Last modified: July 15, 2024
This article is written in: πΊπΈ
Geometric Distribution (Discrete)
A discrete random variable X follows a geometric distribution if it represents the number of trials needed to get the first success in a sequence of Bernoulli trials. The geometric distribution is denoted as , where p is the probability of success on each trial.
Probability Mass Function (PMF)
The PMF of a geometric distribution is given by:
where , representing the number of trials until the first success.
Cumulative Distribution Function (CDF)
The CDF of a geometric distribution is given by:
Expected Value and Variance
The expected value (mean) of a geometric distribution is:
The variance of a geometric distribution is:
Moment Generating Function (MGF)
The moment generating function (MGF) of a geometric distribution is:
Example: Flipping a Coin
Consider flipping a fair coin (probability of heads p = 0.5
) until the first head appears.
I. What is the probability that the first head appears on the 3rd flip?
For the first head on the 3rd flip:
II. What is the expected number of flips to get the first head?
Expected number of flips:
III. What is the variance in the number of flips?
Variance in the number of flips:
Applications
Geometric distributions are useful in scenarios where you are waiting for the first success in a series of independent trials, such as in quality control testing, network packet delivery analysis, and reliability testing in engineering.