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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 11:40:46 -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/t12598658782ivtrgdspcuxqx4.htm/, Retrieved Fri, 29 Mar 2024 12:27:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63054, Retrieved Fri, 29 Mar 2024 12:27:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126
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] [SHW WS9] [2009-12-03 18:40:46] [b7e46d23597387652ca7420fdeb9acca] [Current]
-   PD        [Decomposition by Loess] [loess] [2009-12-04 15:46:02] [ba905ddf7cdf9ecb063c35348c4dab2e]
-   PD          [Decomposition by Loess] [loess] [2009-12-06 20:04:01] [ba905ddf7cdf9ecb063c35348c4dab2e]
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Dataseries X:
1.59
1.26
1.13
1.92
2.61
2.26
2.41
2.26
2.03
2.86
2.55
2.27
2.26
2.57
3.07
2.76
2.51
2.87
3.14
3.11
3.16
2.47
2.57
2.89
2.63
2.38
1.69
1.96
2.19
1.87
1.6
1.63
1.22
1.21
1.49
1.64
1.66
1.77
1.82
1.78
1.28
1.29
1.37
1.12
1.51
2.24
2.94
3.09
3.46
3.64
4.39
4.15
5.21
5.8
5.91
5.39
5.46
4.72
3.14
2.63




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63054&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]1 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=63054&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63054&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal601061
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 601 & 0 & 61 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63054&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]601[/C][C]0[/C][C]61[/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=63054&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63054&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
Seasonal601061
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
11.591.74790183426246-0.02605650607533511.458154671812870.157901834262461
21.261.04135619993061-0.09144132071060421.57008512077999-0.218643800069386
31.130.642810864905706-0.06482643465281191.68201556974711-0.487189135094294
41.922.043909316561590.005693772437416251.790396911001000.123909316561587
52.613.093008144638320.2282136031067891.898778252254890.483008144638324
62.262.257441944289670.2575516326730592.00500642303727-0.00255805571032708
72.412.411875738712950.2968896674674042.111234593819650.00187573871294688
82.262.237575358189810.06567441211217012.21675022969802-0.0224246418101863
92.031.74527513189478-0.007540997471159262.32226586557638-0.284724868105223
102.863.35682392545861-0.04295434495668332.406130419498070.496823925458609
112.552.87437202630348-0.2643669997232482.489994973419770.324372026303482
122.272.35585056723804-0.3568371649638912.540986597725860.0858505672380359
132.261.95407828404339-0.02605650607533512.59197822203195-0.305921715956611
142.572.59380770808559-0.09144132071060422.637633612625020.0238077080855863
153.073.52153743143472-0.06482643465281192.683289003218090.451537431434722
162.762.800920051110020.005693772437416252.713386176452560.0409200511100245
172.512.048303047206180.2282136031067892.74348334968703-0.461696952793818
