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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 01 May 2017 12:17:22 +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/May/01/t1493637466d2ieg99tikh78dx.htm/, Retrieved Sat, 18 Apr 2026 22:01:03 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 18 Apr 2026 22:01:03 +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	'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.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]'George Udny Yule' @ yule.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	'George Udny Yule' @ yule.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	78.46	NA	NA	1.00052	NA
2	78.59	NA	NA	1.00179	NA
3	81.37	NA	NA	1.00275	NA
4	83.61	NA	NA	1.0029	NA
5	83.85	NA	NA	1.00093	NA
6	84.08	NA	NA	1.00112	NA
7	84.56	84.0284	83.9179	1.00132	1.00633
8	84.65	84.5786	84.6221	0.999486	1.00084
9	85.41	85.0687	85.2558	0.997805	1.00401
10	85.75	85.4691	85.7192	0.997083	1.00329
11	86.21	85.737	86.0983	0.995803	1.00552
12	86.38	86.3446	86.4742	0.998502	1.00041
13	86.65	86.8799	86.8346	1.00052	0.997354
14	87.3	87.3367	87.1808	1.00179	0.999579
15	87.87	87.7409	87.5	1.00275	1.00147
16	88.23	88.0423	87.7879	1.0029	1.00213
17	88.33	88.1453	88.0638	1.00093	1.0021
18	88.62	88.4309	88.3321	1.00112	1.00214
19	88.67	88.74	88.6233	1.00132	0.999211
20	88.85	88.861	88.9067	0.999486	0.999876
21	88.87	88.9876	89.1833	0.997805	0.998679
22	89.2	89.2314	89.4925	0.997083	0.999648
23	89.38	89.4381	89.815	0.995803	0.999351
24	89.65	89.9954	90.1304	0.998502	0.996162
25	90.37	90.4959	90.4487	1.00052	0.998609
26	90.38	90.954	90.7917	1.00179	0.993689
27	91.43	91.4001	91.1492	1.00275	1.00033
28	92.09	91.7693	91.5042	1.0029	1.00349
29	92.21	91.9368	91.8517	1.00093	1.00297
30	92.31	92.3807	92.2775	1.00112	0.999235
31	92.62	92.9422	92.82	1.00132	0.996534
32	93.13	93.3783	93.4263	0.999486	0.997341
33	93.17	93.8124	94.0188	0.997805	0.993152
34	93.42	94.2721	94.5479	0.997083	0.990961
35	93.5	94.6569	95.0558	0.995803	0.987778
36	95.75	95.4193	95.5625	0.998502	1.00347
37	97.29	96.1076	96.0575	1.00052	1.0123
38	98.01	96.7039	96.5313	1.00179	1.01351
39	98.02	97.2691	97.0021	1.00275	1.00772
40	98.2	97.7574	97.475	1.0029	1.00453
41	98.29	98.042	97.9513	1.00093	1.00253
42	98.39	98.4529	98.3429	1.00112	0.999361
43	98.42	98.7081	98.5783	1.00132	0.997082
44	98.7	98.676	98.7267	0.999486	1.00024
45	98.9	98.6497	98.8667	0.997805	1.00254
46	99.04	98.7266	99.0154	0.997083	1.00317
47	99.31	98.7542	99.1704	0.995803	1.00563
