Mathematical Decision Making: Predictive Models and Optimization

Professor Stevens discusses the applications of operations research and predictive analytics.

Linear regression can help individuals analyze several problems.

Professor Stevens covers more complex examples of linear regression.

It is possible to extract information from historical data.

Professor Stevens provides insight into data mining.

It is time to learn how to discover relevant connections between variables.

To learn how to model an optimization problem, one must ask four key questions.

Linear programming is a key element of certain solutions to optimization problems.

Multiperiod planning can be quite complex.

A graphical method is perhaps the most efficient way to find answers to certain problems.

The simplex algorithm is a famous method for solving linear programming problems.

Sensitivity analysis has multiple valuable applications.

Integer programs present various special challenges.

A popular technique simplifies comparisons to a great extent.

The professor goes over weighted averages, soft constraints with penalties, and goal prioritization.

The professor introduces learners to nonlinear programming.

The professor teaches learners how to solve two practical nonlinear programming problems.

Simple challenges can help learners gain a grasp on elementary probability.

Decision trees and probability analysis are two efficient decision-making tools.

The Bayesian theorem is believed to have helped locate the wreckage of Air France Flight 447.

Markov analysis can help individuals peer into the future.

Delving deeper into Markov analysis, the professor compares a queue's random arrivals to popping popcorn, and explains how small changes in workforce or equipment can reduce waiting times.

The professor elaborates on one of the most widely used techniques in operations research.

Predictive analytics and optimization can help individuals stay ahead of the competition.