two students look at a mathematical proof on a chalkboard

Carleton’s Mathematics and Statistics department offers two majors and a minor. The mathematics curriculum provides essential skills for students across many disciplines, and instills majors with a deep understanding of the history and current practice of mathematics. Statistics focuses on organizing and analyzing data. Students gain experience with statistical software and learn to apply numbers to real-world problems.

two students look at a mathematical proof on a chalkboard

About Mathematics and Statistics

Mathematics is an art, a pure science, a language, and an analytical tool for the natural and social sciences, a means of exploring philosophical questions, and a beautiful edifice that is a tribute to human creativity. The mathematics curriculum is designed to provide essential skills for students in a variety of disciplines and to provide mathematics majors with a deep understanding of mathematics as it has evolved over the past two thousand years and how it is practiced today.

Statistics is the science of giving meaning to data in the context of uncertainty. Statisticians are involved in data collection and study design, data analysis, and the communication of information to a broad audience. The statistics curriculum is designed to balance both statistical theory and application, and will provide students the opportunity to work on real world data problems and enhance their communication skills.

Students who wish to major in both Mathematics and Statistics should note the College policy that double majors may count no more than four courses toward both majors. Courses for which a student earns AP Credit, such as calculus, are included among these four courses.

Math Skills Center

The Math Skills Center supports all Carleton students in any mathematics or math-related course they are taking. The center’s tutors help students with mathematical concepts and with the mathematical tools needed to succeed in their courses.

Requirements for the Mathematics Major

The Mathematics major requires 72 credits:

  •  A. Required Core Courses (take either Mathematics 101 or 111 and either Mathematics 210 or 211 and all of remaining courses listed):
  •  B. Electives (36 credits): Six courses from among:
  • CS 252: Algorithms
  • CS 254: Computability and Complexity
  • CS 352: Advanced Algorithms (not offered 2023-24)
  • MATH 240: Probability
  • MATH 241: Ordinary Differential Equations
  • MATH 244: Geometries
  • MATH 251: Chaotic Dynamics (not offered 2023-24)
  • MATH 261: Functions of a Complex Variable (not offered 2023-24)
  • MATH 265: Probability (not offered 2023-24)
  • MATH 271: Computational Mathematics
  • MATH 275: Introduction to Statistical Inference (not offered 2023-24)
  • MATH 282: Elementary Theory of Numbers
  • MATH 295: Introduction to Computational Algebraic Geometry
  • MATH 312: Elementary Theory of Numbers (not offered 2023-24)
  • MATH 315: Topics Probability/Statistics: Bayesian Statistics (not offered 2023-24)
  • MATH 321: Real Analysis I
  • MATH 331: Real Analysis II (not offered 2023-24)
  • MATH 332: Advanced Linear Algebra
  • MATH 333: Combinatorial Theory (not offered 2023-24)
  • MATH 341: Partial Differential Equations
  • MATH 342: Abstract Algebra I
  • MATH 344: Differential Geometry (not offered 2023-24)
  • MATH 349: Methods of Teaching Mathematics
  • MATH 352: Galois Theory
  • MATH 354: Topology
  • MATH 361: Complex Analysis
  • MATH 395: Algebraic Geometry Seminar (not offered 2023-24)
  • STAT 250: Introduction to Statistical Inference
  • STAT 320: Time Series Analysis
  • STAT 340: Bayesian Statistics (not offered 2023-24)

At least four of these electives must be Carleton courses with a MATH designation. At least three of the following five areas of mathematics must be represented by the six electives (36 credits).

