1. **State the problem:** We need to determine the regression line, which is the line of best fit for a set of data points. 2. **Formula used:** The regression line is given by the equation $$y = mx + b$$ where $m$ is the slope and $b$ is the y-intercept. 3. **Calculate the slope $m$:** The formula for the slope is $$m = \frac{n\sum xy - \sum x \sum y}{n\sum x^2 - (\sum x)^2}$$ where $n$ is the number of data points, $\sum xy$ is the sum of the product of $x$ and $y$ values, $\sum x$ and $\sum y$ are the sums of $x$ and $y$ values respectively, and $\sum x^2$ is the sum of squares of $x$ values. 4. **Calculate the intercept $b$:** The formula for the intercept is $$b = \frac{\sum y - m \sum x}{n}$$. 5. **Interpretation:** The regression line minimizes the sum of squared vertical distances between the data points and the line. 6. **Summary:** To determine the regression line, you need the data points to compute the sums and then apply the formulas above to find $m$ and $b$, resulting in the equation $$y = mx + b$$. Since no data points were provided, this is the general method to find the regression line.