ii. iii. iv. Mount Pleasant Home Subdivison One of the most important problems in real estate is determining the selling price for a house. Obviously, there are so many factorssize, age, and style of the home; number of bedrooms and bathrooms; size of the lot; and so onwhich makes setting a price a challenging task. In this project, we will try to help realtors in this task by determining how different characteristics of homes relate to home prices, identifying the key variables in pricing, and building multiple- variable regression models to predict prices based on property characteristics. The analysis will be based on 245 properties in three communities in the suburban town of Mount Pleasant, South Carolina, in 2017. To ensure the data contains comparable properties, you may want to eliminate duplexes and properties whose prices are outliers. What limitations does this impose on our analysis? For each subdivision, calculate standard deviation, median and average selling price of a home. For each subdivision, construct scatter plots that depict the relationship between (i) List price and square footage, (ii) List price and number of bedrooms, (iii) List price and age; iv) List Price and number of Fire Places. What do you conclude from the charts about the relationship between List Price and the variables in parts (i)-(iv) Determine whether there is a signicant relationship between List Price and the four independent variables (square footage, age, number of re places, and number of bedrooms) at the 0.05 level of signicance. Provide an economic interpretation of each of the estimated regression coefcients. Which of the independent variables (if any) is statistically signicant (at the 0.05 signicance level) in explaining the selling price? ___- a .. .. . - a . . _~ _ . . a ._ _