Multicollinearity, a high degree of linear intercorrelation among explanatory variables in a multiple regression model, can lead to incorrect statistical results. Diagnostic tools for multicollinearity include the variance inflation factor (VIF), condition index, condition number, and variance decomposition proportion (VDP). The VIF is proportional to the variance of the regression coefficients, and values above 5 to 10 indicate strong multicollinearity. The condition index and condition number are measures of the severity of multicollinearity, with values above 10 to 30 indicating its presence. VDPs help identify multicollinear variables by showing the extent of variance inflation for each condition index. When two or more VDPs corresponding to a common condition index are above 0.8 to 0.9, the associated explanatory variables are multicollinear. Excluding multicollinear variables can lead to more stable and reliable regression models. The article also discusses the impact of multicollinearity on the reliability of regression coefficients, including increased standard errors, wider confidence intervals, and reduced statistical significance.Multicollinearity, a high degree of linear intercorrelation among explanatory variables in a multiple regression model, can lead to incorrect statistical results. Diagnostic tools for multicollinearity include the variance inflation factor (VIF), condition index, condition number, and variance decomposition proportion (VDP). The VIF is proportional to the variance of the regression coefficients, and values above 5 to 10 indicate strong multicollinearity. The condition index and condition number are measures of the severity of multicollinearity, with values above 10 to 30 indicating its presence. VDPs help identify multicollinear variables by showing the extent of variance inflation for each condition index. When two or more VDPs corresponding to a common condition index are above 0.8 to 0.9, the associated explanatory variables are multicollinear. Excluding multicollinear variables can lead to more stable and reliable regression models. The article also discusses the impact of multicollinearity on the reliability of regression coefficients, including increased standard errors, wider confidence intervals, and reduced statistical significance.