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100.70 97.90 96.50 96.60 96.60 95.50 91.80 89.30 87.00 85.90 88.00 87.90 89.20 90.90 91.60 90.20 89.10 87.50 86.30 86.00 84.40 86.10 91.00 92.70 88.00 84.30 82.20 80.80 79.40 80.20 82.20 82.20 81.20 82.10 88.10 88.50 92.10 98.60 100.90 100.60 101.10 102.10 103.60 102.80 108.30 104.00 106.10 106.30 109.00 111.00 113.70 112.70 110.30 114.50 119.30 121.80 125.40 129.70 129.40 134.50 141.20 141.40 152.20 167.70 173.30 168.70 172.60 169.80 172.00 179.40 174.60 172.50 172.60 176.30 178.90 179.60 179.90 180.30 180.90 177.70
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R Code
library(MASS) par1 <- as.numeric(par1) if (par2 == '0') par2 = 'Sturges' else par2 <- as.numeric(par2) x <- as.ts(x) #otherwise the fitdistr function does not work properly r <- fitdistr(x,'normal') r bitmap(file='test1.png') myhist<-hist(x,col=par1,breaks=par2,main=main,ylab=ylab,xlab=xlab,freq=F) curve(1/(r$estimate[2]*sqrt(2*pi))*exp(-1/2*((x-r$estimate[1])/r$estimate[2])^2),min(x),max(x),add=T) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Parameter',1,TRUE) a<-table.element(a,'Estimated Value',1,TRUE) a<-table.element(a,'Standard Deviation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,r$estimate[1]) a<-table.element(a,r$sd[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'standard deviation',header=TRUE) a<-table.element(a,r$estimate[2]) a<-table.element(a,r$sd[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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