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Data:
7.5 2.5 6.0 6.5 1.0 1.0 5.5 8.5 6.5 4.5 2.0 5.0 0.5 5.0 5.0 2.5 5.0 5.5 3.5 3.0 4.0 0.5 6.5 4.5 7.5 5.5 4.0 7.5 7.0 4.0 5.5 2.5 5.5 0.5 3.5 2.5 4.5 4.5 4.5 6.0 2.5 5.0 0.0 5.0 6.5 5.0 6.0 4.5 5.5 1.0 7.5 6.0 5.0 1.0 5.0 6.5 7.0 4.5 0.0 8.5 3.5 7.5 3.5 6.0 1.5 9.0 3.5 3.5 4.0 6.5 7.5 6.0 5.0 5.5 3.5 7.5 1.0 6.5 6.5 6.5 7.0 3.5 1.5 4.0 7.5 4.5 0.0 3.5 5.5 5.0 4.5 2.5 7.5 7.0 0.0 4.5 3.0 1.5 3.5 2.5 5.5 8.0 1.0 5.0 4.5 3.0 3.0 8.0 2.5 7.0 0.0 1.0 3.5 5.5 5.5 0.5 7.5 9 9.5 8.5 7 8 10 7 8.5 9 9.5 4 6 8 5.5 9.5 7.5 7 7.5 8 7 7 6 10 2.5 9 8 6 8.5 6 9 8 8 9 5.5 5 7 5.5 9 2 8.5 9 8.5 9 7.5 10 9 7.5 6 10.5 8.5 8 10 10.5 6.5 9.5 8.5 7.5 5 8 10 7 7.5 7.5 9.5 6 10 7 3 6 7 10 7 3.5 8 10 5.5 6 6.5 6.5 8.5 4 9.5 8 8.5 5.5 7 9 8 10 8 6 8 5 9 4.5 8.5 7 9.5 8.5 7.5 7.5 5 7 8 5.5 8.5 7.5 9.5 7 8 8.5 3.5 6.5 6.5 10.5 8.5 8 10 10 9.5 9 10 7.5 4.5 4.5 0.5 6.5 4.5 5.5 5 6 4 8 10.5 8.5 6.5 8 8.5 5.5 7 5 3.5 5 9 8.5 5 9.5 3 1.5 6 0.5 6.5 7.5 4.5 8 9 7.5 8.5 7 9.5 6.5 9.5 6 8 9.5 8 8 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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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