Jason W. Osborne's Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne's applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.

A Brief Overview of Probit Regression

“To probit or to logit … that is the question. ‘Tis better to use a logit or probit link function than to inappropriately use ordinary least squares regression with binary or categorical dependent variables …”

—attributed to Warren Shakespeare, William's younger statistician brother

You may have encountered this creature called “probit” regression, which sounds a bit like the topic of our book—logistic regression. Indeed, if you come across it in the literature, it looks to be dealing with a similar issue, binary dependent variables, in a similar way to logistic regression. Indeed, probit does handle similar issues in a very similar manner—with a link function that appropriately addresses the issues raised by these types of outcome variables.

It ...

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