Instructor(s):  Staff

Required text(s):  Angel, Allen and Dennis Runde. Intermediate Algebra for College Students (packaged with MyMathLab). 8th ed. ISBN-13: 9780321709042. Upper Saddle River, NJ: Pearson-Prentice Hall, 2010. Print.

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  None

Course description:  Fundamentals of algebra. Graphs of linear equations, polynomials and factoring, first and second-degree equations and inequalities, radicals and exponents, and systems of equations. Word problems emphasized throughout the course.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Consortium for Mathematics and Its Applications (COMAP), S. Garfunkel, ed. For All Practical Purposes: Mathematical Literacy in Today's World. 9th ed. ISBN-13: 978-1429243162. New York: W. H. Freeman, 2011. Print.

Prerequisites:  None

Course description:  An introduction to mathematical modeling. Topics chosen from linear programming, probability theory, Markov chains, scheduling problems, coding theory, social choice, voting theory, geometric concepts, game theory, graph theory, combinatorics, networks. Emphasis placed upon demonstrating the usefulness of mathematical models in other disciplines, especially the social science and business.

Syllabus:  Common

Instructor(s):  staff

Required text(s):  Axler, Sheldon. Precalculus: A Prelude to Calculus. Binder Ready Version w/ Wileyplus ISBN: 9781118562390 or Paperback w/ Wileyplus ISBN: 978-1-118-55625-2. 2nd ed. Hoboken, NJ: Wiley, 2012. Print.

Textbook notes:  Students are required to have access to WileyPLUS for this course. Students buying used textbooks should arrange to purchase WileyPLUS separately.

Prerequisites:  MATH 100 or Math Diagnostic Test

Course description:  Inverse functions, quadratic functions, complex numbers. Detailed study of polynomial functions including zeros, factor theorem, and graphs. Rational functions, exponential and logarithmic functions and their applications. Systems of equations, inequalities, partial fractions, linear programming, sequences and series. Word problems are emphasized throughout the course.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Axler, Sheldon. Precalculus: A Prelude to Calculus, 2nd Edition Binder Ready Version w/ Wileyplus ISBN: 9781118562390 or Paperback w/ Wileyplus ISBN: 978-1-118-55625-2. Hoboken, NJ: Wiley, 2012. Print.

Prerequisites:  MATH 117 or Math Diagnostic Test

Course description:  Functions and change with an emphasis on linear, quadratic, exponential, and logarithmic functions and their graphs. Specific geometric topics include concavity and how transformations affect graphs. Topics in trigonometry include radians, sinusoidal functions, identities, sum/difference formulas, double/half angle formulas, and trigonometric equations. Other topics include polar coordinates.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Hughes-Hallett, Deborah, et al. Applied & Single Variable Calculus for Loyola University Chicago with WebAssign Custom (packaged with WebAssign). 4th ed. ISBN-13: 9781118762202. Hoboken, NJ: Wiley, 2009. Print.

Textbook notes:  Students are required to have access to WebAssign for this course. Students buying used textbooks should arrange to purchase WebAssign separately.

Prerequisites:  MATH 118 or Math Diagnostic Test

Course description:  An introduction to differential and integral calculus, with an emphasis on applications. This course is intended for students in the life and social sciences, computer science, and business. Topics include: modeling change using functions including exponential and trigonometric functions, the concept of the derivative, computing the derivative, applications of the derivative to business and life, social, and computer sciences, and an introduction to integration. This course is not a substitute for MATH 161.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Hughes-Hallett, Deborah, et al. Applied & Single Variable Calculus for Loyola University Chicago with WebAssign Custom (packaged with WebAssign). 4th ed. ISBN-13: 9781118762202. Hoboken, NJ: Wiley, 2009. Print.

Textbook notes:  Students are required to have access to WebAssign for this course. Students buying used textbooks should arrange to purchase WebAssign separately. SPECIAL NOTE: this is a change from previous semesters, which required WileyPLUS.

Prerequisites:  MATH 131 or MATH 161

Course description:  A continuation of MATH 131. Topics include: properties of the integral, techniques of integration, numerical methods, improper integrals, applications to geometry, physics, economics, and probability theory, introduction to differential equations and mathematical modeling, systems of differential equations, power series. This course is not a substitute for MATH 162.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Thomas, George B., Maurice D. Weir, and Joel R. Hass. Thomas' Calculus: Early Transcendentals (Single Variable) (packaged with MyMathLab). 13th ed. ISBN-13: 9780321705402. Boston: Addison-Wesley, 2009. Print.

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  MATH 118 or Math Diagnostic Test

Course description:  A traditional introduction to differential and integral calculus. Functions, limits, continuity, differentiation, intermediate and mean-value theorems, curve sketching, optimization problems, related rates, definite and indefinite integrals, fundamental theorem of calculus, logarithmic and exponential functions. Applications to physics and other disciplines.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Thomas, George B., Maurice D. Weir, and Joel R. Hass. Thomas' Calculus: Early Transcendentals (Single Variable) (packaged with MyMathLab). 13th ed. ISBN-13: 9780321705402. Boston: Addison-Wesley, 2009. Print.

