96	101.129	-0.169181	0.150014
41	101.2	101.108	101.155	-0.0470139	0.0920139
42	101.15	101.166	101.177	-0.0110972	-0.0155694
43	100.92	100.941	101.218	-0.277514	-0.0208194
44	101.1	101.19	101.283	-0.0929306	-0.0904028
45	101.22	101.414	101.354	0.0596528	-0.193819
46	101.25	101.751	101.431	0.320069	-0.500903
47	101.39	101.969	101.508	0.460653	-0.578986
48	101.43	102.058	101.588	0.469903	-0.627819
49	101.95	101.576	101.66	-0.0837639	0.374181
50	101.92	101.354	101.703	-0.348931	0.566014
51	102.05	101.444	101.724	-0.279847	0.605681
52	102.07	101.577	101.746	-0.169181	0.492931
53	102.1	101.728	101.775	-0.0470139	0.372431
54	102.16	101.79	101.801	-0.0110972	0.370264
55	101.63	101.566	101.843	-0.277514	0.0641806
56	101.43	101.808	101.901	-0.0929306	-0.378319
57	101.4	102.015	101.956	0.0596528	-0.615486
58	101.6	102.338	102.018	0.320069	-0.738403
59	101.72	102.553	102.092	0.460653	-0.833153
60	101.73	102.638	102.168	0.469903	-0.908236
61	102.67	102.157	102.241	-0.0837639	0.512931
62	102.59	101.967	102.316	-0.348931	0.622681
63	102.69	102.119	102.399	-0.279847	0.570681
64	102.93	102.31	102.48	-0.169181	0.619597
65	103.02	102.508	102.555	-0.0470139	0.512431
66	103.06	102.624	102.635	-0.0110972	0.435681
67	102.47	NA	NA	-0.277514	NA
68	102.4	NA	NA	-0.0929306	NA
69	102.42	NA	NA	0.0596528	NA
70	102.51	NA	NA	0.320069	NA
71	102.61	NA	NA	0.460653	NA
72	102.78	NA	NA	0.469903	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 = 12 ;

Parameters (R input):

par1 = additive ; 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