Mike Lynch manages a real estate firm in Myrtle Beach, South Carolina, and would like to construct a model to help him predict the selling price of beach properties for his customers. Mike has collected the following data from a random sample of homes that have recently sold: a home’s selling price (in $ 000), asking price (in $ 000), days on the market, size in square feet, age, number of bedrooms, number of bathrooms, and property setting (golf course, or wooded). These data can be found in the Excel file Myrtle Beach Homes.xlsx. Develop a best subsets regression model to predict a home’s selling price using α = 0.01 for a home that is 18 years old, has been on the market for 57 days, has an asking price of $ 499,000, with 4 bedrooms, 3.5 bathrooms, 2,800 square feet, and is on a wooded lot. Provide a complete analysis of your findings.