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MATH 426 - Applied Longitudinal Analysis |

Studies and surveys where data are sampled in clusters or repeatedly sampled from subjects over time (e.g. panel studies) have become popular tools in many fields of research from the biomedical sciences to economics to public policy. The resulting data are typically correlated and potentially time dependent---issues that require a special set of modeling tools. This course covers the modern methods for the analysis of these types of data as well as the unbalanced and incomplete datasets common to such studies. Topics include an introduction to the analysis of correlated data, response profiles, parametric curves, covariance pattern models, random effects, and growth curve models. If time allows, the course will also explore generalized estimating equations (GEEs) and generalized linear mixed effects models (GLMMs). Applications will include real datasets from research in biology, biomedical science, economics, and political science. Analyses will be conducted using the statistical software, R. Textbook: Applied Longitudinal Analysis, 2nd Edition Garrett M. Fitzmaurice, Nan M. Laird, James H. Ware ISBN: 978-0-470-38027-7 Restrictions: Must be enrolled in one of the following Levels: MN or MC Graduate Must be enrolled in one of the following Majors: Mathematics and Statistics 3.000 Credit hours 3.000 Lecture hours Levels: MN or MC Graduate, Undergraduate Schedule Types: Lecture Mathematics Department Course Attributes: Mean Grade is Calculated |

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