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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Wed, 25 Nov 2015 21:43:07 +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/2015/Nov/25/t1448487802kctimfaw2j1u5ie.htm/, Retrieved Wed, 15 May 2024 04:56:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284164, Retrieved Wed, 15 May 2024 04:56:59 +0000

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-       [Classical Decomposition] [] [2015-11-25 21:43:07] [baf7db162d56d42e62a4d339fc25c05c] [Current]

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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	'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284164&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284164&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284164&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	79.21	NA	NA	1.00305	NA
2	79.08	NA	NA	1.00765	NA
3	79.88	NA	NA	1.01957	NA
4	80.57	NA	NA	1.02806	NA
5	80.9	NA	NA	1.02344	NA
6	80.89	NA	NA	1.00462	NA
7	80.61	82.1044	82.3596	0.996902	0.981798
8	80.98	82.806	83.805	0.98808	0.977948
9	81.68	83.9466	85.3571	0.983476	0.972999
10	83.28	84.9524	86.8737	0.977884	0.980313
11	83.94	86.3662	88.285	0.978266	0.971908
12	89.25	88.358	89.3417	0.98899	1.0101
13	95.3	90.3301	90.055	1.00305	1.05502
14	97.68	91.3726	90.6788	1.00765	1.06903
15	98.53	92.988	91.2029	1.01957	1.0596
16	98.32	94.1178	91.5492	1.02806	1.04465
17	97.02	93.9288	91.7771	1.02344	1.03291
18	90.13	92.1716	91.7475	1.00462	0.97785
19	88.49	90.9299	91.2125	0.996902	0.973167
20	88.07	89.2821	90.3592	0.98808	0.986424
21	87.17	87.9141	89.3912	0.983476	0.991536
22	86.1	86.427	88.3817	0.977884	0.996216
23	86.59	85.5065	87.4063	0.978266	1.01267
24	85.89	85.7838	86.7388	0.98899	1.00124
25	85.82	86.7057	86.4417	1.00305	0.989785
26	86.68	86.9356	86.2754	1.00765	0.99706
27	86.3	87.885	86.1979	1.01957	0.981965
28	86.32	88.5517	86.135	1.02806	0.974798
29	85.61	88.0478	86.0308	1.02344	0.972313
30	85.52	86.3917	85.9942	1.00462	0.98991
31	85.97	85.8553	86.1221	0.996902	1.00134
32	86.6	85.2898	86.3188	0.98808	1.01536
33	86.78	85.3681	86.8025	0.983476	1.01654
34	84.98	85.8655	87.8075	0.977884	0.989687
35	85.21	87.1875	89.1246	0.978266	0.977319
36	86.39	89.5588	90.5558	0.98899	0.964617
37	88.39	92.3278	92.0467	1.00305	0.95735
38	88.83	94.2981	93.5821	1.00765	0.942012
39	95.76	96.9898	95.1279	1.01957	0.98732
40	100.98	99.4551	96.7408	1.02806	1.01533
41	102.56	100.68	98.3742	1.02344	1.01867
42	102.92	100.392	99.93	1.00462	1.02518
43	104.35	101.058	101.372	0.996902	1.03257
