Refer to the previous exercise for a description of the problem and data set.
a. Based on his past experience, Jerry knows that whether the individual has a credit card or not has nothing to do with his or her travel plans and would like to remove this variable. Remove the variable CreditCard from the data set.

b. In order to better understand his customers with high incomes, Jerry wants to create a subset of the data that only includes customers with annual incomes higher than $75,000 and who plan to travel within the next year. Subset the data to build the list of customers who meet these criteria. How many observations are in this subset?
c. Return to the original data set. Use the imputation strategy to handle missing values. If there are missing values for the FoodSpend variable, impute the missing values using the mean of the variable. If there are missing values for the Income variable, impute the missing values using the median of the variable. What are the average values of FoodSpend and Income after imputation?

Data from Exercises 26

Jerry Stevenson is the manager of a travel agency. He wants to build a model that can predict whether or not a customer will travel within the next year. He has compiled a data set that contains the following variables: whether the individual has a college degree (College), whether the individual has a credit card (CreditCard), annual household spending on food (FoodSpend in $), annual income (Income in $), and whether the customer has plans to travel within the next year (TravelPlan, 1 = have travel plans; 0 = do not have travel plans). A portion of the Travel_Plan data is shown in the accompanying table.

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College Credit Card Yes No ⠀ No No No ⠀ Yes FoodSpend 1706.89 2892.9 ⠀ 2617 Income 49412 55416 ⠀ 50900 TravelPlan 1 0 ⠀ 0