Final answer:
Clusters are areas where data points are concentrated, and outliers are points that deviate notably from other data. A statistical analysis including scatter plots and standard deviation calculations would help identify these. The removal of outliers depends on context and reason for their existence.
Step-by-step explanation:
Upon examining the data that showcases the relationship between Sales ($) and Temperature, it is possible to determine if there are any clusters or outliers present within the dataset. To identify these, one may look for patterns or points that deviate significantly from the rest. Clusters would be indicated by concentrations of data points in certain temperature or sales ranges, while outliers would stand out as data points that do not fit the pattern established by the majority. Observations that show extremely high or low sales relative to the temperatures observed could be considered outliers. For instance, a sales value might be much higher or lower than what would be expected given a specific temperature compared to the rest of the data.
Statistical analysis, such as constructing a scatter plot or calculating statistical measures like the mean or standard deviation, can provide more insight into the presence of outliers or clusters. Whether any outliers should be removed from the analysis is a question that requires further investigation; it depends on the context and the potential reasons why those outliers are present, considering factors such as data errors, unusual events, or anomalies that are relevant to the study.