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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?


Definitions:

Total Variable Costs

The sum of all costs that vary directly with the level of production or output in the short term.

Output

The quantity of goods or services produced within a given time frame by a firm, sector, or country.

Equilibrium Price

The market value at which there's a perfect match between quantity of goods available and quantity desired by buyers.

Cockatoos

A type of large, often colorful and noisy parrot native to Australia and surrounding islands.

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