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Applied regression analysis and generalized linear models

By: Fox, John.
Publisher: Los Angeles Sage Publications 2008Edition: 2nd ed.Description: xxi, 665 p.ISBN: 9780761930426.Subject(s): Regression analysis | Linear models (Statistics) | Social sciences - Statistical methodsDDC classification: 300.1519536 Summary: 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 (Source:www.sagepub.com)
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Revised adition of Applied regression analysis, lenear models and related methods

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 (Source:www.sagepub.com)

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