Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Population size
(?)
Margin of Error
(?)
Confidence
Population Variance
Power
(?)
Chart options
R Code
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))) (z1 <- abs(qnorm(1-par3)) + abs(qnorm(1-par5))) 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) } bitmap(file='pic1.png') 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() par2sq <- par2 * par2 num <- par1 * z24 denom <- z24 + (par1 - 1) * par2sq (n <- num/denom) num1 <- par1 * z24one denom1 <- z24one + (par1 - 1) * par2sq (n1 <- num1/denom1) load(file='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='mytable.tab') (ni <- z24 / (par2sq)) (ni1 <- z24one / (par2sq)) 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='mytable.tab') (z <- abs(qt((1-par3)/2,n-1)) + abs(qt(1-par5,n-1))) (z1 <- abs(qt(1-par3,n1-1)) + abs(qt(1-par5,n1-1))) 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) num1 <- par1 * z24one denom1 <- z24one + (par1 - 1) * par2sq (n1 <- num1/denom1) 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='mytable.tab') (z <- abs(qt((1-par3)/2,ni-1)) + abs(qt(1-par5,ni-1))) (z1 <- abs(qt(1-par3,ni1-1)) + abs(qt(1-par5,ni1-1))) z2 <- z*z z2one <- z1*z1 z24 <- z2 * par4 z24one <- z2one * par4 (ni <- z24 / (par2sq)) (ni1 <- z24one / (par2sq)) 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='mytable.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation