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Data X:
11178.4 9516.4 12102.8 12989.0 11610.2 10205.5 11356.2 11307.1 12648.6 11947.2 11714.1 12192.5 11268.8 9097.4 12639.8 13040.1 11687.3 11191.7 11391.9 11793.1 13933.2 12778.1 11810.3 13698.4 11956.6 10723.8 13938.9 13979.8 13807.4 12973.9 12509.8 12934.1 14908.3 13772.1 13012.6 14049.9 11816.5 11593.2 14466.2 13615.9 14733.9 13880.7 13527.5 13584.0 16170.2 13260.6 14741.9 15486.5 13154.5 12621.2 15031.6 15452.4 15428 13105.9 14716.8 14180.0 16202.2 14392.4 15140.6 15960.1 14351.3 13230.2 15202.1 17157.3 16159.1 13405.7 17224.7 17338.4 17370.6 18817.8 16593.2 17979.5 17015.2
Data Y:
1.3 1.4 1.3 1 0.8 0.7 0.6 0.8 0.9 1 1.2 1.3 1.3 1.4 1.4 1.8 1.9 2 2.4 2.5 2.5 2.3 1.7 1.1 0.7 0.2 0.3 1.1 1.6 2.2 3 3.8 4.6 5.1 5.3 5.5 5.7 5.9 6.1 6.1 6.3 6.5 6.7 6.6 6.5 6.4 6.3 6.3 6.3 6.2 6 5.6 5.3 5.1 4.5 4 3.5 3.5 3.3 3.1 2.9 2.5 2.6 2.8 2.8 2.9 3.1 3.3 3.5 3.4 3.5 3.7 3.8
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R Code
library('Kendall') k <- Kendall(x,y) bitmap(file='test1.png') par(bg=rgb(0.2,0.4,0.6)) plot(x,y,main='Scatterplot',xlab=xlab,ylab=ylab) grid() dev.off() bitmap(file='test2.png') par(bg=rgb(0.2,0.4,0.6)) plot(rank(x),rank(y),main='Scatterplot of Ranks',xlab=xlab,ylab=ylab) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Kendall tau Rank Correlation',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Kendall tau',header=TRUE) a<-table.element(a,k$tau) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'2-sided p-value',header=TRUE) a<-table.element(a,k$sl) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Score',header=TRUE) a<-table.element(a,k$S) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Var(Score)',header=TRUE) a<-table.element(a,k$varS) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Denominator',header=TRUE) a<-table.element(a,k$D) 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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