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  MATH 161

Course description:  A continuation of MATH 161. Calculus of logarithmic, exponential, inverse trigonometric, and hyperbolic functions. Techniques of integration. Applications of integration to volume, surface area, arc length, center of mass, and work. Numerical sequences and series. Study of power series and the theory of convergence. Taylor's theorem with remainder.

Syllabus:  Common

Instructor(s):  Dr. Emily Peters

Required text(s):  Burton, David. Elementary Number Theory, 7th ed. ISBN-13: 978-0073383149. New York: McGraw-Hill, 2011. Print.

Prerequisites:  Math 161

Course description:  This bridge course to higher level mathematics serves as an introduction both to number theory in particular, and to the art of mathematical argument in general. In exploring fundamental properties of integers and rational numbers, students will learn how to understand and write mathematical proofs. A central role in number theory is played by the prime numbers, whose infinitude was known already to Euclid circa 300 B.C., but whose exact distribution among integers is still so deeply mysterious now in the 21st century as to serve as the basis for secure data transmission. Topics include: representation of numbers, divisibility, prime numbers, Diophantine equations, congruence of numbers, methods of solving congruences, public-key cryptography, Fermat's Last Theorem.

Instructor(s):  Dr. E.N.Barron

Required text(s):  David Lay, Linear Algebra and its Applications, 4th Edition, Pearson Publishing, the unbound book + MML: ISBN: 9780321836144, Or MyMathLab by itself which comes with an electronic copy of the book: MML: ISBN 9780321199911

Prerequisites:  Math 162 or 132.

Course description:  Linear algebra is widely used in mathematics, science, engineering, and the social sciences. For example, statisticians and economists often employ linear models when trying to analyze problems with many variables. And linear algebra is an important tool in many areas of mathematics itself. Much of functional analysis is devoted to the study of functions preserving linearity and field theory uses linear algebra in the proofs of many results. The course starts with the problem of solving simultaneous linear equations using the Gaussian elimination algorithm. The solution of this important practical problem motivates the definition of many linear algebra concepts: matrices, vectors and vector spaces, linear independence, dimension, and vector subspaces. The emphasis then shifts to general vector spaces and proofs using an axiom system. Most of the results will be for finite dimensional spaces and we will always attempt to visualize theorems in 2 or 3 dimensional Euclidean space. Topics to be covered include: linear transformations, change of basis, determinants, eigenvalues and eigenvectors, and diagonalization. We will have at least 2 exams plus a final and weekly online homework on MyMathLab. A TI-83 or higher calculator will also be required for this course.

Instructor(s):  Dr. Anne Hupert

Required text(s):  Anton, Howard. Elementary Linear Algebra. 10th ed. ISBN-13: 978-0470458211. Hoboken: Wiley, 2010. Print.

Prerequisites:  MATH 132 or MATH 162

Course description:  An introduction to linear algebra in abstract vector spaces with particular emphasis on Rⁿ. Topics include: Gaussian elimination, matrix algebra, linear independence, span, basis, linear transformations, determinants, eigenvalues, eigenvectors, and diagonalization. Some of the basic theorems will be proved rigorously; other results will be demonstrated informally. Software such as Mathematica may be utilized.

Instructor(s):  Dr. Cristina Popovici (Section 001 - MoWeFr 8:15AM - 10:10AM)
Dr. Danut Arama (Section 002 - MoWeFr 1:40PM - 3:35PM)

Required text(s):  Thomas, George B., Maurice D. Weir, and Joel R. Hass. Thomas' Calculus, Multivariable (packaged with MyMathLab), 12th ed. ISBN-13: 9780321651952. New York: Pearson, 2009. Print.

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  MATH 162

Course description:  Vectors and vector algebra, curves and surfaces in space, functions of several variables, partial derivatives, the chain rule, the gradient vector, LaGrange multipliers, multiple integrals, volume, surface area, the Change of Variables theorem, line integrals, surface integrals, Green's theorem, the Divergence Theorem, and Stokes' Theorem.

Instructor(s):  Dr. Joseph Mayne

Required text(s):  Nagle, Saff, and Snider, Fundamentals of Differential Equations, 8th Edition, Pearson, 2012. ISBN-10: 0321747739.

Prerequisites:  Math 263

Course description:  A differential equation is often used to model a situation that involves change. Such problems as population growth, the amount of liquid flowing out of the bottom of a tank, the concentration of a drug in a patient’s bloodstream, and the position of an object dropped from above the surface of the earth can all be modeled using a differential equation. This course will concentrate on ordinary differential equations. For what equations does a solution exist and when is a solution unique? Can we solve an equation explicitly in mathematical terms? Can we find a numerical solution? What about studying equations from a qualitative viewpoint rather than a quantitative one? Topics will include first order equations, second order linear equations, Laplace Transforms, series solutions, and systems of equations. Applications to modeling will be emphasized.

Instructor(s):  Dr. Richard Lucas

Required text(s):  Nagle, R. Kent, Saff, Edward B., and Edward B. Snider. Fundamentals of Differential Equations. 8th ed. ISBN-13: 978-0321747730. Boston: Addison-Wesley, 2012. Print.

Prerequisites:  MATH 263

Course description:  A differential equation can be used to model a situation that involves change. Examples come from Ecology, Economics, Medicine, Physics, Biology and Chemistry. This course will concentrate on ordinary differential equations. For what equations does a solution exist and when is a solution unique? Can we solve an equation explicitly in mathematical terms? Can we find a numerical solution? Topics will include first order equations, second order linear equations, Laplace Transforms, series solutions, and systems of equations. Applications to modeling will be emphasized.

