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(1684800.00
+ ,1622400.00
+ ,1716000.00
+ ,1372800.00
+ ,1778400.00
+ ,1747200.00
+ ,1872000.00
+ ,1934400.00
+ ,2152800.00
+ ,1872000.00
+ ,1778400.00
+ ,2215200.00
+ ,1872000.00
+ ,1404000.00
+ ,1653600.00
+ ,1248000.00
+ ,1747200.00
+ ,1435200.00
+ ,1903200.00
+ ,1716000.00
+ ,1809600.00
+ ,2028000.00
+ ,1996800.00
+ ,2371200.00
+ ,1716000.00
+ ,1435200.00
+ ,1591200.00
+ ,1154400.00
+ ,1653600.00
+ ,1279200.00
+ ,1809600.00
+ ,1716000.00
+ ,1528800.00
+ ,2184000.00
+ ,1965600.00
+ ,2246400.00
+ ,1684800.00
+ ,1560000.00
+ ,1404000.00
+ ,1154400.00
+ ,1528800.00
+ ,1372800.00
+ ,1872000.00
+ ,1809600.00
+ ,1560000.00
+ ,2090400.00
+ ,1934400.00
+ ,2496000.00
+ ,1996800.00
+ ,1216800.00
+ ,1216800.00
+ ,1216800.00
+ ,1435200.00
+ ,1435200.00
+ ,1934400.00
+ ,1778400.00
+ ,1591200.00
+ ,1996800.00
+ ,1840800.00
+ ,2652000.00
+ ,2090400.00
+ ,1216800.00
+ ,1279200.00
+ ,1060800.00
+ ,1466400.00
+ ,1684800.00
+ ,2121600.00
+ ,2090400.00
+ ,1684800.00
+ ,1965600.00
+ ,1747200.00
+ ,2496000.00
+ ,1903200.00
+ ,1528800.00
+ ,1372800.00
+ ,1029600.00
+ ,1528800.00
+ ,1840800.00
+ ,2152800.00
+ ,2028000.00
+ ,1497600.00
+ ,2152800.00
+ ,1684800.00
+ ,2589600.00
+ ,2152800.00
+ ,1560000.00
+ ,1435200.00
+ ,967200.00
+ ,1528800.00
+ ,1466400.00
+ ,2215200.00
+ ,2215200.00
+ ,1684800.00
+ ,2184000.00
+ ,1622400.00
+ ,2527200.00
+ ,2152800.00
+ ,1591200.00
+ ,1216800.00
+ ,842400.00
+ ,1653600.00
+ ,1591200.00
+ ,2090400.00
+ ,2402400.00
+ ,1778400.00
+ ,1996800.00
+ ,1497600.00
+ ,2589600.00)
> 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] 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,] 1684800 1872000 1716000 1684800 1996800 2090400 1903200 2152800 2152800
 [2,] 1622400 1404000 1435200 1560000 1216800 1216800 1528800 1560000 1591200
 [3,] 1716000 1653600 1591200 1404000 1216800 1279200 1372800 1435200 1216800
 [4,] 1372800 1248000 1154400 1154400 1216800 1060800 1029600  967200  842400
 [5,] 1778400 1747200 1653600 1528800 1435200 1466400 1528800 1528800 1653600
 [6,] 1747200 1435200 1279200 1372800 1435200 1684800 1840800 1466400 1591200
 [7,] 1872000 1903200 1809600 1872000 1934400 2121600 2152800 2215200 2090400
 [8,] 1934400 1716000 1716000 1809600 1778400 2090400 2028000 2215200 2402400
 [9,] 2152800 1809600 1528800 1560000 1591200 1684800 1497600 1684800 1778400
[10,] 1872000 2028000 2184000 2090400 1996800 1965600 2152800 2184000 1996800
[11,] 1778400 1996800 1965600 1934400 1840800 1747200 1684800 1622400 1497600
[12,] 2215200 2371200 2246400 2496000 2652000 2496000 2589600 2527200 2589600
> 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] 1812200 1765400 1690000 1705600 1692600 1742000 1775800 1796600 1783600
> arr.sd
[1] 225951.3 308135.2 330680.8 362226.3 428025.5 430850.4 421847.3 453198.0
[9] 494360.6
> arr.range
[1]  842400 1123200 1092000 1341600 1435200 1435200 1560000 1560000 1747200
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  5.721e+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)  
       31.469         -1.296  

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

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

Coefficients:
(Intercept)     arr.mean  
  9.581e+05    2.229e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/104io1438953091.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/24sdb1438953091.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/3kn7f1438953091.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4ggfj1438953091.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5ueil1438953091.tab") 
> 
> try(system("convert tmp/104io1438953091.ps tmp/104io1438953091.png",intern=TRUE))
character(0)
> try(system("convert tmp/24sdb1438953091.ps tmp/24sdb1438953091.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.915   0.175   1.093