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Data:
40.477 9.5545 4.0401 9.7672 18.195 24.272 16.302 38.43 15.653 2.8307 1.5373 6.6275 9.2229 9.6092 6.9105 23.104 17.517 3.6727 4.9834 8.6153 0.99686 15.017 6.3066 9.7166 2.2162 4.2027 8.9102 5.1038 6.4457 9.4839 15.145 2.8825 8.1872 7.0022 9.3093 16.827 10.591 10.572 7.8118 18.945 12.39 5.056 8.9831 21.718 8.2692 7.3192 22.893 20.765 4.584
minimum value of shape parameter
maximum value of shape parameter
Chart options
R Code
library(MASS) PPCCWeibull <- function(shape, scale, x) { x <- sort(x) pp <- ppoints(x) cor(qweibull(pp, shape=shape, scale=scale), x) } par1 <- as.numeric(par1) par2 <- as.numeric(par2) if (par1 < 0.1) par1 <- 0.1 if (par1 > 50) par1 <- 50 if (par2 < 0.1) par2 <- 0.1 if (par2 > 50) par2 <- 50 par1h <- par1*10 par2h <- par2*10 sortx <- sort(x) c <- array(NA,dim=c(par2h)) for (i in par1h:par2h) { c[i] <- cor(qweibull(ppoints(x), shape=i/10,scale=2),sortx) } bitmap(file='test1.png') plot((par1h:par2h)/10,c[par1h:par2h],xlab='shape',ylab='correlation',main='PPCC Plot - Weibull') dev.off() f<-fitdistr(x, 'weibull') f$estimate f$sd xlab <- paste('Weibull(shape=',round(f$estimate[[1]],2)) xlab <- paste(xlab,', scale=') xlab <- paste(xlab,round(f$estimate[[2]],2)) xlab <- paste(xlab,')') bitmap(file='test2.png') qqplot(qweibull(ppoints(x), shape=f$estimate[[1]], scale=f$estimate[[2]]), x, main='QQ plot (Weibull)', xlab=xlab ) 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,'shape',header=TRUE) a<-table.element(a,f$estimate[1]) a<-table.element(a,f$sd[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'scale',header=TRUE) a<-table.element(a,f$estimate[2]) a<-table.element(a,f$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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