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Statistical Models: Theory and Practice Paperback – Apr 27 2009


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Review

"At last, a second course in statistics that is serious, correct, and interesting. The book teaches regression, causal modeling, maximum likelihood, and the bootstrap. Everyone who analyzes real data should read this book, and we are extremely fortunate to now have the revised edition."
Persi Diaconis, Professor of Mathematics and Statistics, Stanford University

"A pleasure to read, this newly revised edition of Statistical Models shows the field's most elegant writer at the height of his powers. While most textbooks hurry past core assumptions in order to explicate technique, this book places the spotlight on the core assumptions, challenging readers to think critically about how they are invoked in practice."
Donald Green, Professor of Political Science, Yale University

"For three decades, David Freedman has been the conscience of statistics as applied to important scientific, policy, and legal issues. This book is his legacy, and it is our great good fortune to have the new edition. It should be required reading for any user of multivariate models -- statistician or otherwise -- whose ultimate concern is not with statistical technique but rather with the substantive conclusions, if any, licensed by the data and the analysis."
James M. Robins, Professor of Epidemiology and Biostatistics, Harvard School of Public Health

"Statistical models: theory and practice is lucid, helpful, insightful and a joy to read. It focuses on the most common tools of applied statistics with a clear and simple presentation. This revised edition organizes the chapters differently, making reading much easier. Moreover, it includes many new examples and exercises. In summary, it is a nice and extremely useful addition to the statistical literature."
Heleno Balfarine, Mathematical Reviews

Book Description

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.

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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Amazon.com: 13 reviews
55 of 55 people found the following review helpful
Very well-written...very rigorous. Fairly conventional. Nov. 24 2007
By Alexander C. Zorach - Published on Amazon.com
Format: Paperback
This book is a very well-written, but ultimately fairly conventional textbook on linear models in statistics. It offers a very clear elementary introduction to the mathematics of the material, with an emphasis on both applications and rigor. It is to-the-point and does not cover very much material, instead choosing to cover material thoroughly and demonstrate the application of the material in practical situations.

I have heard this book described as "skeptical". It is not unduly skeptical; the author is just being the way every statistician ought to be. Any statistician who is not "skeptical" in this sense is accepting sloppy work.

The writing style in this book is very clear. Freedman is an outstanding writer! The book makes use of a decent amount of linear algebra and other mathematical notation that can be difficult for people to get through, but Freedman provides a very gentle introduction to the notation both through the text and through exercises (broken into small pieces, with a smooth gradient of difficulty). If you take your time and work through the book, you will not find it difficult to read.

Still, this book is not the be-all and end-all of texts on statistical models. It is particularly lacking on philosophical depth when it comes to the mathematical theory. This book describes techniques that are common practice and teaches you how to use them properly and evaluate them critically. It does not probe very deeply into how or why these techniques were developed. It does not encourage the reader to question the techniques themselves or to create new techniques or new theory. In my opinion, this is a shortcoming worth mentioning.

Also, there are a wide variety of topics that this book seems to ignore. By ignore, I not only mean that it does not cover them but that it is written almost as if these subjects do not exist. These subjects include, among others, causal inference, Bayesian statistics, and decision theory. For example, the book accepts squared error loss as a given, and other options, such as mean absolute error loss leading to quantile regression, are not even mentioned. I think the author should at least acknowledge these other perspectives and branches of statistics, briefly discuss how they relate to the material covered in the book, and point the reader to other texts to cover such material.

Is this a good book? I see it on many peoples' shelves. Personally, I found it immensely useful for learning linear regression properly. It is outstanding for self-study and would make a good textbook as well. But it does not stand on its own, even if all one wants to learn is regression. For what it is, this book is simply amazing; know its limitations, however, before buying it.
81 of 84 people found the following review helpful
The Best Statistics Book I've Seen May 2 2007
By N N Taleb - Published on Amazon.com
Format: Paperback
The Best Statistics Book I've Seen

I spent my life focusing on the errors of statistics and how they sometimes fail us in real life, because of the misinterpretation of what the techniques can do for you. This book is outstanding in the following two aspects: 1) It is of immense clarity, embedding everything in real situations, 2) It uses the real-life situation to critique the statistical model and show you the limit of statistic. For instance, he shows a few anecdotes here and there to illustrate how correlation between two variables might not mean anything causal, or how asymptotic properties may not be relevant in real life.

