Assess learners' understanding of OLS linear regression including the classical assumptions (linearity, independence, homoscedasticity, normality of errors, absence of perfect multicollinearity), correct interpretation of coefficients (intercept, slopes, units, standardized coefficients, marginal effects, statistical significance), and evaluation of model fit (R², adjusted R², RMSE, F-test and basic information criteria); include items that require diagnosing assumption violations from residual output and selecting appropriate goodness-of-fit measures for applied scenarios.