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*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Sun, 25 May 2008 09:05:44 -0600

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/2008/May/25/t12117280004u39u4epb1fva69.htm/, Retrieved Wed, 15 May 2024 15:45:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=13162, Retrieved Wed, 15 May 2024 15:45:29 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

159

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

-       [Classical Decomposition] [Multiplicatief mo...] [2008-05-25 15:05:44] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

1,43
1,43
1,43
1,43
1,43
1,43
1,43
1,43
1,44
1,47
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,48
1,57
1,58
1,58
1,58
1,58
1,59
1,6
1,6
1,6
1,6
1,61
1,61
1,62
1,63
1,63
1,63
1,63
1,63
1,64
1,64
1,64
1,65
1,65

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13162&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13162&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13162&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	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1.43	NA	NA	0.996178632348646	NA
2	1.43	NA	NA	0.993432222712521	NA
3	1.43	NA	NA	0.990716579490285	NA
4	1.43	NA	NA	1.00777262243222	NA
5	1.43	NA	NA	1.00762954729244	NA
6	1.43	NA	NA	1.00543719316130	NA
7	1.43	1.44931124704778	1.44458333333333	1.00327285633535	0.986675569456103
8	1.43	1.45026764719836	1.44875	1.00104755630603	0.986024891862196
9	1.44	1.45417460650757	1.45291666666667	1.00086580315978	0.990252472815758
10	1.47	1.45787377995512	1.45708333333333	1.00054248552825	1.00831774342306
11	1.48	1.45818878106801	1.46125	0.997905068310017	1.01495774704563
12	1.48	1.45838183566280	1.46541666666667	0.995199432923153	1.01482339110962
13	1.48	1.46396751512236	1.46958333333333	0.996178632348646	1.01095139387454
14	1.48	1.46407073822258	1.47375	0.993432222712521	1.01088011758008
15	1.48	1.46378374619690	1.4775	0.990716579490285	1.01107831252071
16	1.48	1.49108357594034	1.47958333333333	1.00777262243222	0.992566764117595
17	1.48	1.49129172999281	1.48	1.00762954729244	0.992428221946307
18	1.48	1.48804704587873	1.48	1.00543719316130	0.994592210037301
19	1.48	1.48484382737632	1.48	1.00327285633535	0.996737820310114
20	1.48	1.48155038333292	1.48	1.00104755630603	0.99895353991983
21	1.48	1.48128138867648	1.48	1.00086580315978	0.999134945806872
22	1.48	1.48080287858181	1.48	1.00054248552825	0.999457808602735
23	1.48	1.47689950109883	1.48	0.997905068310017	1.00209932964218
24	1.48	1.47289516072627	1.48	0.995199432923153	1.00482372368596
25	1.48	1.47434437587600	1.48	0.996178632348646	1.00383602651900
26	1.48	1.47027968961453	1.48	0.993432222712521	1.00661119816463
27	1.48	1.46626053764562	1.48	0.990716579490285	1.00937040996578
28	1.48	1.49150348119969	1.48	1.00777262243222	0.992287325276346
29	1.48	1.49129172999281	1.48	1.00762954729244	0.992428221946307
30	1.48	1.48804704587873	1.48	1.00543719316130	0.994592210037301
31	1.48	1.48484382737632	1.48	1.00327285633535	0.996737820310114
32	1.48	1.48155038333292	1.48	1.00104755630603	0.99895353991983
33	1.48	1.48128138867648	1.48	1.00086580315978	0.999134945806872
