<p><strong>Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R </strong>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 appreciati
Pris: kr 986.00 fra Norli
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Presenting topics in the form of questions and answers, this popular supplemental text offers a brief introduction on multiple regression on a conceptual level. Author Paul D. Allison answers the most essential questions (such as how to read and interpret...
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<p>"I think that the coverage of the text is excellent. It carves out a seriously neglected area and it very thoroughly covers the topic. The authors are very knowledgeable concerning the literature. This is an excellent text that provides...
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<p>This text introduces the fundamental linear regression models used in quantitative research. It covers both the theory and application of these statistical models, and illustrates them with illuminating graphs. The author offers guidence...
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<p><b>Praise for the </b><b><i>Third Edition</i></b></p><p><b>"...this is an excellent book which could easily be used as a course text..."<br/> ¿International Statistical Institute</b></p><p>The...
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