2017-18 Academic Catalog

Many data sets violate a central assumption underlying modeling via multiple regression, namely that the observations be independent. For example, longitudinal studies of test scores of children at different ages, analysis of birthweights of pups from the same litters, and electrical activity on different parts of the brain measured on a sample of patients all involve observations that are correlated. In this course, we will earn methods to address this problem; we will also learn about general linear models of which logistic and Poisson regression are special cases. Prerequisite: Mathematics 245 and Mathematics 275 or permission of instructor. Familiarity with matrix algebra helpful but not required