Quantitative Methods involves topics in both applied optimization and applied statistics. Applied optimization explores resource allocation issues that frequently arise in managerial decision making. In an era of dwindling resources and increasing competition, optimization questions have assumed a new and urgent importance. Doctoral seminars focus on advanced optimization applications and methodologies. Related courses are available from areas such as industrial and electrical engineering and computer sciences. Faculty collaboration with other areas of management and related engineering programs enables students to participate in research on a stimulating range of optimization applications. Current areas of faculty interest in applied optimization include transportation, communication, distribution, and manufacturing systems. Other application domains include auditing, scheduling and quality control.<br><br>Statistics and its applications address managerial problems in which randomness or uncertainty complicates the decision environment. This specialization emphasizes in-depth study of the relevant methodology with the flexibility to apply these methods to any areas of management. Courses from departments such as economics, statistics, and industrial engineering as well as other areas of management offer the student a rich variety of topics for applied statistics research. Current faculty research interests in applied statistics include data mining, reliability theory, stochastic marketing models, auditing and acceptance sampling, statistical decision theory, and statistical quality and process control.