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R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Tue, 01 Dec 2009 12:51:41 -0700

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/2009/Dec/01/t1259697173t95vtrqdrr5aqcr.htm/, Retrieved Fri, 26 Apr 2024 16:23:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62223, Retrieved Fri, 26 Apr 2024 16:23:00 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

141

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Decomposition by Loess] [] [2009-11-27 15:00:29] [b98453cac15ba1066b407e146608df68]
-    D      [Decomposition by Loess] [Seizoenale decomp...] [2009-12-01 19:51:41] [6c304092df7982e5e12293b2743450a3] [Current]
-    D        [Decomposition by Loess] [Ad hoc forecasting] [2009-12-04 16:14:42] [34d27ebe78dc2d31581e8710befe8733] 
-   P           [Decomposition by Loess] [loess techniek] [2009-12-16 22:56:12] [34d27ebe78dc2d31581e8710befe8733] 
-    D        [Decomposition by Loess] [seizoenale decomp...] [2009-12-04 19:15:18] [4f1a20f787b3465111b61213cdeef1a9] 
-    D          [Decomposition by Loess] [Seizoenale decomp...] [2009-12-11 15:57:17] [4f1a20f787b3465111b61213cdeef1a9] 
-    D            [Decomposition by Loess] [Seizoenale decomp...] [2009-12-11 16:44:09] [4f1a20f787b3465111b61213cdeef1a9] 

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Dataseries X:

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Histogram

Boxplots

8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.4
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.5
8.2
8.1
7.9
8.6
8.7
8.7
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8
8.2
8.1
8.1
8
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.4
6.1
6.5
7.7
7.9
7.5
6.9
6.6
6.9
7.7
8
8
7.7
7.3
7.4
8.1
8.3
8.2

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	'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62223&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62223&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62223&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	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	731	0	74
Trend	19	1	2
Low-pass	13	1	2

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 731 & 0 & 74 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62223&T=1

