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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Thu, 24 Nov 2016 15:47:59 +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/2016/Nov/24/t1480002518i5qjx0y9ukdjv9s.htm/, Retrieved Tue, 07 May 2024 09:36:54 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 07 May 2024 09:36:54 +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	'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 & 2 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]2 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

As an alternative you can also use a QR Code:

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	'George Udny Yule' @ yule.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	90.89	NA	NA	-0.627413	NA
2	91.1	NA	NA	-0.164601	NA
3	91.35	NA	NA	0.20842	NA
4	91.52	NA	NA	0.12092	NA
5	91.45	NA	NA	0.0919618	NA
6	91.88	NA	NA	0.0959201	NA
7	91.9	91.5101	91.7708	-0.260747	0.389913
8	91.92	92.077	91.8821	0.194878	-0.156962
9	92	92.0535	91.9862	0.0672743	-0.0535243
10	92	92.2719	92.0879	0.183941	-0.271858
11	92.2	92.3541	92.1938	0.160399	-0.154149
12	92.34	92.2174	92.2883	-0.0709549	0.122622
13	92.29	91.7384	92.3658	-0.627413	0.55158
14	92.37	92.2841	92.4488	-0.164601	0.0858507
15	92.58	92.7509	92.5425	0.20842	-0.17092
16	92.73	92.7909	92.67	0.12092	-0.0609201
17	92.78	92.9274	92.8354	0.0919618	-0.147378
18	92.82	93.0893	92.9933	0.0959201	-0.269253
19	92.82	92.858	93.1188	-0.260747	-0.0380035
20	92.99	93.519	93.3242	0.194878	-0.529045
21	93.18	93.7723	93.705	0.0672743	-0.592274
22	93.88	94.3677	94.1837	0.183941	-0.487691
23	94.29	94.865	94.7046	0.160399	-0.574983
24	94.04	95.1874	95.2583	-0.0709549	-1.14738
25	93.6	95.1984	95.8258	-0.627413	-1.59842
26	95.99	96.3383	96.5029	-0.164601	-0.348316
27	98.1	97.4988	97.2904	0.20842	0.601163
28	98.7	98.188	98.0671	0.12092	0.511997
29	99.31	98.917	98.825	0.0919618	0.393038
30	99.58	99.6972	99.6012	0.0959201	-0.11717
31	99.68	100.114	100.375	-0.260747	-0.433837
32	102.38	101.226	101.031	0.194878	1.15429
33	102.69	101.579	101.512	0.0672743	1.11106
34	103.01	102.068	101.884	0.183941	0.942309
35	103.35	102.377	102.217	0.160399	0.972517
36	103.61	102.456	102.527	-0.0709549	1.15387
37	102.59	102.189	102.817	-0.627413	0.400747
38	102.75	102.822	102.986	-0.164601	-0.0716493
39	102.88	103.242	103.034	0.20842	-0.36217
40	102.85	103.201	103.08	0.12092	-0.350503
41	103.16	103.216	103.124	0.0919618	-0.0561285
42	103.17	103.245	103.149	0.0959201	-0.0750868
43	103.04	102.965	103.226	-0.260747	0.0749132
44	103.09	103.565	103.37	0.194878	-0.475295