Instructor(s):  Alan Saleski

Required text(s):  Dimitri Bertsekas & John Tsitsiklis, Introduction to Probability, second edition, Athena Scientific (2008)

Prerequisites:  MATH 263

Course description:  This course is a calculus-based introduction to mathematical probability theory. Topics include: fundamentals and axioms, combinatorial probability, conditional probability and independence, Bayes' theorem, the binomial, Poisson, and normal distributions, random variables and generating functions, the law of large numbers, and the central limit theorem. A student completing this course should be prepared for the first actuarial exam (called Exam P by the Society of Actuaries and Exam 1 by the Casualty Actuarial Society). This course provides a solid mathematical foundation for students who wish to study Statistics in MATH/STAT 305. There will be weekly homework assignments, three tests and a final exam.

Instructor(s):  Dr. John G. Del Greco

Required text(s):  Larson, Richard J. and Morris L. Marx. Introduction to Mathematical Statistics and Its Applications. 5th ed. ISBN-13: 978-0321693945. Boston: Prentice-Hall, 2012. Print.

Prerequisites:  MATH/STAT 304

Course description:  This course will be a mathematically rigorous introduction to statistics and will require an extensive background in probability. The successful student will need a firm grasp of the following topics from probability theory: axiomatic probability, conditional probability, independence, combinatorial probability, random variables, families of discrete probability distributions (hypergeometric, binomial, Poisson, geometric, negative-binomial), families of continuous distributions (exponential, normal, gammma, beta), expected values, variance, covariance, joint densities, conditional densities, transformations of random variables, order statistics, and moment-generating functions.

Stat 305 will cover the following topics: methods of estimation, properties of estimators (unbiasedness, consistency, sufficiency, efficiency, etc.), minimum-variance unbiased estimators and the Cramer-Rao lower bound, Bayesian estimation, hypothesis testing, uniformly most powerful tests, Neyman-Pearson Lemma, sampling distributions and inferences involving the normal distribution, two-sample tests, goodness-of-fit tests, analysis of variance.

Instructor(s):  Dr. Anthony Giaquinto

Required text(s):  Pinter, Charles C. A Book of Abstract Algebra. Dover ed. ISBN-13: 978-0486474175. New York: McGraw-Hill, 1990. Print.

Prerequisites:  MATH 201 and MATH 212

Course description:  This course provides a rigorous introduction to abstract algebra. We will consider various algebraic structures including groups, rings, and fields, but will mainly focus on groups. We will cover the basic structure theory: homomorphisms, subgroups, cosets, factor groups, and isomorphism theorems. Various examples and special types of groups (Abelian groups, permutation groups, symmetry groups...) will be discussed in detail. We will also consider some applications of abstract algebra, but for the most part this class will be concerned with "pure" mathematics questions about the algebraic structures themselves.

Instructor(s):  Dr. Peter Tingley

Required text(s):  Rotman, Joseph. Galois Theory, second edition. Springer, 2000. ISBN-13: 9780387973050 Pinter, Charles. A book of abstract algebra, second edition. Dover, 1990. ISBN-13: 9780486474175

Textbook notes:  We will use Pinter's book for the first few weeks, then switch to Rotman as we move towards Galois theory. Much of the material is covered in both, and it will be useful to have 2 sources. Both books are available quite cheaply online (look for used copies of Rotman).

Prerequisites:  MATH 313

Course description:  This course is a continuation of Math 313, and I will assume people are familiar with group theory. We will now explore other abstract algebraic structures including rings and fields, with the goal of reaching the fundamental theorems of Galois theory. Along the way we will cover such topics as integral domains, polynomial rings, finite fields, field extensions and algebraic closure.

Syllabus:  This course will be assessed based on 2 midterms, a final, and 8-10 written assignments. The written assignments account for 25% of your grade and are an integral part of the class.

Instructor(s):  Dr. Aaron Greicius

Required text(s):  Bóna, Miklós. A Walk Through Combinatorics: an Introduction to Enumeration and Graph Theory, 3d Edition. Hackensack, NJ: World Scientific Publishing, 2011.

Hardcover: ISBN: 978-981-4335232.
Paperback: ISBN: 978-981-4460002.

Prerequisites:  MATH 162

Course description:  Narrowly construed, combinatorics is the science of counting--an operation that is both fundamental to, and pervasive throughout mathematics. In this course we will develop a powerful toolbox of general counting techniques (pigeonhole principle, mathematical induction, inclusion-exclusion principle, recurrence relations, generating functions, etc.), investigate important combinatorial structures (permutations, combinations, partitions, graphs, trees, etc.), and take a tour of some famous results in the field. This course is cross-listed with MATH 418 and COMP 418.

Instructor(s):  Dr. W. Cary Huffman

Required text(s):  Huffman, W. Cary, and Vera Pless. Fundamentals of Error-Correcting Codes. Cambridge University Press, 2003.

Hardcover: ISBN-10: 0521782805; ISBN-13: 978-0521782807
Paperback: ISBN-10: 0521131707; ISBN-13: 978-0521131704

Prerequisites:  MATH 212 (Linear Algebra) or the equivalent or permission of the instructor.

