GEE is an extension of the generalized linear modeling (GLM) framework for dependent data. GEE can be used with longitudinal data, clustered data, and other forms of dependent data where a GLM may not be appropriate. In this workshop we will discuss fitting models using GEE in Python with the Statsmodels package. We will briefly […]
Prerequisites: Familiarity with basic statistical calculations and graphs is helpful. In this one-day, six-hour workshop we will discuss the basics of using SAS for data analysis. The workshop is held in a computer lab and will alternate between instructor presentations and attendee work sessions. After this course the attendee will be able to load data […]
In the R by Example series of workshops, we’ll discuss example analyses in R as a vehicle for learning commonly used tools and programming patterns. The “Functional Programming with dplyr” workshop will initially focus on analyzing winter home temperatures in the US using data from the Residential Energy Consumption Survey (https://www.eia.gov/consumption/residential/). We’ll use the dplyr package for data […]
 
Audience: Those who have never used Stata before but wish to learn. By the end of the workshop, participants will be able to: Work with Stata, including using Do-files and using the help system Get data into Stata and manage your data files Establish familiarity with your data Clean the data to prepare it for […]
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. […]
In this workshop we will use Make to manage build dependency in a multi-file, multi-language software project. We will learn how to use Make functions, automatically generate dependencies, and inquire the operating system about available packages and libraries. Also, we will briefly review alternative build dependency managers. At the end of the workshop you will […]
This workshop will be heavy on conceptual understanding of basic regression modeling, but with demonstration of activities both essential and tangential to good modeling practice. GLM, model interpretation, model comparison, model debugging, model criticism and more will be covered. Prereq: Some experience using R is required (R I, preferably R II workshops), as well as […]
This workshop will introduce you to the NumPy library in Python, which is useful in scientific computing. We will cover NumPy’s n-dimensional array object and associated functions in depth, along with related linear algebra and random number capabilities. Some familiarity with Python is expected. Computers will be available to complete exercises.
This workshop will cover basic concepts and tools available in QGIS and R for visualizing spatial data. We will cover vector data but will also touch upon the visualization of raster and spatial network data. Participants should have some familiarity with R, but exposure to QGIS is not required.