Case 4 – house price. a small data set contains information on house price (y) in dollars, as well as predictors: number of cars the garage can hold (x1), the age of the house in years (x2), and the number of rooms in the house (x3). consider the regression output below and determine the missing values a through f. dependent variable is: price r squared = r squared (adjusted) = 57.3% s = with 22 - 4 = 18 degrees of freedom source sum of squares df mean square f-ratio regression 6892069096 2297356365 residual 3975520758 18 220862264 variable coefficient s. e. of coeff t-ratio prob constant -26737.5 21074 -1.27 0.2207 cars 6185.10 6640 0.932 0.3639 age -333.303 757.8 rooms 11154.6 2524 4.42 0.0003
key:
(a) = 63.4%
(b) = 14861
(c) = 10.4
(d) = 3
(e) = –0.44
(f) = 0.66