Course description:  Error-correcting codes are used to recover data distorted by noise or by deterioration over time when retransmission or reconstruction of data is impossible. One of the first codes, developed by Richard Hamming in the late 1940s, helped to prevent the shutdown of the Bell Laboratories Model V computer when an error was encountered during the execution of a program. The code he developed became the foundation for the extensive and important field known today as coding theory. Without error-correcting codes CDs would sound much like phonographs, distorting the sound whenever there is a bit of dust on the CD or the slightest flaw in the material. Deep space satellite communication would be virtually impossible without error-correcting capabilities. In order to insure high fidelity reception, error-correcting codes are built into the communication standards for digital television, for optical and audio communication systems, and for multimedia broadcast/multicast service.

In this course we will study the major types of error-correcting codes, how to encode and decode them, and their main properties. The codes we examine will include the Hamming, Golay, BCH, cyclic, quadratic residue, Reed-Solomon, and Reed-Muller codes. As time permits, we will examine applications of coding theory, for instance to its use with CD players. There will be weekly homework assignments, a (probably takehome) midterm, and (probably takehome) final.

This course will require students to understand proofs. A few proofs will be included in homework and exams, but computation will be emphasized. It would be advisable to review the basics of Linear Algebra, although I will attempt to remind you of what is necessary as we go along.

Instructor(s):  Dr. Stephen Doty

Required text(s):  None

Prerequisites:  MATH 201 Elementary Number Theory & MATH 212 Linear Algebra.

Course description:  The course offers a rigorous treatment of properties and applications of real numbers and real-valued functions of a real variable. Topics include: sequences, limits, the Bolzano-Weierstrass theorem, compactness and the Heine-Borel theorem, connectedness, topology, continuity, uniform continuity, fixed-point theorems, derivatives. The focus is on theory and proof. This is a time-intensive course. Outcome: Students will obtain an understanding of the fundamentals of real analysis that will prepare them for advanced work in mathematics.

Instructor(s):  Rafal Goebel

Required text(s):  Mattuck, Arthur. Introduction to Analysis. ISBN-10: 0130811327. ISBN-13: 978-0130811325. New York: Pearson. Published 1998, Copyright 1999.

Prerequisites:  MATH 351

Course description:  This is a natural continuation of Real Analysis I, where convergence of sequences and series, continuity of functions, and some differentiability properties have been covered. Real Analysis II will cover approximation of functions, for example by Taylor series; integration of functions; continuity of functions of more than one variable; elements of topology, including open, closed, and compact sets in two or more dimensions; and more. Students taking the course for graduate credit may also see topics like fixed point theory, convexity, or metric spaces. Topics like approximation of functions by Taylor series, differentiation of integrals, and compactness in two or more dimensions are essential in further study of mathematics and in many applications of mathematics in physics, engineering, and more. Students will be expected to solve problems and, even more importantly, to read, understand, and formulate mathematical arguments and proofs.

Instructor(s):  David Slavsky

Required text(s):  Customized version of Mary Boas' text on Mathematical Methods in the Physical Sciences. ISBN TBA.

Prerequisites:  Math 264

Course description:  Vector calculus, matrices, series solutions of differential equations, special functions; Fourier series, Fourier and Laplace transforms; Partial differential equations, Green's functions, Einstein notation and extensive work in Mathematica.

Instructor(s):  Dr. John G. Del Greco

Required text(s): 

(1) Vanderbei, Robert J. Linear Programming: Foundations and Extensions (International Series in Operations Research & Management Science). 4th ed. ISBN-13: 978-1461476290. New York: Springer, 2014. Print.

(2) Fourer, Robert, David M. Gay, and Brian W. Kernighan. AMPL: A Modeling Language for Mathematical Programming. 2nd ed. ISBN-13: 978-0534388096. Belmont, CA: Brooks/Cole, 2003. Print.

Prerequisites:  MATH 212

Course description:  Math 358 will be an introductory course on operations research methodology which will focus primarily on the linear programming problem and its many extensions and applications.

We will study the following topics from linear programming:

• Simplex Method for solving linear programming problems
• Duality theory and the dual simplex method
• Revised simplex method
• Sensitivity and parametric analysis
• Convex analysis
• Applications to game theory
• Financial applications: portfolio selection and options pricing
• Spanning trees and bases
• Primal and dual network simplex method
• Transportation and assignment problems
• Shortest path problem
• Integer programming models
• Traveling salesman problem
• Branch and bound algorithm for integer programming

Instructor(s):  Dr. Martin Buntinas

Required text(s):  Dennis Wackerly, William Mendenhall, Richard L. Scheaffer, Mathematical Statistics with Applications, 7th edition (2007), Duxbury/Brooks/Cole/Thomson. ISBN-10: 0-495-11081-7 (ISBN-13:978-0-49-511081-1).

Textbook notes:  It is important to have the 7th Edition. The "International Edition" is not the same.

Prerequisites:  MATH/STAT 404

Course description:  This is a continuation of MATH/STAT 404. Limit theorems, point and interval estimation, hypothesis testing, maximum likelihood, Neyman-Pearson Lemma, likelihood ratio, nonparametric statistics. There will be two quizzes, a midterm exam and a final exam. Homework will be assigned every class, collected, and graded.

