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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decomposeloess.wasp
Title produced by softwareDecomposition by Loess
Date of computationThu, 03 Dec 2009 11:15:22 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/03/t1259864252yzsx4097ucbgxs0.htm/, Retrieved Fri, 29 Mar 2024 14:56:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63021, Retrieved Fri, 29 Mar 2024 14:56:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
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]
- R  D      [Decomposition by Loess] [] [2009-12-03 18:15:22] [bcaf453a09027aa0f995cb78bdc3c98a] [Current]
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Dataseries X:
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
8.1
8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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=63021&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=63021&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63021&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal611062
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 611 & 0 & 62 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63021&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]611[/C][C]0[/C][C]62[/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=63021&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63021&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
ComponentWindowDegreeJump
Seasonal611062
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
18.18.39879041351494-0.03711396359245317.838323550077510.298790413514941
27.77.82207879298828-0.2219894501135747.79991065712530.122078792988280
37.57.55617707684427-0.3176748410173477.761497764173080.0561770768442695
47.67.57245126837392-0.1018888371932827.72943756881936-0.0275487316260774
57.87.668725609206460.2338970173279047.69737737346564-0.131274390793544
67.87.581666958363360.3478610487571427.6704719928795-0.218333041636645
77.87.694608295078330.2618250926283047.64356661229337-0.105391704921669
87.57.254522172349980.1246363969116447.62084143073838-0.245477827650022
97.57.49443621136643-0.09255246054981877.59811624918339-0.00556378863357487
107.16.88858905660276-0.2961297031285727.60754064652581-0.211410943397238
117.57.302741901839070.08029305429269677.61696504386823-0.197258098160926
127.57.324083095282930.01883712284132697.65707978187575-0.175916904717073
137.67.53991944370919-0.03711396359245317.69719451988326-0.0600805562908091
147.77.88201863101455-0.2219894501135747.739970819099030.182018631014545
157.77.93492772270255-0.3176748410173477.78274711831480.234927722702549
167.98.11076165705711-0.1018888371932827.791127180136170.210761657057111
178.18.166595740714550.2338970173279047.799507241957550.0665957407145497
188.28.304609472565870.3478610487571427.747529478676980.104609472565873
198.28.442623191975270.2618250926283047.695551715396420.242623191975272
208.28.662461580067280.1246363969116447.612902023021080.462461580067277
217.98.36230012990409-0.09255246054981877.530252330645730.462300129904084
227.37.45129036062526-0.2961297031285727.444839342503310.151290360625257
236.96.360280591346410.08029305429269677.3594263543609-0.539719408653591
246.65.914353849384840.01883712284132697.26680902777384-0.685646150615162
256.76.26292226240568-0.03711396359245317.17419170118678-0.437077737594322
266.96.93239184719101-0.2219894501135747.089597602922560.0323918471910121
2777.312671336359-0.3176748410173477.005003504658350.312671336358996
287.17.34617664490531-0.1018888371932826.955712192287970.246176644905312
