Suppose that you fit a simple linear regression of the housing price of houses in Oakland, CA (y) against the number of bathrooms in the house (x): Yi Bo + Bixi + i You assume that the error terms in your model are normally distributed with mean zero and common variance o. You fit the model to your observed data of n = 50 observations and find the point estimators Bo=900,000, 1 =20,000, and 2 = 130,000. a) Suppose that the mean number of bathrooms is 1.5. What is the average house price in your sample? b) Rather than buying a house in Oakland, you decide to buy in Berkeley. Historical data has shown that houses in Berkeley sell for approximately 1.3 times the selling price for houses in Oakland. You see a house that is about to go on the market in Berkeley that has 2 bathrooms. Based on the fitted model, what is your best guess for the selling price of the house in Berkeley?