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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Tue, 10 Jan 2017 09:54:23 +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/2017/Jan/10/t1484042309q0nhwcez4gqt7nz.htm/, Retrieved Wed, 15 May 2024 18:20:20 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 15 May 2024 18:20:20 +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	'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 & 2 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]2 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	2 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	92.64	NA	NA	-5.54549	NA
2	91.48	NA	NA	1.04426	NA
3	92.65	NA	NA	1.82176	NA
4	93.91	NA	NA	1.2146	NA
5	90.49	NA	NA	1.6611	NA
6	94.56	NA	NA	1.72343	NA
7	95.11	94.0351	92.46	1.5751	1.0749
8	93.07	92.5713	92.2446	0.326681	0.498736
9	93.26	90.422	92.4992	-2.07715	2.83799
10	92.92	94.3523	92.8587	1.49351	-1.43226
11	90.9	90.3064	93.4112	-3.10482	0.593569
12	91.77	93.8753	94.0083	-0.132986	-2.10535
13	86.16	88.8091	94.3546	-5.54549	-2.6491
14	92.79	95.743	94.6988	1.04426	-2.95301
15	97.45	96.9138	95.0921	1.82176	0.536153
16	97.74	96.9988	95.7842	1.2146	0.741236
17	99.92	98.5148	96.8537	1.6611	1.40515
18	99.46	99.7118	97.9883	1.72343	-0.251764
19	98.52	100.529	98.9538	1.5751	-2.00885
20	97.92	100.23	99.9033	0.326681	-2.31001
21	97.85	98.737	100.814	-2.07715	-0.887014
22	104.94	103.068	101.575	1.49351	1.8719
23	104.55	99.1273	102.232	-3.10482	5.42274
24	105.35	102.72	102.853	-0.132986	2.63007
25	95.75	97.9245	103.47	-5.54549	-2.17451
26	105.99	105.135	104.09	1.04426	0.855319
27	106.11	106.279	104.457	1.82176	-0.168847
28	107.33	105.65	104.435	1.2146	1.67999
29	106.11	105.722	104.061	1.6611	0.388069
30	108.17	105.309	103.586	1.72343	2.86074
31	104.62	105.061	103.486	1.5751	-0.441347
32	106.71	103.713	103.386	0.326681	2.99707
33	97.86	101.145	103.222	-2.07715	-3.28451
34	104.41	104.343	102.85	1.49351	0.0669028
35	96.09	99.3452	102.45	-3.10482	-3.25518
36	102.41	102.087	102.22	-0.132986	0.322986
37	96.3	96.4903	102.036	-5.54549	-0.190347
38	103.04	102.826	101.781	1.04426	0.214486
39	105.11	103.495	101.673	1.82176	1.6149
40	99.4	102.936	101.721	1.2146	-3.53585
41	104.45	103.169	101.508	1.6611	1.28099
42	104.31	102.98	101.256	1.72343	1.33032
43	104.06	102.669	101.094	1.5751	1.39074
44	101.16	101.299	100.972	0.326681	-0.139181
45	100.82	98.6103	100.687	-2.07715	2.20965
46	102.6	102.043	100.55	1.49351	0.556903
47	92.78	97.2885	100.393	-3.10482	-4.50851
48	99.68	99.7578	99.8908	-0.132986	-0.0778472
