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Data X:
0.310084596 0.278403759 0.488085568 0.524136573 0.413733736 0.4681831 0.476715297 0.488284498 0.473341212 0.485132292 0.354395181 0.472117215 0.313855946 0.463167042 0.35890907 0.455290318 0.344383836 0.461334035 0.48928383 0.50146836 0.480779797 0.498951763 0.483351648 0.490403205
Data Y:
-0.00362546 -0.081538157 0.02321837 -0.007356826 0.034326162 -0.018621966 -0.049985063 -0.020844341 0.003152273 0.009700787 -0.017549096 0.030149978 0.086366074 -0.673017327 0.10498804 -0.1117681 0.028627252 -0.055901968 -1.084365526 1.90499258 -0.08324419 0.03202561 0.228846121 0.431890398
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R Code
k <- cor.test(x,y,method='spearman') bitmap(file='test1.png') plot(x,y,main='Scatterplot',xlab=xlab,ylab=ylab) grid() dev.off() bitmap(file='test2.png') 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,'Spearman Rank Correlation',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'rho',header=TRUE) a<-table.element(a,k$estimate) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'2-sided p-value',header=TRUE) a<-table.element(a,k$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'S',header=TRUE) a<-table.element(a,k$statistic) 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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