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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 computationFri, 25 Nov 2011 05:56:01 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/25/t13222186456so2e8j44gazb29.htm/, Retrieved Mon, 24 Jun 2024 14:45:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147287, Retrieved Mon, 24 Jun 2024 14:45:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Decomposition by Loess] [WS8] [2011-11-25 10:56:01] [84449ea5bbe6e767918d59f07903f9b5] [Current]
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Dataseries X:
89924
31795
27922
59954
52150
39964
34604
51106
52593
68794
47124
32315
42248
36088
52744
72586
92334
80761
71078
63713
57122
55243
62143
62708
62474
64250
71866
69886
58724
55298
52594
54854
54694
49298
44659
43657
47002
47042
48959
49750
54048
60067
68929
74617
75940
72762
75621
73008
74196
78878
83812
91624
89388
110410
113857
112060
117236
132810
137699
146409




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147287&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147287&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147287&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' @ jenkins.wessa.net







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=147287&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=147287&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147287&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
189924124007.7321146671824.4253166454154015.842568687534083.7321146671
23179521327.6688873722-10766.283807110453028.6149197382-10467.3311126278
32792210151.4151546412-6348.8024254300252041.3872707888-17770.5848453588
45995464496.40181638574222.7248637582851188.87331985614542.40181638567
55215050299.9773924063663.663238670750336.3593689233-1850.02260759396
63996427773.26517977342588.3712025683849566.3636176582-12190.7348202266
73460419957.3493499716454.28278363520448796.3678663932-14646.6506500284
85110651574.63856765112386.5015053221648250.8599270267468.63856765112
95259355972.52678507061508.1212272690847705.35198766033379.52678507064
106879484506.48839730944140.4969828513648941.014619839315712.4883973094
114712443895.0351824875176.28756549422850176.6772520182-3228.96481751246
123231515708.2565023576-3849.7790100450152771.5225076874-16606.7434976424
134224827305.2069199981824.4253166454155366.3677633566-14942.793080002
143608825673.4790836959-10766.283807110457268.8047234145-10414.5209163041
155274452665.5607419577-6348.8024254300259171.2416834723-78.4392580422937
167258680656.95466292924222.7248637582860292.32047331268070.95466292916
1792334119590.9374981763663.663238670761413.399263152827256.9374981765
188076196193.86725182922588.3712025683862739.761545602515432.8672518291
197107877635.5933883127454.28278363520464066.12382805216557.59338831266
206371360022.00471528332386.5015053221665017.4937793946-3690.99528471673
215712246767.01504199391508.1212272690865968.863730737-10354.9849580061
225524340756.13020210224140.4969828513665589.3728150465-14486.8697978978
236214358899.8305351498176.28756549422865209.881899356-3243.16946485021
246270865152.4404561528-3849.7790100450164113.33855389222444.44045615281
256247460106.77947492621824.4253166454163016.7952084284-2367.22052507382
266425077078.6630347132-10766.283807110462187.620772397212828.6630347132
277186688722.356089064-6348.8024254300261358.44633636616856.356089064
286988675237.57941952754222.7248637582860311.69571671435351.57941952746
