Simulation is about quantifying the outcome of specific "what if" questions. What if the average demand for tickets on a 150-seat aircraft is actually 200? What if people who have purchased a ticket don't show up? What if we offered a different number, or economy and first-class tickets? Perhaps most importantly, what effect do these "what if" scenarios have on total revenue?

As you might guess, many "what if" questions in the real world are fundamentally uncertain; there is no deterministic formula for predicting exactly how many people will not show up for a given flight. You can, however, use historical data to estimate no-show probabilities. Once you conclude that uncertainty plays an important part in your problem, it may be that you will have to turn to a probabilistic simulation. Running many replications of the simulation will then help you statistically analyze the system's behavior and assess the effects of different design choices.

In this course, you will explore the intricacies of designing andanalyzing probabilistic simulations. You will also run simulations using packages in the free and open-source statistical programming language R to solve real-world logistical business problems. The focus will be on making these methods accessible for youin your own work.
You are required to have completed the following courses or have equivalent experience before taking this course:

• Understanding Data Analytics
• Finding Patterns in Data: Association Rules, PCA, and Factor Analysis
• Finding Patterns in Data: Cluster and Hotspot Analysis
• Regression Analysis and Discrete Choice Models
• Supervised Learning Techniques
• Neural Networks and Machine Learning
• Making Data-Driven Recommendations Using Optimization