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0.38 0.8 0.11 0.72 0.37 0.41 0.1 0.92 0.89 0.42 0.45 0.11 0.84 0.41 0.36 0.38 0.45 0.78 0.45 0.76 0.11 0.38 0.09 0.46 0.4 0.89 0.82 0.4 0.41 0.38 0.09 0.45 0.4 0.45 0.84 0.1 0.38 0.45 0.11 0.41 0.87 0.45 0.4 0.1 0.09 0.84 0.4 0.57 0.4 0.43 0.13 0.44 0.38 0.39 0.1 0.37 0.11 0.1 0.38 0.85 0.85 0.11 0.1 0.36 0.11 0.81 0.43 0.9 0.76 0.43 0.74 0.09 0.45 0.09 0.11 0.11 0.1 0.4 0.43 0.39 0.45 0.38 0.79 0.84 0.11 0.11 0.17 0.44 0.37 0.1 0.4 0.89 0.42 0.46 0.09 0.37 0.1 0.1 0.11
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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') print(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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