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6802.96 7132.68 7073.29 7264.5 7105.33 7218.71 7225.72 7354.25 7745.46 8070.26 8366.33 8667.51 8854.34 9218.1 9332.9 9358.31 9248.66 9401.2 9652.04 9957.38 10110.63 10169.26 10343.78 10750.21 11337.5 11786.96 12083.04 12007.74 11745.93 11051.51 11445.9 11924.88 12247.63 12690.91 12910.7 13202.12 13654.67 13862.82 13523.93 14211.17 14510.35 14289.23 14111.82 13086.59 13351.54 13747.69 12855.61 12926.93 12121.95 11731.65 11639.51 12163.78 12029.53 11234.18 9852.13 9709.04 9332.75 7108.6 6691.49 6143.05
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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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