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.
Using Model C, what is the conclusion for testing the joint significance of the two dummy variables at the 1% significance level?

Question

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.
Using Model C, what is the conclusion for testing the joint significance of the two dummy variables at the 1% significance level?

Quizplus · Accepted Answer

The Answer of 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. Using Model C, what is the conclusion for testing the joint significance of the two dummy variables at the 1% significance level?

A Realtor Wants to Predict and Compare the Prices of Homes