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WS5: Loess (unemployment)

*The author of this computation has been verified*
R Software Module: /rwasp_decomposeloess.wasp (opens new window with default values)
Title produced by software: Decomposition by Loess
Date of computation: Thu, 09 Dec 2010 15:56:20 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa.htm/, Retrieved Thu, 09 Dec 2010 16:56:28 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
235.1 280.7 264.6 240.7 201.4 240.8 241.1 223.8 206.1 174.7 203.3 220.5 299.5 347.4 338.3 327.7 351.6 396.6 438.8 395.6 363.5 378.8 357 369 464.8 479.1 431.3 366.5 326.3 355.1 331.6 261.3 249 205.5 235.6 240.9 264.9 253.8 232.3 193.8 177 213.2 207.2 180.6 188.6 175.4 199 179.6 225.8 234 200.2 183.6 178.2 203.2 208.5 191.8 172.8 148 159.4 154.5 213.2 196.4 182.8 176.4 153.6 173.2 171 151.2 161.9 157.2 201.7 236.4 356.1 398.3 403.7 384.6 365.8 368.1 367.9 347 343.3 292.9 311.5 300.9 366.9 356.9 329.7 316.2 269 289.3 266.2 253.6 233.8 228.4 253.6 260.1 306.6 309.2 309.5 271 279.9 317.9 298.4 246.7 227.3 209.1 259.9 266 320.6 308.5 282.2 262.7 263.5 313.1 284.3 252.6 250.3 246.5 312.7 333.2 446.4 511.6 515.5 506.4 483.2 522.3 509.8 460.7 405.8 375 378.5 406.8 467.8 469.8 429.8 355.8 332.7 378 360.5 334.7 319.5 323.1 363.6 352.1 411.9 388.6 416.4 360.7 338 417.2 388. etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal37210373
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1235.1214.82909453061945.7272473679813209.6436581014-20.2709054693811
2280.7293.79906118565654.383674531868213.21726428247613.099061185656
3264.6281.5335347772830.8755947591674216.79087046355216.9335347772804
4240.7269.75280229795-9.14411997212565220.79131767417629.0528022979499
5201.4208.788200694984-30.7799655797835224.7917648847997.38820069498436
6240.8211.24207795285141.0692991453643229.288622901785-29.5579220471488
7241.1230.99274984362117.4217692376092233.78548091877-10.1072501563793
8223.8223.647314722719-14.5248380372195238.477523314501-0.152685277280995
9206.1201.463179816058-32.4327455262893243.169565710231-4.63682018394167
10174.7147.954837313879-48.7799560450433250.225118731164-26.7451626861207
11203.3176.933596335576-27.6142680876735257.280671752097-26.3664036644236
12220.5196.77363111899-26.2016910964048270.428059977415-23.7263688810099
13299.5269.69730442928645.7272473679813283.575448202732-29.8026955707136
14347.4341.18966480895254.383674531868299.22666065918-6.2103351910481
15338.3330.84653212520530.8755947591674314.877873115628-7.45346787479531
16327.7334.024311334212-9.14411997212565330.5198086379136.32431133421221
17351.6387.818221419585-30.7799655797835346.16174416019936.2182214195845
18396.6392.44168442109841.0692991453643359.689016433538-4.15831557890209
19438.8486.96194205551417.4217692376092373.21628870687748.1619420555141
20395.6423.056856557164-14.5248380372195382.66798148005627.4568565571639
21363.5367.313071273055-32.4327455262893392.1196742532353.81307127305456
22378.8411.165256850869-48.7799560450433395.21469919417532.3652568508687
23357343.304543952559-27.6142680876735398.309724135114-13.695456047441
24369369.971265782356-26.2016910964048394.2304253140490.97126578235617
25464.8493.72162613903645.7272473679813390.15112649298328.921626139036
26479.1523.19178163574954.383674531868380.62454383238344.0917816357488
27431.3460.62644406904930.8755947591674371.09796117178429.3264440690488
28366.5383.426685670162-9.14411997212565358.71743430196416.9266856701619
29326.3337.04305814764-30.7799655797835346.33690743214410.7430581476398
30355.1337.28500342411341.0692991453643331.845697430523-17.8149965758874
31331.6328.42374333348817.4217692376092317.354487428903-3.1762566665119
32261.3235.82392099443-14.5248380372195301.300917042789-25.4760790055699
33249245.185398869613-32.4327455262893285.247346656676-3.81460113038702
34205.5188.960995855916-48.7799560450433270.818960189128-16.5390041440843
35235.6242.423694366095-27.6142680876735256.3905737215796.82369436609463
36240.9262.947178934096-26.2016910964048245.05451216230922.0471789340961
37264.9250.3543020289845.7272473679813233.718450603038-14.5456979710198
