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Data:
55.5 63 77.2 71.1 90.1 91.5 76.1 87.8 81 77.2 73.8 68.9 68.4 65.2 78.7 77 97.6 88.1 98.7 93.4 68 87.9 75.8 66.3 68.4 71.3 77.4 87.1 88.5 85.9 92.7 88.5 80.2 81.8 70.4 82.2 72.8 69 83 92.4 92.3 100.5 106.9 99.5 85.9 92.6 77.4 84.1 75.3 73.8 100.1 90.7 96.5 111.8 97.4 100.8 93.7 82 86 84.3 73.1 75.4 97.9 97.5 106 112.8 99.5 100.8 102.9 88.8 91.3 88.3 77.4 80.5 96.7 93.8 105 117.1 111.1 105.8 95.7 97.1 91 90.9 83.5 82.3 101.7 108.3 114 118.2 103.4 106.8 95.4 101.8 95.6 94.8 94 82.4 95.8 106.7 114.1 103.9 117.4 105.9 101.7 98.7 91.3 102.3 80.5 86.7 102.6 107.3 108 124.3 117.1 103.9 104.7 95.9 94.2 102.7 70.3 90.2 107.3 104.6 102.7 124.5 117.8 104.2 99.9 91.5 95.7 91.4 86.2 91.5 115.5 113.9 131.9 121.2 105.2 107.5 113.8 100.5 104.8 103.8 93.1 106.2 117.5 109.9 123.6 131.7 111 122 110.9 108 103.6 107.3 94.4 85.2 113.2 111.7 124.3 124 133.4 112.6 115.8 112.3 103.6 111.4 95.1 93.4 117.3 121.5 123.1 139.3 125.8 108.6 121 111.6 99.7 116.7 90.3 90.4 117.3 121.6 114.6 133.3 127.4 115 112.6 108.3 107.6 109 89 102.5 124.5 124.2 130.8 138.7 127.6 130.9 136.9 125.2 131.3 124.1 103.2 118.1 136.5 117.8 145.1 158.8 136.9 132.7
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Seasonal Period
FALSE
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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