34	1.48	1.48455491290254	1.48375	1.00054248552825	0.996931798976949
35	1.48	1.48854172689578	1.49166666666667	0.997905068310017	0.994261681253915
36	1.48	1.49279914938473	1.5	0.995199432923153	0.991426074036816
37	1.48	1.50256943712587	1.50833333333333	0.996178632348646	0.984979438175553
38	1.48	1.50670553778066	1.51666666666667	0.993432222712521	0.982275542824384
39	1.48	1.51125558229747	1.52541666666667	0.990716579490285	0.97931813608261
40	1.57	1.54693097543346	1.535	1.00777262243222	1.01491276917516
41	1.58	1.55678765056682	1.545	1.00762954729244	1.01491041467648
42	1.58	1.56345483536582	1.555	1.00543719316130	1.01058243849449
43	1.58	1.57012202016483	1.565	1.00327285633535	1.00629121794887
44	1.58	1.57706700433045	1.57541666666667	1.00104755630603	1.00185977872944
45	1.59	1.58762338026220	1.58625	1.00086580315978	1.00149696695535
46	1.6	1.59461458631065	1.59375	1.00054248552825	1.00337725098941
47	1.6	1.59456914040371	1.59791666666667	0.997905068310017	1.00340584767300
48	1.6	1.59439242482897	1.60208333333333	0.995199432923153	1.00351706084632
49	1.6	1.60011192821001	1.60625	0.996178632348646	0.99993004976212
50	1.61	1.59983980865996	1.61041666666667	0.993432222712521	1.00635075542254
51	1.61	1.59918167872723	1.61416666666667	0.990716579490285	1.00676491071444
52	1.62	1.63007221678412	1.6175	1.00777262243222	0.993820999658535
53	1.63	1.63319955790317	1.62083333333333	1.00762954729244	0.998040926543432
54	1.63	1.63299757455948	1.62416666666667	1.00543719316130	0.998164372926096
55	1.63	1.63324460404259	1.62791666666667	1.00327285633535	0.998013399808847
56	1.63	1.63337592937267	1.63166666666667	1.00104755630603	0.997933158367307
57	1.63	NA	NA	1.00086580315978	NA
58	1.64	NA	NA	1.00054248552825	NA
59	1.64	NA	NA	0.997905068310017	NA
60	1.64	NA	NA	0.995199432923153	NA
61	1.65	NA	NA	NA	NA
62	1.65	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1.43 & NA & NA & 0.996178632348646 & NA \tabularnewline
2 & 1.43 & NA & NA & 0.993432222712521 & NA \tabularnewline
3 & 1.43 & NA & NA & 0.990716579490285 & NA \tabularnewline
4 & 1.43 & NA & NA & 1.00777262243222 & NA \tabularnewline
5 & 1.43 & NA & NA & 1.00762954729244 & NA \tabularnewline
6 & 1.43 & NA & NA & 1.00543719316130 & NA \tabularnewline
7 & 1.43 & 1.44931124704778 & 1.44458333333333 & 1.00327285633535 & 0.986675569456103 \tabularnewline
8 & 1.43 & 1.45026764719836 & 1.44875 & 1.00104755630603 & 0.986024891862196 \tabularnewline
9 & 1.44 & 1.45417460650757 & 1.45291666666667 & 1.00086580315978 & 0.990252472815758 \tabularnewline
10 & 1.47 & 1.45787377995512 & 1.45708333333333 & 1.00054248552825 & 1.00831774342306 \tabularnewline
11 & 1.48 & 1.45818878106801 & 1.46125 & 0.997905068310017 & 1.01495774704563 \tabularnewline
12 & 1.48 & 1.45838183566280 & 1.46541666666667 & 0.995199432923153 & 1.01482339110962 \tabularnewline
13 & 1.48 & 1.46396751512236 & 1.46958333333333 & 0.996178632348646 & 1.01095139387454 \tabularnewline
14 & 1.48 & 1.46407073822258 & 1.47375 & 0.993432222712521 & 1.01088011758008 \tabularnewline
15 & 1.48 & 1.46378374619690 & 1.4775 & 0.990716579490285 & 1.01107831252071 \tabularnewline
