1. Each catalog costs approximately $2 to mail (including printing, postage, and mailing costs). Estimate the...

 

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1. Each catalog costs approximately $2 to mail (including printing, postage, and mailing costs). Estimate the gross profit that the firm could expect from the remaining 180,000 names if it selects them randomly from the pool. 2. Develop a model for classification of a customer as a purchaser or non- purchaser. a. Partition the data into training data on the basis of the partition vari- able, which has 800 t's, 700 v's, and 500 s's (training data, validation data, and test data, respectively) assigned randomly to cases. b. Using the "best-subset" option in logistic regression, implement the full logistic regression model, select the best subset of variables, then implement a regression model with just those variables to classify the data into purchasers and nonpurchasers. (Logistic regression is used because it yields an estimated "probability of purchase," which is required later in the analysis.) 3. Develop a model for predicting spending among the purchasers. a. Make a copy of the data sheet (call it data2), sort by the "Purchase" variable, and remove the records where Purchase = 0 (the resulting spreadsheet will contain only purchasers). b. Partition this dataset into training and validation partitions on the basis 1. Each catalog costs approximately $2 to mail (including printing, postage, and mailing costs). Estimate the gross profit that the firm could expect from the remaining 180,000 names if it selects them randomly from the pool. 2. Develop a model for classification of a customer as a purchaser or non- purchaser. a. Partition the data into training data on the basis of the partition vari- able, which has 800 t's, 700 v's, and 500 s's (training data, validation data, and test data, respectively) assigned randomly to cases. b. Using the "best-subset" option in logistic regression, implement the full logistic regression model, select the best subset of variables, then implement a regression model with just those variables to classify the data into purchasers and nonpurchasers. (Logistic regression is used because it yields an estimated "probability of purchase," which is required later in the analysis.) 3. Develop a model for predicting spending among the purchasers. a. Make a copy of the data sheet (call it data2), sort by the "Purchase" variable, and remove the records where Purchase = 0 (the resulting spreadsheet will contain only purchasers). b. Partition this dataset into training and validation partitions on the basis

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1 To estimate the gross profit from the remaining 180000 names we need additional information Specifically we need to know the conversion rate of reci... View the full answer