OLS Assumptions, Violations, and Diagnostics

Consider the linear regression model $Y = X\beta + \epsilon$, estimated by ordinary least squares (OLS). 1. State the five classical OLS assumptions (Gauss-Markov plus normality). 2. For each assumption, explain what goes wrong when it is violated -- specifically, does it affect bias, efficiency, or the validity of standard errors and hypothesis tests? 3. For each violation, name a diagnostic test to detect it and a practical remedy.