Statistics: Statistics: Multiple Linear Regression Please Solve problem. Page 1 of 4 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), x (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 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 Cwere 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 og to equal 1 if campaign Bis used in a sales period and 0 otherwise. Furthermore, we define the dummy variable De to equal 1 if campaign C is used in a sales period and 0 otherwise. Table presents the JMP output of a regression analysis of the Fresh demand data by using the model Historical Data Concerning Demand for Fresh Detergent Sales Period Price for Average Industry Advertising Expenditure Fresh, x1 1 3.85 Price, x2 for Fresh, x3 for Fresh, y Demand 3.80 5.50 7.38 2 3.75 4.00 6.75 8.51 3 3.70 4.30 7.25 9.52 4 3.70 3.70 5.50 7.50 3.60 3.85 7.00 9.33 6 3.60 3.80 6.50 8.28 7 3.60 3.75 6.75 8.75 8 3.80 3.85 5.25 7.87 9 3.80 3.65 5.25 7.10 10 3.85 4.00 6.00 8.00 11 3.90 4.10 6.50 7.89 12 3.90 4.00 6.25 8.15 13 3.70 4.10 7.00 9.10 14 3.75 4.20 6.90 8.86 15 3.75 4.10 6.80 8.90 16 3.80 4.10 6.80 8.87 17 3.70 4.20 7.10 9.26 18 3.80 4.30 7.00 9.00 19 3.70 4.10 6.80 8.75 20 3.80 3.75 6.50 7.95 21 3.80 3.75 6.25 7.65 22 3.75 3.65 6.00 7.27 23 3.70 3.90 6.50 8.00 24 3.55 3.65 7.00 8.50 25 3.60 4.10 6.80 8.75 26 3.65 4.25 6.80 9.21 27 3.70 3.65 6.50 8.27 28 3.75 3.75 5.75 7.67 29 3.80 3.85 5.80 7.93 30 3.70 4.25 6.80 9.26 Advertising Campaigns Used by Enter prise Industries Sales Period 1 Advertising Campaign B 4 5 9 10 11 12 13 14 15 16 17 18 19 B 20 21 30 A Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum wgts) Analysis of Variance 0.959727 0.951337 0.15028 8.382667 Sum of Mean Source DF Squares Square F Ratio Model 5 12.916568 2.58331 Error 24 0.542019 0.02258 C. Total 29 13.458587 114.3862 Prob > F |t| 5.50