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

Sun, 04 Jan 2015 08:00:34 +0000

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/2015/Jan/04/t14203584505encb2971d9542g.htm/, Retrieved Tue, 14 May 2024 15:00:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271912, Retrieved Tue, 14 May 2024 15:00:32 +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

Alessio De Looze

Estimated Impact

137

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

-       [Classical Decomposition] [] [2015-01-04 08:00:34] [072d4f39c76834f6beee313555a90f83] [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 Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271912&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271912&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271912&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 Ronald Aylmer Fisher' @ fisher.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	164.88	NA	NA	1.00915	NA
2	164.88	NA	NA	0.997411	NA
3	164.57	NA	NA	0.998526	NA
4	164.53	NA	NA	0.997797	NA
5	165.03	NA	NA	0.995848	NA
6	165.92	NA	NA	0.996507	NA
7	165.92	164.872	165.49	0.996268	1.00636
8	165.92	165.247	165.55	0.998168	1.00407
9	165.92	165.265	165.638	0.997751	1.00396
10	166.12	165.694	165.751	0.999657	1.00257
11	166.34	166.759	165.857	1.00543	0.997489
12	165.48	167.166	165.924	1.00748	0.989916
13	165.61	167.481	165.962	1.00915	0.988831
14	165.61	165.575	166.005	0.997411	1.00021
15	165.94	165.793	166.038	0.998526	1.00089
16	165.88	165.687	166.053	0.997797	1.00116
17	166.23	165.437	166.127	0.995848	1.00479
18	166.32	165.749	166.33	0.996507	1.00344
19	166.43	165.968	166.59	0.996268	1.00278
20	166.43	166.538	166.843	0.998168	0.999353
21	166.2	166.711	167.087	0.997751	0.996933
22	166.21	167.268	167.326	0.999657	0.993672
23	168.02	168.456	167.545	1.00543	0.997413
24	168.68	169.009	167.754	1.00748	0.998051
25	168.65	169.509	167.972	1.00915	0.994933
26	168.65	167.751	168.186	0.997411	1.00536
27	168.75	168.159	168.408	0.998526	1.00351
28	168.8	168.306	168.678	0.997797	1.00293
29	168.58	168.281	168.983	0.995848	1.00178
30	168.98	168.696	169.287	0.996507	1.00168
31	169	168.976	169.609	0.996268	1.00014
32	169	169.631	169.942	0.998168	0.996279
33	168.94	169.889	170.272	0.997751	0.994416
34	169.96	170.525	170.583	0.999657	0.996688
35	171.59	171.801	170.873	1.00543	0.998769
36	172.41	172.418	171.137	1.00748	0.999955
37	172.65	172.948	171.38	1.00915	0.998274
38	172.65	171.175	171.62	0.997411	1.00862
39	172.65	171.61	171.863	0.998526	1.00606
40	172.38	171.692	172.071	0.997797	1.00401
41	171.95	171.464	172.179	0.995848	1.00283
42	171.95	171.614	172.216	0.996507	1.00196
43	171.87	171.609	172.252	0.996268	1.00152
44	171.87	171.64	171.955	0.998168	1.00134
45	171.91	170.93	171.315	0.997751	1.00574
46	171.99	170.605	170.664	0.999657	1.00812
47	172.15	170.89	169.966	1.00543	1.00737
48	172.73	170.493	169.226	1.00748	1.01312
49	173.2	169.969	168.428	1.00915	1.01901
50	164.97	167.167	167.601	0.997411	0.986858
51	164.97	166.539	166.785	0.998526	0.99058
52	164.43	165.599	165.965	0.997797	0.992941
53	163.16	164.535	165.221	0.995848	0.991642
54	162.98	163.971	164.545	0.996507	0.993958
