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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 computationWed, 30 Nov 2011 09:54:25 -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/30/t1322664885mid6jmvavapwtni.htm/, Retrieved Thu, 28 Mar 2024 22:33:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149024, Retrieved Thu, 28 Mar 2024 22:33:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD    [Decomposition by Loess] [Seasonal window = 6] [2011-11-30 14:54:25] [5bed06029c084e4c7aa2cb341d7631f9] [Current]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149024&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149024&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149024&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'George Udny Yule' @ yule.wessa.net







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal601
Trend2513
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 6 & 0 & 1 \tabularnewline
Trend & 25 & 1 & 3 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149024&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]6[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]Trend[/C][C]25[/C][C]1[/C][C]3[/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=149024&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149024&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
Seasonal601
Trend2513
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
197009648.7896381318194.113955011399557.09640685682-51.2103618682067
290819128.72732119467-515.8665658336289549.1392446389647.7273211946722
390848782.89947601814-156.0815584392419541.1820824211-301.100523981857
497439754.19380355555198.5812762412139533.2249202032411.1938035555486
585878381.29527316322-730.2012920131889522.90601884996-205.704726836777
697319788.37379592734161.0390865759749512.587117496757.3737959273367
795639719.9268542358-96.19507037921239502.26821614341156.926854235797
8999810479.674482808225.1730378021779491.15247938966481.674482808168
994379522.88844393629-128.9251865721869480.036742635985.8884439362882
10100389946.22706751443660.851926603439468.92100588214-91.7729324855663
11991810041.572026567337.0513066986119457.37666673434123.572026567046
1292529004.3263579620453.84131445140879445.83232758655-247.673642037958
1397379855.14685931089184.5651522503559434.28798843875118.146859310889
1490359182.68699638795-522.3161868593969409.62919047145147.686996387949
1591339013.69196969873-132.6623622028689384.97039250414-119.308030301268
1694879417.23114424422196.457261218959360.31159453683-69.7688557557794
1787008773.44551109998-715.1651862366949341.7196751367173.4455110999825
1896279775.28947089163155.5827733717789323.1277557366148.28947089163
1989478692.53270858419-103.0685449206669304.53583633647-254.467291415807
2092839288.19955951162-17.02633574521949294.82677623365.19955951162228
2188298482.71656021038-109.8342763410949285.11771613072-346.283439789622
2299479959.85589490995658.7354490622159275.4086560278412.8558949099461
2396289645.24477148803337.2658541726179273.4893743393517.2447714880309
2493189289.3446392636275.08526808551569271.57009265086-28.6553607363785
2596059766.741169359173.6080196786169269.65081096238161.741169359007
2686408533.67379026937-530.1385656596679276.4647753903-106.326209730632
2792149229.957513254-85.23625307221769283.2787398182215.9575132539958
2895679655.53852845528188.3687672985689290.0927042461588.5385284552849
2985478455.63042265412-662.9433061890749301.31288353496-91.369577345884
3091858908.21134938882149.2555877874129312.53306282377-276.788650611181
3194709748.91154150943-132.6647836220099323.75324211258278.911541509427
3291239049.88758924884-141.0688080234719337.18121877463-73.1124107511605
3392789286.55752347024-81.1667189069249350.609195436688.55752347024463
341017010319.3613742596656.6014536416429364.03717209873149.361374259628
