As part of the quarterly reviews, the manager of a retail store analyzes the quality of customer service based on the periodic customer satisfaction ratings (on a scale of 1 to 10 with 1 = Poor and 10 = Excellent). To understand the level of service quality, which includes th waiting times of the customers in the checkout section, he collected the data shown below on 100 customers who visited the store. Customer Customer Wait Time Purchase Customer Satisfaction Number (min) Amount ($) Age Rating 2.3 436 42 2.8 408 33 CO N 3.2 432 38 3.4 431 40 3.4 456 29 4.2 537 46 3.2 456 42 1.4 430 40 6.4 663 24 10 7.8 839 37 11 6.5 659 52 12 9.8 836 43 13 5 543 56 14 1.8 419 35 15 6.1 700 39 16 3.4 432 44 17 7.8 845 33 18 2.8 467 42 19 1.2 425 46 20 9.5 848 50 21 8.2 808 55 22 7.6 674 35 23 5.4 547 52 24 6.7 691 38 25 9.6 847 5386 9.4 845 48 87 4.9 534 36 88 7.1 693 44 4 89 5.4 512 39 3 90 6.7 665 49 5 91 8.6 825 36 92 4.5 548 30 93 6.1 704 31 94 5.3 509 31 95 6.7 672 35 96 B.1 824 36 4 97 6.3 632 30 98 7.4 689 35 2 99 8.8 839 50 100 9.6 847 35 a. Using the data given, apply hierarchical clustering with five clusters using Wait Time (min) and Customer Satisfaction Rating as variables. Be sure to Normalize input data in Step 2 of the XLMiner Hierarchical Clustering procedure and specify single linkage as the clustering method. Analyze the resulting clusters by computing the cluster size. It may be helpful to use a PivotTable on the data in the HC_Clusters worksheet generated by XLMiner to compute descriptive measures of the Wait Time and Customer Satisfaction Rating variables in each cluster. You can also visualize the clusters by creating a scatter plot with Wait Time (min) as the X-variable and Customer Satisfaction Rating as the y-variable. b. Repeat part a using average linkage as the clustering method. Compare the clusters to the previous method