Mathematics and Statistics

Study session for 120 with an emphasis on problem solving. (I)

Offered on a CR/NC basis only.

{Fall, Spring}

Study session for 121 with an emphasis on problem solving. (I)

Offered on a CR/NC basis only.

{Fall, Spring}

Study session for 180 with an emphasis on problem-solving. (I)

Offered on a CR/NC basis only.

{Fall, Spring}

Course offers an in-depth look at the representations of rational numbers, including base-ten and decimal numbers, integers, fractions, and arithmetic operations on these sets. Problem solving is emphasized throughout. (T)

Prerequisite: 120 or 121 or 123 or 150 or 162 or 180 or STAT 145 or ISM 100 or ACT=>19 or SAT=>450 or Compass Pre-Algebra >56 or Algebra >33

This course develops basic geometric concepts including rigid transformations and congruence; dilations and similarity; length, area and volume; systems of measurement and unit conversions; connections to coordinate geometry. Problem solving is emphasized throughout. (T)

Prerequisite: 111

Selected topics from algebra, geometry and trigonometry. (I)

Restriction: permission of the department.

Offered on a CR/NC basis only.

Note: See Restrictions earlier in Mathematics and Statistics.

Preparation for MATH 121, 129 and STAT 145.  Covers linear equations and inequalities, polynomials, factoring, exponents, radicals, fractional expressions and equations, quadratic equations, perimeters, areas of simple geometric shapes, and logarithms. Emphasis on problem solving skills. Acceptable as credit toward graduation, but not acceptable to satisfy UNM core or group requirements.  (I)

Prerequisite: ACT=>19 or SAT=>450 or ISM 100 or Compass Pre-Algebra >56 or Algebra >33

Note: See Restrictions earlier in Mathematics and Statistics.

Preparation for MATH 150 and 180.  The study of equations, functions and graphs, especially linear and quadratic functions. Introduction to polynomial, rational, exponential and logarithmic functions. Applications involving simple geometric objects. Emphasizes algebraic problem solving skills. Meets New Mexico Lower-Division General Education Common Core Curriculum Area II: Mathematics (NMCCN 1113).  (I)

Prerequisite: ACT=>22 or SAT=>510 or MATH 120 or Compass Algebra >54 or College Algebra >33.

Note: See Restrictions earlier in Mathematics and Statistics.

Definition of the trigonometric functions, radian and degree measure, graphs, basic trigonometric identities, inverse trigonometric functions, complex numbers, polar coordinates and graphs, vectors in 2 dimensions. May be taken concurrently with MATH 150. Meets New Mexico Lower-Division General Education Common Core Curriculum Area II: Mathematics (NMCCN 1113).  (I)

Prerequisite: ACT=>25 or SAT=>570 or MATH 121 or Compass College Algebra >54.

An introduction to some of the great ideas of mathematics, including logic, systems of numbers, sequences and series, geometry and probability. Emphasizes general problem-solving skills. Meets New Mexico Lower-Division General Education Common Core Curriculum Area II: Mathematics.  (I)

Prerequisite: ACT=>22 or SAT=>510 or MATH 120 or 121 or 123 or 150 or 162 or 163 or 180 or 181 or 264.

Note: See Restrictions earlier in Mathematics and Statistics.

In-depth study of polynomial, rational, exponential and logarithmic functions and their graphs. Includes the fundamental theorem of algebra, systems of equations, conic sections, parametric equations and applications in geometry. Exploration of the graphing calculator. May be taken concurrently with MATH 123. Meets New Mexico Lower-Division General Education Common Core Curriculum Area II: Mathematics.  (I)

Prerequisite: ACT=>25 or SAT=>570 or MATH 121 or Compass College Algebra >54.

Note: See Restrictions earlier in Mathematics and Statistics.

Derivative as a rate of change, intuitive, numerical and theoretical concepts, applications to graphing, linearization and optimization. Integral as a sum, relation between integral and derivative, and applications of definite integral. Meets New Mexico Lower-Division General Education Common Core Curriculum Area II: Mathematics (NMCCN 1614).  (I)

Prerequisite: (ACT=28-31 or SAT=640-700 or MATH 150 or Compass College Algebra >66) and (MATH 123 or Compass Trig >59) or (ACT=>32 or SAT=>700)

Note: See Restrictions earlier in Mathematics and Statistics.

Transcendental functions, techniques of integration, numerical integration, improper integrals, sequences and series with applications, complex variables and parmetrization of curves.  (I)

Prerequisite: MATH 162.

