Consider the 2013 rejected loan data from LendingClub titled “DAA Chapter 1-2 Data”. To prepare the dataset for analysis, let’s scrub the risk score data. First, because our analysis requires risk scores, debt-to-income data, and employment length, we need to make sure each of them has valid data. Open the file in Excel. Sort the file based on risk score and remove those observations (the complete row or record) that have a missing score or a score of zero, if needed. Assign each risk score to a risk score bucket similar to the chapter. That is, classify the sample according to this breakdown into excellent, very good, good, fair, poor, and very bad credit according to their credit score noted in Exhibit 1-13. Classify those with a score greater than 850 as “Excellent.” Consider using nested if–then statements to complete this. Or sort by risk score and manually input into appropriate risk score buckets. Run a PivotTable analysis that shows the number of loans in each risk score bucket. Required: After removing the observations with a zero or missing risk score, which group (Excellent, Very Good, Good, Fair, Poor, Bad) had the most rejected loans (most observations)? Which group had the least rejected loans (least observations)? Is it similar to Exhibit 1-14 performed on years 2007–2012?

FINANCIAL ACCOUNTING

10th Edition

ISBN:9781259964947

Author:Libby

Publisher:Libby

Chapter1: Financial Statements And Business Decisions

Section: Chapter Questions

Problem 1Q

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Consider the 2013 rejected loan data from LendingClub titled “DAA Chapter 1-2 Data”. To prepare the dataset for analysis, let’s scrub the risk score data. First, because our analysis requires risk scores, debt-to-income data, and employment length, we need to make sure each of them has valid data. Open the file in Excel. Sort the file based on risk score and remove those observations (the complete row or record) that have a missing score or a score of zero, if needed. Assign each risk score to a risk score bucket similar to the chapter. That is, classify the sample according to this breakdown into excellent, very good, good, fair, poor, and very bad credit according to their credit score noted in Exhibit 1-13. Classify those with a score greater than 850 as “Excellent.” Consider using nested if–then statements to complete this. Or sort by risk score and manually input into appropriate risk score buckets. Run a PivotTable analysis that shows the number of loans in each risk score bucket. Required: After removing the observations with a zero or missing risk score, which group (Excellent, Very Good, Good, Fair, Poor, Bad) had the most rejected loans (most observations)? Which group had the least rejected loans (least observations)? Is it similar to Exhibit 1-14 performed on years 2007–2012?

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