Xiwei Tang headshot
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Xiwei Tang

Assistant Professor
Unit: College of Arts and Sciences
Department: Department of Statistics
Office location and address
Halsey Hall - B004
148 Amphitheater Way
Charlottesville, Virginia 22904
Education
Master of Science - University of Virginia
PhD in Statistics - University of Illinois - Champaign-Urbana
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 5120: Applied Linear Models
Credits: 3
Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite:STAT 3120, and either MATH 3351 or APMA 3080
STAT 5330: Data Mining
Credits: 3
This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisites: Previous or concurrent enrollment in STAT 5120 or STAT 6120.
STAT 5630: 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 5120, STAT 6120, or ECON 3720, and previous experience with R Prerequisite: STAT 5120, STAT 6120, or ECON 3720, and previous experience with R
STAT 6160: Experimental Design
Credits: 3
This course develops fundamental concepts and methodology in the design and analysis of experiments. Topics include analysis of variance, multiple comparison tests, completely randomized designs, the general linear model approach to ANOVA, randomized block designs, Latin square and related designs, completely randomized factorial designs with two or more treatments, hierarchical designs, split-plot and confounded factorial designs, and analysis of covariance. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.
STAT 9999: Non-Topical Research
Credits: 1–12
For doctoral research, taken under the supervision of a dissertation director.