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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Tue, 02 May 2017 09:49:39 +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/02/t14937150452dft8d39zg2md8s.htm/, Retrieved Fri, 17 May 2024 08:07:24 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 17 May 2024 08:07:24 +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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.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]'Gwilym Jenkins' @ jenkins.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	3 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	86.88	NA	NA	0.937135	NA
2	90.65	NA	NA	0.969256	NA
3	90.68	NA	NA	0.984	NA
4	89.64	NA	NA	1.03825	NA
5	102.62	NA	NA	1.04304	NA
6	101.84	NA	NA	1.07395	NA
7	92.51	94.6721	93.6183	1.01126	0.977162
8	94.29	92.1977	93.47	0.986388	1.02269
9	94.68	92.0989	93.3675	0.986413	1.02803
10	96.94	94.8181	93.48	1.01431	1.02238
11	94.03	92.2365	93.1963	0.989701	1.01945
12	89.65	89.4986	92.6204	0.966294	1.00169
13	84.9	86.8247	92.6492	0.937135	0.977832
14	89.07	89.7858	92.6338	0.969256	0.992027
15	89.8	90.7757	92.2517	0.984	0.989252
16	93.22	95.3701	91.8562	1.03825	0.977455
17	92.23	95.5672	91.6242	1.04304	0.96508
18	98.41	98.418	91.6408	1.07395	0.999919
19	96.63	92.7432	91.7108	1.01126	1.04191
20	89.8	90.4513	91.6996	0.986388	0.992799
21	90	90.3772	91.6221	0.986413	0.995827
22	92.13	92.7598	91.4508	1.01431	0.99321
23	93.27	90.4047	91.3454	0.989701	1.03169
24	90.81	88.1953	91.2717	0.966294	1.02965
25	85.42	85.3753	91.1025	0.937135	1.00052
26	88.28	88.4095	91.2137	0.969256	0.998535
27	88.73	90.2213	91.6883	0.984	0.98347
28	90.18	95.8893	92.3562	1.03825	0.94046
29	92.74	96.9623	92.9617	1.04304	0.956454
30	96.13	100.29	93.3842	1.07395	0.958518
31	94.85	94.9911	93.9338	1.01126	0.998514
32	94.25	93.4105	94.6996	0.986388	1.00899
33	96.94	94.1835	95.4808	0.986413	1.02927
34	101.22	97.6218	96.2442	1.01431	1.03686
35	98.71	96.2674	97.2692	0.989701	1.02537
36	95.51	95.0979	98.415	0.966294	1.00433
37	93.91	92.9216	99.155	0.937135	1.01064
38	98.17	96.5274	99.5892	0.969256	1.01702
39	97.59	98.398	99.9979	0.984	0.991789
40	99.64	104.032	100.199	1.03825	0.957784
41	107.88	104.442	100.132	1.04304	1.03292
42	108.49	107.431	100.033	1.07395	1.00986
43	100.25	101.153	100.027	1.01126	0.991077
44	99.27	98.4978	99.8571	0.986388	1.00784
45	101.73	98.2442	99.5975	0.986413	1.03548
46	101.25	101.236	99.8075	1.01431	1.00014
47	97.09	98.6571	99.6838	0.989701	0.984115
