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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sat, 26 Nov 2016 10:16:22 +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/26/t1480155399zszohqibhr9w1qz.htm/, Retrieved Sun, 19 May 2024 02:27:51 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sun, 19 May 2024 02:27:51 +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	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	97.78	NA	NA	0.997485	NA
2	97.73	NA	NA	0.996931	NA
3	97.61	NA	NA	0.996594	NA
4	97.69	NA	NA	0.995738	NA
5	97.68	NA	NA	0.995207	NA
6	97.67	NA	NA	0.995264	NA
7	97.67	98.0604	98.3254	0.997305	0.996019
8	97.96	98.2695	98.4937	0.997723	0.99685
9	98.27	98.8054	98.6792	1.00128	0.994581
10	99.52	99.6966	98.885	1.00821	0.998228
11	99.59	99.9844	99.0917	1.00901	0.996056
12	99.75	100.211	99.2921	1.00926	0.995396
13	99.75	99.2427	99.4929	0.997485	1.00511
14	99.8	99.3749	99.6808	0.996931	1.00428
15	99.99	99.5419	99.8821	0.996594	1.0045
16	100.25	99.6646	100.091	0.995738	1.00587
17	100.08	99.7989	100.28	0.995207	1.00282
18	100.08	99.9888	100.465	0.995264	1.00091
19	100.08	100.377	100.649	0.997305	0.997036
20	100.06	100.6	100.83	0.997723	0.994632
21	101	101.133	101.003	1.00128	0.998689
22	101.81	101.996	101.166	1.00821	0.998174
23	101.82	102.239	101.326	1.00901	0.995901
24	101.96	102.443	101.503	1.00926	0.995284
25	101.96	101.436	101.692	0.997485	1.00517
26	101.93	101.568	101.88	0.996931	1.00357
27	102.03	101.701	102.049	0.996594	1.00323
28	102.11	101.74	102.175	0.995738	1.00364
29	102.07	101.803	102.293	0.995207	1.00262
30	102.34	101.935	102.42	0.995264	1.00397
31	102.34	102.087	102.363	0.997305	1.00248
32	102.33	101.895	102.128	0.997723	1.00427
33	102.77	102.025	101.894	1.00128	1.00731
34	103.08	102.482	101.648	1.00821	1.00584
35	103.38	102.307	101.394	1.00901	1.01049
36	103.44	102.071	101.135	1.00926	1.01341
37	99.1	100.653	100.907	0.997485	0.984568
38	99.15	100.421	100.73	0.996931	0.987345
39	99.21	100.211	100.553	0.996594	0.990013
40	99.01	99.9621	100.39	0.995738	0.990475
41	99.08	99.7599	100.24	0.995207	0.993184
42	99.11	99.6068	100.081	0.995264	0.995012
43	100.11	99.7966	100.066	0.997305	1.00314
44	100.31	99.9731	100.201	0.997723	1.00337
45	100.55	100.455	100.327	1.00128	1.00094
46	101.38	101.264	100.44	1.00821	1.00115
47	101.49	101.455	100.549	1.00901	1.00035
48	101.5	101.586	100.654	1.00926	0.999153
49	100.69	100.491	100.745	0.997485	1.00198
50	100.8	100.51	100.819	0.996931	1.00289
51	100.58	100.544	100.888	0.996594	1.00036
52	100.34	100.516	100.947	0.995738	0.998245
53	100.38	100.519	101.003	0.995207	0.99862
54	100.33	100.59	101.069	0.995264	0.997411
55	101.06	100.882	101.155	0.997305	1.00176
56	101.15	101.017	101.247	0.997723	1.00132
57	101.36	101.47	101.34	1.00128	0.998919
58	101.98	102.281	101.449	1.00821	0.997053
59	102.24	102.481	101.566	1.00901	0.99765
60	102.34	102.628	101.687	1.00926	0.997192
61	101.91	101.543	101.799	0.997485	1.00361
