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 Practical Guide to Testing Assumptions and Cleaning Data for Logistic Regression

Logistic regression is a nonparametric technique. Does that mean that data cleaning is less important (or not important at all)?

There is good news and bad news. The good news is that parametric assumptions like normality and homoscedasticity are not relevant in logistic regression. The bad news is that basics like data cleaning (e.g., outliers), missing data, linearity, independence of observations, perfect measurement, and sparseness of data still matter. In the course of this chapter, we will cover how these basic data cleaning chores can be accomplished and how your analyses can benefit if you do so.

You have now learned that logistic regression contains some conceptual and procedural similarities to ordinary least ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles