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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 30 Apr 2017 19:52:26 +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/t1493578468ajwlh404kr4z1mu.htm/, Retrieved Mon, 13 May 2024 18:24:11 +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 18:24:11 +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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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	3 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	96.4	NA	NA	0.99196	NA
2	96.9	NA	NA	1.00131	NA
3	98.1	NA	NA	1.00763	NA
4	99.2	NA	NA	1.00742	NA
5	100	NA	NA	1.0064	NA
6	100.3	NA	NA	1.00419	NA
7	100.3	100.393	100.383	1.00009	0.999078
8	100.8	101.054	101.208	0.998475	0.997486
9	101.3	102.109	102.104	1.00005	0.992078
10	101.4	102.748	103.029	0.997274	0.986877
11	101.9	103.283	103.908	0.993981	0.986611
12	103.4	103.786	104.704	0.991227	0.996285
13	105.6	104.631	105.479	0.99196	1.00926
14	107.5	106.339	106.2	1.00131	1.01092
15	109	107.686	106.871	1.00763	1.0122
16	110.5	108.327	107.529	1.00742	1.02006
17	109.8	108.838	108.146	1.0064	1.00884
18	109.6	109.139	108.683	1.00419	1.00423
19	109.6	109.139	109.129	1.00009	1.00422
20	108.8	109.362	109.529	0.998475	0.99486
21	109.4	109.905	109.9	1.00005	0.995404
22	109.1	109.904	110.204	0.997274	0.992687
23	109	109.81	110.475	0.993981	0.992623
24	109.2	109.754	110.725	0.991227	0.994956
25	110.5	110.05	110.942	0.99196	1.00409
26	112.2	111.379	111.233	1.00131	1.00737
27	113.2	112.451	111.6	1.00763	1.00666
28	113.6	112.81	111.979	1.00742	1.007
29	113.2	113.09	112.371	1.0064	1.00098
30	112.2	113.235	112.762	1.00419	0.990861
31	112.2	113.115	113.104	1.00009	0.991915
32	113.2	113.165	113.337	0.998475	1.00031
33	113.8	113.476	113.471	1.00005	1.00285
34	113.8	113.174	113.483	0.997274	1.00553
35	113.7	112.73	113.412	0.993981	1.00861
36	113.9	112.38	113.375	0.991227	1.01352
37	114	112.48	113.392	0.99196	1.01351
38	114.3	113.503	113.354	1.00131	1.00702
39	114.3	114.076	113.212	1.00763	1.00196
40	112.8	113.847	113.008	1.00742	0.990803
41	112.3	113.492	112.771	1.0064	0.989496
42	112.2	113.005	112.533	1.00419	0.992879
43	112.6	112.281	112.271	1.00009	1.00284
44	111.9	111.796	111.967	0.998475	1.00093
45	111.7	111.626	111.621	1.00005	1.00066
46	111	110.972	111.275	0.997274	1.00026
47	110.8	110.311	110.979	0.993981	1.00443
48	111.1	109.754	110.725	0.991227	1.01227
49	110.5	109.562	110.45	0.99196	1.00856
50	110.5	110.29	110.146	1.00131	1.0019
51	109.8	110.696	109.858	1.00763	0.991903
52	109	110.413	109.6	1.00742	0.987199
53	109	110.016	109.317	1.0064	0.990766
54	109.4	109.293	108.837	1.00419	1.00098
55	108.8	108.156	108.146	1.00009	1.00596
56	108.4	107.282	107.446	0.998475	1.01042
57	108.3	106.909	106.904	1.00005	1.01301
58	108.2	106.218	106.508	0.997274	1.01866
59	106.8	105.561	106.2	0.993981	1.01174
60	103.6	104.967	105.896	0.991227	0.986979
