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Data:
71.97 72.32 74.07 77.95 81.75 80.81 74.1 71.37 75.21 76.9 74.44 74.76 76.23 76.97 78.4 78.6 80.08 81.12 80.31 84.59 81.34 80.95 80.48 75.26 76.32 78.92 80.47 83.14 85.42 81.53 87.31 86.01 85.1 79.91 78.6 78.6 79.37 82.89 84.43 85.32 87.71 84.68 80.62 84.79 85.49 81.68 77.69 78.31 79.18 80.91 83.91 86.3 89.76 85.11 83.81 85.36 85.89 82.59 80.87 80.27 81.36 84.81 90.3 95.43 97.59 97.8 99.48 97.52 104.39 97.74 91.37 92.42 96.9 101.58 105.46 110.06 107.9 102.87 96.28 98.59 103.22 98.6 91.79 93.83 95.17 95.19 99.44 109.18 109.15 109.72 108.41 102.96 107.64 97.28 97.25 91.84 94.12 97.86 98.83 102.29 104.49 102.11 102.14 101.28 101.21 94.2 88.47 88.08 88.02 92.95 97.05 101.44 100.34 99.98 94.17 94.54 95.12 98.04 93.72 93.83 93.03 95.81 99.1 100.12 100.67 103.87 102.39 107.21 105.71 99.79 96.12 96.17 97.23 98.08 99.84 99.72 99.92 102.7 102.06 102.36 102.43 100.6 98.4 98.61 103.03 104.7 107.45 109.67 110.54 112.05 113.19 114.2 112.56 107.36 103.93 103.83 104.74 107.5 109.53 109.42 108.6 110.72 105.1 105.19 102.55 101.25 101.56 101.62 101.7 102.94 104.37 106.93 107.82 110.83 106.86 109.46 108.8 108.69 107.77 108.64 108.5 113.84 114.59 116.27 113.63 112.29 110.31 108.47 110.67 109.1 107.02 108.12 106.69 109.87 110.82 114.14 113.31 115.16 111.06 111.13 115.96 117.57 114.69 119.42 118.4 123.32 123.39 127.04 129.35 127.12 122.1 120.22 121.53 119.01 114.27 114.46
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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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