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Parameters[/C][/ROW]
[ROW][C]Component[/C][C]Window[/C][C]Degree[/C][C]Jump[/C][/ROW]
[ROW][C]Seasonal[/C][C]731[/C][C]0[/C][C]74[/C][/ROW]
[ROW][C]Trend[/C][C]19[/C][C]1[/C][C]2[/C][/ROW]
[ROW][C]Low-pass[/C][C]13[/C][C]1[/C][C]2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62223&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62223&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	731	0	74
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.4	8.21824182560907	0.203999433653392	8.37775874073754	-0.181758174390929
2	8.4	8.45631184937522	-0.0265008185498972	8.37018896917468	0.0563118493752199
3	8.4	8.62568943959978	-0.188308637211597	8.36261919761182	0.225689439599780
4	8.6	8.87511299426585	-0.0368314319854665	8.36171843771962	0.275112994265847
5	8.9	9.19120332686493	0.247978995307642	8.36081767782743	0.291203326864933
6	8.8	8.96404324431652	0.268549128065039	8.36740762761844	0.164043244316522
7	8.3	8.1368830403866	0.089119382203946	8.37399757740946	-0.163116959613401
8	7.5	6.8234800387219	-0.203390058843816	8.37991002012191	-0.676519961278096
9	7.2	6.47674389085377	-0.462566353688139	8.38582246283437	-0.723256109146228
10	7.4	6.92438481993173	-0.506455823511046	8.38207100357931	-0.475615180068266
11	8.8	8.97202585521855	0.249654600457193	8.37831954432426	0.172025855218552
12	9.3	9.84355417940868	0.364751801760567	8.39169401883075	0.543554179408682
13	9.3	9.99093207300936	0.203999433653392	8.40506849333724	0.690932073009364
14	8.7	8.98882426222657	-0.0265008185498972	8.43767655632332	0.288824262226575
15	8.2	8.1180240179022	-0.188308637211597	8.4702846193094	-0.0819759820978021
16	8.3	8.15801382399703	-0.0368314319854665	8.47881760798844	-0.141986176002971
17	8.5	8.26467040802488	0.247978995307642	8.48735059666748	-0.235329591975121
18	8.6	8.46881576469307	0.268549128065039	8.4626351072419	-0.131184235306932
19	8.5	8.47296099997975	0.089119382203946	8.4379196178163	-0.0270390000202543
20	8.2	8.17663117457046	-0.203390058843816	8.42675888427336	-0.023368825429543
21	8.1	8.24696820295773	-0.462566353688139	8.4155981507304	0.146968202957732
22	7.9	7.87883272649662	-0.506455823511046	8.42762309701443	-0.0211672735033819
23	8.6	8.51069735624436	0.249654600457193	8.43964804329845	-0.0893026437556426
24	8.7	8.58252684297707	0.364751801760567	8.45272135526236	-0.117473157022927
25	8.7	8.73020589912034	0.203999433653392	8.46579466722627	0.0302058991203413
26	8.5	8.54682133700419	-0.0265008185498972	8.4796794815457	0.0468213370041912
27	8.4	8.49474434134645	-0.188308637211597	8.49356429586514	0.0947443413464537
28	8.5	8.54280840639079	-0.0368314319854665	8.49402302559468	0.0428084063907921
29	8.7	8.65753924936815	0.247978995307642	8.4944817553242	-0.0424607506318484
30	8.7	8.6678200488189	0.268549128065039	8.46363082311606	-0.0321799511811030
31	8.6	8.67810072688813	0.089119382203946	8.43277989090792	0.0781007268881311
32	8.5	8.82097793432891	-0.203390058843816	8.3824121245149	0.320977934328912
33	8.3	8.73052199556626	-0.462566353688139	8.33204435812188	0.430521995566258
34	8	8.23166138088492	-0.506455823511046	8.27479444262613	0.231661380884921
35	8.2	7.93280087241244	0.249654600457193	8.21754452713037	-0.267199127587563
36	8.1	7.68074954334786	0.364751801760567	8.15449865489157	-0.419250456652139
37	8.1	7.90454778369383	0.203999433653392	8.09145278265277	-0.195452216306165
38	8	7.98872107569708	-0.0265008185498972	8.03777974285282	-0.0112789243029248
39	7.9	8.00420193415873	-0.188308637211597	7.98410670305287	0.104201934158727
40	7.9	7.89839009178393	-0.0368314319854665	7.93844134020154	-0.00160990821607143
41	8	7.85924502734215	0.247978995307642	7.8927759773502	-0.140754972657848
42	8	7.90076492499584	0.268549128065039	7.83068594693912	-0.0992350750041577
43	7.9	7.94228470126802	0.089119382203946	7.76859591652803	0.0422847012680228
44	8	8.51626069801739	-0.203390058843816	7.68712936082642	0.516260698017391
45	7.7	8.25690354856332	-0.462566353688139	7.60566280512482	0.556903548563321
46	7.2	7.38088922324254	-0.506455823511046	7.5255666002685	0.180889223242540
47	7.5	7.30487500413061	0.249654600457193	7.4454703954122	-0.195124995869389
48	7.3	6.87929194823939	0.364751801760567	7.35595625000005	-0.420708051760615
49	7	6.52955846175871	0.203999433653392	7.2664421045879	-0.470441538241293
50	7	6.85931116838803	-0.0265008185498972	7.16718965016187	-0.140688831611972
51	7	7.12037144147576	-0.188308637211597	7.06793719573584	0.120371441475759
52	7.2	7.42019738490436	-0.0368314319854665	7.01663404708111	0.220197384904356
53	7.3	7.38669010626598	0.247978995307642	6.96533089842638	0.0866901062659764
54	7.1	6.95794233289403	0.268549128065039	6.97350853904093	-0.142057667105969
55	6.8	6.52919443814058	0.089119382203946	6.98168617965548	-0.270805561859424
56	6.4	6.01218898278418	-0.203390058843816	6.99120107605964	-0.387811017215824
57	6.1	5.66185038122434	-0.462566353688139	7.0007159724638	-0.438149618775664
58	6.5	6.49315254802738	-0.506455823511046	7.01330327548367	-0.00684745197262338
59	7.7	8.12445482103927	0.249654600457193	7.02589057850354	0.424454821039268
60	7.9	8.35101750030259	0.364751801760567	7.08423069793684	0.451017500302593
61	7.5	7.65342974897646	0.203999433653392	7.14257081737014	0.153429748976465
62	6.9	6.59863518183475	-0.0265008185498972	7.22786563671515	-0.301364818165252
63	6.6	6.07514818115144	-0.188308637211597	7.31316045606016	-0.52485181884856
64	6.9	6.44807727400544	-0.0368314319854665	7.38875415798003	-0.451922725994565
65	7.7	7.68767314479245	0.247978995307642	7.4643478598999	-0.0123268552075455
66	8	8.19441757991445	0.268549128065039	7.53703329202051	0.194417579914455
67	8	8.30116189365495	0.089119382203946	7.60971872414111	0.301161893654945
68	7.7	7.91623916239845	-0.203390058843816	7.68715089644537	0.216239162398446
69	7.3	7.29798328493851	-0.462566353688139	7.76458306874963	-0.00201671506149204
70	7.4	7.46205579550579	-0.506455823511046	7.84440002800525	0.0620557955057937
71	8.1	8.02612841228193	0.249654600457193	7.92421698726088	-0.0738715877180693
72	8.3	8.2315020885679	0.364751801760567	8.00374610967154	-0.0684979114321083
73	8.2	8.1127253342644	0.203999433653392	8.0832752320822	-0.0872746657356007