61	101.4	104.689	105.538	0.99196	0.968583
62	102.8	105.242	105.104	1.00131	0.976797
63	104.5	105.419	104.621	1.00763	0.991284
64	104.8	104.91	104.137	1.00742	0.998948
65	105.8	104.347	103.683	1.0064	1.01393
66	105.3	103.737	103.304	1.00419	1.01507
67	104.3	NA	NA	1.00009	NA
68	102.5	NA	NA	0.998475	NA
69	102.6	NA	NA	1.00005	NA
70	102.3	NA	NA	0.997274	NA
71	101.8	NA	NA	0.993981	NA
72	99.5	NA	NA	0.991227	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 96.4 & NA & NA & 0.99196 & NA \tabularnewline
2 & 96.9 & NA & NA & 1.00131 & NA \tabularnewline
3 & 98.1 & NA & NA & 1.00763 & NA \tabularnewline
4 & 99.2 & NA & NA & 1.00742 & NA \tabularnewline
5 & 100 & NA & NA & 1.0064 & NA \tabularnewline
6 & 100.3 & NA & NA & 1.00419 & NA \tabularnewline
7 & 100.3 & 100.393 & 100.383 & 1.00009 & 0.999078 \tabularnewline
8 & 100.8 & 101.054 & 101.208 & 0.998475 & 0.997486 \tabularnewline
9 & 101.3 & 102.109 & 102.104 & 1.00005 & 0.992078 \tabularnewline
10 & 101.4 & 102.748 & 103.029 & 0.997274 & 0.986877 \tabularnewline
11 & 101.9 & 103.283 & 103.908 & 0.993981 & 0.986611 \tabularnewline
12 & 103.4 & 103.786 & 104.704 & 0.991227 & 0.996285 \tabularnewline
13 & 105.6 & 104.631 & 105.479 & 0.99196 & 1.00926 \tabularnewline
14 & 107.5 & 106.339 & 106.2 & 1.00131 & 1.01092 \tabularnewline
15 & 109 & 107.686 & 106.871 & 1.00763 & 1.0122 \tabularnewline
16 & 110.5 & 108.327 & 107.529 & 1.00742 & 1.02006 \tabularnewline
17 & 109.8 & 108.838 & 108.146 & 1.0064 & 1.00884 \tabularnewline
18 & 109.6 & 109.139 & 108.683 & 1.00419 & 1.00423 \tabularnewline
19 & 109.6 & 109.139 & 109.129 & 1.00009 & 1.00422 \tabularnewline
20 & 108.8 & 109.362 & 109.529 & 0.998475 & 0.99486 \tabularnewline
21 & 109.4 & 109.905 & 109.9 & 1.00005 & 0.995404 \tabularnewline
22 & 109.1 & 109.904 & 110.204 & 0.997274 & 0.992687 \tabularnewline
23 & 109 & 109.81 & 110.475 & 0.993981 & 0.992623 \tabularnewline
24 & 109.2 & 109.754 & 110.725 & 0.991227 & 0.994956 \tabularnewline
25 & 110.5 & 110.05 & 110.942 & 0.99196 & 1.00409 \tabularnewline
26 & 112.2 & 111.379 & 111.233 & 1.00131 & 1.00737 \tabularnewline
27 & 113.2 & 112.451 & 111.6 & 1.00763 & 1.00666 \tabularnewline
28 & 113.6 & 112.81 & 111.979 & 1.00742 & 1.007 \tabularnewline
29 & 113.2 & 113.09 & 112.371 & 1.0064 & 1.00098 \tabularnewline
30 & 112.2 & 113.235 & 112.762 & 1.00419 & 0.990861 \tabularnewline
31 & 112.2 & 113.115 & 113.104 & 1.00009 & 0.991915 \tabularnewline
32 & 113.2 & 113.165 & 113.337 & 0.998475 & 1.00031 \tabularnewline
33 & 113.8 & 113.476 & 113.471 & 1.00005 & 1.00285 \tabularnewline
34 & 113.8 & 113.174 & 113.483 & 0.997274 & 1.00553 \tabularnewline
35 & 113.7 & 112.73 & 113.412 & 0.993981 & 1.00861 \tabularnewline
36 & 113.9 & 112.38 & 113.375 & 0.991227 & 1.01352 \tabularnewline
37 & 114 & 112.48 & 113.392 & 0.99196 & 1.01351 \tabularnewline
38 & 114.3 & 113.503 & 113.354 & 1.00131 & 1.00702 \tabularnewline
