Michigan State University 3 credits Statistics and Probability
Building, understanding, and using predictive models. Fundamentals of simple and multiple linear regression models and their application in both a Bayesian and Frequentist paradigm. Assessing validity of model assumptions. Other regression models including logistic regression, Poisson models, Quasi-Poisson models, and other generalized linear models. Prerequisite: (STT 442 or STT 380 or approval of department) and (STT 180 or CMSE 201 or CSE 231).
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