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Data X:
90.70 89.53 90.70 90.70 89.53 87.21 82.56 80.23 82.56 84.88 87.21 84.88 80.23 76.74 77.91 77.91 80.23 82.56 83.72 82.56 81.40 79.07 81.40 84.88 88.37 93.02 94.19 91.86 90.70 90.70 91.86 93.02 93.02 93.02 93.02 94.19 97.67 100.00 98.84 98.84 98.84 98.84 98.84 98.84 98.84 98.84 97.67 98.84 98.84 100.00 97.67 98.84 97.67 96.51 96.51 96.51 100.00 103.49 103.49 100.00 93.02 90.70 90.70 96.51 98.84 100.00 98.84 97.67 96.51 95.35 94.19 94.19 94.19 94.19 94.19 95.35 95.35 94.19 91.86 90.70 88.37 88.37 88.37 88.37 86.05 84.88 84.88 86.05 86.05 86.05 86.05 84.88 82.56 76.74 72.09 72.09 75.58 76.74 75.58 72.09 70.93 72.09 74.42 77.91 79.07 79.07 81.40 79.07 80.23 80.23 81.40 80.23 81.40 83.72 87.21 89.53 91.86 94.19 97.67
Data Y:
91.46 90.24 93.90 96.34 93.90 86.59 75.61 70.73 74.39 84.15 89.02 87.80 74.39 70.73 74.39 78.05 82.93 82.93 79.27 75.61 76.83 78.05 80.49 81.71 78.05 82.93 85.37 84.15 86.59 87.80 86.59 85.37 84.15 81.71 80.49 84.15 89.02 96.34 100.00 100.00 100.00 98.78 96.34 93.90 93.90 92.68 91.46 91.46 86.59 91.46 91.46 95.12 95.12 95.12 92.68 91.46 93.90 98.78 97.56 92.68 80.49 79.27 82.93 91.46 97.56 100.00 98.78 96.34 96.34 92.68 91.46 92.68 89.02 91.46 92.68 91.46 92.68 95.12 96.34 95.12 91.46 80.49 76.83 76.83 73.17 76.83 78.05 76.83 76.83 78.05 81.71 81.71 82.93 75.61 70.73 68.29 65.85 69.51 70.73 67.07 65.85 65.85 65.85 67.07 68.29 69.51 70.73 65.85 59.76 63.41 67.07 71.95 76.83 79.27 78.05 78.05 80.49 82.93 87.80
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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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