62	101.8	101.56	101.873	0.996931	1.00236
63	101.8	101.563	101.91	0.996594	1.00233
64	101.73	101.506	101.94	0.995738	1.00221
65	101.8	101.476	101.965	0.995207	1.00319
66	101.81	101.492	101.975	0.995264	1.00313
67	102.28	NA	NA	0.997305	NA
68	101.7	NA	NA	0.997723	NA
69	101.7	NA	NA	1.00128	NA
70	102.37	NA	NA	1.00821	NA
71	102.43	NA	NA	1.00901	NA
72	102.41	NA	NA	1.00926	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 97.78 & NA & NA & 0.997485 & NA \tabularnewline
2 & 97.73 & NA & NA & 0.996931 & NA \tabularnewline
3 & 97.61 & NA & NA & 0.996594 & NA \tabularnewline
4 & 97.69 & NA & NA & 0.995738 & NA \tabularnewline
5 & 97.68 & NA & NA & 0.995207 & NA \tabularnewline
6 & 97.67 & NA & NA & 0.995264 & NA \tabularnewline
7 & 97.67 & 98.0604 & 98.3254 & 0.997305 & 0.996019 \tabularnewline
8 & 97.96 & 98.2695 & 98.4937 & 0.997723 & 0.99685 \tabularnewline
9 & 98.27 & 98.8054 & 98.6792 & 1.00128 & 0.994581 \tabularnewline
10 & 99.52 & 99.6966 & 98.885 & 1.00821 & 0.998228 \tabularnewline
11 & 99.59 & 99.9844 & 99.0917 & 1.00901 & 0.996056 \tabularnewline
12 & 99.75 & 100.211 & 99.2921 & 1.00926 & 0.995396 \tabularnewline
13 & 99.75 & 99.2427 & 99.4929 & 0.997485 & 1.00511 \tabularnewline
14 & 99.8 & 99.3749 & 99.6808 & 0.996931 & 1.00428 \tabularnewline
15 & 99.99 & 99.5419 & 99.8821 & 0.996594 & 1.0045 \tabularnewline
16 & 100.25 & 99.6646 & 100.091 & 0.995738 & 1.00587 \tabularnewline
17 & 100.08 & 99.7989 & 100.28 & 0.995207 & 1.00282 \tabularnewline
18 & 100.08 & 99.9888 & 100.465 & 0.995264 & 1.00091 \tabularnewline
19 & 100.08 & 100.377 & 100.649 & 0.997305 & 0.997036 \tabularnewline
20 & 100.06 & 100.6 & 100.83 & 0.997723 & 0.994632 \tabularnewline
21 & 101 & 101.133 & 101.003 & 1.00128 & 0.998689 \tabularnewline
22 & 101.81 & 101.996 & 101.166 & 1.00821 & 0.998174 \tabularnewline
23 & 101.82 & 102.239 & 101.326 & 1.00901 & 0.995901 \tabularnewline
24 & 101.96 & 102.443 & 101.503 & 1.00926 & 0.995284 \tabularnewline
25 & 101.96 & 101.436 & 101.692 & 0.997485 & 1.00517 \tabularnewline
26 & 101.93 & 101.568 & 101.88 & 0.996931 & 1.00357 \tabularnewline
27 & 102.03 & 101.701 & 102.049 & 0.996594 & 1.00323 \tabularnewline
28 & 102.11 & 101.74 & 102.175 & 0.995738 & 1.00364 \tabularnewline
29 & 102.07 & 101.803 & 102.293 & 0.995207 & 1.00262 \tabularnewline
30 & 102.34 & 101.935 & 102.42 & 0.995264 & 1.00397 \tabularnewline
31 & 102.34 & 102.087 & 102.363 & 0.997305 & 1.00248 \tabularnewline
32 & 102.33 & 101.895 & 102.128 & 0.997723 & 1.00427 \tabularnewline
33 & 102.77 & 102.025 & 101.894 & 1.00128 & 1.00731 \tabularnewline
34 & 103.08 & 102.482 & 101.648 & 1.00821 & 1.00584 \tabularnewline
35 & 103.38 & 102.307 & 101.394 & 1.00901 & 1.01049 \tabularnewline
36 & 103.44 & 102.071 & 101.135 & 1.00926 & 1.01341 \tabularnewline
37 & 99.1 & 100.653 & 100.907 & 0.997485 & 0.984568 \tabularnewline
38 & 99.15 & 100.421 & 100.73 & 0.996931 & 0.987345 \tabularnewline
