A Hypergeometric Distribution Problem

In summary, the problem involves finding the probability that n white balls are drawn before m black balls, given an urn with N white balls and M black balls. The solution involves using a hypergeometric random variable with parameters n, N, and m, and its probability mass function. The probability sought is the sum of the probabilities of several events, including drawing out n balls that are all white, drawing out n + 1 balls with all but one being black, and so on. The book's answer involves calculating the probability that at least n white balls are selected from the first n + m - 1 withdrawn balls, which is represented by a hypergeometric random variable. This is because if at least n white balls are selected from
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Homework Statement


Balls are randomly withdrawn, one at a time without replacement, from an urn that initially has N white and M black balls. Find the probability that n white balls are drawn before m black balls, n <= N, m <= M.


Homework Equations


A hypergeometric random variable with parameters n, N, m represents the number of white balls selected when n balls are randomly chosen from an urn that contains N balls, of which m are white. Its probability mass function is given by

p(i) = C(m, i) * C(N - m, n - i) / C(N, n)


The Attempt at a Solution


How can n white balls be withdrawn before m black balls? It could happen in the following ways:

- I draw out n balls and the all happen to be white,
- I draw out n + 1 balls and all but one is black,
- I draw out n + 2 balls and all but two are black, ...,
- I draw out n + m - 1 balls and all but m - 1 are black

The probability sought is the sum of the probabilities of each of the events described above right? If I draw k balls, n <= k < n + m, then the probability that n are white is C(N, n) * C(M, k - n) / C(N + M, k).

Here's the books answer, which I disagree with:

A total of n white balls will be withdrawn before a total of m black balls if and only if there are at least n white balls in the first n + m - 1 withdrawls. With X equal to the nuber of balls amount the first n + m - 1 withdrawn balls, then X is a hypergeometric random variable and

[tex]P\{X \ge n\} = \sum_{i=n}^{n+m-1} \frac{\binom{N}{i} \binom{M}{n+m-1-i}}{\binom{N+M}{n+m-1}}[/tex]

I don't understand why the answer is P{X >= n} since this is the probability that at least n white balls are selected from n + m - 1 withdrawn balls.
 
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  • #2
If at least n white balls are selected from the first n + m - 1 withdrawls, then it doesn't neccesarily mean all n white balls were withdrawn before m black balls were withdrawn. Could someone explain why the book answer is correct?
 

What is a hypergeometric distribution problem?

A hypergeometric distribution problem is a type of probability problem that involves selecting a sample of items from a population without replacement. Unlike other probability distributions, the hypergeometric distribution takes into account the size of the population and the number of desired items when calculating probabilities.

What are the key components of a hypergeometric distribution problem?

The key components of a hypergeometric distribution problem are the population size, the number of desired items in the population, and the sample size. These components are used to calculate the probability of obtaining a certain number of desired items in the sample.

What is the formula for calculating probabilities in a hypergeometric distribution problem?

The formula for calculating probabilities in a hypergeometric distribution problem is P(x) = (N1C1 * N2C2) / N3C3, where N1 is the number of desired items in the population, N2 is the number of non-desired items in the population, N3 is the sample size, C1 is the number of desired items in the sample, and C2 is the number of non-desired items in the sample.

How is a hypergeometric distribution problem different from a binomial distribution problem?

A hypergeometric distribution problem is different from a binomial distribution problem in that it takes into account the size of the population and the number of desired items, while a binomial distribution problem assumes a fixed probability of success for each trial. Additionally, a hypergeometric distribution problem involves sampling without replacement, while a binomial distribution problem involves sampling with replacement.

What are some real-world applications of hypergeometric distribution problems?

Hypergeometric distribution problems are commonly used in quality control, market research, and epidemiology. For example, a company may use a hypergeometric distribution to test a sample of their product for defects, or a researcher may use it to determine the likelihood of a certain disease occurring in a population. It can also be used in genetics to study the distribution of different traits in a population.

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