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

Mon, 28 Nov 2016 22:41:52 +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/2016/Nov/28/t14803729332cjavutvkcpr1jj.htm/, Retrieved Sat, 04 May 2024 16:33:40 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 16:33:40 +0200

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

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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.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]'Gertrude Mary Cox' @ cox.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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1.336	NA	NA	0.994036	NA
2	1.3649	NA	NA	1.00193	NA
3	1.3999	NA	NA	0.990767	NA
4	1.4442	NA	NA	0.993911	NA
5	1.4349	NA	NA	0.997071	NA
6	1.4388	NA	NA	0.99917	NA
7	1.4264	1.38781	1.38981	0.998556	1.02781
8	1.4343	1.3938	1.38615	1.00552	1.02906
9	1.377	1.38261	1.38105	1.00113	0.995941
10	1.3706	1.38432	1.37239	1.00869	0.990086
11	1.3556	1.36342	1.36056	1.0021	0.994265
12	1.3179	1.35587	1.3463	1.00711	0.971997
13	1.2905	1.32237	1.33031	0.994036	0.975896
14	1.3224	1.31652	1.31398	1.00193	1.00447
15	1.3201	1.29005	1.30207	0.990767	1.02329
16	1.3162	1.28733	1.29522	0.993911	1.02243
17	1.2789	1.28536	1.28913	0.997071	0.994976
18	1.2526	1.28478	1.28585	0.99917	0.974951
19	1.2288	1.28534	1.2872	0.998556	0.956014
20	1.24	1.29647	1.28935	1.00552	0.956441
21	1.2856	1.29039	1.28893	1.00113	0.996291
22	1.2974	1.29857	1.28738	1.00869	0.999101
23	1.2828	1.29032	1.28761	1.0021	0.994172
24	1.3119	1.30036	1.29118	1.00711	1.00888
25	1.3288	1.2895	1.29724	0.994036	1.03047
26	1.3359	1.30685	1.30433	1.00193	1.02223
27	1.2964	1.29808	1.31018	0.990767	0.998708
28	1.3026	1.30697	1.31498	0.993911	0.996654
29	1.2982	1.31664	1.3205	0.997071	0.985998
30	1.3189	1.32461	1.32571	0.99917	0.995688
31	1.308	1.32757	1.32949	0.998556	0.985257
32	1.331	1.33943	1.33208	1.00552	0.993704
33	1.3348	1.33842	1.3369	1.00113	0.997299
34	1.3635	1.35544	1.34376	1.00869	1.00595
35	1.3493	1.353	1.35016	1.0021	0.997267
36	1.3704	1.36459	1.35496	1.00711	1.00426
37	1.361	1.35045	1.35855	0.994036	1.00781
38	1.3658	1.36312	1.36049	1.00193	1.00196
39	1.3823	1.34611	1.35865	0.990767	1.02689
40	1.3812	1.34455	1.35278	0.993911	1.02726
41	1.3732	1.34058	1.34452	0.997071	1.02433
42	1.3592	1.33344	1.33455	0.99917	1.01932
43	1.3539	1.31863	1.32054	0.998556	1.02675
44	1.3316	1.30983	1.30263	1.00552	1.01662
45	1.2901	1.28203	1.28058	1.00113	1.0063
46	1.2673	1.26642	1.2555	1.00869	1.0007
