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Data:
120.7 134.1 143.6 115.1 135.1 123.6 110.7 104.3 143.7 149.7 143.3 115.3 136.2 137.4 147.6 123.7 131.6 132.7 123 108.2 140.9 149.2 134.5 103.2 136.2 135.6 139.7 131 124.4 123.6 125.1 106.2 144.4 153.7 131.5 105.5 136.3 133.4 129.8 129.1 113 117.1 115.4 96.5 141 141.3 121 106 121.9 122.4 137.4 118.9 106.7 126.5 110.8 99.3 138.7 128.9 121.9 106 113.1 124.2 129.2 116.5 105.7 122 105.1 100.8 131.8 119.9 127.1 107.1 115.8 122.9 137.5 108.9 114.9 129.7 111.8 103.4 140.3 140.7 136.1 106.3 127.7 136.4 145.1 116.5 117.6 129 117.4 107.2 130.9 145.1 127.8 96.6 126 130.1 124.5 137.4 105.6 113.3 108.4 83.5 116.2 115.6 95.6 83.5 95.3 95.8 100.4 90.9 80 93.8 92.3 74.3 101.4 103.7 92.4 83.4 91.6 101.2 109.2 100.3 91 110.9 96.3 80.4 114.5 109.9 104.1 90.7 94.6 100.4 115.9 94.4 102.5 97.3 90 81.1 107.3 100.5 95.4 81.1 92.2 98.4 98.6 81.4 85.5 90.4 83.7 73.3 89.8 101.6 87.5 65.3 87.1 89.9 91.5 84.7 84.1 86.7 89.6 65.7 92.9 97.7 84.4 68.1 95 96.3 94.7 89.7 81.3 89.3 94.2 68.7 105.7 102 84.3 74.9 92.9 100.4 99.4 94.6 84 102.2 91.4 79.8 101 97.5 87.8 77.1 89.6 100.9 97.8 90.5 84.2 96.8 82.9 75.6 91.9 85.4 90.4 74 93.1 94.9 102.9 80.7 91.7 95.5 84.8 74.4
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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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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