This is the first statistics book I've seen that cares about presenting statistics as a tool to GET TO THE TRUTH.

Please buy it.

Nassim Nicholas Taleb
34 of 35 people found the following review helpful
NOW i get it July 15 2006
By Michele Abernathy - Published on Amazon.com
Format: Hardcover
The formal reviews say this book is very well written. That is an understatement. Freedman uses plain English and interesting examples to explain the concepts behind the statistical jargon. This book is certainly good for those who will go on to advanced statistics and those who can read mathematical notation more easily than words. For those of us who need to apply the results of statistical studies but who do not wish to gain graduate degrees in statistics, Freedman gives us the background to understand studies we have to use, an understanding of whether regression is an appropriate model for specific situations, and the tools to ensure we are making appropriate comparisons. This book IS well written because it leads to understanding concepts rather than mechanical memorization.
17 of 17 people found the following review helpful
a special book March 30 2010
By Michael R. Chernick - Published on Amazon.com
Format: Paperback Verified Purchase
The late Professor David A. Freedman possessed the rare skill of being at the top of his profession in both theoretical and applied statistics. His introductory text with the simple title "Statistics" has been praised as one of the best ever written. On the other side he could write very deep mathematical books as was demonstrated in his trilogy on Markov processes and diffusions. In the real world he contributed to the application of critical thinking about the pros and cons of statistical models and was steadfast in his position against the adjustment of the decennial US Census even though most prominent statisticians stood on the other side. He did consulting which grounded him into real applications particularly in Econometrics. As a Berkeley professor he collaborated with many of the top theoretical statisticians in the world. Many of which were at Berkeley or Stanford.

I concur with the enthusiasm for this book that is shown by the other 4 customer reviews. Persi Diaconis from Stanford was a long-time collaborator with Freedman and the late Erich Lehmann long-time Berkeley colleague. I think the praise for this book shown by them is far more important to hear that some of the nice things I might say.

Diaconis: "At last, a second course in statistics that is serious, correct, and interesting. The book teaches regression, causal mdoeling, maximum likelihood, and the bootstrap. Everyone who analyzes real data should read this book."

Lehmann: "This book is outstanding for clarity of its thought and writng. It prepares readers for a critical assessment of the technical literature in the social and health sciences, and it provides a welcome antidote to the standard formulaic approach to statistics."

Lehmann was a great writer himself and in addition to his research contributions to parametric and nonparametric statistics he presented and extended the Neyman-Pearson theory of hypothesis testing in his first book "Testing Statistical Hypotheses" and its subsequent revisions. With that in mind Lehmann's comments about Freedman's clarity of exposition should be taken very seriously.

In addition to covering applications and hitting the mostimportant topics in applied statistics in the eight chapters Freedman reproduces completely articles that applied statistics in the sociology, economics and political science journals. he devotes a complete chapter (Chapter 7) to bootstrap methods form estimating bias and standard errors. As an author of a book on the bootstrap I know how difficult it is to explain the bootstrap in a technically accurate way without pouring on the asymptotic theory that goes away from intuition. Freedman, who was a major contributor to the asymptotic theory of the bootstrap and its application in regression and simultaneous equation models that are so often used in econometrics, uses this knowledge and his gift of writing to present this in a way that I will want to learn to emulate.
9 of 10 people found the following review helpful
A critical guide to critical thinking Aug. 10 2006
By Cynthia Taeuber - Published on Amazon.com
Format: Paperback
Whether we know it or not, assertions about causality based on regression models from the social and health fields guide decisionmakers to make or break policies that affect all of us. Students learn the mechanics of how to do a regression model without paying much attention to the assumptions behind the model and when it is appropriate to claim causality. The rest of us use the results without questioning whether the assumptions are justified in a particular case.

You don't have to be a hardcore mathematician to understand David Freedman's explanations about the "how" of statistical modelling, and most importantly, the "why," and the "when" of modelling. Dr. Freedman's writing style is direct and he provides many useful examples of when the techniques are appropriate. He provides exercises followed by detailed explanations of the correct answers. This book is certainly of great value to students but I also recommend it to those who use the causality statements from the models to make decisions.


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