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Data X:
19.2 26.6 26.6 31.4 31.2 26.4 20.7 20.7 15 13.3 8.7 10.2 4.3 -0.1 -4.6 -3.9 -3.5 -3.4 -2.5 -1.1 0.3 -0.9 3.6 2.7 -0.2 -1 5.8 6.4 9.6 13.2 10.6 10.9 12.9 15.9 12.2 9.1 9 17.4 14.7 17 13.7 9.5 14.8 13.6 12.6 8.9 10.2 12.7 16 10.4 9.9 9.5 8.6 10 3.5 -4.2 -4.4 -1.5 -0.1 0.8
Data Y:
4.8 5.5 5.4 5.9 5.8 5.1 4.1 4.4 3.6 3.5 3.1 2.9 2.2 1.4 1.2 1.3 1.3 1.3 1.8 1.8 1.8 1.7 2.1 2 1.7 1.9 2.3 2.4 2.5 2.8 2.6 2.2 2.8 2.8 2.8 2.3 2.2 3 2.9 2.7 2.7 2.3 2.4 2.8 2.3 2 1.9 2.3 2.7 1.8 2 2.1 2 2.4 1.7 1 1.2 1.4 1.7 1.8
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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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