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Applied Regression Analysis and Generalized Linear Models / Edition 2

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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 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: 875,828
  • Product dimensions: 7.10 (w) x 10.10 (h) x 1.70 (d)

Meet the Author

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..
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Table of Contents

1 Statistical Models and Social Science 1

Pt. I Data Craft 11

2 What Is Regression Analysis? 13

3 Examining Data 26

4 Transforming Data 50

Pt. II Linear Models and Least Squares 75

5 Linear Least-Squares Regression 77

6 Statistical Inference for Regression 100

7 Dummy-Variable Regression 120

8 Analysis of Variance 143

9 Statistical Theory for Linear Models 187

10 The Vector Geometry of Linear Models 220

Pt. III Linear-Model Diagnostics 239

11 Unusual and Influential Data 241

12 Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity 267

13 Collinearity and Its Purported Remedies 307

Pt. IV Generalized Linear Models 333

14 Logit and Probit Models for Categorical Response Variables 335

15 Generalized Linear Models 279

Pt. V Extending Linear and Generalized Linear Models 425

16 Time-Series Regression and Generalized Least Squares 427

17 Nonlinear Regression 451

18 Nonparametric Regression 476

19 Robust Regression 530

20 Missing Data in Regression Models 548

21 Bootstrapping Regression Models 587

22 Model Selection, Averaging, and Validation 607

App. A Notation 636

References 638

Author Index 648

Subject Index 652

Data Set Index 664

About the Author 665

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