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Data X:
106.5789 101.3158 98.68421 100 102.6316 102.6316 102.6316 98.68421 98.68421 93.42105 98.68421 98.68421 100 101.3158 101.3158 103.9474 106.5789 107.8947 107.8947 107.8947 103.9474 96.05263 90.78947 86.84211 88.15789 90.78947 92.10526 93.42105 94.73684 93.42105 90.78947 92.10526 89.47368 84.21053 88.15789 86.84211 84.21053 82.89474 81.57895 85.52632 89.47368 89.47368 84.21053 80.26316 76.31579 80.26316 94.73684 96.05263 90.78947 80.26316 76.31579 81.57895 93.42105 101.3158 103.9474 101.3158 97.36842 98.68421 105.2632 106.5789
Data Y:
87.60884 80.37508 73.94508 73.94508 76.35633 77.16008 76.35633 73.14133 71.53382 72.33758 81.17883 82.78634 81.98259 77.16008 73.94508 74.74883 75.55258 75.55258 73.94508 72.33758 72.33758 72.33758 78.76758 80.37508 78.76758 74.74883 72.33758 72.33758 73.14133 73.14133 73.14133 73.94508 70.73007 66.71132 67.51507 65.10382 61.88881 63.49632 63.49632 64.30007 63.49632 61.08506 57.06631 54.65506 52.2438 55.45881 65.90757 69.92632 66.71132 63.49632 60.28131 62.69257 66.71132 67.51507 65.90757 61.88881 57.87006 58.67381 65.10382 68.31882
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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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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