182.872.72120040953960.2575516326730592.76124795778734-0.148799590460401
193.143.204097766644940.2968896674674042.779012565887660.064097766644939
203.113.38946281843670.06567441211217012.764862769451130.279462818436696
213.163.57682802445655-0.007540997471159262.750712973014610.416828024456549
222.472.28921742507168-0.04295434495668332.69373691988500-0.180782574928321
232.572.76760613296785-0.2643669997232482.63676086675540.197606132967848
242.893.59523814563133-0.3568371649638912.541599019332560.705238145631335
252.632.83961933416562-0.02605650607533512.446437171909710.209619334165621
262.382.53749990213549-0.09144132071060422.313941418575120.157499902135485
271.691.26338076941229-0.06482643465281192.18144566524052-0.426619230587710
281.961.864384650450830.005693772437416252.04992157711176-0.0956153495491732
292.192.233388907910220.2282136031067891.918397488982990.0433889079102185
301.871.658268633407190.2575516326730591.82417973391975-0.21173136659281
311.61.173148353676090.2968896674674041.72996197885651-0.426851646323914
321.631.506013248028830.06567441211217011.688312339859-0.123986751971172
331.220.800878296609666-0.007540997471159261.64666270086149-0.419121703390334
341.210.833905028535982-0.04295434495668331.6290493164207-0.376094971464018
351.491.63293106774334-0.2643669997232481.611435931979910.142931067743338
361.642.04977183864735-0.3568371649638911.587065326316540.409771838647354
371.661.78336178542217-0.02605650607533511.562694720653160.123361785422172
381.772.08366310068207-0.09144132071060421.547778220028540.313663100682066
391.822.17196471524890-0.06482643465281191.532861719403910.351964715248898
401.781.984779930133410.005693772437416251.569526297429170.204779930133414
411.280.7255955214387850.2282136031067891.60619087545443-0.554404478561215
421.290.609179693107090.2575516326730591.71326867421985-0.68082030689291
431.370.622763859547320.2968896674674041.82034647298528-0.74723614045268
441.120.1680365036136580.06567441211217012.00628908427417-0.951963496386342
451.510.835309301908092-0.007540997471159262.19223169556307-0.674690698091908
462.242.05611680604887-0.04295434495668332.46683753890782-0.183883193951132
472.943.40292361747068-0.2643669997232482.741443382252560.462923617470683
483.093.4418956377759-0.3568371649638913.094941527187990.351895637775901
493.463.49761683395192-0.02605650607533513.448439672123410.0376168339519203
503.643.58511597870935-0.09144132071060423.78632534200126-0.0548840212906532
514.394.72061542277371-0.06482643465281194.12421101187910.330615422773712
524.154.059256858157320.005693772437416254.23504936940526-0.0907431418426805
535.215.845898669961780.2282136031067894.345887726931430.63589866996178
545.86.932611374757360.2575516326730594.409836992569581.13261137475736
555.917.049324074324870.2968896674674044.473786258207731.13932407432487
565.396.186591948931830.06567441211217014.527733638956000.796591948931826
575.466.34585997776688-0.007540997471159264.581681019704280.885859977766878
584.724.86728947278879-0.04295434495668334.61566487216790.147289472788787
593.141.89471827509174-0.2643669997232484.64964872463151-1.24528172490826
602.630.95560274889564-0.3568371649638914.66123441606825-1.67439725110436

