R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(40927.00
+ ,40856.00
+ ,40778.00
+ ,40635.00
+ ,42103.00
+ ,42032.00
+ ,40927.00
+ ,40194.00
+ ,40265.00
+ ,40265.00
+ ,40336.00
+ ,40486.00
+ ,40856.00
+ ,40414.00
+ ,40856.00
+ ,40486.00
+ ,41661.00
+ ,42181.00
+ ,39973.00
+ ,39381.00
+ ,39894.00
+ ,39823.00
+ ,39381.00
+ ,39453.00
+ ,40336.00
+ ,40194.00
+ ,40336.00
+ ,40336.00
+ ,41298.00
+ ,41440.00
+ ,38790.00
+ ,38790.00
+ ,39823.00
+ ,39310.00
+ ,38427.00
+ ,38790.00
+ ,39674.00
+ ,39232.00
+ ,39161.00
+ ,38206.00
+ ,39602.00
+ ,39894.00
+ ,37023.00
+ ,36952.00
+ ,38427.00
+ ,37615.00
+ ,36218.00
+ ,36810.00
+ ,37465.00
+ ,37615.00
+ ,37173.00
+ ,36290.00
+ ,38128.00
+ ,38128.00
+ ,34893.00
+ ,34673.00
+ ,35556.00
+ ,33939.00
+ ,32314.00
+ ,32835.00
+ ,33939.00
+ ,33055.00
+ ,32464.00
+ ,31210.00
+ ,32906.00
+ ,32977.00
+ ,29743.00
+ ,29664.00
+ ,30256.00
+ ,28418.00
+ ,26430.00
+ ,27235.00
+ ,28339.00
+ ,27164.00
+ ,27093.00
+ ,25910.00
+ ,27826.00
+ ,28197.00
+ ,24585.00
+ ,23780.00
+ ,24293.00
+ ,22305.00
+ ,20246.00
+ ,20909.00
+ ,22156.00
+ ,20688.00
+ ,20909.00
+ ,20026.00
+ ,21864.00
+ ,22084.00
+ ,17668.00
+ ,17375.00
+ ,18180.00
+ ,16050.00
+ ,14134.00
+ ,14797.00
+ ,16414.00
+ ,14504.00
+ ,14355.00
+ ,12880.00
+ ,14504.00
+ ,15017.00
+ ,10451.00
+ ,10451.00
+ ,11113.00
+ ,9347.00
+ ,7359.00
+ ,8392.00
+ ,10230.00
+ ,8242.00
+ ,9055.00
+ ,7950.00
+ ,9717.00
+ ,10308.00
+ ,5592.00
+ ,5229.00
+ ,5963.00
+ ,4196.00
+ ,2800.00
+ ,3384.00)
> 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,] 40927 40856 40336 39674 37465 33939 28339 22156 16414 10230
 [2,] 40856 40414 40194 39232 37615 33055 27164 20688 14504  8242
 [3,] 40778 40856 40336 39161 37173 32464 27093 20909 14355  9055
 [4,] 40635 40486 40336 38206 36290 31210 25910 20026 12880  7950
 [5,] 42103 41661 41298 39602 38128 32906 27826 21864 14504  9717
 [6,] 42032 42181 41440 39894 38128 32977 28197 22084 15017 10308
 [7,] 40927 39973 38790 37023 34893 29743 24585 17668 10451  5592
 [8,] 40194 39381 38790 36952 34673 29664 23780 17375 10451  5229
 [9,] 40265 39894 39823 38427 35556 30256 24293 18180 11113  5963
[10,] 40265 39823 39310 37615 33939 28418 22305 16050  9347  4196
[11,] 40336 39381 38427 36218 32314 26430 20246 14134  7359  2800
[12,] 40486 39453 38790 36810 32835 27235 20909 14797  8392  3384
> 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] 40817.000 40363.250 39822.500 38234.500 35750.750 30691.417 25053.917
 [8] 18827.583 12065.583  6888.833
> arr.sd
 [1]  641.6489  900.2103 1005.9239 1285.5570 2035.1635 2469.6734 2827.9942
 [8] 2860.3301 2932.1890 2697.6386
> arr.range
 [1] 1909 2800 3013 3676 5814 7509 8093 8022 9055 7508
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 3764.21911     -0.06234  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
      14.0305        -0.6484  

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

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

Coefficients:
(Intercept)     arr.mean  
 10870.9388      -0.1778  

> postscript(file="/var/wessaorg/rcomp/tmp/1eejb1470935325.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/28pbe1470935325.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/3fplq1470935325.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/47wnv1470935325.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5no1w1470935325.tab") 
> 
> try(system("convert tmp/1eejb1470935325.ps tmp/1eejb1470935325.png",intern=TRUE))
character(0)
> try(system("convert tmp/28pbe1470935325.ps tmp/28pbe1470935325.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.822   0.079   0.910