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Data:
52.20 63.90 70.30 64.30 77.20 71.90 46.30 61.50 73.30 75.00 74.40 74.70 71.70 66.60 75.10 67.50 74.60 76.40 53.90 70.10 76.10 79.40 74.80 65.30 63.50 64.40 70.30 74.50 69.40 74.50 52.80 61.50 73.90 79.40 69.80 77.40 69.40 75.00 76.40 75.90 70.30 89.50 62.50 59.00 89.50 83.50 76.00 85.80 66.90 75.40 84.60 81.80 75.00 92.60 66.40 75.70 91.30 88.60 85.80 86.70 71.00 83.20 85.00 79.30 77.50 96.50 56.50 75.20 86.30 84.80 91.60 110.70 81.00 81.50 91.00 81.30 93.50 100.70 68.50 77.60 102.70 113.10 98.50 108.20 89.60 93.30 104.60 94.30 100.70 111.80 76.10 102.10 149.20 172.30 125.60 132.20 106.50 116.60 110.80 121.90 117.20 123.90 98.00 93.50 136.30 131.00 113.20 101.00 88.70 96.90 105.80 95.20 88.00 107.70 71.10 72.30 101.50 103.20 103.00 88.30 78.00 91.80 111.50 100.20 94.30 118.20 80.50 92.60 113.10 111.80 101.70 106.50 88.90 101.20 119.00 104.60 120.20 112.60 88.10 99.20 126.50 113.20 114.20 128.10 109.20 107.00 142.30 106.00 115.20 129.70 90.40 97.50 118.30 121.20 117.50 105.50 97.30 98.00 114.80 109.80 121.90 123.00 104.10 99.90 128.50 127.70 116.70 112.10 102.80 110.80 117.80 122.40 120.40 119.20 101.30 101.20 136.10 133.60 109.60 115.80 104.30 115.00 124.60 123.10 120.00 132.00 107.20 101.00 153.10 144.50 125.80 125.40 111.70 118.40 135.60 130.70 128.50 137.10 92.10 103.70 139.00 125.00 130.20 116.40 106.40 121.20 147.60 116.00 137.50 136.40 95.80 127.00
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Seasonal Period
12
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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Big Analytics Cloud Computing Center
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