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Instruction 13.18 One of the Most Common Questions of Prospective House Buyers

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Instruction 13.18
One of the most common questions of prospective house buyers pertains to the average 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 Celsius (X1) , the amount of insulation in cm (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.
Instruction 13.18 One of the most common questions of prospective house buyers pertains to the average 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 Celsius (X<sub>1</sub>) , the amount of insulation in cm (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 Microsoft Excel outputs of two regression models. -Referring to Instruction 13.18 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,...,4 using Model 1? A) Reject H<sub>0</sub> and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs. B) Do not reject H<sub>0</sub> and conclude that the four independent variables have significant individual linear effects on heating costs. C) Reject H<sub>0</sub> and conclude that the four independent variables taken as a group have significant linear effects on average heating costs. D) Do not reject H<sub>0</sub> and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs. Instruction 13.18 One of the most common questions of prospective house buyers pertains to the average 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 Celsius (X<sub>1</sub>) , the amount of insulation in cm (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 Microsoft Excel outputs of two regression models. -Referring to Instruction 13.18 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,...,4 using Model 1? A) Reject H<sub>0</sub> and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs. B) Do not reject H<sub>0</sub> and conclude that the four independent variables have significant individual linear effects on heating costs. C) Reject H<sub>0</sub> and conclude that the four independent variables taken as a group have significant linear effects on average heating costs. D) Do not reject H<sub>0</sub> and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs.
-Referring to Instruction 13.18 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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