44	105.07	101.479	102.703	0.98808	1.03539
45	105.41	101.984	103.697	0.983476	1.03359
46	105.06	101.842	104.145	0.977884	1.0316
47	104.33	101.946	104.211	0.978266	1.02339
48	104.61	102.913	104.058	0.98899	1.01649
49	104.78	104.092	103.775	1.00305	1.00661
50	104.38	104.032	103.242	1.00765	1.00334
51	104.08	104.459	102.454	1.01957	0.996372
52	103.4	104.5	101.648	1.02806	0.989475
53	101.72	103.315	100.949	1.02344	0.984558
54	100.1	100.78	100.316	1.00462	0.993253
55	100.37	99.3542	99.6629	0.996902	1.01022
56	96.27	97.8376	99.0179	0.98808	0.983977
57	95.28	96.7867	98.4129	0.983476	0.984433
58	95.85	95.7271	97.8921	0.977884	1.00128
59	96.76	95.4412	97.5617	0.978266	1.01382
60	97	96.3585	97.4312	0.98899	1.00666
61	96.71	97.6536	97.3562	1.00305	0.990337
62	96.97	98.1818	97.4362	1.00765	0.987658
63	96.97	99.5744	97.6629	1.01957	0.973845
64	98.01	100.539	97.795	1.02806	0.974847
65	99.18	100.116	97.8225	1.02344	0.990652
66	99.51	98.2421	97.79	1.00462	1.01291
67	99.16	NA	NA	0.996902	NA
68	99.4	NA	NA	0.98808	NA
69	97.59	NA	NA	0.983476	NA
70	96.71	NA	NA	0.977884	NA
71	96.56	NA	NA	0.978266	NA
72	96.42	NA	NA	0.98899	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 79.21 & NA & NA & 1.00305 & NA \tabularnewline
2 & 79.08 & NA & NA & 1.00765 & NA \tabularnewline
3 & 79.88 & NA & NA & 1.01957 & NA \tabularnewline
4 & 80.57 & NA & NA & 1.02806 & NA \tabularnewline
5 & 80.9 & NA & NA & 1.02344 & NA \tabularnewline
6 & 80.89 & NA & NA & 1.00462 & NA \tabularnewline
7 & 80.61 & 82.1044 & 82.3596 & 0.996902 & 0.981798 \tabularnewline
8 & 80.98 & 82.806 & 83.805 & 0.98808 & 0.977948 \tabularnewline
9 & 81.68 & 83.9466 & 85.3571 & 0.983476 & 0.972999 \tabularnewline
10 & 83.28 & 84.9524 & 86.8737 & 0.977884 & 0.980313 \tabularnewline
11 & 83.94 & 86.3662 & 88.285 & 0.978266 & 0.971908 \tabularnewline
12 & 89.25 & 88.358 & 89.3417 & 0.98899 & 1.0101 \tabularnewline
13 & 95.3 & 90.3301 & 90.055 & 1.00305 & 1.05502 \tabularnewline
14 & 97.68 & 91.3726 & 90.6788 & 1.00765 & 1.06903 \tabularnewline
15 & 98.53 & 92.988 & 91.2029 & 1.01957 & 1.0596 \tabularnewline
16 & 98.32 & 94.1178 & 91.5492 & 1.02806 & 1.04465 \tabularnewline
17 & 97.02 & 93.9288 & 91.7771 & 1.02344 & 1.03291 \tabularnewline
18 & 90.13 & 92.1716 & 91.7475 & 1.00462 & 0.97785 \tabularnewline
19 & 88.49 & 90.9299 & 91.2125 & 0.996902 & 0.973167 \tabularnewline
20 & 88.07 & 89.2821 & 90.3592 & 0.98808 & 0.986424 \tabularnewline
21 & 87.17 & 87.9141 & 89.3912 & 0.983476 & 0.991536 \tabularnewline
22 & 86.1 & 86.427 & 88.3817 & 0.977884 & 0.996216 \tabularnewline
