Multicollinearity occurs when two or more independent variables are highly correlated. This poses several challenges:

• It is difficult to determine which factors influence the dependent variable
• The individual p values can be misleading—a p-value can be high even if the variable is important
• The confidence intervals for the regression coefficients will be excessive, possibly even including zero, making it impossible to determine the effect of an independent variable on the outcome

There is no formal or theory-based solution that corrects for multicollinearity. Instead, try to remove one or more of the correlated input variables, or increase the sample size.