# 16: Logit Regression

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• 16.1: Generalized Linear Models
Generalized Linear Models (GLMs) provide a modeling structure that can relate a linear model to response variables that do not have normal distributions. The distribution of YY is assumed to belong to one of an exponential family of distributions, including the Gaussian, Binomial, and Poisson distributions. GLMs are fit to the data by the method of maximum likelihood.
• 16.2: Logit Estimation
Logit is used when predicting limited dependent variables. By virtue of the binary dependent variable, these models do not meet the key assumptions of OLS. Logit uses maximum likelihood estimation (MLE), which is a counterpart to minimizing least squares. MLE identifies the probability of obtaining the sample as a function of the model parameters. It answers the question, what are the values for BB’s that make the sample most likely?
• 16.3: Summary

This page titled 16: Logit Regression is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Jenkins-Smith et al. (University of Oklahoma Libraries) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.