Note: See Restrictions earlier in Mathematics and Statistics.

Limits of functions and continuity, intuitive concepts and basic properties; derivative as rate of change, basic differentiation techniques; application of differential calculus to graphing and minima-maxima problems; exponential and logarithmic functions with applications. Meets New Mexico Lower-Division General Education Common Core Curriculum Area II: Mathematics (NMCCN 1613).  (I)

Prerequisite: ACT=>26 or SAT=>600 or MATH 121 or MATH 150 or Compass College Algebra >66.

Note: See Restrictions earlier in Mathematics and Statistics.

Includes the definite integral, multivariate calculus, simple differential equations, basic review of trigonometry and its relation to calculus.  (I)

Prerequisite: 180

Algebra from the viewpoint of the elementary curriculum with emphasis on proportional and linear relationships. Also included: topics from probability and statistics with connections to other topics in the elementary curriculum. Problem solving is emphasized throughout. (T)

Prerequisite: 112

Vector operations, vector representation of planes and curves, functions of several variables, partial derivatives, gradient, tangent planes, optimization, multiple integrals in Cartesian cylindrical and spherical coordinates, vector fields, line integrals and Green’s theorem.  (I)

Prerequisite: C (not C-) or better in 163.

Differential and integral calculus with an emphasis on conceptual understanding.  (I)

Prerequisite: Grade of at least A- in 180 or 162

Use of computers and graphing utilities in the mathematics classroom. Introduction to hardware and commercial software. Applications of selected programming languages to the teaching of mathematics. (T)

Prerequisite: 162 or 181

A penetrating look at functions, derivatives, intergrals, and the Fundamental Theorem of Calculus that makes explicit how topics in the secondary school curriculum come to fruition in this foundational subject. (T)

Prerequisite: 163

Restriction: permission of instructor

A survey of mathematical developments prior to 1800; emphasis on problem solving techniques; comparison of older and more modern methods. (T)

Prerequisite: 163

{Fall}

An axiomatic approach to fundamentals of geometry, both Euclidean and non-Euclidean. Emphasis on historical development of geometry. (T)

{Spring}

An experience in mathematical invention and discovery at the level of high school geometry and algebra that includes a deeper look at sequences, series, and recursions. (T)

Prerequisite: 180 or 162. Corequisite: 306

{Offered upon demand}

An experience in mathematical invention and discovery at the level of high school geometry and algebra that includes a deeper look at sequences, series, and recursions. (T)

Prerequisite: 181 or 163

Vector algebra, lines, planes; vector valued functions, curves, tangent lines, arc length, line integrals; directional derivative and gradient; divergence, curl, Gauss’ and Stokes’ theorems, geometric interpretations.

Prerequisite: 264

Solution methods for partial differential equations; science and engineering applications; heat and wave equations, Laplace’s equation; separation of variables; Fourier series and transforms; special functions.

Prerequisite: 264 and 316

Theory of functions of a complex variable with application to physical and engineering problems. Although not required, skill in vector analysis will be helpful in taking this course.

Prerequisite: 264

Note: See Restrictions earlier in Mathematics and Statistics.

System of linear equations, matrices, linear transformations, determinants, eigenvalues and eigenvectors. Efficient computational methods emphasized.

Prerequisite: (163 or 181) and CS 151L

Introduction to algorithmic theory of ordinary differential equations. Topics covered: elementary theory of ordinary differential equations, numerical methods, phase-plane analysis, and introduction to Laplace transformations. Third-level calculus is helpful for this class.

Prerequisite: 163 and CS 151L

Basic enumeration including combinations, permutations, set and integer partitions, distributions, and rearrangements, binomial and multinomial theorems together with pigeon-hole and inclusion-exclusion principles and mathematical induction principles. Discrete probability, elementary ordinary generating functions, recurrence relations, and sorting algorithms.

Prerequisite: 163 or 181

{Fall}

Trees, connectivity, planarity, colorability, and digraphs; algorithms and models involving these concepts. Ability in linear algebra is helpful when taking this course.

{Spring}

Divisibility, congruences, primitive roots, quadratic residues, diophantine equations, continued fractions, partitions, number theoretic functions.

{Spring}

Note: See Restrictions earlier in Mathematics and Statistics.

Linear transformations, matrices, eigenvalues and eigenvectors, inner product spaces.