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 8.4 & 8.21824182560907 & 0.203999433653392 & 8.37775874073754 & -0.181758174390929 \tabularnewline
2 & 8.4 & 8.45631184937522 & -0.0265008185498972 & 8.37018896917468 & 0.0563118493752199 \tabularnewline
3 & 8.4 & 8.62568943959978 & -0.188308637211597 & 8.36261919761182 & 0.225689439599780 \tabularnewline
4 & 8.6 & 8.87511299426585 & -0.0368314319854665 & 8.36171843771962 & 0.275112994265847 \tabularnewline
5 & 8.9 & 9.19120332686493 & 0.247978995307642 & 8.36081767782743 & 0.291203326864933 \tabularnewline
6 & 8.8 & 8.96404324431652 & 0.268549128065039 & 8.36740762761844 & 0.164043244316522 \tabularnewline
7 & 8.3 & 8.1368830403866 & 0.089119382203946 & 8.37399757740946 & -0.163116959613401 \tabularnewline
8 & 7.5 & 6.8234800387219 & -0.203390058843816 & 8.37991002012191 & -0.676519961278096 \tabularnewline
9 & 7.2 & 6.47674389085377 & -0.462566353688139 & 8.38582246283437 & -0.723256109146228 \tabularnewline
10 & 7.4 & 6.92438481993173 & -0.506455823511046 & 8.38207100357931 & -0.475615180068266 \tabularnewline
11 & 8.8 & 8.97202585521855 & 0.249654600457193 & 8.37831954432426 & 0.172025855218552 \tabularnewline
12 & 9.3 & 9.84355417940868 & 0.364751801760567 & 8.39169401883075 & 0.543554179408682 \tabularnewline
13 & 9.3 & 9.99093207300936 & 0.203999433653392 & 8.40506849333724 & 0.690932073009364 \tabularnewline
14 & 8.7 & 8.98882426222657 & -0.0265008185498972 & 8.43767655632332 & 0.288824262226575 \tabularnewline
15 & 8.2 & 8.1180240179022 & -0.188308637211597 & 8.4702846193094 & -0.0819759820978021 \tabularnewline
16 & 8.3 & 8.15801382399703 & -0.0368314319854665 & 8.47881760798844 & -0.141986176002971 \tabularnewline
17 & 8.5 & 8.26467040802488 & 0.247978995307642 & 8.48735059666748 & -0.235329591975121 \tabularnewline
18 & 8.6 & 8.46881576469307 & 0.268549128065039 & 8.4626351072419 & -0.131184235306932 \tabularnewline
19 & 8.5 & 8.47296099997975 & 0.089119382203946 & 8.4379196178163 & -0.0270390000202543 \tabularnewline
20 & 8.2 & 8.17663117457046 & -0.203390058843816 & 8.42675888427336 & -0.023368825429543 \tabularnewline
21 & 8.1 & 8.24696820295773 & -0.462566353688139 & 8.4155981507304 & 0.146968202957732 \tabularnewline
22 & 7.9 & 7.87883272649662 & -0.506455823511046 & 8.42762309701443 & -0.0211672735033819 \tabularnewline
23 & 8.6 & 8.51069735624436 & 0.249654600457193 & 8.43964804329845 & -0.0893026437556426 \tabularnewline
24 & 8.7 & 8.58252684297707 & 0.364751801760567 & 8.45272135526236 & -0.117473157022927 \tabularnewline
25 & 8.7 & 8.73020589912034 & 0.203999433653392 & 8.46579466722627 & 0.0302058991203413 \tabularnewline
26 & 8.5 & 8.54682133700419 & -0.0265008185498972 & 8.4796794815457 & 0.0468213370041912 \tabularnewline
27 & 8.4 & 8.49474434134645 & -0.188308637211597 & 8.49356429586514 & 0.0947443413464537 \tabularnewline
28 & 8.5 & 8.54280840639079 & -0.0368314319854665 & 8.49402302559468 & 0.0428084063907921 \tabularnewline
29 & 8.7 & 8.65753924936815 & 0.247978995307642 & 8.4944817553242 & -0.0424607506318484 \tabularnewline
30 & 8.7 & 8.6678200488189 & 0.268549128065039 & 8.46363082311606 & -0.0321799511811030 \tabularnewline
31 & 8.6 & 8.67810072688813 & 0.089119382203946 & 8.43277989090792 & 0.0781007268881311 \tabularnewline
32 & 8.5 & 8.82097793432891 & -0.203390058843816 & 8.3824121245149 & 0.320977934328912 \tabularnewline
33 & 8.3 & 8.73052199556626 & -0.462566353688139 & 8.33204435812188 & 0.430521995566258 \tabularnewline
34 & 8 & 8.23166138088492 & -0.506455823511046 & 8.27479444262613 & 0.231661380884921 \tabularnewline
35 & 8.2 & 7.93280087241244 & 0.249654600457193 & 8.21754452713037 & -0.267199127587563 \tabularnewline
36 & 8.1 & 7.68074954334786 & 0.364751801760567 & 8.15449865489157 & -0.419250456652139 \tabularnewline
