Covers the general addition rule for computing union probabilities and the concept of statistical independence, then the multiplication rule for joint probabilities of dependent and independent events. Students build and read probability trees to solve multi-stage sequential problems, culminating in a lab that models a two-stage quality inspection pipeline.

Covers the complement rule for efficiently solving 'at least one' problems, then introduces conditional probability as a restriction of the sample space and the formula that connects it to joint and marginal probabilities. Students build contingency tables and extract marginal, joint, and conditional probabilities, closing with a lab that investigates a counterintuitive fraud-detection scenario.

Covers the Fundamental Counting Principle for multi-stage sequential choices, then permutations for ordered arrangements and combinations for unordered selections. Students learn to select the correct counting formula for a given business problem and combine multiple formulas in multi-stage counting problems, closing with a lab spanning scheduling, security, and portfolio-construction challenges.

Introduces the Law of Total Probability for computing unconditional probabilities from conditional rates, then derives Bayes' Theorem for updating beliefs given new evidence. Students apply Bayes' Theorem to medical testing (sensitivity, specificity, PPV, NPV) and business contexts (fraud detection, churn, spam filtering), including sequential updating, closing with a lab on sequential quality testing across suppliers.