What is the role of Mean Squared Error (MSE) in the standard error of a coefficient formula?

The Mean Squared Error (MSE) represents the average squared difference between the observed values and the predicted values in the regression model. It's an estimate of the variance of the error term. A lower MSE indicates a better fit of the model to the data, and consequently, smaller standard errors for the coefficients, suggesting more precise estimates.