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Data:
78 100.1 113.2 93.1 115.4 103.3 45.1 104.7 111.3 111.5 100.9 82.1 85.4 97.7 106.6 92.6 109.2 110 52.5 105.3 102.3 118.5 100 74.4 89.2 91.9 107 103.6 101.8 105.1 55.5 92.1 109.8 112.7 98.5 70.3 84.5 91.1 107.6 102.2 96 107.3 59.9 90.2 116.3 115.6 92 76.5 87.9 95.8 116.9 102.9 95.8 117.3 52.8 100.1 116.3 111.8 98.5 86.2 79.9 92.3 100.5 112.5 101.1 121.5 49.6 104.8 120.4 108.3 105.2 85.7 86.8 95.1 117 100.1 112.3 119.6 51.8 105.5 119.9 115.4 112.8 85.1 96.2 103.6 119.9 103.7 109 119.6 57 109.2 112.6 126 109.7 80.1 105.8 114.1 98.3 125.3 111.6 119.7 65 99 124.5 119 98.8 81.8 90.3 102 119.3 104.3 102.8 118.8 60.9 101 122.6 122.2 95 75.6 83.1 89.8 126.1 108.6 98.9 124.3 56.8 102.7 121.7 118.2 101 69 88.6 109.6 128.2 102 122.7 110.5 54 108.1 125 114.1 112.4 87.3 95.4 96.9 125.8 102 112.5 118.9 62.7 110 114.7 124.4 111.9 77 84.1 96.5 106.8 107.9 107.5 114.3 66.6 97.9 117.8 123.8 103.3 84.2 103.6 103.6 112.2 102.7 100.8 109.4 63.5 92.3 119.2 121.5 97.6 78.3 95.6 97.9 114.4 100.9 94.4 117.2 61 95.8 116.2 118.5 94.3 74.4 94.9 102 102.9 109.5 99.7 118.3 56.2 100.3 116.9 108.7 93.9 85.3 85.3 102.4 121.6 91.4 110.2 112.7 55.7 100.1
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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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