Binomial Distribution
This page delves into the binomial distribution, a crucial concept in probability theory and statistics.
Definition: The binomial distribution models the number of successes in a fixed number of independent trials, each with the same probability of success.
The binomial distribution is characterized by two parameters:
- n: the number of trials
- p: the probability of success on each trial
Vocabulary: An experiment with only two possible outcomes successorfailure is called a Bernoulli trial.
The probability mass function for a binomial distribution X ~ Bn,p is:
PX=k = nchoosek × p^k × 1−p^n−k
Where:
- k is the number of successes
- nchoosek is the binomial coefficient
Example: In a series of 10 coin flips n=10 with a fair coin p=0.5, the probability of getting exactly 3 heads can be calculated using the binomial distribution formula.
Key properties of the binomial distribution include:
- Expected value: EX = np
- Variance: VX = np1−p
- Standard deviation: σX = √np(1−p)
Highlight: The binomial distribution is widely used in various fields, including quality control, epidemiology, and finance, making it a crucial topic in Loi binomiale - Terminale courses.
Understanding the binomial distribution is essential for solving problems involving repeated independent trials with fixed probabilities, which are common in many Loi binomiale exercices corrigés PDF resources.