What does the term "residual" refer to in the context of regression analysis?

In the context of regression analysis, the term "residual" specifically refers to the difference between the actual value of the dependent variable and the value predicted by the regression model. Residuals are essential in assessing the accuracy of a regression model; they help to identify how well the model fits the data. By calculating the residuals for each observation, researchers can analyze patterns in the errors, potentially revealing areas where the model could be improved or showing whether there are underlying trends that the model isn't capturing.

Understanding the concept of residuals is fundamental for diagnosing issues such as non-linearity, heteroscedasticity, or outliers in the data. These insights can guide further refinement of the model, ultimately enhancing its predictive power. In contrast, the other options do not accurately capture the definition of residuals, focusing instead on different aspects of data analysis or statistical measures.