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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 01 May 2017 16:38:58 +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/01/t1493653151jb0xls1i43sho8s.htm/, Retrieved Wed, 15 May 2024 20:48:20 +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 20:48:20 +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	3 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.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]'Herman Ole Andreas Wold' @ wold.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	3 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	99.49	NA	NA	-1.14346	NA
2	99.84	NA	NA	-0.732318	NA
3	100.9	NA	NA	-0.311068	NA
4	101.31	NA	NA	-0.116068	NA
5	100.09	NA	NA	0.0921615	NA
6	99.28	NA	NA	0.369245	NA
7	99.57	101.019	100.435	0.583828	-1.44883
8	101.04	101.198	100.479	0.719141	-0.157891
9	101.87	101.149	100.48	0.668203	0.72138
10	101.39	100.658	100.401	0.256641	0.732109
11	100.3	100.238	100.312	-0.0742969	0.0617969
12	99.95	99.9663	100.278	-0.312005	-0.0163281
13	99.87	99.1524	100.296	-1.14346	0.71763
14	100.51	99.5456	100.278	-0.732318	0.964401
15	100.27	99.8977	100.209	-0.311068	0.372318
16	100.04	99.9923	100.108	-0.116068	0.0477344
17	99.23	100.107	100.015	0.0921615	-0.876745
18	99.32	100.362	99.9929	0.369245	-1.04216
19	99.95	100.582	99.9983	0.583828	-0.632161
20	100.23	100.696	99.9767	0.719141	-0.465807
21	101.02	100.617	99.9483	0.668203	0.403464
22	99.83	100.214	99.9575	0.256641	-0.384141
23	99.61	99.9507	100.025	-0.0742969	-0.340703
24	100.12	99.8197	100.132	-0.312005	0.300339
25	99.83	99.122	100.265	-1.14346	0.708047
26	100.03	99.651	100.383	-0.732318	0.378984
27	100.07	100.097	100.408	-0.311068	-0.026849
28	100.46	100.306	100.422	-0.116068	0.154401
29	100.43	100.55	100.458	0.0921615	-0.120078
30	100.68	100.778	100.409	0.369245	-0.0984115
31	101.8	100.803	100.219	0.583828	0.997005
32	101.21	100.702	99.9825	0.719141	0.508359
33	100.63	100.496	99.8275	0.668203	0.134297
34	100.55	99.9612	99.7046	0.256641	0.588776
35	99.76	99.5282	99.6025	-0.0742969	0.231797
36	98.8	99.2205	99.5325	-0.312005	-0.420495
37	96.59	98.2786	99.4221	-1.14346	-1.68862
38	97.59	98.5339	99.2662	-0.732318	-0.943932
39	98.79	98.751	99.0621	-0.311068	0.0389844
40	98.79	98.6998	98.8158	-0.116068	0.0902344
41	99.65	98.7155	98.6233	0.0921615	0.934505
42	99.78	98.8897	98.5204	0.369245	0.890339
43	100.05	99.0959	98.5121	0.583828	0.954089
44	99.22	99.2346	98.5154	0.719141	-0.0145573
45	97.72	99.109	98.4408	0.668203	-1.38904
46	97.55	98.6166	98.36	0.256641	-1.06664
