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.
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Multilevel and Longitudinal Modeling Using Stata, Second Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels.
Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. The applications and exercises span a wide range of disciplines, making the book suitable for courses on multilevel and longitudinal modeling in the medical, social, and behavioral sciences and in applied statistics. This extensively revised second edition includes 3 new chapters, comprehensive updates for Stata 10, 38 new exercises, and 27 new data sets.
The authors teach multilevel and longitudinal modeling at their universities and frequently hold workshops at international conferences. They have been developing a general modeling framework, GLLAMM, and Stata software gllamm for multilevel and latent variable modeling. This work has been published in their highly acclaimed book Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models and in many journals, including Biometrics, Psychometrika, Journal of Econometrics, and Journal of the Royal Statistical Society.
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