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Informationen zum Autor 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. Klappentext 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. Zusammenfassung Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research. Inhaltsverzeichnis 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 Asses...
