First City Real Estate executives wish to build a model to predict sales prices for residential properties. Such a model will be valuable when collaborating with potential sellers who might list their homes with First City. The company has provided the following data Property Price $10,000 $133.500 $112.500 $141.750 $195.250 $132.250 $135,000 $162.750 Square Feet 1000 14:00 1106 1078 952 1100 Age 43 58 78 33 13 Bedrooms 3 W Garage 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 (foothills-1, and o othenvise). Area 0 A. Develop the multiple regression model to predict property prices. B. At a 5% level of significance, is the overall model significant? C. Is each of the independent variables statistically significant in explaining property prices? D. Formulate the mall and alternative hypotheses E. State whether you would reject the null Irypothesis F Interpret the slope coefficients G. Interpret the intercept. H. Determine the price of a property with the following characteristics 200 square feet, 15 years old. 4 bedrooms, a garages, and not located at the foothills 1. What percentage of the variability in compensations is accounted for in the model?