R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(1263600,1216800,1287000,1029600,1333800,1310400,1404000,1450800,1614600,1404000,1333800,1661400,1404000,1053000,1240200,936000,1310400,1076400,1427400,1287000,1357200,1521000,1497600,1778400,1287000,1076400,1193400,865800,1240200,959400,1357200,1287000,1146600,1638000,1474200,1684800,1263600,1170000,1053000,865800,1146600,1029600,1404000,1357200,1170000,1567800,1450800,1872000,1497600,912600,912600,912600,1076400,1076400,1450800,1333800,1193400,1497600,1380600,1989000,1567800,912600,959400,795600,1099800,1263600,1591200,1567800,1263600,1474200,1310400,1872000,1427400,1146600,1029600,772200,1146600,1380600,1614600,1521000,1123200,1614600,1263600,1942200,1614600,1170000,1076400,725400,1146600,1099800,1661400,1661400,1263600,1638000,1216800,1895400,1614600,1193400,912600,631800,1240200,1193400,1567800,1801800,1333800,1497600,1123200,1942200)
> 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] 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,] 1263600 1404000 1287000 1263600 1497600 1567800 1427400 1614600 1614600
 [2,] 1216800 1053000 1076400 1170000  912600  912600 1146600 1170000 1193400
 [3,] 1287000 1240200 1193400 1053000  912600  959400 1029600 1076400  912600
 [4,] 1029600  936000  865800  865800  912600  795600  772200  725400  631800
 [5,] 1333800 1310400 1240200 1146600 1076400 1099800 1146600 1146600 1240200
 [6,] 1310400 1076400  959400 1029600 1076400 1263600 1380600 1099800 1193400
 [7,] 1404000 1427400 1357200 1404000 1450800 1591200 1614600 1661400 1567800
 [8,] 1450800 1287000 1287000 1357200 1333800 1567800 1521000 1661400 1801800
 [9,] 1614600 1357200 1146600 1170000 1193400 1263600 1123200 1263600 1333800
[10,] 1404000 1521000 1638000 1567800 1497600 1474200 1614600 1638000 1497600
[11,] 1333800 1497600 1474200 1450800 1380600 1310400 1263600 1216800 1123200
[12,] 1661400 1778400 1684800 1872000 1989000 1872000 1942200 1895400 1942200
> 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] 1359150 1324050 1267500 1279200 1269450 1306500 1331850 1347450 1337700
> arr.sd
[1] 169463.5 231101.4 248010.6 271669.7 321019.1 323137.8 316385.5 339898.5
[9] 370770.5
> arr.range
[1]  631800  842400  819000 1006200 1076400 1076400 1170000 1170000 1310400
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  4.291e+05   -1.074e-01  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       30.809         -1.296  

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

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

Coefficients:
(Intercept)     arr.mean  
  7.185e+05    2.229e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/1ioz31436878458.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/2vmys1436878458.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/3bvxn1436878458.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4n7vo1436878458.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5ppzc1436878458.tab") 
> 
> try(system("convert tmp/1ioz31436878458.ps tmp/1ioz31436878458.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vmys1436878458.ps tmp/2vmys1436878458.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.845   0.172   1.023