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In linear regression the dependent variable must be continuous. We look at logit and probit models, which we can use when the dependent variable is binary. We learn to interpret logit using odds ratios as well as predicted probabilities, discrete changes, and marginal effects, and we consider the inverted link function. We also examine model fit.

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Logistic Regression

  • 章节

  • 发表日期：

    2026年

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  • Benjamin E. Schlegel

摘要

In linear regression the dependent variable must be continuous. We look at logit and probit models, which we can use when the dependent variable is binary. We learn to interpret logit using odds ratios as well as predicted probabilities, discrete changes, and marginal effects, and we consider the inverted link function. We also examine model fit.