In regression analysis, heteroskedasticity and autocorrelation significantly impact model accuracy. Heteroskedasticity involves variable error variances, while autocorrelation means time-correlated residuals, both requiring tests like Breusch-Pagan and Durbin-Watson for detection and correction.