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MATH 50015 - APPLIED STATISTICS |

(Slashed with MATH 40015) Course is based on classical linear regression techniques with an emphasis on real data using the principles of sound data analysis. Close attention is given to issues of interpretation, diagnostics, outliers and influential points, goodness of fit and model selection. Topics include simple and multiple linear regression, transformation and modifications of covariates and responses, design matrices, variable selection and logistic regression. Prerequisite: Applied mathematics or pure mathematics major; and graduate standing.
3.000 Credit hours 3.000 Lecture hours Levels: Graduate Schedule Types: Lecture Mathematical Sciences Department Restrictions: Must be enrolled in one of the following Levels: Graduate Must be enrolled in one of the following Majors: Applied Mathematics Pure Mathematics |

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