This lively and engaging book 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 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 modelling, 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. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
This lively and engaging book 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 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 modelling, 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. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
1. Observational studies and experiments; 2. The regression line; 3. Matrix algebra; 4. Multiple regression; 5. Multiple regression: special topics; 6. Path models; 7. Maximum likelihood; 8. The bootstrap; 9. Simultaneous equations; 10. Issues in statistical modeling.
Explains the basic ideas of association and regression, taking you through the current models that link these ideas to causality.
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.
'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.' Persi Diaconis, Stanford
University
'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
'In Statistical Models, David Freedman explains the main
statistical techniques used in causal modeling - and where the
skeletons are buried. Complex statistical ideas are clearly
presented and vividly illustrated with interesting examples. Both
newcomers and practitioners will benefit from reading this book.'
Alan Krueger, Princeton University
'Regression techniques are often applied to observational data with
the intent of drawing causal conclusions. In what circumstances is
this justified? What are the assumptions underlying the analysis?
Statistical Models answers these questions. The book is essential
reading for anybody who uses regression to do more than summarize
data. The treatment is original, and extremely well written.
Critical discussions of research papers from the social sciences
are most insightful. I highly recommend this book to anybody who
engages in statistical modeling, or teaches regression, and most
certainly to all of my students.' Aad van der Vaart, Vrije
Universiteit Amsterdam
'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
'Statistical Models, a modern introduction to the subject,
discusses graphical models and simultaneous equations among other
topics. There are plenty of instructive exercises and computer
labs. Especially valuable is the critical assessment of the main
'philosophers's stones' in applied statistics. This is an inspiring
book and a very good read, for teachers as well as students.'
Gesine Reinert, Oxford University
'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.'
Mathematical Reviews
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