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MATH 105 and 106 are designed for students whose background and algebraic skills are inadequate for the fast pace of MATH 111. In addition to covering the usual material of beginning calculus, these courses will have an extensive review of algebra and trigonometry. There will be a special emphasis on solving word problems.

MATH 105 starts with a quick review of algebraic manipulations, inequalities, absolute values and straight lines. Then the basic ideas of calculus—limits, derivatives, and integrals—are introduced, but only in the context of polynomial and rational functions. As various applications are studied, the algebraic techniques involved will be reviewed in more detail. When covering related rates and maximum-minimum problems, time will be spent learning how to approach, analyze and solve word problems. Four class meetings per week, one of which is an 80-minute group-work day.

Note: While MATH 105 and 106 are sufficient for any course with a MATH 111 requisite, MATH 105 alone is not. However, students who plan to take MATH 121 should consider taking MATH 105 and then MATH 111, rather than MATH 106. Students cannot register for both MATH 105 and CHEM 151 in the same semester.

Fall semester. The Department.

MATH 106 is a continuation of MATH 105. Trigonometric, logarithmic and exponential functions will be studied from the point of view of both algebra and calculus. The applications encountered in MATH 105 will reappear in problems involving these new functions. The basic ideas and theorems of calculus will be reviewed in detail, with more attention being paid to rigor.

Requisite: MATH 105. Spring semester. Professor D. Benedetto.

Basic concepts of limits, derivatives, anti-derivatives; applications, including max/min problems and related rates; the definite integral, simple applications; trigonometric functions; logarithms and exponential functions.

Requisite: Math Placement into 111, or consent of the Department. Limited to 30 students per section. Fall and spring semesters. In the fall semester, the intensive section (Section 01) is open only to students listed as eligible on the Mathematics placement list. The intensive section includes one additional weekly class meeting. The Department.

A continuation of MATH 111. Inverse trigonometric and hyperbolic functions; methods of integration, both exact and approximate; applications of integration to volume and arc length; improper integrals; l’Hôpital’s rule; infinite series, power series and the Taylor development; and polar coordinates.

Requisite: A grade of C or better in MATH 111 or placement into MATH 121 or consent of the Department. Limited to 30 students per section. Fall and spring semesters.The Department.

(Offered as STAT 135 and MATH 135) This course is an introductory statistics course that uses modeling as a unifying framework. The course provides a basic foundation in statistics with a major emphasis on constructing models from data. Students learn important concepts of statistics by mastering powerful and relatively advanced statistical techniques using computational tools. Topics include descriptive and inferential statistics, visualization, probability, study design, and multiple regression.

Students who have taken a semester of calculus (MATH 111 or higher, or equivalent placement) or who are majoring or planning to major in mathematics and/or statistics should take this course instead of STAT 111. (Students who have taken STAT 111, STAT136, or PSYC 122 may not also receive credit for STAT/MATH 135. Students who have taken ECON 360/361 will be admitted only with consent of the instructor.) No prior experience with statistical software is expected.

Requisite:

Student has completed or is in process of completing MATH 111 or has placement in MATH 121 or above, or has statistics placement of STAT135 or has consent of the instructor. Limited to 24 students per section. Fall and spring semesters. The Department.

Mathematical modeling is the process of translating a real world problem into a mathematical expression, analyzing it using mathematical tools and numerical simulations, and then interpreting the results in the context of the original problem. Discussion of basic modeling principles and case studies will be followed by several projects from areas including biology and the social sciences (e.g., flocking and schooling behavior, disease spread in populations, generation of artificial societies). This course has no requisites; projects will be tailored to each student’s level of mathematical preparation.

Limited to 24 students. Spring semester. Professor Moore.

The outcomes of many elections, whether to elect the next United States president or to rank college football teams, can displease many of the voters. How can perfectly fair elections produce results that nobody likes? We will analyze different voting systems, including majority rule, plurality rule, Borda count, and approval voting, and assess a voter’s power to influence the election under each system, for example, by calculating the Banzhaf power index. We will prove Arrow’s Theorem and discuss its implications. After exploring the pitfalls of various voting systems through both theoretical analysis and case studies, we will try to answer some pressing questions: Which voting system best reflects the will of the voters? Which is least susceptible to manipulation? What properties should we seek in a voting system, and how can we best attain them?