  • Algebra:
  • MATH 282: Elementary Theory of Numbers
  • MATH 312: Elementary Theory of Numbers (not offered 2023-24)
  • MATH 332: Advanced Linear Algebra
  • MATH 342: Abstract Algebra I
  • MATH 352: Galois Theory
  • MATH 395: Algebraic Geometry Seminar (not offered 2023-24)
  • Analysis:
  • MATH 251: Chaotic Dynamics (not offered 2023-24)
  • MATH 261: Functions of a Complex Variable (not offered 2023-24)
  • MATH 321: Real Analysis I
  • MATH 331: Real Analysis II (not offered 2023-24)
  • MATH 361: Complex Analysis
  • MATH 395: Algebraic Geometry Seminar (not offered 2023-24)
  • Applied Mathematics:
  • MATH 240: Probability
  • MATH 241: Ordinary Differential Equations
  • MATH 265: Probability (not offered 2023-24)
  • MATH 271: Computational Mathematics
  • MATH 275: Introduction to Statistical Inference (not offered 2023-24)
  • MATH 295: Introduction to Computational Algebraic Geometry
  • MATH 315: Topics Probability/Statistics: Bayesian Statistics (not offered 2023-24)
  • MATH 341: Partial Differential Equations
  • STAT 250: Introduction to Statistical Inference
  • STAT 320: Time Series Analysis
  • STAT 340: Bayesian Statistics (not offered 2023-24)
  • Discrete Structures:
  • CS 252: Algorithms
  • CS 254: Computability and Complexity
  • CS 352: Advanced Algorithms (not offered 2023-24)
  • MATH 333: Combinatorial Theory (not offered 2023-24)
  • Geometry and Topology:

Of the six advanced courses, at least four must be Carleton courses with a Mathematics designation. Advanced courses substituted for Mathematics 232 or Mathematics 236 must also be Carleton courses with a Mathematics designation.

In addition, each senior major must complete an integrative exercise, Mathematics 400 (6 credits) which can be either a group or individual project. Majors must also accumulate eight talk credits during their junior and senior year by attending colloquia and the comps talks of their fellow mathematics or statistics majors. Students who major in both Mathematics and Statistics must accumulate a total of thirteen talk credits. We encourage majors to participate in the numerous activities that take place in the department.

Potential majors with especially strong preparation may petition the department for permission to substitute an advanced course for Mathematics 232 and/or for Mathematics 236. Advanced courses substituted for Mathematics 232 or Mathematics 236 must also be Carleton courses with a Mathematics designation.

There are many patterns of courses for the major depending upon a student’s mathematical interests and career goals. A guide for majors, which supplies information about suitable patterns of courses, is available on the Mathematics and Statistics Department website.

Major under Combined Plan in Engineering:

In addition to completing requirements for the mathematics major listed above including Mathematics 241 and 341, the student should take the following courses required for admission to engineering schools: Two terms of 100-level Physics, Chemistry 123, 224, and Computer Science 111.

Requirements for the Statistics Major

The requirements for the Statistics Major are 74 credits:

  • A.  Supporting Courses (30 credits) Take either Mathematics 101 or 111 and either Mathematics 210 or 211 and all of remaining courses listed:
  • B.  Required Core (18 credits): All of the following, of which at least two must be taken at Carleton
  • MATH 240: Probability
  • MATH 245: Applied Regression Analysis (not offered 2023-24)
  • MATH 265: Probability (not offered 2023-24)
  • MATH 275: Introduction to Statistical Inference (not offered 2023-24)
  • STAT 230: Applied Regression Analysis
  • STAT 250: Introduction to Statistical Inference
  • C. Electives (18 credits): Three electives, of which at least two must be Carleton courses with a Statistics designation
  • CS 314: Data Visualization
  • CS 320: Machine Learning
  • CS 362: Computational Biology
  • MATH 271: Computational Mathematics
  • MATH 285: Introduction to Data Science (not offered 2023-24)
  • MATH 295: Introduction to Computational Algebraic Geometry
  • MATH 315: Topics Probability/Statistics: Bayesian Statistics (not offered 2023-24)
  • MATH 345: Advanced Statistical Modeling (not offered 2023-24)
  • STAT 220: Introduction to Data Science
  • STAT 260: Introduction to Sampling Techniques
  • STAT 310: Spatial Statistics
  • STAT 320: Time Series Analysis
  • STAT 330: Advanced Statistical Modeling (not offered 2023-24)
  • STAT 340: Bayesian Statistics (not offered 2023-24)
  • D. Statistical Practice (2 credits):
    • STAT 285: Statistical Consulting Statistical Consulting 

In addition, each senior major must complete an integrative exercise. Statistics 400 (6 credits), which can be either a group or individual project. Majors must accumulate eight talk credits during their junior and senior year by attending department colloquia and the comps talks of their fellow mathematics or statistics majors. Students who major in both Mathematics and Statistics must accumulate a total of thirteen talk credits. We encourage majors to participate in the numerous activities that take place in the department.

