R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(95320.00
+ ,94965.00
+ ,94605.00
+ ,93860.00
+ ,101230.00
+ ,100840.00
+ ,95320.00
+ ,91650.00
+ ,92005.00
+ ,92005.00
+ ,92400.00
+ ,93110.00
+ ,94215.00
+ ,94215.00
+ ,93505.00
+ ,91650.00
+ ,101230.00
+ ,102690.00
+ ,100485.00
+ ,95320.00
+ ,97530.00
+ ,94215.00
+ ,95710.00
+ ,96425.00
+ ,97170.00
+ ,95320.00
+ ,95710.00
+ ,93110.00
+ ,101230.00
+ ,103795.00
+ ,101590.00
+ ,97530.00
+ ,101945.00
+ ,97170.00
+ ,101590.00
+ ,101230.00
+ ,102335.00
+ ,98275.00
+ ,102690.00
+ ,102335.00
+ ,108960.00
+ ,107465.00
+ ,101590.00
+ ,98630.00
+ ,102690.00
+ ,97170.00
+ ,101230.00
+ ,101945.00
+ ,103440.00
+ ,100130.00
+ ,101945.00
+ ,103050.00
+ ,107110.00
+ ,103795.00
+ ,99380.00
+ ,94605.00
+ ,99025.00
+ ,86875.00
+ ,92755.00
+ ,96065.00
+ ,99380.00
+ ,94605.00
+ ,94605.00
+ ,94605.00
+ ,97170.00
+ ,93505.00
+ ,88695.00
+ ,84670.00
+ ,87590.00
+ ,76190.00
+ ,83175.00
+ ,87235.00
+ ,87980.00
+ ,83920.00
+ ,84275.00
+ ,83175.00
+ ,86875.00
+ ,84275.00
+ ,79150.00
+ ,75445.00
+ ,81710.00
+ ,68105.00
+ ,76940.00
+ ,80965.00
+ ,80965.00
+ ,76190.00
+ ,71775.00
+ ,71420.00
+ ,75445.00
+ ,71775.00
+ ,64795.00
+ ,59985.00
+ ,65150.00
+ ,53005.00
+ ,64045.00
+ ,69920.00
+ ,71775.00
+ ,67715.00
+ ,62585.00
+ ,66255.00
+ ,67715.00
+ ,66610.00
+ ,55565.00
+ ,50440.00
+ ,54105.00
+ ,43065.00
+ ,54465.00
+ ,58525.00
+ ,61835.00
+ ,56315.00
+ ,51150.00
+ ,54105.00
+ ,55565.00
+ ,52645.00
+ ,41605.00
+ ,36795.00
+ ,41210.00
+ ,29065.00
+ ,42315.00
+ ,50440.00)
> par1 = '12'
> par1 <- '12'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P. (2012), Standard Deviation-Mean Plot (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_smp.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 120
> (np <- floor(n / par1))
[1] 10
> 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,]  95320  94215  97170 102335 103440 99380 87980 80965 71775 61835
 [2,]  94965  94215  95320  98275 100130 94605 83920 76190 67715 56315
 [3,]  94605  93505  95710 102690 101945 94605 84275 71775 62585 51150
 [4,]  93860  91650  93110 102335 103050 94605 83175 71420 66255 54105
 [5,] 101230 101230 101230 108960 107110 97170 86875 75445 67715 55565
 [6,] 100840 102690 103795 107465 103795 93505 84275 71775 66610 52645
 [7,]  95320 100485 101590 101590  99380 88695 79150 64795 55565 41605
 [8,]  91650  95320  97530  98630  94605 84670 75445 59985 50440 36795
 [9,]  92005  97530 101945 102690  99025 87590 81710 65150 54105 41210
[10,]  92005  94215  97170  97170  86875 76190 68105 53005 43065 29065
[11,]  92400  95710 101590 101230  92755 83175 76940 64045 54465 42315
[12,]  93110  96425 101230 101945  96065 87235 80965 69920 58525 50440
> 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]  94775.83  96432.50  98949.17 102109.58  99014.58  90118.75  81067.92
 [8]  68705.83  59901.67  47753.75
> arr.sd
 [1] 3211.793 3407.929 3342.744 3420.072 5632.039 6714.903 5522.182 7696.680
 [9] 8594.376 9511.604
> arr.range
 [1]  9580 11040 10685 11790 20235 23190 19875 27960 28710 32770
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 15196.9395      -0.1132  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       24.244         -1.386  

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

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

Coefficients:
(Intercept)     arr.mean  
 52991.7013      -0.3983  

> postscript(file="/var/wessaorg/rcomp/tmp/1bax41439751515.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/2ydpe1439751515.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/34hvl1439751515.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4lvgq1439751515.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/57sig1439751515.tab") 
> 
> try(system("convert tmp/1bax41439751515.ps tmp/1bax41439751515.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ydpe1439751515.ps tmp/2ydpe1439751515.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.936   0.132   1.070