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(161949,161634,161287,160652,167176,166856,161949,158687,159007,159007,159323,159990,159990,157043,155745,157043,161634,160967,154763,149510,148527,146563,147892,149510,148874,147545,144950,147545,149856,149190,141656,138394,135132,132505,132190,134150,131523,130541,129559,135132,135768,132505,123670,119745,113541,110910,112208,114172,114172,112559,112208,117465,121710,119745,113190,109932,103061,98817,102079,105341,105341,101097,100781,106319,109932,108630,102079,97835,88652,85075,86372,91946,92261,84092,87039,94226,97488,95523,86692,80484,73297,67724,70004,74910,73613,66426,68706,75893,79821,77541,68706,64782,58893,52684,53666,58573,59208,53319,54302,62502,64462,61173,49075,42871,34671,26502,29129,32706,32075,25835,29444,38280,42204,40244,32391,26186,19631,12093,13427,15707)
> 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,] 161949 167176 159007 159990 161634 148527 148874 149856 135132 131523
[2,] 161634 166856 159007 157043 160967 146563 147545 149190 132505 130541
[3,] 161287 161949 159323 155745 154763 147892 144950 141656 132190 129559
[4,] 160652 158687 159990 157043 149510 149510 147545 138394 134150 135132
      [,11]  [,12]  [,13]  [,14]  [,15]  [,16]  [,17] [,18] [,19] [,20] [,21]
[1,] 135768 113541 114172 121710 103061 105341 109932 88652 92261 97488 73297
[2,] 132505 110910 112559 119745  98817 101097 108630 85075 84092 95523 67724
[3,] 123670 112208 112208 113190 102079 100781 102079 86372 87039 86692 70004
[4,] 119745 114172 117465 109932 105341 106319  97835 91946 94226 80484 74910
     [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30]
[1,] 73613 79821 58893 59208 64462 34671 32075 42204 19631
[2,] 66426 77541 52684 53319 61173 26502 25835 40244 12093
[3,] 68706 68706 53666 54302 49075 29129 29444 32391 13427
[4,] 75893 64782 58573 62502 42871 32706 38280 26186 15707
> 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] 161380.50 163667.00 159331.75 157455.25 156718.50 148123.00 147228.50
 [8] 144774.00 133494.25 131688.75 127922.00 112707.75 114101.00 116144.25
[15] 102324.50 103384.50 104619.00  88011.25  89404.50  90046.75  71483.75
[22]  71159.50  72712.50  55954.00  57332.75  54395.25  30752.00  31408.50
[29]  35256.25  15214.50
> arr.sd
 [1]   555.8504  4092.0543   463.4274  1797.2027  5715.4278  1234.7666
 [7]  1643.1248  5649.6048  1389.4743  2431.5022  7472.3142  1451.4348
[13]  2400.1910  5515.5120  2708.2917  2854.8235  5680.0923  3011.1749
[19]  4662.7853  7917.5344  3232.7720  4353.2122  7136.5271  3236.4964
[25]  4302.4544 10138.8814  3645.6964  5246.7418  7385.3001  3300.8558
> arr.range
 [1]  1297  8489   983  4245 12124  2947  3924 11462  2942  5573 16023  3262
[13]  5257 11778  6524  5538 12097  6871 10134 17004  7186  9467 15039  6209
[25]  9183 21591  8169 12445 16018  7538
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 6277.69035     -0.02221  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
      13.8832        -0.5092  

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

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

Coefficients:
(Intercept)     arr.mean  
  1.375e+04   -4.958e-02  

> postscript(file="/var/wessaorg/rcomp/tmp/190721312201344.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/2280t1312201344.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/382it1312201344.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4k03z1312201344.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5p7nw1312201344.tab") 
> 
> try(system("convert tmp/190721312201344.ps tmp/190721312201344.png",intern=TRUE))
character(0)
> try(system("convert tmp/2280t1312201344.ps tmp/2280t1312201344.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.650   0.127   0.768