Logistic Regression From A - Z! This book has it all.
The author lays out clear, concise methodologies to build robust predictive models using SAS. The nice thing is this book lays out the process step by step with SAS code examples. You do not have to be a statistics major to understand how to use the built in SAS functionality.
The modeling methods are unbelievably detailed including topics like defining the objective function, testing variables for predictability using chi squared, fitting continuous variables using the most linear variable transformation format (squared, cubed, cubed root, log, exponent, tangent, sine, cosine, etc... 19 total formats), changing categorical variables to continuous indicator variables for logistic regression use, using stepwise, backward, and score regression methods to further eliminate less predictive variables, defining deciles, and model testing methods like bootstrapping, jackknifing and gains tables to validate the model.
I do not fully understand the mathematical concepts involved throughout the entire process nor do I want to. The book provides a consistent repeatable programming methodology to follow that is broken down into very quantifiable steps.
I would recommend this book for anyone with limited statistical knowledge and a need to understand predictive modeling programming methodologies. Knowledge of the SAS programming language is essential to make full use of this material. The book uses real life examples from the banking, insurance, and marketing industries and contains additional valuable information related to these fields.