Description

The Caesars case describes the development of different models to forecast guest arrivals to the Caesars Palace hotel in Las Vegas, Nevada. The case allows students to understand how such a model is developed within an organization and to evaluate the models presented.

Key Questions for Case Reading

1. Evaluate the two forecasting models described in the case for predicting daily check-in volume.

a. What are the strengths and weaknesses of the moving averages model? Do you find any of the results from the moving average model surprising?

b. What are the strengths and weaknesses of the linear regression models? Evaluate the choice of independent variables. Do you find any of the results from the linear regression model surprising?

2. For each of the independent variables in the regression model, answer the following questions (Please note, a one-page summary of the regression output is attached that may make it easier to take notes about each variable):

a. Why do you think would this variable be relevant?

b. Why do you think the Caesars Analytics team included this variable?

c. What is your hypothesis for this variable? That is, what is your a priori expectation about the relationship between this variable and the dependent variable?

d. What does the regression model tell us about the relationship between this variable and the dependent variable?

i. How should we interpret this variables coefficient in the regression model?

ii. What do we learn from the regression model that helps us refine our understanding of Caesars business and its customers behavior?

e. Do you have any concerns about this variable or the way it is defined and used in the regression model? If so, do you have any suggestions on how better to capture the desired effect?

3. Which forecasting model provides a better basis for staffing Caesars front desks? Why?

4. Should Caesars analytics group move to more advanced time series forecasting models? Why or why not?