MEM 4230 Probability, Statistics and Reliability for Engineers 3.0 Credits
This course introduces probabilistic modeling and statistical inference for engineering decision‑making, then applies them to the reliability of components and systems. Topics include random variables and common distributions; estimation, confidence intervals, and hypothesis tests; linear regression and design of experiments; Monte Carlo simulation and Bayesian updating; life‑data analysis (e.g., Weibull, lognormal), hazard functions, and accelerated testing; system reliability (series/parallel/k‑of‑n), block diagrams, FMEA, and fault trees. Students learn to quantify uncertainty, model lifetimes, assess risk, and use data to design, validate, and improve engineered systems.
Repeat Status: Not repeatable for credit
Prerequisites: MATH 2201 [Min Grade: D] or MATH 201 [Min Grade: D] or MATH 261 [Min Grade: D]