Explain how heteroscedasticity affects the standard deviation of the error term and what can be done to address it.

Heteroscedasticity occurs when the variance of the error term is not constant across all levels of the independent variables. This violates a key assumption of linear regression. It results in an inaccurate standard deviation of the error term and biased standard errors. To address it, transformations of the dependent variable (e.g., logarithmic) or weighted least squares regression can be used to equalize the variance of the error term.