Unfortunately, there are many different ways to calculate an R 2 for logistic regression, and no consensus on which one is best. Along the way, I’m going to retract one of my long-standing recommendations regarding these measures. In this post, I’m going to focus on R 2 measures of predictive power. In a later post, I’ll discuss the second approach to model fit, and I’ll explain why I don’t like the Hosmer-Lemeshow goodness-of-fit test. The other is to test whether the model needs to be more complex, specifically, whether it needs additional nonlinearities and interactions to satisfactorily represent the data. One is to get a measure of how well you can predict the dependent variable based on the independent variables. One of the most frequent questions I get about logistic regression is “How can I tell if my model fits the data?” There are two general approaches to answering this question.
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