Exhibit 17.8.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 + β₁Size + β₂Age + ε,
Model B: Price = β₀ + β₁Size + β₂Loc1 + β₃Loc2 + ε,
Model C: Price = β₀ + β₁Size + β₂Age + β₃Loc1 + β₄Loc2 + ε,
where,
Price = the price of a home (in $thousands),
Size = the square footage (in square 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.
Refer to Exhibit 17.8.Using Model B,what is the predicted price of a 2,500 square feet home in Location 1?

Question

Exhibit 17.8.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 + β₁Size + β₂Age + ε,
Model B: Price = β₀ + β₁Size + β₂Loc1 + β₃Loc2 + ε,
Model C: Price = β₀ + β₁Size + β₂Age + β₃Loc1 + β₄Loc2 + ε,
where,
Price = the price of a home (in $thousands),
Size = the square footage (in square 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.
Refer to Exhibit 17.8.Using Model B,what is the predicted price of a 2,500 square feet home in Location 1?

Quizplus · Accepted Answer

The Answer of Exhibit 17.8.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 + β₁Size + β₂Age + ε, Model B: Price = β₀ + β₁Size + β₂Loc1 + β₃Loc2 + ε, Model C: Price = β₀ + β₁Size + β₂Age + β₃Loc1 + β₄Loc2 + ε, where, Price = the price of a home (in $thousands), Size = the square footage (in square 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. Refer to Exhibit 17.8.Using Model B,what is the predicted price of a 2,500 square feet home in Location 1?