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SCENARIO 18-2 One of the Most Common Questions of Prospective House Buyers

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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,what can we say about Model 1 ? A) The model explains 77.7% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 75.1% of the sample variability of heating costs. B) The model explains 75.1% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 77.7% of the sample variability of heating costs. C) The model explains 80.8% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 75.7% of the sample variability of heating costs. D) The model explains 75.7% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 80.8% of the sample variability of heating costs.
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,what can we say about Model 1 ? A) The model explains 77.7% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 75.1% of the sample variability of heating costs. B) The model explains 75.1% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 77.7% of the sample variability of heating costs. C) The model explains 80.8% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 75.7% of the sample variability of heating costs. D) The model explains 75.7% of the sample variability of heating costs;after correcting for the degrees of freedom,the model explains 80.8% of the sample variability of heating costs.
-Referring to Scenario 18-2,what can we say about Model 1 ?


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