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Hypergeometric Distributions
Let X be a random variable following a Hypergeometric distribution. All Hypergeometric distributions have three parameters: sample size, population size, and number of successes in the population. For this problem, let X be a sample of size 13 taken from a population of size 34, in which there are 26 successes. An example of where such a distribution may arise is the following:
Statistics, Inc., wants to determine how well people like its candies. To determine this, they randomly select 13 people from its workforce of 34. (In this workforce, only 26 of those 34 actually like the candies.) Let X be the number of employees surveyed who like the candies.
From the description, we can tell that X has a Hypergeometric distribution. We know this because X is the number of successes in a finite population where subjects are not be measured more than once (i.e., “without replacement”).
For those who like pictures, here is a graphic of the probability mass function of X. Note that it has a negative skew (left skew), that its sample space is integers between 5 and 13, inclusive. In other words, S = {5, 6, 7, …, 13}.
Here is the probability function of the Hypergeometric distribution described in the example:
Continuing the candy example, let us determine the expected value of X, the number of people in the survey who respond that they like the candies. In symbols, calculate E[X].
In the box below, please enter the value of E[X], where X ~ H(n=13, N=34, K=26), then click on the “Check your answer!” button. Please round your answer to the ten-thousandths place.
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