23 & 86.59 & 85.5065 & 87.4063 & 0.978266 & 1.01267 \tabularnewline
24 & 85.89 & 85.7838 & 86.7388 & 0.98899 & 1.00124 \tabularnewline
25 & 85.82 & 86.7057 & 86.4417 & 1.00305 & 0.989785 \tabularnewline
26 & 86.68 & 86.9356 & 86.2754 & 1.00765 & 0.99706 \tabularnewline
27 & 86.3 & 87.885 & 86.1979 & 1.01957 & 0.981965 \tabularnewline
28 & 86.32 & 88.5517 & 86.135 & 1.02806 & 0.974798 \tabularnewline
29 & 85.61 & 88.0478 & 86.0308 & 1.02344 & 0.972313 \tabularnewline
30 & 85.52 & 86.3917 & 85.9942 & 1.00462 & 0.98991 \tabularnewline
31 & 85.97 & 85.8553 & 86.1221 & 0.996902 & 1.00134 \tabularnewline
32 & 86.6 & 85.2898 & 86.3188 & 0.98808 & 1.01536 \tabularnewline
33 & 86.78 & 85.3681 & 86.8025 & 0.983476 & 1.01654 \tabularnewline
34 & 84.98 & 85.8655 & 87.8075 & 0.977884 & 0.989687 \tabularnewline
35 & 85.21 & 87.1875 & 89.1246 & 0.978266 & 0.977319 \tabularnewline
36 & 86.39 & 89.5588 & 90.5558 & 0.98899 & 0.964617 \tabularnewline
37 & 88.39 & 92.3278 & 92.0467 & 1.00305 & 0.95735 \tabularnewline
38 & 88.83 & 94.2981 & 93.5821 & 1.00765 & 0.942012 \tabularnewline
39 & 95.76 & 96.9898 & 95.1279 & 1.01957 & 0.98732 \tabularnewline
40 & 100.98 & 99.4551 & 96.7408 & 1.02806 & 1.01533 \tabularnewline
41 & 102.56 & 100.68 & 98.3742 & 1.02344 & 1.01867 \tabularnewline
42 & 102.92 & 100.392 & 99.93 & 1.00462 & 1.02518 \tabularnewline
43 & 104.35 & 101.058 & 101.372 & 0.996902 & 1.03257 \tabularnewline
44 & 105.07 & 101.479 & 102.703 & 0.98808 & 1.03539 \tabularnewline
45 & 105.41 & 101.984 & 103.697 & 0.983476 & 1.03359 \tabularnewline
46 & 105.06 & 101.842 & 104.145 & 0.977884 & 1.0316 \tabularnewline
47 & 104.33 & 101.946 & 104.211 & 0.978266 & 1.02339 \tabularnewline
48 & 104.61 & 102.913 & 104.058 & 0.98899 & 1.01649 \tabularnewline
49 & 104.78 & 104.092 & 103.775 & 1.00305 & 1.00661 \tabularnewline
50 & 104.38 & 104.032 & 103.242 & 1.00765 & 1.00334 \tabularnewline
51 & 104.08 & 104.459 & 102.454 & 1.01957 & 0.996372 \tabularnewline
52 & 103.4 & 104.5 & 101.648 & 1.02806 & 0.989475 \tabularnewline
53 & 101.72 & 103.315 & 100.949 & 1.02344 & 0.984558 \tabularnewline
54 & 100.1 & 100.78 & 100.316 & 1.00462 & 0.993253 \tabularnewline
55 & 100.37 & 99.3542 & 99.6629 & 0.996902 & 1.01022 \tabularnewline
56 & 96.27 & 97.8376 & 99.0179 & 0.98808 & 0.983977 \tabularnewline
57 & 95.28 & 96.7867 & 98.4129 & 0.983476 & 0.984433 \tabularnewline
58 & 95.85 & 95.7271 & 97.8921 & 0.977884 & 1.00128 \tabularnewline
59 & 96.76 & 95.4412 & 97.5617 & 0.978266 & 1.01382 \tabularnewline
60 & 97 & 96.3585 & 97.4312 & 0.98899 & 1.00666 \tabularnewline