297.27.259682102754510.2338970173279046.906420879917590.0596821027545102
307.16.976478475723060.3478610487571426.87566047551979-0.123521524276935
316.96.69327483624970.2618250926283046.844900071122-0.206725163750303
3277.076500355783070.1246363969116446.798863247305280.0765003557830735
336.86.93972603706125-0.09255246054981876.752826423488570.139726037061252
346.46.38887781381601-0.2961297031285726.70725188931257-0.0111221861839930
356.76.658029590570740.08029305429269676.66167735513657-0.0419704094292621
366.66.559460615462320.01883712284132696.62170226169635-0.0405393845376816
376.46.25538679533631-0.03711396359245316.58172716825614-0.144613204663688
386.36.29127126110243-0.2219894501135746.53071818901114-0.00872873889756853
396.26.23796563125120-0.3176748410173476.479709209766140.037965631251204
406.56.64847237986478-0.1018888371932826.45341645732850.148472379864779
416.86.938979277781230.2338970173279046.427123704890860.138979277781233
426.86.808151779982950.3478610487571426.44398717125990.00815177998295358
436.46.077324269742750.2618250926283046.46085063762894-0.322675730257249
446.15.601449121482130.1246363969116446.47391448160622-0.498550878517865
455.85.20557413496632-0.09255246054981876.4869783255835-0.59442586503368
466.15.99688144065977-0.2961297031285726.4992482624688-0.103118559340230
477.27.80818874635320.08029305429269676.51151819935410.608188746353196
487.38.005702651558490.01883712284132696.575460225600190.705702651558486
496.97.19771171174619-0.03711396359245316.639402251846270.297711711746187
506.15.6774338568925-0.2219894501135746.74455559322107-0.422566143107498
515.85.06796590642147-0.3176748410173476.84970893459588-0.732034093578531
526.25.53849237410057-0.1018888371932826.96339646309271-0.661507625899429
537.16.889018991082550.2338970173279047.07708399158954-0.210981008917448
547.77.851573328926250.3478610487571427.200565622316610.151573328926246
557.98.214127654328020.2618250926283047.324047253043680.314127654328017
567.77.820993181219750.1246363969116447.454370421868610.120993181219746
577.47.30785886985628-0.09255246054981877.58469359069354-0.0921411301437214
587.57.57501258395088-0.2961297031285727.721117119177690.0750125839508815
5988.062166298045460.08029305429269677.857540647661840.0621662980454607
608.18.183492382730970.01883712284132697.99767049442770.0834923827309684
6187.89931362239889-0.03711396359245318.13780034119356-0.100686377601111

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 8.1 & 8.39879041351494 & -0.0371139635924531 & 7.83832355007751 & 0.298790413514941 \tabularnewline
2 & 7.7 & 7.82207879298828 & -0.221989450113574 & 7.7999106571253 & 0.122078792988280 \tabularnewline
3 & 7.5 & 7.55617707684427 & -0.317674841017347 & 7.76149776417308 & 0.0561770768442695 \tabularnewline
4 & 7.6 & 7.57245126837392 & -0.101888837193282 & 7.72943756881936 & -0.0275487316260774 \tabularnewline
5 & 7.8 & 7.66872560920646 & 0.233897017327904 & 7.69737737346564 & -0.131274390793544 \tabularnewline
6 & 7.8 & 7.58166695836336 & 0.347861048757142 & 7.6704719928795 & -0.218333041636645 \tabularnewline
7 & 7.8 & 7.69460829507833 & 0.261825092628304 & 7.64356661229337 & -0.105391704921669 \tabularnewline
8 & 7.5 & 7.25452217234998 & 0.124636396911644 & 7.62084143073838 & -0.245477827650022 \tabularnewline
9 & 7.5 & 7.49443621136643 & -0.0925524605498187 & 7.59811624918339 & -0.00556378863357487 \tabularnewline
10 & 7.1 & 6.88858905660276 & -0.296129703128572 & 7.60754064652581 & -0.211410943397238 \tabularnewline
11 & 7.5 & 7.30274190183907 & 0.0802930542926967 & 7.61696504386823 & -0.197258098160926 \tabularnewline
