This is a course in multivariate data analysis. Students will be introduced to modern multivariate techniques, their applications and interpretations, and will learn how to use these methods to understand relationships between variables, extract patterns, or identify clusters or classifications in a rich data set involving multiple variables. Topics covered during the semester include both dependence techniques (e.g. multiple linear regression, binary logistic regression, multinomial logistic regression, principal component analysis, linear discriminant analysis, decision trees, etc) and interdependence techniques (e.g. factor analysis, cluster analysis, etc). A selection of topics in machine learning and data mining are also introduced during the semester. Statistical language R is used in this class.
Max Enrollment: 20
Prerequisites: MATH 205 and STAT 218 (or STAT 260).
Distribution Requirements: MM - Mathematical Modeling and Problem Solving
Semesters Offered this Academic Year: Spring