Statistical Models: Theory And Practice 2nd Edition
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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
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If you're in the social and health sciences just make sure that you have the mathematical background necessary. There are lots of proofs given in this book. The exercises often ask you to prove things. The math is not tucked neatly away. You need to be comfortable with basic undergrad linear algebra and probability. Otherwise, save yourself the frustration and don't buy this book just yet!
The book also just falls a little flat in some places. Particularly, no derivation for OLS is given and the motivation for the chapter on maximum likelihood is really weak and no discussion of regularity conditions. For other concepts too like bootstrap, I think many other books do a better job at motivating and explaining these concepts.
If there's one reason to buy this book, its how much stress freedman places on understanding the limitations of statistics. He's done a great job here. I have great respect for him but its a shame the book just doesn't shine anywhere else and just reads like a typical dry statistics book.
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.