48	99.34	99.2032	99.3521	0.998502	1.00138
49	99.35	99.6161	99.5642	1.00052	0.997329
50	99.51	99.9651	99.7867	1.00179	0.995447
51	99.88	100.273	99.9979	1.00275	0.996078
52	99.91	100.501	100.211	1.0029	0.994118
53	100.3	100.513	100.42	1.00093	0.997885
54	100.74	100.737	100.625	1.00112	1.00003
55	101.16	100.971	100.838	1.00132	1.00187
56	101.3	101.025	101.077	0.999486	1.00272
57	101.37	101.103	101.325	0.997805	1.00264
58	101.68	101.28	101.576	0.997083	1.00395
59	101.68	101.401	101.828	0.995803	1.00275
60	101.89	101.93	102.083	0.998502	0.999608
61	101.93	102.398	102.345	1.00052	0.99543
62	102.66	102.783	102.6	1.00179	0.998803
63	102.68	103.152	102.868	1.00275	0.995429
64	103.13	103.466	103.167	1.0029	0.996753
65	103.14	103.602	103.506	1.00093	0.995543
66	104.01	103.983	103.867	1.00112	1.00026
67	104.17	NA	NA	1.00132	NA
68	104.41	NA	NA	0.999486	NA
69	104.71	NA	NA	0.997805	NA
70	105.51	NA	NA	0.997083	NA
71	105.98	NA	NA	0.995803	NA
72	106.25	NA	NA	0.998502	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 78.46 & NA & NA & 1.00052 & NA \tabularnewline
2 & 78.59 & NA & NA & 1.00179 & NA \tabularnewline
3 & 81.37 & NA & NA & 1.00275 & NA \tabularnewline
4 & 83.61 & NA & NA & 1.0029 & NA \tabularnewline
5 & 83.85 & NA & NA & 1.00093 & NA \tabularnewline
6 & 84.08 & NA & NA & 1.00112 & NA \tabularnewline
7 & 84.56 & 84.0284 & 83.9179 & 1.00132 & 1.00633 \tabularnewline
8 & 84.65 & 84.5786 & 84.6221 & 0.999486 & 1.00084 \tabularnewline
9 & 85.41 & 85.0687 & 85.2558 & 0.997805 & 1.00401 \tabularnewline
10 & 85.75 & 85.4691 & 85.7192 & 0.997083 & 1.00329 \tabularnewline
11 & 86.21 & 85.737 & 86.0983 & 0.995803 & 1.00552 \tabularnewline
12 & 86.38 & 86.3446 & 86.4742 & 0.998502 & 1.00041 \tabularnewline
13 & 86.65 & 86.8799 & 86.8346 & 1.00052 & 0.997354 \tabularnewline
14 & 87.3 & 87.3367 & 87.1808 & 1.00179 & 0.999579 \tabularnewline
15 & 87.87 & 87.7409 & 87.5 & 1.00275 & 1.00147 \tabularnewline
16 & 88.23 & 88.0423 & 87.7879 & 1.0029 & 1.00213 \tabularnewline
17 & 88.33 & 88.1453 & 88.0638 & 1.00093 & 1.0021 \tabularnewline
18 & 88.62 & 88.4309 & 88.3321 & 1.00112 & 1.00214 \tabularnewline
19 & 88.67 & 88.74 & 88.6233 & 1.00132 & 0.999211 \tabularnewline
20 & 88.85 & 88.861 & 88.9067 & 0.999486 & 0.999876 \tabularnewline
21 & 88.87 & 88.9876 & 89.1833 & 0.997805 & 0.998679 \tabularnewline
22 & 89.2 & 89.2314 & 89.4925 & 0.997083 & 0.999648 \tabularnewline
23 & 89.38 & 89.4381 & 89.815 & 0.995803 & 0.999351 \tabularnewline
24 & 89.65 & 89.9954 & 90.1304 & 0.998502 & 0.996162 \tabularnewline