45	103.12	103.592	103.525	0.0672743	-0.472274
46	103.68	103.87	103.686	0.183941	-0.189774
47	103.75	104	103.84	0.160399	-0.250399
48	103.81	103.946	104.017	-0.0709549	-0.136128
49	104.23	103.591	104.218	-0.627413	0.63908
50	104.58	104.253	104.418	-0.164601	0.327101
51	104.76	104.835	104.627	0.20842	-0.0750868
52	104.83	104.938	104.817	0.12092	-0.107587
53	104.88	105.077	104.985	0.0919618	-0.196545
54	105.7	105.246	105.15	0.0959201	0.454497
55	105.34	NA	NA	-0.260747	NA
56	105.57	NA	NA	0.194878	NA
57	105.66	NA	NA	0.0672743	NA
58	105.7	NA	NA	0.183941	NA
59	105.76	NA	NA	0.160399	NA
60	105.76	NA	NA	-0.0709549	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 90.89 & NA & NA & -0.627413 & NA \tabularnewline
2 & 91.1 & NA & NA & -0.164601 & NA \tabularnewline
3 & 91.35 & NA & NA & 0.20842 & NA \tabularnewline
4 & 91.52 & NA & NA & 0.12092 & NA \tabularnewline
5 & 91.45 & NA & NA & 0.0919618 & NA \tabularnewline
6 & 91.88 & NA & NA & 0.0959201 & NA \tabularnewline
7 & 91.9 & 91.5101 & 91.7708 & -0.260747 & 0.389913 \tabularnewline
8 & 91.92 & 92.077 & 91.8821 & 0.194878 & -0.156962 \tabularnewline
9 & 92 & 92.0535 & 91.9862 & 0.0672743 & -0.0535243 \tabularnewline
10 & 92 & 92.2719 & 92.0879 & 0.183941 & -0.271858 \tabularnewline
11 & 92.2 & 92.3541 & 92.1938 & 0.160399 & -0.154149 \tabularnewline
12 & 92.34 & 92.2174 & 92.2883 & -0.0709549 & 0.122622 \tabularnewline
13 & 92.29 & 91.7384 & 92.3658 & -0.627413 & 0.55158 \tabularnewline
14 & 92.37 & 92.2841 & 92.4488 & -0.164601 & 0.0858507 \tabularnewline
15 & 92.58 & 92.7509 & 92.5425 & 0.20842 & -0.17092 \tabularnewline
16 & 92.73 & 92.7909 & 92.67 & 0.12092 & -0.0609201 \tabularnewline
17 & 92.78 & 92.9274 & 92.8354 & 0.0919618 & -0.147378 \tabularnewline
18 & 92.82 & 93.0893 & 92.9933 & 0.0959201 & -0.269253 \tabularnewline
19 & 92.82 & 92.858 & 93.1188 & -0.260747 & -0.0380035 \tabularnewline
20 & 92.99 & 93.519 & 93.3242 & 0.194878 & -0.529045 \tabularnewline
21 & 93.18 & 93.7723 & 93.705 & 0.0672743 & -0.592274 \tabularnewline
22 & 93.88 & 94.3677 & 94.1837 & 0.183941 & -0.487691 \tabularnewline
23 & 94.29 & 94.865 & 94.7046 & 0.160399 & -0.574983 \tabularnewline
24 & 94.04 & 95.1874 & 95.2583 & -0.0709549 & -1.14738 \tabularnewline
25 & 93.6 & 95.1984 & 95.8258 & -0.627413 & -1.59842 \tabularnewline
26 & 95.99 & 96.3383 & 96.5029 & -0.164601 & -0.348316 \tabularnewline
27 & 98.1 & 97.4988 & 97.2904 & 0.20842 & 0.601163 \tabularnewline
28 & 98.7 & 98.188 & 98.0671 & 0.12092 & 0.511997 \tabularnewline
