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Data X:
1.5291 1.5358 1.5355 1.5287 1.5334 1.5225 1.5135 1.5144 1.4913 1.4793 1.4663 1.4749 1.4745 1.4775 1.4678 1.4658 1.4572 1.4721 1.4624 1.4636 1.4649 1.465 1.4673 1.4679 1.4621 1.4674 1.4695 1.4964 1.5155 1.5411 1.5476 1.54 1.5474 1.5485 1.559 1.5544 1.5657 1.5734 1.567 1.5547 1.54 1.5192 1.527 1.5387 1.5431 1.5426 1.5216 1.5364 1.5469 1.5501 1.5494 1.5475 1.5448 1.5391 1.5578 1.5528 1.5496 1.549 1.5449 1.5479
Data Y:
1.6891 1.7236 1.8072 1.7847 1.6813 1.6469 1.689 1.7169 1.8036 1.7955 1.7172 1.7348 1.7094 1.6963 1.6695 1.6537 1.6662 1.6793 1.7922 1.8045 1.7927 1.7831 1.7847 1.8076 1.8218 1.8112 1.795 1.7813 1.7866 1.7552 1.7184 1.7114 1.6967 1.6867 1.6337 1.6626 1.6374 1.626 1.637 1.6142 1.7033 1.7483 1.7135 1.7147 1.7396 1.7049 1.6867 1.7462 1.7147 1.667 1.6806 1.6738 1.6571 1.5875 1.6002 1.6144 1.6009 1.5937 1.603 1.5979
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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='Zwitserse Frank',ylab='Australische Dollar') 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='Zwitserse Frank',ylab='Australische Dollar') 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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