R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(206010
+ ,198112
+ ,194519
+ ,185705
+ ,180173
+ ,176142
+ ,203401
+ ,221902
+ ,197378
+ ,185001
+ ,176356
+ ,180449
+ ,180144
+ ,173666
+ ,165688
+ ,161570
+ ,156145
+ ,153730
+ ,182698
+ ,200765
+ ,176512
+ ,166618
+ ,158644
+ ,159585
+ ,163095
+ ,159044
+ ,155511
+ ,153745
+ ,150569
+ ,150605
+ ,179612
+ ,194690
+ ,189917
+ ,184128
+ ,175335
+ ,179566
+ ,181140
+ ,177876
+ ,175041
+ ,169292
+ ,166070
+ ,166972
+ ,206348
+ ,215706
+ ,202108
+ ,195411
+ ,193111
+ ,195198
+ ,198770
+ ,194163
+ ,190420
+ ,189733
+ ,186029
+ ,191531
+ ,232571
+ ,243477
+ ,227247
+ ,217859
+ ,208679
+ ,213188
+ ,216234
+ ,213586
+ ,209465
+ ,204045
+ ,200237
+ ,203666
+ ,241476
+ ,260307
+ ,243324
+ ,244460
+ ,233575
+ ,237217
+ ,235243
+ ,230354
+ ,227184
+ ,221678
+ ,217142
+ ,219452
+ ,256446
+ ,265845
+ ,248624
+ ,241114
+ ,229245
+ ,231805
+ ,219277
+ ,219313
+ ,212610
+ ,214771
+ ,211142
+ ,211457
+ ,240048
+ ,240636
+ ,230580
+ ,208795
+ ,197922
+ ,194596
+ ,194581
+ ,185686
+ ,178106
+ ,172608
+ ,167302
+ ,168053
+ ,202300
+ ,202388
+ ,182516
+ ,173476
+ ,166444
+ ,171297
+ ,169701
+ ,164182
+ ,161914
+ ,159612
+ ,151001
+ ,158114
+ ,186530
+ ,187069
+ ,174330
+ ,169362
+ ,166827
+ ,178037
+ ,186413
+ ,189226
+ ,191563
+ ,188906
+ ,186005
+ ,195309
+ ,223532
+ ,226899
+ ,214126
+ ,206903
+ ,204442
+ ,220375
+ ,214320
+ ,212588
+ ,205816
+ ,202196
+ ,195722
+ ,198563
+ ,229139
+ ,229527
+ ,211868
+ ,203555
+ ,195770)
> 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] 143
> (np <- floor(n / par1))
[1] 11
> 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,] 206010 180144 163095 181140 198770 216234 235243 219277 194581 169701
 [2,] 198112 173666 159044 177876 194163 213586 230354 219313 185686 164182
 [3,] 194519 165688 155511 175041 190420 209465 227184 212610 178106 161914
 [4,] 185705 161570 153745 169292 189733 204045 221678 214771 172608 159612
 [5,] 180173 156145 150569 166070 186029 200237 217142 211142 167302 151001
 [6,] 176142 153730 150605 166972 191531 203666 219452 211457 168053 158114
 [7,] 203401 182698 179612 206348 232571 241476 256446 240048 202300 186530
 [8,] 221902 200765 194690 215706 243477 260307 265845 240636 202388 187069
 [9,] 197378 176512 189917 202108 227247 243324 248624 230580 182516 174330
[10,] 185001 166618 184128 195411 217859 244460 241114 208795 173476 169362
[11,] 176356 158644 175335 193111 208679 233575 229245 197922 166444 166827
[12,] 180449 159585 179566 195198 213188 237217 231805 194596 171297 178037
       [,11]
 [1,] 186413
 [2,] 189226
 [3,] 191563
 [4,] 188906
 [5,] 186005
 [6,] 195309
 [7,] 223532
 [8,] 226899
 [9,] 214126
[10,] 206903
[11,] 204442
[12,] 220375
> 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] 192095.7 169647.1 169651.4 187022.8 207805.6 225632.7 235344.3 216762.2
 [9] 180396.4 168889.9 202808.2
> arr.sd
 [1] 13983.87 13685.35 15989.73 16556.47 19141.27 20014.54 15066.83 14480.89
 [9] 13184.57 11090.92 15307.62
> arr.range
 [1] 45760 47035 44121 49636 57448 60070 48703 46040 35944 36068 40894
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  3774.3249       0.0589  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       0.1274         0.7797  

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

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

Coefficients:
(Intercept)     arr.mean  
  9455.0741       0.1891  

> postscript(file="/var/www/rcomp/tmp/1ea301292768029.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/rcomp/tmp/2ea301292768029.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/rcomp/tmp/30t251292768029.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/4lb0b1292768029.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/rcomp/tmp/56uzh1292768029.tab") 
> 
> try(system("convert tmp/1ea301292768029.ps tmp/1ea301292768029.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ea301292768029.ps tmp/2ea301292768029.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.620   0.370   0.988