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12710.3 12120.8 12469.5 12054.6 12112.9 9617.2 12645.8 13581.3 12162.3 10969.7 11880.0 11887.6 12926.9 12300.0 12092.8 12380.8 12196.9 9455.0 13168.0 13427.9 11980.5 11884.8 11691.7 12233.8 14341.4 13130.7 12421.1 14285.8 12864.6 11160.2 14316.2 14388.7 14013.9 13419.0 12769.6 13315.5 15332.9 14243.0 13824.4 14962.9 13202.9 12199.0 15508.9 14199.8 15169.6 14058.0 13786.2 14147.9 16541.7 13587.5 15582.4 15802.8 14130.5 12923.2 15612.2 16033.7 16036.6 14037.8 15330.6 15038.3 17401.8
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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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