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Data X:
145.3 143.6 142.8 155.9 156.2 149.8 152.7 155.5 159.3 143 141.4 142.8 146.4 152.3 164.3 168 171.3 162.7 150.2 142.5 138.2 138 145.1 138.4 131.8 130.8 126.3 123 124 120.8 122.1 106.5 104.3 108.7 113.8 112.5 106.1 98.4 96 99.3 97.5 95.3 88 94.7 99.4 98.9 96.4 95.3 99.5 101.6 103.9 106.6 108.3 102 93.8 91.6 97.7 94.8 98 103.8 97.8 91.2 89.3 87.5 90.4 94.2 102.2 101.3 96 90.8 93.2 90.9 91.1 90.2 94.3 96 99 103.3 113.1 112.8 112.1 107.4 111 110.5 110.8 112.4 111.5 116.2 122.5 121.3 113.9 110.7 120.8 141.1 147.4 148 158.1 165 187 190.3 182.4 168.8 151.2 120.1 112.5 106.2 107.1 108.5 106.5 108.3 125.6 124 127.2 136.9 135.8 124.3 115.4 113.6 114.4 118.4 117 116.5 115.4 113.6 117.4 116.9 116.4 111.1 110.2 118.9 131.8 130.6 138.3 148.4 148.7 144.3 152.5 162.9 167.2 166.5 185.6
Data Y:
174.1 180.4 182.6 207.1 213.7 186.5 179.1 168.3 156.5 144.3 138.9 137.8 136.3 140.3 149.1 149.2 140.4 129 124.7 130.8 130.1 133.2 130.1 126.6 124.8 125.3 126.9 120.1 118.7 117.7 113.4 107.5 107.6 114.3 114.9 111.2 109.9 108.6 109.2 106.4 103.7 103 96.9 104.7 102.2 99 95.8 94.5 102.7 103.2 105.6 103.9 107.2 100.7 92.1 90.3 93.4 98.5 100.8 102.3 104.7 101.1 101.4 99.5 98.4 96.3 100.7 101.2 100.3 97.8 97.4 98.6 99.7 99 98.1 97 98.5 103.8 114.4 124.5 134.2 131.8 125.6 119.9 114.9 115.5 112.5 111.4 115.3 110.8 103.7 111.1 113 111.2 117.6 121.7 127.3 129.8 137.1 141.4 137.4 130.7 117.2 110.8 111.4 108.2 108.8 110.2 109.5 109.5 116 111.2 112.1 114 119.1 114.1 115.1 115.4 110.8 116 119.2 126.5 127.8 131.3 140.3 137.3 143 134.5 139.9 159.3 170.4 175 175.8 180.9 180.3 169.6 172.3 184.8 177.7 184.6 211.4
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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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