This book is primarily intended for advanced undergraduates or beginning graduate students in statistics. It should also be of interest to many students and professionals in the social and health sciences. Although written as a textbook, it can be read on its own. The focus is on applications of linear models, including generalized least squares, two-stage least squares, probits and logits. The bootstrap is explained as a technique for estimating bias and computing standard errors.

The contents of the book can fairly be described as what you have to know in order to start reading empirical papers that use statistical models. The emphasis throughout is on the connection—or lack of connection—between the models and the real phenomena. Much of the discussion is organized around published studies; the key papers are reprinted for ease of reference. Some observers may find the tone of the discussion too skeptical. If you are among them, I would make an unusual request: suspend belief until you finish reading the book. (Suspension of disbelief is all too easily obtained, but that is a topic for another day.)

The first chapter contrasts observational studies with experiments, and introduces regression as a technique that may help to adjust for confounding in observational studies. There is a chapter that explains the regression line, and another chapter with a quick review of matrix algebra. (At Berkeley, half the statistics majors need these chapters.)