3594349157.31642162733330.4777313272889380.20584704539-276.683578372675
3696559775.99018453992137.6352934680369396.37452199205120.990184539918
3794299311.8876575746133.5691454866899412.5431969387-117.112342425391
3887398617.06531936522-572.2304708830539433.16515151784-121.934680634784
3995529622.5419177450327.67097615799429453.7871060969870.5419177450294
4096879755.04749859592144.5434407279679474.4090606761168.0474985959208
4190199206.04126557087-667.1977753985049499.15650982764187.041265570866
4296729635.40435348419184.6916875366479523.90395897917-36.5956465158142
4392069018.98264715072-155.6340552814159548.6514081307-187.01735284928
4490698741.35032221922-178.9677978993369575.61747568012-327.649677780784
4597889961.1421362750612.27432049539229602.58354322954173.142136275063
461031210460.6143696453533.8360195757279629.54961077897148.614369645305
471010510186.4552548612369.4211559550949654.1235891836881.4552548612282
4898639830.6595688932216.6428635184079678.6975675884-32.340431106797
4996569430.56145642105178.1669975858469703.2715459931-225.438543578948
5092959433.84963411472-568.0661895221379724.21655540742138.84963411472
51994610228.0724458634-81.23401068511379745.16156482173282.072445863383
5297019513.695388544122.1980372199629766.10657423604-187.304611456006
5390499022.04344860256-706.393158487219782.34970988465-26.9565513974394
541019010348.1679258633233.2392286034689798.59284553326158.167925863276
5597069796.58797108659-199.4239522684489814.8359811818690.5879710865865
5697659797.0680936497-96.69046916693389829.6223755172332.0680936497065
5798939901.1779639030340.41326624437719844.40876985268.1779639030301
5899949677.27770924774451.5271265643029859.19516418796-316.722290752261
591043310585.6257951497403.081667674059877.29253717628152.62579514967
60100739995.94473453016254.6653553052389895.3899101646-77.0552654698404
611011210115.3544680698195.158248777299913.487283152923.35446806978689
6292669155.73384380986-561.7623527587319938.02850894888-110.266156190144
6398209807.02848542068-129.5982201655049962.56973474483-12.9715145793216
641009710099.1661988534107.7228406057869987.110960540782.16619885343607
6591158939.84779151278-719.53700210680410009.689210594-175.152208487225
661041110536.5515934051253.18094594764710032.2674606473125.551593405071
6796789515.83287395915-214.67858465967910054.8457107005-162.167126040855
681040810812.278702805-72.19758095691910075.9188781519404.278702805048
691015310158.051218515150.956735881679710096.99204560325.05121851511103
701036810201.7664337577416.16835318775410118.0652130545-166.233566242299
711058110602.3081474939420.27056433187810139.421288174221.3081474939154
721059710768.9578386878264.26479801834510160.7773632939171.957838687789
731068010975.6243404436202.24222114282910182.1334384135295.624340443645
7497389831.51885538244-558.77230258737110203.253447204993.5188553824428
7595569037.47953667355-149.85299266987510224.3734559963-518.520463326455

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 9700 & 9648.7896381318 & 194.11395501139 & 9557.09640685682 & -51.2103618682067 \tabularnewline
2 & 9081 & 9128.72732119467 & -515.866565833628 & 9549.13924463896 & 47.7273211946722 \tabularnewline
3 & 9084 & 8782.89947601814 & -156.081558439241 & 9541.1820824211 & -301.100523981857 \tabularnewline
4 & 9743 & 9754.19380355555 & 198.581276241213 & 9533.22492020324 & 11.1938035555486 \tabularnewline
5 & 8587 & 8381.29527316322 & -730.201292013188 & 9522.90601884996 & -205.704726836777 \tabularnewline
6 & 9731 & 9788.37379592734 & 161.039086575974 & 9512.5871174967 & 57.3737959273367 \tabularnewline
7 & 9563 & 9719.9268542358 & -96.1950703792123 & 9502.26821614341 & 156.926854235797 \tabularnewline
8 & 9998 & 10479.6744828082 & 25.173037802177 & 9491.15247938966 & 481.674482808168 \tabularnewline