38253.8227.35680015873454.383674531868225.859525309398-26.4431998412664
39232.3215.72380522507430.8755947591674218.000600015758-16.5761947749258
40193.8183.23812197173-9.14411997212565213.505998000396-10.56187802827
41177175.768569594751-30.7799655797835209.011395985033-1.2314304052494
42213.2178.85448915752541.0692991453643206.476211697111-34.3455108424754
43207.2193.03720335320117.4217692376092203.941027409189-14.1627966467986
44180.6173.434705223465-14.5248380372195202.290132813754-7.16529477653486
45188.6208.99350730797-32.4327455262893200.63923821831920.39350730797
46175.4200.008141107515-48.7799560450433199.57181493752924.6081411075147
47199227.109876430936-27.6142680876735198.50439165673828.1098764309357
48179.6187.797222560776-26.2016910964048197.6044685356288.19722256077628
49225.8209.168207217545.7272473679813196.704545414519-16.6317927825005
50234218.11136778348354.383674531868195.504957684649-15.8886322165168
51200.2175.21903528605430.8755947591674194.305369954778-24.9809647139458
52183.6183.576379040694-9.14411997212565192.767740931431-0.0236209593056742
53178.2195.949853671699-30.7799655797835191.23011190808417.7498536716993
54203.2175.39237057170241.0692991453643189.938330282934-27.8076294282984
55208.5210.93168210460717.4217692376092188.6465486577842.43168210460689
56191.8211.120421708298-14.5248380372195187.00441632892219.3204217082977
57172.8192.67046152623-32.4327455262893185.3622840000619.8704615262295
58148161.531109431451-48.7799560450433183.24884661359213.5311094314512
59159.4165.278858860549-27.6142680876735181.1354092271245.87885886054912
60154.5156.979150209213-26.2016910964048178.2225408871922.47915020921317
61213.2205.3630800847645.7272473679813175.309672547259-7.83691991524014
62196.4165.58847329643954.383674531868172.827852171693-30.811526703561
63182.8164.37837344470530.8755947591674170.346031796127-18.4216265552946
64176.4190.713879133249-9.14411997212565171.23024083887714.3138791332485
65153.6165.865515698157-30.7799655797835172.11444988162712.2655156981566
66173.2125.90256144793941.0692991453643179.428139406697-47.297438552061
67171137.83640183062417.4217692376092186.741828931766-33.1635981693756
68151.2115.949008974743-14.5248380372195200.975829062477-35.2509910252572
69161.9141.022916333102-32.4327455262893215.209829193187-20.8770836668977
70157.2129.322647644386-48.7799560450433233.857308400658-27.8773523556143
71201.7178.509480479545-27.6142680876735252.504787608128-23.1905195204548
72236.4227.525189593787-26.2016910964048271.476501502618-8.87481040621276
73356.1376.02453723491245.7272473679813290.44821539710719.9245372349118
74398.3435.31159205189654.383674531868306.90473341623637.0115920518963
75403.7453.16315380546830.8755947591674323.36125143536449.4631538054682
76384.6443.862755685216-9.14411997212565334.48136428690959.2627556852162
77365.8416.778488441329-30.7799655797835345.60147713845450.978488441329
78368.1345.65740234397441.0692991453643349.473298510662-22.4425976560265
79367.9365.03311087952117.4217692376092353.34511988287-2.86688912047919
80347358.175673701868-14.5248380372195350.34916433535111.175673701868
81343.3371.679536738456-32.4327455262893347.35320878783328.3795367384563
82292.9293.325431908523-48.7799560450433341.2545241365210.425431908522626
83311.5315.458428602465-27.6142680876735335.1558394852083.95842860246518
84300.9300.305102354594-26.2016910964048327.696588741811-0.59489764540632
85366.9367.83541463360545.7272473679813320.2373379984140.935414633604864
86356.9347.2618381974754.383674531868312.154487270662-9.63816180253048
87329.7324.45276869792130.8755947591674304.071636542911-5.24723130207849
88316.2344.230682837504-9.14411997212565297.31343713462228.0306828375036
89269278.224727853451-30.7799655797835290.5552377263339.22472785345053
90289.3251.62118366501341.0692991453643285.909517189623-37.6788163349869
91266.2233.71443410947817.4217692376092281.263796652912-32.4855658905216
92253.6243.823300807488-14.5248380372195277.901537229731-9.77669919251173
93233.8225.493467719739-32.4327455262893274.53927780655-8.3065322802608
94228.4232.083657756684-48.7799560450433273.4962982883593.68365775668389
