Resampling Methods Explained | Jackknife, Bootstrap & Cross-Validation

In this lecture, we explore **resampling methods in statistics**: the jackknife, bootstrap, and cross-validation. We develop the mathematical ideas behind each method, derive important formulas, work through a numerical jackknife example, and use simulations to understand how resampling estimates uncertainty in practice. This lecture connects statistical theory with computational methods and applications in data science and machine learning. 🎯 Why Learn Resampling Methods? Resampling methods are valuable when theoretical formulas for uncertainty are difficult to obtain or when we want to assess the stability of statistical procedures computationally. They play important roles in regression, statistical inference, portfolio analysis, model selection, and machine learning. This lecture combines mathematical definitions, worked examples, and simulations to help you understand both the principles and practical uses of resampling. *Scope note:* This video focuses on resampling methods. Although CART (Classification and Regression Trees) appears in the original title slide, CART itself is not covered in this lecture. If you find this lecture useful, please *like, share, and subscribe* for more lessons in statistics, probability, optimization, numerical analysis, and learning from data. 🔖 Hashtags #Resampling #Jackknife #Bootstrap #CrossValidation #Statistics #StatisticalInference #MachineLearning #DataScience #Regression #LearningFromData