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Graduate Courses by Interest Area

Algebra, Discrete Mathematics and Number Theory


The core courses of an algebra, discrete mathematics, and number theory concentration are matrix analysis (853) and abstract algebra I and II (851-52). Matrix analysis is a basic course in linear algebra dealing with topics such as similarity of matrices, eigenvalues, and canonical forms just to name a few. Abstract algebra I and II abstract the familiar structures of the integers, rational numbers, matrices, etc. into the concepts of groups, rings, fields, and modules. One also studies one of the crowning achievements of the subject, Galois theory. In addition to the department's broad course requirements, it is expected a student in algebra, discrete mathematics, and number theory will gain a deeper level of understanding of each of the concentrations listed below as well as taking significant advanced courses in that student's particular concentration.


MTHSC 850: Computational Algebraic Geometry, 3 cr. (3 and 0)
Covers algebraic geometry and commutative algevra via Grobner bases. Includes ideals and varieties (affine and projective), Grobner bases, elimination theory, dimensions, solving plynomial systems via eigenvalues aand eigenvectors. Selected applications may include coding theory, computer vision, geometric theorem proving, integer programming, or statistics.
Prerequisite: MTHSC 311, 412.

MTHSC 851: Abstract Algebra I, 3 cr. (3 and 0)
Basic algebraic structures: groups, rings and fields; permutation groups, Sylow theorems, finite abelian groups, polynomial domains, factorization theory and elementary field theory.

MTHSC 852: Abstract Algebra II, 3 cr. (3 and 0)
A continuation of MTHSC 851 including selected topics from ring theory and field theory.

MTHSC 853: Matrix Analysis, 3 cr. (3 and 0)
Topics in matrix analysis that support an applied curriculum: similarity and eigenvalues; Hermitian and normal matrices; canonical forms; norms; eigenvalue localizations; singular value decompositions; definite matrices.
Prerequisite: MTHSC 311, 453 or 463.

MTHSC 854: Theory of Graphs, 3 cr. (3 and 0)
Connectedness; path problems; trees; matching theorems; directed graphs; fundamental numbers of the theory of graphs; groups and graphs.
Prerequisite: permission of instructor.

MTHSC 855: Combinatorial Analysis, 3 cr. (3 and 0)
Combinations; permutations; permutations with restricted position; Polya's theorem; principle of inclusion and exclusion; partitions; recurrence relations; generating functions; Mobius inversion; enumeration techniques; Ramsey numbers; finite projective and affine geometries; Latin rectangles; orthogonal arrays; block designs; error detecting and error correcting codes.
Prerequisite: MTHSC 311.

MTHSC 856: Applicable Algebra, 3 cr. (3 and 0)
Applied algebraic ideas in lattice theory and Boolean Algebra; finite-state sequential machines; group theory as applied to network complexity and combinatorial enumeration; algebraic coding theory. Topics vary with background and interests of students.
Prerequisites: MTHSC 851 and 853 or permission of instructor.

MTHSC 857: Cryptography, 3 cr. (3 and 0)
Classical and modern cryptography and their uses in modern communication systems are covered. Topics include entropy, Shannon's perfect secrecy theorem, Advanced Encryption Standard (AES), integer factorization, RSA cryptosystem, discrete logarithm problem, Diffie-Hellman key exchange, digital signatures, elliptic curve cryptosystems, hash functions, and identification schemes.
Prerequisite: MTHSC 311, 400 or 600, 412 or 851.

MTHSC 951: Algebraic Number Theory, 3 cr. (3 and 0)
Covers arithmetic of number fields and number rings. Covers prime decomposition, ideal class groups, unit groups of number fields and distribution of prime ideals in number fields. Provides an overview of completions absolute values and valuation theory.
Prerequisite: MTHSC 851.

MTHSC 954: Advanced Graph Theory, 3 cr. (3 and 0)
Continuation of MTHSC 854; topics not covered in 854 including the four-color theorem, domination numbers, Ramsey theory, graph isomorphism, embeddings, algebraic graph theory and tournaments; research papers are also examined.
Prerequisite: MTHSC 854 or permission of instructor.

