Pricing real estate Jules is a local commercial real estate agent. He just got a call from a potential customer, Ms. Wagner, who wants to lease her property and was inquiring how much monthly rent she might be able to get. In particular, she wondered how likely it would be that she could get at least $10,000 dollars per month. Jules checked the public records and found that the size of Ms. Wagner's property is 15,000 square feet. Then, he pulled out his database of recent commercial real estate deals and quickly built two regression models in order to forecast the likely rent price for Ms. Wagner's property. A brief description of the regression models are as follows: Regression 1. This model tries to explain and forecast rent (variable name: TotalRent) as a linear function of the square footage of a property (variable name: SquareFeet). Regression 2. This model tries to forecast rent per square foot (computed by dividing the total monthly rent for the property by the total square footage; variable name: RENT) as a linear function of the square footage (variable name: SquareFeet). The regression outputs for these two regressions are available in the worksheet CommercialREOutputs. (There is no data available to you, so you cannot rerun these regressions and need to work with provided outputs.)