In regression analysis, what can multicollinearity lead to?

In regression analysis, multicollinearity refers to a situation where two or more independent variables are highly correlated, making it difficult to determine the individual effect of each variable on the dependent variable. When multicollinearity occurs, it can lead to reduced predictive accuracy because the estimations of the regression coefficients become unstable and highly sensitive to changes in the model. This instability causes confidence intervals to widen, leading to less reliable predictions and making it challenging to assess the individual contribution of each predictor in the model.

Additionally, while multicollinearity does not eliminate the possibility of making predictions, it introduces complexity into the model that can diminish the effectiveness of those predictions, as overlaps in information between variables skew the results. This increased uncertainty and imprecision in estimating the relationships can ultimately reduce the overall effectiveness of the regression model, affecting the conclusions drawn from the analysis.