R version 3.2.4 (2016-03-10) -- "Very Secure Dishes"
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> x <- c(92.09,93.77,94.44,94.91,94.78,94.51,94.36,96.6,96.72,96.71,97.44,97.83,98.92,97.98,98.76,99.76,99.87,100.09,100.07,99.46,100.4,101.25,102.29,102.1,105.91,108.95,110.07,109.92,109.87,110.54,110.79,110.32,110.76,110.24,110.27,110.11,110.39,111.05,110.85,110.24,108.7,109.93,109.53,109.83,107.86,104.61,103.61,103.11,102.59,102.91,101.94,101.8,102.25,102.6,102.49,102.13,100.76,100.86,101.12,100.74,99.99,99.39,99.52,99.21,99.38,99.37,99.38,99.26,99.36,99.2,98.53,98.65,99.15,100.17,99.98,100.07,99.94,100.05,99.13,98.74,98.64,98.44,98.81,98.88,99.63,100.08,100.07,100.55,99.98,99.89,99.86,99.61,100.12,100.24,100.1,99.86,97.99,97.57,98.28,97.97,97.99,97.84,97.33,96.7,96.79,96.76,96.23,96.29,96.46,97.23,97.59,97.13,97.37,96.12,96.96,96.7,97,97.15,96.51,96.68)
> 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,] 92.09  98.92 105.91 110.39 102.59 99.99  99.15  99.63 97.99 96.46
 [2,] 93.77  97.98 108.95 111.05 102.91 99.39 100.17 100.08 97.57 97.23
 [3,] 94.44  98.76 110.07 110.85 101.94 99.52  99.98 100.07 98.28 97.59
 [4,] 94.91  99.76 109.92 110.24 101.80 99.21 100.07 100.55 97.97 97.13
 [5,] 94.78  99.87 109.87 108.70 102.25 99.38  99.94  99.98 97.99 97.37
 [6,] 94.51 100.09 110.54 109.93 102.60 99.37 100.05  99.89 97.84 96.12
 [7,] 94.36 100.07 110.79 109.53 102.49 99.38  99.13  99.86 97.33 96.96
 [8,] 96.60  99.46 110.32 109.83 102.13 99.26  98.74  99.61 96.70 96.70
 [9,] 96.72 100.40 110.76 107.86 100.76 99.36  98.64 100.12 96.79 97.00
[10,] 96.71 101.25 110.24 104.61 100.86 99.20  98.44 100.24 96.76 97.15
[11,] 97.44 102.29 110.27 103.61 101.12 98.53  98.81 100.10 96.23 96.51
[12,] 97.83 102.10 110.11 103.11 100.74 98.65  98.88  99.86 96.29 96.68
> 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]  95.34667 100.07917 109.81250 108.30917 101.84917  99.27000  99.33333
 [8]  99.99917  97.31167  96.90833
> arr.sd
 [1] 1.7046372 1.2956182 1.3203039 2.8858320 0.7877177 0.3787779 0.6555821
 [8] 0.2587543 0.7262586 0.4238961
> arr.range
 [1] 5.74 4.31 4.88 7.94 2.17 1.46 1.73 0.94 2.05 1.47
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    -7.9695       0.0894  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
      -34.425          7.419  

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

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

Coefficients:
(Intercept)     arr.mean  
     -22.94         0.26  

> postscript(file="/var/wessaorg/rcomp/tmp/1dqht1458629666.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/2k2l91458629666.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/3k8gk1458629666.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4u2ly1458629666.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5ykxx1458629666.tab") 
> 
> try(system("convert tmp/1dqht1458629666.ps tmp/1dqht1458629666.png",intern=TRUE))
character(0)
> try(system("convert tmp/2k2l91458629666.ps tmp/2k2l91458629666.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.832   0.159   0.988