R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
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Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(1.65,1.66,1.66,1.67,1.68,1.68,1.68,1.68,1.69,1.7,1.7,1.71,1.72,1.73,1.74,1.74,1.75,1.75,1.75,1.76,1.79,1.83,1.84,1.85,1.87,1.87,1.87,1.88,1.88,1.88,1.88,1.89,1.89,1.89,1.9,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.89,1.9,1.9,1.92,1.93,1.92,1.95,1.96,1.96,1.96,1.96,1.96,1.97,1.97,1.97,1.97,1.97,1.97,1.98,1.98,1.98,1.98,1.98,1.98,1.97,1.98,1.98,1.99,2,2)
> 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] 84
> (np <- floor(n / par1))
[1] 7
> 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]
 [1,] 1.65 1.72 1.87 1.89 1.89 1.95 1.98
 [2,] 1.66 1.73 1.87 1.89 1.89 1.96 1.98
 [3,] 1.66 1.74 1.87 1.89 1.89 1.96 1.98
 [4,] 1.67 1.74 1.88 1.89 1.89 1.96 1.98
 [5,] 1.68 1.75 1.88 1.89 1.89 1.96 1.98
 [6,] 1.68 1.75 1.88 1.89 1.89 1.96 1.98
 [7,] 1.68 1.75 1.88 1.89 1.89 1.97 1.97
 [8,] 1.68 1.76 1.89 1.89 1.90 1.97 1.98
 [9,] 1.69 1.79 1.89 1.89 1.90 1.97 1.98
[10,] 1.70 1.83 1.89 1.89 1.92 1.97 1.99
[11,] 1.70 1.84 1.90 1.89 1.93 1.97 2.00
[12,] 1.71 1.85 1.89 1.89 1.92 1.97 2.00
> 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] 1.680000 1.770833 1.882500 1.890000 1.900000 1.964167 1.983333
> arr.sd
[1] 0.018090681 0.045218326 0.009653073 0.000000000 0.014770979 0.006685579
[7] 0.008876254
> arr.range
[1] 0.06 0.13 0.03 0.00 0.04 0.02 0.03
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    0.16185     -0.07877  

> (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