Modeling Discrete Choice: Categorical Dependent Variables, Logistic Regression, and Maximum Likelihood Estimation Harvard Case Solution & Analysis

It draws on the analogy between building and modeling distinct choice a regression model with a dummy variable that is dependent and on an example that exemplifies the requirement for estimating the chance of a pick instead of the choice itself, which leads to a unique type of regression - logistic regression. The note presents the notions of utility along with a random utility choice model, of which the logistic regression model is the most commonly used. It shows how choice probabilities may be constructed from utilities resulting in the logit model. It then presents the maximum-likelihood estimation (MLE) procedure of fitting the logit model to the choice data. Working through a comprehensive example using Solver and accompanying spreadsheet model, the note gives students deep understanding for how MLE works and the way that it's different and similar to the standard least-squared approximation in linear regression.

Modeling Discrete Choice Categorical Dependent Variables, Logistic Regression, and Maximum Likelihood Estimation Case Study Solution

The note concludes by presenting the results of estimation using StatTools, a commercial statistical software. The note avoids the usage of heavy mathematical machinery but however demands rudimentary knowledge of exponent and logarithmic functions, probability, and optimization with Solver, as well as familiarity with the "standard" linear regression. Applications include construction of models for conjoint analysis, estimating cost elasticity, cost optimization, product versioning, product line design, and consumer selection.

PUBLICATION DATE: November 07, 2011 PRODUCT #: UV6335-PDF-ENG

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