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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sat, 29 Apr 2017 14:20:26 +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/29/t14934720551gnvn2416xb2c2i.htm/, Retrieved Sun, 12 May 2024 21:35:32 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sun, 12 May 2024 21:35:32 +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	'Sir Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.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]'Sir Ronald Aylmer Fisher' @ fisher.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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	74.16	NA	NA	-1.84483	NA
2	73.21	NA	NA	-0.712576	NA
3	77.14	NA	NA	1.33642	NA
4	80.9	NA	NA	0.75659	NA
5	82.76	NA	NA	0.119424	NA
6	83.33	NA	NA	-0.43316	NA
7	81.94	81.4694	81.7912	-0.321826	0.470576
8	82.38	83.7168	83.2996	0.417174	-1.33676
9	82.81	86.4927	84.9767	1.51601	-3.68267
10	83.17	86.9531	86.6042	0.348924	-3.78309
11	84.07	87.9474	88.0204	-0.0729931	-3.87742
12	87.33	88.1988	89.3079	-1.10916	-0.868757
13	90.75	88.7764	90.6212	-1.84483	1.97358
14	92.82	91.2258	91.9383	-0.712576	1.59424
15	97.78	94.6068	93.2704	1.33642	3.17316
16	99.32	95.4333	94.6767	0.75659	3.88674
17	98.33	96.3107	96.1912	0.119424	2.01933
18	98.66	97.1539	97.5871	-0.43316	1.50608
19	98.13	98.4315	98.7533	-0.321826	-0.301507
20	97.8	100.204	99.7871	0.417174	-2.40426
21	99.36	102.141	100.625	1.51601	-2.78059
22	100.37	101.626	101.277	0.348924	-1.25642
23	103.22	101.701	101.774	-0.0729931	1.51924
24	101.68	100.943	102.052	-1.10916	0.737076
25	104.39	100.394	102.239	-1.84483	3.99608
26	103.99	101.989	102.701	-0.712576	2.00133
27	106.71	104.74	103.404	1.33642	1.96983
28	106.06	104.812	104.055	0.75659	1.24841
29	103.5	104.532	104.412	0.119424	-1.03192
30	100.17	104.092	104.525	-0.43316	-3.92184
31	101.1	104.149	104.47	-0.321826	-3.04859
32	105.93	104.751	104.334	0.417174	1.17866
33	108.09	105.618	104.102	1.51601	2.47191
34	107.27	104.011	103.662	0.348924	3.25899
35	104.9	103.095	103.167	-0.0729931	1.80549
36	102.7	101.695	102.804	-1.10916	1.00541
37	102.06	100.783	102.627	-1.84483	1.27733
38	103.05	101.59	102.303	-0.712576	1.45966
39	102.08	103.15	101.814	1.33642	-1.07017
40	100.13	101.998	101.241	0.75659	-1.86784
41	97.56	100.719	100.6	0.119424	-3.15901
42	97.38	99.7189	100.152	-0.43316	-2.33892
43	99.66	99.4953	99.8171	-0.321826	0.164743
44	99.58	99.8405	99.4233	0.417174	-0.260507
45	102.7	100.525	99.0092	1.51601	2.17483
46	98.92	99.0131	98.6642	0.348924	-0.0930903
47	97.85	98.3999	98.4729	-0.0729931	-0.549924
