A random sample of 22 residential properties was used in a regression of price on nine different independent variables. The variables used in this study were as follows:
PRICE = selling price (dollars)
BATHS 5 number of baths (powder room = 1/ 2 bath)
BEDA = dummy variable for number of bedrooms (1 = 2 bedrooms, 0 = otherwise)
BEDB = dummy variable for number of bedrooms (1 = 3 bedrooms, 0 = otherwise)
BEDC = dummy variable for number of bedrooms (1 = 4 bedrooms, 0 = otherwise)
CARA = dummy variable for type of garage (1 = no garage, 0 = otherwise)
CARB = dummy variable for type of garage (1 = one- car garage, 0 = otherwise)
AGE = age in years
LOT = lot size in square yards
DOM = days on the market
In this study, homes had two, three, four, or five bedrooms and either no garage or one- or two- car garages. Hence, we are using two dummy variables to code for the three categories of garage. Fit a full regression model ( nine independent variables), and then estimate the average difference in selling price between
a. Properties with no garage and properties with a one- car garage.
b. Properties with a one- car garage and properties with a two- car garage.
c. Properties with no garage and properties with a two- car garage.

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Property PRICE BATHS BEDA BEDB卫RDC CARA CA R AGE 23 7 1.0 1.0 2.5 LOTDOM 164 25750 37950 46450 46550 2.5 47950 49950 52450 54050 20 9680 1889 9 1941 315 61 234 116 162 80 1 8 1.5 1.5 2.5 1813 1583 1533 1667 5 3450 10 po 6 4 2 3 5 3 3 7 31113 13225222152222 0q00-000000000 00000010010011 0200040000 040 42430, 0 3 2 9 210 7 901234567890-2