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 1.59 & 1.74790183426246 & -0.0260565060753351 & 1.45815467181287 & 0.157901834262461 \tabularnewline
2 & 1.26 & 1.04135619993061 & -0.0914413207106042 & 1.57008512077999 & -0.218643800069386 \tabularnewline
3 & 1.13 & 0.642810864905706 & -0.0648264346528119 & 1.68201556974711 & -0.487189135094294 \tabularnewline
4 & 1.92 & 2.04390931656159 & 0.00569377243741625 & 1.79039691100100 & 0.123909316561587 \tabularnewline
5 & 2.61 & 3.09300814463832 & 0.228213603106789 & 1.89877825225489 & 0.483008144638324 \tabularnewline
6 & 2.26 & 2.25744194428967 & 0.257551632673059 & 2.00500642303727 & -0.00255805571032708 \tabularnewline
7 & 2.41 & 2.41187573871295 & 0.296889667467404 & 2.11123459381965 & 0.00187573871294688 \tabularnewline
8 & 2.26 & 2.23757535818981 & 0.0656744121121701 & 2.21675022969802 & -0.0224246418101863 \tabularnewline
9 & 2.03 & 1.74527513189478 & -0.00754099747115926 & 2.32226586557638 & -0.284724868105223 \tabularnewline
10 & 2.86 & 3.35682392545861 & -0.0429543449566833 & 2.40613041949807 & 0.496823925458609 \tabularnewline
11 & 2.55 & 2.87437202630348 & -0.264366999723248 & 2.48999497341977 & 0.324372026303482 \tabularnewline
12 & 2.27 & 2.35585056723804 & -0.356837164963891 & 2.54098659772586 & 0.0858505672380359 \tabularnewline
13 & 2.26 & 1.95407828404339 & -0.0260565060753351 & 2.59197822203195 & -0.305921715956611 \tabularnewline
14 & 2.57 & 2.59380770808559 & -0.0914413207106042 & 2.63763361262502 & 0.0238077080855863 \tabularnewline
15 & 3.07 & 3.52153743143472 & -0.0648264346528119 & 2.68328900321809 & 0.451537431434722 \tabularnewline
16 & 2.76 & 2.80092005111002 & 0.00569377243741625 & 2.71338617645256 & 0.0409200511100245 \tabularnewline
17 & 2.51 & 2.04830304720618 & 0.228213603106789 & 2.74348334968703 & -0.461696952793818 \tabularnewline
18 & 2.87 & 2.7212004095396 & 0.257551632673059 & 2.76124795778734 & -0.148799590460401 \tabularnewline
19 & 3.14 & 3.20409776664494 & 0.296889667467404 & 2.77901256588766 & 0.064097766644939 \tabularnewline
20 & 3.11 & 3.3894628184367 & 0.0656744121121701 & 2.76486276945113 & 0.279462818436696 \tabularnewline
21 & 3.16 & 3.57682802445655 & -0.00754099747115926 & 2.75071297301461 & 0.416828024456549 \tabularnewline
22 & 2.47 & 2.28921742507168 & -0.0429543449566833 & 2.69373691988500 & -0.180782574928321 \tabularnewline
23 & 2.57 & 2.76760613296785 & -0.264366999723248 & 2.6367608667554 & 0.197606132967848 \tabularnewline
24 & 2.89 & 3.59523814563133 & -0.356837164963891 & 2.54159901933256 & 0.705238145631335 \tabularnewline
25 & 2.63 & 2.83961933416562 & -0.0260565060753351 & 2.44643717190971 & 0.209619334165621 \tabularnewline
26 & 2.38 & 2.53749990213549 & -0.0914413207106042 & 2.31394141857512 & 0.157499902135485 \tabularnewline
27 & 1.69 & 1.26338076941229 & -0.0648264346528119 & 2.18144566524052 & -0.426619230587710 \tabularnewline
28 & 1.96 & 1.86438465045083 & 0.00569377243741625 & 2.04992157711176 & -0.0956153495491732 \tabularnewline
29 & 2.19 & 2.23338890791022 & 0.228213603106789 & 1.91839748898299 & 0.0433889079102185 \tabularnewline
