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1.8 2.1 2.2 2.3 2.1 2.7 2.1 2.4 2.9 2.2 2.1 2.2 2.2 2.7 1.9 2 2.5 2.2 2.3 1.9 2.1 3.5 2.1 2.3 2.3 2.2 3.5 1.9 1.9 1.9 1.9 2.1 2 3.2 2.3 2.5 1.8 2.4 2.8 2.3 2 2.5 2.3 1.8 1.9 2.6 2 2.6 1.6 2.2 2.1 1.8 1.8 1.9 2.4 1.9 2 2.1 1.7 1.9 2.1 2.4 1.8 2.3 2.1 2 2.8 2 2.7 2.1 2.9 2 1.8 2.6 2.1 2.3 2.2 2 2.2 2.1 2.1 1.9 2 1.7 2.2 2.2 2.3 2.4 2.1 1.9 1.7 1.8 1.5 1.9 1.9 1.7 1.9 1.9 1.8 2.4 1.8 1.9 1.8 2.1 1.9 2.2 2 1.7 1.7 1.8 1.9 1.8 1 1 4 4 3 2 4 4 4 2 4 1 3 3 4 3 4 3 3 4 3 3 2 2 3 1 4 3 2 4 4 4 4 4 3 3 4 4 4 3 4 4 2 2 4 3 3 2 3 2 4 1 4 1 4 3 3 2 3 3 4 4 4 3 3 4 4 1 2 3 4 3 4 3 3 3 3 1 1 3 2 3 2 2 4 2 2 3 4 2 4 3 4 2 1 1 4 3 1 4 3 2 4 3 3 4 1 3 4 1 3 4 4 1 4 2 3 4 4 4 2 4 2 1 1 4 2 2 3 2 3 4 2 3 4 3 4 4 4 2 2 2 4 3 2 2 3 3 1 2 2 3 3 2 2 3 3 1 3 2 2 3 3 3 3 1
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R Code
library(MASS) library(car) 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$estimate[2])^2),min(x),max(x),add=T) dev.off() bitmap(file='test3.png') qqPlot(x,dist='norm',main='QQ plot (Normal) with confidence intervals') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Parameter',1,TRUE) a<-table.element(a,'Estimated Value',1,TRUE) a<-table.element(a,'Standard Deviation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,r$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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