Regression Homoscedasticity

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In linear regression, what does the assumption of homoscedasticity mean, and what happens when it is violated?

Multiple Choice

Residuals must be normally distributed; violation causes biased coefficients The variance of residuals is constant across predictor values; violation makes standard errors and hypothesis tests unreliable Predictors must be uncorrelated; violation causes multicollinearity The relationship must be linear; violation causes omitted variable bias

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Regression Homoscedasticity

Multiple Choice

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