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 14.6. 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 * = the price (in dollars) of Fresh as offered by Enterprise Industries in the sales period x, = the average industry price (in dollars) of com- petitors' similar detergents in the sales period .x; = Enterprise Industries' advertising expenditure (in hundreds of thousands of dollars) to pro- mote Fresh in the sales period Figure 14.6 on the next page gives the Excel output of a regression analysis of the Fresh Detergent demand data in Table 14.6 using the model a Find (on the computer out- put) and report the values of bj, by, and by, the least squares point estimates of BI, B2, and B3- Interpret by, b2, and by- b Consider the demand for Fresh Detergent in a fu- ture sales period when En- terprise Industries' price for Fresh will be x, = 3.70. the average price of competitors' similar de- lergents will be x, = 3.90, and Enterprise Industries' advertising expenditure for Fresh will be x, = 6.50. The point prediction of this demand is given at the bottom of Figure 14.6 in the MegaStat output. Report this point predict tion and show (within rounding) how it has been calculated.