Course Schedule and Catalog
Course Schedule - Fall Semester 2018
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
MGMP 689 001 (CRN: 14100)
DECISION MODELS
Long Title: DECISION MODELS
Department: Management
Instructor: Cohen, Dinah
Meeting: 12:30PM - 2:00PM MW (22-OCT-2018 - 3-DEC-2018)
Part of Term: MBA Term II
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 1.5
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Program(s):
MBA for Professional Extended
MBA for Professionals
MBA for Professional Weekend
MBA - Executive Program
MBA
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 65
Section Enrolled: 56
Enrollment data as of: 20-APR-2024 11:39AM
Final Exam: GR Course-Dept Schedules Exam
Description: Successful management requires the ability to recognize a decision situation, understand its essential features, and make a choice. However, many of these situations - particularly those involving uncertainty and/or complex interactions - may be too difficult to grasp intuitively, and the stakes may be too high to learn by experience. This course introduces spreadsheet modeling, simulation, decision analysis and optimization to represent and analyze such complex problems. The skills learned in this course are applicable in almost all aspects of business and should be helpful in future courses. The course is divided into two parts. In the first part, we discuss the use of decision trees for structuring decision problems under uncertainty. In the second part of the course, we discuss Monte Carlo simulation, a technique for simulating complex, uncertain systems. Throughout the course, we will use Microsoft Excel as a modeling environment, using add-in programs as necessary. Familiarity with Excel is an important prerequisite for this course. Repeatable for Credit.