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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 06:27:56 -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/t12598469191hohhih9qclgnki.htm/, Retrieved Fri, 29 Mar 2024 10:10:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62725, Retrieved Fri, 29 Mar 2024 10:10:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsJSSHWWS9P8
Estimated Impact117
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]
- RMPD    [Decomposition by Loess] [Decomposition by ...] [2009-12-03 13:27:56] [c8fd62404619100d8e91184019148412] [Current]
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Dataseries X:
11.1
10.9
10
9.2
9.2
9.5
9.6
9.5
9.1
8.9
9
10.1
10.3
10.2
9.6
9.2
9.3
9.4
9.4
9.2
9
9
9
9.8
10
9.8
9.3
9
9
9.1
9.1
9.1
9.2
8.8
8.3
8.4
8.1
7.7
7.9
7.9
8
7.9
7.6
7.1
6.8
6.5
6.9
8.2
8.7
8.3
7.9
7.5
7.8
8.3
8.4
8.2
7.7
7.2
7.3
8.1




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=62725&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=62725&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62725&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=62725&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=62725&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62725&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
111.111.49124459290110.7245834280744319.984171979024510.391244592901057
210.911.37699428665340.4987639680642679.924241745282330.476994286653403
31010.04274386277430.09294462568558249.864311511540150.0427438627742696
49.28.84154750338496-0.2526699420005279.81112243861556-0.358452496615037
59.28.7603513792587-0.1182847449496829.75793336569098-0.439648620741297
69.59.203978984754740.0888385703396979.70718244490556-0.296021015245259
79.69.447606498759590.09596197712026589.65643152412015-0.152393501240415
89.59.46811270729899-0.07567271635475829.60756000905577-0.0318872927010148
99.18.94861870671568-0.3073072007070749.5586884939914-0.151381293284324
108.98.81753033943586-0.560625949845119.54309561040925-0.0824696605641417
1198.9864421420683-0.5139448688954039.5275027268271-0.0135578579317031
1210.110.35066526937410.3274128551483199.521921875477630.250665269374053
1310.310.35907554779740.7245834280744319.516341024128150.0590755477974216
1410.210.39968441967470.4987639680642679.501551612261020.199684419674716
159.69.620293173920530.09294462568558249.486762200393890.0202931739205319
169.29.1798535332355-0.2526699420005279.47281640876502-0.0201464667644942
179.39.25941412781353-0.1182847449496829.45887061713615-0.0405858721864707
189.49.26987397070960.0888385703396979.4412874589507-0.1301260292904
199.49.280333722114480.09596197712026589.42370430076526-0.119666277885521
209.29.07200354455857-0.07567271635475829.40366917179618-0.127996455441425
2198.92367315787997-0.3073072007070749.3836340428271-0.0763268421200323
2299.19598933027585-0.560625949845119.364636619569260.19598933027585
2399.16830567258399-0.5139448688954039.345639196311420.168305672583987
249.89.948058195334630.3274128551483199.324528949517050.148058195334629
25109.971997869202880.7245834280744319.30341870272269-0.0280021307971214
269.89.81678626630820.4987639680642679.284449765627540.0167862663081966
279.39.241574545782030.09294462568558249.26548082853238-0.0584254542179661
2899.01890920521249-0.2526699420005279.233760736788040.0189092052124877
2998.91624409990599-0.1182847449496829.2020406450437-0.0837559000940118
309.18.994882101386150.0888385703396979.11627932827416-0.105117898613853
