DATA 405 - Design of Experiments

Upper Division

Prerequisites
DATA 305 and DATA 320; Minimum grade C-.

Experimental design is a fundamental component of any investigation on the causal effects of treatment factors on a response. This course will provide a unique treatment of the design and analysis of experiments based on the modern Rubin Causal Model, and the classical contributions of Sir Ronald Aylmer Fisher and Jerzy Neyman. This distinct perspective forms the foundation for conventional inferential techniques, and more importantly, can be effectively applied to address complex real-life problems that are not amenable to standard techniques. Topics include: randomization inference, completely randomized and randomized block designs, Latin square designs and the Neyman-Fisher controversy of 1935, rerandomization, factorial and fractional factorial designs, and the analysis of experiments with noncompliance. Specific topics and the course outline are subject to change as the semester progresses. All topics will be motivated by real-life problems from the physical, life, social, and
management sciences, as well as engineering. Conceptual understanding, not memorization or theoretical derivations, is required and emphasized throughout the course.

Repeatable
No

Additional Notes
Previous course number: DATA 151

Course credits: 4