47	1.2472	1.2347	1.23211	1.0021	1.01012
48	1.2331	1.22005	1.21144	1.00711	1.0107
49	1.1621	1.18383	1.19093	0.994036	0.981647
50	1.135	1.17353	1.17126	1.00193	0.96717
51	1.0838	1.14453	1.15519	0.990767	0.946943
52	1.0779	1.13525	1.1422	0.993911	0.949486
53	1.115	1.12567	1.12898	0.997071	0.990523
54	1.1213	1.11476	1.11568	0.99917	1.00587
55	1.0996	NA	NA	0.998556	NA
56	1.1139	NA	NA	1.00552	NA
57	1.1221	NA	NA	1.00113	NA
58	1.1235	NA	NA	1.00869	NA
59	1.0736	NA	NA	1.0021	NA
60	1.0877	NA	NA	1.00711	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1.336 & NA & NA & 0.994036 & NA \tabularnewline
2 & 1.3649 & NA & NA & 1.00193 & NA \tabularnewline
3 & 1.3999 & NA & NA & 0.990767 & NA \tabularnewline
4 & 1.4442 & NA & NA & 0.993911 & NA \tabularnewline
5 & 1.4349 & NA & NA & 0.997071 & NA \tabularnewline
6 & 1.4388 & NA & NA & 0.99917 & NA \tabularnewline
7 & 1.4264 & 1.38781 & 1.38981 & 0.998556 & 1.02781 \tabularnewline
8 & 1.4343 & 1.3938 & 1.38615 & 1.00552 & 1.02906 \tabularnewline
9 & 1.377 & 1.38261 & 1.38105 & 1.00113 & 0.995941 \tabularnewline
10 & 1.3706 & 1.38432 & 1.37239 & 1.00869 & 0.990086 \tabularnewline
11 & 1.3556 & 1.36342 & 1.36056 & 1.0021 & 0.994265 \tabularnewline
12 & 1.3179 & 1.35587 & 1.3463 & 1.00711 & 0.971997 \tabularnewline
13 & 1.2905 & 1.32237 & 1.33031 & 0.994036 & 0.975896 \tabularnewline
14 & 1.3224 & 1.31652 & 1.31398 & 1.00193 & 1.00447 \tabularnewline
15 & 1.3201 & 1.29005 & 1.30207 & 0.990767 & 1.02329 \tabularnewline
16 & 1.3162 & 1.28733 & 1.29522 & 0.993911 & 1.02243 \tabularnewline
17 & 1.2789 & 1.28536 & 1.28913 & 0.997071 & 0.994976 \tabularnewline
18 & 1.2526 & 1.28478 & 1.28585 & 0.99917 & 0.974951 \tabularnewline
19 & 1.2288 & 1.28534 & 1.2872 & 0.998556 & 0.956014 \tabularnewline
20 & 1.24 & 1.29647 & 1.28935 & 1.00552 & 0.956441 \tabularnewline
21 & 1.2856 & 1.29039 & 1.28893 & 1.00113 & 0.996291 \tabularnewline
22 & 1.2974 & 1.29857 & 1.28738 & 1.00869 & 0.999101 \tabularnewline
23 & 1.2828 & 1.29032 & 1.28761 & 1.0021 & 0.994172 \tabularnewline
24 & 1.3119 & 1.30036 & 1.29118 & 1.00711 & 1.00888 \tabularnewline
25 & 1.3288 & 1.2895 & 1.29724 & 0.994036 & 1.03047 \tabularnewline
26 & 1.3359 & 1.30685 & 1.30433 & 1.00193 & 1.02223 \tabularnewline
27 & 1.2964 & 1.29808 & 1.31018 & 0.990767 & 0.998708 \tabularnewline
28 & 1.3026 & 1.30697 & 1.31498 & 0.993911 & 0.996654 \tabularnewline
29 & 1.2982 & 1.31664 & 1.3205 & 0.997071 & 0.985998 \tabularnewline
