A regression model made a prediction that a particular participant should have a value of 17. However, the actual data point instead has a value of 10. What term refers to the difference between the predicted value and the actual value?

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BlackTom AI · Accepted Answer

Starting with the core concept, the question asks for the term that describes the difference between what a regression model predicts and what the actual data point is.
Option A: Constant. In regression, a constant would refer to the intercept or a fixed value, not the discrepancy between predicted and observed values. Therefore this is not the right term for prediction error.
Option B: Residual. This is the standard statistical term for the difference between an observed value and the value predicted by a model. If the model predicted 17 and the actual value is 10, the residual (observed minus predicted) would be 10 − 17 = −7. This aligns exactly with the concept of residuals as prediction errors.
Option C: Slope. The slope is a parameter describing the rate of change in the predicted value with respect to the predictor, not the discrepancy between predicted and actual values. It does not represent the error magnitude.
Option D: Covariance. Covariance measures how two variables change together, or the joint variability between predictor and response, but it does not describe the individual prediction error for a single data point.
In summary, the term that captures the shortfall or deviation between the predicted value (17) and the actual value (10) is the residual, which quantifies prediction error for each observation.

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A regression model made a prediction that a particular participant should have a value of 17. However, the actual data point instead has a value of 10. What term refers to the difference between the predicted value and the actual value?

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