95253.6262.360949317505-27.6142680876735272.4533187701698.76094931750475
96260.1272.779548006722-26.2016910964048273.62214308968312.6795480067219
97306.6292.68178522282245.7272473679813274.790967409197-13.9182147771784
98309.2288.56465004171854.383674531868275.451675426414-20.6353499582819
99309.5312.01202179720230.8755947591674276.1123834436312.51202179720195
100271275.341180501594-9.14411997212565275.8029394705314.34118050159435
101279.9315.086470082351-30.7799655797835275.49349549743235.1864700823514
102317.9319.15552598858541.0692991453643275.5751748660511.25552598858462
103298.4303.72137652772117.4217692376092275.656854234675.32137652772064
104246.7232.761139025409-14.5248380372195275.163699011811-13.9388609745915
105227.3212.362201737338-32.4327455262893274.670543788952-14.9377982626625
106209.1193.492634656657-48.7799560450433273.487321388387-15.6073653433434
107259.9275.110169099852-27.6142680876735272.30409898782215.2101690998517
108266286.454346617531-26.2016910964048271.74734447887420.4543466175311
109320.6324.28216266209345.7272473679813271.1905899699263.68216266209305
110308.5290.81918269950754.383674531868271.797142768625-17.6808173004925
111282.2261.12070967350930.8755947591674272.403695567323-21.0792903264907
112262.7259.697274628193-9.14411997212565274.846845343933-3.00272537180729
113263.5280.489970459241-30.7799655797835277.28999512054316.9899704592409
114313.1300.85078715063741.0692991453643284.279913703998-12.2492128493627
115284.3259.90839847493617.4217692376092291.269832287454-24.3916015250635
116252.6214.327883580446-14.5248380372195305.396954456774-38.2721164195545
117250.3213.508668900196-32.4327455262893319.524076626094-36.7913310998044
118246.5202.864031697316-48.7799560450433338.915924347727-43.6359683026841
119312.7294.706496018312-27.6142680876735358.307772069361-17.9935039816878
120333.2313.671619249713-26.2016910964048378.930071846691-19.5283807502867
121446.4447.52038100799745.7272473679813399.5523716240221.12038100799691
122511.6551.52514867951454.383674531868417.29117678861839.9251486795142
123515.5565.09442328761930.8755947591674435.02998195321449.5944232876187
124506.4576.219814193543-9.14411997212565445.72430577858269.8198141935435
125483.2540.761335975833-30.7799655797835456.4186296039557.5613359758332
126522.3544.46958918242441.0692991453643459.06111167221222.1695891824235
127509.8540.47463702191717.4217692376092461.70359374047430.6746370219168
128460.7478.988211220689-14.5248380372195456.9366268165318.2882112206891
129405.8391.863085633702-32.4327455262893452.169659892587-13.9369143662975
130375356.707163207141-48.7799560450433442.072792837903-18.2928367928593
131378.5352.638342304455-27.6142680876735431.975925783218-25.8616576955449
132406.8419.400479886466-26.2016910964048420.40121120993912.6004798864657
133467.8481.04625599535945.7272473679813408.8264966366613.2462559953589
134469.8486.06529600581254.383674531868399.1510294623216.2652960058122
135429.8439.24884295285330.8755947591674389.475562287989.44884295285289
136355.8337.498881393316-9.14411997212565383.245238578809-18.3011186066836
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138378342.20777041894141.0692991453643372.722930435695-35.7922295810592
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140334.7318.16864624025-14.5248380372195365.756191796969-16.5313537597499
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142323.1331.782040314158-48.7799560450433363.1979157308858.68204031415814
143363.6391.499874218091-27.6142680876735363.31439386958227.8998742180912
144352.1365.138959637451-26.2016910964048365.26273145895413.0389596374505
145411.9410.86168358369245.7272473679813367.211069048326-1.03831641630757
146388.6353.79846615534154.383674531868369.017859312791-34.8015338446592
147416.4431.09975566357630.8755947591674370.82464957725614.6997556635764
148360.7357.110730835223-9.14411997212565373.433389136903-3.58926916477731
149338330.737836883234-30.7799655797835376.04212869655-7.2621631167662
150417.2410.61296505073641.0692991453643382.7177358039-6.58703494926408
