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 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.