Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R
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Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. Dedication Contents Preface 1 Review of Multiple Linear Regression 2 Beyond Least Squares: Using Likelihoods 3 Distribution Theory 4 Poisson Regression 5 Generalized Linear Models: A Unifying Theory 6 Logistic Regression 7 Correlated Data 8 Introduction to Multilevel Models 9 Two-Level Longitudinal Data 10 Multilevel Data With More Than Two Levels 11 Multilevel Generalized Linear Models Bibliography Index
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