319.19.073520011375110.09596197712026589.03051801150462-0.0264799886248888
329.19.37641259527792-0.07567271635475828.899260121076840.276412595277916
339.29.93930497005801-0.3073072007070748.768002230649060.739304970058013
348.89.52136262258848-0.560625949845118.639263327256640.721362622588476
358.38.6034204450312-0.5139448688954038.510524423864210.303420445031195
368.48.09201058852150.3274128551483198.38057655633018-0.307989411478502
378.17.224787883129410.7245834280744318.25062868879616-0.87521211687059
387.76.805846722658550.4987639680642678.09538930927718-0.894153277341448
397.97.766905444556210.09294462568558247.9401499297582-0.133094555443786
407.98.2447042142546-0.2526699420005277.807965727745920.344704214254609
4188.44250321921605-0.1182847449496827.675781525733630.442503219216049
427.98.07986582697180.0888385703396977.63129560268850.179865826971799
437.67.517228343236360.09596197712026587.58680967964338-0.0827716567636418
447.16.68591094964692-0.07567271635475827.58976176670784-0.414089050353083
456.86.31459334693477-0.3073072007070747.59271385377231-0.485406653065234
466.55.96354072518522-0.560625949845117.59708522465989-0.536459274814781
476.96.71248827334793-0.5139448688954037.60145659554747-0.187511726652071
488.28.430372910199480.3274128551483197.64221423465220.230372910199481
498.78.992444698168640.7245834280744317.682971873756920.292444698168644
508.38.343344646934130.4987639680642677.75789138500160.0433446469341323
517.97.874244478068140.09294462568558247.83281089624628-0.0257555219318615
527.57.39000338229118-0.2526699420005277.86266655970934-0.109996617708816
537.87.82576252177727-0.1182847449496827.89252222317240.0257625217772732
548.38.597092828643080.0888385703396977.914068601017220.297092828643083
558.48.76842304401770.09596197712026587.935614978862030.3684230440177
568.28.520766857479-0.07567271635475827.954905858875760.320766857478998
577.77.73311046181759-0.3073072007070747.974196738889480.0331104618175910
587.26.97215588303982-0.560625949845117.9884700668053-0.227844116960184
597.37.1112014741743-0.5139448688954038.0027433947211-0.188798525825701
608.17.86057212876780.3274128551483198.01201501608388-0.239427871232195

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 11.1 & 11.4912445929011 & 0.724583428074431 & 9.98417197902451 & 0.391244592901057 \tabularnewline
2 & 10.9 & 11.3769942866534 & 0.498763968064267 & 9.92424174528233 & 0.476994286653403 \tabularnewline
3 & 10 & 10.0427438627743 & 0.0929446256855824 & 9.86431151154015 & 0.0427438627742696 \tabularnewline
4 & 9.2 & 8.84154750338496 & -0.252669942000527 & 9.81112243861556 & -0.358452496615037 \tabularnewline
5 & 9.2 & 8.7603513792587 & -0.118284744949682 & 9.75793336569098 & -0.439648620741297 \tabularnewline
6 & 9.5 & 9.20397898475474 & 0.088838570339697 & 9.70718244490556 & -0.296021015245259 \tabularnewline
7 & 9.6 & 9.44760649875959 & 0.0959619771202658 & 9.65643152412015 & -0.152393501240415 \tabularnewline
8 & 9.5 & 9.46811270729899 & -0.0756727163547582 & 9.60756000905577 & -0.0318872927010148 \tabularnewline
9 & 9.1 & 8.94861870671568 & -0.307307200707074 & 9.5586884939914 & -0.151381293284324 \tabularnewline
10 & 8.9 & 8.81753033943586 & -0.56062594984511 & 9.54309561040925 & -0.0824696605641417 \tabularnewline
11 & 9 & 8.9864421420683 & -0.513944868895403 & 9.5275027268271 & -0.0135578579317031 \tabularnewline
12 & 10.1 & 10.3506652693741 & 0.327412855148319 & 9.52192187547763 & 0.250665269374053 \tabularnewline
