Applied Engineering Statistics and Statistical Inference

Engineering decisions rely on data, but raw numbers are meaningless without the tools to analyze them accurately. This text-based course guides you through the foundational concepts of statistical inference, helping you draw reliable conclusions from engineering data. By working through clear explanations and practical technical scenarios, you will transition from basic probability to applied statistical modeling, learning how to formulate hypotheses, estimate parameters, and make data-driven decisions with confidence. What you'll learn: Understand the core principles of statistical inference and estimation; Formulate and conduct hypothesis tests for engineering parameters; Construct confidence intervals to quantify uncertainty in experimental data; Apply regression analysis to model relationships between engineering variables; Explore modern computational statistics techniques, such as simulation-based inference; Analyze real-world technical datasets to make informed design and manufacturing decisions. Starting with essential terminology and foundational sampling distributions, the course progresses systematically through estimation, hypothesis testing, and regression modeling. This course is designed for engineering students, technical professionals, and data enthusiasts who have a basic grasp of introductory probability and want to master statistical inference. Start reading today to make your engineering decisions truly data-driven.