III : Probability
Modules I and II described data you already have in hand. This module turns to data you don’t have yet: probability, the mathematics of uncertainty, and the foundation for everything that follows in this book. It starts with sample spaces, events, and the classical, empirical, and subjective definitions of probability, then builds through the addition and multiplication rules to conditional probability and Bayes’ Theorem, the tool for updating a belief once new evidence arrives, and finally to full probability distributions, Binomial and Poisson for discrete outcomes, and the Normal distribution for continuous data. These ideas underpin quality control, fraud and spam detection, medical testing, credit risk, and the sampling and hypothesis-testing techniques covered later in this book.