48	99.01	97.2996	98.4088	-1.10916	1.71041
49	97.71	96.4935	98.3383	-1.84483	1.21649
50	97.95	97.4974	98.21	-0.712576	0.452576
51	97.24	99.226	97.8896	1.33642	-1.98601
52	96.69	98.2253	97.4687	0.75659	-1.53534
53	96.41	97.1978	97.0783	0.119424	-0.787757
54	96.99	95.9352	96.3683	-0.43316	1.05483
55	98.36	94.7953	95.1171	-0.321826	3.56474
56	97.8	94.1272	93.71	0.417174	3.67283
57	96.79	94.1235	92.6075	1.51601	2.66649
58	94.73	92.0064	91.6575	0.348924	2.72358
59	92.67	90.7174	90.7904	-0.0729931	1.95258
60	87.15	88.8842	89.9933	-1.10916	-1.73417
61	79.54	87.1535	88.9983	-1.84483	-7.61351
62	82.35	87.0078	87.7204	-0.712576	-4.65784
63	86.38	87.6168	86.2804	1.33642	-1.23684
64	84.75	85.632	84.8754	0.75659	-0.882007
65	87.54	83.7307	83.6112	0.119424	3.80933
66	86.73	82.1802	82.6133	-0.43316	4.54983
67	84.74	NA	NA	-0.321826	NA
68	80.75	NA	NA	0.417174	NA
69	79.28	NA	NA	1.51601	NA
70	78.52	NA	NA	0.348924	NA
71	78.54	NA	NA	-0.0729931	NA
72	77.33	NA	NA	-1.10916	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 74.16 & NA & NA & -1.84483 & NA \tabularnewline
2 & 73.21 & NA & NA & -0.712576 & NA \tabularnewline
3 & 77.14 & NA & NA & 1.33642 & NA \tabularnewline
4 & 80.9 & NA & NA & 0.75659 & NA \tabularnewline
5 & 82.76 & NA & NA & 0.119424 & NA \tabularnewline
6 & 83.33 & NA & NA & -0.43316 & NA \tabularnewline
7 & 81.94 & 81.4694 & 81.7912 & -0.321826 & 0.470576 \tabularnewline
8 & 82.38 & 83.7168 & 83.2996 & 0.417174 & -1.33676 \tabularnewline
9 & 82.81 & 86.4927 & 84.9767 & 1.51601 & -3.68267 \tabularnewline
10 & 83.17 & 86.9531 & 86.6042 & 0.348924 & -3.78309 \tabularnewline
11 & 84.07 & 87.9474 & 88.0204 & -0.0729931 & -3.87742 \tabularnewline
12 & 87.33 & 88.1988 & 89.3079 & -1.10916 & -0.868757 \tabularnewline
13 & 90.75 & 88.7764 & 90.6212 & -1.84483 & 1.97358 \tabularnewline
14 & 92.82 & 91.2258 & 91.9383 & -0.712576 & 1.59424 \tabularnewline
15 & 97.78 & 94.6068 & 93.2704 & 1.33642 & 3.17316 \tabularnewline
16 & 99.32 & 95.4333 & 94.6767 & 0.75659 & 3.88674 \tabularnewline
17 & 98.33 & 96.3107 & 96.1912 & 0.119424 & 2.01933 \tabularnewline
18 & 98.66 & 97.1539 & 97.5871 & -0.43316 & 1.50608 \tabularnewline
19 & 98.13 & 98.4315 & 98.7533 & -0.321826 & -0.301507 \tabularnewline
20 & 97.8 & 100.204 & 99.7871 & 0.417174 & -2.40426 \tabularnewline
21 & 99.36 & 102.141 & 100.625 & 1.51601 & -2.78059 \tabularnewline
22 & 100.37 & 101.626 & 101.277 & 0.348924 & -1.25642 \tabularnewline
23 & 103.22 & 101.701 & 101.774 & -0.0729931 & 1.51924 \tabularnewline
24 & 101.68 & 100.943 & 102.052 & -1.10916 & 0.737076 \tabularnewline