We recommend statistics majors also take courses in a discipline in which statistics can be applied. Students interested in data science should consider taking additional computer science courses.

Students considering graduate school in statistics or biostatistics are strongly encouraged to take Mathematics 236 (Mathematical Structures) and Mathematics 321 (Real Analysis). Consult a statistics faculty member for more information specific to your choice of program.

Requirements for the Mathematics Minor

To earn a minor in Mathematics, a student must earn 42 credits from courses taken in the Department of Mathematics and Statistics at Carleton. (Students who place out of courses based on work done outside of Carleton are still required to earn 42 credits from courses taken in the Department of Mathematics and Statistics at Carleton.) At least 36 of the required 42 credits must come from courses with a Mathematics designation. In addition, the only Statistics courses which can be counted toward the Mathematics minor are Statistics 250, 320 and 340.

Students who wish to major in Statistics and minor in Mathematics should note the College policy that a student may not fulfill more than half the credits for a minor from the courses counted toward their major or majors.

Mathematics Courses

  • MATH 101 Calculus with Problem Solving

    An introduction to the central ideas of calculus with review and practice of those skills needed for the continued study of calculus. Problem solving strategies will be emphasized. In addition to regular MWF class time, students will be expected to attend two problem-solving sessions each week, one on Monday or Tuesday, and one on Wednesday or Thursday. Details will be provided on the first day of class.

  • MATH 111 Introduction to Calculus

    An introduction to the differential and integral calculus. Derivatives, antiderivatives, the definite integral, applications, and the fundamental theorem of calculus.

  • MATH 120 Calculus 2

    Inverse functions, integration by parts, improper integrals, modeling with differential equations, vectors, calculus of functions of two independent variables including directional derivatives and double integrals, Lagrange multipliers.

    • Fall 2023, Winter 2024, Spring 2024
    • 6
    • Formal or Statistical Reasoning

    • Mathematics 101, 111, score of 4 or 5 on Calculus AB Exam or placement via a Carleton placement exam. Not open to students who have received credit for Mathematics 211 or have a score of 4 or 5 on the AP Calculus BC exam
    • Claudio Gómez-Gonzáles 🏫 👤 · Corey Brooke 🏫 👤 · Sunrose Shrestha 🏫 👤

  • MATH 206 A Tour of Mathematics

    A series of eight lectures intended for students considering a Mathematics major. The emphasis will be on presenting various striking ideas, concepts and results in modern mathematics, rather than on developing extensive knowledge or techniques in any particular subject area.

  • MATH 210 Calculus 3

    Vectors, curves, calculus of functions of three independent variables, including directional derivatives and triple integrals, cylindrical and spherical coordinates, line integrals, Green’s theorem, sequences and series, power series, Taylor series.

  • MATH 211 Introduction to Multivariable Calculus

    Vectors, curves, partial derivatives, gradient, multiple and iterated integrals, line integrals, Green’s theorem.

  • MATH 215 Introduction to Statistics

    Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 265-275 Probability-Statistics sequence.

    Not offered in 2023-24

  • MATH 232 Linear Algebra

    Linear algebra centers on the study of highly structured functions called linear transformations. Given the abundance of nonlinear functions in mathematics, it may come as a surprise that restricting to linear ones opens the door to a rich and powerful theory that finds applications throughout mathematics, statistics, computer science, and the natural and social sciences. Linear transformations are everywhere, once we know what to look for. They appear in calculus as the functions that are used to define lines and planes in Euclidean space. In fact, differentiation is also a linear transformation that takes one function to another. The course focuses on developing geometric intuition as well as computational matrix methods. Topics include kernel and image of a linear transformation, vector spaces, determinants, eigenvectors and eigenvalues.

  • MATH 236 Mathematical Structures

    Basic concepts and techniques used throughout mathematics. Topics include logic, mathematical induction and other methods of proof, problem solving, sets, cardinality, equivalence relations, functions and relations, and the axiom of choice. Other topics may include: algebraic structures, graph theory, and basic combinatorics.

