Abstract
Machine Learning and Generative AI is all the rage, but these technologies struggle to support the most common question that academics and practitioners still face: ‘why?’. Many people analytics practitioners want to be able to use analytic methods that are highly explainable and interpretable to help them communicate with their clients. This hands on lab session will provide an introduction to (or a refresher on) foundational explanatory statistical methods in R and their applications in people analytics. Using content from Keith McNulty’s Handbook of Regression Modeling, the session will cover the foundations of, implementation of and interpretation of hypothesis testing, linear regression, binomial and ordinal logistic regression. The session will involve walkthroughs of typical people analytics problems and data sets, choosing the right methodology for the problem, live assisted coding, and discussions about the articulation of results to stakeholders. Participants will need a computer, but no special configuration or pre-work will be required.
Facilitators
Find biographies on the Workshop Facilitators webpage.
Learning Objectives
After attending this program, participants will be able to:
- Learn the basic principles of classical statistics, including basic hypothesis testing, using people analytics datasets
- Learn how to choose a statistical method based on the problem being solved and the data available.
- Learn how to implement the method in R
- Learn how to interpret the results of statistical models
- Learn how to communicate those results to stakeholders
Lunch will be provided for workshop attendees.
Date
October 23, 2025
Time
8:30 a.m. - 12:00 p.m.
Delivery Type
In-Person
Number of Credits
This workshop is currently being reviewed for continuing education credit. Details can be found on the Continuing Education webpage.
Workshop Coordinator
