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TABLE 13-6 One of the Most Common Questions of Prospective House Buyers

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TABLE 13-6
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 four 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 Microsoft Excel outputs of two regression models.
TABLE 13-6 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 four 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 Microsoft Excel outputs of two regression models.        -Referring to Table 13-6, 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 13-6 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 four 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 Microsoft Excel outputs of two regression models.        -Referring to Table 13-6, 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 13-6, what can we say about Model 1?


Definitions:

FOB Shipping Point

A term used in shipping agreements indicating that the buyer is responsible for the goods and the cost of shipping as soon as the goods leave the seller's premises.

Goods In Transit

Items being transported from one location to another, which have not yet reached the recipient or destination point.

Purchaser's Inventory

Goods and materials that a business holds for the ultimate goal of resale.

LIFO Conformity Rule

A tax regulation requiring companies that use the Last In, First Out (LIFO) method for tax reporting to also use it for financial reporting.

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