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TABLE 17-2 One of the Most Common Questions of Prospective House Buyers

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TABLE 17-2
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 (X2) ,the number of windows in the house (X3) ,and the age of the furnace in years (X4) .Given below are the EXCEL outputs of two regression models. TABLE 17-2 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 Table 17-2,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. TABLE 17-2 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 Table 17-2,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 Table 17-2,what can we say about Model 1?


Definitions:

Absorption Costing

An accounting method that includes all direct costs and allotted indirect costs associated with manufacturing a product.

Ending Inventory

The total value of goods available for sale at the end of an accounting period.

Manufacturing Costs

Manufacturing costs encompass all expenses directly involved in the production of a good, including raw materials, labor, and factory overhead.

Direct Materials

Raw materials that are traceable directly to the manufacturing of a product.

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