Limited to 24 students. Omitted 2023-24.

Elementary vector calculus; introduction to partial derivatives; multiple integrals in two and three dimensions; line integrals in the plane; Green’s theorem.

Requisite: A grade of C or better in MATH 121 or placement into MATH 211 or consent of the Department. Limited to 30 students per section. Fall and spring semesters. The Department.

This course serves as an introduction to mathematical reasoning and pays particular attention to helping students learn how to write proofs. The topics covered may include logic, elementary set theory, functions, relations and equivalence relations, mathematical induction, sequences, and quantifiers. Additional topics may vary from semester to semester.

Limited to 25 students. Spring and fall semesters. The Department.

This course is a mathematical treatment of fractal geometry, a field of mathematics partly developed by Benoit Mandelbrot (1924–2010) that continues to be actively researched in the present day. Fractal geometry is a mathematical examination of the concepts of self-similarity, fractals, and chaos, and their applications to the modeling of natural phenomena. In particular, we will develop the iterated function system (IFS) method for describing fractals, study the concept of fractal dimension among other theoretical concepts, and examine Julia and Mandelbrot sets (time permitting). Through the teaching of these concepts, the course will also lend itself to familiarizing students with some of the formalisms and rigor of mathematical proofs.

Requisite: MATH 211 or consent of the instructor. Limited to 25 students. Professor Folsom.

An introduction to the theory of rational integers; divisibility, the unique factorization theorem; congruences, quadratic residues. Selections from the following topics: cryptology; Diophantine equations; asymptotic prime number estimates; continued fractions; algebraic integers.

Requisite: MATH 121 or consent of the instructor. Limited to 25 students. Spring semester. Professor Call.

Many security problems arise when two computers must communicate on a channel with eavesdroppers or malicious attackers. Public-key cryptography applies ideas from number theory and abstract algebra to address these problems. This course concerns the mathematical theory and algorithms needed to construct the most commonly-used public-key ciphers and digital signature schemes, as well as the attacks that must be anticipated when designing such systems. Several topics from number theory, abstract algebra, and algorithms will be introduced, including discrete logarithms, integer factorization algorithms, and elliptic curves. Depending on time and student interest, we may cover some newer systems that are believed to be secure against attacks by quantum computers but not yet commonly implemented in practice. Students will write short programs to implement the systems and to break badly implemented systems. No prior programming experience is expected; basic aspects of programming in Python will be taught in class. Four class hours per week.

Requisite: Experience writing proofs, such as MATH 220/221 or 271/272, or consent of the instructor. Spring semester. Professor Pflueger.

This course is a survey of geometry in dimensions 2, 3, 4, and higher. We will consider questions such as: How do we know the angles of a triangle add up to 180 degrees? (Spoiler: usually they don't.) What are the different ways we could tile our kitchen floor? How many tennis balls fit in a bucket? How many regular polyhedra are there in four dimensions? And what shape is the universe? We will examine how mathematicians and scientists, from Euclid to Einstein, have tried to give definitive answers to these questions, and we will explore some of what is still unknown about how the mathematical world reflects what we see around us.

Requisite: MATH 211 or instructor permission. Limited to 25 students. Fall semester. Professor Ching.

The study of differential equations is an important part of mathematics that involves many topics, both theoretical and practical. The course will cover first- and second-order ordinary differential equations, basic theorems concerning existence and uniqueness of solutions and continuous dependence on parameters, long-term behavior of solutions and approximate solutions.

Requisite: MATH 211 or consent of the instructor. Limited to 25 students. Fall semester.

The study of vector spaces over the real and complex numbers, introducing the concepts of subspace, linear independence, basis, and dimension; systems of linear equations and their solution by Gaussian elimination; matrix operations; linear transformations and their representations by matrices; eigenvalues and eigenvectors; and inner product spaces. MATH 271 will feature both proofs and applications, with special attention paid to the theoretical development of the subject.

Requisite: MATH 211 or 220, or consent of the instructor. This course and MATH 272 may not both be taken for credit. Limited to 25 students. Fall and spring semesters. The Department.

The study of vector spaces over the real and complex numbers, introducing the concepts of subspace, linear independence, basis, and dimension; systems of linear equations and their solution by Gaussian elimination; matrix operations; linear transformations and their representations by matrices; eigenvalues and eigenvectors; and inner product spaces. This course will feature both proofs and applications, with special attention paid to applied topics such as least squares and singular value decomposition.