55	161.69	163.239	163.851	0.996268	0.990508
56	162.19	NA	NA	0.998168	NA
57	162	NA	NA	0.997751	NA
58	162.22	NA	NA	0.999657	NA
59	164.08	NA	NA	1.00543	NA
60	164.58	NA	NA	1.00748	NA
61	164.68	NA	NA	1.00915	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 164.88 & NA & NA & 1.00915 & NA \tabularnewline
2 & 164.88 & NA & NA & 0.997411 & NA \tabularnewline
3 & 164.57 & NA & NA & 0.998526 & NA \tabularnewline
4 & 164.53 & NA & NA & 0.997797 & NA \tabularnewline
5 & 165.03 & NA & NA & 0.995848 & NA \tabularnewline
6 & 165.92 & NA & NA & 0.996507 & NA \tabularnewline
7 & 165.92 & 164.872 & 165.49 & 0.996268 & 1.00636 \tabularnewline
8 & 165.92 & 165.247 & 165.55 & 0.998168 & 1.00407 \tabularnewline
9 & 165.92 & 165.265 & 165.638 & 0.997751 & 1.00396 \tabularnewline
10 & 166.12 & 165.694 & 165.751 & 0.999657 & 1.00257 \tabularnewline
11 & 166.34 & 166.759 & 165.857 & 1.00543 & 0.997489 \tabularnewline
12 & 165.48 & 167.166 & 165.924 & 1.00748 & 0.989916 \tabularnewline
13 & 165.61 & 167.481 & 165.962 & 1.00915 & 0.988831 \tabularnewline
14 & 165.61 & 165.575 & 166.005 & 0.997411 & 1.00021 \tabularnewline
15 & 165.94 & 165.793 & 166.038 & 0.998526 & 1.00089 \tabularnewline
16 & 165.88 & 165.687 & 166.053 & 0.997797 & 1.00116 \tabularnewline
17 & 166.23 & 165.437 & 166.127 & 0.995848 & 1.00479 \tabularnewline
18 & 166.32 & 165.749 & 166.33 & 0.996507 & 1.00344 \tabularnewline
19 & 166.43 & 165.968 & 166.59 & 0.996268 & 1.00278 \tabularnewline
20 & 166.43 & 166.538 & 166.843 & 0.998168 & 0.999353 \tabularnewline
21 & 166.2 & 166.711 & 167.087 & 0.997751 & 0.996933 \tabularnewline
22 & 166.21 & 167.268 & 167.326 & 0.999657 & 0.993672 \tabularnewline
23 & 168.02 & 168.456 & 167.545 & 1.00543 & 0.997413 \tabularnewline
24 & 168.68 & 169.009 & 167.754 & 1.00748 & 0.998051 \tabularnewline
25 & 168.65 & 169.509 & 167.972 & 1.00915 & 0.994933 \tabularnewline
26 & 168.65 & 167.751 & 168.186 & 0.997411 & 1.00536 \tabularnewline
27 & 168.75 & 168.159 & 168.408 & 0.998526 & 1.00351 \tabularnewline
28 & 168.8 & 168.306 & 168.678 & 0.997797 & 1.00293 \tabularnewline
29 & 168.58 & 168.281 & 168.983 & 0.995848 & 1.00178 \tabularnewline
30 & 168.98 & 168.696 & 169.287 & 0.996507 & 1.00168 \tabularnewline
31 & 169 & 168.976 & 169.609 & 0.996268 & 1.00014 \tabularnewline
32 & 169 & 169.631 & 169.942 & 0.998168 & 0.996279 \tabularnewline
33 & 168.94 & 169.889 & 170.272 & 0.997751 & 0.994416 \tabularnewline
34 & 169.96 & 170.525 & 170.583 & 0.999657 & 0.996688 \tabularnewline
35 & 171.59 & 171.801 & 170.873 & 1.00543 & 0.998769 \tabularnewline
36 & 172.41 & 172.418 & 171.137 & 1.00748 & 0.999955 \tabularnewline
37 & 172.65 & 172.948 & 171.38 & 1.00915 & 0.998274 \tabularnewline
38 & 172.65 & 171.175 & 171.62 & 0.997411 & 1.00862 \tabularnewline
39 & 172.65 & 171.61 & 171.863 & 0.998526 & 1.00606 \tabularnewline
40 & 172.38 & 171.692 & 172.071 & 0.997797 & 1.00401 \tabularnewline
41 & 171.95 & 171.464 & 172.179 & 0.995848 & 1.00283 \tabularnewline
42 & 171.95 & 171.614 & 172.216 & 0.996507 & 1.00196 \tabularnewline
43 & 171.87 & 171.609 & 172.252 & 0.996268 & 1.00152 \tabularnewline