9 & 9437 & 9522.88844393629 & -128.925186572186 & 9480.0367426359 & 85.8884439362882 \tabularnewline
10 & 10038 & 9946.22706751443 & 660.85192660343 & 9468.92100588214 & -91.7729324855663 \tabularnewline
11 & 9918 & 10041.572026567 & 337.051306698611 & 9457.37666673434 & 123.572026567046 \tabularnewline
12 & 9252 & 9004.32635796204 & 53.8413144514087 & 9445.83232758655 & -247.673642037958 \tabularnewline
13 & 9737 & 9855.14685931089 & 184.565152250355 & 9434.28798843875 & 118.146859310889 \tabularnewline
14 & 9035 & 9182.68699638795 & -522.316186859396 & 9409.62919047145 & 147.686996387949 \tabularnewline
15 & 9133 & 9013.69196969873 & -132.662362202868 & 9384.97039250414 & -119.308030301268 \tabularnewline
16 & 9487 & 9417.23114424422 & 196.45726121895 & 9360.31159453683 & -69.7688557557794 \tabularnewline
17 & 8700 & 8773.44551109998 & -715.165186236694 & 9341.71967513671 & 73.4455110999825 \tabularnewline
18 & 9627 & 9775.28947089163 & 155.582773371778 & 9323.1277557366 & 148.28947089163 \tabularnewline
19 & 8947 & 8692.53270858419 & -103.068544920666 & 9304.53583633647 & -254.467291415807 \tabularnewline
20 & 9283 & 9288.19955951162 & -17.0263357452194 & 9294.8267762336 & 5.19955951162228 \tabularnewline
21 & 8829 & 8482.71656021038 & -109.834276341094 & 9285.11771613072 & -346.283439789622 \tabularnewline
22 & 9947 & 9959.85589490995 & 658.735449062215 & 9275.40865602784 & 12.8558949099461 \tabularnewline
23 & 9628 & 9645.24477148803 & 337.265854172617 & 9273.48937433935 & 17.2447714880309 \tabularnewline
24 & 9318 & 9289.34463926362 & 75.0852680855156 & 9271.57009265086 & -28.6553607363785 \tabularnewline
25 & 9605 & 9766.741169359 & 173.608019678616 & 9269.65081096238 & 161.741169359007 \tabularnewline
26 & 8640 & 8533.67379026937 & -530.138565659667 & 9276.4647753903 & -106.326209730632 \tabularnewline
27 & 9214 & 9229.957513254 & -85.2362530722176 & 9283.27873981822 & 15.9575132539958 \tabularnewline
28 & 9567 & 9655.53852845528 & 188.368767298568 & 9290.09270424615 & 88.5385284552849 \tabularnewline
29 & 8547 & 8455.63042265412 & -662.943306189074 & 9301.31288353496 & -91.369577345884 \tabularnewline
30 & 9185 & 8908.21134938882 & 149.255587787412 & 9312.53306282377 & -276.788650611181 \tabularnewline
31 & 9470 & 9748.91154150943 & -132.664783622009 & 9323.75324211258 & 278.911541509427 \tabularnewline
32 & 9123 & 9049.88758924884 & -141.068808023471 & 9337.18121877463 & -73.1124107511605 \tabularnewline
33 & 9278 & 9286.55752347024 & -81.166718906924 & 9350.60919543668 & 8.55752347024463 \tabularnewline
34 & 10170 & 10319.3613742596 & 656.601453641642 & 9364.03717209873 & 149.361374259628 \tabularnewline
35 & 9434 & 9157.31642162733 & 330.477731327288 & 9380.20584704539 & -276.683578372675 \tabularnewline
36 & 9655 & 9775.99018453992 & 137.635293468036 & 9396.37452199205 & 120.990184539918 \tabularnewline
37 & 9429 & 9311.8876575746 & 133.569145486689 & 9412.5431969387 & -117.112342425391 \tabularnewline
38 & 8739 & 8617.06531936522 & -572.230470883053 & 9433.16515151784 & -121.934680634784 \tabularnewline
39 & 9552 & 9622.54191774503 & 27.6709761579942 & 9453.78710609698 & 70.5419177450294 \tabularnewline
40 & 9687 & 9755.04749859592 & 144.543440727967 & 9474.40906067611 & 68.0474985959208 \tabularnewline
41 & 9019 & 9206.04126557087 & -667.197775398504 & 9499.15650982764 & 187.041265570866 \tabularnewline
42 & 9672 & 9635.40435348419 & 184.691687536647 & 9523.90395897917 & -36.5956465158142 \tabularnewline
43 & 9206 & 9018.98264715072 & -155.634055281415 & 9548.6514081307 & -187.01735284928 \tabularnewline
44 & 9069 & 8741.35032221922 & -178.967797899336 & 9575.61747568012 & -327.649677780784 \tabularnewline
45 & 9788 & 9961.14213627506 & 12.2743204953922 & 9602.58354322954 & 173.142136275063 \tabularnewline
46 & 10312 & 10460.6143696453 & 533.836019575727 & 9629.54961077897 & 148.614369645305 \tabularnewline
