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Data X:
31/08/2001 30/09/2001 31/10/2001 30/11/2001 31/12/2001 31/01/2002 28/02/2002 31/03/2002 30/04/2002 31/05/2002 30/06/2002 31/07/2002 31/08/2002 30/09/2002 31/10/2002 30/11/2002 31/12/2002 31/01/2003 28/02/2003 31/03/2003 30/04/2003 31/05/2003 30/06/2003 31/07/2003 31/08/2003 30/09/2003 31/10/2003 30/11/2003 31/12/2003 31/01/2004 29/02/2004 31/03/2004 30/04/2004 31/05/2004 30/06/2004 31/07/2004 31/08/2004 30/09/2004 31/10/2004 30/11/2004 31/12/2004 31/01/2005 28/02/2005 31/03/2005 30/04/2005 31/05/2005 30/06/2005 31/07/2005 31/08/2005 30/09/2005 31/10/2005 30/11/2005 31/12/2005 31/01/2006 28/02/2006 31/03/2006 30/04/2006 31/05/2006 30/06/2006 31/07/2006 31/08/2006 30/09/2006 31/10/2006 30/11/2006 31/12/2006 31/01/2007 28/02/2007 31/03/2007 30/04/2007 31/05/2007 30/06/2007 31/07/2007 31/08/2007 30/09/2007 31/10/2007 30/11/2007 31/12/2007 31/01/2008 29/02/2008 31/03/2008 30/04/2008 31/05/2008 30/06/2008 31/07/2008 31/08/2008
Data Y:
78.4 114.6 113.3 117 99.6 99.4 101.9 115.2 108.5 113.8 121 92.2 90.2 101.5 126.6 93.9 89.8 93.4 101.5 110.4 105.9 108.4 113.9 86.1 69.4 101.2 100.5 98 106.6 90.1 96.9 125.9 112 100 123.9 79.8 83.4 113.6 112.9 104 109.9 99 106.3 128.9 111.1 102.9 130 87 87.5 117.6 103.4 110.8 112.6 102.5 112.4 135.6 105.1 127.7 137 91 90.5 122.4 123.3 124.3 120 118.1 119 142.7 123.6 129.6 151.6 110.4 99.2 130.5 136.2 129.7 128 121.6 135.8 143.8 147.5 136.2 156.6 123.3 100.4
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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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