Casualty Actuarial Society MAS-1 Practice Exam

The mean squared error is driven by the residuals between observed and predicted values. If the model equation is misspecified, predictions don’t capture the true relationship, leaving large and systematic residuals that inflate MSE. When errors are heteroscedastic, their variance changes with the level of the data, so the usual assumption of constant variance is violated and the standard interpretation of MSE as a uniform measure of predictive error becomes unreliable. Outliers push squared residuals up dramatically, disproportionately distorting the MSE and giving a misleading view of typical predictive performance. Multicollinearity, while it makes coefficient estimates unstable and inflates their standard errors, does not directly distort the overall mean squared error of the model’s predictions. Thus, multicollinearity is not a direct cause of unreliable MSE estimates.