R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(98.6,98.8,99.9,100.3,100.2,100.2,100.6,100.4,100.7,100.9,99.7,99.7,96.8,99.2,99.9,99.3,98.9,98.9,98.7,98.4,98.6,98.5,98.1,98.3,98.1,97.9,99.1,98.5,98.2,97.8,98,98,97.6,97.6,97.6,97.5,96.1,96.1,96.3,96.3,96.3,96,96,95.2,96,96.1,95.3,95.1,94.8,94.5,94.7,94.8,94.5,94.5,92.8,92.8,94.5,94.4,94.2,94.1,92.9,93.3,93.6,93.6,94,94,94.2,93.3,93,93,94.7,95.6,95.8,96,95.4,95.3,94.4,94.4,94.3,93.9,94.5,93.6,93.9,93.9,93.7,94.6,94.4,94,91.1,91.1,90.7,90.8,89.8,90.7,90.3,89.7,89,88.4,89.3,89.3,89.3,89.3,88.4,89.4,91.3,90.9,91,89.3,88.1,89,90.1,90.6,90.6,90.2,89.5,90.5,90.4,89.7,90,90.2,89.3,89.6,89.8,89.4,89.3,89.4,89.5,89.2,90,88,88.3,89.1)
> 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] 132
> (np <- floor(n / par1))
[1] 11
> 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]
 [1,]  98.6 96.8 98.1 96.1 94.8 92.9 95.8 93.7 89.0  88.1  89.3
 [2,]  98.8 99.2 97.9 96.1 94.5 93.3 96.0 94.6 88.4  89.0  89.6
 [3,]  99.9 99.9 99.1 96.3 94.7 93.6 95.4 94.4 89.3  90.1  89.8
 [4,] 100.3 99.3 98.5 96.3 94.8 93.6 95.3 94.0 89.3  90.6  89.4
 [5,] 100.2 98.9 98.2 96.3 94.5 94.0 94.4 91.1 89.3  90.6  89.3
 [6,] 100.2 98.9 97.8 96.0 94.5 94.0 94.4 91.1 89.3  90.2  89.4
 [7,] 100.6 98.7 98.0 96.0 92.8 94.2 94.3 90.7 88.4  89.5  89.5
 [8,] 100.4 98.4 98.0 95.2 92.8 93.3 93.9 90.8 89.4  90.5  89.2
 [9,] 100.7 98.6 97.6 96.0 94.5 93.0 94.5 89.8 91.3  90.4  90.0
[10,] 100.9 98.5 97.6 96.1 94.4 93.0 93.6 90.7 90.9  89.7  88.0
[11,]  99.7 98.1 97.6 95.3 94.2 94.7 93.9 90.3 91.0  90.0  88.3
[12,]  99.7 98.3 97.5 95.1 94.1 95.6 93.9 89.7 89.3  90.2  89.1
> 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] 100.00000  98.63333  97.99167  95.90000  94.21667  93.76667  94.61667
 [8]  91.74167  89.57500  89.90833  89.24167
> arr.sd
 [1] 0.7122308 0.7595852 0.4561864 0.4390071 0.6939129 0.7946793 0.8077278
 [8] 1.8603315 0.9658957 0.7427937 0.5712161
> arr.range
 [1] 2.3 3.1 1.6 1.2 2.0 2.7 2.4 4.9 2.9 2.5 2.0
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    3.86169     -0.03252  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       14.952         -3.356  

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

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

Coefficients:
(Intercept)     arr.mean  
     8.8538      -0.0674  

> postscript(file="/var/www/rcomp/tmp/167fb1323294208.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/rcomp/tmp/2lo0q1323294208.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/rcomp/tmp/3mvu81323294208.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/4cv0l1323294208.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/rcomp/tmp/5296y1323294208.tab") 
> 
> try(system("convert tmp/167fb1323294208.ps tmp/167fb1323294208.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lo0q1323294208.ps tmp/2lo0q1323294208.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.510   0.070   0.573