295872454519.39166426683663.663238670759264.9450970625-4204.60833573321
305529850442.87777477112588.3712025683857564.7510226605-4855.12222522891
315259448869.1602681063454.28278363520455864.5569482585-3724.83973189375
325485453126.64473631432386.5015053221654194.8537583635-1727.35526368566
335469455354.72820426251508.1212272690852525.1505684685660.728204262457
344929842905.63112740464140.4969828513651549.8718897441-6392.36887259544
354465938567.1192234861176.28756549422850574.5932110197-6091.88077651394
364365740301.2983104479-3849.7790100450150862.4806995971-3355.70168955206
374700241029.20649518021824.4253166454151150.3681881744-5972.79350481984
384704252258.9694709344-10766.283807110452591.3143361765216.96947093442
394895950234.5419412525-6348.8024254300254032.26048417751275.54194125254
404975039029.01532888114222.7248637582856248.2598073606-10720.9846711189
415404845968.07763078553663.663238670758464.2591305438-8079.92236921447
426006756577.50713421662588.3712025683860968.121663215-3489.49286578341
436892973931.7330204785454.28278363520463471.98419588635002.73302047851
447461780561.91184678042386.5015053221666285.58664789755944.91184678038
457594081272.68967282221508.1212272690869099.18909990875332.68967282225
467276269178.04891106274140.4969828513672205.454106086-3583.95108893732
477562175753.9933222425176.28756549422875311.7191122633132.993322242502
487300871197.4924163381-3849.7790100450178668.2865937069-1810.50758366191
497419664542.7206082041824.4253166454182024.8540751506-9653.27939179598
507887882832.4287849642-10766.283807110485689.85502214623954.42878496421
518381284617.9464562882-6348.8024254300289354.8559691418805.946456288249
529162484416.70537016264222.7248637582894608.5697660792-7207.29462983744
538938875250.05319831273663.663238670799862.2835630166-14137.9468016873
54110410112832.3309187922588.37120256838105399.297878642422.3309187915
55113857116323.405022101454.282783635204110936.3121942642466.4050221011
56112060105141.8958669812386.50150532216116591.602627697-6918.10413301895
57117236110716.9857116011508.12122726908122246.89306113-6519.01428839896
58132810133443.0427033744140.49698285136128036.460313775633.0427033741
59137699141395.684868087176.287565494228133826.0275664193696.68486808657
60146409156924.967747978-3849.77901004501139742.81126206710515.9677479782

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 89924 & 124007.732114667 & 1824.42531664541 & 54015.8425686875 & 34083.7321146671 \tabularnewline
2 & 31795 & 21327.6688873722 & -10766.2838071104 & 53028.6149197382 & -10467.3311126278 \tabularnewline
3 & 27922 & 10151.4151546412 & -6348.80242543002 & 52041.3872707888 & -17770.5848453588 \tabularnewline
4 & 59954 & 64496.4018163857 & 4222.72486375828 & 51188.8733198561 & 4542.40181638567 \tabularnewline
5 & 52150 & 50299.977392406 & 3663.6632386707 & 50336.3593689233 & -1850.02260759396 \tabularnewline
6 & 39964 & 27773.2651797734 & 2588.37120256838 & 49566.3636176582 & -12190.7348202266 \tabularnewline
7 & 34604 & 19957.3493499716 & 454.282783635204 & 48796.3678663932 & -14646.6506500284 \tabularnewline
8 & 51106 & 51574.6385676511 & 2386.50150532216 & 48250.8599270267 & 468.63856765112 \tabularnewline
9 & 52593 & 55972.5267850706 & 1508.12122726908 & 47705.3519876603 & 3379.52678507064 \tabularnewline
10 & 68794 & 84506.4883973094 & 4140.49698285136 & 48941.0146198393 & 15712.4883973094 \tabularnewline