47 & 10105 & 10186.4552548612 & 369.421155955094 & 9654.12358918368 & 81.4552548612282 \tabularnewline
48 & 9863 & 9830.6595688932 & 216.642863518407 & 9678.6975675884 & -32.340431106797 \tabularnewline
49 & 9656 & 9430.56145642105 & 178.166997585846 & 9703.2715459931 & -225.438543578948 \tabularnewline
50 & 9295 & 9433.84963411472 & -568.066189522137 & 9724.21655540742 & 138.84963411472 \tabularnewline
51 & 9946 & 10228.0724458634 & -81.2340106851137 & 9745.16156482173 & 282.072445863383 \tabularnewline
52 & 9701 & 9513.695388544 & 122.198037219962 & 9766.10657423604 & -187.304611456006 \tabularnewline
53 & 9049 & 9022.04344860256 & -706.39315848721 & 9782.34970988465 & -26.9565513974394 \tabularnewline
54 & 10190 & 10348.1679258633 & 233.239228603468 & 9798.59284553326 & 158.167925863276 \tabularnewline
55 & 9706 & 9796.58797108659 & -199.423952268448 & 9814.83598118186 & 90.5879710865865 \tabularnewline
56 & 9765 & 9797.0680936497 & -96.6904691669338 & 9829.62237551723 & 32.0680936497065 \tabularnewline
57 & 9893 & 9901.17796390303 & 40.4132662443771 & 9844.4087698526 & 8.1779639030301 \tabularnewline
58 & 9994 & 9677.27770924774 & 451.527126564302 & 9859.19516418796 & -316.722290752261 \tabularnewline
59 & 10433 & 10585.6257951497 & 403.08166767405 & 9877.29253717628 & 152.62579514967 \tabularnewline
60 & 10073 & 9995.94473453016 & 254.665355305238 & 9895.3899101646 & -77.0552654698404 \tabularnewline
61 & 10112 & 10115.3544680698 & 195.15824877729 & 9913.48728315292 & 3.35446806978689 \tabularnewline
62 & 9266 & 9155.73384380986 & -561.762352758731 & 9938.02850894888 & -110.266156190144 \tabularnewline
63 & 9820 & 9807.02848542068 & -129.598220165504 & 9962.56973474483 & -12.9715145793216 \tabularnewline
64 & 10097 & 10099.1661988534 & 107.722840605786 & 9987.11096054078 & 2.16619885343607 \tabularnewline
65 & 9115 & 8939.84779151278 & -719.537002106804 & 10009.689210594 & -175.152208487225 \tabularnewline
66 & 10411 & 10536.5515934051 & 253.180945947647 & 10032.2674606473 & 125.551593405071 \tabularnewline
67 & 9678 & 9515.83287395915 & -214.678584659679 & 10054.8457107005 & -162.167126040855 \tabularnewline
68 & 10408 & 10812.278702805 & -72.197580956919 & 10075.9188781519 & 404.278702805048 \tabularnewline
69 & 10153 & 10158.0512185151 & 50.9567358816797 & 10096.9920456032 & 5.05121851511103 \tabularnewline
70 & 10368 & 10201.7664337577 & 416.168353187754 & 10118.0652130545 & -166.233566242299 \tabularnewline
71 & 10581 & 10602.3081474939 & 420.270564331878 & 10139.4212881742 & 21.3081474939154 \tabularnewline
72 & 10597 & 10768.9578386878 & 264.264798018345 & 10160.7773632939 & 171.957838687789 \tabularnewline
73 & 10680 & 10975.6243404436 & 202.242221142829 & 10182.1334384135 & 295.624340443645 \tabularnewline
74 & 9738 & 9831.51885538244 & -558.772302587371 & 10203.2534472049 & 93.5188553824428 \tabularnewline
75 & 9556 & 9037.47953667355 & -149.852992669875 & 10224.3734559963 & -518.520463326455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149024&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]9700[/C][C]9648.7896381318[/C][C]194.11395501139[/C][C]9557.09640685682[/C][C]-51.2103618682067[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]9128.72732119467[/C][C]-515.866565833628[/C][C]9549.13924463896[/C][C]47.7273211946722[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]8782.89947601814[/C][C]-156.081558439241[/C][C]9541.1820824211[/C][C]-301.100523981857[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9754.19380355555[/C][C]198.581276241213[/C][C]9533.22492020324[/C][C]11.1938035555486[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]8381.29527316322[/C][C]-730.201292013188[/C][C]9522.90601884996[/C][C]-205.704726836777[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9788.37379592734[/C][C]161.039