jäännösvirhe

Jäännösvirhe, also known as residual error or bias, refers to the part of the error in a statistical model that cannot be explained by the independent variables included in the model. It represents the systematic difference between the observed values and the values predicted by the model. In simpler terms, it's what's left over after the model has done its best to explain the data.

This unexplained variation can arise from several sources. It might be due to omitted variables that have a significant influence on the dependent variable but were not included in the model. It could also stem from incorrect model specification, such as assuming a linear relationship when the true relationship is non-linear, or from measurement errors in the dependent variable. Furthermore, unobserved heterogeneity among the subjects or entities being studied can contribute to jäännösvirhe.

The presence of significant jäännösvirhe can indicate that the model is not a good fit for the data. It violates the assumption of homoscedasticity and independence of errors in many regression models, potentially leading to biased parameter estimates and incorrect inferences. Researchers often try to minimize jäännösvirhe by including more relevant variables, choosing a more appropriate functional form for the model, or employing more sophisticated modeling techniques. Analyzing the pattern of the jäännösvirhe (often visualized through residual plots) can provide valuable insights into the model's limitations and suggest avenues for improvement.