Please use R for implementation

dataset:https://goo.gl/oaWKgt.

The simulated dataset for a hotel combines customers' responses to a satisfaction survey with basic account information from their hotel stays. These are the sort of data that you might acquire from an email survey is sent to users, where a disguised identifier links the surveys responses to account data. Another common source of similar data is an online system where a pop-up survey asks satisfaction questions, and the answers can be related to the user's account.

These data include 18 items asking about satisfaction with various aspects of the hotel (cleanliness, dining experience, staff, satisfaction with elite status perks, and so forth), each on a 7 point rating scale. In addition to the survey responses, the data include each respondent's corresponding number of nights stayed at the hotel, the distance traveled, reason for visiting, their elite membership level, and the average amounts spent per night on the room, dining, and WiFi.

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 is associated with changes in it. How do you interpret the relationship?

2) We might wish to control the previous satPerks model for other influences, such as satisfaction with the Front Staff (satFrontStaff) and with the city location (satCity). How do you change the previous model? Model and interpret the result. Is the answer different than in the model with only Perks? Why or why not?

3) Now we are examining ways to improve revenue in the restaurant. Management wants to understand the relationship of average food spend per night with elite status (eliteStatus) and satisfaction with food price (satDiningPrice). Model this and interpret it.

4) How does satisfaction relate to spending in our restaurant? On one side, we might expect dining satisfaction to be higher when food costs less, because customers are often happy about lower prices. However, we might also expect the exact opposite relationship, where satisfied diners spend more. Which relationship is better supported by these data?