Statistical Models: Theory and Practice
   by David A. Freedman     2009 Revised Edition  

This lively and engaging textbook explains the things you have to know
in order to read empirical papers in the social and health sciences, as well as
the techniques you need to build statistical models of your own. The author,
David A. Freedman, explains the basic ideas of association and regression,
and takes you through the current models that link these ideas to causality.

The focus is on applications of linear models, including generalized
least squares and two-stage least squares, with probits and logits for binary
variables. The bootstrap is developed as a technique for estimating bias and
computing standard errors. Careful attention is paid to the principles of statistical
inference. There is background material on study design, bivariate regression,
and matrix algebra. To develop technique, there are computer labs
with sample computer programs. The book is rich in exercises, most with
answers.

Target audiences include advanced undergraduates and beginning graduate
students in statistics, as well as students and professionals in the social
and health sciences. The discussion in the book is organized around published
studies, as are many of the exercises. Relevant journal articles are reprinted
at the back of the book. Freedman makes a thorough appraisal of the statistical
methods in these papers and in a variety of other examples. He illustrates
the principles of modeling, and the pitfalls. The discussion shows you how
to think about the critical issues—including the connection (or lack of it)
between the statistical models and the real phenomena.