37 & 8.1 & 7.90454778369383 & 0.203999433653392 & 8.09145278265277 & -0.195452216306165 \tabularnewline
38 & 8 & 7.98872107569708 & -0.0265008185498972 & 8.03777974285282 & -0.0112789243029248 \tabularnewline
39 & 7.9 & 8.00420193415873 & -0.188308637211597 & 7.98410670305287 & 0.104201934158727 \tabularnewline
40 & 7.9 & 7.89839009178393 & -0.0368314319854665 & 7.93844134020154 & -0.00160990821607143 \tabularnewline
41 & 8 & 7.85924502734215 & 0.247978995307642 & 7.8927759773502 & -0.140754972657848 \tabularnewline
42 & 8 & 7.90076492499584 & 0.268549128065039 & 7.83068594693912 & -0.0992350750041577 \tabularnewline
43 & 7.9 & 7.94228470126802 & 0.089119382203946 & 7.76859591652803 & 0.0422847012680228 \tabularnewline
44 & 8 & 8.51626069801739 & -0.203390058843816 & 7.68712936082642 & 0.516260698017391 \tabularnewline
45 & 7.7 & 8.25690354856332 & -0.462566353688139 & 7.60566280512482 & 0.556903548563321 \tabularnewline
46 & 7.2 & 7.38088922324254 & -0.506455823511046 & 7.5255666002685 & 0.180889223242540 \tabularnewline
47 & 7.5 & 7.30487500413061 & 0.249654600457193 & 7.4454703954122 & -0.195124995869389 \tabularnewline
48 & 7.3 & 6.87929194823939 & 0.364751801760567 & 7.35595625000005 & -0.420708051760615 \tabularnewline
49 & 7 & 6.52955846175871 & 0.203999433653392 & 7.2664421045879 & -0.470441538241293 \tabularnewline
50 & 7 & 6.85931116838803 & -0.0265008185498972 & 7.16718965016187 & -0.140688831611972 \tabularnewline
51 & 7 & 7.12037144147576 & -0.188308637211597 & 7.06793719573584 & 0.120371441475759 \tabularnewline
52 & 7.2 & 7.42019738490436 & -0.0368314319854665 & 7.01663404708111 & 0.220197384904356 \tabularnewline
53 & 7.3 & 7.38669010626598 & 0.247978995307642 & 6.96533089842638 & 0.0866901062659764 \tabularnewline
54 & 7.1 & 6.95794233289403 & 0.268549128065039 & 6.97350853904093 & -0.142057667105969 \tabularnewline
55 & 6.8 & 6.52919443814058 & 0.089119382203946 & 6.98168617965548 & -0.270805561859424 \tabularnewline
56 & 6.4 & 6.01218898278418 & -0.203390058843816 & 6.99120107605964 & -0.387811017215824 \tabularnewline
57 & 6.1 & 5.66185038122434 & -0.462566353688139 & 7.0007159724638 & -0.438149618775664 \tabularnewline
58 & 6.5 & 6.49315254802738 & -0.506455823511046 & 7.01330327548367 & -0.00684745197262338 \tabularnewline
59 & 7.7 & 8.12445482103927 & 0.249654600457193 & 7.02589057850354 & 0.424454821039268 \tabularnewline
60 & 7.9 & 8.35101750030259 & 0.364751801760567 & 7.08423069793684 & 0.451017500302593 \tabularnewline
61 & 7.5 & 7.65342974897646 & 0.203999433653392 & 7.14257081737014 & 0.153429748976465 \tabularnewline
62 & 6.9 & 6.59863518183475 & -0.0265008185498972 & 7.22786563671515 & -0.301364818165252 \tabularnewline
63 & 6.6 & 6.07514818115144 & -0.188308637211597 & 7.31316045606016 & -0.52485181884856 \tabularnewline
64 & 6.9 & 6.44807727400544 & -0.0368314319854665 & 7.38875415798003 & -0.451922725994565 \tabularnewline
65 & 7.7 & 7.68767314479245 & 0.247978995307642 & 7.4643478598999 & -0.0123268552075455 \tabularnewline
66 & 8 & 8.19441757991445 & 0.268549128065039 & 7.53703329202051 & 0.194417579914455 \tabularnewline
67 & 8 & 8.30116189365495 & 0.089119382203946 & 7.60971872414111 & 0.301161893654945 \tabularnewline
68 & 7.7 & 7.91623916239845 & -0.203390058843816 & 7.68715089644537 & 0.216239162398446 \tabularnewline
69 & 7.3 & 7.29798328493851 & -0.462566353688139 & 7.76458306874963 & -0.00201671506149204 \tabularnewline
70 & 7.4 & 7.46205579550579 & -0.506455823511046 & 7.84440002800525 & 0.0620557955057937 \tabularnewline
71 & 8.1 & 8.02612841228193 & 0.249654600457193 & 7.92421698726088 & -0.0738715877180693 \tabularnewline
72 & 8.3 & 8.2315020885679 & 0.364751801760567 & 8.00374610967154 & -0.0684979114321083 \tabularnewline
73 & 8.2 & 8.1127253342644 & 0.203999433653392 & 8.0832752320822 & -0.0872746657356007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62223&T=2