48	94.74	95.5919	98.9262	0.966294	0.991088
49	94.53	92.1176	98.2971	0.937135	1.02619
50	93.48	94.5974	97.5979	0.969256	0.988188
51	96.05	94.8773	96.42	0.984	1.01236
52	106.22	98.8215	95.1804	1.03825	1.07487
53	98.33	98.3161	94.2596	1.04304	1.00014
54	99.86	100.485	93.5658	1.07395	0.993777
55	93.78	94.0426	92.9958	1.01126	0.997207
56	88.96	91.3769	92.6379	0.986388	0.97355
57	83.77	91.3254	92.5833	0.986413	0.91727
58	89.46	93.8439	92.5196	1.01431	0.953285
59	86.78	91.8072	92.7625	0.989701	0.945242
60	88.4	90.2861	93.4354	0.966294	0.979109
61	87.19	88.0094	93.9133	0.937135	0.990689
62	92.23	91.3076	94.2038	0.969256	1.0101
63	95.99	93.2849	94.8017	0.984	1.029
64	104.75	99.2606	95.6033	1.03825	1.0553
65	105.63	100.478	96.3325	1.04304	1.05127
66	108.71	104.149	96.9771	1.07395	1.04379
67	96.4	NA	NA	1.01126	NA
68	93.31	NA	NA	0.986388	NA
69	93.77	NA	NA	0.986413	NA
70	98.7	NA	NA	1.01431	NA
71	95.04	NA	NA	0.989701	NA
72	95.61	NA	NA	0.966294	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 86.88 & NA & NA & 0.937135 & NA \tabularnewline
2 & 90.65 & NA & NA & 0.969256 & NA \tabularnewline
3 & 90.68 & NA & NA & 0.984 & NA \tabularnewline
4 & 89.64 & NA & NA & 1.03825 & NA \tabularnewline
5 & 102.62 & NA & NA & 1.04304 & NA \tabularnewline
6 & 101.84 & NA & NA & 1.07395 & NA \tabularnewline
7 & 92.51 & 94.6721 & 93.6183 & 1.01126 & 0.977162 \tabularnewline
8 & 94.29 & 92.1977 & 93.47 & 0.986388 & 1.02269 \tabularnewline
9 & 94.68 & 92.0989 & 93.3675 & 0.986413 & 1.02803 \tabularnewline
10 & 96.94 & 94.8181 & 93.48 & 1.01431 & 1.02238 \tabularnewline
11 & 94.03 & 92.2365 & 93.1963 & 0.989701 & 1.01945 \tabularnewline
12 & 89.65 & 89.4986 & 92.6204 & 0.966294 & 1.00169 \tabularnewline
13 & 84.9 & 86.8247 & 92.6492 & 0.937135 & 0.977832 \tabularnewline
14 & 89.07 & 89.7858 & 92.6338 & 0.969256 & 0.992027 \tabularnewline
15 & 89.8 & 90.7757 & 92.2517 & 0.984 & 0.989252 \tabularnewline
16 & 93.22 & 95.3701 & 91.8562 & 1.03825 & 0.977455 \tabularnewline
17 & 92.23 & 95.5672 & 91.6242 & 1.04304 & 0.96508 \tabularnewline
18 & 98.41 & 98.418 & 91.6408 & 1.07395 & 0.999919 \tabularnewline
19 & 96.63 & 92.7432 & 91.7108 & 1.01126 & 1.04191 \tabularnewline
20 & 89.8 & 90.4513 & 91.6996 & 0.986388 & 0.992799 \tabularnewline
21 & 90 & 90.3772 & 91.6221 & 0.986413 & 0.995827 \tabularnewline
22 & 92.13 & 92.7598 & 91.4508 & 1.01431 & 0.99321 \tabularnewline
23 & 93.27 & 90.4047 & 91.3454 & 0.989701 & 1.03169 \tabularnewline
24 & 90.81 & 88.1953 & 91.2717 & 0.966294 & 1.02965 \tabularnewline
