Model

This prediction was based on a formula that weighed factors such as age, income, education level, mortgage status, and whether the personal loan was accepted or not. The largest factors influencing loan acceptance were income and education level, with higher income and education levels increasing the likelihood of loan acceptance.

Formula

\[ Personal.Loan_i = \beta_0 + \beta_1 \cdot Age_i + \beta_2 \cdot Income_i + \beta_3 \cdot Education_i + \beta_4 \cdot Mortgage_i + \beta_5 \cdot Treatment_i + \epsilon_i \]

where (Personal.Loan_i) is the likelihood of accepting a personal loan for individual (i), (Age_i) is the age of individual (i), (Income_i) is the income of individual (i), (Education_i) is the education level of individual (i), (Mortgage_i) is the mortgage status of individual (i), and (Treatment_i) is the treatment variable indicating if the loan was accepted or not.

After fitting the model, we generate a summary table of the fixed effects to understand the influence of each variable on the likelihood of accepting a personal loan.

While this model provides insights into factors affecting personal loan acceptance, it is important to note the limitations and consider other potential variables not included in this analysis. Factors such as credit history and employment status could further enhance the model’s predictive accuracy.