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

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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 ( 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 ( ) , the amount of insulation in inches ( ) , the number of windows in the house ( ) , and the age of the furnace in years ( ) .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. ) , the amount of insulation in inches ( 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 ( ) , the amount of insulation in inches ( ) , the number of windows in the house ( ) , and the age of the furnace in years ( ) .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. ) , the number of windows in the house ( 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 ( ) , the amount of insulation in inches ( ) , the number of windows in the house ( ) , and the age of the furnace in years ( ) .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. ) , and the age of the furnace in years ( 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 ( ) , the amount of insulation in inches ( ) , the number of windows in the house ( ) , and the age of the furnace in years ( ) .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. ) .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 ( ) , the amount of insulation in inches ( ) , the number of windows in the house ( ) , and the age of the furnace in years ( ) .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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