Environmental Data Analysis with MATLAB: Fitting Probability Distributions

Environmental engineers and data analysts must constantly assess risks and predict natural events by fitting observational data to theoretical probability models. This text-based course guides you through the foundational concepts of statistical modeling in environmental engineering, teaching you how to systematically compare empirical sample data with theoretical Cumulative Distribution Functions (CDFs). You will start with the fundamental mathematics of probability distributions before diving into practical MATLAB implementation techniques. By the end of this course, you will be able to write and execute scripts to evaluate data fits, determine model confidence, and make informed engineering decisions based on environmental datasets. What you will learn: Learn the core concepts of empirical and theoretical Cumulative Distribution Functions (CDFs); Understand how to structure and pre-process raw environmental data for statistical analysis in MATLAB; Apply parameter estimation techniques to fit distributions like Normal, Lognormal, and Gumbel to sample data; Practice writing clean, reusable MATLAB code to automate the comparison of sample and specified CDFs; Configure goodness-of-fit tests to quantitatively evaluate how well your data matches theoretical models. The course begins with essential terminology and statistical definitions, then moves step-by-step through dataset preparation, distribution fitting, and quantitative evaluation techniques. This course is designed for beginner to intermediate students, engineers, and researchers in civil and environmental fields who want to strengthen their data analysis skills; no advanced programming experience is required. Start mastering environmental data fitting and make more reliable engineering predictions today.