Megan Hanson, a Realtor in Brownsburg, Indiana, would like to use estimates from a multiple linear 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); and (4) the lot size (LTSZ) in acres. She randomly collects online listings for 50 single-family homes. A portion of the data is presented in the accompanying table; the entire data set, labeled Indiana_RealEstate, can be found on the text website.

In a report, use the sample information to:
1. Provide summary statistics on the asking price, the square footage, the number of bedrooms, the number of bathrooms, and the lot size.
2. Estimate a multiple linear regression model where Price is the response variable and
SQFT, Bed, Bath, and LTSZ are the explanatory variables.
3. Interpret the resulting coefficient of determination.
4. Conduct joint and individual significance tests at the 5% significancelevel.

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Data for Case Study 12.2 Real Estate Data for Brownsburg, Indiana Price 399,900 375,000 SQFT 5026 3200 Bed Bath LTSZ 0.3 4.5 4 102,900 1938 0.1