Homoscedasticity is a fundamental assumption in linear regression analysis, which refers to the equal variance of the residuals (the differences between the observed values and the predicted values) across all levels of the independent variable(s). This concept is crucial in ensuring the reliability and validity of the regression model's inferences.
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