This case describes the introduction of a regression analysis model for forecasting guest arrivals to Caesars Palace hotel in Las Vegas, Nevada. The company will use the forecast to staff the front desk in the hotel. The staff is unionized and the company has little flexibility to change staffing levels on a short-term basis. The case is set in the context of industry overcapacity and lower customer demand. The case describes several models that could be used to forecast guest arrivals, including a moving average technique and a multiple regression model. The multiple regression model includes over 40 independent variables, including dummy variables (e.g., to represent day of week, month, year, holidays, paydays) as well as continuous variables to represent customer segment and average daily room rate. The case contains tables showing the output of the regression model, and compares the fit of the moving average and regression models. The case allows students to understand how such a model is developed within an organization and to evaluate the models presented. Students may work with a data file with several years of historical data or they may work with the model description and output results in the case.