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Data X:
21 11 16 9 22 22 28 29 43 61 43 14 33 20 25 36 32 35 21 41 61 17 42 12 167 22 17 20 10 21 46 16 4.97 19 15 40 17 32 14 43 28 78 67 25 6
Data Y:
349.27 417.34 427.88 342.7 375.63 299.075 337.75 306.48 349.7967 435.13 318.46 372.5 297.34 283.94 394.18 310.31 187.53 353.96 342.195 259.4 431.5 323.3 319.34 395.45 382.9 389.79 328.16 315.63 334.61 264.38 255.91 422.47 345.54 235.18 324.64 505.368 300.2 167.76 376 436 320.37 303.746 415.89 314.2 429.173
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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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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