Nonparametric Statistics

Spring 2022

Nonparametric Statistics

Listed in: Mathematics and Statistics, as STAT-225

Moodle site: Course (Login required)

Faculty

Ryan P. McShane (Section 01)

Description

This course is an introduction to nonparametric and distribution-free statistical procedures and techniques. These methods rely heavily on counting and ranking techniques and will be explored through both theoretical and applied perspectives. One- and two-sample procedures will provide students with alternatives to traditional parametric procedures, such as the t-test. We will also investigate correlation and regression in a nonparametric setting. A variety of other topics may be explored in the nonparametric setting including resampling techniques (for example, bootstrapping), categorical data and contingency tables, density estimation, and the one-way and two-way layouts for analysis of variance. The course will emphasize data analysis (with appropriate use of statistical software) and the intuitive nature of nonparametric statistics.

Requisite: STAT 111 or STAT 135. Limited to 24 students.  Professor McShane. 

Expectations

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, quizzes or exams, use of computational software, project Students with documented disabilities who will require accommodations in this course should be in consultation with Accessibility Services and reach out to the faculty member as soon as possible to ensure that accommodations can be made in a timely manner.

If Overenrolled: Priority for Statistics majors.

Offerings

2022-23: Not offered
Other years: Offered in Spring 2015, Spring 2018, Spring 2020, Spring 2022