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Data X:
124.06 124.58 122.00 124.02 124.16 124.29 123.93 124.62 121.81 124.14 124.31 125.15 125.35 125.48 124.17 125.33 124.46 123.39 123.14 122.24 119.31 120.87 120.43 119.41 118.85 119.08 117.25 118.51 118.42 118.56 117.97 117.98 115.25 117.23 117.08 116.83 117.17 117.73 115.74 116.99 116.90 116.49 115.84 115.92 113.32 114.84 114.75 114.84 115.03 115.03 112.99 114.15 113.77 113.57 113.38 112.71 110.27 111.73 112.12 112.31 111.73 111.83 109.99 111.15 111.25 110.87 110.27 110.18 108.15 109.60 109.60 109.41 109.80 109.60 107.76 109.02 108.62 109.02 109.22 108.92 106.69 107.76 107.66 107.85 107.95 107.85 106.30 107.37 107.66 107.46 107.37 107.18 105.43 106.39 106.50 106.50 106.69 106.50 105.14 106.50 106.20 105.72 104.76 104.55 102.71 104.36 104.65 104.46 104.65 103.88 102.32 103.39 103.00 102.71 102.51 102.04 100.00
Data Y:
83.33 83.33 78.33 77.50 76.67 74.17 72.50 72.50 75.83 71.67 74.17 78.33 85.00 83.33 81.67 83.33 85.00 86.67 90.00 90.00 87.50 89.17 85.83 91.67 90.83 90.83 91.67 93.33 94.17 94.17 91.67 93.33 91.67 85.83 93.33 94.17 90.83 90.83 90.83 90.83 87.50 89.17 88.33 90.83 91.67 88.33 85.00 85.83 80.83 84.17 83.33 83.33 83.33 88.33 90.83 90.00 87.50 87.50 86.67 87.50 90.83 90.83 89.17 92.50 87.50 89.17 90.00 91.67 90.00 87.50 87.50 80.00 88.33 83.33 81.67 84.17 85.00 83.33 77.50 81.67 85.00 85.83 89.17 90.00 90.00 90.00 91.67 92.50 93.33 92.50 94.17 93.33 91.67 85.83 77.50 80.83 89.17 92.50 95.83 95.83 95.00 95.00 98.33 99.17 103.33 105.00 104.17 104.17 100.83 105.83 103.33 105.00 103.33 102.50 103.33 101.67 100.00
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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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