1. **Problem Statement:** We have odometer readings (independent variable $x$) and retail values (dependent variable $y$). We want to find the least-squares regression line, predict a value, find the correlation coefficient, and interpret it. 2. **Least-Squares Line Formula:** The regression line is given by $$\hat{y} = b_0 + b_1 x$$ where $b_1$ is the slope and $b_0$ is the intercept. 3. **Given:** The least-squares line is $$\hat{y} = 54428.01 - 0.16x$$ rounded to two decimal places. 4. **Prediction:** For $x=30000$, substitute into the equation: $$\hat{y} = 54428.01 - 0.16 \times 30000 = 54428.01 - 4800 = 49628.01$$ Rounded to the nearest 100, this is $49600$. 5. **Correlation Coefficient:** Given as $r = -0.9926$, rounded to four decimal places. 6. **Interpretation:** A negative $r$ means as odometer reading increases, retail value decreases. The strong magnitude near 1 indicates a strong negative linear relationship. **Note:** The user’s prediction value $6400$ seems inconsistent with the regression line; based on the line, the predicted retail value at 30000 miles is approximately $49600$.