13 & 10.3 & 10.3590755477974 & 0.724583428074431 & 9.51634102412815 & 0.0590755477974216 \tabularnewline
14 & 10.2 & 10.3996844196747 & 0.498763968064267 & 9.50155161226102 & 0.199684419674716 \tabularnewline
15 & 9.6 & 9.62029317392053 & 0.0929446256855824 & 9.48676220039389 & 0.0202931739205319 \tabularnewline
16 & 9.2 & 9.1798535332355 & -0.252669942000527 & 9.47281640876502 & -0.0201464667644942 \tabularnewline
17 & 9.3 & 9.25941412781353 & -0.118284744949682 & 9.45887061713615 & -0.0405858721864707 \tabularnewline
18 & 9.4 & 9.2698739707096 & 0.088838570339697 & 9.4412874589507 & -0.1301260292904 \tabularnewline
19 & 9.4 & 9.28033372211448 & 0.0959619771202658 & 9.42370430076526 & -0.119666277885521 \tabularnewline
20 & 9.2 & 9.07200354455857 & -0.0756727163547582 & 9.40366917179618 & -0.127996455441425 \tabularnewline
21 & 9 & 8.92367315787997 & -0.307307200707074 & 9.3836340428271 & -0.0763268421200323 \tabularnewline
22 & 9 & 9.19598933027585 & -0.56062594984511 & 9.36463661956926 & 0.19598933027585 \tabularnewline
23 & 9 & 9.16830567258399 & -0.513944868895403 & 9.34563919631142 & 0.168305672583987 \tabularnewline
24 & 9.8 & 9.94805819533463 & 0.327412855148319 & 9.32452894951705 & 0.148058195334629 \tabularnewline
25 & 10 & 9.97199786920288 & 0.724583428074431 & 9.30341870272269 & -0.0280021307971214 \tabularnewline
26 & 9.8 & 9.8167862663082 & 0.498763968064267 & 9.28444976562754 & 0.0167862663081966 \tabularnewline
27 & 9.3 & 9.24157454578203 & 0.0929446256855824 & 9.26548082853238 & -0.0584254542179661 \tabularnewline
28 & 9 & 9.01890920521249 & -0.252669942000527 & 9.23376073678804 & 0.0189092052124877 \tabularnewline
29 & 9 & 8.91624409990599 & -0.118284744949682 & 9.2020406450437 & -0.0837559000940118 \tabularnewline
30 & 9.1 & 8.99488210138615 & 0.088838570339697 & 9.11627932827416 & -0.105117898613853 \tabularnewline
31 & 9.1 & 9.07352001137511 & 0.0959619771202658 & 9.03051801150462 & -0.0264799886248888 \tabularnewline
32 & 9.1 & 9.37641259527792 & -0.0756727163547582 & 8.89926012107684 & 0.276412595277916 \tabularnewline
33 & 9.2 & 9.93930497005801 & -0.307307200707074 & 8.76800223064906 & 0.739304970058013 \tabularnewline
34 & 8.8 & 9.52136262258848 & -0.56062594984511 & 8.63926332725664 & 0.721362622588476 \tabularnewline
35 & 8.3 & 8.6034204450312 & -0.513944868895403 & 8.51052442386421 & 0.303420445031195 \tabularnewline
36 & 8.4 & 8.0920105885215 & 0.327412855148319 & 8.38057655633018 & -0.307989411478502 \tabularnewline
37 & 8.1 & 7.22478788312941 & 0.724583428074431 & 8.25062868879616 & -0.87521211687059 \tabularnewline
38 & 7.7 & 6.80584672265855 & 0.498763968064267 & 8.09538930927718 & -0.894153277341448 \tabularnewline
39 & 7.9 & 7.76690544455621 & 0.0929446256855824 & 7.9401499297582 & -0.133094555443786 \tabularnewline
40 & 7.9 & 8.2447042142546 & -0.252669942000527 & 7.80796572774592 & 0.344704214254609 \tabularnewline
41 & 8 & 8.44250321921605 & -0.118284744949682 & 7.67578152573363 & 0.442503219216049 \tabularnewline
42 & 7.9 & 8.0798658269718 & 0.088838570339697 & 7.6312956026885 & 0.179865826971799 \tabularnewline
43 & 7.6 & 7.51722834323636 & 0.0959619771202658 & 7.58680967964338 & -0.0827716567636418 \tabularnewline
44 & 7.1 & 6.68591094964692 & -0.0756727163547582 & 7.58976176670784 & -0.414089050353083 \tabularnewline
45 & 6.8 & 6.31459334693477 & -0.307307200707074 & 7.59271385377231 & -0.485406653065234 \tabularnewline
46 & 6.5 & 5.96354072518522 & -0.56062594984511 & 7.59708522465989 & -0.536459274814781 \tabularnewline
