16: Logit Regression

Quantitative Research Methods for Political Science, Public Policy and Public Administration · LibreTexts

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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 was last modified on Wed, 09 Nov 2022 01:40:33 GMT and 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.