Enterprise Industries produces Fresh, a brand of liquid laundry detergent. In order to manage its inventory more effectively and make revenue projections, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 30 sales periods (each sales period is defined to be a four- week period). The demand data are presented in Table 14.5. Here, for each sales period,
y = the demand for the large size bottle of Fresh (in hundreds of thousands of bottles) in the sales period
x1 = the price (in dollars) of Fresh as offered by Enterprise Industries in the sales period
x2 = the average industry price (in dollars) of competitors’ similar detergents in the sales period
x3 = Enterprise Industries’ advertising expenditure (in hundreds of thousands of dollars) to promote Fresh in the sales period
Figure on the next page gives the Excel output of a regression analysis of the Fresh Detergent demand data in Table using the model
y = β0 + β1x1 + β2x2 + β3x3 + ε
a. Find (on the output) and report the values of b1, b2, and b3, the least squares point estimates of b1, b2, and b3. Interpret b1, b2, and b3.
b. Consider the demand for Fresh Detergent in a future sales period when Enterprise Industries’ price for Fresh will be x1 = 3.70, the average price of competitors’ similar detergents will be x2 = 3.90, and Enterprise Industries’ advertising expenditure for Fresh will be x3 = 6.50. The point prediction of this demand is given at the bottom of the Excel add- in output. Report this point prediction and show (within rounding) how it has been calculated.