Scenario 1

Use the scenario described below to answer the following questions.

A realty company in Phoenix, Arizona is interested in analyzing how the square footage (Sqft) and number of bedrooms (Bedrooms) influence a home's sales price in dollars (Price). They collected data on homes that recently sold using Truilia.com and Zillow.com and used it to obtain the following estimated regression equations:

Model 1: Price=79,758.20+8,702ln(Sqft)+7,588.99BedroomsPrice^=79,758.20+8,702ln(Sqft)+7,588.99Bedrooms, S_e = 4,450, R² = 0.29

Model 2: ln(Price)=10.97+0.0005Sqft+0.0881Bedroomsln(Price)^=10.97+0.0005Sqft+0.0881Bedrooms, S_e = 0.267, R² = 0.288

Model 3: Price=43,328.28+67.12Sqft+5,589.20BedroomsPrice^=43,328.28+67.12Sqft+5,589.20Bedrooms, S_e = 4,550, R² = 0.27

Which of the following would be the correct interpretation for the coefficient in front of the ln(Sqft) variable in Model 1? Assume the variable is statistically significant at the 5% level. Hint, make sure to identify which type of log-transformed model this is before trying to interpret the coefficient.

1. As the natural log of the square footage of the home increases by 1%, the sales price of the home increases by $87.02, on average and holding the number of bedrooms constant.
2. As the square footage of the home increases by 1%, the sales price of the home increases by $8702, on average and holding the number of bedrooms constant.
3. As the square footage of the home increases by 1%, the sales price of the home increases by $87.02, on average and holding the number of bedrooms constant.
4. As the square footage of the home increases by 1 square foot, the sales price of the home increases by $8702, on average and holding the number of bedrooms constant.
5. As the square footage of the home increases by 1 square foot, the sales price of the home increases by $87.02, on average and holding the number of bedrooms constant.