30 & 1.3189 & 1.32461 & 1.32571 & 0.99917 & 0.995688 \tabularnewline
31 & 1.308 & 1.32757 & 1.32949 & 0.998556 & 0.985257 \tabularnewline
32 & 1.331 & 1.33943 & 1.33208 & 1.00552 & 0.993704 \tabularnewline
33 & 1.3348 & 1.33842 & 1.3369 & 1.00113 & 0.997299 \tabularnewline
34 & 1.3635 & 1.35544 & 1.34376 & 1.00869 & 1.00595 \tabularnewline
35 & 1.3493 & 1.353 & 1.35016 & 1.0021 & 0.997267 \tabularnewline
36 & 1.3704 & 1.36459 & 1.35496 & 1.00711 & 1.00426 \tabularnewline
37 & 1.361 & 1.35045 & 1.35855 & 0.994036 & 1.00781 \tabularnewline
38 & 1.3658 & 1.36312 & 1.36049 & 1.00193 & 1.00196 \tabularnewline
39 & 1.3823 & 1.34611 & 1.35865 & 0.990767 & 1.02689 \tabularnewline
40 & 1.3812 & 1.34455 & 1.35278 & 0.993911 & 1.02726 \tabularnewline
41 & 1.3732 & 1.34058 & 1.34452 & 0.997071 & 1.02433 \tabularnewline
42 & 1.3592 & 1.33344 & 1.33455 & 0.99917 & 1.01932 \tabularnewline
43 & 1.3539 & 1.31863 & 1.32054 & 0.998556 & 1.02675 \tabularnewline
44 & 1.3316 & 1.30983 & 1.30263 & 1.00552 & 1.01662 \tabularnewline
45 & 1.2901 & 1.28203 & 1.28058 & 1.00113 & 1.0063 \tabularnewline
46 & 1.2673 & 1.26642 & 1.2555 & 1.00869 & 1.0007 \tabularnewline
47 & 1.2472 & 1.2347 & 1.23211 & 1.0021 & 1.01012 \tabularnewline
48 & 1.2331 & 1.22005 & 1.21144 & 1.00711 & 1.0107 \tabularnewline
49 & 1.1621 & 1.18383 & 1.19093 & 0.994036 & 0.981647 \tabularnewline
50 & 1.135 & 1.17353 & 1.17126 & 1.00193 & 0.96717 \tabularnewline
51 & 1.0838 & 1.14453 & 1.15519 & 0.990767 & 0.946943 \tabularnewline
52 & 1.0779 & 1.13525 & 1.1422 & 0.993911 & 0.949486 \tabularnewline
53 & 1.115 & 1.12567 & 1.12898 & 0.997071 & 0.990523 \tabularnewline
54 & 1.1213 & 1.11476 & 1.11568 & 0.99917 & 1.00587 \tabularnewline
55 & 1.0996 & NA & NA & 0.998556 & NA \tabularnewline
56 & 1.1139 & NA & NA & 1.00552 & NA \tabularnewline
57 & 1.1221 & NA & NA & 1.00113 & NA \tabularnewline
58 & 1.1235 & NA & NA & 1.00869 & NA \tabularnewline
59 & 1.0736 & NA & NA & 1.0021 & NA \tabularnewline
60 & 1.0877 & NA & NA & 1.00711 & 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]1.336[/C][C]NA[/C][C]NA[/C][C]0.994036[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1.3649[/C][C]NA[/C][C]NA[/C][C]1.00193[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.3999[/C][C]NA[/C][C]NA[/C][C]0.990767[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.4442[/C][C]NA[/C][C]NA[/C][C]0.993911[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1.4349[/C][C]NA[/C][C]NA[/C][C]0.997071[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.4388[/C][C]NA[/C][C]NA[/C][C]0.99917[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.4264[/C][C]1.38781[/C][C]1.38981[/C][C]0.998556[/C][C]1.02781[/C][/ROW]
