4. First City Real Estate executives wish to build a model to precict sales prices for residential properties. Such a model will be valuable when collaborating with potential selless who might list their homes with First City, The comparry has provided the following data: Page 2 of 3 The price of the property depends on square footage, the age of the property, the number of bedrooms, the number of garages, and the area (foothalls 1, and o ctberwise). A. Develop the multiple regression model to predict property prices. B. At a 5% level of significance, ts the overall model significant?: C. Is each of the independent variables statistically significant in explaining ptoperty frices? D. Formulate the null and alternative hypotheses. E. State whether you would reject the null hypothesis. F. Interpret the slope coefficients. G. Interpret the intercept. 13. Determine the price of a property with the following characteristics: 2,100 square foet, 15 years old, 4 bedrooms, 2 garages, and not located at the foothills. 1. What percentage of the variability in compencutions is accounted for in the mobel