49	95.14	93.9137	99.4592	-5.54549	1.22632
50	101.28	100.215	99.1704	1.04426	1.06532
51	100.03	100.654	98.8325	1.82176	-0.624264
52	101.17	99.6967	98.4821	1.2146	1.47332
53	98.93	100.194	98.5333	1.6611	-1.26443
54	97.77	100.381	98.6575	1.72343	-2.61093
55	100.24	100.255	98.68	1.5751	-0.0150972
56	98.05	99.0963	98.7696	0.326681	-1.04626
57	95.82	96.6958	98.7729	-2.07715	-0.875764
58	99.19	100.253	98.7596	1.49351	-1.0631
59	97.42	95.6723	98.7771	-3.10482	1.74774
60	98.02	98.7895	98.9225	-0.132986	-0.769514
61	97.34	93.552	99.0975	-5.54549	3.78799
62	101.23	100.412	99.3675	1.04426	0.818236
63	100.16	101.518	99.6958	1.82176	-1.3576
64	100.72	101.078	99.8637	1.2146	-0.358347
65	99.8	101.609	99.9483	1.6611	-1.80943
66	100.39	101.718	99.9946	1.72343	-1.32801
67	101.82	NA	NA	1.5751	NA
68	102.95	NA	NA	0.326681	NA
69	98.8	NA	NA	-2.07715	NA
70	100.24	NA	NA	1.49351	NA
71	98.4	NA	NA	-3.10482	NA
72	98.15	NA	NA	-0.132986	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 92.64 & NA & NA & -5.54549 & NA \tabularnewline
2 & 91.48 & NA & NA & 1.04426 & NA \tabularnewline
3 & 92.65 & NA & NA & 1.82176 & NA \tabularnewline
4 & 93.91 & NA & NA & 1.2146 & NA \tabularnewline
5 & 90.49 & NA & NA & 1.6611 & NA \tabularnewline
6 & 94.56 & NA & NA & 1.72343 & NA \tabularnewline
7 & 95.11 & 94.0351 & 92.46 & 1.5751 & 1.0749 \tabularnewline
8 & 93.07 & 92.5713 & 92.2446 & 0.326681 & 0.498736 \tabularnewline
9 & 93.26 & 90.422 & 92.4992 & -2.07715 & 2.83799 \tabularnewline
10 & 92.92 & 94.3523 & 92.8587 & 1.49351 & -1.43226 \tabularnewline
11 & 90.9 & 90.3064 & 93.4112 & -3.10482 & 0.593569 \tabularnewline
12 & 91.77 & 93.8753 & 94.0083 & -0.132986 & -2.10535 \tabularnewline
13 & 86.16 & 88.8091 & 94.3546 & -5.54549 & -2.6491 \tabularnewline
14 & 92.79 & 95.743 & 94.6988 & 1.04426 & -2.95301 \tabularnewline
15 & 97.45 & 96.9138 & 95.0921 & 1.82176 & 0.536153 \tabularnewline
16 & 97.74 & 96.9988 & 95.7842 & 1.2146 & 0.741236 \tabularnewline
17 & 99.92 & 98.5148 & 96.8537 & 1.6611 & 1.40515 \tabularnewline
18 & 99.46 & 99.7118 & 97.9883 & 1.72343 & -0.251764 \tabularnewline
19 & 98.52 & 100.529 & 98.9538 & 1.5751 & -2.00885 \tabularnewline
20 & 97.92 & 100.23 & 99.9033 & 0.326681 & -2.31001 \tabularnewline
21 & 97.85 & 98.737 & 100.814 & -2.07715 & -0.887014 \tabularnewline
22 & 104.94 & 103.068 & 101.575 & 1.49351 & 1.8719 \tabularnewline
23 & 104.55 & 99.1273 & 102.232 & -3.10482 & 5.42274 \tabularnewline
24 & 105.35 & 102.72 & 102.853 & -0.132986 & 2.63007 \tabularnewline
