Quantitative Modeling

  • QST QM 221: Probabilistic and Statistical Decision-Making for Management
    Undergraduate Prerequisites: SM131; CAS MA121 or MA123 previous or concurrent.
    Exposes students to the fundamentals of probability, decision analysis, and statistics, and their application to business. Topics include probability, decision analysis, distributions, sampling, estimation, hypothesis testing, and chi-square. Please note: Students may not receive credit for both QST QM 221 and CAS EC 305.
  • QST QM 222: Modeling Business Decisions and Market Outcomes
    Undergraduate Prerequisites: Sophomore standing; QST QM221, CAS EC101.
    Examines the use of economic and statistical tools for making business decisions. Topics include optimization (including linear programming), multiple regression, demand modeling, cost modeling, industry analysis (including models of perfect competition, monopoly, and oligopoly), and game theory. The course emphasizes modeling with spreadsheets.
  • QST QM 323: Analytics
    Undergraduate Prerequisites: QST AC222, IS223, OB221, QM222, and ES275 or SM151
    Component of QST SM 323, The Cross Functional Core. Teaches quantitative methods and modeling techniques that will improve the student's ability to make informed decisions in an uncertain world. The two major modules of the course are models for optimal decision-making and decision- making under uncertainty. The first module focuses on methods and predictive models for decision-making; how optimization models are used to identify the best choice; and how choices change in response to changes in the model's parameters (sensitivity analysis). The second module covers the measurement and management of risk and Monte Carlo simulation. Throughout the semester, we will perform hands-on analysis that will improve Excel modeling skills; discuss the ethical use of data analytics; and learn to recognize pitfalls and biases in quantitative decision-making. cr. N
  • QST QM 711: Topics in Business Analytics
    This condensed analytics course focuses on how to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.
  • QST QM 716: Business Analytics: Data Analysis and Risk
    The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.
  • QST QM 717: Data Analysis for Managerial Decision-Making
    Graduate Prerequisites: OB712/713/715
    The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.
  • QST QM 880: Business Analytics: Spreadsheet Optimization and Simulation
    Graduate Prerequisites: QM711/716/717
    The modeling process illustrated throughout the course will significantly improve students? abilities to structure complex problems and derive insights about the value of alternatives. You will develop the skills to formulate and analyze a wide range of models that can aid in managerial decision-making in the functional areas of business. These areas include finance (capital budgeting, cash planning, portfolio optimization, valuing options, hedging investments), marketing (pricing, sales force allocation, planning advertising budgets) and operations (production planning, workforce scheduling, facility location, project management). The course will be taught almost entirely by example, using problems from the main functional areas of business. This course is not for people who want a general introduction to or review of Excel. This course is for students who are already comfortable using Excel and would like to use it to create optimization and simulation models.
  • QST QM 898: Directed Study: Quantitative Methods
    Graduate Prerequisites: Consent of instructor and the department chairman
    Graduate-level directed study in Quantitative Methods. 1, 2, or 3 cr. Application available on the Graduate Program Office website.
  • QST QM 998: Directed Study: Quantitative Methods
    Graduate Prerequisites: Consent of instructor and the department chairman
    PhD-level directed study in Quantitative Methods. 1, 2, or 3 cr. Application available on the Graduate Program Office website.