11 & 47124 & 43895.0351824875 & 176.287565494228 & 50176.6772520182 & -3228.96481751246 \tabularnewline
12 & 32315 & 15708.2565023576 & -3849.77901004501 & 52771.5225076874 & -16606.7434976424 \tabularnewline
13 & 42248 & 27305.206919998 & 1824.42531664541 & 55366.3677633566 & -14942.793080002 \tabularnewline
14 & 36088 & 25673.4790836959 & -10766.2838071104 & 57268.8047234145 & -10414.5209163041 \tabularnewline
15 & 52744 & 52665.5607419577 & -6348.80242543002 & 59171.2416834723 & -78.4392580422937 \tabularnewline
16 & 72586 & 80656.9546629292 & 4222.72486375828 & 60292.3204733126 & 8070.95466292916 \tabularnewline
17 & 92334 & 119590.937498176 & 3663.6632386707 & 61413.3992631528 & 27256.9374981765 \tabularnewline
18 & 80761 & 96193.8672518292 & 2588.37120256838 & 62739.7615456025 & 15432.8672518291 \tabularnewline
19 & 71078 & 77635.5933883127 & 454.282783635204 & 64066.1238280521 & 6557.59338831266 \tabularnewline
20 & 63713 & 60022.0047152833 & 2386.50150532216 & 65017.4937793946 & -3690.99528471673 \tabularnewline
21 & 57122 & 46767.0150419939 & 1508.12122726908 & 65968.863730737 & -10354.9849580061 \tabularnewline
22 & 55243 & 40756.1302021022 & 4140.49698285136 & 65589.3728150465 & -14486.8697978978 \tabularnewline
23 & 62143 & 58899.8305351498 & 176.287565494228 & 65209.881899356 & -3243.16946485021 \tabularnewline
24 & 62708 & 65152.4404561528 & -3849.77901004501 & 64113.3385538922 & 2444.44045615281 \tabularnewline
25 & 62474 & 60106.7794749262 & 1824.42531664541 & 63016.7952084284 & -2367.22052507382 \tabularnewline
26 & 64250 & 77078.6630347132 & -10766.2838071104 & 62187.6207723972 & 12828.6630347132 \tabularnewline
27 & 71866 & 88722.356089064 & -6348.80242543002 & 61358.446336366 & 16856.356089064 \tabularnewline
28 & 69886 & 75237.5794195275 & 4222.72486375828 & 60311.6957167143 & 5351.57941952746 \tabularnewline
29 & 58724 & 54519.3916642668 & 3663.6632386707 & 59264.9450970625 & -4204.60833573321 \tabularnewline
30 & 55298 & 50442.8777747711 & 2588.37120256838 & 57564.7510226605 & -4855.12222522891 \tabularnewline
31 & 52594 & 48869.1602681063 & 454.282783635204 & 55864.5569482585 & -3724.83973189375 \tabularnewline
32 & 54854 & 53126.6447363143 & 2386.50150532216 & 54194.8537583635 & -1727.35526368566 \tabularnewline
33 & 54694 & 55354.7282042625 & 1508.12122726908 & 52525.1505684685 & 660.728204262457 \tabularnewline
34 & 49298 & 42905.6311274046 & 4140.49698285136 & 51549.8718897441 & -6392.36887259544 \tabularnewline
35 & 44659 & 38567.1192234861 & 176.287565494228 & 50574.5932110197 & -6091.88077651394 \tabularnewline
36 & 43657 & 40301.2983104479 & -3849.77901004501 & 50862.4806995971 & -3355.70168955206 \tabularnewline
37 & 47002 & 41029.2064951802 & 1824.42531664541 & 51150.3681881744 & -5972.79350481984 \tabularnewline
38 & 47042 & 52258.9694709344 & -10766.2838071104 & 52591.314336176 & 5216.96947093442 \tabularnewline
39 & 48959 & 50234.5419412525 & -6348.80242543002 & 54032.2604841775 & 1275.54194125254 \tabularnewline
40 & 49750 & 39029.0153288811 & 4222.72486375828 & 56248.2598073606 & -10720.9846711189 \tabularnewline
41 & 54048 & 45968.0776307855 & 3663.6632386707 & 58464.2591305438 & -8079.92236921447 \tabularnewline
42 & 60067 & 56577.5071342166 & 2588.37120256838 & 60968.121663215 & -3489.49286578341 \tabularnewline
43 & 68929 & 73931.7330204785 & 454.282783635204 & 63471.9841958863 & 5002.73302047851 \tabularnewline
44 & 74617 & 80561.9118467804 & 2386.50150532216 & 66285.5866478975 & 5944.91184678038 \tabularnewline
