Data, Inference, and Decisions
Heteroscedasticity refers to a condition in regression analysis where the variance of the errors, or residuals, is not constant across all levels of the independent variable(s). This phenomenon can indicate that the model may not be appropriately capturing the relationship between the variables, potentially leading to inefficient estimates and unreliable statistical tests. Recognizing and visualizing heteroscedasticity is crucial because it can significantly affect the validity of conclusions drawn from the data analysis.
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