New York University 2 credits Applied Statistics
This is a course in intermediate and advanced foundations of statistical inference in the context of applied research and covers Bayesian workflow, conjugate models, MCMC, prediction and model evaluation, Bayesian estimation of GLMs, and introduction to hierarchical/multilevel models. Through this course, students gain an understanding of mathematical theory, implement related statistical algorithms in R and Stan, and interpret models and parameters in the context of applied statistical analysis of real data. Prior exposure to probability and statistics required. Prerequisites: APSTA-GE 2351. Corequisites: APSTA-GE 2122 AND.
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