In regression analysis, what does a high standard error indicate?

In regression analysis, a high standard error indicates a high level of unaccounted variability in the data. The standard error measures the average distance that the observed values fall from the regression line, which reflects how well the model fits the data. When the standard error is high, it suggests that there is substantial variation in the data that the model is unable to explain, leading to less precise estimates of the regression coefficients.

This situation may imply that the chosen model does not capture all the relevant factors influencing the dependent variable, or that the data itself has a great deal of noise or randomness. It affects the reliability and confidence of the predictions made by the model, as large standard errors can result in wider confidence intervals and a lower power to detect significant relationships between variables. Thus, a high standard error serves as an indicator of uncertainty in the predictive capability of the model.