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Data X:
75.4 105.5 112.3 102.5 93.5 86.7 95.2 103.8 97.0 95.5 101.0 67.5 64.0 106.7 100.6 101.2 93.1 84.2 85.8 91.8 92.4 80.3 79.7 62.5 57.1 100.8 100.7 86.2 83.2 71.7 77.5 89.8 80.3 78.7 93.8 57.6 60.6 91.0 85.3 77.4 77.3 68.3 69.9 81.7 75.1 69.9 84.0 54.3 60.0 89.9 77.0 85.3 77.6 69.2 75.5 85.7 72.2 79.9 85.3 52.2 61.2 82.4 85.4 78.2 70.2 70.2 69.3 77.5 66.1 69.0 79.2 56.2 63.3 77.8 92.0 78.1 65.1 71.1 70.9 72.0 81.9 70.6 72.5 65.1 61.1
Data Y:
98.4 107.4 117.7 105.7 97.5 99.9 98.2 104.5 100.8 101.5 103.9 99.6 98.4 112.7 118.4 108.1 105.4 114.6 106.9 115.9 109.8 101.8 114.2 110.8 108.4 127.5 128.6 116.6 127.4 105.0 108.3 125.0 111.6 106.5 130.3 115.0 116.1 134.0 126.5 125.8 136.4 114.9 110.9 125.5 116.8 116.8 125.5 104.2 115.1 132.8 123.3 124.8 122.0 117.4 117.9 137.4 114.6 124.7 129.6 109.4 120.9 134.9 136.3 133.2 127.2 122.7 120.5 137.8 119.1 124.3 134.4 121.1 122.2 127.7 137.4 132.2 129.2 124.9 124.8 128.2 134.4 118.6 132.6 123.2 112.3
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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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