A mathematical and applied course in probability, statistical inference, Bayesian methods, experimental design, regression, generalized linear models, nonparametric methods, power, mixed models, and multivariate analysis.
What you will learn
Students will formulate probability models; derive estimators and sampling distributions; conduct likelihood and Bayesian inference; design experiments; analyze factorial and hierarchical data; fit GLMs; plan power; and produce reproducible statistical analyses.
How the course is organized
Each lecture session has a full set of written notes: learning objectives, formal development with derivations, worked examples, a problem set, a computational lab and readings. Problem sets with solution guidance and examination specifications are under Assignments and Exams.
Part I · Probability and asymptotics Lectures 1–3
Part II · Estimation and inference Lectures 4–6
Part III · Experimental design and ANOVA Lectures 7–8
Part IV · Statistical modeling Lectures 9–13
Part V · Statistical workflow Lecture 14
Assessment
Problem sets 25%; design/power report 20%; computational labs 20%; midterm 15%; final project 20%.
Core references
Casella and Berger, Statistical Inference; Lehmann and Romano, Testing Statistical Hypotheses; van der Vaart, Asymptotic Statistics; Gelman et al., Bayesian Data Analysis; Montgomery, Design and Analysis of Experiments; McCullagh and Nelder, Generalized Linear Models.