R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(36.80,35.40,33.00,28.73,26.70,26.46,24.60,28.00,31.60,33.50,34.50,35.00,34.76,33.50,32.74,34.40,31.93,29.24,25.75,26.03,26.08,23.80,20.61,19.70,18.18,19.60,20.60,20.03,23.00,23.60,22.56,22.55,23.75,24.92,24.50,30.58,28.07,27.70,27.00,25.23,26.86,25.60,24.55,23.96,23.50,23.64,21.55,21.05,21.89,21.98,21.45,22.15,22.58,23.80,23.30,22.38,23.00,21.96,22.40,20.80,20.40,16.00,12.78,9.75,7.50,11.24,12.24,12.75,12.52,14.49,14.21,14.32,22.15,22.58,23.80,23.30,22.38,23.00,21.96,22.40,20.80,20.40,16.00,12.78,9.75,7.50,11.24,12.24,12.75,12.52,14.49,14.21,14.32)
> par1 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 93
> (np <- floor(n / par1))
[1] 23
> 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] [,11] [,12]
[1,] 36.80 26.70 31.6 34.76 31.93 26.08 18.18 23.00 23.75 28.07 26.86 23.50
[2,] 35.40 26.46 33.5 33.50 29.24 23.80 19.60 23.60 24.92 27.70 25.60 23.64
[3,] 33.00 24.60 34.5 32.74 25.75 20.61 20.60 22.56 24.50 27.00 24.55 21.55
[4,] 28.73 28.00 35.0 34.40 26.03 19.70 20.03 22.55 30.58 25.23 23.96 21.05
     [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23]
[1,] 21.89 22.58 23.00 20.40  7.50 12.52 22.15 22.38 20.80  9.75 12.75
[2,] 21.98 23.80 21.96 16.00 11.24 14.49 22.58 23.00 20.40  7.50 12.52
[3,] 21.45 23.30 22.40 12.78 12.24 14.21 23.80 21.96 16.00 11.24 14.49
[4,] 22.15 22.38 20.80  9.75 12.75 14.32 23.30 22.40 12.78 12.24 14.21
> 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] 33.4825 26.4400 33.6500 33.8500 28.2375 22.5475 19.6025 22.9275 25.9375
[10] 27.0000 25.2425 22.4350 21.8675 23.0150 22.0400 14.7325 10.9325 13.8850
[19] 22.9575 22.4350 17.4950 10.1825 13.4925
> arr.sd
 [1] 3.5356129 1.4008569 1.5022206 0.9101648 2.9269025 2.9387909 1.0330013
 [8] 0.4950000 3.1326067 1.2606612 1.2739015 1.3276169 0.2984823 0.6557184
[15] 0.9300896 4.5594106 2.3727252 0.9172604 0.7352267 0.4278240 3.8222288
[22] 2.0602973 1.0011452
> arr.range
 [1]  8.07  3.40  3.40  2.02  6.18  6.38  2.42  1.05  6.83  2.84  2.90  2.59
[13]  0.70  1.42  2.20 10.65  5.25  1.97  1.65  1.04  8.02  4.74  1.97
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    2.02052     -0.01352  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       1.0311        -0.2406  

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

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

Coefficients:
(Intercept)     arr.mean  
    4.45759     -0.02884  

> postscript(file="/var/www/html/rcomp/tmp/1zpp21244400893.ps",horizontal=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/www/html/rcomp/tmp/29y861244400893.ps",horizontal=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/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/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/www/html/rcomp/tmp/3hd4y1244400893.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4lxuq1244400893.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/www/html/rcomp/tmp/5vcwm1244400893.tab") 
> 
> system("convert tmp/1zpp21244400893.ps tmp/1zpp21244400893.png")
> system("convert tmp/29y861244400893.ps tmp/29y861244400893.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.504   0.297   0.639