From the course: Predictive Analytics with Categorical Data: Advanced Regression Methods for Real-World Applications
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Probit regression
From the course: Predictive Analytics with Categorical Data: Advanced Regression Methods for Real-World Applications
Probit regression
- [Instructor] Probit regression, like logistic regression, models binary outcomes by predicting the probability that an observation belongs to one of two categories based on explanatory variables. Although very similar to logistic regression, a key difference is that probit coefficients cannot be easily transformed into odds ratios, leaving users with raw coefficients that lack direct interpretation, or having to compute more complicated marginal effects. And that's why many people prefer logistic regression, because it offers an additional option for presenting results. The key difference between logistic and probit regression lies in the link function that is used. Probit regression uses the cumulative distribution function of the standard normal distribution as the link function, whereas logistic regression uses the logistic distribution. Here's how both models are commonly laid out mathematically. F represents the cumulative density function of the logistic distribution, and phi…
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