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Data:
100.21 100.36 100.62 100.78 100.93 100.70 100.00 100.20 99.68 99.56 100.06 100.50 99.30 99.37 99.20 98.11 97.60 97.76 98.06 98.25 98.50 97.39 98.09 97.78 98.12 97.50 97.30 97.64 96.88 97.40 98.27 97.94 98.61 98.72 98.62 98.56 98.06 97.40 97.76 97.05 97.85 97.40 97.27 97.93 98.60 98.70 98.88 98.27 97.85 97.70 96.97 97.72 97.66 99.00 98.86 99.56 100.19 100.37 100.01 99.68 99.78 99.36 99.21 99.26 99.26 100.43 101.50 102.27 102.69 103.47 104.02 103.55 103.77 104.19 103.64 103.68 105.39 106.61 108.12 109.22 110.17 110.31 111.06 111.14 111.39 112.51 111.28 112.22 113.19 114.32 115.34 116.61 117.83 117.70 118.51 118.82 119.49 119.57 120.00 121.96 121.45 123.41 124.44 126.25 127.41 127.63 129.19 129.82 130.45 132.02 132.72 132.96 135.06 137.04 137.83 139.17 140.35 141.01 141.89 143.28 142.90 143.37 145.03 146.05 147.39 149.58 151.02 153.57 155.60 157.18 158.77 159.95 161.34 161.95 163.36 165.00 166.65 168.65 170.29 172.70 173.79 176.45 177.58 179.19 181.01 184.08 185.63 188.51 190.18 192.19 193.47 196.73 200.39 203.24 205.53 208.21 208.88 212.85 216.41 216.23 219.27 222.02 224.89 230.37 232.29 235.53 236.92 242.37 242.75 244.19 247.94 248.80 250.18 251.55 254.40 255.72 257.69 258.37 258.22 258.59 257.45 257.45 256.73 258.82 257.99 262.85 262.58 261.55 261.25 259.78 256.26 254.29 248.50 241.88 238.53 232.24 232.46 225.79 221.63 219.62 215.94 211.81 205.57 201.25 194.70 187.94 185.61 181.15 186.50 183.21 182.61 187.09 189.10 191.25 190.74 190.79
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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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Big Analytics Cloud Computing Center
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