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Data X:
131.6 132.05 132.4 132.57 133.02 133.47 133.66 133.96 134.19 134.93 134.9 135.05 135.16 135.23 135.15 135.12 137.29 137.41 137.44 137.62 137.78 137.98 138.06 138.16 138.28 138.33 138.43 138.44 138.41 138.55 138.64 138.72 138.9 139.02 139.04 139.15 139.3 140.73 141.84 141.95 142.1 142.36 142.58 142.75 142.85 143.03 143.19 143.62 143.89 144.69 147.51 147.78 148.04 148.21 148.29 148.34 148.33 148.38 148.37 148.37
Data Y:
128.6 128.9 129.06 129.23 129.27 129.33 129.35 129.31 129.4 129.49 129.47 129.46 129.45 129.28 129.2 129.25 129.14 129.11 129.02 129.08 128.99 129.11 129.08 129.19 129.23 129.25 129.31 129.33 129.39 129.55 129.43 129.45 129.57 129.76 129.92 130.08 130.41 130.84 131.24 131.49 131.74 132.34 133.5 134.43 136.5 137.41 138.02 138.15 138.24 138.2 138.31 138.65 139.3 139.8 140.52 141.57 141.77 141.66 141.36 141.17
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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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Big Analytics Cloud Computing Center
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