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 prowided the following data: The price of the property depends ou square footage, the age of the property, the number of bedrooms, the mumber of garages, and the area (foothills−1, and o othenvise). A. Develop the multiple regression model to preduct property prices:- B. At a 5 fiblevel of signiffance, is the vverall model vignificati? C. Is each of the independent variables statistically sizusticant in explaining property prices? D. Formulate the nul and altemative bypotheses. E. State whether you would reiect the zull lypotheis. F. Interpret the slope coefficients. G. Interptet the itutetcept. H. Determine the pricn of a property with the following churacteriviok zoo square feet, 15 ynats ald. thedrooms, maragis, and not located at the focthills. I. What percentage of this variobility in compenuisons as accounted for in the model?