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This second course in statistics for graduate students is intended to further expand students' background in the statistical methods that will assist them in the analysis of data. Course covers many fundamental analysis methods currently used to analyze a wide array of data, mostly arising from designed experiments. Topics include multiple regression models, factorial effects models, general linear models, mixed effect models, logistic regression analysis, and basic repeated measures analysis. This is a calculus-based course. Statistical software is used, however, there is no lab associated with the course. Credit not given for this course and ³§°ÕÌý512 or ³§°ÕÌý514 or ³§°ÕÌý516. Note: this course will be offered in person (Spring) and online (Fall and Spring).

Prerequisite: ³§°ÕÌý517

Typically offered in Fall and Spring