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As a direct marketer of specialty books, the BookBinders Book Club has achieved steady growth in their customer base. Yet while sales have grown steadily, profits began falling when the database got larger and when the company diversified its book selection and increased the number of offers sent to customers. The falling profits have led Dave Lawton, BookBinders’ marketing director, to experiment with different database marketing approaches in order improve BookBinder’s mailing yields and profits. Dave began a series of live market tests, each involving a random sample of customers from the database. An offer for the current book selection is sent to the sample and then the sample customers’ responses, either purchase or no purchase, are recorded and used to calibrate a response model for the current offering. The response model’s results are then used to “score” the remaining customers in the database and select customers from the full customer database for the ‘rollout’ mailing campaign. Dave’s team continues to debate the best method that will best identify potential targets for its new book, ‘The Art History of Florence’. The team is discussing whether to develop a predictive model using Logistic Regression Analysis.Dave has a dataset containing the responses of a random sample of 50,000 customers to “The Art History of Florence” campaign. He is eager to assess the potential value of a predictive model in predicting customer response and has asked you to complete the following analysis.