30 & 1.87 & 1.65826863340719 & 0.257551632673059 & 1.82417973391975 & -0.21173136659281 \tabularnewline
31 & 1.6 & 1.17314835367609 & 0.296889667467404 & 1.72996197885651 & -0.426851646323914 \tabularnewline
32 & 1.63 & 1.50601324802883 & 0.0656744121121701 & 1.688312339859 & -0.123986751971172 \tabularnewline
33 & 1.22 & 0.800878296609666 & -0.00754099747115926 & 1.64666270086149 & -0.419121703390334 \tabularnewline
34 & 1.21 & 0.833905028535982 & -0.0429543449566833 & 1.6290493164207 & -0.376094971464018 \tabularnewline
35 & 1.49 & 1.63293106774334 & -0.264366999723248 & 1.61143593197991 & 0.142931067743338 \tabularnewline
36 & 1.64 & 2.04977183864735 & -0.356837164963891 & 1.58706532631654 & 0.409771838647354 \tabularnewline
37 & 1.66 & 1.78336178542217 & -0.0260565060753351 & 1.56269472065316 & 0.123361785422172 \tabularnewline
38 & 1.77 & 2.08366310068207 & -0.0914413207106042 & 1.54777822002854 & 0.313663100682066 \tabularnewline
39 & 1.82 & 2.17196471524890 & -0.0648264346528119 & 1.53286171940391 & 0.351964715248898 \tabularnewline
40 & 1.78 & 1.98477993013341 & 0.00569377243741625 & 1.56952629742917 & 0.204779930133414 \tabularnewline
41 & 1.28 & 0.725595521438785 & 0.228213603106789 & 1.60619087545443 & -0.554404478561215 \tabularnewline
42 & 1.29 & 0.60917969310709 & 0.257551632673059 & 1.71326867421985 & -0.68082030689291 \tabularnewline
43 & 1.37 & 0.62276385954732 & 0.296889667467404 & 1.82034647298528 & -0.74723614045268 \tabularnewline
44 & 1.12 & 0.168036503613658 & 0.0656744121121701 & 2.00628908427417 & -0.951963496386342 \tabularnewline
45 & 1.51 & 0.835309301908092 & -0.00754099747115926 & 2.19223169556307 & -0.674690698091908 \tabularnewline
46 & 2.24 & 2.05611680604887 & -0.0429543449566833 & 2.46683753890782 & -0.183883193951132 \tabularnewline
47 & 2.94 & 3.40292361747068 & -0.264366999723248 & 2.74144338225256 & 0.462923617470683 \tabularnewline
48 & 3.09 & 3.4418956377759 & -0.356837164963891 & 3.09494152718799 & 0.351895637775901 \tabularnewline
49 & 3.46 & 3.49761683395192 & -0.0260565060753351 & 3.44843967212341 & 0.0376168339519203 \tabularnewline
50 & 3.64 & 3.58511597870935 & -0.0914413207106042 & 3.78632534200126 & -0.0548840212906532 \tabularnewline
51 & 4.39 & 4.72061542277371 & -0.0648264346528119 & 4.1242110118791 & 0.330615422773712 \tabularnewline
52 & 4.15 & 4.05925685815732 & 0.00569377243741625 & 4.23504936940526 & -0.0907431418426805 \tabularnewline
53 & 5.21 & 5.84589866996178 & 0.228213603106789 & 4.34588772693143 & 0.63589866996178 \tabularnewline
54 & 5.8 & 6.93261137475736 & 0.257551632673059 & 4.40983699256958 & 1.13261137475736 \tabularnewline
55 & 5.91 & 7.04932407432487 & 0.296889667467404 & 4.47378625820773 & 1.13932407432487 \tabularnewline
56 & 5.39 & 6.18659194893183 & 0.0656744121121701 & 4.52773363895600 & 0.796591948931826 \tabularnewline
57 & 5.46 & 6.34585997776688 & -0.00754099747115926 & 4.58168101970428 & 0.885859977766878 \tabularnewline
58 & 4.72 & 4.86728947278879 & -0.0429543449566833 & 4.6156648721679 & 0.147289472788787 \tabularnewline
59 & 3.14 & 1.89471827509174 & -0.264366999723248 & 4.64964872463151 & -1.24528172490826 \tabularnewline
