Students learn to use models developed through quantitative methods of problem solving. Key topics include linear programming, network analysis, queuing theory, and decision theory. Math 350 Differential Equations Prerequisite, Math 211. Students gain a solid foundation in ordinary differential equations and introduce applications of differential equations to the sciences. Math 351 Applied Mathematics Prerequisite, Math 350.
Students gain a solid foundation in applied mathematics and mathematical modeling.
Mathematical statistics, beginning with the elementary parts of the theory of probability, including probability spaces, random variables, probability distributions, expectation, the law of large numbers and the central limit theorem, which are fundamental to the study of statistics, and proceeding from there to the statistical concepts of sampling, point estimation, interval estimation, tests of hypotheses, regression and correlation, and analysis of variance. Appropriate applications of concept are provided. Math 403 Statistics for Applied Sciences Prerequisite, Math 203 or equivalent. A second course in applied statistics for research and development in the applied sciences and clinical practice. Beginning with the basic notions of linear regression and the analysis of variance, the course progresses to the principles of experimental design, survey methods and the elements of observational studies.