25 & 85.42 & 85.3753 & 91.1025 & 0.937135 & 1.00052 \tabularnewline
26 & 88.28 & 88.4095 & 91.2137 & 0.969256 & 0.998535 \tabularnewline
27 & 88.73 & 90.2213 & 91.6883 & 0.984 & 0.98347 \tabularnewline
28 & 90.18 & 95.8893 & 92.3562 & 1.03825 & 0.94046 \tabularnewline
29 & 92.74 & 96.9623 & 92.9617 & 1.04304 & 0.956454 \tabularnewline
30 & 96.13 & 100.29 & 93.3842 & 1.07395 & 0.958518 \tabularnewline
31 & 94.85 & 94.9911 & 93.9338 & 1.01126 & 0.998514 \tabularnewline
32 & 94.25 & 93.4105 & 94.6996 & 0.986388 & 1.00899 \tabularnewline
33 & 96.94 & 94.1835 & 95.4808 & 0.986413 & 1.02927 \tabularnewline
34 & 101.22 & 97.6218 & 96.2442 & 1.01431 & 1.03686 \tabularnewline
35 & 98.71 & 96.2674 & 97.2692 & 0.989701 & 1.02537 \tabularnewline
36 & 95.51 & 95.0979 & 98.415 & 0.966294 & 1.00433 \tabularnewline
37 & 93.91 & 92.9216 & 99.155 & 0.937135 & 1.01064 \tabularnewline
38 & 98.17 & 96.5274 & 99.5892 & 0.969256 & 1.01702 \tabularnewline
39 & 97.59 & 98.398 & 99.9979 & 0.984 & 0.991789 \tabularnewline
40 & 99.64 & 104.032 & 100.199 & 1.03825 & 0.957784 \tabularnewline
41 & 107.88 & 104.442 & 100.132 & 1.04304 & 1.03292 \tabularnewline
42 & 108.49 & 107.431 & 100.033 & 1.07395 & 1.00986 \tabularnewline
43 & 100.25 & 101.153 & 100.027 & 1.01126 & 0.991077 \tabularnewline
44 & 99.27 & 98.4978 & 99.8571 & 0.986388 & 1.00784 \tabularnewline
45 & 101.73 & 98.2442 & 99.5975 & 0.986413 & 1.03548 \tabularnewline
46 & 101.25 & 101.236 & 99.8075 & 1.01431 & 1.00014 \tabularnewline
47 & 97.09 & 98.6571 & 99.6838 & 0.989701 & 0.984115 \tabularnewline
48 & 94.74 & 95.5919 & 98.9262 & 0.966294 & 0.991088 \tabularnewline
49 & 94.53 & 92.1176 & 98.2971 & 0.937135 & 1.02619 \tabularnewline
50 & 93.48 & 94.5974 & 97.5979 & 0.969256 & 0.988188 \tabularnewline
51 & 96.05 & 94.8773 & 96.42 & 0.984 & 1.01236 \tabularnewline
52 & 106.22 & 98.8215 & 95.1804 & 1.03825 & 1.07487 \tabularnewline
53 & 98.33 & 98.3161 & 94.2596 & 1.04304 & 1.00014 \tabularnewline
54 & 99.86 & 100.485 & 93.5658 & 1.07395 & 0.993777 \tabularnewline
55 & 93.78 & 94.0426 & 92.9958 & 1.01126 & 0.997207 \tabularnewline
56 & 88.96 & 91.3769 & 92.6379 & 0.986388 & 0.97355 \tabularnewline
57 & 83.77 & 91.3254 & 92.5833 & 0.986413 & 0.91727 \tabularnewline
58 & 89.46 & 93.8439 & 92.5196 & 1.01431 & 0.953285 \tabularnewline
59 & 86.78 & 91.8072 & 92.7625 & 0.989701 & 0.945242 \tabularnewline
60 & 88.4 & 90.2861 & 93.4354 & 0.966294 & 0.979109 \tabularnewline
61 & 87.19 & 88.0094 & 93.9133 & 0.937135 & 0.990689 \tabularnewline