16 & 1.48 & 1.49108357594034 & 1.47958333333333 & 1.00777262243222 & 0.992566764117595 \tabularnewline
17 & 1.48 & 1.49129172999281 & 1.48 & 1.00762954729244 & 0.992428221946307 \tabularnewline
18 & 1.48 & 1.48804704587873 & 1.48 & 1.00543719316130 & 0.994592210037301 \tabularnewline
19 & 1.48 & 1.48484382737632 & 1.48 & 1.00327285633535 & 0.996737820310114 \tabularnewline
20 & 1.48 & 1.48155038333292 & 1.48 & 1.00104755630603 & 0.99895353991983 \tabularnewline
21 & 1.48 & 1.48128138867648 & 1.48 & 1.00086580315978 & 0.999134945806872 \tabularnewline
22 & 1.48 & 1.48080287858181 & 1.48 & 1.00054248552825 & 0.999457808602735 \tabularnewline
23 & 1.48 & 1.47689950109883 & 1.48 & 0.997905068310017 & 1.00209932964218 \tabularnewline
24 & 1.48 & 1.47289516072627 & 1.48 & 0.995199432923153 & 1.00482372368596 \tabularnewline
25 & 1.48 & 1.47434437587600 & 1.48 & 0.996178632348646 & 1.00383602651900 \tabularnewline
26 & 1.48 & 1.47027968961453 & 1.48 & 0.993432222712521 & 1.00661119816463 \tabularnewline
27 & 1.48 & 1.46626053764562 & 1.48 & 0.990716579490285 & 1.00937040996578 \tabularnewline
28 & 1.48 & 1.49150348119969 & 1.48 & 1.00777262243222 & 0.992287325276346 \tabularnewline
29 & 1.48 & 1.49129172999281 & 1.48 & 1.00762954729244 & 0.992428221946307 \tabularnewline
30 & 1.48 & 1.48804704587873 & 1.48 & 1.00543719316130 & 0.994592210037301 \tabularnewline
31 & 1.48 & 1.48484382737632 & 1.48 & 1.00327285633535 & 0.996737820310114 \tabularnewline
32 & 1.48 & 1.48155038333292 & 1.48 & 1.00104755630603 & 0.99895353991983 \tabularnewline
33 & 1.48 & 1.48128138867648 & 1.48 & 1.00086580315978 & 0.999134945806872 \tabularnewline
34 & 1.48 & 1.48455491290254 & 1.48375 & 1.00054248552825 & 0.996931798976949 \tabularnewline
35 & 1.48 & 1.48854172689578 & 1.49166666666667 & 0.997905068310017 & 0.994261681253915 \tabularnewline
36 & 1.48 & 1.49279914938473 & 1.5 & 0.995199432923153 & 0.991426074036816 \tabularnewline
37 & 1.48 & 1.50256943712587 & 1.50833333333333 & 0.996178632348646 & 0.984979438175553 \tabularnewline
38 & 1.48 & 1.50670553778066 & 1.51666666666667 & 0.993432222712521 & 0.982275542824384 \tabularnewline
39 & 1.48 & 1.51125558229747 & 1.52541666666667 & 0.990716579490285 & 0.97931813608261 \tabularnewline
40 & 1.57 & 1.54693097543346 & 1.535 & 1.00777262243222 & 1.01491276917516 \tabularnewline
41 & 1.58 & 1.55678765056682 & 1.545 & 1.00762954729244 & 1.01491041467648 \tabularnewline
42 & 1.58 & 1.56345483536582 & 1.555 & 1.00543719316130 & 1.01058243849449 \tabularnewline
43 & 1.58 & 1.57012202016483 & 1.565 & 1.00327285633535 & 1.00629121794887 \tabularnewline
44 & 1.58 & 1.57706700433045 & 1.57541666666667 & 1.00104755630603 & 1.00185977872944 \tabularnewline
45 & 1.59 & 1.58762338026220 & 1.58625 & 1.00086580315978 & 1.00149696695535 \tabularnewline
46 & 1.6 & 1.59461458631065 & 1.59375 & 1.00054248552825 & 1.00337725098941 \tabularnewline
47 & 1.6 & 1.59456914040371 & 1.59791666666667 & 0.997905068310017 & 1.00340584767300 \tabularnewline
48 & 1.6 & 1.59439242482897 & 1.60208333333333 & 0.995199432923153 & 1.00351706084632 \tabularnewline
