Foundations of Point Estimation

In a data-driven world, making accurate inferences from samples is critical. This course provides a robust foundation in point estimation, equipping you with the statistical tools needed to derive and evaluate estimates from observed data. You will gain a deep understanding of how to quantify unknown population parameters using sound statistical principles. Upon completion, you will be able to confidently construct and assess various types of point estimators, enabling you to apply these powerful statistical techniques in diverse analytical scenarios. What you'll learn: * Understand the fundamental concepts of statistical inference, populations, samples, and parameters. * Learn to construct point estimators using essential methods like the Method of Moments. * Master the principles of Maximum Likelihood Estimation (MLE) for various distributions. * Apply key criteria such as bias, variance, consistency, and efficiency to evaluate estimator performance. * Practice deriving point estimators and assessing their properties through theoretical and practical exercises. * Explore foundational concepts of resampling methods for understanding estimator variability. This course begins with core definitions and theoretical underpinnings, systematically progressing through different estimation techniques and rigorous evaluation criteria, and concluding with practical considerations for applying these concepts. It is designed for beginners in statistics, students, or professionals who need a solid grasp of statistical inference. No prior advanced statistical knowledge is required. Start building your statistical expertise today.