62 & 92.23 & 91.3076 & 94.2038 & 0.969256 & 1.0101 \tabularnewline
63 & 95.99 & 93.2849 & 94.8017 & 0.984 & 1.029 \tabularnewline
64 & 104.75 & 99.2606 & 95.6033 & 1.03825 & 1.0553 \tabularnewline
65 & 105.63 & 100.478 & 96.3325 & 1.04304 & 1.05127 \tabularnewline
66 & 108.71 & 104.149 & 96.9771 & 1.07395 & 1.04379 \tabularnewline
67 & 96.4 & NA & NA & 1.01126 & NA \tabularnewline
68 & 93.31 & NA & NA & 0.986388 & NA \tabularnewline
69 & 93.77 & NA & NA & 0.986413 & NA \tabularnewline
70 & 98.7 & NA & NA & 1.01431 & NA \tabularnewline
71 & 95.04 & NA & NA & 0.989701 & NA \tabularnewline
72 & 95.61 & NA & NA & 0.966294 & 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]86.88[/C][C]NA[/C][C]NA[/C][C]0.937135[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]90.65[/C][C]NA[/C][C]NA[/C][C]0.969256[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]90.68[/C][C]NA[/C][C]NA[/C][C]0.984[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]89.64[/C][C]NA[/C][C]NA[/C][C]1.03825[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]102.62[/C][C]NA[/C][C]NA[/C][C]1.04304[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]101.84[/C][C]NA[/C][C]NA[/C][C]1.07395[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]92.51[/C][C]94.6721[/C][C]93.6183[/C][C]1.01126[/C][C]0.977162[/C][/ROW]
[ROW][C]8[/C][C]94.29[/C][C]92.1977[/C][C]93.47[/C][C]0.986388[/C][C]1.02269[/C][/ROW]
[ROW][C]9[/C][C]94.68[/C][C]92.0989[/C][C]93.3675[/C][C]0.986413[/C][C]1.02803[/C][/ROW]
[ROW][C]10[/C][C]96.94[/C][C]94.8181[/C][C]93.48[/C][C]1.01431[/C][C]1.02238[/C][/ROW]
[ROW][C]11[/C][C]94.03[/C][C]92.2365[/C][C]93.1963[/C][C]0.989701[/C][C]1.01945[/C][/ROW]
[ROW][C]12[/C][C]89.65[/C][C]89.4986[/C][C]92.6204[/C][C]0.966294[/C][C]1.00169[/C][/ROW]
[ROW][C]13[/C][C]84.9[/C][C]86.8247[/C][C]92.6492[/C][C]0.937135[/C][C]0.977832[/C][/ROW]
[ROW][C]14[/C][C]89.07[/C][C]89.7858[/C][C]92.6338[/C][C]0.969256[/C][C]0.992027[/C][/ROW]
[ROW][C]15[/C][C]89.8[/C][C]90.7757[/C][C]92.2517[/C][C]0.984[/C][C]0.989252[/C][/ROW]
[ROW][C]16[/C][C]93.22[/C][C]95.3701[/C][C]91.8562[/C][C]1.03825[/C][C]0.977455[/C][/ROW]
[ROW][C]17[/C][C]92.23[/C][C]95.5672[/C][C]91.6242[/C][C]1.04304[/C][C]0.96508[/C][/ROW]
[ROW][C]18[/C][C]98.41[/C][C]98.418[/C][C]91.6408[/C][C]1.07395[/C][C]0.999919[/C][/ROW]
[ROW][C]19[/C][C]96.63[/C][C]92.7432[/C][C]91.7108[/C][C]1.01126[/C][C]1.04191[/C][/ROW]
[ROW][C]20[/C][C]89.8[/C][C]90.4513[/C][C]91.6996[/C][C]0.986388[/C][C]0.992799[/C][/ROW]