MTHSC 985: Selected Topics in Algebra and Combinatorics, 1-3 cr. (1-3 and 0)
Advanced topics in algebra and combinatorics from current problems of interest. May be repeated for credit, but only if different topics are covered. Sample offerings include:

Introduction to Cryptography
The purpose of this course is to acquaint the students with classical and modern methods of cryptography and their uses in modern communication systems. Main topics: Shannon's theory, conventional cryptosystems, DES, AES, finite fields and elementary number theory, RSA, Diffie-Hellman key exchange scheme, ElGamal cryptosystem, digital signature schemes, elliptic curves and elliptic curve cryptosystems, hash functions, pseudorandom numbers, identification schemes, and zero knowledge proofs.

Coding Theory
This courses covers the basics of coding theory. Topics include cyclic codes, BCH codes, Reed-Solomon codes, and finite geometry.

Finite Fields
This course covers basic finite field theory and applications.

Algebraic Curves
This course covers some basic results about algebraic curves that are useful in constructing error-correcting codes and in implementing public-key cryptosystems. Basic concepts in algebraic geometry and commutative algebra to be covered include varieties, polynomial and rational maps, divisors, (prime) ideals, function fields, valuations, local rings, Riemann-Roch Theorem, etc.

Introduction to Computational Algebra I
The course focuses heavily on the theory and applications of Grobner bases. Coding theory is emphasized as an area of application, including decoding of Reed-Solomon codes and Hermitian codes.

Introduction to Computational Algebra II
Fast Fourier transforms, fast multiplication of polynomials (integers), fast decoding of RS codes, sparse linear systems (from coding theory, cryptography and computer algebra), Krylov subspace methods (Lanczos and bi-orthogonal methods), Wiedemann's method a la Berlekamp-Massey, block algorithms (Coppersmith's and Montgomory's) and their analysis.

MTHSC 986: Selected Topics in Geometry, 1-3 cr. (1-3 and 0)
Advanced topics in Geometry from current problems of interest. May be repeated for credit, but only if different topics are covered.

Course Substitution Policy

. . . back to Algebra and Discrete Mathematics page



A plan of study for students concentrating in analysis will include courses in theoretical analysis, applied analysis, numerical analysis, and physical system modeling.


MTHSC 821: Linear Analysis, 3 cr. (3 and 0)
Metric spaces,completeness of a metric space and the completion of a metric space, infinite dimensional vectors spaces, Zorn's Lemma, normed spaces and compactness, Schauder Basis, linear operators bounded and unbounded, linear functionals, minimization results for normed spaces, inner product spaces, projection theorems and minimization, Hilbert spaces, Riesz - Fischer Theorem and self - adjoint operators, orthogonal systems.
Prerequisites: MTHSC 454/654 or 453 and 853

MTHSC 822: Measure and Integration, 3 cr. (3 and 0)
Riemann and Riemann - Stieljes integration, inner and outer measures, Cantor sets, measurability and additivity, abstract integration and Lebesgue integration, types of convergence and convergence interchange results, Lebesgue spaces; integration and differentiation, product measure, Fubini type results.
Prerequisite: MTHSC 454/654, MTHSC 821

MTHSC 823: Complex Analysis, 3 cr. (3 and 0)
Topological concepts; complex integration; local and global properties of analytic functions; power series; analytic continuation; representation theorems; calculus of residues. Designed for nonengineering majors.
Prerequisite: MTHSC 464/664

MTHSC 825: Introduction to Dynamical Systems Theory, 3 cr. (3 and 0)
Techniques of analysis of dynamical systems; sensitivity analysis, linear systems, stability and control; theory of differential and difference equations.
Prerequisites: MTHSC 454/654 and 311 or 453 and 853

MTHSC 826: Partial Differential Equations, 3 cr. (3 and 0)
First-order equations: elliptic, hyperbolic and parabolic; second-order equations: existence and uniqueness results, maximum principles, finite difference and Hilbert Space methods.
Prerequisite: MTHSC 821 or permission of instructor

MTHSC 827: Dynamical System Neural Networks, 3 cr. (3 and 0)
Scalar and planar maps with applications from biology; existence and uniqueness, bifurcations, periodic equations, stability of equilibria, conservative systems.
Prerequisites: MTHSC 453, MTHSC 454, MTHSC 825 and MTHSC 821

