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SCENARIO 18-2
One of the Most Common Questions of Prospective β\beta

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SCENARIO 18-2
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 ( X 2 ) , the number of windows in the house ( X3 ) , and the age of the furnace in years ( X 4 ) . Given below are the EXCEL outputs of two regression models.
 SCENARIO 18-2 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 Scenario 18-2,the estimated value of the partial regression parameter  \beta <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%.
 SCENARIO 18-2 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 Scenario 18-2,the estimated value of the partial regression parameter  \beta <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 Scenario 18-2,the estimated value of the partial regression parameter β\beta 1 in Model 1 means that


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