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Data:
94.46 96.91 101.92 104.53 105.85 106.39 106.08 106.42 106.51 106.59 105.71 104.41 103.04 100.99 100.93 99.31 99.79 99.57 99.11 98.96 97.72 99.35 98.15 97.64 97.49 97.94 98.03 98.05 97.54 98.71 99.33 99.16 98.41 98.43 97.02 97.89 97.92 97.37 97.42 96.12 96.72 96.64 95.28 95.3 95.02 95.57 94.78 95.27 96.14 96.16 95.08 94.39 94.15 94.58 94.16 95.02 94.86 94.49 94.81 94.16 94.83 96.02 95.97 95.88 95.97 97.55 97.49 98.4 98.2 97.16 96.96 96.37 96.34 96.86 96.32 95.44 92.85 92.56 91.74 92.44 93.19 92.62 94.04 93.8 96.73 98.99 100.38 101.07 99.92 101.78 100.91 100.49 101.17 100.25 98.94 99.4 100.02 99.91 99.22 98.84 98.42 97.59 97.07 96.59 95.96 94.22 94.48 93.14 93.73 94.24 98.52 99.09 99.84 99.13 100.88 100.83 101.6 101.8 102.77 103.14
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R Code
library(Hmisc) m <- mean(x) e <- median(x) bitmap(file='test1.png') op <- par(mfrow=c(2,1)) mydensity1 <- density(x,kernel='gaussian',na.rm=TRUE) plot(mydensity1,main='Density Plot - Gaussian Kernel',xlab='Median (0 -> full line) | Mean (0 -> dashed line)',ylab='density') abline(v=e,lty=1) abline(v=m,lty=5) grid() myseq <- seq(0.01, 0.99, 0.01) hd <- hdquantile(x, probs = myseq, se = TRUE, na.rm = FALSE, names = TRUE, weights=FALSE) plot(myseq,hd,col=2,main='Harrell-Davis Quantiles',xlab='quantiles',ylab='Median (0 -> full) | Mean (0 -> dashed)') abline(h=m,lty=5) abline(h=e,lty=1) grid() par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Median versus Mean',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,mean(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) a<-table.element(a,median(x)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Computing time
1 seconds
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Big Analytics Cloud Computing Center
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