25 & 90.37 & 90.4959 & 90.4487 & 1.00052 & 0.998609 \tabularnewline
26 & 90.38 & 90.954 & 90.7917 & 1.00179 & 0.993689 \tabularnewline
27 & 91.43 & 91.4001 & 91.1492 & 1.00275 & 1.00033 \tabularnewline
28 & 92.09 & 91.7693 & 91.5042 & 1.0029 & 1.00349 \tabularnewline
29 & 92.21 & 91.9368 & 91.8517 & 1.00093 & 1.00297 \tabularnewline
30 & 92.31 & 92.3807 & 92.2775 & 1.00112 & 0.999235 \tabularnewline
31 & 92.62 & 92.9422 & 92.82 & 1.00132 & 0.996534 \tabularnewline
32 & 93.13 & 93.3783 & 93.4263 & 0.999486 & 0.997341 \tabularnewline
33 & 93.17 & 93.8124 & 94.0188 & 0.997805 & 0.993152 \tabularnewline
34 & 93.42 & 94.2721 & 94.5479 & 0.997083 & 0.990961 \tabularnewline
35 & 93.5 & 94.6569 & 95.0558 & 0.995803 & 0.987778 \tabularnewline
36 & 95.75 & 95.4193 & 95.5625 & 0.998502 & 1.00347 \tabularnewline
37 & 97.29 & 96.1076 & 96.0575 & 1.00052 & 1.0123 \tabularnewline
38 & 98.01 & 96.7039 & 96.5313 & 1.00179 & 1.01351 \tabularnewline
39 & 98.02 & 97.2691 & 97.0021 & 1.00275 & 1.00772 \tabularnewline
40 & 98.2 & 97.7574 & 97.475 & 1.0029 & 1.00453 \tabularnewline
41 & 98.29 & 98.042 & 97.9513 & 1.00093 & 1.00253 \tabularnewline
42 & 98.39 & 98.4529 & 98.3429 & 1.00112 & 0.999361 \tabularnewline
43 & 98.42 & 98.7081 & 98.5783 & 1.00132 & 0.997082 \tabularnewline
44 & 98.7 & 98.676 & 98.7267 & 0.999486 & 1.00024 \tabularnewline
45 & 98.9 & 98.6497 & 98.8667 & 0.997805 & 1.00254 \tabularnewline
46 & 99.04 & 98.7266 & 99.0154 & 0.997083 & 1.00317 \tabularnewline
47 & 99.31 & 98.7542 & 99.1704 & 0.995803 & 1.00563 \tabularnewline
48 & 99.34 & 99.2032 & 99.3521 & 0.998502 & 1.00138 \tabularnewline
49 & 99.35 & 99.6161 & 99.5642 & 1.00052 & 0.997329 \tabularnewline
50 & 99.51 & 99.9651 & 99.7867 & 1.00179 & 0.995447 \tabularnewline
51 & 99.88 & 100.273 & 99.9979 & 1.00275 & 0.996078 \tabularnewline
52 & 99.91 & 100.501 & 100.211 & 1.0029 & 0.994118 \tabularnewline
53 & 100.3 & 100.513 & 100.42 & 1.00093 & 0.997885 \tabularnewline
54 & 100.74 & 100.737 & 100.625 & 1.00112 & 1.00003 \tabularnewline
55 & 101.16 & 100.971 & 100.838 & 1.00132 & 1.00187 \tabularnewline
56 & 101.3 & 101.025 & 101.077 & 0.999486 & 1.00272 \tabularnewline
57 & 101.37 & 101.103 & 101.325 & 0.997805 & 1.00264 \tabularnewline
58 & 101.68 & 101.28 & 101.576 & 0.997083 & 1.00395 \tabularnewline
59 & 101.68 & 101.401 & 101.828 & 0.995803 & 1.00275 \tabularnewline
60 & 101.89 & 101.93 & 102.083 & 0.998502 & 0.999608 \tabularnewline
61 & 101.93 & 102.398 & 102.345 & 1.00052 & 0.99543 \tabularnewline
