Teaching

Rose-Hulman operates on a quarter system. Each statistics course meets for four 50-minute periods per week over the course of a 10-week term. While the content for each of my courses is hosted on Moodle (our course management system), a brief summary of each course I teach is provided below. When available, additional information on each course can be obtained by clicking on the title of the course.

I use some form of Specifications Grading and/or Ungrading in each of my courses.

Senior Projects and Theses

The senior capstone experience can fall into one of the following categories:

• Statistical Collaboration: This consists of an analysis of data to advance another scientific discipline; this is generally provided by a client. This is generally viewed as a Senior Project.
• Numerical Investigation of Existing Method: This consists of characterizing the behavior of an existing method in a previously unexplored setting using numerical simulations.
• Development of New Statistical Theory: After researching an open problem in the field, a novel approach is proposed and characterized using theory and/or numerical simulations.
• State of the Art: An in-depth summary of methodological developments for a topic in the field. This extensive literature review should represent a novel contribuion to the field.

Students interested in completing a senior capstone experience in statistics with me should review my Capstone Syllabus; if you would like to proceed after reviewing that document, contact me via email.

A list of past projects/theses I have supervised can be found on my research page.

Courses Taught Regularly

• Engineering Statistics I (MA223)
  Technically calculus-based, this introductory course is designed to capitalize on the strong mathematical background of Rose-Hulman students while emphasizing statistical concepts over mathematical computations. The statistical process is highlighted: formulating a question of interest in terms of parameters that characterize the population, collecting data appropriately for addressing the question of interest, summarizing the information available within the sample, characterizing the variability in our statistics, and performing inference about the parameters using the available sample. We introduce students to sampling methods, confidence intervals, hypothesis testing, simple linear regression analysis, ANOVA, and repeated measures ANOVA.
• Statistical Programming (MA386)
  This course introduces tools and strategies for data management, manipulation and analysis that are common in statistics and data science. These skills are developed primarily in the R computing language. This class encourages students to build their portfolio in applied work.
• Biostatistics (MA482)
  This course introduces the analytical tools commonly used in the biological sciences. Emphasizing modeling, this course covers repeated measures, categorical response variables, censored survival (or reliability), and nonlinear relationships. Other topics discussed as time permits include pooling results from multiple studies, handling missing data, study design and power, and drawing causal conclusions from observational data. The course is not meant to provide an in-depth statistical treatment of these topics, but instead aims to enable students to evaluate the strength of evidence presented in the literature within their field of study. Largely driven by small projects analyzing real data, the course focuses on identifying when the application of these techniques is warranted and interpreting the results produces by these methods (such as those found in the medical literature).

Additional Courses Taught

• Mathematical Statistics (MA481)
  This course introduces students to the analytical tools needed to establish the properties of common statistical methods. Beginning with a discussion of random vectors, including the generation of random variates for use in a simulation study, we explore the properties of a random sample. This includes convergence concepts such as the consistency of a statistic and its limiting distribution (including a proof of the central limit theorem). Maximum likelihood methods for estimation and inference are the climax of the course. We end the course with a short introduction to nonparametric methods for density estimation and the bootstrap. The course combines theory, application and simulation throughout in order to motivate the statistical results we rely on in practice.
• Bayesian Data Analysis (MA483)
  This course offers an introduction to inference under the Bayesian framework. This includes inference about a single sample through generalized linear models. The course also introduces computational tools for Bayesian inference, including Markov Chain Monte Carlo (MCMC) methods.
• Applied Regression (MA485)
  This course explores linear regression modeling, which provides a unifying framework for many standard statistical methods. Regression modeling also provides a foundation for many advanced procedures. We examine questions that can be addressed through regression modeling such as isolating the effect of a predictor, modeling nonlinear relationships, and allowing for effect modifications. We also examine the theory (probability and linear algebra primarily) underlying the model.