45 & 75940 & 81272.6896728222 & 1508.12122726908 & 69099.1890999087 & 5332.68967282225 \tabularnewline
46 & 72762 & 69178.0489110627 & 4140.49698285136 & 72205.454106086 & -3583.95108893732 \tabularnewline
47 & 75621 & 75753.9933222425 & 176.287565494228 & 75311.7191122633 & 132.993322242502 \tabularnewline
48 & 73008 & 71197.4924163381 & -3849.77901004501 & 78668.2865937069 & -1810.50758366191 \tabularnewline
49 & 74196 & 64542.720608204 & 1824.42531664541 & 82024.8540751506 & -9653.27939179598 \tabularnewline
50 & 78878 & 82832.4287849642 & -10766.2838071104 & 85689.8550221462 & 3954.42878496421 \tabularnewline
51 & 83812 & 84617.9464562882 & -6348.80242543002 & 89354.8559691418 & 805.946456288249 \tabularnewline
52 & 91624 & 84416.7053701626 & 4222.72486375828 & 94608.5697660792 & -7207.29462983744 \tabularnewline
53 & 89388 & 75250.0531983127 & 3663.6632386707 & 99862.2835630166 & -14137.9468016873 \tabularnewline
54 & 110410 & 112832.330918792 & 2588.37120256838 & 105399.29787864 & 2422.3309187915 \tabularnewline
55 & 113857 & 116323.405022101 & 454.282783635204 & 110936.312194264 & 2466.4050221011 \tabularnewline
56 & 112060 & 105141.895866981 & 2386.50150532216 & 116591.602627697 & -6918.10413301895 \tabularnewline
57 & 117236 & 110716.985711601 & 1508.12122726908 & 122246.89306113 & -6519.01428839896 \tabularnewline
58 & 132810 & 133443.042703374 & 4140.49698285136 & 128036.460313775 & 633.0427033741 \tabularnewline
59 & 137699 & 141395.684868087 & 176.287565494228 & 133826.027566419 & 3696.68486808657 \tabularnewline
60 & 146409 & 156924.967747978 & -3849.77901004501 & 139742.811262067 & 10515.9677479782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147287&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]89924[/C][C]124007.732114667[/C][C]1824.42531664541[/C][C]54015.8425686875[/C][C]34083.7321146671[/C][/ROW]
[ROW][C]2[/C][C]31795[/C][C]21327.6688873722[/C][C]-10766.2838071104[/C][C]53028.6149197382[/C][C]-10467.3311126278[/C][/ROW]
[ROW][C]3[/C][C]27922[/C][C]10151.4151546412[/C][C]-6348.80242543002[/C][C]52041.3872707888[/C][C]-17770.5848453588[/C][/ROW]
[ROW][C]4[/C][C]59954[/C][C]64496.4018163857[/C][C]4222.72486375828[/C][C]51188.8733198561[/C][C]4542.40181638567[/C][/ROW]
[ROW][C]5[/C][C]52150[/C][C]50299.977392406[/C][C]3663.6632386707[/C][C]50336.3593689233[/C][C]-1850.02260759396[/C][/ROW]
[ROW][C]6[/C][C]39964[/C][C]27773.2651797734[/C][C]2588.37120256838[/C][C]49566.3636176582[/C][C]-12190.7348202266[/C][/ROW]
[ROW][C]7[/C][C]34604[/C][C]19957.3493499716[/C][C]454.282783635204[/C][C]48796.3678663932[/C][C]-14646.6506500284[/C][/ROW]
[ROW][C]8[/C][C]51106[/C][C]51574.6385676511[/C][C]2386.50150532216[/C][C]48250.8599270267[/C][C]468.63856765112[/C][/ROW]
[ROW][C]9[/C][C]52593[/C][C]55972.5267850706[/C][C]1508.12122726908[/C][C]47705.3519876603[/C][C]3379.52678507064[/C][/ROW]
[ROW][C]10[/C][C]68794[/C][C]84506.4883973094[/C][C]4140.49698285136[/C][C]48941.0146198393[/C][C]15712.4883973094[/C][/ROW]
[ROW][C]11[/C][C]47124[/C][C]43895.0351824875[/C][C]176.287565494228[/C][C]50176.6772520182[/C][C]-3228.96481751246[/C][/ROW]
[ROW][C]12[/C][C]32315[/C][C]15708.2565023576[/C][C]-3849.77901004501[/C][C]52771.5225076874[/C][C]-16606.7434976424[/C][/ROW]
[ROW][C]13[/C][C]42248[/C][C]27305.206919998[/C][C]1824.42531664541[/C][C]55366.3677633566[/C][C]-14942.793080002[/C][/ROW]
[ROW][C]14[/C][C]36088[/C][C]25673.4790836959[/C][C]-10766.2838071104[/C][C]57268.8047234145[/C][C]-10414.5209163041[/C][/ROW]
