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159.81 147.75 147.13 140.33 142.87 139.67 135.35 136.32 129.27 126.81 137.17 150.94 173.71 156.68 146.84 144.17 134.81 135.13 131.45 133.77 134.87 140.90 136.57 155.52 160.16 158.04 148.42 150.70 135.26 134.63 132.23 132.55 134.13 136.94 141.73 165.68 162.48 145.86 142.19 137.30 131.71 133.67 133.81 127.48 128.10 134.32 135.83 151.87 158.87 163.86 158.58 140.13 136.87 134.20 126.19 122.52 124.20 133.87 136.53 148.90 151.19 151.29 149.06 138.80 134.77 135.43 141.19 126.77 126.43 131.00 134.00 138.13 151.06 158.61 144.03 139.57 128.74 127.20 125.90 122.06 127.23 135.13 144.83 134.94
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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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