Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: $$\begin{array} { | l | r | r | }
\hline & \text { Coefficierts } & \text { Stardard Error } \
\hline \text { Iratercept } & 16310.0 & 4114.5 \
\hline \text { Nurrber of Bedroorrs } & 7295.3 & 1399 . \
\hline \text { Number of Bathroorrs } & 23473.0 & 4032.0 \
\hline
\end{array}$$ Given these results which additional condition would be sufficient to ensure number of bathrooms satisfies the "primary criteria" for a good control variable in attempting to identify the causal effect of number of bedrooms on house prices?
A) The number of bathrooms is correlated with house prices.
B) The number of bedrooms is correlated with house prices.
C) The number of bathrooms is correlated with the number of bedrooms.
D) The p-value for the number of bedrooms.

Question

Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: $$\begin{array} { | l | r | r | } 
\hline & \text { Coefficierts } & \text { Stardard Error } \
\hline \text { Iratercept } & 16310.0 & 4114.5 \
\hline \text { Nurrber of Bedroorrs } & 7295.3 & 1399 . \
\hline \text { Number of Bathroorrs } & 23473.0 & 4032.0 \
\hline
\end{array}$$ Given these results which additional condition would be sufficient to ensure number of bathrooms satisfies the "primary criteria" for a good control variable in attempting to identify the causal effect of number of bedrooms on house prices?
A) The number of bathrooms is correlated with house prices.
B) The number of bedrooms is correlated with house prices.
C) The number of bathrooms is correlated with the number of bedrooms.
D) The p-value for the number of bedrooms.

Quizplus · Accepted Answer

For a variable to be a good control in identifying the causal effect of another variable on the outcome, it should be correlated with both the independent variable of interest (in this case, the number of bedrooms) and the dependent variable (house prices). This ensures that controlling for this variable helps in isolating the specific effect of the independent variable on the outcome. Option C directly addresses this requirement by stating that the number of bathrooms is correlated with the number of bedrooms, implying it could account for some of the variation in house prices that is due to differences in the number of bedrooms.

Suppose You Have the Following Regression Results from a Regression