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12300.00 12092.80 12380.80 12196.90 9455.00 13168.00 13427.90 11980.50 11884.80 11691.70 12233.80 14341.40 13130.70 12421.10 14285.80 12864.60 11160.20 14316.20 14388.70 14013.90 13419.00 12769.60 13315.50 15332.90 14243.00 13824.40 14962.90 13202.90 12199.00 15508.90 14199.80 15169.60 14058.00 13786.20 14147.90 16541.70 13587.50 15582.40 15802.80 14130.50 12923.20 15612.20 16033.70 16036.60 14037.80 15330.60 15038.30 17401.80 14992.50 16043.70 16929.60 15921.30 14417.20 15961.00 17851.90 16483.90 14215.50 17429.70 17839.50 17629.20
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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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Big Analytics Cloud Computing Center
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