The quantile regression gives a more comprehensive picture of the effect of the independent variables on the dependent variable. Instead of estimating the model with average effects using the OLS linear model, the quantile regression produces different effects along the distribution (quantiles) of the dependent variable. The dependent variable is continuous with no zeros or too many repeated values. Examples include estimating the effects of household income on food expenditures for low- and high-expenditure households; and determining the factors affecting student scores along their score distribution.