R version 3.2.4 Revised (2016-03-16 r70336) -- "Very Secure Dishes"
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> x <- c(16.8,17.2,17.4,17.6,17.7,17.7,17.6,17.6,17.5,17.5,17.6,17.6,17.9,18.2,18.4,18.5,19,19.5,19.7,19.9,19.7,19.5,19.7,19.7,19.7,19.9,20.1,20.1,20.1,20.1,20.2,20.3,20.8,21.1,21.2,21.3,21.6,21.7,21.8,22,21.9,21.9,22,22.1,21,19.7,19.8,19.9,19.8,20,20.2,20.3,20.7,20.9,21,21.2,23.7,23.7,23.7,23.8,24,24,24.1,24.3,24.4,24.4,24.5,24.6,24.7,24.6,24.6,24.6,24.7,24.7,24.8,24.9,25,25.1,25.2,25.3)
> par1 = '12'
> par1 <- '12'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P. (2012), Standard Deviation-Mean Plot (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_smp.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 80
> (np <- floor(n / par1))
[1] 6
> 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
      [,1] [,2] [,3] [,4] [,5] [,6]
 [1,] 16.8 17.9 19.7 21.6 19.8 24.0
 [2,] 17.2 18.2 19.9 21.7 20.0 24.0
 [3,] 17.4 18.4 20.1 21.8 20.2 24.1
 [4,] 17.6 18.5 20.1 22.0 20.3 24.3
 [5,] 17.7 19.0 20.1 21.9 20.7 24.4
 [6,] 17.7 19.5 20.1 21.9 20.9 24.4
 [7,] 17.6 19.7 20.2 22.0 21.0 24.5
 [8,] 17.6 19.9 20.3 22.1 21.2 24.6
 [9,] 17.5 19.7 20.8 21.0 23.7 24.7
[10,] 17.5 19.5 21.1 19.7 23.7 24.6
[11,] 17.6 19.7 21.2 19.8 23.7 24.6
[12,] 17.6 19.7 21.3 19.9 23.8 24.6
> 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
[1] 17.48333 19.14167 20.40833 21.28333 21.58333 24.40000
> arr.sd
[1] 0.2552479 0.7064100 0.5434876 0.9388904 1.6330859 0.2486326
> arr.range
[1] 0.9 2.0 1.6 2.4 4.0 0.7
> (lm1 <- lm(arr.sd~arr.mean))

Call:
lm(formula = arr.sd ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
    0.15365      0.02738  

> (lnlm1 <- lm(log(arr.sd)~log(arr.mean)))

Call:
lm(formula = log(arr.sd) ~ log(arr.mean))

Coefficients:
  (Intercept)  log(arr.mean)  
      -2.4950         0.6436  

> (lm2 <- lm(arr.range~arr.mean))

Call:
lm(formula = arr.range ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
    1.29567      0.03078  

> postscript(file="/var/wessaorg/rcomp/tmp/10kpp1458685479.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/2v8n81458685479.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/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="/var/wessaorg/rcomp/tmp/3ea1h1458685479.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4zq3p1458685479.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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="/var/wessaorg/rcomp/tmp/54f1h1458685479.tab") 
> 
> try(system("convert tmp/10kpp1458685479.ps tmp/10kpp1458685479.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v8n81458685479.ps tmp/2v8n81458685479.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.839   0.162   1.000