Students completing this course should be able to:
- Select appropriate methods of multivariate data analysis, given multivariate data and study objectives;
- Write SAS and/or Minitab programs to carry out multivariate data analyses;
- Interpret results of multivariate data analyses.
Data mining and statistical learning methods use a variety of computational tools for understanding large, complex datasets. In some cases, the focus is on building models to predict a quantitative or qualitative output based on a collection of inputs. In others, the goal is simply to find relationships and structure from data with no specific output variable. This course takes an applied approach to understand the methodology, motivation, assumptions, strengths, and weaknesses of the most widely applicable methods in this field.