In this assignment, you will practice using the regression algorithm to create a model for explaining the interest rate for home loans that were granted in the state of Illinois.

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Overview

In this assignment, you will practice using the regression algorithm to create a model for explaining the interest rate for home loans that were granted in the state of Illinois.

Instructions

1. (1 point) Create a code chunk to complete the following tasks:

a. Load the following packages: tidyverse, magrittr, lubridate, and corrplot. (You may need to install those packages if you have not already done so.)

b. Read in the hmdaInterestRate.rds file.

c. Report the structure of the dataframe. (No need to comment on the structure.)

2. (1 point) Data preparation:

a. Replace the values in the following columns with the same value divided by 1,000: loan_amount, property_value, and income. (This will make it easier to see the impact on the interest rate.)

b. Create a new column, ltp, that is equal to the values in the loan_amount column divided by the values in the property_value column.

c. Filter the data to keep observations for which income is less than 300 (i.e., $300,000).

d. Report a summary of all columns. (No need to comment on the summary of the columns.)

3. (2 points) Create a correlation plot of the following columns: interest_rate, ltp, income, applicant_age, property_value, and loan_amount.

a. Below the plot, identify what variable has the strongest negative correlation with interest_rate. Comment on what might explain why that correlation is negative.

4. (2 points) Regress interest rate on ltp. Interpret the coefficient estimate on ltp.

5. (2 points) Regress interest rate on ltp and loan_amount. Comment on the change in the adjusted R-squared, as well as the change in the coefficient on ltp.

6. (2 points) Regress interest rate on ltp, loan_amount, and aus_1. Interpret the new coefficients.

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