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(152512,151944,151368,150176,161968,161344,152512,146640,147208,147208,147840,148976,150744,150744,149608,146640,161968,164304,160776,152512,156048,150744,153136,154280,155472,152512,153136,148976,161968,166072,162544,156048,163112,155472,162544,161968,163736,157240,164304,163736,174336,171944,162544,157808,164304,155472,161968,163112,165504,160208,163112,164880,171376,166072,159008,151368,158440,139000,148408,153704,159008,151368,151368,151368,155472,149608,141912,135472,140144,121904,133080,139576,140768,134272,134840,133080,139000,134840,126640,120712,130736,108968,123104,129544,129544,121904,114840,114272,120712,114840,103672,95976,104240,84808,102472,111872,114840,108344,100136,106008,108344,106576,88904,80704,86568,68904,87144,93640,98936,90104,81840,86568,88904,84232,66568,58872,65936,46504,67704,80704)
> 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,] 152512 150744 155472 163736 165504 159008 140768 129544 114840 98936
 [2,] 151944 150744 152512 157240 160208 151368 134272 121904 108344 90104
 [3,] 151368 149608 153136 164304 163112 151368 134840 114840 100136 81840
 [4,] 150176 146640 148976 163736 164880 151368 133080 114272 106008 86568
 [5,] 161968 161968 161968 174336 171376 155472 139000 120712 108344 88904
 [6,] 161344 164304 166072 171944 166072 149608 134840 114840 106576 84232
 [7,] 152512 160776 162544 162544 159008 141912 126640 103672  88904 66568
 [8,] 146640 152512 156048 157808 151368 135472 120712  95976  80704 58872
 [9,] 147208 156048 163112 164304 158440 140144 130736 104240  86568 65936
[10,] 147208 150744 155472 155472 139000 121904 108968  84808  68904 46504
[11,] 147840 153136 162544 161968 148408 133080 123104 102472  87144 67704
[12,] 148976 154280 161968 163112 153704 139576 129544 111872  93640 80704
> 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] 151641.33 154292.00 158318.67 163375.33 158423.33 144190.00 129708.67
 [8] 109929.33  95842.67  76406.00
> arr.sd
 [1]  5138.870  5452.686  5348.391  5472.115  9011.263 10743.845  8835.491
 [8] 12314.689 13751.002 15218.567
> arr.range
 [1] 15328 17664 17096 18864 32376 37104 31800 44736 45936 52432
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 24315.1031      -0.1132  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       25.365         -1.386  

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

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

Coefficients:
(Intercept)     arr.mean  
 84786.7220      -0.3983  

> postscript(file="/var/wessaorg/rcomp/tmp/1l4po1438333783.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/2etun1438333783.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/3lqje1438333783.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4n8271438333783.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5rw281438333783.tab") 
> 
> try(system("convert tmp/1l4po1438333783.ps tmp/1l4po1438333783.png",intern=TRUE))
character(0)
> try(system("convert tmp/2etun1438333783.ps tmp/2etun1438333783.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.919   0.165   1.074