29 & 99.31 & 98.917 & 98.825 & 0.0919618 & 0.393038 \tabularnewline
30 & 99.58 & 99.6972 & 99.6012 & 0.0959201 & -0.11717 \tabularnewline
31 & 99.68 & 100.114 & 100.375 & -0.260747 & -0.433837 \tabularnewline
32 & 102.38 & 101.226 & 101.031 & 0.194878 & 1.15429 \tabularnewline
33 & 102.69 & 101.579 & 101.512 & 0.0672743 & 1.11106 \tabularnewline
34 & 103.01 & 102.068 & 101.884 & 0.183941 & 0.942309 \tabularnewline
35 & 103.35 & 102.377 & 102.217 & 0.160399 & 0.972517 \tabularnewline
36 & 103.61 & 102.456 & 102.527 & -0.0709549 & 1.15387 \tabularnewline
37 & 102.59 & 102.189 & 102.817 & -0.627413 & 0.400747 \tabularnewline
38 & 102.75 & 102.822 & 102.986 & -0.164601 & -0.0716493 \tabularnewline
39 & 102.88 & 103.242 & 103.034 & 0.20842 & -0.36217 \tabularnewline
40 & 102.85 & 103.201 & 103.08 & 0.12092 & -0.350503 \tabularnewline
41 & 103.16 & 103.216 & 103.124 & 0.0919618 & -0.0561285 \tabularnewline
42 & 103.17 & 103.245 & 103.149 & 0.0959201 & -0.0750868 \tabularnewline
43 & 103.04 & 102.965 & 103.226 & -0.260747 & 0.0749132 \tabularnewline
44 & 103.09 & 103.565 & 103.37 & 0.194878 & -0.475295 \tabularnewline
45 & 103.12 & 103.592 & 103.525 & 0.0672743 & -0.472274 \tabularnewline
46 & 103.68 & 103.87 & 103.686 & 0.183941 & -0.189774 \tabularnewline
47 & 103.75 & 104 & 103.84 & 0.160399 & -0.250399 \tabularnewline
48 & 103.81 & 103.946 & 104.017 & -0.0709549 & -0.136128 \tabularnewline
49 & 104.23 & 103.591 & 104.218 & -0.627413 & 0.63908 \tabularnewline
50 & 104.58 & 104.253 & 104.418 & -0.164601 & 0.327101 \tabularnewline
51 & 104.76 & 104.835 & 104.627 & 0.20842 & -0.0750868 \tabularnewline
52 & 104.83 & 104.938 & 104.817 & 0.12092 & -0.107587 \tabularnewline
53 & 104.88 & 105.077 & 104.985 & 0.0919618 & -0.196545 \tabularnewline
54 & 105.7 & 105.246 & 105.15 & 0.0959201 & 0.454497 \tabularnewline
55 & 105.34 & NA & NA & -0.260747 & NA \tabularnewline
56 & 105.57 & NA & NA & 0.194878 & NA \tabularnewline
57 & 105.66 & NA & NA & 0.0672743 & NA \tabularnewline
58 & 105.7 & NA & NA & 0.183941 & NA \tabularnewline
59 & 105.76 & NA & NA & 0.160399 & NA \tabularnewline
60 & 105.76 & NA & NA & -0.0709549 & 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]90.89[/C][C]NA[/C][C]NA[/C][C]-0.627413[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]91.1[/C][C]NA[/C][C]NA[/C][C]-0.164601[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]91.35[/C][C]NA[/C][C]NA[/C][C]0.20842[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]91.52[/C][C]NA[/C][C]NA[/C][C]0.12092[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]91.45[/C][C]NA[/C][C]NA[/C][C]0.0919618[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]91.88[/C][C]NA[/C][C]NA[/C][C]0.0959201[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]91.9[/C][C]91.5101[/C][C]91.7708[/C][C]-0.260747[/C][C]0.389913[/C][/ROW]
