Chris Anderson is a professor at the Cornell School of Hotel Administration. Prior to his appointment in 2006, he was on faculty at the Ivey School of Business in London, Ontario, Canada. His main research focus is on revenue management (RM) and service pricing. He actively works with industry, across numerous industry types, in the application and development of RM, having worked with a variety of hotels, airlines, rental car and tour companies, as well as numerous consumer packaged goods and financial services firms. Anderson’s research has been funded by numerous governmental agencies and industrial partners. He serves on the editorial board of the Journal of Revenue and Pricing Management and is the regional editor for the International Journal of Revenue Management. At the School of Hotel Administration, he teaches courses in revenue management and service operations management.
The sheer variety of sources and types of data that can aid in decision making are almost overwhelming. The key to making good use of the data lies in knowing what specifically to pay attention to, understanding the relationships and variables among the data, and making the right connections.
Experience is essential to knowing and making educated guesses about what to pay attention to. Familiarity with statistical methods will provide you with a significant advantage over relying on gut instinct alone.
In this course you will learn to identify uncertainty in a business decision, and to choose variables that help reduce uncertainty. By the end of this course, you will have a robust decision model that you can use to make predictions related to your decision. Along the way, you will clarify and enhance your understanding of the factors that influence possible outcomes from the decision.
- Determine the degree of uncertainty in your decision and determine the impact of this uncertainty
- Identify data relationships to reduce uncertainty
- Create a regression model that looks at attributes of variables driving the decision
- Refine your regression model to improve its validity
- Create a convincing argument for the validity of your model
- Make a prediction or an estimate using your model
How It Works
Who Should Enroll
- Functional Managers
- Any professional that uses data to make business decisions