Suppose You Have the Following Regression Results from a Regression

Multiple Choice

Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: $$\begin{array} { | l | r | r | } \hline & \text { Coefficierts } & \text { Stardard Error } \\\hline \text { Iratercept } & 16310.0 & 4114.5 \\\hline \text { Nurrber of Bedroorrs } & 7295.3 & 1399 . \\\hline \text { Number of Bathroorrs } & 23473.0 & 4032.0 \\\hline\end{array}$$ r-squared = 0.302 Adjusted r-squared = 0.299 If 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 lowers the adjusted r-squared.
B) It increases the r-squared value.
C) It provides a sanity check on our regression.
D) It also leads to more consistent estimates of the treatment effect.