[ROW][C]15[/C][C]52744[/C][C]52665.5607419577[/C][C]-6348.80242543002[/C][C]59171.2416834723[/C][C]-78.4392580422937[/C][/ROW]
[ROW][C]16[/C][C]72586[/C][C]80656.9546629292[/C][C]4222.72486375828[/C][C]60292.3204733126[/C][C]8070.95466292916[/C][/ROW]
[ROW][C]17[/C][C]92334[/C][C]119590.937498176[/C][C]3663.6632386707[/C][C]61413.3992631528[/C][C]27256.9374981765[/C][/ROW]
[ROW][C]18[/C][C]80761[/C][C]96193.8672518292[/C][C]2588.37120256838[/C][C]62739.7615456025[/C][C]15432.8672518291[/C][/ROW]
[ROW][C]19[/C][C]71078[/C][C]77635.5933883127[/C][C]454.282783635204[/C][C]64066.1238280521[/C][C]6557.59338831266[/C][/ROW]
[ROW][C]20[/C][C]63713[/C][C]60022.0047152833[/C][C]2386.50150532216[/C][C]65017.4937793946[/C][C]-3690.99528471673[/C][/ROW]
[ROW][C]21[/C][C]57122[/C][C]46767.0150419939[/C][C]1508.12122726908[/C][C]65968.863730737[/C][C]-10354.9849580061[/C][/ROW]
[ROW][C]22[/C][C]55243[/C][C]40756.1302021022[/C][C]4140.49698285136[/C][C]65589.3728150465[/C][C]-14486.8697978978[/C][/ROW]
[ROW][C]23[/C][C]62143[/C][C]58899.8305351498[/C][C]176.287565494228[/C][C]65209.881899356[/C][C]-3243.16946485021[/C][/ROW]
[ROW][C]24[/C][C]62708[/C][C]65152.4404561528[/C][C]-3849.77901004501[/C][C]64113.3385538922[/C][C]2444.44045615281[/C][/ROW]
[ROW][C]25[/C][C]62474[/C][C]60106.7794749262[/C][C]1824.42531664541[/C][C]63016.7952084284[/C][C]-2367.22052507382[/C][/ROW]
[ROW][C]26[/C][C]64250[/C][C]77078.6630347132[/C][C]-10766.2838071104[/C][C]62187.6207723972[/C][C]12828.6630347132[/C][/ROW]
[ROW][C]27[/C][C]71866[/C][C]88722.356089064[/C][C]-6348.80242543002[/C][C]61358.446336366[/C][C]16856.356089064[/C][/ROW]
[ROW][C]28[/C][C]69886[/C][C]75237.5794195275[/C][C]4222.72486375828[/C][C]60311.6957167143[/C][C]5351.57941952746[/C][/ROW]
[ROW][C]29[/C][C]58724[/C][C]54519.3916642668[/C][C]3663.6632386707[/C][C]59264.9450970625[/C][C]-4204.60833573321[/C][/ROW]
[ROW][C]30[/C][C]55298[/C][C]50442.8777747711[/C][C]2588.37120256838[/C][C]57564.7510226605[/C][C]-4855.12222522891[/C][/ROW]
[ROW][C]31[/C][C]52594[/C][C]48869.1602681063[/C][C]454.282783635204[/C][C]55864.5569482585[/C][C]-3724.83973189375[/C][/ROW]
[ROW][C]32[/C][C]54854[/C][C]53126.6447363143[/C][C]2386.50150532216[/C][C]54194.8537583635[/C][C]-1727.35526368566[/C][/ROW]
[ROW][C]33[/C][C]54694[/C][C]55354.7282042625[/C][C]1508.12122726908[/C][C]52525.1505684685[/C][C]660.728204262457[/C][/ROW]
[ROW][C]34[/C][C]49298[/C][C]42905.6311274046[/C][C]4140.49698285136[/C][C]51549.8718897441[/C][C]-6392.36887259544[/C][/ROW]
[ROW][C]35[/C][C]44659[/C][C]38567.1192234861[/C][C]176.287565494228[/C][C]50574.5932110197[/C][C]-6091.88077651394[/C][/ROW]
[ROW][C]36[/C][C]43657[/C][C]40301.2983104479[/C][C]-3849.77901004501[/C][C]50862.4806995971[/C][C]-3355.70168955206[/C][/ROW]
[ROW][C]37[/C][C]47002[/C][C]41029.2064951802[/C][C]1824.42531664541[/C][C]51150.3681881744[/C][C]-5972.79350481984[/C][/ROW]
[ROW][C]38[/C][C]47042[/C][C]52258.9694709344[/C][C]-10766.2838071104[/C][C]52591.314336176[/C][C]5216.96947093442[/C][/ROW]
[ROW][C]39[/C][C]48959[/C][C]50234.5419412525[/C][C]-6348.80242543002[/C][C]54032.2604841775[/C][C]1275.54194125254[/C][/ROW]
[ROW][C]40[/C][C]49750[/C][C]39029.0153288811[/C][C]4222.72486375828[/C][C]56248.2598073606[/C][C]-10720.9846711189[/C][/ROW]
[ROW][C]41[/C][C]54048[/C][C]45968.0776307855[/C][C]3663.6632386707[/C][C]58464.2591305438[/C][C]-8079.92236921447[/C][/ROW]
