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Data:
66.6 89.1 96.2 78.9 107.5 96.2 36.9 100.3 102.1 101.1 90.6 71.8 69 85.7 95.8 83 100.2 102 42.9 99.9 91.3 107.5 89.7 65.5 74 78 97.1 95.2 95.5 98.1 46.8 88.5 103 104.9 91 61.3 69.8 80.2 99.3 93.9 89.5 101.6 53.6 86.4 109.4 108 86 68.4 74.8 86.9 110.6 96.3 91.1 110.7 48 97 109.9 105.2 91.4 80.6 69.2 83.9 91.5 107.1 95.9 116.9 46.2 103.3 115.9 103 99.8 80.7 77.3 85.1 111.1 93.8 107 114.3 47.3 101 114.8 108.9 103.9 76.9 82.9 93.1 110.9 95.3 102.3 114.2 51.5 105 106 117.9 102.7 74.5 93.9 104.5 92.3 118.3 106.4 113.7 61.3 97.6 118.7 112.1 94.5 77 77.6 93.8 115.6 101.8 102.3 117.6 59.3 104.1 123.4 121.3 91.8 74.1 73.4 84.2 128.3 110.3 102.3 127.7 59 107.2 124.2 120.3 100.3 63 83.4 109.6 130.1 103.1 127 113.6 54.3 113.5 129.8 117.9 114.4 85.6 97.9 96.2 136 111.3 125 128.7 69.4 124.2 125.9 134 123 82.8 76.4 98.2 112.1 117.9 117.6 128.8 69.8 109.5 129.8 130.7 111 88.5 104.8 108.3 122.4 109.5 108.5 118 68 103 128.2 128.7 103.9 80.5 92.2 98.4 119.2 105.2 100.5 123.7 65.3 104.2 122.3 125.4 97.4 76.9 92.6 102.8 107.2 115.7 105.3 124.7 59.7 110 127.1 118.1 98.6 90.6 83.6 107.7 133.1 98.7 121 124 60.3 110.4 121.4
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Seasonal Period
2
12
4
6
12
Chart options
R Code
par1 <- '12' 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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