[ROW][C]8[/C][C]91.92[/C][C]92.077[/C][C]91.8821[/C][C]0.194878[/C][C]-0.156962[/C][/ROW]
[ROW][C]9[/C][C]92[/C][C]92.0535[/C][C]91.9862[/C][C]0.0672743[/C][C]-0.0535243[/C][/ROW]
[ROW][C]10[/C][C]92[/C][C]92.2719[/C][C]92.0879[/C][C]0.183941[/C][C]-0.271858[/C][/ROW]
[ROW][C]11[/C][C]92.2[/C][C]92.3541[/C][C]92.1938[/C][C]0.160399[/C][C]-0.154149[/C][/ROW]
[ROW][C]12[/C][C]92.34[/C][C]92.2174[/C][C]92.2883[/C][C]-0.0709549[/C][C]0.122622[/C][/ROW]
[ROW][C]13[/C][C]92.29[/C][C]91.7384[/C][C]92.3658[/C][C]-0.627413[/C][C]0.55158[/C][/ROW]
[ROW][C]14[/C][C]92.37[/C][C]92.2841[/C][C]92.4488[/C][C]-0.164601[/C][C]0.0858507[/C][/ROW]
[ROW][C]15[/C][C]92.58[/C][C]92.7509[/C][C]92.5425[/C][C]0.20842[/C][C]-0.17092[/C][/ROW]
[ROW][C]16[/C][C]92.73[/C][C]92.7909[/C][C]92.67[/C][C]0.12092[/C][C]-0.0609201[/C][/ROW]
[ROW][C]17[/C][C]92.78[/C][C]92.9274[/C][C]92.8354[/C][C]0.0919618[/C][C]-0.147378[/C][/ROW]
[ROW][C]18[/C][C]92.82[/C][C]93.0893[/C][C]92.9933[/C][C]0.0959201[/C][C]-0.269253[/C][/ROW]
[ROW][C]19[/C][C]92.82[/C][C]92.858[/C][C]93.1188[/C][C]-0.260747[/C][C]-0.0380035[/C][/ROW]
[ROW][C]20[/C][C]92.99[/C][C]93.519[/C][C]93.3242[/C][C]0.194878[/C][C]-0.529045[/C][/ROW]
[ROW][C]21[/C][C]93.18[/C][C]93.7723[/C][C]93.705[/C][C]0.0672743[/C][C]-0.592274[/C][/ROW]
[ROW][C]22[/C][C]93.88[/C][C]94.3677[/C][C]94.1837[/C][C]0.183941[/C][C]-0.487691[/C][/ROW]
[ROW][C]23[/C][C]94.29[/C][C]94.865[/C][C]94.7046[/C][C]0.160399[/C][C]-0.574983[/C][/ROW]
[ROW][C]24[/C][C]94.04[/C][C]95.1874[/C][C]95.2583[/C][C]-0.0709549[/C][C]-1.14738[/C][/ROW]
[ROW][C]25[/C][C]93.6[/C][C]95.1984[/C][C]95.8258[/C][C]-0.627413[/C][C]-1.59842[/C][/ROW]
[ROW][C]26[/C][C]95.99[/C][C]96.3383[/C][C]96.5029[/C][C]-0.164601[/C][C]-0.348316[/C][/ROW]
[ROW][C]27[/C][C]98.1[/C][C]97.4988[/C][C]97.2904[/C][C]0.20842[/C][C]0.601163[/C][/ROW]
[ROW][C]28[/C][C]98.7[/C][C]98.188[/C][C]98.0671[/C][C]0.12092[/C][C]0.511997[/C][/ROW]
[ROW][C]29[/C][C]99.31[/C][C]98.917[/C][C]98.825[/C][C]0.0919618[/C][C]0.393038[/C][/ROW]
[ROW][C]30[/C][C]99.58[/C][C]99.6972[/C][C]99.6012[/C][C]0.0959201[/C][C]-0.11717[/C][/ROW]
[ROW][C]31[/C][C]99.68[/C][C]100.114[/C][C]100.375[/C][C]-0.260747[/C][C]-0.433837[/C][/ROW]
[ROW][C]32[/C][C]102.38[/C][C]101.226[/C][C]101.031[/C][C]0.194878[/C][C]1.15429[/C][/ROW]
