Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: CoefficientsStandard ErrorIntercept16310.04114.5Number of Bedrooms7295.31399.9Number of Bathrooms23473.04032.0r-squared = 0.302 Adjusted r-squared = 0.299If we assume that the proper model to predict the market value of houses is given by this regression, and we also happen to know that number of bathrooms and number of bedrooms is uncorrelated both in the sample and in the target population of house sales, why might we still want to include number of bathrooms in a regression to identify the causal effect of number of bedrooms on home prices?
A. It provides a sanity check on our regression.
B. It increases the r-squared value.
C. It also leads to more consistent estimates of the treatment effect.
D. It lowers the adjusted r-squared.
Answer: A
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