Courses

Gain statistical understanding and skill through our many course offerings. Majors complete seven courses in the department, two in mathematics, and one in computer science. Below is a sample of the types of courses offered by the department.

Statistics Courses

STAT 101 (F, S) LEC Elementary Statistics and Data Analysis

It is impossible to be an informed citizen in today’s world without an understanding of data. Whether it is opinion polls, unemployment rates, salary disparities, the efficacy of vaccines, etc., we need to be able to interpret and gain information from statistics. This course will introduce the common methods used to analyze and present data with an emphasis on interpretation and informed decision making.

Taught by: Benjamin Bradbury Seiler | Catalog details

STAT 161 (F, S) LEC Introductory Statistics for Social Science

This course will cover the basics of modern statistical analysis with a view toward applications in the social sciences. Topics include exploratory data analysis, linear regression, basic statistical inference, and elements of probability theory. The course focuses on the application of statistical tools to solve problems, to make decisions, and the use of statistical thinking to understand the world.

Taught by: Duncan Clark, Anna Plantinga | Catalog details

STAT 18 Pennies and Steamrollers

Last offered NA

This course will explore financial derivatives markets through the lenses of market making and risk management. We will learn how traders price a wide range of vanilla and exotic financial instruments with an emphasis on dynamic hedging and arbitrage. While quantitative at times, this is not a financial mathematics course. We will focus on building an intuitive understanding of derivatives pricing, risks, and market dynamics. We will explore the practical reality over the theoretical. Classes will include real-time trading simulations to help cement that intuition and put our ideas to the test. This course is intended for any students who are curious about trading; you do not need to be planning a career in finance. Preference will be given to students who have not already interned at a broker-dealer, hedge fund, or equivalent.

Taught by: TBA | Catalog details

STAT 201 (F, S) LEC Statistics and Data Analysis

Statistics can be viewed as the art and science of turning data into information. Real world decision-making, whether in business or science, is often based on data and the perceived information it contains. Sherlock Holmes, when prematurely asked the merits of a case by Dr. Watson, snapped back, “Data, data, data! I can’t make bricks without clay.” In this course, we will study the basic methods by which statisticians attempt to extract information from data. These will include many of the standard tools of statistical inference such as hypothesis testing, confidence intervals, and linear regression as well as exploratory and graphical data analysis techniques. This is an accelerated introductory statistics course that involves computational programming and incorporates modern statistical techniques.

Taught by: Shaoyang Ning | Catalog details

STAT 202 (F, S) LEC Introduction to Statistical Modeling

Data come from a variety of sources: sometimes from planned experiments or designed surveys, sometimes by less organized means. In this course we’ll explore the kinds of models and predictions that we can make from both kinds of data, as well as design aspects of collecting data. We’ll focus on model building, especially multiple regression, and talk about its potential to answer questions about the world — and about its limitations. We’ll emphasize applications over theory and analyze real data sets throughout the course.

Taught by: Anna Neufeld | Catalog details

STAT 250 (S) LEC Statistics Foundations: Computation and Communication

Computation and communication are essential for modern statistical practice. In this course students will learn to tidy, visualize, and model data with an emphasis on communication, reproducibility, and ethical responsibility in statistical analysis. Students will engage deeply with statistical computation to work with complex data sources; implement randomization-based statistical tests; design simulation studies to empirically explore theoretical concepts such as sampling variability, model misspecification, and robustness; and train and evaluate statistical/machine learning models. In the process, we will pay attention to best practices for coding (e.g., writing functions, documenting code, using version control) and reproducible research methods (via R Markdown and GitHub). Students will practice combining the collaborative, technical, and communication aspects of data science and statistics throughout the semester by completing data-oriented projects and producing formal written analysis reports. This course counts as the computing requirement for the Statistics major.

Taught by: Anna Plantinga | Catalog details

STAT 335 (F) LEC Introduction to Biostatistics and Epidemiology

Epidemiology is the study of disease and disability in human populations, while biostatistics focuses on the development and application of statistical methods to address questions that arise in medicine, public health, or biology. This course will begin with epidemiological study designs and core concepts in epidemiology, followed by key statistical methods in public health research. Topics will include multiple regression, analysis of categorical data (two sample methods, sets of 2×2 tables, RxC tables, and logistic regression), survival analysis (Cox proportional hazards model), and if time permits, a brief introduction to regression with correlated data.