47 & 6.9 & 6.71248827334793 & -0.513944868895403 & 7.60145659554747 & -0.187511726652071 \tabularnewline
48 & 8.2 & 8.43037291019948 & 0.327412855148319 & 7.6422142346522 & 0.230372910199481 \tabularnewline
49 & 8.7 & 8.99244469816864 & 0.724583428074431 & 7.68297187375692 & 0.292444698168644 \tabularnewline
50 & 8.3 & 8.34334464693413 & 0.498763968064267 & 7.7578913850016 & 0.0433446469341323 \tabularnewline
51 & 7.9 & 7.87424447806814 & 0.0929446256855824 & 7.83281089624628 & -0.0257555219318615 \tabularnewline
52 & 7.5 & 7.39000338229118 & -0.252669942000527 & 7.86266655970934 & -0.109996617708816 \tabularnewline
53 & 7.8 & 7.82576252177727 & -0.118284744949682 & 7.8925222231724 & 0.0257625217772732 \tabularnewline
54 & 8.3 & 8.59709282864308 & 0.088838570339697 & 7.91406860101722 & 0.297092828643083 \tabularnewline
55 & 8.4 & 8.7684230440177 & 0.0959619771202658 & 7.93561497886203 & 0.3684230440177 \tabularnewline
56 & 8.2 & 8.520766857479 & -0.0756727163547582 & 7.95490585887576 & 0.320766857478998 \tabularnewline
57 & 7.7 & 7.73311046181759 & -0.307307200707074 & 7.97419673888948 & 0.0331104618175910 \tabularnewline
58 & 7.2 & 6.97215588303982 & -0.56062594984511 & 7.9884700668053 & -0.227844116960184 \tabularnewline
59 & 7.3 & 7.1112014741743 & -0.513944868895403 & 8.0027433947211 & -0.188798525825701 \tabularnewline
60 & 8.1 & 7.8605721287678 & 0.327412855148319 & 8.01201501608388 & -0.239427871232195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62725&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]11.1[/C][C]11.4912445929011[/C][C]0.724583428074431[/C][C]9.98417197902451[/C][C]0.391244592901057[/C][/ROW]
[ROW][C]2[/C][C]10.9[/C][C]11.3769942866534[/C][C]0.498763968064267[/C][C]9.92424174528233[/C][C]0.476994286653403[/C][/ROW]
[ROW][C]3[/C][C]10[/C][C]10.0427438627743[/C][C]0.0929446256855824[/C][C]9.86431151154015[/C][C]0.0427438627742696[/C][/ROW]
[ROW][C]4[/C][C]9.2[/C][C]8.84154750338496[/C][C]-0.252669942000527[/C][C]9.81112243861556[/C][C]-0.358452496615037[/C][/ROW]
[ROW][C]5[/C][C]9.2[/C][C]8.7603513792587[/C][C]-0.118284744949682[/C][C]9.75793336569098[/C][C]-0.439648620741297[/C][/ROW]
[ROW][C]6[/C][C]9.5[/C][C]9.20397898475474[/C][C]0.088838570339697[/C][C]9.70718244490556[/C][C]-0.296021015245259[/C][/ROW]
[ROW][C]7[/C][C]9.6[/C][C]9.44760649875959[/C][C]0.0959619771202658[/C][C]9.65643152412015[/C][C]-0.152393501240415[/C][/ROW]
[ROW][C]8[/C][C]9.5[/C][C]9.46811270729899[/C][C]-0.0756727163547582[/C][C]9.60756000905577[/C][C]-0.0318872927010148[/C][/ROW]
[ROW][C]9[/C][C]9.1[/C][C]8.94861870671568[/C][C]-0.307307200707074[/C][C]9.5586884939914[/C][C]-0.151381293284324[/C][/ROW]
[ROW][C]10[/C][C]8.9[/C][C]8.81753033943586[/C][C]-0.56062594984511[/C][C]9.54309561040925[/C][C]-0.0824696605641417[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]8.9864421420683[/C][C]-0.513944868895403[/C][C]9.5275027268271[/C][C]-0.0135578579317031[/C][/ROW]
[ROW][C]12[/C][C]10.1[/C][C]10.3506652693741[/C][C]0.327412855148319[/C][C]9.52192187547763[/C][C]0.250665269374053[/C][/ROW]
[ROW][C]13[/C][C]10.3[/C][C]10.3590755477974[/C][C]0.724583428074431[/C][C]9.51634102412815[/C][C]0.0590755477974216[/C][/ROW]
[ROW][C]14[/C][C]10.2[/C][C]10.3996844196747[/C][C]0.498763968064267[/C][C]9.50155161226102[/C][C]0.199684419674716[/C][/ROW]
[ROW][C]15[/C][C]9.6[/C][C]9.62029317392053[/C][C]0.0929446256855824[/C][C]9.48676220039389[/C][C]0.0202931739205319[/C][/ROW]