39 & 114.3 & 114.076 & 113.212 & 1.00763 & 1.00196 \tabularnewline
40 & 112.8 & 113.847 & 113.008 & 1.00742 & 0.990803 \tabularnewline
41 & 112.3 & 113.492 & 112.771 & 1.0064 & 0.989496 \tabularnewline
42 & 112.2 & 113.005 & 112.533 & 1.00419 & 0.992879 \tabularnewline
43 & 112.6 & 112.281 & 112.271 & 1.00009 & 1.00284 \tabularnewline
44 & 111.9 & 111.796 & 111.967 & 0.998475 & 1.00093 \tabularnewline
45 & 111.7 & 111.626 & 111.621 & 1.00005 & 1.00066 \tabularnewline
46 & 111 & 110.972 & 111.275 & 0.997274 & 1.00026 \tabularnewline
47 & 110.8 & 110.311 & 110.979 & 0.993981 & 1.00443 \tabularnewline
48 & 111.1 & 109.754 & 110.725 & 0.991227 & 1.01227 \tabularnewline
49 & 110.5 & 109.562 & 110.45 & 0.99196 & 1.00856 \tabularnewline
50 & 110.5 & 110.29 & 110.146 & 1.00131 & 1.0019 \tabularnewline
51 & 109.8 & 110.696 & 109.858 & 1.00763 & 0.991903 \tabularnewline
52 & 109 & 110.413 & 109.6 & 1.00742 & 0.987199 \tabularnewline
53 & 109 & 110.016 & 109.317 & 1.0064 & 0.990766 \tabularnewline
54 & 109.4 & 109.293 & 108.837 & 1.00419 & 1.00098 \tabularnewline
55 & 108.8 & 108.156 & 108.146 & 1.00009 & 1.00596 \tabularnewline
56 & 108.4 & 107.282 & 107.446 & 0.998475 & 1.01042 \tabularnewline
57 & 108.3 & 106.909 & 106.904 & 1.00005 & 1.01301 \tabularnewline
58 & 108.2 & 106.218 & 106.508 & 0.997274 & 1.01866 \tabularnewline
59 & 106.8 & 105.561 & 106.2 & 0.993981 & 1.01174 \tabularnewline
60 & 103.6 & 104.967 & 105.896 & 0.991227 & 0.986979 \tabularnewline
61 & 101.4 & 104.689 & 105.538 & 0.99196 & 0.968583 \tabularnewline
62 & 102.8 & 105.242 & 105.104 & 1.00131 & 0.976797 \tabularnewline
63 & 104.5 & 105.419 & 104.621 & 1.00763 & 0.991284 \tabularnewline
64 & 104.8 & 104.91 & 104.137 & 1.00742 & 0.998948 \tabularnewline
65 & 105.8 & 104.347 & 103.683 & 1.0064 & 1.01393 \tabularnewline
66 & 105.3 & 103.737 & 103.304 & 1.00419 & 1.01507 \tabularnewline
67 & 104.3 & NA & NA & 1.00009 & NA \tabularnewline
68 & 102.5 & NA & NA & 0.998475 & NA \tabularnewline
69 & 102.6 & NA & NA & 1.00005 & NA \tabularnewline
70 & 102.3 & NA & NA & 0.997274 & NA \tabularnewline
71 & 101.8 & NA & NA & 0.993981 & NA \tabularnewline
72 & 99.5 & NA & NA & 0.991227 & 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]96.4[/C][C]NA[/C][C]NA[/C][C]0.99196[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]96.9[/C][C]NA[/C][C]NA[/C][C]1.00131[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]98.1[/C][C]NA[/C][C]NA[/C][C]1.00763[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]99.2[/C][C]NA[/C][C]NA[/C][C]1.00742[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100[/C][C]NA[/C][C]NA[/C][C]1.0064[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.3[/C][C]NA[/C][C]NA[/C][C]1.00419[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]100.3[/C][C]100.393[/C][C]100.383[/C][C]1.00009[/C][C]0.999078[/C][/ROW]
