R version 3.2.4 (2016-03-10) -- "Very Secure Dishes"
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> x <- c(96.44,96.35,96.4,96.66,96.95,97.14,97.27,97.34,97.42,97.47,97.29,97.36,97.47,97.48,97.84,97.9,97.53,97.61,97.73,97.76,97.87,97.85,98.13,98.21,98.3,98.34,98.38,98.42,98.16,98.18,98.22,98.29,98.45,98.54,98.54,98.78,98.84,99.14,99.2,99.33,98.56,98.65,98.77,98.82,98.9,98.89,98.9,99.07,99.09,99.12,99.03,99,99.21,99.35,99.37,99.39,99.41,99.43,99.6,99.73,99.78,99.8,99.88,99.74,100.15,100.27,100.26,100.36,100.37,100.54,99.8,99.82,99.82,99.82,99.67,99.78,99.44,99.61,99.71,99.71,99.77,99.77,99.89,99.96,100.02,100,100.04,99.99,99.97,99.77,99.93,99.9,100.01,100.08,100.21,100.28,100.48,100.72,100.74,100.88,101.03,101.47,101.46,101.46,101.45,101.74,102.41,102.54,102.67,102.87,102.9,102.88,102.82,102.94,102.97,103.01,103.11,103.21,104.66,104.79)
> 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,] 96.44 97.47 98.30 98.84 99.09  99.78 99.82 100.02 100.48 102.67
 [2,] 96.35 97.48 98.34 99.14 99.12  99.80 99.82 100.00 100.72 102.87
 [3,] 96.40 97.84 98.38 99.20 99.03  99.88 99.67 100.04 100.74 102.90
 [4,] 96.66 97.90 98.42 99.33 99.00  99.74 99.78  99.99 100.88 102.88
 [5,] 96.95 97.53 98.16 98.56 99.21 100.15 99.44  99.97 101.03 102.82
 [6,] 97.14 97.61 98.18 98.65 99.35 100.27 99.61  99.77 101.47 102.94
 [7,] 97.27 97.73 98.22 98.77 99.37 100.26 99.71  99.93 101.46 102.97
 [8,] 97.34 97.76 98.29 98.82 99.39 100.36 99.71  99.90 101.46 103.01
 [9,] 97.42 97.87 98.45 98.90 99.41 100.37 99.77 100.01 101.45 103.11
[10,] 97.47 97.85 98.54 98.89 99.43 100.54 99.77 100.08 101.74 103.21
[11,] 97.29 98.13 98.54 98.90 99.60  99.80 99.89 100.21 102.41 104.66
[12,] 97.36 98.21 98.78 99.07 99.73  99.82 99.96 100.28 102.54 104.79
> 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]  97.00750  97.78167  98.38333  98.92250  99.31083 100.06417  99.74583
 [8] 100.01667 101.36500 103.23583
> arr.sd
 [1] 0.4298652 0.2371740 0.1777809 0.2257160 0.2267341 0.2883325 0.1349383
 [8] 0.1336436 0.6457483 0.7093974
> arr.range
 [1] 1.12 0.74 0.62 0.77 0.73 0.80 0.52 0.51 2.06 2.12
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    -6.6006       0.0695  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       -70.61          15.06  

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

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

Coefficients:
(Intercept)     arr.mean  
   -21.2533       0.2235  

> postscript(file="/var/wessaorg/rcomp/tmp/1qpjm1458316475.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/2m5s11458316475.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/37uqm1458316475.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4upmw1458316475.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5et5f1458316475.tab") 
> 
> try(system("convert tmp/1qpjm1458316475.ps tmp/1qpjm1458316475.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m5s11458316475.ps tmp/2m5s11458316475.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.975   0.189   1.165