[ROW][C]16[/C][C]9.2[/C][C]9.1798535332355[/C][C]-0.252669942000527[/C][C]9.47281640876502[/C][C]-0.0201464667644942[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]9.25941412781353[/C][C]-0.118284744949682[/C][C]9.45887061713615[/C][C]-0.0405858721864707[/C][/ROW]
[ROW][C]18[/C][C]9.4[/C][C]9.2698739707096[/C][C]0.088838570339697[/C][C]9.4412874589507[/C][C]-0.1301260292904[/C][/ROW]
[ROW][C]19[/C][C]9.4[/C][C]9.28033372211448[/C][C]0.0959619771202658[/C][C]9.42370430076526[/C][C]-0.119666277885521[/C][/ROW]
[ROW][C]20[/C][C]9.2[/C][C]9.07200354455857[/C][C]-0.0756727163547582[/C][C]9.40366917179618[/C][C]-0.127996455441425[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.92367315787997[/C][C]-0.307307200707074[/C][C]9.3836340428271[/C][C]-0.0763268421200323[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]9.19598933027585[/C][C]-0.56062594984511[/C][C]9.36463661956926[/C][C]0.19598933027585[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]9.16830567258399[/C][C]-0.513944868895403[/C][C]9.34563919631142[/C][C]0.168305672583987[/C][/ROW]
[ROW][C]24[/C][C]9.8[/C][C]9.94805819533463[/C][C]0.327412855148319[/C][C]9.32452894951705[/C][C]0.148058195334629[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]9.97199786920288[/C][C]0.724583428074431[/C][C]9.30341870272269[/C][C]-0.0280021307971214[/C][/ROW]
[ROW][C]26[/C][C]9.8[/C][C]9.8167862663082[/C][C]0.498763968064267[/C][C]9.28444976562754[/C][C]0.0167862663081966[/C][/ROW]
[ROW][C]27[/C][C]9.3[/C][C]9.24157454578203[/C][C]0.0929446256855824[/C][C]9.26548082853238[/C][C]-0.0584254542179661[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.01890920521249[/C][C]-0.252669942000527[/C][C]9.23376073678804[/C][C]0.0189092052124877[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]8.91624409990599[/C][C]-0.118284744949682[/C][C]9.2020406450437[/C][C]-0.0837559000940118[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]8.99488210138615[/C][C]0.088838570339697[/C][C]9.11627932827416[/C][C]-0.105117898613853[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]9.07352001137511[/C][C]0.0959619771202658[/C][C]9.03051801150462[/C][C]-0.0264799886248888[/C][/ROW]
[ROW][C]32[/C][C]9.1[/C][C]9.37641259527792[/C][C]-0.0756727163547582[/C][C]8.89926012107684[/C][C]0.276412595277916[/C][/ROW]
[ROW][C]33[/C][C]9.2[/C][C]9.93930497005801[/C][C]-0.307307200707074[/C][C]8.76800223064906[/C][C]0.739304970058013[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]9.52136262258848[/C][C]-0.56062594984511[/C][C]8.63926332725664[/C][C]0.721362622588476[/C][/ROW]
[ROW][C]35[/C][C]8.3[/C][C]8.6034204450312[/C][C]-0.513944868895403[/C][C]8.51052442386421[/C][C]0.303420445031195[/C][/ROW]
[ROW][C]36[/C][C]8.4[/C][C]8.0920105885215[/C][C]0.327412855148319[/C][C]8.38057655633018[/C][C]-0.307989411478502[/C][/ROW]
[ROW][C]37[/C][C]8.1[/C][C]7.22478788312941[/C][C]0.724583428074431[/C][C]8.25062868879616[/C][C]-0.87521211687059[/C][/ROW]
[ROW][C]38[/C][C]7.7[/C][C]6.80584672265855[/C][C]0.498763968064267[/C][C]8.09538930927718[/C][C]-0.894153277341448[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.76690544455621[/C][C]0.0929446256855824[/C][C]7.9401499297582[/C][C]-0.133094555443786[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]8.2447042142546[/C][C]-0.252669942000527[/C][C]7.80796572774592[/C][C]0.344704214254609[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]8.44250321921605[/C][C]-0.118284744949682[/C][C]7.67578152573363[/C][C]0.442503219216049[/C][/ROW]