25 & 104.39 & 100.394 & 102.239 & -1.84483 & 3.99608 \tabularnewline
26 & 103.99 & 101.989 & 102.701 & -0.712576 & 2.00133 \tabularnewline
27 & 106.71 & 104.74 & 103.404 & 1.33642 & 1.96983 \tabularnewline
28 & 106.06 & 104.812 & 104.055 & 0.75659 & 1.24841 \tabularnewline
29 & 103.5 & 104.532 & 104.412 & 0.119424 & -1.03192 \tabularnewline
30 & 100.17 & 104.092 & 104.525 & -0.43316 & -3.92184 \tabularnewline
31 & 101.1 & 104.149 & 104.47 & -0.321826 & -3.04859 \tabularnewline
32 & 105.93 & 104.751 & 104.334 & 0.417174 & 1.17866 \tabularnewline
33 & 108.09 & 105.618 & 104.102 & 1.51601 & 2.47191 \tabularnewline
34 & 107.27 & 104.011 & 103.662 & 0.348924 & 3.25899 \tabularnewline
35 & 104.9 & 103.095 & 103.167 & -0.0729931 & 1.80549 \tabularnewline
36 & 102.7 & 101.695 & 102.804 & -1.10916 & 1.00541 \tabularnewline
37 & 102.06 & 100.783 & 102.627 & -1.84483 & 1.27733 \tabularnewline
38 & 103.05 & 101.59 & 102.303 & -0.712576 & 1.45966 \tabularnewline
39 & 102.08 & 103.15 & 101.814 & 1.33642 & -1.07017 \tabularnewline
40 & 100.13 & 101.998 & 101.241 & 0.75659 & -1.86784 \tabularnewline
41 & 97.56 & 100.719 & 100.6 & 0.119424 & -3.15901 \tabularnewline
42 & 97.38 & 99.7189 & 100.152 & -0.43316 & -2.33892 \tabularnewline
43 & 99.66 & 99.4953 & 99.8171 & -0.321826 & 0.164743 \tabularnewline
44 & 99.58 & 99.8405 & 99.4233 & 0.417174 & -0.260507 \tabularnewline
45 & 102.7 & 100.525 & 99.0092 & 1.51601 & 2.17483 \tabularnewline
46 & 98.92 & 99.0131 & 98.6642 & 0.348924 & -0.0930903 \tabularnewline
47 & 97.85 & 98.3999 & 98.4729 & -0.0729931 & -0.549924 \tabularnewline
48 & 99.01 & 97.2996 & 98.4088 & -1.10916 & 1.71041 \tabularnewline
49 & 97.71 & 96.4935 & 98.3383 & -1.84483 & 1.21649 \tabularnewline
50 & 97.95 & 97.4974 & 98.21 & -0.712576 & 0.452576 \tabularnewline
51 & 97.24 & 99.226 & 97.8896 & 1.33642 & -1.98601 \tabularnewline
52 & 96.69 & 98.2253 & 97.4687 & 0.75659 & -1.53534 \tabularnewline
53 & 96.41 & 97.1978 & 97.0783 & 0.119424 & -0.787757 \tabularnewline
54 & 96.99 & 95.9352 & 96.3683 & -0.43316 & 1.05483 \tabularnewline
55 & 98.36 & 94.7953 & 95.1171 & -0.321826 & 3.56474 \tabularnewline
56 & 97.8 & 94.1272 & 93.71 & 0.417174 & 3.67283 \tabularnewline
57 & 96.79 & 94.1235 & 92.6075 & 1.51601 & 2.66649 \tabularnewline
58 & 94.73 & 92.0064 & 91.6575 & 0.348924 & 2.72358 \tabularnewline
59 & 92.67 & 90.7174 & 90.7904 & -0.0729931 & 1.95258 \tabularnewline
60 & 87.15 & 88.8842 & 89.9933 & -1.10916 & -1.73417 \tabularnewline
61 & 79.54 & 87.1535 & 88.9983 & -1.84483 & -7.61351 \tabularnewline
