The Heights Hotel in Tampa, Florida, is a luxury hotel that has 414 rooms. The hotel’s controller imported its monthly occupancy data from the past four years into Excel. The data included date, number of room nights sold, average monthly high outdoor temperature, and monthly occupancy costs. Management would like to be able to predict monthly occupancy costs. The following are the results of two regressions performed to predict monthly occupancy costs:• Regression #1: A regression using Room nights sold as the independent variable• Regression #2: A regression using Room nights sold and Average monthly high outdoor temperature as the independent variables.

Using the output from the two regressions, answer the following questions:1. Describe the steps you would undertake to perform a simple linear regression using Excel.2. Use the output from Regression #1 to construct the cost equation that managers could use to predict monthly occupancy costs.3. Use the output from Regression #2 to construct the cost equation that managers could use to predict monthly occupancy costs.4. Is Regression #1 or Regression #2 a better predictor of monthly occupancy costs? How do you know? What type of regression (simple linear regression or multiple regression) is the better predictor in this case?5. Assume that management expects to sell 10,000 room nights in a month when the average daily high outdoor temperature is expected to be 82 degrees. Predict the hotel’s occupancy costs for the month, using the output given for Regression #2.

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1 SUMMARY OUTPUT 2 Regression #1 3 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error Observations 8 9 10 ANOVA 11 Regression Statistics 12 Regression 13 Residual 14 Total 15 16 17 Intercept 18 Room nights sold B 0.952693 0.807623 0.805615 5382.641 48 df 1 46 47 Coefficients 65757.31 31.97424 1 SUMMARY OUTPUT Regression #2 2 3 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 9 10 ANOVA 11 12 13 Residual 14 Total 15 16 17 Intercept 18 Room nights sold 19 Avg monthly high temp Regression Statistics Regression B 0.946533 0.894956 0.902561 5191.862 48 df 2 45 47 Coefficients 32598.15 25.67059 293.7245