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Data X:
97.3 101 113.2 101 105.7 113.9 86.4 96.5 103.3 114.9 105.8 94.2 98.4 99.4 108.8 112.6 104.4 112.2 81.1 97.1 112.6 113.8 107.8 103.2 103.3 101.2 107.7 110.4 101.9 115.9 89.9 88.6 117.2 123.9 100 103.6 94.1 98.7 119.5 112.7 104.4 124.7 89.1 97 121.6 118.8 114 111.5 97.2 102.5 113.4 109.8 104.9 126.1 80 96.8 117.2 112.3 117.3 111.1 102.2 104.3 122.9 107.6 121.3 131.5 89 104.4 128.9 135.9 133.3 121.3 120.5 120.4 137.9 126.1 133.2 146.6 103.4 117.2
Data Y:
93.5 94.7 112.9 99.2 105.6 113 83.1 81.1 96.9 104.3 97.7 102.6 89.9 96 112.7 107.1 106.2 121 101.2 83.2 105.1 113.3 99.1 100.3 93.5 98.8 106.2 98.3 102.1 117.1 101.5 80.5 105.9 109.5 97.2 114.5 93.5 100.9 121.1 116.5 109.3 118.1 108.3 105.4 116.2 111.2 105.8 122.7 99.5 107.9 124.6 115 110.3 132.7 99.7 96.5 118.7 112.9 130.5 137.9 115 116.8 140.9 120.7 134.2 147.3 112.4 107.1 128.4 137.7 135 151 137.4 132.4 161.3 139.8 146 154.6 142.1 120.5
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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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