[ROW][C]8[/C][C]100.8[/C][C]101.054[/C][C]101.208[/C][C]0.998475[/C][C]0.997486[/C][/ROW]
[ROW][C]9[/C][C]101.3[/C][C]102.109[/C][C]102.104[/C][C]1.00005[/C][C]0.992078[/C][/ROW]
[ROW][C]10[/C][C]101.4[/C][C]102.748[/C][C]103.029[/C][C]0.997274[/C][C]0.986877[/C][/ROW]
[ROW][C]11[/C][C]101.9[/C][C]103.283[/C][C]103.908[/C][C]0.993981[/C][C]0.986611[/C][/ROW]
[ROW][C]12[/C][C]103.4[/C][C]103.786[/C][C]104.704[/C][C]0.991227[/C][C]0.996285[/C][/ROW]
[ROW][C]13[/C][C]105.6[/C][C]104.631[/C][C]105.479[/C][C]0.99196[/C][C]1.00926[/C][/ROW]
[ROW][C]14[/C][C]107.5[/C][C]106.339[/C][C]106.2[/C][C]1.00131[/C][C]1.01092[/C][/ROW]
[ROW][C]15[/C][C]109[/C][C]107.686[/C][C]106.871[/C][C]1.00763[/C][C]1.0122[/C][/ROW]
[ROW][C]16[/C][C]110.5[/C][C]108.327[/C][C]107.529[/C][C]1.00742[/C][C]1.02006[/C][/ROW]
[ROW][C]17[/C][C]109.8[/C][C]108.838[/C][C]108.146[/C][C]1.0064[/C][C]1.00884[/C][/ROW]
[ROW][C]18[/C][C]109.6[/C][C]109.139[/C][C]108.683[/C][C]1.00419[/C][C]1.00423[/C][/ROW]
[ROW][C]19[/C][C]109.6[/C][C]109.139[/C][C]109.129[/C][C]1.00009[/C][C]1.00422[/C][/ROW]
[ROW][C]20[/C][C]108.8[/C][C]109.362[/C][C]109.529[/C][C]0.998475[/C][C]0.99486[/C][/ROW]
[ROW][C]21[/C][C]109.4[/C][C]109.905[/C][C]109.9[/C][C]1.00005[/C][C]0.995404[/C][/ROW]
[ROW][C]22[/C][C]109.1[/C][C]109.904[/C][C]110.204[/C][C]0.997274[/C][C]0.992687[/C][/ROW]
[ROW][C]23[/C][C]109[/C][C]109.81[/C][C]110.475[/C][C]0.993981[/C][C]0.992623[/C][/ROW]
[ROW][C]24[/C][C]109.2[/C][C]109.754[/C][C]110.725[/C][C]0.991227[/C][C]0.994956[/C][/ROW]
[ROW][C]25[/C][C]110.5[/C][C]110.05[/C][C]110.942[/C][C]0.99196[/C][C]1.00409[/C][/ROW]
[ROW][C]26[/C][C]112.2[/C][C]111.379[/C][C]111.233[/C][C]1.00131[/C][C]1.00737[/C][/ROW]
[ROW][C]27[/C][C]113.2[/C][C]112.451[/C][C]111.6[/C][C]1.00763[/C][C]1.00666[/C][/ROW]
[ROW][C]28[/C][C]113.6[/C][C]112.81[/C][C]111.979[/C][C]1.00742[/C][C]1.007[/C][/ROW]
[ROW][C]29[/C][C]113.2[/C][C]113.09[/C][C]112.371[/C][C]1.0064[/C][C]1.00098[/C][/ROW]
[ROW][C]30[/C][C]112.2[/C][C]113.235[/C][C]112.762[/C][C]1.00419[/C][C]0.990861[/C][/ROW]
[ROW][C]31[/C][C]112.2[/C][C]113.115[/C][C]113.104[/C][C]1.00009[/C][C]0.991915[/C][/ROW]
[ROW][C]32[/C][C]113.2[/C][C]113.165[/C][C]113.337[/C][C]0.998475[/C][C]1.00031[/C][/ROW]
[ROW][C]33[/C][C]113.8[/C][C]113.476[/C][C]113.471[/C][C]1.00005[/C][C]1.00285[/C][/ROW]
[ROW][C]34[/C][C]113.8[/C][C]113.174[/C][C]113.483[/C][C]0.997274[/C][C]1.00553[/C][/ROW]
[ROW][C]35[/C][C]113.7[/C][C]112.73[/C][C]113.412[/C][C]0.993981[/C][C]1.00861[/C][/ROW]
[ROW][C]36[/C][C]113.9[/C][C]112.38[/C][C]113.375[/C][C]0.991227[/C][C]1.01352[/C][/ROW]
[ROW][C]37[/C][C]114[/C][C]112.48[/C][C]113.392[/C][C]0.99196[/C][C]1.01351[/C][/ROW]
[ROW][C]38[/C][C]114.3[/C][C]113.503[/C][C]113.354[/C][C]1.00131[/C][C]1.00702[/C][/ROW]
[ROW][C]39[/C][C]114.3[/C][C]114.076[/C][C]113.212[/C][C]1.00763[/C][C]1.00196[/C][/ROW]
[ROW][C]40[/C][C]112.8[/C][C]113.847[/C][C]113.008[/C][C]1.00742[/C][C]0.990803[/C][/ROW]