62 & 102.66 & 102.783 & 102.6 & 1.00179 & 0.998803 \tabularnewline
63 & 102.68 & 103.152 & 102.868 & 1.00275 & 0.995429 \tabularnewline
64 & 103.13 & 103.466 & 103.167 & 1.0029 & 0.996753 \tabularnewline
65 & 103.14 & 103.602 & 103.506 & 1.00093 & 0.995543 \tabularnewline
66 & 104.01 & 103.983 & 103.867 & 1.00112 & 1.00026 \tabularnewline
67 & 104.17 & NA & NA & 1.00132 & NA \tabularnewline
68 & 104.41 & NA & NA & 0.999486 & NA \tabularnewline
69 & 104.71 & NA & NA & 0.997805 & NA \tabularnewline
70 & 105.51 & NA & NA & 0.997083 & NA \tabularnewline
71 & 105.98 & NA & NA & 0.995803 & NA \tabularnewline
72 & 106.25 & NA & NA & 0.998502 & 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]78.46[/C][C]NA[/C][C]NA[/C][C]1.00052[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]78.59[/C][C]NA[/C][C]NA[/C][C]1.00179[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]81.37[/C][C]NA[/C][C]NA[/C][C]1.00275[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]83.61[/C][C]NA[/C][C]NA[/C][C]1.0029[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]83.85[/C][C]NA[/C][C]NA[/C][C]1.00093[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]84.08[/C][C]NA[/C][C]NA[/C][C]1.00112[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]84.56[/C][C]84.0284[/C][C]83.9179[/C][C]1.00132[/C][C]1.00633[/C][/ROW]
[ROW][C]8[/C][C]84.65[/C][C]84.5786[/C][C]84.6221[/C][C]0.999486[/C][C]1.00084[/C][/ROW]
[ROW][C]9[/C][C]85.41[/C][C]85.0687[/C][C]85.2558[/C][C]0.997805[/C][C]1.00401[/C][/ROW]
[ROW][C]10[/C][C]85.75[/C][C]85.4691[/C][C]85.7192[/C][C]0.997083[/C][C]1.00329[/C][/ROW]
[ROW][C]11[/C][C]86.21[/C][C]85.737[/C][C]86.0983[/C][C]0.995803[/C][C]1.00552[/C][/ROW]
[ROW][C]12[/C][C]86.38[/C][C]86.3446[/C][C]86.4742[/C][C]0.998502[/C][C]1.00041[/C][/ROW]
[ROW][C]13[/C][C]86.65[/C][C]86.8799[/C][C]86.8346[/C][C]1.00052[/C][C]0.997354[/C][/ROW]
[ROW][C]14[/C][C]87.3[/C][C]87.3367[/C][C]87.1808[/C][C]1.00179[/C][C]0.999579[/C][/ROW]
[ROW][C]15[/C][C]87.87[/C][C]87.7409[/C][C]87.5[/C][C]1.00275[/C][C]1.00147[/C][/ROW]
[ROW][C]16[/C][C]88.23[/C][C]88.0423[/C][C]87.7879[/C][C]1.0029[/C][C]1.00213[/C][/ROW]
[ROW][C]17[/C][C]88.33[/C][C]88.1453[/C][C]88.0638[/C][C]1.00093[/C][C]1.0021[/C][/ROW]
[ROW][C]18[/C][C]88.62[/C][C]88.4309[/C][C]88.3321[/C][C]1.00112[/C][C]1.00214[/C][/ROW]
[ROW][C]19[/C][C]88.67[/C][C]88.74[/C][C]88.6233[/C][C]1.00132[/C][C]0.999211[/C][/ROW]
[ROW][C]20[/C][C]88.85[/C][C]88.861[/C][C]88.9067[/C][C]0.999486[/C][C]0.999876[/C][/ROW]