Instructor(s):  Dr. Aaron Greicius

Required text(s):  Bóna, Miklós. A Walk Through Combinatorics: an Introduction to Enumeration and Graph Theory, 3d Edition. Hackensack, NJ: World Scientific Publishing, 2011.

Hardcover: ISBN: 978-981-4335232.
Paperback: ISBN: 978-981-4460002.

Prerequisites:  MATH 162

Course description:  Narrowly construed, combinatorics is the science of counting--an operation that is both fundamental to, and pervasive throughout mathematics. In this course we will develop a powerful toolbox of general counting techniques (pigeonhole principle, mathematical induction, inclusion-exclusion principle, recurrence relations, generating functions, etc.), investigate important combinatorial structures (permutations, combinations, partitions, graphs, trees, etc.), and take a tour of some famous results in the field. This course is cross-listed with MATH 418 and COMP 418.

Instructor(s):  Dr. W. Cary Huffman

Required text(s):  Huffman, W. Cary, and Vera Pless. Fundamentals of Error-Correcting Codes. Cambridge University Press, 2003.

Hardcover: ISBN-10: 0521782805; ISBN-13: 978-0521782807
Paperback: ISBN-10: 0521131707; ISBN-13: 978-0521131704

Prerequisites:  MATH 212 (Linear Algebra) or the equivalent or permission of the instructor.

Course description:  Error-correcting codes are used to recover data distorted by noise or by deterioration over time when retransmission or reconstruction of data is impossible. One of the first codes, developed by Richard Hamming in the late 1940s, helped to prevent the shutdown of the Bell Laboratories Model V computer when an error was encountered during the execution of a program. The code he developed became the foundation for the extensive and important field known today as coding theory. Without error-correcting codes CDs would sound much like phonographs, distorting the sound whenever there is a bit of dust on the CD or the slightest flaw in the material. Deep space satellite communication would be virtually impossible without error-correcting capabilities. In order to insure high fidelity reception, error-correcting codes are built into the communication standards for digital television, for optical and audio communication systems, and for multimedia broadcast/multicast service.

In this course we will study the major types of error-correcting codes, how to encode and decode them, and their main properties. The codes we examine will include the Hamming, Golay, BCH, cyclic, quadratic residue, Reed-Solomon, and Reed-Muller codes. As time permits, we will examine applications of coding theory, for instance to its use with CD players. There will be weekly homework assignments, a (probably takehome) midterm, and (probably takehome) final.

This course will require students to understand proofs. A few proofs will be included in homework and exams, but computation will be emphasized. It would be advisable to review the basics of Linear Algebra, although I will attempt to remind you of what is necessary as we go along.

Instructor(s):  Rafal Goebel

Required text(s):  Mattuck, Arthur. Introduction to Analysis. ISBN-10: 0130811327. ISBN-13: 978-0130811325. New York: Pearson. Published 1998, Copyright 1999.

Prerequisites:  MATH 351

Course description:  This is a natural continuation of Real Analysis I, where convergence of sequences and series, continuity of functions, and some differentiability properties have been covered. Real Analysis II will cover approximation of functions, for example by Taylor series; integration of functions; continuity of functions of more than one variable; elements of topology, including open, closed, and compact sets in two or more dimensions; and more. Students taking the course for graduate credit may also see topics like fixed point theory, convexity, or metric spaces. Topics like approximation of functions by Taylor series, differentiation of integrals, and compactness in two or more dimensions are essential in further study of mathematics and in many applications of mathematics in physics, engineering, and more. Students will be expected to solve problems and, even more importantly, to read, understand, and formulate mathematical arguments and proofs.

Instructor(s):  Dr. John G. Del Greco

Required text(s): 

(1) Vanderbei, Robert J. Linear Programming: Foundations and Extensions (International Series in Operations Research & Management Science). 4th ed. ISBN-13: 978-1461476290. New York: Springer, 2014. Print.

(2) Fourer, Robert, David M. Gay, and Brian W. Kernighan. AMPL: A Modeling Language for Mathematical Programming. 2nd ed. ISBN-13: 978-0534388096. Belmont, CA: Brooks/Cole, 2003. Print.

Prerequisites:  MATH 212

Course description:  Math 358 will be an introductory course on operations research methodology which will focus primarily on the linear programming problem and its many extensions and applications.

We will study the following topics from linear programming:

• Simplex Method for solving linear programming problems
• Duality theory and the dual simplex method
• Revised simplex method
• Sensitivity and parametric analysis
• Convex analysis
• Applications to game theory
• Financial applications: portfolio selection and options pricing
• Spanning trees and bases
• Primal and dual network simplex method
• Transportation and assignment problems
• Shortest path problem
• Integer programming models
• Traveling salesman problem
• Branch and bound algorithm for integer programming

Instructor(s):  Staff

Required text(s):  Freedman, David, Robert Pisani, and Roger Purves, Statistics. 4th ed. ISBN-13: 978-0393929720. New York: W. W. Norton & Company, 2007. Print.

Sections 003 and 004 [Dr. Adam Spiegler] will use the following text:

Lock, Robin H. et al. Statistics: Unlocking the Power of Data. ISBN-13: 978-0470601877. Hoboken, NJ: Wiley, 2012.

