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Data X:
100.00 106.54 127.63 141.72 147.95 142.16 147.95 155.82 164.13 159.16 147.14 159.16 178.85 200.44 189.43 160.16 157.02 168.91 173.19 175.83 158.78 166.96 171.24 179.55 191.00 196.41 206.80 208.94 224.86 217.31 229.96 252.36 255.25 290.37 269.67 240.53 252.86 265.51 299.31 297.42 277.09 313.59 335.75 370.67 375.33 358.65 334.80 335.05 364.07 350.47 350.16 393.46 405.29 406.86 426.12 422.97 373.63 335.18 329.89 346.32 100.00 106.54 127.63 141.72 147.95 142.16 147.95 155.82 164.13 159.16 147.14 159.16 178.85 200.44 189.43 160.16 157.02 168.91 173.19 175.83 158.78 166.96 171.24 179.55 191.00 196.41 206.80 208.94 224.86 217.31 229.96 252.36 255.25 290.37 269.67 240.53 252.86 265.51 299.31 297.42 277.09 313.59 335.75 370.67 375.33 358.65 334.80 335.05 364.07 350.47 350.16 393.46 405.29 406.86 426.12 422.97 373.63 335.18 329.89 346.32
Data Y:
100.00 100.28 100.00 98.62 98.35 98.35 104.68 104.13 103.58 104.68 104.41 105.79 107.99 108.54 107.99 109.09 107.99 109.09 115.43 115.98 115.70 115.15 112.95 115.15 117.36 117.91 118.46 116.80 116.53 117.63 121.49 123.69 124.52 127.27 125.34 127.00 127.00 127.55 127.27 125.62 125.34 125.62 130.03 130.03 129.75 128.10 126.45 128.10 128.93 128.65 127.55 126.72 127.27 127.00 131.13 131.13 129.75 124.79 122.04 121.76 100.00 100.28 100.00 98.62 98.35 98.35 104.68 104.13 103.58 104.68 104.41 105.79 107.99 108.54 107.99 109.09 107.99 109.09 115.43 115.98 115.70 115.15 112.95 115.15 117.36 117.91 118.46 116.80 116.53 117.63 121.49 123.69 124.52 127.27 125.34 127.00 127.00 127.55 127.27 125.62 125.34 125.62 130.03 130.03 129.75 128.10 126.45 128.10 128.93 128.65 127.55 126.72 127.27 127.00 131.13 131.13 129.75 124.79 122.04 121.76
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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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