Prerequisite: 264

{Fall, Spring}

Groups, rings, homomorphisms, permutation groups, quotient structure, ideal theory, fields.

Prerequisite: 264

{Fall}

Course will introduce students to the fundamentals of mathematical proof in the context of discrete structures. Topics include logic, sets and relations, functions, integers, induction and recursion, counting, permutations and combinations and algorithms.

Prerequisite: 162 and 163

(Fall)

Topics from secondary mathematics presented from an advanced standpoint and designed to meet the needs of pre- and in-service teachers. Open only to prospective and in-service teachers of mathematics. (T)

Prerequisite: 306 and 327

{Spring}

Presents mathematical topics of concern to elementary and mid-school teachers. Open only to in-service and prospective teachers. (T)

{Offered upon demand}

Presents mathematical topics of concern to secondary teachers. Open only to in-service and prospective teachers. (T)

{Offered upon demand}

(Also offered as PHIL 356)

This is a first course in logical theory. Its primary goal is to study the notion of logical entailment and related concepts, such as consistency and contingency. Formal systems are developed to analyze these notions rigorously.

(Also offered as CS 375)

An introductory course covering such topics as solution of linear and nonlinear equations; interpolation and approximation of functions, including splines; techniques for approximate differentiation and integration; solution of differential equations; familiarization with existing software.

Prerequisite: CS 151L

Advanced problem solving. Especially recommended for students wishing to participate in the Putnam Intercollegiate Mathematical Competition.

Restriction: permission of instructor

{Offered upon demand}

Selected topics from analysis, algebra, geometry, statistics, model building, interdisciplinary studies and problem solving.

{Offered upon demand}

Rigorous treatment of calculus in one variable. Definition and topology of real numbers, sequences, limits, functions, continuity, differentiation and integration. Students will learn how to read, understand and construct mathematical proofs.

Prerequisite: 264 and two courses at the 300+ level.

Generalization of 401/501 to several variables and metric spaces: sequences, limits, compactness and continuity on metric spaces; interchange of limit operations; series, power series; partial derivatives; fixed point, implicit and inverse function theorems; multiple integrals.

Prerequisite: 401

Qualitative study of linear and nonlinear ordinary differential equations and discrete time maps including stability analysis, bifucations, fractal structures and chaos; applications to biology, chemistry, physics and engineering.

Prerequisite: 264 and (314 or 321) or 316

(Also offered as PHIL *415)

A historical survey of principal issues and controversies on the nature of mathematics. Emphasis varies from year to year.

Prerequisite: 163 or 181 or 356

Theory of fields, algebraic field extensions and Galois theory for fields of characteristic zero.

Prerequisite: 322 or 422

{Spring}

Groups, rings and fields. (This course will not be counted in the hours necessary for a mathematics major.)

Prerequisite: 264

{Fall}

Metric spaces, topological spaces, continuity, algebraic topology.

Prerequisite: 401

{Fall}

Elementary theory of surfaces, differential forms, integral geometry and Riemannian geometry.

Prerequisite: 311 or 402

{Offered upon demand}

{Offered upon demand}

(Also offered as STAT 461/561)

Mathematical models for random experiments, random variables, expectation. The common discrete and continuous distributions with application. Joint distributions, conditional probability and expectation, independence. Laws of large numbers and the central limit theorem. Moment generating functions.

Prerequisite: MATH 264

{Fall}

Linear systems. Existence and uniqueness theorems, flows, linearized stability for critical points, stable manifold theorem. Gradient and Hamiltonian systems. Limit sets, attractors, periodic orbits, Floquet theory and the Poincare Map. Introduction to perturbation theory.

Prerequisite: (314 or 321) and 316 and 401

{Fall}

Classification of partial differential equations; properly posed problems; separation of variables, eigenfunctions and Green’s functions; brief survey of numerical methods and variational principles.

Prerequisite: 312 and 313 and (314 or 321) and (311 or 402)

{Spring}

Determinants; theory of linear equations; matrix analysis of differential equations; eigenvalues, eigenvectors and canonical forms; variational principles; generalized inverses.

Prerequisite: 314 or 321

{Fall}

Special functions and advanced mathematical methods for solving differential equations, difference equations and integral equations.

Prerequisite: 311 and 312 and 313 and 316

{Spring}

(Also offered as CS 471)

Introduction to scientific computing fundamentals, exposure to high performance programming language and scientific computing tools, case studies of scientific problem solving techniques.

