Multicollinearity, a statistical phenomenon where predictor variables in a logistic regression model are highly correlated, is a significant issue, especially when there are many covariates. This phenomenon can lead to unstable estimates and inaccurate variances, affecting confidence intervals and hypothesis tests. While examining the correlation matrix can help detect multicollinearity, more effective diagnostics include tolerance, variance inflation factor (VIF), condition indices, and variance proportions. For moderate to large sample sizes, dropping one of the correlated variables is a satisfactory approach to reduce multicollinearity without increasing the sample size. This method can significantly mitigate multicollinearity without the need for larger datasets.Multicollinearity, a statistical phenomenon where predictor variables in a logistic regression model are highly correlated, is a significant issue, especially when there are many covariates. This phenomenon can lead to unstable estimates and inaccurate variances, affecting confidence intervals and hypothesis tests. While examining the correlation matrix can help detect multicollinearity, more effective diagnostics include tolerance, variance inflation factor (VIF), condition indices, and variance proportions. For moderate to large sample sizes, dropping one of the correlated variables is a satisfactory approach to reduce multicollinearity without increasing the sample size. This method can significantly mitigate multicollinearity without the need for larger datasets.