  • MATH 240 Probability

    Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

  • MATH 241 Ordinary Differential Equations

    Ordinary differential equations are a fundamental language used by mathematicians, scientists, and engineers to describe processes involving continuous change. In this course we develop ordinary differential equations as models of real world phenomena and explore the mathematical ideas that arise within these models. Topics include separation of variables; phase portraits; equilibria and their stability; non-dimensionalization; bifurcation analysis; and modeling of physical, biological, chemical, and social processes.

  • MATH 244 Geometries

    Euclidean geometry from an advanced perspective; projective, hyperbolic, inversive, and/or other geometries. Recommended for prospective secondary school teachers.

  • MATH 245 Applied Regression Analysis

    A second course in statistics covering simple linear regression, multiple regression and ANOVA, and logistic regression. Exploratory graphical methods, model building and model checking techniques will be emphasized with extensive use of statistical software to analyze real-life data.

    Not offered in 2023-24

  • MATH 251 Chaotic Dynamics

    An exploration of the behavior of non-linear dynamical systems. Topics include one and two-dimensional dynamics, Sarkovskii’s Theorem, chaos, symbolic dynamics,and the Hénon Map.

    Not offered in 2023-24

  • MATH 261 Functions of a Complex Variable

    Algebra and geometry of complex numbers, analytic functions, complex integration, series, residues, applications. Not open to students who have already received credits for Mathematics 361.

    Not offered in 2023-24

  • MATH 265 Probability

    Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

    Not offered in 2023-24

  • MATH 271 Computational Mathematics

    An introduction to mathematical ideas from numerical approximation, scientific computing, and/or data analysis. Topics will be selected from numerical linear algebra, numerical analysis, and optimization. Theory, implementation, and application of computational methods will be emphasized.

  • MATH 275 Introduction to Statistical Inference

    Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.

    Not offered in 2023-24

  • MATH 280 Statistical Consulting

    Students will apply their statistical knowledge by analyzing data problems solicited from the Northfield community. Students will also learn basic consulting skills, including communication and ethics.

    Not offered in 2023-24

  • MATH 282 Elementary Theory of Numbers

    A first course in number theory, covering properties of the integers. Topics include the Euclidean algorithm, prime factorization, Diophantine equations, congruences, divisibility, Euler’s phi function and other multiplicative functions, primitive roots, and quadratic reciprocity. Along the way we will encounter and explore several famous unsolved problems in number theory. If time permits, we may discuss further topics, including integers as sums of squares, continued fractions, distribution of primes, Mersenne primes, the RSA cryptosystem.

  • MATH 285 Introduction to Data Science

    This course will cover the computational side of data analysis, including data acquisition, management and visualization tools. Topics may include: data scraping, clean up and manipulation, data visualization using packages such as ggplots, understanding and visualizing spatial and network data, and supervised and unsupervised classification methods. We will use the statistics software R in this course.

    Not offered in 2023-24

  • MATH 295 Introduction to Computational Algebraic Geometry

    Classical algebraic geometry is the study of geometric objects defined by polynomial equations. This course will cover fundamental concepts and techniques—varieties, ideals, and Gröbner bases, to name a few—as well as algorithms for solving equations and computing intersections of curves and surfaces. Ultimately, this course will build towards several beautiful results: the 27 lines on a cubic surface, the 28 bitangents on a planar quartic, and the construction of regular polygons. Students will learn to use software such as SageMath to perform computations and practice visualization. While familiarity with Python would be helpful, it is by no means required!

  • MATH 312 Elementary Theory of Numbers

    Properties of the integers. Topics include the Euclidean algorithm, classical unsolved problems in number theory, prime factorization, Diophantine equations, congruences, divisibility, Euler’s phi function and other multiplicative functions, primitive roots, and quadratic reciprocity. Other topics may include integers as sums of squares, continued fractions, distribution of primes, integers in extension fields, p-adic numbers.

    Not offered in 2023-24

  • MATH 315 Topics Probability/Statistics: Bayesian Statistics

    An introduction to statistical inference and modeling in the Bayesian paradigm. Topics include Bayes’ Theorem, common prior and posterior distributions, hierarchical models, Markov chain Monte Carlo methods (e.g., the Metropolis-Hastings algorithm and Gibbs sampler) and model adequacy and posterior predictive checks. The course uses R extensively for simulations.

