TABLE 14-6
One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X₁) the amount of insulation in inches (X₂), the number of windows in the house (X₃), and the age of the furnace in years (X₄). Given below are the Excel outputs of two regression models.
Model 1

Model 2

-Referring to Table 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test H₀: β₁ = β₂ = β₃ = β₄ = 0 vs. H₁: At least one βⱼ ≠ 0, j = 1,2,..., 4 using Model 1?
A) Do not reject H₀ and conclude that the 4 independent variables have significant individual linear effects on heating costs.
B) Reject H₀ and conclude that the 4 independent variables taken as a group have significant linear effects on heating costs.
C) Do not reject H₀ and conclude that the 4 independent variables taken as a group do not have significant linear effects on heating costs.
D) Reject H₀ and conclude that the 4 independent variables taken as a group do not have significant linear effects on heating costs.

An Interaction Term in a Multiple Regression Model May Be

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