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Time Series Components[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Seasonal[/C][C]Trend[/C][C]Remainder[/C][/ROW]
[ROW][C]1[/C][C]8.4[/C][C]8.21824182560907[/C][C]0.203999433653392[/C][C]8.37775874073754[/C][C]-0.181758174390929[/C][/ROW]
[ROW][C]2[/C][C]8.4[/C][C]8.45631184937522[/C][C]-0.0265008185498972[/C][C]8.37018896917468[/C][C]0.0563118493752199[/C][/ROW]
[ROW][C]3[/C][C]8.4[/C][C]8.62568943959978[/C][C]-0.188308637211597[/C][C]8.36261919761182[/C][C]0.225689439599780[/C][/ROW]
[ROW][C]4[/C][C]8.6[/C][C]8.87511299426585[/C][C]-0.0368314319854665[/C][C]8.36171843771962[/C][C]0.275112994265847[/C][/ROW]
[ROW][C]5[/C][C]8.9[/C][C]9.19120332686493[/C][C]0.247978995307642[/C][C]8.36081767782743[/C][C]0.291203326864933[/C][/ROW]
[ROW][C]6[/C][C]8.8[/C][C]8.96404324431652[/C][C]0.268549128065039[/C][C]8.36740762761844[/C][C]0.164043244316522[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]8.1368830403866[/C][C]0.089119382203946[/C][C]8.37399757740946[/C][C]-0.163116959613401[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]6.8234800387219[/C][C]-0.203390058843816[/C][C]8.37991002012191[/C][C]-0.676519961278096[/C][/ROW]
[ROW][C]9[/C][C]7.2[/C][C]6.47674389085377[/C][C]-0.462566353688139[/C][C]8.38582246283437[/C][C]-0.723256109146228[/C][/ROW]
[ROW][C]10[/C][C]7.4[/C][C]6.92438481993173[/C][C]-0.506455823511046[/C][C]8.38207100357931[/C][C]-0.475615180068266[/C][/ROW]
[ROW][C]11[/C][C]8.8[/C][C]8.97202585521855[/C][C]0.249654600457193[/C][C]8.37831954432426[/C][C]0.172025855218552[/C][/ROW]
[ROW][C]12[/C][C]9.3[/C][C]9.84355417940868[/C][C]0.364751801760567[/C][C]8.39169401883075[/C][C]0.543554179408682[/C][/ROW]
[ROW][C]13[/C][C]9.3[/C][C]9.99093207300936[/C][C]0.203999433653392[/C][C]8.40506849333724[/C][C]0.690932073009364[/C][/ROW]
[ROW][C]14[/C][C]8.7[/C][C]8.98882426222657[/C][C]-0.0265008185498972[/C][C]8.43767655632332[/C][C]0.288824262226575[/C][/ROW]
[ROW][C]15[/C][C]8.2[/C][C]8.1180240179022[/C][C]-0.188308637211597[/C][C]8.4702846193094[/C][C]-0.0819759820978021[/C][/ROW]
[ROW][C]16[/C][C]8.3[/C][C]8.15801382399703[/C][C]-0.0368314319854665[/C][C]8.47881760798844[/C][C]-0.141986176002971[/C][/ROW]
[ROW][C]17[/C][C]8.5[/C][C]8.26467040802488[/C][C]0.247978995307642[/C][C]8.48735059666748[/C][C]-0.235329591975121[/C][/ROW]
[ROW][C]18[/C][C]8.6[/C][C]8.46881576469307[/C][C]0.268549128065039[/C][C]8.4626351072419[/C][C]-0.131184235306932[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]8.47296099997975[/C][C]0.089119382203946[/C][C]8.4379196178163[/C][C]-0.0270390000202543[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.17663117457046[/C][C]-0.203390058843816[/C][C]8.42675888427336[/C][C]-0.023368825429543[/C][/ROW]
[ROW][C]21[/C][C]8.1[/C][C]8.24696820295773[/C][C]-0.462566353688139[/C][C]8.4155981507304[/C][C]0.146968202957732[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]7.87883272649662[/C][C]-0.506455823511046[/C][C]8.42762309701443[/C][C]-0.0211672735033819[/C][/ROW]
[ROW][C]23[/C][C]8.6[/C][C]8.51069735624436[/C][C]0.249654600457193[/C][C]8.43964804329845[/C][C]-0.0893026437556426[/C][/ROW]
[ROW][C]24[/C][C]8.7[/C][C]8.58252684297707[/C][C]0.364751801760567[/C][C]8.45272135526236[/C][C]-0.117473157022927[/C][/ROW]
[ROW][C]25[/C][C]8.7[/C][C]8.73020589912034[/C][C]0.203999433653392[/C][C]8.46579466722627[/C][C]0.0302058991203413[/C][/ROW]
[ROW][C]26[/C][C]8.5[/C][C]8.54682133700419[/C][C]-0.0265008185498972[/C][C]8.4796794815457[/C][C]0.0468213370041912[/C][/ROW]
[ROW][C]27[/C][C]8.4[/C][C]8.49474434134645[/C][C]-0.188308637211597[/C][C]8.49356429586514[/C][C]0.0947443413464537[/C][/ROW]
[ROW][C]28[/C][C]8.5[/C][C]8.54280840639079[/C][C]-0.0368314319854665[/C][C]8.49402302559468[/C][C]0.0428084063907921[/C][/ROW]
[ROW][C]29[/C][C]8.7[/C][C]8.65753924936815[/C][C]0.247978995307642[/C][C]8.4944817553242[/C][C]-0.0424607506318484[/C][/ROW]
