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Data X:
2.97 3.04 3.12 3.21 3.34 3.45 3.74 4.02 4.24 4.87 5.62 6.02 5.98 5.89 5.76 5.58 5.39 5.19 5.16 5.2 5.25 5.26 5.21 5.18 5.13 5.03 5.01 4.87 4.86 4.82 4.69 4.65 4.61 4.47 4.37 4.29 4.2 4.19 4.09 3.88 3.87 3.74 3.61 3.43 3.29 3.18 3.07 3.02 2.97 2.98 3.01 3.06 3.12 3.16 3.19 3.21 3.27 3.36 3.45 3.52 3.58 3.62 3.5 3.43 3.41 3.48 3.63 3.76 3.8 3.72 3.67 3.58 3.47 3.43 3.55 3.65 3.7 3.7 3.93 4.15 4.24
Data Y:
4.62 4.64 4.57 4.49 4.48 4.5 4.52 4.63 4.75 4.99 5.28 5.33 5.26 5.14 4.99 4.85 4.83 4.83 4.88 4.91 4.93 4.93 4.95 4.95 4.88 4.78 4.61 4.46 4.42 4.43 4.41 4.4 4.36 4.36 4.38 4.4 4.37 4.32 4.18 4.04 4 3.97 3.94 3.93 3.89 3.89 3.88 3.9 3.9 3.95 4.02 4.07 4.17 4.27 4.32 4.38 4.45 4.71 4.96 4.95 4.78 4.78 4.68 4.65 4.64 4.74 4.76 4.61 4.75 4.73 4.68 4.68 4.75 4.79 4.81 4.92 4.99 5.18 5.29 5.48 5.66
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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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