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Data X:
33.5 29.7 28.9 35.9 24.7 21.3 26.3 30.7 21.7 26.2 30.5 22.1 28.9 29.3 32.7 30.9 31.3 31.6 34.9 28.2 29.6 23.3 30.7 27.6 35.5 30.2 26.4 30.2 27.7 27.4 26.9 28.4 27.0 29.7 32.2 32.6 33.0 27.9 30.2 27.0 32.1 29.8 31.2 31.9 25.7 24.8 28.5 27.3 35.7 31.2 29.5
Data Y:
$42.278 $67.629 $49.254 $44.922 $60.487 $60.940 $70.161 $57.522 $68.277 $46.140 $49.555 $71.223 $53.438 $54.916 $48.060 $57.810 $53.444 $42.786 $42.406 $51.710 $76.165 $63.151 $52.005 $67.244 $35.521 $56.630 $51.102 $56.870 $49.875 $73.397 $65.243 $46.686 $54.310 $46.784 $60.730 $49.644 $47.199 $58.875 $55.173 $58.633 $44.929 $53.053 $43.716 $53.875 $63.383 $60.708 $66.155 $59.068 $39.552 $58.080 $55.690
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R Code
k <- cor.test(x,y,method='spearman') bitmap(file='test1.png') plot(x,y,main='Scatterplot',xlab=xlab,ylab=ylab) grid() dev.off() bitmap(file='test2.png') plot(rank(x),rank(y),main='Scatterplot of Ranks',xlab=xlab,ylab=ylab) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Spearman Rank Correlation',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'rho',header=TRUE) a<-table.element(a,k$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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