Answer: All of the above

**Explanation:** OLS (Ordinary Least Squares) regression requires several key assumptions to draw valid conclusions: 1. **Zero conditional mean assumption**: The expected value of the error term, conditional on the independent variable, is zero (E(εᵢ|Xᵢ) = 0). This ensures that the model is correctly specified and there are no omitted variables. 2. **Independence of observations**: The error terms are uncorrelated across observations. This is equivalent to saying that (X, Y) observations are independent and identically distributed (i.i.d.). 3. **Normality assumption**: The error term is normally distributed. This assumption is particularly important for hypothesis testing and constructing confidence intervals. 4. **Homoscedasticity**: The variance of the error term is constant across all observations. 5. **Linearity**: A linear relationship exists between the independent and dependent variables. 6. **No perfect multicollinearity**: The independent variables are not perfectly correlated with each other. Since all three statements (A, B, and C) are correct assumptions required for OLS regression, option D ("All of the above") is the correct answer. **Additional context from the text:** - The text mentions that OLS requires three key assumptions: zero conditional mean, i.i.d. observations, and no large outliers. - It also lists other assumptions including normality of error terms, linear relationship, and no omitted variables. - All of these are standard assumptions for OLS regression to produce unbiased, consistent, and efficient estimates.

Author: Nikitesh Somanthe