Short Answer
A realtor wants to predict and compare the prices of homes in three neighboring locations. She considers the following linear models:
Model A: Price = β0 + β1 Size + β2 Age + ε
Model B: Price = β0 + β1 Size + β3 Loc1 + β4 Loc2 + ε
Model C: Price = β0 + β1 Size + β2 Age + β3 Loc1 + β4 Loc2 + ε
where,
Price = the price of a home (in $1,000s)
Size = the square footage (in sq. feet)
Loc1 = a dummy variable taking on 1 for Location 1, and 0 otherwise
Loc2 = a dummy variable taking on 1 for Location 2, and 0 otherwise
After collecting data on 52 sales and applying regression, her findings were summarized in the following table. Note: The values of relevant test statistics are shown in parentheses below the estimated coefficients.
Which of these three models would you choose to make the predictions of the home prices?
Correct Answer:

Verified
Correct Answer:
Verified
Q104: For the model y = β<sub>0 </sub>+
Q105: Like any other university, Seton Hall University
Q106: According to the Center for Disease Control
Q107: To examine the differences between salaries of
Q108: A bank manager is interested in assigning
Q110: A researcher wants to examine how the
Q111: Variables employed in a regression model can
Q112: An over-the-counter drug manufacturer wants to examine
Q113: An over-the-counter drug manufacturer wants to examine
Q114: For the linear probability model y =