Statistics is all around us. Just by going to work in the morning we can observe numerous examples of the use of statistics and immediately think of methods/models to analyze the observed data. Freedman'sgreat qualityas an author is the ability to provide you with this statistical vision, if you don't posses it already.
This book is truly an eye opener. It provides essential rigorous insight into statistical modeling. It differs from others in many aspects. Most statistics books, especially the more technical ones, are filled with theorems, proofs and examples you will never encounter in practice, either because of they are too simple or because they are extremely complex and are there to serve only as counterexamples. By contrast, this book provides real examples taken from real studies. The theorems and the corresponding proofs are presented in an elegant and intelligent manner. The author answers the questions the reader/researcher should ask. Among modeling books, this one is a gem.
The topics covered are the usual ones, such as the MLE, logit/probit modeling, path modeling and bootstrap, among the others. All of these are more or less familiar to every statistician and statistics student. Statistical Models is intended as a second course, so it builds onthe standard introductory material, but adding something special: of the theory and complexity of the subject. The author is very careful in presenting the theoretical ideas. He strives to explain almost all the bits and pieces. Rather than just presentinga theorem and its proof he gives the reasoning behind it. This is what should be highly appreciated. The more you read on, the morethis bookslooks like a step-by-step guide to statistical modeling. The writing is so clear and attractive that it doesn't allow you to get confused or lost so that you would stop reading.
I think that the most important part of the book (and generally where the most understanding will potentially come from) are the exercises. These are truly teaching exercises. If you take this book on as means for a second course in statistics by just simply reading the book without doing the exercises you will not get far. It is hard to describe the form of the exercises. Some concentrate on the basic understanding of the subject with questions (very good for class discussions) like "is MLE biased on unbiased?", some are in the form of a study, some analyze the output of the model, some are proofs, etc. There are exercises for everyone's taste, so to speak. And, even better, there are solutions at the end of the book!
Who is this book for? It should definitely find its place on the graduate student's bookshelf as well as on the bookshelf of a serious statistical researcher. Having completed a serious first course in statistics and some linear models the book could be easily used for self-study.
It is definitely not enough to know just how to plug one model into the software and get its output. We also need the "insider information," and this is exactly what this book offers. In any case, it will definitely raise you to the next level.
Ita Cirovic Donev is a PhD candidate at the University of Zagreb. She hold a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical mehods of credit and market risk. Apart from the academic work she does consulting work for financial institutions.