Course Description
Methods for modeling and understanding complex data. Topics include linear regression models for sparse and high dimensional data sets, nonlinear models, tree-based methods, and clustering methods. Prerequisite: Permission of instructor; Note: Same as IAA 622.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- Identify a statistical technique appropriate to address a given research question.
- Determine the implications of study design on the type of statistical inference
- Compare and contrast data analysis tools for prediction
- Apply data analysis tools for prediction
- Apply various variable and model selection techniques
- Communicate clearly and correctly the results of statistical analyses
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Assignments | 40% |
| Labs | 10% |
| Midterm | 20% |
| Final Project | 30% |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
An Introduction to Statistical Learning, with Applications in R, 2nd edition, by James, et.al., 2023, Springer.
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.