Taught by: Anna Neufeld | Catalog details

STAT 341 (F, S) LEC Probability

The historical roots of probability lie in the study of games of chance. Modern probability, however, is a mathematical discipline that has wide applications in a myriad of other mathematical and physical sciences. Drawing on classical gaming examples for motivation, this course will present axiomatic and mathematical aspects of probability. Included will be discussions of random variables (both discrete and continuous), distribution and expectation, independence, laws of large numbers, and the well-known Central Limit Theorem. Many interesting and important applications will also be presented, including some from classical Poisson processes, random walks and Markov Chains.

Taught by: Benjamin Bradbury Seiler, Steven Miller | Catalog details

STAT 342 (F) LEC Introduction to Stochastic Processes

Stochastic processes are mathematical models for random phenomena evolving in time or space. Examples include the number of people in a queue at time t or the accumulated claims paid by an insurance company in an interval of time t. This course introduces the basic concepts and techniques of stochastic processes used to construct models for a variety of problems of practical interest. The theory of Markov chains will guide our discussion as we cover topics such as random walks, Poisson process, birth and death processes, and Brownian motion.

Taught by: Elizabeth Upton | Catalog details

STAT 344 LEC Statistical Design of Experiments

Last offered Spring 2026

When you hear the word experiment you might be picturing white lab coats and pipettes, but businesses, especially e-commerce, are constantly experimenting as well. How do you get the most out of both scientific and business investigations? By doing the right experiment in the first place. We’ll explore the techniques used to plan experiments that are both efficient and statistically sound. We’ll learn how to analyze the data that come from these experiments and the conclusions we can draw from that analysis. We’ll look at both classical tools like fractional factorial designs as well as optimal design, and see how these two frameworks differ in their philosophy and in what they can do. Throughout the course, we’ll make extensive use of both R and JMP software to work with real-world data.

Taught by: Richard De Veaux | Catalog details

STAT 345 (S) LEC Applied Causal Inference

Association is NOT causality, a slogan from introductory statistics that we all know too well. Natural, the next question is: when can we actually draw causal conclusions from data, and how? This course introduces the fundamental concepts and modern methods of causal inference, with an emphasis on real-world applications. Moving beyond association and prediction, students will learn how to rigorously define, identify, estimate, and interpret causal effects from randomized experiments and observational studies. Through hands-on analysis of datasets in R, students develop practical skills to apply causal thinking and analysis to problems in public health, economics, and the social sciences.

Taught by: Shaoyang Ning | Catalog details

STAT 346 (F, S) LEC Regression Theory and Applications

This course provides a comprehensive introduction to regression theory, emphasizing both the theoretical foundations and practical applications of simple and multiple linear regression. Students will develop a deep understanding of regression models through the lens of linear algebra, exploring key concepts such as estimation, inference, and model diagnostics. Throughout the course, they will gain hands on experience using R to implement and assess regression models with real world data. By integrating theory and computation, this course equips students with the skills needed to analyze relationships between variables and make data-driven predictions across various disciplines.

Taught by: Xizhen Cai | Catalog details

STAT 350 (S) LEC An Introduction to the Statistical Analysis of Network Data

This course provides an introduction to the statistical analysis and modeling of network data. Network data arise when observations are inherently connected, representing relationships between entities rather than independent measurements. Examples include social networks, where nodes may represent individuals and edges capture friendships; biological networks, such as protein-protein interaction networks; and communication networks, like the Internet or citation networks. We will explore key concepts such as network representation, centrality measures, community detection, network regression models, and stochastic network models. While the primary focus will be on applications in the social sciences, the techniques covered are broadly applicable across disciplines.

Taught by: Duncan Clark | Catalog details

STAT 355 LEC Multivariate Statistical Analysis

Last offered Fall 2024

To better understand complex processes, we study how variables are related to one another and how they work in combination. In addition, we want to make inferences about more than one variable at a time. Elementary statistical methods might not apply. In this course, we study the tools and the intuition that are necessary to analyze and describe such datasets with more than multiple variables. Topics covered will include data visualization techniques for data sets with more variables, clustering algorithms, parametric and non-parametric techniques to estimate joint distributions, techniques for combining variables, performing dimension reduction, and making inferences.

Taught by: Xizhen Cai | Catalog details

STAT 356 LEC Time Series Analysis

Last offered Spring 2026

We encounter time series data in settings from astronomy to engineering to finance and beyond, but our traditional statistical toolbox is often inadequate in the face of time-dependent observations. This course will introduce a range of specialized statistical and machine learning methods for exploring, modeling, and forecasting time series data. There will be a strong emphasis on application, as we use these techniques on a variety of real-world data using R.

