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Data:
96.1 96.5 96.9 97.8 98.9 100.2 101.2 101 101.6 102.4 103.7 103.7 104.6 104.5 104.5 105.6 106.1 107.6 107.7 108.3 108.1 108.1 108 108.2 108.9 109.8 109.9 109.8 110.9 111.1 112.2 112.7 114.6 114.2 114.7 114.7 116 116.3 116.4 116.6 118.1 117.2 108.3 109.5 110.5 110.6 111.2 111.1 111 112.4 112.5 112.4 111.8 111.6 112.9 112.8 113.7 113.8 114 113.8 113.9 114.4 114.4 114.5 113.8 114.3 115 115.4 115.3 114.9 114.3 114.5 115.5 115.8 115.8 116 114.9 114.1 114.1 113.5 115 114.7 115.4 116.1 116.6 117.2 118.2 118 117.7 118.5 117.5 118 117.7 116.3 115 115.7 113.6 114.8 114.9 117.3 117.3 117.7 120 119.6 119.2 117.3 117.5 119 112.5 118.9 118.4 119.4 120.6 118.6 122 122.6 120.6 117.4 116.4 122.2 121 122.4 124.9 126.1 124.5 123.2 126.4 123.9 116 126.6 125.9 126.6 116.7 126.4 129 128.7 128.4 129.2 133.3 128.9 132.7 127.7 131.8 133.9
# simulations
Significant digits
Bandwidth
(?)
Quantiles
P1 P5 Q1 Q3 P95 P99
P0.5 P2.5 Q1 Q3 P97.5 P99.5
P10 P20 Q1 Q3 P80 P90
Chart options
R Code
par1 <- as.numeric(par1) if (par1 < 10) par1 = 10 if (par1 > 5000) par1 = 5000 library(lattice) library(boot) boot.stat <- function(s,i) { s.mean <- mean(s[i]) s.median <- median(s[i]) s.midrange <- (max(s[i]) + min(s[i])) / 2 c(s.mean, s.median, s.midrange) } (r <- boot(x,boot.stat, R=par1, stype='i')) bitmap(file='plot1.png') plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean') grid() dev.off() bitmap(file='plot2.png') plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median') grid() dev.off() bitmap(file='plot3.png') plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') grid() dev.off() bitmap(file='plot4.png') densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean') dev.off() bitmap(file='plot5.png') densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median') dev.off() bitmap(file='plot6.png') densityplot(~r$t[,3],col='black',main='Density Plot',xlab='midrange') dev.off() z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3])) colnames(z) <- list('mean','median','midrange') bitmap(file='plot7.png') boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'statistic',header=TRUE) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,'Estimate',header=TRUE) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'IQR',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) q1 <- quantile(r$t[,1],0.25)[[1]] q3 <- quantile(r$t[,1],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[1]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,1]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) q1 <- quantile(r$t[,2],0.25)[[1]] q3 <- quantile(r$t[,2],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[2]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,2]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'midrange',header=TRUE) q1 <- quantile(r$t[,3],0.25)[[1]] q3 <- quantile(r$t[,3],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[3]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,3]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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