A student will learn to generate confidence intervals, test hypotheses, create mathematical models and perform regression analysis. Computer assisted methods will be used throughout. Calculus III 4 Vector algebra and geometry, functions of several variables, partial and directional derivatives, gradient, chain rule, optimization, multiple and iterated integrals. Parametric curves and surfaces in 3-space, vector fields, divergence and curl, line and surface integrals. Green's, Stoke's and divergence theorems.
Emphasis is on the concept of theorem and proof.
MATH 320 Probability and Statistics 3 Sample spaces, random variables, central limit theorems, stochastic processes, estimation and testing of hypotheses. MATH 325 Linear Programming and Game Theory 3 Geometric solutions, the simplex method, duality, the revised simplex method, sensitivity analysis, integer programming and applications. MATH 328 Methods of Teaching Secondary School Mathematics 5 Strategies for teaching topics in algebra, geometry, trigonometry, linear algebra, probability and statistics, computing science and the calculus. Mathematical Modeling 3 Methodology of modeling and problem solving, common equations that arise in mathematical models, optimization techniques, probability and stochastic processes, physical and biological applications, social and behavioral applications, hierarchies and priorities, computer-aided modeling and problem solving. Linear Algebra 3 Systems of linear equations, Gaussian elimination, matrices vector spaces, linear independence, basis, dimension, inner products, orthogonal projections, orthonormal bases.