47	98.14	98.2228	98.2971	-0.0742969	-0.0827865
48	97.95	97.9434	98.2554	-0.312005	0.00658854
49	97.24	97.107	98.2504	-1.14346	0.133047
50	97.02	97.5493	98.2817	-0.732318	-0.529349
51	97.57	98.0843	98.3954	-0.311068	-0.514349
52	98.07	98.4923	98.6083	-0.116068	-0.422266
53	98.86	98.9276	98.8354	0.0921615	-0.0675781
54	99.57	99.4497	99.0804	0.369245	0.120339
55	100.14	NA	NA	0.583828	NA
56	99.88	NA	NA	0.719141	NA
57	99.79	NA	NA	0.668203	NA
58	100.59	NA	NA	0.256641	NA
59	100.55	NA	NA	-0.0742969	NA
60	101.42	NA	NA	-0.312005	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 99.49 & NA & NA & -1.14346 & NA \tabularnewline
2 & 99.84 & NA & NA & -0.732318 & NA \tabularnewline
3 & 100.9 & NA & NA & -0.311068 & NA \tabularnewline
4 & 101.31 & NA & NA & -0.116068 & NA \tabularnewline
5 & 100.09 & NA & NA & 0.0921615 & NA \tabularnewline
6 & 99.28 & NA & NA & 0.369245 & NA \tabularnewline
7 & 99.57 & 101.019 & 100.435 & 0.583828 & -1.44883 \tabularnewline
8 & 101.04 & 101.198 & 100.479 & 0.719141 & -0.157891 \tabularnewline
9 & 101.87 & 101.149 & 100.48 & 0.668203 & 0.72138 \tabularnewline
10 & 101.39 & 100.658 & 100.401 & 0.256641 & 0.732109 \tabularnewline
11 & 100.3 & 100.238 & 100.312 & -0.0742969 & 0.0617969 \tabularnewline
12 & 99.95 & 99.9663 & 100.278 & -0.312005 & -0.0163281 \tabularnewline
13 & 99.87 & 99.1524 & 100.296 & -1.14346 & 0.71763 \tabularnewline
14 & 100.51 & 99.5456 & 100.278 & -0.732318 & 0.964401 \tabularnewline
15 & 100.27 & 99.8977 & 100.209 & -0.311068 & 0.372318 \tabularnewline
16 & 100.04 & 99.9923 & 100.108 & -0.116068 & 0.0477344 \tabularnewline
17 & 99.23 & 100.107 & 100.015 & 0.0921615 & -0.876745 \tabularnewline
18 & 99.32 & 100.362 & 99.9929 & 0.369245 & -1.04216 \tabularnewline
19 & 99.95 & 100.582 & 99.9983 & 0.583828 & -0.632161 \tabularnewline
20 & 100.23 & 100.696 & 99.9767 & 0.719141 & -0.465807 \tabularnewline
21 & 101.02 & 100.617 & 99.9483 & 0.668203 & 0.403464 \tabularnewline
22 & 99.83 & 100.214 & 99.9575 & 0.256641 & -0.384141 \tabularnewline
23 & 99.61 & 99.9507 & 100.025 & -0.0742969 & -0.340703 \tabularnewline
24 & 100.12 & 99.8197 & 100.132 & -0.312005 & 0.300339 \tabularnewline
25 & 99.83 & 99.122 & 100.265 & -1.14346 & 0.708047 \tabularnewline
26 & 100.03 & 99.651 & 100.383 & -0.732318 & 0.378984 \tabularnewline
27 & 100.07 & 100.097 & 100.408 & -0.311068 & -0.026849 \tabularnewline
28 & 100.46 & 100.306 & 100.422 & -0.116068 & 0.154401 \tabularnewline
29 & 100.43 & 100.55 & 100.458 & 0.0921615 & -0.120078 \tabularnewline
