How can we know if my standard error is consider as high or low in my regression table?

Question

A high standard error shows that your sample may not closely represent your population.

A low standard error shows that your sample is representative of your population.

How can we know if my standard error is consider as high or low in my regression table?

Answer 1

There is not a straightforward answer or a "cutoff" where standard error is too high or is low enough. Additionally SE is something you can calculate for most parameters (that is, estimates of parameters). So you can calculate SE for the means of variables and their slopes. Additionally, high SE doesn't necessary mean that your sample is not closely representing your population. But you can always calculate confidence intervals or p-values (using SE), and then based on those results estimate if SE looks abnormally high. Additionally, you can calculate coefficient of variation (standard deviation of a variable divided by its mean) for a better insight. Long story short, you should consider SE on a case-by-case basis - and evaluate multiple aspects of your regression model (residuals, fitted values, outliers, leverage, etc).