Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Tue, 31 May 2016 10:10:35 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/May/31/t14646858790zip2ss6juwhsvo.htm/, Retrieved Mon, 06 May 2024 17:13:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=295732, Retrieved Mon, 06 May 2024 17:13:23 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

117

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Classical Decomposition] [] [2016-05-31 09:10:35] [60c466f2753cef60360c0cd0685abd02] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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 Maurice George Kendall' @ kendall.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 Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295732&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 Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295732&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295732&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	93.91	NA	NA	0.99744	NA
2	94.27	NA	NA	1.00042	NA
3	94.55	NA	NA	1.00185	NA
4	94.66	NA	NA	1.00128	NA
5	94.78	NA	NA	1.00042	NA
6	94.91	NA	NA	1.00018	NA
7	95.2	95.1991	95.1954	1.00004	1.00001
8	95.48	95.4205	95.3921	1.0003	1.00062
9	95.56	95.6096	95.5937	1.00017	0.999481
10	95.75	95.8354	95.8058	1.00031	0.999109
11	95.91	95.9632	96.0254	0.999352	0.999445
12	96.16	96.0814	96.2512	0.998236	1.00082
13	96.32	96.2267	96.4738	0.99744	1.00097
14	96.58	96.7344	96.6933	1.00042	0.998404
15	97.08	97.0965	96.9171	1.00185	0.99983
16	97.22	97.2687	97.1442	1.00128	0.999499
17	97.49	97.4126	97.3712	1.00042	1.00079
18	97.62	97.6071	97.5896	1.00018	1.00013
19	97.83	97.8021	97.7983	1.00004	1.00029
20	98.12	98.0396	98.0104	1.0003	1.00082
21	98.29	98.2304	98.2142	1.00017	1.00061
22	98.47	98.4366	98.4062	1.00031	1.00034
23	98.64	98.5241	98.5879	0.999352	1.00118
24	98.67	98.5883	98.7625	0.998236	1.00083
25	98.82	98.6896	98.9429	0.99744	1.00132
26	99.17	99.1638	99.1217	1.00042	1.00006
27	99.38	99.4871	99.3033	1.00185	0.998923
28	99.53	99.628	99.5004	1.00128	0.999017
29	99.54	99.7436	99.7012	1.00042	0.997959
30	99.76	99.9171	99.8992	1.00018	0.998428
31	100.02	100.102	100.098	1.00004	0.999183
32	100.22	100.342	100.312	1.0003	0.998784
33	100.55	100.564	100.547	1.00017	0.999863
34	100.94	100.821	100.79	1.00031	1.00118
35	100.99	100.975	101.04	0.999352	1.00015
36	101.07	101.112	101.291	0.998236	0.999583
37	101.19	101.28	101.54	0.99744	0.999115
38	101.94	101.831	101.788	1.00042	1.00107
39	102.25	102.221	102.032	1.00185	1.00028
40	102.49	102.395	102.264	1.00128	1.00093
41	102.58	102.534	102.49	1.00042	1.00045
42	102.74	102.738	102.72	1.00018	1.00002
43	103.01	102.954	102.95	1.00004	1.00055
44	103.19	103.211	103.18	1.0003	0.999799
45	103.44	103.432	103.415	1.00017	1.00008
46	103.62	103.682	103.65	1.00031	0.999398
47	103.74	103.818	103.885	0.999352	0.999251
48	103.82	103.945	104.129	0.998236	0.998797
49	103.96	104.104	104.371	0.99744	0.998621
50	104.7	104.649	104.604	1.00042	1.00049
51	105.13	105.026	104.832	1.00185	1.00099
52	105.26	105.198	105.064	1.00128	1.00059
