Home » date » 2010 » Aug » 19 »

aantal verkochte goederen

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 19 Aug 2010 12:01:51 +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/Aug/19/t1282219334hnwnyyoz8c4i2pk.htm/, Retrieved Thu, 19 Aug 2010 14:02:51 +0200
 
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/Aug/19/t1282219334hnwnyyoz8c4i2pk.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:
Trouillard Olivier
 
Dataseries X:
» Textbox « » Textfile « » CSV «
268 267 266 264 284 283 268 258 259 259 260 262 255 259 258 258 288 289 271 268 274 284 284 279 273 280 276 271 298 297 278 270 280 289 288 293 285 283 275 268 295 290 267 252 268 278 280 278 261 263 259 265 294 285 255 231 246 258 265 260 238 241 239 233 265 255 224 194 210 222 230 225 206 204 207 195 230 221 195 162 182 203 211 206 187 181 189 174 213 201 177 140 165 192 197 196 176 164 177 165 208 195 164 123 147 173 176 170 157 145 148 135 175 168 140 109 129 150 150 152
 
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'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1268NANA-5.1929012345679NA
2267NANA-5.9104938271605NA
3266NANA-3.72993827160494NA
4264NANA-9.73456790123457NA
5284NANA24.8348765432099NA
6283NANA18.6311728395062NA
7268260.945987654321265.958333333333-5.012345679012347.05401234567904
8258238.816358024691265.083333333333-26.26697530864219.1836419753087
9259254.038580246914264.416666666667-10.37808641975314.96141975308643
10259268.70987654321263.8333333333334.87654320987654-9.7098765432099
11260273.395061728395263.759.64506172839506-13.3950617283951
12262272.404320987654264.1666666666678.23765432098766-10.4043209876544
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15258262.395061728395266.125-3.72993827160494-4.39506172839504
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19271267.987654320988273-5.012345679012343.01234567901236
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21274265.871913580247276.25-10.37808641975318.12808641975306
22284282.418209876543277.5416666666674.876543209876541.58179012345676
23284288.145061728395278.59.64506172839506-4.14506172839504
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41295303.001543209877278.16666666666724.8348765432099-8.00154320987656
42290295.839506172839277.20833333333318.6311728395062-5.83950617283949
43267270.570987654321275.583333333333-5.01234567901234-3.57098765432096
44252247.483024691358273.75-26.2669753086424.51697530864197
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46278276.33487654321271.4583333333334.876543209876541.66512345679013
47280280.936728395062271.2916666666679.64506172839506-0.93672839506172
48278279.279320987654271.0416666666678.23765432098766-1.27932098765427
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51259263.436728395062267.166666666667-3.72993827160494-4.43672839506166
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53294288.793209876543263.95833333333324.83487654320995.20679012345681
54285281.214506172839262.58333333333318.63117283950623.78549382716051
55255255.862654320988260.875-5.01234567901234-0.862654320987644
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58258259.95987654321255.0833333333334.87654320987654-1.95987654320982
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62241238.797839506173244.708333333333-5.91049382716052.20216049382717
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64233228.932098765432238.666666666667-9.734567901234574.0679012345679
65265260.543209876543235.70833333333324.83487654320994.45679012345681
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77230229.043209876543204.20833333333324.83487654320990.956790123456813
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80162173.024691358025199.291666666667-26.266975308642-11.0246913580247
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102195189.547839506173170.91666666666718.63117283950625.45216049382719
103164164.029320987654169.041666666667-5.01234567901234-0.0293209876543301
104123141.191358024691167.458333333333-26.266975308642-18.1913580246914
105147155.080246913580165.458333333333-10.3780864197531-8.08024691358025
106173167.8765432098771634.876543209876545.12345679012344
107176170.020061728395160.3759.645061728395065.97993827160494
108170166.112654320988157.8758.237654320987663.88734567901236
109157150.557098765432155.75-5.19290123456796.44290123456787
110145148.256172839506154.166666666667-5.9104938271605-3.25617283950615
111148149.103395061728152.833333333333-3.72993827160494-1.10339506172838
112135141.390432098765151.125-9.73456790123457-6.39043209876542
113175173.918209876543149.08333333333324.83487654320991.08179012345681
114168165.881172839506147.2518.63117283950622.11882716049385
115140NANA-5.01234567901234NA
116109NANA-26.266975308642NA
117129NANA-10.3780864197531NA
118150NANA4.87654320987654NA
119150NANA9.64506172839506NA
120152NANA8.23765432098766NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/1zfy01282219308.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/1zfy01282219308.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/2zfy01282219308.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/2zfy01282219308.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/3apf31282219308.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/3apf31282219308.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/43gx61282219308.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282219334hnwnyyoz8c4i2pk/43gx61282219308.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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