Mathematical Decision Making: Predictive Models and Optimization Marikina

Mathematical Decision Making: Predictive Models and Optimization
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Mathematical Decision Making: Predictive Models and Optimization
Course No. 1342 – Video + PDF Guidebook
Lecturer: Professor Scott P. Stevens Ph.D.

1 The Operations Research Superhighway
2 Forecasting with Simple Linear Regression
3 Nonlinear Trends and Multiple Regression
4 Time Series Forecasting
5 Data Mining: Exploration and Prediction
6 Data Mining for Affinity and Clustering
7 Optimization: Goals, Decisions, and Constraints
8 Linear Programming and Optimal Network Flow
9 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

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