This is a basic course in mathematical statistics at the graduate level. It assumes knowledge of univariate calculus and a nodding acquaintance with multivariate calculus. The first part of the course includes a brief review of probability theory, distributions functions of univariate and multivariate random variables, sampling theory and convergence concepts. The second part of the course covers statistical inference (parameter estimation and tests of hypothesis). The third part of the course provides an introduction to several statistical methods, including ANOVA and linear regression.
There will be regular homework assignments, a midterm and a final. The midterm and final will be open book. Homework will be an individual effort, but help may be obtained from fellow students.
Kravis Professor of Business
Assaf Zeevi is the Kravis Professor of Business at the Graduate School of Business, Columbia University. His research focuses on the formulation and analysis of mathematical models of complex systems, with particular research and teaching interests that lie in the intersection of Operations Research, Statistics, Computer Science and Economics. Recent application areas have been motivated by problems in healthcare analytics, dynamic pricing, recommendation engines...