Discrete Choice Modeling: Theory and Practical Application

Understanding how individuals make choices among competing alternatives is crucial for effective market analysis, policy planning, and resource allocation. This course provides a structured introduction to the mathematical and statistical framework for analyzing these decisions. By the end of this course, you will be able to confidently apply the Random Utility Maximization (RUM) framework and utilize fundamental discrete choice models to analyze complex behavioral data. What you'll learn: * Understand the Random Utility Maximization theory that underpins all choice modeling. * Learn the structure and assumptions of the Multinomial Logit (MNL) model and when to apply it. * Practice specifying choice models, defining alternatives, and preparing data for estimation. * Apply Maximum Likelihood Estimation concepts to calculate model parameters and assess statistical significance. * Interpret model outputs, including marginal effects, elasticities, and Willingness-to-Pay measures. * Configure basic extensions like Nested Logit or Mixed Logit to account for unobserved heterogeneity and correlation. The material begins with core terminology and utility theory before moving into structured model specification and the practical steps of estimation and validation. You will read detailed explanations of how to translate theoretical concepts into actionable predictive models. This course is designed for beginners in data science, economics, marketing, and planning who need to model consumer or traveler behavior. No prior experience with choice modeling or advanced statistics is required. Build a strong analytical foundation for predicting discrete outcomes.