12 & 7.5 & 7.32408309528293 & 0.0188371228413269 & 7.65707978187575 & -0.175916904717073 \tabularnewline
13 & 7.6 & 7.53991944370919 & -0.0371139635924531 & 7.69719451988326 & -0.0600805562908091 \tabularnewline
14 & 7.7 & 7.88201863101455 & -0.221989450113574 & 7.73997081909903 & 0.182018631014545 \tabularnewline
15 & 7.7 & 7.93492772270255 & -0.317674841017347 & 7.7827471183148 & 0.234927722702549 \tabularnewline
16 & 7.9 & 8.11076165705711 & -0.101888837193282 & 7.79112718013617 & 0.210761657057111 \tabularnewline
17 & 8.1 & 8.16659574071455 & 0.233897017327904 & 7.79950724195755 & 0.0665957407145497 \tabularnewline
18 & 8.2 & 8.30460947256587 & 0.347861048757142 & 7.74752947867698 & 0.104609472565873 \tabularnewline
19 & 8.2 & 8.44262319197527 & 0.261825092628304 & 7.69555171539642 & 0.242623191975272 \tabularnewline
20 & 8.2 & 8.66246158006728 & 0.124636396911644 & 7.61290202302108 & 0.462461580067277 \tabularnewline
21 & 7.9 & 8.36230012990409 & -0.0925524605498187 & 7.53025233064573 & 0.462300129904084 \tabularnewline
22 & 7.3 & 7.45129036062526 & -0.296129703128572 & 7.44483934250331 & 0.151290360625257 \tabularnewline
23 & 6.9 & 6.36028059134641 & 0.0802930542926967 & 7.3594263543609 & -0.539719408653591 \tabularnewline
24 & 6.6 & 5.91435384938484 & 0.0188371228413269 & 7.26680902777384 & -0.685646150615162 \tabularnewline
25 & 6.7 & 6.26292226240568 & -0.0371139635924531 & 7.17419170118678 & -0.437077737594322 \tabularnewline
26 & 6.9 & 6.93239184719101 & -0.221989450113574 & 7.08959760292256 & 0.0323918471910121 \tabularnewline
27 & 7 & 7.312671336359 & -0.317674841017347 & 7.00500350465835 & 0.312671336358996 \tabularnewline
28 & 7.1 & 7.34617664490531 & -0.101888837193282 & 6.95571219228797 & 0.246176644905312 \tabularnewline
29 & 7.2 & 7.25968210275451 & 0.233897017327904 & 6.90642087991759 & 0.0596821027545102 \tabularnewline
30 & 7.1 & 6.97647847572306 & 0.347861048757142 & 6.87566047551979 & -0.123521524276935 \tabularnewline
31 & 6.9 & 6.6932748362497 & 0.261825092628304 & 6.844900071122 & -0.206725163750303 \tabularnewline
32 & 7 & 7.07650035578307 & 0.124636396911644 & 6.79886324730528 & 0.0765003557830735 \tabularnewline
33 & 6.8 & 6.93972603706125 & -0.0925524605498187 & 6.75282642348857 & 0.139726037061252 \tabularnewline
34 & 6.4 & 6.38887781381601 & -0.296129703128572 & 6.70725188931257 & -0.0111221861839930 \tabularnewline
35 & 6.7 & 6.65802959057074 & 0.0802930542926967 & 6.66167735513657 & -0.0419704094292621 \tabularnewline
36 & 6.6 & 6.55946061546232 & 0.0188371228413269 & 6.62170226169635 & -0.0405393845376816 \tabularnewline
37 & 6.4 & 6.25538679533631 & -0.0371139635924531 & 6.58172716825614 & -0.144613204663688 \tabularnewline
38 & 6.3 & 6.29127126110243 & -0.221989450113574 & 6.53071818901114 & -0.00872873889756853 \tabularnewline
39 & 6.2 & 6.23796563125120 & -0.317674841017347 & 6.47970920976614 & 0.037965631251204 \tabularnewline
40 & 6.5 & 6.64847237986478 & -0.101888837193282 & 6.4534164573285 & 0.148472379864779 \tabularnewline
41 & 6.8 & 6.93897927778123 & 0.233897017327904 & 6.42712370489086 & 0.138979277781233 \tabularnewline
42 & 6.8 & 6.80815177998295 & 0.347861048757142 & 6.4439871712599 & 0.00815177998295358 \tabularnewline
43 & 6.4 & 6.07732426974275 & 0.261825092628304 & 6.46085063762894 & -0.322675730257249 \tabularnewline
44 & 6.1 & 5.60144912148213 & 0.124636396911644 & 6.47391448160622 & -0.498550878517865 \tabularnewline
45 & 5.8 & 5.20557413496632 & -0.0925524605498187 & 6.4869783255835 & -0.59442586503368 \tabularnewline
