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166.06 154.50 146.87 145.10 143.32 137.03 132.42 130.71 128.60 130.39 138.43 154.74 184.35 163.39 149.06 147.10 138.06 134.13 139.87 133.68 125.47 137.03 140.50 157.13 159.55 160.36 156.48 153.03 138.03 139.70 138.23 145.68 139.90 142.06 145.77 171.19 171.61 150.21 144.65 140.33 129.61 130.40 128.13 125.35 129.73 136.84 137.80 153.00 165.03 172.25 177.06 142.10 136.16 135.87 119.84 119.84 126.13 133.58 132.27 153.77 161.90 155.11 156.55 138.47 130.16 133.20 152.71 121.87 129.57 127.52 132.90 143.10 154.94 166.86 147.10 142.97 127.77 131.43 126.84 123.10 127.80 133.23 148.90 143.45
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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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Big Analytics Cloud Computing Center
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