What does SSE stand for in regression context?

What does SSE stand for in regression context?

• A. Sum of Square Errors
• B. Sum of Squares Explained
• C. Standard Sum Estimator
• D. Squared Error Sum

Answer: (A)

In regression, SSE represents the sum of squared errors, which measures how far the model’s predictions are from the actual observed values. For each data point, you take the residual (y_i minus the predicted ŷ_i) and square it, then add all those squared residuals together. This total, SSE = sum of (y_i − ŷ_i)², captures the portion of variability in the response that the model fails to explain. SSE is part of the fundamental variance decomposition in regression: SST (total variability) equals SSR (variability explained by the model) plus SSE (unexplained variability). A smaller SSE indicates a better-fitting model. It also relates to R-squared through R² = 1 − SSE/SST, showing how much of the total variation is explained by the model. The term used for SSE is standardly “Sum of Squared Errors.” The option phrasing here is a close match to that concept, while the other choices refer to different ideas (for example, the part of variation explained by the model). The best answer, given the standard terminology, is the one that aligns with Sum of Squared Errors.

In regression, SSE represents the sum of squared errors, which measures how far the model’s predictions are from the actual observed values. For each data point, you take the residual (y_i minus the predicted ŷ_i) and square it, then add all those squared residuals together. This total, SSE = sum of (y_i − ŷ_i)², captures the portion of variability in the response that the model fails to explain.

SSE is part of the fundamental variance decomposition in regression: SST (total variability) equals SSR (variability explained by the model) plus SSE (unexplained variability). A smaller SSE indicates a better-fitting model. It also relates to R-squared through R² = 1 − SSE/SST, showing how much of the total variation is explained by the model.

The term used for SSE is standardly “Sum of Squared Errors.” The option phrasing here is a close match to that concept, while the other choices refer to different ideas (for example, the part of variation explained by the model). The best answer, given the standard terminology, is the one that aligns with Sum of Squared Errors.