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."
E. C. Hedberg
The strength of this text is the unified presentation of several regression topics that provides the student with a global perspective on regression analysis. The student is well served with this unified approach as it facilitates deeper research on any one topic with more advanced texts.
Corey S. Sparks
This text is a one-stop shop for me for my first year stats sequence for students in our program. Those wanting the technical detail will be satisfied; those wanting an excellent explanation of these methods using real-world examples and approachable language will also be satisfied.
Michael S. Lynch
I have enjoyed using previous editions of this text and look forward to using this edition. It covers all key topics, and quite a few advanced ones, in one well-written text.
Journal of the American Statistical Association (review of the second edition)

In summary, this is an excellent text on regression applications and methods, written with authority, lucidity, and eloquence. The second edition provides substantive and topical updates, and makes the book suitable for courses designed to emphasize both the classical and the modern aspects of regression.

Journal of the American Statistical Association (review of the first edition)

Even though the book is written with social scientists as the target audience, the depth of material and how it is conveyed give it far broader appeal. Indeed, I recommend it as a useful learning text and resource for researchers and students in any field that applies regression or linear models (that is, most everyone), including courses for undergraduate statistics majors…. The author is to be commended for giving us this book, which I trust will find a wide and enduring readership.

Chance (review of the first edition)

[T]his wonderfully comprehensive book focuses on regression analysis and linear models… We enthusiastically recommend this book—having used it in class, we know that it is thorough and well-liked by students.

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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: 272,449
  • Product dimensions: 7.10 (w) x 10.10 (h) x 1.70 (d)

Meet the Author

John Fox is professor of sociology at Mc Master University in Hamilton, Ontario, Canada. Fox earned a Ph D in sociology from the University of Michigan in 1972, and prior to arriving at Mc Master, he taught at the University of Alberta and at York University in Toronto, where he was cross-appointed in the sociology and mathematics and statistics departments and directed the university's statistical consulting service. He has delivered numerous lectures and workshops on statistical topics in North and South America, Europe, and Asia, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the Oxford University Spring School in Quantitative Methods for Social Research, and the annual meetings of the American Sociological Association. Much of his recent work has been on formulating methods for visualizing complex statistical models and on developing software in the R statistical computing environment. He is the author and co-author of many articles, in such journals as Sociological Methodology, Sociological Methods and Research, The Journal of the American Statistical Association, The Journal of Statistical Software, The Journal of Computational and Graphical Statistics, Statistical Science, Social Psychology Quarterly, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written a number of other books, including Regression Diagnostics (SAGE, 1991), Nonparametric Simple Regression (SAGE, 2000), Multiple and General-ized Nonparametric Regression (SAGE, 2000), A Mathematical Primer for Social Statistics (SAGE, 2008), and, with Sanford Weisberg, An R Companion to Applied Regression, Second Edition (SAGE, 2010). Fox also edits the SAGE Quantitative Applications in the Social Sciences (QASS) monograph series.

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Table of Contents

1 - Statistical Models and Social Science
2 - What is Regression Analysis?
3 - Examining Data
4 - Transforming Data
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
11 - Unusual and Influential Data
12 - Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity
13 - Collinearity and its Purported Remedies
14 - Logit and Probit Models
15 - 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

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