44 & 171.87 & 171.64 & 171.955 & 0.998168 & 1.00134 \tabularnewline
45 & 171.91 & 170.93 & 171.315 & 0.997751 & 1.00574 \tabularnewline
46 & 171.99 & 170.605 & 170.664 & 0.999657 & 1.00812 \tabularnewline
47 & 172.15 & 170.89 & 169.966 & 1.00543 & 1.00737 \tabularnewline
48 & 172.73 & 170.493 & 169.226 & 1.00748 & 1.01312 \tabularnewline
49 & 173.2 & 169.969 & 168.428 & 1.00915 & 1.01901 \tabularnewline
50 & 164.97 & 167.167 & 167.601 & 0.997411 & 0.986858 \tabularnewline
51 & 164.97 & 166.539 & 166.785 & 0.998526 & 0.99058 \tabularnewline
52 & 164.43 & 165.599 & 165.965 & 0.997797 & 0.992941 \tabularnewline
53 & 163.16 & 164.535 & 165.221 & 0.995848 & 0.991642 \tabularnewline
54 & 162.98 & 163.971 & 164.545 & 0.996507 & 0.993958 \tabularnewline
55 & 161.69 & 163.239 & 163.851 & 0.996268 & 0.990508 \tabularnewline
56 & 162.19 & NA & NA & 0.998168 & NA \tabularnewline
57 & 162 & NA & NA & 0.997751 & NA \tabularnewline
58 & 162.22 & NA & NA & 0.999657 & NA \tabularnewline
59 & 164.08 & NA & NA & 1.00543 & NA \tabularnewline
60 & 164.58 & NA & NA & 1.00748 & NA \tabularnewline
61 & 164.68 & NA & NA & 1.00915 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271912&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]164.88[/C][C]NA[/C][C]NA[/C][C]1.00915[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]164.88[/C][C]NA[/C][C]NA[/C][C]0.997411[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]164.57[/C][C]NA[/C][C]NA[/C][C]0.998526[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]164.53[/C][C]NA[/C][C]NA[/C][C]0.997797[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]165.03[/C][C]NA[/C][C]NA[/C][C]0.995848[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]165.92[/C][C]NA[/C][C]NA[/C][C]0.996507[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]165.92[/C][C]164.872[/C][C]165.49[/C][C]0.996268[/C][C]1.00636[/C][/ROW]
[ROW][C]8[/C][C]165.92[/C][C]165.247[/C][C]165.55[/C][C]0.998168[/C][C]1.00407[/C][/ROW]
[ROW][C]9[/C][C]165.92[/C][C]165.265[/C][C]165.638[/C][C]0.997751[/C][C]1.00396[/C][/ROW]
[ROW][C]10[/C][C]166.12[/C][C]165.694[/C][C]165.751[/C][C]0.999657[/C][C]1.00257[/C][/ROW]
[ROW][C]11[/C][C]166.34[/C][C]166.759[/C][C]165.857[/C][C]1.00543[/C][C]0.997489[/C][/ROW]
[ROW][C]12[/C][C]165.48[/C][C]167.166[/C][C]165.924[/C][C]1.00748[/C][C]0.989916[/C][/ROW]
[ROW][C]13[/C][C]165.61[/C][C]167.481[/C][C]165.962[/C][C]1.00915[/C][C]0.988831[/C][/ROW]
[ROW][C]14[/C][C]165.61[/C][C]165.575[/C][C]166.005[/C][C]0.997411[/C][C]1.00021[/C][/ROW]
[ROW][C]15[/C][C]165.94[/C][C]165.793[/C][C]166.038[/C][C]0.998526[/C][C]1.00089[/C][/ROW]
[ROW][C]16[/C][C]165.88[/C][C]165.687[/C][C]166.053[/C][C]0.997797[/C][C]1.00116[/C][/ROW]
[ROW][C]17[/C][C]166.23[/C][C]165.437[/C][C]166.127[/C][C]0.995848[/C][C]1.00479[/C][/ROW]
[ROW][C]18[/C][C]166.32[/C][C]165.749[/C][C]166.33[/C][C]0.996507[/C][C]1.00344[/C][/ROW]
[ROW][C]19[/C][C]166.43[/C][C]165.968[/C][C]166.59[/C][C]0.996268[/C][C]1.00278[/C][/ROW]
[ROW][C]20[/C][C]166.43[/C][C]166.538[/C][C]166.843[/C][C]0.998168[/C][C]0.999353[/C][/ROW]
[ROW][C]21[/C][C]166.2[/C][C]166.711[/C][C]167.087[/C][C]0.997751[/C][C]0.996933[/C][/ROW]
