Management proposed the following regression model to predict sales at a fast-food outlet.

y=₀+₁x₁+₂x₂+₃x₃+

where

x1	=	number of competitors within one mile
x2	=	population within one mile (1,000s)
x3	=	1 if drive-up window present 0 otherwise
y	=	sales ($1,000s).

The following estimated regression equation was developed after 20 outlets were surveyed.

=10.64.2x₁+ 6.8x₂+15.8x₃

(a)What is the expected amount of sales (in dollars) attributable to the drive-up window?(b)Predict sales (in dollars) for a store withthreecompetitors within one mile, a population of 8,000 within one mile, andnodrive-up window.(c)Predict sales (in dollars) for a store with one competitor within one mile, a population of 3,000 within one mile, andadrive-up window.

Step 1

(a)What is the expected amount of sales (in dollars) attributable to the drive-up window?

For the following variables, an estimated regression equation was determined to be

=10.64.2x₁+ 6.8x₂+15.8x₃.

x1	=	number of competitors within one mile
x2	=	population within one mile (1,000s)
x3	=	1 if drive-up window present 0 otherwise
y	=	sales ($1,000s).

Recall that the parameters

_p

are used to describe how the mean value ofychanges. The drive-up window corresponds to the independent variablex₃.If a drive-up window is present, then the final term of the estimated regression equation is

15.8x₃=15.8(1) = .

Sinceyrepresents the sales in $1,000s, the final term of the estimated regression equation should be ---Select--- divided multiplied by 1,000.

Therefore, a drive-up window will add $ in sales.