What is meant by collinearity in regression analysis?

Collinearity in regression analysis refers to the scenario where two or more features (independent variables) exhibit a strong linear relationship with each other. This means that one independent variable can be predicted from the other(s) with a high degree of accuracy. In practice, this can create challenges in regression, as it complicates the estimation of the individual effects of each feature on the dependent variable. When collinearity is present, it becomes difficult to assess the contribution of each independent variable to the prediction because their effects are intertwined.

Understanding collinearity is crucial for data scientists and statisticians as it can lead to inflated standard errors of the coefficients, making hypothesis tests unreliable. While this term is often mentioned in relation to multicollinearity, which refers specifically to the situation where multiple independent variables are correlated, collinearity broadly addresses the linear relationship between two features.

In contrast, non-linearity between independent variables does not directly relate to collinearity, as collinearity specifically involves linear relationships. The relationship between dependent and independent variables pertains more to the overall regression model rather than the interrelationships of the independent variables themselves. Multicollinearity issues are a specific scenario under the umbrella of collinearity but pertain more to situations where three