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MATH 651 - Linear Regression Models |

This course will focus on the theory and application of regression methods for statistical modeling and data analysis. Emphasis will be in the following areas: simple and multiple regression, inference and prediction, model building and diagnostics, model selection and validation, analysis of variance (ANOVA), analysis of covariance (ANCOVA), generalized linear models, logistic and Poisson regression and other extensions as time permits (e.g. mixed models, nonlinear regression). Practical issues involved in implementation of these methods will be presented using statistical software packages SAS and R based on example problems from a wide range of applications. Text: Kutner, Nachtsheim, Neter, Li Applied Linear Statistical Models with Student CD ISBN: 9780073108742 Publisher: McGraw-Hill Edition: 5th Edition 3.000 Credit hours 3.000 Lecture hours 0.000 Lab hours Levels: MN or MC Graduate Schedule Types: Lecture Mathematics Department 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 |

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