Applied Regression Analysis and Generalized Linear Models
Fox, John
Sold by BookResQ., West Valley City, UT, U.S.A.
AbeBooks Seller since January 19, 2010
Used - Hardcover
Condition: Good
Quantity: 1 available
Add to basketSold by BookResQ., West Valley City, UT, U.S.A.
AbeBooks Seller since January 19, 2010
Condition: Good
Quantity: 1 available
Add to basketEx-library book with typical stickers and stampings. RF tag removed from the back of the book which took some paper with it. Priority Mail is available on this item. No international shipping.
Seller Inventory # B250323uqmug167599
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Second Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material throughout the book.
Key Updates to the Second Edition:
Intended Audience:
This core text will be a valuable resource for graduate students and researchers in the social sciences (particularly sociology, political science, and psychology) and other disciplines that employ linear and related models for data analysis.
John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.
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