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Verkoop Volvo per maand in nederland

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 16 May 2011 17:32:24 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2.htm/, Retrieved Mon, 16 May 2011 19:28:41 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
2435 1379 1511 2021 1614 1680 1630 870 1877 2428 1711 127 3192 1934 2075 1700 1198 1582 1705 911 1817 1168 920 84 2254 1485 1886 1358 1167 1781 1218 779 1418 1641 1196 132 2926 1777 2094 1648 1646 1537 1917 977 1475 2124 1209 135 2917 1981 1398 1171 903 1390 1280 781 1828 1631 1063 186 2275 1342 1070 950 1121 1305 1586 548 1225 1419 880 124 2044 1143 897 1264 1326 1529 1373 587 1137 1426 1016 176
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ www.wessa.org


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12435NANA1194.12326388889NA
21379NANA206.873263888889NA
31511NANA173.644097222222NA
42021NANA-35.7586805555556NA
51614NANA-145.640625NA
61680NANA152.678819444444NA
716301754.331597222221638.45833333333115.873263888889-124.331597222222
88701068.352430555561693.125-624.772569444444-198.352430555556
918771916.5468751739.75176.796875-39.546875
1024282064.692708333331749.875314.817708333333363.307291666667
1117111469.241319444441719.16666666667-249.925347222222241.758680555556
12127419.0399305555551697.75-1278.71006944444-292.039930555555
1331922890.914930555561696.791666666671194.12326388889301.085069444445
1419341908.498263888891701.625206.87326388888925.5017361111115
1520751874.477430555561700.83333333333173.644097222222200.522569444445
1617001610.074652777781645.83333333333-35.758680555555689.9253472222224
1711981414.7343751560.375-145.640625-216.734375
1815821678.303819444441525.625152.678819444444-96.3038194444443
1917051600.623263888891484.75115.873263888889104.376736111111
20911802.1857638888891426.95833333333-624.772569444444108.814236111111
2118171577.1718751400.375176.796875239.828125
2211681693.067708333331378.25314.817708333333-525.067708333333
239201112.782986111111362.70833333333-249.925347222222-192.782986111111
248490.99826388888891369.70833333333-1278.71006944444-6.99826388888891
2522542551.831597222221357.708333333331194.12326388889-297.831597222222
2614851538.789930555561331.91666666667206.873263888889-53.7899305555554
2718861483.435763888891309.79166666667173.644097222222402.564236111111
2813581277.116319444441312.875-35.758680555555680.8836805555557
2911671198.442708333331344.08333333333-145.640625-31.442708333333
3017811510.262152777781357.58333333333152.678819444444270.737847222222
3112181503.456597222221387.58333333333115.873263888889-285.456597222222
32779802.9774305555561427.75-624.772569444444-23.9774305555557
3314181625.380208333331448.58333333333176.796875-207.380208333333
3416411784.151041666671469.33333333333314.817708333333-143.151041666667
3511961251.449652777781501.375-249.925347222222-55.4496527777778
36132232.4565972222221511.16666666667-1278.71006944444-100.456597222222
3729262724.248263888891530.1251194.12326388889201.751736111111
3817771774.373263888891567.5206.8732638888892.62673611111131
3920941751.769097222221578.125173.644097222222342.230902777778
4016481564.866319444441600.625-35.758680555555683.1336805555559
4116461475.651041666671621.29166666667-145.640625170.348958333334
4215371774.637152777781621.95833333333152.678819444444-237.637152777777
4319171737.581597222221621.70833333333115.873263888889179.418402777778
449771005.060763888891629.83333333333-624.772569444444-28.0607638888889
4514751786.130208333331609.33333333333176.796875-311.130208333333
4621241875.276041666671560.45833333333314.817708333333248.723958333333
4712091259.699652777781509.625-249.925347222222-50.6996527777778
48135193.8315972222221472.54166666667-1278.71006944444-58.8315972222222
4929172633.998263888891439.8751194.12326388889283.001736111111
5019811612.039930555561405.16666666667206.873263888889368.960069444445
5113981585.352430555561411.70833333333173.644097222222-187.352430555555
5211711370.116319444441405.875-35.7586805555556-199.116319444444
539031233.6093751379.25-145.640625-330.609375
5413901527.970486111111375.29166666667152.678819444444-137.970486111111
5512801466.539930555561350.66666666667115.873263888889-186.539930555555
56781672.5190972222221297.29166666667-624.772569444444108.480902777778
5718281433.7968751257176.796875394.203125
5816311548.942708333331234.125314.81770833333382.0572916666665
591063984.0746527777781234-249.92534722222278.9253472222224
60186-39.16840277777781239.54166666667-1278.71006944444225.168402777778
6122752442.873263888891248.751194.12326388889-167.873263888889
6213421458.664930555561251.79166666667206.873263888889-116.664930555555
6310701390.602430555561216.95833333333173.644097222222-320.602430555555
649501147.241319444441183-35.7586805555556-197.241319444444
6511211020.901041666671166.54166666667-145.640625100.098958333333
6613051309.012152777781156.33333333333152.678819444444-4.0121527777776
6715861259.998263888891144.125115.873263888889326.001736111111
68548501.4357638888891126.20833333333-624.77256944444446.5642361111111
6912251287.505208333331110.70833333333176.796875-62.505208333333
7014191431.401041666671116.58333333333314.817708333333-12.4010416666667
71880888.2829861111111138.20833333333-249.925347222222-8.28298611111109
72124-122.6267361111111156.08333333333-1278.71006944444246.626736111111
7320442350.664930555561156.541666666671194.12326388889-306.664930555555
7411431356.164930555561149.29166666667206.873263888889-213.164930555556
758971320.894097222221147.25173.644097222222-423.894097222222
7612641108.116319444441143.875-35.7586805555556155.883680555556
7713261004.192708333331149.83333333333-145.640625321.807291666667
7815291310.345486111111157.66666666667152.678819444444218.654513888889
791373NANA115.873263888889NA
80587NANA-624.772569444444NA
811137NANA176.796875NA
821426NANA314.817708333333NA
831016NANA-249.925347222222NA
84176NANA-1278.71006944444NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/13qlq1305567141.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/13qlq1305567141.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/2vtfl1305567141.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/2vtfl1305567141.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/36c6c1305567141.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/36c6c1305567141.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/48cit1305567141.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305566917o7s4qtuckifs1g2/48cit1305567141.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 12 ;
 
Parameters (R input):
par1 = additive ; par2 = 12 ;
 
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
 





Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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