Taught by: Benjamin Bradbury Seiler | Catalog details

STAT 358 LEC Introduction to Categorical Data Analysis

Last offered Spring 2026

This course focuses on methods and models for analyzing categorical response data and has two major parts. The first part will discuss statistical inference for parameters of categorical distributions (Bernoulli, Binomial, Multinomial, Poisson) and for measures of association arising in contingency tables (difference and ratio of proportions and odds ratios). Inferential methods covered include Wald, score and likelihood ratio tests and confidence intervals, and a brief foray into Bayesian methods for inference. The longer second part will focus on statistical modeling of categorical response data via generalized linear models, such as logistic regression or log-linear models, including model formulation and fitting, categorical predictors and interactions, model comparisons, predictions, and residual analysis. Model inference will be based on maximum likelihood. Contrasts will be drawn to traditional Machine Learning algorithms such as Decision Trees and Neural Networks for binary classification. If time permits, we will discuss models for dependent (e.g., repeated or clustered) binary observations via the generalized estimating equation approach (GEE). For all topics examples will be given using the R software, and students are expected to produce R Markdown documents.

Taught by: Bernhard Klingenberg | Catalog details

STAT 360 (F) LEC Statistical Inference

Stat 360 offers a deep dive into the mathematical foundations that underpin the statistical methods covered in previous stat courses. This course explores concepts like likelihood theory, the theory of estimation, and hypothesis testing from a rigorous perspective, providing insights into the assumptions and derivations that make these methods work.

Taught by: Shaoyang Ning | Catalog details

STAT 365 LEC Bayesian Statistics

Last offered Spring 2024

The Bayesian approach to statistical inference represents a reversal of traditional (or frequentist) inference, in which data are viewed as being fixed and model parameters as unknown quantities. Interest and application of Bayesian methods have exploded in recent decades, being facilitated by recent advances in computational power. We begin with an introduction to Bayes’ Theorem, the theoretical underpinning of Bayesian statistics which dates back to the 1700’s, and the concepts of prior and posterior distributions, conjugacy, and closed-form Bayesian inference. Building on this, we introduce modern computational approaches to Bayesian inference, including Markov chain Monte Carlo (MCMC), Metropolis-Hastings sampling, and the theory underlying these simple and powerful methods. Students will become comfortable with modern software tools for MCMC using a variety of applied hierarchical modeling examples, and will use R for all statistical computing.

Taught by: TBA | Catalog details

STAT 368 (S) LEC Modern Nonparametric Statistics

Many statistical procedures and tools are based on a set of assumptions, such as normality or other parametric models. But, what if some or all of these assumptions are not valid and the adopted models are miss-specified? This question leads to an active and fascinating field in modern statistics called nonparametric statistics, where few assumptions are made on data’s distribution or the model structure to ensure great model flexibility and robustness. In this course, we start with a brief overview of classic rank-based tests, and focus primarily on modern nonparametric inferential techniques, such as nonparametric density estimation, nonparametric regression, selection of smoothing parameter (cross-validation), bootstrap, randomization-based inference, clustering, and nonparametric Bayes. Throughout the semester we will examine these new methodologies and apply them on simulated and real datasets using R.

Taught by: Xizhen Cai | Catalog details

STAT 372 (F) LEC Longitudinal Data Analysis

This course explores modern statistical methods for drawing scientific inferences from longitudinal data, i.e., data collected repeatedly on experimental units over time. The independence assumption made for most classical statistical methods does not hold with this data structure because we have multiple measurements on each individual. Topics will include linear and generalized linear models for correlated data, including marginal and random effect models, as well as computational issues and methods for fitting these models. As time permits, we will also address options for treatment of missing data. We will consider many applications in the social and biological sciences.

Taught by: Anna Plantinga | Catalog details

STAT 397 (F, S) IND Independent Study: Statistics

Directed independent study in Statistics.

Taught by: Anna Plantinga | Catalog details

STAT 398 IND Independent Study: Statistics

Last offered Spring 2024

Directed independent study in Statistics.

