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Data X:
106.48 106.83 107.14 107.94 108.46 108.81 108.92 108.99 109.16 109.22 109.43 109.23 109.93 110.09 110.33 110.11 110.35 110.09 110.44 110.39 110.62 110.43 110.46 110.55 110.94 111.56 111.82 111.73 111.57 111.85 112.06 112.2 112.47 112.15 112.36 112.32 112.67 113.02 113.05 113.5 113.67 113.65 114 114.03 114.08 114.49 114.48 114.25 114.68 115.28 115.9 115.87 116.09 116.29 116.76 116.78 116.65 116.46 116.82 116.91
Data Y:
107.97 108.13 108.54 109.86 109.75 109.99 112.01 111.96 111.41 112.11 111.67 111.95 112.31 113.26 113.5 114.43 115.02 115.1 117.11 117.52 116.1 116.39 116.01 116.74 116.68 117.45 117.8 119.37 118.9 119.05 120.46 120.99 119.86 120.18 119.81 120.15 119.8 120.27 120.71 121.87 121.87 121.92 123.72 124.38 123.21 123.17 122.95 123.46 123.24 123.86 124.28 124.78 125.19 125.46 127.6 127.8 126.63 127.06 126.77 127.05
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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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