Loans Data
We will consider data about loans from the peertopeer lender, Lending Club. The loan data includes terms of the loan as well as information about the borrower. The outcome variable we would like to better understand is the interest rate assigned to the loan. For instance, all other characteristics held constant, does it matter how much debt someone already has? Does it matter if their income has been verified?

 interest – Interest rate of the loan the applicant received.
 ccheck – Number of credit checks in the last 12 months.
 bankrupt/brupt – An indicator variable for whether the borrower has a past bankruptcy in their record. This variable takes on the value of 1 if the answer is “yes” and a 0 if the answer is “no”.
 term : The length of the loan, in months.
 cutil: (credit utilized) Of all the credit available to the borrower, what fraction are they utilizing. For example, the credit utilization on a credit card would be the card’s balance divided by the card’s credit limit.
 verified: (income verified status) Categorical variable describing whether the borrower’s income source and amount have been verified, with levels verified, source only, and not.
Questions
Import the loans data set.
Question 1: Construct a model that allows you to assess the relationship between interest rate (the response) and credit utilized (the explanatory). Is there a significant relationship between credit utilization and interest rate?
Question 2: Using your model from Question 1 – What is the estimated interest rate for someone who utilizes 50% of their available credit?
Question 3: Construct a model that allows you to assess the relationship between interest rate (the response) and bankruptcy (the explanatory). How does the average interest rate vary between those who have a history of bankruptcy and those who do not?
Question 4: Using your model from Question 3 – Does bankruptcy seem to contribute to the interest rate of loan?
Question 5: Construct a model that allows you to assess the relationship between interest rate (the response) and income verified status (the explanatory). Those who have their income verified are expected to have an interest rate that is ______ percentage points higher than someone whose income is __________.
Question 6: Using your model from Question 5 – What is the predicted interest rate for someone whose income is not verified?
Question 7: Construct a model attempts to predict interest rate (the response) from all available covariates. Which covariates are significantly related to interest rate of loan?
Question 8: Based on your model in Question 7, what can be said about how number of credit checks relates to interest rate of loan?