Outliers in scatter plots

Scatter plots often have a pattern. We call a data point an outlier if it doesn't fit the pattern.

Consider the scatter plot above, which shows data for students on a backpacking trip. (Each point represents a student.)

Notice how two of the points don't fit the pattern very well. These points have been labeled Brad and Sharon, which are the names of the students they represent.

Sharon could be considered an outlier because she is carrying a much heavier backpack than the pattern predicts.

Brad could be considered an outlier because he is carrying a much lighter backpack than the pattern predicts.

Key idea: There is no special rule that tells us whether or not a point is an outlier in a scatter plot. When doing more advanced statistics, it may become helpful to invent a precise definition of "outlier", but we don't need that yet.

To fully wrap our minds around why certain data points might be considered outliers, let's try a couple of practice problems.

Michelle was researching different computers to buy for college. She looked up the prices and quality ratings for a sample of computers. Her data is shown in the scatter plot to the right, where each point is a computer.

Some high school students in the U.S. take a test called the SAT before applying to colleges. The scatter plot to the right shows what percent of each state's college-bound graduates took the SAT in $2009\text{-}2010$‍, along with that state's average score on the math section.

The three labeled points could be considered outliers.