What indicates multicollinearity among predictor variables?

High correlation among predictor variables is a key indicator of multicollinearity. When predictor variables are highly correlated, it means that they provide overlapping information about the variance in the dependent variable, which can cause complications in statistical analyses, particularly in regression models.

Such high correlation makes it difficult to assess the individual effect of each predictor, leading to unstable coefficient estimates and increased standard errors. As a result, it becomes challenging to determine which predictor is actually influencing the outcome. This can distort the interpretation and strength of relationships identified in the analysis, ultimately affecting the model's predictive accuracy.

Low correlation, independent variance, and non-existent variance do not signify multicollinearity; rather, they indicate a lack of overlapping information among predictors, which is desirable for accurate model fitting and interpretation. Therefore, the presence of high correlation among predictors is the most telling sign of multicollinearity in statistical modeling.