53	105.44	105.353	105.308	1.00042	1.00082
54	105.73	105.577	105.558	1.00018	1.00145
55	105.83	NA	NA	1.00004	NA
56	105.97	NA	NA	1.0003	NA
57	106.13	NA	NA	1.00017	NA
58	106.49	NA	NA	1.00031	NA
59	106.74	NA	NA	0.999352	NA
60	106.82	NA	NA	0.998236	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 93.91 & NA & NA & 0.99744 & NA \tabularnewline
2 & 94.27 & NA & NA & 1.00042 & NA \tabularnewline
3 & 94.55 & NA & NA & 1.00185 & NA \tabularnewline
4 & 94.66 & NA & NA & 1.00128 & NA \tabularnewline
5 & 94.78 & NA & NA & 1.00042 & NA \tabularnewline
6 & 94.91 & NA & NA & 1.00018 & NA \tabularnewline
7 & 95.2 & 95.1991 & 95.1954 & 1.00004 & 1.00001 \tabularnewline
8 & 95.48 & 95.4205 & 95.3921 & 1.0003 & 1.00062 \tabularnewline
9 & 95.56 & 95.6096 & 95.5937 & 1.00017 & 0.999481 \tabularnewline
10 & 95.75 & 95.8354 & 95.8058 & 1.00031 & 0.999109 \tabularnewline
11 & 95.91 & 95.9632 & 96.0254 & 0.999352 & 0.999445 \tabularnewline
12 & 96.16 & 96.0814 & 96.2512 & 0.998236 & 1.00082 \tabularnewline
13 & 96.32 & 96.2267 & 96.4738 & 0.99744 & 1.00097 \tabularnewline
14 & 96.58 & 96.7344 & 96.6933 & 1.00042 & 0.998404 \tabularnewline
15 & 97.08 & 97.0965 & 96.9171 & 1.00185 & 0.99983 \tabularnewline
16 & 97.22 & 97.2687 & 97.1442 & 1.00128 & 0.999499 \tabularnewline
17 & 97.49 & 97.4126 & 97.3712 & 1.00042 & 1.00079 \tabularnewline
18 & 97.62 & 97.6071 & 97.5896 & 1.00018 & 1.00013 \tabularnewline
19 & 97.83 & 97.8021 & 97.7983 & 1.00004 & 1.00029 \tabularnewline
20 & 98.12 & 98.0396 & 98.0104 & 1.0003 & 1.00082 \tabularnewline
21 & 98.29 & 98.2304 & 98.2142 & 1.00017 & 1.00061 \tabularnewline
22 & 98.47 & 98.4366 & 98.4062 & 1.00031 & 1.00034 \tabularnewline
23 & 98.64 & 98.5241 & 98.5879 & 0.999352 & 1.00118 \tabularnewline
24 & 98.67 & 98.5883 & 98.7625 & 0.998236 & 1.00083 \tabularnewline
25 & 98.82 & 98.6896 & 98.9429 & 0.99744 & 1.00132 \tabularnewline
26 & 99.17 & 99.1638 & 99.1217 & 1.00042 & 1.00006 \tabularnewline
27 & 99.38 & 99.4871 & 99.3033 & 1.00185 & 0.998923 \tabularnewline
28 & 99.53 & 99.628 & 99.5004 & 1.00128 & 0.999017 \tabularnewline
29 & 99.54 & 99.7436 & 99.7012 & 1.00042 & 0.997959 \tabularnewline
30 & 99.76 & 99.9171 & 99.8992 & 1.00018 & 0.998428 \tabularnewline
31 & 100.02 & 100.102 & 100.098 & 1.00004 & 0.999183 \tabularnewline
32 & 100.22 & 100.342 & 100.312 & 1.0003 & 0.998784 \tabularnewline
33 & 100.55 & 100.564 & 100.547 & 1.00017 & 0.999863 \tabularnewline
34 & 100.94 & 100.821 & 100.79 & 1.00031 & 1.00118 \tabularnewline
35 & 100.99 & 100.975 & 101.04 & 0.999352 & 1.00015 \tabularnewline
36 & 101.07 & 101.112 & 101.291 & 0.998236 & 0.999583 \tabularnewline
37 & 101.19 & 101.28 & 101.54 & 0.99744 & 0.999115 \tabularnewline
38 & 101.94 & 101.831 & 101.788 & 1.00042 & 1.00107 \tabularnewline
39 & 102.25 & 102.221 & 102.032 & 1.00185 & 1.00028 \tabularnewline
40 & 102.49 & 102.395 & 102.264 & 1.00128 & 1.00093 \tabularnewline
41 & 102.58 & 102.534 & 102.49 & 1.00042 & 1.00045 \tabularnewline
42 & 102.74 & 102.738 & 102.72 & 1.00018 & 1.00002 \tabularnewline