Discrete Fourier and Wavelet Transform. Fourier series and integrals. Expansions in series of orthogond wavelets and other functions. Multiresolution and time/frequency analysis. Applications to signal processing and statistics.

Prerequisite: (314 or 321) or 401

{Offered upon demand}

Guided study, under the supervision of a faculty member, of selected topics not covered in regular courses.

Use of computers and graphing utilities in the mathematics classroom. Introduction to hardware and commercial software. Applications of selected programming languages to the teaching of mathematics.

Prerequisite: 162 or 181

Restriction: College of Education graduate students

Rigorous treatment of calculus in one variable. Definition and topology of real numbers, sequences, limits, functions, continuity, differentiation and integration. Students will learn how to read, understand and construct mathematical proofs.

Prerequisite: 264 and two courses at the 300+ level.

Restriction: College of Education graduate students.

Generalization of 401/501 to several variables and metric spaces: sequences, limits, compactness and continuity on metric spaces; interchange of limit operations; series, power series; partial derivatives; fixed point, implicit and inverse function theorems; multiple integrals.

Prerequisite: 501

Restriction: College of Education graduate students.

A penetrating look at functions, derivatives, integrals, and the Fundamental Theorem of Calculus that makes explicit how topics in the secondary school curriculum come to fruition in this foundational subject.

Restriction: permission of instructor

(Also offered as CS 575)

Direct and iterative methods of the solution of linear systems of equations and least squares problems. Error analysis and numerical stability. The eigenvalue problem. Descent methods for function minimization, time permitting.

Prerequisite: 464, 514

{Spring}

(Also offered as CS 576)

Numerical approximation of functions. Interpolation by polynomials, splines and trigonometric functions. Numerical integration and solution of ordinary differential equations. An introduction to finite difference and finite element methods, time permitting.

Prerequisite: 316, 401

{Fall}

An axiomatic approach to fundamentals of geometry, both Euclidean and non-Euclidean. Emphasis on historical development of geometry.

Restriction: College of Education graduate students

{Spring}

A survey of mathematical developments prior to 1800; emphasis on problem solving techniques; comparison of older and more modern methods.

Prerequisite: 163

Restriction: College of Education graduate students

{Fall}

An experience in mathematical invention and discovery at the level of high school geometry and algebra that includes a deeper look at sequences, series, and recursions.

Prerequisite: 180 or 162. Corequisite: 306

Restriction: College of Education graduate students

{Offered upon demand}

An experience in mathematical invention and discovery at the level of high school geometry and algebra that includes a deeper look at sequences, series, and recursions.

Prerequisite: 181 or 163

Restriction: College of Education graduate students

Real number fields, sets and mappings. Basic point set topology, sequences, series, convergence issues. Continuous functions, differentiation, Riemann integral. General topology and applications: Weierstrass and Stone-Weierstrass approximation theorems, elements of Founier Analysis (time permitting).

Prerequisite: 321, 401

{Fall}

Continuation of 510. Differentiation in Rⁿ. Inverse and implicit function theorems, integration in Rⁿ, differential forms and Stokes theorem.

Prerequisite: 510

{Spring}

Linear systems. Existence and uniqueness theorems, flows, linearized stability for critical points, stable manifold theorem. Gradient and Hamiltonian systems. Limit sets, attractors, periodic orbits, Floquet theory and the Poincare Map. Introduction to perturbation theory.

Prerequisite: 314, or 321, 316, 401

{Fall}

Classification of partial differential equations; properly posed problems; separation of variables, eigenfunctions and Green’s functions; brief survey of numerical methods and variational principles.

Prerequisite: 312, 313, 314 or 321, one of 311 or 402

{Spring}

Determinants; theory of linear equations; matrix analysis of differential equations; eigenvalues, eigenvectors and canonical forms; variational principles; generalized inverses.

Prerequisite: 314 or 321

{Fall}

Theory of groups, permutation groups, Sylow theorems. Introduction to ring theory, polynomial rings. Principal ideal domains.

Prerequisite: 322

{Fall}

Continuation of 520. Module theory, field theory, Galois theory.

Prerequisite: 321, 520

{Spring}

Basic theory of complex affine and projective varieties. Smooth and singular points, dimension, regular and rational mappings between varieties, Chow’s theorem.

Prerequisite: 431, 521, 561

{Alternate Falls}

Continuation of 530. Degree of a variety and linear systems. Detailed study of curves and surfaces.

Prerequisite: 530

{Alternate Springs}

Introduction to homology and cohomology theories. Homotopy theory, CW complexes.

