Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
178.6 224.7 206.7 149.7 160.1 154.7 155 233.6 211.9 186.7 156.5 142 207 232.3 230.4 159.4 158.4 164.2 179.2 242.9 211.7 188.6 151.9 134.8 218 233.4 218.5 163.7 150.8 145.6 190.3 235.9 203.7 185.3 150.9 136 213.9 234.1 194.8 154.2 138.5 133.7 186.8 221.3 211.7 171.4 124.5 129.2 173.3 190.9 175 113.8 98.4 116.4 153.9 199.7 168.8 132.8 118.8 112.7 150.5 203.5 184.3 113.5 102.4 119.3 152.4 218.5 154.6 124.9 124 113.8 162.5 184.8 177.3 91.4 85.2 120.9 159.8 200.1 171.8 139.5 115.7 96.8 169.9 212.3 182.3 95.2 96.9 100.3 131.3 172.3 130.6 129.5 96.3 91.4 140.7 160.2 158.8 193.6 80.8 102 119.5 129.6 113.8 102.5 78.4 95.7 143.7 149.3 121.7 81 68.1 92.3 107.7 114.4 98.6 106.7 73.9 85.9 118.4 144.2 118.4 82.6 68 99.8 93.4 107.9 101.1 100.4 76.7 89.1 105.3 124.8 111.9 89 88.6 84.5 91.1 118.1 103.6 92.6 70.2 70.2 114.3 125.3 98.9 65.4 66 71.2 84.6 102.6 91.8 97.4 64.1 62.3 96.2 104.9 90.3 65.2 57.8 70.5 93.2 74.2 91.1 85 58.9 68.3 98.1 110.5 77.6 55.1 49.8 58.5 86.5 88.8 94 65 52.2 70.9 88.4 107.8 75.2 58 58.3 71.6 72.4 119.8 83.4 60.6 47.1 65.5 76.1 115.2 73.5 50.7 53.5 66.7 84.5 96.4 63.6 40.4 56.3 58.4 103 104.5 84.9 50.8 57.9 56.9 82.8 96
Sample Range:
(leave blank to include all observations)
From:
To:
Seasonal Period
FALSEFALSEFALSEFALSEFALSEFALSEFALSEadditiveFALSEFALSEFALSEadditivemultiplicativeadditivemultiplicativemultiplicativeadditivemultiplicative12additiveFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE12additive12additivemultiplicativeadditive12additiveFALSEFALSEFALSEFALSEadditiveFALSEadditive1212121212additiveadditive1212
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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation