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.

Estimation and Model Fit

Estimation and Model Fit

The last chapter focused on logistic regression coefficients, emphasizing the linear and additive relationships with the logged odds, the multiplicative relationships with the odds, and the nonlinear and nonadditive relationships with the probabilities. Although the discussion focused on the probability of experiencing an event or having a characteristic, data on individuals usually include values of only 0 and 1 for the dependent variable rather than the actual probabilities. Without known probabilities, the estimation procedure must use observed values of 0 and 1 on binary dependent variables to obtain predicted probabilities.

As discussed earlier, the binary dependent variable may make estimation using ordinary least squares problematic. Logistic regression relies instead on maximum likelihood procedures to obtain the coefficient estimates. As a general ...

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