Ann Perkins, a realtor in Brownsburg, Indiana, would like to use estimates from a multiple regression model to help prospective sellers determine a reasonable asking price for their homes. She believes that the following four factors influence the asking price (Price) of a house: 1) The square footage of the house (SQFT) 2) The number of bedrooms (Bed) 3) The number of bathrooms (Bath) 4) The lot size (LTSZ) in acres She randomly collects online listings for 50 single-family homes. The data file is located in the Blackboard "Case Study Indiana Real Estate Data File Excel" within the Case Study folder. Requirements and associated point values: Part 1- Provide summary statistics (with Excel Data Analysis) by calculating the mean and standard deviation on the asking price, square footage, the number of bedrooms, the number of bathrooms, and the lot size. Explain each factor's mean and standard deviation. What does each of these summary statistics tell us. A total of ten calculations 2 points each for a total of 20 points. Part 2- Estimate and Interpret a multiple regression model where the asking price is the response variable and the other four factors are the explanatory variables. The end result should be a Excel Regression Output SUMMARY OUTPUT Regression Statistics Multiple R R Square Adj. R Square Standard Error Observation ANOVA Significanc Of 55 MS F e F Regression Residual