This course covers the mathematical foundations of classical and modern statistical inference. Topics include sampling distributions, confidence intervals, hypothesis testing, likelihood theory, and bootstrap methods as well as linear models. Emphasis is placed on conceptual understanding, simulation-based intuition, and practical implementation using R. It is foundational for understanding statistical learning and data science.
Mathematical Statistics with Resampling and R (3rd Edition) by Laura M. Chihara and Tim C. Hesterberg