Consider the 2013 declined loan data from LendingClub titled RejectStatsB2013. Similar to the analysis done in the chapter, lets scrub the debt-to-income data. Because our analysis requires risk scores, debt-to-income data, and employment length, we need to make sure each of them has valid data.

a. Sort the file based on debt-to-income and remove those observations (the complete row or record) that have a missing score, a score of zero, or a negative score.

b. Assign each valid debt-to-income ratio into three buckets (labeled DTI bucket) by classifying each debt-to-income ratio into high (>20 percent), medium (1020 percent), and low (<10 percent) buckets. Consider using if-then statements to complete this. Or sort the row and manually input.

c.Run a PivotTable analysis that shows the number of loans in each DTI bucket. Any interpretation of why these loans were declined based on debt-to-income ratios?