Credit Cards

Preregistration of analyses

Analysis #1

For this part we are going to make a scatter plot using the numerical variables (income and age) and how many months due each individual applicant was on their credit card payments. Ideally for the first plot, the x-axis will be income and the y-axis would be the months. For the second plot, the age would go on the x-axis and the months would be the y-axis once more. From both of these plots, we would hopefully create a linear regression model to show the linear relationship between income and age to the months overdue and see whether there is a relationship or not.

Analysis #2

We will then create another analysis using 3 new categorical variables: Education Level, Gender, and Marital Status. We will be interpreting their effects on whether they pay their credit card dues on time or late. We can explore this by having different graphs such as histograms or bar graphs with these variables and the overdue/on-time dependent categorical variable. We can analyze the trends of whether any one of these variables has a greater effect on likelihood of paying credit card dues on time or not, which may be significant and will play a role in our conclusions. This is our second analysis!