62 & 82.35 & 87.0078 & 87.7204 & -0.712576 & -4.65784 \tabularnewline
63 & 86.38 & 87.6168 & 86.2804 & 1.33642 & -1.23684 \tabularnewline
64 & 84.75 & 85.632 & 84.8754 & 0.75659 & -0.882007 \tabularnewline
65 & 87.54 & 83.7307 & 83.6112 & 0.119424 & 3.80933 \tabularnewline
66 & 86.73 & 82.1802 & 82.6133 & -0.43316 & 4.54983 \tabularnewline
67 & 84.74 & NA & NA & -0.321826 & NA \tabularnewline
68 & 80.75 & NA & NA & 0.417174 & NA \tabularnewline
69 & 79.28 & NA & NA & 1.51601 & NA \tabularnewline
70 & 78.52 & NA & NA & 0.348924 & NA \tabularnewline
71 & 78.54 & NA & NA & -0.0729931 & NA \tabularnewline
72 & 77.33 & NA & NA & -1.10916 & 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]74.16[/C][C]NA[/C][C]NA[/C][C]-1.84483[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]73.21[/C][C]NA[/C][C]NA[/C][C]-0.712576[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]77.14[/C][C]NA[/C][C]NA[/C][C]1.33642[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]80.9[/C][C]NA[/C][C]NA[/C][C]0.75659[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]82.76[/C][C]NA[/C][C]NA[/C][C]0.119424[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]83.33[/C][C]NA[/C][C]NA[/C][C]-0.43316[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]81.94[/C][C]81.4694[/C][C]81.7912[/C][C]-0.321826[/C][C]0.470576[/C][/ROW]
[ROW][C]8[/C][C]82.38[/C][C]83.7168[/C][C]83.2996[/C][C]0.417174[/C][C]-1.33676[/C][/ROW]
[ROW][C]9[/C][C]82.81[/C][C]86.4927[/C][C]84.9767[/C][C]1.51601[/C][C]-3.68267[/C][/ROW]
[ROW][C]10[/C][C]83.17[/C][C]86.9531[/C][C]86.6042[/C][C]0.348924[/C][C]-3.78309[/C][/ROW]
[ROW][C]11[/C][C]84.07[/C][C]87.9474[/C][C]88.0204[/C][C]-0.0729931[/C][C]-3.87742[/C][/ROW]
[ROW][C]12[/C][C]87.33[/C][C]88.1988[/C][C]89.3079[/C][C]-1.10916[/C][C]-0.868757[/C][/ROW]
[ROW][C]13[/C][C]90.75[/C][C]88.7764[/C][C]90.6212[/C][C]-1.84483[/C][C]1.97358[/C][/ROW]
[ROW][C]14[/C][C]92.82[/C][C]91.2258[/C][C]91.9383[/C][C]-0.712576[/C][C]1.59424[/C][/ROW]
[ROW][C]15[/C][C]97.78[/C][C]94.6068[/C][C]93.2704[/C][C]1.33642[/C][C]3.17316[/C][/ROW]
[ROW][C]16[/C][C]99.32[/C][C]95.4333[/C][C]94.6767[/C][C]0.75659[/C][C]3.88674[/C][/ROW]
[ROW][C]17[/C][C]98.33[/C][C]96.3107[/C][C]96.1912[/C][C]0.119424[/C][C]2.01933[/C][/ROW]
[ROW][C]18[/C][C]98.66[/C][C]97.1539[/C][C]97.5871[/C][C]-0.43316[/C][C]1.50608[/C][/ROW]
[ROW][C]19[/C][C]98.13[/C][C]98.4315[/C][C]98.7533[/C][C]-0.321826[/C][C]-0.301507[/C][/ROW]
[ROW][C]20[/C][C]97.8[/C][C]100.204[/C][C]99.7871[/C][C]0.417174[/C][C]-2.40426[/C][/ROW]