086575974[/C][C]9512.5871174967[/C][C]57.3737959273367[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9719.9268542358[/C][C]-96.1950703792123[/C][C]9502.26821614341[/C][C]156.926854235797[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]10479.6744828082[/C][C]25.173037802177[/C][C]9491.15247938966[/C][C]481.674482808168[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9522.88844393629[/C][C]-128.925186572186[/C][C]9480.0367426359[/C][C]85.8884439362882[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]9946.22706751443[/C][C]660.85192660343[/C][C]9468.92100588214[/C][C]-91.7729324855663[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]10041.572026567[/C][C]337.051306698611[/C][C]9457.37666673434[/C][C]123.572026567046[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]9004.32635796204[/C][C]53.8413144514087[/C][C]9445.83232758655[/C][C]-247.673642037958[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9855.14685931089[/C][C]184.565152250355[/C][C]9434.28798843875[/C][C]118.146859310889[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]9182.68699638795[/C][C]-522.316186859396[/C][C]9409.62919047145[/C][C]147.686996387949[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9013.69196969873[/C][C]-132.662362202868[/C][C]9384.97039250414[/C][C]-119.308030301268[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9417.23114424422[/C][C]196.45726121895[/C][C]9360.31159453683[/C][C]-69.7688557557794[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]8773.44551109998[/C][C]-715.165186236694[/C][C]9341.71967513671[/C][C]73.4455110999825[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9775.28947089163[/C][C]155.582773371778[/C][C]9323.1277557366[/C][C]148.28947089163[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]8692.53270858419[/C][C]-103.068544920666[/C][C]9304.53583633647[/C][C]-254.467291415807[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9288.19955951162[/C][C]-17.0263357452194[/C][C]9294.8267762336[/C][C]5.19955951162228[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]8482.71656021038[/C][C]-109.834276341094[/C][C]9285.11771613072[/C][C]-346.283439789622[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]9959.85589490995[/C][C]658.735449062215[/C][C]9275.40865602784[/C][C]12.8558949099461[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9645.24477148803[/C][C]337.265854172617[/C][C]9273.48937433935[/C][C]17.2447714880309[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9289.34463926362[/C][C]75.0852680855156[/C][C]9271.57009265086[/C][C]-28.6553607363785[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9766.741169359[/C][C]173.608019678616[/C][C]9269.65081096238[/C][C]161.741169359007[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]8533.67379026937[/C][C]-530.138565659667[/C][C]9276.4647753903[/C][C]-106.326209730632[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9229.957513254[/C][C]-85.2362530722176[/C][C]9283.27873981822[/C][C]15.9575132539958[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9655.53852845528[/C][C]188.368767298568[/C][C]9290.09270424615[/C][C]88.5385284552849[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]8455.63042265412[/C][C]-662.943306189074[/C][C]9301.31288353496[/C][C]-91.369577345884[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]8908.21134938882[/C][C]149.255587787412[/C][C]9312.53306282377[/C][C]-276.788650611181[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9748.91154150943[/C][C]-132.664783622009[/C][C]9323.75324211258[/C][C]278.911541509427[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]9049.88758924884[/C][C]-141.068808023471[/C][C]9337.18121877463[/C][C]-73.1124107511605[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9286.55752347024[/C][C]-81.166718906924[/C][C]9350.60919543668[/C][C]8.55752347024463[