61 & 96.71 & 97.6536 & 97.3562 & 1.00305 & 0.990337 \tabularnewline
62 & 96.97 & 98.1818 & 97.4362 & 1.00765 & 0.987658 \tabularnewline
63 & 96.97 & 99.5744 & 97.6629 & 1.01957 & 0.973845 \tabularnewline
64 & 98.01 & 100.539 & 97.795 & 1.02806 & 0.974847 \tabularnewline
65 & 99.18 & 100.116 & 97.8225 & 1.02344 & 0.990652 \tabularnewline
66 & 99.51 & 98.2421 & 97.79 & 1.00462 & 1.01291 \tabularnewline
67 & 99.16 & NA & NA & 0.996902 & NA \tabularnewline
68 & 99.4 & NA & NA & 0.98808 & NA \tabularnewline
69 & 97.59 & NA & NA & 0.983476 & NA \tabularnewline
70 & 96.71 & NA & NA & 0.977884 & NA \tabularnewline
71 & 96.56 & NA & NA & 0.978266 & NA \tabularnewline
72 & 96.42 & NA & NA & 0.98899 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284164&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]79.21[/C][C]NA[/C][C]NA[/C][C]1.00305[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]79.08[/C][C]NA[/C][C]NA[/C][C]1.00765[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]79.88[/C][C]NA[/C][C]NA[/C][C]1.01957[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]80.57[/C][C]NA[/C][C]NA[/C][C]1.02806[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]80.9[/C][C]NA[/C][C]NA[/C][C]1.02344[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]80.89[/C][C]NA[/C][C]NA[/C][C]1.00462[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]80.61[/C][C]82.1044[/C][C]82.3596[/C][C]0.996902[/C][C]0.981798[/C][/ROW]
[ROW][C]8[/C][C]80.98[/C][C]82.806[/C][C]83.805[/C][C]0.98808[/C][C]0.977948[/C][/ROW]
[ROW][C]9[/C][C]81.68[/C][C]83.9466[/C][C]85.3571[/C][C]0.983476[/C][C]0.972999[/C][/ROW]
[ROW][C]10[/C][C]83.28[/C][C]84.9524[/C][C]86.8737[/C][C]0.977884[/C][C]0.980313[/C][/ROW]
[ROW][C]11[/C][C]83.94[/C][C]86.3662[/C][C]88.285[/C][C]0.978266[/C][C]0.971908[/C][/ROW]
[ROW][C]12[/C][C]89.25[/C][C]88.358[/C][C]89.3417[/C][C]0.98899[/C][C]1.0101[/C][/ROW]
[ROW][C]13[/C][C]95.3[/C][C]90.3301[/C][C]90.055[/C][C]1.00305[/C][C]1.05502[/C][/ROW]
[ROW][C]14[/C][C]97.68[/C][C]91.3726[/C][C]90.6788[/C][C]1.00765[/C][C]1.06903[/C][/ROW]
[ROW][C]15[/C][C]98.53[/C][C]92.988[/C][C]91.2029[/C][C]1.01957[/C][C]1.0596[/C][/ROW]
[ROW][C]16[/C][C]98.32[/C][C]94.1178[/C][C]91.5492[/C][C]1.02806[/C][C]1.04465[/C][/ROW]
[ROW][C]17[/C][C]97.02[/C][C]93.9288[/C][C]91.7771[/C][C]1.02344[/C][C]1.03291[/C][/ROW]
[ROW][C]18[/C][C]90.13[/C][C]92.1716[/C][C]91.7475[/C][C]1.00462[/C][C]0.97785[/C][/ROW]
[ROW][C]19[/C][C]88.49[/C][C]90.9299[/C][C]91.2125[/C][C]0.996902[/C][C]0.973167[/C][/ROW]
[ROW][C]20[/C][C]88.07[/C][C]89.2821[/C][C]90.3592[/C][C]0.98808[/C][C]0.986424[/C][/ROW]
