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Understanding Maximum Likelihood Estimates in Logistic Regression and Probit Models

This lecture focuses on the concepts of logistic regression and probit regression, emphasizing the importance of maximum likelihood estimates (MLE) in model fitting. We will review additive probability models, discuss their limitations due to the bounded nature of probabilities, and explore the properties of log-odds transformation. The session will include key equations and examples to clarify how these models can be applied effectively in statistical analysis, particularly in multiple regression settings.

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Understanding Maximum Likelihood Estimates in Logistic Regression and Probit Models

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    1. G89.2229 Lect 11W Logistic Regression Review Maximum Likelihood Estimates Probit Regression and Example Model Fit G89.2229 Multiple Regression Week 11 (Wednesday)

    2. G89.2229 Lect 11W Logistic Regression Review Additive probability models are often not ideal. p is bounded by [0,1]. Log odds of p [w(p)] has nice properties. Unbounded, with w(p)=0 for p=.5. Important equations

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