R version 3.2.4 (2016-03-10) -- "Very Secure Dishes"
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> x <- c(-15,-15,-13,-8,-13,-9,-7,-4,-4,-2,0,-2,-3,1,-2,-1,1,-3,-4,-9,-9,-7,-14,-12,-16,-20,-12,-12,-10,-10,-13,-16,-14,-17,-24,-25,-23,-17,-24,-20,-19,-18,-16,-12,-7,-6,-6,-5,-4,-4,-8,-9,-6,-7,-10,-11,-11,-12,-14,-12,-9,-5,-6,-6,-3,-2,-6,-6,-10,-8,-4,-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] 72
> (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,]  -15   -3  -16  -23   -4   -9
 [2,]  -15    1  -20  -17   -4   -5
 [3,]  -13   -2  -12  -24   -8   -6
 [4,]   -8   -1  -12  -20   -9   -6
 [5,]  -13    1  -10  -19   -6   -3
 [6,]   -9   -3  -10  -18   -7   -2
 [7,]   -7   -4  -13  -16  -10   -6
 [8,]   -4   -9  -16  -12  -11   -6
 [9,]   -4   -9  -14   -7  -11  -10
[10,]   -2   -7  -17   -6  -12   -8
[11,]    0  -14  -24   -6  -14   -4
[12,]   -2  -12  -25   -5  -12   -3
> 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]  -7.666667  -5.166667 -15.750000 -14.416667  -9.000000  -5.666667
> arr.sd
[1] 5.365434 4.969605 5.029459 6.947313 3.247377 2.461830
> arr.range
[1] 15 15 15 19 10  8
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
      2.748       -0.200  

> (lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
Calls: lm -> lm.fit
In addition: Warning message:
In log(arr.mean) : NaNs produced
Execution halted