Final answer:
A point on a residual plot represents how much the actual arm span of a student deviates from the arm span predicted by the regression line for a given height. Without more context, we cannot determine if the point is an outlier, as it requires the point to be more than two standard deviations away from the predicted value.
Step-by-step explanation:
When interpreting a point on a residual plot in the context of measuring student heights and arm spans, a point at given coordinates represents the difference between the actual arm span and the value predicted by the regression line for a given height. Specifically, this point on the residual plot does not represent the arm span itself (predicted, average, maximum, or outlier). Therefore, the correct interpretation of this point is:
The point represents how much the actual arm span of a student deviates from the arm span predicted by the regression line given their height.
Identifying Outliers
In regression analysis, an outlier can significantly affect the slope and correlation coefficient of the regression line. To identify outliers, we can use a rule of thumb that flags any point more than two standard deviations of the residuals from its predicted value as an outlier. If a coordinate is mentioned on the residual plot that fits this criterion, then it would represent an outlier in the data. However, without specific values or a context indicating that this point is beyond two standard deviations, we cannot immediately conclude it is an outlier from the statement alone.