Q3

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 F 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 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 balance 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 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 D to equal 1 if campaign Bis 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 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 Average Advertising Sales Price for Industry Expenditure Demand Period Fresh, *1 Price, X2 for Fresh, X3 for Fresh, y 3.91 3.84 5.51 7.38 3.73 4.04 6.70 8.59 3.78 4.31 7.20 9.20 3.72 3.78 5.56 7.51 3.63 3.81 7.09 9.38 3 . 87 6.55 8.29 3.68 3.78 6.74 8.74 3.84 3.86 5.25 7.84 3.86 3.63 5.24 7.11 10 4.04 6.07 8.03 3.91 4.15 3.91 4.02 6.51 7.80 6.25 8.13 3.76 4.14 7.07 9.16 4.27 6.94 8.81 6.82 8.95 6.84 8.82 4.28 7.10 9.23 7.06 9.01 1.12 6.81 6.50 8.72 7.92 3.70 6 .22 7.64 3. 60 6.03 7.23 3.77 6 .58 8.06 3.59 7.89 8.57 3.56 4.18 6.89 8.73 9.28 3.74 3.69 6.54 8.22 5.72 7.63 3.78 7.91 4.21 6.84 9.24 Advertising Campaigns Used by Enter prise Industries Sales Advertising eriod Campaign onanapp Summary of Fit RSquare 0.960270 RSquare Adj 0.951993 Root Mean Square Error 0. 14828 Mean of Response 3.382667 Observations (or Sum Wgts) Analysis of Variance Sum of Mean DF Squares Model 12.754955 Square Ratio Error 24 2.55099 116.0167 Prob > F C. Total 29 0.527715 0.02199 13.282670 <.0001 term estimate std error t ratio proboltl lower upper intercept price indprice advexp db dc predicted demand mean indiv y="60" in this model the parameter represents effect on of advertising campaign b compared to a and c a. use regression output find report point each above effects test significance effects. also percent confidence interval for interpret your results. answers decimal places. is b4="The" b5="The" probably most effective even though intervals overlap. prediction results at bottom correspond future period when fresh will be x1="3.77," average similar detergents x2="3.90," expenditure t3="6.58," used. show how calculated. then an individual y-hat consider alternative here da equals if used o otherwise. describe represented by>