In the linear regression method, what do the coefficients correspond to?

• A. Prediction Error
• B. Feature Importance
• C. Attribute Weight
• D. Intercepts

Answer: (C)

In linear regression, the coefficients are the weights assigned to each input feature in the prediction. They tell you how much the predicted value would change for a one-unit change in that feature, holding all other features constant. This is why they’re described as the influence or weight of each attribute on the outcome. The intercept is a separate term that sets the baseline prediction when all features are zero. The prediction error is the difference between what the model predicts and what’s observed, so it isn’t what the coefficients represent. Keep in mind that coefficients reflect feature scales—standardizing features makes their magnitudes more directly comparable as measures of relative influence.