What needed to be understood.
Investment behavior reflected several competing household pressures. The analysis needed to separate salary effects from children, mortgage obligations, and debt.
How the analysis was built.
- Explored 194 household records using descriptive statistics and scatterplots.
- Calculated correlations between investing and each predictor.
- Built a multiple-regression model using salary, children, debt, and mortgage.
- Translated statistical results into audience-focused financial-education recommendations.
What the evidence showed.
- Salary showed a positive relationship with retirement investing.
- Children and debt showed negative relationships of similar magnitude in the submitted analysis.
- Mortgage also had a negative but weaker relationship.
- The multiple-regression model explained about 45% of variation in the sample.
What should happen next.
- Target education and simplified contribution options toward lower-income and high-debt households.
- Promote early, small, recurring contributions rather than all-or-nothing messaging.
- Use additional variables such as age, employer match, and financial literacy in future models.
What this project does—and does not—prove.
The analysis identified association, not causation, and the sample contained 194 couples rather than a representative population.