[ROW][C]30[/C][C]8.7[/C][C]8.6678200488189[/C][C]0.268549128065039[/C][C]8.46363082311606[/C][C]-0.0321799511811030[/C][/ROW]
[ROW][C]31[/C][C]8.6[/C][C]8.67810072688813[/C][C]0.089119382203946[/C][C]8.43277989090792[/C][C]0.0781007268881311[/C][/ROW]
[ROW][C]32[/C][C]8.5[/C][C]8.82097793432891[/C][C]-0.203390058843816[/C][C]8.3824121245149[/C][C]0.320977934328912[/C][/ROW]
[ROW][C]33[/C][C]8.3[/C][C]8.73052199556626[/C][C]-0.462566353688139[/C][C]8.33204435812188[/C][C]0.430521995566258[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]8.23166138088492[/C][C]-0.506455823511046[/C][C]8.27479444262613[/C][C]0.231661380884921[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]7.93280087241244[/C][C]0.249654600457193[/C][C]8.21754452713037[/C][C]-0.267199127587563[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]7.68074954334786[/C][C]0.364751801760567[/C][C]8.15449865489157[/C][C]-0.419250456652139[/C][/ROW]
[ROW][C]37[/C][C]8.1[/C][C]7.90454778369383[/C][C]0.203999433653392[/C][C]8.09145278265277[/C][C]-0.195452216306165[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]7.98872107569708[/C][C]-0.0265008185498972[/C][C]8.03777974285282[/C][C]-0.0112789243029248[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]8.00420193415873[/C][C]-0.188308637211597[/C][C]7.98410670305287[/C][C]0.104201934158727[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]7.89839009178393[/C][C]-0.0368314319854665[/C][C]7.93844134020154[/C][C]-0.00160990821607143[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]7.85924502734215[/C][C]0.247978995307642[/C][C]7.8927759773502[/C][C]-0.140754972657848[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]7.90076492499584[/C][C]0.268549128065039[/C][C]7.83068594693912[/C][C]-0.0992350750041577[/C][/ROW]
[ROW][C]43[/C][C]7.9[/C][C]7.94228470126802[/C][C]0.089119382203946[/C][C]7.76859591652803[/C][C]0.0422847012680228[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]8.51626069801739[/C][C]-0.203390058843816[/C][C]7.68712936082642[/C][C]0.516260698017391[/C][/ROW]
[ROW][C]45[/C][C]7.7[/C][C]8.25690354856332[/C][C]-0.462566353688139[/C][C]7.60566280512482[/C][C]0.556903548563321[/C][/ROW]
[ROW][C]46[/C][C]7.2[/C][C]7.38088922324254[/C][C]-0.506455823511046[/C][C]7.5255666002685[/C][C]0.180889223242540[/C][/ROW]
[ROW][C]47[/C][C]7.5[/C][C]7.30487500413061[/C][C]0.249654600457193[/C][C]7.4454703954122[/C][C]-0.195124995869389[/C][/ROW]
[ROW][C]48[/C][C]7.3[/C][C]6.87929194823939[/C][C]0.364751801760567[/C][C]7.35595625000005[/C][C]-0.420708051760615[/C][/ROW]
[ROW][C]49[/C][C]7[/C][C]6.52955846175871[/C][C]0.203999433653392[/C][C]7.2664421045879[/C][C]-0.470441538241293[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]6.85931116838803[/C][C]-0.0265008185498972[/C][C]7.16718965016187[/C][C]-0.140688831611972[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]7.12037144147576[/C][C]-0.188308637211597[/C][C]7.06793719573584[/C][C]0.120371441475759[/C][/ROW]
[ROW][C]52[/C][C]7.2[/C][C]7.42019738490436[/C][C]-0.0368314319854665[/C][C]7.01663404708111[/C][C]0.220197384904356[/C][/ROW]
[ROW][C]53[/C][C]7.3[/C][C]7.38669010626598[/C][C]0.247978995307642[/C][C]6.96533089842638[/C][C]0.0866901062659764[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]6.95794233289403[/C][C]0.268549128065039[/C][C]6.97350853904093[/C][C]-0.142057667105969[/C][/ROW]
[ROW][C]55[/C][C]6.8[/C][C]6.52919443814058[/C][C]0.089119382203946[/C][C]6.98168617965548[/C][C]-0.270805561859424[/C][/ROW]
[ROW][C]56[/C][C]6.4[/C][C]6.01218898278418[/C][C]-0.203390058843816[/C][C]6.99120107605964[/C][C]-0.387811017215824[/C][/ROW]
[ROW][C]57[/C][C]6.1[/C][C]5.66185038122434[/C][C]-0.462566353688139[/C][C]7.0007159724638[/C][C]-0.438149618775664[/C][/ROW]
[ROW][C]58[/C][C]6.5[/C][C]6.49315254802738[/C][C]-0.506455823511046[/C][C]7.01330327548367[/C][C]-0.00684745197262338[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]8.12445482103927[/C][C]0.249654600457193[/C][C]7.02589057850354[/C][C]0.424454821039268[/C][/ROW]
