STA 199
Schedule Syllabus Project Sakai Gradescope Help
Schedule
Week 01: Intro
Week 02: Data viz
Week 03: Data wrangle
Week 04: Probability
Week 05: Bootstrap + Data Ethics
Week 06: Sim-based testing
Week 07: Central Limit Theorem
Week 08: Two-sample-inference
Week 09: Linear models
Week 10: Multiple linear regression
Week 11: Classification + Text analysis
Week 12: Spatial data
Week 13: Web scraping + functions
Week 14
Week 15

Week 07: Central Limit Theorem

  • Lectures
  • Readings
  • Assignments
  • Announcements

Lectures πŸ”—οΈŽ

Slides Videos Application Exercise (AE)
Monday Central Limit Theorem (CLT) Central Limit Theorem AE 13: Bone density
Wednesday Inference with the CLT Inference with the CLT AE 14: Pokemon

Readings πŸ”—οΈŽ

Introduction to Modern Statistics: 5.3 Mathematical models Required
Infer examples Optional

Assignments πŸ”—οΈŽ

Lab 06 due Oct 7 at 11:59p
Project proposal due Oct 9 at 11:59p

Announcements πŸ”—οΈŽ

Tea with a TA!

Hang out with the TAs from STA 199! This is a casual conversation and a fun opportunity to meet the members of the STA 199 teaching time. The only rule is these can’t turn into office hours!

Tea with a TA counts as a statistics experience.

Upcoming Tea with a TA events

Matty Pahren, October 2 1p - 2p

  • Click here to sign up. Zoom details will be emailed before the event.

Matty Pahren is a current senior at Duke University. She plans on earning a B.S. in Statistical Science as well as minors in Psychology and Economics. She enjoys using data to contextualize real world problems and find solutions to them. Last summer, she served as a Business Analytics Intern at 2nd Order Solutions, a financial consulting firm in her hometown of Richmond, VA. There she worked on a debt collections project for a client, and she wrote code to help expedite future modeling projects. In her free time, she enjoys listening to music, watching sports, or going to trivia events with her friends.

Ezinne Nwankwo, October 8, 2:30p - 3:30p

  • Click here to sign up. Zoom details will be emailed before the event.

Ezinne Nwankwo is a Ph.D. candidate in statistical sciences with a focus on machine learning and computational social sciences at Duke University. Her research focuses on using statistical and machine learning methods to better understand society (using social data) and to aid in decision making processes. She is a passionate advocate for students and researchers from underrepresented backgrounds as an organizer and mentor with Black in AI.

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Week 06: Sim-based testing
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Week 08: Two-sample-inference
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