39 & 99.21 & 100.211 & 100.553 & 0.996594 & 0.990013 \tabularnewline
40 & 99.01 & 99.9621 & 100.39 & 0.995738 & 0.990475 \tabularnewline
41 & 99.08 & 99.7599 & 100.24 & 0.995207 & 0.993184 \tabularnewline
42 & 99.11 & 99.6068 & 100.081 & 0.995264 & 0.995012 \tabularnewline
43 & 100.11 & 99.7966 & 100.066 & 0.997305 & 1.00314 \tabularnewline
44 & 100.31 & 99.9731 & 100.201 & 0.997723 & 1.00337 \tabularnewline
45 & 100.55 & 100.455 & 100.327 & 1.00128 & 1.00094 \tabularnewline
46 & 101.38 & 101.264 & 100.44 & 1.00821 & 1.00115 \tabularnewline
47 & 101.49 & 101.455 & 100.549 & 1.00901 & 1.00035 \tabularnewline
48 & 101.5 & 101.586 & 100.654 & 1.00926 & 0.999153 \tabularnewline
49 & 100.69 & 100.491 & 100.745 & 0.997485 & 1.00198 \tabularnewline
50 & 100.8 & 100.51 & 100.819 & 0.996931 & 1.00289 \tabularnewline
51 & 100.58 & 100.544 & 100.888 & 0.996594 & 1.00036 \tabularnewline
52 & 100.34 & 100.516 & 100.947 & 0.995738 & 0.998245 \tabularnewline
53 & 100.38 & 100.519 & 101.003 & 0.995207 & 0.99862 \tabularnewline
54 & 100.33 & 100.59 & 101.069 & 0.995264 & 0.997411 \tabularnewline
55 & 101.06 & 100.882 & 101.155 & 0.997305 & 1.00176 \tabularnewline
56 & 101.15 & 101.017 & 101.247 & 0.997723 & 1.00132 \tabularnewline
57 & 101.36 & 101.47 & 101.34 & 1.00128 & 0.998919 \tabularnewline
58 & 101.98 & 102.281 & 101.449 & 1.00821 & 0.997053 \tabularnewline
59 & 102.24 & 102.481 & 101.566 & 1.00901 & 0.99765 \tabularnewline
60 & 102.34 & 102.628 & 101.687 & 1.00926 & 0.997192 \tabularnewline
61 & 101.91 & 101.543 & 101.799 & 0.997485 & 1.00361 \tabularnewline
62 & 101.8 & 101.56 & 101.873 & 0.996931 & 1.00236 \tabularnewline
63 & 101.8 & 101.563 & 101.91 & 0.996594 & 1.00233 \tabularnewline
64 & 101.73 & 101.506 & 101.94 & 0.995738 & 1.00221 \tabularnewline
65 & 101.8 & 101.476 & 101.965 & 0.995207 & 1.00319 \tabularnewline
66 & 101.81 & 101.492 & 101.975 & 0.995264 & 1.00313 \tabularnewline
67 & 102.28 & NA & NA & 0.997305 & NA \tabularnewline
68 & 101.7 & NA & NA & 0.997723 & NA \tabularnewline
69 & 101.7 & NA & NA & 1.00128 & NA \tabularnewline
70 & 102.37 & NA & NA & 1.00821 & NA \tabularnewline
71 & 102.43 & NA & NA & 1.00901 & NA \tabularnewline
72 & 102.41 & NA & NA & 1.00926 & 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]97.78[/C][C]NA[/C][C]NA[/C][C]0.997485[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]97.73[/C][C]NA[/C][C]NA[/C][C]0.996931[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]97.61[/C][C]NA[/C][C]NA[/C][C]0.996594[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]97.69[/C][C]NA[/C][C]NA[/C][C]0.995738[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]97.68[/C][C]NA[/C][C]NA[/C][C]0.995207[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]97.67[/C][C]NA[/C][C]NA[/C][C]0.995264[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]97.67[/C][C]98.0604[/C][C]98.3254[/C][C]0.997305[/C][C]0.996019[/C][/ROW]
