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Mathematical Decision Making: Predictive Models and Optimization

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Overview

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Handle complex decisions with ease and confidence using powerful mathematical concept in this course taught by an award-winning mathematician.

Syllabus

  • 01: The Operations Research Superhighway
  • 02: Forecasting with Simple Linear Regression
  • 03: Nonlinear Trends and Multiple Regression
  • 04: Time Series Forecasting
  • 05: Data Mining-Exploration and Prediction
  • 06: Data Mining for Affinity and Clustering
  • 07: Optimization-Goals, Decisions, and Constraints
  • 08: Linear Programming and Optimal Network Flow
  • 09: Scheduling and Multiperiod Planning
  • 10: Visualizing Solutions to Linear Programs
  • 11: Solving Linear Programs in a Spreadsheet
  • 12: Sensitivity Analysis-Trust the Answer?
  • 13: Integer Programming-All or Nothing
  • 14: Where Is the Efficiency Frontier?
  • 15: Programs with Multiple Goals
  • 16: Optimization in a Nonlinear Landscape
  • 17: Nonlinear Models-Best Location, Best Pricing
  • 18: Randomness, Probability, and Expectation
  • 19: Decision Trees-Which Scenario Is Best?
  • 20: Bayesian Analysis of New Information
  • 21: Markov Models-How a Random Walk Evolves
  • 22: Queuing-Why Waiting Lines Work or Fail
  • 23: Monte Carlo Simulation for a Better Job Bid
  • 24: Stochastic Optimization and Risk
  • By This Professor

Taught by

Scott P. Stevens

Reviews

4.6 rating at The Great Courses Plus based on 64 ratings

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