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Data X:
325.87 302.25 294.00 285.43 286.19 276.70 267.77 267.03 257.87 257.19 275.60 305.68 358.06 320.07 295.90 291.27 272.87 269.27 271.32 267.45 260.33 277.94 277.07 312.65 319.71 318.39 304.90 303.73 273.29 274.33 270.45 278.23 274.03 279.00 287.50 336.87 334.10 296.07 286.84 277.63 261.32 264.07 261.94 252.84 257.83 271.16 273.63 304.87 323.90 336.11 335.65 282.23 273.03 270.07 246.03 242.35 250.33 267.45 268.80 302.68 313.10 306.39 305.61 277.27 264.94 268.63 293.90 248.65 256.00 258.52 266.90 281.23 306.00 325.46 291.13 282.53 256.52 258.63 252.74 245.16 255.03 268.35 293.73 278.39
Data Y:
5.70 3.40 4.80 6.50 8.50 13.60 15.70 18.80 19.20 12.90 14.40 6.20 2.40 4.60 7.10 7.80 9.90 13.90 17.10 17.80 18.30 14.70 10.50 8.60 4.40 2.30 2.80 8.80 10.70 12.80 19.30 19.50 20.30 15.30 7.90 8.30 4.50 3.20 5.00 6.60 11.10 13.40 16.30 17.40 18.90 15.80 11.70 6.40 2.90 4.70 2.40 7.00 10.60 12.80 17.70 18.20 16.50 16.20 13.90 6.60 3.60 1.40 2.60 4.30 8.80 14.50 16.80 22.70 15.70 18.20 14.20 9.10 5.90 7.00 6.20 7.80 14.30 14.60 17.30 17.10 17.00 13.90 10.30 6.70
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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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