25 & 95.75 & 97.9245 & 103.47 & -5.54549 & -2.17451 \tabularnewline
26 & 105.99 & 105.135 & 104.09 & 1.04426 & 0.855319 \tabularnewline
27 & 106.11 & 106.279 & 104.457 & 1.82176 & -0.168847 \tabularnewline
28 & 107.33 & 105.65 & 104.435 & 1.2146 & 1.67999 \tabularnewline
29 & 106.11 & 105.722 & 104.061 & 1.6611 & 0.388069 \tabularnewline
30 & 108.17 & 105.309 & 103.586 & 1.72343 & 2.86074 \tabularnewline
31 & 104.62 & 105.061 & 103.486 & 1.5751 & -0.441347 \tabularnewline
32 & 106.71 & 103.713 & 103.386 & 0.326681 & 2.99707 \tabularnewline
33 & 97.86 & 101.145 & 103.222 & -2.07715 & -3.28451 \tabularnewline
34 & 104.41 & 104.343 & 102.85 & 1.49351 & 0.0669028 \tabularnewline
35 & 96.09 & 99.3452 & 102.45 & -3.10482 & -3.25518 \tabularnewline
36 & 102.41 & 102.087 & 102.22 & -0.132986 & 0.322986 \tabularnewline
37 & 96.3 & 96.4903 & 102.036 & -5.54549 & -0.190347 \tabularnewline
38 & 103.04 & 102.826 & 101.781 & 1.04426 & 0.214486 \tabularnewline
39 & 105.11 & 103.495 & 101.673 & 1.82176 & 1.6149 \tabularnewline
40 & 99.4 & 102.936 & 101.721 & 1.2146 & -3.53585 \tabularnewline
41 & 104.45 & 103.169 & 101.508 & 1.6611 & 1.28099 \tabularnewline
42 & 104.31 & 102.98 & 101.256 & 1.72343 & 1.33032 \tabularnewline
43 & 104.06 & 102.669 & 101.094 & 1.5751 & 1.39074 \tabularnewline
44 & 101.16 & 101.299 & 100.972 & 0.326681 & -0.139181 \tabularnewline
45 & 100.82 & 98.6103 & 100.687 & -2.07715 & 2.20965 \tabularnewline
46 & 102.6 & 102.043 & 100.55 & 1.49351 & 0.556903 \tabularnewline
47 & 92.78 & 97.2885 & 100.393 & -3.10482 & -4.50851 \tabularnewline
48 & 99.68 & 99.7578 & 99.8908 & -0.132986 & -0.0778472 \tabularnewline
49 & 95.14 & 93.9137 & 99.4592 & -5.54549 & 1.22632 \tabularnewline
50 & 101.28 & 100.215 & 99.1704 & 1.04426 & 1.06532 \tabularnewline
51 & 100.03 & 100.654 & 98.8325 & 1.82176 & -0.624264 \tabularnewline
52 & 101.17 & 99.6967 & 98.4821 & 1.2146 & 1.47332 \tabularnewline
53 & 98.93 & 100.194 & 98.5333 & 1.6611 & -1.26443 \tabularnewline
54 & 97.77 & 100.381 & 98.6575 & 1.72343 & -2.61093 \tabularnewline
55 & 100.24 & 100.255 & 98.68 & 1.5751 & -0.0150972 \tabularnewline
56 & 98.05 & 99.0963 & 98.7696 & 0.326681 & -1.04626 \tabularnewline
57 & 95.82 & 96.6958 & 98.7729 & -2.07715 & -0.875764 \tabularnewline
58 & 99.19 & 100.253 & 98.7596 & 1.49351 & -1.0631 \tabularnewline
59 & 97.42 & 95.6723 & 98.7771 & -3.10482 & 1.74774 \tabularnewline
60 & 98.02 & 98.7895 & 98.9225 & -0.132986 & -0.769514 \tabularnewline
61 & 97.34 & 93.552 & 99.0975 & -5.54549 & 3.78799 \tabularnewline
