What defines multicollinearity in regression analysis?

The scenario where independent variables are highly correlated with each other defines multicollinearity in regression analysis. This condition occurs when two or more predictors in a regression model demonstrate a strong linear relationship, making it difficult to ascertain the individual effect of each independent variable on the dependent variable. High multicollinearity can lead to inflated standard errors, making estimates of the coefficients unstable and difficult to interpret, which negatively affects the overall reliability of the model.

In practical terms, when multicollinearity is present, it can cause issues such as reduced statistical power to detect the effect of individual predictors, leading to a misleading understanding of which variables are truly significant. Identifying and addressing multicollinearity is crucial for ensuring valid and reliable regression analyses.