[ROW][C]22[/C][C]166.21[/C][C]167.268[/C][C]167.326[/C][C]0.999657[/C][C]0.993672[/C][/ROW]
[ROW][C]23[/C][C]168.02[/C][C]168.456[/C][C]167.545[/C][C]1.00543[/C][C]0.997413[/C][/ROW]
[ROW][C]24[/C][C]168.68[/C][C]169.009[/C][C]167.754[/C][C]1.00748[/C][C]0.998051[/C][/ROW]
[ROW][C]25[/C][C]168.65[/C][C]169.509[/C][C]167.972[/C][C]1.00915[/C][C]0.994933[/C][/ROW]
[ROW][C]26[/C][C]168.65[/C][C]167.751[/C][C]168.186[/C][C]0.997411[/C][C]1.00536[/C][/ROW]
[ROW][C]27[/C][C]168.75[/C][C]168.159[/C][C]168.408[/C][C]0.998526[/C][C]1.00351[/C][/ROW]
[ROW][C]28[/C][C]168.8[/C][C]168.306[/C][C]168.678[/C][C]0.997797[/C][C]1.00293[/C][/ROW]
[ROW][C]29[/C][C]168.58[/C][C]168.281[/C][C]168.983[/C][C]0.995848[/C][C]1.00178[/C][/ROW]
[ROW][C]30[/C][C]168.98[/C][C]168.696[/C][C]169.287[/C][C]0.996507[/C][C]1.00168[/C][/ROW]
[ROW][C]31[/C][C]169[/C][C]168.976[/C][C]169.609[/C][C]0.996268[/C][C]1.00014[/C][/ROW]
[ROW][C]32[/C][C]169[/C][C]169.631[/C][C]169.942[/C][C]0.998168[/C][C]0.996279[/C][/ROW]
[ROW][C]33[/C][C]168.94[/C][C]169.889[/C][C]170.272[/C][C]0.997751[/C][C]0.994416[/C][/ROW]
[ROW][C]34[/C][C]169.96[/C][C]170.525[/C][C]170.583[/C][C]0.999657[/C][C]0.996688[/C][/ROW]
[ROW][C]35[/C][C]171.59[/C][C]171.801[/C][C]170.873[/C][C]1.00543[/C][C]0.998769[/C][/ROW]
[ROW][C]36[/C][C]172.41[/C][C]172.418[/C][C]171.137[/C][C]1.00748[/C][C]0.999955[/C][/ROW]
[ROW][C]37[/C][C]172.65[/C][C]172.948[/C][C]171.38[/C][C]1.00915[/C][C]0.998274[/C][/ROW]
[ROW][C]38[/C][C]172.65[/C][C]171.175[/C][C]171.62[/C][C]0.997411[/C][C]1.00862[/C][/ROW]
[ROW][C]39[/C][C]172.65[/C][C]171.61[/C][C]171.863[/C][C]0.998526[/C][C]1.00606[/C][/ROW]
[ROW][C]40[/C][C]172.38[/C][C]171.692[/C][C]172.071[/C][C]0.997797[/C][C]1.00401[/C][/ROW]
[ROW][C]41[/C][C]171.95[/C][C]171.464[/C][C]172.179[/C][C]0.995848[/C][C]1.00283[/C][/ROW]
[ROW][C]42[/C][C]171.95[/C][C]171.614[/C][C]172.216[/C][C]0.996507[/C][C]1.00196[/C][/ROW]
[ROW][C]43[/C][C]171.87[/C][C]171.609[/C][C]172.252[/C][C]0.996268[/C][C]1.00152[/C][/ROW]
[ROW][C]44[/C][C]171.87[/C][C]171.64[/C][C]171.955[/C][C]0.998168[/C][C]1.00134[/C][/ROW]
[ROW][C]45[/C][C]171.91[/C][C]170.93[/C][C]171.315[/C][C]0.997751[/C][C]1.00574[/C][/ROW]
[ROW][C]46[/C][C]171.99[/C][C]170.605[/C][C]170.664[/C][C]0.999657[/C][C]1.00812[/C][/ROW]
[ROW][C]47[/C][C]172.15[/C][C]170.89[/C][C]169.966[/C][C]1.00543[/C][C]1.00737[/C][/ROW]
[ROW][C]48[/C][C]172.73[/C][C]170.493[/C][C]169.226[/C][C]1.00748[/C][C]1.01312[/C][/ROW]
[ROW][C]49[/C][C]173.2[/C][C]169.969[/C][C]168.428[/C][C]1.00915[/C][C]1.01901[/C][/ROW]
[ROW][C]50[/C][C]164.97[/C][C]167.167[/C][C]167.601[/C][C]0.997411[/C][C]0.986858[/C][/ROW]
[ROW][C]51[/C][C]164.97[/C][C]166.539[/C][C]166.785[/C][C]0.998526[/C][C]0.99058[/C][/ROW]
[ROW][C]52[/C][C]164.43[/C][C]165.599[/C][C]165.965[/C][C]0.997797[/C][C]0.992941[/C][/ROW]
[ROW][C]53[/C][C]163.16[/C][C]164.535[/C][C]165.221[/C][C]0.995848[/C][C]0.991642[/C][/ROW]
[ROW][C]54[/C][C]162.98[/C][C]163.971[/C][C]164.545[/C][C]0.996507[/C][C]0.993958[/C][/ROW]
[ROW][C]55[/C][C]161.69[/C][C]163.239[/C][C]163.851[/C][C]0.996268[/C][C]0.990508[/C][/ROW]
