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Data:
112.1 104.2 102.4 100.3 102.6 101.5 103.4 99.4 97.9 98 90.2 87.1 91.8 94.8 91.8 89.3 91.7 86.2 82.8 82.3 79.8 79.4 85.3 87.5 88.3 88.6 94.9 94.7 92.6 91.8 96.4 96.4 107.1 111.9 107.8 109.2 115.3 119.2 107.8 106.8 104.2 94.8 97.5 98.3 100.6 94.9 93.6 98 104.3 103.9 105.3 102.6 103.3 107.9 107.8 109.8 110.6 110.8 119.3 128.1 127.6 137.9 151.4 143.6 143.4 141.9 135.2 133.1 129.6 134.1 136.8 143.5 162.5 163.1 157.2 158.8 155.4 148.5 154.2 153.3 149.4 147.9 156 163 159.1 159.5 157.3 156.4 156.6 162.4 166.8 162.6 168.1
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R Code
gp <- function(lambda, p) { (p^lambda-(1-p)^lambda)/lambda } sortx <- sort(x) c <- array(NA,dim=c(201)) for (i in 1:201) { if (i != 101) c[i] <- cor(gp(ppoints(x), lambda=(i-101)/100),sortx) } bitmap(file='test1.png') plot((-100:100)/100,c[1:201],xlab='lambda',ylab='correlation',main='PPCC Plot - Tukey lambda') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tukey Lambda - Key Values',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Distribution (lambda)',1,TRUE) a<-table.element(a,'Correlation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Cauchy (lambda=-1)',header=TRUE) a<-table.element(a,c[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exact Logistic (lambda=0)',header=TRUE) a<-table.element(a,(c[100]+c[102])/2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Normal (lambda=0.14)',header=TRUE) a<-table.element(a,c[115]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'U-shaped (lambda=0.5)',header=TRUE) a<-table.element(a,c[151]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exactly Uniform (lambda=1)',header=TRUE) a<-table.element(a,c[201]) 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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