Requisite: MATH 211 or 220, or consent of the instructor. This course and MATH 271 may not both be taken for credit. Limited to 25 students. Fall and Spring semester: The Department.

This course is a continuation of the material in MATH 271 and 272, providing more insight into abstract vector spaces and operator theory. Topics may include least squares estimates, singular value decompositions, Jordan canonical forms, inner product spaces, linear functionals and duals, orthogonal polynomials, vector and matrix norms, the spectral theorem, eigenvalue inequalities, and error-correcting codes. Time permitting, applications to graph theory and discrete dynamical systems may be explored. Four class hours per week.

Requisites: MATH 271, MATH 272, or consent of the instructor. Spring semester. Limited to 25 students.

A graph is a collection of points with edges drawn between them. Graph theory was first introduced by Leonhard Euler in his solution to the Königsberg bridge problem in 1736. Since then, graph theory has become an active area of study in mathematics due both to its wide array of real life applications in biology, chemistry, social sciences and computer networking, and to its interactions with other branches of mathematics.

The course will start with an overview of the fundamental concepts and general results in graph theory, followed by explorations of a variety of topics in graph theory and their applications, including: connectivity, planar graphs, directed graphs, greedy algorithms, matchings, vertex and edge colorings. The course will end with the introduction of a more advanced topic.

Requisite: MATH 271 or 272 or consent of the instructor. MATH 220 or other prior experience with mathematical proofs is recommended. Limited to 30 students. Fall semester. Professor Contreras.

Optimization is a branch of applied mathematics focused on algorithms to determine maxima and minima of functions, often under constraints. Applications range from economics and finance to machine learning and information retrieval. This course will first develop advanced linear algebra tools, and then will study methods of convex optimization. Possible topics include linear, quadratic, second-order cone, and semidefinite models. Several applications will be explored, and algorithms will be implemented using mathematical software to aid numerical experimentation.

Requisite: MATH 211 and 271 or 272, or consent of the instructor. Limited to 30 students. Omitted 2021-22.

An introduction to analytic functions; complex numbers, derivatives, conformal mappings, integrals. Cauchy’s theorem; power series, singularities, Laurent series, analytic continuation; special functions.

Requisite: MATH 211 and prior experience with mathematical proofs, or consent of the instructor. Limited to 25 students. Fall semester. Professor Rasheed.

A brief consideration of properties of sets, mappings, and the system of integers, followed by an introduction to the theory of groups and rings including the principal theorems on homomorphisms and the related quotient structures; integral domains, fields, polynomial rings.

Requisite: MATH 211 and either MATH 271 or 272, or consent of the instructor. Students with a grade of B+ or lower in linear algebra are encouraged to take another 200-level course with proofs before taking MATH 350.

Limited to 25 students. Fall semester: Professor Call. Spring semester: Professor Contreras.

Completeness of the real numbers; topology of n-space including the Bolzano-Weierstrass and Heine-Borel theorems; sequences, properties of continuous functions on sets; infinite series, uniform convergence.

Requisite: MATH 211 and either MATH 271 or 272, or consent of the instructor. Students with a grade of B+ or lower in linear algebra are encouraged to take another 200-level course with proofs before taking MATH 355.

Limited to 25 students. The Department.

(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance. Distributions covered include the binomial, hypergeometric, Poisson, normal, Gamma, Beta, multinomial, and bivariate normal. Other topics include generating functions, order statistics, and limit theorems.

Requisite:

Student has completed or is in the process of completing MATH 121, or has MATH 211 placement, or has consent of the instructor. Limited to 24 students. Fall semester. Professor Horton.

(Offered as STAT 370 and MATH 370) This course examines the theory underlying common statistical procedures including visualization, exploratory analysis, estimation, hypothesis testing, modeling, and Bayesian inference. Topics include maximum likelihood estimators, sufficient statistics, confidence intervals, hypothesis testing and test selection, non-parametric procedures, and linear models.

Requisite:

Student has completed or is in the process of completing: STAT 111 or MATH/STAT 135 or STAT136 or has STAT 230 placement, and STAT/MATH 360, or has consent of the instructor. Limited to 25 students. Spring semester. Professor Donges.

