This workshop will cover regression analysis using linear models and least squares in Python.  We will discuss the goals and main use-cases for linear regression, and how to interpret a fitted linear model.  We will then discuss methods for fitting more complex models with larger data sets, including the use of interactions, dummy-coding of categorical variables, and splines.  Finally we will discuss some aspects of statistical inference and model selection for linear regression. Several case studies using open-access data sets will be used to illustrate the approaches. 

Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.