Final answer:
Outliers are observed values that are significantly different from the other data points and may hold valuable information or signify errors in a dataset. They can be identified by being far from the least squares regression line.
Step-by-step explanation:
An outlier is an observed value that is far from the least squares regression line. A rule of thumb is that a point more than two standard deviations of the residuals from its predicted value on the least squares regression line is an outlier.
A potential outlier is a data point that is significantly different from the other data points. These special data points may be errors or some kind of abnormality, or they may be a key to understanding the data.
Identifying outliers can be done by looking at the scatter plot and using the guideline that any point located farther than two standard deviations above or below the best-fit line can be considered an outlier.