R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(5400,5200,5500,4400,5700,5600,6000,6200,6900,6000,5700,7100,6000,4500,5300,4000,5600,4600,6100,5500,5800,6500,6400,7600,5500,4600,5100,3700,5300,4100,5800,5500,4900,7000,6300,7200,5400,5000,4500,3700,4900,4400,6000,5800,5000,6700,6200,8000,6400,3900,3900,3900,4600,4600,6200,5700,5100,6400,5900,8500,6700,3900,4100,3400,4700,5400,6800,6700,5400,6300,5600,8000,6100,4900,4400,3300,4900,5900,6900,6500,4800,6900,5400,8300,6900,5000,4600,3100,4900,4700,7100,7100,5400,7000,5200,8100,6900,5100,3900,2700,5300,5100,6700,7700,5700,6400,4800,8300)
> par1 = '12'
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 108
> (np <- floor(n / par1))
[1] 9
> 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]
 [1,] 5400 6000 5500 5400 6400 6700 6100 6900 6900
 [2,] 5200 4500 4600 5000 3900 3900 4900 5000 5100
 [3,] 5500 5300 5100 4500 3900 4100 4400 4600 3900
 [4,] 4400 4000 3700 3700 3900 3400 3300 3100 2700
 [5,] 5700 5600 5300 4900 4600 4700 4900 4900 5300
 [6,] 5600 4600 4100 4400 4600 5400 5900 4700 5100
 [7,] 6000 6100 5800 6000 6200 6800 6900 7100 6700
 [8,] 6200 5500 5500 5800 5700 6700 6500 7100 7700
 [9,] 6900 5800 4900 5000 5100 5400 4800 5400 5700
[10,] 6000 6500 7000 6700 6400 6300 6900 7000 6400
[11,] 5700 6400 6300 6200 5900 5600 5400 5200 4800
[12,] 7100 7600 7200 8000 8500 8000 8300 8100 8300
> 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] 5808.333 5658.333 5416.667 5466.667 5425.000 5583.333 5691.667 5758.333
[9] 5716.667
> arr.sd
[1]  724.2028  987.6127 1059.8742 1160.9818 1371.8766 1380.9307 1352.0747
[8] 1452.5578 1584.4892
> arr.range
[1] 2700 3600 3500 4300 4600 4600 5000 5000 5600
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  1833.7141      -0.1074  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       18.282         -1.296  

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

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

Coefficients:
(Intercept)     arr.mean  
  3070.7183       0.2229  

> postscript(file="/var/wessaorg/rcomp/tmp/1lj751470658762.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/2i5ol1470658762.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/3jv8a1470658762.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/40nue1470658762.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5bfhc1470658762.tab") 
> 
> try(system("convert tmp/1lj751470658762.ps tmp/1lj751470658762.png",intern=TRUE))
character(0)
> try(system("convert tmp/2i5ol1470658762.ps tmp/2i5ol1470658762.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.915   0.096   1.023