Applied Statistics and Econometrics for Decision Making

How do we prove that one event actually caused another, rather than it just being a coincidence? In a world flooded with data, the ability to draw accurate, causal conclusions is one of the most highly valued skills in policy, finance, and business. This structured text-only course guides you from statistical foundations to practical econometric modeling. You will transition from simply reading data to building robust models that can forecast trends, evaluate policy impacts, and uncover hidden relationships. What you'll learn: Understand foundational probability, statistical distributions, and hypothesis testing; Build and interpret linear regression models to analyze relationships between variables; Address real-world data challenges like multicollinearity, heteroscedasticity, and autocorrelation; Apply causal inference techniques to distinguish correlation from causation in observational data; Analyze time-series data to identify trends, seasonality, and make reliable forecasts; Evaluate econometric models using modern diagnostic tools and best practices. You will begin with essential terminology and the core mathematical concepts of probability and statistics. From there, you will progress step-by-step through simple and multiple regression analysis, diagnostic testing, and modern causal inference frameworks, practicing your skills with clear written scenarios and data-modeling exercises. This course is designed for beginners, aspiring data analysts, economics students, and professionals looking to build a strong quantitative foundation. No prior background in advanced statistics or econometrics is required. Start reading today to unlock the power of econometric modeling and make data-backed decisions with confidence.