43 & 103.01 & 102.954 & 102.95 & 1.00004 & 1.00055 \tabularnewline
44 & 103.19 & 103.211 & 103.18 & 1.0003 & 0.999799 \tabularnewline
45 & 103.44 & 103.432 & 103.415 & 1.00017 & 1.00008 \tabularnewline
46 & 103.62 & 103.682 & 103.65 & 1.00031 & 0.999398 \tabularnewline
47 & 103.74 & 103.818 & 103.885 & 0.999352 & 0.999251 \tabularnewline
48 & 103.82 & 103.945 & 104.129 & 0.998236 & 0.998797 \tabularnewline
49 & 103.96 & 104.104 & 104.371 & 0.99744 & 0.998621 \tabularnewline
50 & 104.7 & 104.649 & 104.604 & 1.00042 & 1.00049 \tabularnewline
51 & 105.13 & 105.026 & 104.832 & 1.00185 & 1.00099 \tabularnewline
52 & 105.26 & 105.198 & 105.064 & 1.00128 & 1.00059 \tabularnewline
53 & 105.44 & 105.353 & 105.308 & 1.00042 & 1.00082 \tabularnewline
54 & 105.73 & 105.577 & 105.558 & 1.00018 & 1.00145 \tabularnewline
55 & 105.83 & NA & NA & 1.00004 & NA \tabularnewline
56 & 105.97 & NA & NA & 1.0003 & NA \tabularnewline
57 & 106.13 & NA & NA & 1.00017 & NA \tabularnewline
58 & 106.49 & NA & NA & 1.00031 & NA \tabularnewline
59 & 106.74 & NA & NA & 0.999352 & NA \tabularnewline
60 & 106.82 & NA & NA & 0.998236 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295732&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]93.91[/C][C]NA[/C][C]NA[/C][C]0.99744[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]94.27[/C][C]NA[/C][C]NA[/C][C]1.00042[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]94.55[/C][C]NA[/C][C]NA[/C][C]1.00185[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]94.66[/C][C]NA[/C][C]NA[/C][C]1.00128[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]94.78[/C][C]NA[/C][C]NA[/C][C]1.00042[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]94.91[/C][C]NA[/C][C]NA[/C][C]1.00018[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]95.2[/C][C]95.1991[/C][C]95.1954[/C][C]1.00004[/C][C]1.00001[/C][/ROW]
[ROW][C]8[/C][C]95.48[/C][C]95.4205[/C][C]95.3921[/C][C]1.0003[/C][C]1.00062[/C][/ROW]
[ROW][C]9[/C][C]95.56[/C][C]95.6096[/C][C]95.5937[/C][C]1.00017[/C][C]0.999481[/C][/ROW]
[ROW][C]10[/C][C]95.75[/C][C]95.8354[/C][C]95.8058[/C][C]1.00031[/C][C]0.999109[/C][/ROW]
[ROW][C]11[/C][C]95.91[/C][C]95.9632[/C][C]96.0254[/C][C]0.999352[/C][C]0.999445[/C][/ROW]
[ROW][C]12[/C][C]96.16[/C][C]96.0814[/C][C]96.2512[/C][C]0.998236[/C][C]1.00082[/C][/ROW]
[ROW][C]13[/C][C]96.32[/C][C]96.2267[/C][C]96.4738[/C][C]0.99744[/C][C]1.00097[/C][/ROW]
[ROW][C]14[/C][C]96.58[/C][C]96.7344[/C][C]96.6933[/C][C]1.00042[/C][C]0.998404[/C][/ROW]
[ROW][C]15[/C][C]97.08[/C][C]97.0965[/C][C]96.9171[/C][C]1.00185[/C][C]0.99983[/C][/ROW]
[ROW][C]16[/C][C]97.22[/C][C]97.2687[/C][C]97.1442[/C][C]1.00128[/C][C]0.999499[/C][/ROW]
[ROW][C]17[/C][C]97.49[/C][C]97.4126[/C][C]97.3712[/C][C]1.00042[/C][C]1.00079[/C][/ROW]
[ROW][C]18[/C][C]97.62[/C][C]97.6071[/C][C]97.5896[/C][C]1.00018[/C][C]1.00013[/C][/ROW]
[ROW][C]19[/C][C]97.83[/C][C]97.8021[/C][C]97.7983[/C][C]1.00004[/C][C]1.00029[/C][/ROW]
[ROW][C]20[/C][C]98.12[/C][C]98.0396[/C][C]98.0104[/C][C]1.0003[/C][C]1.00082[/C][/ROW]