Mathematicians confirm their answers to mathematical questions by writing proofs. But what, exactly, is a proof? This course begins with a precise definition specifying what counts as a mathematical proof. This definition makes it possible to carry out a mathematical study of what can be accomplished by means of deductive reasoning and, perhaps more interestingly, what cannot be accomplished. Topics will include the propositional and predicate calculi, completeness, compactness, and decidability. At the end of the course we will study Gödel’s famous Incompleteness Theorem, which shows that there are statements about the positive integers that are true but impossible to prove.

Requisite: MATH 220, 271, 272, or 355, or consent of the instructor. Spring semester. Professor Ching.

Lie groups and Lie algebras appear naturally in the study of symmetries of geometric objects. Lie algebras carry local information and can be studied using tools from linear algebra. Finite dimensional Lie groups can be constructed using techniques from calculus and group theory.

This course serves as a first introduction to Lie groups and Lie algebras. We will examine the structure of finite dimensional matrix Lie groups, the exponential and differentiation maps, as well as compact Lie groups. We will also study Lie algebras, ideals and homomorphisms, nilpotent and solvable Lie algebras, Cartan subalgebras, semisimplicity and root systems, and the classification of semisimple Lie algebras. This classification is not only a fundamental result in Lie Theory, but is also an archetype of classifications that appear in other areas of math. More amazingly, this classification is embodied in simple combinatorial pictures called Dynkin diagrams, which underlie surprisingly disparate fields, such as geometric group theory, quiver representation theory, and string theory. Four class meetings per week.

Requisite: MATH 350 or consent of the instructor. Fall semester.

The quadratic formula shows us that the roots of a quadratic polynomial possess a certain symmetry. Galois Theory is the study of the corresponding symmetry for higher degree polynomials. We will develop this theory starting from a basic knowledge of groups, rings, and fields. One of our main goals will be to prove that there is no general version of the quadratic formula for a polynomial of degree five or more. Along the way, we will also show that a circular cake can be divided into 17 (but not 7) equal slices using only a straight-edged knife.

Requisite: MATH 350 or consent of the instructor. Spring semester. Prof. Daniels.

An introduction to Lebesgue measure and integration; topology of the real numbers; inner and outer measures and measurable set; the approximation of continuous and measurable functions; the Lebesgue integral and associated convergence theorems; the Fundamental Theorem of Calculus.

Requisite: MATH 355. Professor R. Benedetto.

This course will explore the geometry of curves, surfaces and higher dimensional geometric objects in *n*-dimensional Euclidean space. For curves, the key concepts are curvature and torsion, while for surfaces, the key players are Gaussian curvature, geodesics, and the Gauss-Bonnet Theorem. Other topics covered may include (time permitting) two-dimensional Riemmanian geometry, differential forms and manifolds. MATH 378 and MATH 408 may not both be taken for credit.

Spring semester. Professor Contreras

An introduction to general topology: the topology of Euclidean, metric and abstract spaces, with emphasis on such notions as continuous mappings, compactness, connectedness, completeness, separable spaces, separation axioms, and metrizable spaces. Additional topics may be selected to illustrate applications of topology in analysis or to introduce the student briefly to algebraic topology. Four class hours per week. Offered in alternate years.

Requisite: MATH 355. Spring semester. Prof. Rasheed.

This course is an introduction to Analytic Number Theory, a foundational subject in mathematics which dates back to the 1800s and is still a major research area today. The subject generally uses tools and techniques which are analytic in nature to solve problems primarily related to integers. Asymptotic and summation results and methods are of great significance in Analytic Number Theory. Two primary course objectives are to state and prove two major theorems: Dirichlet's Theorem on Primes in Arithmetic Progressions, and the Prime Number Theorem. In particular, we will study Selberg's "elementary" proof of the Prime Number Theorem. Additional topics may include: arithmetic functions, especially their averages, their asymptotics, and related summation formulae; Dirichlet convolutions; characters and Gauss sums; and an introduction to Dirichlet series, such as the Riemann zeta-function and L-functions. Further topics may vary.

Requisite: MATH 355. Limited to 25 students. Fall semester. Professor Folsom.

Fall and spring semesters. The Department.

Open to seniors with the consent of the Department. Fall semester. The Department.

