Foundations of Point Estimation with Modern Statistical Applications

To make sense of vast datasets, you must first understand how to estimate unknown population parameters from limited samples. This course provides a clear, step-by-step path through the core theories and formulas of point estimation, helping you move from raw data to confident mathematical conclusions. You will start by mastering foundational concepts like estimators, estimates, and sampling distributions, ensuring you have a strong theoretical base before tackling advanced scenarios. By reading through structured explanations and analyzing practical code snippets, you will learn how to evaluate and compare different estimators using real-world criteria. You will also explore modern computational approaches, such as bootstrap resampling and numerical optimization, which are essential for today's data science workflows. What you'll learn: - Understand the fundamental differences between estimators and estimates using clear definitions. - Analyze estimator properties including bias, consistency, efficiency, and sufficiency. - Derive estimators using the Method of Moments and Maximum Likelihood Estimation. - Evaluate the Mean Squared Error to determine the quality of your statistical models. - Apply modern bootstrap resampling techniques to estimate variance and confidence intervals. - Practice solving statistical estimation problems through step-by-step written exercises. The course begins with essential terminology and the mathematical foundations of probability distributions. From there, you will progress through classical estimation methods, analyze their properties, and conclude with modern computational techniques used in contemporary data analysis. This course is designed for beginners in statistics, data science enthusiasts, and students looking for a solid introduction to parameter estimation with no advanced prerequisites. Ready to master the core of statistical inference? Start reading today.