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TABLE 17-2 One of the Most Common Questions of Prospective House Buyers

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TABLE 17-2
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. TABLE 17-2 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 (X<sub>1</sub>) ,the amount of insulation in inches (X<sub>2</sub>) ,the number of windows in the house (X<sub>3</sub>) ,and the age of the furnace in years (X<sub>4</sub>) .Given below are the EXCEL outputs of two regression models.     -Referring to Table 17-2 and allowing for a 1% probability of committing a type I error,what is the decision and conclusion for the test H<sub>0</sub> : β<sub>1</sub> = β<sub>2</sub> = β<sub>3</sub> = β<sub>4</sub> = 0 vs.H<sub>1</sub> : At least one β<sub>j</sub> ≠ 0,j = 1,2,<sup>...</sup>,4 using Model 1? A) Do not reject H<sub>0</sub> and conclude that the 4 independent variables have significant individual linear effects on heating costs. B) Reject H<sub>0</sub> and conclude that the 4 independent variables taken as a group have significant linear effects on heating costs. C) Do not reject H<sub>0</sub> and conclude that the 4 independent variables taken as a group do not have significant linear effects on heating costs. D) Reject H<sub>0</sub> and conclude that the 4 independent variables taken as a group do not have significant linear effects on heating costs. TABLE 17-2 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 (X<sub>1</sub>) ,the amount of insulation in inches (X<sub>2</sub>) ,the number of windows in the house (X<sub>3</sub>) ,and the age of the furnace in years (X<sub>4</sub>) .Given below are the EXCEL outputs of two regression models.     -Referring to Table 17-2 and allowing for a 1% probability of committing a type I error,what is the decision and conclusion for the test H<sub>0</sub> : β<sub>1</sub> = β<sub>2</sub> = β<sub>3</sub> = β<sub>4</sub> = 0 vs.H<sub>1</sub> : At least one β<sub>j</sub> ≠ 0,j = 1,2,<sup>...</sup>,4 using Model 1? A) Do not reject H<sub>0</sub> and conclude that the 4 independent variables have significant individual linear effects on heating costs. B) Reject H<sub>0</sub> and conclude that the 4 independent variables taken as a group have significant linear effects on heating costs. C) Do not reject H<sub>0</sub> and conclude that the 4 independent variables taken as a group do not have significant linear effects on heating costs. D) Reject H<sub>0</sub> and conclude that the 4 independent variables taken as a group do not have significant linear effects on heating costs.
-Referring to Table 17-2 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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