62 & 101.23 & 100.412 & 99.3675 & 1.04426 & 0.818236 \tabularnewline
63 & 100.16 & 101.518 & 99.6958 & 1.82176 & -1.3576 \tabularnewline
64 & 100.72 & 101.078 & 99.8637 & 1.2146 & -0.358347 \tabularnewline
65 & 99.8 & 101.609 & 99.9483 & 1.6611 & -1.80943 \tabularnewline
66 & 100.39 & 101.718 & 99.9946 & 1.72343 & -1.32801 \tabularnewline
67 & 101.82 & NA & NA & 1.5751 & NA \tabularnewline
68 & 102.95 & NA & NA & 0.326681 & NA \tabularnewline
69 & 98.8 & NA & NA & -2.07715 & NA \tabularnewline
70 & 100.24 & NA & NA & 1.49351 & NA \tabularnewline
71 & 98.4 & NA & NA & -3.10482 & NA \tabularnewline
72 & 98.15 & NA & NA & -0.132986 & 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]92.64[/C][C]NA[/C][C]NA[/C][C]-5.54549[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]91.48[/C][C]NA[/C][C]NA[/C][C]1.04426[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]92.65[/C][C]NA[/C][C]NA[/C][C]1.82176[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]93.91[/C][C]NA[/C][C]NA[/C][C]1.2146[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]90.49[/C][C]NA[/C][C]NA[/C][C]1.6611[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]94.56[/C][C]NA[/C][C]NA[/C][C]1.72343[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]95.11[/C][C]94.0351[/C][C]92.46[/C][C]1.5751[/C][C]1.0749[/C][/ROW]
[ROW][C]8[/C][C]93.07[/C][C]92.5713[/C][C]92.2446[/C][C]0.326681[/C][C]0.498736[/C][/ROW]
[ROW][C]9[/C][C]93.26[/C][C]90.422[/C][C]92.4992[/C][C]-2.07715[/C][C]2.83799[/C][/ROW]
[ROW][C]10[/C][C]92.92[/C][C]94.3523[/C][C]92.8587[/C][C]1.49351[/C][C]-1.43226[/C][/ROW]
[ROW][C]11[/C][C]90.9[/C][C]90.3064[/C][C]93.4112[/C][C]-3.10482[/C][C]0.593569[/C][/ROW]
[ROW][C]12[/C][C]91.77[/C][C]93.8753[/C][C]94.0083[/C][C]-0.132986[/C][C]-2.10535[/C][/ROW]
[ROW][C]13[/C][C]86.16[/C][C]88.8091[/C][C]94.3546[/C][C]-5.54549[/C][C]-2.6491[/C][/ROW]
[ROW][C]14[/C][C]92.79[/C][C]95.743[/C][C]94.6988[/C][C]1.04426[/C][C]-2.95301[/C][/ROW]
[ROW][C]15[/C][C]97.45[/C][C]96.9138[/C][C]95.0921[/C][C]1.82176[/C][C]0.536153[/C][/ROW]
[ROW][C]16[/C][C]97.74[/C][C]96.9988[/C][C]95.7842[/C][C]1.2146[/C][C]0.741236[/C][/ROW]
[ROW][C]17[/C][C]99.92[/C][C]98.5148[/C][C]96.8537[/C][C]1.6611[/C][C]1.40515[/C][/ROW]
[ROW][C]18[/C][C]99.46[/C][C]99.7118[/C][C]97.9883[/C][C]1.72343[/C][C]-0.251764[/C][/ROW]
[ROW][C]19[/C][C]98.52[/C][C]100.529[/C][C]98.9538[/C][C]1.5751[/C][C]-2.00885[/C][/ROW]
[ROW][C]20[/C][C]97.92[/C][C]100.23[/C][C]99.9033[/C][C]0.326681[/C][C]-2.31001[/C][/ROW]
[ROW][C]21[/C][C]97.85[/C][C]98.737[/C][C]100.814[/C][C]-2.07715[/C][C]-0.887014[/C][/ROW]