[ROW][C]60[/C][C]7.9[/C][C]8.35101750030259[/C][C]0.364751801760567[/C][C]7.08423069793684[/C][C]0.451017500302593[/C][/ROW]
[ROW][C]61[/C][C]7.5[/C][C]7.65342974897646[/C][C]0.203999433653392[/C][C]7.14257081737014[/C][C]0.153429748976465[/C][/ROW]
[ROW][C]62[/C][C]6.9[/C][C]6.59863518183475[/C][C]-0.0265008185498972[/C][C]7.22786563671515[/C][C]-0.301364818165252[/C][/ROW]
[ROW][C]63[/C][C]6.6[/C][C]6.07514818115144[/C][C]-0.188308637211597[/C][C]7.31316045606016[/C][C]-0.52485181884856[/C][/ROW]
[ROW][C]64[/C][C]6.9[/C][C]6.44807727400544[/C][C]-0.0368314319854665[/C][C]7.38875415798003[/C][C]-0.451922725994565[/C][/ROW]
[ROW][C]65[/C][C]7.7[/C][C]7.68767314479245[/C][C]0.247978995307642[/C][C]7.4643478598999[/C][C]-0.0123268552075455[/C][/ROW]
[ROW][C]66[/C][C]8[/C][C]8.19441757991445[/C][C]0.268549128065039[/C][C]7.53703329202051[/C][C]0.194417579914455[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]8.30116189365495[/C][C]0.089119382203946[/C][C]7.60971872414111[/C][C]0.301161893654945[/C][/ROW]
[ROW][C]68[/C][C]7.7[/C][C]7.91623916239845[/C][C]-0.203390058843816[/C][C]7.68715089644537[/C][C]0.216239162398446[/C][/ROW]
[ROW][C]69[/C][C]7.3[/C][C]7.29798328493851[/C][C]-0.462566353688139[/C][C]7.76458306874963[/C][C]-0.00201671506149204[/C][/ROW]
[ROW][C]70[/C][C]7.4[/C][C]7.46205579550579[/C][C]-0.506455823511046[/C][C]7.84440002800525[/C][C]0.0620557955057937[/C][/ROW]
[ROW][C]71[/C][C]8.1[/C][C]8.02612841228193[/C][C]0.249654600457193[/C][C]7.92421698726088[/C][C]-0.0738715877180693[/C][/ROW]
[ROW][C]72[/C][C]8.3[/C][C]8.2315020885679[/C][C]0.364751801760567[/C][C]8.00374610967154[/C][C]-0.0684979114321083[/C][/ROW]
[ROW][C]73[/C][C]8.2[/C][C]8.1127253342644[/C][C]0.203999433653392[/C][C]8.0832752320822[/C][C]-0.0872746657356007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62223&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62223&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.4	8.21824182560907	0.203999433653392	8.37775874073754	-0.181758174390929
2	8.4	8.45631184937522	-0.0265008185498972	8.37018896917468	0.0563118493752199
3	8.4	8.62568943959978	-0.188308637211597	8.36261919761182	0.225689439599780
4	8.6	8.87511299426585	-0.0368314319854665	8.36171843771962	0.275112994265847
5	8.9	9.19120332686493	0.247978995307642	8.36081767782743	0.291203326864933
6	8.8	8.96404324431652	0.268549128065039	8.36740762761844	0.164043244316522
7	8.3	8.1368830403866	0.089119382203946	8.37399757740946	-0.163116959613401
8	7.5	6.8234800387219	-0.203390058843816	8.37991002012191	-0.676519961278096
9	7.2	6.47674389085377	-0.462566353688139	8.38582246283437	-0.723256109146228
10	7.4	6.92438481993173	-0.506455823511046	8.38207100357931	-0.475615180068266
11	8.8	8.97202585521855	0.249654600457193	8.37831954432426	0.172025855218552
12	9.3	9.84355417940868	0.364751801760567	8.39169401883075	0.543554179408682
13	9.3	9.99093207300936	0.203999433653392	8.40506849333724	0.690932073009364
14	8.7	8.98882426222657	-0.0265008185498972	8.43767655632332	0.288824262226575
15	8.2	8.1180240179022	-0.188308637211597	8.4702846193094	-0.0819759820978021
16	8.3	8.15801382399703	-0.0368314319854665	8.47881760798844	-0.141986176002971
17	8.5	8.26467040802488	0.247978995307642	8.48735059666748	-0.235329591975121
18	8.6	8.46881576469307	0.268549128065039	8.4626351072419	-0.131184235306932
19	8.5	8.47296099997975	0.089119382203946	8.4379196178163	-0.0270390000202543
20	8.2	8.17663117457046	-0.203390058843816	8.42675888427336	-0.023368825429543
21	8.1	8.24696820295773	-0.462566353688139	8.4155981507304	0.146968202957732
22	7.9	7.87883272649662	-0.506455823511046	8.42762309701443	-0.0211672735033819
23	8.6	8.51069735624436	0.249654600457193	8.43964804329845	-0.0893026437556426
24	8.7	8.58252684297707	0.364751801760567	8.45272135526236	-0.117473157022927
25	8.7	8.73020589912034	0.203999433653392	8.46579466722627	0.0302058991203413
26	8.5	8.54682133700419	-0.0265008185498972	8.4796794815457	0.0468213370041912