[ROW][C]8[/C][C]97.96[/C][C]98.2695[/C][C]98.4937[/C][C]0.997723[/C][C]0.99685[/C][/ROW]
[ROW][C]9[/C][C]98.27[/C][C]98.8054[/C][C]98.6792[/C][C]1.00128[/C][C]0.994581[/C][/ROW]
[ROW][C]10[/C][C]99.52[/C][C]99.6966[/C][C]98.885[/C][C]1.00821[/C][C]0.998228[/C][/ROW]
[ROW][C]11[/C][C]99.59[/C][C]99.9844[/C][C]99.0917[/C][C]1.00901[/C][C]0.996056[/C][/ROW]
[ROW][C]12[/C][C]99.75[/C][C]100.211[/C][C]99.2921[/C][C]1.00926[/C][C]0.995396[/C][/ROW]
[ROW][C]13[/C][C]99.75[/C][C]99.2427[/C][C]99.4929[/C][C]0.997485[/C][C]1.00511[/C][/ROW]
[ROW][C]14[/C][C]99.8[/C][C]99.3749[/C][C]99.6808[/C][C]0.996931[/C][C]1.00428[/C][/ROW]
[ROW][C]15[/C][C]99.99[/C][C]99.5419[/C][C]99.8821[/C][C]0.996594[/C][C]1.0045[/C][/ROW]
[ROW][C]16[/C][C]100.25[/C][C]99.6646[/C][C]100.091[/C][C]0.995738[/C][C]1.00587[/C][/ROW]
[ROW][C]17[/C][C]100.08[/C][C]99.7989[/C][C]100.28[/C][C]0.995207[/C][C]1.00282[/C][/ROW]
[ROW][C]18[/C][C]100.08[/C][C]99.9888[/C][C]100.465[/C][C]0.995264[/C][C]1.00091[/C][/ROW]
[ROW][C]19[/C][C]100.08[/C][C]100.377[/C][C]100.649[/C][C]0.997305[/C][C]0.997036[/C][/ROW]
[ROW][C]20[/C][C]100.06[/C][C]100.6[/C][C]100.83[/C][C]0.997723[/C][C]0.994632[/C][/ROW]
[ROW][C]21[/C][C]101[/C][C]101.133[/C][C]101.003[/C][C]1.00128[/C][C]0.998689[/C][/ROW]
[ROW][C]22[/C][C]101.81[/C][C]101.996[/C][C]101.166[/C][C]1.00821[/C][C]0.998174[/C][/ROW]
[ROW][C]23[/C][C]101.82[/C][C]102.239[/C][C]101.326[/C][C]1.00901[/C][C]0.995901[/C][/ROW]
[ROW][C]24[/C][C]101.96[/C][C]102.443[/C][C]101.503[/C][C]1.00926[/C][C]0.995284[/C][/ROW]
[ROW][C]25[/C][C]101.96[/C][C]101.436[/C][C]101.692[/C][C]0.997485[/C][C]1.00517[/C][/ROW]
[ROW][C]26[/C][C]101.93[/C][C]101.568[/C][C]101.88[/C][C]0.996931[/C][C]1.00357[/C][/ROW]
[ROW][C]27[/C][C]102.03[/C][C]101.701[/C][C]102.049[/C][C]0.996594[/C][C]1.00323[/C][/ROW]
[ROW][C]28[/C][C]102.11[/C][C]101.74[/C][C]102.175[/C][C]0.995738[/C][C]1.00364[/C][/ROW]
[ROW][C]29[/C][C]102.07[/C][C]101.803[/C][C]102.293[/C][C]0.995207[/C][C]1.00262[/C][/ROW]
[ROW][C]30[/C][C]102.34[/C][C]101.935[/C][C]102.42[/C][C]0.995264[/C][C]1.00397[/C][/ROW]
[ROW][C]31[/C][C]102.34[/C][C]102.087[/C][C]102.363[/C][C]0.997305[/C][C]1.00248[/C][/ROW]
[ROW][C]32[/C][C]102.33[/C][C]101.895[/C][C]102.128[/C][C]0.997723[/C][C]1.00427[/C][/ROW]
[ROW][C]33[/C][C]102.77[/C][C]102.025[/C][C]101.894[/C][C]1.00128[/C][C]1.00731[/C][/ROW]
[ROW][C]34[/C][C]103.08[/C][C]102.482[/C][C]101.648[/C][C]1.00821[/C][C]1.00584[/C][/ROW]
[ROW][C]35[/C][C]103.38[/C][C]102.307[/C][C]101.394[/C][C]1.00901[/C][C]1.01049[/C][/ROW]
[ROW][C]36[/C][C]103.44[/C][C]102.071[/C][C]101.135[/C][C]1.00926[/C][C]1.01341[/C][/ROW]
[ROW][C]37[/C][C]99.1[/C][C]100.653[/C][C]100.907[/C][C]0.997485[/C][C]0.984568[/C][/ROW]
[ROW][C]38[/C][C]99.15[/C][C]100.421[/C][C]100.73[/C][C]0.996931[/C][C]0.987345[/C][/ROW]
[ROW][C]39[/C][C]99.21[/C][C]100.211[/C][C]100.553[/C][C]0.996594[/C][C]0.990013[/C][/ROW]
[ROW][C]40[/C][C]99.01[/C][C]99.9621[/C][C]100.39[/C][C]0.995738[/C][C]0.990475[/C][/ROW]
