Crucial Exams

CompTIA DataX DY0-001 (V1) Practice Question

A data scientist develops an Ordinary Least Squares (OLS) regression model. Upon reviewing the diagnostic plots, they observe that a scatter plot of the model's residuals versus the fitted values shows a distinct funnel shape, where the variance of the residuals increases as the fitted values increase. This pattern indicates a violation of a key OLS assumption. Which OLS assumption is violated, and what is the primary consequence of this violation?

  • The assumption of independence of errors is violated; the coefficient estimates become inefficient, and the model is susceptible to autocorrelation.

  • The assumption of homoscedasticity is violated; the coefficient estimates remain unbiased, but their standard errors are no longer reliable.

  • The assumption of linearity is violated; the coefficient estimates become biased, and the model will systematically mis-predict the outcome.

  • The assumption of normality of errors is violated; the coefficient estimates are no longer the Best Linear Unbiased Estimators (BLUE).

CompTIA DataX DY0-001 (V1)
Machine Learning
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