Parameter Estimation and Uncertainty in Engineering

Engineers must make critical decisions based on incomplete or uncertain data. Understanding how to estimate unknown parameters from physical measurements is essential for designing safe, resilient infrastructure and environmental systems. This text-based course guides you from foundational statistical concepts to practical parameter estimation techniques used in modern engineering analysis. You will learn how to formulate mathematical models, evaluate estimator performance, and make sense of real-world data variability. What you will learn: Understand the core principles of probability, random variables, and parameter estimation; Compare the bias and efficiency of estimators, including the upper limit of uniform distributions; Apply maximum likelihood estimation and the method of moments to engineering data; Quantify uncertainty and construct confidence intervals for structural and environmental parameters; Evaluate model fit using modern statistical validation techniques. The course begins with essential definitions of probability distributions and estimator properties, then moves step-by-step through practical engineering scenarios with written code examples and analysis. This course is designed for engineering students, practicing civil and environmental engineers, and data analysts looking to build a solid foundation in statistical estimation without any prior advanced statistics background. Start building your quantitative engineering toolkit today.