[ROW][C]22[/C][C]104.94[/C][C]103.068[/C][C]101.575[/C][C]1.49351[/C][C]1.8719[/C][/ROW]
[ROW][C]23[/C][C]104.55[/C][C]99.1273[/C][C]102.232[/C][C]-3.10482[/C][C]5.42274[/C][/ROW]
[ROW][C]24[/C][C]105.35[/C][C]102.72[/C][C]102.853[/C][C]-0.132986[/C][C]2.63007[/C][/ROW]
[ROW][C]25[/C][C]95.75[/C][C]97.9245[/C][C]103.47[/C][C]-5.54549[/C][C]-2.17451[/C][/ROW]
[ROW][C]26[/C][C]105.99[/C][C]105.135[/C][C]104.09[/C][C]1.04426[/C][C]0.855319[/C][/ROW]
[ROW][C]27[/C][C]106.11[/C][C]106.279[/C][C]104.457[/C][C]1.82176[/C][C]-0.168847[/C][/ROW]
[ROW][C]28[/C][C]107.33[/C][C]105.65[/C][C]104.435[/C][C]1.2146[/C][C]1.67999[/C][/ROW]
[ROW][C]29[/C][C]106.11[/C][C]105.722[/C][C]104.061[/C][C]1.6611[/C][C]0.388069[/C][/ROW]
[ROW][C]30[/C][C]108.17[/C][C]105.309[/C][C]103.586[/C][C]1.72343[/C][C]2.86074[/C][/ROW]
[ROW][C]31[/C][C]104.62[/C][C]105.061[/C][C]103.486[/C][C]1.5751[/C][C]-0.441347[/C][/ROW]
[ROW][C]32[/C][C]106.71[/C][C]103.713[/C][C]103.386[/C][C]0.326681[/C][C]2.99707[/C][/ROW]
[ROW][C]33[/C][C]97.86[/C][C]101.145[/C][C]103.222[/C][C]-2.07715[/C][C]-3.28451[/C][/ROW]
[ROW][C]34[/C][C]104.41[/C][C]104.343[/C][C]102.85[/C][C]1.49351[/C][C]0.0669028[/C][/ROW]
[ROW][C]35[/C][C]96.09[/C][C]99.3452[/C][C]102.45[/C][C]-3.10482[/C][C]-3.25518[/C][/ROW]
[ROW][C]36[/C][C]102.41[/C][C]102.087[/C][C]102.22[/C][C]-0.132986[/C][C]0.322986[/C][/ROW]
[ROW][C]37[/C][C]96.3[/C][C]96.4903[/C][C]102.036[/C][C]-5.54549[/C][C]-0.190347[/C][/ROW]
[ROW][C]38[/C][C]103.04[/C][C]102.826[/C][C]101.781[/C][C]1.04426[/C][C]0.214486[/C][/ROW]
[ROW][C]39[/C][C]105.11[/C][C]103.495[/C][C]101.673[/C][C]1.82176[/C][C]1.6149[/C][/ROW]
[ROW][C]40[/C][C]99.4[/C][C]102.936[/C][C]101.721[/C][C]1.2146[/C][C]-3.53585[/C][/ROW]
[ROW][C]41[/C][C]104.45[/C][C]103.169[/C][C]101.508[/C][C]1.6611[/C][C]1.28099[/C][/ROW]
[ROW][C]42[/C][C]104.31[/C][C]102.98[/C][C]101.256[/C][C]1.72343[/C][C]1.33032[/C][/ROW]
[ROW][C]43[/C][C]104.06[/C][C]102.669[/C][C]101.094[/C][C]1.5751[/C][C]1.39074[/C][/ROW]
[ROW][C]44[/C][C]101.16[/C][C]101.299[/C][C]100.972[/C][C]0.326681[/C][C]-0.139181[/C][/ROW]
[ROW][C]45[/C][C]100.82[/C][C]98.6103[/C][C]100.687[/C][C]-2.07715[/C][C]2.20965[/C][/ROW]
[ROW][C]46[/C][C]102.6[/C][C]102.043[/C][C]100.55[/C][C]1.49351[/C][C]0.556903[/C][/ROW]
[ROW][C]47[/C][C]92.78[/C][C]97.2885[/C][C]100.393[/C][C]-3.10482[/C][C]-4.50851[/C][/ROW]
[ROW][C]48[/C][C]99.68[/C][C]99.7578[/C][C]99.8908[/C][C]-0.132986[/C][C]-0.0778472[/C][/ROW]
