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 below concerning y (demand for Fresh liquid laundry detergent), x1 (the price of Fresh), x2 (the average industry price of competitors' similar detergents), and x3 (Enterprise Industries' advertising expenditure for Fresh). To ultimately increase the demand for Fresh, Enterprise Industries' marketing department is comparing the effectiveness of three different advertising campaigns. These campaigns are denoted as campaigns A, B, and C. Campaign A consists entirely of television commercials, campaign B consists of a balanced mixture of television and radio commercials, and campaign C consists of a balanced mixture of television, radio, newspaper, and magazine ads. To conduct the study, Enterprise Industries has randomly selected one advertising campaign to be used in each of the 30 sales periods in table below. Although logic would indicate that each of campaigns A, B, and C should be used in 10 of the 30 sales periods, Enterprise Industries has made previous commitments to the advertising media involved in the study. As a result, campaigns A, B, and C were randomly assigned to, respectively, 9, 11, and 10 sales periods. Furthermore, advertising was done in only the first three weeks of each sales period, so that the carryover effect of the campaign used in a sales period to the next sales period would be minimized. Table lists the campaigns used in the sales periods. To compare the effectiveness of advertising campaigns A, B, and C, we define two dummy variables. Specifically, we define the dummy variable DB to equal 1 if campaign B is used in a sales period and 0 otherwise. Furthermore, we define the dummy variable DC to equal 1 if campaign C is used in a sales period and O otherwise. Table presents the JMP output of a regression analysis of the Fresh demand data by using the modelAverage Advertising Sales Price for Industry Expenditure Demand Period Fresh, x1 Price, x2 for Fresh, X3 for Fresh, y 3.91 3. 81 5.55 7.38 3. 74 4 . 05 6. 76 8. 55 3. 73 4.38 7. 25 9. 21 3. 74 3. 75 5.59 7.52 3. 64 3. 83 7. 08 9 .35 3. 61 3 . 86 6.53 8. 27 3 . 60 3. 71 6. 74 8 . 74 3.83 3. 83 5 . 26 7.83 3 . 86 3. 61 5.23 7. 15 3. 82 4 .05 6 . 06 8 . 06 3. 91 4 . 15 6.51 7.80 12 3. 91 4 . 04 6. 26 8. 19 13 3. 76 4. 13 7.00 9. 10 14 3. 77 4. 26 6.92 8 . 8 8 15 3.73 4. 16 6.80 8. 90 16 3. 82 4. 17 6.89 8 . 87 17 3.75 4. 21 7.10 9. 29 18 3. 86 4 .35 7. 05 9 . 02 19 3.73 4. 13 6.80 8. 77 20 3.84 3. 74 6.52 7.93 21 3.83 3. 77 6. 22 7. 62 22 3. 73 3 . 60 6. 01 7.29 23 3 . 78 3.97 6.52 8. 03 24 3 . 58 3 . 64 7. 07 8 . 58 25 3. 65 4 . 18 6. 89 8. 77 26 3. 69 4. 27 6.82 9 . 25 27 3. 75 3 . 68 6.53 8. 21 28 3. 76 3. 77 5 . 76 7. 62 29 3 . 88 3. 81 5 . 88 7.94 30 3. 79 4.24 6.87 9 . 21\f40 29 not 30Summary of Fit RSquare 0 . 929386 RSquare Adj 0. 914675 Root Mean Square Error 0 . 19578 Mean of Response 8. 382667 Observations (or Sum Wgts) 30 Analysis of Variance Sum of Mean Source DF Squares Square F Ratio Model 5 12 . 107614 2 . 42152 63. 1755 Error 24 0 . 919922 0 . 03833 Prob > F C. Total 29 13 . 027537 <. term estimate std error t ratio prob> | t| Lower 95% Upper 95% Intercept 8. 101920 2 . 110668 3. 84 0. 000791539* 3. 7457143 12 . 458126 Price (X1) -2. 407396 0 . 560729 -4.29 0. 000250506* -3 .56468 -1 . 250110 IndPrice (X2) 1 . 3533192 0 . 236909 5. 71