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Data X:
0.25 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.75 0.75 0.75 0.75 0.75 0.75 1 1 1 1 1 1 1.15 1.15 1.25 1.25 1.25 1.4 1.6 1.6 1.6 1.6 1.6 1.6 2 2.1 2.3 2.3 2.65 2.9 3.1 3.35 3.35 3.35 3.35 3.35 3.35 3.35 3.35 3.35 3.8 4.25 4.25 4.25 4.25 4.25 4.25 4.75 5.15 5.15 5.15 5.15 5.15 5.15 5.15 5.15 5.15 5.15 5.85 6.55 7.25 7.25 7.25 7.25 7.25 7.25 7.25 7.25
Data Y:
35 34.5 35.5 35.5 36.5 36.25 37.25 38.25 43 42 40.5 40.25 40 38.7 35.5 35.25 35.15 35.2 35.25 35.25 35.25 36.5 35.5 35.35 35.25 35.35 35.5 35.4 35.5 43.5 41 38.9 44.6 63.84 106.48 183.77 139.29 133.77 161.1 208.1 459 594.9 400 447 380 308 327 390.9 486.5 410.15 401 386.2 353.15 333 391.75 383.25 387 369 287.05 288.7 290.25 272.65 276.5 342.75 417.25 435.6 513 635.7 836.5 869.75 1087.5 1420.25 1531 1664 1204.5 1199.25 1060 1340 1248
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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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