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(323898,323268,322574,321304,334352,333712,323898,317374,318014,318014,318646,319980,319980,314086,311490,314086,323268,321934,309526,299020,297054,293126,295784,299020,297748,295090,289900,295090,299712,298380,283312,276788,270264,265010,264380,268300,263046,261082,259118,270264,271536,265010,247340,239490,227082,221820,224416,228344,228344,225118,224416,234930,243420,239490,226380,219864,206122,197634,204158,210682,210682,202194,201562,212638,219864,217260,204158,195670,177304,170150,172744,183892,184522,168184,174078,188452,194976,191046,173384,160968,146594,135448,140008,149820,147226,132852,137412,151786,159642,155082,137412,129564,117786,105368,107332,117146,118416,106638,108604,125004,128924,122346,98150,85742,69342,53004,58258,65412,64150,51670,58888,76560,84408,80488,64782,52372,39262,24186,26854,31414)
> 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,] 323898 319980 297748 263046 228344 210682 184522 147226 118416 64150
 [2,] 323268 314086 295090 261082 225118 202194 168184 132852 106638 51670
 [3,] 322574 311490 289900 259118 224416 201562 174078 137412 108604 58888
 [4,] 321304 314086 295090 270264 234930 212638 188452 151786 125004 76560
 [5,] 334352 323268 299712 271536 243420 219864 194976 159642 128924 84408
 [6,] 333712 321934 298380 265010 239490 217260 191046 155082 122346 80488
 [7,] 323898 309526 283312 247340 226380 204158 173384 137412  98150 64782
 [8,] 317374 299020 276788 239490 219864 195670 160968 129564  85742 52372
 [9,] 318014 297054 270264 227082 206122 177304 146594 117786  69342 39262
[10,] 318014 293126 265010 221820 197634 170150 135448 105368  53004 24186
[11,] 318646 295784 264380 224416 204158 172744 140008 107332  58258 26854
[12,] 319980 299020 268300 228344 210682 183892 149820 117146  65412 31414
> 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] 322919.50 308197.83 283664.50 248212.33 221713.17 197343.17 167290.00
 [8] 133217.33  94986.67  54586.17
> arr.sd
 [1]  5702.707 10914.830 13997.769 19064.324 14468.503 17407.899 20651.570
 [8] 18360.247 27651.656 20748.391
> arr.range
 [1] 16978 30142 35332 49716 45786 49714 59528 54274 75920 60222
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  2.819e+04   -5.558e-02  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
      16.0671        -0.5294  

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

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

Coefficients:
(Intercept)     arr.mean  
 81364.6462      -0.1654  

> postscript(file="/var/fisher/rcomp/tmp/163h51374834108.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/20a7z1374834108.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/3bkn61374834108.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/4qd9o1374834108.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5nz8d1374834108.tab") 
> 
> try(system("convert tmp/163h51374834108.ps tmp/163h51374834108.png",intern=TRUE))
character(0)
> try(system("convert tmp/20a7z1374834108.ps tmp/20a7z1374834108.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.364   0.347   1.688