The marketing manager of a supermarket chain is trying to see if the number of shelves can predict sales for breakfast cereal. A random sample of 12 equal-sized stores is selected with the following...

The marketing manager of a supermarket chain is trying to see if the number of shelves can predict sales for breakfast cereal. A random sample of 12 equal-sized stores is selected with the following results.

Note in the solution for this problem we don’t actually need to use the aisle location data. Be careful to identify the key variables for each regression model. The independent variable is the number of shelves and our variable of interest is the dependent variable, sales.

(a) Construct a scatter plot

(b) Determine the coefficient of determination, R²
, and interpret its meaning.

Answer:

Therefore, 68 % of sales can be explained by number of shelves.

(c) How useful do you think this regression model is for predicting sales?

This regression is helpful in predicting sales because it helps the marketing manager understand that for each increase in shelf space of an additional foot, weekly sales are estimated to increase by
 (from the slope).