In this module, you will examine the core ideas behind random variables and their distributions. You will distinguish outcomes from variables, classify discrete and continuous random variables, and relate relative frequency, repeated trials, and probability distributions. You will also use mean and variance to describe distributions and connect these concepts to stock returns, including log return, probability of loss, normal-distribution quantiles, and the distribution patterns discussed in finance.

In this module, you will examine how sample data is used to draw conclusions about a larger population in a financial context. You will compare populations and samples, distinguish common sampling approaches, and review key ideas behind unbiased estimation, sampling distributions, the central limit theorem, confidence intervals, and hypothesis testing, including the role of p-values in evaluating investment return claims.

In this module, you will examine how linear regression is used in financial analysis. You will distinguish covariance and correlation, interpret relationships between variables through visualization, and identify responses and predictors in simple linear regression from a financial perspective. You will also review best-fit modeling, model significance and assumptions, train-test splitting, and how regression can be applied to stock signal generation and performance evaluation.