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Data X:
83.33 83.33 78.33 77.50 76.67 74.17 72.50 72.50 75.83 71.67 74.17 78.33 85.00 83.33 81.67 83.33 85.00 86.67 90.00 90.00 87.50 89.17 85.83 91.67 90.83 90.83 91.67 93.33 94.17 94.17 91.67 93.33 91.67 85.83 93.33 94.17 90.83 90.83 90.83 90.83 87.50 89.17 88.33 90.83 91.67 88.33 85.00 85.83 80.83 84.17 83.33 83.33 83.33 88.33 90.83 90.00 87.50 87.50 86.67 87.50 90.83 90.83 89.17 92.50 87.50 89.17 90.00 91.67 90.00 87.50 87.50 80.00 88.33 83.33 81.67 84.17 85.00 83.33 77.50 81.67 85.00 85.83 89.17 90.00 90.00 90.00 91.67 92.50 93.33 92.50 94.17 93.33 91.67 85.83 77.50 80.83 89.17 92.50 95.83 95.83 95.00 95.00 98.33 99.17 103.33 105.00 104.17 104.17 100.83 105.83 103.33 105.00 103.33 102.50 103.33 101.67 100.00
Data Y:
241.66 251.25 230.26 240.91 211.20 188.19 177.01 167.85 174.03 170.09 203.42 254.97 342.84 386.29 440.51 433.58 408.13 370.32 355.51 332.62 314.62 301.73 306.31 282.98 266.48 249.97 259.87 246.24 238.36 238.04 224.19 214.71 203.11 221.00 211.73 209.39 217.48 242.19 244.64 232.07 235.80 230.37 209.82 206.41 209.60 192.24 186.17 193.41 202.36 203.00 190.64 185.43 171.58 179.57 180.42 162.10 157.95 146.66 154.43 163.38 150.92 151.98 144.74 140.37 143.36 135.79 134.73 126.42 124.72 117.90 114.07 112.26 105.44 110.77 107.68 105.76 102.03 100.22 111.62 118.11 111.72 103.42 97.13 103.10 104.91 100.22 98.52 95.32 96.92 96.60 92.55 82.75 80.84 79.13 79.77 85.10 96.39 97.56 96.39 101.18 103.52 100.11 99.26 104.48 101.29 100.33 115.24 113.64 115.35 108.42 105.65 108.64 104.80 95.43 104.48 103.84 100.01
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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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