A second edition textbook explaining the basic ideas of association and regression, which takes you through the current models that link these ideas to causality.
A second edition textbook explaining the basic ideas of association and regression, which takes 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.
Inhaltsangabe
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.