MTHSC 831: Fourier Series, 3 cr. (3 and 0)
Fourier series with applications to solution of boundary value problems in partial differential equations of physics and engineering; introduction to Bessel functions and Legendre polynominals.
Prerequisite: MTHSC 464/664

MTHSC 837: Calculus of Variations and Optimal Control, 3 cr. (3 and 0)
Fundamental theory of the calculus of variations; variable end points; the parametric problem; the isoperimetric problem; constraint inequalities; introduction to the theory of optimal control; connections with the calculus of variations; geometric concepts.
Prerequisite: MTHSC 453/653 or 463/663

MTHSC 841: Applied Mathematics I, 3 cr. (3 and 0)
Derivation of equations from conservation laws, dimensional analysis, scaling and simplification; methods such as steepest descent, stationary phase, perturbation series, boundary layer theory, WKB theory, multiple-scale analysis and ray theory applied to problems in diffusion processes, wave propagation, fluid dynamics and mechanics.
Prerequisites: MTHSC 208 and 453/653 or 463/663

MTHSC 861: Advanced Numerical Analysis I, 3 cr. (3 and 0)
Interpolation and approximation; numerical quadrature; numerical solution of functional differential equations; integral equations and overdetermined linear systems; eigenvalue problems; approximation using splines.
Prerequisites: MTHSC 453 and 460.

MTHSC 927: Functional Analysis, 3 cr. (3 and 0)
Topological Vector Spaces, Hahn - Banach Theorems, Closed and Open Mapping Theorems, Linear operators on specific spaces and spectral theory, distributions and Sobolev spaces.
Prerequisite: MTHSC 821, MTHSC 822

MTHSC 974: Mathematical Models in Investment Science, 3 cr. (3 and 0)
The course deals with a collection of concepts, constructs, and mathematical models that have been created to help deal with (in a rational manner) a portion of the myriad of problems that arise in the financial arena. There are two major themes in the course:

  • How to decide the best course of action in an investment situation, e.g. how to devise the best portfolio, how to devise the optimal investment strategy for managing an investment, how to select a group of investment projects.
  • How to determine the correct arbitrage-free, fair, or equilibrium value of an asset, e.g. the value of a firm, the value of a bond, the value of a derivative such as a put or call option.

Prerequisites: Individuals should have a technical background roughly equivalent to a bachelor's degree in engineering, mathematics, science, or economics; or have some familiarity with basic calculus, linear algebra, and probability theory. Most of the mathematics is at the level of undergraduate calculus.

MTHSC 982: Selected Topics in Analysis, 1-3 cr. (1-3 and 0)
Advanced analysis topics from current problems of interest. May be repeated for credit if different topics are covered. Sample Offerings:

Stochastic Calculus for Finance
This special topics course is intended as an introduction to some basic ideas for modeling and simulation in finance. The course begins with a discussion of simple random walks and the analysis of certain gambling games. These topics are used to motivate the theory of martingales and continuous time stochastic processes. The course will then take up the Ito integral and enough of the theory of the diffusion equation to be able to solve the Black-Scholes PDE and prove the uniqueness of the solution. The foundations for the martingale theory of arbitrage pricing are then prefaced by a well-motivated development of the martingale representation theorems and Girsanov theory.
Prerequisites: Some analysis beyond calculus, an introduction to linear algebra, and basic methods from probability and statistics.

Computational Finance
This special topics course is intended provide hands on familiarity with simulation of financial models. This will be a "soft" computing course, i.e., we will not prove convergence of the approximations used in the simulations. The course will make use of Maple and MatLab programs from the literature: e.g.,

  • D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Review 43 (2001), 525-546.
  • D. J. Higham and P. E. Kloeden, Maple and MatLab for stochastic differential equations in finance, research report.
  • D. J. Higham, Nine ways to implement the binomial method of option valuation in MatLab, research report.
  • S. Cygnowski, L. Grüne, and P. E. Kloeden, Maple for stochastic differential equations, research report.

Prerequisites: Basic concepts from probability and stochastic processes. Familiarity with martingales, MatLab, and Maple would be helpful but not necessary.

