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Interpreting Interactions in Logistic Regression

Cornell Statistical Consulting UnitInterpreting Interactions in Logistic Regression Statnews #84 Cornell Statistical Consulting Unit Created October 2012. Last updated September 2020 Introduction Logistic Regression is useful when modeling a binary ( two category) response variable. This newsletter focuses on how to interpret an interaction term between a continuous predictor and a categorical predictor in a Logistic Regression model. We suggest two techniques to aid in interpretation of such Interactions : 1) numerical summaries of a series of odds ratios and 2) plotting predicted probabilities.

categorical predictor in a logistic regression model. We suggest two techniques to aid in interpretation of such interactions: 1) numerical summaries of a series of odds ratios and 2) plotting predicted probabilities. For an introduction to logistic regression or interpreting coefficients of interaction terms in

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