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Event Series Event Series: Discrete Choice Modeling (Online)

Discrete Choice Modeling (Online)

July 15 @ 9:30 am - 12:00 pm

REGISTER HERE Registration will close at 12:01am 7/12/2024. No late registrations will be accepted.

This 2-part (7/15/24 & 7/17/24) 5-hour course will be offered via Zoom, over two mornings. Attendance is required as the course will not be recorded.


This course introduces participants to discrete choice models. These econometric models are used to explain how people choose between discrete outcomes, such as mode of travel to work or type of treatment for pain. The course will cover the subset of discrete choice models known as random utility models, namely the multinomial logit and nested logit. These models are often used in disciplines such as economics, transportation, and public health. No prior knowledge of discrete choice modeling is expected. Hands-on exercises will be conducted in Python.

Why Take This Course?

Random utility models are used across many disciplines. They allow one to use regression techniques to model choices between multiple outcomes, something not possible with many other models. Unlike many other models of discrete outcomes, random utility models are interpretable—it is easy to see which predictor variables are associated with which choices. Random utility models are also consistent with rational economic theory, meaning that properly specified estimates can be interpreted as willingness-to-pay and transformed into dollar amounts to understand the welfare impacts of policy. This course will prepare participants both to estimate these models and to interpret and evaluate them when encountered in practice.

What Will Participants Learn?

This course will combine lecture and hands-on coding experiences. Participants can expect to learn:

  • The theory underlying random utility modeling
  • How to interpret estimates from random utility models
  • Common pitfalls in random utility modeling
  • How to structure and estimate random utility models

Prerequisites and Requirements

Participants should be familiar with linear regression. Some understanding of binary logistic regression, as well as experience using Python, will be helpful but is not required.

Instructor: Matthew Wigginton Bhagat-Conway

Matthew Wigginton Bhagat-Conway is an Assistant Professor in the Department of City and Regional Planning and a consultant in the Odum Institute for Research in Social Science. His research interests are in travel behavior, urban transportation, and statistical methods for transportation data analysis. He is available to assist researchers with statistics and data analysis.

Dr. Bhagat-Conway has a PhD and MA in Geography from Arizona State University, and a BA in Geography from the University of California, Santa Barbara. Prior to graduate school, he was a software developer and project manager for Conveyal, a public transport planning consulting firm, and a fellow in the Data Science for Social Good fellowship at the University of Chicago.

Registration fees:

  • UNC Chapel Hill Students: $0, with a $35 deposit to hold your spot (deposit is refundable upon your attendance for at least 66% of the course)
  • UNC Chapel Hill Faculty/Staff/Postdoc/Resident/Visiting Scholars: $80
  • University (Non UNC Chapel Hill) Student/Employee (must have active university email): $105
  • Government/Non-Profit/Corporate: $130

Additional course information:

  • Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
  • Zoom link will be sent prior to the course. Registration must be made at least 1 day prior to the course date to receive the Zoom link.


For questions regarding this class, please contact Jill Stevens at


July 15
9:30 am - 12:00 pm
Event Categories:


NC United States