[ROW][C]41[/C][C]112.3[/C][C]113.492[/C][C]112.771[/C][C]1.0064[/C][C]0.989496[/C][/ROW]
[ROW][C]42[/C][C]112.2[/C][C]113.005[/C][C]112.533[/C][C]1.00419[/C][C]0.992879[/C][/ROW]
[ROW][C]43[/C][C]112.6[/C][C]112.281[/C][C]112.271[/C][C]1.00009[/C][C]1.00284[/C][/ROW]
[ROW][C]44[/C][C]111.9[/C][C]111.796[/C][C]111.967[/C][C]0.998475[/C][C]1.00093[/C][/ROW]
[ROW][C]45[/C][C]111.7[/C][C]111.626[/C][C]111.621[/C][C]1.00005[/C][C]1.00066[/C][/ROW]
[ROW][C]46[/C][C]111[/C][C]110.972[/C][C]111.275[/C][C]0.997274[/C][C]1.00026[/C][/ROW]
[ROW][C]47[/C][C]110.8[/C][C]110.311[/C][C]110.979[/C][C]0.993981[/C][C]1.00443[/C][/ROW]
[ROW][C]48[/C][C]111.1[/C][C]109.754[/C][C]110.725[/C][C]0.991227[/C][C]1.01227[/C][/ROW]
[ROW][C]49[/C][C]110.5[/C][C]109.562[/C][C]110.45[/C][C]0.99196[/C][C]1.00856[/C][/ROW]
[ROW][C]50[/C][C]110.5[/C][C]110.29[/C][C]110.146[/C][C]1.00131[/C][C]1.0019[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]110.696[/C][C]109.858[/C][C]1.00763[/C][C]0.991903[/C][/ROW]
[ROW][C]52[/C][C]109[/C][C]110.413[/C][C]109.6[/C][C]1.00742[/C][C]0.987199[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]110.016[/C][C]109.317[/C][C]1.0064[/C][C]0.990766[/C][/ROW]
[ROW][C]54[/C][C]109.4[/C][C]109.293[/C][C]108.837[/C][C]1.00419[/C][C]1.00098[/C][/ROW]
[ROW][C]55[/C][C]108.8[/C][C]108.156[/C][C]108.146[/C][C]1.00009[/C][C]1.00596[/C][/ROW]
[ROW][C]56[/C][C]108.4[/C][C]107.282[/C][C]107.446[/C][C]0.998475[/C][C]1.01042[/C][/ROW]
[ROW][C]57[/C][C]108.3[/C][C]106.909[/C][C]106.904[/C][C]1.00005[/C][C]1.01301[/C][/ROW]
[ROW][C]58[/C][C]108.2[/C][C]106.218[/C][C]106.508[/C][C]0.997274[/C][C]1.01866[/C][/ROW]
[ROW][C]59[/C][C]106.8[/C][C]105.561[/C][C]106.2[/C][C]0.993981[/C][C]1.01174[/C][/ROW]
[ROW][C]60[/C][C]103.6[/C][C]104.967[/C][C]105.896[/C][C]0.991227[/C][C]0.986979[/C][/ROW]
[ROW][C]61[/C][C]101.4[/C][C]104.689[/C][C]105.538[/C][C]0.99196[/C][C]0.968583[/C][/ROW]
[ROW][C]62[/C][C]102.8[/C][C]105.242[/C][C]105.104[/C][C]1.00131[/C][C]0.976797[/C][/ROW]
[ROW][C]63[/C][C]104.5[/C][C]105.419[/C][C]104.621[/C][C]1.00763[/C][C]0.991284[/C][/ROW]
[ROW][C]64[/C][C]104.8[/C][C]104.91[/C][C]104.137[/C][C]1.00742[/C][C]0.998948[/C][/ROW]
[ROW][C]65[/C][C]105.8[/C][C]104.347[/C][C]103.683[/C][C]1.0064[/C][C]1.01393[/C][/ROW]
[ROW][C]66[/C][C]105.3[/C][C]103.737[/C][C]103.304[/C][C]1.00419[/C][C]1.01507[/C][/ROW]
[ROW][C]67[/C][C]104.3[/C][C]NA[/C][C]NA[/C][C]1.00009[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.5[/C][C]NA[/C][C]NA[/C][C]0.998475[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]102.6[/C][C]NA[/C][C]NA[/C][C]1.00005[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]102.3[/C][C]NA[/C][C]NA[/C][C]0.997274[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]101.8[/C][C]NA[/C][C]NA[/C][C]0.993981[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]99.5[/C][C]NA[/C][C]NA[/C][C]0.991227[/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	96.4	NA	NA	0.99196	NA
