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Data:
78.70 88.60 104.20 88.20 94.70 112.00 78.90 111.40 132.50 121.60 116.10 123.30 107.90 107.00 115.80 91.80 93.50 107.10 80.50 100.50 100.20 100.30 96.60 86.00 76.90 79.70 93.10 79.50 80.30 88.80 72.40 75.50 92.90 101.50 94.70 93.00 79.80 82.20 87.60 83.20 81.60 85.90 71.90 71.80 98.30 93.60 86.10 96.20 78.60 82.10 94.40 86.40 82.20 96.70 84.20 73.60 94.90 96.90 90.20 104.20 78.40 81.50 96.70 87.50 86.20 105.10 72.90 76.40 100.50 92.40 96.30 103.60 75.10 78.80 93.70 82.50 88.30 95.70 73.30 72.40 94.00 96.90 92.40 90.90 93.50 92.00 115.90 97.80 97.70 116.90 96.70 97.70 103.90 124.10 117.30 113.80 100.00 114.20 116.30 111.40 103.40 125.30 92.50 92.00 121.60 113.30 92.50 100.30 83.20 81.20 94.50 87.70 82.30 99.00 72.40 80.80 105.50 98.40 94.50 109.20 84.10 88.40 111.30 93.20 86.30 111.40 85.40 89.70 110.90 119.40 109.30 110.70 101.30 99.00 117.90 89.30 105.40 99.90 79.50 88.30 116.20 110.60 99.30 105.40 89.90 100.70 122.50 97.40 97.90 124.30 94.70 85.20 101.90 110.90 102.00 95.80 86.90 90.30 97.90 91.90 90.40 98.90 81.30 79.80 93.70 101.50 88.60 94.60 84.20 86.50 92.60 84.20 85.90 90.00 79.10 75.60 97.00 96.40 85.20 100.30 76.70 79.00 94.40 82.80 74.60 92.80 69.70 68.90 97.50 92.90 93.40 92.10 80.60 86.00 93.60 90.30 81.30 98.40 73.30 77.10 91.40 89.00 94.10 94.70 80.70 85.20 107.90 81.60 83.80 98.80 75.60 80.70
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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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