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Data X:
18 38 15 20 18 36 20 43 45 65 43 38 33 10 50 10 50 15 53 60 18 38 15 20 18 36 20 43 45 65 43 38 33 10 50 10 50 15 53 15 37 15 18 11 35 20 40 50 36 50 38 10 75 10 85 13 50 58 58 48 12 63 10 63 13 28 35 63 13 45 9 20 18 35 20 38 50 70 40 21 19 10 33 16 5 32 23 30 45 33 25 12 53 36 5 63 43 25 73 45 52 9 30 22 56 15 45
Data Y:
20.2 56 12.5 21.2 15.5 39 21 38.2 55.6 81.9 39.5 56.4 40.5 14.3 81.5 13.7 81.5 20.5 56 80.7 20 56.5 12.1 19.6 15.5 38.8 19.5 38 55 80 38.5 55.8 38.8 12.5 80.4 12.7 80.9 20.5 55 19 55.5 12.3 18.4 11.5 38 18.5 38 55.3 38.7 54.5 38 12 81.7 11.5 80 18.3 55.3 80.2 80.7 55.8 15 81 12 81.4 12.5 38.2 54.2 79.3 18.2 55.5 11.4 19.5 15.5 37.5 19.5 37.5 55.5 80 37.5 15.5 23.7 9.8 40.8 17.5 4.3 36.5 26.3 30.4 50.2 30.1 25.5 13.8 58.9 40 6 72.5 38.8 19.4 81.5 77.4 54.6 6.8 32.6 19.8 58.8 12.9 49
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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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1 seconds
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Big Analytics Cloud Computing Center
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