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(69731,68504,67277,64823,89654,88427,69731,57316,58542,58542,59770,62357,54862,47354,41207,41207,64823,67277,48581,27431,38620,38620,47354,52396,51168,38620,44900,42434,63584,58542,38620,23738,37392,41207,44900,49808,39846,31246,34939,36166,68504,68504,49808,47354,54862,51168,61130,73546,76012,58542,53622,48581,82280,84746,78466,84746,83507,73546,84746,97162,102203,87200,77238,84746,117084,127046,124592,129499,128273,115858,137008,142049,149423,127046,118312,128273,152010,173160,168119,168119,170585,161971,184361,184361,180546,159384,163199,165665,181895,203045,188041,195550,189269,185587,214246,207965,199230,186815,199230,205511,213006,222967,213006,219154,211657,210431,241542,244129,234168,216700,231581,237850,245357,256546,245357,254092,250277,236622,265279,265279)
> 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,] 69731 54862 51168 39846 76012 102203 149423 180546 199230 234168
 [2,] 68504 47354 38620 31246 58542  87200 127046 159384 186815 216700
 [3,] 67277 41207 44900 34939 53622  77238 118312 163199 199230 231581
 [4,] 64823 41207 42434 36166 48581  84746 128273 165665 205511 237850
 [5,] 89654 64823 63584 68504 82280 117084 152010 181895 213006 245357
 [6,] 88427 67277 58542 68504 84746 127046 173160 203045 222967 256546
 [7,] 69731 48581 38620 49808 78466 124592 168119 188041 213006 245357
 [8,] 57316 27431 23738 47354 84746 129499 168119 195550 219154 254092
 [9,] 58542 38620 37392 54862 83507 128273 170585 189269 211657 250277
[10,] 58542 38620 41207 51168 73546 115858 161971 185587 210431 236622
[11,] 59770 47354 44900 61130 84746 137008 184361 214246 241542 265279
[12,] 62357 52396 49808 73546 97162 142049 184361 207965 244129 265279
> 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]  67889.50  47477.67  44576.08  51422.75  75496.33 114399.67 157145.00
 [8] 186199.33 213889.83 244925.67
> arr.sd
 [1] 10852.34 11322.82 10445.14 14282.43 14583.67 21570.47 22393.64 17490.22
 [9] 16626.21 14416.19
> arr.range
 [1] 32338 39846 39846 42300 48581 64811 66049 54862 57314 48579
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  1.213e+04    2.720e-02  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       6.5273         0.2678  

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

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

Coefficients:
(Intercept)     arr.mean  
  3.908e+04    8.623e-02  

> postscript(file="/var/wessaorg/rcomp/tmp/1at3o1375362088.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/2qjs41375362088.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/3jzvh1375362088.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4gyvl1375362088.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5p5ql1375362088.tab") 
> 
> try(system("convert tmp/1at3o1375362088.ps tmp/1at3o1375362088.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qjs41375362088.ps tmp/2qjs41375362088.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.914   0.514   2.409