Editing Multicollinearity (section)

=== Resolution ===
Perfect collinearity is typically caused by including redundant variables in a regression. For example, a dataset may include variables for income, expenses, and savings. However, because income is equal to expenses plus savings by definition, it is incorrect to include all 3 variables in a regression simultaneously. Similarly, including a [[Dummy variable (statistics)|dummy variable]] for every category (e.g., summer, autumn, winter, and spring) as well as an intercept term will result in perfect collinearity. This is known as the dummy variable trap.<ref>{{Cite web |title=Dummy Variable Trap - What is the Dummy Variable Trap? |work=LearnDataSci (www.learndatasci.com) |url=https://www.learndatasci.com/glossary/dummy-variable-trap/ |access-date=2024-01-18 |first=Fatih |last=Karabiber }}</ref>

The other common cause of perfect collinearity is attempting to use [[ordinary least squares]] when working with very wide datasets (those with more variables than observations). These require more advanced data analysis techniques like [[Hierarchical linear model|Bayesian hierarchical modeling]] to produce meaningful results.{{fact|date=March 2024}}