Sample Curricula

Sample Program for M.S. Concentration in Analysis
  • Fall: 810, 853, 825/826
  • Spring: 805, 821, 860
  • Summer: 803
  • Fall: 841, 861, 825/826
  • Spring: 809, 811, 831, 892
Sample Program for M.S. Concentration in Financial Mathematics
  • Fall: 805, 810, 853
  • Spring: 821, 860, 974
  • Summer: 803
  • Fall: 804, 982, MBA 846
  • Spring: 809/811, ECON 855, 982, 892

Course Substitution Policy

. . . back to Analysis page

Computational Mathematics


Data Structures, Graph Algorithms, Computational Problems in Discrete Structures, Numerical Linear Algebra, Numerical Approximation Theory, Numerical Solution of Ordinary and Partial Differential Equations, Digital Models, Introduction to Scientific Computing. Some of the courses in computer science at the graduate level offered by the Department of Computer Science which may be chosen as electives are: Theory of Computation, Introduction to Artificial Intelligence, Design and Analysis of Algorithms, and Software Development Methodology. Students often take a graduate course in engineering or science which supports their graduate research.


MTHSC 860: An Introduction to Scientific Computing, 3 cr. (3 and 0)
Floating point models, conditioning and numerical stability, numerical linear algebra, integration, systems of ordinary differential equations and zero finding; emphasis is on the use of existing scientific software.
Prerequisites: MTHSC 208, 311 and CP SC 110.

MTHSC 861: Advanced Numerical Analysis I, 3 cr. (3 and 0)
Interpolation and approximation; numerical quadrature; numerical solution of functional differential equations; integral equations and overdetermined linear systems; eigenvalue problems; approximation using splines.
Prerequisites: MTHSC 453 and 460.

MTHSC 863: Digital Models I, 3 cr. (3 and 0)
Experimental mathematics; pseudostochastic processes; analytical and algebraic formulations of time-independent simulation; continuous-time simulation and discrete-time simulation;digital optimization; Fibonacci search; ravine search; gradient methods; current research in digital analysis. Offered Fall semester only.
Prerequisite: MTHSC 311, 453, and digital computer experience.

MTHSC 865: Data Structures, 3 cr. (3 and 0)
Representation and transformation of information; formal description of processes and data structures; tree and list structures; pushdown stacks; string and formula manipulation; hashing techniques; interrelation between data structure and program structure; storage allocation methods.
Prerequisites: Computational maturity and permission of instructor.

MTHSC 866: Finite Element Method, 3 cr. (3 and 0)
Discusses the basic theory of the finite element method (FEM) for the numerical approximation of partial differential equations. Topics include Sobolev spaces, error estimation, and implementation of FEM in one and higher dimensions.
Prerequisite: MTHSC 860 or consent of instructor.

MTHSC 983: Selected Topics in Computational Mathematics, 1-3 cr. (1-3 and 0)
Advanced topics in computational mathematics and numerical analysis from current problems of interest. May be repeated for credit if different topics are covered.Sample Offerings:

Scientific Simulations in Java
Because of its Object Orientation, its Platform Independence, and its tight specifications on arithmetic operations, Java is an appealing language for the development of Scientific Simulations. Since Java-based simulations can be distributed in the context of web-based documentation, such Java-based simulations can greatly enhance the dissemination of scientific knowledge and can substantially improve scientific training particularly in the area of understanding complex models. This course focuses on developing Scientific Simulations written in Java and distributed through the World Wide Web. The course is project oriented and involves the development of interactive web-based simulations of scientific topics chosen by the students.

Fiber and Film Systems: Modeling and Simulation
This course, cross-listed as ChE 845, ME 893, and MTHSC 983, is team-taught by Math Sciences and Chemical Engineering faculty. The course presents a systems perspective of fiber and film processes using existing and new models developed by the Center for Advanced Engineering Fibers and Films. Constitutive equations are developed and applied to specific geometries and flow problems encountered in the production of fibers and films. Specific objectives are to develop the governing equations for polymeric fluids, derive various constitutive equations including those based on molecular models, explore analytical and numerical solution of the governing equations for special cases, develop an understanding for the strengths and weaknesses of the models to be discussed, and apply constitutive equations to fiber and film processing geometries.

Analysis of Finite Element Methods
Topics include classification of partial differential equations, the finite element method in one and higher dimensions, Sobolev spaces, interpolation theory, finite element spaces, and development of error estimates. Related topics are considered as time permits, including application of finite element methods to fluid flow problems which arise in science and engineering.