This course will provide a rigorous presentation of fundamental statistical principles and ethics. We will discuss standards for relationships between statisticians and policymakers, researchers, the press, and other institutions, as well as the standards for interactions between statisticians and their employers/clients, colleagues and research subjects. The course will explore how the interplay of institutions (e.g., organizations, systems, laws, codes of professional ethics) and the broader sociopolitical culture affect the production of reliable, high quality statistics. Students will also explore the implications of statistical principles and ethics for the operation of national, regional, and international official statistical systems. In addition, we will investigate the proper place of official statistics within a government system that operates with separate branches. Students will gain a strong foundation in international statistical principles and professional ethics as well as an understanding and the tools to assess the quality of the statistics they use. The course is designed to make students responsible and effective supporters of reliable, high quality statistics in their professions. Students will particularly learn how to assess the quality of official statistics produced by governments and how to identify areas for improvement. Examples, case studies, readings from statistical practice, and discussion will provide a full appreciation of real world applications. The course is also intended for non-majors interested in an introduction to quantitative social science and the use of data in public policy.

Limited to 30 students. Fall semester. Visiting Scholar Andreas Georgiou.

Introduction to Statistics provides a basic foundation in descriptive and inferential statistics, including constructing models from data. Students will learn to think critically about data, produce meaningful graphical and numerical summaries of data, apply basic probability models, and utilize statistical inference procedures using computational tools. Topics include basic descriptive and inferential statistics, visualization, study design, and multiple regression. S

Students who have taken a semester of calculus (MATH 111 or higher, or equivalent placement) or who are planning to major in statistics should take STAT 135/MATH 135 or STAT 136 instead of this course. (Students who have taken STAT/MATH 135, STAT136, PSYC 122, or ECON 360/361 may not also receive credit for STAT 111. STAT 111 does not count towards the major in Mathematics.)

Limited to 24 students per section. Permission with consent of the instructor. Fall semester. Professor Matheson.

This is an interactive course designed to help students understand inequities in mental health issues via statistics. We will begin the course by examining mental health stigmas and practice self-care exercises to train our “happy muscles” together. We will discover the scientific evidence behind those self-care practices and explore existing racial disparities in mental health care systems, while learning about important statistical concepts and mastering our data analysis skills using R (a popular statistical software package). Statistical topics covered include descriptive statistics, visualization, study design, simulation-based inferences, and multiple regression. Students are expected to play an active role in co-creating the course and co-building an inclusive learning community with their peers and the professor. Course components include weekly reading and discussion, regular self-reflections and problem sets, and collaborative work in groups. We will use an OER (Open-Educational-Resources) textbook in this course. No prior experience with statistical software is expected.

This course is an alternative to STAT135 (Introduction to Statistics via Modeling) with a special focus on mental health issues. Students may not receive credit for both this course and STAT 111, STAT 135, or PSYC122. Limited to 24 students.

Fall semester. Prof. Liao.

This course will focus on the use of text analytics to explore the rich history of Holyoke, MA. Holyoke has been a site of rapid industrialization, multiple waves of immigration and migration, urban development, rapid changes in its workforce, and ongoing creativity, activism, and innovation. Students will develop the skills to mine textual data from archives at the Wistariahurst Museum, the Holyoke Public Library, Holyoke Community College, and other repositories to address important questions regarding the development and history of this planned community. Topics include sentiment analysis, regular expressions, document-term-matrices, named entity recognition, and Latent Dirichlet analysis. Requisite:

Student has completed or is in the process of completing: STAT 111 or MATH/STAT 135 or STAT136 or PSYC 122 or has a placement of STAT230.

Recommended requisite: HIST 351 or COSC 111.

Professor Horton.

This course is an introduction to nonparametric and distribution-free statistical procedures and techniques. These methods often rely on counting and ranking techniques and will be explored through both theoretical and applied perspectives. One- and two-sample procedures will provide students with alternatives to traditional parametric procedures, such as the t-test. A variety of other topics may be explored in the nonparametric setting depending on the instructor. Potential topics include but are not limited to: nonparametric correlation and regression, resampling techniques (e.g. bootstrapping and permutation procedures), categorical data and contingency tables, density estimation, and the one-way and two-way layouts for analysis of variance. The course will emphasize data analysis (with appropriate use of statistical software) and the intuitive nature of nonparametric statistics.