[ROW][C]8[/C][C]1.4343[/C][C]1.3938[/C][C]1.38615[/C][C]1.00552[/C][C]1.02906[/C][/ROW]
[ROW][C]9[/C][C]1.377[/C][C]1.38261[/C][C]1.38105[/C][C]1.00113[/C][C]0.995941[/C][/ROW]
[ROW][C]10[/C][C]1.3706[/C][C]1.38432[/C][C]1.37239[/C][C]1.00869[/C][C]0.990086[/C][/ROW]
[ROW][C]11[/C][C]1.3556[/C][C]1.36342[/C][C]1.36056[/C][C]1.0021[/C][C]0.994265[/C][/ROW]
[ROW][C]12[/C][C]1.3179[/C][C]1.35587[/C][C]1.3463[/C][C]1.00711[/C][C]0.971997[/C][/ROW]
[ROW][C]13[/C][C]1.2905[/C][C]1.32237[/C][C]1.33031[/C][C]0.994036[/C][C]0.975896[/C][/ROW]
[ROW][C]14[/C][C]1.3224[/C][C]1.31652[/C][C]1.31398[/C][C]1.00193[/C][C]1.00447[/C][/ROW]
[ROW][C]15[/C][C]1.3201[/C][C]1.29005[/C][C]1.30207[/C][C]0.990767[/C][C]1.02329[/C][/ROW]
[ROW][C]16[/C][C]1.3162[/C][C]1.28733[/C][C]1.29522[/C][C]0.993911[/C][C]1.02243[/C][/ROW]
[ROW][C]17[/C][C]1.2789[/C][C]1.28536[/C][C]1.28913[/C][C]0.997071[/C][C]0.994976[/C][/ROW]
[ROW][C]18[/C][C]1.2526[/C][C]1.28478[/C][C]1.28585[/C][C]0.99917[/C][C]0.974951[/C][/ROW]
[ROW][C]19[/C][C]1.2288[/C][C]1.28534[/C][C]1.2872[/C][C]0.998556[/C][C]0.956014[/C][/ROW]
[ROW][C]20[/C][C]1.24[/C][C]1.29647[/C][C]1.28935[/C][C]1.00552[/C][C]0.956441[/C][/ROW]
[ROW][C]21[/C][C]1.2856[/C][C]1.29039[/C][C]1.28893[/C][C]1.00113[/C][C]0.996291[/C][/ROW]
[ROW][C]22[/C][C]1.2974[/C][C]1.29857[/C][C]1.28738[/C][C]1.00869[/C][C]0.999101[/C][/ROW]
[ROW][C]23[/C][C]1.2828[/C][C]1.29032[/C][C]1.28761[/C][C]1.0021[/C][C]0.994172[/C][/ROW]
[ROW][C]24[/C][C]1.3119[/C][C]1.30036[/C][C]1.29118[/C][C]1.00711[/C][C]1.00888[/C][/ROW]
[ROW][C]25[/C][C]1.3288[/C][C]1.2895[/C][C]1.29724[/C][C]0.994036[/C][C]1.03047[/C][/ROW]
[ROW][C]26[/C][C]1.3359[/C][C]1.30685[/C][C]1.30433[/C][C]1.00193[/C][C]1.02223[/C][/ROW]
[ROW][C]27[/C][C]1.2964[/C][C]1.29808[/C][C]1.31018[/C][C]0.990767[/C][C]0.998708[/C][/ROW]
[ROW][C]28[/C][C]1.3026[/C][C]1.30697[/C][C]1.31498[/C][C]0.993911[/C][C]0.996654[/C][/ROW]
[ROW][C]29[/C][C]1.2982[/C][C]1.31664[/C][C]1.3205[/C][C]0.997071[/C][C]0.985998[/C][/ROW]
[ROW][C]30[/C][C]1.3189[/C][C]1.32461[/C][C]1.32571[/C][C]0.99917[/C][C]0.995688[/C][/ROW]
[ROW][C]31[/C][C]1.308[/C][C]1.32757[/C][C]1.32949[/C][C]0.998556[/C][C]0.985257[/C][/ROW]
[ROW][C]32[/C][C]1.331[/C][C]1.33943[/C][C]1.33208[/C][C]1.00552[/C][C]0.993704[/C][/ROW]