60 & 2.63 & 0.95560274889564 & -0.356837164963891 & 4.66123441606825 & -1.67439725110436 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63054&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]1.59[/C][C]1.74790183426246[/C][C]-0.0260565060753351[/C][C]1.45815467181287[/C][C]0.157901834262461[/C][/ROW]
[ROW][C]2[/C][C]1.26[/C][C]1.04135619993061[/C][C]-0.0914413207106042[/C][C]1.57008512077999[/C][C]-0.218643800069386[/C][/ROW]
[ROW][C]3[/C][C]1.13[/C][C]0.642810864905706[/C][C]-0.0648264346528119[/C][C]1.68201556974711[/C][C]-0.487189135094294[/C][/ROW]
[ROW][C]4[/C][C]1.92[/C][C]2.04390931656159[/C][C]0.00569377243741625[/C][C]1.79039691100100[/C][C]0.123909316561587[/C][/ROW]
[ROW][C]5[/C][C]2.61[/C][C]3.09300814463832[/C][C]0.228213603106789[/C][C]1.89877825225489[/C][C]0.483008144638324[/C][/ROW]
[ROW][C]6[/C][C]2.26[/C][C]2.25744194428967[/C][C]0.257551632673059[/C][C]2.00500642303727[/C][C]-0.00255805571032708[/C][/ROW]
[ROW][C]7[/C][C]2.41[/C][C]2.41187573871295[/C][C]0.296889667467404[/C][C]2.11123459381965[/C][C]0.00187573871294688[/C][/ROW]
[ROW][C]8[/C][C]2.26[/C][C]2.23757535818981[/C][C]0.0656744121121701[/C][C]2.21675022969802[/C][C]-0.0224246418101863[/C][/ROW]
[ROW][C]9[/C][C]2.03[/C][C]1.74527513189478[/C][C]-0.00754099747115926[/C][C]2.32226586557638[/C][C]-0.284724868105223[/C][/ROW]
[ROW][C]10[/C][C]2.86[/C][C]3.35682392545861[/C][C]-0.0429543449566833[/C][C]2.40613041949807[/C][C]0.496823925458609[/C][/ROW]
[ROW][C]11[/C][C]2.55[/C][C]2.87437202630348[/C][C]-0.264366999723248[/C][C]2.48999497341977[/C][C]0.324372026303482[/C][/ROW]
[ROW][C]12[/C][C]2.27[/C][C]2.35585056723804[/C][C]-0.356837164963891[/C][C]2.54098659772586[/C][C]0.0858505672380359[/C][/ROW]
[ROW][C]13[/C][C]2.26[/C][C]1.95407828404339[/C][C]-0.0260565060753351[/C][C]2.59197822203195[/C][C]-0.305921715956611[/C][/ROW]
[ROW][C]14[/C][C]2.57[/C][C]2.59380770808559[/C][C]-0.0914413207106042[/C][C]2.63763361262502[/C][C]0.0238077080855863[/C][/ROW]
[ROW][C]15[/C][C]3.07[/C][C]3.52153743143472[/C][C]-0.0648264346528119[/C][C]2.68328900321809[/C][C]0.451537431434722[/C][/ROW]
[ROW][C]16[/C][C]2.76[/C][C]2.80092005111002[/C][C]0.00569377243741625[/C][C]2.71338617645256[/C][C]0.0409200511100245[/C][/ROW]
[ROW][C]17[/C][C]2.51[/C][C]2.04830304720618[/C][C]0.228213603106789[/C][C]2.74348334968703[/C][C]-0.461696952793818[/C][/ROW]
[ROW][C]18[/C][C]2.87[/C][C]2.7212004095396[/C][C]0.257551632673059[/C][C]2.76124795778734[/C][C]-0.148799590460401[/C][/ROW]
[ROW][C]19[/C][C]3.14[/C][C]3.20409776664494[/C][C]0.296889667467404[/C][C]2.77901256588766[/C][C]0.064097766644939[/C][/ROW]
[ROW][C]20[/C][C]3.11[/C][C]3.3894628184367[/C][C]0.0656744121121701[/C][C]2.76486276945113[/C][C]0.279462818436696[/C][/ROW]
[ROW][C]21[/C][C]3.16[/C][C]3.57682802445655[/C][C]-0.00754099747115926[/C][C]2.75071297301461[/C][C]0.416828024456549[/C][/ROW]
[ROW][C]22[/C][C]2.47[/C][C]2.28921742507168[/C][C]-0.0429543449566833[/C][C]2.69373691988500[/C][C]-0.180782574928321[/C][/ROW]
[ROW][C]23[/C][C]2.57[/C][C]2.76760613296785[/C][C]-0.264366999723248[/C][C]2.6367608667554[/C][C]0.197606132967848[/C][/ROW]