[ROW][C]42[/C][C]60067[/C][C]56577.5071342166[/C][C]2588.37120256838[/C][C]60968.121663215[/C][C]-3489.49286578341[/C][/ROW]
[ROW][C]43[/C][C]68929[/C][C]73931.7330204785[/C][C]454.282783635204[/C][C]63471.9841958863[/C][C]5002.73302047851[/C][/ROW]
[ROW][C]44[/C][C]74617[/C][C]80561.9118467804[/C][C]2386.50150532216[/C][C]66285.5866478975[/C][C]5944.91184678038[/C][/ROW]
[ROW][C]45[/C][C]75940[/C][C]81272.6896728222[/C][C]1508.12122726908[/C][C]69099.1890999087[/C][C]5332.68967282225[/C][/ROW]
[ROW][C]46[/C][C]72762[/C][C]69178.0489110627[/C][C]4140.49698285136[/C][C]72205.454106086[/C][C]-3583.95108893732[/C][/ROW]
[ROW][C]47[/C][C]75621[/C][C]75753.9933222425[/C][C]176.287565494228[/C][C]75311.7191122633[/C][C]132.993322242502[/C][/ROW]
[ROW][C]48[/C][C]73008[/C][C]71197.4924163381[/C][C]-3849.77901004501[/C][C]78668.2865937069[/C][C]-1810.50758366191[/C][/ROW]
[ROW][C]49[/C][C]74196[/C][C]64542.720608204[/C][C]1824.42531664541[/C][C]82024.8540751506[/C][C]-9653.27939179598[/C][/ROW]
[ROW][C]50[/C][C]78878[/C][C]82832.4287849642[/C][C]-10766.2838071104[/C][C]85689.8550221462[/C][C]3954.42878496421[/C][/ROW]
[ROW][C]51[/C][C]83812[/C][C]84617.9464562882[/C][C]-6348.80242543002[/C][C]89354.8559691418[/C][C]805.946456288249[/C][/ROW]
[ROW][C]52[/C][C]91624[/C][C]84416.7053701626[/C][C]4222.72486375828[/C][C]94608.5697660792[/C][C]-7207.29462983744[/C][/ROW]
[ROW][C]53[/C][C]89388[/C][C]75250.0531983127[/C][C]3663.6632386707[/C][C]99862.2835630166[/C][C]-14137.9468016873[/C][/ROW]
[ROW][C]54[/C][C]110410[/C][C]112832.330918792[/C][C]2588.37120256838[/C][C]105399.29787864[/C][C]2422.3309187915[/C][/ROW]
[ROW][C]55[/C][C]113857[/C][C]116323.405022101[/C][C]454.282783635204[/C][C]110936.312194264[/C][C]2466.4050221011[/C][/ROW]
[ROW][C]56[/C][C]112060[/C][C]105141.895866981[/C][C]2386.50150532216[/C][C]116591.602627697[/C][C]-6918.10413301895[/C][/ROW]
[ROW][C]57[/C][C]117236[/C][C]110716.985711601[/C][C]1508.12122726908[/C][C]122246.89306113[/C][C]-6519.01428839896[/C][/ROW]
[ROW][C]58[/C][C]132810[/C][C]133443.042703374[/C][C]4140.49698285136[/C][C]128036.460313775[/C][C]633.0427033741[/C][/ROW]
[ROW][C]59[/C][C]137699[/C][C]141395.684868087[/C][C]176.287565494228[/C][C]133826.027566419[/C][C]3696.68486808657[/C][/ROW]
[ROW][C]60[/C][C]146409[/C][C]156924.967747978[/C][C]-3849.77901004501[/C][C]139742.811262067[/C][C]10515.9677479782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147287&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147287&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
189924124007.7321146671824.4253166454154015.842568687534083.7321146671
23179521327.6688873722-10766.283807110453028.6149197382-10467.3311126278
32792210151.4151546412-6348.8024254300252041.3872707888-17770.5848453588
45995464496.40181638574222.7248637582851188.87331985614542.40181638567
55215050299.9773924063663.663238670750336.3593689233-1850.02260759396
63996427773.26517977342588.3712025683849566.3636176582-12190.7348202266
73460419957.3493499716454.28278363520448796.3678663932-14646.6506500284
85110651574.63856765112386.5015053221648250.8599270267468.63856765112
95259355972.52678507061508.1212272690847705.35198766033379.52678507064
106879484506.48839730944140.4969828513648941.014619839315712.4883973094
114712443895.0351824875176.28756549422850176.6772520182-3228.96481751246
123231515708.2565023576-3849.7790100450152771.5225076874-16606.7434976424
134224827305.2069199981824.4253166454155366.3677633566-14942.793080002
