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; References; Answers to exercises; The computer labs; Appendix: sample MATLAB code; Reprints; Index.
Textbook for undergraduates and beginning graduate students in statistics, and students and professionals in the social and health sciences.
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 150 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 U.S. 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.
'… 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' stones' in applied
statistics. This is an inspiring book and a very good read, for
teachers as well as students.' Professor Gesine Reinert, Oxford
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, Professor of
Statistics, 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, Director of the Institution for Social and
Policy Studies, Yale University
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