This volume helps readers understand the intuitive logic behind logistic regression through nontechnical language and simple examples. The Second Edition presents results from several statistical packages to help interpret the meaning of logistic regression coefficients, presents more detail on variations in logistic regression for multicategory outcomes, and describes some potential problems in interpreting logistic regression coefficients. A companion website includes the three data sets and Stata, SPSS, and R commands needed to reproduce all the tables and figures in the book. Finally, the Appendix reviews the meaning of logarithms, and helps readers understand the use of logarithms in logistic regression as well as in other types of models.

Interpreting Logistic Regression Coefficients

Interpreting Logistic Regression Coefficients

As is true for nonlinear transformations more generally, the effects of the independent variables in logistic regression have multiple interpretations. Effects exist for probabilities, odds, and logged odds, and the interpretation of each effect varies.

To preview, the effects of the independent variables on the logged odds are linear and additive—each X variable has the same effect on the logged odds regardless of its level or the level of other X variables—but the units of the dependent variable, logged odds, have little intuitive meaning. The effects of the independent variables on the probabilities have intuitive meaning but are nonlinear and nonadditive—each X variable has a different effect on the probability depending on its level and the level of the other independent ...

  • 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