49 & 1.6 & 1.60011192821001 & 1.60625 & 0.996178632348646 & 0.99993004976212 \tabularnewline
50 & 1.61 & 1.59983980865996 & 1.61041666666667 & 0.993432222712521 & 1.00635075542254 \tabularnewline
51 & 1.61 & 1.59918167872723 & 1.61416666666667 & 0.990716579490285 & 1.00676491071444 \tabularnewline
52 & 1.62 & 1.63007221678412 & 1.6175 & 1.00777262243222 & 0.993820999658535 \tabularnewline
53 & 1.63 & 1.63319955790317 & 1.62083333333333 & 1.00762954729244 & 0.998040926543432 \tabularnewline
54 & 1.63 & 1.63299757455948 & 1.62416666666667 & 1.00543719316130 & 0.998164372926096 \tabularnewline
55 & 1.63 & 1.63324460404259 & 1.62791666666667 & 1.00327285633535 & 0.998013399808847 \tabularnewline
56 & 1.63 & 1.63337592937267 & 1.63166666666667 & 1.00104755630603 & 0.997933158367307 \tabularnewline
57 & 1.63 & NA & NA & 1.00086580315978 & NA \tabularnewline
58 & 1.64 & NA & NA & 1.00054248552825 & NA \tabularnewline
59 & 1.64 & NA & NA & 0.997905068310017 & NA \tabularnewline
60 & 1.64 & NA & NA & 0.995199432923153 & NA \tabularnewline
61 & 1.65 & NA & NA & NA & NA \tabularnewline
62 & 1.65 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13162&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]1.43[/C][C]NA[/C][C]NA[/C][C]0.996178632348646[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1.43[/C][C]NA[/C][C]NA[/C][C]0.993432222712521[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.43[/C][C]NA[/C][C]NA[/C][C]0.990716579490285[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]NA[/C][C]NA[/C][C]1.00777262243222[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1.43[/C][C]NA[/C][C]NA[/C][C]1.00762954729244[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]NA[/C][C]NA[/C][C]1.00543719316130[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.43[/C][C]1.44931124704778[/C][C]1.44458333333333[/C][C]1.00327285633535[/C][C]0.986675569456103[/C][/ROW]
[ROW][C]8[/C][C]1.43[/C][C]1.45026764719836[/C][C]1.44875[/C][C]1.00104755630603[/C][C]0.986024891862196[/C][/ROW]
[ROW][C]9[/C][C]1.44[/C][C]1.45417460650757[/C][C]1.45291666666667[/C][C]1.00086580315978[/C][C]0.990252472815758[/C][/ROW]
[ROW][C]10[/C][C]1.47[/C][C]1.45787377995512[/C][C]1.45708333333333[/C][C]1.00054248552825[/C][C]1.00831774342306[/C][/ROW]
[ROW][C]11[/C][C]1.48[/C][C]1.45818878106801[/C][C]1.46125[/C][C]0.997905068310017[/C][C]1.01495774704563[/C][/ROW]
[ROW][C]12[/C][C]1.48[/C][C]1.45838183566280[/C][C]1.46541666666667[/C][C]0.995199432923153[/C][C]1.01482339110962[/C][/ROW]
[ROW][C]13[/C][C]1.48[/C][C]1.46396751512236[/C][C]1.46958333333333[/C][C]0.996178632348646[/C][C]1.01095139387454[/C][/ROW]
[ROW][C]14[/C][C]1.48[/C][C]1.46407073822258[/C][C]1.47375[/C][C]0.993432222712521[/C][C]1.01088011758008[/C][/ROW]
[ROW][C]15[/C][C]1.48[/C][C]1.46378374619690[/C][C]1.4775[/C][C]0.990716579490285[/C][C]1.01107831252071[/C][/ROW]
[ROW][C]16[/C][C]1.48[/C][C]1.49108357594034[/C][C]1.47958333333333[/C][C]1.00777262243222[/C][C]0.992566764117595[/C][/ROW]
[ROW][C]17[/C][C]1.48[/C][C]1.49129172999281[/C][C]1.48[/C][C]1.00762954729244[/C][C]0.992428221946307[/C][/ROW]
