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Data:
5.2 7.9 8.7 8.9 15.3 15.4 18.1 19.7 13 12.6 6.2 3.5 3.4 0 9.5 8.9 10.4 13.2 18.9 19 16.3 10.6 5.8 3.6 2.6 5 7.3 9.2 15.7 16.8 18.4 18.1 14.6 7.8 7.6 3.8 5.6 2.2 6.8 11.8 14.9 16.7 16.7 15.9 13.6 9.2 2.8 2.5 4.8 2.8 7.8 9 12.9 16.4 21.8 17.8 13.5 10 10.4 5.5 4 6.8 5.7 9.1 13.6 15 20.9 20.4 14 13.7 7.1 0.8 2.1 1.3 3.9 10.7 11.1 16.4 17.1 17.3 12.9 10.9 5.3 0.7 -0.2 6.5 8.6 8.5 13.3 16.2 17.5 21.2 14.8 10.3 7.3 5.1 4.4 6.2 7.7 9.3 15.6 16.3 16.6 17.4 15.3 9.7 3.7 4.6 5.4 3.1 7.9 10.1 15 15.6 19.7 18.1 17.7 10.7 6.2 4.2 4 5.9 7.1 10.5 15.1 16.8 15.3 18.4 16.1 11.3 7.9 5.6 3.4 4.8 6.5 8.5 15.1 15.7 18.7 19.2 12.9 14.4 6.2 3.3 4.6 7.2 7.8 9.9 13.6 17.1 17.8 18.6 14.7 10.5 8.6 4.4 2.3 2.8 8.8 10.7 13.9 19.3 19.5 20.4 15.3 7.9 8.3 4.5 3.2 5 6.6 11.1 12.8 16.3 17.4 18.9 15.8 11.7 6.4 2.9 4.7 2.4 7.2 10.7 13.4 18.5 18.3 16.8 16.6 14.1 6.1 3.5 1.7 2.3 4.5 9.3 14.2 17.3 23 16.3 18.4 14.2 9.1 5.9 7.2 6.8 8 14.3 14.6 17.5 17.2 17.2 14.1 10.5 6.8 4.1 6.5 6.1 6.3 9.3 16.4 16.1 18 17.6 14 10.5 6.9 2.8 0.7 3.6 6.7 12.5 14.4 16.5 18.7 19.4 15.8 11.3 9.7 2.9
Sample Range:
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Seasonal Period
12
12
4
6
12
Chart options
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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