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MATH 411 Scientific Data Analysis II 3.0 Credits

Scientific data analysis and experimental design. Topics include multiple regression and model selection, nonlinear and logistic regression, analysis of covariance, multi-factor analysis of variance, nested, factorial and repeated measures experimental designs, random effects, and introduction to bootstrap methods and randomization tests. Programming statistical applications in R will be included.

College/Department: College of Arts and Sciences
Repeat Status: Not repeatable for credit
Prerequisites: MATH 410 [Min Grade: C-]

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