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Course Criteria
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4.00 Credits
4 hours Propositional and predicate logic, methods of proof, induction, recursion and recurrence relations, sets and combinatorics, binary relations (including equivalence relations and partial orderings), functions, Boolean algebra and computer logic, and finite state machines. Prerequisite: MATH 152 or above; or CS 150, 151, or CS 220; or consent of instructor. (Same as CS 220.) (Quant)
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4.00 Credits
4 hours Model building. Analytic tools useful to management chosen from linear programming, simplex algorithm, sensitivity analysis, duality; integer linear programming; goal programming; dynamic programming; networks, PERT-CPM, maximum flow, shortest path; simulation; nonlinear programming. Offered alternate years. Prerequisite: MATH 140 or MATH 151. (Quant)
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4.00 Credits
4 hours Matrices, abstract vector spaces, subspaces, spanning sets, linear independence, bases, linear transformations, isomorphisms, eigenvalues and eigenvectors, inner product spaces. Prerequisite: MATH 152, or consent of instructor. (Quant)
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4.00 Credits
4 hours Vector valued functions: limits, continuity, derivatives, and integrals. Length of space curves, tangents and normals to curves. Functions of several variables: limits, continuity, partial derivatives, directional derivatives, the gradient, tangent plane approximation and differentials, extreme value, multiple integrals, vector fields, line integrals, Green's theorem, surface integrals, Stokes' theorem, the divergence theorem. Prerequisite: MATH 240. (Quant)
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4.00 Credits
4 hours Divisibility theory in the integers, prime numbers, Euclidean algorithm, Diophantine equations, congruences, divisibility tests, Euler's theorem, public key cryptography, primitive roots, quadratic reciprocity law. Usually offered in alternate years. Prerequisites: MATH 220 or MATH 240. (Quant)
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4.00 Credits
4 hours Axioms and laws of probability, independence, conditional probability, combinatorics, discrete and continuous random variables, mathematical expectation, central limit theorem, descriptive statistics, confidence intervals. Only two of MATH 321, 322, 327, and MATH 328 may apply toward the math major. Prerequisite: MATH 152. (Quant)
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4.00 Credits
4 hours Sampling distribution theory, theory of estimation and hypothesis testing, confidence intervals, inferences for means and proportions, correlation and regression, chisquare tests. Only two of MATH 321, 322, 327, and MATH 328 may apply toward the math major. Prerequisite: MATH 321. (Quant)
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4.00 Credits
4 hours Regression Analysis: Least square estimates, simple linear regression, multiple linear regression, hypothesis testing and confidence intervals for linear regression models, prediction intervals, and ANOVA. Model diagnostics including tests of constant variance assumptions, serial correlation, and multicollinearity. Time series: Linear time series, moving average, autoregressive and ARIMA models. Estimation and forecasting. Forecast errors and confidence intervals. Prerequisite: college-level statistics course. (Quant)
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4.00 Credits
4 hours Design and analysis of experiments; analysis of variance techniques; fixed, random, and mixed models; repeated measures. Only two of MATH 321, 322, 327, and MATH 328 may apply toward the math major. Prerequisite: MATH 327. (Quant)
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4.00 Credits
4 hours An introduction to first and second order differential equations, existence and uniqueness theorems, higher order linear differential equations, Laplace transforms, power series solutions, boundary value problems, systems of linear differential equations, and applications in the physical, biological, and social sciences. Prerequisite: MATH 240. (Quant)
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