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Hypergeometric Question on Training Enrollment

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I can do the Hyper formula f(x) = r N-r
x n-x divided by N
n
but I cant figure out all my needed numbers from the problem.
Problem states 20% enrolled in training dropped out without completing their training and the likelihood of dropping out is the same for all participants.
If new group had 20 whats the expected value and standard deviation for number of drop outs? Usually E(x) = np, but I'm not sure if 0 is my p and 20 is my n.
Whats the probability that no one will drop out?
According to the book, x= probability of x successes in n trials, n= number of trials, N = number of elements in the population, r= number of elements in the population labeled success.
I think x = 0, n = 20, but I don't know the N for my population size or the number for the r.
Can you help?

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Solution Summary

The solution determines the expected value and standard deviation for number of drop outs.

Solution Preview

This is NOT about a hypergeometric distribution, BUT a binomial distribution. Let X be the number of drop outs. Then we know that X follows a ...

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  • BSc , Wuhan Univ. China
  • MA, Shandong Univ.
Recent Feedback
  • "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
  • "excellent work"
  • "Thank you so much for all of your help!!! I will be posting another assignment. Please let me know (once posted), if the credits I'm offering is enough or you ! Thanks again!"
  • "Thank you"
  • "Thank you very much for your valuable time and assistance!"
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