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Data X:
92.3 95.5 92.5 89.6 84.3 76.3 80.7 96.3 81.0 82.9 90.3 74.8 70.1 86.7 86.4 89.9 88.1 78.8 81.1 85.4 82.6 80.3 81.2 68.0 67.4 91.3 94.9 82.8 88.6 73.1 76.7 93.2 84.9 83.8 93.5 91.9 69.6 87.0 90.2 82.7 91.4 74.6 76.1 87.1 78.4 81.3 99.3 71.0 73.2 95.6 84.0 90.8 93.6 80.9 84.4 97.3 83.5 88.8 100.7 69.4 74.6 96.6 96.6 93.1 91.8 85.7 79.1 91.3 84.2 85.8 94.6 77.1 76.5
Data Y:
95.5 98.7 115.9 110.4 109.5 92.3 102.1 112.8 110.2 98.9 119.0 104.3 98.8 109.4 170.3 118.0 116.9 111.7 116.8 116.1 114.8 110.8 122.8 104.7 86.0 127.2 126.1 114.6 127.8 105.2 113.1 161.0 126.9 117.7 144.9 119.4 107.1 142.8 126.2 126.9 179.2 105.3 114.8 125.4 113.2 134.4 150.0 100.9 101.8 137.7 138.7 135.4 153.8 119.5 123.3 166.4 137.5 142.2 167.0 112.3 120.6 154.9 153.4 156.2 175.8 131.7 130.1 161.1 128.2 140.3 174.9 111.8 136.6
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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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