Consider the case where Apple computer tracks online transactions at its iStore and is interested in learning about the purchase patterns of its customers in order to provide recommendations as a customer browses its Web site. A sample of the shopping cart data in binary matrix format resides in the file AppleCart (posted on Questromtools in the Problem Set 2 folder.). Each row indicates which iPad features and accessories a customer selected. Using a minimum support of 10 percent of the total number of transactions and a minimum confidence of 50 percent, use XLMiner to generate a list of association rules.1 (Note: select all the data in the spreadsheet before clicking "Associate." Then, select "Data in binary matrix format.") a) Interpret the support count of the item set involved in the rule with the largest lift ratio. b) Interpret the confidence of the rule with the largest lift ratio. c) Interpret what the rule with the largest lift ratio is saying about the relationship between the antecedent item set and consequent item set.