Requisite: STAT 111 or MATH/STAT 135 or STAT 136 or equivalent. Limited to 24 students. Fall semester. Professor Wagaman.

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111, STAT 135, or STAT 136. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: Student has completed or is in process of completing any of the following course(s): STAT 111 or MATH/STAT 135 or STAT 136 or PSYC 122 or has a STAT 230 placement or has consent of the instructor. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. The Department.

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools.

Requisite:

STAT 111 or STAT 135 or STAT136 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department.

Making sense of a complex, high-dimensional data set is not an easy task. The analysis chosen is ultimately based on the research question(s) being asked. This course will explore how to visualize and extract meaning from large data sets through a variety of analytical methods. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering). Additional methods covered may include factor analysis, dimension reduction methods, or network analysis at instructor discretion. This course will feature hands-on data analysis with statistical software, emphasizing application over theory.

The course is expected to include small group work, interactive labs, peer interactions such as peer review and short presentations, and a personal project, to foster student engagement in the course and with each other.

Requisite:

Requisite: STAT 111 or 135 or 136 or PSYC 122. Limited to 24 students. Omitted 2023-24.

Statistical Communication is an important component of the capacity to "think with data." This course will integrate theoretical and practical aspects of statistics with a focus on communicating results and their implications. Students will gain experience clearly synthesizing and explaining complex data using diverse predictive and explanatory models. Learning objectives include: understanding the role of a statistician, developing communications skills, working collaboratively on group projects, designing studies to collect information, acquiring existing data resources, utilizing publications in statistics, creating reproducible research and developing oral arguments, relevant project reports, and dynamic graphical displays. Emphasis will be placed on the use of statistical software, data management, visual presentation, and oral and written communication skills that are necessary for communicating technical content.

Requisite:

Student has completed or is in the process of completing STAT 230 or has consent of the instructor. Limited to 24 students. Fall semester. Omitted 2021-22. Professor Matheson.

Epidemiology is the study of the distribution and determinants of disease and health in human populations. It typically involves the analysis of multivariate observational data that pose challenges when trying to make causal conclusions. The course will focus on reasoning about cause and effect, study design, bias and missing data, models and analysis of risk, detection and classification, and modern approaches to confounding and causal inference. Topics include: Measures of disease (incidence and prevalence); Measures of association (relative risk, odds ratio, relative hazard, excess risk, attributable risk); Study designs (exposure and disease base sampling); Assessing significance in a 2x2 table; Assessing significance in a 2x2x2 table; Missing data; Introduction to confounding; Matching; Propensity score adjustment; Unmeasured confounding; Introduction to causal inference and counterfactuals; Causal graphs; and D-separation.

Requisite: STAT-230 (or PSYC 122 and PSYC 200 and consent of the instructor). Omitted 2021-22.. Professor Horton.

Real world datasets are plagued by missing observations. Statistical software packages often ignore these cases by default, but there are better ways to approach the problem. This course will introduce students to the different missing data mechanisms and explore naive and modern methods for handling missing data. It will prepare students to read the current literature in this area and have broad appreciation for the implications of missing data.

This course is intended for students who have experience with standard statistical methods for complete data and want to extend them to handle missing data in practice.

Requisite:

Student has completed or is in the process of completing STAT 230 and STAT/MATH 370, or has consent of the instructor. Fall semester: Professor Correia.

Linear regression and logistic regression are powerful tools for statistical analysis, but they are only a subset of a broader class of generalized linear models. This course will explore the theory behind and practical application of generalized linear models for responses that do not have a normal distribution, including counts, categories, and proportions. We will also delve into extensions of these models for dependent responses such as repeated measures over time.

Requisite:

Student has completed or is in the process of completing: STAT 230 and STAT/MATH 360. Limited to 20 students. Spring semester. Professor Bailey.

Fall and spring semesters. The Department.

Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and interpretation of complex and real-world datasets that go beyond textbook problems. Course topics will vary from year to year depending on the instructor and selected case studies. Potential topics include but are not limited to: visualization techniques to summarize and display high dimensional data, advanced topics in design and linear regression, ethics, selected topics in machine learning and data mining, nonparametric analysis, spatial data, and analysis of network data. Students will enhance their capacity to think and compute with data, undertake and assess analyses, and effectively communicate their results.

Fall semester. The Department.