[ROW][C]41[/C][C]99.08[/C][C]99.7599[/C][C]100.24[/C][C]0.995207[/C][C]0.993184[/C][/ROW]
[ROW][C]42[/C][C]99.11[/C][C]99.6068[/C][C]100.081[/C][C]0.995264[/C][C]0.995012[/C][/ROW]
[ROW][C]43[/C][C]100.11[/C][C]99.7966[/C][C]100.066[/C][C]0.997305[/C][C]1.00314[/C][/ROW]
[ROW][C]44[/C][C]100.31[/C][C]99.9731[/C][C]100.201[/C][C]0.997723[/C][C]1.00337[/C][/ROW]
[ROW][C]45[/C][C]100.55[/C][C]100.455[/C][C]100.327[/C][C]1.00128[/C][C]1.00094[/C][/ROW]
[ROW][C]46[/C][C]101.38[/C][C]101.264[/C][C]100.44[/C][C]1.00821[/C][C]1.00115[/C][/ROW]
[ROW][C]47[/C][C]101.49[/C][C]101.455[/C][C]100.549[/C][C]1.00901[/C][C]1.00035[/C][/ROW]
[ROW][C]48[/C][C]101.5[/C][C]101.586[/C][C]100.654[/C][C]1.00926[/C][C]0.999153[/C][/ROW]
[ROW][C]49[/C][C]100.69[/C][C]100.491[/C][C]100.745[/C][C]0.997485[/C][C]1.00198[/C][/ROW]
[ROW][C]50[/C][C]100.8[/C][C]100.51[/C][C]100.819[/C][C]0.996931[/C][C]1.00289[/C][/ROW]
[ROW][C]51[/C][C]100.58[/C][C]100.544[/C][C]100.888[/C][C]0.996594[/C][C]1.00036[/C][/ROW]
[ROW][C]52[/C][C]100.34[/C][C]100.516[/C][C]100.947[/C][C]0.995738[/C][C]0.998245[/C][/ROW]
[ROW][C]53[/C][C]100.38[/C][C]100.519[/C][C]101.003[/C][C]0.995207[/C][C]0.99862[/C][/ROW]
[ROW][C]54[/C][C]100.33[/C][C]100.59[/C][C]101.069[/C][C]0.995264[/C][C]0.997411[/C][/ROW]
[ROW][C]55[/C][C]101.06[/C][C]100.882[/C][C]101.155[/C][C]0.997305[/C][C]1.00176[/C][/ROW]
[ROW][C]56[/C][C]101.15[/C][C]101.017[/C][C]101.247[/C][C]0.997723[/C][C]1.00132[/C][/ROW]
[ROW][C]57[/C][C]101.36[/C][C]101.47[/C][C]101.34[/C][C]1.00128[/C][C]0.998919[/C][/ROW]
[ROW][C]58[/C][C]101.98[/C][C]102.281[/C][C]101.449[/C][C]1.00821[/C][C]0.997053[/C][/ROW]
[ROW][C]59[/C][C]102.24[/C][C]102.481[/C][C]101.566[/C][C]1.00901[/C][C]0.99765[/C][/ROW]
[ROW][C]60[/C][C]102.34[/C][C]102.628[/C][C]101.687[/C][C]1.00926[/C][C]0.997192[/C][/ROW]
[ROW][C]61[/C][C]101.91[/C][C]101.543[/C][C]101.799[/C][C]0.997485[/C][C]1.00361[/C][/ROW]
[ROW][C]62[/C][C]101.8[/C][C]101.56[/C][C]101.873[/C][C]0.996931[/C][C]1.00236[/C][/ROW]
[ROW][C]63[/C][C]101.8[/C][C]101.563[/C][C]101.91[/C][C]0.996594[/C][C]1.00233[/C][/ROW]
[ROW][C]64[/C][C]101.73[/C][C]101.506[/C][C]101.94[/C][C]0.995738[/C][C]1.00221[/C][/ROW]
[ROW][C]65[/C][C]101.8[/C][C]101.476[/C][C]101.965[/C][C]0.995207[/C][C]1.00319[/C][/ROW]
[ROW][C]66[/C][C]101.81[/C][C]101.492[/C][C]101.975[/C][C]0.995264[/C][C]1.00313[/C][/ROW]
[ROW][C]67[/C][C]102.28[/C][C]NA[/C][C]NA[/C][C]0.997305[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]101.7[/C][C]NA[/C][C]NA[/C][C]0.997723[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]101.7[/C][C]NA[/C][C]NA[/C][C]1.00128[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]102.37[/C][C]NA[/C][C]NA[/C][C]1.00821[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.43[/C][C]NA[/C][C]NA[/C][C]1.00901[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]102.41[/C][C]NA[/C][C]NA[/C][C]1.00926[/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	97.78	NA	NA	0.997485	NA
