Answer: Homoscedasticity

## Explanation When the variance of the error term is an increasing function of the explanatory variable, this indicates **heteroscedasticity** rather than homoscedasticity. ### Key Concepts: - **Homoscedasticity**: Assumes constant variance of error terms across all values of explanatory variables - **Heteroscedasticity**: Occurs when the variance of error terms changes with the explanatory variables - **Violation**: The described scenario clearly shows heteroscedasticity, which violates the homoscedasticity assumption ### Other Options Analysis: - **B. Multicollinearity**: Refers to high correlation between explanatory variables, not variance patterns - **C. Model is linear**: The linearity assumption relates to the functional form, not variance properties - **D. No autocorrelation**: Concerns correlation between error terms over time, not variance patterns **Correct Answer: A** - Homoscedasticity assumption is violated

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