1. Mid City housing price data The Midcity. txt data set, available on the class website, contains data on 128 sales ofhouses in a midwest town over a given period of ti me. For each sale, the le shows an indicator of neighborhood quality, the number of offers made on the house, the square footage, whether the house is made out of brick, the number ofbathrooms, the number of bedrooms, and the eventual selling price. Use regression models to estimate the pricing structure ofhouses in this town. Consider, in particular, the following questions. (a) Should you include the Offers variable in the regression? Why or why not? (b) Is there a premium for brick houses, everything else being equal? What is this premium and does it change depending on which other variables you include? Why? (C) Does combining neighborhoods 1 and 2 into a single category diminish 'le ability of the model to predict house prices as measured by the R2? ((1) Give a mean prediction and 90% plug-in prediction interval for a 1,500 square foot brick house with two bedrooms and two bathrooms in the nice neighborhood