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Data:
62.4 67.4 76.1 67.4 74.5 72.6 60.5 66.1 76.5 76.8 77 71 74.8 73.7 80.5 71.8 76.9 79.9 65.9 69.5 75.1 79.6 75.2 68 72.8 71.5 78.5 76.8 75.3 76.7 69.7 67.8 77.5 82.5 75.3 70.9 76 73.7 79.7 77.8 73.3 78.3 71.9 67 82 83.7 74.8 80 74.3 76.8 89 81.9 76.8 88.9 75.8 75.5 89.1 88 85.9 89.3 82.9 81.2 90.5 86.4 81.8 91.3 73.4 76.6 91 87 89.7 90.7 86.5 86.6 98.8 84.4 91.4 95.7 78.5 81.7 94.3 98.5 95.4 91.7 92.8 90.5 102.2 91.8 95 102 88.9 89.6 97.9 108.6 100.8 95.1 101 100.9 102.5 105.4 98.4 105.3 96.5 88.1 107.9 107 92.5 95.7 85.2 85.5 94.7 86.2 88.8 93.4 83.4 82.9 96.7 96.2 92.8 92.8 90 95.4 108.3 96.3 95 109 92 92.3 107 105.5 105.4 103.9 99.2 102.2 121.5 102.3 110 105.9 91.9 100 111.7 104.9 103.3 101.8 100.8 104.2 116.5 97.9 100.7 107 96.3 96 104.5 107.4 102.4 94.9 98.8 96.8 108.2 103.8 102.3 107.2 102 92.6 105.2 113 105.6 101.6 101.7 102.7 109 105.5 103.3 108.6 98.2 90 112.4 111.9 102.1 102.4 101.7 98.7 114 105.1 98.3 110 96.5 92.2 112 111.4 107.5 103.4 103.5 107.4 117.6 110.2 104.3 115.9 98.9 101.9 113.5 109.5 110 114.2 106.9 109.2 124.2 104.7 111.9 119 102.9 106.3
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Seasonal Period
12
12
4
6
12
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R Code
par1 <- as.numeric(par1) (n <- length(x)) (np <- floor(n / par1)) arr <- array(NA,dim=c(par1,np)) j <- 0 k <- 1 for (i in 1:(np*par1)) { j = j + 1 arr[j,k] <- x[i] if (j == par1) { j = 0 k=k+1 } } arr arr.mean <- array(NA,dim=np) arr.sd <- array(NA,dim=np) arr.range <- array(NA,dim=np) for (j in 1:np) { arr.mean[j] <- mean(arr[,j],na.rm=TRUE) arr.sd[j] <- sd(arr[,j],na.rm=TRUE) arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE) } arr.mean arr.sd arr.range (lm1 <- lm(arr.sd~arr.mean)) (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) (lm2 <- lm(arr.range~arr.mean)) bitmap(file='test1.png') plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation') dev.off() bitmap(file='test2.png') plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range') dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Section',header=TRUE) a<-table.element(a,'Mean',header=TRUE) a<-table.element(a,'Standard Deviation',header=TRUE) a<-table.element(a,'Range',header=TRUE) a<-table.row.end(a) for (j in 1:np) { a<-table.row.start(a) a<-table.element(a,j,header=TRUE) a<-table.element(a,arr.mean[j]) a<-table.element(a,arr.sd[j] ) a<-table.element(a,arr.range[j] ) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'alpha',header=TRUE) a<-table.element(a,lm1$coefficients[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'beta',header=TRUE) a<-table.element(a,lm1$coefficients[[2]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,summary(lm1)$coefficients[2,2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,summary(lm1)$coefficients[2,3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,summary(lm1)$coefficients[2,4]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'alpha',header=TRUE) a<-table.element(a,lnlm1$coefficients[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'beta',header=TRUE) a<-table.element(a,lnlm1$coefficients[[2]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,summary(lnlm1)$coefficients[2,2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,summary(lnlm1)$coefficients[2,3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,summary(lnlm1)$coefficients[2,4]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Lambda',header=TRUE) a<-table.element(a,1-lnlm1$coefficients[[2]]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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Big Analytics Cloud Computing Center
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