Mathematical Decision Making: Predictive Models and Optimization

by Scott P. Stevens

Streaming video, 2014

Status

Available

Call number

519.542

Collection

Publication

Great Courses (2014), 12 hours, 24 lectures, 263 pages

Description

Not so long ago, executives faced with complex problems made decisions based on experience, intuition, and no small measure of luck. But now there's a better way. In recent decades, mathematics and computer science have perfected formerly top-secret techniques for predicting the best possible outcomes when faced with conflicting options. This field goes by different names--analytics, operations research, linear and nonlinear programming, management science--but its purpose is simple: to apply quantitative methods to help business managers, public servants, investors, scientific researchers, and problem solvers of all kinds make better decisions.

Language

Original language

English

Local notes

[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
Page: 0.4086 seconds