    Not offered in 2023-24

  • MATH 321 Real Analysis I

    A systematic study of concepts basic to calculus, such as topology of the real numbers, limits, differentiation, integration, convergence of sequences, and series of functions.

  • MATH 331 Real Analysis II

    Further topics in analysis such as measure theory, Lebesgue integration or Banach and Hilbert spaces.

    Not offered in 2023-24

  • MATH 332 Advanced Linear Algebra

    Selected topics beyond the material of Mathematics 232. Topics may include the Cayley-Hamilton theorem, the spectral theorem, factorizations, canonical forms, determinant functions, estimation of eigenvalues, inner product spaces, dual vector spaces, unitary and Hermitian matrices, operators, infinite-dimensional spaces, and various applications.

  • MATH 333 Combinatorial Theory

    The study of structures involving finite sets. Counting techniques, including generating functions, recurrence relations, and the inclusion-exclusion principle; existence criteria, including Ramsey’s theorem and the pigeonhole principle. Some combinatorial identities and bijective proofs. Other topics may include graph and/or network theory, Hall’s (“marriage”) theorem, partitions, and hypergeometric series.

    Not offered in 2023-24

  • MATH 341 Partial Differential Equations

    An introduction to partial differential equations with emphasis on the heat equation, wave equation, and Laplace’s equation. Topics include the method of characteristics, separation of variables, Fourier series, Fourier transforms and existence/uniqueness of solutions.

  • MATH 342 Abstract Algebra I

    Introduction to algebraic structures, including groups, rings, and fields. Homomorphisms and quotient structures, polynomials, unique factorization. Other topics may include applications such as Burnside’s counting theorem, symmetry groups, polynomial equations, or geometric constructions.

  • MATH 344 Differential Geometry

    Local and global theory of curves, Frenet formulas. Local theory of surfaces, normal curvature, geodesics, Gaussian and mean curvatures, Theorema Egregium.

    Not offered in 2023-24

  • MATH 345 Advanced Statistical Modeling

    Topics include linear mixed effects models for repeated measures, longitudinal or hierarchical data and generalized linear models (of which logistic and Poisson regression are special cases) including zero-inflated Poisson models. Depending on time, additional topics could include survival analysis, generalized additive models or models for spatial data.

    Not offered in 2023-24

  • MATH 349 Methods of Teaching Mathematics

    Methods of teaching mathematics in grades 7-12. Issues in contemporary mathematics education. Regular visits to school classrooms and teaching a class are required.

  • MATH 352 Galois Theory

    In the nineteenth century, Évariste Galois discovered a deep connection between field theory and group theory. Now known as Galois theory, this led to the resolution of several centuries-old problems, including whether there is a version of the quadratic formula for higher-degree polynomials, and whether the circle can be squared. Today Galois theory is a fundamental concept for many mathematical fields, from topology to algebra to number theory. This course develops the theory in a modern framework, and explores several applications. Topics include field extensions, classical constructions, splitting fields, the Galois correspondence, Galois groups of polynomials, and solvability by radicals.

  • MATH 354 Topology

    An introduction to the study of topological spaces. We develop concepts from point-set and algebraic topology in order to distinguish between different topological spaces up to homeomorphism. Topics include methods of construction of topological spaces; continuity, connectedness, compactness, Hausdorff condition; fundamental group, homotopy of maps.

  • MATH 361 Complex Analysis

    The theoretical foundations for the calculus of functions of a complex variable.

    • Winter 2024
    • 6
    • Formal or Statistical Reasoning

    • Mathematics 321 or instructor permission. Students who have already received credit for Mathematics 261 may only take this course with instructor permission
    • Caroline Turnage-Butterbaugh 🏫 👤

  • MATH 395 Algebraic Geometry Seminar

    An exploration of topics in algebraic geometry and related commutative algebra. Participants will each give at least one substantive presentation at the blackboard. Topics will definitely include the Hilbert basis theorem and the Nullstellensatz, but beyond that substantial variation is possible, depending on the interests and mathematical backgrounds of the participants.  

    Not offered in 2023-24

  • MATH 399 Senior Seminar

    As part of their senior capstone experience, majors will work together in teams (typically three to four students per team) to develop advanced knowledge in a faculty-specified area or application of mathematics, and to design and implement the first stage of a project completed the following term.

