• Theory and application of modeling and statistical methods for analysis and forecasting of demand for facilities, services, and products. Topics include: review of probability and statistics, estimation and testing of linear regression models, theory of individual choice behavior, derivation, estimation, and testing of discrete choice models, estimation under various sample designs and data collection methods (including revealed and stated preferences), sampling, aggregate and disaggregate forecasting methods, iterative proportional fitting, and related methods. Introductions to advanced topics are covered including Bayesian estimation and combining discrete choice analysis and machine learning. Lectures reinforced with case studies, which require specification, estimation, testing, and analysis of models using data sets from actual applications. Lab hours are for workbook case studies.

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