Applied Regression Analysis and Generalized Linear Models

ISBN-10: 0761930426

ISBN-13: 9780761930426

Edition: 2nd 2008

Authors: John Fox

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Description:

Linear models, their variants, and extensions are among the most useful and widely used statistical tools for social research. The Second Edition of Applied Regression Analysis and Generalized Linear Models provides an accessible, in-depth, modern treatment of regression analysis, linear models, and closely related methods. Author John Fox makes the text as user-friendly as possible: With the exception of three chapters, several sections, and a few shorter passages, the prerequisite for reading the book is a course in basic applied statistics that covers the elements of statistical data analysis and inference. Even relatively advanced topics (such as methods for handling missing data and bootstrapping) are presented in a manner consistent with this prerequisite. Key Features of the Second Edition Covers regression modelssuch as generalized linear models, limited-dependent-variable-models, mixed models and Cox regressionand methods that are increasingly being used in social science research Contains a more robust Web site with extensive appendices of background material (matrices, linear algebra, vector geometry; calculus; probability and estimation); data sets used in the book and for data analytic exercises; and the data-analytic exercises themselves. Incorporates real data from the social sciences that is similar to data readers are likely to encounter. This book should be of interest to students and researchers in the social sciences, as well as other disciplines that employ linear models for data analysis, and in courses on applied regression and linear models where the subject matter of applications is not of special concern.
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Book details

List price: $136.00
Edition: 2nd
Copyright year: 2008
Publisher: SAGE Publications, Incorporated
Publication date: 4/16/2008
Binding: Hardcover
Pages: 688
Size: 7.25" wide x 10.25" long x 1.75" tall
Weight: 1.826
Language: English

John Fox is the Senator William McMaster Professor of Social Statistics in the Sociology Department of McMaster University in Hamilton, Ontario, Canada. Professor Fox earned a Ph.D. in sociology from the University of Michigan in 1972. He has delivered numerous lectures and workshops on statistical topics, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the annual meetings of the American Sociological Association, and the Oxford Spring School in Quantitative Methods for Social Research. He has written many articles on statistics, sociology, and social psychology, and is the author of several books on statistics, including most recently Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008) and A Mathematical Primer for Social Statistics (Sage, 2009), and (with Sanford Weisberg) An R Companion to Applied Regression, Second Edition (Sage, 2011). Professor Fox is an active contributor to the R Project for Statistical Computing and is a member of the R Foundation. His work on this book was partly supported by a grant from the Social Sciences and Humanities Research Council of Canada.

Preface
Statistical Models - Social Science
Data Craft
What is Regression Analysis?
Examining Data
Transforming Data
Linear Models - Least Squares
Linear Least-Squares Regression
Statistical Inference for Regression
Dummy-Variable Regression
Analysis of Variance
Statistical Theory for Linear Models
The Vector Geometry of Linear Models
Linear-Model Diagnostics
Unusual and Influential Data
Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity
Collinearity and its Purported Remedies
Generalized Linear Models
Logit and Probit Models
Generalized Linear Models
Extending Linear - Generalized Linear Models
Time-Series Regression
Nonlinear Regression
Nonparametric Regression
Robust Regression
Missing Data in Regression Models
Bootstrapping Regression Models
Model Selection, Averaging, and Validation
A Notation
References
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