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Data X:
1 1.04 1.02 1.07 1.12 1.08 1.02 1.01 1.04 0.98 0.95 0.94 0.94 0.96 0.97 1.03 1.01 0.99 1 1 1.02 1.01 0.99 0.98 1.01 1.03 1.03 1 0.96 0.97 0.98 1.02 1.04 1.01 1.01 1 1.01 1.02 1.03 1.06 1.12 1.12 1.13 1.13 1.13 1.17 1.14 1.08 1.07 1.12 1.14 1.21 1.2 1.23 1.29 1.31 1.37 1.35 1.26 1.26 1.28 1.28 1.27 1.35 1.37 1.37 1.4 1.4 1.28 1.23 1.23 1.25 1.21 1.22 1.29 1.32 1.36 1.36 1.37 1.32
Data Y:
0.76 0.77 0.76 0.77 0.78 0.79 0.78 0.76 0.78 0.76 0.74 0.73 0.72 0.71 0.73 0.75 0.75 0.72 0.72 0.72 0.74 0.78 0.74 0.74 0.75 0.78 0.81 0.75 0.7 0.71 0.71 0.73 0.74 0.74 0.75 0.74 0.74 0.73 0.76 0.8 0.83 0.81 0.83 0.88 0.89 0.93 0.91 0.9 0.86 0.88 0.93 0.98 0.97 1.03 1.06 1.06 1.08 1.09 1.04 1 1.01 1.02 1.04 1.06 1.06 1.06 1.06 1.06 1.02 0.98 0.99 0.99 0.94 0.96 0.98 1.01 1.01 1.02 1.04 1.03
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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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