Applied Regression Analysis and Generalized Linear Models / Edition 2

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Overview

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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Editorial Reviews

The Political Methodologist
"helps to bridge the divide between introductory and intermediate to advanced methods courses. The book is written in a clear, concise manner and organized in such a way as to help facilitate comprehension of the material...Together [with] theR and S-plus Companion to Applied Regression [has] made a fantastic contribution to the world of quantitative social science methology. "— Ryan Baker
Joseph Cavanaugh
"This is an excellent text on regression applications and methods, written with authority, lucidity, and eloquence. "
The Political Methodologist - Ryan Baker
"helps to bridge the divide between introductory and intermediate to advanced methods courses. The book is written in a clear, concise manner and organized in such a way as to help facilitate comprehension of the material...Together [with] theR and S-plus Companion to Applied Regression [has] made a fantastic contribution to the world of quantitative social science methology. "
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Product Details

  • ISBN-13: 9780761930426
  • Publisher: SAGE Publications
  • Publication date: 4/16/2008
  • Edition description: Second Edition
  • Edition number: 2
  • Pages: 688
  • Sales rank: 678,062
  • Product dimensions: 7.10 (w) x 10.10 (h) x 1.70 (d)

Meet the Author

John Fox is the Senator William Mc Master Professor of Social Statistics in the Sociology Department of Mc Master 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..

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Table of Contents

Preface
1 - Statistical Models and Social Science
I - DATA CRAFT
2 - What is Regression Analysis?
3 - Examining Data
4 - Transforming Data
II - LINEAR MODELS AND LEAST SQUARES
5 - Linear Least-Squares Regression
6 - Statistical Inference for Regression
7 - Dummy-Variable Regression
8 - Analysis of Variance
9 - Statistical Theory for Linear Models
10 - The Vector Geometry of Linear Models
III - LINEAR-MODEL DIAGNOSTICS
11 - Unusual and Influential Data
12 - Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity
13 - Collinearity and its Purported Remedies
IV - GENERALIZED LINEAR MODELS
14 - Logit and Probit Models
15 - Generalized Linear Models
V - EXTENDING LINEAR AND GENERALIZED LINEAR MODELS
16 - Time-Series Regression
17 - Nonlinear Regression
18 - Nonparametric Regression
19 - Robust Regression
20 - Missing Data in Regression Models
21 - Bootstrapping Regression Models
22 - Model Selection, Averaging, and Validation
A Notation
References

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