What happens if the assumption of homoscedasticity is violated in simple linear regression?

It has no effect on the regression model
It makes the regression model more accurate
It makes the regression model perfectly fit the data
It makes the standard errors and confidence intervals invalid

Homoscedasticity is the assumption that the variance of the residuals is constant across all levels of the independent variable. If this assumption is violated (a condition known as heteroscedasticity), it can lead to unreliable and inefficient estimates of the standard errors. This, in turn, can make the confidence intervals and hypothesis tests invalid.

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