46 & 6.1 & 5.99688144065977 & -0.296129703128572 & 6.4992482624688 & -0.103118559340230 \tabularnewline
47 & 7.2 & 7.8081887463532 & 0.0802930542926967 & 6.5115181993541 & 0.608188746353196 \tabularnewline
48 & 7.3 & 8.00570265155849 & 0.0188371228413269 & 6.57546022560019 & 0.705702651558486 \tabularnewline
49 & 6.9 & 7.19771171174619 & -0.0371139635924531 & 6.63940225184627 & 0.297711711746187 \tabularnewline
50 & 6.1 & 5.6774338568925 & -0.221989450113574 & 6.74455559322107 & -0.422566143107498 \tabularnewline
51 & 5.8 & 5.06796590642147 & -0.317674841017347 & 6.84970893459588 & -0.732034093578531 \tabularnewline
52 & 6.2 & 5.53849237410057 & -0.101888837193282 & 6.96339646309271 & -0.661507625899429 \tabularnewline
53 & 7.1 & 6.88901899108255 & 0.233897017327904 & 7.07708399158954 & -0.210981008917448 \tabularnewline
54 & 7.7 & 7.85157332892625 & 0.347861048757142 & 7.20056562231661 & 0.151573328926246 \tabularnewline
55 & 7.9 & 8.21412765432802 & 0.261825092628304 & 7.32404725304368 & 0.314127654328017 \tabularnewline
56 & 7.7 & 7.82099318121975 & 0.124636396911644 & 7.45437042186861 & 0.120993181219746 \tabularnewline
57 & 7.4 & 7.30785886985628 & -0.0925524605498187 & 7.58469359069354 & -0.0921411301437214 \tabularnewline
58 & 7.5 & 7.57501258395088 & -0.296129703128572 & 7.72111711917769 & 0.0750125839508815 \tabularnewline
59 & 8 & 8.06216629804546 & 0.0802930542926967 & 7.85754064766184 & 0.0621662980454607 \tabularnewline
60 & 8.1 & 8.18349238273097 & 0.0188371228413269 & 7.9976704944277 & 0.0834923827309684 \tabularnewline
61 & 8 & 7.89931362239889 & -0.0371139635924531 & 8.13780034119356 & -0.100686377601111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63021&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]8.1[/C][C]8.39879041351494[/C][C]-0.0371139635924531[/C][C]7.83832355007751[/C][C]0.298790413514941[/C][/ROW]
[ROW][C]2[/C][C]7.7[/C][C]7.82207879298828[/C][C]-0.221989450113574[/C][C]7.7999106571253[/C][C]0.122078792988280[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]7.55617707684427[/C][C]-0.317674841017347[/C][C]7.76149776417308[/C][C]0.0561770768442695[/C][/ROW]
[ROW][C]4[/C][C]7.6[/C][C]7.57245126837392[/C][C]-0.101888837193282[/C][C]7.72943756881936[/C][C]-0.0275487316260774[/C][/ROW]
[ROW][C]5[/C][C]7.8[/C][C]7.66872560920646[/C][C]0.233897017327904[/C][C]7.69737737346564[/C][C]-0.131274390793544[/C][/ROW]
[ROW][C]6[/C][C]7.8[/C][C]7.58166695836336[/C][C]0.347861048757142[/C][C]7.6704719928795[/C][C]-0.218333041636645[/C][/ROW]
[ROW][C]7[/C][C]7.8[/C][C]7.69460829507833[/C][C]0.261825092628304[/C][C]7.64356661229337[/C][C]-0.105391704921669[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]7.25452217234998[/C][C]0.124636396911644[/C][C]7.62084143073838[/C][C]-0.245477827650022[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.49443621136643[/C][C]-0.0925524605498187[/C][C]7.59811624918339[/C][C]-0.00556378863357487[/C][/ROW]
[ROW][C]10[/C][C]7.1[/C][C]6.88858905660276[/C][C]-0.296129703128572[/C][C]7.60754064652581[/C][C]-0.211410943397238[/C][/ROW]
[ROW][C]11[/C][C]7.5[/C][C]7.30274190183907[/C][C]0.0802930542926967[/C][C]7.61696504386823[/C][C]-0.197258098160926[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]7.32408309528293[/C][C]0.0188371228413269[/C][C]7.65707978187575[/C][C]-0.175916904717073[/C][/ROW]
[ROW][C]13[/C][C]7.6[/C][C]7.53991944370919[/C][C]-0.0371139635924531[/C][C]7.69719451988326[/C][C]-0.0600805562908091[/C][/ROW]
[ROW][C]14[/C][C]7.7[/C][C]7.88201863101455[/C][C]-0.221989450113574[/C][C]7.73997081909903[/C][C]0.182018631014545[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.93492772270255[/C][C]-0.317674841017347[/C][C]7.7827471183148[/C][C]0.234927722702549[/C][/ROW]
