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A Realtor Wants to Predict and Compare the Prices of Homes

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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. A realtor wants to predict and compare the prices of homes in three neighboring locations. She considers the following linear models: Model A: Price = β<sub>0</sub> + β<sub>1</sub> Size + β<sub>2</sub> Age + ε Model B: Price = β<sub>0</sub> + β<sub>1</sub> Size + β<sub>3</sub> Loc1 + β<sub>4</sub> Loc2 + ε Model C: Price = β<sub>0</sub> + β<sub>1</sub> Size + β<sub>2</sub> Age + β<sub>3</sub> Loc1 + β<sub>4</sub> 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. Using Model B, compute the predicted price of a 2,500-square-foot home in Location 1. Note: The values of relevant test statistics are shown in parentheses below the estimated coefficients.
Using Model B, compute the predicted price of a 2,500-square-foot home in Location 1.


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