Load the data into R using the following command: hotel.df = read.csv("https://goo.gl/oaWKgt")

1. Using the above data, answer the following questions:
   1. Management wants to know whether satisfaction with elite membership perks (satPerks) predicts overall satisfaction (satOverall). Assume that satPerks is a predictor and we want to know how satOverall changes in response to it. How do you interpret the relationship?
   2. We might wish to control the perks model for other influences, such as satisfaction with the front staff (satFrontStaff) and with the hotel city (satCity). How do you change the previous model to do this? Model and interpret the result. Is the answer different than in the model with only perks? Why or why not?
   3. Suppose we have a business strategy to maximize satisfaction with elite recognition (satRecognition) among our Gold and Platinum elite members. Suppose we might invest more in the front staff, room cleanliness, the points that we give, or the membership perks. Which of those seems best to consider, to increase Gold and Platinum member satisfaction with elite recognition?
   4. From these data and the business question and model in the previous question, would you recommend investing more in cleanliness? Why or why not?
   5. Now suppose we want to improve revenues in the restaurant, and to understand the relationship of average food spend per night as it relates to elite status (eliteStatus) and satisfaction with food price (satDiningPrice). Model and interpret the relationships.