[ROW][C]16[/C][C]7.9[/C][C]8.11076165705711[/C][C]-0.101888837193282[/C][C]7.79112718013617[/C][C]0.210761657057111[/C][/ROW]
[ROW][C]17[/C][C]8.1[/C][C]8.16659574071455[/C][C]0.233897017327904[/C][C]7.79950724195755[/C][C]0.0665957407145497[/C][/ROW]
[ROW][C]18[/C][C]8.2[/C][C]8.30460947256587[/C][C]0.347861048757142[/C][C]7.74752947867698[/C][C]0.104609472565873[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]8.44262319197527[/C][C]0.261825092628304[/C][C]7.69555171539642[/C][C]0.242623191975272[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.66246158006728[/C][C]0.124636396911644[/C][C]7.61290202302108[/C][C]0.462461580067277[/C][/ROW]
[ROW][C]21[/C][C]7.9[/C][C]8.36230012990409[/C][C]-0.0925524605498187[/C][C]7.53025233064573[/C][C]0.462300129904084[/C][/ROW]
[ROW][C]22[/C][C]7.3[/C][C]7.45129036062526[/C][C]-0.296129703128572[/C][C]7.44483934250331[/C][C]0.151290360625257[/C][/ROW]
[ROW][C]23[/C][C]6.9[/C][C]6.36028059134641[/C][C]0.0802930542926967[/C][C]7.3594263543609[/C][C]-0.539719408653591[/C][/ROW]
[ROW][C]24[/C][C]6.6[/C][C]5.91435384938484[/C][C]0.0188371228413269[/C][C]7.26680902777384[/C][C]-0.685646150615162[/C][/ROW]
[ROW][C]25[/C][C]6.7[/C][C]6.26292226240568[/C][C]-0.0371139635924531[/C][C]7.17419170118678[/C][C]-0.437077737594322[/C][/ROW]
[ROW][C]26[/C][C]6.9[/C][C]6.93239184719101[/C][C]-0.221989450113574[/C][C]7.08959760292256[/C][C]0.0323918471910121[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]7.312671336359[/C][C]-0.317674841017347[/C][C]7.00500350465835[/C][C]0.312671336358996[/C][/ROW]
[ROW][C]28[/C][C]7.1[/C][C]7.34617664490531[/C][C]-0.101888837193282[/C][C]6.95571219228797[/C][C]0.246176644905312[/C][/ROW]
[ROW][C]29[/C][C]7.2[/C][C]7.25968210275451[/C][C]0.233897017327904[/C][C]6.90642087991759[/C][C]0.0596821027545102[/C][/ROW]
[ROW][C]30[/C][C]7.1[/C][C]6.97647847572306[/C][C]0.347861048757142[/C][C]6.87566047551979[/C][C]-0.123521524276935[/C][/ROW]
[ROW][C]31[/C][C]6.9[/C][C]6.6932748362497[/C][C]0.261825092628304[/C][C]6.844900071122[/C][C]-0.206725163750303[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]7.07650035578307[/C][C]0.124636396911644[/C][C]6.79886324730528[/C][C]0.0765003557830735[/C][/ROW]
[ROW][C]33[/C][C]6.8[/C][C]6.93972603706125[/C][C]-0.0925524605498187[/C][C]6.75282642348857[/C][C]0.139726037061252[/C][/ROW]
[ROW][C]34[/C][C]6.4[/C][C]6.38887781381601[/C][C]-0.296129703128572[/C][C]6.70725188931257[/C][C]-0.0111221861839930[/C][/ROW]
[ROW][C]35[/C][C]6.7[/C][C]6.65802959057074[/C][C]0.0802930542926967[/C][C]6.66167735513657[/C][C]-0.0419704094292621[/C][/ROW]
[ROW][C]36[/C][C]6.6[/C][C]6.55946061546232[/C][C]0.0188371228413269[/C][C]6.62170226169635[/C][C]-0.0405393845376816[/C][/ROW]
[ROW][C]37[/C][C]6.4[/C][C]6.25538679533631[/C][C]-0.0371139635924531[/C][C]6.58172716825614[/C][C]-0.144613204663688[/C][/ROW]
[ROW][C]38[/C][C]6.3[/C][C]6.29127126110243[/C][C]-0.221989450113574[/C][C]6.53071818901114[/C][C]-0.00872873889756853[/C][/ROW]
[ROW][C]39[/C][C]6.2[/C][C]6.23796563125120[/C][C]-0.317674841017347[/C][C]6.47970920976614[/C][C]0.037965631251204[/C][/ROW]
[ROW][C]40[/C][C]6.5[/C][C]6.64847237986478[/C][C]-0.101888837193282[/C][C]6.4534164573285[/C][C]0.148472379864779[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]6.93897927778123[/C][C]0.233897017327904[/C][C]6.42712370489086[/C][C]0.138979277781233[/C][/ROW]
[ROW][C]42[/C][C]6.8[/C][C]6.80815177998295[/C][C]0.347861048757142[/C][C]6.4439871712599[/C][C]0.00815177998295358[/C][/ROW]
