Suppose you fit a multiple regression equation to predict the price of a house(Y

Question

Suppose you fit a multiple regression equation to predict the price of a house(Y) in thousands of dollars using the number of bedrooms (BED) and square footage (SIZE). The fitted model is cap y = 81 + 11.9* BED + 0.04* SIZE How would you interpret the coefficient on SIZE (that is, what does "0.04" mean in the context of predicting house size based on bedrooms and square footage? We estimate that the mean selling price of a house with 0 bedrooms is $40. For every one square-foot increase in the size of the house, we estimate that the mean selling price of a house increases by $0.04 thousand (or $40), for any fixed number of bedrooms. For every one square-foot increase in the size of the house, we estimate that the mean selling price of a house increases by $81, for any fixed number of bedrooms. For every one square-foot increase in the size of the house, we estimate that the mean selling price of a house increases by $0.04 thousand (or $40). For every one square-foot increase in the size of the house, we estimate that the mean selling price of a house increases by $11, 900, for any fixed number of bedrooms.

Explanation / Answer

Option-B) for every one squared foot increase in the size of the house , we estimate that the mean selling price of a house increases by $0.04 thousand (or $40) for any fixed number of bedrooms

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