outlier

(noun)

a value in a statistical sample which does not fit a pattern that describes most other data points; specifically, a value that lies 1.5 IQR beyond the upper or lower quartile

Related Terms

Examples of outlier in the following topics:

  • Outliers

    • Estimators capable of coping with outliers are said to be robust.
    • Outliers can have many anomalous causes.
    • Outliers that cannot be readily explained demand special attention.
    • There is no rigid mathematical definition of what constitutes an outlier.
    • The application should use a classification algorithm that is robust to outliers to model data with naturally occurring outlier points.

  • Outliers

    • Outliers need to be examined closely.
    • It is possible that an outlier is a result of erroneous data.
    • Note: There is no rigid mathematical definition of what constitutes an outlier; determining whether or not an observation is an outlier is ultimately a subjective exercise.
    • Outliers can have many anomalous causes.
    • Outliers that cannot be readily explained demand special attention.

  • Types of outliers in linear regression

    • Outliers in regression are observations that fall far from the "cloud" of points.
    • In these cases, the outliers influenced the slope of the least squares lines.
    • It is tempting to remove outliers.
    • Don't ignore outliers when fitting a final model.
    • All data sets have at least one outlier.

  • Types of outliers in linear regression exercises

    • Identify the outliers in the scatterplots shown below, and determine what type of outliers they are.
    • Identify the outliers in the scatterplots shown below and determine what type of outliers they are.
    • What type of outlier is this observation?
    • 7.25 (a) The outlier is in the upper-left corner.
    • (c) The outlier is in the upper-middle of the plot.

  • Outliers

    • Outliers need to be examined closely.
    • It is possible that an outlier is a result of erroneous data.
    • Any points that are outside these two lines are outliers.
    • We call that point a potential outlier.
    • (Remember, we do not always delete an outlier. )

  • Examining the Central Limit Theorem

    • The uniform distribution is symmetric, the exponential distribution may be considered as having moderate skew since its right tail is relatively short (few outliers), and the log-normal distribution is strongly skewed and will tend to produce more apparent outliers.
    • There are two outliers, one very extreme, which suggests the data are very strongly skewed or very distant outliers may be common for this type of data.
    • Strong skew is often identified by the presence of clear outliers.
    • These data include some very clear outliers.
    • For example, outliers are often an indicator of very strong skew.

  • Student Learning Outcomes

  • Lab: Descriptive Statistics

    • Are there any potential outliers?
    • Use a formula to check the end values to determine if they are potential outliers.
    • Using the box plot, how can you determine if there are potential outliers?
    • How does the standard deviation help you to determine concentration of the data and whether or not there are potential outliers?

  • Lab 2: Regression (Textbook Cost)

    • Are there any outliers?
    • If so, which point(s) is an outlier?
    • Should the outlier, if it exists, be removed?

  • The normality condition

    • For example, ask: would I expect this distribution to be symmetric, and am I confident that outliers are rare?
    • Data with strong skew or outliers require a more cautious analysis.