R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> par5 = '0.50'
> par4 = '0.13'
> par3 = '0.95'
> par2 = '0.05'
> par1 = '105'
> par1 <- as.numeric(par1)
> par2 <- as.numeric(par2)
> par3 <- as.numeric(par3)
> par4 <- as.numeric(par4)
> par5 <- as.numeric(par5)
> (z <- abs(qnorm((1-par3)/2)) + abs(qnorm(1-par5)))
[1] 1.959964
> (z1 <- abs(qnorm(1-par3)) + abs(qnorm(1-par5)))
[1] 1.644854
> z2 <- z*z
> z2one <- z1*z1
> z24 <- z2 * par4
> z24one <- z2one * par4
> npop <- array(NA, 200)
> ppop <- array(NA, 200)
> for (i in 1:200)
+ {
+ ppop[i] <- i * 100
+ npop[i] <- ppop[i] * z24 / (z24 + (ppop[i] - 1) * par2*par2)
+ }
> postscript(file="/var/www/html/freestat/rcomp/tmp/1fklb1288086824.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(ppop,npop, xlab='population size', ylab='sample size (2 sided test)', main = paste('Confidence',par3))
> dumtext <- paste('Margin of error = ',par2)
> dumtext <- paste(dumtext,' Population Var. = ')
> dumtext <- paste(dumtext, par4)
> mtext(dumtext)
> grid()
> dev.off()
null device 
          1 
> par2sq <- par2 * par2
> num <- par1 * z24
> denom <- z24 + (par1 - 1) * par2sq
> (n <- num/denom)
[1] 69.05008
> num1 <- par1 * z24one
> denom1 <- z24one + (par1 - 1) * par2sq
> (n1 <- num1/denom1)
[1] 60.37179
> 
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Size',header=TRUE)
> a<-table.element(a,par1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Margin of Error',header=TRUE)
> a<-table.element(a,par2)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Confidence',header=TRUE)
> a<-table.element(a,par3)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Power',header=TRUE)
> a<-table.element(a,par5)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Variance',header=TRUE)
> a<-table.element(a,par4)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'z(alpha/2) + z(beta)',header=TRUE)
> a<-table.element(a,z)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'z(alpha) + z(beta)',header=TRUE)
> a<-table.element(a,z1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
> a<-table.element(a,n)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
> a<-table.element(a,n1)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/2wczq1288086824.tab") 
> (ni <- z24 / (par2sq))
[1] 199.7559
> (ni1 <- z24one / (par2sq))
[1] 140.6883
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (for Infinite Populations)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Size',header=TRUE)
> a<-table.element(a,'infinite')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Margin of Error',header=TRUE)
> a<-table.element(a,par2)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Confidence',header=TRUE)
> a<-table.element(a,par3)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Power',header=TRUE)
> a<-table.element(a,par5)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Variance',header=TRUE)
> a<-table.element(a,par4)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'z(alpha/2) + z(beta)',header=TRUE)
> a<-table.element(a,z)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'z(alpha) + z(beta)',header=TRUE)
> a<-table.element(a,z1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
> a<-table.element(a,ni)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
> a<-table.element(a,ni1)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/3smxz1288086824.tab") 
> (z <- abs(qt((1-par3)/2,n-1)) + abs(qt(1-par5,n-1)))
[1] 1.995442
> (z1 <- abs(qt(1-par3,n1-1)) + abs(qt(1-par5,n1-1)))
[1] 1.670926
> z2 <- z*z
> z2one <- z1*z1
> z24 <- z2 * par4
> z24one <- z2one * par4
> par2sq <- par2 * par2
> num <- par1 * z24
> denom <- z24 + (par1 - 1) * par2sq
> (n <- num/denom)
[1] 69.89345
> num1 <- par1 * z24one
> denom1 <- z24one + (par1 - 1) * par2sq
> (n1 <- num1/denom1)
[1] 61.17691
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (Unknown Population Variance)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Size',header=TRUE)
> a<-table.element(a,par1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Margin of Error',header=TRUE)
> a<-table.element(a,par2)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Confidence',header=TRUE)
> a<-table.element(a,par3)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Power',header=TRUE)
> a<-table.element(a,par5)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Variance',header=TRUE)
> a<-table.element(a,'unknown')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t(alpha/2) + t(beta)',header=TRUE)
> a<-table.element(a,z)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t(alpha) + t(beta)',header=TRUE)
> a<-table.element(a,z1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
> a<-table.element(a,n)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
> a<-table.element(a,n1)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/4sete1288086824.tab") 
> (z <- abs(qt((1-par3)/2,ni-1)) + abs(qt(1-par5,ni-1)))
[1] 1.971971
> (z1 <- abs(qt(1-par3,ni1-1)) + abs(qt(1-par5,ni1-1)))
[1] 1.655835
> z2 <- z*z
> z2one <- z1*z1
> z24 <- z2 * par4
> z24one <- z2one * par4
> (ni <- z24 / (par2sq))
[1] 202.2109
> (ni1 <- z24one / (par2sq))
[1] 142.5731
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size<br />(Infinite Population, Unknown Population Variance)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Size',header=TRUE)
> a<-table.element(a,'infinite')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Margin of Error',header=TRUE)
> a<-table.element(a,par2)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Confidence',header=TRUE)
> a<-table.element(a,par3)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Power',header=TRUE)
> a<-table.element(a,par5)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Population Variance',header=TRUE)
> a<-table.element(a,'unknown')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t(alpha/2) + t(beta)',header=TRUE)
> a<-table.element(a,z)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t(alpha) + t(beta)',header=TRUE)
> a<-table.element(a,z1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
> a<-table.element(a,ni)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
> a<-table.element(a,ni1)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/566r41288086824.tab") 
> 
> try(system("convert tmp/1fklb1288086824.ps tmp/1fklb1288086824.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.691   0.288   0.722