Jeffrey Woo headshot

Yong Ming Ming Woo

Assistant Professor
Unit: College of Arts and Sciences
Department: Department of Statistics
Office location and address
Halsey 106
148 Amphitheater Way
Charlottesville, Virginia 22904
B.Sc., University of Michigan
Ph.D., Pennsylvania State University
STAT 2120: Introduction to Statistical Analysis
Credits: 4
Introduction to the probability and statistical theory underlying the estimation of parameters and testing of statistical hypotheses, including those arising in the context of simple and multiple regression models. Students will use computers and statistical programs to analyze data. Examples and applications are drawn from economics, business, and other fields. Students will not receive credit for both STAT 2120 and ECON 3710. Prerequisite: MATH 1210 or equivalent; co-requisite: Concurrent enrollment in a discussion section of STAT 2120.
STAT 4630: Statistical Machine Learning
Credits: 3
Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated throughout. Prerequisite: STAT 3220, STAT 5120, or ECON 3720, and previous experience with R.
STAT 4993: Independent Study
Credits: 1–4
Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and direct the program.
STAT 5170: Applied Time Series
Credits: 3
Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 3120
STAT 6021: Linear Models for Data Science
Credits: 3
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components. The primary software is R. Prerequisite: A previous statistics course, a previous linear algebra course, and permission of instructor.