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Data X:
100.30 101.90 102.10 103.20 103.70 106.20 107.70 109.90 111.70 114.90 116.00 118.30 120.40 126.00 128.10 130.10 130.80 133.60 134.20 135.50 136.20 139.10 139.00 139.60 138.70 140.90 141.30 141.80 142.00 144.50 144.60 145.50 146.80 149.50 149.90 150.10 150.90 152.80 153.10 154.00 154.90 156.90 158.40 159.70 160.20 163.20 163.70 164.40 163.70 165.50 165.60 166.80 167.50 170.60 170.90 172.00 171.80 173.90 174.00 173.80 173.90 176.00 176.60 178.20 179.20 181.30 181.80 182.90 183.80 186.30 187.40 189.20 189.70 191.90 192.60 193.70 194.20 197.60 199.30 201.40 203.00 206.30 207.10 209.80 211.10 215.30 217.40 215.50 210.90 212.60
Data Y:
100.00 102.83 109.50 115.91 107.94 110.86 118.89 123.38 113.33 116.38 122.04 125.47 115.62 117.91 122.40 125.05 114.18 114.74 120.63 123.68 112.84 115.64 122.32 124.59 116.33 117.45 125.64 128.38 119.87 121.22 128.98 131.35 121.35 123.72 131.06 134.55 125.93 128.90 136.19 140.34 130.48 134.68 141.05 145.44 136.21 139.85 147.13 151.44 143.62 148.55 153.54 159.79 152.55 155.84 160.38 164.22 156.40 160.05 165.60 171.15 161.90 167.21 171.34 176.83 166.27 172.30 176.71 182.99 172.07 178.17 182.20 188.49 176.88 182.13 185.32 192.86 180.27 184.92 187.82 194.94 184.36 188.80 193.42 199.76 188.78 191.49 194.87 198.28 183.24 204.87
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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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