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Data X:
0 0 0 5937098.5 0 2898816.667 0 10064966.67 0 124928.1667 4265127.767 62745103.33 0 1880150 0 7562716.667 4120556 193585.3167 0 27151993.33 0 0 0 0 171967373.3 17171995.87 16492062.5 0 456315 14617078.17 0 0 22624366.67 22624366.67 9495636.65 9495636.65 0 0 0 91366125 0 0 0 0 8310883.333 140240103.3 2405900 0 0 1805566.667 3577851.667 0 26480560.33 0 8906686.667 370.065 2.86E+06
Data Y:
644315.634 0 0 4490672.84 0 66628420 211617800 117626.2 556163.93 6495448.6 122740176.4 23317800 0 4292881.6 0 23086774.2 6762189.4 15008680.6 0 45678360 0 0 0 9994940 181996860 101934426 13249210 0 59603333 14884118 402161.4 402161.4 39875028 39875028 19180988 19180988 0 0 0 42999682 772028.5383 120559.8 0 6618272 58547600 162395540 60372804 3055546 108263626 2861663.969 1241558.42 565354.2 58999216 0 27111222 6722324 1.60E+07
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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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