151388.4369.98488785114117.4217692376092389.39334291125-18.4151121488591
152371.1356.231897386813-14.5248380372195400.492940650407-14.8681026131873
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162531.3549.54177056845541.0692991453643471.98893028618118.2417705684546
163496.1507.25727977649717.4217692376092467.52095098589411.157279776497
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166386.3379.078691590792-48.7799560450433442.301264454251-7.22130840920806
167394.1382.171526370035-27.6142680876735433.642741717638-11.9284736299649
168404.1408.466851475277-26.2016910964048425.9348396211284.3668514752772
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170448.1429.70306660530254.383674531868412.11325886283-18.396933394698
171432.3427.72482503978930.8755947591674405.999580201043-4.57517496021052
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173395.2423.557290332539-30.7799655797835397.62267524724428.3572903325393
174421.9406.76149385414141.0692991453643395.969207000495-15.1385061458589
175382.9354.06249200864617.4217692376092394.315738753745-28.8375079913542
176384.2387.891409338233-14.5248380372195395.0334286989863.69140933823331
177345.5327.681626882062-32.4327455262893395.751118644227-17.8183731179381
178323.4297.945930911204-48.7799560450433397.63402513384-25.4540690887963
179372.6373.297336464222-27.6142680876735399.5169316234520.697336464221678
180376376.513021945883-26.2016910964048401.6886691505210.513021945883395
181462.7475.81234595442845.7272473679813403.86040667759113.1123459544277
182487514.37574970678554.383674531868405.24057576134727.375749706785
183444.2450.9036603957330.8755947591674406.6207448451036.70366039572963
184399.3401.029952789863-9.14411997212565406.7141671822621.72995278986326
185394.9413.772376060362-30.7799655797835406.80758951942218.8723760603616
186455.4463.8567573611541.0692991453643405.8739434934858.45675736115044
187414405.63793329484217.4217692376092404.940297467549-8.36206670515787
188375.5362.369098573574-14.5248380372195403.155739463645-13.1309014264256
189347325.061564066548-32.4327455262893401.371181459741-21.9384359334522
190339.4328.083789888226-48.7799560450433399.496166156817-11.316210111774
191385.8401.59311723378-27.6142680876735397.62115085389315.7931172337804
192378.8387.993042812954-26.2016910964048395.808648283459.19304281295445
193451.8463.87660691901145.7272473679813393.99614571300812.0766069190111
194446.1445.94145747044754.383674531868391.874867997685-0.158542529553017
195422.5424.3708149584730.8755947591674389.7535902823621.87081495847013
196383.1388.661329376842-9.14411997212565386.6827905952845.5613293768422
197352.8352.767974671579-30.7799655797835383.611990908205-0.0320253284210139
198445.3469.69518597935741.0692991453643379.83551487527924.3951859793569
199367.5341.51919192003817.4217692376092376.059038842353-25.9808080799622
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201326.2316.130635116307-32.4327455262893368.702110409982-10.0693648836933
202319.8322.818100072876-48.7799560450433365.5618559721673.01810007287617
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205394.1385.260779698845.7272473679813357.211972933219-8.83922030120033
206417.2425.63332454445254.383674531868354.383000923688.43332454445152
207369.9357.37037632669130.8755947591674351.554028914142-12.5296236733093
208349.2359.784946522622-9.14411997212565347.75917344950410.5849465226221
209321.4329.615647594918-30.7799655797835343.9643179848658.21564759491815
210405.7431.4833624702641.0692991453643338.84733838437625.7833624702596
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212316.5320.461945683398-14.5248380372195327.0628923538223.96194568339797
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214270.9276.501932724054-48.7799560450433314.0780233209895.60193272405382
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225250.3244.750178365694-32.4327455262893288.282567160595-5.54982163430623
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232266.4251.232088456701-9.14411997212565290.712031515424-15.1679115432987
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234362.8388.1301124410941.0692991453643296.40058841354625.3301124410899
