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Data X:
111.28 111.41 111.62 111.76 111.89 112.04 112.12 112.30 112.47 112.59 112.78 112.73 112.99 113.10 113.33 113.38 113.68 113.65 113.81 113.88 114.02 114.25 114.28 114.38 114.73 114.97 115.05 115.29 115.37 115.54 115.76 115.92 116.02 116.21 116.26 116.51 104.71 105.35 106.31 106.26 106.97 107.04 106.98 107.05 107.33 107.36 107.28 107.58 109.03 110.43 111.01 111.01 110.76 111.13 111.07 111.09 110.96 110.64 110.62 110.58 111.33
Data Y:
5.66 5.48 5.29 5.18 4.99 4.92 4.81 4.79 4.75 4.68 4.68 4.73 4.75 4.61 4.76 4.74 4.64 4.65 4.68 4.78 4.78 4.95 4.96 4.71 4.45 4.38 4.32 4.27 4.17 4.07 4.02 3.95 3.90 3.90 3.88 3.89 3.89 3.93 3.94 3.97 4.00 4.04 4.18 4.32 4.37 4.40 4.38 4.36 4.36 4.40 4.41 4.43 4.42 4.46 4.61 4.78 4.88 4.95 4.95 4.93 4.93
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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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