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TABLE 14-6 One of the Most Common Questions of Prospective House Buyers

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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
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.
Model 2
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.
-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?


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