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Data X:
1.4 1.5 1.8 1.8 1.8 1.7 1.5 1.1 1.3 1.6 1.9 1.9 2 2.2 2.2 2 2.3 2.6 3.2 3.2 3.1 2.8 2.3 1.9 1.9 2 2 1.8 1.6 1.4 0.2 0.3 0.4 0.7 1 1.1 0.8 0.8 1 1.1 1 0.8 1.6 1.5 1.6 1.6 1.6 1.9 2 1.9 2 2.1 2.3 2.3 2.6 2.6 2.7 2.6 2.6 2.4 2.5 2.5 2.5 2.4 2.1 2.1 2.3 2.3 2.3 2.9 2.8 2.9 3 3 2.9 2.6 2.8 2.9 3.1 2.8 2.4 1.6 1.5 1.7
Data Y:
0.0013999990894105 0.0876771622176185 0.253920154859331 -0.0329407507841036 0.00427395855379008 -0.0889668134958849 -0.165281368150775 -0.33220433089751 0.219927126513421 0.236701919529402 0.234525437835356 -0.0304289243382355 0.0953913592916723 0.222671044416315 0.0914666084830592 -0.206034580848975 0.317983507811223 0.209887369047125 0.47505736015715 -0.222062521324981 0.0119408639911758 -0.172481528037084 -0.342720660187122 -0.343867756086308 0.088435724392957 0.207149951929822 0.0343760117343465 -0.301707672057424 -0.0110173444677831 -0.0691482212286872 -0.925838573157798 0.137673245546374 0.0727565374056945 0.201173883123843 0.0856783035058499 -0.109577212438584 -0.260551934204086 0.147885882041203 0.212413648519748 -0.0832851309957541 -0.115216896092894 -0.221533847078893 0.33793754507397 -0.134446101735477 0.164631582429395 0.0888222485881065 0.0469533479921545 0.241738416214495 -0.0752540261587926 -0.0299598595890917 0.225881103587503 0.0412636104361186 0.128108276344458 -0.141250457347375 0.481091241511557 -0.11016711751824 0.191801843198123 -0.066757083135655 0.0394798962983962 -0.0743907512944395 0.0864476825544102 -0.0321810065262648 0.121406171476103 -0.0784677853924106 -0.219288911182277 -0.0373619502368736 0.449190378168538 -0.083495394142657 0.104571843583723 0.564217976443139 -0.156913984252991 0.0837627464269975 0.129666943943497 -0.0279018187223396 -0.0344326851573588 -0.328655799589801 0.121385421475263 0.0494710079304724 0.428353319793773 -0.368885964978714 -0.302589451991359 -0.45541145100303 -0.0851366312257987 0.244537840690697
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R Code
n <- length(x) c <- array(NA,dim=c(401)) l <- array(NA,dim=c(401)) mymin = min(x) if(mymin < 0) { x <- x - mymin +1 } 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) if(mymin < 0) { a<-table.element(a,'added constant term to x',header=TRUE) a<-table.element(a,-mymin+1) 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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