In multiple regression analysis, multicollinearity refers to a situation where two or more independent variables are highly correlated with each other. This can make it difficult to assess the effect of each independent variable on the dependent variable. The presence of multicollinearity does not bias the estimated coefficients, but it can inflate the standard errors, leading to less reliable statistical tests.
Which of the following statements best describes the impact of multicollinearity in a multiple regression analysis?