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(209704.00
+ ,208923.00
+ ,208131.00
+ ,206492.00
+ ,222706.00
+ ,221848.00
+ ,209704.00
+ ,201630.00
+ ,202411.00
+ ,202411.00
+ ,203280.00
+ ,204842.00
+ ,207273.00
+ ,207273.00
+ ,205711.00
+ ,201630.00
+ ,222706.00
+ ,225918.00
+ ,221067.00
+ ,209704.00
+ ,214566.00
+ ,207273.00
+ ,210562.00
+ ,212135.00
+ ,213774.00
+ ,209704.00
+ ,210562.00
+ ,204842.00
+ ,222706.00
+ ,228349.00
+ ,223498.00
+ ,214566.00
+ ,224279.00
+ ,213774.00
+ ,223498.00
+ ,222706.00
+ ,225137.00
+ ,216205.00
+ ,225918.00
+ ,225137.00
+ ,239712.00
+ ,236423.00
+ ,223498.00
+ ,216986.00
+ ,225918.00
+ ,213774.00
+ ,222706.00
+ ,224279.00
+ ,227568.00
+ ,220286.00
+ ,224279.00
+ ,226710.00
+ ,235642.00
+ ,228349.00
+ ,218636.00
+ ,208131.00
+ ,217855.00
+ ,191125.00
+ ,204061.00
+ ,211343.00
+ ,218636.00
+ ,208131.00
+ ,208131.00
+ ,208131.00
+ ,213774.00
+ ,205711.00
+ ,195129.00
+ ,186274.00
+ ,192698.00
+ ,167618.00
+ ,182985.00
+ ,191917.00
+ ,193556.00
+ ,184624.00
+ ,185405.00
+ ,182985.00
+ ,191125.00
+ ,185405.00
+ ,174130.00
+ ,165979.00
+ ,179762.00
+ ,149831.00
+ ,169268.00
+ ,178123.00
+ ,178123.00
+ ,167618.00
+ ,157905.00
+ ,157124.00
+ ,165979.00
+ ,157905.00
+ ,142549.00
+ ,131967.00
+ ,143330.00
+ ,116611.00
+ ,140899.00
+ ,153824.00
+ ,157905.00
+ ,148973.00
+ ,137687.00
+ ,145761.00
+ ,148973.00
+ ,146542.00
+ ,122243.00
+ ,110968.00
+ ,119031.00
+ ,94743.00
+ ,119823.00
+ ,128755.00
+ ,136037.00
+ ,123893.00
+ ,112530.00
+ ,119031.00
+ ,122243.00
+ ,115819.00
+ ,91531.00
+ ,80949.00
+ ,90662.00
+ ,63943.00
+ ,93093.00
+ ,110968.00)
> 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] 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,] 209704 207273 213774 225137 227568 218636 193556 178123 157905 136037
 [2,] 208923 207273 209704 216205 220286 208131 184624 167618 148973 123893
 [3,] 208131 205711 210562 225918 224279 208131 185405 157905 137687 112530
 [4,] 206492 201630 204842 225137 226710 208131 182985 157124 145761 119031
 [5,] 222706 222706 222706 239712 235642 213774 191125 165979 148973 122243
 [6,] 221848 225918 228349 236423 228349 205711 185405 157905 146542 115819
 [7,] 209704 221067 223498 223498 218636 195129 174130 142549 122243  91531
 [8,] 201630 209704 214566 216986 208131 186274 165979 131967 110968  80949
 [9,] 202411 214566 224279 225918 217855 192698 179762 143330 119031  90662
[10,] 202411 207273 213774 213774 191125 167618 149831 116611  94743  63943
[11,] 203280 210562 223498 222706 204061 182985 169268 140899 119823  93093
[12,] 204842 212135 222706 224279 211343 191917 178123 153824 128755 110968
> 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] 208506.8 212151.5 217688.2 224641.1 217832.1 198261.2 178349.4 151152.8
 [9] 131783.7 105058.2
> arr.sd
 [1]  7065.946  7497.444  7354.038  7524.159 12390.486 14772.787 12148.800
 [8] 16932.697 18907.628 20925.529
> arr.range
 [1] 21076 24288 23507 25938 44517 51018 43725 61512 63162 72094
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 33433.2668      -0.1132  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       26.125         -1.386  

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

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

Coefficients:
(Intercept)     arr.mean  
  1.166e+05   -3.983e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/197ck1439466043.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/2nil11439466043.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/366js1439466043.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4k8d31439466043.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5yrf01439466043.tab") 
> 
> try(system("convert tmp/197ck1439466043.ps tmp/197ck1439466043.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nil11439466043.ps tmp/2nil11439466043.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.916   0.165   1.085