A strong negative correlation in a scatter diagram appears as a tight cluster of points that slopes downward from left to right.

To elaborate, a scatter diagram is a graphical representation that illustrates the relationship between two variables. Each point on the graph corresponds to a pair of values. When we refer to a strong negative correlation, we indicate that as one variable increases, the other variable decreases in a consistent and predictable manner. This relationship is visually represented by the points forming a distinct downward slope.

For instance, consider plotting the heights and weights of a group of individuals. If taller individuals tend to weigh less, the points on the scatter diagram would start high on the left side (representing tall individuals with lower weights) and descend to the right side (representing shorter individuals with higher weights). The points would be closely clustered along this downward trend, indicating a strong negative correlation.

In mathematical terms, the correlation coefficient, commonly denoted as $r$, quantifies the strength and direction of this relationship. For a strong negative correlation, the value of $r$ would be close to $-1$. This indicates that the points are not randomly scattered but instead follow a clear, tight pattern that slopes downward.

Understanding scatter diagrams and correlations is crucial for analyzing data and identifying trends. For example, if you were investigating the relationship between study time and exam scores, a strong negative correlation would suggest that increased study time is associated with lower exam scores, potentially highlighting an issue that merits further investigation.