Applied Regression Analysis covers linear regression for prediction and interpretation, including feature selection, assumption checking, and extensions like mixed and generalized linear models using R.
Offering recorded · Fall 2026
Weekly homework is described as long and unrelated to lectures, while the final project is considered very important and strictly graded. Students report dealing with shifting deadlines and vague test questions that rely on the professor's interpretation.
STAT 371 introduces applied statistical practice for the life sciences using R, covering EDA, inference, ANOVA, and regression.
Offering recorded · Fall 2026
Historical reviews indicate varied workloads. John Davis reported short homework, whereas Cecile Ane assigned a heavy group project and required extra practice. Vivak Patel's course was noted as highly difficult.
STAT 424 introduces statistical experimental design, covering randomization, blocking, factorial designs, and response surface methodology with engineering applications.
Offering recorded · Fall 2026
Workload involves writing R code for homework, while exams are often predictable and similar to practice problems, though self-study via the textbook is recommended.
STAT 601 provides a thorough grounding in modern statistical methods, covering data collection, exploration, probability, and analysis.
Offering recorded · Fall 2026
Historical reviews of Vivak Patel: Reviewers describe a heavy workload with difficult projects and expect students to do significant independent work, noting that graduate-level rigor requires hard work.