Based on the uploaded scatterplots, let’s match the correlation coefficient $r$ values:

1. Scatterplot 1: Displays a strong negative correlation (points tightly clustered in a downward slope). This corresponds to $r = -0.996$.

2. Scatterplot 2: Displays a strong positive correlation (points tightly clustered in an upward slope). This corresponds to $r = 0.996$.

3. Scatterplot 3: Displays no clear trend or pattern, indicating a very weak correlation. This corresponds to $r = 0.293$.

4. Scatterplot 4: Shows a moderate negative correlation (points loosely clustered in a downward slope). This corresponds to $r = -0.678$.

5. Scatterplot 5: Shows a perfect positive correlation (points form a straight upward line). This corresponds to $r = 1$.

If you have further questions or would like clarifications, feel free to ask!

Here are 5 relative questions for expansion:

1. What factors influence the strength of a correlation coefficient?
2. How do we interpret the values of $r$ in terms of strength and direction?
3. Can a scatterplot with a weak correlation still have significant patterns in the data?
4. How is the correlation coefficient affected by outliers in the dataset?
5. In real-world scenarios, what are examples of data that might produce perfect correlations?

Tip: When estimating $r$, remember that tighter clustering of points along a line (positive or negative) indicates a stronger correlation.