[ROW][C]33[/C][C]1.3348[/C][C]1.33842[/C][C]1.3369[/C][C]1.00113[/C][C]0.997299[/C][/ROW]
[ROW][C]34[/C][C]1.3635[/C][C]1.35544[/C][C]1.34376[/C][C]1.00869[/C][C]1.00595[/C][/ROW]
[ROW][C]35[/C][C]1.3493[/C][C]1.353[/C][C]1.35016[/C][C]1.0021[/C][C]0.997267[/C][/ROW]
[ROW][C]36[/C][C]1.3704[/C][C]1.36459[/C][C]1.35496[/C][C]1.00711[/C][C]1.00426[/C][/ROW]
[ROW][C]37[/C][C]1.361[/C][C]1.35045[/C][C]1.35855[/C][C]0.994036[/C][C]1.00781[/C][/ROW]
[ROW][C]38[/C][C]1.3658[/C][C]1.36312[/C][C]1.36049[/C][C]1.00193[/C][C]1.00196[/C][/ROW]
[ROW][C]39[/C][C]1.3823[/C][C]1.34611[/C][C]1.35865[/C][C]0.990767[/C][C]1.02689[/C][/ROW]
[ROW][C]40[/C][C]1.3812[/C][C]1.34455[/C][C]1.35278[/C][C]0.993911[/C][C]1.02726[/C][/ROW]
[ROW][C]41[/C][C]1.3732[/C][C]1.34058[/C][C]1.34452[/C][C]0.997071[/C][C]1.02433[/C][/ROW]
[ROW][C]42[/C][C]1.3592[/C][C]1.33344[/C][C]1.33455[/C][C]0.99917[/C][C]1.01932[/C][/ROW]
[ROW][C]43[/C][C]1.3539[/C][C]1.31863[/C][C]1.32054[/C][C]0.998556[/C][C]1.02675[/C][/ROW]
[ROW][C]44[/C][C]1.3316[/C][C]1.30983[/C][C]1.30263[/C][C]1.00552[/C][C]1.01662[/C][/ROW]
[ROW][C]45[/C][C]1.2901[/C][C]1.28203[/C][C]1.28058[/C][C]1.00113[/C][C]1.0063[/C][/ROW]
[ROW][C]46[/C][C]1.2673[/C][C]1.26642[/C][C]1.2555[/C][C]1.00869[/C][C]1.0007[/C][/ROW]
[ROW][C]47[/C][C]1.2472[/C][C]1.2347[/C][C]1.23211[/C][C]1.0021[/C][C]1.01012[/C][/ROW]
[ROW][C]48[/C][C]1.2331[/C][C]1.22005[/C][C]1.21144[/C][C]1.00711[/C][C]1.0107[/C][/ROW]
[ROW][C]49[/C][C]1.1621[/C][C]1.18383[/C][C]1.19093[/C][C]0.994036[/C][C]0.981647[/C][/ROW]
[ROW][C]50[/C][C]1.135[/C][C]1.17353[/C][C]1.17126[/C][C]1.00193[/C][C]0.96717[/C][/ROW]
[ROW][C]51[/C][C]1.0838[/C][C]1.14453[/C][C]1.15519[/C][C]0.990767[/C][C]0.946943[/C][/ROW]
[ROW][C]52[/C][C]1.0779[/C][C]1.13525[/C][C]1.1422[/C][C]0.993911[/C][C]0.949486[/C][/ROW]
[ROW][C]53[/C][C]1.115[/C][C]1.12567[/C][C]1.12898[/C][C]0.997071[/C][C]0.990523[/C][/ROW]
[ROW][C]54[/C][C]1.1213[/C][C]1.11476[/C][C]1.11568[/C][C]0.99917[/C][C]1.00587[/C][/ROW]
[ROW][C]55[/C][C]1.0996[/C][C]NA[/C][C]NA[/C][C]0.998556[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]1.1139[/C][C]NA[/C][C]NA[/C][C]1.00552[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]1.1221[/C][C]NA[/C][C]NA[/C][C]1.00113[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]1.1235[/C][C]NA[/C][C]NA[/C][C]1.00869[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]1.0736[/C][C]NA[/C][C]NA[/C][C]1.0021[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]1.0877[/C][C]NA[/C][C]NA[/C][C]1.00711[/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	1.336	NA	NA	0.994036	NA
