Biostatistics 601 - Probability&StatInfer/HealthDS

The goal of this course is to introduce fundamentals of probability theory, statistical inference tools and their application to biostatistics and health data science. The course is intended for first-year graduate students in Biostatistics MS program and students who are interested in learning probability and statistical inference. The topics in this course include basic concepts of probability, random variables, important probability distributions (e.g., normal, exponential, binomial and Poisson), marginal distribution, conditional distribution, joint distribution, expectation and variance, conditional expectation, law of large numbers, central limit theorem, sampling distributions, point estimation, maximum likelihood estimation, method of moments and estimating equations, interval estimation, hypothesis testing. Examples from biomedical applications will be used whenever possible. Simple simulations with R software will be used to illustrate some concepts in probability and statistical inference.

Open to MS-BIOSTATS students. This course is offered in person on the Amherst campus. A synchronous Zoom option may be available for all or selected class meetings; please check with the instructor for details.

Students who cannot self-enroll please submit a request here: https://forms.gle/srQ98i4bHmoeadFH6