Engineering Probability and Statistics: Point Estimation and Bayesian Analysis

Engineers must make critical decisions under uncertainty, relying on robust statistical methods to interpret data and model real-world systems. This text-based course provides a clear, foundational path to understanding how to estimate distribution parameters and apply statistical reasoning to engineering problems. You will learn to transform raw engineering data into reliable probabilistic models that inform design and safety decisions. By working through structured explanations and practical written exercises, you will develop the analytical skills required to analyze datasets, estimate unknown parameters, and update beliefs using modern statistical frameworks. What you'll learn: - Understand foundational probability concepts and statistical distributions used in engineering - Apply the Method of Moments to estimate distribution parameters from sample data - Formulate and solve Maximum Likelihood Estimation problems for common probability models - Implement Bayesian analysis to update probability distributions as new data becomes available - Evaluate the reliability and uncertainty of point estimators in engineering contexts The course begins with essential definitions of probability, random variables, and sampling distributions. From there, you will progress through classical point estimation techniques before exploring the powerful framework of Bayesian inference, testing your understanding with step-by-step mathematical exercises along the way. This course is designed for engineering students, practicing engineers, and technical professionals who want a solid, beginner-friendly introduction to statistical estimation. No advanced prior background in statistics is required, though a basic understanding of calculus is helpful. Start reading today to build a strong analytical foundation in engineering probability and estimation.