This course explores the fascinating field of nonparametric statistics, focusing on inferential methods that make minimal assumptions about the underlying data distribution. Moving beyond the limitations of parametric methods which often rely on assumptions like normality, we will examine a wide range of techniques including classical rank-based and randomization-based tests. Furthermore, the course will delve into modern nonparametric methods, encompassing nonparametric distribution and density estimation, nonparametric regression, selection of smoothing parameters through cross-validation, and resampling methods like bootstrap and jackknife. Throughout the semester, students will gain practical experience by applying these methodologies to real-world datasets using the R programming language, cultivating a robust understanding of nonparametric statistical analysis and its practical applications.

Units: 1

Max Enrollment: 15

Prerequisites: MATH 205, and one of (STAT 218, STAT 160, ECON 103/SOC 190, PSYC 105, BISC 198, or POL 299), and either (STAT 260 or STAT 318), and MATH 220/STAT 220.

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes: