This text addresses the complex strategies involved in developing multivariable statistical models using real-world data. It moves beyond isolated techniques to emphasize problem-solving approaches for issues encountered in practical data analysis, including imputation for missing data, handling nonlinear relationships, formalizing transformations, and managing situations with numerous variables and limited observations. The book also covers powerful model validation techniques, particularly those based on the bootstrap, to realistically assess model uncertainty and its impact on inference, aiming for robust data mining.

The book is recognized for its emphasis on practical strategies for multivariable modeling, moving beyond isolated statistical tools to address real-world data analysis challenges. It is particularly noted for its comprehensive coverage of issues such as missing data imputation, nonlinear relationships, and model validation using the bootstrap. The text aims to provide a realistic approach to model uncertainty and its impact on inference, guiding users toward safer data mining practices.