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Data:
18.09 18.13 18 17.72 17.62 17.13 17.39 17.09 17.14 17.38 16.8 16.51 16.01 15.05 13.56 15.22 14.91 15.13 15.25 14.61 14.87 15.1 15.22 15.46 14.96 14 14.2 13.9 13.63 13.32 13.8 14.5 14.12 13.88 14.11 14.26 14.71 14.52 14.32 14.69 15.25 15.04 14.82 14.5 14.72 14.6 14.58 14 14.75 14.41 15.19 14.96 14.83 14.25 14.32 14.93 14.65 15.65 15.65 15.61 15.95 15.83 15.77 16.7 16.69 16.4 16.77 16.78 16.84 16.68 16.67 16.3 16.37 16.6 16.72 16.82 17.5 17.2 17.29 17.2 17.2 17.32 17.16 17.41 17.31 17.3 17.34 17.19 17.05 17.07 17.07 16.81 16.81 16.96 17.05 17 16.77 16.66 16.2 16.26 15.84 15.85 15.71 15.84 15.73 15.77 15.3 15.41 15.4 15.61 15 14.12 14.01 13.46 13.85 13.92 13.59 13.67 13.05 12.87 12.28 11.88 12.49 11.9 10.8 10.99 10.15 10.07 10.05 10.31 9.94 9.65 9.74 9.85 9.96 9.63 9.43 8.77 9.53 9.5 9.78 9.9 9.93 10.35 9.79 9.63 9.02 9.25 9.11 8.95 9.3 9.13 9.75 9.65 9.27 9.59 9.58 9.98 9.57 9.6 9.64 9.46 9.19 9.02 8.9 9.12 8.86 8.94 9 9.23 9.39 9.62 9.9 9.8 9.2 9.87 9.6 9.37 9.21 9.15 8.7 8.2 8.1 6.68 7.7 8.2 7.55 7.53 7.02 6.6 6 3.95 4.91 5.15 5.7 1.93 1.36 1.1 0.98 1 1.1 1.06 1.01 0.93 0.89 0.9 0.88 0.85 0.84 0.94 1 1.1 1.15 1.05 1.06 0.99 0.93 0.84 0.9 0.86 0.78 0.77 0.6 0.57 0.62 0.62 0.58 0.6 0.73 0.75 0.63 0.71 0.68 0.64 0.66 0.69 0.72 0.92 0.85 0.95 1 1.15 1.07 1.01 0.99 0.95 0.92 0.94 0.96 1.05 1.04 1.1 1.14 1.12 1.19 1.35 1.62 1.43 1.45 1.47 1.35 1.15 1.46 1.3 1.3 1.5 1.52 1.63 1.9 1.65 1.5 1.38 1.39
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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