Question 2: Tayko Software is a software catalog firm that sells games and educational software. It...

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Question 2: Tayko Software is a software catalog firm that sells games and educational software. It started out as a software manufacturer and then added third-party titles to its offerings. It recently revised its collection of items in a new catalog, which it mailed out to its customers. This mailing yielded 2000 purchases. Based on these data, Tayko wants to devise a model for predicting the spending amount that a purchasing customer will yield. The file Tayko.csv contains information on 2000 purchases. Table below describes the variables to be used in the problem (the Excel file contains additional variables). Description of Variables for Tayko Software Example FREQ LAST_UPDATE WEB GENDER ADDRESS_RES ADDRESS_US Number of transactions in the preceding year Number of days since last update to customer record Whether customer purchased by Web order at least once Male or female Whether it is a residential address Whether it is a US address SPENDING (response) Amount spent by customer in test mailing (in dollars) a. Prepare the data to contain only the above listed variables. b. Explore the spending amount by creating a pivot table for the categorical variables and computing the average and standard deviation of spending in each category. c. Explore the relationship between spending and each of the two continuous predictors by creating two scatterplots (Spending vs. Freq, and Spending vs. last_update_days_ago. Does there seem to be a linear relationship? d. To fit a predictive model for Spending: i. ii. iii. Partition the 2000 records into training (75%) and validation (25%) sets. Fit at least 2 prediction models (e.g. linear regression, stepwise regression, neural networks, etc.) to the training data. Evaluate the predictive accuracy of the model by examining its performance on the validation set. iv. Discuss the predictive performance of both models. Question 2: Tayko Software is a software catalog firm that sells games and educational software. It started out as a software manufacturer and then added third-party titles to its offerings. It recently revised its collection of items in a new catalog, which it mailed out to its customers. This mailing yielded 2000 purchases. Based on these data, Tayko wants to devise a model for predicting the spending amount that a purchasing customer will yield. The file Tayko.csv contains information on 2000 purchases. Table below describes the variables to be used in the problem (the Excel file contains additional variables). Description of Variables for Tayko Software Example FREQ LAST_UPDATE WEB GENDER ADDRESS_RES ADDRESS_US Number of transactions in the preceding year Number of days since last update to customer record Whether customer purchased by Web order at least once Male or female Whether it is a residential address Whether it is a US address SPENDING (response) Amount spent by customer in test mailing (in dollars) a. Prepare the data to contain only the above listed variables. b. Explore the spending amount by creating a pivot table for the categorical variables and computing the average and standard deviation of spending in each category. c. Explore the relationship between spending and each of the two continuous predictors by creating two scatterplots (Spending vs. Freq, and Spending vs. last_update_days_ago. Does there seem to be a linear relationship? d. To fit a predictive model for Spending: i. ii. iii. Partition the 2000 records into training (75%) and validation (25%) sets. Fit at least 2 prediction models (e.g. linear regression, stepwise regression, neural networks, etc.) to the training data. Evaluate the predictive accuracy of the model by examining its performance on the validation set. iv. Discuss the predictive performance of both models.