30 & 100.68 & 100.778 & 100.409 & 0.369245 & -0.0984115 \tabularnewline
31 & 101.8 & 100.803 & 100.219 & 0.583828 & 0.997005 \tabularnewline
32 & 101.21 & 100.702 & 99.9825 & 0.719141 & 0.508359 \tabularnewline
33 & 100.63 & 100.496 & 99.8275 & 0.668203 & 0.134297 \tabularnewline
34 & 100.55 & 99.9612 & 99.7046 & 0.256641 & 0.588776 \tabularnewline
35 & 99.76 & 99.5282 & 99.6025 & -0.0742969 & 0.231797 \tabularnewline
36 & 98.8 & 99.2205 & 99.5325 & -0.312005 & -0.420495 \tabularnewline
37 & 96.59 & 98.2786 & 99.4221 & -1.14346 & -1.68862 \tabularnewline
38 & 97.59 & 98.5339 & 99.2662 & -0.732318 & -0.943932 \tabularnewline
39 & 98.79 & 98.751 & 99.0621 & -0.311068 & 0.0389844 \tabularnewline
40 & 98.79 & 98.6998 & 98.8158 & -0.116068 & 0.0902344 \tabularnewline
41 & 99.65 & 98.7155 & 98.6233 & 0.0921615 & 0.934505 \tabularnewline
42 & 99.78 & 98.8897 & 98.5204 & 0.369245 & 0.890339 \tabularnewline
43 & 100.05 & 99.0959 & 98.5121 & 0.583828 & 0.954089 \tabularnewline
44 & 99.22 & 99.2346 & 98.5154 & 0.719141 & -0.0145573 \tabularnewline
45 & 97.72 & 99.109 & 98.4408 & 0.668203 & -1.38904 \tabularnewline
46 & 97.55 & 98.6166 & 98.36 & 0.256641 & -1.06664 \tabularnewline
47 & 98.14 & 98.2228 & 98.2971 & -0.0742969 & -0.0827865 \tabularnewline
48 & 97.95 & 97.9434 & 98.2554 & -0.312005 & 0.00658854 \tabularnewline
49 & 97.24 & 97.107 & 98.2504 & -1.14346 & 0.133047 \tabularnewline
50 & 97.02 & 97.5493 & 98.2817 & -0.732318 & -0.529349 \tabularnewline
51 & 97.57 & 98.0843 & 98.3954 & -0.311068 & -0.514349 \tabularnewline
52 & 98.07 & 98.4923 & 98.6083 & -0.116068 & -0.422266 \tabularnewline
53 & 98.86 & 98.9276 & 98.8354 & 0.0921615 & -0.0675781 \tabularnewline
54 & 99.57 & 99.4497 & 99.0804 & 0.369245 & 0.120339 \tabularnewline
55 & 100.14 & NA & NA & 0.583828 & NA \tabularnewline
56 & 99.88 & NA & NA & 0.719141 & NA \tabularnewline
57 & 99.79 & NA & NA & 0.668203 & NA \tabularnewline
58 & 100.59 & NA & NA & 0.256641 & NA \tabularnewline
59 & 100.55 & NA & NA & -0.0742969 & NA \tabularnewline
60 & 101.42 & NA & NA & -0.312005 & 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]99.49[/C][C]NA[/C][C]NA[/C][C]-1.14346[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]99.84[/C][C]NA[/C][C]NA[/C][C]-0.732318[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.9[/C][C]NA[/C][C]NA[/C][C]-0.311068[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]101.31[/C][C]NA[/C][C]NA[/C][C]-0.116068[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100.09[/C][C]NA[/C][C]NA[/C][C]0.0921615[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]99.28[/C][C]NA[/C][C]NA[/C][C]0.369245[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]99.57[/C][C]101.019[/C][C]100.435[/C][C]0.583828[/C][C]-1.44883[/C][/ROW]