[ROW][C]21[/C][C]99.36[/C][C]102.141[/C][C]100.625[/C][C]1.51601[/C][C]-2.78059[/C][/ROW]
[ROW][C]22[/C][C]100.37[/C][C]101.626[/C][C]101.277[/C][C]0.348924[/C][C]-1.25642[/C][/ROW]
[ROW][C]23[/C][C]103.22[/C][C]101.701[/C][C]101.774[/C][C]-0.0729931[/C][C]1.51924[/C][/ROW]
[ROW][C]24[/C][C]101.68[/C][C]100.943[/C][C]102.052[/C][C]-1.10916[/C][C]0.737076[/C][/ROW]
[ROW][C]25[/C][C]104.39[/C][C]100.394[/C][C]102.239[/C][C]-1.84483[/C][C]3.99608[/C][/ROW]
[ROW][C]26[/C][C]103.99[/C][C]101.989[/C][C]102.701[/C][C]-0.712576[/C][C]2.00133[/C][/ROW]
[ROW][C]27[/C][C]106.71[/C][C]104.74[/C][C]103.404[/C][C]1.33642[/C][C]1.96983[/C][/ROW]
[ROW][C]28[/C][C]106.06[/C][C]104.812[/C][C]104.055[/C][C]0.75659[/C][C]1.24841[/C][/ROW]
[ROW][C]29[/C][C]103.5[/C][C]104.532[/C][C]104.412[/C][C]0.119424[/C][C]-1.03192[/C][/ROW]
[ROW][C]30[/C][C]100.17[/C][C]104.092[/C][C]104.525[/C][C]-0.43316[/C][C]-3.92184[/C][/ROW]
[ROW][C]31[/C][C]101.1[/C][C]104.149[/C][C]104.47[/C][C]-0.321826[/C][C]-3.04859[/C][/ROW]
[ROW][C]32[/C][C]105.93[/C][C]104.751[/C][C]104.334[/C][C]0.417174[/C][C]1.17866[/C][/ROW]
[ROW][C]33[/C][C]108.09[/C][C]105.618[/C][C]104.102[/C][C]1.51601[/C][C]2.47191[/C][/ROW]
[ROW][C]34[/C][C]107.27[/C][C]104.011[/C][C]103.662[/C][C]0.348924[/C][C]3.25899[/C][/ROW]
[ROW][C]35[/C][C]104.9[/C][C]103.095[/C][C]103.167[/C][C]-0.0729931[/C][C]1.80549[/C][/ROW]
[ROW][C]36[/C][C]102.7[/C][C]101.695[/C][C]102.804[/C][C]-1.10916[/C][C]1.00541[/C][/ROW]
[ROW][C]37[/C][C]102.06[/C][C]100.783[/C][C]102.627[/C][C]-1.84483[/C][C]1.27733[/C][/ROW]
[ROW][C]38[/C][C]103.05[/C][C]101.59[/C][C]102.303[/C][C]-0.712576[/C][C]1.45966[/C][/ROW]
[ROW][C]39[/C][C]102.08[/C][C]103.15[/C][C]101.814[/C][C]1.33642[/C][C]-1.07017[/C][/ROW]
[ROW][C]40[/C][C]100.13[/C][C]101.998[/C][C]101.241[/C][C]0.75659[/C][C]-1.86784[/C][/ROW]
[ROW][C]41[/C][C]97.56[/C][C]100.719[/C][C]100.6[/C][C]0.119424[/C][C]-3.15901[/C][/ROW]
[ROW][C]42[/C][C]97.38[/C][C]99.7189[/C][C]100.152[/C][C]-0.43316[/C][C]-2.33892[/C][/ROW]
[ROW][C]43[/C][C]99.66[/C][C]99.4953[/C][C]99.8171[/C][C]-0.321826[/C][C]0.164743[/C][/ROW]
[ROW][C]44[/C][C]99.58[/C][C]99.8405[/C][C]99.4233[/C][C]0.417174[/C][C]-0.260507[/C][/ROW]
[ROW][C]45[/C][C]102.7[/C][C]100.525[/C][C]99.0092[/C][C]1.51601[/C][C]2.17483[/C][/ROW]
[ROW][C]46[/C][C]98.92[/C][C]99.0131[/C][C]98.6642[/C][C]0.348924[/C][C]-0.0930903[/C][/ROW]
[ROW][C]47[/C][C]97.85[/C][C]98.3999[/C][C]98.4729[/C][C]-0.0729931[/C][C]-0.549924[/C][/ROW]
[ROW][C]48[/C][C]99.01[/C][C]97.2996[/C][C]98.4088[/C][C]-1.10916[/C][C]1.71041[/C][/ROW]
