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(132.1,125,127.1,101.5,85.7,79.3,70.9,77.1,83.9,96.2,111.7,127.2,143.6,134.9,135.6,105.3,86.4,74.6,67.6,73.4,78.5,98.2,118.6,136.9,137.9,115.6,119.3,98.5,84.3,73.5,60.7,69.5,77.9,113.9,126.3,135.1,130.5,113.1,110,90.8,85.4,72.5,64.7,67.2,77.9,105.2,107.2,120.7,121.3,107.9,105.6,81.3,71.7,64.8,57.3,61.9,70.1,88.8,106.8,110.7,114.1,108,111.5,86.8,78.4,68,57.3,65.3,73.3,88.6,101.3,122.9,126.6,114.1,124.7,93.3,77.2,66.5,57.9,63.7,65.8,85,101,105.3,121,117.9,106,86.6,79.9,65.2,61.2,67.6,78.9,95.5,113.1,124.4,122,110.3,114,93.3,75.5,65.4,59.2,63.8,74.2,91.7,107,120.7,127.4,119.7,112.7,84.4,75.6,66.5,59.9,64.8,74.3,100.4,105.9,131.1)
> par1 = '12'
> #'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] 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,] 132.1 143.6 137.9 130.5 121.3 114.1 126.6 121.0 122.0 127.4
 [2,] 125.0 134.9 115.6 113.1 107.9 108.0 114.1 117.9 110.3 119.7
 [3,] 127.1 135.6 119.3 110.0 105.6 111.5 124.7 106.0 114.0 112.7
 [4,] 101.5 105.3  98.5  90.8  81.3  86.8  93.3  86.6  93.3  84.4
 [5,]  85.7  86.4  84.3  85.4  71.7  78.4  77.2  79.9  75.5  75.6
 [6,]  79.3  74.6  73.5  72.5  64.8  68.0  66.5  65.2  65.4  66.5
 [7,]  70.9  67.6  60.7  64.7  57.3  57.3  57.9  61.2  59.2  59.9
 [8,]  77.1  73.4  69.5  67.2  61.9  65.3  63.7  67.6  63.8  64.8
 [9,]  83.9  78.5  77.9  77.9  70.1  73.3  65.8  78.9  74.2  74.3
[10,]  96.2  98.2 113.9 105.2  88.8  88.6  85.0  95.5  91.7 100.4
[11,] 111.7 118.6 126.3 107.2 106.8 101.3 101.0 113.1 107.0 105.9
[12,] 127.2 136.9 135.1 120.7 110.7 122.9 105.3 124.4 120.7 131.1
> 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] 101.47500 104.46667 101.04167  95.43333  87.35000  89.62500  90.09167
 [8]  93.10833  91.42500  93.55833
> arr.sd
 [1] 22.40240 28.50121 27.08621 21.99869 22.30346 21.66795 24.38175 22.98163
 [9] 23.22444 25.71326
> arr.range
 [1] 61.2 76.0 77.2 65.8 64.0 65.6 68.7 63.2 62.8 71.2
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    -0.1666       0.2553  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
      -1.1905         0.9595  

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

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

Coefficients:
(Intercept)     arr.mean  
    20.3129       0.4987  

> postscript(file="/var/www/html/rcomp/tmp/1plpi1291556935.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/www/html/rcomp/tmp/2zcol1291556935.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/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/3ld4r1291556935.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4ov3x1291556935.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5ae131291556935.tab") 
> 
> try(system("convert tmp/1plpi1291556935.ps tmp/1plpi1291556935.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zcol1291556935.ps tmp/2zcol1291556935.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.533   0.284   1.161