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Data:
106.2 101.6 99 96.4 94 93.2 103 103.6 103.2 102.2 100 99.6 98.8 95.2 91.6 88.6 86 84.8 95.2 96.2 94 92 90.2 90 88.8 85.8 84.2 80 77.8 76.8 86.4 89.2 86.2 84.6 83.2 83.2 82.6 79.8 77.2 74.8 73 73 83.6 85.6 84.8 84.2 83.4 84.6 84.6 83.8 81.2 79.6 78 78.2 88.8 92 91 91.2 90.4 91.8 92.2 90.2 88.6 87.8 86 87.2 97.6 101.2 100.4 100.2 100.2 103 104.2 104 102.4 101.8 101 102.2 114 118.4 118.8 117.2 117.2 118.4 118.8 117.2 114.4 112.6 111 110.8 120.2 124.4 123.4 121.2 119 119.8 120 118.4 115 113.4 111 111 121.6 126.2 125.8 124.8 122 123.2 124.2 120.8 116.8 114.8 111 109 119.8 124 121.6 118 115.8 116 115.8 114.4 112 110.2 107.4 108.2 117.6 121.4 119.8 115.6 112.6 113.2 112.2 110.8 108 105.2 102.4 101 110.8 116.8 113.8 108 104.4 105.2 105.4 103.2 100.6 97.8 95.8 95 104.8 110.4 106.4 102.2 98.4 98.4 98.6 96.2 92.4 91.4 88.4 87.8 97.6 104.2 100.2 97 92.8 92 93.4 92 89.6 88.6 87.2 86.2 96.8 102 102.6 100.6 94.2 94.2 95.2 95 94 92.2 91 91.2 103.4 105 104.6 103.8 101.8 102.4 103.8 103.4 102 101.8 100.2 101.4 113.8 116 115.6 113 109.4 111 112.4 112.2 111 108.8 107.4 108.6 118.8 122.2 122.6 122.2 118.8 119 118.2 117.8 116.8 114.6 113.4 113.8 124.2 125.8 125.6 122.4 119 119.4 118.6 118 116 114.8 114.6 114.6 124 125.2 124 117.6 113.2 111.4 112.2 109.8 106.4 105.2 102.2 99.8 111 113 108.4 105.4 102 102.8
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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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