[ROW][C]42[/C][C]7.9[/C][C]8.0798658269718[/C][C]0.088838570339697[/C][C]7.6312956026885[/C][C]0.179865826971799[/C][/ROW]
[ROW][C]43[/C][C]7.6[/C][C]7.51722834323636[/C][C]0.0959619771202658[/C][C]7.58680967964338[/C][C]-0.0827716567636418[/C][/ROW]
[ROW][C]44[/C][C]7.1[/C][C]6.68591094964692[/C][C]-0.0756727163547582[/C][C]7.58976176670784[/C][C]-0.414089050353083[/C][/ROW]
[ROW][C]45[/C][C]6.8[/C][C]6.31459334693477[/C][C]-0.307307200707074[/C][C]7.59271385377231[/C][C]-0.485406653065234[/C][/ROW]
[ROW][C]46[/C][C]6.5[/C][C]5.96354072518522[/C][C]-0.56062594984511[/C][C]7.59708522465989[/C][C]-0.536459274814781[/C][/ROW]
[ROW][C]47[/C][C]6.9[/C][C]6.71248827334793[/C][C]-0.513944868895403[/C][C]7.60145659554747[/C][C]-0.187511726652071[/C][/ROW]
[ROW][C]48[/C][C]8.2[/C][C]8.43037291019948[/C][C]0.327412855148319[/C][C]7.6422142346522[/C][C]0.230372910199481[/C][/ROW]
[ROW][C]49[/C][C]8.7[/C][C]8.99244469816864[/C][C]0.724583428074431[/C][C]7.68297187375692[/C][C]0.292444698168644[/C][/ROW]
[ROW][C]50[/C][C]8.3[/C][C]8.34334464693413[/C][C]0.498763968064267[/C][C]7.7578913850016[/C][C]0.0433446469341323[/C][/ROW]
[ROW][C]51[/C][C]7.9[/C][C]7.87424447806814[/C][C]0.0929446256855824[/C][C]7.83281089624628[/C][C]-0.0257555219318615[/C][/ROW]
[ROW][C]52[/C][C]7.5[/C][C]7.39000338229118[/C][C]-0.252669942000527[/C][C]7.86266655970934[/C][C]-0.109996617708816[/C][/ROW]
[ROW][C]53[/C][C]7.8[/C][C]7.82576252177727[/C][C]-0.118284744949682[/C][C]7.8925222231724[/C][C]0.0257625217772732[/C][/ROW]
[ROW][C]54[/C][C]8.3[/C][C]8.59709282864308[/C][C]0.088838570339697[/C][C]7.91406860101722[/C][C]0.297092828643083[/C][/ROW]
[ROW][C]55[/C][C]8.4[/C][C]8.7684230440177[/C][C]0.0959619771202658[/C][C]7.93561497886203[/C][C]0.3684230440177[/C][/ROW]
[ROW][C]56[/C][C]8.2[/C][C]8.520766857479[/C][C]-0.0756727163547582[/C][C]7.95490585887576[/C][C]0.320766857478998[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]7.73311046181759[/C][C]-0.307307200707074[/C][C]7.97419673888948[/C][C]0.0331104618175910[/C][/ROW]
[ROW][C]58[/C][C]7.2[/C][C]6.97215588303982[/C][C]-0.56062594984511[/C][C]7.9884700668053[/C][C]-0.227844116960184[/C][/ROW]
[ROW][C]59[/C][C]7.3[/C][C]7.1112014741743[/C][C]-0.513944868895403[/C][C]8.0027433947211[/C][C]-0.188798525825701[/C][/ROW]
[ROW][C]60[/C][C]8.1[/C][C]7.8605721287678[/C][C]0.327412855148319[/C][C]8.01201501608388[/C][C]-0.239427871232195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62725&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62725&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
111.111.49124459290110.7245834280744319.984171979024510.391244592901057
210.911.37699428665340.4987639680642679.924241745282330.476994286653403
31010.04274386277430.09294462568558249.864311511540150.0427438627742696
49.28.84154750338496-0.2526699420005279.81112243861556-0.358452496615037
59.28.7603513792587-0.1182847449496829.75793336569098-0.439648620741297
69.59.203978984754740.0888385703396979.70718244490556-0.296021015245259
79.69.447606498759590.09596197712026589.65643152412015-0.152393501240415
89.59.46811270729899-0.07567271635475829.60756000905577-0.0318872927010148
99.18.94861870671568-0.3073072007070749.5586884939914-0.151381293284324
108.98.81753033943586-0.560625949845119.54309561040925-0.0824696605641417
1198.9864421420683-0.5139448688954039.5275027268271-0.0135578579317031
1210.110.35066526937410.3274128551483199.521921875477630.250665269374053
1310.310.35907554779740.7245834280744319.516341024128150.0590755477974216
