The owner of a fashion retailer has been tracking how many customers use coupons in his store after he mailed out sale fliers. The fliers offer customers a variety of choices, including 20% off the entire order, buy one and get one free, and 50% off a single item. Customers may use only one of the coupons, but the owner knows that some customers will make separate purchases in order to use more than one coupon or keep one coupon and give one away. The data are given in the table below.

Number of Fliers

Mailed (x)Number of Coupons Used

During Sale (y)10,0007,46510,50011,46611,00011,13211,50013,64912,00012,00312,5008,72713,00014,82513,50010,11114,00013,13614,50013,68415,00015,00315,50012,479

(a)

Look at a scatterplot of the data. Is a linear model appropriate?

Yes

No

(b)

What is the least squares regression equation for this model? (Round your answers to four decimal places.)

=x+

(c)

What is the best interpretation of they-intercept?

For every additional one flier mailed, the owner can expect approximately 0.7182 coupons used.

If the owner does not send out any fliers, he still expects 2,815.8019 coupons used.

They-intercept does not make sense in the context of the problem because if the owner does not send out any coupons, no customer will be using them.

(d)

The owner has been offered a two-for-one sale on his fliers and decides to mail out29,700of them. Use your equation to determine how many coupons he can expect to be used.

This can't be determined as it is extrapolating, 29,700 is way too far out of the range ofxvalues in the original data set.

You cannot have 0.8 of a coupon, so the owner can expect approximately 21,332.54 used coupons.

Exactly 21,333.34 used coupons

Approximately 21,333.34 used coupons