What is the primary effect of multicollinearity in regression analysis?

The primary effect of multicollinearity in regression analysis is that it creates a greater probability of incorrectly concluding that a variable is significant. Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with one another. This high correlation can obscure the individual effect of each variable on the dependent variable, leading to inflated standard errors for the coefficients. Consequently, this makes it more difficult to determine whether a predictor is truly significant or just appears to be significant due to the shared variance with other predictors.

When multicollinearity is present, the estimated coefficients can become unstable and sensitive to small changes in the model or the data, which can lead to misleading conclusions about the importance of predictors. As a result, even if a particular variable is included in the model, the statistical tests used to assess its significance may not provide reliable results. This highlights the critical impact of multicollinearity on the reliability of regression analysis results.