STATISTICAL EXPERIMENTAL DESIGN
Introduction to statistical design and analysis of experiments. Topics include: principles of randomization, blocking and replication, randomized blocking designs, Latin square designs, full factorial and fractional factorial designs and response surface methodology. Substantial focus will be devoted to engineering applications. A brief review on using design of experiments for digital transformation and A/B testing will be provided.
Topics
- principles of randomization
- blocking and replication
- randomized blocking designs
- Latin square designs
- full factorial and fractional factorial designs and response surface methodology
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: ( MATH/STAT 310 , STAT 333 , or 340 ) or (declared in Mechanical Engineering BS and one of STAT 240 , 301 , 302, 312 , 324 , 371 , or I SY E 210 ), or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Describe key concepts in the design and analysis of experiments. Audience: Undergraduate 2. Generate experimental designs and apply appropriate analysis techniques. Audience: Undergraduate 3. Compare different experimental design and analysis methods in various scenarios. Audience: Undergraduate 4. Apply experimental design and analysis methods in real-world projects. Audience: Undergraduate 5. Implement experimental design and analysis methods in statistical software like R. Audience: Undergraduate Learning Outcomes: 1. Describe key concepts in the design and analysis of experiments. Audience: Undergraduate 2. Generate experimental designs and apply appropriate analysis techniques. Audience: Undergraduate 3. Compare different experimental design and analysis methods in various scenarios. Audience: Undergraduate 4. Apply experimental design and analysis methods in real-world projects. Audience: Undergraduate 5. Implement experimental design and analysis methods in statistical software like R. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027