Sample Curricula

Sample Program for M.S. Concentration in Computational Mathematics
  • Fall: 805, 810, 865
  • Spring: 860, 821, 853
  • Summer: 803
  • Fall: 822, 825, 861
  • Spring: 927, 983, modeling course in another department, 892

Course Substitution Policy

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Operations Research


Operations Research often approaches a particular problem from several modeling perspectives and uses various analytical techniques. Because of the diversity and broad scope of decision problems, the successful OR practitioner requires training in a number of mathematical concepts and techniques. Areas in the mathematical sciences that relate directly to OR are optimization (linear, nonlinear, integer, network programming, calculus of variations, control theory); applied probability (stochastic processes, queueing, reliability); and applied statistics (simulation, econometrics, time series). Computational mathematics also plays an important role in the effective application of OR because of the need to structure and analyze vast amounts of data and to solve large-scale problems efficiently. Other areas of the mathematical sciences related to OR are combinatorics, graph theory, financial mathematics, and dynamical systems.


MTHSC 800: Probability, 3 cr. (3 and 0)
Basic probability theory with emphasis on results and techniques useful in operations research and statistics; axiomatic probability, advanced combinatorial probability, conditional informative expectation, functions of random variables, moment generating functions, distribution theory and limit theorems.
Prerequisite: MTHSC 206

MTHSC 803: Stochastic Processes, 3 cr. (3 and 0)
Theory and analysis of time series; recurrent events; Markov chains; random walks; renewal theory; application to communication theory; operations research.
Prerequisite: MTHSC 400/600 or MTHSC 800

MTHSC 810: Mathematical Programming, 3 cr. (3 and 0)
Formulation and solution of linear programming models; mathematical development of the simplex method; revised simplex method; duality; sensitivity analysis; parametric programming, implementation, software packages.
Prerequisite: MTHSC 311

MTHSC 811: Nonlinear Programming, 3 cr. (3 and 0)
Theoretical development of nonlinear optimization with applications; classical optimization; convex and concave functions; separable programming; quadratic programming; gradient methods.
Prerequisites: MTHSC 440 and 454

MTHSC 812: Discrete Optimization, 3 cr. (3 and 0)
Principal methods used in integer programming and discrete optimization; branch and bound, implicit enumeration, cutting planes, group knapsack, Lagrangian relaxation, surrogate constraints, heuristics (performance analysis), separation/branching strategies and polynomial time algorithms for specific problems on special structures.
Prerequisite: MTHSC 810 or equivalent

MTHSC 813: Advanced Linear Programming, 3 cr. (3 and 0)
Development of linear programming theory using inequality systems, convex cones, polyhedra and duality; solution algorithms and computational considerations for large scale and special structured problems using techniques of upper bounded variables, decomposition, partitioning and column generation; game theory; nonlinear representations and other methods such as ellipsoid and Karmarkar.
Prerequisite: MTHSC 440/640, 810 or equivalent

MTHSC 814: Network Flow Programming, 3 cr. (3 and 0)
Max-flow/min-cut theorem; combinatorial applications; minimum cost flow problems (transportation, shortest path, transshipment); solution algorithms (including the out-of-kilter method); implementation and computational considerations.
Prerequisite: MTHSC 440/640, 810 or equivalent

MTHSC 816: Network Algorithms and Data Structures, 3 cr. (3 and 0)
Design, analysis and implementation of algorithms and data structures associated with the solution of problems formulated as networks and graphs; applications to graph theory, combinatorial optimization and network programming.
Corequisite: MTHSC 640, 810, 854, 863 or permission of instructor

MTHSC 817: Stochastic Models in Operations Research I, 3 cr. (3 and 0)
Stochastic control; structure of sequential decision processes; stochastic inventory models; recursive computation of optimal policies; discrete parameter finite Markov decision processes; various optimality criteria; computation by policy improvement and other methods; existence of optimal stationary policies; stopping-rule problems; examples from financial management, maintenance and reliability, search, queuing and shortest path.
Prerequisite: MTHSC 803

