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Applied Regression Analysis and Generalized Linear Models John Fox

Applied Regression Analysis and Generalized Linear Models By John Fox

Applied Regression Analysis and Generalized Linear Models by John Fox

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Summary

The Second Edition extends coverage to regression models such as: generalized linear models; limited-dependent-variable-models; mixed models and Cox regression among others.

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Applied Regression Analysis and Generalized Linear Models Summary

Applied Regression Analysis and Generalized Linear Models by John Fox

The new Second Edition will extend coverage to regression models such as: generalized linear models; limited-dependent-variable-models; mixed models and Cox regression among other methods.

Applied Regression Analysis and Generalized Linear Models Reviews

This is an excellent text on regression applications and methods, written with authority, lucidity, and eloquence.

-- Joseph Cavanaugh
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] the R and S-plus Companion to Applied Regression [has] made a fantastic contribution to the world of quantitative social science methology. -- Ryan Baker * The Political Methodologist *

About John Fox

John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \\emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.

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

Additional information

CIN0761930426G
9780761930426
0761930426
Applied Regression Analysis and Generalized Linear Models by John Fox
John Fox
Used - Good
Hardback
SAGE Publications Inc
20080626
688
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in good condition, but if you are not entirely satisfied please get in touch with us

Customer Reviews - Applied Regression Analysis and Generalized Linear Models