Review the attached excel data set. Each file contains information on unit sales and price for four brands that are named as brand1, brand2, brand3 and brand4. The unit sales have been summed up across several stores, whereas marketing activity variables are weighted averages across those stores. The data set contains the following variables: Week = Week number (1 to 75) Unitsj = Total units of brand \"j\" sold (j=1 to 4) across stores Pricej = Shelf price of brand \"j\" (weighted average across stores) Featj = Newspaper feature, (1 = brand "j" is featured, 0 = otherwise) Dispj = Weighted average of in In-store display, (1 = there is an in-store display for brand "j" in a store, 0 = otherwise) Brands 1 Tide 2 Wisk 3 Era 4 Surf 1) Formulate (Be very specific) and estimate models (log-log model or hybrid models only) to compute and compare own price elasticities for all the brands. Comment on the results. 2) Do the results have face validity (hint: look at summary statistics of the four brands)? Why? What are the managerial implications that one can infer from your results? Be specific. 3) Consider the best models (for Tide and Wisk only) from Part A. For these two focal brands, how can you improve the models to account for the effect of competition (which is the effect of price of a competing brand on the sales of a focal brand)? Formulate models (by extending the best fitting models from Question 1) for Tide and Wisk. Estimate and interpret the models. 4) The above models are based on analysis of aggregate data. What are the advantages and limitations of working with aggregate data? From a marketing manager's perspective, elaborate on how disaggregate data can be useful in understanding the effect of a price cut on sales. Be specific. What are the advantages and limitations of working with disaggregate data? Explain with an example. qattachments_41add3ef502efd32b9e729b2c748b274fd792621.xlsx WEEK UNITS1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 59 78 73 79 55 100 343 690 397 139 147 93 91 64 82 60 138 126 112 63 74 86 86 43 53 36 52 63 84 60 29 35 62 123 100 151 126 159 94 103 109 86 124 64 63 26 42 237 4776 1422 1147 689 UNITS2 72 63 69 137 61 101 51 78 42 1478 1774 439 12 15 8 7 27 6 10 36 80 99 1236 840 423 299 180 173 137 200 223 148 62 1119 605 200 185 229 718 370 280 112 519 318 132 166 138 591 254 67 123 162 UNITS3 73 79 133 188 148 161 133 120 90 82 129 119 91 51 55 56 141 90 138 71 93 69 74 86 80 81 43 51 64 74 69 76 59 77 58 76 51 64 48 30 47 44 34 32 40 63 36 53 48 51 59 72 UNITS4 30 60 85 86 68 80 94 121 95 69 104 68 72 52 78 106 129 82 124 130 192 186 78 52 62 51 45 66 96 109 88 96 58 72 51 51 37 71 28 48 45 49 69 59 57 48 35 44 36 16 36 28 PRICE1 FEAT1 4.1334 4.0569 4.13 4.13 4.13 4.13 3.3397 3.3654 3.38 3.4016 3.4463 3.4445 3.5531 3.5222 3.4155 3.38 3.38 3.38 3.5943 3.9871 4.1199 4.13 4.0515 4.13 4.13 3.9842 4.1156 4.13 3.5764 3.505 3.4576 3.6586 3.5252 3.4044 3.38 3.38 3.3769 3.417 3.9104 4.0855 4.116 4.1243 4.166 4.1744 4.1652 4.1692 4.19 3.5256 3.2501 3.2211 3.1915 3.183 Page 1 DISP1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0.8029 0.8615 0.8058 0 0 0 0 0 0 0.5507 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9826 0.9923 0.7315 0.508 PRICE2 3.454 3.4641 3.6794 4.1026 4.13 4.13 4.13 4.13 4.13 2.9105 2.912 3.2982 4.035 3.7267 4.0775 4.13 4.13 4.13 4.13 4.13 3.6808 3.1381 2.685 2.7534 2.893 2.8619 2.8729 2.8951 2.9218 2.8238 2.7316 3.063 3.3316 2.4011 2.442 3.048 3.0195 3.0191 2.5645 2.7212 2.7999 3.2188 2.8963 2.9475 2.9986 3.1411 3.2748 2.8141 2.8159 3.0043 3.0118 2.9962 qattachments_41add3ef502efd32b9e729b2c748b274fd792621.xlsx 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 530 404 211 432 226 199 200 159 132 232 243 186 102 140 101 121 192 59 74 60 51 49 71 76 100 110 98 39 77 61 74 64 46 139 69 69 69 44 68 128 48 75 61 56 48 94 56 72 74 80 76 117 91 94 64 54 46 54 52 61 27 41 78 33 51 39 39 42 58 309 934 299 156 100 89 57 69 84 42 30 36 44 26 33 45 75 35 40 24 22 26 27 Page 2 3.1774 3.2264 3.2935 3.2902 3.3519 3.5207 3.353 3.3713 3.5023 3.3346 3.1215 3.3352 3.5179 3.5418 3.5648 3.5987 3.4752 3.4756 3.592 3.5025 3.5635 3.642 3.6228 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.4035 0.5687 0.9352 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.9842 3.006 3.0245 3.0839 3.1336 3.429 3.6064 3.5008 3.6088 3.7465 3.5123 3.2625 3.2161 3.2045 3.19 3.19 3.1978 3.19 3.19 3.19 3.19 3.19 3.19 qattachments_41add3ef502efd32b9e729b2c748b274fd792621.xlsx FEAT2 DISP2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9932 0.4624 0 0 0 0 0 0 0 0 0 0 0.9806 0.8131 0.5319 0.4649 0 0 0 0 0 0 0 0.9401 0 0.675 0.6378 0 0.3231 0.3216 0 0 0 0 0 0 0 0.3807 0 0 0 0 PRICE3 PRICE4 FEAT4 4.1763 4.104 4.0872 3.6733 3.9834 3.8773 3.4957 2.9858 3.38 2.9965 3.38 2.9 3.38 3.1355 3.38 2.8288 3.38 2.8007 3.5538 2.8228 3.8102 3.9775 4.0922 3.7409 4.1135 3.7131 4.0586 3.9687 4.068 4.0014 4.13 4.0719 4.1247 4.0681 4.13 3.9622 4.13 3.3203 4.13 3.1268 4.13 3.0608 4.13 3.0775 4.13 3.3915 4.13 3.9138 3.8878 3.9471 3.4685 3.7778 3.7058 3.5149 3.6829 3.3079 4.13 3.4217 4.13 3.3961 4.13 3.4141 4.13 3.2863 4.13 3.2583 4.13 3.1486 4.13 3.3575 4.13 3.4943 4.13 3.8597 4.1703 4.1469 4.165 4.0129 4.3317 4.2127 4.4657 4.07 