- Paperback: 424 pages
- Publisher: Cambridge University Press; F First Edition edition (August 8, 2005)
- Language: English
- ISBN-10: 0521671051
- ISBN-13: 978-0521671057
- Product Dimensions: 6.5 x 1 x 9.5 inches
- Shipping Weight: 1.4 pounds
- Average Customer Review: 4.4 out of 5 stars See all reviews (16 customer reviews)
- Amazon Best Sellers Rank: #2,201,470 in Books (See Top 100 in Books)
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Statistical Models: Theory and Practice F First Edition Edition
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"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."
"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
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.
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Top customer reviews
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.
I would recommend this book to anyone who wants a solid background with statistical modeling.
In short buy this book if you are in an academic path and want good mathematical foundations on linear regressions and probit models. You will still need assistance though because formula explanations are reduced to a bare minimum.