AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

The Residual Sum of Squares (RSS) is a fundamental concept in statistical modeling, particularly in regression analysis. It measures the discrepancy between the data and the estimation model. The correct equation for calculating RSS is derived from how we quantify these discrepancies.

RSS is calculated as the sum of the squared differences between the actual values and the predicted values from the model. Mathematically, this is represented as the summation of the squared differences:

RSS = Σ(predicted - actual)^2.

This formulation is crucial because squaring the differences ensures that negative discrepancies do not cancel out positive ones, thus providing a cumulative measure of error regardless of direction. It allows for a clear quantification of how well the model's predictions align with the actual data.

The formulation focusing on the difference between predicted and actual values captures the essence of model accuracy—how well the model is performing in comparison to the true values. By focusing solely on the squared terms of these deviations, RSS serves as a key statistic in assessing the fit of a regression model. This makes it an important component in models that aim to minimize the error between predicted and observed data.