Applied Regression Analysis and Generalized Linear Models
About this ebook
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:
- Provides greatly enhanced coverage of generalized linear models, with an emphasis on models for categorical and count data
- Offers new chapters on missing data in regression models and on methods of model selection
- Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression
- Incorporates new examples using larger data sets
- Includes an extensive Web site at http://www.sagepub.com/fox that presents appendixes, data sets used in the book and for data-analytic exercises, and the data-analytic exercises themselves
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.
About the author
John Fox is professor of sociology at McMaster University in Hamilton, Ontario, Canada. Fox earned a PhD in sociology from the University of Michigan in 1972, and prior to arriving at McMaster, he taught at the University of Alberta and at York University in Toronto, where he was cross-appointed in the sociology and mathematics and statistics departments and directed the university's statistical consulting service. He has delivered numerous lectures and workshops on statistical topics in North and South America, Europe, and Asia, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the Oxford University Spring School in Quantitative Methods for Social Research, and the annual meetings of the American Sociological Association. Much of his recent work has been on formulating methods for visualizing complex statistical models and on developing software in the R statistical computing environment. He is the author and co-author of many articles, in such journals as Sociological Methodology, Sociological Methods and Research, The Journal of the American Statistical Association, The Journal of Statistical Software, The Journal of Computational and Graphical Statistics, Statistical Science, Social Psychology Quarterly, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written a number of other books, including Regression Diagnostics (SAGE, 1991), Nonparametric Simple Regression (SAGE, 2000), Multiple and General-ized Nonparametric Regression (SAGE, 2000), A Mathematical Primer for Social Statistics (SAGE, 2008), and, with Sanford Weisberg, An R Companion to Applied Regression, Second Edition (SAGE, 2010). Fox also edits the SAGE Quantitative Applications in the Social Sciences (QASS) monograph series.