MATH 291
Erin Leatherman joined the Kenyon faculty in 2018 after spending five years teaching at West Virginia University. Her statistical research is related to an experimental methodology that incorporates deterministic computer simulators that implement mathematical models of physical processes. Simulators can be used as a stand-alone experimental platform when a traditional physical experiment is not possible or they can be used in conjunction with physical experiments; elements of Erin’s research intersect both areas. Additionally, she has worked with various collaborators on interdisciplinary projects that include subject areas such as biomedical engineering, workplace safety and medicine.
Erin has enjoyed teaching a wide variety of statistics courses ranging from introductory- to Ph.D.-level courses. These courses have included theory, application and computational topics. In each of her courses, Erin seeks to empower students to be critical thinkers who are capable of solving hard problems…
Read MoreErin Leatherman joined the Kenyon faculty in 2018 after spending five years teaching at West Virginia University. Her statistical research is related to an experimental methodology that incorporates deterministic computer simulators that implement mathematical models of physical processes. Simulators can be used as a stand-alone experimental platform when a traditional physical experiment is not possible or they can be used in conjunction with physical experiments; elements of Erin’s research intersect both areas. Additionally, she has worked with various collaborators on interdisciplinary projects that include subject areas such as biomedical engineering, workplace safety and medicine.
Erin has enjoyed teaching a wide variety of statistics courses ranging from introductory- to Ph.D.-level courses. These courses have included theory, application and computational topics. In each of her courses, Erin seeks to empower students to be critical thinkers who are capable of solving hard problems and to be good communicators who can share results in written and oral forms.
Computer experiments, including prediction and design for simulator and physical experiments. Use and development of statistical methodology in collaborative scientific settings.
2013 — Doctor of Philosophy from The Ohio State University
2008 — Master of Science from Bowling Green State University
2006 — Bachelor of Arts from Bluffton University
Special Topic
Individual study is a privilege reserved for students who want to pursue a course of reading or complete a research project on a topic not regularly offered in the curriculum. It is intended to supplement, not take the place of, coursework. Individual study cannot be used to fulfill requirements for the major. Individual studies will earn 0.25–0.50 units of credit. To qualify, a student must identify a member of the mathematics department willing to direct the project. The professor, in consultation with the student, will create a tentative syllabus (including a list of readings and/or problems, goals and tasks) and describe in some detail the methods of assessment (e.g., problem sets to be submitted for evaluation biweekly; a 20-page research paper submitted at the course's end, with rough drafts due at given intervals, and so on). The department expects the student to meet regularly with his or her instructor for at least one hour per week. All standard enrollment/registration deadlines for regular college courses apply. Because students must enroll for individual studies by the end of the seventh class day of each semester, they should begin discussion of the proposed individual study preferably the semester before, so that there is time to devise the proposal and seek departmental approval before the registrar's deadline. Permission of instructor and department chair required. No prerequisite.\n\n
This is a basic course in statistics. The topics to be covered are the nature of statistical reasoning, graphical and descriptive statistical methods, design of experiments, sampling methods, probability, probability distributions, sampling distributions, estimation and statistical inference. Confidence intervals and hypothesis tests for means and proportions will be studied in the one- and two-sample settings. The course concludes with inference regarding correlation, linear regression, chi-square tests for two-way tables and one-way ANOVA. Statistical software will be used throughout the course, and students will be engaged in a wide variety of hands-on projects. No prerequisite. Offered every semester.
This course focuses on choosing, fitting, assessing and using statistical models. Simple linear regression, multiple regression, analysis of variance, general linear models, logistic regression and discrete data analysis will provide the foundation for the course. Classical interference methods that rely on the normality of the error terms will be thoroughly discussed, and general approaches for dealing with data where such conditions are not met will be provided. For example, distribution-free techniques and computer-intensive methods, such as bootstrapping and permutation tests, will be presented. Students will use statistical software throughout the course to write and present statistical reports. The culminating project will be a complete data analysis report for a real problem chosen by the student. The MATH 106–206 sequence provides a thorough foundation for statistical work in economics, psychology, biology, political science and many other fields. Prerequisite: STAT 106 or 116 or a score of 4 or 5 on the Statistics AP exam. Offered every semester.
This course will focus on linear regression models. Simple linear regression with one predictor variable will serve as the starting point. Models, inferences, diagnostics and remedial measures for dealing with invalid assumptions will be examined. The matrix approach to simple linear regression will be presented and used to develop more general multiple regression models. Building and evaluating models for real data will be the ultimate goal of the course. Time series models, nonlinear regression models and logistic regression models also may be studied if time permits. Prerequisite: STAT 106 and MATH 224. Offered every other spring.