2	1.3649	NA	NA	1.00193	NA
3	1.3999	NA	NA	0.990767	NA
4	1.4442	NA	NA	0.993911	NA
5	1.4349	NA	NA	0.997071	NA
6	1.4388	NA	NA	0.99917	NA
7	1.4264	1.38781	1.38981	0.998556	1.02781
8	1.4343	1.3938	1.38615	1.00552	1.02906
9	1.377	1.38261	1.38105	1.00113	0.995941
10	1.3706	1.38432	1.37239	1.00869	0.990086
11	1.3556	1.36342	1.36056	1.0021	0.994265
12	1.3179	1.35587	1.3463	1.00711	0.971997
13	1.2905	1.32237	1.33031	0.994036	0.975896
14	1.3224	1.31652	1.31398	1.00193	1.00447
15	1.3201	1.29005	1.30207	0.990767	1.02329
16	1.3162	1.28733	1.29522	0.993911	1.02243
17	1.2789	1.28536	1.28913	0.997071	0.994976
18	1.2526	1.28478	1.28585	0.99917	0.974951
19	1.2288	1.28534	1.2872	0.998556	0.956014
20	1.24	1.29647	1.28935	1.00552	0.956441
21	1.2856	1.29039	1.28893	1.00113	0.996291
22	1.2974	1.29857	1.28738	1.00869	0.999101
23	1.2828	1.29032	1.28761	1.0021	0.994172
24	1.3119	1.30036	1.29118	1.00711	1.00888
25	1.3288	1.2895	1.29724	0.994036	1.03047
26	1.3359	1.30685	1.30433	1.00193	1.02223
27	1.2964	1.29808	1.31018	0.990767	0.998708
28	1.3026	1.30697	1.31498	0.993911	0.996654
29	1.2982	1.31664	1.3205	0.997071	0.985998
30	1.3189	1.32461	1.32571	0.99917	0.995688
31	1.308	1.32757	1.32949	0.998556	0.985257
32	1.331	1.33943	1.33208	1.00552	0.993704
33	1.3348	1.33842	1.3369	1.00113	0.997299
34	1.3635	1.35544	1.34376	1.00869	1.00595
35	1.3493	1.353	1.35016	1.0021	0.997267
36	1.3704	1.36459	1.35496	1.00711	1.00426
37	1.361	1.35045	1.35855	0.994036	1.00781
38	1.3658	1.36312	1.36049	1.00193	1.00196
39	1.3823	1.34611	1.35865	0.990767	1.02689
40	1.3812	1.34455	1.35278	0.993911	1.02726
41	1.3732	1.34058	1.34452	0.997071	1.02433
42	1.3592	1.33344	1.33455	0.99917	1.01932
43	1.3539	1.31863	1.32054	0.998556	1.02675
44	1.3316	1.30983	1.30263	1.00552	1.01662
45	1.2901	1.28203	1.28058	1.00113	1.0063
46	1.2673	1.26642	1.2555	1.00869	1.0007
47	1.2472	1.2347	1.23211	1.0021	1.01012
48	1.2331	1.22005	1.21144	1.00711	1.0107
49	1.1621	1.18383	1.19093	0.994036	0.981647
50	1.135	1.17353	1.17126	1.00193	0.96717
51	1.0838	1.14453	1.15519	0.990767	0.946943
52	1.0779	1.13525	1.1422	0.993911	0.949486
53	1.115	1.12567	1.12898	0.997071	0.990523
54	1.1213	1.11476	1.11568	0.99917	1.00587
55	1.0996	NA	NA	0.998556	NA
56	1.1139	NA	NA	1.00552	NA
57	1.1221	NA	NA	1.00113	NA
58	1.1235	NA	NA	1.00869	NA
59	1.0736	NA	NA	1.0021	NA
60	1.0877	NA	NA	1.00711	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