Final answer:
A wrongly classified point that lies at least twice the standard deviation away from the regression line is considered an outlier, indicating significant deviation from the predicted values.
Step-by-step explanation:
The distance of a wrongly classified point from the line of best fit in a data set indicates the degree to which the point deviates from expected values based on the model. Specifically, if this vertical distance is at least twice the standard deviation (2s), the point is considered a potential outlier, which may significantly influence the regression results. Identifying such outliers is crucial for accurate statistical analysis and is often done graphically using tools like a TI-83, 83+, or 84+ graphing calculator, where lines representing two standard deviations above and below the regression line (Y2 and Y3) help in this identification process. Points lying outside these lines are flagged as outliers, indicating that they are significantly farther than typical from the predicted values.