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For Problems $9,10,$ and $11,$ we will be cleaning a data file in preparation for subsequent analysis.

The analysis performed on LendingClub data in the chapter was for the years $20072012.$ We now use LendingClub data for $2013.$

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.

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.

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 $10.$

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 $9.$

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?

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.