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SCENARIO 14-6 One of the Most Common Questions of Prospective House Buyers

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SCENARIO 14-6
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 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X1 ) and the amount of insulation in inches ( X 2 ) . Given below is EXCEL output of the regression model.
SCENARIO 14-6 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 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X<sub>1</sub> )  and the amount of insulation in inches ( X <sub>2</sub> ) . Given below is EXCEL output of the regression model.       Also SSR (X<sub>1</sub> | X<sub>2</sub>)  = 8343.3572 and SSR (X<sub>2</sub> | X<sub>1</sub>)  = 4199.2672 -Referring to Scenario 14-6,what can we say about the regression model? A) The model explains 17.12% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 27.78% of the sample variability of heating costs. B) The model explains 19.28% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 27.78% of the sample variability of heating costs. C) The model explains 27.78% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 19.28% of the sample variability of heating costs. D) The model explains 19.28% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 17.12% of the sample variability of heating costs.
SCENARIO 14-6 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 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X<sub>1</sub> )  and the amount of insulation in inches ( X <sub>2</sub> ) . Given below is EXCEL output of the regression model.       Also SSR (X<sub>1</sub> | X<sub>2</sub>)  = 8343.3572 and SSR (X<sub>2</sub> | X<sub>1</sub>)  = 4199.2672 -Referring to Scenario 14-6,what can we say about the regression model? A) The model explains 17.12% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 27.78% of the sample variability of heating costs. B) The model explains 19.28% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 27.78% of the sample variability of heating costs. C) The model explains 27.78% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 19.28% of the sample variability of heating costs. D) The model explains 19.28% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 17.12% of the sample variability of heating costs.
SCENARIO 14-6 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 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X<sub>1</sub> )  and the amount of insulation in inches ( X <sub>2</sub> ) . Given below is EXCEL output of the regression model.       Also SSR (X<sub>1</sub> | X<sub>2</sub>)  = 8343.3572 and SSR (X<sub>2</sub> | X<sub>1</sub>)  = 4199.2672 -Referring to Scenario 14-6,what can we say about the regression model? A) The model explains 17.12% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 27.78% of the sample variability of heating costs. B) The model explains 19.28% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 27.78% of the sample variability of heating costs. C) The model explains 27.78% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 19.28% of the sample variability of heating costs. D) The model explains 19.28% of the variability of heating costs;after correcting for the degrees of freedom,the model explains 17.12% of the sample variability of heating costs.
Also SSR (X1 | X2) = 8343.3572 and SSR (X2 | X1) = 4199.2672
-Referring to Scenario 14-6,what can we say about the regression model?

Develop an understanding of personal strategies for managing health care costs, the importance of health insurance in financial planning, government and private sources of health and disability insurance, and the basic types of health insurance.
Understand the various types of insurance coverage and their purposes.
Recognize the factors affecting insurance rates and coverage levels.
Identify risk management strategies and their applications.

Definitions:

Hourly Wage Rate

The amount of money paid for each hour of work, commonly used to compensate employees in many occupations.

Marginal Utility

The augmented enjoyment or usefulness that comes from the consumption of an additional unit of a product or service.

Optimal Labor Supply

The amount of labor hours that maximizes an individual's or firm's net benefits or utility.

Worth of Goods

The total value or market price of all goods considered or evaluated.

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