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Consider the 2013 rejected loan data from LendingClub titled "DAA Chapter 1-2 Data." Similar to the analysis done in the chapter, let's scrub the employment length. 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 employment length and remove those observations (the complete row or record) that have a missing score ("NA"). Note that we are including the employment lengths of zero, different than the analysis in the chapter text. b. 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, similar to that done in Problem 10. c. 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, similar to that done in Problem 9. d. There should now be 669,993 observations. Any thoughts on what biases are imposed when we remove observations? Is there another way to do this? e. Run a PivotTable analysis to show the number of Excellent Risk Scores but High DTI Bucket loans in each Employment year bucket. Required: After completing the steps above, answer the following questions by inputting the corresponding employment level. Question Which employment year group had the most observations to go along with excellent risk scores but high debt-to-income? Which employment year group had the least observations to go along with excellent risk scores but high debt-to-income? Level employment years employment years

Consider the 2013 rejected loan data from LendingClub titled "DAA Chapter 1-2 Data." Similar to the analysis done in the chapter, let's scrub the employment length. 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 employment length and remove those observations (the complete row or record) that have a missing score ("NA"). Note that we are including the employment lengths of zero, different than the analysis in the chapter text. b. 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, similar to that done in Problem 10. c. 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, similar to that done in Problem 9. d. There should now be 669,993 observations. Any thoughts on what biases are imposed when we remove observations? Is there another way to do this? e. Run a PivotTable analysis to show the number of Excellent Risk Scores but High DTI Bucket loans in each Employment year bucket. Required: After completing the steps above, answer the following questions by inputting the corresponding employment level. Question Which employment year group had the most observations to go along with excellent risk scores but high debt-to-income? Which employment year group had the least observations to go along with excellent risk scores but high debt-to-income? Level employment years employment years

FINANCIAL ACCOUNTING
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." Similar to the analysis done in the chapter, let's scrub the employment length. 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 employment length and remove those observations (the complete row or record) that have a missing score ("NA"). Note that we are including the employment lengths of zero, different than the analysis in the chapter text. b. 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, similar to that done in Problem 10. c. 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, similar to that done in Problem 9. d. There should now be 669,993 observations. Any thoughts on what biases are imposed when we remove observations? Is there another way to do this? e. Run a PivotTable analysis to show the number of Excellent Risk Scores but High DTI Bucket loans in each Employment year bucket. Required: After completing the steps above, answer the following questions by inputting the corresponding employment level. Question Which employment year group had the most observations to go along with excellent risk scores but high debt-to-income? Which employment year group had the least observations to go along with excellent risk scores but high debt-to-income? Level employment years employment years
Transcribed Image Text:Consider the 2013 rejected loan data from LendingClub titled "DAA Chapter 1-2 Data." Similar to the analysis done in the chapter, let's scrub the employment length. 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 employment length and remove those observations (the complete row or record) that have a missing score ("NA"). Note that we are including the employment lengths of zero, different than the analysis in the chapter text. b. 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, similar to that done in Problem 10. c. 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, similar to that done in Problem 9. d. There should now be 669,993 observations. Any thoughts on what biases are imposed when we remove observations? Is there another way to do this? e. Run a PivotTable analysis to show the number of Excellent Risk Scores but High DTI Bucket loans in each Employment year bucket. Required: After completing the steps above, answer the following questions by inputting the corresponding employment level. Question Which employment year group had the most observations to go along with excellent risk scores but high debt-to-income? Which employment year group had the least observations to go along with excellent risk scores but high debt-to-income? Level employment years employment years
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