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Activity Number: 506 - Categorical Data
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #306915 Presentation
Title: An Overview of the Assessment of Logistic Regression Models
Author(s): Justin Shang* and Tim Robinson and Shaun Wulff
Companies: University of Wyoming; Covance Inc. and University of Wyoming and University of Wyoming
Keywords: goodness-of-fit; model selection; logistic regression; chi-square; residuals; binary response

The logistic regression model is frequently used in many practical applications to fit a binary response. Model specification depends upon a number of issues including response selection, link specification, and the choice of predictors. Model evaluation includes model selection, predictive ability, and goodness-of-fit. As a result, the art of logistic regression modeling involves many choices and multiple criteria for the data modeler to consider. Particular emphasis will be given to a thorough review of the model selection procedures and the goodness-of-fit testing. In logistic regression, goodness-of-fit assessments sometimes can be challenging, depending on the covariates in the model and the number of covariate patterns. Goodness-of-fit tests can involve chi-square based tests, raw residuals, and transformed residuals. We detail these approaches for assessing the quality of logistic regression models.

Authors who are presenting talks have a * after their name.

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