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]10319.3613742596[/C][C]656.601453641642[/C][C]9364.03717209873[/C][C]149.361374259628[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9157.31642162733[/C][C]330.477731327288[/C][C]9380.20584704539[/C][C]-276.683578372675[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9775.99018453992[/C][C]137.635293468036[/C][C]9396.37452199205[/C][C]120.990184539918[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9311.8876575746[/C][C]133.569145486689[/C][C]9412.5431969387[/C][C]-117.112342425391[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]8617.06531936522[/C][C]-572.230470883053[/C][C]9433.16515151784[/C][C]-121.934680634784[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9622.54191774503[/C][C]27.6709761579942[/C][C]9453.78710609698[/C][C]70.5419177450294[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9755.04749859592[/C][C]144.543440727967[/C][C]9474.40906067611[/C][C]68.0474985959208[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]9206.04126557087[/C][C]-667.197775398504[/C][C]9499.15650982764[/C][C]187.041265570866[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9635.40435348419[/C][C]184.691687536647[/C][C]9523.90395897917[/C][C]-36.5956465158142[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9018.98264715072[/C][C]-155.634055281415[/C][C]9548.6514081307[/C][C]-187.01735284928[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]8741.35032221922[/C][C]-178.967797899336[/C][C]9575.61747568012[/C][C]-327.649677780784[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]9961.14213627506[/C][C]12.2743204953922[/C][C]9602.58354322954[/C][C]173.142136275063[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]10460.6143696453[/C][C]533.836019575727[/C][C]9629.54961077897[/C][C]148.614369645305[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]10186.4552548612[/C][C]369.421155955094[/C][C]9654.12358918368[/C][C]81.4552548612282[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9830.6595688932[/C][C]216.642863518407[/C][C]9678.6975675884[/C][C]-32.340431106797[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9430.56145642105[/C][C]178.166997585846[/C][C]9703.2715459931[/C][C]-225.438543578948[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9433.84963411472[/C][C]-568.066189522137[/C][C]9724.21655540742[/C][C]138.84963411472[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]10228.0724458634[/C][C]-81.2340106851137[/C][C]9745.16156482173[/C][C]282.072445863383[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9513.695388544[/C][C]122.198037219962[/C][C]9766.10657423604[/C][C]-187.304611456006[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9022.04344860256[/C][C]-706.39315848721[/C][C]9782.34970988465[/C][C]-26.9565513974394[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]10348.1679258633[/C][C]233.239228603468[/C][C]9798.59284553326[/C][C]158.167925863276[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9796.58797108659[/C][C]-199.423952268448[/C][C]9814.83598118186[/C][C]90.5879710865865[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9797.0680936497[/C][C]-96.6904691669338[/C][C]9829.62237551723[/C][C]32.0680936497065[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9901.17796390303[/C][C]40.4132662443771[/C][C]9844.4087698526[/C][C]8.1779639030301[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]9677.27770924774[/C][C]451.527126564302[/C][C]9859.19516418796[/C][C]-316.722290752261[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]10585.6257951497[/C][C]403.08166767405[/C][C]9877.29253717628[/C][C]152.62579514967[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9995.94473453016[/C][C]254.665355305238[/C][C]9895.3899101646[/C][C]-77.0552654698404[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10115.3544680698[/C][C]195