Statistical Models: Theory and Practice 2nd Edition by David A. Freedman (PDF)

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. The focus is on applications of linear models, including generalized least squares and two-stage least squares, with probits and logits for binary variables. The bootstrap is developed as a technique for estimating bias and computing standard errors. Careful attention is paid to the principles of statistical inference. There is background material on study design, bivariate regression, and matrix algebra. To develop technique, there are computer labs with sample computer programs. The book is rich in exercises, most with answers. Target audiences include advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modeling, and the pitfalls. The discussion shows you how to think about the critical issues – including the connection (or lack of it) between the statistical models and the real phenomena. Features of the book: • authoritative guidance from a well-known author with wide experience in teaching, research, and consulting • careful analysis of statistical issues in substantive applications • no-nonsense, direct style • versatile structure, enabling the text to be used as a text in a course, or read on its own • text that has been thoroughly class-tested at Berkeley • background material on regression and matrix algebra • plenty of exercises, most with solutions • extra material for instructors, including data sets and code for lab projects (available from Cambridge University Press) • many new exercises and examples • reorganized, restructured, and revised chapters to aid teaching and understanding

User’s Reviews

Editorial Reviews: 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 Explains the basic ideas of association and regression, taking you through the current models that link these ideas to causality. About the Author David A. Freedman is Professor of Statistics at the University of California, Berkeley. He has also taught in Athens, Caracas, Jerusalem, Kuwait, London, Mexico City, and Stanford. He has written several previous books, including a widely used elementary text. He is one of the leading researchers in probability and statistics, with 200 papers in the professional literature. He is a member of the American Academy of Arts and Sciences. In 2003, he received the John J. Carty Award for the Advancement of Science from the National Academy of Sciences, recognizing his ‘profound contributions to the theory and practice of statistics’. Freedman has consulted for the Carnegie Commission, the City of San Francisco, and the Federal Reserve, as well as several departments of the US government. He has testified as an expert witness on statistics in law cases that involve employment discrimination, fair loan practices, duplicate signatures on petitions, railroad taxation, ecological inference, flight patterns of golf balls, price scanner errors, sampling techniques, and census adjustment. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐If you’re a student in math or statistics, the book is a good secondary text but not the best standalone. Written at a semi-rigorous level, it will develop a solid foundation for statistics. Even for machine learning people I would recommend it with the caveat that the applications and real-world examples in the book will probably bore you.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.

⭐The genius of this book cannot be overstated. This book supplies the ideas, motivations, and insights at the heart of statistical thinking and removes all of the confusing jargon and technicality that burdens most traditional treatments. Its rigor is in ideas, not the pseudo-rigor of pages of poorly motivated formulas. A more advanced follow up on Freedman’s “Statistics”, these two books solidify for me Freedman’s place as a visionary in exposition.

⭐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.

⭐Great book and is easy to read with clear, clever examples.

⭐This book is a great balance between intuitive examples and solid mathematical theories. I hope I could read this books several years ago. During my several years’ research experience, I made many mistakes and actually most of them are well discussed in this book. Also, for one of my recent research topic, this book provided me with very direct solution and great help!I would recommend this book to anyone who wants a solid background with statistical modeling.

⭐Writing is concise, clear, enough material to link concepts together without babying the reader. As a mathematical textbook, you can’t get better than this. Also, answers for almost all of the exercises are provided. Note that this book assumes you are comfortable with linear algebra. As a statistic student, this is a must-have reference.

⭐It seems the author is on a quest to document statistical models pitfalls and express his frustrations against some of the misconceptions of statistical analysis. Certainly this is the most complete statistics book I have seen in terms of mathematical proofs, but the title “Theory and Practice” seemed to imply a text oriented towards applications. There are a number of classic case studies presented, but statistical analysis has evolved since then and I for one knew that an imperfect tool it is, not a mean to predict the future or express causality under every circumstance.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.

⭐Clear, concise coverage of core statistical topics. Lots of examples to work through, with answers to most of them included.

⭐The book is easy to read and lots of interesting information is contained in between the lines. Ideal for practitioners who need to do statistics in their jobs but would never consider studying mathematics in their youth. The few theorems are given without proofs albeit with good references. Only a master in his subject can write a book which is all of it: correct, clear, useful and entertaining.

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