[ROW][C]21[/C][C]88.87[/C][C]88.9876[/C][C]89.1833[/C][C]0.997805[/C][C]0.998679[/C][/ROW]
[ROW][C]22[/C][C]89.2[/C][C]89.2314[/C][C]89.4925[/C][C]0.997083[/C][C]0.999648[/C][/ROW]
[ROW][C]23[/C][C]89.38[/C][C]89.4381[/C][C]89.815[/C][C]0.995803[/C][C]0.999351[/C][/ROW]
[ROW][C]24[/C][C]89.65[/C][C]89.9954[/C][C]90.1304[/C][C]0.998502[/C][C]0.996162[/C][/ROW]
[ROW][C]25[/C][C]90.37[/C][C]90.4959[/C][C]90.4487[/C][C]1.00052[/C][C]0.998609[/C][/ROW]
[ROW][C]26[/C][C]90.38[/C][C]90.954[/C][C]90.7917[/C][C]1.00179[/C][C]0.993689[/C][/ROW]
[ROW][C]27[/C][C]91.43[/C][C]91.4001[/C][C]91.1492[/C][C]1.00275[/C][C]1.00033[/C][/ROW]
[ROW][C]28[/C][C]92.09[/C][C]91.7693[/C][C]91.5042[/C][C]1.0029[/C][C]1.00349[/C][/ROW]
[ROW][C]29[/C][C]92.21[/C][C]91.9368[/C][C]91.8517[/C][C]1.00093[/C][C]1.00297[/C][/ROW]
[ROW][C]30[/C][C]92.31[/C][C]92.3807[/C][C]92.2775[/C][C]1.00112[/C][C]0.999235[/C][/ROW]
[ROW][C]31[/C][C]92.62[/C][C]92.9422[/C][C]92.82[/C][C]1.00132[/C][C]0.996534[/C][/ROW]
[ROW][C]32[/C][C]93.13[/C][C]93.3783[/C][C]93.4263[/C][C]0.999486[/C][C]0.997341[/C][/ROW]
[ROW][C]33[/C][C]93.17[/C][C]93.8124[/C][C]94.0188[/C][C]0.997805[/C][C]0.993152[/C][/ROW]
[ROW][C]34[/C][C]93.42[/C][C]94.2721[/C][C]94.5479[/C][C]0.997083[/C][C]0.990961[/C][/ROW]
[ROW][C]35[/C][C]93.5[/C][C]94.6569[/C][C]95.0558[/C][C]0.995803[/C][C]0.987778[/C][/ROW]
[ROW][C]36[/C][C]95.75[/C][C]95.4193[/C][C]95.5625[/C][C]0.998502[/C][C]1.00347[/C][/ROW]
[ROW][C]37[/C][C]97.29[/C][C]96.1076[/C][C]96.0575[/C][C]1.00052[/C][C]1.0123[/C][/ROW]
[ROW][C]38[/C][C]98.01[/C][C]96.7039[/C][C]96.5313[/C][C]1.00179[/C][C]1.01351[/C][/ROW]
[ROW][C]39[/C][C]98.02[/C][C]97.2691[/C][C]97.0021[/C][C]1.00275[/C][C]1.00772[/C][/ROW]
[ROW][C]40[/C][C]98.2[/C][C]97.7574[/C][C]97.475[/C][C]1.0029[/C][C]1.00453[/C][/ROW]
[ROW][C]41[/C][C]98.29[/C][C]98.042[/C][C]97.9513[/C][C]1.00093[/C][C]1.00253[/C][/ROW]
[ROW][C]42[/C][C]98.39[/C][C]98.4529[/C][C]98.3429[/C][C]1.00112[/C][C]0.999361[/C][/ROW]
[ROW][C]43[/C][C]98.42[/C][C]98.7081[/C][C]98.5783[/C][C]1.00132[/C][C]0.997082[/C][/ROW]
[ROW][C]44[/C][C]98.7[/C][C]98.676[/C][C]98.7267[/C][C]0.999486[/C][C]1.00024[/C][/ROW]
[ROW][C]45[/C][C]98.9[/C][C]98.6497[/C][C]98.8667[/C][C]0.997805[/C][C]1.00254[/C][/ROW]
[ROW][C]46[/C][C]99.04[/C][C]98.7266[/C][C]99.0154[/C][C]0.997083[/C][C]1.00317[/C][/ROW]
[ROW][C]47[/C][C]99.31[/C][C]98.7542[/C][C]99.1704[/C][C]0.995803[/C][C]1.00563[/C][/ROW]
[ROW][C]48[/C][C]99.34[/C][C]99.2032[/C][C]99.3521[/C][C]0.998502[/C][C]1.00138[/C][/ROW]