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240272275.136447655772-26.2016910964048295.0652434406333.13644765577152
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242328.7312.3166136528354.383674531868290.699711815302-16.3833863471703
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269338.4322.219078075544-30.7799655797835385.36088750424-16.1809219244564
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273429.2444.425808560585-32.4327455262893446.40693696570415.2258085605849
274425.9444.072916637119-48.7799560450433456.50703940792418.172916637119
275460.7482.407126237529-27.6142680876735466.60714185014421.7071262375293
276463.6479.828155289703-26.2016910964048473.57353580670216.228155289703
277541.4556.53282286875945.7272473679813480.53992976325915.1328228687593
278544.2548.42147978803454.383674531868485.5948456800984.221479788034
279517.5513.47464364389630.8755947591674490.649761596937-4.02535635610417
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285484499.171930798104-32.4327455262893501.26081472818515.1719307981042
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287481.5490.279612445601-27.6142680876735500.3346556420728.779612445601
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289544.7546.3624339943945.7272473679813497.3103186376291.6624339943898
290541.2532.42099228699754.383674531868495.595333181135-8.77900771300335
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292469.7456.638818458819-9.14411997212565491.905301513306-13.0611815411807
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294542.6557.8870815167441.0692991453643486.24361933789615.28708151674
295517.3534.6212473885717.4217692376092482.55698337382117.3212473885702
296485.7508.482353032142-14.5248380372195477.44248500507822.7823530321417
297465.8491.704758889954-32.4327455262893472.32798663633525.9047588899543
298447476.283353657492-48.7799560450433466.49660238755129.2833536574919
299426.6420.149049948906-27.6142680876735460.665218138768-6.4509500510942
300411.6394.754195906013-26.2016910964048454.647495190391-16.8458040939865
301467.5440.64298039000445.7272473679813448.629772242015-26.8570196099963
302484.5471.16993125320754.383674531868443.446394214925-13.3300687467932
303451.2433.26138905299730.8755947591674438.263016187835-17.9386109470028
304417.4408.588920891701-9.14411997212565435.355199080424-8.81107910829854
305379.9358.13258360677-30.7799655797835432.447381973013-21.7674163932296
306484.7495.79391952251941.0692991453643432.53678133211611.0939195225194
307455459.95205007117117.4217692376092432.626180691224.9520500711713
308420.8421.629786515983-14.5248380372195434.4950515212360.829786515983244
309416.5429.068823175036-32.4327455262893436.36392235125312.5688231750361
310376.3362.737391673691-48.7799560450433438.642564371353-13.5626083263094
311405.6397.893061696221-27.6142680876735440.921206391452-7.70693830377883
312405.8393.205936461851-26.2016910964048444.595754634554-12.5940635381495
313500.8507.60244975436345.7272473679813448.2703028776566.8024497543625
314514518.7520696954254.383674531868454.8642557727124.75206969542046
315475.5458.66619657306630.8755947591674461.458208667767-16.8338034269343
316430.1396.985507890067-9.14411997212565472.358612082059-33.1144921099329
317414.4376.320950083433-30.7799655797835483.25901549635-38.0790499165669
318538533.60938039926341.0692991453643501.321320455373-4.39061960073718
319526515.19460534799517.4217692376092519.383625414396-10.8053946520047
320488.5445.929816598918-14.5248380372195545.595021438301-42.5701834010817
321520.2501.026328064082-32.4327455262893571.806417462207-19.1736719359176
322504.4455.492843514321-48.7799560450433602.087112530722-48.9071564856791
323568.5532.246460488436-27.6142680876735632.367807599238-36.2535395115642
324610.6586.173699944293-26.2016910964048661.227991152112-24.4263000557074
325818900.18457792703245.7272473679813690.08817470498782.1845779270318
326830.9893.21468141787454.383674531868714.20164405025862.3146814178742
327835.9902.60929184530430.8755947591674738.31511339552966.7092918453038