[ROW][C]21[/C][C]98.29[/C][C]98.2304[/C][C]98.2142[/C][C]1.00017[/C][C]1.00061[/C][/ROW]
[ROW][C]22[/C][C]98.47[/C][C]98.4366[/C][C]98.4062[/C][C]1.00031[/C][C]1.00034[/C][/ROW]
[ROW][C]23[/C][C]98.64[/C][C]98.5241[/C][C]98.5879[/C][C]0.999352[/C][C]1.00118[/C][/ROW]
[ROW][C]24[/C][C]98.67[/C][C]98.5883[/C][C]98.7625[/C][C]0.998236[/C][C]1.00083[/C][/ROW]
[ROW][C]25[/C][C]98.82[/C][C]98.6896[/C][C]98.9429[/C][C]0.99744[/C][C]1.00132[/C][/ROW]
[ROW][C]26[/C][C]99.17[/C][C]99.1638[/C][C]99.1217[/C][C]1.00042[/C][C]1.00006[/C][/ROW]
[ROW][C]27[/C][C]99.38[/C][C]99.4871[/C][C]99.3033[/C][C]1.00185[/C][C]0.998923[/C][/ROW]
[ROW][C]28[/C][C]99.53[/C][C]99.628[/C][C]99.5004[/C][C]1.00128[/C][C]0.999017[/C][/ROW]
[ROW][C]29[/C][C]99.54[/C][C]99.7436[/C][C]99.7012[/C][C]1.00042[/C][C]0.997959[/C][/ROW]
[ROW][C]30[/C][C]99.76[/C][C]99.9171[/C][C]99.8992[/C][C]1.00018[/C][C]0.998428[/C][/ROW]
[ROW][C]31[/C][C]100.02[/C][C]100.102[/C][C]100.098[/C][C]1.00004[/C][C]0.999183[/C][/ROW]
[ROW][C]32[/C][C]100.22[/C][C]100.342[/C][C]100.312[/C][C]1.0003[/C][C]0.998784[/C][/ROW]
[ROW][C]33[/C][C]100.55[/C][C]100.564[/C][C]100.547[/C][C]1.00017[/C][C]0.999863[/C][/ROW]
[ROW][C]34[/C][C]100.94[/C][C]100.821[/C][C]100.79[/C][C]1.00031[/C][C]1.00118[/C][/ROW]
[ROW][C]35[/C][C]100.99[/C][C]100.975[/C][C]101.04[/C][C]0.999352[/C][C]1.00015[/C][/ROW]
[ROW][C]36[/C][C]101.07[/C][C]101.112[/C][C]101.291[/C][C]0.998236[/C][C]0.999583[/C][/ROW]
[ROW][C]37[/C][C]101.19[/C][C]101.28[/C][C]101.54[/C][C]0.99744[/C][C]0.999115[/C][/ROW]
[ROW][C]38[/C][C]101.94[/C][C]101.831[/C][C]101.788[/C][C]1.00042[/C][C]1.00107[/C][/ROW]
[ROW][C]39[/C][C]102.25[/C][C]102.221[/C][C]102.032[/C][C]1.00185[/C][C]1.00028[/C][/ROW]
[ROW][C]40[/C][C]102.49[/C][C]102.395[/C][C]102.264[/C][C]1.00128[/C][C]1.00093[/C][/ROW]
[ROW][C]41[/C][C]102.58[/C][C]102.534[/C][C]102.49[/C][C]1.00042[/C][C]1.00045[/C][/ROW]
[ROW][C]42[/C][C]102.74[/C][C]102.738[/C][C]102.72[/C][C]1.00018[/C][C]1.00002[/C][/ROW]
[ROW][C]43[/C][C]103.01[/C][C]102.954[/C][C]102.95[/C][C]1.00004[/C][C]1.00055[/C][/ROW]
[ROW][C]44[/C][C]103.19[/C][C]103.211[/C][C]103.18[/C][C]1.0003[/C][C]0.999799[/C][/ROW]
[ROW][C]45[/C][C]103.44[/C][C]103.432[/C][C]103.415[/C][C]1.00017[/C][C]1.00008[/C][/ROW]
[ROW][C]46[/C][C]103.62[/C][C]103.682[/C][C]103.65[/C][C]1.00031[/C][C]0.999398[/C][/ROW]
[ROW][C]47[/C][C]103.74[/C][C]103.818[/C][C]103.885[/C][C]0.999352[/C][C]0.999251[/C][/ROW]
[ROW][C]48[/C][C]103.82[/C][C]103.945[/C][C]104.129[/C][C]0.998236[/C][C]0.998797[/C][/ROW]
[ROW][C]49[/C][C]103.96[/C][C]104.104[/C][C]104.371[/C][C]0.99744[/C][C]0.998621[/C][/ROW]
[ROW][C]50[/C][C]104.7[/C][C]104.649[/C][C]104.604[/C][C]1.00042[/C][C]1.00049[/C][/ROW]
[ROW][C]51[/C][C]105.13[/C][C]105.026[/C][C]104.832[/C][C]1.00185[/C][C]1.00099[/C][/ROW]
[ROW][C]52[/C][C]105.26[/C][C]105.198[/C][C]105.064[/C][C]1.00128[/C][C]1.00059[/C][/ROW]
[ROW][C]53[/C][C]105.44[/C][C]105.353[/C][C]105.308[/C][C]1.00042[/C][C]1.00082[/C][/ROW]
[ROW][C]54[/C][C]105.73[/C][C]105.577[/C][C]105.558[/C][C]1.00018[/C][C]1.00145[/C][/ROW]