[ROW][C]33[/C][C]102.69[/C][C]101.579[/C][C]101.512[/C][C]0.0672743[/C][C]1.11106[/C][/ROW]
[ROW][C]34[/C][C]103.01[/C][C]102.068[/C][C]101.884[/C][C]0.183941[/C][C]0.942309[/C][/ROW]
[ROW][C]35[/C][C]103.35[/C][C]102.377[/C][C]102.217[/C][C]0.160399[/C][C]0.972517[/C][/ROW]
[ROW][C]36[/C][C]103.61[/C][C]102.456[/C][C]102.527[/C][C]-0.0709549[/C][C]1.15387[/C][/ROW]
[ROW][C]37[/C][C]102.59[/C][C]102.189[/C][C]102.817[/C][C]-0.627413[/C][C]0.400747[/C][/ROW]
[ROW][C]38[/C][C]102.75[/C][C]102.822[/C][C]102.986[/C][C]-0.164601[/C][C]-0.0716493[/C][/ROW]
[ROW][C]39[/C][C]102.88[/C][C]103.242[/C][C]103.034[/C][C]0.20842[/C][C]-0.36217[/C][/ROW]
[ROW][C]40[/C][C]102.85[/C][C]103.201[/C][C]103.08[/C][C]0.12092[/C][C]-0.350503[/C][/ROW]
[ROW][C]41[/C][C]103.16[/C][C]103.216[/C][C]103.124[/C][C]0.0919618[/C][C]-0.0561285[/C][/ROW]
[ROW][C]42[/C][C]103.17[/C][C]103.245[/C][C]103.149[/C][C]0.0959201[/C][C]-0.0750868[/C][/ROW]
[ROW][C]43[/C][C]103.04[/C][C]102.965[/C][C]103.226[/C][C]-0.260747[/C][C]0.0749132[/C][/ROW]
[ROW][C]44[/C][C]103.09[/C][C]103.565[/C][C]103.37[/C][C]0.194878[/C][C]-0.475295[/C][/ROW]
[ROW][C]45[/C][C]103.12[/C][C]103.592[/C][C]103.525[/C][C]0.0672743[/C][C]-0.472274[/C][/ROW]
[ROW][C]46[/C][C]103.68[/C][C]103.87[/C][C]103.686[/C][C]0.183941[/C][C]-0.189774[/C][/ROW]
[ROW][C]47[/C][C]103.75[/C][C]104[/C][C]103.84[/C][C]0.160399[/C][C]-0.250399[/C][/ROW]
[ROW][C]48[/C][C]103.81[/C][C]103.946[/C][C]104.017[/C][C]-0.0709549[/C][C]-0.136128[/C][/ROW]
[ROW][C]49[/C][C]104.23[/C][C]103.591[/C][C]104.218[/C][C]-0.627413[/C][C]0.63908[/C][/ROW]
[ROW][C]50[/C][C]104.58[/C][C]104.253[/C][C]104.418[/C][C]-0.164601[/C][C]0.327101[/C][/ROW]
[ROW][C]51[/C][C]104.76[/C][C]104.835[/C][C]104.627[/C][C]0.20842[/C][C]-0.0750868[/C][/ROW]
[ROW][C]52[/C][C]104.83[/C][C]104.938[/C][C]104.817[/C][C]0.12092[/C][C]-0.107587[/C][/ROW]
[ROW][C]53[/C][C]104.88[/C][C]105.077[/C][C]104.985[/C][C]0.0919618[/C][C]-0.196545[/C][/ROW]
[ROW][C]54[/C][C]105.7[/C][C]105.246[/C][C]105.15[/C][C]0.0959201[/C][C]0.454497[/C][/ROW]
[ROW][C]55[/C][C]105.34[/C][C]NA[/C][C]NA[/C][C]-0.260747[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]105.57[/C][C]NA[/C][C]NA[/C][C]0.194878[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]105.66[/C][C]NA[/C][C]NA[/C][C]0.0672743[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]105.7[/C][C]NA[/C][C]NA[/C][C]0.183941[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]105.76[/C][C]NA[/C][C]NA[/C][C]0.160399[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]105.76[/C][C]NA[/C][C]NA[/C][C]-0.0709549[/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	90.89	NA	NA	-0.627413	NA
