To interpret the given scatterplot based on your description, we can identify both patterns of clustering and potential outliers as follows:
Patterns of Clustering:
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Tightly Packed Cluster: There is a significant concentration of data points between temperatures of 30°F to 37°F and coffee sales of 42 to 48 cups. This indicates that at this temperature range, a moderate number of coffee cups are being sold, suggesting that this temperature may be ideal for coffee sales.
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Loosely Packed Cluster: Another cluster is observed between temperatures of 40°F to 51°F, with coffee sales ranging from 30 to 38 cups. The data points in this range are more spread out compared to the previous cluster, which may imply that coffee sales are more variable at higher temperatures compared to the lower range.
Outliers:
- Outlier Identification: There is one data point that appears to be an outlier, specifically at 62°F on the horizontal axis and 5 cups sold on the vertical axis. This point stands apart from the rest of the data. It suggests that even though the temperature is relatively high, very few cups of coffee were sold, which could be an atypical observation that deviates significantly from the general trend indicated by the other data points.
Conclusion:
The scatterplot illustrates a clear relationship between the temperature and the number of coffee cups sold, with clusters indicating preferred selling temperatures. However, the significant outlier highlights an exception that may warrant further investigation, as it does not fit the pattern established by the majority of the data. Understanding these patterns can help make better predictions about coffee sales based on temperature.