R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(84728,84412,84092,83430,89981,89635,84728,81466,81781,81781,82133,82764,83746,83746,83115,81466,89981,91279,89319,84728,86692,83746,85075,85710,86372,84728,85075,82764,89981,92261,90301,86692,90617,86372,90301,89981,90964,87355,91279,90964,96852,95523,90301,87670,91279,86372,89981,90617,91946,89004,90617,91599,95208,92261,88337,84092,88021,77221,82448,85390,88337,84092,84092,84092,86372,83115,78839,75261,77857,67724,73933,77541,78204,74595,74910,73933,77221,74910,70355,67062,72630,60537,68390,71968,71968,67724,63799,63484,67062,63799,57595,53319,57911,47115,56928,62150,63799,60191,55631,58893,60191,59208,49391,44835,48093,38280,48413,52022,54964,50057,45466,48093,49391,46795,36982,32706,36631,25835,37613,44835)
> 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] 120
> (np <- floor(n / par1))
[1] 10
> 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]  [,7]  [,8]  [,9] [,10]
 [1,] 84728 83746 86372 90964 91946 88337 78204 71968 63799 54964
 [2,] 84412 83746 84728 87355 89004 84092 74595 67724 60191 50057
 [3,] 84092 83115 85075 91279 90617 84092 74910 63799 55631 45466
 [4,] 83430 81466 82764 90964 91599 84092 73933 63484 58893 48093
 [5,] 89981 89981 89981 96852 95208 86372 77221 67062 60191 49391
 [6,] 89635 91279 92261 95523 92261 83115 74910 63799 59208 46795
 [7,] 84728 89319 90301 90301 88337 78839 70355 57595 49391 36982
 [8,] 81466 84728 86692 87670 84092 75261 67062 53319 44835 32706
 [9,] 81781 86692 90617 91279 88021 77857 72630 57911 48093 36631
[10,] 81781 83746 86372 86372 77221 67724 60537 47115 38280 25835
[11,] 82133 85075 90301 89981 82448 73933 68390 56928 48413 37613
[12,] 82764 85710 89981 90617 85390 77541 71968 62150 52022 44835
> 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] 84244.25 85716.92 87953.75 90763.08 88012.00 80104.58 72059.58 61071.17
 [9] 53245.58 42447.33
> arr.sd
 [1] 2854.904 3029.084 2971.164 3039.963 5006.361 5968.608 4908.553 6841.491
 [9] 7639.316 8454.752
> arr.range
 [1]  8515  9813  9497 10480 17987 20613 17667 24853 25519 29129
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 13508.4190      -0.1132  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       23.963         -1.386  

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

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

Coefficients:
(Intercept)     arr.mean  
 47103.4453      -0.3983  

> postscript(file="/var/fisher/rcomp/tmp/1rz9i1375970974.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/fisher/rcomp/tmp/2ru8x1375970974.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/3sm0n1375970974.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/4vw8w1375970974.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/fisher/rcomp/tmp/5lyzf1375970975.tab") 
> 
> try(system("convert tmp/1rz9i1375970974.ps tmp/1rz9i1375970974.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ru8x1375970974.ps tmp/2ru8x1375970974.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.248   0.282   1.511