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Data X:
6.9 6.8 6.7 6.6 6.5 6.5 7.0 7.5 7.6 7.6 7.6 7.8 8.0 8.0 8.0 7.9 7.9 8.0 8.5 9.2 9.4 9.5 9.5 9.6 9.7 9.7 9.6 9.5 9.4 9.3 9.6 10.2 10.2 10.1 9.9 9.8 9.8 9.7 9.5 9.3 9.1 9.0 9.5 10.0 10.2 10.1 10.0 9.9 10.0 9.9 9.7 9.5 9.2 9.0 9.3 9.8 9.8 9.6 9.4 9.3 9.2 9.2 9.0 8.8 8.7 8.7 9.1 9.7 9.8 9.6 9.4 9.4 9.5 9.4 9.3 9.2 9.0 8.9 9.2 9.8 9.9 9.6 9.2 9.1 9.1 9.0 8.9 8.7 8.5 8.3 8.5 8.7 8.4 8.1 7.8 7.7 7.5 7.2 6.8 6.7 6.4 6.3 6.8 7.3 7.1 7.0 6.8 6.6 6.3 6.1 6.1 6.3 6.3 6.0 6.2 6.4 6.8 7.5 7.5 7.6 7.6 7.4 7.3 7.1 6.9 6.8 7.5 7.6 7.8 8.0 8.1 8.2 8.3 8.2 8.0 7.9 7.6 7.6 8.3 8.4 8.4 8.4 8.4 8.6 8.9 8.8 8.3 7.5 7.2 7.4 8.8 9.3 9.3 8.7 8.2 8.3 8.5 8.6 8.5 8.2 8.1 7.9 8.6 8.7 8.7 8.5 8.4 8.5 8.7 8.7 8.6 8.5 8.3 8.0 8.2 8.1 8.1 8.0 7.9 7.9
Data Y:
4.8 4.8 4.7 4.7 4.7 4.6 5.0 5.4 5.5 5.6 5.6 5.8 6.0 6.1 6.1 6.0 6.0 6.1 6.5 7.1 7.4 7.4 7.5 7.6 7.8 7.8 7.7 7.6 7.5 7.3 7.6 8.0 8.8 7.9 7.8 7.7 7.8 7.7 7.5 7.3 7.1 7.0 7.3 7.8 7.9 7.9 7.8 7.8 7.9 7.8 7.6 7.4 7.2 6.9 7.1 7.5 7.6 7.4 7.3 7.2 7.3 7.2 7.1 7.0 6.9 6.8 7.2 7.6 7.7 7.6 7.5 7.5 7.6 7.6 7.6 7.5 7.3 7.2 7.4 8.0 8.2 8.0 7.7 7.7 7.8 7.8 7.7 7.5 7.3 7.1 7.1 7.2 6.8 6.6 6.4 6.4 6.5 6.3 5.9 5.5 5.2 4.9 5.4 5.8 5.7 5.6 5.5 5.4 5.4 5.4 5.5 5.8 5.7 5.4 5.6 5.8 6.2 6.8 6.7 6.7 6.4 6.3 6.3 6.4 6.3 6.0 6.3 6.3 6.6 7.5 7.8 7.9 7.8 7.6 7.5 7.6 7.5 7.3 7.6 7.5 7.6 7.9 7.9 8.1 8.2 8.0 7.5 6.8 6.5 6.6 7.6 8.0 8.1 7.7 7.5 7.6 7.8 7.8 7.8 7.5 7.5 7.1 7.5 7.5 7.6 7.7 7.7 7.9 8.1 8.2 8.2 8.2 7.9 7.3 6.9 6.6 6.7 6.9 7.0 7.1
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R Code
n <- length(x) c <- array(NA,dim=c(401)) l <- array(NA,dim=c(401)) mx <- 0 mxli <- -999 for (i in 1:401) { l[i] <- (i-201)/100 if (l[i] != 0) { x1 <- (x^l[i] - 1) / l[i] } else { x1 <- log(x) } c[i] <- cor(x1,y) if (mx < abs(c[i])) { mx <- abs(c[i]) mxli <- l[i] } } c mx mxli if (mxli != 0) { x1 <- (x^mxli - 1) / mxli } else { x1 <- log(x) } r<-lm(y~x) se <- sqrt(var(r$residuals)) r1 <- lm(y~x1) se1 <- sqrt(var(r1$residuals)) bitmap(file='test1.png') plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation') grid() dev.off() bitmap(file='test2.png') plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y') abline(r) grid() mtext(paste('Residual Standard Deviation = ',se)) dev.off() bitmap(file='test3.png') plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y') abline(r1) grid() mtext(paste('Residual Standard Deviation = ',se1)) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Box-Cox Linearity Plot',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations x',header=TRUE) a<-table.element(a,n) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum correlation',header=TRUE) a<-table.element(a,mx) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'optimal lambda(x)',header=TRUE) a<-table.element(a,mxli) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (orginial)',header=TRUE) a<-table.element(a,se) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (transformed)',header=TRUE) a<-table.element(a,se1) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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1 seconds
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Big Analytics Cloud Computing Center
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