[ROW][C]18[/C][C]1.48[/C][C]1.48804704587873[/C][C]1.48[/C][C]1.00543719316130[/C][C]0.994592210037301[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.48484382737632[/C][C]1.48[/C][C]1.00327285633535[/C][C]0.996737820310114[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.48155038333292[/C][C]1.48[/C][C]1.00104755630603[/C][C]0.99895353991983[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.48128138867648[/C][C]1.48[/C][C]1.00086580315978[/C][C]0.999134945806872[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.48080287858181[/C][C]1.48[/C][C]1.00054248552825[/C][C]0.999457808602735[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.47689950109883[/C][C]1.48[/C][C]0.997905068310017[/C][C]1.00209932964218[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.47289516072627[/C][C]1.48[/C][C]0.995199432923153[/C][C]1.00482372368596[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.47434437587600[/C][C]1.48[/C][C]0.996178632348646[/C][C]1.00383602651900[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.47027968961453[/C][C]1.48[/C][C]0.993432222712521[/C][C]1.00661119816463[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.46626053764562[/C][C]1.48[/C][C]0.990716579490285[/C][C]1.00937040996578[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.49150348119969[/C][C]1.48[/C][C]1.00777262243222[/C][C]0.992287325276346[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.49129172999281[/C][C]1.48[/C][C]1.00762954729244[/C][C]0.992428221946307[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.48804704587873[/C][C]1.48[/C][C]1.00543719316130[/C][C]0.994592210037301[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.48484382737632[/C][C]1.48[/C][C]1.00327285633535[/C][C]0.996737820310114[/C][/ROW]
[ROW][C]32[/C][C]1.48[/C][C]1.48155038333292[/C][C]1.48[/C][C]1.00104755630603[/C][C]0.99895353991983[/C][/ROW]
[ROW][C]33[/C][C]1.48[/C][C]1.48128138867648[/C][C]1.48[/C][C]1.00086580315978[/C][C]0.999134945806872[/C][/ROW]
[ROW][C]34[/C][C]1.48[/C][C]1.48455491290254[/C][C]1.48375[/C][C]1.00054248552825[/C][C]0.996931798976949[/C][/ROW]
[ROW][C]35[/C][C]1.48[/C][C]1.48854172689578[/C][C]1.49166666666667[/C][C]0.997905068310017[/C][C]0.994261681253915[/C][/ROW]
[ROW][C]36[/C][C]1.48[/C][C]1.49279914938473[/C][C]1.5[/C][C]0.995199432923153[/C][C]0.991426074036816[/C][/ROW]
[ROW][C]37[/C][C]1.48[/C][C]1.50256943712587[/C][C]1.50833333333333[/C][C]0.996178632348646[/C][C]0.984979438175553[/C][/ROW]
[ROW][C]38[/C][C]1.48[/C][C]1.50670553778066[/C][C]1.51666666666667[/C][C]0.993432222712521[/C][C]0.982275542824384[/C][/ROW]
[ROW][C]39[/C][C]1.48[/C][C]1.51125558229747[/C][C]1.52541666666667[/C][C]0.990716579490285[/C][C]0.97931813608261[/C][/ROW]
[ROW][C]40[/C][C]1.57[/C][C]1.54693097543346[/C][C]1.535[/C][C]1.00777262243222[/C][C]1.01491276917516[/C][/ROW]
[ROW][C]41[/C][C]1.58[/C][C]1.55678765056682[/C][C]1.545[/C][C]1.00762954729244[/C][C]1.01491041467648[/C][/ROW]
[ROW][C]42[/C][C]1.58[/C][C]1.56345483536582[/C][C]1.555[/C][C]1.00543719316130[/C][C]1.01058243849449[/C][/ROW]
[ROW][C]43[/C][C]1.58[/C][C]1.57012202016483[/C][C]1.565[/C][C]1.00327285633535[/C][C]1.00629121794887[/C][/ROW]