  • MATH 400 Integrative Exercise

    Either a supervised small-group research project or an individual, independent reading. Required of all senior majors.

    • Fall 2023, Winter 2024, Spring 2024
    • S/NC
    • 3

    • Mathematics 236 and successful completion of three courses from among: Mathematics courses numbered above 236, Computer Science 252, Computer Science 254, Computer Science 352, Statistics 250, Statistics 320, Statistics 340
    • Deanna Haunsperger 🏫 👤 · Caroline Turnage-Butterbaugh 🏫 👤 · Staff Rob Thompson 🏫 👤 · Rafe Jones 🏫 👤 · Sunrose Shrestha 🏫 👤

Statistics Courses

  • STAT 120 Introduction to Statistics

    Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of the statistical software R, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 Probability/Statistical Inference sequence.

  • STAT 220 Introduction to Data Science

    This course will cover the computational side of data analysis, including data acquisition, management, and visualization tools. Topics may include: data scraping, data wrangling, data visualization using packages such as ggplots, interactive graphics using tools such as Shiny, supervised and unsupervised classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.

  • STAT 230 Applied Regression Analysis

    A second course in statistics covering simple linear regression, multiple regression and ANOVA, and logistic regression. Exploratory graphical methods, model building and model checking techniques will be emphasized with extensive use of statistical software to analyze real-life data.

  • STAT 250 Introduction to Statistical Inference

    Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.

  • STAT 260 Introduction to Sampling Techniques

    Covers sampling design issues beyond the basic simple random sample: stratification, clustering, domains, and complex designs like two-phase and multistage designs. Inference and estimation techniques for most of these designs will be covered and the idea of sampling weights for a survey will be introduced. We may also cover topics like graphing complex survey data and exploring relationships in complex survey data using regression and chi-square tests.

  • STAT 285 Statistical Consulting

    Students will apply their statistical knowledge by analyzing data problems solicited from the Northfield community. Students will also learn basic consulting skills, including communication and ethics.

  • STAT 310 Spatial Statistics

    Spatial data is becoming increasingly available in a wide range of disciplines, including social sciences such as political science and criminology, as well as natural sciences such as geosciences and ecology. This course will introduce methods for exploring and analyzing spatial data. Methods will be covered to describe and analyze three main types of spatial data: areal, point process, and point-referenced (geostatistical) data. The course will also extensively cover tools for working with spatial data in R. The goals are that by the end of the course, students will be able to read, explore, plot, and describe spatial data in R, determine appropriate methods for analyzing a given spatial dataset, and work with their own spatial dataset(s) in R and derive conclusions about an application through statistical inference.

  • STAT 320 Time Series Analysis

    Models and methods for characterizing dependence in data that are ordered in time. Emphasis on univariate, quantitative data observed over evenly spaced intervals. Topics include perspectives from both the time domain (e.g., autoregressive and moving average models, and their extensions) and the frequency domain (e.g., periodogram smoothing and parametric models for the spectral density).

  • STAT 330 Advanced Statistical Modeling

    (Formerly MATH 315) Topics include linear mixed effects models for repeated measures, longitudinal or hierarchical data and generalized linear models (of which logistic and Poisson regression are special cases) including zero-inflated Poisson models. Depending on time, additional topics could include survival analysis, generalized additive models or models for spatial data.

    Not offered in 2023-24

  • STAT 340 Bayesian Statistics

    Formerly MATH 315) An introduction to statistical inference and modeling in the Bayesian paradigm. Topics include Bayes’ Theorem, common prior and posterior distributions, hierarchical models, Markov chain Monte Carlo methods (e.g., the Metropolis-Hastings algorithm and Gibbs sampler) and model adequacy and posterior predictive checks. The course uses R extensively for simulations.

    Not offered in 2023-24

  • STAT 400 Integrative Exercise

    Either a supervised small-group research project or an individual, independent reading. Required of all senior majors.

    • Fall 2023, Winter 2024, Spring 2024
    • S/NC
    • 3

    • Senior Statistics major. Students are strongly encouraged to complete Statistics 230 and Statistics 250 before starting this course
    • Deanna Haunsperger 🏫 👤 · Staff Claire Kelling 🏫 👤 · Andy Poppick 🏫 👤