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Data X:
7.3 7.2 7.1 6.9 6.8 6.7 6.8 6.8 6.7 6.8 6.8 6.7 6.3 6.2 6.2 6.5 6.5 6.4 6.2 6.2 6.3 7.5 7.4 7.4 7.4 7.4 7.4 7.2 7.2 7.2 7.5 7.4 7.5 8.0 8.0 8.0 8.1 8.1 8.1 7.9 7.9 8.0 8.2 8.1 8.2 8.5 8.5 8.6 8.4 8.4 8.4 7.7 7.8 7.9 8.8 8.8 8.9 8.5 8.5 8.5 8.4 8.5 8.4 8.3 8.4 8.4 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.3 8.3 8.3 8.2 8.1 8.1 8.2 8.0 7.9 7.9 7.8 7.7 7.9 7.7 7.6
Data Y:
-12.7 -2.4 7.1 -3.9 9.5 5 -16.1 -10.8 7 13.6 8.1 -8.1 4.9 -0.8 4.3 4 1.5 5.4 -11.3 -16.4 -2 8.9 -7.2 -18 1.3 6.3 -6 2.8 2 5.1 -7.6 -18.6 5.8 20.3 0.7 -11.2 -5.7 -0.1 3.4 3.3 -1.2 4.2 -8.8 -25.3 8.5 14.5 -3.1 -10.4 -2.9 0.3 22.6 15.4 9 29.1 2.8 -3.8 27.7 28.9 26.5 19.8 13.2 14.1 34.1 30 21.8 32.1 5.3 3 17.1 26.3 38.1 19.5 38 35.5 78.6 62.2 76.9 104.9 32.2 42.5 64.3 74.9 75.4 43 58.7 55.4 76.6 63.3 78.9 82.7
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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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