Outliers

Definition

Outliers are data points that differ significantly from other observations in a dataset. They can affect the results of statistical analyses, such as linear regression and correlation.

5 Must Know Facts For Your Next Test

1. Outliers can skew the results of a linear regression analysis by pulling the regression line toward themselves.
2. Identifying outliers often involves using statistical methods like calculating Z-scores or using the interquartile range (IQR).
3. Removing or keeping outliers should be justified by the context and purpose of your analysis.
4. Outliers can indicate variability in your data but may also suggest measurement error or data entry mistakes.
5. Correlation coefficients can be sensitive to outliers, potentially inflating or deflating the perceived strength of a relationship.

Review Questions

• How do outliers affect the slope and intercept of a regression line?
• What is one method for identifying outliers in a dataset?
• Why is it important to consider whether to keep or remove an outlier from your analysis?

Related terms

Linear Regression:

A statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

Correlation Coefficient:

A measure that determines the degree to which two variables' movements are associated, ranging from -1 to +1.

Interquartile Range (IQR):

A measure of statistical dispersion, being equal to the difference between the upper and lower quartiles, used to identify potential outliers.