Dealing with differences in the variability of data when studying the quality of life of older people is important. Using ordinary least squares for this can give unreliable results. Other methods like weighted least squares and heteroskedasticity-consistent covariance matrix estimators can provide more accurate estimates. By comparing these methods using real data, researchers found better ways to model quality of life in older individuals.