Current course offerings

Please Note: Course profiles marked as not available may still be in development.

Course description

Our understanding of biology is uncertain because biological systems are subject to stochasticity, and because our ability to quantitatively observe biological systems is imperfect. Statistical modeling is the approach that allows us to 'peer through' (and quantify) this uncertainty to understand how biological systems work. Therefore, the goal of this course is to provide you with a solid foundation in statistical modeling in a biological context. We will cover some basic probability (the 'language' of uncertainty) in the context of methods of estimation (ordinary least squares and maximum likelihood) and we will then begin with a deep dive into simple linear regression models assuming Gaussian errors and including both metric and nominal predictor variables. We will then build on this foundation by learning how to fit statistical models to data with non-Gaussian errors via so-called generalized linear models (GLMs). Next we will learn methods to account for correlation/non-independence in data by including so-called random effects in our statistical models (i.e. we will learn mixed/multilevel/hierarchical modeling). Finally, we will introduce an alternative statistical philosophy and modeling approach based on Bayesian (rather than Frequentist) statistical methods. The course will be very applied, providing lots of opportunities to learn by doing. An important focus of the course will be to develop an intuition for the iterative process of statistical modeling from question or hypothesis through data exploration, model fitting, model diagnostics, model selection, and visualization, interpretation and presentation of results. Indeed, much of the labyrinthine world of statistical modeling can be navigated by carefully implementing a relatively consistent modeling process. Once you become comfortable with the process, you can problem solve the details for the rest of your career in quantitative biology.

Archived offerings