Applied Regression Analysis and Generalized Linear Models (3rd Revised edition)

Applied Regression Analysis and Generalized Linear Models (3rd Revised edition)

By: John Fox (author)Hardback

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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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About Author

John Fox is professor of sociology at McMaster University in Hamilton, Ontario, Canada. Fox earned a PhD in sociology from the University of Michigan in 1972, and prior to arriving at McMaster, 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.


Preface About the Author 1. Statistical Models and Social Science 1.1 Statistical Models and Social Reality 1.2 Observation and Experiment 1.3 Populations and Samples I. DATA CRAFT 2. What Is Regression Analysis? 2.1 Preliminaries 2.2 Naive Nonparametric Regression 2.3 Local Averaging 3. Examining Data 3.1 Univariate Displays 3.2 Plotting Bivariate Data 3.3 Plotting Multivariate Data 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* II. LINEAR MODELS AND LEAST SQUARES 5. Linear Least-Squares Regression 5.1 Simple Regression 5.2 Multiple Regression 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* 7. Dummy-Variable Regression 7.1 A Dichotomous Factor 7.2 Polytomous Factors 7.3 Modeling Interactions 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 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 Two-Stage Least Squares 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 III. LINEAR-MODEL DIAGNOSTICS 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* 12. Non-Normality, Nonconstant Error 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 13. Collinearity and Its Purported Remedies 13.1 Detecting Collinearity 13.2 Coping With Collinearity: No Quick Fix IV. GENERALIZED LINEAR MODELS 14. Logit and Probit Models for Categorical Response Variables 14.1 Models for Dichotomous Data 14.2 Models for Polytomous Data 14.3 Discrete Explanatory Variables and Contingency Tables 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 Analyzing Data From Complex Sample Surveys V. EXTENDING LINEAR AND GENERALIZED LINEAR MODELS 16. Time-Series Regression and Generalized Leasr Squares* 16.1 Generalized Least-Squares Estimation 16.2 Serially Correlated Errors 16.3 GLS Estimation With Autocorrelated Errors 16.4 Correcting OLS Inference for Autocorrelated Errors 16.5 Diagnosing Serially Correlated Errors 16.6 Concluding Remarks 17. Nonlinear Regression 17.1 Polynomial Regression 17.2 Piece-wise Polynomials and Regression Splines 17.3 Transformable Nonlinearity 17.4 Nonlinear Least Squares* 18. Nonparametric Regression 18.1 Nonparametric Simple Regression: Scatterplot Smoothing 18.2 Nonparametric Multiple Regression 18.3 Generalized Nonparametric Regression 19. Robust Regression* 19.1 M Estimation 19.2 Bounded-Influence Regression 19.3 Quantile Regression 19.4 Robust Estimation of Generalized Linear Models 19.5 Concluding Remarks 20. Missing Data in Regression Models 20.1 Missing Data Basics 20.2 Traditional Approaches to Missing Data 20.3 Maximum-Likelihood Estimation for Data Missing at Random* 20.4 Bayesian Multiple Imputation 20.5 Selection Bias and Censoring 21. Bootstrapping Regression Models 21.1 Bootstrapping Basics 21.2 Bootstrap Confidence Intervals 21.3 Bootstrapping Regression Models 21.4 Bootstrap Hypothesis Tests* 21.5 Bootstrapping Complex Sampling Designs 21.6 Concluding Remarks 22. Model Selection, Averaging, and Validation 22.1 Model Selection 22.2 Model Averaging* 22.3 Model Validation VI. MIXED-EFFECT MODELS 23. Linear Mixed-Effects Models for Hierarchical and Longitudinal Data 23.1 Hierarchical and Longitudinal Data 23.2 The Linear Mixed-Effects Model 23.3 Modeling Hierarchical Data 23.4 Modeling Longitudinal Data 23.5 Wald Tests for Fixed Effects 23.6 Likelihood-Ratio Tests of Variance and Covariance Components 23.7 Centering Explanatory Variables, Contextual Effects, and Fixed-Effects Models 23.8 BLUPs 23.9 Statistical Details* 24. Generalized Linear and Nonlinear Mixed-Effects Models 24.1 Generalized Linear Mixed Models 24.2 Nonlinear Mixed Models Appendix A References Author Index Subject Index Data Set Index

Product Details

  • publication date: 12/05/2015
  • ISBN13: 9781452205663
  • Format: Hardback
  • Number Of Pages: 816
  • ID: 9781452205663
  • weight: 1406
  • ISBN10: 1452205663
  • edition: 3rd Revised edition

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