[ROW][C]49[/C][C]99.35[/C][C]99.6161[/C][C]99.5642[/C][C]1.00052[/C][C]0.997329[/C][/ROW]
[ROW][C]50[/C][C]99.51[/C][C]99.9651[/C][C]99.7867[/C][C]1.00179[/C][C]0.995447[/C][/ROW]
[ROW][C]51[/C][C]99.88[/C][C]100.273[/C][C]99.9979[/C][C]1.00275[/C][C]0.996078[/C][/ROW]
[ROW][C]52[/C][C]99.91[/C][C]100.501[/C][C]100.211[/C][C]1.0029[/C][C]0.994118[/C][/ROW]
[ROW][C]53[/C][C]100.3[/C][C]100.513[/C][C]100.42[/C][C]1.00093[/C][C]0.997885[/C][/ROW]
[ROW][C]54[/C][C]100.74[/C][C]100.737[/C][C]100.625[/C][C]1.00112[/C][C]1.00003[/C][/ROW]
[ROW][C]55[/C][C]101.16[/C][C]100.971[/C][C]100.838[/C][C]1.00132[/C][C]1.00187[/C][/ROW]
[ROW][C]56[/C][C]101.3[/C][C]101.025[/C][C]101.077[/C][C]0.999486[/C][C]1.00272[/C][/ROW]
[ROW][C]57[/C][C]101.37[/C][C]101.103[/C][C]101.325[/C][C]0.997805[/C][C]1.00264[/C][/ROW]
[ROW][C]58[/C][C]101.68[/C][C]101.28[/C][C]101.576[/C][C]0.997083[/C][C]1.00395[/C][/ROW]
[ROW][C]59[/C][C]101.68[/C][C]101.401[/C][C]101.828[/C][C]0.995803[/C][C]1.00275[/C][/ROW]
[ROW][C]60[/C][C]101.89[/C][C]101.93[/C][C]102.083[/C][C]0.998502[/C][C]0.999608[/C][/ROW]
[ROW][C]61[/C][C]101.93[/C][C]102.398[/C][C]102.345[/C][C]1.00052[/C][C]0.99543[/C][/ROW]
[ROW][C]62[/C][C]102.66[/C][C]102.783[/C][C]102.6[/C][C]1.00179[/C][C]0.998803[/C][/ROW]
[ROW][C]63[/C][C]102.68[/C][C]103.152[/C][C]102.868[/C][C]1.00275[/C][C]0.995429[/C][/ROW]
[ROW][C]64[/C][C]103.13[/C][C]103.466[/C][C]103.167[/C][C]1.0029[/C][C]0.996753[/C][/ROW]
[ROW][C]65[/C][C]103.14[/C][C]103.602[/C][C]103.506[/C][C]1.00093[/C][C]0.995543[/C][/ROW]
[ROW][C]66[/C][C]104.01[/C][C]103.983[/C][C]103.867[/C][C]1.00112[/C][C]1.00026[/C][/ROW]
[ROW][C]67[/C][C]104.17[/C][C]NA[/C][C]NA[/C][C]1.00132[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]104.41[/C][C]NA[/C][C]NA[/C][C]0.999486[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]104.71[/C][C]NA[/C][C]NA[/C][C]0.997805[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]105.51[/C][C]NA[/C][C]NA[/C][C]0.997083[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]105.98[/C][C]NA[/C][C]NA[/C][C]0.995803[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]106.25[/C][C]NA[/C][C]NA[/C][C]0.998502[/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	78.46	NA	NA	1.00052	NA
2	78.59	NA	NA	1.00179	NA
3	81.37	NA	NA	1.00275	NA
4	83.61	NA	NA	1.0029	NA
5	83.85	NA	NA	1.00093	NA
6	84.08	NA	NA	1.00112	NA
7	84.56	84.0284	83.9179	1.00132	1.00633
8	84.65	84.5786	84.6221	0.999486	1.00084
9	85.41	85.0687	85.2558	0.997805	1.00401
10	85.75	85.4691	85.7192	0.997083	1.00329
11	86.21	85.737	86.0983	0.995803	1.00552
