1. The problem involves interpreting diagnostic plots from a linear regression model where the dependent variable is $\log(\text{price})$ and the predictors are mileage and year. 2. The Residuals vs Fitted plot (top-left) helps check for non-linearity, unequal error variances, and outliers. Ideally, residuals should be randomly scattered around zero without patterns. 3. The Q-Q Residuals plot (top-right) assesses if residuals follow a normal distribution. Points should lie approximately on the diagonal line. 4. The Scale-Location plot (bottom-left) shows if residuals have constant variance (homoscedasticity). A horizontal line with equally spread points is ideal. 5. The Residuals vs Leverage plot (bottom-right) identifies influential points that might disproportionately affect the model. Points with high leverage and large residuals are potential outliers. 6. Points labeled 16461, 166700, and 116060 appear in multiple plots, indicating they may be influential or outliers. 7. Interpretation summary: Check if residuals are randomly scattered, normally distributed, and have constant variance. Investigate points 16461, 166700, and 116060 for influence or outlier status. This analysis helps validate the assumptions of the linear regression model and identify data points that may need further attention.