Mixed models are an extremely useful modeling tool for situations in which there is some dependency among observations in the data, where the correlation typically arises from the observations being clustered in some way. For example, it is quite common to have data in which we have repeated measurements for the units of observation, or […]

We'll discuss mixed model regression (also known as multi-level models or hierarchical linear models) in this session which is used for repeated measures data or data which has a clustering element.  We'll start with a theoretical overview, discuss choosing an appropriate model, fitting the models, checking assumptions and post-hoc analysis. We'll also cover diagnosing convergence […]

Learn data analysis with Python. We’ll be using pandas, the go-to Python library used for data wrangling and analysis. We’ll be practicing with several different real-world datasets (e.g. time-series, text) and learning how to read, write, clean, transform, merge and reshape data. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.6 […]

Multilevel modeling is the state-of-the-art approach for handling data with complex dependence structure in a regression analysis.  This workshop will discuss fitting multilevel models in Python using the Statsmodels package. We will discuss the motivation and main use cases for multilevel modeling, and illustrate by example how to fit linear and generalized linear mixed models. […]