R version 3.0.1 (2013-05-16) -- "Good Sport"
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Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(109.77,109.77,109.77,109.77,109.77,109.77,109.77,109.77,109.77,109.77,109.77,109.77,109.86,110.12,110.5,113.73,119.84,119.83,113.06,112.45,110.07,110.09,110.72,109.9,109.9,110.06,110.52,116.16,118.54,118.77,113.71,106.98,106.98,106.98,106.98,106.98,106.98,107.43,107.93,111.99,115.4,115.53,115.22,102.75,102.75,102.75,102.75,102.75,102.75,102.87,103.13,108.52,111.6,111.32,108.77,100.05,100.05,100.05,100.05,100.05,100.05,100.07,100.07,109.26,110,110,109.26,99.42,99.42,99.42,99.42,99.42)
> 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,] 109.77 109.86 109.90 106.98 102.75 100.05
 [2,] 109.77 110.12 110.06 107.43 102.87 100.07
 [3,] 109.77 110.50 110.52 107.93 103.13 100.07
 [4,] 109.77 113.73 116.16 111.99 108.52 109.26
 [5,] 109.77 119.84 118.54 115.40 111.60 110.00
 [6,] 109.77 119.83 118.77 115.53 111.32 110.00
 [7,] 109.77 113.06 113.71 115.22 108.77 109.26
 [8,] 109.77 112.45 106.98 102.75 100.05  99.42
 [9,] 109.77 110.07 106.98 102.75 100.05  99.42
[10,] 109.77 110.09 106.98 102.75 100.05  99.42
[11,] 109.77 110.72 106.98 102.75 100.05  99.42
[12,] 109.77 109.90 106.98 102.75 100.05  99.42
> 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] 109.7700 112.5142 111.0467 107.8525 104.1008 102.9842
> arr.sd
[1] 0.000000 3.664405 4.615134 5.380360 4.632028 4.920518
> arr.range
[1]  0.00  9.98 11.79 12.78 11.55 10.58
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    24.4998      -0.1909  

> (lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  NA/NaN/Inf in 'y'
Calls: lm -> lm.fit
Execution halted