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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,the estimated value of the partial regression parameter β<sub>1</sub>in Model 1 means that A) holding the effect of the other independent variables constant,an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 4.51 degrees. B) holding the effect of the other independent variables constant,a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $4.51. C) holding the effect of the other independent variables constant,a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by $4.51. D) holding the effect of the other independent variables constant,a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by 4.51%. 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,the estimated value of the partial regression parameter β<sub>1</sub>in Model 1 means that A) holding the effect of the other independent variables constant,an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 4.51 degrees. B) holding the effect of the other independent variables constant,a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $4.51. C) holding the effect of the other independent variables constant,a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by $4.51. D) holding the effect of the other independent variables constant,a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by 4.51%.
-Referring to Table 17-2,the estimated value of the partial regression parameter β1in Model 1 means that

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