Regression modeling strategies

This book presents information on regressions modeling strategies that address many issues arising when developing multivariable models using (real data) examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with “too many variables to analyze and not enough observations”, and powerful model validation techniques based on the bootstrap. The text deals with model uncertainty and its effect on inference. It also presents many graphical methods for communicating complex regression models to nonstatisticians.