[ROW][C]56[/C][C]162.19[/C][C]NA[/C][C]NA[/C][C]0.998168[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]162[/C][C]NA[/C][C]NA[/C][C]0.997751[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]162.22[/C][C]NA[/C][C]NA[/C][C]0.999657[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]164.08[/C][C]NA[/C][C]NA[/C][C]1.00543[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]164.58[/C][C]NA[/C][C]NA[/C][C]1.00748[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]164.68[/C][C]NA[/C][C]NA[/C][C]1.00915[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271912&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271912&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	164.88	NA	NA	1.00915	NA
2	164.88	NA	NA	0.997411	NA
3	164.57	NA	NA	0.998526	NA
4	164.53	NA	NA	0.997797	NA
5	165.03	NA	NA	0.995848	NA
6	165.92	NA	NA	0.996507	NA
7	165.92	164.872	165.49	0.996268	1.00636
8	165.92	165.247	165.55	0.998168	1.00407
9	165.92	165.265	165.638	0.997751	1.00396
10	166.12	165.694	165.751	0.999657	1.00257
11	166.34	166.759	165.857	1.00543	0.997489
12	165.48	167.166	165.924	1.00748	0.989916
13	165.61	167.481	165.962	1.00915	0.988831
14	165.61	165.575	166.005	0.997411	1.00021
15	165.94	165.793	166.038	0.998526	1.00089
16	165.88	165.687	166.053	0.997797	1.00116
17	166.23	165.437	166.127	0.995848	1.00479
18	166.32	165.749	166.33	0.996507	1.00344
19	166.43	165.968	166.59	0.996268	1.00278
20	166.43	166.538	166.843	0.998168	0.999353
21	166.2	166.711	167.087	0.997751	0.996933
22	166.21	167.268	167.326	0.999657	0.993672
23	168.02	168.456	167.545	1.00543	0.997413
24	168.68	169.009	167.754	1.00748	0.998051
25	168.65	169.509	167.972	1.00915	0.994933
26	168.65	167.751	168.186	0.997411	1.00536
27	168.75	168.159	168.408	0.998526	1.00351
28	168.8	168.306	168.678	0.997797	1.00293
29	168.58	168.281	168.983	0.995848	1.00178
30	168.98	168.696	169.287	0.996507	1.00168
31	169	168.976	169.609	0.996268	1.00014
32	169	169.631	169.942	0.998168	0.996279
33	168.94	169.889	170.272	0.997751	0.994416
34	169.96	170.525	170.583	0.999657	0.996688
35	171.59	171.801	170.873	1.00543	0.998769
36	172.41	172.418	171.137	1.00748	0.999955
37	172.65	172.948	171.38	1.00915	0.998274
38	172.65	171.175	171.62	0.997411	1.00862
39	172.65	171.61	171.863	0.998526	1.00606
40	172.38	171.692	172.071	0.997797	1.00401
41	171.95	171.464	172.179	0.995848	1.00283
42	171.95	171.614	172.216	0.996507	1.00196
43	171.87	171.609	172.252	0.996268	1.00152
44	171.87	171.64	171.955	0.998168	1.00134
45	171.91	170.93	171.315	0.997751	1.00574
46	171.99	170.605	170.664	0.999657	1.00812
47	172.15	170.89	169.966	1.00543	1.00737
48	172.73	170.493	169.226	1.00748	1.01312
49	173.2	169.969	168.428	1.00915	1.01901
50	164.97	167.167	167.601	0.997411	0.986858
51	164.97	166.539	166.785	0.998526	0.99058
52	164.43	165.599	165.965	0.997797	0.992941
53	163.16	164.535	165.221	0.995848	0.991642
54	162.98	163.971	164.545	0.996507	0.993958
55	161.69	163.239	163.851	0.996268	0.990508
56	162.19	NA	NA	0.998168	NA
57	162	NA	NA	0.997751	NA
58	162.22	NA	NA	0.999657	NA
59	164.08	NA	NA	1.00543	NA
60	164.58	NA	NA	1.00748	NA
61	164.68	NA	NA	1.00915	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