.15824877729[/C][C]9913.48728315292[/C][C]3.35446806978689[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9155.73384380986[/C][C]-561.762352758731[/C][C]9938.02850894888[/C][C]-110.266156190144[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9807.02848542068[/C][C]-129.598220165504[/C][C]9962.56973474483[/C][C]-12.9715145793216[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]10099.1661988534[/C][C]107.722840605786[/C][C]9987.11096054078[/C][C]2.16619885343607[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]8939.84779151278[/C][C]-719.537002106804[/C][C]10009.689210594[/C][C]-175.152208487225[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]10536.5515934051[/C][C]253.180945947647[/C][C]10032.2674606473[/C][C]125.551593405071[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9515.83287395915[/C][C]-214.678584659679[/C][C]10054.8457107005[/C][C]-162.167126040855[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]10812.278702805[/C][C]-72.197580956919[/C][C]10075.9188781519[/C][C]404.278702805048[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]10158.0512185151[/C][C]50.9567358816797[/C][C]10096.9920456032[/C][C]5.05121851511103[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10201.7664337577[/C][C]416.168353187754[/C][C]10118.0652130545[/C][C]-166.233566242299[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10602.3081474939[/C][C]420.270564331878[/C][C]10139.4212881742[/C][C]21.3081474939154[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10768.9578386878[/C][C]264.264798018345[/C][C]10160.7773632939[/C][C]171.957838687789[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10975.6243404436[/C][C]202.242221142829[/C][C]10182.1334384135[/C][C]295.624340443645[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]9831.51885538244[/C][C]-558.772302587371[/C][C]10203.2534472049[/C][C]93.5188553824428[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]9037.47953667355[/C][C]-149.852992669875[/C][C]10224.3734559963[/C][C]-518.520463326455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149024&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149024&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
197009648.7896381318194.113955011399557.09640685682-51.2103618682067
290819128.72732119467-515.8665658336289549.1392446389647.7273211946722
390848782.89947601814-156.0815584392419541.1820824211-301.100523981857
497439754.19380355555198.5812762412139533.2249202032411.1938035555486
585878381.29527316322-730.2012920131889522.90601884996-205.704726836777
697319788.37379592734161.0390865759749512.587117496757.3737959273367
795639719.9268542358-96.19507037921239502.26821614341156.926854235797
8999810479.674482808225.1730378021779491.15247938966481.674482808168
994379522.88844393629-128.9251865721869480.036742635985.8884439362882
10100389946.22706751443660.851926603439468.92100588214-91.7729324855663
11991810041.572026567337.0513066986119457.37666673434123.572026567046
1292529004.3263579620453.84131445140879445.83232758655-247.673642037958
1397379855.14685931089184.5651522503559434.28798843875118.146859310889
1490359182.68699638795-522.3161868593969409.62919047145147.686996387949
1591339013.69196969873-132.6623622028689384.97039250414-119.308030301268
1694879417.23114424422196.457261218959360.31159453683-69.7688557557794
1787008773.44551109998-715.1651862366949341.7196751367173.4455110999825
1896279775.28947089163155.5827733717789323.1277557366148.28947089163
1989478692.53270858419-103.0685449206669304.53583633647-254.467291415807
2092839288.19955951162-17.02633574521949294.82677623365.19955951162228
2188298482.71656021038-109.8342763410949285.11771613072-346.283439789622
2299479959.85589490995658.7354490622159275.4086560278412.8558949099461
2396289645.24477148803337.2658541726179273.4893743393517.2447714880309
