How can you reduce the standard errors of coefficients in a linear regression model?

Several strategies can reduce standard errors: increasing the sample size, reducing measurement error in the data, addressing multicollinearity (e.g., by removing highly correlated variables or using regularization techniques), and improving the model fit by adding relevant predictors or transforming variables.