4.2905 3.4031 4.3729 3.232 4.0747 3.1197 4.1555 3.1525 4.1605 3.2017 4.1583 3.134 4.1674 3.1327 4.1713 3.2083 4.0282 3.1188 3.802 3.1161 3.5075 3.1357 DISP4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Page 3 0 0 0 0 0 0 0 0.8029 0.8615 0.8058 0 0 0 0 0 0 0.5507 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9826 0.9923 0.7315 0.508 FEAT3 DISP3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 qattachments_41add3ef502efd32b9e729b2c748b274fd792621.xlsx 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.4491 3.3913 3.4068 3.4396 3.44 3.499 3.6559 4.1319 4.1553 4.1644 4.1665 4.1644 4.1692 4.187 4.19 4.19 4.19 4.19 4.19 4.19 4.19 4.19 4.19 3.0029 3.1342 3.0886 3.0262 3.1485 3.2529 3.5058 3.5088 3.8686 4.19 4.19 4.0511 3.6218 3.4977 3.493 3.4789 3.47 3.19 3.365 3.6483 3.2355 3.2285 3.3011 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Page 4 1 0.4035 0.5687 0.9352 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Regression analysis 1) Formulate (Be very specific) and estimate models (log-log model or hybrid models only) to compute and compare own price elasticities for all the brands. Comment on the results. From the results, the price elasticities for the brands are; i. Brand 1= -6.2818 ii. Brand 2= -6.5104 iii. Brand 3= -2.7691 iv. Brand 4= -1.5177 2) Do the results have face validity (hint: look at summary statistics of the four brands)? Why? What are the managerial implications that one can infer from your results? Be specific From the summary statistics, we can see that quantity and price vary significantly. We can therefore conclude that the results have a face validity. The results can help in decision making. 3) Consider the best models (for Tide and Wisk only) from Part A. For these two focal brands, how can you improve the models to account for the effect of competition (which is the effect of price of a competing brand on the sales of a focal brand)? Formulate models (by extending the best fitting models from Question 1) for Tide and Wisk. Estimate and interpret the models. From the models for tide and Wisk, the best model is that of the Wisk. This is because the variables are strongly related compared to that of tide model. The models are; i. Tide Ln (UNITS1) =12.8771-6.2818Ln (PRICE1) ii. Wisk Ln (UNITS2) =12.3575-6.5104Ln (PRICE2) From the models, the coefficients are the price elasticities. 4) The above models are based on analysis of aggregate data. What are the advantages and limitations of working with aggregate data? From a marketing manager's perspective, elaborate on how disaggregate data can be useful in understanding the effect of a price cut on sales. Be specific. What are the advantages and limitations of working with disaggregate data? Explain with an example. The data is easier to analyze and interpret the results. This becomes the wider advantage of aggregate data. One shortcoming for this a data is that it becomes difficult for making conclusions on a single level analysis. Disaggregated data is very important when carrying out forecasting. However, it cannot be used for trend analysis. Regression analysis 1) Formulate (Be very specific) and estimate models (log-log model or hybrid models only) to compute and compare own price elasticities for all the brands. Comment on the results. From the results, the price elasticities for the brands are; i. Brand 1= -6.2818 ii. Brand 2= -6.5104 iii. Brand 3= -2.7691 iv. Brand 4= -1.5177 2) Do the results have face validity (hint: look at summary statistics of the four brands)? Why? What are the managerial implications that one can infer from your results? Be specific From the summary statistics, we can see that quantity and price vary significantly. We can therefore conclude that the results have a face validity. The results can help in decision making. 3) Consider the best models (for Tide and Wisk only) from Part A. For these two focal brands, how can you improve the models to account for the effect of competition (which is the effect of price of a competing brand on the sales of a focal brand)? Formulate models (by extending the best fitting models from Question 1) for Tide and Wisk. Estimate and interpret the models. From the models for tide and Wisk, the best model is that of the Wisk. This is because the variables are strongly related compared to that of tide model. The models are; i. Tide Ln (UNITS1) =12.8771-6.2818Ln (PRICE1) ii. Wisk Ln (UNITS2) =12.3575-6.5104Ln (PRICE2) From the models, the coefficients are the price elasticities. 4) The above models are based on analysis of aggregate data. What are the advantages and limitations of working with aggregate data? From a marketing manager's perspective, elaborate on how disaggregate data can be useful in understanding the effect of a price cut on sales. Be specific. What are the advantages and limitations of working with disaggregate data? Explain with an example. The data is easier to analyze and interpret the results. This becomes the wider advantage of aggregate data. One shortcoming for this a data is that it becomes difficult for making conclusions on a single level analysis. Disaggregated data is very important when carrying out forecasting. However, it cannot be used for trend analysis