[ROW][C]8[/C][C]101.04[/C][C]101.198[/C][C]100.479[/C][C]0.719141[/C][C]-0.157891[/C][/ROW]
[ROW][C]9[/C][C]101.87[/C][C]101.149[/C][C]100.48[/C][C]0.668203[/C][C]0.72138[/C][/ROW]
[ROW][C]10[/C][C]101.39[/C][C]100.658[/C][C]100.401[/C][C]0.256641[/C][C]0.732109[/C][/ROW]
[ROW][C]11[/C][C]100.3[/C][C]100.238[/C][C]100.312[/C][C]-0.0742969[/C][C]0.0617969[/C][/ROW]
[ROW][C]12[/C][C]99.95[/C][C]99.9663[/C][C]100.278[/C][C]-0.312005[/C][C]-0.0163281[/C][/ROW]
[ROW][C]13[/C][C]99.87[/C][C]99.1524[/C][C]100.296[/C][C]-1.14346[/C][C]0.71763[/C][/ROW]
[ROW][C]14[/C][C]100.51[/C][C]99.5456[/C][C]100.278[/C][C]-0.732318[/C][C]0.964401[/C][/ROW]
[ROW][C]15[/C][C]100.27[/C][C]99.8977[/C][C]100.209[/C][C]-0.311068[/C][C]0.372318[/C][/ROW]
[ROW][C]16[/C][C]100.04[/C][C]99.9923[/C][C]100.108[/C][C]-0.116068[/C][C]0.0477344[/C][/ROW]
[ROW][C]17[/C][C]99.23[/C][C]100.107[/C][C]100.015[/C][C]0.0921615[/C][C]-0.876745[/C][/ROW]
[ROW][C]18[/C][C]99.32[/C][C]100.362[/C][C]99.9929[/C][C]0.369245[/C][C]-1.04216[/C][/ROW]
[ROW][C]19[/C][C]99.95[/C][C]100.582[/C][C]99.9983[/C][C]0.583828[/C][C]-0.632161[/C][/ROW]
[ROW][C]20[/C][C]100.23[/C][C]100.696[/C][C]99.9767[/C][C]0.719141[/C][C]-0.465807[/C][/ROW]
[ROW][C]21[/C][C]101.02[/C][C]100.617[/C][C]99.9483[/C][C]0.668203[/C][C]0.403464[/C][/ROW]
[ROW][C]22[/C][C]99.83[/C][C]100.214[/C][C]99.9575[/C][C]0.256641[/C][C]-0.384141[/C][/ROW]
[ROW][C]23[/C][C]99.61[/C][C]99.9507[/C][C]100.025[/C][C]-0.0742969[/C][C]-0.340703[/C][/ROW]
[ROW][C]24[/C][C]100.12[/C][C]99.8197[/C][C]100.132[/C][C]-0.312005[/C][C]0.300339[/C][/ROW]
[ROW][C]25[/C][C]99.83[/C][C]99.122[/C][C]100.265[/C][C]-1.14346[/C][C]0.708047[/C][/ROW]
[ROW][C]26[/C][C]100.03[/C][C]99.651[/C][C]100.383[/C][C]-0.732318[/C][C]0.378984[/C][/ROW]
[ROW][C]27[/C][C]100.07[/C][C]100.097[/C][C]100.408[/C][C]-0.311068[/C][C]-0.026849[/C][/ROW]
[ROW][C]28[/C][C]100.46[/C][C]100.306[/C][C]100.422[/C][C]-0.116068[/C][C]0.154401[/C][/ROW]
[ROW][C]29[/C][C]100.43[/C][C]100.55[/C][C]100.458[/C][C]0.0921615[/C][C]-0.120078[/C][/ROW]
[ROW][C]30[/C][C]100.68[/C][C]100.778[/C][C]100.409[/C][C]0.369245[/C][C]-0.0984115[/C][/ROW]
[ROW][C]31[/C][C]101.8[/C][C]100.803[/C][C]100.219[/C][C]0.583828[/C][C]0.997005[/C][/ROW]
[ROW][C]32[/C][C]101.21[/C][C]100.702[/C][C]99.9825[/C][C]0.719141[/C][C]0.508359[/C][/ROW]