[ROW][C]55[/C][C]105.83[/C][C]NA[/C][C]NA[/C][C]1.00004[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]105.97[/C][C]NA[/C][C]NA[/C][C]1.0003[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]106.13[/C][C]NA[/C][C]NA[/C][C]1.00017[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]106.49[/C][C]NA[/C][C]NA[/C][C]1.00031[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]106.74[/C][C]NA[/C][C]NA[/C][C]0.999352[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]106.82[/C][C]NA[/C][C]NA[/C][C]0.998236[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295732&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295732&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	93.91	NA	NA	0.99744	NA
2	94.27	NA	NA	1.00042	NA
3	94.55	NA	NA	1.00185	NA
4	94.66	NA	NA	1.00128	NA
5	94.78	NA	NA	1.00042	NA
6	94.91	NA	NA	1.00018	NA
7	95.2	95.1991	95.1954	1.00004	1.00001
8	95.48	95.4205	95.3921	1.0003	1.00062
9	95.56	95.6096	95.5937	1.00017	0.999481
10	95.75	95.8354	95.8058	1.00031	0.999109
11	95.91	95.9632	96.0254	0.999352	0.999445
12	96.16	96.0814	96.2512	0.998236	1.00082
13	96.32	96.2267	96.4738	0.99744	1.00097
14	96.58	96.7344	96.6933	1.00042	0.998404
15	97.08	97.0965	96.9171	1.00185	0.99983
16	97.22	97.2687	97.1442	1.00128	0.999499
17	97.49	97.4126	97.3712	1.00042	1.00079
18	97.62	97.6071	97.5896	1.00018	1.00013
19	97.83	97.8021	97.7983	1.00004	1.00029
20	98.12	98.0396	98.0104	1.0003	1.00082
21	98.29	98.2304	98.2142	1.00017	1.00061
22	98.47	98.4366	98.4062	1.00031	1.00034
23	98.64	98.5241	98.5879	0.999352	1.00118
24	98.67	98.5883	98.7625	0.998236	1.00083
25	98.82	98.6896	98.9429	0.99744	1.00132
26	99.17	99.1638	99.1217	1.00042	1.00006
27	99.38	99.4871	99.3033	1.00185	0.998923
28	99.53	99.628	99.5004	1.00128	0.999017
29	99.54	99.7436	99.7012	1.00042	0.997959
30	99.76	99.9171	99.8992	1.00018	0.998428
31	100.02	100.102	100.098	1.00004	0.999183
32	100.22	100.342	100.312	1.0003	0.998784
33	100.55	100.564	100.547	1.00017	0.999863
34	100.94	100.821	100.79	1.00031	1.00118
35	100.99	100.975	101.04	0.999352	1.00015
36	101.07	101.112	101.291	0.998236	0.999583
37	101.19	101.28	101.54	0.99744	0.999115
38	101.94	101.831	101.788	1.00042	1.00107
39	102.25	102.221	102.032	1.00185	1.00028
40	102.49	102.395	102.264	1.00128	1.00093
41	102.58	102.534	102.49	1.00042	1.00045
42	102.74	102.738	102.72	1.00018	1.00002
43	103.01	102.954	102.95	1.00004	1.00055
44	103.19	103.211	103.18	1.0003	0.999799
45	103.44	103.432	103.415	1.00017	1.00008
46	103.62	103.682	103.65	1.00031	0.999398
47	103.74	103.818	103.885	0.999352	0.999251
48	103.82	103.945	104.129	0.998236	0.998797
49	103.96	104.104	104.371	0.99744	0.998621
50	104.7	104.649	104.604	1.00042	1.00049
51	105.13	105.026	104.832	1.00185	1.00099
52	105.26	105.198	105.064	1.00128	1.00059
53	105.44	105.353	105.308	1.00042	1.00082
54	105.73	105.577	105.558	1.00018	1.00145
55	105.83	NA	NA	1.00004	NA
56	105.97	NA	NA	1.0003	NA
57	106.13	NA	NA	1.00017	NA
58	106.49	NA	NA	1.00031	NA
59	106.74	NA	NA	0.999352	NA
60	106.82	NA	NA	0.998236	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