Computing the Mean of a Geometric Distribution

In summary, the conversation discusses problem H-10, which involves computing the mean of the geometric distribution. It is also possible to compute the variance by finding E(X^2) and then using the formula Var(X) = E(X^2) - (E(X))^2. The conversation then goes through the steps of solving for E(X), showing that it can be rewritten as a geometric series, evaluating the series, and simplifying to get the final answer involving p and q.
  • #1
shawn26
3
0

Homework Statement


Problem H-10. We will compute the mean of the geometric distribution. (Note: It's also possible to
compute E(X^2) and then Var(X) = E(X^2)−(E(X))^2 by steps similar to these.)

(a) Show that
E(X) = (k=1 to infinity summation symbol) (k *q^k−1* p)
where q = 1−p.

(b) Show that the above summation can be rewritten as follows:
E(X) = p* d/dq (k=1 to infinity summation symbol) q^k

(c) The sum in part (b) is a geometric series. Evaluate the geometric series; replace the sum in (b) by this value; and do the derivative d/dq. The final answer will be a quotient of polynomials involving p
and q; there will not be an in nite sum remaining.

(d) Plug in q = 1−p and simplify to get the final answer.
 
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  • #2
hi shawn,

Welcome to the forums.

You need to show an attempt at a solution before we can help you.
 
  • #3
kinda how no clue how to go about it let me think about it a little more and get back
 

Related to Computing the Mean of a Geometric Distribution

What is the formula for computing the mean of a geometric distribution?

The mean of a geometric distribution is calculated by dividing 1 by the probability of success (p). This can be represented as μ = 1/p.

What does the mean of a geometric distribution represent?

The mean of a geometric distribution represents the average number of trials needed to achieve a single success. It is also known as the expected value of the distribution.

How does the mean of a geometric distribution change with different values of probability (p)?

The mean of a geometric distribution increases as the probability of success (p) decreases. This means that the higher the chance of success, the fewer trials are needed on average to achieve a single success.

Can the mean of a geometric distribution be a decimal or a negative number?

Yes, the mean of a geometric distribution can be a decimal or a negative number. This is because the formula for calculating the mean involves dividing 1 by the probability of success, which can result in a decimal or a negative number depending on the value of p.

How is the mean of a geometric distribution used in real-life scenarios?

The mean of a geometric distribution is commonly used in fields such as statistics, probability, and data analysis. It can be used to make predictions about the number of trials needed to achieve a certain number of successes, such as in quality control processes or in predicting the number of attempts needed to win a game of chance.

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