Spring 2019

Multivariate Data Analysis

Listed in: Mathematics and Statistics, as STAT-240


Amy S. Wagaman (Section 01)


Making sense of a complex, high-dimensional data set is not an easy task. The analysis chosen is ultimately based on the research question(s) being asked. This course will explore how to visualize and extract meaning from large data sets through a variety of analytical methods. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering). Additional methods covered may include factor analysis, dimension reduction methods, or network analysis at instructor discretion. This course will feature hands-on data analysis with statistical software, emphasizing application over theory.

Requisite: STAT 111 or 135. Limited to 24 students. Spring semester. Professor Wagaman.

If Overenrolled: Priority for sophomores then STAT majors


Quantitative Reasoning


2022-23: Not offered
Other years: Offered in Fall 2016, Spring 2019, Fall 2020