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TABLE 13-6 One of the Most Common Questions of Prospective House Buyers

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TABLE 13-6
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 four 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 Microsoft Excel outputs of two regression models.
TABLE 13-6 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 four 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 Microsoft Excel outputs of two regression models.        -Referring to Table 13-6, what is your decision and conclusion for the test H₀: β₂ = 0 vs. H₁: β₂ < 0 at the α = 0.01 level of significance using Model 1? A)  Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating cots. B)  Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)  Reject H₀ and conclude that the amount of insulation has a negative linear effect on heating costs. D)  Do not reject H₀ and conclude that the amount of insulation has a negative linear effect on heating costs.
TABLE 13-6 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 four 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 Microsoft Excel outputs of two regression models.        -Referring to Table 13-6, what is your decision and conclusion for the test H₀: β₂ = 0 vs. H₁: β₂ < 0 at the α = 0.01 level of significance using Model 1? A)  Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating cots. B)  Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)  Reject H₀ and conclude that the amount of insulation has a negative linear effect on heating costs. D)  Do not reject H₀ and conclude that the amount of insulation has a negative linear effect on heating costs.
-Referring to Table 13-6, what is your decision and conclusion for the test H₀: β₂ = 0 vs. H₁: β₂ < 0 at the α = 0.01 level of significance using Model 1?


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