[ROW][C]49[/C][C]97.71[/C][C]96.4935[/C][C]98.3383[/C][C]-1.84483[/C][C]1.21649[/C][/ROW]
[ROW][C]50[/C][C]97.95[/C][C]97.4974[/C][C]98.21[/C][C]-0.712576[/C][C]0.452576[/C][/ROW]
[ROW][C]51[/C][C]97.24[/C][C]99.226[/C][C]97.8896[/C][C]1.33642[/C][C]-1.98601[/C][/ROW]
[ROW][C]52[/C][C]96.69[/C][C]98.2253[/C][C]97.4687[/C][C]0.75659[/C][C]-1.53534[/C][/ROW]
[ROW][C]53[/C][C]96.41[/C][C]97.1978[/C][C]97.0783[/C][C]0.119424[/C][C]-0.787757[/C][/ROW]
[ROW][C]54[/C][C]96.99[/C][C]95.9352[/C][C]96.3683[/C][C]-0.43316[/C][C]1.05483[/C][/ROW]
[ROW][C]55[/C][C]98.36[/C][C]94.7953[/C][C]95.1171[/C][C]-0.321826[/C][C]3.56474[/C][/ROW]
[ROW][C]56[/C][C]97.8[/C][C]94.1272[/C][C]93.71[/C][C]0.417174[/C][C]3.67283[/C][/ROW]
[ROW][C]57[/C][C]96.79[/C][C]94.1235[/C][C]92.6075[/C][C]1.51601[/C][C]2.66649[/C][/ROW]
[ROW][C]58[/C][C]94.73[/C][C]92.0064[/C][C]91.6575[/C][C]0.348924[/C][C]2.72358[/C][/ROW]
[ROW][C]59[/C][C]92.67[/C][C]90.7174[/C][C]90.7904[/C][C]-0.0729931[/C][C]1.95258[/C][/ROW]
[ROW][C]60[/C][C]87.15[/C][C]88.8842[/C][C]89.9933[/C][C]-1.10916[/C][C]-1.73417[/C][/ROW]
[ROW][C]61[/C][C]79.54[/C][C]87.1535[/C][C]88.9983[/C][C]-1.84483[/C][C]-7.61351[/C][/ROW]
[ROW][C]62[/C][C]82.35[/C][C]87.0078[/C][C]87.7204[/C][C]-0.712576[/C][C]-4.65784[/C][/ROW]
[ROW][C]63[/C][C]86.38[/C][C]87.6168[/C][C]86.2804[/C][C]1.33642[/C][C]-1.23684[/C][/ROW]
[ROW][C]64[/C][C]84.75[/C][C]85.632[/C][C]84.8754[/C][C]0.75659[/C][C]-0.882007[/C][/ROW]
[ROW][C]65[/C][C]87.54[/C][C]83.7307[/C][C]83.6112[/C][C]0.119424[/C][C]3.80933[/C][/ROW]
[ROW][C]66[/C][C]86.73[/C][C]82.1802[/C][C]82.6133[/C][C]-0.43316[/C][C]4.54983[/C][/ROW]
[ROW][C]67[/C][C]84.74[/C][C]NA[/C][C]NA[/C][C]-0.321826[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]80.75[/C][C]NA[/C][C]NA[/C][C]0.417174[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]79.28[/C][C]NA[/C][C]NA[/C][C]1.51601[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]78.52[/C][C]NA[/C][C]NA[/C][C]0.348924[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]78.54[/C][C]NA[/C][C]NA[/C][C]-0.0729931[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]77.33[/C][C]NA[/C][C]NA[/C][C]-1.10916[/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	74.16	NA	NA	-1.84483	NA
2	73.21	NA	NA	-0.712576	NA
3	77.14	NA	NA	1.33642	NA
4	80.9	NA	NA	0.75659	NA
5	82.76	NA	NA	0.119424	NA
6	83.33	NA	NA	-0.43316	NA
7	81.94	81.4694	81.7912	-0.321826	0.470576
8	82.38	83.7168	83.2996	0.417174	-1.33676
9	82.81	86.4927	84.9767	1.51601	-3.68267
10	83.17	86.9531	86.6042	0.348924	-3.78309
11	84.07	87.9474	88.0204	-0.0729931	-3.87742
