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(1474200,1419600,1501500,1201200,1556100,1528800,1638000,1692600,1883700,1638000,1556100,1938300,1638000,1228500,1446900,1092000,1528800,1255800,1665300,1501500,1583400,1774500,1747200,2074800,1501500,1255800,1392300,1010100,1446900,1119300,1583400,1501500,1337700,1911000,1719900,1965600,1474200,1365000,1228500,1010100,1337700,1201200,1638000,1583400,1365000,1829100,1692600,2184000,1747200,1064700,1064700,1064700,1255800,1255800,1692600,1556100,1392300,1747200,1610700,2320500,1829100,1064700,1119300,928200,1283100,1474200,1856400,1829100,1474200,1719900,1528800,2184000,1665300,1337700,1201200,900900,1337700,1610700,1883700,1774500,1310400,1883700,1474200,2265900,1883700,1365000,1255800,846300,1337700,1283100,1938300,1938300,1474200,1911000,1419600,2211300,1883700,1392300,1064700,737100,1446900,1392300,1829100,2102100,1556100,1747200,1310400,2265900)
> 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,] 1474200 1638000 1501500 1474200 1747200 1829100 1665300 1883700 1883700
 [2,] 1419600 1228500 1255800 1365000 1064700 1064700 1337700 1365000 1392300
 [3,] 1501500 1446900 1392300 1228500 1064700 1119300 1201200 1255800 1064700
 [4,] 1201200 1092000 1010100 1010100 1064700  928200  900900  846300  737100
 [5,] 1556100 1528800 1446900 1337700 1255800 1283100 1337700 1337700 1446900
 [6,] 1528800 1255800 1119300 1201200 1255800 1474200 1610700 1283100 1392300
 [7,] 1638000 1665300 1583400 1638000 1692600 1856400 1883700 1938300 1829100
 [8,] 1692600 1501500 1501500 1583400 1556100 1829100 1774500 1938300 2102100
 [9,] 1883700 1583400 1337700 1365000 1392300 1474200 1310400 1474200 1556100
[10,] 1638000 1774500 1911000 1829100 1747200 1719900 1883700 1911000 1747200
[11,] 1556100 1747200 1719900 1692600 1610700 1528800 1474200 1419600 1310400
[12,] 1938300 2074800 1965600 2184000 2320500 2184000 2265900 2211300 2265900
> 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] 1585675 1544725 1478750 1492400 1481025 1524250 1553825 1572025 1560650
> arr.sd
[1] 197707.4 269618.3 289345.7 316948.0 374522.3 376994.1 369116.4 396548.3
[9] 432565.6
> arr.range
[1]  737100  982800  955500 1173900 1255800 1255800 1365000 1365000 1528800
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  5.006e+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)  
       31.163         -1.296  

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

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

Coefficients:
(Intercept)     arr.mean  
  8.383e+05    2.229e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/13itp1439753195.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/2e7jz1439753195.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/3pl4p1439753195.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4s3rw1439753195.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5o92i1439753195.tab") 
> 
> try(system("convert tmp/13itp1439753195.ps tmp/13itp1439753195.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e7jz1439753195.ps tmp/2e7jz1439753195.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.885   0.189   1.065