Prerequisite: 431, 521

{Alternate Falls}

Continuation of 532. Duality theorems, universal coefficients, spectral sequence.

Prerequisite: 532

{Alternate Springs}

Elementary theory of surfaces, differential forms, integral geometry and Riemannian geometry.

Prerequisite: 311 or 402

{Offered upon demand}

Basic point set topology. Separation axioms, metric spaces, topological manifolds, fundamental group and covering spaces.

Prerequisite: 401

{Fall}

Concept of a manifold, differential structures, vector bundles, tangent and cotangent bundles, embedding, immersions and submersions, transversality, Stokes’ theorem.

Prerequisite: 511

{Spring}

Theory of connections, curvature, Riemannian metrics, Hopf-Rinow theorem, geodesics. Riemannian submanifolds.

Prerequisite: 536

{Alternate Falls}

Continuation of MATH 537 with emphasis on adding more structures. Riemannian submersions, Bochner theorems with relation to topology of manifolds, Riemannian Foliations, Complex and Kaehler geometry, Sasakian and contact geometry.

Prerequisite: 537

{Alternate Springs}

(Also offered as STAT 565)

Markov chains and processes with applications. Classification of states. Decompositions. Stationary distributions. Probability of absorption, the gambler’s ruin and mean time problems. Queuing and branching processes. Introduction to continuous time Markov processes. Jump processes and Brownian motion.

Prerequisite: STAT 527

{Offered on demand}

(Also offered as STAT 567)

A measure theoretic introduction to probability theory. Construction of probability measures. Distribution and characteristic functions, independence and zero-one laws. Sequences of independent random variables, strong law of large numbers and central limit theorem. Conditional expectation. Martingales.

Prerequisite: 563

{Alternate Springs}

Topics from secondary mathematics presented from an advanced standpoint and designed to meet the needs of pre- and in-service teachers. Open only to prospective and in-service teachers of mathematics.

Restriction: College of Education graduate students

Prerequisite: 306 and 322 and 327

{Fall}

Presents mathematical topics of concern to elementary and mid-school teachers. Open only to in-service and prospective teachers. May be repeated for credit by permission of instructor.

Restriction: College of Education graduate students

{Offered upon demand}

(Also offered as STAT 569)

Presents mathematical topics of concern to secondary teachers. Open only to in-service and prospective teachers.  May be repeated for credit by permission of instructor.

Restriction: College of Education graduate students

{Offered upon demand}

(Also offered as CS 557)

Possible topics include approximation theory, two point boundary value problems, quadrature, integral equations and roots of nonlinear equations.

Analyticity, Cauchy theorem and formulas, Taylor and Laurent series, singularities and residues, conformal mapping, selected topics.

Prerequisite: 311 or 402

{Fall}

The Mittag-Leffler theorem, series and product expansions, introduction to asymptotics and the properties of the gamma and zeta functions. The Riemann mapping theorem, harmonic functions and Dirichlet’s problem. Introduction to elliptic functions. Selected topics.

Prerequisite: 561

{Fall}

Functions of one and several real variables, measure theory, starting with Lebesque measure and integration. Product measures. Measure on spaces of functions.

Prerequisite: 401 or 510

{Fall}

Fourier analysis on the circle, real line and on compact and locally compact groups.

Prerequisite: 563

{Offered upon demand}

Basic theory of stochastic differential equations with applications. The presentation will be at a level accessible to scientists, engineers and applied mathematicians.

Prerequisite: 316 and 441

{Offered upon demand}

Singularly perturbed boundary value problems, layer type expansions and matching. Initial value problems and multiscaling methods for ordinary and partial differential equations. Phase plane and qualitative ideas. Applications. Perturbations of Hamiltonian systems.

Prerequisite: 462, 463

{Alternate Springs}

Existence and uniqueness of solutions, linear systems, asymptotic behavior of solutions to nonlinear systems, integral manifolds and linearizations, perturbation theory, bifurcation theory, dichotomies for solutions of linear systems.

Prerequisite: 462

{Alternate Springs}

Discrete Fourier and Wavelet Transform. Fourier series and integrals. Expansions in series of orthogond wavelets and other functions. Multiresolution and time/frequency analysis. Applications to signal processing and statistics.

Prerequisite: 314, 321 or 401

{Offered upon demand}

Equations of first order, classification of equations and systems, elliptic equations and introduction to potential theory, hyperbolic equations and systems, parabolic equations.

