To determine which graph shows the line of best fit for the data, let's examine the alignment of each line in relation to the data points:

1. Top-left graph: The line has a strong negative slope and passes through the center of the data cluster, aligning well with the general downward trend. This seems to represent the data well.

2. Top-right graph: The line has a slight negative slope but doesn’t appear to match the downward trend of the data points as closely. It seems more horizontal than what the data suggests.

3. Bottom-left graph: This line also has a negative slope and is well-aligned with the data points, similar to the top-left graph. It seems like a good candidate as well.

4. Bottom-right graph: This line is nearly horizontal and does not capture the downward trend observed in the data points.

Based on these observations, the top-left or bottom-left graph shows the best fit because both lines have a negative slope that matches the downward trend in the data.

If forced to choose one, the top-left graph would be preferable due to a slightly stronger alignment with the entire range of points.

Would you like further details or have any questions?

Here are related questions for further exploration:

1. How is the line of best fit determined in statistical analysis?
2. What are some methods for calculating the slope of a line of best fit?
3. How does the line of best fit help in predicting values in a dataset?
4. What is the difference between interpolation and extrapolation using a line of best fit?
5. How can residuals be used to assess the accuracy of a line of best fit?

Tip: In general, the line of best fit should pass as close as possible to all points, minimizing the vertical distance (residuals) from each point to the line.