[ROW][C]21[/C][C]87.17[/C][C]87.9141[/C][C]89.3912[/C][C]0.983476[/C][C]0.991536[/C][/ROW]
[ROW][C]22[/C][C]86.1[/C][C]86.427[/C][C]88.3817[/C][C]0.977884[/C][C]0.996216[/C][/ROW]
[ROW][C]23[/C][C]86.59[/C][C]85.5065[/C][C]87.4063[/C][C]0.978266[/C][C]1.01267[/C][/ROW]
[ROW][C]24[/C][C]85.89[/C][C]85.7838[/C][C]86.7388[/C][C]0.98899[/C][C]1.00124[/C][/ROW]
[ROW][C]25[/C][C]85.82[/C][C]86.7057[/C][C]86.4417[/C][C]1.00305[/C][C]0.989785[/C][/ROW]
[ROW][C]26[/C][C]86.68[/C][C]86.9356[/C][C]86.2754[/C][C]1.00765[/C][C]0.99706[/C][/ROW]
[ROW][C]27[/C][C]86.3[/C][C]87.885[/C][C]86.1979[/C][C]1.01957[/C][C]0.981965[/C][/ROW]
[ROW][C]28[/C][C]86.32[/C][C]88.5517[/C][C]86.135[/C][C]1.02806[/C][C]0.974798[/C][/ROW]
[ROW][C]29[/C][C]85.61[/C][C]88.0478[/C][C]86.0308[/C][C]1.02344[/C][C]0.972313[/C][/ROW]
[ROW][C]30[/C][C]85.52[/C][C]86.3917[/C][C]85.9942[/C][C]1.00462[/C][C]0.98991[/C][/ROW]
[ROW][C]31[/C][C]85.97[/C][C]85.8553[/C][C]86.1221[/C][C]0.996902[/C][C]1.00134[/C][/ROW]
[ROW][C]32[/C][C]86.6[/C][C]85.2898[/C][C]86.3188[/C][C]0.98808[/C][C]1.01536[/C][/ROW]
[ROW][C]33[/C][C]86.78[/C][C]85.3681[/C][C]86.8025[/C][C]0.983476[/C][C]1.01654[/C][/ROW]
[ROW][C]34[/C][C]84.98[/C][C]85.8655[/C][C]87.8075[/C][C]0.977884[/C][C]0.989687[/C][/ROW]
[ROW][C]35[/C][C]85.21[/C][C]87.1875[/C][C]89.1246[/C][C]0.978266[/C][C]0.977319[/C][/ROW]
[ROW][C]36[/C][C]86.39[/C][C]89.5588[/C][C]90.5558[/C][C]0.98899[/C][C]0.964617[/C][/ROW]
[ROW][C]37[/C][C]88.39[/C][C]92.3278[/C][C]92.0467[/C][C]1.00305[/C][C]0.95735[/C][/ROW]
[ROW][C]38[/C][C]88.83[/C][C]94.2981[/C][C]93.5821[/C][C]1.00765[/C][C]0.942012[/C][/ROW]
[ROW][C]39[/C][C]95.76[/C][C]96.9898[/C][C]95.1279[/C][C]1.01957[/C][C]0.98732[/C][/ROW]
[ROW][C]40[/C][C]100.98[/C][C]99.4551[/C][C]96.7408[/C][C]1.02806[/C][C]1.01533[/C][/ROW]
[ROW][C]41[/C][C]102.56[/C][C]100.68[/C][C]98.3742[/C][C]1.02344[/C][C]1.01867[/C][/ROW]
[ROW][C]42[/C][C]102.92[/C][C]100.392[/C][C]99.93[/C][C]1.00462[/C][C]1.02518[/C][/ROW]
[ROW][C]43[/C][C]104.35[/C][C]101.058[/C][C]101.372[/C][C]0.996902[/C][C]1.03257[/C][/ROW]
[ROW][C]44[/C][C]105.07[/C][C]101.479[/C][C]102.703[/C][C]0.98808[/C][C]1.03539[/C][/ROW]
[ROW][C]45[/C][C]105.41[/C][C]101.984[/C][C]103.697[/C][C]0.983476[/C][C]1.03359[/C][/ROW]
[ROW][C]46[/C][C]105.06[/C][C]101.842[/C][C]104.145[/C][C]0.977884[/C][C]1.0316[/C][/ROW]
[ROW][C]47[/C][C]104.33[/C][C]101.946[/C][C]104.211[/C][C]0.978266[/C][C]1.02339[/C][/ROW]
[ROW][C]48[/C][C]104.61[/C][C]102.913[/C][C]104.058[/C][C]0.98899[/C][C]1.01649[/C][/ROW]