[ROW][C]24[/C][C]2.89[/C][C]3.59523814563133[/C][C]-0.356837164963891[/C][C]2.54159901933256[/C][C]0.705238145631335[/C][/ROW]
[ROW][C]25[/C][C]2.63[/C][C]2.83961933416562[/C][C]-0.0260565060753351[/C][C]2.44643717190971[/C][C]0.209619334165621[/C][/ROW]
[ROW][C]26[/C][C]2.38[/C][C]2.53749990213549[/C][C]-0.0914413207106042[/C][C]2.31394141857512[/C][C]0.157499902135485[/C][/ROW]
[ROW][C]27[/C][C]1.69[/C][C]1.26338076941229[/C][C]-0.0648264346528119[/C][C]2.18144566524052[/C][C]-0.426619230587710[/C][/ROW]
[ROW][C]28[/C][C]1.96[/C][C]1.86438465045083[/C][C]0.00569377243741625[/C][C]2.04992157711176[/C][C]-0.0956153495491732[/C][/ROW]
[ROW][C]29[/C][C]2.19[/C][C]2.23338890791022[/C][C]0.228213603106789[/C][C]1.91839748898299[/C][C]0.0433889079102185[/C][/ROW]
[ROW][C]30[/C][C]1.87[/C][C]1.65826863340719[/C][C]0.257551632673059[/C][C]1.82417973391975[/C][C]-0.21173136659281[/C][/ROW]
[ROW][C]31[/C][C]1.6[/C][C]1.17314835367609[/C][C]0.296889667467404[/C][C]1.72996197885651[/C][C]-0.426851646323914[/C][/ROW]
[ROW][C]32[/C][C]1.63[/C][C]1.50601324802883[/C][C]0.0656744121121701[/C][C]1.688312339859[/C][C]-0.123986751971172[/C][/ROW]
[ROW][C]33[/C][C]1.22[/C][C]0.800878296609666[/C][C]-0.00754099747115926[/C][C]1.64666270086149[/C][C]-0.419121703390334[/C][/ROW]
[ROW][C]34[/C][C]1.21[/C][C]0.833905028535982[/C][C]-0.0429543449566833[/C][C]1.6290493164207[/C][C]-0.376094971464018[/C][/ROW]
[ROW][C]35[/C][C]1.49[/C][C]1.63293106774334[/C][C]-0.264366999723248[/C][C]1.61143593197991[/C][C]0.142931067743338[/C][/ROW]
[ROW][C]36[/C][C]1.64[/C][C]2.04977183864735[/C][C]-0.356837164963891[/C][C]1.58706532631654[/C][C]0.409771838647354[/C][/ROW]
[ROW][C]37[/C][C]1.66[/C][C]1.78336178542217[/C][C]-0.0260565060753351[/C][C]1.56269472065316[/C][C]0.123361785422172[/C][/ROW]
[ROW][C]38[/C][C]1.77[/C][C]2.08366310068207[/C][C]-0.0914413207106042[/C][C]1.54777822002854[/C][C]0.313663100682066[/C][/ROW]
[ROW][C]39[/C][C]1.82[/C][C]2.17196471524890[/C][C]-0.0648264346528119[/C][C]1.53286171940391[/C][C]0.351964715248898[/C][/ROW]
[ROW][C]40[/C][C]1.78[/C][C]1.98477993013341[/C][C]0.00569377243741625[/C][C]1.56952629742917[/C][C]0.204779930133414[/C][/ROW]
[ROW][C]41[/C][C]1.28[/C][C]0.725595521438785[/C][C]0.228213603106789[/C][C]1.60619087545443[/C][C]-0.554404478561215[/C][/ROW]
[ROW][C]42[/C][C]1.29[/C][C]0.60917969310709[/C][C]0.257551632673059[/C][C]1.71326867421985[/C][C]-0.68082030689291[/C][/ROW]
[ROW][C]43[/C][C]1.37[/C][C]0.62276385954732[/C][C]0.296889667467404[/C][C]1.82034647298528[/C][C]-0.74723614045268[/C][/ROW]
[ROW][C]44[/C][C]1.12[/C][C]0.168036503613658[/C][C]0.0656744121121701[/C][C]2.00628908427417[/C][C]-0.951963496386342[/C][/ROW]
[ROW][C]45[/C][C]1.51[/C][C]0.835309301908092[/C][C]-0.00754099747115926[/C][C]2.19223169556307[/C][C]-0.674690698091908[/C][/ROW]
[ROW][C]46[/C][C]2.24[/C][C]2.05611680604887[/C][C]-0.0429543449566833[/C][C]2.46683753890782[/C][C]-0.183883193951132[/C][/ROW]
[ROW][C]47[/C][C]2.94[/C][C]3.40292361747068[/C][C]-0.264366999723248[/C][C]2.74144338225256[/C][C]0.462923617470683[/C][/ROW]
[ROW][C]48[/C][C]3.09[/C][C]3.4418956377759[/C][C]-0.356837164963891[/C][C]3.09494152718799[/C][C]0.351895637775901[/C][/ROW]
