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(40801,49081,52431,59650,75428,78705,68870,70641,80074,76464,69976,92917,92559,73981,71107,96942,86270,69610,57768,80077,71454,70382,69881,84530,79322,80181,82137,88439,91575,82909,73282,94089,108112,95653,85273,105093,102275,99308,79687,93263,114918,103374,65124,104045,101183,95492,85035,90692,107486,98179,82551,106804,110898,89950,65184,95357,98280,92146,77874,100039,104777,102341,71316,88838,85457,70784,70522,88629,88452,98886,79601,108135,113835,101617,68698,79182,86003,84165,68550,90385,100368,99081,81288,103491,111695,82504,62237,78249,92341,84412,75102,90461,106451,98379,72615,98367,116949,95832,68060,83923,87653,78054,57566,78784,88916,84662,63442,77773,88102,87972,61790,95276,104418,95420,82141,104064,96287,78426,59111,76837,76615,65860,57703,68656,77955,65856,60947,69885,80550,73694,67538,76326,84727)
> 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] 133
> (np <- floor(n / par1))
[1] 33
> 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,] 40801 75428 80074 92559 86270 71454 79322 91575 108112 102275 114918
[2,] 49081 78705 76464 73981 69610 70382 80181 82909  95653  99308 103374
[3,] 52431 68870 69976 71107 57768 69881 82137 73282  85273  79687  65124
[4,] 59650 70641 92917 96942 80077 84530 88439 94089 105093  93263 104045
      [,12]  [,13]  [,14]  [,15]  [,16] [,17]  [,18]  [,19] [,20]  [,21]  [,22]
[1,] 101183 107486 110898  98280 104777 85457  88452 113835 86003 100368 111695
[2,]  95492  98179  89950  92146 102341 70784  98886 101617 84165  99081  82504
[3,]  85035  82551  65184  77874  71316 70522  79601  68698 68550  81288  62237
[4,]  90692 106804  95357 100039  88838 88629 108135  79182 90385 103491  78249
     [,23]  [,24]  [,25] [,26] [,27] [,28]  [,29] [,30] [,31] [,32] [,33]
[1,] 92341 106451 116949 87653 88916 88102 104418 96287 76615 77955 80550
[2,] 84412  98379  95832 78054 84662 87972  95420 78426 65860 65856 73694
[3,] 75102  72615  68060 57566 63442 61790  82141 59111 57703 60947 67538
[4,] 90461  98367  83923 78784 77773 95276 104064 76837 68656 69885 76326
> 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] 50490.75 73411.00 79857.75 83647.25 73431.25 74061.75 82519.75 85463.75
 [9] 98532.75 93633.25 96865.25 93100.50 98755.00 90347.25 92084.75 91818.00
[17] 78848.00 93768.50 90833.00 82275.75 96057.00 83671.25 85579.00 93953.00
[25] 91191.00 75514.25 78698.25 83285.00 96510.75 77665.25 67208.50 68660.75
[33] 74527.00
> arr.sd
 [1]  7821.660  4486.584  9656.730 12998.255 12502.518  7009.609  4118.227
 [8]  9428.045 10310.397 10025.262 21812.239  6877.481 11603.421 18980.498
[15] 10059.827 15361.007  9551.566 12403.875 20583.899  9515.212 10018.600
[22] 20619.739  7760.756 14726.202 20598.385 12736.176 11159.122 14730.813
[29] 10444.387 15190.946  7804.923  7193.757  5448.331
> arr.range
 [1] 18849  9835 22941 25835 28502 14649  9117 20807 22839 22588 49794 16148
[13] 24935 45714 22165 33461 18107 28534 45137 21835 22203 49458 17239 33836
[25] 48889 30087 25474 33486 22277 37176 18912 17008 13012
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 -3200.9919       0.1762  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       -4.611          1.226  

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

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

Coefficients:
(Intercept)     arr.mean  
 -4875.3441       0.3716  

> postscript(file="/var/www/html/rcomp/tmp/1s3si1273754524.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/2s3si1273754524.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/3wlr61273754524.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4zm7t1273754524.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5k4nh1273754524.tab") 
> 
> try(system("convert tmp/1s3si1273754524.ps tmp/1s3si1273754524.png",intern=TRUE))
character(0)
> try(system("convert tmp/2s3si1273754524.ps tmp/2s3si1273754524.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.541   0.299   3.205