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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:
99.5 98.2 108.9 100.0 105.0 108.4 96.7 100.5 115.6 114.9 110.7 107.7 113.5 106.9 119.6 109.4 106.9 118.7 108.9 113.1 125.1 126.5 122.7 127.5 107.1 112.0 122.1 111.5 113.2 128.2 115.1 117.4 132.0 130.8 128.0 132.7 117.0 110.9 123.5 117.4 122.7 123.5 111.5 113.8 131.2 127.0 126.2 121.2 118.8 117.9 135.2 120.7 126.4 129.6 113.4 120.5 135.5 137.6 130.6 133.1 121.5 120.5 136.9 123.7 128.5 135.0 120.9 121.1 132.2 134.5 133.6 136.1 124.5 124.6 133.5 132.3 125.3 135.5 121.2 117.5 135.9
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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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