2	91.1	NA	NA	-0.164601	NA
3	91.35	NA	NA	0.20842	NA
4	91.52	NA	NA	0.12092	NA
5	91.45	NA	NA	0.0919618	NA
6	91.88	NA	NA	0.0959201	NA
7	91.9	91.5101	91.7708	-0.260747	0.389913
8	91.92	92.077	91.8821	0.194878	-0.156962
9	92	92.0535	91.9862	0.0672743	-0.0535243
10	92	92.2719	92.0879	0.183941	-0.271858
11	92.2	92.3541	92.1938	0.160399	-0.154149
12	92.34	92.2174	92.2883	-0.0709549	0.122622
13	92.29	91.7384	92.3658	-0.627413	0.55158
14	92.37	92.2841	92.4488	-0.164601	0.0858507
15	92.58	92.7509	92.5425	0.20842	-0.17092
16	92.73	92.7909	92.67	0.12092	-0.0609201
17	92.78	92.9274	92.8354	0.0919618	-0.147378
18	92.82	93.0893	92.9933	0.0959201	-0.269253
19	92.82	92.858	93.1188	-0.260747	-0.0380035
20	92.99	93.519	93.3242	0.194878	-0.529045
21	93.18	93.7723	93.705	0.0672743	-0.592274
22	93.88	94.3677	94.1837	0.183941	-0.487691
23	94.29	94.865	94.7046	0.160399	-0.574983
24	94.04	95.1874	95.2583	-0.0709549	-1.14738
25	93.6	95.1984	95.8258	-0.627413	-1.59842
26	95.99	96.3383	96.5029	-0.164601	-0.348316
27	98.1	97.4988	97.2904	0.20842	0.601163
28	98.7	98.188	98.0671	0.12092	0.511997
29	99.31	98.917	98.825	0.0919618	0.393038
30	99.58	99.6972	99.6012	0.0959201	-0.11717
31	99.68	100.114	100.375	-0.260747	-0.433837
32	102.38	101.226	101.031	0.194878	1.15429
33	102.69	101.579	101.512	0.0672743	1.11106
34	103.01	102.068	101.884	0.183941	0.942309
35	103.35	102.377	102.217	0.160399	0.972517
36	103.61	102.456	102.527	-0.0709549	1.15387
37	102.59	102.189	102.817	-0.627413	0.400747
38	102.75	102.822	102.986	-0.164601	-0.0716493
39	102.88	103.242	103.034	0.20842	-0.36217
40	102.85	103.201	103.08	0.12092	-0.350503
41	103.16	103.216	103.124	0.0919618	-0.0561285
42	103.17	103.245	103.149	0.0959201	-0.0750868
43	103.04	102.965	103.226	-0.260747	0.0749132
44	103.09	103.565	103.37	0.194878	-0.475295
45	103.12	103.592	103.525	0.0672743	-0.472274
46	103.68	103.87	103.686	0.183941	-0.189774
47	103.75	104	103.84	0.160399	-0.250399
48	103.81	103.946	104.017	-0.0709549	-0.136128
49	104.23	103.591	104.218	-0.627413	0.63908
50	104.58	104.253	104.418	-0.164601	0.327101
51	104.76	104.835	104.627	0.20842	-0.0750868
52	104.83	104.938	104.817	0.12092	-0.107587
53	104.88	105.077	104.985	0.0919618	-0.196545
54	105.7	105.246	105.15	0.0959201	0.454497
55	105.34	NA	NA	-0.260747	NA
56	105.57	NA	NA	0.194878	NA
57	105.66	NA	NA	0.0672743	NA
58	105.7	NA	NA	0.183941	NA
59	105.76	NA	NA	0.160399	NA
60	105.76	NA	NA	-0.0709549	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