Complete SAS Programming Guide - Learn SAS and Become a Data Ninja - Bivariate Analysis

The video tutorial covers bivariate analysis, focusing on how predictor variables correlate with a dependent variable, specifically loan status. It examines categorical variables like gender and marital status, and numeric variables such as income and loan amount, to understand their impact on loan approval. The analysis reveals insights into approval trends based on these variables.

What is the primary focus of bivariate analysis in the context of loan approval?

• To calculate the average loan amount

• To identify the highest income bracket

• To compare the proportions of different categories

• To determine the total number of applicants

Which group of applicants is more likely to have their loans approved according to the categorical analysis?

• Married applicants

• Self-employed applicants

• Single applicants

• Applicants with no dependents

What surprising finding was observed regarding applicant income and loan approval?

• Higher income always leads to higher approval rates

• Higher income does not necessarily mean higher approval rates

• Lower income applicants are more likely to be approved

• Income has no impact on loan approval

Why might co-applicant income appear to have a high approval rate when it is low?

• Most applicants have high co-applicant income

• Co-applicant income is not considered in approval

• Many applicants do not have co-applicants

• Co-applicant income is always zero

What is the relationship between loan amount and approval likelihood?

• Loan amount does not affect approval

• Higher loan amounts are more likely to be approved

• Lower loan amounts are more likely to be approved

• Approval is based solely on income