Data Analytics For Accounting
Data Analytics For Accounting
19th Edition
ISBN: 9781260375190
Author: RICHARDSON, Vernon J., Teeter, Ryan, Terrell, Katie
Publisher: Mcgraw-hill Education,
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Chapter 1, Problem 6P

Consider the 2013 declined loan data from LendingClub titled “RejectStatsB2013.” Similar to the analysis done in the chapter, let’s 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.

  1. 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.
  2. 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 (10–20 percent), and low (<10 percent) buckets. Consider using if-then statements to complete this. Or sort the row and manually input.
  3. 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?
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