Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. Authors Jeff Gill and Michelle Torres provide examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on the multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models.
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Jeff Gill is Distinguished Professor in the Department of Government, Professor in the Depart- ment of Mathematics & Statistics, and a Member of the Center for Behavioral Neuroscience at American University. He is also the inaugural director of the Center for Data Science and Co-Director of the graduate program in Data Science there. In additional to theoretical and methodological work in Bayesian statistics and statistical computing, his applied work centers on studying human beings from social, political, and biomedical perspectives.
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