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(29054.50
+ ,28543.50
+ ,28032.00
+ ,27009.50
+ ,37356.00
+ ,36844.50
+ ,29054.50
+ ,23881.50
+ ,24392.50
+ ,24392.50
+ ,24904.00
+ ,25982.00
+ ,22859.00
+ ,19731.00
+ ,17169.50
+ ,17169.50
+ ,27009.50
+ ,28032.00
+ ,20242.00
+ ,11429.50
+ ,16091.50
+ ,16091.50
+ ,19731.00
+ ,21831.50
+ ,21320.00
+ ,16091.50
+ ,18708.50
+ ,17681.00
+ ,26493.50
+ ,24392.50
+ ,16091.50
+ ,9891.00
+ ,15580.00
+ ,17169.50
+ ,18708.50
+ ,20753.50
+ ,16602.50
+ ,13019.00
+ ,14558.00
+ ,15069.00
+ ,28543.50
+ ,28543.50
+ ,20753.50
+ ,19731.00
+ ,22859.00
+ ,21320.00
+ ,25471.00
+ ,30644.00
+ ,31671.50
+ ,24392.50
+ ,22342.50
+ ,20242.00
+ ,34283.50
+ ,35311.00
+ ,32694.00
+ ,35311.00
+ ,34794.50
+ ,30644.00
+ ,35311.00
+ ,40484.00
+ ,42584.50
+ ,36333.50
+ ,32182.50
+ ,35311.00
+ ,48785.00
+ ,52936.00
+ ,51913.50
+ ,53958.00
+ ,53447.00
+ ,48274.00
+ ,57086.50
+ ,59187.00
+ ,62259.50
+ ,52936.00
+ ,49296.50
+ ,53447.00
+ ,63337.50
+ ,72150.00
+ ,70049.50
+ ,70049.50
+ ,71077.00
+ ,67488.00
+ ,76817.00
+ ,76817.00
+ ,75227.50
+ ,66410.00
+ ,67999.50
+ ,69027.00
+ ,75789.50
+ ,84602.00
+ ,78350.50
+ ,81479.00
+ ,78862.00
+ ,77328.00
+ ,89269.00
+ ,86652.00
+ ,83012.50
+ ,77839.50
+ ,83012.50
+ ,85629.50
+ ,88752.50
+ ,92903.00
+ ,88752.50
+ ,91314.00
+ ,88190.50
+ ,87679.50
+ ,100642.50
+ ,101720.50
+ ,97570.00
+ ,90291.50
+ ,96492.00
+ ,99104.00
+ ,102232.00
+ ,106894.00
+ ,102232.00
+ ,105871.50
+ ,104282.00
+ ,98592.50
+ ,110533.00
+ ,110533.00)
> 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]
 [1,] 29054.5 22859.0 21320.0 16602.5 31671.5 42584.5 62259.5 75227.5  83012.5
 [2,] 28543.5 19731.0 16091.5 13019.0 24392.5 36333.5 52936.0 66410.0  77839.5
 [3,] 28032.0 17169.5 18708.5 14558.0 22342.5 32182.5 49296.5 67999.5  83012.5
 [4,] 27009.5 17169.5 17681.0 15069.0 20242.0 35311.0 53447.0 69027.0  85629.5
 [5,] 37356.0 27009.5 26493.5 28543.5 34283.5 48785.0 63337.5 75789.5  88752.5
 [6,] 36844.5 28032.0 24392.5 28543.5 35311.0 52936.0 72150.0 84602.0  92903.0
 [7,] 29054.5 20242.0 16091.5 20753.5 32694.0 51913.5 70049.5 78350.5  88752.5
 [8,] 23881.5 11429.5  9891.0 19731.0 35311.0 53958.0 70049.5 81479.0  91314.0
 [9,] 24392.5 16091.5 15580.0 22859.0 34794.5 53447.0 71077.0 78862.0  88190.5
[10,] 24392.5 16091.5 17169.5 21320.0 30644.0 48274.0 67488.0 77328.0  87679.5
[11,] 24904.0 19731.0 18708.5 25471.0 35311.0 57086.5 76817.0 89269.0 100642.5
[12,] 25982.0 21831.5 20753.5 30644.0 40484.0 59187.0 76817.0 86652.0 101720.5
         [,10]
 [1,]  97570.0
 [2,]  90291.5
 [3,]  96492.0
 [4,]  99104.0
 [5,] 102232.0
 [6,] 106894.0
 [7,] 102232.0
 [8,] 105871.5
 [9,] 104282.0
[10,]  98592.5
[11,] 110533.0
[12,] 110533.0
> 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]  28287.25  19782.29  18573.42  21426.17  31456.79  47666.54  65477.04
 [8]  77583.00  89120.75 102052.29
> arr.sd
 [1] 4521.855 4717.844 4352.163 5951.057 6076.552 8987.650 9330.671 7287.561
 [9] 6927.618 6006.785
> arr.range
 [1] 13474.5 16602.5 16602.5 17625.0 20242.0 27004.5 27520.5 22859.0 23881.0
[10] 20241.5
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  5052.1906       0.0272  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       5.8863         0.2678  

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

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

Coefficients:
(Intercept)     arr.mean  
  1.628e+04    8.623e-02  

> postscript(file="/var/wessaorg/rcomp/tmp/1si4k1470783743.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/2ezr01470783743.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/35n1i1470783743.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4atn11470783743.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5yvjo1470783743.tab") 
> 
> try(system("convert tmp/1si4k1470783743.ps tmp/1si4k1470783743.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ezr01470783743.ps tmp/2ezr01470783743.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.888   0.062   0.966