Prerequisites:  None

Course description:  An introduction to statistical reasoning. Students learn how statistics has helped to solve major problems in economics, education, genetics, medicine, physics, political science, and psychology. Topics include: design of experiments, descriptive statistics, mean and standard deviation, the normal distribution, the binomial distribution, correlation and regression, sampling, estimation, and testing of hypothesis. A TI-83 (or equivalent) calculator is required for this course.

Syllabus:  Common

Instructor(s):  Dr. E.N.Barron

Required text(s):  Walpole, Myers, Myers, and Ye, Essentials of Probability and Statistics, ISBN-13 978-0-321-78373-8, Pearson Publishing Co., 2013

Textbook notes:  Student Solution manual available, ISBN-13 978-0-321-78399-8

Prerequisites:  Math 132 or Math 162

Course description:  This is a one semester foundation class in probability and statistics. The class is meant to introduce the student to the basics of probability and statistics for science and engineering majors. It introduces the probability needed to form a foundation for the statistics methods which are the focus of the course. Statistics is the scientific method used in order to be able to reach a decision about the result of an experiment which has random outcomes. Such experiments arise in all areas of science and medicine. The scientific method involves controlled randomized experiments so that the method of comparison can be used to reach a conclusion about the compared groups. Statistics and probability is the mathematical basis for the method of comparison. We will have at least two exams plus a final, as well as assigned homework from the book and quizzes on WebWork. The class requires two semesters of calculus as a prerequisite.

Syllabus:  1. Probability 2. Random Variables and Distributions 3. Sampling Distributions 4. One and Two Sample Estimation 5. One and Two Sample Tests of Hypotheses 6. Linear Regression 7. One Factor Experiments

Instructor(s):  Dr. Michael Perry

Required text(s):  R. Cody & Smith, Applied Statistics and the SAS Programming

Recommended text(s):  Any general stats book

Textbook notes:  Students may want to have a general statistics book as reference.

Additional notes:  Students will be graded on homework, quizzes, one take home test and one project with a presentation. The course will require a significant amount of computer coding.

Prerequisites:  STAT 103 or 203 or 335.

Course description:  This course is an introduction to writing and executing SAS programs under the Windows environment in the context of applied statistics problems. SAS procedures are used to read and analyze various types of data sets as they apply to t-tests, simple and multiple regressions, ANOVA, categorical analysis, and repeated measures.

Instructor(s):  Alan Saleski

Required text(s):  Dimitri Bertsekas & John Tsitsiklis, Introduction to Probability, second edition, Athena Scientific (2008)

Prerequisites:  MATH 263

Course description:  This course is a calculus-based introduction to mathematical probability theory. Topics include: fundamentals and axioms, combinatorial probability, conditional probability and independence, Bayes' theorem, the binomial, Poisson, and normal distributions, random variables and generating functions, the law of large numbers, and the central limit theorem. A student completing this course should be prepared for the first actuarial exam (called Exam P by the Society of Actuaries and Exam 1 by the Casualty Actuarial Society). This course provides a solid mathematical foundation for students who wish to study Statistics in MATH/STAT 305. There will be weekly homework assignments, three tests and a final exam.

Instructor(s):  Dr. John G. Del Greco

Required text(s):  Larson, Richard J. and Morris L. Marx. Introduction to Mathematical Statistics and Its Applications. 5th ed. ISBN-13: 978-0321693945. Boston: Prentice-Hall, 2012. Print.

Prerequisites:  MATH/STAT 304

Course description:  This course will be a mathematically rigorous introduction to statistics and will require an extensive background in probability. The successful student will need a firm grasp of the following topics from probability theory: axiomatic probability, conditional probability, independence, combinatorial probability, random variables, families of discrete probability distributions (hypergeometric, binomial, Poisson, geometric, negative-binomial), families of continuous distributions (exponential, normal, gammma, beta), expected values, variance, covariance, joint densities, conditional densities, transformations of random variables, order statistics, and moment-generating functions.

Stat 305 will cover the following topics: methods of estimation, properties of estimators (unbiasedness, consistency, sufficiency, efficiency, etc.), minimum-variance unbiased estimators and the Cramer-Rao lower bound, Bayesian estimation, hypothesis testing, uniformly most powerful tests, Neyman-Pearson Lemma, sampling distributions and inferences involving the normal distribution, two-sample tests, goodness-of-fit tests, analysis of variance.

Instructor(s):  Dr. Changwon Lim

Required text(s):  Statistical Analysis of Designed Experiments Theory and Applications by Ajit C. Tamhane, John Wiley & Sons, Inc (2009). ISBN 978-0-471-75043-7.

Prerequisites:  STAT 307: STAT 203 or 335. STAT 407: Some background in basic statistical methods or biostatistics, or permission of instructor.

Course description:  This course provides students with a thorough introduction to statistical experimental design, and to the statistical methods used to analyze the resulting data. The concepts of comparative experiments, ANOVA and mean separation procedures will be reviewed; blocking (complete and incomplete) will be discussed, as will be factorial designs, fractional factorial designs, and confounding. The course will focus on biometric applications such as clinical trials, HIV studies, and environmental and agricultural research, but industrial and other examples will occasionally be provided to show the breadth of application of experimental design ideas. There will be two midterm exams, a final exam, and multiple written homework assignments during the semester which may include computer exercises using R or Minitab. This course is a combined course and the graduate students will do more theoretical works.

