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Data X:
100.00 98.04 95.10 92.16 90.20 89.22 94.12 100.00 100.00 98.04 97.06 97.06 97.06 95.10 93.14 92.16 91.18 91.18 97.06 102.94 103.92 103.92 102.94 103.92 105.88 105.88 104.90 103.92 103.92 105.88 111.76 119.61 121.57 121.57 120.59 121.57 122.55 122.55 121.57 120.59 119.61 118.63 123.53 129.41 131.37 129.41 126.47 125.49 124.51 123.53 121.57 118.63 117.65 116.67 122.55 129.41 131.37 130.39 127.45 126.47 127.45 126.47 123.53 121.57 118.63 116.67 120.59 127.45 127.45 123.53 119.61 118.63 117.65 115.69 113.73 111.76 109.80 109.80 115.69 122.55 123.53 121.57 118.63 117.65 117.65 116.67 115.69 112.75 110.78 109.80 113.73 119.61 119.61 114.71 109.80 107.84 106.86 105.88 102.94 100.00 98.04 97.06 100.98 104.90 101.96 99.02 95.10 92.16 87.25 82.35 79.41 81.37 79.41 78.43 85.29 90.20 88.24 87.25 83.33 79.41 73.53 69.61 67.65 69.61 68.63 65.69 68.63 71.57 75.49 82.35 82.35 86.27 89.22 88.24 84.31 77.45 75.49 76.47 90.20 92.16 90.20 85.29 82.35 84.31 88.24 89.22 85.29 80.39 77.45 77.45 89.22 92.16 92.16 89.22 88.24 91.18 97.06 96.08 91.18 81.37 78.43 83.33 101.96 108.82 106.86 98.04 90.20 90.20 93.14 94.12 93.14 89.22 87.25 88.24 99.02 100.98 100.00 94.12 90.20 91.18 92.16 92.16 90.20 88.24 88.24 88.24 96.08 98.04 96.08 91.18 88.24 88.24 89.22 89.22 89.22 90.20 86.27 81.37 82.35 79.41 75.49 77.45 77.45 78.43 77.45 74.51 69.61 66.67 63.73 67.65 80.39 85.29 81.37 77.45 73.53 76.47 81.37 82.35 80.39 75.49 70.59 71.57 79.41 83.33
Data Y:
100.00 100.78 101.24 102.35 102.41 102.67 102.54 102.74 103.07 103.39 103.72 103.91 104.37 104.89 105.61 106.00 106.13 106.20 106.26 106.33 106.98 107.44 107.50 107.57 107.63 108.15 108.35 109.07 109.39 109.78 109.98 110.18 110.31 110.57 110.63 110.70 110.76 111.15 111.48 112.13 112.13 112.20 112.20 112.46 112.85 113.31 113.37 113.37 113.44 113.89 114.16 114.74 114.81 114.22 114.55 114.74 115.26 114.87 114.87 114.94 115.00 115.98 116.44 117.03 117.09 117.16 117.22 117.22 117.29 117.35 117.55 117.68 117.68 118.26 118.66 119.05 119.18 119.44 119.77 119.83 119.90 119.96 120.09 120.29 120.35 121.27 121.72 122.37 122.50 122.57 122.64 122.83 122.90 122.96 122.96 123.03 123.03 123.55 123.94 125.18 125.57 125.90 126.22 126.48 126.68 127.14 127.14 127.14 127.14 127.53 127.85 127.98 127.98 127.98 127.98 128.51 128.96 129.16 129.29 129.48 129.55 130.14 130.46 131.31 131.90 132.35 132.75 132.75 133.07 133.14 133.27 133.40 133.59 133.79 134.18 134.70 134.96 135.09 135.55 135.68 136.01 136.07 136.33 136.40 136.79 136.86 136.92 136.99 137.51 137.90 138.10 138.29 138.42 138.55 138.88 139.20 139.40 139.60 139.79 140.12 140.25 140.83 141.16 141.49 141.68 141.88 142.14 142.27 142.60 142.79 143.05 143.77 144.10 144.16 144.68 144.81 144.94 145.14 145.40 145.53 145.73 146.12 146.84 147.10 147.36 147.62 147.75 148.08 148.27 148.60 148.79 148.99 149.05 149.25 149.38 149.45 149.58 149.77 149.97 150.03 150.16 150.55 150.68 151.14 151.27 151.99 153.36 153.95 154.53 154.66 154.92 155.38 155.84 155.97 156.56 156.69 156.88 157.01 157.27 157.47 157.99 158.45 158.64 159.10
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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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