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Data:
71.97 72.32 74.07 77.95 81.75 80.81 74.1 71.37 75.21 76.9 74.44 74.76 76.23 76.97 78.4 78.6 80.08 81.12 80.31 84.59 81.34 80.95 80.48 75.26 76.32 78.92 80.47 83.14 85.42 81.53 87.31 86.01 85.1 79.91 78.6 78.6 79.37 82.89 84.43 85.32 87.71 84.68 80.62 84.79 85.49 81.68 77.69 78.31 79.18 80.91 83.91 86.3 89.76 85.11 83.81 85.36 85.89 82.59 80.87 80.27 81.36 84.81 90.3 95.43 97.59 97.8 99.48 97.52 104.39 97.74 91.37 92.42 96.9 101.58 105.46 110.06 107.9 102.87 96.28 98.59 103.22 98.6 91.79 93.83 95.17 95.19 99.44 109.18 109.15 109.72 108.41 102.96 107.64 97.28 97.25 91.84 94.12 97.86 98.83 102.29 104.49 102.11 102.14 101.28 101.21 94.2 88.47 88.08 88.02 92.95 97.05 101.44 100.34 99.98 94.17 94.54 95.12 98.04 93.72 93.83 93.03 95.81 99.1 100.12 100.67 103.87 102.39 107.21 105.71 99.79 96.12 96.17 97.23 98.08 99.84 99.72 99.92 102.7 102.06 102.36 102.43 100.6 98.4 98.61 103.03 104.7 107.45 109.67 110.54 112.05 113.19 114.2 112.56 107.36 103.93 103.83 104.74 107.5 109.53 109.42 108.6 110.72 105.1 105.19 102.55 101.25 101.56 101.62 101.7 102.94 104.37 106.93 107.82 110.83 106.86 109.46 108.8 108.69 107.77 108.64 108.5 113.84 114.59 116.27 113.63 112.29 110.31 108.47 110.67 109.1 107.02 108.12 106.69 109.87 110.82 114.14 113.31 115.16 111.06 111.13 115.96 117.57 114.69 119.42 118.4 123.32 123.39 127.04 129.35 127.12 122.1 120.22 121.53 119.01 114.27 114.46
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R Code
par1 <- as.numeric(par1) (n <- length(x)) (np <- floor(n / par1)) arr <- array(NA,dim=c(par1,np+1)) darr <- array(NA,dim=c(par1,np+1)) ari <- array(0,dim=par1) dx <- diff(x) j <- 0 for (i in 1:n) { j = j + 1 ari[j] = ari[j] + 1 arr[j,ari[j]] <- x[i] darr[j,ari[j]] <- dx[i] if (j == par1) j = 0 } ari arr darr arr.mean <- array(NA,dim=par1) arr.median <- array(NA,dim=par1) arr.midrange <- array(NA,dim=par1) for (j in 1:par1) { arr.mean[j] <- mean(arr[j,],na.rm=TRUE) arr.median[j] <- median(arr[j,],na.rm=TRUE) arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2 } overall.mean <- mean(x) overall.median <- median(x) overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2 bitmap(file='plot1.png') plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.mean,0) dev.off() bitmap(file='plot2.png') plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.median,0) dev.off() bitmap(file='plot3.png') plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.midrange,0) dev.off() bitmap(file='plot4.png') z <- data.frame(t(arr)) names(z) <- c(1:par1) (boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries')) dev.off() bitmap(file='plot4b.png') z <- data.frame(t(darr)) names(z) <- c(1:par1) (boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries')) dev.off() bitmap(file='plot5.png') z <- data.frame(arr) names(z) <- c(1:np) (boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks')) dev.off() bitmap(file='plot6.png') z <- data.frame(cbind(arr.mean,arr.median,arr.midrange)) names(z) <- list('mean','median','midrange') (boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots')) dev.off()
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1 seconds
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Big Analytics Cloud Computing Center
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