Listed in: Mathematics and Statistics, as STAT-404
Katharine F. Correia (Section 01)
Real world datasets are plagued by missing observations. Statistical software packages often ignore these cases by default, but there are better ways to approach the problem. This course will introduce students to the different missing data mechanisms and explore naive and modern methods for handling missing data. It will prepare students to read the current literature in this area and have broad appreciation for the implications of missing data.
This course is intended for students who have experience with standard statistical methods for complete data and want to extend them to handle missing data in practice.
Student has completed or is in the process of completing STAT 230 and STAT/MATH 370, or has consent of the instructor. Fall semester: Professor Correia.
How to handle overenrollment: null
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, problem sets, reading research articles, group work, use of computational software, projects