Prerequisite: 463

{Alternate Falls}

Selected advanced topics in numerical linear algebra.

Prerequisite: 504

{Alternate Springs}

Numerical methods for initial value and/or boundary value problems.

Prerequisite: 462, 504, 505

{Offered upon demand}

Introduction to the numerical analysis of partial differential equations.

Prerequisite: 463, 504, 505

{Alternate Falls}

Normed vector spaces, including Hilbert and Banach spaces. Linear operators on these spaces, with an emphasis on applications.

Prerequisite: 510

{Offered upon demand}

Advanced topics in function spaces and linear operators.

Prerequisite: 581

Approximation in Hilbert spaces, basic operator theory, integral equations, distribution theory, Green’s functions, differential operators, boundary value problems and nonlinear problems.

Prerequisite: 312, 314, 316, 401

{Alternate Falls}

Eigenfunction expansions for ordinary and partial differential operators, Euler-Lagrange equations, Hamilton’s principle, calculus of variations, brief complex variable theory, special functions, transform and spectral theory, asymptotic expansions.

Prerequisite: 312 and 314 and 316 and 401

{Alternate Springs}

Practicum involves a project of an applied nature which may be done in conjunction with an industrial laboratory, a research institution or another department of the University. It is expected the student will become acquainted with a field of application in science or engineering and complete a project of use and interest to workers in that field. A final written report is required.

Offered on a CR/NC basis only.

Students present their current research.

(Also offered as STAT 649)

Offered on a CR/NC basis only.

Techniques for the visual presentation of numerical data, descriptive statistics, introduction to probability and basic probability models used in statistics, introduction to sampling and statistical inference, illustrated by examples from a variety of fields.

Prerequisite: ACT = >22 or SAT = >510 or MATH 120 or 121 or 123 or 150 or 162 or 163 or 180 or 181 or 264.

{Summer, Fall, Spring}

An introduction to probability including combinatorics, Bayes’ theorem, probability densities, expectation, variance and correlation. An introduction to estimation, confidence intervals and hypothesis testing.

Prerequisite: MATH 181 or MATH 163

A detailed introduction to the SAS® programming language. Topics covered include reading data, storing data, manipulating data, data presentation, graphing, and macro programming. SAS® software will be used.

Prerequisite: 345 and 427

Statistical tools for scientific research, including parametric and non-parametric methods for ANOVA and group comparisons, simple linear and multiple linear regression, and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as Minitab® and SAS®.

Prerequisite: 145

{Fall}

A continuation of 427 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principal components, discriminant analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression.

Prerequisite: 427

This course examines the use of log-linear models to analyze count data. It also uses graphical models to examine dependence structures for both count data and measurement data.

Prerequisite: 345 and 427

Simple regression and multiple regression. Residual analysis and transformations. Matrix approach to general linear models. Model selection procedures, nonlinear least squares, logistic regression. Computer applications.

Prerequisite: 427

{Fall}

A data-analytic course. Multifactor ANOVA. Principles of experimental design. Analysis of randomized blocks, Latin squares, split plots, etc. Random and mixed models. Extensive use of computer packages with interpretation, diagnostics.

Prerequisite: 440

{Spring}

Transformations of univariate and multivariate distributions to obtain the special distributions important in statistics. Concepts of estimation and hypothesis testing in both large and small samples with emphasis on the statistical properties of the more commonly used procedures, including student’s t-tests, F-tests and chi-square tests. Confidence intervals. Performance of procedures under non-standard conditions (i.e., robustness).

Prerequisite: 461

{Spring}

(Also offered as MATH 441)

Mathematical models for random experiments, random variables, expectation. The common discrete and continuous distributions with application. Joint distributions, conditional probability and expectation, independence. Laws of large numbers and the central limit theorem. Moment generating functions.

Prerequisite: MATH 264

{Fall}

Basic ideas of statistical quality control and improvement. Topics covered: Deming’s 14 points and deadly diseases, Pareto charts, histograms, cause and effect diagrams, control charts, sampling, prediction, reliability, experimental design, fractional factorials, Taguchi methods, response surfaces.

Prerequisite: 345

Basic methods of survey sampling; simple random sampling, stratified sampling, cluster sampling, systematic sampling and general sampling schemes; estimation based on auxiliary information; design of complex samples and case studies.