[ROW][C]44[/C][C]1.58[/C][C]1.57706700433045[/C][C]1.57541666666667[/C][C]1.00104755630603[/C][C]1.00185977872944[/C][/ROW]
[ROW][C]45[/C][C]1.59[/C][C]1.58762338026220[/C][C]1.58625[/C][C]1.00086580315978[/C][C]1.00149696695535[/C][/ROW]
[ROW][C]46[/C][C]1.6[/C][C]1.59461458631065[/C][C]1.59375[/C][C]1.00054248552825[/C][C]1.00337725098941[/C][/ROW]
[ROW][C]47[/C][C]1.6[/C][C]1.59456914040371[/C][C]1.59791666666667[/C][C]0.997905068310017[/C][C]1.00340584767300[/C][/ROW]
[ROW][C]48[/C][C]1.6[/C][C]1.59439242482897[/C][C]1.60208333333333[/C][C]0.995199432923153[/C][C]1.00351706084632[/C][/ROW]
[ROW][C]49[/C][C]1.6[/C][C]1.60011192821001[/C][C]1.60625[/C][C]0.996178632348646[/C][C]0.99993004976212[/C][/ROW]
[ROW][C]50[/C][C]1.61[/C][C]1.59983980865996[/C][C]1.61041666666667[/C][C]0.993432222712521[/C][C]1.00635075542254[/C][/ROW]
[ROW][C]51[/C][C]1.61[/C][C]1.59918167872723[/C][C]1.61416666666667[/C][C]0.990716579490285[/C][C]1.00676491071444[/C][/ROW]
[ROW][C]52[/C][C]1.62[/C][C]1.63007221678412[/C][C]1.6175[/C][C]1.00777262243222[/C][C]0.993820999658535[/C][/ROW]
[ROW][C]53[/C][C]1.63[/C][C]1.63319955790317[/C][C]1.62083333333333[/C][C]1.00762954729244[/C][C]0.998040926543432[/C][/ROW]
[ROW][C]54[/C][C]1.63[/C][C]1.63299757455948[/C][C]1.62416666666667[/C][C]1.00543719316130[/C][C]0.998164372926096[/C][/ROW]
[ROW][C]55[/C][C]1.63[/C][C]1.63324460404259[/C][C]1.62791666666667[/C][C]1.00327285633535[/C][C]0.998013399808847[/C][/ROW]
[ROW][C]56[/C][C]1.63[/C][C]1.63337592937267[/C][C]1.63166666666667[/C][C]1.00104755630603[/C][C]0.997933158367307[/C][/ROW]
[ROW][C]57[/C][C]1.63[/C][C]NA[/C][C]NA[/C][C]1.00086580315978[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]1.64[/C][C]NA[/C][C]NA[/C][C]1.00054248552825[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]1.64[/C][C]NA[/C][C]NA[/C][C]0.997905068310017[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]1.64[/C][C]NA[/C][C]NA[/C][C]0.995199432923153[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]1.65[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]62[/C][C]1.65[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13162&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13162&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	1.43	NA	NA	0.996178632348646	NA
2	1.43	NA	NA	0.993432222712521	NA
3	1.43	NA	NA	0.990716579490285	NA
4	1.43	NA	NA	1.00777262243222	NA
5	1.43	NA	NA	1.00762954729244	NA
6	1.43	NA	NA	1.00543719316130	NA
7	1.43	1.44931124704778	1.44458333333333	1.00327285633535	0.986675569456103
8	1.43	1.45026764719836	1.44875	1.00104755630603	0.986024891862196
9	1.44	1.45417460650757	1.45291666666667	1.00086580315978	0.990252472815758
10	1.47	1.45787377995512	1.45708333333333	1.00054248552825	1.00831774342306
11	1.48	1.45818878106801	1.46125	0.997905068310017	1.01495774704563
12	1.48	1.45838183566280	1.46541666666667	0.995199432923153	1.01482339110962
13	1.48	1.46396751512236	1.46958333333333	0.996178632348646	1.01095139387454
14	1.48	1.46407073822258	1.47375	0.993432222712521	1.01088011758008
15	1.48	1.46378374619690	1.4775	0.990716579490285	1.01107831252071
16	1.48	1.49108357594034	1.47958333333333	1.00777262243222	0.992566764117595