12	87.33	88.1988	89.3079	-1.10916	-0.868757
13	90.75	88.7764	90.6212	-1.84483	1.97358
14	92.82	91.2258	91.9383	-0.712576	1.59424
15	97.78	94.6068	93.2704	1.33642	3.17316
16	99.32	95.4333	94.6767	0.75659	3.88674
17	98.33	96.3107	96.1912	0.119424	2.01933
18	98.66	97.1539	97.5871	-0.43316	1.50608
19	98.13	98.4315	98.7533	-0.321826	-0.301507
20	97.8	100.204	99.7871	0.417174	-2.40426
21	99.36	102.141	100.625	1.51601	-2.78059
22	100.37	101.626	101.277	0.348924	-1.25642
23	103.22	101.701	101.774	-0.0729931	1.51924
24	101.68	100.943	102.052	-1.10916	0.737076
25	104.39	100.394	102.239	-1.84483	3.99608
26	103.99	101.989	102.701	-0.712576	2.00133
27	106.71	104.74	103.404	1.33642	1.96983
28	106.06	104.812	104.055	0.75659	1.24841
29	103.5	104.532	104.412	0.119424	-1.03192
30	100.17	104.092	104.525	-0.43316	-3.92184
31	101.1	104.149	104.47	-0.321826	-3.04859
32	105.93	104.751	104.334	0.417174	1.17866
33	108.09	105.618	104.102	1.51601	2.47191
34	107.27	104.011	103.662	0.348924	3.25899
35	104.9	103.095	103.167	-0.0729931	1.80549
36	102.7	101.695	102.804	-1.10916	1.00541
37	102.06	100.783	102.627	-1.84483	1.27733
38	103.05	101.59	102.303	-0.712576	1.45966
39	102.08	103.15	101.814	1.33642	-1.07017
40	100.13	101.998	101.241	0.75659	-1.86784
41	97.56	100.719	100.6	0.119424	-3.15901
42	97.38	99.7189	100.152	-0.43316	-2.33892
43	99.66	99.4953	99.8171	-0.321826	0.164743
44	99.58	99.8405	99.4233	0.417174	-0.260507
45	102.7	100.525	99.0092	1.51601	2.17483
46	98.92	99.0131	98.6642	0.348924	-0.0930903
47	97.85	98.3999	98.4729	-0.0729931	-0.549924
48	99.01	97.2996	98.4088	-1.10916	1.71041
49	97.71	96.4935	98.3383	-1.84483	1.21649
50	97.95	97.4974	98.21	-0.712576	0.452576
51	97.24	99.226	97.8896	1.33642	-1.98601
52	96.69	98.2253	97.4687	0.75659	-1.53534
53	96.41	97.1978	97.0783	0.119424	-0.787757
54	96.99	95.9352	96.3683	-0.43316	1.05483
55	98.36	94.7953	95.1171	-0.321826	3.56474
56	97.8	94.1272	93.71	0.417174	3.67283
57	96.79	94.1235	92.6075	1.51601	2.66649
58	94.73	92.0064	91.6575	0.348924	2.72358
59	92.67	90.7174	90.7904	-0.0729931	1.95258
60	87.15	88.8842	89.9933	-1.10916	-1.73417
61	79.54	87.1535	88.9983	-1.84483	-7.61351
62	82.35	87.0078	87.7204	-0.712576	-4.65784
63	86.38	87.6168	86.2804	1.33642	-1.23684
64	84.75	85.632	84.8754	0.75659	-0.882007
65	87.54	83.7307	83.6112	0.119424	3.80933
66	86.73	82.1802	82.6133	-0.43316	4.54983
67	84.74	NA	NA	-0.321826	NA
68	80.75	NA	NA	0.417174	NA
69	79.28	NA	NA	1.51601	NA
70	78.52	NA	NA	0.348924	NA
71	78.54	NA	NA	-0.0729931	NA
72	77.33	NA	NA	-1.10916	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