Prerequisite: 345

{Alternate Falls}

A detailed overview of methods commonly used to analyze medical and epidemiological data. Topics include the Kaplan-Meier estimate of the survivor function, models for censored survival data, the Cox proportional hazards model, methods for categorical response data including logistic regression and probit analysis, generalized linear models.

Prerequisite: 428 or 440

Tools for multivariate analysis including multivariate ANOVA, principal components analysis, discriminant analysis, cluster analysis, factor analysis, structural equations modeling, canonical correlations and multidimensional scaling.

Prerequisite: 428 or 440

{Offered upon demand}

An introduction to Bayesian methodology and applications. Topics covered include: probability review, Bayes’ theorem, prior elicitation, Markov chain Monte Carlo techniques. The free software programs WinBUGS and R will be used for data analysis.

Prerequisite: 461 and (427 or 440)

{Alternate Springs}

Modern topics not covered in regular course offerings.

Introduction to time domain and frequency domain models of time series. Data analysis with emphasis on Box-Jenkins methods. Topics such as multivariate models; linear filters; linear prediction; forecasting and control.

Prerequisite: 461

{Alternate Springs}

Guided study, under the supervision of a faculty member, of selected topics not covered in regular course offerings.

(Also offered as ANTH 620, BIOL 520, CS 520, ECE 620)

Varying interdisciplinary topics taught by collaborative scientists from UNM, SFI, and LANL.

(Also offered as BIOL 524, CS 524, ANTH 624, ECE 524)

Course designed to develop the methods content and assessment of effective interdisciplinary biological courses; Students will develop and teach an undergraduate interdisciplinary topics course. Topics vary.

Restriction: permission of instructor.

A detailed introduction to the SAS® programming language. Topics covered include reading data, storing data, manipulating data, data presentation, graphing, and macro programming. SAS® software will be used.

Prerequisite: 345, 427

Statistical tools for scientific research, including parametric and non-parametric methods for ANOVA and group comparisons, simple linear and multiple linear regression and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as Minitab® and SAS®. Course cannot be counted in the hours needed for graduate degrees in Mathematics and Statistics.

Prerequisite: 145

{Fall}

A continuation of 527 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principal components, discriminate analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression.

Prerequisite: 527

A detailed examination of the statistical methods used in analyzing genetic data. Topics covered include the estimation of allele frequencies, testing for Hardy-Weinberg equilibrium, classical and complex segregation analysis, linkage analysis for Mendelian and complex diseases, and the detection of allelic association. Popular genetic software will be used for data analysis.

Prerequisite: 345, 427

{Alternate Falls}

A continuation of 531. Topics covered include statistical methods for describing variation in quantitative traits, methods of mapping and characterizing quantitative trait loci and other current topics in statistical genetics, including the analysis of microarray data and phylogenetic methods. Popular genetic software will be used for data analysis.

Prerequisite: 531

{Alternate Springs}

This course examines the use of log-linear models to analyze count data. It also uses graphical models to examine dependence structures for both count data and measurement data.

Prerequisite: 345, 427

Covers basic statistical methods including statistical summaries and inference. Methods of summarizing data include graphical displays and numerical summaries. Statistical inference includes hypothesis testing and confidence intervals. Methods for continuous and categorical data are studied.

Prerequisite: B or better in MATH 121

{Fall}

Covers basic models used in the statistical analysis of studies in the medical sciences and public health field, with an emphasis on epidemiology. Linear regression, analysis of variance, logistic regression, and survival models are studied.

Prerequisite: 538

{Spring}

Simple regression and multiple regression. Residual analysis and transformations. Matrix approach to general linear models. Model selection procedures, nonlinear least squares, logistic regression. Computer applications.

Prerequisite: 527

{Fall}

A data-analytic course. Multifactor ANOVA. Principles of experimental design. Analysis of randomized blocks, Latin squares, split plots, etc. Random and mixed models. Extensive use of computer packages with interpretation, diagnostics.

Prerequisite: 540

{Spring}

Theory of the Linear Models discussed in 440/540 and 445/545. Linear spaces, matrices, projections, multivariate normal distribution and theory of quadratic forms. Non-full rank models and estimability. Gauss-Markov theorem. Distribution theory for normality assumptions. Hypothesis testing and confidence regions.

Prerequisite: 553, 545, linear algebra.

{Alternate Falls}

Hotelling T2, multivariate ANOVA and Regression, classification and discrimination, principal components and factor analysis, clustering, graphical and computational techniques, topics in linear models.