Instructor(s):  Dr. Changwon Lim

Required text(s):  Agresti, Alan. An Introduction to Categorical Data Analysis, 2nd Ed, Wiley, 2007, ISBN: 0471226185

Prerequisites:  STAT 203 or 335 including some background in basic statistical methods or biostatistics including chi-square tests and simple regression, and maturity to get through somewhat sophisticated material.

Course description:  Normally distributed response variables lead data scientists to use simple linear models procedures such as simple and multiple regression methods, one- or two-way ANOVA or ANOCOV; but other types of data can leave practitioners applying these methods incorrectly. Thus, these simple (regression) techniques have been generalized to handle nominal, ordinal, count and binary data under the general heading of categorical data analysis. Modern-day extensions to the chi-square test include logistic regression and log-linear modeling techniques, which are the focus of this course, as will the unified perspective, based on generalized linear models, that connects these methods with standard regression methods for normally-distributed data. Applications of these methods are widespread in business, predictive modelling, and biostatistics. In this larger framework, this course addresses the fundamental questions encountered with regression and ANOVA for count data. Specialized methods for ordinal data, small samples, multi-category data, and matched pairs will also be discussed. The focus throughout this course will be on applications and real-life data sets; as such, theorems and proofs will not be emphasized, although graduate students will be expected to solve occasional theoretical exercises on homework assignments or exams.

Instructor(s):  Section 001 (MWF 8:15am): Dr. Matthew Bourque
Section 002 (MWF 11:30 am): Mr. Bret Longman
Section 003 (TuTh 8:30am): Mr. Bret Longman
Section 004 (TuTh 11:30pm): Dr. Martin Buntinas
Section 005 (MWF 8:15am): Dr. Michael Perry

Required text(s):  Samuels, Myra L., Jeffrey A. Witmer, and Andrew A. Schaffner. Statistics for the Life Sciences. 4th ed. ISBN-13: 978-0321652805. New York: Prentice Hall, 2012. Print.

Additional notes:  Students may not receive credit for both STAT 203 and STAT 335

Prerequisites:  BIOL 102 and MATH 162 or MATH 132

Course description:  An introduction to statistical methods used in designing biological experiments and in data analysis. Topics include frequency distributions, probability and sampling distribution, design of biological experiments, interval estimation, tests of hypotheses, analysis of variance, correlation and regression. This course will have two quizzes, two exams, regularly assigned homework, a course project, and computer laboratory assignments in MINITAB with biological data.

Instructor(s):  Dr. Molly K. Walsh

Required text(s):  D’Agostino, Ralph, Lisa Sullivan, and Alexa Beiser. Introductory Applied Biostatistics. 1st ed. ISBN-13: 978-0534423995. New York: Brooks/Cole, 2006. Print.

Prerequisites:  STAT 335 or permission of instructor

Course description:  This course covers the basics of hypothesis testing, sample size and power calculations, categorical data techniques, experimental design and ANOVA, repeated measures ANOVA, simple and multiple linear regression, analysis of covariance (ANCOVA), generalized linear models, maximum likelihood estimation, logistic regression, survival analysis, and if time allows, relative potency and drug synergy. The emphasis is on applications instead of statistical theory, and students are required to analyze real-life datasets using output from statistical packages such as Minitab and SAS, although no previous programming experience is assumed. Practice problems will be given for every topic studied, and random quizzes based on the practice problems will be given during most weeks of the course. Two quiz scores may be dropped. Quizzes make up 40% of the final grade. Students (in small groups) will complete an original data analysis project using at least one of the methods covered during the course. Use of computer software such as Minitab and/or SAS is highly recommended for computations. The project represents 20% of the final grade. A midterm and a final exam will be given, and each is worth 20% of the final grade.

Instructor(s):  Dr. Timothy E. O'Brien

Required text(s):  Deonier, Richard C., Simon Tavaré and Michael S. Waterman, Computational Genome Analysis: An Introduction, 2005, New York: Springer, ISBN: 0-387-98785-1.

Prerequisites:  Some exposure to basic statistical/biostatistical methods (e.g. as in BIOL/STAT335 or equivalent) as well as differential and integral calculus (MATH 131 and MATH 132 or MATH 161 and MATH 162)

Course description:  Clearly, predicting which conditions and diseases will develop in animals and human subjects based on its gene and protein characteristics must involve drawing conclusions from well-designed studies. As such, meaningful decisions hinge upon the correct use of statistical hypothesis testing, prediction and estimation. The most likely conclusions are also drawn from probabilistic and stochastic arguments, and so a wisely chosen experimental design removes any biases and allows researchers to generalize from small studies to the larger population. This course explores recently developed mathematical, probabilistic and statistical methods currently used in the fields of bioinformatics and DNA microarray and protein array data analysis. These include stochastic processes, (hidden and traditional) Markov chains, microarray and other techniques. Our focus in this course is on the application of these techniques and on meaningful interpretation of results. Students will be required to analyze real-life data sets using the Minitab, R and SAS statistical packages. Grading will be based on homework assignments, two exams and a final, and a course project.

