R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(24514,24442,24364,24222,25689,25618,24514,23780,23851,23851,23922,24072,24514,24735,25105,25397,26722,26573,25468,23851,24143,24442,24364,24735,24442,24955,25176,25247,26872,26573,25468,23851,24143,23922,24293,25105,25027,24884,25247,25468,26722,26793,25468,23559,23409,23851,23481,24663,24663,24222,24806,25176,26430,26793,25247,23409,23409,22818,22376,23338,22968,22084,22676,23189,24735,25326,23702,22526,22526,22084,21792,22376,21643,21493,21864,22376,23922,24222,22305,20909,20246,19584,19213,19947,19506,19584,19947,20246,21714,21935,19584,18480,17375,16635,16122,16855,16492,17076,17297,17518,18480,19064,16122,15388,13543,12367,11997,13030,12439,13179,13179,13251,14134,14725,11854,10821,9126,8022,7437,8905)
> 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,] 24514 24514 24442 25027 24663 22968 21643 19506 16492 12439
 [2,] 24442 24735 24955 24884 24222 22084 21493 19584 17076 13179
 [3,] 24364 25105 25176 25247 24806 22676 21864 19947 17297 13179
 [4,] 24222 25397 25247 25468 25176 23189 22376 20246 17518 13251
 [5,] 25689 26722 26872 26722 26430 24735 23922 21714 18480 14134
 [6,] 25618 26573 26573 26793 26793 25326 24222 21935 19064 14725
 [7,] 24514 25468 25468 25468 25247 23702 22305 19584 16122 11854
 [8,] 23780 23851 23851 23559 23409 22526 20909 18480 15388 10821
 [9,] 23851 24143 24143 23409 23409 22526 20246 17375 13543  9126
[10,] 23851 24442 23922 23851 22818 22084 19584 16635 12367  8022
[11,] 23922 24364 24293 23481 22376 21792 19213 16122 11997  7437
[12,] 24072 24735 25105 24663 23338 22376 19947 16855 13030  8905
> 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] 24403.25 25004.08 25003.92 24881.00 24390.58 22998.67 21477.00 18998.58
 [9] 15697.83 11422.67
> arr.sd
 [1]  641.6544  903.2380  969.0921 1162.7685 1385.7346 1088.4146 1597.5107
 [8] 1924.8525 2417.7203 2493.8712
> arr.range
 [1] 1909 2871 3021 3384 4417 3534 5009 5813 7067 7288
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  4181.7729      -0.1271  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       21.197         -1.408  

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

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

Coefficients:
(Intercept)     arr.mean  
 12089.7048      -0.3574  

> postscript(file="/var/wessaorg/rcomp/tmp/1dvcg1408299855.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/2jud21408299855.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/37pw91408299855.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4jitr1408299855.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5p63z1408299855.tab") 
> 
> try(system("convert tmp/1dvcg1408299855.ps tmp/1dvcg1408299855.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jud21408299855.ps tmp/2jud21408299855.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.872   0.132   0.995