Statistical Models: Theory and Practice [Paperback]

David Freedman (Author)

Book Description

ISBN-10: 0521671051 | ISBN-13: 978-0521671057 | Publication Date: August 8, 2005

Explaining the things you need to know in order to read empirical papers in the social and health sciences, as well as techniques needed to build personal statistical models, this user-friendly volume includes background material on study design, bivariate regression, and matrix algebra. To develop technique, Freedman also includes computer labs, with sample computer programs, and illustrates the principles and pitfalls of modeling. The book is rich in exercises with answers. Target audiences include undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.

Editorial Reviews

Review

"This is an insightful and authoritative textbook. It is also a clarion call for quantitative researchers to clean up their act. Whether you are a newcomer to statistics or a long-time practitioner, working your way through Freedman's extensive exercises and examples will deepen your understanding of how statistical models can reflect -- and distort -- reality."
Larry M. Bartels, Princeton University

"Master of a conversational style that is precise and clear, David Freedman is the statistics professor we all deserved but weren't lucky enough to get. His new book, Statistical Models, makes up for what we missed. It skillfully guides the reader through the complexities of theory and the nuts-and-bolts of practice, with cogent explanations and lively applications."
Shari Seidman Diamond, Northwestern University

"A pleasure to read, 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, Yale University

"Freedman is a master of exposition-concise, rigorous, and sometimes wickedly funny. The essential mathematics are here with real, not just toy, examples. A unique feature is Freedman's wise advice against misusing models. All students and users of statistical models should read this book. It is a methodological gold mine."
Paul Humphreys, University of Virginia

"This book is outstanding for the clarity of its thought and writing. It prepares readers for a critical assessment of the technical literature in the social and health sciences, and provides a welcome antidote to the standard formulaic approach to statistics."
Erich L. Lehmann, University of California, Berkeley

"Freedman brings unmatched clarity to the enterprise of statistical modeling. A concise presentation illuminates the mathematics, while case studies lead to a thoughtful analysis of the link between theory and practice. The exercises and computer labs make the text eminently suited to self-study as well as to the classroom. There is no other book like it."
Russell D. Lyons, Indiana University

"Statistical models are everywhere, often developed by analysts who do not understand the underlying theory. Freedman's book brings modeling down to earth. The book covers the theory and the assumptions, with many examples drawn from social science and medicine. It will find an immediate audience as a text for advanced undergraduates and beginning graduate students, because it is so practical. The wider audience will be those who make policy based on statistical models, and those who want to think about the basis for the policies."
Diana B. Petitti, Senior Scientific Advisor, Kaiser Permanent Southern California

"A cogent introduction to the use of linear models for casual assessment, this book deftly investigates the interacting role of statistical methods and subject-matter theory. Four reprints from the social-science literature are included; this is most unusual but eminently sensible. Each article is examined carefully to elucidate the assumptions behind the methodology. It is hard to imagine the student of statistics or quantitative sociology who would not benefit from this book."
Michael Stein, University of Chicago

"This book is truly an eye opener. It provides essential rigorous insight into statistical modeling...provides real examples taken from real studies...The author answers the questions the reader/researcher should ask. Among modeling books, this one is a gem...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."
MAA Reviews

"David A. Freedman invites us in a charming way to further contemplate the underlying theories of various statistical models and their application, especially about their assumptions, the interpretation of results, and their connection to the real phenomena. He also reminds us to be away of the dangers of the sloppy use of statistical models in observational and experimental studies. All of this makes the book a surely unique and highly recommendable textbook."
Karin Bammann, Bremen Institute for Prevention Research and Social Medicine, Biometrics

Book Description

Statistical Models is a lively and engaging textbook that explains the things you have to know in order to read empirical papers in the social and health sciences, as well as techniques you need to build statistical models of your own. Freedman illustrates the principles of modeling, and the pitfalls. There are computer labs, with sample computer programs. The book is rich in exercises, most with answers. Target audiences include undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.

See all Editorial Reviews

Product Details

• Paperback: 424 pages
• Publisher: Cambridge University Press (August 8, 2005)
• Language: English
• ISBN-10: 0521671051
• ISBN-13: 978-0521671057
• Product Dimensions: 9.2 x 6.2 x 1.2 inches
• Shipping Weight: 1.4 pounds
• Average Customer Review: 4.2 out of 5 stars  See all reviews (8 customer reviews)
• Amazon Best Sellers Rank: #1,140,520 in Books (See Top 100 in Books)

More About the Author

David A. Freedman (1938-2008) was a Professor of Statistics at the University of California, Berkeley. A distinguished mathematical statistician, he revolutionized the teaching of statistics with his undergraduate (new edition, 2007) and graduate (new edition, 2009) textbooks that emphasize clear reasoning over mere technique and that use numerous illustrations and empirical examples that are vivid, real, and up-to-date. Freedman also published widely on the application--and misapplication--of statistics in the social sciences. This major aspect of his work is synthesized in his book "Statistical Models and Causal Inference" (2009). Freedman was a member of the American Academy of Arts and Sciences and in 2003 received the National Academy of Science's John J. Carty Award for his "profound contributions to the theory and practice of statistics."

Customer Reviews

Most Helpful Customer Reviews

55 of 58 people found the following review helpful:

5.0 out of 5 stars The Best Statistics Book I've Seen, May 2, 2007

By 

N N Taleb "Nassim N Taleb" - See all my reviews

This review is from: Statistical Models: Theory and Practice (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

37 of 38 people found the following review helpful:

5.0 out of 5 stars Very well-written...very rigorous. Fairly conventional., November 24, 2007

By 

Alexander C. Zorach "Alex Zorach" (Lancaster, PA) - See all my reviews
(REAL NAME)   

This review is from: Statistical Models: Theory and Practice (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.

24 of 25 people found the following review helpful:

5.0 out of 5 stars NOW i get it, July 15, 2006

By 

Forrest Mcilwain - See all my reviews

This review is from: Statistical Models: Theory and Practice (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.