Answer: I and IV only

## Explanation Let's analyze each statement: **Statement I**: "In the ordinary least squares (OLS) model, the random error term is assumed to have zero mean and constant variance." - **CORRECT** - This is one of the key assumptions of OLS regression: E(ε) = 0 (zero mean) and Var(ε) = σ² (constant variance/homoscedasticity) **Statement II**: "In the OLS model, the variance of the independent variable is assumed to be positively correlated with the variance of the error term." - **INCORRECT** - This is not an assumption of OLS. In fact, if the variance of the error term depends on the independent variable, it violates the homoscedasticity assumption. **Statement III**: "In the OLS model, it is assumed that the correlation between the dependent variable and the random error term is zero." - **INCORRECT** - The correct assumption is that the independent variable(s) are uncorrelated with the error term, not the dependent variable. The dependent variable is by definition correlated with the error term. **Statement IV**: "In the OLS model, the variance of the residuals is assumed to be constant." - **CORRECT** - This is the homoscedasticity assumption, which means the variance of the error term (and therefore the residuals) is constant across all observations. Therefore, only statements I and IV are correct, making option C the correct answer.

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