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15.060 Data, Models, and Decisions, Fall 2007

Author(s)
Gamarnik, David; Freund, Robert; Schulz, Andreas
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Download15-060-fall-2007/contents/index.htm (34.83Kb)
Alternative title
Data, Models, and Decisions
Terms of use
Usage Restrictions: This site (c) Massachusetts Institute of Technology 2015. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license") unless otherwise noted. The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. Usage Restrictions: Attribution-NonCommercial-ShareAlike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/
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Abstract
This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice.
Date issued
2007-12
URI
http://hdl.handle.net/1721.1/99935
Department
Sloan School of Management
Other identifiers
15.060-Fall2007
local: 15.060
local: IMSCP-MD5-79005dac82afd47cb9c5f02e60018edc
Keywords
decision analysis, discrete probability distributions, continuous probability distributions, normal probability distribution, statistical sampling, regression models, linear optimization, nonlinear optimization, discrete optimization

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