Instructor(s):  Dr. Martin Buntinas

Required text(s):  Dennis Wackerly, William Mendenhall, Richard L. Scheaffer, Mathematical Statistics with Applications, 7th edition (2007), Duxbury/Brooks/Cole/Thomson. ISBN-10: 0-495-11081-7 (ISBN-13:978-0-49-511081-1).

Textbook notes:  It is important to have the 7th Edition. The "International Edition" is not the same.

Prerequisites:  MATH/STAT 404

Course description:  This is a continuation of MATH/STAT 404. Limit theorems, point and interval estimation, hypothesis testing, maximum likelihood, Neyman-Pearson Lemma, likelihood ratio, nonparametric statistics. There will be two quizzes, a midterm exam and a final exam. Homework will be assigned every class, collected, and graded.

Instructor(s):  Dr. Changwon Lim

Required text(s):  Statistical Analysis of Designed Experiments Theory and Applications by Ajit C. Tamhane, John Wiley & Sons, Inc (2009). ISBN 978-0-471-75043-7.

Prerequisites:  STAT 307: STAT 203 or 335. STAT 407: Some background in basic statistical methods or biostatistics, or permission of instructor.

Course description:  This course provides students with a thorough introduction to statistical experimental design, and to the statistical methods used to analyze the resulting data. The concepts of comparative experiments, ANOVA and mean separation procedures will be reviewed; blocking (complete and incomplete) will be discussed, as will be factorial designs, fractional factorial designs, and confounding. The course will focus on biometric applications such as clinical trials, HIV studies, and environmental and agricultural research, but industrial and other examples will occasionally be provided to show the breadth of application of experimental design ideas. There will be two midterm exams, a final exam, and multiple written homework assignments during the semester which may include computer exercises using R or Minitab. This course is a combined course and the graduate students will do more theoretical works.

Instructor(s):  Dr. Changwon Lim

Required text(s):  Agresti, Alan. An Introduction to Categorical Data Analysis, 2nd Ed, Wiley, 2007, ISBN: 0471226185

Prerequisites:  STAT 203 or 335 including some background in basic statistical methods or biostatistics including chi-square tests and simple regression, and maturity to get through somewhat sophisticated material.

Course description:  Normally distributed response variables lead data scientists to use simple linear models procedures such as simple and multiple regression methods, one- or two-way ANOVA or ANOCOV; but other types of data can leave practitioners applying these methods incorrectly. Thus, these simple (regression) techniques have been generalized to handle nominal, ordinal, count and binary data under the general heading of categorical data analysis. Modern-day extensions to the chi-square test include logistic regression and log-linear modeling techniques, which are the focus of this course, as will the unified perspective, based on generalized linear models, that connects these methods with standard regression methods for normally-distributed data. Applications of these methods are widespread in business, predictive modelling, and biostatistics. In this larger framework, this course addresses the fundamental questions encountered with regression and ANOVA for count data. Specialized methods for ordinal data, small samples, multi-category data, and matched pairs will also be discussed. The focus throughout this course will be on applications and real-life data sets; as such, theorems and proofs will not be emphasized, although graduate students will be expected to solve occasional theoretical exercises on homework assignments or exams.

Instructor(s):  Dr. Timothy E. O'Brien

Required text(s):  Pre-publication version of Dr. O’Brien’s Springer text, Intermediate Methods in Applied Statistics and Biostatistics. This will be provided to students free of charge.

Prerequisites:  Some background in basic statistical methods or biostatistics, or permission of instructor.

Course description:  This course covers experimental design (including interaction, analysis of covariance, and crossover designs) and the analysis of designed studies, simple and multiple linear regression, generalized linear and nonlinear regression, bioassay, relative potency and drug synergy, multivariate analysis (including MANOVA and multivariate regression), repeated measures (designs and analysis), and survival analysis (Cox proportional odds, log-rank tests, Kaplan-Meier estimation) of censored data. The emphasis of the course will be on applications instead of statistical theory, and students will be required to analyze real-life datasets using statistical packages such as Minitab and SAS, though no previous programming experience will be assumed.

Instructor(s):  Dr. Timothy E. O'Brien

Required text(s):  Deonier, Richard C., Simon Tavaré and Michael S. Waterman, Computational Genome Analysis: An Introduction, 2005, New York: Springer, ISBN: 0-387-98785-1.

Prerequisites:  Some exposure to basic statistical/biostatistical methods (e.g. as in BIOL/STAT335 or equivalent) as well as differential and integral calculus (MATH 131 and MATH 132 or MATH 161 and MATH 162)

Course description:  Clearly, predicting which conditions and diseases will develop in animals and human subjects based on its gene and protein characteristics must involve drawing conclusions from well-designed studies. As such, meaningful decisions hinge upon the correct use of statistical hypothesis testing, prediction and estimation. The most likely conclusions are also drawn from probabilistic and stochastic arguments, and so a wisely chosen experimental design removes any biases and allows researchers to generalize from small studies to the larger population. This course explores recently developed mathematical, probabilistic and statistical methods currently used in the fields of bioinformatics and DNA microarray and protein array data analysis. These include stochastic processes, (hidden and traditional) Markov chains, microarray and other techniques. Our focus in this course is on the application of these techniques and on meaningful interpretation of results. Students will be required to analyze real-life data sets using the Minitab, R and SAS statistical packages. Grading will be based on homework assignments, two exams and a final, and a course project.