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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 average 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 average 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. Model 1    Note: 2.96869E-05 = 2.96869×10<sup>-5</sup> Model 2    Note: 2.9036E-06 = 2.9036×10<sup>-6</sup> -Referring to Table 14-6, the estimated value of the partial regression parameter β<sub>1</sub> in Model 1 means that A)  all else equal, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 4.51 degrees. B)  all else equal, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $4.51. C)  all else equal, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by $4.51. D)  all else equal, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by 4.51%. Note: 2.96869E-05 = 2.96869×10-5
Model 2
TABLE 14-6 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 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. Model 1    Note: 2.96869E-05 = 2.96869×10<sup>-5</sup> Model 2    Note: 2.9036E-06 = 2.9036×10<sup>-6</sup> -Referring to Table 14-6, the estimated value of the partial regression parameter β<sub>1</sub> in Model 1 means that A)  all else equal, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 4.51 degrees. B)  all else equal, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $4.51. C)  all else equal, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by $4.51. D)  all else equal, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by 4.51%. Note: 2.9036E-06 = 2.9036×10-6
-Referring to Table 14-6, the estimated value of the partial regression parameter β1 in Model 1 means that


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