Assignment 3: Case Study - Rooms Forecast (10%) R Use the following data to prepare a rooms forecast for the first week of May for The Times Hotel: Number of rooms available = 600 Number of rooms occupied on April 30 = 300 May 1: Departures - 200 rooms Arrivals = 200 rooms (70 percent confirmed, 30 percent guaranteed) Walk-ins = 40 rooms No-shows = 0.02 percent of expected arrivals May 2: Departures = 50 rooms Arrivals = 100 rooms (60 percent confirmed, 40 percent guaranteed) Walk-ins - 10 rooms No-shows = 0.02 percent of expected arrivals May 3: Departures 200 rooms Arrivals = 100 rooms (50 percent confirmed, 50 percent guaranteed) Walk-ins - 20 rooms No-shows - 0.02 percent of expected arrivals May 4: Departures = 50 rooms Arrivals - 100 rooms (20 percent confirmed, 80 percent guaranteed) Walk-ins 10 rooms No-shows = 0.01 percent of expected arrivals May 5: Departures - 300 rooms Arrivals = 70 rooms (30 percent confirmed, 70 percent guaranteed) Walk-ins 25 rooms No-shows = 0.0143 percent of expected arrivals May 6: Departures - 50 rooms Arrivals - 175 rooms (92 percent confirmed, 8 percent guaranteed) Walk-ins = 10 rooms No-shows = 0,04 percent of expected arrivals May 7: Departures -- 200 rooms Arrivals = 180 rooms (10 percent confirmed, 90 percent guaranteed) Walk-ins 25 rooms No-shows = 0.0223 percent of expected arrivals