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Data:
8357.00 7454.00 8076.00 7248.00 7339.00 7292.00 7359.00 7537.00 7441.00 8057.00 8037.00 8257.00 8692.00 8119.00 8236.00 7432.00 7669.00 7453.00 7566.00 7731.00 7657.00 8130.00 8401.00 8737.00 9009.00 7919.00 8228.00 7903.00 7912.00 7857.00 7965.00 8091.00 8024.00 8772.00 8656.00 8953.00 9014.00 8103.00 8876.00 8231.00 8173.00 8087.00 8296.00 8007.00 8382.00 9168.00 9137.00 9321.00 9234.00 8451.00 9101.00 8279.00 8284.00 8225.00 8597.00 8305.00 8620.00 9102.00 9258.00 9652.00 9522.00 8874.00 9415.00 8525.00 8862.00 8421.00 8626.00 8750.00 8852.00 9412.00 9570.00 9513.00 9986.00 8907.00 9663.00 8799.00 8931.00 8732.00 8936.00 9127.00 9070.00 9773.00 9670.00 9929.00 10095.00 9025.00 9659.00 8954.00 9022.00 8855.00 9034.00 9196.00 9038.00 9650.00 9715.00 10052.00 10436.00 9314.00 9717.00 8997.00 9062.00 8885.00 9058.00 9095.00 9149.00 9857.00 9848.00 10269.00 10341.00 9690.00 10125.00 9349.00 9224.00 9224.00 9454.00 9347.00 9430.00 9933.00 10148.00 10677.00 10735.00 9760.00 10567.00 9333.00 9409.00 9502.00 9348.00 9319.00 9594.00 10160.00 10182.00 10810.00 11105.00 9874.00 10958.00 9311.00 9610.00 9398.00 9784.00 9425.00 9557.00 10166.00 10337.00 10770.00 11265.00 10183.00 10941.00 9628.00 9709.00 9637.00 9579.00 9741.00 9754.00 10508.00 10749.00 11079.00 11608.00 10668.00 10933.00 9703.00 9799.00 9656.00 9648.00 9712.00 9766.00 10540.00 10564.00 10911.00 11218.00 10230.00 10410.00 9227.00 9378.00 9105.00 9128.00
Sample Range:
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From:
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Seasonal Period
4
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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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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