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Suppose that a researcher, by using a sample of transactions of residential houses, wants to estimate how the price of the houses is affected by the absence of an elevator in the building. To this end, he regresses the price of the house in thousands euro, price, on the dummy no_elevator indicating the absence of the elevator (dummy equal to 1) or the presence (dummy equal to 0): price; = ao +a1 no_elevator; + u; The OLS estimates are (standard errors in round brackets below the corresponding coefficient): price, = 199.937 - 73.068 no_elevator; (5.482) R2 = 0.036 (10.735) The poor fit of the model suggests the researcher to insert an additional regressor, namely the surface of the houses, area, in square meters. Supposing that area and no_elevator are negatively correlated, what do you think will happen to the previously estimated coefficient of no_elevator?

Suppose that a researcher, by using a sample of transactions of residential houses, wants to estimate how the price of the houses is affected by the absence of an elevator in the building. To this end, he regresses the price of the house in thousands euro, price, on the dummy no_elevator indicating the absence of the elevator (dummy equal to 1) or the presence (dummy equal to 0): price; = ao +a1 no_elevator; + u; The OLS estimates are (standard errors in round brackets below the corresponding coefficient): price, = 199.937 - 73.068 no_elevator; (5.482) R2 = 0.036 (10.735) The poor fit of the model suggests the researcher to insert an additional regressor, namely the surface of the houses, area, in square meters. Supposing that area and no_elevator are negatively correlated, what do you think will happen to the previously estimated coefficient of no_elevator?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Suppose that a researcher, by using a sample of transactions of residential houses, wants to estimate how the price of the houses is affected by the absence of an elevator in the building. To this end, he regresses the price of the house in thousands euro, price, on the dummy no_elevator indicating the absence of the elevator (dummy equal to 1) or the presence (dummy equal to 0): price; = a0 +a1 no_elevator; + u; The OLS estimates are (standard errors in round brackets below the corresponding coefficient): R2 = 0.036 price; = 199.937 - 73.068 no_elevator; (10.735) (5.482) The poor fit of the model suggests the researcher to insert an additional regressor, namely the surface of the houses, area, in square meters. Supposing that area and no_elevator are negatively correlated, what do you think will happen to the previously estimated coefficient of no_elevator? O (a) We cannot say given the so poor fit of the model O (b) It will be larger than -73.068 (i.e. -30) O (c) It will be smaller than -73.068 (i.e. -110) O (d) It will be equal to -73.068
Transcribed Image Text:Suppose that a researcher, by using a sample of transactions of residential houses, wants to estimate how the price of the houses is affected by the absence of an elevator in the building. To this end, he regresses the price of the house in thousands euro, price, on the dummy no_elevator indicating the absence of the elevator (dummy equal to 1) or the presence (dummy equal to 0): price; = a0 +a1 no_elevator; + u; The OLS estimates are (standard errors in round brackets below the corresponding coefficient): R2 = 0.036 price; = 199.937 - 73.068 no_elevator; (10.735) (5.482) The poor fit of the model suggests the researcher to insert an additional regressor, namely the surface of the houses, area, in square meters. Supposing that area and no_elevator are negatively correlated, what do you think will happen to the previously estimated coefficient of no_elevator? O (a) We cannot say given the so poor fit of the model O (b) It will be larger than -73.068 (i.e. -30) O (c) It will be smaller than -73.068 (i.e. -110) O (d) It will be equal to -73.068
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