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(1755000,1690000,1787500,1430000,1852500,1820000,1950000,2015000,2242500,1950000,1852500,2307500,1950000,1462500,1722500,1300000,1820000,1495000,1982500,1787500,1885000,2112500,2080000,2470000,1787500,1495000,1657500,1202500,1722500,1332500,1885000,1787500,1592500,2275000,2047500,2340000,1755000,1625000,1462500,1202500,1592500,1430000,1950000,1885000,1625000,2177500,2015000,2600000,2080000,1267500,1267500,1267500,1495000,1495000,2015000,1852500,1657500,2080000,1917500,2762500,2177500,1267500,1332500,1105000,1527500,1755000,2210000,2177500,1755000,2047500,1820000,2600000,1982500,1592500,1430000,1072500,1592500,1917500,2242500,2112500,1560000,2242500,1755000,2697500,2242500,1625000,1495000,1007500,1592500,1527500,2307500,2307500,1755000,2275000,1690000,2632500,2242500,1657500,1267500,877500,1722500,1657500,2177500,2502500,1852500,2080000,1560000,2697500)
> 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,] 1755000 1950000 1787500 1755000 2080000 2177500 1982500 2242500 2242500
 [2,] 1690000 1462500 1495000 1625000 1267500 1267500 1592500 1625000 1657500
 [3,] 1787500 1722500 1657500 1462500 1267500 1332500 1430000 1495000 1267500
 [4,] 1430000 1300000 1202500 1202500 1267500 1105000 1072500 1007500  877500
 [5,] 1852500 1820000 1722500 1592500 1495000 1527500 1592500 1592500 1722500
 [6,] 1820000 1495000 1332500 1430000 1495000 1755000 1917500 1527500 1657500
 [7,] 1950000 1982500 1885000 1950000 2015000 2210000 2242500 2307500 2177500
 [8,] 2015000 1787500 1787500 1885000 1852500 2177500 2112500 2307500 2502500
 [9,] 2242500 1885000 1592500 1625000 1657500 1755000 1560000 1755000 1852500
[10,] 1950000 2112500 2275000 2177500 2080000 2047500 2242500 2275000 2080000
[11,] 1852500 2080000 2047500 2015000 1917500 1820000 1755000 1690000 1560000
[12,] 2307500 2470000 2340000 2600000 2762500 2600000 2697500 2632500 2697500
> 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] 1887708 1838958 1760417 1776667 1763125 1814583 1849792 1871458 1857917
> arr.sd
[1] 235365.9 320974.1 344459.1 377319.1 445859.9 448802.5 439424.3 472081.3
[9] 514959.0
> arr.range
[1]  877500 1170000 1137500 1397500 1495000 1495000 1625000 1625000 1820000
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  5.960e+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)  
       31.563         -1.296  

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

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

Coefficients:
(Intercept)     arr.mean  
  9.980e+05    2.229e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/1jp3d1439292883.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/22sth1439292883.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/3c8ai1439292883.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4z9iv1439292883.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5y67k1439292883.tab") 
> 
> try(system("convert tmp/1jp3d1439292883.ps tmp/1jp3d1439292883.png",intern=TRUE))
character(0)
> try(system("convert tmp/22sth1439292883.ps tmp/22sth1439292883.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.895   0.176   1.077