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Explaining the things you need to know in order to read empirical papers in the social and health sciences, as well as techniques needed to build personal statistical models, this user-friendly volume includes background material on study design, bivariate regression, and matrix algebra. To develop technique, Freedman also includes computer labs, with sample computer programs, and illustrates the principles and pitfalls of modeling. The book is rich in exercises with answers. Target audiences include undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
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Statistical Models is a lively and engaging 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. Freedman illustrates the principles of modeling, and the pitfalls. There are computer labs, with sample computer programs. The book is rich in exercises, most with answers. Target audiences include undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.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 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.
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