MTHSC 818: Stochastic Models in Operations Research II, 3 cr. (3 and 0)
Introduction to queuing theory: Markovian queues, repairman problems, queues with an embedded Markov structure, the queue GI/G/1, queues with a large number of servers, decision making in queues; introduction to reliability theory; failure distributions; stochastic models for complex systems; maintenance and replacement policies; reliability properties of multicomponent structures.
Prerequisite: MTHSC 817

MTHSC 819: Multicriteria Optimization, 3 cr. (3 and 0)
Theory and methodology of optimization problems with vector-valued objective functions; preference orders and domination structures; generating efficient solutions; solving mulitcriteria decision-making problems, noninteractive and interactive methods with applications.
Prerequisite: MTHSC 810 or equivalent

MTHSC 820: Complementarity Models, 3 cr. (3 and 0)
Theory, algorithms and applications of linear and nonlinear complementarity; classes of matrices and functions and corresponding algorithms; applications to economics, mechanics and networks; generalizations to fixed-point problems and nonlinear systems of equations.
Prerequisite: MTHSC 810

MTHSC 988: Selected Topics in Operations Research, 1-3 cr. (1-3 and 0)
Advanced topics in operations research from current problems of interest. May be repeated for credit, but only if different topic covered.

Sample Curricula

Sample Program for M.S. Concentration in Optimization
  • Fall: 800, 810, 853
  • Spring: 805, 821, 860
  • Summer: 803
  • Fall: 812/819, 814, 817
  • Spring: 811, 813, 988, 892
Sample Program for M.S. Concentration in Stochastics
  • Fall: 800, 810, 853
  • Spring: 805, 821, 860
  • Summer: 803
  • Fall: 817, 901, 988/simulation
  • Spring: 811, 809, 818, 892

Course Substitution Policy

. . . back to Operations Research page

Statistics and Probability


Whether one is interested in applying statistical methods to problems in government or industry, or would like to engage in teaching and research at a university, a program can be tailored to meet these objectives within the constructs of the graduate program at Clemson. In addition to comprehensive training in statistical theory and methodology, students are exposed to areas such as combinatorics, mathematical programming, and scientific computing. While these areas are not part of a traditional statistics program, knowledge of them is becoming essential to the application and development of statistical methods. Thus, the Mathematical Sciences Department at Clemson is an ideal place to pursue the study of statistics. Students who choose to pursue the PhD degree may do research within the Department of Mathematical Sciences or they may enroll in the Management Science PhD program which is jointly administered by Mathematical Sciences and the Department of Management. That program stresses the use of analytic models and quantitative methods for decision making.


MTHSC 801: General Linear Hypothesis I, 3 cr. (3 and 0)
Least-square estimates; Gauss-Markov theorem; confidence ellipsoids and confidence intervals for estimable functions; tests of hypotheses; one-, two- and higher-way layouts; analysis of variance for other models.
Prerequisites: MTHSC 403/603 and 311

MTHSC 802: General Linear Hypothesis II, 3 cr. (3 and 0)
Continuation of MTHSC 801.
Prerequisite: MTHSC 801

MTHSC 804: Statistical Inference, 3 cr. (3 and 0)
Sampling distributions; maximum likelihood estimation and likelihood ratio tests; asymptotic confidence intervals for Binomial, Poisson and Exponential parameters; two sample methods; nonparametric tests; ANOVA; regression and model building.
Prerequisite: MTHSC 400/600 or equivalent or permission of instructor.

MTHSC 805: Data Analysis, 3 cr. (3 and 0)
Methodology in analysis of statistical data emphasizing applications to real problems using computer-oriented techniques: computer plots, transformations, criteria for selecting variables, error analysis, multiple and stepwise regression, analysis of residuals, model building in time series and ANOVA problems, jackknife and random subsampling, multidimensional scaling, clustering.
Prerequisites: MTHSC 301 and 400/600, or MTHSC 401/601 and 800.

MTHSC 806: Nonparametric Statistics, 3 cr. (3 and 0)
Order statistics; tolerance limits; rank-order statistics; Kolmogorov-Smirnov one-sample statistics; Chi-square goodness-of-fit test; two-sample problem; linear rank statistics; asymptotic relative efficiency.
Prerequisite: MTHSC 600 or 800.

