SUMMARY
The area under the curve of a probability density function (PDF) represents probability due to the fundamental definition of continuous random variables. Specifically, for a nonnegative function f(x), the probability P(a ≤ X ≤ b) is calculated using the integral of f(x) from a to b, denoted as P(a ≤ X ≤ b) = ∫_a^b f(x) dx. The cumulative distribution function (CDF) is defined as P(X ≤ x) = ∫_{-∞}^{x} f(t) dt, leading to the conclusion that the total area under the curve equals 1, thereby establishing the relationship between area and probability.
PREREQUISITES
- Understanding of probability density functions (PDFs)
- Basic knowledge of calculus, specifically integration
- Familiarity with cumulative distribution functions (CDFs)
- Concept of continuous random variables
NEXT STEPS
- Study the properties of probability density functions (PDFs)
- Learn about cumulative distribution functions (CDFs) in depth
- Explore applications of integrals in probability theory
- Investigate different types of distributions, such as normal and exponential distributions
USEFUL FOR
Students of statistics, mathematicians, data scientists, and anyone seeking to understand the mathematical foundations of probability and its applications in various fields.