Home » date » 2010 » Dec » 09 »

Inschrijvingen personenwagens (Opgave 9, deel 1)

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 09 Dec 2010 17:34:04 +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/t1291916371kga2rg42dckjg5o.htm/, Retrieved Thu, 09 Dec 2010 18:39:31 +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/t1291916371kga2rg42dckjg5o.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:
KDGP2W91
 
Dataseries X:
» Textbox « » Textfile « » CSV «
41086 39690 43129 37863 35953 29133 24693 22205 21725 27192 21790 13253 37702 30364 32609 30212 29965 28352 25814 22414 20506 28806 22228 13971 36845 35338 35022 34777 26887 23970 22780 17351 21382 24561 17409 11514 31514 27071 29462 26105 22397 23843 21705 18089 20764 25316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
141086NANA8179.45347222222NA
239690NANA5047.23680555556NA
343129NANA8069.20347222222NA
437863NANA5544.53680555556NA
535953NANA578.453472222222NA
629133NANA1266.33680555556NA
72469326610.670138888929668.3333333333-3057.66319444444-1917.67013888889
82220523213.103472222229138.75-5925.64652777778-1008.10347222222
92172524050.020138888928311.8333333333-4261.81319444444-2325.02013888889
102719228287.478472222227554.7083333333732.770138888889-1095.47847222222
112179021855.528472222226986.4166666667-5130.88819444444-65.5284722222241
121325315662.395138888926704.375-11041.9798611111-2409.39513888889
133770234897.995138888926718.54166666678179.453472222222804.00486111111
143036431821.195138888926773.95833333335047.23680555556-1457.19513888889
153260934801.078472222226731.8758069.20347222222-2192.07847222222
163021232292.870138888926748.33333333335544.53680555556-2080.87013888888
172996527412.286805555626833.8333333333578.4534722222222552.71319444444
182835228148.3368055556268821266.33680555556203.663194444449
192581423818.545138888926876.2083333333-3057.663194444441995.45486111111
202241421122.103472222227047.75-5925.646527777781291.89652777778
212050623093.728472222227355.5416666667-4261.81319444444-2587.72847222222
222880628379.061805555627646.2916666667732.770138888889426.938194444447
232222822577.361805555627708.25-5130.88819444444-349.361805555556
241397116355.436805555627397.4166666667-11041.9798611111-2384.43680555555
253684535267.870138888927088.41666666678179.453472222221577.12986111112
263533831798.278472222226751.04166666675047.236805555563539.72152777778
273502234645.786805555626576.58333333338069.20347222222376.213194444445
283477731980.745138888926436.20833333335544.536805555562796.25486111111
292688726636.995138888926058.5416666667578.453472222222250.004861111112
302397027021.711805555625755.3751266.33680555556-3051.71180555555
312278022373.211805555625430.875-3057.66319444444406.788194444449
321735118938.645138888924864.2916666667-5925.64652777778-1587.64513888889
332138220026.353472222224288.1666666667-4261.813194444441355.64652777778
342456124427.936805555623695.1666666667732.770138888889133.063194444447
351740918015.861805555623146.75-5130.88819444444-606.861805555556
361151411912.395138888922954.375-11041.9798611111-398.395138888885
373151431083.745138888922904.29166666678179.45347222222430.254861111109
382707127937.486805555522890.255047.23680555556-866.486805555549
392946230964.453472222222895.258069.20347222222-1502.45347222222
402610528445.495138888922900.95833333335544.53680555556-2340.49513888889
412239723523.161805555622944.7083333333578.453472222222-1126.16180555556
422384324391.420138888923125.08333333331266.33680555556-548.420138888887
432170520090.295138888923147.9583333333-3057.663194444441614.70486111111
441808917173.436805555623099.0833333333-5925.64652777778915.563194444447
452076419224.270138888923486.0833333333-4261.813194444441539.72986111111
462531624756.561805555624023.7916666667732.770138888889559.438194444443
471770419151.695138888924282.5833333333-5130.88819444444-1447.69513888889
481554813272.186805555624314.1666666667-11041.97986111112275.81319444445
492802932359.245138888924179.79166666678179.45347222222-4330.24513888889
502938329049.820138888924002.58333333335047.23680555556333.179861111112
513643832032.2034722222239638069.203472222224405.79652777778
523203429385.370138888923840.83333333335544.536805555562648.62986111111
532267924374.745138888923796.2916666667578.453472222222-1695.74513888889
542431925051.128472222223784.79166666671266.33680555556-732.128472222219
551800420726.003472222223783.6666666667-3057.66319444444-2722.00347222222
561753717771.436805555623697.0833333333-5925.64652777778-234.436805555557
572036618971.353472222223233.1666666667-4261.813194444441394.64652777778
582278223428.686805555622695.9166666667732.770138888889-646.686805555553
591916917322.278472222222453.1666666667-5130.888194444441846.72152777778
601380711513.311805555622555.2916666667-11041.97986111112293.68819444444
612974330846.870138888922667.41666666678179.45347222222-1103.87013888889
622559127762.945138888922715.70833333335047.23680555556-2171.94513888889
632909630806.2034722222227378069.20347222222-1710.20347222222
642648228128.245138888922583.70833333335544.53680555556-1646.24513888889
652240523008.536805555622430.0833333333578.453472222222-603.536805555552
662704423538.128472222222271.79166666671266.336805555563505.87152777778
6717970NANA-3057.66319444444NA
6818730NANA-5925.64652777778NA
6919684NANA-4261.81319444444NA
7019785NANA732.770138888889NA
7118479NANA-5130.88819444444NA
7210698NANA-11041.9798611111NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/113a71291916041.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/113a71291916041.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/213a71291916041.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/213a71291916041.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/3uu9s1291916041.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/3uu9s1291916041.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/4uu9s1291916041.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916371kga2rg42dckjg5o/4uu9s1291916041.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')
 





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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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