[ROW][C]49[/C][C]95.14[/C][C]93.9137[/C][C]99.4592[/C][C]-5.54549[/C][C]1.22632[/C][/ROW]
[ROW][C]50[/C][C]101.28[/C][C]100.215[/C][C]99.1704[/C][C]1.04426[/C][C]1.06532[/C][/ROW]
[ROW][C]51[/C][C]100.03[/C][C]100.654[/C][C]98.8325[/C][C]1.82176[/C][C]-0.624264[/C][/ROW]
[ROW][C]52[/C][C]101.17[/C][C]99.6967[/C][C]98.4821[/C][C]1.2146[/C][C]1.47332[/C][/ROW]
[ROW][C]53[/C][C]98.93[/C][C]100.194[/C][C]98.5333[/C][C]1.6611[/C][C]-1.26443[/C][/ROW]
[ROW][C]54[/C][C]97.77[/C][C]100.381[/C][C]98.6575[/C][C]1.72343[/C][C]-2.61093[/C][/ROW]
[ROW][C]55[/C][C]100.24[/C][C]100.255[/C][C]98.68[/C][C]1.5751[/C][C]-0.0150972[/C][/ROW]
[ROW][C]56[/C][C]98.05[/C][C]99.0963[/C][C]98.7696[/C][C]0.326681[/C][C]-1.04626[/C][/ROW]
[ROW][C]57[/C][C]95.82[/C][C]96.6958[/C][C]98.7729[/C][C]-2.07715[/C][C]-0.875764[/C][/ROW]
[ROW][C]58[/C][C]99.19[/C][C]100.253[/C][C]98.7596[/C][C]1.49351[/C][C]-1.0631[/C][/ROW]
[ROW][C]59[/C][C]97.42[/C][C]95.6723[/C][C]98.7771[/C][C]-3.10482[/C][C]1.74774[/C][/ROW]
[ROW][C]60[/C][C]98.02[/C][C]98.7895[/C][C]98.9225[/C][C]-0.132986[/C][C]-0.769514[/C][/ROW]
[ROW][C]61[/C][C]97.34[/C][C]93.552[/C][C]99.0975[/C][C]-5.54549[/C][C]3.78799[/C][/ROW]
[ROW][C]62[/C][C]101.23[/C][C]100.412[/C][C]99.3675[/C][C]1.04426[/C][C]0.818236[/C][/ROW]
[ROW][C]63[/C][C]100.16[/C][C]101.518[/C][C]99.6958[/C][C]1.82176[/C][C]-1.3576[/C][/ROW]
[ROW][C]64[/C][C]100.72[/C][C]101.078[/C][C]99.8637[/C][C]1.2146[/C][C]-0.358347[/C][/ROW]
[ROW][C]65[/C][C]99.8[/C][C]101.609[/C][C]99.9483[/C][C]1.6611[/C][C]-1.80943[/C][/ROW]
[ROW][C]66[/C][C]100.39[/C][C]101.718[/C][C]99.9946[/C][C]1.72343[/C][C]-1.32801[/C][/ROW]
[ROW][C]67[/C][C]101.82[/C][C]NA[/C][C]NA[/C][C]1.5751[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.95[/C][C]NA[/C][C]NA[/C][C]0.326681[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]98.8[/C][C]NA[/C][C]NA[/C][C]-2.07715[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]100.24[/C][C]NA[/C][C]NA[/C][C]1.49351[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]98.4[/C][C]NA[/C][C]NA[/C][C]-3.10482[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]98.15[/C][C]NA[/C][C]NA[/C][C]-0.132986[/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	92.64	NA	NA	-5.54549	NA
2	91.48	NA	NA	1.04426	NA
3	92.65	NA	NA	1.82176	NA
4	93.91	NA	NA	1.2146	NA
5	90.49	NA	NA	1.6611	NA
6	94.56	NA	NA	1.72343	NA
7	95.11	94.0351	92.46	1.5751	1.0749
8	93.07	92.5713	92.2446	0.326681	0.498736
9	93.26	90.422	92.4992	-2.07715	2.83799
10	92.92	94.3523	92.8587	1.49351	-1.43226
11	90.9	90.3064	93.4112	-3.10482	0.593569