MTHSC 807: Applied Multivariate Analysis, 3 cr. (3 and 0)
Applied multivariate analysis: computer plots of multivariate observations; multidimensional scaling; multivariate tests of means, covariances and equality of distributions; univariate and multivariate regressions and their comparisons; MANOVA; principle components analysis; factor analysis; analytic rotations; canonical correlations.
Prerequisites: MTHSC 403/603 and 805 or permission of instructor.

MTHSC 808: Reliability and Life Testing, 3 cr. (3 and 0)
Probability models and statistical methods relevant to parametric and nonparametric analysis of reliability and life testing data.
Prerequisites: MTHSC 400/600 and 401/601 or equivalent.

MTHSC 809: Time Series Analysis, Forecasting and Control, 3 cr. (3 and 0)
Modeling and forecasting random processes; autocorrelation functions and spectral densities; model identification, estimation and diagnostic checking; transfer function models; feedforward and feedback control schemes.
Prerequisites: MTHSC 600 and 605, or MTHSC 800 and 605 or equivalent.

MTHSC 881: Mathematical Statistics, 3 cr. (3 and 0)
Fundamental concepts of sufficiency, hypothesis testing and estimation; robust estimation; resampling (jackknife, bootstrap, etc.) methods; asymptotic theory; two-stage and sequential sampling problems; ranking and selection procedures.
Prerequisite: MTHSC 403/603 or equivalent.

MTHSC 884: Statistics for Experimenters, 3 cr. (3 and 0)
Statistical methods for students who are conducting experiments; introduction to descriptive statistics, estimation, and hypothesis testing as they relate to design of experiments; higher-order layouts, factorial and fracitonal factorial designs, and response surface models. Offered Fall semester only.
Prerequisite: MTHSC 206 or equivalent.

MTHSC 885: Advanced Data Analysis, 3 cr. (3 and 0)
Continuation of MTHSC 805, covering alternatives to ordinary least squares, influence and diagnostic considerations, robustness, special statistical computation methods.
Prerequisites: MTHSC 603, 800 and 805.

MTHSC 901: Probability Theory I, 3 cr. (3 and 0)
Axiomatic theory of probability; distribution functions; expectation; Cartesian product of infinitely many probability spaces, and the Kolmogorov consistency theorem; models of convergence; weak and strong laws of large numbers.
Prerequisite: MTHSC 400 and 822, or MTHSC 800 and 822 or consent of instructor.

MTHSC 902: Probability Theory II, 3 cr. (3 and 0)
Continuation of MTHSC 901; characteristic functions, infinitely divisible distributions, central limit theorems, laws of large numbers, conditioning, and limit properties of sums of dependent random variables, conditioning, matingales.
Prerequisite: MTHSC 901.

MTHSC 981: Selected Topics in Mathematical Statistics and Probability, 1-3 cr. (1-3 and 0)
Advanced topics in mathematical statistics and probability of current interest. May be repeated for credit, but only if different topics are covered.

Sample Curricula

Sample M.S. Program for Well-Prepared Students
  • Fall: 800, 804, 853
  • Spring: 805, 860, 881
  • Summer: 821
  • Fall: 801, 807/809, 810
  • Spring: 802, 803, 806/808/981, 892
Sample M.S. Program for Students Lacking Advanced Calculus*
  • Fall: 800, 804, 653
  • Spring: 853, 805, 881
  • Summer:  860
  • Fall:  801, 807/809, 810
  • Spring:  802, 806/808/981, 821, 892

*If lacking any other prerequisite course, substitute this for 653 in the Fall.

Course Substitution Policy

. . . back to Statistics page

Course Substitution Policy

For courses outside the Depatment of Mathematical Sciences:

You must get prior approval from the Graduate Coordinator for courses to be taken outside the department if they are to be counted towards the course requirements for the degree (MS or PhD). Your committee chair should send the Graduate Coordinator a brief statement (email is fine) that is endorsed by the whole committee.

For courses within the Department of Mathematical Sciences:

If you plan to count a course in one area for a required course in another area, your advisor should send the Graduate Coordinator an email justifying the reason. For example, 803 is not a statistics course and therefore does not satisfy the statistics breadth requirement.

If you have any further questions, please refer to the Long Range Plan on the web or contact the Graduate Corrdinator at