143608825673.4790836959-10766.283807110457268.8047234145-10414.5209163041
155274452665.5607419577-6348.8024254300259171.2416834723-78.4392580422937
167258680656.95466292924222.7248637582860292.32047331268070.95466292916
1792334119590.9374981763663.663238670761413.399263152827256.9374981765
188076196193.86725182922588.3712025683862739.761545602515432.8672518291
197107877635.5933883127454.28278363520464066.12382805216557.59338831266
206371360022.00471528332386.5015053221665017.4937793946-3690.99528471673
215712246767.01504199391508.1212272690865968.863730737-10354.9849580061
225524340756.13020210224140.4969828513665589.3728150465-14486.8697978978
236214358899.8305351498176.28756549422865209.881899356-3243.16946485021
246270865152.4404561528-3849.7790100450164113.33855389222444.44045615281
256247460106.77947492621824.4253166454163016.7952084284-2367.22052507382
266425077078.6630347132-10766.283807110462187.620772397212828.6630347132
277186688722.356089064-6348.8024254300261358.44633636616856.356089064
286988675237.57941952754222.7248637582860311.69571671435351.57941952746
295872454519.39166426683663.663238670759264.9450970625-4204.60833573321
305529850442.87777477112588.3712025683857564.7510226605-4855.12222522891
315259448869.1602681063454.28278363520455864.5569482585-3724.83973189375
325485453126.64473631432386.5015053221654194.8537583635-1727.35526368566
335469455354.72820426251508.1212272690852525.1505684685660.728204262457
344929842905.63112740464140.4969828513651549.8718897441-6392.36887259544
354465938567.1192234861176.28756549422850574.5932110197-6091.88077651394
364365740301.2983104479-3849.7790100450150862.4806995971-3355.70168955206
374700241029.20649518021824.4253166454151150.3681881744-5972.79350481984
384704252258.9694709344-10766.283807110452591.3143361765216.96947093442
394895950234.5419412525-6348.8024254300254032.26048417751275.54194125254
404975039029.01532888114222.7248637582856248.2598073606-10720.9846711189
415404845968.07763078553663.663238670758464.2591305438-8079.92236921447
426006756577.50713421662588.3712025683860968.121663215-3489.49286578341
436892973931.7330204785454.28278363520463471.98419588635002.73302047851
447461780561.91184678042386.5015053221666285.58664789755944.91184678038
457594081272.68967282221508.1212272690869099.18909990875332.68967282225
467276269178.04891106274140.4969828513672205.454106086-3583.95108893732
477562175753.9933222425176.28756549422875311.7191122633132.993322242502
487300871197.4924163381-3849.7790100450178668.2865937069-1810.50758366191
497419664542.7206082041824.4253166454182024.8540751506-9653.27939179598
507887882832.4287849642-10766.283807110485689.85502214623954.42878496421
518381284617.9464562882-6348.8024254300289354.8559691418805.946456288249
529162484416.70537016264222.7248637582894608.5697660792-7207.29462983744
538938875250.05319831273663.663238670799862.2835630166-14137.9468016873
54110410112832.3309187922588.37120256838105399.297878642422.3309187915
55113857116323.405022101454.282783635204110936.3121942642466.4050221011
56112060105141.8958669812386.50150532216116591.602627697-6918.10413301895
57117236110716.9857116011508.12122726908122246.89306113-6519.01428839896
58132810133443.0427033744140.49698285136128036.460313775633.0427033741
59137699141395.684868087176.287565494228133826.0275664193696.68486808657
60146409156924.967747978-3849.77901004501139742.81126206710515.9677479782



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')