Taught by: Richard De Veaux | Catalog details

STAT 440 LEC Categorical Data Analysis

Last offered Spring 2024

This course focuses on methods for analyzing categorical response data. Traditional tools of statistical data analysis for continuous response data are not designed to handle such data and pose inappropriate assumptions. We will develop methods specifically designed to address the discrete nature of the observations and consider many applications in the social and biological sciences as well as in medicine, engineering and economics. The first part of the course will discuss statistical inference for parameters of categorical distributions and arising in contingency tables. The longer second part will focus on statistical modeling via generalized linear models for binary, multinomial, ordinal and count response variables, using maximum likelihood.

Taught by: TBA | Catalog details

STAT 441 LEC Information Theory and Applications

Last offered Fall 2021

What is information? And how do we communicate information effectively? This course will introduce students to the fundamental ideas of Information Theory including entropy, communication channels, mutual information, and Kolmogorov complexity. These ideas have surprising connections to a fields as diverse as physics (statistical mechanics, thermodynamics), mathematics (ergodic theory and number theory), statistics and machine learning (Fisher information, Occam’s razor), and electrical engineering (communication theory).

Taught by: Richard De Veaux | Catalog details

STAT 442 (S) LEC Statistical Learning and Data Mining

In both science and industry today, the ability to collect and store data can outpace our ability to analyze it. Traditional techniques in statistics are often unable to cope with the size and complexity of today’s data bases and data warehouses. New methodologies in Statistics have recently been developed, designed to address these inadequacies, emphasizing visualization, exploration and empirical model building at the expense of traditional hypothesis testing. In this course we will examine these new techniques and apply them to a variety of real data sets.

Taught by: Benjamin Bradbury Seiler | Catalog details

STAT 458 (F) LEC Generalized Linear Models- Theory and Applications

This course will explore generalized linear models (GLMs)–the extension of linear models, discussed in Stat346, to response variables that have specific non-normal distributions, such as counts and proportions. We will consider the general structure and theory of GLMs and see their use in a range of applications. As time permits, we will also examine extensions of these models for clustered data such as mixed effects models and generalized estimating equations.

Taught by: Elizabeth Upton | Catalog details

STAT 465 LEC Bayesian Statistics

Last offered Fall 2025

Empirical science is, by definition, based on the gathering of data. Once the data are gathered and analyzed, the beliefs of the scientist are updated. This is a natural framing of human learning, and precisely how Bayesian inference works. Given that data generating processes are random, Bayesian inference develops a mathematically and philosophically rigorous method for updating prior beliefs, depending on the data observed. The key idea, formulated by Bayes’ Theorem, dates back to 18th century. Bayesian inference is one of oldest schools of statistics (more than a century earlier than the Frequentism!). Yet, it was not until the recent developments in sampling algorithms and computational methods that Bayesian inference became practical. Bayesian, and Bayesian inspired methods, with their flexibilities in modeling generative processes of data, interpretability with posterior probability statements, and coherent principles to incorporate empirical evidence a priori, have played key roles in modern data analysis, especially for “big data” with enhanced complexity and connectivity. This course is designed to provide students a comprehensive introduction to Bayesian Statistics, with a focus on methods that are actually useful. Students will be introduced to classic Bayesian models, basic computational algorithms/methods for Bayesian inference, as well as their applications in various fields, and comparisons with classic Frequentist methods. As Bayesian inference finds its roots and merits particularly in application, this course puts great emphasis on enhancing students’ skills in statistical computation and data analysis.

Taught by: Duncan Clark | Catalog details

STAT 493 (F, S) HON Senior Thesis: Statistics

Each student carries out an individual research project under the direction of a faculty member that culminates in a thesis. See description under The Degree with Honors in Statistics.

Taught by: Anna Plantinga | Catalog details

STAT 494 HON Senior Thesis: Statistics

Last offered Spring 2026

Each student carries out an individual research project under the direction of a faculty member that culminates in a thesis. See description under The Degree with Honors in Statistics.

Taught by: Richard De Veaux | Catalog details

STAT 497 (F, S) IND Independent Study: Statistics

Directed independent study in Statistics.

Taught by: Anna Plantinga | Catalog details

STAT 498 IND Independent Study: Statistics

Last offered Spring 2026

Directed independent study in Statistics.

Taught by: Richard De Veaux | Catalog details

STAT 499 (F, S) SEM Statistics Colloquium

Statistics senior colloquium. Meets every week for an hour both fall and spring. Senior statistics majors must participate. This colloquium is in addition to the regular four semester-courses taken by all students.

Taught by: Anna Plantinga | Catalog details