Management of a fast-food chain proposed the following regression model to predict sales at outlets: y = β0 + β1x1 + β2x2 + β3x3 + ε, where y = sales ($1000s) x1 = number of competitors within one mile x2 = population (in 1000s) within one mile x3 is 1 if a drive-up window is present, 0 otherwise The following estimated regression equation was developed after 20 outlets were surveyed: begin mathsize 14px style y with hat on top end style = 14.8 − 3.5x1 + 3.2x2 + 15.8x3 Use this equation to predict sales for a store with 3 competitors, a population of 5100 within one mile, and no drive-up window. Note: omit the $ symbol in your answer

y [ Expected Sales ] = 20.62 thousands

Step-by-step explanation:

Regression line : y = 14.8 − 3.5x1 + 3.2x2 + 15.8x3 ; where

y = sales (in 1000s), x1 = number of competitors within one mile, x2 =  population (in 1000s) within one mile, x3 = 1 (if a drive-up window is present) or x3 = 0 (if a drive-up window is not present)

x1 (competitors) = 3 , x2 (population) = 5100 = 5.1 thousands , x3 (no drive up window) = 0

y = 14.8 − 3.5 (3)  + 3.2 (5.1) + 15.8 (0)

y = 14.8 - 10.5 + 16.32

y [ Expected Sales ] = 20.62 thousands

to plot points   for x just start at the origin (middle) and go to the left however many times it say(if the number is a positive) if the number is a negative then go right however many times it says if it is 0 go nowhere

now keep you pencil there and go up however many times it says on y(if it is positive) if it is negative then go down however many times it says. if it says 0 go nowhere.

you have just plotted a point! now when you finish plotting all of them connect them with a ruler

plot each point as (-3, 7), (0,4), (3,1), (8,-4)   in the parenthesis on the left of the comma is x and the right is y.