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Data X:
-999.0 6.3 -999.0 -999.0 2.1 9.1 15.8 5.2 10.9 8.3 11.0 3.2 7.6 -999.0 6.3 8.6 6.6 9.5 4.8 12.0 -999.0 3.3 11.0 -999.0 4.7 -999.0 10.4 7.4 2.1 -999.0 -999.0 7.7 17.9 6.1 8.2 8.4 11.9 10.8 13.8 14.3 -999.0 15.2 10.0 11.9 6.5 7.5 -999.0 10.6 7.4 8.4 5.7 4.9 -999.0 3.2 -999.0 8.1 11.0 4.9 13.2 9.7 12.8 -999.0
Data Y:
645.0 42.0 60.0 25.0 624.0 180.0 35.0 392.0 63.0 230.0 112.0 281.0 -999.0 365.0 42.0 28.0 42.0 120.0 -999.0 -999.0 400.0 148.0 16.0 252.0 310.0 63.0 28.0 68.0 336.0 100.0 33.0 21.5 50.0 267.0 30.0 45.0 19.0 30.0 12.0 120.0 440.0 140.0 170.0 17.0 115.0 31.0 63.0 21.0 52.0 164.0 225.0 225.0 150.0 151.0 90.0 -999.0 60.0 200.0 46.0 210.0 14.0 38.0
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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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