Applied Regression Analysis and Generalized Linear Models (inbunden)
Inbunden (Hardback)
Antal sidor
SAGE Publications, Inc
Fox, John
257 x 180 x 36 mm
1407 g
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Applied Regression Analysis and Generalized Linear Models (inbunden)

Applied Regression Analysis and Generalized Linear Models

Inbunden Engelska, 2015-05-26
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Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.
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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.

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.

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.


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.


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.


[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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Övrig information

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.



Chapter 1: Statistical Models and Social Science 1.1 Statistical Models and Social Reality 1.2 Observation and Experiment 1.3 Populations and Samples Part I: Data Craft Chapter 2: What Is Regression Analysis? 2.1 Preliminaries 2.2 Naive Nonparametric Regression 2.3 Local Averaging Chapter 3: Examining Data 3.1 Univariate Displays 3.2 Plotting Bivariate Data 3.3 Plotting Multivariate Data Chapter 4: Transforming Data 4.1 The Family of Powers and Roots 4.2 Transforming Skewness 4.3 Transforming Nonlinearity 4.4 Transforming Nonconstant Spread 4.5 Transforming Proportions 4.6 Estimating Transformations as Parameters Part II: Linear Models and Least Squares Chapter 5: Linear Least-Squares Regression 5.1 Simple Regression 5.2 Multiple Regression Chapter 6: Statistical Inference for Regression 6.1 Simple Regression 6.2 Multiple Regression 6.3 Empirical Versus Structural Relations 6.4 Measurement Error in Explanatory Variables Chapter 7: Dummy-Variable Regression 7.1 A Dichotomous Factor 7.2 Polytomous Factors 7.3 Modeling Interactions Chapter 8: Analysis of Variance 8.1 One-Way Analysis of Variance 8.2 Two-Way Analysis of Variance 8.3 Higher-Way Analysis of Variance 8.4 Analysis of Covariance 8.5 Linear Contrasts of Means Chapter 9: Statistical Theory for Linear Models 9.1 Linear Models in Matrix Form 9.2 Least-Squares Fit 9.3 Properties of the Least-Squares Estimator 9.4 Statistical Inference for Linear Models 9.5 Multivariate Linear Models 9.6 Random Regressors 9.7 Specification Error 9.8 Instrumental Variables and 2SLS Chapter 10: The Vector Geometry of Linear Models 10.1 Simple Regression 10.2 Multiple Regression 10.3 Estimating the Error Variance 10.4 Analysis-of-Variance Models Part III: Linear-Model Diagnostics Chapter 11: Unusual and Influential Data 11.1 Outliers, Leverage, and Influence 11.2 Assessing Leverage: Hat-Values 11.3 Detecting Outliers: Studentized Residuals 11.4 Measuring Influence 11.5 Numerical Cutoffs for Diagnostic Statistics 11.6 Joint Influence 11.7 Should Unusual Data Be Discarded? 11.8 Some Statistical Details Chapter 12: Non-Normality, Nonconstant Variance, Nonlinearity 12.1 Non-Normally Distributed Errors 12.2 Nonconstant Error Variance 12.3 Nonlinearity 12.4 Discrete Data 12.5 Maximum-Likelihood Methods 12.6 Structural Dimension Chapter 13: Collinearity and Its Purported Remedies 13.1 Detecting Collinearity 13.2 Coping With Collinearity: No Quick Fix Part IV: Generalized Linear Models Chapter 14: Logit and Probit Models 14.1 Models for Dichotomous Data 14.2 Models for Polytomous Data 14.3 Discrete Explanatory Variables and Contingency Tables Chapter 15: Generalized Linear Models 15.1 The Structure of Generalized Linear Models 15.2 Generalized Linear Models for Counts 15.3 Statistical Theory for Generalized Linear Models 15.4 Diagnostics for Generalized Linear Models 15.5 Complex Sample Surveys Part V: Extending Linear and Generalized Linear Models Chapter 16: Time-Series Regression and GLS 16.1 Generalized Least-Squares Estimation 16.2 Serially Correlated Errors 16.3 GLS Estimation With Autocorrelated Errors 16.4 Diagnosing Serially Correlated Errors Chapter 17: Nonlinear Regression 17.1 Polynomial Regression 17.2 Piecewise Polynomials and Regression Splines 17.3 Transformable Nonlinearity 17.4 Nonlinear Least Squares Chapter 18: Nonparametric Regression 18.1 Nonparametric Simple Regression: Scatterplot Smoothing 18.2 Nonparametric Multiple Regression 18.3 Generalized Nonparametric Regression Chapter 19: Robust Regression 19.1 M Estimation 19.2 Bounded-Inuence Regression 19.3 Quantile Regression 19.4 Robust Estimation of Generalized Linear Models 19.5 Concluding Remarks Chapter 20: Missing Data in Regression Models 20.1 Missing Data Basics 20.2 Traditional