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Data X:
3134.5 3510.5 4047.4 3580.8 3567.3 3920.1 3764.8 3139.3 4126.1 3920 3868.3 3414 3423.4 3819 4482.7 4040.4 3720.3 4405 3916.6 3540.5 4486.4 4213.6 4521.7 4102.3 3854.1 4106.5 4870.9 4559.7 4072.1 4687.7 4096.1 4107.2 4888 4256.2 4593.8 3888.2 4232.7 4386.2 5203.6 4456.6 4828.4 5244.6 4407.6 4809.3 5226.8 5290.2 5068.8 4425.2 4971 4806.9 5565.8 4754.9 5220 5684.3 4815.3 5114.4 5273.9 5602.6 5609.7 4168.9
Data Y:
2236 2084.9 2409.5 2199.3 2203.5 2254.1 1975.8 1742.2 2520.6 2438.1 2126.3 2267.5 2201.1 2128.5 2596 2458.2 2210.5 2621.2 2231.4 2103.6 2685.8 2539.3 2462.4 2693.3 2307.7 2385.9 2737.6 2653.9 2545.4 2848.8 2359.5 2488.3 2861.1 2717.9 2844 2749 2652.9 2660.2 3187.1 2774.1 3158.2 3244.6 2665.5 2820.8 2983.4 3077.4 3024.8 2731.8 3046.2 2834.8 3292.8 2946.1 3196.9 3284.2 3003 2979 3137.4 3647.7 3283 2947.3
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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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