Quantitative Modeling
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GSM QM 716: Data Analysis for Managerial Decision-Making
Graduate Prerequisites: OB712/713, AC710/711
Managers deal with a large amount of information in quantitative form. Effective managers must understand the conditions under which quantitative techniques may be appropriately applied for decision-making. In this course, students develop skills in using the computer to examine and report data. The focus is on deriving meaning from particular data sets, and the use of statistical estimation, hypothesis testing, and regression/correlation analysis in decision-making. -
GSM QM 717: Data Analysis for Managerial Decision-Making
Graduate Prerequisites: OB712/713
Managers deal with a large amount of information in quantitative form. Effective managers must understand the conditions under which quantitative techniques may be appropriately applied for decision-making. In this course, students develop skills in using the computer to examine and report data. The focus is on deriving meaning from particular data sets, and the use of statistical estimation, hypothesis testing, and regression/correlation analysis in decision-making. -
GSM QM 880: Business Analytics: Spreadsheet Optimization and Simulation
Graduate Prerequisites: QM716/717
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. -
SMG QM 450: Business Analytics
Undergraduate Prerequisites: SMG FE 323 ; SMG IS 323 ; SMG MK 323 ; SMG OM 323.
Improves students' ability to think logically about and to structure complex managerial problems; and to develop Excel-based spreadsheet models that can be used to significantly improve managerial decision-making. The course is taught almost entirely by example, using problems from the main functional areas of business: Finance, Operations, and Marketing. Students learn about the two main types of modeling approaches: optimization models which can help find the "best" solution, and simulation models which allow explicit consideration of risk trade-offs associated with alternatives. 4 cr. -
SMG SM 221: Probabilistic and Statistical Decision Making for Management
Undergraduate Prerequisites: Sophomore standing; SM121/122 or SM299; CAS MA121 or MA123 previous or concurrent
Sophomore requirement. 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 SMG SM 221 and CAS EC 305. -
SMG SM 222: Modeling Business Decisions and Market Outcomes
Undergraduate Prerequisites: CAS EC 101 and SMG SM 221; and sophomore standing.
Sophomore requirement. Examines the use of economic and statistical tools for making business decisions. Topics include optimization (including linear programing), 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. (There is also an Honors Program section for this course offered each Spring, numbered SMG SM 224).
