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(1.4272,1.4614,1.4914,1.4816,1.4562,1.4268,1.4088,1.4016,1.365,1.319,1.305,1.2785,1.3239,1.3449,1.2732,1.3322,1.4369,1.4975,1.577,1.5553,1.5557,1.575,1.5527,1.4748,1.4718,1.457,1.4684,1.4227,1.3896,1.3622,1.3716,1.3419,1.3511,1.3516,1.3242,1.3074,1.2999,1.3213,1.2881,1.2611,1.2727,1.2811,1.2684,1.265,1.277,1.2271,1.202,1.1938,1.2103,1.1856,1.1786,1.2015,1.2256,1.2292,1.2037,1.2165,1.2694,1.2938,1.3201,1.3014,1.3119,1.3408,1.2991,1.249,1.2218,1.2176,1.2266,1.2138,1.2007,1.1985,1.2262,1.2646)
> 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] 72
> (np <- floor(n / par1))
[1] 18
> 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,] 1.4272 1.4562 1.3650 1.3239 1.4369 1.5557 1.4718 1.3896 1.3511 1.2999
[2,] 1.4614 1.4268 1.3190 1.3449 1.4975 1.5750 1.4570 1.3622 1.3516 1.3213
[3,] 1.4914 1.4088 1.3050 1.2732 1.5770 1.5527 1.4684 1.3716 1.3242 1.2881
[4,] 1.4816 1.4016 1.2785 1.3322 1.5553 1.4748 1.4227 1.3419 1.3074 1.2611
      [,11]  [,12]  [,13]  [,14]  [,15]  [,16]  [,17]  [,18]
[1,] 1.2727 1.2770 1.2103 1.2256 1.2694 1.3119 1.2218 1.2007
[2,] 1.2811 1.2271 1.1856 1.2292 1.2938 1.3408 1.2176 1.1985
[3,] 1.2684 1.2020 1.1786 1.2037 1.3201 1.2991 1.2266 1.2262
[4,] 1.2650 1.1938 1.2015 1.2165 1.3014 1.2490 1.2138 1.2646
> 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] 1.465400 1.423350 1.316875 1.318550 1.516675 1.539550 1.454975 1.366325
 [9] 1.333575 1.292600 1.271800 1.224975 1.194000 1.218750 1.296175 1.300200
[17] 1.219950 1.222500
> arr.sd
 [1] 0.028364767 0.024329612 0.036213200 0.031442487 0.062882768 0.044283217
 [7] 0.022428312 0.019859066 0.021641376 0.025096348 0.006954615 0.037464861
[13] 0.014487466 0.011367937 0.020994027 0.038331449 0.005507268 0.030753536
> arr.range
 [1] 0.0642 0.0546 0.0865 0.0717 0.1401 0.1002 0.0491 0.0477 0.0442 0.0602
[11] 0.0161 0.0832 0.0317 0.0255 0.0507 0.0918 0.0128 0.0661
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
   -0.07307      0.07497  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       -4.979          4.243  

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

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

Coefficients:
(Intercept)     arr.mean  
    -0.1625       0.1677  

> postscript(file="/var/www/html/rcomp/tmp/1hzzs1274912121.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/2a8gv1274912121.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/3w9xj1274912121.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/49tr61274912121.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5536f1274912121.tab") 
> 
> try(system("convert tmp/1hzzs1274912121.ps tmp/1hzzs1274912121.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a8gv1274912121.ps tmp/2a8gv1274912121.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.506   0.296   0.778