In common practices, heteroscedasticity and non-normality are frequently encountered when fitting linear
regression models. Several methods have been proposed to handle these problems. In this research, we applied four
different estimation methods: ordinary least squares (OLS), transform both sides (TBS), power of the mean function
(POM) and exponential variance function (VEXP), dealing with three different forms of the non-constant variances
under four symmetric distributions. In order to study the performance of the four methods in estimating the studied
model parameters, a simulation study with sample sizes of 20, 50 and 100 was conducted. Relative bias, mean squared
error (MSE) and coverage probability of the nominal 95% confidence interval for regression parameters were accessed.
The simulation results and application to real life data suggest that each estimation method performed differently
on different variance structures and different distributions whereas the sample size did not give much effect on each
estimation method. In overall, the TBS method performed best in terms of smallest bias and MSE, especially under
extreme heteroscedasticity. On the other hand, the OLS method was very accurate in maintaining the nominal coverage
probabilities although it had relatively poor performance in terms of bias.