27	8.4	8.49474434134645	-0.188308637211597	8.49356429586514	0.0947443413464537
28	8.5	8.54280840639079	-0.0368314319854665	8.49402302559468	0.0428084063907921
29	8.7	8.65753924936815	0.247978995307642	8.4944817553242	-0.0424607506318484
30	8.7	8.6678200488189	0.268549128065039	8.46363082311606	-0.0321799511811030
31	8.6	8.67810072688813	0.089119382203946	8.43277989090792	0.0781007268881311
32	8.5	8.82097793432891	-0.203390058843816	8.3824121245149	0.320977934328912
33	8.3	8.73052199556626	-0.462566353688139	8.33204435812188	0.430521995566258
34	8	8.23166138088492	-0.506455823511046	8.27479444262613	0.231661380884921
35	8.2	7.93280087241244	0.249654600457193	8.21754452713037	-0.267199127587563
36	8.1	7.68074954334786	0.364751801760567	8.15449865489157	-0.419250456652139
37	8.1	7.90454778369383	0.203999433653392	8.09145278265277	-0.195452216306165
38	8	7.98872107569708	-0.0265008185498972	8.03777974285282	-0.0112789243029248
39	7.9	8.00420193415873	-0.188308637211597	7.98410670305287	0.104201934158727
40	7.9	7.89839009178393	-0.0368314319854665	7.93844134020154	-0.00160990821607143
41	8	7.85924502734215	0.247978995307642	7.8927759773502	-0.140754972657848
42	8	7.90076492499584	0.268549128065039	7.83068594693912	-0.0992350750041577
43	7.9	7.94228470126802	0.089119382203946	7.76859591652803	0.0422847012680228
44	8	8.51626069801739	-0.203390058843816	7.68712936082642	0.516260698017391
45	7.7	8.25690354856332	-0.462566353688139	7.60566280512482	0.556903548563321
46	7.2	7.38088922324254	-0.506455823511046	7.5255666002685	0.180889223242540
47	7.5	7.30487500413061	0.249654600457193	7.4454703954122	-0.195124995869389
48	7.3	6.87929194823939	0.364751801760567	7.35595625000005	-0.420708051760615
49	7	6.52955846175871	0.203999433653392	7.2664421045879	-0.470441538241293
50	7	6.85931116838803	-0.0265008185498972	7.16718965016187	-0.140688831611972
51	7	7.12037144147576	-0.188308637211597	7.06793719573584	0.120371441475759
52	7.2	7.42019738490436	-0.0368314319854665	7.01663404708111	0.220197384904356
53	7.3	7.38669010626598	0.247978995307642	6.96533089842638	0.0866901062659764
54	7.1	6.95794233289403	0.268549128065039	6.97350853904093	-0.142057667105969
55	6.8	6.52919443814058	0.089119382203946	6.98168617965548	-0.270805561859424
56	6.4	6.01218898278418	-0.203390058843816	6.99120107605964	-0.387811017215824
57	6.1	5.66185038122434	-0.462566353688139	7.0007159724638	-0.438149618775664
58	6.5	6.49315254802738	-0.506455823511046	7.01330327548367	-0.00684745197262338
59	7.7	8.12445482103927	0.249654600457193	7.02589057850354	0.424454821039268
60	7.9	8.35101750030259	0.364751801760567	7.08423069793684	0.451017500302593
61	7.5	7.65342974897646	0.203999433653392	7.14257081737014	0.153429748976465
62	6.9	6.59863518183475	-0.0265008185498972	7.22786563671515	-0.301364818165252
63	6.6	6.07514818115144	-0.188308637211597	7.31316045606016	-0.52485181884856
64	6.9	6.44807727400544	-0.0368314319854665	7.38875415798003	-0.451922725994565
65	7.7	7.68767314479245	0.247978995307642	7.4643478598999	-0.0123268552075455
66	8	8.19441757991445	0.268549128065039	7.53703329202051	0.194417579914455
67	8	8.30116189365495	0.089119382203946	7.60971872414111	0.301161893654945
68	7.7	7.91623916239845	-0.203390058843816	7.68715089644537	0.216239162398446
69	7.3	7.29798328493851	-0.462566353688139	7.76458306874963	-0.00201671506149204
70	7.4	7.46205579550579	-0.506455823511046	7.84440002800525	0.0620557955057937
71	8.1	8.02612841228193	0.249654600457193	7.92421698726088	-0.0738715877180693
72	8.3	8.2315020885679	0.364751801760567	8.00374610967154	-0.0684979114321083
73	8.2	8.1127253342644	0.203999433653392	8.0832752320822	-0.0872746657356007

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 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par4 = ; par5 = 1 ; par6 = ; par7 = 1 ; par8 = FALSE ;

R code (references can be found in the software module):

par1 <- as.numeric(par1) #seasonal period
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code