2	96.9	NA	NA	1.00131	NA
3	98.1	NA	NA	1.00763	NA
4	99.2	NA	NA	1.00742	NA
5	100	NA	NA	1.0064	NA
6	100.3	NA	NA	1.00419	NA
7	100.3	100.393	100.383	1.00009	0.999078
8	100.8	101.054	101.208	0.998475	0.997486
9	101.3	102.109	102.104	1.00005	0.992078
10	101.4	102.748	103.029	0.997274	0.986877
11	101.9	103.283	103.908	0.993981	0.986611
12	103.4	103.786	104.704	0.991227	0.996285
13	105.6	104.631	105.479	0.99196	1.00926
14	107.5	106.339	106.2	1.00131	1.01092
15	109	107.686	106.871	1.00763	1.0122
16	110.5	108.327	107.529	1.00742	1.02006
17	109.8	108.838	108.146	1.0064	1.00884
18	109.6	109.139	108.683	1.00419	1.00423
19	109.6	109.139	109.129	1.00009	1.00422
20	108.8	109.362	109.529	0.998475	0.99486
21	109.4	109.905	109.9	1.00005	0.995404
22	109.1	109.904	110.204	0.997274	0.992687
23	109	109.81	110.475	0.993981	0.992623
24	109.2	109.754	110.725	0.991227	0.994956
25	110.5	110.05	110.942	0.99196	1.00409
26	112.2	111.379	111.233	1.00131	1.00737
27	113.2	112.451	111.6	1.00763	1.00666
28	113.6	112.81	111.979	1.00742	1.007
29	113.2	113.09	112.371	1.0064	1.00098
30	112.2	113.235	112.762	1.00419	0.990861
31	112.2	113.115	113.104	1.00009	0.991915
32	113.2	113.165	113.337	0.998475	1.00031
33	113.8	113.476	113.471	1.00005	1.00285
34	113.8	113.174	113.483	0.997274	1.00553
35	113.7	112.73	113.412	0.993981	1.00861
36	113.9	112.38	113.375	0.991227	1.01352
37	114	112.48	113.392	0.99196	1.01351
38	114.3	113.503	113.354	1.00131	1.00702
39	114.3	114.076	113.212	1.00763	1.00196
40	112.8	113.847	113.008	1.00742	0.990803
41	112.3	113.492	112.771	1.0064	0.989496
42	112.2	113.005	112.533	1.00419	0.992879
43	112.6	112.281	112.271	1.00009	1.00284
44	111.9	111.796	111.967	0.998475	1.00093
45	111.7	111.626	111.621	1.00005	1.00066
46	111	110.972	111.275	0.997274	1.00026
47	110.8	110.311	110.979	0.993981	1.00443
48	111.1	109.754	110.725	0.991227	1.01227
49	110.5	109.562	110.45	0.99196	1.00856
50	110.5	110.29	110.146	1.00131	1.0019
51	109.8	110.696	109.858	1.00763	0.991903
52	109	110.413	109.6	1.00742	0.987199
53	109	110.016	109.317	1.0064	0.990766
54	109.4	109.293	108.837	1.00419	1.00098
55	108.8	108.156	108.146	1.00009	1.00596
56	108.4	107.282	107.446	0.998475	1.01042
57	108.3	106.909	106.904	1.00005	1.01301
58	108.2	106.218	106.508	0.997274	1.01866
59	106.8	105.561	106.2	0.993981	1.01174
60	103.6	104.967	105.896	0.991227	0.986979
61	101.4	104.689	105.538	0.99196	0.968583
62	102.8	105.242	105.104	1.00131	0.976797
63	104.5	105.419	104.621	1.00763	0.991284
64	104.8	104.91	104.137	1.00742	0.998948
65	105.8	104.347	103.683	1.0064	1.01393
66	105.3	103.737	103.304	1.00419	1.01507
67	104.3	NA	NA	1.00009	NA
68	102.5	NA	NA	0.998475	NA
69	102.6	NA	NA	1.00005	NA
70	102.3	NA	NA	0.997274	NA
71	101.8	NA	NA	0.993981	NA
72	99.5	NA	NA	0.991227	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