Residual is defined as:

Residual is defined as:

• A. The difference between the observed value and the mean of all observations.
• B. The average residual across observations.
• C. The sum of squared errors.
• D. The difference between the value a model predicts and the value observed in the data on which the model is based.

Answer: (D)

Residuals measure the error for each observation: how far the actual value is from what the model predicts for that same observation. For each data point, the residual is the difference between the observed value and the model’s predicted value. This tells you the discrepancy between what happened and what the model forecasted. The option describing the residual as the difference between the value a model predicts and the value observed captures that same idea of a discrepancy between prediction and reality. It’s not the difference from the overall mean, which would be deviation from the mean, and it isn’t the average residual or the sum of squared errors, which are summaries rather than a single residual.

Residuals measure the error for each observation: how far the actual value is from what the model predicts for that same observation. For each data point, the residual is the difference between the observed value and the model’s predicted value. This tells you the discrepancy between what happened and what the model forecasted. The option describing the residual as the difference between the value a model predicts and the value observed captures that same idea of a discrepancy between prediction and reality. It’s not the difference from the overall mean, which would be deviation from the mean, and it isn’t the average residual or the sum of squared errors, which are summaries rather than a single residual.