Statistical Models: Theory and Practice / Edition 2

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Overview

"Statistical Models is a 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." "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. He focuses on applications of linear models, including generalized least squares and two-stage least squares, with probits and logits for binary variables." Target audiences include 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.
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Editorial Reviews

From the Publisher
"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

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Product Details

  • ISBN-13: 9780521112437
  • Publisher: Cambridge University Press
  • Publication date: 4/27/2009
  • Edition description: Second Edition
  • Edition number: 2
  • Pages: 456
  • Product dimensions: 6.70 (w) x 9.60 (h) x 1.20 (d)

Meet 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.
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Table of Contents

1 Observational studies and experiments
2 The regression line
3 Matrix algebra
4 Multiple regression
5 Path models
6 Maximum likelihood
7 The bootstrap
8 Simultaneous equations
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