R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(192528,190773,188996,185320,221698,219771,192528,174414,176163,176163,178112,181616,192528,188996,194449,203412,254400,254400,243516,232604,241567,252473,254400,259853,276217,265306,265306,281670,327033,330709,321580,299762,316121,316121,317876,327033,334241,337917,337917,348823,390681,401565,403314,376071,390681,385228,374317,397889,403314,394185,396112,408773,456085,479624,479624,468740,485082,468740,459588,494239,499664,486832,519533,532366,570521,595842,592339,590389,604999,603222,581432,614128,625040,614128,659491,681309,732102,752143,746712,735800,744935,755841,719441,748461,766753,759373,806657,822993,892101,904761,888424,897554,903007,908460,873809,906511,924624,906511,959275,975618,1046468,1057380,1060884,1079170,1079170,1086378,1053676,1070041,1080925,1060884,1119079,1129986,1202597,1215430,1233543,1249908,1251657,1253584,1220883,1253584)
> 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] 120
> (np <- floor(n / par1))
[1] 30
> 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,] 192528 221698 176163 192528 254400 241567 276217 327033 316121 334241
[2,] 190773 219771 176163 188996 254400 252473 265306 330709 316121 337917
[3,] 188996 192528 178112 194449 243516 254400 265306 321580 317876 337917
[4,] 185320 174414 181616 203412 232604 259853 281670 299762 327033 348823
      [,11]  [,12]  [,13]  [,14]  [,15]  [,16]  [,17]  [,18]  [,19]  [,20]
[1,] 390681 390681 403314 456085 485082 499664 570521 604999 625040 732102
[2,] 401565 385228 394185 479624 468740 486832 595842 603222 614128 752143
[3,] 403314 374317 396112 479624 459588 519533 592339 581432 659491 746712
[4,] 376071 397889 408773 468740 494239 532366 590389 614128 681309 735800
      [,21]  [,22]  [,23]  [,24]  [,25]   [,26]   [,27]   [,28]   [,29]   [,30]
[1,] 744935 766753 892101 903007 924624 1046468 1079170 1080925 1202597 1251657
[2,] 755841 759373 904761 908460 906511 1057380 1086378 1060884 1215430 1253584
[3,] 719441 806657 888424 873809 959275 1060884 1053676 1119079 1233543 1220883
[4,] 748461 822993 897554 906511 975618 1079170 1070041 1129986 1249908 1253584
> 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]  189404.2  202102.8  178013.5  194846.2  246230.0  252073.2  272124.8
 [8]  319771.0  319287.8  339724.5  392907.8  387028.8  400596.0  471018.2
[15]  476912.2  509598.8  587272.8  600945.2  644992.0  741689.2  742169.5
[22]  788944.0  895710.0  897946.8  941507.0 1060975.5 1072316.2 1097718.5
[29] 1225369.5 1244927.0
> arr.sd
 [1]  3081.074 22763.154  2571.407  6140.836 10432.824  7669.841  8182.278
 [8] 13856.515  5229.358  6308.343 12538.907  9935.043  6719.574 11199.855
[15] 15640.507 20281.857 11393.435 13858.431 30982.868  9329.300 15819.035
[22] 30767.782  7104.731 16249.225 31564.843 13594.648 14110.781 32332.979
[29] 20706.879 16055.053
> arr.range
 [1]  7208 47284  5453 14416 21796 18286 16364 30947 10912 14582 27243 23572
[13] 14588 23539 34651 45534 25321 32696 67181 20041 36400 63620 16337 34651
[25] 69107 32702 32702 69102 47311 32701
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  6313.3979       0.0134  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       0.6601         0.6649  

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

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

Coefficients:
(Intercept)     arr.mean  
  1.377e+04    2.956e-02  

> postscript(file="/var/wessaorg/rcomp/tmp/1zs0e1312383200.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/wessaorg/rcomp/tmp/2mxfl1312383200.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/39xtj1312383200.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/44l541312383200.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/wessaorg/rcomp/tmp/5g0cm1312383200.tab") 
> 
> try(system("convert tmp/1zs0e1312383200.ps tmp/1zs0e1312383200.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mxfl1312383200.ps tmp/2mxfl1312383200.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.642   0.125   0.758