Applied Discrete Choice Models: Analyzing and Predicting Human Decisions

Understanding how individuals make choices is the key to designing better products, pricing strategies, and public policies. Discrete choice modeling provides the mathematical framework to predict these disaggregate decisions with precision. In this written course, you will progress from foundational probability concepts to advanced choice modeling techniques. You will learn how to structure choice data, estimate complex models, and interpret results to predict real-world human behavior. What you'll learn: - Understand the core mathematical foundations of random utility theory and discrete decision-making. - Formulate and estimate multinomial logit, nested logit, and advanced choice models. - Prepare and structure disaggregate datasets for choice analysis using modern data standards. - Interpret model parameters, elasticities, and marginal effects to derive actionable business insights. - Explore the integration of machine learning techniques with traditional econometric choice models. - Practice evaluating model fit and validating predictive performance through guided written exercises. The course begins with a clear introduction to choice theory and key terminology before guiding you step-by-step through advanced model specifications and practical estimation techniques. You will work through detailed written walkthroughs and scenario-based analyses. This course is designed for data analysts, economists, and researchers who have a basic understanding of regression and want to specialize in behavioral prediction. No advanced mathematical background is required to start. Read on to master the science of predicting human decisions and elevate your analytical toolkit today.