The trend in home building in recent years has been to emphasize open spaces and great rooms, rather than smaller living rooms and family rooms. A builder of speculative homes in the college community of Oxford, Ohio, had been building such homes, but his homes had been taking many months to sell and selling for substantially less than the asking price. In order to determine what types of homes would attract residents of the community, the builder contacted a statistician at a local college. The statistician went to a local real estate agency and obtained the data in Table 14.14. This table presents the sales price y, square footage xx, number of rooms x2, number of bedrooms .v,. and age x4 for each of 63 single-family residences recently sold in the community. When we perform a regression analysis of these data using the model

y = β0 + β1x1 + β2x2 + β3x3 + β4x4 + ε

We find that the least squares point estimates of the model parameters and their associated p values (given in parentheses) are b0 = 10.3676(.3710), b1 = .0500(<.001 b2="6.3218(.0152)," b3="-11.1032(.0635)." and b4="-.4319(.0()02)." discuss why the estimates suggest that it might be more profitable when building a house of specified square footage to include both living room family rather than great not increase number bedrooms cost another type would normally included in footage.>Based on the statistical results, the builder realized that there are many families with children in a college town and that the parents in such families would rather have one living area for the children (the family room) and a separate living area for themselves (the living room). The builder started modifying his open-space homes accordingly and greatly increased his profits.

TABLE 14.14

Measurements Taken on 63 Single-Family Residences

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