Module 1: Statistical Modeling and Revenue Management
• Descriptive Statistics
This session provides an introduction to the statistical methods used in decision modeling, including: (1) review of the mean, median, mode, standard deviation, normal curve, graphs and charts; (2) sampling - why sample, uses of sampling, comparison of sample survey methods on 10 dimensions, sample design and choosing random samples; (3) making estimates from samples - central limit theorem, confidence intervals for means and proportions; and (4) computing sample size for estimating means and proportions.
• Regression Models
Once we are familiar with the basic concepts of descriptive statistics we can take on more complex tasks. In this session students investigate how to implement these concepts in Excel and how to use statistics to generate conclusions. The first topic covered here is hypothesis tests; the primary decision-making tool in statistics. We begin by applying hypothesis tests to one-variable problems. More realistic models require multiple variables. In working with multiple variables we build regression models. The objective is to develop a model where the dependent variable is explained by one or more independent variables. This allows identification of the factors that determine the outcome of the dependent variable. Students will become familiar with implementing regression in Excel and interpreting the Excel output.
• Revenue Management
This lecture is designed to introduce you to some of the models and methods used in the emerging field of revenue management (RM). The problem that motivates RM is ancient: how does a company manage its capacity and/or prices to extract the greatest possible revenue from the marketplace? The RM approach is to exploit differences in customer segments and their willingness to pay. For example, a person who books a room in an upscale hotel three days in advance is typically willing to pay much more than someone booking the same room three months in advance. RM focuses on how to manage these different types of customer demands to maximize revenues.