[ROW][C]43[/C][C]6.4[/C][C]6.07732426974275[/C][C]0.261825092628304[/C][C]6.46085063762894[/C][C]-0.322675730257249[/C][/ROW]
[ROW][C]44[/C][C]6.1[/C][C]5.60144912148213[/C][C]0.124636396911644[/C][C]6.47391448160622[/C][C]-0.498550878517865[/C][/ROW]
[ROW][C]45[/C][C]5.8[/C][C]5.20557413496632[/C][C]-0.0925524605498187[/C][C]6.4869783255835[/C][C]-0.59442586503368[/C][/ROW]
[ROW][C]46[/C][C]6.1[/C][C]5.99688144065977[/C][C]-0.296129703128572[/C][C]6.4992482624688[/C][C]-0.103118559340230[/C][/ROW]
[ROW][C]47[/C][C]7.2[/C][C]7.8081887463532[/C][C]0.0802930542926967[/C][C]6.5115181993541[/C][C]0.608188746353196[/C][/ROW]
[ROW][C]48[/C][C]7.3[/C][C]8.00570265155849[/C][C]0.0188371228413269[/C][C]6.57546022560019[/C][C]0.705702651558486[/C][/ROW]
[ROW][C]49[/C][C]6.9[/C][C]7.19771171174619[/C][C]-0.0371139635924531[/C][C]6.63940225184627[/C][C]0.297711711746187[/C][/ROW]
[ROW][C]50[/C][C]6.1[/C][C]5.6774338568925[/C][C]-0.221989450113574[/C][C]6.74455559322107[/C][C]-0.422566143107498[/C][/ROW]
[ROW][C]51[/C][C]5.8[/C][C]5.06796590642147[/C][C]-0.317674841017347[/C][C]6.84970893459588[/C][C]-0.732034093578531[/C][/ROW]
[ROW][C]52[/C][C]6.2[/C][C]5.53849237410057[/C][C]-0.101888837193282[/C][C]6.96339646309271[/C][C]-0.661507625899429[/C][/ROW]
[ROW][C]53[/C][C]7.1[/C][C]6.88901899108255[/C][C]0.233897017327904[/C][C]7.07708399158954[/C][C]-0.210981008917448[/C][/ROW]
[ROW][C]54[/C][C]7.7[/C][C]7.85157332892625[/C][C]0.347861048757142[/C][C]7.20056562231661[/C][C]0.151573328926246[/C][/ROW]
[ROW][C]55[/C][C]7.9[/C][C]8.21412765432802[/C][C]0.261825092628304[/C][C]7.32404725304368[/C][C]0.314127654328017[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]7.82099318121975[/C][C]0.124636396911644[/C][C]7.45437042186861[/C][C]0.120993181219746[/C][/ROW]
[ROW][C]57[/C][C]7.4[/C][C]7.30785886985628[/C][C]-0.0925524605498187[/C][C]7.58469359069354[/C][C]-0.0921411301437214[/C][/ROW]
[ROW][C]58[/C][C]7.5[/C][C]7.57501258395088[/C][C]-0.296129703128572[/C][C]7.72111711917769[/C][C]0.0750125839508815[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]8.06216629804546[/C][C]0.0802930542926967[/C][C]7.85754064766184[/C][C]0.0621662980454607[/C][/ROW]
[ROW][C]60[/C][C]8.1[/C][C]8.18349238273097[/C][C]0.0188371228413269[/C][C]7.9976704944277[/C][C]0.0834923827309684[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]7.89931362239889[/C][C]-0.0371139635924531[/C][C]8.13780034119356[/C][C]-0.100686377601111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63021&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63021&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
tObservedFittedSeasonalTrendRemainder
18.18.39879041351494-0.03711396359245317.838323550077510.298790413514941
27.77.82207879298828-0.2219894501135747.79991065712530.122078792988280
37.57.55617707684427-0.3176748410173477.761497764173080.0561770768442695
47.67.57245126837392-0.1018888371932827.72943756881936-0.0275487316260774
57.87.668725609206460.2338970173279047.69737737346564-0.131274390793544
67.87.581666958363360.3478610487571427.6704719928795-0.218333041636645
77.87.694608295078330.2618250926283047.64356661229337-0.105391704921669
87.57.254522172349980.1246363969116447.62084143073838-0.245477827650022
97.57.49443621136643-0.09255246054981877.59811624918339-0.00556378863357487
107.16.88858905660276-0.2961297031285727.60754064652581-0.211410943397238
117.57.302741901839070.08029305429269677.61696504386823-0.197258098160926
127.57.324083095282930.01883712284132697.65707978187575-0.175916904717073
137.67.53991944370919-0.03711396359245317.69719451988326-0.0600805562908091