12	91.77	93.8753	94.0083	-0.132986	-2.10535
13	86.16	88.8091	94.3546	-5.54549	-2.6491
14	92.79	95.743	94.6988	1.04426	-2.95301
15	97.45	96.9138	95.0921	1.82176	0.536153
16	97.74	96.9988	95.7842	1.2146	0.741236
17	99.92	98.5148	96.8537	1.6611	1.40515
18	99.46	99.7118	97.9883	1.72343	-0.251764
19	98.52	100.529	98.9538	1.5751	-2.00885
20	97.92	100.23	99.9033	0.326681	-2.31001
21	97.85	98.737	100.814	-2.07715	-0.887014
22	104.94	103.068	101.575	1.49351	1.8719
23	104.55	99.1273	102.232	-3.10482	5.42274
24	105.35	102.72	102.853	-0.132986	2.63007
25	95.75	97.9245	103.47	-5.54549	-2.17451
26	105.99	105.135	104.09	1.04426	0.855319
27	106.11	106.279	104.457	1.82176	-0.168847
28	107.33	105.65	104.435	1.2146	1.67999
29	106.11	105.722	104.061	1.6611	0.388069
30	108.17	105.309	103.586	1.72343	2.86074
31	104.62	105.061	103.486	1.5751	-0.441347
32	106.71	103.713	103.386	0.326681	2.99707
33	97.86	101.145	103.222	-2.07715	-3.28451
34	104.41	104.343	102.85	1.49351	0.0669028
35	96.09	99.3452	102.45	-3.10482	-3.25518
36	102.41	102.087	102.22	-0.132986	0.322986
37	96.3	96.4903	102.036	-5.54549	-0.190347
38	103.04	102.826	101.781	1.04426	0.214486
39	105.11	103.495	101.673	1.82176	1.6149
40	99.4	102.936	101.721	1.2146	-3.53585
41	104.45	103.169	101.508	1.6611	1.28099
42	104.31	102.98	101.256	1.72343	1.33032
43	104.06	102.669	101.094	1.5751	1.39074
44	101.16	101.299	100.972	0.326681	-0.139181
45	100.82	98.6103	100.687	-2.07715	2.20965
46	102.6	102.043	100.55	1.49351	0.556903
47	92.78	97.2885	100.393	-3.10482	-4.50851
48	99.68	99.7578	99.8908	-0.132986	-0.0778472
49	95.14	93.9137	99.4592	-5.54549	1.22632
50	101.28	100.215	99.1704	1.04426	1.06532
51	100.03	100.654	98.8325	1.82176	-0.624264
52	101.17	99.6967	98.4821	1.2146	1.47332
53	98.93	100.194	98.5333	1.6611	-1.26443
54	97.77	100.381	98.6575	1.72343	-2.61093
55	100.24	100.255	98.68	1.5751	-0.0150972
56	98.05	99.0963	98.7696	0.326681	-1.04626
57	95.82	96.6958	98.7729	-2.07715	-0.875764
58	99.19	100.253	98.7596	1.49351	-1.0631
59	97.42	95.6723	98.7771	-3.10482	1.74774
60	98.02	98.7895	98.9225	-0.132986	-0.769514
61	97.34	93.552	99.0975	-5.54549	3.78799
62	101.23	100.412	99.3675	1.04426	0.818236
63	100.16	101.518	99.6958	1.82176	-1.3576
64	100.72	101.078	99.8637	1.2146	-0.358347
65	99.8	101.609	99.9483	1.6611	-1.80943
66	100.39	101.718	99.9946	1.72343	-1.32801
67	101.82	NA	NA	1.5751	NA
68	102.95	NA	NA	0.326681	NA
69	98.8	NA	NA	-2.07715	NA
70	100.24	NA	NA	1.49351	NA
71	98.4	NA	NA	-3.10482	NA
72	98.15	NA	NA	-0.132986	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