kimberly f. sellers

Teaching

I teach undergraduate and graduate (Masters level) courses in an effort to interest and excite students about the power and significance of statistics (no pun intended). My teaching philosophy revolves around an organized class format and structure, interaction with the students (both in and outside of the classroom), and overall general conduct. In some of my courses, I further incorporate real-world training by having students complete a data analytic project in lieu of a final examination. Through these efforts, both the students and I can feel proud of the accomplishments and progress made as a result of a well-organized and interesting course.

Technology In and Outside of the Classroom

I maintain a digital course management system to supply a detailed semester schedule (including deadlines) where students can access and download my lecture notes, homework assignments and solutions, relevant data, project materials, etc. All courses use the statistical computing software, R. I further use technology to remain in contact with students, monitor their progress, anticipate points of confusion, address misunderstandings, and provide follow-up notes to resolve any misconceptions or diculties; this includes conducting class lectures via an online video conferencing service to maintain instructional continuity when necessary (e.g. inclement weather).

MATH-040

Probability & Statistics

This course is intended to be an introduction to statistics, serving a broad emphasis in various applications. You will use MINITAB to handle some calculations; however, you are still expected to understand how the computations are determined and interpreted.

Taught individual section format: Fall 2006, Fall 2007, Spring 2008
Taught in large lecture format: Fall 2008, Fall 2009, Spring 2010

MATH-140

Intro. to Mathematical Statistics

This course provides an introduction to probability theory and statistical inference. Probability topics include basic probability rules, properties of random variables, common discrete and continuous distribution functions, and expected values. Meanwhile, statistical concepts include the central limit theorem, maximum likelihood estimation, confidence intervals, hypothesis testing, and linear regression. The overarching learning goal for this course is for students to gain an appreciation of probability and statistics, and its significance in a wide array of disciplines and applications.

Taught Spring 2012

MATH-240

Regression Analysis

This course provides an in-depth coverage of regression analysis. After reviewing matrix algebra and simple linear regression using matrix notation, the course will focus on inference and model building in multiple linear regression. Regression inference, handling of categorical regressors, variable selection, interaction effects, multicollinearity, model diagnostics will be thoroughly covered. The course concludes with one-way and multi-way analysis of variance (ANOVA) models. Statistical concepts will be accompanied by hands-on data analysis using the R statistical software.

Taught Spring 2012, Fall 2013, Spring 2014, Spring 2017, Spring 2020 (two sections) , Spring 2022, Fall 2022, Spring 2023

MATH-340

Advanced Statistical Methods

This course builds on the principles and methods of statistical reasoning that were developed through MATH 240 and introduces the student to various upper-level statistical concepts and methods. Topics include generalized linear models (e.g. logistic regression, Poisson regression, alternatives for over-dispersed data, etc.), ridge regression, and smoothing methods.

Taught Fall 2012, Spring 2013, Fall 2013, Spring 2014, Fall 2015, Fall 2016, Fall 2017, Fall 2018, Fall 2019, Fall 2021, Fall 2022

MATH-503

Mathematical Statistics

This is a first course in the mathematical theory of statistical inference. The emphasis is on classical methods, with appropriate attention also to Bayesian methods. Topics include principles of data reduction (sufficiency and sufficient statistics, likelihood, invariance); point estimation (method of moments, maximum likelihood, Bayes estimators) and associated criteria (mean squared error, unbiasedness, consistency); some asymptotic properties of point estimators; construction of and criteria for hypothesis tests (error probabilities and power, most powerful tests, bias); asymptotics of some large sample tests; construction of and criteria for interval estimate; and elements of decision theory and applications to statistical inference (Bayes rules, minimax), analysis of variance (one-way ANOVA, F-test, contrasts), linear regression (least squares, tests for model parameters, pointwise and simultaneous estimation and prediction), and nonparametric methods (e.g., sign test, rank sum test, Wilcoxin test, Mann-Whitney test).

Taught Spring 2007, Spring 2008, Spring 2009, Spring 2011, Spring 2013, Spring 2016, Spring 2019, Fall 2021, Spring 2022

MATH-651

Regression & Generalized Linear Models

This course will focus on the theory and application of regression methods for statistical modeling and data analysis. Emphasis will be in the following areas: simple and multiple regression, inference and prediction, model building and diagnostics, model selection and validation, analysis of variance (ANOVA), analysis of covariance (ANCOVA), generalized linear models, logistic and Poisson regression and other extensions as time permits (e.g. mixed models, nonlinear regression). Practical issues involved in implementation of these methods will be presented using statistical software packages (R) based on example problems from a wide range of applications.

Taught (all Fall semesters) 2015, 2016, 2017, 2018, 2019

MATH-652

Applied Multivariate Analysis

This course is an introduction to the analysis of multivariate data. Topics include matrix algebra and random vectors, the multivariate normal distribution, Hotelling's T-squared, multivariate linear regression models, principal components, factor analysis, and discriminant analysis. The overarching learning goal for this course is for students to acquire an understanding and ability to analyze multivariate data.

Taught Spring 2009, Fall 2012, Spring 2017, Spring 2023

UROP-MATH 18

Undergraduate Mentored Research

Time permitting, I advise undergraduates through the Georgetown Undergraduate Research Opportunities Program (GUROP). The primary research focus is on developing statistical methods to analyze dispersed count data. Classical statistical methods involving count data are motivated by a Poisson model. Real data, however, rarely adhere to its equi-dispersion constraint (i.e. that the variance equals the mean). Therefore, we develop statistical methods that circumvent this issue via a more generalized count distribution model that can handle over- or under-dispersion.

Advised Fall 2009- Spring 2010, Calendar Year 2013, Spring 2014, 2017-2023

Testimonials

Testimonials from undergraduate & graduate course teaching evaluations.

"I wish every professor was like Professor Sellers.... She is definitely one of the best professors that I have had."

"[Professor Sellers] is very helpful in providing outside resources for conferences and job opportunities, and makes it apparent that she wants to see her students succeed."

"Professor Sellers's classes were highly organized. Her powerpoints are easy to follow, and her presentation style is very engaging. She is very good at answering questions both in and out of class. Because of her, I wish I had more space in my schedule to take more statistics classes!"

"Professor Sellers is one of the best instructors I've had in the math/stat program and I always leave her classes with new skills that I feel comfortable applying."