147.77.88201863101455-0.2219894501135747.739970819099030.182018631014545
157.77.93492772270255-0.3176748410173477.78274711831480.234927722702549
167.98.11076165705711-0.1018888371932827.791127180136170.210761657057111
178.18.166595740714550.2338970173279047.799507241957550.0665957407145497
188.28.304609472565870.3478610487571427.747529478676980.104609472565873
198.28.442623191975270.2618250926283047.695551715396420.242623191975272
208.28.662461580067280.1246363969116447.612902023021080.462461580067277
217.98.36230012990409-0.09255246054981877.530252330645730.462300129904084
227.37.45129036062526-0.2961297031285727.444839342503310.151290360625257
236.96.360280591346410.08029305429269677.3594263543609-0.539719408653591
246.65.914353849384840.01883712284132697.26680902777384-0.685646150615162
256.76.26292226240568-0.03711396359245317.17419170118678-0.437077737594322
266.96.93239184719101-0.2219894501135747.089597602922560.0323918471910121
2777.312671336359-0.3176748410173477.005003504658350.312671336358996
287.17.34617664490531-0.1018888371932826.955712192287970.246176644905312
297.27.259682102754510.2338970173279046.906420879917590.0596821027545102
307.16.976478475723060.3478610487571426.87566047551979-0.123521524276935
316.96.69327483624970.2618250926283046.844900071122-0.206725163750303
3277.076500355783070.1246363969116446.798863247305280.0765003557830735
336.86.93972603706125-0.09255246054981876.752826423488570.139726037061252
346.46.38887781381601-0.2961297031285726.70725188931257-0.0111221861839930
356.76.658029590570740.08029305429269676.66167735513657-0.0419704094292621
366.66.559460615462320.01883712284132696.62170226169635-0.0405393845376816
376.46.25538679533631-0.03711396359245316.58172716825614-0.144613204663688
386.36.29127126110243-0.2219894501135746.53071818901114-0.00872873889756853
396.26.23796563125120-0.3176748410173476.479709209766140.037965631251204
406.56.64847237986478-0.1018888371932826.45341645732850.148472379864779
416.86.938979277781230.2338970173279046.427123704890860.138979277781233
426.86.808151779982950.3478610487571426.44398717125990.00815177998295358
436.46.077324269742750.2618250926283046.46085063762894-0.322675730257249
446.15.601449121482130.1246363969116446.47391448160622-0.498550878517865
455.85.20557413496632-0.09255246054981876.4869783255835-0.59442586503368
466.15.99688144065977-0.2961297031285726.4992482624688-0.103118559340230
477.27.80818874635320.08029305429269676.51151819935410.608188746353196
487.38.005702651558490.01883712284132696.575460225600190.705702651558486
496.97.19771171174619-0.03711396359245316.639402251846270.297711711746187
506.15.6774338568925-0.2219894501135746.74455559322107-0.422566143107498
515.85.06796590642147-0.3176748410173476.84970893459588-0.732034093578531
526.25.53849237410057-0.1018888371932826.96339646309271-0.661507625899429
537.16.889018991082550.2338970173279047.07708399158954-0.210981008917448
547.77.851573328926250.3478610487571427.200565622316610.151573328926246
557.98.214127654328020.2618250926283047.324047253043680.314127654328017
567.77.820993181219750.1246363969116447.454370421868610.120993181219746
577.47.30785886985628-0.09255246054981877.58469359069354-0.0921411301437214
587.57.57501258395088-0.2961297031285727.721117119177690.0750125839508815
5988.062166298045460.08029305429269677.857540647661840.0621662980454607
608.18.183492382730970.01883712284132697.99767049442770.0834923827309684
6187.89931362239889-0.03711396359245318.13780034119356-0.100686377601111



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')