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Data:
46.8 52.8 58.3 54.5 64.7 58.3 57.5 56.7 56 66.2 58.2 53.9 53.1 54.4 59.2 57.8 61.5 60.1 60.1 58.4 56.8 63.8 53.9 63.1 55.7 54.9 64.6 60.2 63.9 69.9 58.5 52 66.7 72 68.4 70.8 56.5 62.6 66.5 69.2 63.7 73.6 64.1 53.8 72.2 80.2 69.1 72 66.3 72.5 88.9 88.6 73.7 86 70 71.6 90.5 85.7 84.8 81.1 70.8 65.7 86.2 76.1 79.8 85.2 75.8 69.4 85 75 77.7 68.5 68.4 65 73.2 67.9 76.5 85.5 71.7 57.9 75.5 78.2 75.7 67.1 74.6 66.2 74.9 69.5 76.1 82.3 82.1 60.5 71.2 81.4 74.5 61.4 83.8 85.4 91.6 91.9 86.3 96.8 81 70.8 98.8 94.5 84.5 92.8 81.2 75.7 86.7 87.5 87.8 103.1 96.4 77.1 106.5 95.7 95.3 86.6 89.6 81.9 98.4 92.9 83.9 121.8 103.9 87.5 118.9 109 112.2 100.1 111.3 102.7 122.6 124.8 120.3 118.3 108.7 100.7 124 103.1 115 112.7 101.7 111.5 114.4 112.5 107.2 136.7 107.8 94.6 110.7 126.6 127.9 109.2 87.1 90.8 94.5 103.3 103.2 105.4 103.9 79.8 105.6 113 87.7 110 90.3 108.9 105.1 113 100.4 110.1 114.7 88.6 117.2 127.7 107.8 102.8 100.2 108.4 114.2 94.4 92.2 115.3 102 86.3 112 112.5 109.5 105.9 115.3 126.2 112.2 112.5 106.9 90.6 75.6 78.8 101.8 93.9 100 89.2 97.7 121.1 108.8 92.9 113.6 112.6 98.8 78
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Seasonal Period
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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