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Data X:
103.1 103.1 103.3 103.5 103.3 103.5 103.8 103.9 103.9 104.2 104.6 104.9 105.2 105.2 105.6 105.6 106.2 106.3 106.4 106.9 107.2 107.3 107.3 107.4 107.55 107.87 108.37 108.38 107.92 108.03 108.14 108.3 108.64 108.66 109.04 109.03 109.03 109.54 109.75 109.83 109.65 109.82 109.95 110.12 110.15 110.2 109.99 110.14 110.14 110.81 110.97 110.99 109.73 109.81 110.02 110.18 110.21 110.25 110.36 110.51 110.64 110.95 111.18 111.19 111.69 111.7 111.83 111.77 111.73 112.01 111.86 112.04
Data Y:
152.60 153.32 165.50 139.18 136.53 115.92 96.65 83.77 84.66 106.03 86.92 54.66 151.66 121.27 132.95 119.64 122.16 117.44 106.69 87.45 80.98 110.30 87.01 55.73 146.00 137.54 138.54 135.62 107.27 99.04 91.36 68.35 82.59 98.41 71.25 47.58 130.83 113.60 125.69 113.60 97.12 104.43 91.84 75.11 89.24 110.23 78.42 68.45 122.81 129.66 159.06 139.03 102.16 113.59 81.46 77.36 87.57 101.23 87.21 64.94 133.12 117.99 135.90 125.67 108.03 128.31 84.74 86.38 92.24 95.83 92.33 54.27
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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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