Chartered Financial Analyst (CFA) Practice Exam Level 2

What does Root Mean Square Error (RMSE) measure?

1. The variability of the independent variable

2. The accuracy of predictive models

3. The degree of multicollinearity

4. The value of the coefficients in a regression

The correct answer is: The accuracy of predictive models

Root Mean Square Error (RMSE) serves as a crucial metric in evaluating the accuracy of predictive models. It quantifies the difference between predicted values generated by a model and the actual observed values. By taking the square root of the average of the squared differences, RMSE provides a clear numerical expression of the model's predictive performance, making it easier to understand the error in units similar to the original data. A key point is that a lower RMSE value indicates a better fit of the model to the data, meaning the predictions are closer to the actual outcomes. This makes RMSE a preferred choice for assessing the performance of regression models, where the goal is to make accurate predictions. In contrast, other options like the variability of the independent variable or the degree of multicollinearity do not pertain to the evaluation of a model's accuracy, and the value of the coefficients in a regression relates to the strength and direction of predictor variable relationships rather than the model’s predictive accuracy. Hence, the focus of RMSE specifically on model accuracy aligns perfectly with its definition and application in predictive analytics.