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Data:
82 96.5 104.8 87.2 98.6 98.7 75 86.8 105 109.8 108.2 99 89.6 97.8 104.8 87 87.9 93.9 84.3 84 104.3 104.4 102.3 89.4 78.7 86.9 93.7 87 83.9 95.3 73.7 76.6 94.7 97.7 90 82.4 77.4 85 90.3 82.1 79.6 86.2 73.4 66.7 96.7 98.6 83.2 84 75.8 83.2 95.7 87.3 83.8 98.7 80.8 74.2 96.1 99.4 91.8 89.7 82.9 90 98.5 93.4 89.1 103 74.7 79 101.3 96.7 99.1 92.3 90.6 95.2 107.6 97.6 104 112 90.6 84.9 112.7 115.2 110.1 95.7 104.2 103.3 116.1 106.9 105.9 120.2 96.2 91.5 108.3 121.1 111.4 95.6 98.7 117.7 124.5 114.8 108 120.7 95.6 84.3 122.2 117.1 97.2 99.5 90.1 87.3 97.4 90.1 83.6 97.8 79.7 75.1 106.1 103.5 94.5 100.9 89.7 91.4 110.2 102.8 89.8 112.8 84 86.5 107.3 120.2 105.5 99.9 100.4 99.6 118.6 96 105.3 105.8 80.1 89.3 120.4 111.3 98.1 102.9 95.4 108.7 123 107.7 97.2 127.7 100.6 89.7 108.3 110 105.2 87.7 91.4 92.8 97.5 95.7 93.5 97.3 84.1 87.8 96.2 94.6 88.7 76.5 83.9 88.1 93 81.8 84.1 89.1 75.8 71.4 93.8 88.5 78.1 83.6 78.2 76.2 92 79.5 69.5 86.4 72.3 65 86 83.4 87.2 76.4 76.3 76.9 92.7 83.3 73.8 94 73.1 69.8 86 78.8 89.4 83.8 74.1 77.2 103.6 78 80.2 88.8 72.9 73.6
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Seasonal Period
FALSE
12
4
6
12
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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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