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Data X:
1190.8 728.8 995.6 1260.3 994 957.3 975.6 884.9 908.4 1022.8 958.6 825.1 1116.6 724.2 1004.5 1058.9 854.7 943.4 792.4 873.2 1101.4 987.1 1038.8 1060.7 1047.7 840 1044 1097.4 987.5 934 977 881.1 1083.3 1074.7 1182.2 1117.5 1117.4 936.2 1246.3 1175.1 1177.7 1035.8 1091.6 998.7 1247.9 1034.7 1287.7 994.0 1122.8 1017.3 1106.0 1191.8 1030.1 989.4 979.6 1088.0 1389.2 1043.9 1182.1 1109.6 1463.3 1276.2 1082.4 1360.4 1130.2 1019.6 1077.0 958.8 959.6 907.2 880.8 759.6 1137.2
Data Y:
0.9922 0.9778 0.9808 0.9811 1.0014 1.0183 1.0622 1.0773 1.0807 1.0848 1.1582 1.1663 1.1372 1.1139 1.1222 1.1692 1.1702 1.2286 1.2613 1.2646 1.2262 1.1985 1.2007 1.2138 1.2266 1.2176 1.2218 1.249 1.2991 1.3408 1.3119 1.3014 1.3201 1.2938 1.2694 1.2165 1.2037 1.2292 1.2256 1.2015 1.1786 1.1856 1.2103 1.1938 1.202 1.2271 1.277 1.265 1.2684 1.2811 1.2727 1.2611 1.2881 1.3213 1.2999 1.3074 1.3242 1.3516 1.3511 1.3419 1.3716 1.3622 1.3896 1.4227 1.4684 1.457 1.4718 1.4748 1.5527 1.575 1.5557 1.5553 1.577
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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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1 seconds
R Server
Big Analytics Cloud Computing Center
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