1410.210.39968441967470.4987639680642679.501551612261020.199684419674716
159.69.620293173920530.09294462568558249.486762200393890.0202931739205319
169.29.1798535332355-0.2526699420005279.47281640876502-0.0201464667644942
179.39.25941412781353-0.1182847449496829.45887061713615-0.0405858721864707
189.49.26987397070960.0888385703396979.4412874589507-0.1301260292904
199.49.280333722114480.09596197712026589.42370430076526-0.119666277885521
209.29.07200354455857-0.07567271635475829.40366917179618-0.127996455441425
2198.92367315787997-0.3073072007070749.3836340428271-0.0763268421200323
2299.19598933027585-0.560625949845119.364636619569260.19598933027585
2399.16830567258399-0.5139448688954039.345639196311420.168305672583987
249.89.948058195334630.3274128551483199.324528949517050.148058195334629
25109.971997869202880.7245834280744319.30341870272269-0.0280021307971214
269.89.81678626630820.4987639680642679.284449765627540.0167862663081966
279.39.241574545782030.09294462568558249.26548082853238-0.0584254542179661
2899.01890920521249-0.2526699420005279.233760736788040.0189092052124877
2998.91624409990599-0.1182847449496829.2020406450437-0.0837559000940118
309.18.994882101386150.0888385703396979.11627932827416-0.105117898613853
319.19.073520011375110.09596197712026589.03051801150462-0.0264799886248888
329.19.37641259527792-0.07567271635475828.899260121076840.276412595277916
339.29.93930497005801-0.3073072007070748.768002230649060.739304970058013
348.89.52136262258848-0.560625949845118.639263327256640.721362622588476
358.38.6034204450312-0.5139448688954038.510524423864210.303420445031195
368.48.09201058852150.3274128551483198.38057655633018-0.307989411478502
378.17.224787883129410.7245834280744318.25062868879616-0.87521211687059
387.76.805846722658550.4987639680642678.09538930927718-0.894153277341448
397.97.766905444556210.09294462568558247.9401499297582-0.133094555443786
407.98.2447042142546-0.2526699420005277.807965727745920.344704214254609
4188.44250321921605-0.1182847449496827.675781525733630.442503219216049
427.98.07986582697180.0888385703396977.63129560268850.179865826971799
437.67.517228343236360.09596197712026587.58680967964338-0.0827716567636418
447.16.68591094964692-0.07567271635475827.58976176670784-0.414089050353083
456.86.31459334693477-0.3073072007070747.59271385377231-0.485406653065234
466.55.96354072518522-0.560625949845117.59708522465989-0.536459274814781
476.96.71248827334793-0.5139448688954037.60145659554747-0.187511726652071
488.28.430372910199480.3274128551483197.64221423465220.230372910199481
498.78.992444698168640.7245834280744317.682971873756920.292444698168644
508.38.343344646934130.4987639680642677.75789138500160.0433446469341323
517.97.874244478068140.09294462568558247.83281089624628-0.0257555219318615
527.57.39000338229118-0.2526699420005277.86266655970934-0.109996617708816
537.87.82576252177727-0.1182847449496827.89252222317240.0257625217772732
548.38.597092828643080.0888385703396977.914068601017220.297092828643083
558.48.76842304401770.09596197712026587.935614978862030.3684230440177
568.28.520766857479-0.07567271635475827.954905858875760.320766857478998
577.77.73311046181759-0.3073072007070747.974196738889480.0331104618175910
587.26.97215588303982-0.560625949845117.9884700668053-0.227844116960184
597.37.1112014741743-0.5139448688954038.0027433947211-0.188798525825701
608.17.86057212876780.3274128551483198.01201501608388-0.239427871232195



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')