2493189289.3446392636275.08526808551569271.57009265086-28.6553607363785
2596059766.741169359173.6080196786169269.65081096238161.741169359007
2686408533.67379026937-530.1385656596679276.4647753903-106.326209730632
2792149229.957513254-85.23625307221769283.2787398182215.9575132539958
2895679655.53852845528188.3687672985689290.0927042461588.5385284552849
2985478455.63042265412-662.9433061890749301.31288353496-91.369577345884
3091858908.21134938882149.2555877874129312.53306282377-276.788650611181
3194709748.91154150943-132.6647836220099323.75324211258278.911541509427
3291239049.88758924884-141.0688080234719337.18121877463-73.1124107511605
3392789286.55752347024-81.1667189069249350.609195436688.55752347024463
341017010319.3613742596656.6014536416429364.03717209873149.361374259628
3594349157.31642162733330.4777313272889380.20584704539-276.683578372675
3696559775.99018453992137.6352934680369396.37452199205120.990184539918
3794299311.8876575746133.5691454866899412.5431969387-117.112342425391
3887398617.06531936522-572.2304708830539433.16515151784-121.934680634784
3995529622.5419177450327.67097615799429453.7871060969870.5419177450294
4096879755.04749859592144.5434407279679474.4090606761168.0474985959208
4190199206.04126557087-667.1977753985049499.15650982764187.041265570866
4296729635.40435348419184.6916875366479523.90395897917-36.5956465158142
4392069018.98264715072-155.6340552814159548.6514081307-187.01735284928
4490698741.35032221922-178.9677978993369575.61747568012-327.649677780784
4597889961.1421362750612.27432049539229602.58354322954173.142136275063
461031210460.6143696453533.8360195757279629.54961077897148.614369645305
471010510186.4552548612369.4211559550949654.1235891836881.4552548612282
4898639830.6595688932216.6428635184079678.6975675884-32.340431106797
4996569430.56145642105178.1669975858469703.2715459931-225.438543578948
5092959433.84963411472-568.0661895221379724.21655540742138.84963411472
51994610228.0724458634-81.23401068511379745.16156482173282.072445863383
5297019513.695388544122.1980372199629766.10657423604-187.304611456006
5390499022.04344860256-706.393158487219782.34970988465-26.9565513974394
541019010348.1679258633233.2392286034689798.59284553326158.167925863276
5597069796.58797108659-199.4239522684489814.8359811818690.5879710865865
5697659797.0680936497-96.69046916693389829.6223755172332.0680936497065
5798939901.1779639030340.41326624437719844.40876985268.1779639030301
5899949677.27770924774451.5271265643029859.19516418796-316.722290752261
591043310585.6257951497403.081667674059877.29253717628152.62579514967
60100739995.94473453016254.6653553052389895.3899101646-77.0552654698404
611011210115.3544680698195.158248777299913.487283152923.35446806978689
6292669155.73384380986-561.7623527587319938.02850894888-110.266156190144
6398209807.02848542068-129.5982201655049962.56973474483-12.9715145793216
641009710099.1661988534107.7228406057869987.110960540782.16619885343607
6591158939.84779151278-719.53700210680410009.689210594-175.152208487225
661041110536.5515934051253.18094594764710032.2674606473125.551593405071
6796789515.83287395915-214.67858465967910054.8457107005-162.167126040855
681040810812.278702805-72.19758095691910075.9188781519404.278702805048
691015310158.051218515150.956735881679710096.99204560325.05121851511103
701036810201.7664337577416.16835318775410118.0652130545-166.233566242299
711058110602.3081474939420.27056433187810139.421288174221.3081474939154
721059710768.9578386878264.26479801834510160.7773632939171.957838687789
731068010975.6243404436202.24222114282910182.1334384135295.624340443645
7497389831.51885538244-558.77230258737110203.253447204993.5188553824428
7595569037.47953667355-149.85299266987510224.3734559963-518.520463326455



Parameters (Session):
Parameters (R input):
par1 = 12 ; par2 = 6 ; 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')