In the context of regression analysis, what does ε represent?

In regression analysis, ε, often referred to as "epsilon," represents the error term. This term captures the difference between the observed values and the values predicted by the regression model. Essentially, it accounts for the variability in the dependent variable that cannot be explained by the independent variables included in the model.

The error term is crucial because it reflects the influence of other factors not included in the regression analysis, measurement errors, and random variations in the data. By analyzing the error term, one can assess the model's fit and its ability to explain the variability in the data. A small error term indicates that the model's predictions closely align with the actual observations, while a larger error term suggests more discrepancy between predicted and actual values.

Understanding the concept of the error term is fundamental in regression analysis, as it helps in evaluating the overall performance of the model and its assumptions.