2	97.73	NA	NA	0.996931	NA
3	97.61	NA	NA	0.996594	NA
4	97.69	NA	NA	0.995738	NA
5	97.68	NA	NA	0.995207	NA
6	97.67	NA	NA	0.995264	NA
7	97.67	98.0604	98.3254	0.997305	0.996019
8	97.96	98.2695	98.4937	0.997723	0.99685
9	98.27	98.8054	98.6792	1.00128	0.994581
10	99.52	99.6966	98.885	1.00821	0.998228
11	99.59	99.9844	99.0917	1.00901	0.996056
12	99.75	100.211	99.2921	1.00926	0.995396
13	99.75	99.2427	99.4929	0.997485	1.00511
14	99.8	99.3749	99.6808	0.996931	1.00428
15	99.99	99.5419	99.8821	0.996594	1.0045
16	100.25	99.6646	100.091	0.995738	1.00587
17	100.08	99.7989	100.28	0.995207	1.00282
18	100.08	99.9888	100.465	0.995264	1.00091
19	100.08	100.377	100.649	0.997305	0.997036
20	100.06	100.6	100.83	0.997723	0.994632
21	101	101.133	101.003	1.00128	0.998689
22	101.81	101.996	101.166	1.00821	0.998174
23	101.82	102.239	101.326	1.00901	0.995901
24	101.96	102.443	101.503	1.00926	0.995284
25	101.96	101.436	101.692	0.997485	1.00517
26	101.93	101.568	101.88	0.996931	1.00357
27	102.03	101.701	102.049	0.996594	1.00323
28	102.11	101.74	102.175	0.995738	1.00364
29	102.07	101.803	102.293	0.995207	1.00262
30	102.34	101.935	102.42	0.995264	1.00397
31	102.34	102.087	102.363	0.997305	1.00248
32	102.33	101.895	102.128	0.997723	1.00427
33	102.77	102.025	101.894	1.00128	1.00731
34	103.08	102.482	101.648	1.00821	1.00584
35	103.38	102.307	101.394	1.00901	1.01049
36	103.44	102.071	101.135	1.00926	1.01341
37	99.1	100.653	100.907	0.997485	0.984568
38	99.15	100.421	100.73	0.996931	0.987345
39	99.21	100.211	100.553	0.996594	0.990013
40	99.01	99.9621	100.39	0.995738	0.990475
41	99.08	99.7599	100.24	0.995207	0.993184
42	99.11	99.6068	100.081	0.995264	0.995012
43	100.11	99.7966	100.066	0.997305	1.00314
44	100.31	99.9731	100.201	0.997723	1.00337
45	100.55	100.455	100.327	1.00128	1.00094
46	101.38	101.264	100.44	1.00821	1.00115
47	101.49	101.455	100.549	1.00901	1.00035
48	101.5	101.586	100.654	1.00926	0.999153
49	100.69	100.491	100.745	0.997485	1.00198
50	100.8	100.51	100.819	0.996931	1.00289
51	100.58	100.544	100.888	0.996594	1.00036
52	100.34	100.516	100.947	0.995738	0.998245
53	100.38	100.519	101.003	0.995207	0.99862
54	100.33	100.59	101.069	0.995264	0.997411
55	101.06	100.882	101.155	0.997305	1.00176
56	101.15	101.017	101.247	0.997723	1.00132
57	101.36	101.47	101.34	1.00128	0.998919
58	101.98	102.281	101.449	1.00821	0.997053
59	102.24	102.481	101.566	1.00901	0.99765
60	102.34	102.628	101.687	1.00926	0.997192
61	101.91	101.543	101.799	0.997485	1.00361
62	101.8	101.56	101.873	0.996931	1.00236
63	101.8	101.563	101.91	0.996594	1.00233
64	101.73	101.506	101.94	0.995738	1.00221
65	101.8	101.476	101.965	0.995207	1.00319
66	101.81	101.492	101.975	0.995264	1.00313
67	102.28	NA	NA	0.997305	NA
68	101.7	NA	NA	0.997723	NA
69	101.7	NA	NA	1.00128	NA
70	102.37	NA	NA	1.00821	NA
71	102.43	NA	NA	1.00901	NA
72	102.41	NA	NA	1.00926	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