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Data X:
97.7 101.5 119.6 108.1 117.8 125.5 89.2 92.3 104.6 122.8 96.0 94.6 93.3 101.1 114.2 104.7 113.3 118.2 83.6 73.9 99.5 97.7 103.0 106.3 92.2 101.8 122.8 111.8 106.3 121.5 81.9 85.4 110.9 117.3 106.3 105.5 101.3 105.9 126.3 111.9 108.9 127.2 94.2 85.7 116.2 107.2 110.6 112.0 104.5 112.0 132.8 110.8 128.7 136.8 94.9 88.8 123.2 125.3 122.7 125.7 116.3 118.7 142.0 127.9 131.9 152.3 110.8 99.1 135.0 133.2 131.0 133.9 119.9 136.9 148.9 145.1 142.4 159.6 120.7 109.0 142.0
Data Y:
98.5 97.0 103.3 99.6 100.1 102.9 95.9 94.5 107.4 116.0 102.8 99.8 109.6 103.0 111.6 106.3 97.9 108.8 103.9 101.2 122.9 123.9 111.7 120.9 99.6 103.3 119.4 106.5 101.9 124.6 106.5 107.8 127.4 120.1 118.5 127.7 107.7 104.5 118.8 110.3 109.6 119.1 96.5 106.7 126.3 116.2 118.8 115.2 110.0 111.4 129.6 108.1 117.8 122.9 100.6 111.8 127.0 128.6 124.8 118.5 114.7 112.6 128.7 111.0 115.8 126.0 111.1 113.2 120.1 130.6 124.0 119.4 116.7 116.5 119.6 126.5 111.3 123.5 114.2 103.7 129.5
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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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