[ROW][C]33[/C][C]100.63[/C][C]100.496[/C][C]99.8275[/C][C]0.668203[/C][C]0.134297[/C][/ROW]
[ROW][C]34[/C][C]100.55[/C][C]99.9612[/C][C]99.7046[/C][C]0.256641[/C][C]0.588776[/C][/ROW]
[ROW][C]35[/C][C]99.76[/C][C]99.5282[/C][C]99.6025[/C][C]-0.0742969[/C][C]0.231797[/C][/ROW]
[ROW][C]36[/C][C]98.8[/C][C]99.2205[/C][C]99.5325[/C][C]-0.312005[/C][C]-0.420495[/C][/ROW]
[ROW][C]37[/C][C]96.59[/C][C]98.2786[/C][C]99.4221[/C][C]-1.14346[/C][C]-1.68862[/C][/ROW]
[ROW][C]38[/C][C]97.59[/C][C]98.5339[/C][C]99.2662[/C][C]-0.732318[/C][C]-0.943932[/C][/ROW]
[ROW][C]39[/C][C]98.79[/C][C]98.751[/C][C]99.0621[/C][C]-0.311068[/C][C]0.0389844[/C][/ROW]
[ROW][C]40[/C][C]98.79[/C][C]98.6998[/C][C]98.8158[/C][C]-0.116068[/C][C]0.0902344[/C][/ROW]
[ROW][C]41[/C][C]99.65[/C][C]98.7155[/C][C]98.6233[/C][C]0.0921615[/C][C]0.934505[/C][/ROW]
[ROW][C]42[/C][C]99.78[/C][C]98.8897[/C][C]98.5204[/C][C]0.369245[/C][C]0.890339[/C][/ROW]
[ROW][C]43[/C][C]100.05[/C][C]99.0959[/C][C]98.5121[/C][C]0.583828[/C][C]0.954089[/C][/ROW]
[ROW][C]44[/C][C]99.22[/C][C]99.2346[/C][C]98.5154[/C][C]0.719141[/C][C]-0.0145573[/C][/ROW]
[ROW][C]45[/C][C]97.72[/C][C]99.109[/C][C]98.4408[/C][C]0.668203[/C][C]-1.38904[/C][/ROW]
[ROW][C]46[/C][C]97.55[/C][C]98.6166[/C][C]98.36[/C][C]0.256641[/C][C]-1.06664[/C][/ROW]
[ROW][C]47[/C][C]98.14[/C][C]98.2228[/C][C]98.2971[/C][C]-0.0742969[/C][C]-0.0827865[/C][/ROW]
[ROW][C]48[/C][C]97.95[/C][C]97.9434[/C][C]98.2554[/C][C]-0.312005[/C][C]0.00658854[/C][/ROW]
[ROW][C]49[/C][C]97.24[/C][C]97.107[/C][C]98.2504[/C][C]-1.14346[/C][C]0.133047[/C][/ROW]
[ROW][C]50[/C][C]97.02[/C][C]97.5493[/C][C]98.2817[/C][C]-0.732318[/C][C]-0.529349[/C][/ROW]
[ROW][C]51[/C][C]97.57[/C][C]98.0843[/C][C]98.3954[/C][C]-0.311068[/C][C]-0.514349[/C][/ROW]
[ROW][C]52[/C][C]98.07[/C][C]98.4923[/C][C]98.6083[/C][C]-0.116068[/C][C]-0.422266[/C][/ROW]
[ROW][C]53[/C][C]98.86[/C][C]98.9276[/C][C]98.8354[/C][C]0.0921615[/C][C]-0.0675781[/C][/ROW]
[ROW][C]54[/C][C]99.57[/C][C]99.4497[/C][C]99.0804[/C][C]0.369245[/C][C]0.120339[/C][/ROW]
[ROW][C]55[/C][C]100.14[/C][C]NA[/C][C]NA[/C][C]0.583828[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]99.88[/C][C]NA[/C][C]NA[/C][C]0.719141[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]99.79[/C][C]NA[/C][C]NA[/C][C]0.668203[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]100.59[/C][C]NA[/C][C]NA[/C][C]0.256641[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]100.55[/C][C]NA[/C][C]NA[/C][C]-0.0742969[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]101.42[/C][C]NA[/C][C]NA[/C][C]-0.312005[/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	99.49	NA	NA	-1.14346	NA