[ROW][C]49[/C][C]104.78[/C][C]104.092[/C][C]103.775[/C][C]1.00305[/C][C]1.00661[/C][/ROW]
[ROW][C]50[/C][C]104.38[/C][C]104.032[/C][C]103.242[/C][C]1.00765[/C][C]1.00334[/C][/ROW]
[ROW][C]51[/C][C]104.08[/C][C]104.459[/C][C]102.454[/C][C]1.01957[/C][C]0.996372[/C][/ROW]
[ROW][C]52[/C][C]103.4[/C][C]104.5[/C][C]101.648[/C][C]1.02806[/C][C]0.989475[/C][/ROW]
[ROW][C]53[/C][C]101.72[/C][C]103.315[/C][C]100.949[/C][C]1.02344[/C][C]0.984558[/C][/ROW]
[ROW][C]54[/C][C]100.1[/C][C]100.78[/C][C]100.316[/C][C]1.00462[/C][C]0.993253[/C][/ROW]
[ROW][C]55[/C][C]100.37[/C][C]99.3542[/C][C]99.6629[/C][C]0.996902[/C][C]1.01022[/C][/ROW]
[ROW][C]56[/C][C]96.27[/C][C]97.8376[/C][C]99.0179[/C][C]0.98808[/C][C]0.983977[/C][/ROW]
[ROW][C]57[/C][C]95.28[/C][C]96.7867[/C][C]98.4129[/C][C]0.983476[/C][C]0.984433[/C][/ROW]
[ROW][C]58[/C][C]95.85[/C][C]95.7271[/C][C]97.8921[/C][C]0.977884[/C][C]1.00128[/C][/ROW]
[ROW][C]59[/C][C]96.76[/C][C]95.4412[/C][C]97.5617[/C][C]0.978266[/C][C]1.01382[/C][/ROW]
[ROW][C]60[/C][C]97[/C][C]96.3585[/C][C]97.4312[/C][C]0.98899[/C][C]1.00666[/C][/ROW]
[ROW][C]61[/C][C]96.71[/C][C]97.6536[/C][C]97.3562[/C][C]1.00305[/C][C]0.990337[/C][/ROW]
[ROW][C]62[/C][C]96.97[/C][C]98.1818[/C][C]97.4362[/C][C]1.00765[/C][C]0.987658[/C][/ROW]
[ROW][C]63[/C][C]96.97[/C][C]99.5744[/C][C]97.6629[/C][C]1.01957[/C][C]0.973845[/C][/ROW]
[ROW][C]64[/C][C]98.01[/C][C]100.539[/C][C]97.795[/C][C]1.02806[/C][C]0.974847[/C][/ROW]
[ROW][C]65[/C][C]99.18[/C][C]100.116[/C][C]97.8225[/C][C]1.02344[/C][C]0.990652[/C][/ROW]
[ROW][C]66[/C][C]99.51[/C][C]98.2421[/C][C]97.79[/C][C]1.00462[/C][C]1.01291[/C][/ROW]
[ROW][C]67[/C][C]99.16[/C][C]NA[/C][C]NA[/C][C]0.996902[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]99.4[/C][C]NA[/C][C]NA[/C][C]0.98808[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]97.59[/C][C]NA[/C][C]NA[/C][C]0.983476[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]96.71[/C][C]NA[/C][C]NA[/C][C]0.977884[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]96.56[/C][C]NA[/C][C]NA[/C][C]0.978266[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]96.42[/C][C]NA[/C][C]NA[/C][C]0.98899[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284164&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284164&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	79.21	NA	NA	1.00305	NA
2	79.08	NA	NA	1.00765	NA
3	79.88	NA	NA	1.01957	NA
4	80.57	NA	NA	1.02806	NA
5	80.9	NA	NA	1.02344	NA
6	80.89	NA	NA	1.00462	NA
7	80.61	82.1044	82.3596	0.996902	0.981798
8	80.98	82.806	83.805	0.98808	0.977948
9	81.68	83.9466	85.3571	0.983476	0.972999