[ROW][C]49[/C][C]3.46[/C][C]3.49761683395192[/C][C]-0.0260565060753351[/C][C]3.44843967212341[/C][C]0.0376168339519203[/C][/ROW]
[ROW][C]50[/C][C]3.64[/C][C]3.58511597870935[/C][C]-0.0914413207106042[/C][C]3.78632534200126[/C][C]-0.0548840212906532[/C][/ROW]
[ROW][C]51[/C][C]4.39[/C][C]4.72061542277371[/C][C]-0.0648264346528119[/C][C]4.1242110118791[/C][C]0.330615422773712[/C][/ROW]
[ROW][C]52[/C][C]4.15[/C][C]4.05925685815732[/C][C]0.00569377243741625[/C][C]4.23504936940526[/C][C]-0.0907431418426805[/C][/ROW]
[ROW][C]53[/C][C]5.21[/C][C]5.84589866996178[/C][C]0.228213603106789[/C][C]4.34588772693143[/C][C]0.63589866996178[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]6.93261137475736[/C][C]0.257551632673059[/C][C]4.40983699256958[/C][C]1.13261137475736[/C][/ROW]
[ROW][C]55[/C][C]5.91[/C][C]7.04932407432487[/C][C]0.296889667467404[/C][C]4.47378625820773[/C][C]1.13932407432487[/C][/ROW]
[ROW][C]56[/C][C]5.39[/C][C]6.18659194893183[/C][C]0.0656744121121701[/C][C]4.52773363895600[/C][C]0.796591948931826[/C][/ROW]
[ROW][C]57[/C][C]5.46[/C][C]6.34585997776688[/C][C]-0.00754099747115926[/C][C]4.58168101970428[/C][C]0.885859977766878[/C][/ROW]
[ROW][C]58[/C][C]4.72[/C][C]4.86728947278879[/C][C]-0.0429543449566833[/C][C]4.6156648721679[/C][C]0.147289472788787[/C][/ROW]
[ROW][C]59[/C][C]3.14[/C][C]1.89471827509174[/C][C]-0.264366999723248[/C][C]4.64964872463151[/C][C]-1.24528172490826[/C][/ROW]
[ROW][C]60[/C][C]2.63[/C][C]0.95560274889564[/C][C]-0.356837164963891[/C][C]4.66123441606825[/C][C]-1.67439725110436[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63054&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63054&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
11.591.74790183426246-0.02605650607533511.458154671812870.157901834262461
21.261.04135619993061-0.09144132071060421.57008512077999-0.218643800069386
31.130.642810864905706-0.06482643465281191.68201556974711-0.487189135094294
41.922.043909316561590.005693772437416251.790396911001000.123909316561587
52.613.093008144638320.2282136031067891.898778252254890.483008144638324
62.262.257441944289670.2575516326730592.00500642303727-0.00255805571032708
72.412.411875738712950.2968896674674042.111234593819650.00187573871294688
82.262.237575358189810.06567441211217012.21675022969802-0.0224246418101863
92.031.74527513189478-0.007540997471159262.32226586557638-0.284724868105223
102.863.35682392545861-0.04295434495668332.406130419498070.496823925458609
112.552.87437202630348-0.2643669997232482.489994973419770.324372026303482
122.272.35585056723804-0.3568371649638912.540986597725860.0858505672380359
132.261.95407828404339-0.02605650607533512.59197822203195-0.305921715956611
142.572.59380770808559-0.09144132071060422.637633612625020.0238077080855863
153.073.52153743143472-0.06482643465281192.683289003218090.451537431434722
162.762.800920051110020.005693772437416252.713386176452560.0409200511100245
172.512.048303047206180.2282136031067892.74348334968703-0.461696952793818
182.872.72120040953960.2575516326730592.76124795778734-0.148799590460401
193.143.204097766644940.2968896674674042.779012565887660.064097766644939
