Variable Selection in Low and High-dimensional Data Analysis

Monday, May 16

This workshop is designed to introduce tools and techniques of variable selection in linear regression modeling. We will begin with the basics of variable selection in low-dimensional linear regression, then introduce some techniques for variable selection in high-dimensional linear regression model, including

  • What research questions one can answer using model selection
  • When to use different types of variable selection techniques
  • Model assumptions in high-dimensional data settings
  • Implementation of variable selection in real data examples

Emphasis will be on practical issues to help researchers better apply variable selection techniques to help address their research questions and better understand and report their results.

Prerequisites: Regression Analysis workshop or similar background. A basic knowledge of simple and multiple linear regression is assumed.

Instructor: Dr. Xiaoli Gao is Associate Professor in the Department of Mathematics and Statistics. She teaches undergraduate and graduate level courses in statistical methodology. Her research interests include high-dimensional data analysis and statistical genetics. More information can be found at









Contact Us

Quantitative Methodology Series

The University of North Carolina at Greensboro
Department of Mathematics & Statistics
116 Petty Building
PO Box 26170
Greensboro, NC 27402-6170

Scott Richter, Ph.D.

Professor, Department of Mathematics and Statistics
Director of Statistical Consulting Center

John Willse, PSY.D.

Associate Professor and Department Chair, Educational Research Methodology