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In the scatterplot showing the number of cups of coffee sold versus temperature, we can observe the following:
1. Outliers:
- There is a single data point located at 62 on the horizontal axis (temperature) and 5 on the vertical axis (number of cups of coffee sold). This data point is considered an outlier as it significantly deviates from the clustering of other data points. It is far from the main cluster of data points and may represent an unusual or extreme observation.
2. Patterns of Clustering:
- Tightly packed cluster:
A tightly packed cluster of data points is observed between 30 to 37 on the horizontal axis (temperature) and 42 to 48 on the vertical axis (number of cups of coffee sold). This tightly packed cluster suggests a strong correlation or relationship between temperature in this range and the number of cups of coffee sold. The data points in this range are closely grouped together, indicating a consistent pattern.
- Loosely packed cluster:
A loosely packed cluster of data points is seen between 40 to 51 on the horizontal axis (temperature) and 30 to 38 on the vertical axis (number of cups of coffee sold). The data points in this range are more spread out compared to the tightly packed cluster, suggesting a weaker correlation or relationship between temperature in this range and the number of cups of coffee sold. The variability in the data points indicates less consistency in the pattern compared to the tightly packed cluster.
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5. Overall Trend:
- Despite the presence of outliers and varying degrees of clustering, an overall upward trend can be observed in the scatterplot. As temperature increases, there is a general increase in the number of cups of coffee sold. This trend suggests that higher temperatures are associated with higher sales of coffee, indicating a positive relationship between these two variables. The outliers and different patterns of clustering provide additional insights into the nuances of this relationship.