R version 3.2.2 (2015-08-14) -- "Fire Safety"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(250.71,251.57,260.85,265.47,262.37,272.39,277.49,274.41,274.42,267.1,258.84,253.97,253.88,253.3,249.86,246,248.42,250.29,246.9,255.2,253.33,251.02,254.5,253.18,256.03,262.15,259.94,253.75,247.69,242.42,231.82,235.88,240.68,260.15,265.32,265.02,279.86,298.3,304.14,295.26,281.93,280.46,272.06,270.05,271.84,268.49,270.92,273.22,269.43,271.21,265.4,265.53,276.78,281.49,283.75,281.45,282.1,274.01,275.51,277.62,275.33,271.15,270.89,265.29,266.96,266.87,267.68,272.37,285.05,296.79,309.15,304.19,307.33,290.68,292.26,294.81,293.67,293.57,286.28,278.93,284.22,282.09,282.26,285.79,294.01,292.73,303.01,298.67,292.38,295.7,294.9,299.46,299.75,294.76,297.68,300.24,302.48,310.2,311.49,307.37,304.58,305.87,309.81,313.91,313.2,307.85,306.89,310.83)
> 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,] 250.71 253.88 256.03 279.86 269.43 275.33 307.33 294.01 302.48
 [2,] 251.57 253.30 262.15 298.30 271.21 271.15 290.68 292.73 310.20
 [3,] 260.85 249.86 259.94 304.14 265.40 270.89 292.26 303.01 311.49
 [4,] 265.47 246.00 253.75 295.26 265.53 265.29 294.81 298.67 307.37
 [5,] 262.37 248.42 247.69 281.93 276.78 266.96 293.67 292.38 304.58
 [6,] 272.39 250.29 242.42 280.46 281.49 266.87 293.57 295.70 305.87
 [7,] 277.49 246.90 231.82 272.06 283.75 267.68 286.28 294.90 309.81
 [8,] 274.41 255.20 235.88 270.05 281.45 272.37 278.93 299.46 313.91
 [9,] 274.42 253.33 240.68 271.84 282.10 285.05 284.22 299.75 313.20
[10,] 267.10 251.02 260.15 268.49 274.01 296.79 282.09 294.76 307.85
[11,] 258.84 254.50 265.32 270.92 275.51 309.15 282.26 297.68 306.89
[12,] 253.97 253.18 265.02 273.22 277.62 304.19 285.79 300.24 310.83
> 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] 264.1325 251.3233 251.7375 280.5442 275.3567 279.3100 289.3242 296.9408
[9] 308.7067
> arr.sd
[1]  9.302061  3.055125 11.691060 12.211866  6.379452 15.633639  7.744554
[8]  3.344957  3.472435
> arr.range
[1] 26.78  9.20 33.50 35.65 18.35 43.86 28.40 10.63 11.43
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
   25.25002     -0.06183  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       14.795         -2.287  

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

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

Coefficients:
(Intercept)     arr.mean  
    61.7704      -0.1354  

> postscript(file="/var/wessaorg/rcomp/tmp/1bprl1447938524.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/23rq61447938524.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/307ei1447938524.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4vmvf1447938524.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5r15f1447938524.tab") 
> 
> try(system("convert tmp/1bprl1447938524.ps tmp/1bprl1447938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/23rq61447938524.ps tmp/23rq61447938524.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.849   0.159   1.015