If a scatter plot shows points that are closely clustered together, what can you infer about the correlation between the variables?

When a scatter plot displays points that are closely clustered together, it typically indicates a **strong correlation** between the two variables being analyzed. Here's what you can infer based on the clustering pattern: 1. **Direction of the Correlation:** - **Positive Correlation:** If the tightly clustered points slope upwards from left to right, it suggests that as one variable increases, the other variable also tends to increase. - **Negative Correlation:** If the cluster slopes downwards from left to right, it indicates that as one variable increases, the other tends to decrease. 2. **Strength of the Correlation:** - **Tight Clustering:** Indicates a **strong correlation**, meaning the relationship between the variables is consistent and predictable. - **Loose Clustering:** Would suggest a weaker correlation, where the variables do not move together as consistently. 3. **Nature of the Relationship:** - **Linear Relationship:** If the clustered points form a pattern that closely follows a straight line (either ascending or descending), the correlation is likely linear and strong. - **Non-Linear Relationship:** If the points are tightly clustered but follow a curvilinear pattern, there may be a strong non-linear relationship. **Key Takeaway:** Close clustering of points on a scatter plot signifies that there is a strong and consistent relationship between the two variables, with the direction (positive or negative) determined by the slope of the clustering pattern.