solve

Suppose you have a data set that includes the sale price of homes (price), the area of the home in square feet (area), the number of bedrooms, the lot size in square feet, and the distance in miles from the city center (miles). Model A price = 6) + B, area + Bybedrooms + G3lot + 84miles + e Model B log(price) = 89 + B, area + Bybedrooms + (slot + Bymiles + Model C log(price) = By + B, area + Bybedrooms + f3log(lot) + G4miles + Model D price = Jy + B area + Bybedrooms + 3lot + 6ymiles + ; miles\" + Suppose the R? of model B is larger than for model A. What do you conclude? Model A is the better model. You should not evaluate the models based on the R? value because the dependent variable in model B is transformed R? are not comparable. Model B is the better model. You made a mistake, R? values should be the same