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Data X:
7.8 8 8.1 8.2 8.3 8.2 8 7.9 7.6 7.6 8.3 8.4 8.4 8.4 8.4 8.6 8.9 8.8 8.3 7.5 7.2 7.4 8.8 9.3 9.3 8.7 8.2 8.3 8.5 8.6 8.5 8.2 8.1 7.9 8.6 8.7 8.7 8.5 8.4 8.5 8.7 8.7 8.6 8.5 8.3 8 8.2 8.1 8.1 8 7.9 7.9 8 8 7.9 8 7.7 7.2 7.5 7.3 7 7 7 7.2 7.3 7.1 6.8 6.4 6.1 6.5 7.7 7.9 7.5 6.9 6.6 6.9 7.7 8
Data Y:
123.10 136.60 112.10 95.10 96.30 105.70 115.80 105.70 105.70 111.10 82.40 60.00 107.30 99.30 113.50 108.90 100.20 103.90 138.70 120.20 100.20 143.20 70.90 85.20 133.00 136.60 117.90 106.30 122.30 125.50 148.40 126.30 99.60 140.40 80.30 92.60 138.50 110.90 119.60 105.00 109.00 129.40 148.60 101.40 134.80 143.70 81.60 90.30 141.50 140.70 140.20 100.20 125.70 119.60 134.70 109.00 116.30 146.90 97.40 89.40 132.10 139.80 129.00 112.50 121.90 121.70 123.10 131.60 119.30 132.50 98.30 85.10 131.70 129.30 90.70 78.60 68.90 79.10
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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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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