# 1.202 Demand Modeling

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 (including logit, nested logit, GEV, probit, and mixture models), estimation under various sample designs and data collection methods (including revealed and stated preferences), sampling, aggregate forecasting methods, and iterative proportional fitting and related methods. Lectures reinforced with case studies, which require specification, estimation, testing, and analysis of models using data sets from actual applications.

This class has 1.201 as a prerequisite.

1.202 will not be offered this semester. It will be available in the Spring semester.

Lecture occurs 2:30 PM to 4:00 PM on Mondays and Wednesdays in 2-105.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+1.202&btnG=Google+Search&inurl=https site or on the 1.202 Stellar site.

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*Introduction to Probability, 2nd Edition* by Dimitri P. Bertsekas

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*Probability and Statistics (4th Edition)* by Morris H. DeGroot

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*Introduction to Mathematical Statistics and Its Applications (5th Edition)* by Richard J. Larsen

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*An Introduction to Mathematical Statistics and Its Applications (6th Edition)* by Richard J. Larsen

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*Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)* by Kevin P. Murphy

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*Access Card for Interactive Labs with Chapter Highlights for: Probability and Statistics by Morris H. DeGroot* by Robert Powell

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*Probability and Statistics* by Morris H. DeGroot

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*Econometric Analysis* by William H. Greene

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*Introduction to Mathematical Statistics and Its Applications, An* by Richard J. Larsen

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*Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)* by Kevin P. Murphy