Data from a random sample of 107 home sales in 2003 yielded the regression , where is price or value (Rs. 1000), = bedrooms, = baths, = house size (sq. ft.), = plot size (sq. ft.), = age (years), and if the house is in poor condition and otherwise. If a homeowner adds a new bathroom which increases the house size by 100 sq. ft., what is the expected increase in the value of the house?
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Data from a random sample of 107 home sales in 2003 yielded the regression P̂=119.2+0.485 BD+23.4 BA+0.156 HS+0.002 PS+0.090 A-35.6 PC, where P is price or value (Rs. 1000), BD = bedrooms, BA = baths, HS = house size (sq. ft.), PS = plot size (sq. ft.), A = age (years), and PC=1 if the house is in poor condition and 0 otherwise. If a homeowner adds a new bathroom which increases the house size by 100 sq. ft., what is the expected increase in the value of the house? (A) Rs. 37,000 (B) Rs. 39,450 (C) Rs. 39,000 (D) Rs. 37,200
Also written as: Data from a random sample of 107 home sales in 2003 yielded the regression P_hat=119.2+0.485 BD+23.4 BA+0.156 HS+0.002 PS+0.090 A-35.6 PC, where P is price or value (Rs. 1000), BD = bedrooms, BA = baths, HS = house size (sq. ft.), PS = plot size (sq. ft.), A = age (years), and PC=1 if the house is in poor condition and 0 otherwise. If a homeowner adds a new bathroom which increases the house size by 100 sq. ft., what is the expected increase in the value of the house?
Linear regression and OLS has come up 74 times in 7 exams since 2008.
This question is on Interpreting slopes, intercepts and rescaling, part of Linear regression and OLS in Econometrics.
Reading a fitted coefficient, and what changes when variables are rescaled.
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Question from the DSE 2019 paper. Solutions are TestStop's own. Not affiliated with or endorsed by any examination authority.