[ROW][C]21[/C][C]90[/C][C]90.3772[/C][C]91.6221[/C][C]0.986413[/C][C]0.995827[/C][/ROW]
[ROW][C]22[/C][C]92.13[/C][C]92.7598[/C][C]91.4508[/C][C]1.01431[/C][C]0.99321[/C][/ROW]
[ROW][C]23[/C][C]93.27[/C][C]90.4047[/C][C]91.3454[/C][C]0.989701[/C][C]1.03169[/C][/ROW]
[ROW][C]24[/C][C]90.81[/C][C]88.1953[/C][C]91.2717[/C][C]0.966294[/C][C]1.02965[/C][/ROW]
[ROW][C]25[/C][C]85.42[/C][C]85.3753[/C][C]91.1025[/C][C]0.937135[/C][C]1.00052[/C][/ROW]
[ROW][C]26[/C][C]88.28[/C][C]88.4095[/C][C]91.2137[/C][C]0.969256[/C][C]0.998535[/C][/ROW]
[ROW][C]27[/C][C]88.73[/C][C]90.2213[/C][C]91.6883[/C][C]0.984[/C][C]0.98347[/C][/ROW]
[ROW][C]28[/C][C]90.18[/C][C]95.8893[/C][C]92.3562[/C][C]1.03825[/C][C]0.94046[/C][/ROW]
[ROW][C]29[/C][C]92.74[/C][C]96.9623[/C][C]92.9617[/C][C]1.04304[/C][C]0.956454[/C][/ROW]
[ROW][C]30[/C][C]96.13[/C][C]100.29[/C][C]93.3842[/C][C]1.07395[/C][C]0.958518[/C][/ROW]
[ROW][C]31[/C][C]94.85[/C][C]94.9911[/C][C]93.9338[/C][C]1.01126[/C][C]0.998514[/C][/ROW]
[ROW][C]32[/C][C]94.25[/C][C]93.4105[/C][C]94.6996[/C][C]0.986388[/C][C]1.00899[/C][/ROW]
[ROW][C]33[/C][C]96.94[/C][C]94.1835[/C][C]95.4808[/C][C]0.986413[/C][C]1.02927[/C][/ROW]
[ROW][C]34[/C][C]101.22[/C][C]97.6218[/C][C]96.2442[/C][C]1.01431[/C][C]1.03686[/C][/ROW]
[ROW][C]35[/C][C]98.71[/C][C]96.2674[/C][C]97.2692[/C][C]0.989701[/C][C]1.02537[/C][/ROW]
[ROW][C]36[/C][C]95.51[/C][C]95.0979[/C][C]98.415[/C][C]0.966294[/C][C]1.00433[/C][/ROW]
[ROW][C]37[/C][C]93.91[/C][C]92.9216[/C][C]99.155[/C][C]0.937135[/C][C]1.01064[/C][/ROW]
[ROW][C]38[/C][C]98.17[/C][C]96.5274[/C][C]99.5892[/C][C]0.969256[/C][C]1.01702[/C][/ROW]
[ROW][C]39[/C][C]97.59[/C][C]98.398[/C][C]99.9979[/C][C]0.984[/C][C]0.991789[/C][/ROW]
[ROW][C]40[/C][C]99.64[/C][C]104.032[/C][C]100.199[/C][C]1.03825[/C][C]0.957784[/C][/ROW]
[ROW][C]41[/C][C]107.88[/C][C]104.442[/C][C]100.132[/C][C]1.04304[/C][C]1.03292[/C][/ROW]
[ROW][C]42[/C][C]108.49[/C][C]107.431[/C][C]100.033[/C][C]1.07395[/C][C]1.00986[/C][/ROW]
[ROW][C]43[/C][C]100.25[/C][C]101.153[/C][C]100.027[/C][C]1.01126[/C][C]0.991077[/C][/ROW]
[ROW][C]44[/C][C]99.27[/C][C]98.4978[/C][C]99.8571[/C][C]0.986388[/C][C]1.00784[/C][/ROW]
[ROW][C]45[/C][C]101.73[/C][C]98.2442[/C][C]99.5975[/C][C]0.986413[/C][C]1.03548[/C][/ROW]
[ROW][C]46[/C][C]101.25[/C][C]101.236[/C][C]99.8075[/C][C]1.01431[/C][C]1.00014[/C][/ROW]
[ROW][C]47[/C][C]97.09[/C][C]98.6571[/C][C]99.6838[/C][C]0.989701[/C][C]0.984115[/C][/ROW]
[ROW][C]48[/C][C]94.74[/C][C]95.5919[/C][C]98.9262[/C][C]0.966294[/C][C]0.991088[/C][/ROW]