2	99.84	NA	NA	-0.732318	NA
3	100.9	NA	NA	-0.311068	NA
4	101.31	NA	NA	-0.116068	NA
5	100.09	NA	NA	0.0921615	NA
6	99.28	NA	NA	0.369245	NA
7	99.57	101.019	100.435	0.583828	-1.44883
8	101.04	101.198	100.479	0.719141	-0.157891
9	101.87	101.149	100.48	0.668203	0.72138
10	101.39	100.658	100.401	0.256641	0.732109
11	100.3	100.238	100.312	-0.0742969	0.0617969
12	99.95	99.9663	100.278	-0.312005	-0.0163281
13	99.87	99.1524	100.296	-1.14346	0.71763
14	100.51	99.5456	100.278	-0.732318	0.964401
15	100.27	99.8977	100.209	-0.311068	0.372318
16	100.04	99.9923	100.108	-0.116068	0.0477344
17	99.23	100.107	100.015	0.0921615	-0.876745
18	99.32	100.362	99.9929	0.369245	-1.04216
19	99.95	100.582	99.9983	0.583828	-0.632161
20	100.23	100.696	99.9767	0.719141	-0.465807
21	101.02	100.617	99.9483	0.668203	0.403464
22	99.83	100.214	99.9575	0.256641	-0.384141
23	99.61	99.9507	100.025	-0.0742969	-0.340703
24	100.12	99.8197	100.132	-0.312005	0.300339
25	99.83	99.122	100.265	-1.14346	0.708047
26	100.03	99.651	100.383	-0.732318	0.378984
27	100.07	100.097	100.408	-0.311068	-0.026849
28	100.46	100.306	100.422	-0.116068	0.154401
29	100.43	100.55	100.458	0.0921615	-0.120078
30	100.68	100.778	100.409	0.369245	-0.0984115
31	101.8	100.803	100.219	0.583828	0.997005
32	101.21	100.702	99.9825	0.719141	0.508359
33	100.63	100.496	99.8275	0.668203	0.134297
34	100.55	99.9612	99.7046	0.256641	0.588776
35	99.76	99.5282	99.6025	-0.0742969	0.231797
36	98.8	99.2205	99.5325	-0.312005	-0.420495
37	96.59	98.2786	99.4221	-1.14346	-1.68862
38	97.59	98.5339	99.2662	-0.732318	-0.943932
39	98.79	98.751	99.0621	-0.311068	0.0389844
40	98.79	98.6998	98.8158	-0.116068	0.0902344
41	99.65	98.7155	98.6233	0.0921615	0.934505
42	99.78	98.8897	98.5204	0.369245	0.890339
43	100.05	99.0959	98.5121	0.583828	0.954089
44	99.22	99.2346	98.5154	0.719141	-0.0145573
45	97.72	99.109	98.4408	0.668203	-1.38904
46	97.55	98.6166	98.36	0.256641	-1.06664
47	98.14	98.2228	98.2971	-0.0742969	-0.0827865
48	97.95	97.9434	98.2554	-0.312005	0.00658854
49	97.24	97.107	98.2504	-1.14346	0.133047
50	97.02	97.5493	98.2817	-0.732318	-0.529349
51	97.57	98.0843	98.3954	-0.311068	-0.514349
52	98.07	98.4923	98.6083	-0.116068	-0.422266
53	98.86	98.9276	98.8354	0.0921615	-0.0675781
54	99.57	99.4497	99.0804	0.369245	0.120339
55	100.14	NA	NA	0.583828	NA
56	99.88	NA	NA	0.719141	NA
57	99.79	NA	NA	0.668203	NA
58	100.59	NA	NA	0.256641	NA
59	100.55	NA	NA	-0.0742969	NA
60	101.42	NA	NA	-0.312005	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