Understanding continuous random variables is essential in statistics, as they form the foundation for modelling and analysing real-world data that varies smoothly rather than in steps. Use this page ...

So far, you learned about discrete random variables and how to calculate or visualize their distribution functions. In this lesson, you'll learn about continuous variables and probability density ...

The probability density function (pdf) describes the distribution of a continuous random variable. The probability that a random variable assumes an outcome in a given interval are computed by finding ...

The next statement shows how to compute the probability that continuous random variable X with pdf f (x) lies in the interval [a,b]. The cumulative density function (cdf) for random variable X with ...

The Probability Density Function (PDF) Introduction So far, you learned about discrete random variables and how to calculate or visualize their distribution functions. In this lesson, you'll learn ...

A continuous bivariate joint density function defines the probability distribution for a pair of random variables. For example, the function f (x,y) = 1 when both x and y are in the interval [0,1] and ...

Fundamental notions such as random variables, distribution functions and probability density functions facilitate the analysis of both discrete and continuous outcomes.

A random variable is one whose value is unknown or a function that assigns values to each of an experiment’s outcomes. A random variable can be discrete or continuous.