10	83.28	84.9524	86.8737	0.977884	0.980313
11	83.94	86.3662	88.285	0.978266	0.971908
12	89.25	88.358	89.3417	0.98899	1.0101
13	95.3	90.3301	90.055	1.00305	1.05502
14	97.68	91.3726	90.6788	1.00765	1.06903
15	98.53	92.988	91.2029	1.01957	1.0596
16	98.32	94.1178	91.5492	1.02806	1.04465
17	97.02	93.9288	91.7771	1.02344	1.03291
18	90.13	92.1716	91.7475	1.00462	0.97785
19	88.49	90.9299	91.2125	0.996902	0.973167
20	88.07	89.2821	90.3592	0.98808	0.986424
21	87.17	87.9141	89.3912	0.983476	0.991536
22	86.1	86.427	88.3817	0.977884	0.996216
23	86.59	85.5065	87.4063	0.978266	1.01267
24	85.89	85.7838	86.7388	0.98899	1.00124
25	85.82	86.7057	86.4417	1.00305	0.989785
26	86.68	86.9356	86.2754	1.00765	0.99706
27	86.3	87.885	86.1979	1.01957	0.981965
28	86.32	88.5517	86.135	1.02806	0.974798
29	85.61	88.0478	86.0308	1.02344	0.972313
30	85.52	86.3917	85.9942	1.00462	0.98991
31	85.97	85.8553	86.1221	0.996902	1.00134
32	86.6	85.2898	86.3188	0.98808	1.01536
33	86.78	85.3681	86.8025	0.983476	1.01654
34	84.98	85.8655	87.8075	0.977884	0.989687
35	85.21	87.1875	89.1246	0.978266	0.977319
36	86.39	89.5588	90.5558	0.98899	0.964617
37	88.39	92.3278	92.0467	1.00305	0.95735
38	88.83	94.2981	93.5821	1.00765	0.942012
39	95.76	96.9898	95.1279	1.01957	0.98732
40	100.98	99.4551	96.7408	1.02806	1.01533
41	102.56	100.68	98.3742	1.02344	1.01867
42	102.92	100.392	99.93	1.00462	1.02518
43	104.35	101.058	101.372	0.996902	1.03257
44	105.07	101.479	102.703	0.98808	1.03539
45	105.41	101.984	103.697	0.983476	1.03359
46	105.06	101.842	104.145	0.977884	1.0316
47	104.33	101.946	104.211	0.978266	1.02339
48	104.61	102.913	104.058	0.98899	1.01649
49	104.78	104.092	103.775	1.00305	1.00661
50	104.38	104.032	103.242	1.00765	1.00334
51	104.08	104.459	102.454	1.01957	0.996372
52	103.4	104.5	101.648	1.02806	0.989475
53	101.72	103.315	100.949	1.02344	0.984558
54	100.1	100.78	100.316	1.00462	0.993253
55	100.37	99.3542	99.6629	0.996902	1.01022
56	96.27	97.8376	99.0179	0.98808	0.983977
57	95.28	96.7867	98.4129	0.983476	0.984433
58	95.85	95.7271	97.8921	0.977884	1.00128
59	96.76	95.4412	97.5617	0.978266	1.01382
60	97	96.3585	97.4312	0.98899	1.00666
61	96.71	97.6536	97.3562	1.00305	0.990337
62	96.97	98.1818	97.4362	1.00765	0.987658
63	96.97	99.5744	97.6629	1.01957	0.973845
64	98.01	100.539	97.795	1.02806	0.974847
65	99.18	100.116	97.8225	1.02344	0.990652
66	99.51	98.2421	97.79	1.00462	1.01291
67	99.16	NA	NA	0.996902	NA
68	99.4	NA	NA	0.98808	NA
69	97.59	NA	NA	0.983476	NA
70	96.71	NA	NA	0.977884	NA
71	96.56	NA	NA	0.978266	NA
72	96.42	NA	NA	0.98899	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