ENGINEERING APPLICATIONS OF STATISTICS

Prerequisites & course information

Requisites: Graduate/professional standing or declared in Capstone Certificate in Artificial Intelligence for Engineering Data Analytics Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Apply statistical thinking, regression analysis, and descriptive analytics to evaluate data and assess process or product performance Audience: Graduate 2. Evaluate process performance against output requirements and identify improvement opportunities using process capability evaluation and design of experiments Audience: Graduate 3. Detect changes in process signals with noise and address factors impacting performance Audience: Graduate 4. Design and implement experiments to identify and model input-output relationships and optimize performance Audience: Graduate 5. Build traditional and artificial intelligence (AI) models to enhance process capability and develop actionable plans to drive measurable improvements Audience: Graduate Learning Outcomes: 1. Apply statistical thinking, regression analysis, and descriptive analytics to evaluate data and assess process or product performance Audience: Graduate 2. Evaluate process performance against output requirements and identify improvement opportunities using process capability evaluation and design of experiments Audience: Graduate 3. Detect changes in process signals with noise and address factors impacting performance Audience: Graduate 4. Design and implement experiments to identify and model input-output relationships and optimize performance Audience: Graduate 5. Build traditional and artificial intelligence (AI) models to enhance process capability and develop actionable plans to drive measurable improvements Audience: Graduate