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Data X:
21863 20403 18792 17931 16475 16205 25134 31896 26537 22801 20200 19666 19809 18799 17884 17512 16327 16880 26537 31867 29427 25800 22041 21759 21333 20462 19594 18564 17640 18614 32562 35640 31865 28117 25508 25006 24452 22643 21474 20500 19505 21769 36062 38633 34629 30184 27271 26841 26482 25538 23789 22386 21087 22891 36192 38922 34669 30197 27001 25891 24879 23662 22741 21615 20305 21877 35369 37941 33480 29757 26323 25359 22207 21763 19944 19662 18624 19902 31726 32860 28894 22949 19758 18420 18245 16761 15341 14271 13418 15218 26485 27457 21402 17879 15607 15626 15303 14296 13686 12948 11609 14602 23629 24680
Data Y:
60804 57907 54355 52536 49081 48877 64599 75314 71209 65210 59829 57656 57428 55315 52790 51050 48519 48354 65333 73990 72755 67424 59214 57427 56681 55437 53600 51641 49478 50124 71313 76208 74387 69520 64735 63413 62553 60109 57764 55667 53103 55301 76795 80928 79213 72759 67802 66940 66396 67539 67776 68014 68251 68488 68725 68962 69200 69437 68212 65444 63181 61198 59010 56388 53723 55340 75352 79817 78289 71892 66448 64167 61250 59580 56417 54662 53349 55385 73546 77683 74995 67282 60742 57283 57314 54704 51578 49962 46252 47234 64708 68753 62970 57474 52494 51831 51663 49637 46679 45557 41630 44417 60070 63157
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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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Big Analytics Cloud Computing Center
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