STA 440: Case Studies

STA 440 is an intensive applied course that asks you to analyze timely real-world data across diverse domains in a principled, data-driven way. There may be more than one reasonable approach for any given situation, and you may be introduced to new material and techniques you haven't yet seen. Along the way, you'll work with a team of peers, develop critical thinking and communication skills, develop best-practices in version-control and reproducibility, and strive to become a creative and well-rounded practicing statistician.


Course info
Lecture T/Th 10:15 - 11:30 AM See Sakai for Zoom
Lab F 1:45 - 3:00 PM See Sakai for Zoom

Due to the global pandemic, STA 440 for the Spring Semester is an only-online course, with live lecture and lab sessions listed above.
Instructor Yue Jiang T/Th 1:00 - 2:00 PM See Sakai for Zoom
Teaching Assistant Anna Yanchenko W 6:00 - 7:00 PM See Sakai for Zoom
Teaching Assistant Carol Wang M 10:00 - 11:00 AM See Sakai for Zoom

As well, Carol will attend Tuesday lecture sessions and Anna will attend Thursday sections. Additional instructor office hours are available by appointment.

Topics covered

This semester's case studies include:

  • comparing the safety and efficacy of two drugs in preventing opioid-relapse following rehabilitation,
  • evaluating whether associations exist between batting average and physical characteristics of professional baseball players, and
  • whether there is still evidence of racial disparities in stop-question-frisk events and outcomes despite recent efforts at reform.

In addition, each student will conduct an individual case study project of their own interest and provide meaningful, detailed reviews and critiques of case projects from peers.

To develop your skills as a practicing statistician, throughout the course you will:

  • solidify skills in reproducible research and programming, including version-control and collaboration via GitHub,
  • critically think about reasonable analysis approaches in the context of recent real-world data,
  • express statistical models clearly and correctly,
  • develop scientific writing skills by providing clear, concise, data-driven conclusions suitable for allied researchers, and
  • effectively and concisely communicate results to allied researchers.