328782818.767234243615-9.14411997212565754.37688572851136.7672342436149
329762.3784.94130751829-30.7799655797835770.43865806149322.6413075182908
330856.9896.47021830389741.0692991453643776.26048255073939.5702183038967
331820.9842.29592372240617.4217692376092782.08230703998521.3959237224055
332769.6774.491826208535-14.5248380372195779.2330118286844.89182620853535
333752.2760.449028908906-32.4327455262893776.3837166173838.24902890890621
334724.4728.719956043671-48.7799560450433768.8600000013724.31995604367148
335723.1712.477984702313-27.6142680876735761.33628338536-10.6220152976869
336719.5711.792069242995-26.2016910964048753.40962185341-7.70793075700453
337817.4843.5897923105645.7272473679813745.48296032145826.1897923105603
338803.3812.05615568618454.383674531868740.1601697819488.75615568618377
339752.5739.28702599839530.8755947591674734.837379242438-13.2129740016054
340689655.172295290788-9.14411997212565731.971824681338-33.8277047092123
341630.4562.473695459546-30.7799655797835729.106270120238-67.9263045404545
342765.5761.99356103171541.0692991453643727.937139822921-3.50643896828501
343757.7771.21022123678717.4217692376092726.76800952560313.5102212367874
344732.2751.82654245377-14.5248380372195727.0982955834519.6265424537694
345702.6710.204163884993-32.4327455262893727.4285816412977.60416388499254
346683.3688.558515066544-48.7799560450433726.82144097855.25851506654385
347709.5720.399967771972-27.6142680876735726.21430031570210.8999677719715
348702.2707.7667497505-26.2016910964048722.8349413459045.56674975050055
349784.8804.41717025591245.7272473679813719.45558237610719.6171702559119
350810.9853.30055489880754.383674531868714.11577056932542.4005548988072
351755.6771.5484464782930.8755947591674708.77595876254315.9484464782897
352656.8620.197589074521-9.14411997212565702.546530897604-36.6024109254785
353615.1564.662862547118-30.7799655797835696.317103032665-50.4371374528818
354745.3761.0757585931241.0692991453643688.45494226151515.7757585931204
355694.1690.18544927202617.4217692376092680.592781490365-3.91455072797442
356675.7693.6588622882-14.5248380372195672.2659757490217.9588622881995
357643.7655.893575518614-32.4327455262893663.93917000767512.1935755186144
358622.1637.039310150026-48.7799560450433655.94064589501714.9393101500264
359634.6648.872146305315-27.6142680876735647.94212178235914.2721463053147
360588561.989746209274-26.2016910964048640.21194488713-26.0102537907258
361689.7701.19098464011645.7272473679813632.48176799190211.4909846401163
362673.9667.74424540596254.383674531868625.67208006217-6.15575459403772
363647.9646.06201310839630.8755947591674618.862392132437-1.83798689160437
364568.8531.505368588922-9.14411997212565615.238751383204-37.2946314110783
365545.7510.564854945813-30.7799655797835611.615110633971-35.1351450541873
366632.6616.09556261465141.0692991453643608.035138239985-16.5044373853489
367643.8665.72306491639217.4217692376092604.45516584599821.9230649163925
368593.1599.405807160758-14.5248380372195601.3190308764626.30580716075758
369579.7593.649849619364-32.4327455262893598.18289590692613.9498496193636
370546545.275613718931-48.7799560450433595.504342326112-0.7243862810692
371562.9560.588479342374-27.6142680876735592.825788745299-2.31152065762558
372572.5580.789109242784-26.2016910964048590.412581853628.28910924278443
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/16b2l1291910171.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/16b2l1291910171.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/26b2l1291910171.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/26b2l1291910171.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/3gljo1291910171.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/3gljo1291910171.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/4gljo1291910171.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910181mv6ovb8dmou5sqa/4gljo1291910171.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
Parameters (R input):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; 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')
 





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