Statistics 590SM - Adv. Stats w/Impact Mindset

Spring
2025
01
4.00
Krista Gile,Kelsey Shoub,David Cort

TU TH 10:00AM 11:15AM; M 9:05AM 9:55AM

UMass Amherst
52913
Integ. Learning Center S231
gile@cns.umass.edu
kshoub@umass.edu
dcort@soc.umass.edu
52929,53060
This course introduces students to advanced statistical methods in the context of applications with high social impact. It helps students learn about the technical aspects of these methods, as well as the critical statistical thinking skills necessary to relate methods to applied contexts. It also includes horizontally- and vertically-integrated components to support communication skills across disciplines and experience levels. In this course, advanced statistical methods are methods that follow or build on multiple linear regression. The specific technical topics will vary by semester, but will include multilevel/mixed effect/hierarchical models, and generalized linear models (such as logistic or Poisson regression), and may include other topics such as spatial modeling, networks, causal inference, mediation analysis, or latent class analysis. The methods will be introduced in the context of research data related to some subset of disparities in policing, legal cynicism and violence, global health, STEM student success, segregation, inequality in access to nature, political beliefs and extremism, and disparities in college sex and dating. Students from both technical and applied majors will work together in horizontally-integrated teams to deepen content learning and support learning collaboration across disciplines.

STATISTC 525 Students who have taken an introduction to statistics, and not linear regression, can complete a free bridge course in winter term to be eligible to take this class.

Registration requires permission of instructor. More information and registration request form are here: https://websites.umass.edu/gile/

Review of registration requests will begin at 9am on 11/8/24.

Permission is required for interchange registration during all registration periods.