Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
168.67 164.83 184.38 180.81 190.54 181.41 155.67 135.99 125.88 126.09 114.86 127.98 127.98 125.11 125.93 128.2 125.93 111.94 120.01 124.09 126.02 136.41 143.79 141.67 143.9 155 144.83 141.4 137 141.02 131.11 132.83 136.73 141.18 137.86 133.79 128.53 125.87 124.27 123.96 128.15 126.4 127.86 129.31 132.56 141.28 145.55 146.54 143.14 145.72 148.21 150.4 149.94 146.66 143.37 145.29 140.24 136.12 140.25 140.64 145.58 143.73 141.27 140.66 141.94 141.16 134.31 132.93 133.07 140.48 154.85 196.77 235.3 226.52 237.62 224.07 208.74 174.54 170.63 172.23 198.36 175.91 154.63 134.31 121.75 119.6 102.04 106.3 116.38 103.72 98.56 100.9 110 118.26 124.77 125.22 126.38 137.14 134.74 134.3 136.39 141.83 139.24 128.89 134.83 130.43 132.09 144.95 149.5 137.57 139.38 143.06 138.65 123.21 85.91 77.4 77.84 67.76 70.72 72.55 75.83 84.01 93.96 93.73 92.02 88.26 86.48 94.42 94.92 91.41 84.84 89.89 86.32 89.57 93.72 92.27 87.59 85.5 82.81 81.62 87.45 79.86 78.52 75.1 72.99 67.88 70.14 65.43 60.26 58.38 57.68 52.42 52.73 61.4 67.13 77.46 68.66 67.46 62.77 56.88 61.48 61.99 71.56 76.56 79.82 75.05 77.07 80 77.21 82.16 85.57 89.23 121.98 142.56 217.67 198.07 220.1 198.68 181.64 167.47 172.33 168.71 178.22 172.81 168.83 152.25 143.83 151.41 131.87 125.38 123.23 103.99 109.38 123.79 119.05 122.01 128.56 127.91 120.47 122.49 114.05 120.62 119.61 115.01 131.83 167.2 193.82 204.43 264.5 212.55 186.52 185.17 184.38 161.45 154.15 174.25 175.04 175.87 154.82 147.08 134.35 121.56 113.86 119.89 108.07 107.07 115.14 116.03 111.48 103.24 103.23 99.69 108.91 104.21 90.85 87.64 81.06 92.2 114.02 123.56 109.17 101.65 97.95 92.56 91.76 84.1 84.67 74.52 73.83 75.37 70.47 64.5 64.98 66.94 65.93 65.51 68.94 63.67 58.47 59.68 57.71 56.53 58.96 55.6 57.34 60.51 66.38 65.78 58.43 55.16 53.09 52.02 57.58 64.05 70.18 63.86 65.22 67.6 61.66 65.32 66.18 61.34 62.29 63.6 65.51 62.58 62.36 64.88 73.73 77.51 77.47 74.34 75.81 82.16 73.96 73.17 80.99 79.81 89.51 102.57 107.11 122.23 134.69 128.79 126.16 119.98 108.45 108.43 98.17 106.09 108.81 103.03 124.36 118.52 112.2 114.71 107.96 101.21 102.77 112.13 109.36 110.91 123.57 129.95 124.46 122.34 116.61 114.59 112.52 118.67 116.8 123.63 128.04 134.57 130.33 136.47 139.05 158.21 148.07 137.74 139.74 144.08 145.35 145.77 140.56 121.41 120.44 116.97 128.03 128.51 127.76 134.58 147.64 144.46 137.6 146.87 145.67 151.95 150.23 155.86 154.4 156.36 162.13 171.06 174.01 193.52 205.26 212.8 222.1
Sample Range:
(leave blank to include all observations)
From:
To:
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')
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