203.113.38946281843670.06567441211217012.764862769451130.279462818436696
213.163.57682802445655-0.007540997471159262.750712973014610.416828024456549
222.472.28921742507168-0.04295434495668332.69373691988500-0.180782574928321
232.572.76760613296785-0.2643669997232482.63676086675540.197606132967848
242.893.59523814563133-0.3568371649638912.541599019332560.705238145631335
252.632.83961933416562-0.02605650607533512.446437171909710.209619334165621
262.382.53749990213549-0.09144132071060422.313941418575120.157499902135485
271.691.26338076941229-0.06482643465281192.18144566524052-0.426619230587710
281.961.864384650450830.005693772437416252.04992157711176-0.0956153495491732
292.192.233388907910220.2282136031067891.918397488982990.0433889079102185
301.871.658268633407190.2575516326730591.82417973391975-0.21173136659281
311.61.173148353676090.2968896674674041.72996197885651-0.426851646323914
321.631.506013248028830.06567441211217011.688312339859-0.123986751971172
331.220.800878296609666-0.007540997471159261.64666270086149-0.419121703390334
341.210.833905028535982-0.04295434495668331.6290493164207-0.376094971464018
351.491.63293106774334-0.2643669997232481.611435931979910.142931067743338
361.642.04977183864735-0.3568371649638911.587065326316540.409771838647354
371.661.78336178542217-0.02605650607533511.562694720653160.123361785422172
381.772.08366310068207-0.09144132071060421.547778220028540.313663100682066
391.822.17196471524890-0.06482643465281191.532861719403910.351964715248898
401.781.984779930133410.005693772437416251.569526297429170.204779930133414
411.280.7255955214387850.2282136031067891.60619087545443-0.554404478561215
421.290.609179693107090.2575516326730591.71326867421985-0.68082030689291
431.370.622763859547320.2968896674674041.82034647298528-0.74723614045268
441.120.1680365036136580.06567441211217012.00628908427417-0.951963496386342
451.510.835309301908092-0.007540997471159262.19223169556307-0.674690698091908
462.242.05611680604887-0.04295434495668332.46683753890782-0.183883193951132
472.943.40292361747068-0.2643669997232482.741443382252560.462923617470683
483.093.4418956377759-0.3568371649638913.094941527187990.351895637775901
493.463.49761683395192-0.02605650607533513.448439672123410.0376168339519203
503.643.58511597870935-0.09144132071060423.78632534200126-0.0548840212906532
514.394.72061542277371-0.06482643465281194.12421101187910.330615422773712
524.154.059256858157320.005693772437416254.23504936940526-0.0907431418426805
535.215.845898669961780.2282136031067894.345887726931430.63589866996178
545.86.932611374757360.2575516326730594.409836992569581.13261137475736
555.917.049324074324870.2968896674674044.473786258207731.13932407432487
565.396.186591948931830.06567441211217014.527733638956000.796591948931826
575.466.34585997776688-0.007540997471159264.581681019704280.885859977766878
584.724.86728947278879-0.04295434495668334.61566487216790.147289472788787
593.141.89471827509174-0.2643669997232484.64964872463151-1.24528172490826
602.630.95560274889564-0.3568371649638914.66123441606825-1.67439725110436



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')