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(98.4,96.5,97.4,99.2,100.8,101.8,102.7,100,100.8,101.7,99,101.7,100.2,101.2,99.5,100.8,100.7,99.5,99.4,101.1,97.2,98.1,97.8,95.5,96.3,93.6,96.7,95.1,97.7,96.5,98.1,97.3,97,93.7,95.6,94.6,95.1,94.5,93.6,92.1,95.9,98.1,98.2,96.2,94.1,95,93.4,95.4,93.5,94.5,94.3,95.7,98.4,99.4,99.2,99,99.4,99.3,98.6,98.7,96,98.7,100.1,100,101.5,101.5,103.8,104.1,101,104.9,104.4,105.6,103.4,101.7,103.5,101.2,105.4,105.4,108.6,110.6,110.2,106.2,108.6,107.5,106.9,108.4,109.9,108.6,106.5,105.7,105.6,104.2,105.1,102.7,108.3,104.2,105.4,104.6,106.4,111,111.7,113.8,115.9,117.3,113.6,113.6,114.6,113.2,112.8,109.6,111.1,109.7,113,111,113.3,111.8,107.2,106.4,110,108.2)
> 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,]  98.4 100.2 96.3 95.1 93.5  96.0 103.4 106.9 105.4 112.8
 [2,]  96.5 101.2 93.6 94.5 94.5  98.7 101.7 108.4 104.6 109.6
 [3,]  97.4  99.5 96.7 93.6 94.3 100.1 103.5 109.9 106.4 111.1
 [4,]  99.2 100.8 95.1 92.1 95.7 100.0 101.2 108.6 111.0 109.7
 [5,] 100.8 100.7 97.7 95.9 98.4 101.5 105.4 106.5 111.7 113.0
 [6,] 101.8  99.5 96.5 98.1 99.4 101.5 105.4 105.7 113.8 111.0
 [7,] 102.7  99.4 98.1 98.2 99.2 103.8 108.6 105.6 115.9 113.3
 [8,] 100.0 101.1 97.3 96.2 99.0 104.1 110.6 104.2 117.3 111.8
 [9,] 100.8  97.2 97.0 94.1 99.4 101.0 110.2 105.1 113.6 107.2
[10,] 101.7  98.1 93.7 95.0 99.3 104.9 106.2 102.7 113.6 106.4
[11,]  99.0  97.8 95.6 93.4 98.6 104.4 108.6 108.3 114.6 110.0
[12,] 101.7  95.5 94.6 95.4 98.7 105.6 107.5 104.2 113.2 108.2
> 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  99.25000  96.01667  95.13333  97.50000 101.80000 106.02500
 [8] 106.34167 111.75833 110.34167
> arr.sd
 [1] 1.925900 1.770208 1.501413 1.810240 2.287118 2.865151 3.160301 2.155525
 [9] 4.155272 2.257697
> arr.range
 [1]  6.2  5.7  4.5  6.1  5.9  9.6  9.4  7.2 12.7  6.9
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
    -7.5959       0.0975  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
      -17.332          3.924  

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

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

Coefficients:
(Intercept)     arr.mean  
   -23.4464       0.3014  

> postscript(file="/var/www/html/rcomp/tmp/135b71293625140.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/2webs1293625140.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/37nsv1293625140.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4lf841293625140.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5og7a1293625140.tab") 
> 
> try(system("convert tmp/135b71293625140.ps tmp/135b71293625140.png",intern=TRUE))
character(0)
> try(system("convert tmp/2webs1293625140.ps tmp/2webs1293625140.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.521   0.309  20.288