Prerequisite: 546

{Alternate Springs}

Transformations of univariate and multivariate distributions to obtain the special distributions important in statistics. Concepts of estimation and hypothesis testing in both large and small samples with emphasis on the statistical properties of the more commonly used procedures, including student’s t-tests, F-tests and chi-square tests. Confidence intervals. Performance of procedures under non-standard conditions (i.e., robustness).

Prerequisite: 561

{Spring}

Theory and methods of point estimation, sufficiency and its applications.

Prerequisite: 553, 561 and MATH 510

{Alternate Falls}

Standard limit theorems, hypothesis testing, confidence intervals and decision theory.

Prerequisite: 556

{Alternate Springs}

Mathematical models for random experiments, random variables, expectation. The common discrete and continuous distributions with application. Joint distributions, conditional probability and expectation, independence. Laws of large numbers and the central limit theorem. Moment generating functions.

Prerequisite: MATH 264

{Fall}

(Also offered as MATH 540)

Markov chains and processes with applications. Classification of states. Decompositions. Stationary distributions. Probability of absorption, the gambler’s ruin and mean time problems. Queuing and branching processes. Introduction to continuous time Markov processes. Jump processes and Brownian motion.

Prerequisite: 561

{Offered on demand}

(Also offered as MATH 541)

A measure theoretic introduction to probability theory. Construction of probability measures. Distribution and characteristic functions, independence and zero-one laws. Sequences of independent random variables, strong law of large numbers and central limit theorem. Conditional expectation. Martingales.

Prerequisite: MATH 563

{Alternate Springs}

(Also offered as MATH 549)

Basic ideas of statistical quality control and improvement. Topics covered: Deming’s 14 points and deadly diseases, Pareto charts, histograms, cause and effect diagrams, control charts, sampling, prediction, reliability, experimental design, fractional factorials, Taguchi methods, response surfaces.

Prerequisite: 345

Basic methods of survey sampling; simple random sampling, stratified sampling, cluster sampling, systematic sampling and general sampling schemes; estimation based on auxiliary information; design of complex samples and case studies.

Prerequisite: 345

{Alternate Falls}

A detailed overview of methods commonly used to analyze medical and epidemiological data. Topics include the Kaplan-Meier estimate of the survivor function, models for censored survival data, the Cox proportional hazards model, methods for categorical response data including logistic regression and probit analysis, generalized linear models.

Prerequisite: 528 or 540

Tools for multivariate analysis including multivariate ANOVA, principal components analysis, discriminant analysis, cluster analysis, factor analysis, structural equations modeling, canonical correlations and multidimensional scaling.

Prerequisite: 528 or 540

{Offered upon demand}

An introduction to Bayesian methodology and applications. Topics covered include: probability review, Bayes’ theorem, prior elicitation, Markov chain Monte Carlo techniques. The free software programs WinBUGS and R will be used for data analysis.

Prerequisite: 561 and (527 or 540)

{Alternate Springs}

Introduction to time domain and frequency domain models of time series. Data analysis with emphasis on Box-Jenkins methods. Topics such as multivariate models; linear filters; linear prediction; forecasting and control.

Prerequisite: 561

{Alternate Springs}

Time series models in the time and spectral domains. Linear filters. Multivariate models. Autoregressive and moving average models. Filtering and prediction. Distribution theory. Design of experiments.

Prerequisite: 581

{Alternate Falls}

Statistical methods that are insensitive to the distribution of the data. Sign tests, Kolmogorov-Smirnov tests, rank tests including the Wilcoxon, Mann-Whitney, Kruskal-Wallis and Friedman tests. Robust estimation including M estimators, L estimators and R estimators.

Prerequisite: 561

{Offered upon demand}

Nonparametric regression, density estimation, filtering, spectral density estimation, image reconstruction and pattern recognition. Tools include orthogonal series, kernels, splines, wavelets and neural networks. Applications to medicine, engineering, biostatistics and economics.

Prerequisite: 561

{Offered upon demand}

A detailed examination of essential statistical computing skills needed for research and industrial work. Students will use S-Plus, Matlab and SAS® to develop algorithms for solving a variety of statistical problems using resampling and simulation techniques such as the bootstrap, Monte Carlo methods and Markov chain methods for approximating probability distributions. Applications to linear and non-linear models will be stressed.

Prerequisite: 528

Provides experience in statistical consulting and analysis of real data.

Prerequisite: 528

Offered on a CR/NC basis only.

Students present their current research.

(Also offered as MATH 649)

Offered on a CR/NC basis only.