PMF for Dice Roll

Probability · Axiom Academy

Working with probability mass functions for discrete random variables Excellent work! You've completed this PMF example. Here's what we learned: PMF Definition: The probability mass function P(X = x) gives the probability that a discrete random variable takes on a specific value. PMF Properties: All probabilities must be non-negative, and the sum of all probabilities must equal 1. Calculating Probabilities: To find P(X ≥ 4), we sum the individual probabilities: P(X = 4) + P(X = 5) + P(X = 6). Fair Die: For a fair six-sided die, each outcome has equal probability of 1/6. Understanding PMFs is fundamental for working with discrete random variables in probability!

This is the written version of the interactive lesson above. See the full Probability course.