R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(284.4,212.8,226.9,308.4,262,227.9,236.1,320.4,271.9,232.8,237,313.4,261.4,226.8,249.9,314.3,286.1,226.5,260.4,311.4,294.7,232.6,257.2,339.2,279.1,249.8,269.8,345.7,293.8,254.7,277.5,363.4,313.4,272.8,300.1,369.5,330.8,287.8,305.9,386.1,335.2,288,308.3,402.3,352.8,316.1,324.9,404.8,393,318.9,327,442.3,383.1,331.6,361.4,445.9,386.6,357.2,373.6,466.2,409.6,369.8,378.6,487,419.2,376.7,392.8,506.1,458.4,387.4,426.9,565,464.8,444.5,449.5,556.1,499.6,451.9,434.9,553.8,510,432.9,453.2,547.6,485.8,452.6,456.6,565.7,514.8,464.3,430.9,588.3,503.1,442.6,448,554.5,504.5,427.3,473.1,526.2,547.5,440.2,468.7,574.5,492.6,432.6,479.8,575.7,474.6,405.3,434.6,535.1,452.6,429.5,417.2,551.8,464,416.6,422.9,553.6,458.6,427.6,429.2,534.2,481.7,416,440.2,538.7,473.8,439.9,446.8,597.5,467.2,439.4,447.4,568.5,485.9,442.1,430.5,600,464.5,423.6,437,574,443,410,420,532,432,420,411,512)
> 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] 152
> (np <- floor(n / par1))
[1] 38
> 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] [,12]
[1,] 284.4 262.0 271.9 261.4 286.1 294.7 279.1 293.8 313.4 330.8 335.2 352.8
[2,] 212.8 227.9 232.8 226.8 226.5 232.6 249.8 254.7 272.8 287.8 288.0 316.1
[3,] 226.9 236.1 237.0 249.9 260.4 257.2 269.8 277.5 300.1 305.9 308.3 324.9
[4,] 308.4 320.4 313.4 314.3 311.4 339.2 345.7 363.4 369.5 386.1 402.3 404.8
     [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,] 393.0 383.1 386.6 409.6 419.2 458.4 464.8 499.6 510.0 485.8 514.8 503.1
[2,] 318.9 331.6 357.2 369.8 376.7 387.4 444.5 451.9 432.9 452.6 464.3 442.6
[3,] 327.0 361.4 373.6 378.6 392.8 426.9 449.5 434.9 453.2 456.6 430.9 448.0
[4,] 442.3 445.9 466.2 487.0 506.1 565.0 556.1 553.8 547.6 565.7 588.3 554.5
     [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
[1,] 504.5 547.5 492.6 474.6 452.6 464.0 458.6 481.7 473.8 467.2 485.9 464.5
[2,] 427.3 440.2 432.6 405.3 429.5 416.6 427.6 416.0 439.9 439.4 442.1 423.6
[3,] 473.1 468.7 479.8 434.6 417.2 422.9 429.2 440.2 446.8 447.4 430.5 437.0
[4,] 526.2 574.5 575.7 535.1 551.8 553.6 534.2 538.7 597.5 568.5 600.0 574.0
     [,37] [,38]
[1,]   443   432
[2,]   410   420
[3,]   420   411
[4,]   532   512
> 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] 258.125 261.600 263.775 263.100 271.100 280.925 286.100 297.350 313.950
[10] 327.650 333.450 349.650 370.300 380.500 395.900 411.250 423.700 459.425
[19] 478.725 485.050 485.925 490.175 499.575 487.050 482.775 507.725 495.175
[28] 462.400 462.775 464.275 462.400 469.150 489.500 480.625 489.625 474.775
[37] 451.250 443.750
> arr.sd
 [1] 45.63371 41.80726 37.43887 37.04169 36.29848 46.48981 41.57138 46.86239
 [9] 40.70729 42.76841 49.80499 39.95668 58.35563 48.44220 48.38581 53.30700
[17] 57.65940 76.14155 52.30120 53.39229 52.49123 52.47961 68.47021 52.62385
[25] 42.92935 63.57024 59.56209 56.17645 61.13724 63.15016 49.94317 53.72067
[33] 73.47095 59.73733 77.35396 68.30570 55.57802 46.30605
> arr.range
 [1]  95.6  92.5  80.6  87.5  84.9 106.6  95.9 108.7  96.7  98.3 114.3  88.7
[13] 123.4 114.3 109.0 117.2 129.4 177.6 111.6 118.9 114.7 113.1 157.4 111.9
[25]  98.9 134.3 143.1 129.8 134.6 137.0 106.6 122.7 157.6 129.1 169.5 150.4
[37] 122.0 101.0
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
   16.62034      0.08896  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       0.1060         0.6420  

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

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

Coefficients:
(Intercept)     arr.mean  
    42.2118       0.1858  

> postscript(file="/var/www/html/rcomp/tmp/1iqco1233074381.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/2mh711233074381.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/3r1wy1233074381.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4jn4s1233074381.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5kaex1233074381.tab") 
> 
> system("convert tmp/1iqco1233074381.ps tmp/1iqco1233074381.png")
> system("convert tmp/2mh711233074381.ps tmp/2mh711233074381.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.394   0.453   1.489