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

question 18

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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 18-1,when the builder used a simple linear regression model with house size (House) as the dependent variable and education (School) as the independent variable,he obtained an r<sup>2</sup> value of 23.0%.What additional percentage of the total variation in house size has been explained by including family size and income in the multiple regression? A) 2.8% B) 51.8% C) 72.6% D) 74.8%
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 18-1,when the builder used a simple linear regression model with house size (House) as the dependent variable and education (School) as the independent variable,he obtained an r<sup>2</sup> value of 23.0%.What additional percentage of the total variation in house size has been explained by including family size and income in the multiple regression? A) 2.8% B) 51.8% C) 72.6% D) 74.8%
-Referring to Scenario 18-1,when the builder used a simple linear regression model with house size (House) as the dependent variable and education (School) as the independent variable,he obtained an r2 value of 23.0%.What additional percentage of the total variation in house size has been explained by including family size and income in the multiple regression?


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