17	1.48	1.49129172999281	1.48	1.00762954729244	0.992428221946307
18	1.48	1.48804704587873	1.48	1.00543719316130	0.994592210037301
19	1.48	1.48484382737632	1.48	1.00327285633535	0.996737820310114
20	1.48	1.48155038333292	1.48	1.00104755630603	0.99895353991983
21	1.48	1.48128138867648	1.48	1.00086580315978	0.999134945806872
22	1.48	1.48080287858181	1.48	1.00054248552825	0.999457808602735
23	1.48	1.47689950109883	1.48	0.997905068310017	1.00209932964218
24	1.48	1.47289516072627	1.48	0.995199432923153	1.00482372368596
25	1.48	1.47434437587600	1.48	0.996178632348646	1.00383602651900
26	1.48	1.47027968961453	1.48	0.993432222712521	1.00661119816463
27	1.48	1.46626053764562	1.48	0.990716579490285	1.00937040996578
28	1.48	1.49150348119969	1.48	1.00777262243222	0.992287325276346
29	1.48	1.49129172999281	1.48	1.00762954729244	0.992428221946307
30	1.48	1.48804704587873	1.48	1.00543719316130	0.994592210037301
31	1.48	1.48484382737632	1.48	1.00327285633535	0.996737820310114
32	1.48	1.48155038333292	1.48	1.00104755630603	0.99895353991983
33	1.48	1.48128138867648	1.48	1.00086580315978	0.999134945806872
34	1.48	1.48455491290254	1.48375	1.00054248552825	0.996931798976949
35	1.48	1.48854172689578	1.49166666666667	0.997905068310017	0.994261681253915
36	1.48	1.49279914938473	1.5	0.995199432923153	0.991426074036816
37	1.48	1.50256943712587	1.50833333333333	0.996178632348646	0.984979438175553
38	1.48	1.50670553778066	1.51666666666667	0.993432222712521	0.982275542824384
39	1.48	1.51125558229747	1.52541666666667	0.990716579490285	0.97931813608261
40	1.57	1.54693097543346	1.535	1.00777262243222	1.01491276917516
41	1.58	1.55678765056682	1.545	1.00762954729244	1.01491041467648
42	1.58	1.56345483536582	1.555	1.00543719316130	1.01058243849449
43	1.58	1.57012202016483	1.565	1.00327285633535	1.00629121794887
44	1.58	1.57706700433045	1.57541666666667	1.00104755630603	1.00185977872944
45	1.59	1.58762338026220	1.58625	1.00086580315978	1.00149696695535
46	1.6	1.59461458631065	1.59375	1.00054248552825	1.00337725098941
47	1.6	1.59456914040371	1.59791666666667	0.997905068310017	1.00340584767300
48	1.6	1.59439242482897	1.60208333333333	0.995199432923153	1.00351706084632
49	1.6	1.60011192821001	1.60625	0.996178632348646	0.99993004976212
50	1.61	1.59983980865996	1.61041666666667	0.993432222712521	1.00635075542254
51	1.61	1.59918167872723	1.61416666666667	0.990716579490285	1.00676491071444
52	1.62	1.63007221678412	1.6175	1.00777262243222	0.993820999658535
53	1.63	1.63319955790317	1.62083333333333	1.00762954729244	0.998040926543432
54	1.63	1.63299757455948	1.62416666666667	1.00543719316130	0.998164372926096
55	1.63	1.63324460404259	1.62791666666667	1.00327285633535	0.998013399808847
56	1.63	1.63337592937267	1.63166666666667	1.00104755630603	0.997933158367307
57	1.63	NA	NA	1.00086580315978	NA
58	1.64	NA	NA	1.00054248552825	NA
59	1.64	NA	NA	0.997905068310017	NA
60	1.64	NA	NA	0.995199432923153	NA
61	1.65	NA	NA	NA	NA
62	1.65	NA	NA	NA	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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
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