What is meant by heteroscedasticity? What are its consequences? How do you detect the presence of heteroscedasticity in a data set?

Heteroscedasticity refers to the condition in which the variance of the error terms in a regression model is not constant across all levels of the independent variable(s). In an ideal ordinary least squares (OLS) regression model, one of the key assumptions is homoscedasticity, meaning that the residuals (errors) exhibit constant variance. When this assumption is violated, heteroscedasticity occurs, leading to inefficiencies in the estimation process.

The consequences of heteroscedasticity can be significant. First, it leads to inefficient OLS estimates because the standard errors of the regression coefficients become unreliable. In the presence of heteroscedasticity, OLS estimators remain unbiased but no longer have minimum variance, violating the _______ ________ _______ __________ ___ ______.
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