[ROW][C]49[/C][C]94.53[/C][C]92.1176[/C][C]98.2971[/C][C]0.937135[/C][C]1.02619[/C][/ROW]
[ROW][C]50[/C][C]93.48[/C][C]94.5974[/C][C]97.5979[/C][C]0.969256[/C][C]0.988188[/C][/ROW]
[ROW][C]51[/C][C]96.05[/C][C]94.8773[/C][C]96.42[/C][C]0.984[/C][C]1.01236[/C][/ROW]
[ROW][C]52[/C][C]106.22[/C][C]98.8215[/C][C]95.1804[/C][C]1.03825[/C][C]1.07487[/C][/ROW]
[ROW][C]53[/C][C]98.33[/C][C]98.3161[/C][C]94.2596[/C][C]1.04304[/C][C]1.00014[/C][/ROW]
[ROW][C]54[/C][C]99.86[/C][C]100.485[/C][C]93.5658[/C][C]1.07395[/C][C]0.993777[/C][/ROW]
[ROW][C]55[/C][C]93.78[/C][C]94.0426[/C][C]92.9958[/C][C]1.01126[/C][C]0.997207[/C][/ROW]
[ROW][C]56[/C][C]88.96[/C][C]91.3769[/C][C]92.6379[/C][C]0.986388[/C][C]0.97355[/C][/ROW]
[ROW][C]57[/C][C]83.77[/C][C]91.3254[/C][C]92.5833[/C][C]0.986413[/C][C]0.91727[/C][/ROW]
[ROW][C]58[/C][C]89.46[/C][C]93.8439[/C][C]92.5196[/C][C]1.01431[/C][C]0.953285[/C][/ROW]
[ROW][C]59[/C][C]86.78[/C][C]91.8072[/C][C]92.7625[/C][C]0.989701[/C][C]0.945242[/C][/ROW]
[ROW][C]60[/C][C]88.4[/C][C]90.2861[/C][C]93.4354[/C][C]0.966294[/C][C]0.979109[/C][/ROW]
[ROW][C]61[/C][C]87.19[/C][C]88.0094[/C][C]93.9133[/C][C]0.937135[/C][C]0.990689[/C][/ROW]
[ROW][C]62[/C][C]92.23[/C][C]91.3076[/C][C]94.2038[/C][C]0.969256[/C][C]1.0101[/C][/ROW]
[ROW][C]63[/C][C]95.99[/C][C]93.2849[/C][C]94.8017[/C][C]0.984[/C][C]1.029[/C][/ROW]
[ROW][C]64[/C][C]104.75[/C][C]99.2606[/C][C]95.6033[/C][C]1.03825[/C][C]1.0553[/C][/ROW]
[ROW][C]65[/C][C]105.63[/C][C]100.478[/C][C]96.3325[/C][C]1.04304[/C][C]1.05127[/C][/ROW]
[ROW][C]66[/C][C]108.71[/C][C]104.149[/C][C]96.9771[/C][C]1.07395[/C][C]1.04379[/C][/ROW]
[ROW][C]67[/C][C]96.4[/C][C]NA[/C][C]NA[/C][C]1.01126[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]93.31[/C][C]NA[/C][C]NA[/C][C]0.986388[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]93.77[/C][C]NA[/C][C]NA[/C][C]0.986413[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]98.7[/C][C]NA[/C][C]NA[/C][C]1.01431[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]95.04[/C][C]NA[/C][C]NA[/C][C]0.989701[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]95.61[/C][C]NA[/C][C]NA[/C][C]0.966294[/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	86.88	NA	NA	0.937135	NA
2	90.65	NA	NA	0.969256	NA
3	90.68	NA	NA	0.984	NA
4	89.64	NA	NA	1.03825	NA
5	102.62	NA	NA	1.04304	NA
6	101.84	NA	NA	1.07395	NA
7	92.51	94.6721	93.6183	1.01126	0.977162
8	94.29	92.1977	93.47	0.986388	1.02269
9	94.68	92.0989	93.3675	0.986413	1.02803
10	96.94	94.8181	93.48	1.01431	1.02238
11	94.03	92.2365	93.1963	0.989701	1.01945
