The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data and the author includes his Web address where additional resources can be found.
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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.
Enables researchers to decide how to select the best way to adapt their data to Generalized Linear Models (GLM). Examples are provided throughout which illustrate the application of actual data.
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