Answer: Stratification

## Explanation This question tests knowledge of common regression problems and violations of classical linear regression assumptions. **Common Regression Problems:** 1. **Multicollinearity** - When independent variables are highly correlated with each other - Violates assumption of no perfect multicollinearity - Causes unstable coefficient estimates and high standard errors 2. **Heteroscedasticity** - When error terms have non-constant variance - Violates assumption of homoscedasticity - Leads to inefficient estimates and incorrect standard errors 3. **Autocorrelation** - When error terms are correlated across observations - Violates assumption of no serial correlation - Common in time series data **Stratification** is NOT a regression problem: - Stratification is a sampling technique where the population is divided into subgroups (strata) - It's used in survey sampling and experimental design - It's not a violation of regression assumptions **Key Regression Assumptions (CLRM):** 1. Linearity in parameters 2. Random sampling 3. No perfect multicollinearity 4. Zero conditional mean 5. Homoscedasticity 6. No autocorrelation 7. Normality of errors (for inference)

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