12	89.65	89.4986	92.6204	0.966294	1.00169
13	84.9	86.8247	92.6492	0.937135	0.977832
14	89.07	89.7858	92.6338	0.969256	0.992027
15	89.8	90.7757	92.2517	0.984	0.989252
16	93.22	95.3701	91.8562	1.03825	0.977455
17	92.23	95.5672	91.6242	1.04304	0.96508
18	98.41	98.418	91.6408	1.07395	0.999919
19	96.63	92.7432	91.7108	1.01126	1.04191
20	89.8	90.4513	91.6996	0.986388	0.992799
21	90	90.3772	91.6221	0.986413	0.995827
22	92.13	92.7598	91.4508	1.01431	0.99321
23	93.27	90.4047	91.3454	0.989701	1.03169
24	90.81	88.1953	91.2717	0.966294	1.02965
25	85.42	85.3753	91.1025	0.937135	1.00052
26	88.28	88.4095	91.2137	0.969256	0.998535
27	88.73	90.2213	91.6883	0.984	0.98347
28	90.18	95.8893	92.3562	1.03825	0.94046
29	92.74	96.9623	92.9617	1.04304	0.956454
30	96.13	100.29	93.3842	1.07395	0.958518
31	94.85	94.9911	93.9338	1.01126	0.998514
32	94.25	93.4105	94.6996	0.986388	1.00899
33	96.94	94.1835	95.4808	0.986413	1.02927
34	101.22	97.6218	96.2442	1.01431	1.03686
35	98.71	96.2674	97.2692	0.989701	1.02537
36	95.51	95.0979	98.415	0.966294	1.00433
37	93.91	92.9216	99.155	0.937135	1.01064
38	98.17	96.5274	99.5892	0.969256	1.01702
39	97.59	98.398	99.9979	0.984	0.991789
40	99.64	104.032	100.199	1.03825	0.957784
41	107.88	104.442	100.132	1.04304	1.03292
42	108.49	107.431	100.033	1.07395	1.00986
43	100.25	101.153	100.027	1.01126	0.991077
44	99.27	98.4978	99.8571	0.986388	1.00784
45	101.73	98.2442	99.5975	0.986413	1.03548
46	101.25	101.236	99.8075	1.01431	1.00014
47	97.09	98.6571	99.6838	0.989701	0.984115
48	94.74	95.5919	98.9262	0.966294	0.991088
49	94.53	92.1176	98.2971	0.937135	1.02619
50	93.48	94.5974	97.5979	0.969256	0.988188
51	96.05	94.8773	96.42	0.984	1.01236
52	106.22	98.8215	95.1804	1.03825	1.07487
53	98.33	98.3161	94.2596	1.04304	1.00014
54	99.86	100.485	93.5658	1.07395	0.993777
55	93.78	94.0426	92.9958	1.01126	0.997207
56	88.96	91.3769	92.6379	0.986388	0.97355
57	83.77	91.3254	92.5833	0.986413	0.91727
58	89.46	93.8439	92.5196	1.01431	0.953285
59	86.78	91.8072	92.7625	0.989701	0.945242
60	88.4	90.2861	93.4354	0.966294	0.979109
61	87.19	88.0094	93.9133	0.937135	0.990689
62	92.23	91.3076	94.2038	0.969256	1.0101
63	95.99	93.2849	94.8017	0.984	1.029
64	104.75	99.2606	95.6033	1.03825	1.0553
65	105.63	100.478	96.3325	1.04304	1.05127
66	108.71	104.149	96.9771	1.07395	1.04379
67	96.4	NA	NA	1.01126	NA
68	93.31	NA	NA	0.986388	NA
69	93.77	NA	NA	0.986413	NA
70	98.7	NA	NA	1.01431	NA
71	95.04	NA	NA	0.989701	NA
72	95.61	NA	NA	0.966294	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