12	86.38	86.3446	86.4742	0.998502	1.00041
13	86.65	86.8799	86.8346	1.00052	0.997354
14	87.3	87.3367	87.1808	1.00179	0.999579
15	87.87	87.7409	87.5	1.00275	1.00147
16	88.23	88.0423	87.7879	1.0029	1.00213
17	88.33	88.1453	88.0638	1.00093	1.0021
18	88.62	88.4309	88.3321	1.00112	1.00214
19	88.67	88.74	88.6233	1.00132	0.999211
20	88.85	88.861	88.9067	0.999486	0.999876
21	88.87	88.9876	89.1833	0.997805	0.998679
22	89.2	89.2314	89.4925	0.997083	0.999648
23	89.38	89.4381	89.815	0.995803	0.999351
24	89.65	89.9954	90.1304	0.998502	0.996162
25	90.37	90.4959	90.4487	1.00052	0.998609
26	90.38	90.954	90.7917	1.00179	0.993689
27	91.43	91.4001	91.1492	1.00275	1.00033
28	92.09	91.7693	91.5042	1.0029	1.00349
29	92.21	91.9368	91.8517	1.00093	1.00297
30	92.31	92.3807	92.2775	1.00112	0.999235
31	92.62	92.9422	92.82	1.00132	0.996534
32	93.13	93.3783	93.4263	0.999486	0.997341
33	93.17	93.8124	94.0188	0.997805	0.993152
34	93.42	94.2721	94.5479	0.997083	0.990961
35	93.5	94.6569	95.0558	0.995803	0.987778
36	95.75	95.4193	95.5625	0.998502	1.00347
37	97.29	96.1076	96.0575	1.00052	1.0123
38	98.01	96.7039	96.5313	1.00179	1.01351
39	98.02	97.2691	97.0021	1.00275	1.00772
40	98.2	97.7574	97.475	1.0029	1.00453
41	98.29	98.042	97.9513	1.00093	1.00253
42	98.39	98.4529	98.3429	1.00112	0.999361
43	98.42	98.7081	98.5783	1.00132	0.997082
44	98.7	98.676	98.7267	0.999486	1.00024
45	98.9	98.6497	98.8667	0.997805	1.00254
46	99.04	98.7266	99.0154	0.997083	1.00317
47	99.31	98.7542	99.1704	0.995803	1.00563
48	99.34	99.2032	99.3521	0.998502	1.00138
49	99.35	99.6161	99.5642	1.00052	0.997329
50	99.51	99.9651	99.7867	1.00179	0.995447
51	99.88	100.273	99.9979	1.00275	0.996078
52	99.91	100.501	100.211	1.0029	0.994118
53	100.3	100.513	100.42	1.00093	0.997885
54	100.74	100.737	100.625	1.00112	1.00003
55	101.16	100.971	100.838	1.00132	1.00187
56	101.3	101.025	101.077	0.999486	1.00272
57	101.37	101.103	101.325	0.997805	1.00264
58	101.68	101.28	101.576	0.997083	1.00395
59	101.68	101.401	101.828	0.995803	1.00275
60	101.89	101.93	102.083	0.998502	0.999608
61	101.93	102.398	102.345	1.00052	0.99543
62	102.66	102.783	102.6	1.00179	0.998803
63	102.68	103.152	102.868	1.00275	0.995429
64	103.13	103.466	103.167	1.0029	0.996753
65	103.14	103.602	103.506	1.00093	0.995543
66	104.01	103.983	103.867	1.00112	1.00026
67	104.17	NA	NA	1.00132	NA
68	104.41	NA	NA	0.999486	NA
69	104.71	NA	NA	0.997805	NA
70	105.51	NA	NA	0.997083	NA
71	105.98	NA	NA	0.995803	NA
72	106.25	NA	NA	0.998502	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