To determine the correct line of best fit in this image, we need to identify which line best represents the trend of the data points. A line of best fit should:

1. Minimize the overall distance (residuals) between the data points and the line.
2. Reflect the overall direction (slope) of the data.

Here’s a quick analysis of each line:

• Line A seems to have an upward slope, which doesn’t match the overall downward trend of the data points.
• Line B has a downward slope and closely passes through or near the center of the data points, suggesting it fits the general trend.
• Line C also has a downward slope, but it appears too steep and doesn’t pass close to most of the data points.

Thus, the best answer is B, as Line B most accurately represents the line of best fit for this set of data.

Would you like more details on the calculation or reasoning? Here are some related questions to deepen your understanding:

1. What is the mathematical formula for a line of best fit?
2. How do we calculate the residuals for a given line?
3. What is the least squares method used for?
4. Can a line of best fit ever perfectly match all data points?
5. How do outliers affect the line of best fit?

Tip: The line of best fit balances the distances between the data points and minimizes errors in prediction, making it a useful tool in regression analysis.